Datapy

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Product Details

Name

Datapy

Tagline

Effortless Insights, Empowered Growth

Tech-Savvy Entrepreneur

Name: Tech-Savvy Entrepreneur
Description: The Tech-Savvy Entrepreneur embraces innovative solutions to drive business growth. They thrive on leveraging data analytics to optimize processes and enhance customer experiences. Their typical day involves sifting through data dashboards on Datapy to identify trends, experiment with strategies, and pivot quickly to achieve their goals. They engage with Datapy to extract valuable insights that shape their business trajectory, seeking out tools that enable scalability and efficiency.
Demographics: Age: 32-45, Gender: Male/Female, Education: Bachelor's degree or higher in Business or Tech-related field, Occupation: Business Owner/Entrepreneur, Income Level: $80,000-$150,000 annually.
Background: Growing up in a tech-centric environment, this persona often found themselves experimenting with new software and gadgets. They pursued a degree in business administration with a focus on technology, allowing them to integrate tech solutions into their business strategies effectively. Their hobbies include attending startup workshops and networking events to fuel their entrepreneurial journey. Challenges faced include resource constraints and the constant need for adaptation in a fast-paced market.
Psychographics: Values innovation, seeks continuous improvement and efficiency, motivated by growth and success, enjoys collaboration and knowledge-sharing, holds a strong belief in the power of data-driven decision-making.
Needs: Needs intuitive data visualization tools to easily interpret complex datasets, desires real-time insights for responsive decision-making, and requires collaboration features to share findings with their teams efficiently.
Pain: Struggles with data overload, finds it challenging to extract actionable insights from voluminous data, and experiences difficulty in educating team members on analytics tools.
Channels: Primarily uses online business forums, LinkedIn for networking, and industry-specific events. Also engages on social media platforms to connect with fellow entrepreneurs and discover tech trends.
Usage: Utilizes Datapy daily, especially for post-analysis after marketing campaigns or product launches. Engages the platform for around 1-2 hours per day, focusing on real-time monitoring and decision-making processes.
Decision: The decision-making process involves risk assessment, analyzing data-backed reports from Datapy, and considering industry standards and competitor actions before making informed choices.

Growth-Focused Sales Director

Name: Growth-Focused Sales Director
Description: The Growth-Focused Sales Director is dedicated to enhancing sales performance and building lasting customer relationships. They frequently access Datapy to track sales metrics, evaluate team performance, and strategize on improving efficiency. Their typical day revolves around analyzing KPIs and aligning their team’s efforts with broader business objectives using insights gained from the platform.
Demographics: Age: 35-50, Gender: Male/Female, Education: Bachelor's degree in Marketing or Business, Occupation: Sales Director, Income Level: $90,000-$175,000 annually.
Background: With a passion for sales from an early age, this persona built a career in sales management. They hold a degree in marketing and have taken various professional development courses focused on sales strategy. Their interests include studying market trends, engaging in sales strategy discussions, and participating in seminars, helping them lead their teams effectively while facing challenges like market competition and changing consumer preferences.
Psychographics: Values team collaboration, customer satisfaction, and personal growth, motivated by achieving targets and cultivating trust in client relationships, embraces a data-driven mindset to enhance sales efforts.
Needs: Requires robust metrics tracking to assess team performance in real-time, desires integration tools to sync sales data quickly, and needs competitive insights to outmaneuver competitors.
Pain: Frustrated by fragmented sales data across platforms, struggles with aligning team efforts towards common sales goals, and often encounters delays in communication and reporting processes.
Channels: Primarily engages with CRM platforms, LinkedIn for prospecting, emailed newsletters for industry insights, and sales conferences or webinars for networking.
Usage: Regular user of Datapy, typically spending 1-3 hours multiple times a week analyzing sales data, team performance, and market trends to inform strategies and adjust tactics as necessary.
Decision: They rely heavily on data-driven reports, prioritize actionable insights from the platform, and will consult their team for feedback and insights before finalizing strategies.

Data-Driven Product Manager

Name: Data-Driven Product Manager
Description: The Data-Driven Product Manager leverages analytics to guide product development and enhancements. They use Datapy to monitor user feedback, engagement metrics, and product performance, ensuring that the products meet customer expectations and market demands. Their days are spent dissecting data to prioritize features and improvements accordingly.
Demographics: Age: 28-42, Gender: Male/Female, Education: Bachelor's or Master's in Product Management or Engineering, Occupation: Product Manager, Income Level: $85,000-$160,000 annually.
Background: Having earned a degree in engineering, this persona began in a technical role before transitioning into product management, where they discovered a passion for combining tech and user experience. They regularly read product management literature and attend webinars to stay current on industry practices. Hobbies include user experience research and networking with other product managers to share insights and strategies, facing challenges like balancing stakeholder input and user needs.
Psychographics: Values user-centered design, thrives on teamwork and cross-functional collaboration, motivated by product success and user satisfaction, believes in creating solutions that solve real-life problems.
Needs: Needs comprehensive analytics tools to represent user insights effectively, requires the ability to track product performance metrics, and desires integration with user feedback channels.
Pain: Overwhelmed by conflicting feedback from various stakeholders, struggles with isolating the most critical data that directly impacts product success, and finds it challenging to communicate data insights to non-technical stakeholders.
Channels: Favors online forums and communities like ProductHunt, LinkedIn for professional growth, and online platforms such as Reddit for user interactions and feedback collection.
Usage: Uses Datapy frequently for bi-weekly product reviews, dedicating around 2-4 hours per week to analyze user metrics and product feedback, assessing the impact of updates and features on the user base.
Decision: Their decisions rely on user feedback and behavioral data drawn from Datapy, engaging in discussions with the development team to ensure alignment with product strategy and vision.

Analytical Customer Support Lead

Name: Analytical Customer Support Lead
Description: The Analytical Customer Support Lead strives for exceptional customer experiences by analyzing support metrics and feedback. They leverage Datapy to track response times, ticket volume, and customer satisfaction scores, ensuring the support team meets service level agreements and continuously improves processes. Their routine is structured around monitoring data and collaborating with team members to implement improvements.
Demographics: Age: 30-45, Gender: Male/Female, Education: Bachelor's degree in Communications or Business, Occupation: Customer Support Lead, Income Level: $60,000-$120,000 annually.
Background: Starting their career in front-line customer service, this persona naturally progressed to a leadership role after excelling in support environments. They hold a degree in communication and regularly attend training sessions to strengthen their team's capabilities. Interests include customer psychology and process optimization, facing challenges in team performance and maintaining high customer satisfaction levels.
Psychographics: Values customer-centric approaches and transparency, driven by a commitment to excellence in service delivery and personal development, enjoys exploring innovative solutions to improve customer interactions.
Needs: Requires real-time data monitoring tools to assess team performance, seeks advanced reporting capabilities to analyze support quality, and desires collaborative tools to engage team feedback effectively.
Pain: Struggles with efficiently managing high ticket volumes, faces challenges in ensuring timely responses and high quality of service, and often deals with miscommunication within the team regarding customer feedback and resolution strategies.
Channels: Primarily uses help desk software, customer feedback forums, and industry blogs for best practices. Engages with decision-makers via company communication channels.
Usage: Utilizes Datapy on a daily basis, spending around 1 hour analyzing support trends and team performance metrics, fostering an environment of continuous improvement within the support team.
Decision: Decisions are driven by data insights from Datapy, regular team discussions are held to develop strategic and operational adjustments based on observed trends.

Innovative Business Strategist

Name: Innovative Business Strategist
Description: The Innovative Business Strategist focuses on aligning business objectives with analytical insights to drive growth. They harness Datapy to explore market conditions, competitive positioning, and internal performance metrics, shaping strategic initiatives accordingly. Their typical day revolves around engaging with data to identify opportunities and inform decision-making processes.
Demographics: Age: 35-55, Gender: Male/Female, Education: Bachelor's or Master's degree in Business or Economics, Occupation: Business Strategist, Income Level: $100,000-$200,000 annually.
Background: Having built a successful career as a strategy consultant, this persona transitioned into an internal role to create lasting impact. They hold an MBA and enjoy studying market dynamics, ensuring they stay ahead of trends. Their interests include strategic analysis and process optimization, facing challenges around resource allocation and aligning team efforts with strategic goals.
Psychographics: Values strategic foresight and analytical rigor, driven by a passion for leveraging data to create sustainable strategies, committed to fostering a culture of innovation within their organization.
Needs: Requires comprehensive market analysis tools to inform strategic decisions, seeks integration capabilities for cross-departmental data sharing, and desires collaborative decision-making platforms.
Pain: Frustrated by data silos and lack of real-time insights, struggles to communicate strategy effectively across teams, and encounters difficulties in validating assumptions made during decision-making processes.
Channels: Engages with industry reports, business strategy seminars, LinkedIn for professional connections, and publications focused on market trends and strategic insights.
Usage: Uses Datapy 3-4 times a week, focusing on in-depth data analysis sessions that can last anywhere from 1-3 hours as they prepare and refine strategic recommendations for stakeholders.
Decision: Relies on in-depth analytics reports, often engages in team brainstorming sessions, and validates decisions based on a combination of analytical insights and market research.

Product Ideas

Insightful Onboarding Experience: A comprehensive onboarding module for new users that leverages interactive tutorials and AI-driven guidance to help them quickly navigate Datapy's features and dashboards. This would enable users to fully utilize the platform's capabilities, resulting in faster adoption and improved user satisfaction.
Custom Analytics API: An API that allows users to integrate their existing systems and data sources directly with Datapy. This feature would enable seamless data flow and enhance the analytics capabilities by allowing businesses to pull and push data in real-time, creating a more cohesive data ecosystem.
Predictive Trend Alerts: An AI-powered notification system that monitors critical metrics to alert users of significant changes or emerging trends in their data. This proactive feature would empower users to take timely action based on insights generated from their analytics, enhancing decision-making.
Collaborative Insights Hub: A shared workspace within Datapy where teams can collaboratively analyze data, share insights, and brainstorm strategies. This would include features for commenting, tagging, and version control, enhancing teamwork and communication around data-driven decisions.
Customer Feedback Integrator: An integrated tool that allows users to gather customer feedback directly within Datapy, utilizing surveys and feedback forms. This would streamline the process of accessing customer sentiments and integrating qualitative data into quantitative analytics.
Enhanced Visualization Toolkit: A suite of advanced visualization options for users to better present their data. This toolkit would include customizable chart types, advanced filtering, and interactive dashboards, allowing users to tell compelling stories with their data insights.
Data Security Shield: An advanced security feature that enhances data protection through additional layers of authentication, encryption, and regular security audits. This feature would provide users with peace of mind regarding data privacy and compliance with regulations.

Product Features

Interactive Tutorial Sessions

Engaging, step-by-step tutorials that guide new users through the core functionalities of Datapy. Each session focuses on essential tasks, ensuring users gain confidence in using the platform's features while minimizing the learning curve.

Requirements

Interactive Onboarding

User Story: As a new user, I want to participate in interactive onboarding sessions so that I can quickly learn how to use Datapy's core features without feeling overwhelmed.
Description: The Interactive Onboarding requirement involves designing a series of engaging, step-by-step tutorials tailored for new users of Datapy. These tutorials will guide users through core functionalities such as data input, visualization, and analytics tools. The primary benefit is to enhance user confidence and competence in using the platform, significantly reducing the learning curve. This feature will be seamlessly integrated into the user dashboard, prompting new users to enroll in sessions that are relevant to their assigned tasks. Expected outcomes include improved user retention, increased satisfaction, and a proactive approach to utilizing Datapy's features effectively.
Acceptance Criteria: New user logs into Datapy for the first time and chooses to engage with the Interactive Onboarding feature.

Given the user is a new Datapy user, when they log in for the first time, then they should see a prompt to start the Interactive Onboarding sessions on their dashboard.

A user follows the onboarding tutorial to complete a specific task, such as importing data into Datapy.

Given the user is in an onboarding tutorial, when they follow the steps to import data, then they should successfully import data into Datapy without errors and receive confirmation of completion.

A user finishes the onboarding session and explores further features of Datapy independently.

Given the user has completed the onboarding session, when they navigate away from the tutorial, then they should be able to access and utilize other features in Datapy without any issues.

A user encounters difficulty during a step of the onboarding session and seeks assistance.

Given the user is engaged in the onboarding tutorial, when they click on the help icon during any step, then they should see relevant help articles or a support chat option.

A user completes all onboarding sessions and provides feedback on their experience.

Given the user has completed all onboarding tutorials, when they are prompted for feedback, then they should be able to submit a rating and comments on their onboarding experience.

A user returns to the dashboard after completing the onboarding sessions.

Given the user has finished the onboarding sessions, when they return to their dashboard, then they should see a summary of their completed sessions and suggestions for next steps.

A user attempts to skip the onboarding sessions upon first login.

Given the user is a new Datapy user, when they log in for the first time and choose to skip the onboarding sessions, then they should see an option to re-engage with the onboarding sessions later from their dashboard.

Progress Tracking System

User Story: As a user, I want to track my progress in the interactive tutorials so that I can understand what I have learned and identify my next steps in mastering Datapy.
Description: The Progress Tracking System requirement consists of implementing a feature that allows users to monitor their progress through the interactive tutorials. Users will receive feedback on completed sessions, areas for improvement, and recommendations for additional tutorials based on their usage patterns. This feature will be embedded within the tutorial interface and will utilize data analytics to provide personalized user experiences. The expected benefit is to enhance user engagement, motivation, and retention by making learning outcomes visible and measurable.
Acceptance Criteria: User views their tutorial progress for the first time after completing the initial tutorial session.

Given a user has completed an interactive tutorial session, when they access the progress tracking system, then they should see a visual representation of their completed tutorials and overall progress percentage.

User receives feedback on areas for improvement after completing tutorials.

Given a user has completed multiple tutorials, when they access the progress tracking system, then they should receive personalized feedback highlighting areas where they struggled or scored lower in assessments.

User is recommended additional tutorials based on their progress and feedback.

Given a user has finished a certain number of tutorials, when they access the progress tracking system, then they should see a list of recommended tutorials tailored to their learning needs and previous performance.

User checks their progress tracking system on a mobile device.

Given a user accesses the progress tracking system on a mobile device, when they view their progress, then it should be optimized for mobile, maintaining usability and visual clarity.

User provides feedback on the effectiveness of the progress tracking system.

Given a user has been using the progress tracking system for a month, when they submit feedback through a survey, then the system should collect and categorize the feedback for further analysis to improve user experience.

User who skips sessions is reminded of incomplete tutorials.

Given that a user has not completed a tutorial session for a defined period, when they log into the platform, then they should receive a notification prompting them to complete the missed tutorial.

User can visually track their tutorial completion status over time.

Given that a user has been using the progress tracking system for at least two weeks, when they check their progress history, then they should see a timeline graph showing their tutorial completion rates over the selected period.

Feedback Mechanism

User Story: As a user, I want to provide feedback on the tutorials so that I can help improve the experience for future users and share what worked well or needs refinement.
Description: The Feedback Mechanism requirement involves creating a system that collects user feedback at various stages of the interactive tutorial sessions. This could include ratings, open-ended questions, and suggestions for additional content. The goal is to continuously improve the tutorial experience based on user input. The integration of this feature will include a survey pop-up after each tutorial session and analytics dashboard to review feedback trends. This requirement is essential for evolving the tutorials to meet user needs better and for ensuring a high quality of educational content.
Acceptance Criteria: User completes an interactive tutorial session and is presented with the feedback survey at the end.

Given a user completes the tutorial session, When the tutorial ends, Then a feedback survey appears prompting the user for their feedback, including ratings and open-ended questions.

Admin accesses the analytics dashboard to review collected user feedback.

Given the feedback mechanism has collected responses, When the admin accesses the feedback analytics dashboard, Then the system displays feedback trends, including average ratings, common comments, and suggestions.

User submits feedback through the survey and confirms successful submission.

Given a user fills out the feedback survey, When they click the submit button, Then a confirmation message is displayed, and their responses are saved in the database.

Tutorial sessions are updated based on user feedback after review by the content team.

Given collected user feedback indicates specific areas for improvement, When the content team reviews the feedback and implements changes, Then updated tutorial sessions reflect the changes based on user suggestions.

The feedback mechanism is functional across multiple tutorial sessions.

Given a user completes any tutorial session, When they finish, Then the feedback survey should be implemented and displayed in each session uniformly and correctly.

Users are able to provide additional content suggestions through the feedback mechanism.

Given a user wants to suggest new tutorial content, When they fill in the open-ended feedback question with suggestions, Then their suggestions are captured and saved as part of the feedback data.

User receives prompt to complete feedback survey after each tutorial, regardless of their performance in the tutorial.

Given a user has completed a tutorial session, When they are prompted to complete the feedback survey, Then they should receive this prompt regardless of their tutorial completion score.

Language Support

User Story: As a non-English speaker, I want the tutorials to be available in my preferred language so that I can understand the platform better and utilize its features effectively.
Description: The Language Support requirement aims to incorporate multiple language options for the interactive tutorials, enabling users from diverse backgrounds to engage with the content comfortably. This feature will enhance accessibility and inclusivity, ensuring that language barriers do not hinder the learning process. The implementation consists of translating all tutorial content and enabling users to select their preferred language in the settings. This will significantly broaden the user base and enhance user satisfaction across different demographics.
Acceptance Criteria: User selects a preferred language for the interactive tutorials during the initial setup process.

Given a new user goes through the setup process, when they reach the language selection screen, then they should see a dropdown list of available languages to choose from, and the selected language should be saved for future sessions.

User accesses the interactive tutorial in their selected language after changing their language preference in settings.

Given a user has changed their language preference in the settings, when they navigate to any interactive tutorial, then the content of the tutorial should be displayed in the selected language without any errors or omissions.

All tutorial content is translated and accessible in multiple languages.

Given the interactive tutorials exist in the application, when the user navigates through various tutorials, then all text, audio, and video content in each tutorial should be fully translated into the selected language and accurately reflect the original content's intent.

User experiences a seamless transition between languages while using the tutorials.

Given the user is in the middle of a tutorial, when they change their language preference, then the tutorial should reload, presenting the content immediately in the new selected language without requiring any additional input from the user.

Quality assurance checks on language support for all tutorials.

Given all interactive tutorials are available in multiple languages, when the QA team conducts a review, then all tutorials should meet predefined language quality standards, including grammar, context accuracy, and cultural relevance.

User receives feedback or error messages related to language support.

Given a user attempts to access a tutorial in a language that is not supported, when this occurs, then the system should notify the user with an appropriate message indicating the limitation without crashing or freezing the application.

Gamification Elements

User Story: As a user, I want to earn badges and points for completing tutorials so that I feel motivated to progress and engage with Datapy's features further.
Description: The Gamification Elements requirement focuses on integrating game-like features into the interactive tutorials, such as badges, points, and challenges. This will create a more engaging learning environment and motivate users to complete more tutorials and explore the platform extensively. The gamification approach aims to make learning fun while providing users with tangible rewards for their efforts. This feature will be seamlessly integrated within the onboarding experience and visible on user profiles, fostering a community of friendly competition.
Acceptance Criteria: User completes a tutorial session and earns points for finished tasks.

Given a user completes a tutorial session, when they finish all tasks, then the user should receive the corresponding points awarded for each task according to the defined scoring system.

User achieves a specific milestone during the interactive tutorial.

Given a user reaches a milestone in the tutorial, when they complete the milestone task, then they should be awarded a badge reflecting their achievement next to their profile.

Users engage in friendly competitions through leaderboard visibility.

Given multiple users have completed tutorials, when a leaderboard is displayed, then it should accurately show the top users ranked by accumulated points and badges earned in the community of users.

Users receive notifications for newly earned rewards.

Given a user earns a badge or points, when they log in to their profile, then they should see a notification alerting them about their recent rewards and progress updates.

Users can view their progress and reward status in their profiles.

Given a user accesses their profile, when they check the gamification section, then they should see the total points earned, badges obtained, and completed tutorial sessions.

Users can share achievements on social media platforms.

Given a user earns a badge, when they choose to share that achievement, then they should have an option to post it on social media with predefined hashtags and links to their profile.

Users can provide feedback on the gamification elements and tutorial experience.

Given a user completes a tutorial, when they submit feedback through a designated section, then their feedback should be successfully recorded and submitted for review.

AI-Driven Walkthroughs

Personalized onboarding experiences powered by AI that adapt to the user’s role and needs. This feature assesses the user’s pace and proficiency, offering tailored guidance to enhance understanding and facilitate quick mastery of dashboards and tools.

Requirements

Personalized Walkthrough Engine

User Story: As a new user of Datapy, I want to receive a personalized onboarding walkthrough so that I can quickly understand how to use the dashboards and tools relevant to my role without feeling overwhelmed.
Description: The Personalized Walkthrough Engine will utilize AI algorithms to assess a user’s role, pace, and proficiency across the Datapy platform. It will dynamically adapt the onboarding experience, providing custom guidance tailored to individual needs. This engine will track user interactions continuously, allowing for real-time adjustments to the content and guidance provided. By personalizing the learning pathway, the engine enhances user understanding of dashboards and tools, reducing the learning curve and improving overall user satisfaction with the platform.
Acceptance Criteria: User logs into Datapy for the first time and initiates the onboarding process, where the Personalized Walkthrough Engine assesses their role as a data analyst and modifies the guidance accordingly.

Given a new user with the role of a data analyst, when they log into Datapy for the first time and start the onboarding, then the Personalized Walkthrough Engine should present a tailored introduction focusing on data visualization tools relevant to their role.

A user with prior experience in analytics tools logs into Datapy and initiates the onboarding process, where the Personalized Walkthrough Engine evaluates their proficiency level and adjusts the content of the walkthrough.

Given a returning user familiar with analytics tools, when they access the onboarding walkthrough, then the engine should recognize their proficiency level and skip foundational lessons, providing advanced insights instead.

During a session, the user utilizes the dashboard but struggles with a particular feature. The Personalized Walkthrough Engine recognizes this through user interaction analytics and dynamically offers additional support.

Given a user interacting with a feature on the dashboard, when the system detects a significant drop in user engagement on that feature, then the Personalized Walkthrough Engine should provide context-sensitive help for that specific feature in real-time.

The user finishes a walkthrough and receives feedback on their understanding and mastery of the tools based on their interactions and responses during the session.

Given that the user completes the onboarding walkthrough, when they submit feedback, then the system should generate a personalized report indicating their proficiency and areas for improvement based on their actions throughout the session.

An administrator reviews user engagement within the onboarding process to measure the effectiveness of the Personalized Walkthrough Engine across different user roles.

Given an administrator accessing analytics, when they review the engagement metrics from the onboarding process, then the system should display user satisfaction scores and completion rates segmented by user roles to assess the effectiveness of the walkthroughs.

A user with limited experience in data analytics begins the onboarding process and requires additional contextual help from the Personalized Walkthrough Engine.

Given a new user with limited analytics experience, when they initiate the onboarding process, then the Personalized Walkthrough Engine should provide extra explanatory texts and examples for all dashboard functionalities during the walkthrough.

After completing the personalized walkthrough, the user receives a follow-up email summary reinforcing the key concepts learned and actionable steps to implement the insights gained.

Given that the user has finished the onboarding walkthrough, when they exit the session, then they should receive a follow-up email summarizing the key concepts covered, including tips and links to further resources relevant to their learning path.

Adaptive Learning Pathways

User Story: As a user who is less familiar with analytics tools, I want my learning pathway to adapt based on my progress so that I can focus on areas where I need improvement without being stuck on topics I already understand.
Description: Adaptive Learning Pathways will be a core component that enables the onboarding process to evolve based on user interactions and feedback. This feature will analyze user data to understand areas where users struggle or excel, creating a personalized pathway that helps direct them to further resources or advanced features as they progress. By integrating with existing analytics tools within the platform, this capability ensures that users are continually engaged and receive content that directly addresses their learning needs, promoting an effective and efficient onboarding experience.
Acceptance Criteria: User completes the onboarding process with AI-driven adaptive learning pathways that adjust based on their interactions and feedback.

Given a user begins the onboarding process, when they engage with the adaptive learning pathways, then the system should analyze their engagement data and modify the learning path accordingly within 2 minutes.

User receives personalized resource recommendations based on their progress and identified areas for improvement from the adaptive learning pathway.

Given a user has completed several onboarding tasks, when the system analyzes their performance trends, then it should present a list of at least 3 personalized resources relating to their learning needs.

Users can track their progress through the adaptive learning pathways on their dashboard.

Given a user is logged into the platform, when they access their dashboard, then they should see a visual representation of their learning path progress, including completed tasks and suggested next steps.

Feedback collected from users is integrated into the AI model for ongoing enhancements of adaptive learning pathways.

Given feedback is submitted by users about the onboarding process, when this feedback is reviewed, then the AI model should update its learning suggestions based on new insights within a week.

Users can provide feedback on the effectiveness of the adaptive learning pathways.

Given a user has completed the onboarding process, when they access the feedback mechanism, then they should be able to submit their feedback easily and receive a confirmation of receipt immediately.

The adaptive learning pathways can easily adapt to different user roles and proficiency levels.

Given a user logs in with a specific role, when they begin the onboarding process, then the adaptive learning pathways should adjust to reflect the appropriate level of complexity for that role within 5 minutes.

AI Feedback Mechanism

User Story: As a user going through the onboarding process, I want to provide feedback on the walkthrough guidance so that I can contribute to improving the onboarding experience for future users.
Description: The AI Feedback Mechanism will allow users to provide real-time feedback during the onboarding process. By implementing a simple feedback tool embedded within walkthroughs, users can rate the helpfulness of guidance and suggest improvements. This feedback will be analyzed by the AI system to refine the onboarding content, enhancing the relevance and quality of future walkthroughs. This continuous improvement cycle will ensure that the onboarding process remains user-centered and aligns with user expectations and preferences.
Acceptance Criteria: User provides feedback during their onboarding walk-through using the embedded tool while navigating through the dashboard.

Given a user is on a walkthrough, when they submit feedback about the helpfulness of guidance, then the feedback should be successfully recorded and acknowledged.

User suggests improvements while using the guided walkthrough feature.

Given a user is prompted to suggest improvements, when they enter their suggestions, then the suggestions should be saved and displayed for future analysis.

Admin reviews the collected feedback from users after the onboarding has been implemented.

Given that feedback has been collected, when the admin accesses the dashboard displaying the feedback insights, then they should see organized summaries of ratings and suggestions by users.

AI system analyzes user feedback to identify trends and areas for improvement.

Given a dataset of user feedback, when the AI conducts an analysis, then it should highlight common issues and propose updates to the onboarding content based on user needs.

Users receive an enhanced walkthrough based on previous feedback received.

Given that feedback has been implemented, when a user completes a new onboarding session, then they should experience improvements in the walkthrough that address their prior feedback.

User feedback is displayed in real-time during the onboarding process.

Given that feedback has been submitted, when the user checks the feedback summary dashboard during their session, then it should reflect real-time updates of feedback responses.

Multi-Role Support System

User Story: As an Admin user of Datapy, I want my onboarding experience to focus on administrative tools and features so that I can quickly start managing the platform effectively without irrelevant information.
Description: The Multi-Role Support System will facilitate different onboarding experiences based on user roles within Datapy. By defining roles such as Admin, Analyst, and Viewer, each role will receive tailored walkthroughs that focus on the specific features and functionalities most relevant to their responsibilities. This requirement is crucial as it enhances engagement and ensures that users receive the most pertinent information. By aligning the onboarding experience with job functions, users are more likely to remember and apply what they learn effectively.
Acceptance Criteria: Admin User Onboarding Experience

Given an admin user is onboarded to Datapy, when they access the AI-Driven Walkthrough, then they should receive detailed guidance on managing user permissions, setting up integrations, and configuring dashboard settings specific to their role.

Analyst User Onboarding Experience

Given an analyst user is onboarded to Datapy, when they engage with the AI-Driven Walkthrough, then they should receive personalized instructions on data analysis tools, generating reports, and utilizing predictive analytics features that correlate with their responsibilities.

Viewer User Onboarding Experience

Given a viewer user is onboarded to Datapy, when they initiate the AI-Driven Walkthrough, then they should be guided through the navigation of available dashboards, understanding basic data visualizations, and accessing shared reports relevant to their role.

Customized Walkthrough Adaptation

Given a user interacts with the AI-Driven Walkthrough, when they provide feedback on their understanding of the features, then the walkthrough should dynamically adjust to offer more simplified or advanced guidance as needed.

Role-Based Content Relevance

Given multiple roles exist in Datapy, when a user selects their role during onboarding, then the AI-Driven Walkthrough should present content and features that are exclusively relevant to that specific role without any irrelevant information.

User Progress Tracking

Given a user is undergoing the AI-Driven Walkthrough, when they complete specific sections, then their progress should be tracked, and they should be able to receive a summary of completed topics along with next recommended steps based on their pace.

Feedback Collection Mechanism

Given a user completes the onboarding walkthrough, when they provide feedback through a built-in survey, then the system should capture their suggestions and ratings, enabling continuous improvement of the onboarding process.

Interactive Tutorial Mode

User Story: As a hands-on learner, I want to participate in interactive tutorials so that I can practice using Datapy's features before applying them in real scenarios, helping to build my confidence.
Description: The Interactive Tutorial Mode will provide a hands-on learning experience for new users within the platform. This mode will simulate common tasks and scenarios that users are likely to encounter, allowing them to practice in a safe environment without affecting actual data. By offering interactive tutorials, users can gain significant confidence as they explore the features of Datapy. This feature is crucial for hands-on learners and is expected to drastically reduce the initial hesitance when acclimating to the platform.
Acceptance Criteria: User initiates Interactive Tutorial Mode to learn how to create a new dashboard within Datapy.

Given a new user is logged in, when they access the 'Interactive Tutorial Mode', then they should be presented with step-by-step guidance to create a new dashboard, including tooltips and prompts for each action.

User completes the Interactive Tutorial Mode for editing existing reports in Datapy.

Given a user has selected an existing report, when they follow the Interactive Tutorial Mode for editing, then they should be able to successfully modify report elements and see the changes reflected in a preview without affecting actual data.

User engages with Interactive Tutorial Mode to understand data visualization features.

Given a user is in Interactive Tutorial Mode, when they complete the visualization segment, then they should be able to create at least three different types of visualizations (e.g., pie chart, bar graph, and line chart) using provided sample data.

User navigates through Interactive Tutorial Mode to learn about collaborative tools in Datapy.

Given a user is participating in the Interactive Tutorial, when they reach the collaborative tools section, then they should be able to invite another user to collaborate on a project within the tutorial without any errors.

User utilizes the Interactive Tutorial Mode to practice exporting reports in Datapy.

Given a user is in the Interactive Tutorial Mode for report exporting, when they complete the steps, then they should be able to download a sample report in a specified format (e.g., PDF, CSV) without accessing live data.

User completes a feedback survey at the end of the Interactive Tutorial Mode.

Given a user has finished the Interactive Tutorial Mode, when they are prompted to fill out a feedback survey, then they should be able to submit their feedback successfully and receive a confirmation of submission.

User tries to exit the Interactive Tutorial Mode before completion.

Given a user is in the Interactive Tutorial Mode, when they attempt to exit prematurely, then they should be prompted with a confirmation dialog to either continue or exit, ensuring they can safely choose their preferred action.

Gamified Learning Modules

An interactive, game-like onboarding experience that rewards new users for completing various onboarding tasks. This feature fosters engagement and motivation, making the learning process enjoyable and encouraging users to explore Datapy's full potential.

Requirements

Interactive Task Checklist

User Story: As a new user, I want an interactive checklist to guide me through the onboarding process so that I can learn how to effectively use Datapy step by step while being rewarded for my progress.
Description: Create an interactive checklist that guides new users through the onboarding process. Each task on the checklist should be designed to familiarize users with different aspects of Datapy’s functionality. Completing each task should trigger rewards, such as points or badges, which contribute to a user’s overall progress in the gamified learning module. This checklist is essential for ensuring a structured onboarding experience, as it helps users develop familiarity with the platform while being rewarded for their achievements. The integration of this feature into Datapy’s existing interface will enhance user engagement and retention rates, as users will feel a sense of accomplishment and motivation throughout their onboarding journey.
Acceptance Criteria: New user begins the onboarding process using the interactive task checklist to familiarize themselves with Datapy's functionalities.

Given a new user is logged into Datapy, when they access the interactive task checklist, then they should see a list of onboarding tasks, each with clear instructions and visual indicators of progress.

The new user completes a task on the interactive checklist.

Given a user completes a task on the interactive checklist, when the task is marked as complete, then the user should receive a reward notification immediately, including points or a badge.

A user has completed all tasks in the interactive task checklist.

Given a user has completed all tasks in the interactive checklist, when they reach the end of the checklist, then they should see a summary of their achievements and a congratulatory message, along with an invitation to explore further features of Datapy.

Integration of the interactive task checklist into Datapy's existing interface.

Given the interactive task checklist is integrated into the Datapy interface, when a user navigates through the interface, then they should not encounter any usability issues or performance lag.

Data tracking for user engagement with the interactive task checklist.

Given the checklist is being used, when multiple users interact with it, then the system should accurately log task completion rates and user engagement statistics for analytics.

Visual feedback for the completion of tasks in the checklist.

Given a user marks a task as complete, when they view the checklist again, then the completed tasks should be visually distinct (e.g., crossed out or grayed out) to indicate progress.

User ability to restart or reset the interactive task checklist if needed.

Given a user feels they need to restart the onboarding process, when they select the reset option, then they should receive a confirmation prompt and, upon confirmation, the checklist should revert to its original state.

Reward System Integration

User Story: As a new user, I want to receive rewards for completing onboarding tasks so that I feel motivated to engage and explore more features of Datapy.
Description: Develop and integrate a reward system that allocates points, badges, and other incentives to users as they complete onboarding tasks within the gamified learning modules. The reward system should recognize various types of achievements, such as completing a specific number of tasks, mastering a feature, or providing feedback on the onboarding experience. This integration will not only enhance user motivation but also encourage deeper exploration of the platform’s features, ultimately leading to higher engagement. The rewards should be easily accessible within user profiles and visible to foster a sense of achievement among users.
Acceptance Criteria: User accesses the gamified learning module and completes their first onboarding task, receiving points and a badge as a reward.

Given a user has completed their first task, when they check their profile, then they should see at least 10 points allocated and a 'First Task Completed' badge.

A user completes five onboarding tasks and accesses their rewards section to check their accumulated points and badges.

Given a user has completed five tasks, when they access the rewards section, then they should see at least 50 points and five badges reflecting their achievements.

A user masters a key feature of Datapy during onboarding and submits feedback about their experience.

Given a user completes a specific feature mastery task, when they submit feedback, then they should receive an additional 20 points and a 'Feedback Contributor' badge in their profile.

A user refers another friend to Datapy as part of the onboarding challenge.

Given a user completes the referral task, when they enter their friend's email, then they should receive 30 points and a 'Referral Reward' badge.

A user explores all features of the gamified learning module and reaches the final task.

Given a user has completed all tasks in the onboarding module, when they reach the final task, then they should receive a total of 100 points and a 'Master of Datapy' badge.

Progress Tracking Dashboard

User Story: As a new user, I want a visual dashboard to track my onboarding progress so that I can see how much I have achieved and what tasks are upcoming.
Description: Implement a progress tracking dashboard that visualizes a user's advancement through the onboarding process. This dashboard should display the tasks completed, upcoming tasks, and levels achieved within the gamified learning context. Additionally, there should be options to share achievements on social media or within team channels, enhancing community engagement. The progress tracking dashboard will provide users with a clear representation of their journey, helping them understand how far they have come and what remains, thereby sustaining motivation to complete the onboarding process.
Acceptance Criteria: User logs into Datapy for the first time and navigates to the onboarding process, expecting to see a visual representation of their progress through the gamified learning modules.

Given a user is logged in, when they access the progress tracking dashboard, then the dashboard should display their current progress including completed tasks, upcoming tasks, and achieved levels in a clear and visually engaging format.

A user completes a task in the onboarding process and expects the progress tracking dashboard to update accordingly without requiring a page refresh.

Given a user has completed a task, when they return to the progress tracking dashboard, then the dashboard should automatically update to reflect the newly completed task and any changes in level or metrics without manual intervention.

While using Datapy on their mobile device, a user wants to check their progress in the onboarding process and ensure it is formatted correctly for smaller screens.

Given a user accesses the progress tracking dashboard on a mobile device, when they view the dashboard, then the layout should be responsive, with all information clearly visible and easy to interact with on the smaller screen.

User wants to share their newly achieved level from the progress tracking dashboard on social media to celebrate their onboarding success with their friends and colleagues.

Given a user has achieved a new level, when they select the share option on the progress tracking dashboard, then the platform should display a confirmation message and provide an easily accessible link to share their achievement on supported social media platforms.

A user is interested in seeing detailed analytics on their onboarding tasks and expects the dashboard to provide actionable insights based on their progress.

Given a user views the progress tracking dashboard, when they access the analytics section, then the dashboard should present detailed insights into their task completion rates, time taken for each task, and any areas where they may need additional focus or improvement.

In-Platform Tutorials and Tips

User Story: As a new user, I want in-platform tutorials and tips while onboarding so that I can receive guidance as I interact with different features of Datapy.
Description: Introduce in-platform tutorials and contextual tips that pop up during key interactions throughout the onboarding process. These tutorials should break down complex tasks into manageable steps, providing users with the support they need as they engage with different features of Datapy. This requirement aims to create a more supportive learning environment, ensuring that users are not just motivated by gamification but also have the necessary guidance to understand the platform's capabilities. Integrating these tutorials alongside gamified elements will facilitate a comprehensive and enjoyable learning experience.
Acceptance Criteria: User a new user discovers the in-platform tutorial for the first time while attempting to create their first dashboard in Datapy.

Given the user is on the dashboard creation page, When they hover over the 'Help' icon, Then an interactive tutorial pop-up appears that guides them through the steps to create a dashboard.

A user encounters a complex data analysis feature for the first time and needs immediate guidance to understand key functions in Datapy.

Given the user clicks on the 'Data Analysis' feature, When they engage with the feature for the first time, Then a contextual tip appears explaining the main functions available within the feature.

An onboarding user is utilizing gamified elements while completing tasks, and they need instant support during this process.

Given the user is completing a gamified task, When they achieve a milestone, Then an in-platform tutorial automatically starts that elaborates on the features they just unlocked.

An experienced user revisits the onboarding module after a software update and wants to see the latest features through tutorials.

Given the user accesses the onboarding module, When they select the 'Latest Features' section, Then they receive a tutorial showcasing all updates and enhancements in Datapy.

A user feels overwhelmed by the amount of information provided and needs help breaking down complex tasks into simpler steps.

Given the user is navigating a complex feature, When they click on 'Need Help?', Then a series of step-by-step tutorials are displayed, allowing them to choose which aspect they need assistance with.

A user completes a series of onboarding tasks but feels they need further clarification on certain features post-tutorials.

Given the user has completed the initial onboarding tutorials, When they return to the dashboard, Then they can access a list of additional tips and tutorials related to features they interacted with during onboarding.

Social Sharing Capabilities

User Story: As a new user, I want to share my onboarding achievements on social media so that I can celebrate my progress and inspire others to explore Datapy.
Description: Enable social sharing functionalities that allow users to share their achievements and progress within the gamified learning modules on popular social media platforms and within organizational teams. This feature aims to create a sense of community and competition among users by showcasing their accomplishments publicly. Integrating social sharing will not only enhance user engagement but also promote Datapy organically as users share their positive experiences and achievements with their networks.
Acceptance Criteria: Users can share their achievements on Facebook after completing the first onboarding task in the gamified learning module.

Given the user has completed the first onboarding task, when the user clicks the 'Share on Facebook' button, then their achievement should be displayed on their Facebook timeline with a link back to Datapy.

Users can post their progress to Twitter within the gamified learning modules.

Given the user has earned a badge for completing three onboarding tasks, when the user selects the 'Share on Twitter' option, then a tweet should be generated displaying the badge and a link to their profile in Datapy.

Team members can view shared achievements within the Datapy platform.

Given a user has shared their achievement on a team board, when another team member views the board, then they should see the user's achievement along with options to congratulate them or comment.

Users can choose which social media platforms to share their achievements on.

Given the user has completed any onboarding task, when they access the sharing options, then they should see buttons for at least three social media platforms (Facebook, Twitter, LinkedIn) to choose from before sharing.

Achievement sharing is tracked for analytics purposes.

Given a user shares an achievement on any social media platform, when the event is triggered, then the sharing action must be recorded in the Datapy analytics dashboard to quantify user engagement.

Users can customize their shared achievement messages before posting.

Given the user is in the sharing modal after completing an onboarding task, when they edit the pre-populated message, then the updated message should be reflected in the shared post.

Users can view a history of their shared achievements within their profile in Datapy.

Given the user has shared at least one achievement in the past, when they visit their profile section, then they should see a list of all their shared achievements with timestamps.

Feedback Mechanism

User Story: As a new user, I want to provide feedback on the onboarding experience so that my insights can contribute to improving the learning modules for future users.
Description: Create a feedback mechanism that enables users to provide immediate feedback on their experience with the onboarding process and gamified learning modules. This system should allow users to submit suggestions, report bugs, or highlight positive experiences directly within the platform. The feedback mechanism will be critical for continuous improvement of the onboarding experience, ensuring that it evolves based on user insights and preferences. Analyzing user feedback will provide valuable data for future enhancements and adaptations of the learning modules.
Acceptance Criteria: New users navigate to the gamified learning modules onboarding section and complete their first task, receiving prompts to provide feedback regarding their experience.

Given the feedback mechanism is present in the gamified learning module, When users complete a task, Then they can submit feedback through a clearly accessible button that opens a feedback form.

Users complete multiple onboarding tasks and have different experiences; they wish to provide feedback about the onboarding process and gamified modules after they have finished.

Given users have completed the onboarding tasks, When they access the feedback mechanism, Then they can categorize their feedback as suggestion, bug report, or positive experience and submit it successfully.

Users want to report a bug encountered during the onboarding process via the feedback mechanism provided within the platform.

Given a user encounters a bug, When they use the feedback mechanism, Then they can provide specific details about the bug, which are saved in the system for review by the development team.

The system admin reviews feedback submitted by users regarding the onboarding process to identify areas needing improvement.

Given the admin views the feedback dashboard, When feedback submissions are filtered by type and date, Then they can easily identify trends and prioritize tasks for enhancements based on user input.

Users want to feel valued and ensure their voice matters; they are interested in seeing how their feedback impacts future updates of the onboarding modules.

Given users submit feedback, When they return to the platform after some time, Then they receive an update on how their feedback influenced changes or improvements in the onboarding experience.

A user wants to provide feedback but is unsure of what information is helpful; they need guidance on how to articulate their feedback effectively.

Given a user opens the feedback form, When they read the instructions provided within the form, Then they understand the types of information that would be helpful to submit for improving the onboarding experience.

Contextual Help Tooltips

Real-time tooltips that provide context-sensitive help as users navigate Datapy. These tooltips explain features and functionalities at the moment they are needed, ensuring that new users have immediate access to assistance without overwhelming them.

Requirements

Dynamic Tooltip Content

User Story: As a new user, I want the tooltips to provide relevant help based on what I'm currently viewing so that I can better understand how to use the features without feeling lost.
Description: The Contextual Help Tooltips must dynamically generate content based on the user's current action and the interface component in focus. This functionality ensures that the tooltips are relevant and provide specific assistance tailored to the user’s needs at that moment, enhancing user experience and reducing confusion during navigation. The tooltips should be easily customizable to fit different user roles and levels of expertise, allowing for a personalized introduction to the product's features without overwhelming users with irrelevant information.
Acceptance Criteria: User accesses the dashboard for the first time and hovers over a graph to get more information about its data points.

Given a user is on the dashboard, when they hover over any graph, then a tooltip should appear providing specific details about the data points in that graph, including definitions and metrics related to those data points.

A new user clicks on the 'Settings' icon to configure their preferences and needs help understanding each option.

Given a user clicks on the 'Settings' icon, when they hover over any settings option, then the tooltip should display a concise description of the setting purpose along with a recommended action for new users.

An advanced user is editing a data report and requires assistance understanding a complex feature within the report builder.

Given an advanced user is in the report builder, when they focus on the complex feature, then the tooltip should provide a detailed explanation of that feature, along with examples of its application within the report.

A user is reviewing their customizable dashboard options and wants to understand the implications of each customization.

Given a user is in the customizable dashboard section, when they hover over a customization option, then the tooltip should explain the functionality and impact of that option on their dashboard experience.

A user with limited experience is managing team collaboration settings and needs clarification on permissions.

Given a user is on the collaboration settings page, when they focus on the permissions section, then the tooltip should provide clear definitions of each permission level and its implications for team members.

A user is integrating their Datapy account with third-party applications and seeks help on the integration options.

Given a user is on the integration settings page, when they hover over an integration option, then the tooltip should describe the integration's purpose and steps needed to successfully connect.

Tooltip Timing and Behaviour Configuration

User Story: As a user, I want to customize how tooltips behave so that I can tailor my interaction experience according to my preferences and workflow.
Description: The tooltips should have configurable display timings and behaviors. Users should be able to set preferences for how long tooltips stay visible, when they appear (on hover or click), and whether they fade out or stay anchored. This customization allows for a more user-friendly experience, catering to different speeds of user engagement and interaction styles, ultimately leading to increased satisfaction and efficiency when using the platform.
Acceptance Criteria: User Configures Tooltip Display Timing for New Dashboard Feature

Given that the user is in the dashboard settings, when they set the tooltip display duration to 5 seconds, then the tooltips should remain visible for 5 seconds before fading out.

User Chooses Tooltip Trigger Method for Inline Chart Features

Given that the user is configuring tooltip behavior, when they select the 'on click' trigger option, then the tooltip should only appear when the user clicks on the chart feature, not on hover.

User Sets Tooltip Fade-out Behavior for Contextual Help

Given that the user has selected a fade-out behavior for tooltips, when the tooltip is triggered, then the tooltip should smoothly fade out over a duration of 1 second after the set display time.

User Tests Different Tooltip Preferences in Real-Time

Given that the user has set multiple tooltip display preferences, when they navigate through the application, then each tooltip should display according to the configured preferences of visibility duration, trigger method, and fade behavior without any errors.

User Resolves Conflicting Tooltip Settings

Given that the user has conflicting settings for tooltip timing (5 seconds) and trigger (on hover) that overlap, when they review those settings, then they should receive a prompt indicating the potential conflict and a suggestion to reconcile the settings.

User Resets Tooltip Preferences to Default Settings

Given that the user wants to revert to default tooltip settings, when they click on the 'Reset to Default' button in the tooltip configuration, then all custom settings should revert to the application’s predefined defaults without error.

Searchable Tooltip Index

User Story: As a user, I want to search for specific help topics within tooltips so that I can find information quickly when I’m struggling with a feature.
Description: A searchable index of all tooltips must be created to allow users to quickly find the assistance they need without waiting for the contextual tooltips to show. This feature will provide a comprehensive view of all available help topics and functionalities, further equipping users with the knowledge they need to utilize Datapy effectively and fostering a sense of self-sufficiency.
Acceptance Criteria: User initiates a search for specific tooltips in the searchable index from the Datapy interface.

Given the user is on the Datapy interface, when they enter a keyword in the searchable tooltip index, then the system returns a list of relevant tooltips that match the search term.

User accesses the tooltip index to view all available tooltips and their descriptions.

Given the user opens the searchable tooltip index, when they view the index, then all tooltips and their corresponding descriptions are displayed in a clear and organized manner.

User searches for a tooltip that does not exist in the index.

Given the user enters a non-existent keyword in the searchable tooltip index, when they execute the search, then a message indicating 'No results found' is displayed.

User utilizes filtering options in the searchable tooltip index.

Given the user opens the searchable tooltip index, when they apply filters to the search, then only tooltips that meet the filtering criteria are displayed in the results.

User finds a desired tooltip and views the associated content.

Given the user has performed a search in the tooltip index, when they click on a tooltip from the search results, then the detailed content for that tooltip is displayed without any errors.

User evaluates the load time of the searchable tooltip index.

Given the user clicks to open the searchable tooltip index, when the index loads, then it should load within 2 seconds to ensure a responsive user experience.

User navigates through the tooltip index without errors.

Given the user is interacting with the searchable tooltip index, when they scroll through the tooltips, then the interface should remain responsive and free of any glitches or errors.

Multilingual Support for Tooltips

User Story: As a non-English speaking user, I want tooltips to be available in my native language so that I can understand the features more easily and effectively use the platform.
Description: The tooltips must support multiple languages to cater to a diverse user base. This will involve translating tooltip content and implementing locale detection to present users with the help content in their preferred language. Ensuring that language barriers are minimized will empower users from different regions and enhance overall user satisfaction with the platform.
Acceptance Criteria: As a new user from Spain who prefers to interact with the platform in Spanish, I want to see the contextual help tooltips in my chosen language so that I can understand the features without language barriers during my first experience with Datapy.

Given a user is logged in with Spanish as their preferred language, when they hover over any interactive element, then the tooltip displayed must be in Spanish and contain accurate translations of the respective feature explanations.

As an international user switching my display language, I want the tooltip content to automatically update to the selected language so that I can seamlessly transition between languages while using the platform.

Given a user has selected a different language from the settings, when they refresh or navigate to a new page within the platform, then all contextual tooltips must immediately display in the newly selected language without requiring additional actions.

As a user accessing Datapy from Mexico, I want to receive contextual help tooltips that reflect local terminology and phrases used within the Mexican context to improve my understanding of the platform's features.

Given a user has their locale set to Mexico, when they hover over any feature, then the tooltips must utilize local vernacular and correctly translate terms relevant to the Mexican market.

As a user, I want to confirm that the tooltip translations are clear, accurate, and relevant so that I can fully utilize the contextual help feature without confusion stemming from language errors.

Given a user is accessing tooltips in their chosen language, when the tooltips are presented, then they must pass a clarity and accuracy review by native speakers to ensure meaningful and proper translations.

As a diverse user group, we want to validate that the multilingual support for tooltips includes at least five different languages to ensure broad accessibility for various user demographics.

Given that the system needs to support multilingual tooltips, when tested, then the tooltips must demonstrate support for English, Spanish, French, German, and Mandarin, with correct translations available during the hover interaction.

User Feedback Mechanism

User Story: As a user, I want to provide feedback on the tooltip help so that I can share my thoughts on its usefulness and contribute to improving the product.
Description: A feedback mechanism should be integrated within the tooltips to allow users to rate the usefulness of the information provided or suggest improvements. This will not only enhance the content over time but also engage users in the development process, making them feel valued and ensuring that the help content continuously evolves to meet user needs effectively.
Acceptance Criteria: User Interaction with Tooltips

Given a user navigates to a feature in Datapy, when the tooltip appears, then the tooltip should contain a rating mechanism (1-5 stars) that is easily accessible.

Feedback Submission Confirmation

Given a user submits feedback on a tooltip, when the feedback is successfully sent, then the user should receive a confirmation message indicating their feedback was received.

Rating Average Calculation

Given multiple users have rated a tooltip using the rating mechanism, when the ratings are submitted, then the tooltip should display the average rating to all users who view it.

User Suggestion Implementation Tracking

Given a user provides a suggestion for improving a tooltip, when the suggestion is submitted, then it should be logged for review in the admin panel with a timestamp and user ID.

Tooltip Content Update Based on Feedback

Given that user feedback has been collected on tooltips, when feedback indicates need for changes, then the tooltip content should be reviewed and updated based on user suggestions every quarter.

Accessibility of Feedback Mechanism

Given a user is viewing a tooltip, when they utilize any device (desktop, tablet, mobile), then the feedback mechanism should be accessible and function correctly on all devices.

Contextual Video/Media Integration

User Story: As a visual learner, I want tooltips to include videos or media that explain features so that I can understand them better through demonstration.
Description: Tooltips should allow for the integration of short tutorials or media links related to the feature being described. This could involve embedding brief videos, gifs, or screenshots that provide visual assistance alongside textual help. The use of multimedia will cater to varied learning preferences and enhance comprehension of complex functionalities.
Acceptance Criteria: User is navigating the Datapy platform for the first time and encounters a new feature for the first time. They hover over the feature for a brief moment and see a tooltip that displays a short video tutorial, explaining how to use that feature effectively.

Given the user hovers over the feature, When the tooltip appears, Then the tooltip must display a video link that starts playing a tutorial about the feature within 2 seconds.

While using Datapy, a user clicks on a data visualization tool that they are not familiar with. They require instant help to understand the functionalities of this tool. A tooltip displays, providing a link to a graphic showing usage examples.

Given the user clicks on the visualization tool, When the tooltip appears, Then the tooltip must include a clear, relevant graphic that illustrates how to utilize the tool effectively, and the graphic should load within 1 second.

A user is reviewing a complex data report within Datapy. They are unsure about the significance of a specific metric. The user hovers over the metric label and receives a tooltip with a brief GIF demonstrating its meaning and implications.

Given the user hovers over the metric label, When the tooltip appears, Then the tooltip must display an animated GIF that conveys the meaning of the metric and must not exceed 5 seconds in length.

A user exploring Datapy is confused about a collaboration feature. Upon hovering over the related icon, they expect to see a tooltip with both text and an image to clarify its use and capabilities.

Given the user hovers over the collaboration feature icon, When the tooltip appears, Then the tooltip must contain a concise text description along with a static image and must load completely within 2 seconds.

During a training session on Datapy, the instructor uses a feature, and participants request a quick recap via tooltips that provide multimedia support. The tooltips should assist them while maintaining the flow of the session.

Given the instructor demonstrates a feature, When participants hover over the same feature during the session, Then all participants should see the tooltips that incorporate both visual and textual help, and the tooltips must be accessible for the duration of the session without lag.

Onboarding Checklist

A customizable checklist that guides users through essential setup steps in Datapy. This interactive checklist ensures that users don’t miss critical actions during their onboarding process, fostering a structured and comprehensive introduction to the platform.

Requirements

Interactive Steps Guide

User Story: As a new user, I want an interactive checklist that guides me through the setup process so that I can quickly and efficiently get started with Datapy without missing important steps.
Description: The Interactive Steps Guide requirement outlines the need for a dynamic onboarding checklist that provides users with a series of sequential tasks designed to walk them through the essential setup stages within Datapy. This guide will facilitate an organized onboarding experience by highlighting key actions, ensuring that new users understand major functionalities and configurations required to utilize the platform effectively. The checklist will integrate seamlessly with the user interface, leveraging tooltips and progressive disclosure to accommodate diverse user skill levels. It is crucial to minimize user onboarding time and enhance overall satisfaction with the platform by preventing overlooked setup tasks.
Acceptance Criteria: User Initiates Onboarding Process in Datapy

Given the user is logged into Datapy, when they visit the onboarding section, then the interactive checklist should be displayed with a list of essential setup tasks organized in a sequential manner.

User Completes a Step in the Onboarding Checklist

Given the user is viewing the onboarding checklist, when they complete a task and mark it as done, then the checklist should update to reflect the completed task and display the next actionable step.

User Receives Helpful Tooltips During Onboarding

Given the user is on a task within the interactive checklist, when they hover over specific action items, then relevant tooltips should appear, providing additional information and guidance pertinent to that action item.

User Re-enters the Onboarding Checklist After Closing It

Given the user has previously started the onboarding checklist and then closed it, when they navigate back to the onboarding section, then the checklist should restore their progress and display the last completed task accordingly.

Admin Customizes the Onboarding Checklist

Given the admin is accessing the Datapy admin panel, when they navigate to the onboarding checklist settings, then they should be able to add, modify, or remove items in the checklist based on user feedback and requirements.

User Sees a Progress Indicator During Onboarding

Given the user is engaging with the interactive checklist, when they complete tasks, then a progress indicator should dynamically update, showing the percentage of tasks completed and motivating them to finish the onboarding process.

User Feedback is Collected Post-Onboarding

Given the user has completed all tasks in the onboarding checklist, when they reach the end of the checklist, then they should be prompted to provide feedback on the onboarding experience, which is recorded for further analysis.

Customizable Checklist Templates

User Story: As a user, I want to customize my onboarding checklist so that it reflects the unique setup processes and requirements of my business, ensuring I cover all necessary actions specific to my industry.
Description: The Customizable Checklist Templates requirement specifies that users should be able to modify their onboarding checklists according to their specific business needs. This feature allows users to add, remove, or reorder steps within the checklist, enabling a personalized onboarding experience. The edits to the templates can accommodate various industry standards or best practices that users may need to incorporate. This customization capability will significantly enhance user engagement and adherence to the checklist.
Acceptance Criteria: User customizes the onboarding checklist by adding new steps specific to their business needs.

Given the user is on the onboarding checklist page, when they click the 'Add Step' button and input a new step, then the new step should be added to the checklist and displayed correctly according to the user's specifications.

User modifies the order of steps in their onboarding checklist.

Given the user has an existing checklist, when they drag and drop a step to a new position, then the checklist should reflect the new order immediately without errors.

User removes a step from their onboarding checklist.

Given the user is viewing their checklist, when they click the 'Remove' button on a step, then that step should be removed from the checklist, and the remaining steps should adjust accordingly.

User saves their customized checklist and accesses it later.

Given the user has customized their checklist, when they click on the 'Save' button, then the checklist should be saved successfully, and upon reopening the checklist, the user's customization should be intact and displayed correctly.

User's customized checklist is displayed correctly on different devices and screen sizes.

Given the user accesses their checklist from different devices (desktop, tablet, mobile), when they view the checklist, then it should be responsive and maintain its layout and functionality across all devices.

User receives feedback prompts during the checklist customization process.

Given the user is customizing their checklist, when they complete a step, then a feedback prompt should appear allowing them to rate the helpfulness of that step.

User can reset their checklist to the default template.

Given the user has made customizations to their checklist, when they click the 'Reset to Default' option, then the checklist should revert back to the original default template, removing all customizations made by the user.

Progress Tracking and Feedback

User Story: As a user, I want to track my progress through the onboarding checklist and provide feedback so that I can see how far I've come and help improve the experience for future users.
Description: The Progress Tracking and Feedback requirement entails implementing a real-time progress indicator that allows users to monitor their completion status on the onboarding checklist. Additionally, users should be able to provide feedback regarding their onboarding experience through quick ratings or comments. This requirement will help Datapy collect valuable insights on the onboarding process and identify areas of improvement, thereby enhancing future user experiences. It also serves to motivate users by offering visibility into their progress and next steps.
Acceptance Criteria: User accesses the onboarding checklist for the first time during their initial setup process.

Given the user is on the onboarding checklist page, When the user views their progress bar, Then it should display the percentage of checklist items completed out of the total items in real-time.

User completes an action on the onboarding checklist and wants to see their updated progress.

Given the user completes an action from the onboarding checklist, When the user refreshes the checklist page, Then the progress indicator should update to reflect the new completion status immediately.

User wants to provide feedback after finishing the onboarding checklist.

Given the user has completed the onboarding checklist, When they attempt to submit feedback, Then they should see a feedback form that allows them to rate their experience and leave comments.

User views their onboarding checklist and wants to see what actions are next to take.

Given the user is on the onboarding checklist, When they reach the end of the checklist, Then they should see a message prompting them on their next steps and how to continue using Datapy effectively.

User submits feedback after using the feedback form.

Given the user has filled out the feedback form, When they submit their feedback, Then a confirmation message should appear indicating successful submission and thanking them for their input.

Admins review the feedback submitted by users on the onboarding process.

Given the admin is logged into the Datapy admin dashboard, When they access the feedback section, Then they should see a consolidated report of user feedback with ratings and comments sortable by date and rating.

Integrated Help Resources

User Story: As a new user, I want access to integrated help resources within the onboarding checklist so that I can easily find answers to my questions without getting stuck during the setup process.
Description: The Integrated Help Resources requirement necessitates the inclusion of contextual help within the onboarding checklist that links users to relevant support documents, video tutorials, or FAQs right at the moment they need assistance. This integration will empower users by providing them immediate, relevant resources without having to navigate away from the checklist, thus improving the onboarding experience and ensuring users can resolve any questions or confusions promptly.
Acceptance Criteria: Onboarding Checklist displays Integrated Help Resources link for each checklist item

Given a user is accessing the onboarding checklist, when they click on a checklist item, then the Integrated Help Resources link should be visible and clickable, providing access to relevant support documents, video tutorials, or FAQs.

Information is displayed correctly in the Integrated Help Resources link

Given a user accesses the Integrated Help Resources link, when they select a resource, then the user should be redirected to the correct external document, tutorial or FAQ that corresponds to the checklist item.

Integrated Help Resources link enhances user experience during onboarding

Given a user is completing the onboarding checklist, when they utilize the Integrated Help Resources link, then they should report increased satisfaction in their ability to resolve questions or confusions as evidenced by a post-onboarding survey scoring above 75% satisfaction.

Integrated Help Resources link functions across all devices

Given a user is on the onboarding checklist, when they access the Integrated Help Resources link from any device (desktop, tablet, mobile), then the resource should load successfully without errors on each device.

Help resources are updated regularly within the onboarding checklist

Given a user is viewing the onboarding checklist, when new resources are added, then these should be reflected in the Integrated Help Resources link within 48 hours of being published.

Completion Certification

User Story: As a user, I want to receive a completion certificate after I finish the onboarding checklist so that I can acknowledge my progress and share my achievement with others.
Description: The Completion Certification requirement allows users to receive a digital certificate upon completing all steps of the onboarding checklist. This feature adds a gamification element to the onboarding process, recognizing the user’s accomplishment and encouraging engagement with the platform. The certificate can be shared via social media or printed for office display, fostering a sense of achievement and motivation for new users as they get started with Datapy.
Acceptance Criteria: User successfully completes all steps in the onboarding checklist and receives the completion certificate.

Given the user has completed all items in the onboarding checklist, When the user clicks on the 'Receive Certificate' button, Then a digital certificate is generated and displayed with the user's name and completion date.

User shares the digital completion certificate via social media platforms.

Given the user has received the digital certificate, When the user clicks on the 'Share on Social Media' button, Then the certificate is shared on the selected social media platform with a predefined message.

User opts to print the completion certificate for office display.

Given the user has received the digital certificate, When the user selects the 'Print Certificate' option, Then the certificate is formatted properly for printing and sent to the printer without errors.

User receives a notification upon successful completion of the onboarding checklist.

Given the user has completed all items in the onboarding checklist, When the completion is finalized, Then a notification is sent to the user confirming completion and availability of the certificate.

User accesses the digital certificate from their user profile after completion.

Given the user has completed the onboarding checklist, When the user navigates to their profile section, Then the completion certificate is accessible and can be downloaded as a PDF.

System records the completion status of the onboarding checklist in the user database.

Given the user has completed the onboarding checklist, When the user submits completion, Then the system updates the user’s profile in the database to reflect the newly completed status with a timestamp.

User can customize the name displayed on the completion certificate before generating it.

Given that the user is on the completion certificate screen, When the user enters a preferred name in the customization field and confirms, Then the digital certificate reflects the customized name upon generation.

Community Support Portal

An integrated platform where new users can access FAQs, forums, and user-contributed tutorials. This feature promotes self-service support and encourages community engagement, allowing users to learn from shared experiences and best practices.

Requirements

Integrated FAQ Section

User Story: As a new user, I want to access an FAQ section so that I can quickly find answers to my questions without needing to wait for support.
Description: The integrated FAQ section will provide users with quick answers to the most common questions related to Datapy's functionalities, best practices, and troubleshooting. This requirement aims to enhance user self-service capabilities, reducing the need for direct support while fostering a deeper understanding of the platform. By categorizing FAQs based on user feedback and inquiries, it will serve as a comprehensive resource that directly integrates into the Community Support Portal for easy navigation and accessibility, ultimately empowering users to resolve issues independently and efficiently.
Acceptance Criteria: FAQ Page Accessibility

Given a user is logged into the Community Support Portal, when they navigate to the FAQ section, then they should be able to view the integrated FAQ section without encountering any broken links or loading errors.

Search Functionality in FAQ Section

Given a user is on the FAQ section, when they enter a keyword in the search bar, then the results should display relevant FAQs within five seconds based on the entered keyword.

FAQ Categorization Based on User Feedback

Given the FAQ section is populated, when a user accesses the section, then they should see FAQs categorized accurately according to predefined topics, such as 'Troubleshooting', 'Best Practices', and 'Features'.

User Contribution to FAQs

Given a logged-in user views an FAQ, when they click on the 'Contribute' button, then they should be able to submit their own FAQ suggestion, and receive confirmation that their suggestion has been received.

Mobile Responsiveness of FAQ Section

Given a user accesses the Community Support Portal via mobile, when they navigate to the FAQ section, then the layout should adjust accordingly for optimal readability and usability on mobile devices.

Feedback Mechanism for FAQs

Given a user has read an FAQ, when they click on the 'Was this helpful?' prompt, then they should be able to provide feedback that gets recorded in the system for review.

User Forum Creation

User Story: As an experienced user, I want to participate in a forum so that I can share my expertise and help others while learning from their experiences.
Description: The user forum will allow users to create discussions, post questions, and share experiences regarding Datapy's features and use cases. This requirement will provide a collaborative environment where users can interact, share solutions, and enhance their overall experience with the platform. The forum will be categorized by topics, enabling users to find relevant conversations easily. Moderators will oversee content to maintain quality, and features will include voting on useful replies and notifications for new posts. This forum promotes community engagement and knowledge sharing among users, which is vital for a user-friendly experience.
Acceptance Criteria: New user creates a discussion thread in the forum to seek help for a specific Datapy feature.

Given the user is logged into the Community Support Portal, when they navigate to the user forum and click on 'Create Discussion', then they should be able to fill out the thread title and description and submit it successfully.

Moderators review a new discussion thread for content quality and relevance.

Given a moderator views a newly created discussion thread, when they assess the content, then they should have the ability to approve, reject, or request edits, while ensuring the thread is categorized properly.

Users vote on replies to determine which ones are most helpful for specific questions posed in the forum.

Given a user is viewing a discussion thread with multiple replies, when they click the 'Vote' button next to a reply, then their vote should be recorded, and the reply should reflect the updated vote count immediately.

Users receive notifications for new posts in threads they are following.

Given a user has opted in to receive notifications for a specific discussion thread, when a new post is made in that thread, then they should receive an email notification about the new post within 5 minutes.

Users categorize their discussions according to predefined topics in the forum.

Given the user creates a new discussion thread, when they select a category from a dropdown menu, then the thread should be successfully tagged with that category and displayed in the appropriate section of the forum.

Users search for existing discussions related to common issues or features of Datapy.

Given the user enters a keyword in the forum search bar, when they click the 'Search' button, then they should receive a list of relevant discussion threads that match the keyword within 3 seconds.

Users access FAQs and tutorials to assist with their questions before posting in the forum.

Given a user is new to the Community Support Portal, when they click on the 'FAQs' or 'Tutorials' section, then they should be able to view a categorized list of frequently asked questions and user-contributed tutorials that are relevant to Datapy.

User-Contributed Tutorial Repository

User Story: As a new user, I want to find user-generated tutorials so that I can learn how to use Datapy more effectively and from real-world examples.
Description: The user-contributed tutorial repository will provide a platform for users to upload, share, and access tutorials related to Datapy functionalities and usage. This requirement seeks to tap into the knowledge and creativity of the user community, allowing them to contribute valuable insights and resources. The repository will be organized by themes and use cases, making it easy for new users to find pertinent tutorials. Users can rate and comment on tutorials, promoting quality content and aiding in the continuous improvement of shared knowledge. This feature will elevate user engagement while fostering a culture of collaboration.
Acceptance Criteria: User accesses the tutorial repository to find a specific tutorial on data visualization techniques within Datapy.

Given a user is logged into the Community Support Portal, when they search for 'data visualization techniques', then they should see a list of at least 5 relevant tutorials ranked by user ratings and most recent uploads.

A user uploads a new tutorial on best practices for using Datapy's predictive analytics features.

Given a user is logged into the Community Support Portal, when they submit a new tutorial with an appropriate title and content, then the tutorial should be available in the repository and categorized under 'Predictive Analytics' within 5 minutes.

A user rates an existing tutorial in the repository.

Given a user has accessed a tutorial, when they submit a rating between 1 to 5 stars, then the average rating for that tutorial should update immediately and reflect the new average rating.

A user searches for tutorials and finds content relevant to their current needs.

Given a user accesses the tutorial repository, when they input a keyword that matches tutorial metadata (title, description, or tags), then the system should return relevant tutorials sorted by relevance and date uploaded.

Users discuss content within a tutorial's comment section.

Given a user has accessed a tutorial, when they submit a comment relevant to the tutorial topic, then that comment should appear instantly in the comment section and notify the tutorial author of the new comment.

A new user accesses the tutorial repository for the first time.

Given a new user visits the tutorial repository, when they navigate through the categories, then they should be able to view at least 3 categories of tutorials with a brief description of each category's content.

Users are able to report a tutorial that violates community guidelines.

Given a user views a tutorial, when they click on the 'Report' button and provide a reason, then the tutorial should be flagged for review and the user should receive a confirmation message that their report has been submitted.

Community Engagement Metrics

User Story: As a product manager, I want to track community engagement metrics so that I can identify areas of success and improvement within the Community Support Portal.
Description: The community engagement metrics will provide insights into user interactions within the Community Support Portal, allowing both users and administrators to track participation levels in forums, FAQ views, and tutorial contributions. By analyzing these metrics, Datapy can identify active community members, popular topics, and areas needing improvement. This requirement highlights the importance of user involvement in shaping the community and allows for targeted initiatives to encourage ongoing participation. Furthermore, metrics will help gauge the effectiveness and relevance of the Community Support Portal as a resource for users.
Acceptance Criteria: User Accessibility and Interaction Monitoring in Community Support Portal

Given a user accesses the Community Support Portal, when they interact with forums, FAQs, and tutorials, then the system should accurately record and display user interaction metrics such as posts created, views counted, and tutorial contributions.

Active Community Member Identification

Given the recorded user interactions within the Community Support Portal, when metrics are analyzed, then the system should generate a list of active community members based on predefined thresholds of participation levels.

Popular Topics and Content Analysis

Given the collected engagement data from the Community Support Portal, when the system analyzes the metrics, then it should identify and display the top three most engaged topics or tutorial contributions in the dashboard.

Reporting Metrics to Administrators

Given the engagement metrics tracked within the Community Support Portal, when an administrator requests a report, then the system should generate and display a comprehensive report of community interactions, including participation rates and popular content trends.

User Feedback Collection on Support Resources

Given the presence of FAQs, forums, and tutorials in the Community Support Portal, when users provide feedback on these resources, then the system should capture and display feedback metrics to assess user satisfaction and areas for improvement.

Real-time Metric Updates

Given user activity in the Community Support Portal, when interactions occur, then the system should update engagement metrics in real-time to provide immediate insights into community engagement levels.

Search Functionality Within the Portal

User Story: As a user, I want to search for specific content within the community portal so that I can quickly find the information I need without browsing multiple pages.
Description: The search functionality will enable users to easily locate specific content within the Community Support Portal, including FAQs, forum posts, and tutorials. This requirement is critical to enhancing user experience, as it reduces the time spent searching for information and increases the efficiency of utilizing available resources. Advanced filtering options will allow users to narrow down results based on categories, topics, or date, ensuring relevant and timely information is quickly accessible. This will significantly improve user satisfaction and streamline interactions within the community.
Acceptance Criteria: User initiates a search in the Community Support Portal to find a specific FAQ related to data analytics.

Given a logged-in user on the Community Support Portal, when they enter a keyword in the search bar and click 'search', then the search results should display relevant FAQs that include the keyword in the title or content.

A user wants to filter forum posts by topic within the Community Support Portal.

Given any user on the Community Support Portal, when they select a topic filter and initiate a search, then the results should display only the forum posts related to that topic.

A user attempts to find a user-contributed tutorial by a specific date in the Community Support Portal.

Given a user on the Community Support Portal, when they apply a date filter and search for tutorials, then the results should include only the tutorials posted within the selected date range.

A new user wants to find resources about getting started with Datapy in the Community Support Portal.

Given a new user on the Community Support Portal, when they type 'getting started with Datapy' in the search bar, then the results should prioritize and display tutorials, FAQs, and forum discussions specifically about starting with Datapy.

A user expects the search functionality to provide instant feedback as they type in the Community Support Portal.

Given any user in the Community Support Portal, when they start typing in the search box, then the system should display auto-suggested results based on the input dynamically without requiring an additional search action.

A user wants to reset the search filters after conducting a search in the Community Support Portal.

Given a user who has applied filters and performed a search, when they click on the 'reset filters' button, then all previous filters should be cleared and the original search results should be displayed.

An administrator tests the performance of the search functionality under heavy load in the Community Support Portal.

Given multiple users are conducting searches simultaneously, when the search functionality is tested, then it should respond within 2 seconds and deliver accurate results without errors.

Community Guidelines and Moderation System

User Story: As a user, I want to understand the community guidelines so that I can engage positively and constructively in discussions and contributions.
Description: The community guidelines and moderation system will establish clear rules for interaction within the Community Support Portal, ensuring a respectful and constructive environment for all users. This requirement involves creating a set of guidelines that all users must adhere to, as well as implementing moderation tools that allow designated community moderators to manage content effectively. The system will include features for reporting inappropriate content, notifying violators, and handling complaints. This is essential for maintaining a positive community experience and encouraging active participation.
Acceptance Criteria: Establishing Community Guidelines for User Interaction

Given a new user accesses the Community Support Portal, when they navigate to the community guidelines section, then they should be able to see clearly defined rules outlined in a user-friendly manner, ensuring they understand the expected behavior within the community.

Moderation Tools Implementation by Designated Moderators

Given a moderator is assigned to the Community Support Portal, when they log in to the moderation dashboard, then they should have access to tools that allow them to review, approve, or remove user content effectively, ensuring community standards are upheld.

Reporting Inappropriate Content by Users

Given a user identifies inappropriate content in the forum, when they click the 'Report' button on that content, then a notification should be sent to the moderators for review, and the user should receive a confirmation that their report has been submitted.

Notification of Guidelines Violation to Users

Given a user violates community guidelines, when the moderation team reviews the reported content, then the user should receive an automated notification detailing the violation and the corresponding action taken, ensuring transparency in the moderation process.

Handling Complaints Through a Transparent Process

Given a user submits a complaint regarding a fellow community member, when the moderation team processes this complaint, then the user should receive timely updates on the status of their complaint, ensuring their concerns are addressed and fostering community trust.

Encouraging Positive Community Engagement

Given new users have access to community guidelines, when they participate in their first forum discussion, then they should receive encouragement and tips on constructive engagement from the moderation tools, enhancing their experience and integration into the community.

Notifications for Community Interactions

User Story: As a user, I want to receive notifications for my interactions so that I stay updated on discussions and contributions without having to constantly check the portal.
Description: The notifications system will keep users informed about interactions related to their contributions in the Community Support Portal. This requirement encompasses alerts for replies to forum posts, comments on tutorials, and updates within categories of interest. By providing timely notifications, users will remain engaged and informed, fostering a sense of community and encouraging active participation. Implementing various customization options for notification preferences will empower users to control the flow of information they receive, further enhancing their user experience.
Acceptance Criteria: User receives a notification when someone replies to their forum post on the Community Support Portal.

Given a user has created a forum post, when a reply is made to that post, then the user should receive an in-app notification and an email alerting them of the reply.

User can customize notification preferences for receiving alerts about comments on their contributed tutorials.

Given a user is in their notification settings, when they toggle options for receiving comments on their tutorials, then these preferences should be saved and applied to future comments received.

Users receive alerts for category updates in the Community Support Portal.

Given a user follows specific categories within the Community Support Portal, when there is an update in any of those categories, then the user receives a notification indicating the update.

Notification preferences allow users to selectively receive notifications based on their interests.

Given a user is editing their notification preferences, when they select or deselect the types of notifications, then only the selected notifications should be sent to the user.

User receives a summary notification of all interactions related to their contributions at the end of the day.

Given the user has contributed to multiple aspects of the Community Support Portal, when the user logs in the next day, then they should receive a summary notification compiling all interactions from the previous day.

Users can access previously missed notifications through a notifications archive.

Given a user has notifications they have not viewed, when they access the notifications archive, then they should see a complete list of all past notifications organized by date.

Feedback-Driven Improvements

A system that collects feedback from users during onboarding to continuously enhance the onboarding experience. This feature ensures that the onboarding modules evolve based on user needs, optimizing future experiences and increasing overall satisfaction.

Requirements

User Feedback Collection

User Story: As a new user, I want to provide feedback during the onboarding process so that my experience can be improved for future users.
Description: This requirement outlines a mechanism for collecting real-time feedback from users during the onboarding process. The feedback system will include an interactive questionnaire and rating scale, allowing users to share their experiences and suggestions easily. This data will be essential for identifying pain points and areas for improvement. By actively engaging users, this feature will help enhance the onboarding journey and adapt it according to user preferences, ultimately driving user satisfaction and retention.
Acceptance Criteria: User initiates the onboarding process in Datapy and is prompted with an interactive questionnaire for feedback.

Given a user is onboarded, When they complete the onboarding module, Then the interactive questionnaire is displayed for feedback collection.

User rates their onboarding experience on a scale from 1 to 5 immediately after completing a module.

Given a user has finished a module, When the rating prompt appears, Then the user can select a rating from 1 to 5 without any errors.

Users are presented with an option to provide comments and suggestions for improvement post-onboarding.

Given a user has completed all onboarding steps, When they are asked for additional comments, Then they can submit feedback through a text box successfully.

Feedback data from questionnaires is stored and categorized for analysis.

Given feedback has been collected, When it is submitted by users, Then all entries should be stored in a database and categorized by module.

Users receive a confirmation message after successfully submitting their feedback.

Given a user submits their feedback, When the submission is processed, Then a confirmation message is displayed indicating successful feedback submission.

System generates a report summarizing collected feedback over time for assessment.

Given sufficient feedback data has been collected, When a report is requested, Then the system generates a summary report of feedback for review.

Analytics Dashboard Integration

User Story: As a product manager, I want to view user feedback metrics in an analytics dashboard so that I can make informed decisions regarding onboarding improvements.
Description: This requirement specifies the integration of an analytics dashboard that visualizes user feedback data. The dashboard will present metrics such as average ratings, common comments, and trends over time. It will help product managers and developers understand user sentiment and the effectiveness of onboarding modules. By providing actionable insights through the dashboard, stakeholders can prioritize necessary changes and track the impact of improvements over time, fostering an iterative approach to product development.
Acceptance Criteria: Feedback dashboard displays user feedback insights after onboarding.

Given that the feedback has been collected, when a product manager accesses the analytics dashboard, then they should see a visual representation of average ratings over the last month, with the ability to filter by specific onboarding modules.

Dashboard updates in real-time with new feedback submissions.

Given that a user submits feedback during onboarding, when the submission is registered in the system, then the analytics dashboard should refresh automatically within 5 seconds to display the updated metrics.

Ability to view trends in user feedback over time.

Given that user feedback data is available, when a product manager selects a date range on the analytics dashboard, then they should be able to view trends of average ratings and common comments, displayed in a graph format over the selected time period.

Identify common comments from user feedback.

Given that feedback data has been aggregated, when the product manager accesses the analytics dashboard, then they should see a list of the top 5 most common comments provided by users during onboarding.

Measure the effectiveness of onboarding modules based on feedback.

Given that the analytics dashboard is displaying feedback data, when the product manager analyzes the data, then they should be able to correlate average ratings with specific onboarding modules within the dashboard to determine which were the most effective.

Export user feedback data for external analysis.

Given that the feedback data is present on the analytics dashboard, when the product manager clicks on the export button, then they should be able to download the user feedback data in a CSV format for further analysis.

Automated Feedback Analysis

User Story: As a team member, I want to receive automated insights from user feedback so that we can quickly address the most common user issues and improve onboarding.
Description: This requirement entails creating an automated system for analyzing the feedback collected from users. Utilizing natural language processing (NLP) and machine learning algorithms, the system will categorize feedback into themes and highlight frequent suggestions or issues raised by users. This enriches the qualitative data gathered and enables the team to focus on the most pressing user needs, streamlining the process of onboarding enhancement and improving user experience.
Acceptance Criteria: Automated feedback is collected from users during the onboarding process of Datapy after they complete each module, providing insights into their user experience.

Given users have completed an onboarding module, when they submit their feedback, then the feedback is automatically categorized and stored for analysis within 5 minutes.

The system categorizes user feedback into themes such as usability, functionality, and suggestions for improvement, allowing the development team to prioritize enhancements effectively.

Given the feedback received from users, when the automated analysis is performed, then it should categorize at least 90% of feedback into designated themes within an accuracy of 85%.

Frequent suggestions or issues raised by users during onboarding are highlighted in a dashboard that stakeholders can access to review ongoing complaints and areas for enhancement.

Given the categorized feedback, when the analysis is completed, then the dashboard should display the top 5 recurring themes and corresponding suggestions, updated in real-time.

The NLP and machine learning components of the feedback analysis work in synchronization to develop a report summarizing the findings from the collected data.

Given feedback data is available, when the NLP analysis is executed, then a report should be generated that summarizes key insights and trends from the data without manual intervention.

The feedback analysis results are used to inform changes in the onboarding modules to better meet user needs over time.

Given a report has been generated from user feedback, when the development team reviews the findings, then at least one change should be made to the onboarding process based on the insights within one month.

Team members receive alerts for new themes or significant feedback trends that emerge, ensuring prompt action can be taken.

Given new feedback is processed, when a theme exceeds a predefined threshold of 20 responses, then an alert should be triggered for the development team via email within one hour.

Feedback Loop Implementation

User Story: As a user, I want to receive updates regarding changes made from my feedback, so that I feel valued and engaged in the onboarding process.
Description: This requirement describes the process for implementing a feedback loop where users are informed about the changes made based on their feedback. After enhancements to the onboarding experience, users will receive updates and surveys asking for their impressions of the changes. This two-way communication will demonstrate that user input is valued and can reinforce a sense of community, driving further engagement and willingness to participate in feedback activities.
Acceptance Criteria: User receives notification about implemented feedback changes after onboarding.

Given the user has completed onboarding, when they receive an email notification, then they should see specific improvements listed based on their feedback.

User accesses an updated onboarding module reflecting user feedback.

Given that updates have been made to the onboarding module, when the user logs in after the updates, then they should be able to navigate the new features without confusion based on provided user insights.

User participates in a post-update survey regarding the onboarding experience.

Given the user has been informed about the changes, when they complete the post-update survey, then the survey should include questions directly asking for their feedback on the new features they experienced.

System tracks and displays the number of feedback submissions received during onboarding.

Given that users can submit feedback, when the admin checks the feedback dashboard, then it should show a count of submissions along with qualitative data from users.

Users' feedback is publicly acknowledged through the platform's communication channels.

Given that feedback has been implemented, when the user checks the community forum or newsletter, then they should see acknowledgment of their specific feedback contributions and how they led to changes.

User engagement metrics are analyzed post-implementation of feedback changes.

Given that feedback-driven changes have been implemented, when the product team reviews user engagement metrics, then they should see an increase in user feedback submissions and participation rates in further onboarding surveys.

Feedback mechanism is tested for accessibility and ease of use.

Given that the feedback submission mechanism is part of the onboarding process, when a user tries to submit feedback, then they should be able to complete the process easily using various devices without encountering technical issues.

Onboarding Module Update Mechanism

User Story: As a content manager, I want a mechanism to update onboarding modules based on user feedback so that the materials stay current and effective for new users.
Description: This requirement establishes a systematic approach to updating onboarding materials and modules based on user feedback. The mechanism will outline the criteria and procedure for identifying which aspects of the onboarding process require revision. By formalizing the update process and incorporating user feedback, the onboarding experience will remain relevant and effective, leading to continual user satisfaction and a smoother entry for new users into the platform.
Acceptance Criteria: Onboarding feedback collection during the first week of user engagement.

Given a new user is onboarded, when they interact with the onboarding modules, then they should be provided with a feedback form at the end of each module.

Evaluation of user feedback for content improvement after three months.

Given user feedback has been collected for three months, when the feedback is reviewed, then at least 75% of the feedback must be addressed in the next update cycle.

User engagement analytics post-update.

Given an onboarding update has been implemented, when analyzing user engagement metrics, then there should be at least a 30% increase in user satisfaction ratings within one month of the update.

Integration of feedback into the onboarding experience.

Given feedback has been analyzed, when onboarding modules are revised, then the updated modules must reflect at least two significant changes based on the feedback.

Real-time feedback system functionality during onboarding.

Given the onboarding experience is ongoing, when a user submits feedback, then it should be recorded in the system and acknowledged within 24 hours.

User reengagement after feedback implementation.

Given feedback-driven improvements have been made, when reaching out to previous users, then there should be at least a 40% return rate for those who were previously disengaged.

Feedback accessibility for users post-onboarding.

Given a user completes the onboarding process, when they access their profile, then they should see an option to view past feedback and changes made in response.

Feedback Reminder Notifications

User Story: As a product user, I want to receive reminders to provide feedback after onboarding so that I can easily share my experiences and suggestions.
Description: This requirement outlines a system for sending automated reminders to users who have not yet provided feedback after completing the onboarding process. These notifications will prompt users to engage with the feedback system, ensuring that the collection of user sentiments remains robust. By securing higher participation rates in feedback collection, the onboarding experience can be more data-driven, ultimately enhancing the overall satisfaction of future users.
Acceptance Criteria: Feedback Reminder Notifications for Inactive Users

Given a user has completed the onboarding process and not submitted feedback within 7 days, when the reminder notification is sent, then the user should receive an email prompting them to provide feedback.

Multiple Attempts for Feedback Submission

Given a user has received the feedback reminder notification and still not submitted feedback after 14 days, when a second reminder notification is sent, then the user should receive a follow-up email and an in-app notification.

Tracking Feedback Reminder Delivery

Given the reminder notification system is in place, when a feedback reminder is sent, then the system should log the time of delivery and confirm successful dispatch in the activity log.

User Feedback Engagement Metrics

Given that users are receiving feedback reminder notifications, when analyzing user feedback participation rates, then the system should show a 20% increase in participation within 30 days post implementation.

Feedback Submission Confirmation

Given a user submits their feedback after receiving the reminder notification, when they submit the form, then they should receive a confirmation message and a thank you email.

Customizable Reminder Frequency

Given the need to adapt to user preferences, when a user opts into customized notification settings, then they should be able to select the frequency of feedback reminders (daily, weekly, bi-weekly).

Feedback Reminder A/B Testing

Given two different reminder notification formats are designed, when the notifications are sent to a randomized user group, then we should track and compare the feedback submission rates from both formats within one month.

Real-Time Data Sync

This feature enables users to synchronize data between their existing systems and Datapy in real-time. With immediate data updates, users can ensure that their analytics reflect the latest information, allowing for timely decision-making and operational agility.

Requirements

Real-Time Data Integration

User Story: As a business analyst, I want real-time synchronization of data from our CRM into Datapy so that I can analyze the most recent customer trends and make informed recommendations to the sales team without any delay.
Description: The Real-Time Data Integration requirement ensures seamless synchronization of data from various existing systems, such as CRM, ERP, and other databases, into the Datapy platform. This enhancement allows users to access the most current information and insights without manual data uploads or import/export processes. By providing immediate data updates, businesses can make timely decisions based on the latest analytics, increasing operational efficiency and responsiveness to market changes. Leveraging industry-standard APIs, the integration will cater to various data formats and ensure a robust, secure, and scalable connection to facilitate continuous data flow as organizations grow.
Acceptance Criteria: User initiates data synchronization from their ERP system to the Datapy platform during business hours to ensure metrics reflect the latest sales data.

Given the user has initiated data sync from the ERP system, When the synchronization process starts, Then the latest sales data should be reflected in the Datapy dashboard within 5 minutes.

A user wants to view real-time changes in their CRM data reflected on Datapy while conducting an analysis report.

Given the user has made changes in the CRM system, When Datapy detects the changes, Then the updated CRM data should be displayed on the Datapy interface within 2 minutes.

A business user conducts a review meeting with their team, wanting to ensure that the analytics displayed are based on the most current data collected from various sources.

Given that data synchronization is continuously active, When the user accesses the analytics dashboard, Then the dashboard should show the most recent data with a timestamp indicating the last synchronization occurred within the last 5 minutes.

An organization wants to integrate their existing SQL database with Datapy for real-time analytics without manual intervention.

Given that the SQL database connection is properly configured, When a new data entry is made in the SQL database, Then the entry should appear in Datapy analytics within 3 minutes without needing a manual refresh.

A customer wants to ensure that API errors during data synchronization are logged and reported appropriately.

Given that an API error occurs during data sync, When the error status is detected, Then a detailed error log should be generated and sent to the system administrator within 1 minute.

A team member needs to verify the security of data streaming between their systems and Datapy during real-time integration.

Given that real-time data syncing is active, When a security audit is performed, Then all data transfers should be encrypted, and there should be no unauthorized access detected during the sync process.

A user checks the integration compatibility with various file formats and data sources before implementing the integration.

Given the user wants to integrate multiple data sources, When they access the integration setup options, Then they should see compatibility indications for at least 5 different file formats and sources listed.

User Access Management

User Story: As an IT administrator, I want to manage user access levels within Datapy so that I can ensure data security and compliance with company policies while empowering my team with the right tools.
Description: The User Access Management requirement focuses on implementing a comprehensive permission structure that allows administrators to define user roles and access levels across the Datapy platform. This functionality will enable businesses to restrict access to sensitive data or analytical features based on user roles, ensuring data security and compliance with organizational policies. Additionally, it allows for the creation of custom user groups tailored to specific team needs, facilitating collaboration while safeguarding proprietary information. Clear audit logs will track user activities, enabling organizations to monitor access and maintain accountability.
Acceptance Criteria: Administrator grants access to a new user based on their role.

Given an administrator is logged into Datapy, when they navigate to the User Access Management section, then they should be able to assign a new user to a pre-defined role with specified access levels, and the system should display a confirmation of the access granted.

User tries to access a restricted feature based on their role.

Given a user with limited access attempts to access a premium analytics feature, when the user attempts to interact with the feature, then they should receive a notification that access is denied due to their current role permissions.

Administrator creates a custom user group with specific access rights.

Given an administrator is in the User Access Management interface, when they create a custom user group and assign specific permissions to that group, then the new user group should reflect these access levels immediately, and users in that group should only be able to access features corresponding to those permissions.

Audit logs are generated to track user activity.

Given a user performs various actions within Datapy, when the actions include accessing sensitive data, then the system should log these actions in the audit logs along with timestamps and user identification, allowing for a clear record of who accessed what.

Administrator modifies access levels for existing users.

Given an administrator wants to change the access level of an existing user, when they edit the user's role in the User Access Management section, then the user should be immediately notified of the change, and their access levels should be updated in real-time without requiring a platform refresh.

User attempts to access the system with incorrect credentials.

Given a user enters an incorrect username or password, when they attempt to log in to Datapy, then they should receive an error message indicating that the login credentials are incorrect, and the login attempt should be logged in the audit trail.

Customizable Dashboard Widgets

User Story: As a marketing manager, I want to customize my dashboard in Datapy by adding widgets that display key campaign metrics so that I can monitor performance in real-time and adjust strategies as needed.
Description: The Customizable Dashboard Widgets requirement enables users to tailor their dashboard interface by adding, removing, and modifying widgets that display business metrics and KPIs most relevant to them. Users can select from various visualization formats such as graphs, tables, and charts, providing flexibility for individuals to create personalized views that suit their work styles and analytical preferences. This feature will enhance user engagement and productivity, allowing teams to quickly access the information they need, improving their decision-making process with customized insights perfectly aligned with their priorities.
Acceptance Criteria: User Customization of Dashboard Widgets on Initial Setup

Given a new user, when they log into Datapy for the first time, then they should be able to access the customizable dashboard interface, add at least three different types of widgets, remove one widget, and modify the settings of another widget without errors.

Updating Dashboard Widgets with Real-Time Data Sync

Given a user with a customized dashboard, when the real-time data sync occurs, then the metrics displayed in the widgets should update automatically within 5 seconds to reflect the latest data without the user needing to refresh the dashboard.

Saving Customized Dashboard Preferences

Given a user who has customized their dashboard, when they click the 'Save Preferences' button, then their customization settings should persist across sessions, and those settings should load automatically the next time they log in to the platform.

Changing Visualization Formats of Widgets

Given a user has added a widget displaying sales data as a graph, when they change the visualization format to a table, then the widget should update to display the same data in tabular format without loss of data integrity.

User Access and Permissions for Dashboard Customization

Given a user with restricted permissions, when they attempt to customization options on the dashboard, then they should receive an error message indicating that they do not have the necessary permissions to make these changes.

Removing Widgets from the Dashboard

Given a user has four widgets on their dashboard, when they remove one widget, then they should see the remaining three widgets adjust seamlessly to maintain a user-friendly layout without any functionality issues.

Accessing Help for Dashboard Customization

Given a user on the customizable dashboard, when they click on the 'Help' icon, then they should be shown a guide on how to customize their widgets, including step-by-step instructions, examples, and FAQs without any broken links.

Collaborative Analytics Tools

User Story: As a project manager, I want to use collaborative tools within Datapy so that my team can work together on data analytics projects in real-time and make collective decisions faster.
Description: The Collaborative Analytics Tools requirement seeks to integrate features that promote teamwork within the Datapy platform, such as shared workspaces, comments, and real-time notifications. Users can collaborate on data analysis projects, share insights, and provide feedback directly within the platform, fostering a culture of data-driven decision-making. By enhancing communication and collaboration, teams can improve their analytical outcomes and ensure all stakeholders are aligned with business objectives. This feature will also include the ability to export shared insights and visualizations to various formats for reporting and presentations, enhancing the utility of collaborative work.
Acceptance Criteria: User Collaboration on Data Analysis Projects

Given a user accesses the collaborative analytics tool, When they create a shared workspace, Then all invited team members should receive a notification and be able to access the workspace within 2 minutes.

Real-Time Comments and Feedback

Given a user is viewing a shared analysis, When they leave a comment, Then the comment should be visible to all collaborators in real-time without needing to refresh the page.

Exporting Insights for Reporting

Given a user has generated a visualization in the collaborative workspace, When they choose an export option for a report, Then the visualization should be exportable in at least three formats (PDF, CSV, and PNG) with no loss of fidelity.

Notification of Activity Updates

Given a team member makes any changes in the shared workspace, When the changes are saved, Then all other team members should receive an instant notification of the activity.

User Permission Management

Given an admin user manages the shared workspace permissions, When they add or remove a member, Then the changes should be reflected in the workspace access within 1 minute.

Real-time Data Synchronization Confirmation

Given a user initiates data synchronization in the collaborative analytics tool, When the synchronization is completed, Then the user should receive a confirmation message indicating successful sync and updated data availability.

Search and Filter in Shared Workspaces

Given a user is in a shared workspace, When they utilize the search or filter functionality, Then they should be able to locate specific analyses or comments within 3 seconds.

AI-Driven Forecasting Models

User Story: As a business executive, I want to utilize AI-driven forecasting models in Datapy to predict future sales trends so that I can make data-driven decisions that enhance our business strategy.
Description: The AI-Driven Forecasting Models requirement provides users with advanced predictive analytics functionalities powered by machine learning algorithms. By analyzing historical data patterns, these models will generate accurate forecasts that enable businesses to anticipate trends, customer behaviors, and operational needs. Users will have the option to customize the parameters of the models to align with their specific business objectives. This requirement will empower organizations to make proactive, well-informed business decisions, optimize resource allocation, and enhance strategic planning through data-backed predictions, ultimately driving growth and efficiency.
Acceptance Criteria: AI-Driven Forecasting Models Utilization in Quarterly Planning Sessions

Given that the user accesses the AI-Driven Forecasting Models feature, when they input historical sales data and configure the model parameters, then they should be able to generate forecasts for the upcoming quarter that display as a graph and table format.

Customizable Parameters for Forecasting Models

Given that the user wants to customize the AI model, when they change the forecasting parameters (e.g., time frame, data sources), then the model should update and provide an adjusted forecast based on the new settings without errors.

Real-Time Data Sync for Accurate Forecasting

Given that the user has enabled Real-Time Data Sync, when new data is entered into the connected system, then the AI-Driven Forecasting Model should automatically integrate this data and update the forecasts in real-time within five minutes.

User Access Controls for Predictive Analytics Features

Given that an administrator wants to set permissions for the AI-Driven Forecasting models, when they specify user roles and access levels, then users in lower permission tiers should not have visibility or editing capabilities for the forecasting feature.

Visualization of Forecasting Results

Given that the user runs the AI-Driven Forecasting Model, when the results are generated, then the user should see a visually engaging dashboard that includes actionable insights, trends, and a summary of forecasting accuracy metrics.

User Feedback on Forecast Accuracy

Given that a user utilizes the AI-Driven Forecasting Model, when they compare the generated forecast to actual performance data after a specified time period, then they should be able to rate the accuracy of the forecast and provide feedback directly through the interface.

Integration with Collaborative Tools

Given that a team is working on analytics in Datapy, when they generate forecasts using the AI-Driven Forecasting Model, then they should be able to share these forecasts within the collaborative tools and receive real-time comments from team members.

Mobile Application Access

User Story: As a sales executive, I want to access Datapy via a mobile app so that I can track sales metrics and performance while on the road to stay informed and responsive to client needs.
Description: The Mobile Application Access requirement will enable users to access the Datapy platform through a dedicated mobile application, providing them with the flexibility to monitor analytics and business metrics on the go. The mobile app will support core features available on the web platform, including real-time data insights, customizable dashboards, and collaboration tools. This user-centric approach ensures that decision-makers can stay connected and responsive to their business needs, regardless of their location, enhancing the overall utility of Datapy for remote and on-the-move users.
Acceptance Criteria: User accesses the Datapy mobile application to review real-time sales data while commuting to a meeting.

Given the user is logged into the mobile application, when they navigate to the sales dashboard, then the dashboard should display real-time sales metrics updated within the last 5 minutes.

A user collaborates with their team by sharing insights from the mobile application during a remote team meeting.

Given the user is in the collaboration section of the mobile app, when they share a dashboard view, then all invited team members should receive a notification and be able to view the shared data in real-time.

A user customizes their dashboard on the mobile app before presenting to stakeholders.

Given the user modifies the dashboard layout in the mobile application, when they save the customization, then the app should retain the updated layout upon the next login and on the web platform.

User sends feedback on a specific feature through the mobile application while analyzing insights.

Given the user is analyzing data in the mobile app, when they submit feedback using the feedback feature, then the app should confirm submission and record the feedback in the system.

User receives a push notification alert for significant changes in key performance indicators on the mobile application.

Given the user has enabled notifications, when there is a significant change in any tracked KPI, then the user should receive a push notification on their mobile device within 10 minutes of the change.

User logs into the mobile application with biometric authentication.

Given the user has set up biometric authentication, when they attempt to log in, then the application should allow access using their fingerprint or facial recognition without requiring a password.

Custom Data Connectors

Users can create personalized data connectors tailored to their specific systems and applications. This feature simplifies the integration process and enables businesses to seamlessly connect various data sources, enhancing the flexibility and depth of their analytics.

Requirements

Dynamic Connector Configuration

User Story: As a data analyst, I want to dynamically configure my data connectors so that I can connect to my company's unique applications without needing extensive technical support.
Description: This requirement focuses on enabling users to configure and customize data connectors within the Datapy platform. Users should be able to specify connection parameters such as endpoint URLs, authentication methods, and query settings, allowing for a highly tailored integration experience. The feature enhances flexibility, enabling businesses to adapt their analytics setup based on their unique system requirements and data sources. By supporting various authentication mechanisms, including OAuth and API keys, this requirement promotes security and compliance. The expected outcome is significantly streamlined data integration processes, empowering users to connect their systems without depending on technical teams or extensive documentation.
Acceptance Criteria: User successfully configures a custom data connector for their CRM system by entering endpoint URL, selecting authentication method, and specifying query parameters.

Given the user is on the Custom Data Connector configuration page, when they enter valid endpoint URL, select OAuth as the authentication method, and input appropriate query parameters, then the connection should be established successfully without errors.

User attempts to configure a data connector with an invalid endpoint URL and receives a clear error message.

Given the user is on the Custom Data Connector configuration page, when they enter an invalid endpoint URL, then an error message should be displayed indicating 'Invalid endpoint URL. Please check your input.'

User selects API key as the authentication method and correctly enters the key while configuring the connector.

Given the user is configuring the data connector, when they choose API key as the authentication method and enter a valid API key, then the configuration should save successfully, and the connection should be listed as 'Configured' in the connector overview.

User customizes a data connector with various query settings to filter data for their analytics purposes.

Given the user wants to filter data, when they add query conditions such as date range and specific fields in the configuration, then these settings should be reflected in the test query output, confirming appropriate filtering.

User wants to delete a connector they previously created and confirms the deletion process.

Given the user is on the data connector overview page, when they select a connector and choose the delete option, then a confirmation message should be displayed, and upon confirming, the connector should be removed from the list.

User sees a list of previously configured custom data connectors with associated statuses (active/inactive).

Given the user accesses the custom data connectors section, then they should be able to view all previously configured connectors along with their current status (active or inactive) in a clear and organized list.

Pre-built Connector Library

User Story: As a business owner, I want to easily access pre-configured connectors for popular platforms so that I can quickly integrate my data and start analyzing it.
Description: The pre-built connector library requirement entails developing a library of pre-configured data connectors for popular business applications and services. This initiative increases the value of Datapy by offering users immediate access to a wide range of data sources with minimal setup. Users can select connectors for CRM systems, e-commerce platforms, social media, and various databases, thereby reducing the manual work required to integrate their data. The library should be regularly updated to include new connectors based on user demand and emerging market trends. The expected outcome of this requirement is to enhance user experience by significantly shortening the onboarding time for new integrations, fostering rapid insights and decision-making.
Acceptance Criteria: Selection and Deployment of a Pre-built Connector for a CRM System

Given a user has access to the pre-built connector library, when they select a CRM connector and follow the setup process, then the connector should be established with accurate data syncing in less than 5 minutes without requiring additional technical support.

Updating the Pre-built Connector Library with Emerging Market Demands

Given that user feedback is collected on a quarterly basis, when new data sources are identified as high demand, then the pre-built connector library should be updated with those connectors within 30 days of identification, ensuring continued relevance and user satisfaction.

User Experience Evaluation During Connector Deployment

Given a user deployment of a selected pre-built connector, when the user completes the setup, then a satisfaction survey should indicate at least an 80% satisfaction rate regarding ease of use and integration completion.

Accessibility of Connector Documentation and Support

Given the availability of the pre-built connectors, when a user accesses the connector library, then the required documentation and support resources should be easily reachable and have a loading time of under 3 seconds.

Monitoring and Troubleshooting Capability of Connectors

Given that a connector is deployed, when any issues are detected (such as disconnections or data sync failures), then a notification should be sent to the user within 2 minutes along with troubleshooting documentation.

Integration Speed Comparison With Manual Connectors

Given users integrating data manually in the past, when comparing the integration time for manual vs. pre-built connector usage, then deployments using pre-built connectors should demonstrate a time savings of at least 50%.

Connection Health Monitoring

User Story: As a system administrator, I want to monitor the health of my data connections so that I can quickly address any issues and ensure continuous data flow.
Description: This requirement outlines the implementation of tools for monitoring the health and status of data connections established through custom connectors. Users should be notified of connection failures, performance issues, or changes in data schema, ensuring they can promptly address any disruptions to their data flow. This feature may include dashboards displaying connection status, alerts for failures via email or in-app notifications, and logs to track historical issues. The implementation of this requirement is crucial for maintaining data integrity and reliability, empowering users to act before minor issues escalate into significant problems. The expected outcome is enhanced reliability and user confidence in the data they are analyzing and reporting.
Acceptance Criteria: Notification of Connection Failure via Email

Given a user has set up custom data connectors, When a connection fails, Then the user receives an email notification indicating the specific connection that has failed, including a timestamp and error details.

Real-Time Connection Status Dashboard Update

Given a user is viewing their connection status dashboard, When there is a change in connection health, Then the dashboard updates in real-time to reflect the current status of each data connector.

Performance Issue Alert Generation

Given a user has established performance thresholds for data connections, When a connection performance falls below the defined threshold, Then the user receives an in-app alert notifying them of the performance degradation.

Historical Issue Logging

Given a connection has failed, When the failure occurs, Then the system logs the failure with relevant details such as timestamp, error type, and connection ID, accessible in a historical log view.

Data Schema Change Notification

Given a user has active data connectors, When there is a change in the data schema of any connected data source, Then the user is notified via email and in-app notification about the schema change and its potential impact.

Connection Health Summary Report Generation

Given a user accesses the connection health monitoring feature, When the user requests a report, Then the system generates a summary report detailing the health status and performance metrics of all connectors over the specified period.

User Customization of Alert Settings

Given a user is in the connection monitoring settings, When they customize their alert preferences, Then the system saves the preferences and applies them to alert notifications for all data connections.

User-Friendly Connection Wizard

User Story: As a non-technical user, I want an easy-to-follow connection wizard so that I can set up my data connections without feeling overwhelmed or needing help.
Description: The user-friendly connection wizard requirement involves creating an intuitive, step-by-step interface for setting up custom data connectors. This feature should guide users through the integration process, with tooltips and examples to simplify even complex setups. Having such a wizard reduces barriers to entry for users without technical expertise and enhances the overall accessibility of the platform. It should also offer troubleshooting suggestions based on user input, allowing for a smoother integration experience. The desired outcome is to empower all users, regardless of technical skill, to establish their data connections and benefit from the full capabilities of Datapy without external help.
Acceptance Criteria: User initiates the connection wizard to link their custom data source for the first time.

Given a user navigates to the connection wizard, when they select a data source and follow the step-by-step prompts, then they should be able to successfully create a data connector without errors and receive a confirmation message.

User encounters an unknown error while setting up a data connection.

Given the user is in the connection wizard and encounters an error, when they input relevant details and click on 'troubleshoot', then they should receive context-sensitive help suggestions for resolving the issue.

A user is completing the connection process but has questions about certain fields in the wizard.

Given a user is on any step of the connection wizard, when they hover over any field, then a tooltip should appear that provides clear examples and explanations of what is required.

User successfully completes the connection wizard and wishes to review their custom data connector settings.

Given the user completes the connection setup, when they navigate to the dashboard view of their custom connectors, then the newly created connector should be listed with all settings accurately reflecting the inputs given during setup.

User wants to edit an existing data connector through the connection wizard.

Given the user selects an existing data connector in the connection wizard, when they make changes and click save, then the updated connector should reflect the new settings without duplicating the entry.

Integration Testing Framework

User Story: As a developer, I want an integration testing framework so that I can verify my custom data connectors before deploying them to avoid critical data errors.
Description: The integration testing framework requirement implies the development of a robust testing environment for users to validate their custom connectors before going live. This feature should allow users to simulate data pulls, verify transformations, and assess the performance of their connectors, ensuring that data flows correctly and meets the platform's expectations. The framework should provide detailed logs and error messages to assist users in identifying and fixing issues. This requirement is vital for ensuring that users can deploy reliable and high-quality integrations, thereby preventing disruptions in data analysis and reporting. Expected outcomes include improved trust in data integrations and increased user satisfaction via minimized errors.
Acceptance Criteria: User is looking to establish a custom data connector from their ERP system to Datapy for automated data synchronization and analytics.

Given that the user has defined their ERP data structure, when they create a custom connector in Datapy, then the connector should successfully validate the data schema and status without failures.

A user has configured a custom data connector and is ready to test it using the integration testing framework before going live.

Given that the user initiates a test of the custom connector, when the test is executed, then the system should accurately log the retrieval of data from the ERP system, apply the defined transformations, and display the final result in the testing logs with success indication.

User encounters errors while testing their custom connector and needs detailed logs for troubleshooting.

Given that the user has run tests on their custom connector, when an error occurs, then detailed logs should provide clear error messages and structured information about the data flow and transformation for troubleshooting purposes.

User wants to test the performance of their custom connector under heavy data loads to ensure it meets the performance standards of Datapy.

Given that the user simulates heavy data loads through the custom connector, when the tests are run, then the performance metrics should show data retrieval and processing times within acceptable thresholds as per Datapy's performance standards.

The user has completed testing their custom connector and is now looking to deploy it live on the Datapy platform.

Given that all integration tests have passed successfully, when the user attempts to deploy the connector, then the system should permit deployment with a confirmation message and make the connector available in the live environment.

User wants to validate data transformations applied by their custom data connector to ensure accuracy.

Given that the user has tested their custom connector with sample data, when data retrieval and transformations are executed, then the output data should match predefined accuracy benchmarks specified in the connector's configuration.

User needs to assess the overall satisfaction with the integration testing framework experience after utilizing it for their custom connector.

Given that the user has completed several tests using the integration testing framework, when they provide feedback through a satisfaction survey, then at least 85% of responses should indicate a positive experience and improved trust in data integrations.

Custom Alerts for Data Changes

User Story: As a data scientist, I want to set custom alerts for my data sources so that I can quickly respond to any significant changes that might affect my analysis.
Description: This requirement aims to provide users with the capability to set up custom alerts for any significant changes in the data being integrated through custom connectors. Users should be able to define specific triggers, such as data thresholds, structural changes, or missing data notifications. By receiving timely alerts, businesses can react swiftly to data inconsistencies or issues, thus improving their analytical integrity. This feature should include flexible alerting methods, allowing users to receive notifications via email, SMS, or within the Datapy platform. The expected outcome is enhanced responsiveness to data quality issues, enabling users to maintain accurate and reliable analytics.
Acceptance Criteria: User sets up an alert for a significant drop in sales data for a specific product over the last quarter.

Given the user has selected the product and defined the threshold for alerting, when the sales data falls below the specified threshold, then the user receives an immediate notification via email and within the Datapy platform.

User creates a custom alert for structural changes in a connected database, such as missing fields or tables.

Given the user has configured the custom alert for structural integrity monitoring, when a field or table is removed from the database, then the user receives a notification via SMS and within the Datapy platform.

User defines an alert for data quality issues, such as a spike in erroneous entries within a day.

Given the user has set the criteria for acceptable data limits, when erroneous entries exceed the defined threshold within a 24-hour period, then the user is alerted via multiple channels, including email and the Datapy dashboard.

User wants to receive alerts for missing data in customer contact fields after a data sync operation.

Given the user has specified critical fields that must not be empty, when a data sync operation results in missing mandatory customer information, then the system alerts the user via email and SMS notifications.

User adjusts their alert settings to test the functionality of alerts for various data thresholds.

Given the user modifies the threshold settings for existing alerts, when the data changes to meet the new threshold conditions, then the user successfully receives alerts reflecting the new parameters via the chosen notification methods.

User utilizes collaborative tools to notify team members about significant data changes.

Given the user sets a custom alert for data anomalies, when the alert is triggered, then selected team members receive notifications within the Datapy platform, fostering immediate communication about the data issue.

Cross-Platform Compatibility

This feature ensures that the Custom Analytics API works smoothly across multiple platforms, enabling users to integrate various tools and software without compatibility issues. Users can leverage their existing technology stack, maximizing their investment in current systems.

Requirements

Cross-Platform Integration

User Story: As a business analyst, I want to integrate Datapy with my existing software tools so that I can leverage current systems without compatibility issues and improve my data analysis workflow.
Description: The Cross-Platform Integration requirement focuses on ensuring that the Custom Analytics API is fully compatible with a diverse range of operating systems, devices, and software applications. This functionality allows users to seamlessly integrate the Datapy platform with their existing technology stacks, significantly enhancing workflow efficiency and minimizing downtime caused by compatibility issues. By supporting various platforms, this requirement not only extends the usability of Datapy but also maximizes the return on investment for businesses by leveraging their current tools and systems. Additionally, this integration support will facilitate real-time data synchronization across different platforms, ensuring that users have consistent and up-to-date insights regardless of the device or system they are using. This ultimately leads to improved decision-making and stronger collaboration among teams.
Acceptance Criteria: Integration of Datapy with an existing CRM system on a Windows-based desktop environment.

Given a user attempts to connect Datapy to a Windows-based CRM, when they input their API credentials, then the connection should be established successfully without errors and data should synchronize within 5 minutes.

Utilization of Datapy analytics dashboard on a mobile device while traveling.

Given a user accesses the Datapy dashboard on a mobile device, when they log in, then the dashboard should load quickly within 3 seconds and display all relevant metrics accurately.

Syncing data from multiple software tools to Datapy in real-time during a team meeting.

Given that a user initiates data synchronization from various tools into Datapy, when they trigger the sync process, then the data should be updated in Datapy within 1 minute across all connected platforms.

Users accessing Datapy while simultaneously working on different operating systems (e.g., MacOS and Linux).

Given multiple users are logged into Datapy from different operating systems, when they perform actions such as data analysis and report generation, then the platform should function seamlessly without any performance issues.

Integration of Datapy with a third-party marketing automation tool.

Given the user has valid API keys, when they configure Datapy to connect with the marketing tool, then data should flow bi-directionally without latency, allowing for real-time campaign adjustments.

A user attempting to integrate Datapy with a cloud-based accounting software.

Given a user enters the correct integration settings, when they request the connection between Datapy and the accounting software, then the integration should complete successfully and reflect accurate financial reports in Datapy.

Enhanced User Experience

User Story: As a new user, I want an intuitive interface that guides me through data analytics so that I can quickly learn how to use Datapy and make informed decisions based on my analysis.
Description: The Enhanced User Experience requirement is centered around optimizing the user interface and overall user interaction with the Datapy platform. This includes refining navigation, streamlining workflows, and ensuring that key features are easily accessible. By focusing on user-centered design principles, this requirement aims to make the platform intuitive and engaging for users of all technical levels. The goal is to reduce learning curves and enhance user satisfaction by providing a cohesive experience that allows users to harness the full power of data analytics without unnecessary complexity. Through thoughtful design iterations based on user feedback, this requirement will also aid in driving user adoption and retention, ultimately contributing to the platform's success.
Acceptance Criteria: User navigates the Datapy platform for the first time to create a custom dashboard.

Given a new user has logged into Datapy, when they access the dashboard creation tool, then they should see intuitive on-screen guidance and easily navigable options for adding data sources and visual components.

User attempts to integrate Datapy with existing software tools like CRM or Project Management applications.

Given a user selects the integration option in their settings, when they choose a third-party application, then they should be able to complete the integration with no more than three steps and receive a confirmation message.

User wishes to analyze data from multiple sources using the platform's analytics tools.

Given a user has connected at least two data sources, when they open the analytics dashboard, then they should be able to select data from both sources without any redundancy or errors in representation.

User needs to customize their dashboard to focus on specific business metrics.

Given a user is on the dashboard page, when they select the 'Customize' option, then they are presented with a list of available metrics that can easily be dragged and dropped onto their dashboard layout.

User provides feedback regarding the user interface after utilizing the platform for a month.

Given that the user submits feedback through the designated feedback form, when the feedback is reviewed by the design team, then the team should categorize the feedback for analysis and implement suggested improvements in the next version update.

User collaborates with team members on shared analytics reports.

Given a user opens a shared report, when they make real-time edits and comments, then those changes should reflect immediately for all other users viewing the report without delay.

User accesses Datapy on a mobile device after initial setup on a desktop.

Given the user has set up their account on a desktop, when they log into the Datapy mobile app, then they should see a fully functional interface that mirrors the desktop experience without any loss of features or data.

AI-Driven Predictive Analytics

User Story: As a data analyst, I want to use predictive analytics to identify trends and forecast outcomes so that I can make data-informed decisions that contribute to my company’s success.
Description: The AI-Driven Predictive Analytics requirement aims to integrate advanced machine learning algorithms within the Datapy platform to offer users predictive insights based on historical data trends. This feature will enable businesses to anticipate future outcomes, improve performance metrics, and make proactive decisions with confidence. The predictive analytics tool will analyze various data sets and generate actionable recommendations tailored to the unique needs of each user. By harnessing the power of AI, this requirement will enable users to unlock deeper insights, identify opportunities for growth, and mitigate potential risks, thus enhancing the decision-making process and driving strategic initiatives.
Acceptance Criteria: As a data analyst, I want to be able to upload historical sales data to Datapy so that I can generate predictive insights about future sales trends.

Given I have a valid historical sales dataset, when I upload the data to Datapy, then the system should successfully process the upload without errors and display a confirmation message.

As a business manager, I want to view predictive analytics on my dashboard so that I can quickly assess future performance metrics.

Given that I have uploaded relevant historical data, when I access the predictive analytics dashboard, then it should display key performance indicators (KPIs) and forecasts derived from the uploaded data.

As a user, I want the predictive analytics tool to generate actionable recommendations based on my unique business metrics so that I can make data-driven decisions.

Given that the predictive analytics tool processes my data, when it completes the analysis, then it should present tailored recommendations based on identified trends within my data.

As an operations manager, I want to compare predictions with actual outcomes to evaluate the accuracy of the predictive analytics tool over time.

Given I analyze past predictions versus actual outcomes, when I access the historical performance report, then the system should provide a detailed comparison chart showing prediction accuracy over time.

As a team member, I want to share predictive insights with my colleagues easily to foster collaboration in decision-making.

Given that I have generated predictive insights, when I use the sharing functionality, then the system should allow me to share insights via email or direct link with multiple team members.

As a user, I want the AI-driven predictive analytics to run automatically on a scheduled basis to ensure I receive timely updates on my data.

Given that I have set up a schedule for predictive analytics to run, when the scheduled time arrives, then the system should automatically generate and notify me of the updated insights without manual intervention.

Data Transformation Toolkit

Users can utilize a suite of data transformation tools within the API to clean, shape, and modify data as it flows into Datapy. This feature empowers users to manage data quality and relevance, ensuring that analytics are based on accurate and ready-to-use information.

Requirements

Data Cleansing Tool

User Story: As a data analyst, I want a data cleansing tool so that I can ensure my datasets are accurate and reliable for analysis.
Description: The Data Cleansing Tool allows users to identify and correct errors or inconsistencies in their datasets before analysis. This functionality is crucial for maintaining high data quality, as it ensures that analytics are based on error-free information. By incorporating this tool into the API, users can streamline their data preparation processes, making it easier to maintain clean datasets that yield accurate insights. This tool supports various cleansing techniques, including duplicate removal, format standardization, and outlier detection, integrating seamlessly within the existing Datapy framework.
Acceptance Criteria: User initiates the data cleansing tool to analyze a dataset containing duplicate entries, formatting inconsistencies, and outliers prior to performing analytics.

Given a dataset with duplicate entries, When the user applies the data cleansing tool, Then all duplicate records are removed successfully without impacting non-duplicate records.

A user uploads a dataset with inconsistent date formats and runs the data cleansing tool to standardize the formats.

Given a dataset with various date formats, When the user applies the data cleansing tool, Then all dates are standardized to a single format (e.g., YYYY-MM-DD) across the entire dataset.

User utilizes the data cleansing tool to detect and handle outliers in a dataset for accurate analytics.

Given a dataset with numerical values containing outliers, When the user applies the data cleansing tool's outlier detection feature, Then outliers are flagged and the user is presented with options to remove or adjust them accordingly.

A team collaborates on a project, utilizing the data cleansing tool within the API to ensure everyone is working with the same clean dataset.

Given multiple users accessing the same dataset, When one user applies the data cleansing tool, Then all team members have access to the updated, cleansed dataset in real-time.

User needs to ensure that every data entry follows predefined formats and ranges using the data cleansing tool.

Given a dataset with entries that do not conform to specific format requirements (e.g., emails, phone numbers), When the user applies the data cleansing tool, Then all entries are validated against the defined rules and corrected or flagged accordingly.

User wants to generate a report that includes data quality metrics after applying the data cleansing tool.

Given a dataset that has been cleansed, When the user requests a data quality report, Then the report includes metrics on duplicates removed, outliers adjusted, and total records processed.

Custom Data Shaping Functions

User Story: As a data engineer, I want to define custom data shaping functions so that I can tailor the data transformation process to meet the specific needs of my organization.
Description: The Custom Data Shaping Functions empower users to define their own data transformation logic based on their unique business needs. Users can create specific functions for reshaping data, aggregating values, or pivoting datasets, which enhances flexibility and usability in data handling. This requirement supports the diverse use cases of varying businesses and integrates into Datapy's API to be easily accessible for users with different levels of technical expertise. It allows for personalized and precise data manipulation, ensuring that users can obtain relevant insights tailored to their objectives.
Acceptance Criteria: User creates a custom data shaping function to aggregate sales data from multiple datasets into a single dataset for reporting purposes.

Given a user with appropriate permissions, when they define a custom aggregation function in the Datapy API, then the function should successfully execute and return the combined dataset with correct aggregated values.

User utilizes a custom data shaping function to pivot sales data and view metrics by region and product category.

Given a user with a defined pivot function, when they apply the function to the sales data, then the output should display pivoted data correctly grouped by region and product category with valid totals.

User modifies an existing custom data shaping function to include an additional parameter for filtering data before transformation.

Given an existing custom function, when the user adds a new filter parameter, then the modified function should execute without errors and apply the filter correctly to the input data before returning the transformed result.

User accesses documentation on creating custom data shaping functions and follows the steps to implement a new function.

Given the user is on the documentation page, when they follow the provided instructions to create a function, then they should successfully implement the function and see it listed among available functions in the API.

User tests a custom data shaping function using a sample dataset to validate its accuracy in data transformation.

Given the user has a sample dataset, when they run the custom data shaping function on the dataset, then the output should match expected results with no discrepancies in the transformed data.

User shares a custom data shaping function with team members for collaborative use.

Given a user with share permissions, when they share the custom function, then team members should receive access to the function and be able to apply it within their own data workflows without errors.

Real-time Data Updates

User Story: As a business strategist, I want real-time data updates so that I can make timely decisions based on the most current information available.
Description: The Real-time Data Updates feature ensures that any modifications made to the incoming datasets are reflected in Datapy instantly. This capability allows users to work with the most current data without the need for manual refreshes. Implementing real-time updates enhances the user experience by promoting agile decision-making and ensuring that all analytics are grounded in the latest information available. Integrating this feature into the data transformation toolkit will streamline workflows and ensure dynamic, data-driven processes.
Acceptance Criteria: User modifies a dataset by adding new entries through the API, and the changes should appear in Datapy within seconds, ensuring real-time updates.

Given a user has added new data entries to the API, when they access Datapy, then they must see the new entries reflected in their dashboards within 5 seconds.

A user removes an outdated entry in a dataset via the API while a team member is simultaneously viewing the data in Datapy.

Given a user removes an entry from the API, when another team member views the dataset in Datapy, then the removed entry must no longer be visible after 5 seconds.

User applies a filtering condition on a dataset while receiving updates in real-time, ensuring the filtered view reflects the changes immediately.

Given a user applies a filter on a dataset in Datapy, when new data updates occur in real-time, then the filtered dataset must update accordingly within 5 seconds.

A team member is collaborating remotely and relies on the real-time updates to stay informed about ongoing changes made to the data by other team members.

Given multiple users are modifying the dataset simultaneously, when one user makes a change, then all other users must receive a notification of that change in Datapy within 5 seconds.

Users are connected to an external data source via API and expect their modifications to synchronize without interruptions during data transformation tasks.

Given users are transforming data from an external source, when they initiate the transformation, then all modifications must be reflected in Datapy instantly without data loss.

A user runs a scheduled report, expecting it to present the most up-to-date data available, reflecting any real-time changes that occurred just before execution.

Given a user executes a scheduled report, when the report is generated, then it must display data that includes all updates made in the last 5 seconds prior to execution.

Pre-built Transformation Templates

User Story: As a marketing analyst, I want pre-built transformation templates so that I can quickly prepare my datasets without extensive technical knowledge.
Description: The Pre-built Transformation Templates offer users a collection of commonly used data transformation scenarios that can be applied instantly. These templates are designed to assist users who may not have advanced technical skills, enabling them to perform essential transformations quickly and efficiently. The integration of these templates within the data transformation toolkit not only saves time but also enhances user productivity, allowing users to focus on analysis rather than data preparation. This ensures a smoother onboarding process for new users and aids in leveraging the platform effectively.
Acceptance Criteria: User Selection of a Pre-built Transformation Template

Given a user is logged into Datapy, when they navigate to the Data Transformation Toolkit and select a pre-built transformation template, then the selected template should be applied to their dataset without errors and reflect the expected transformations in the preview before execution.

Template Modification and Customization

Given a user has applied a pre-built transformation template, when they modify parameters within that template, then the changes should be saved accurately and reflect in the transformed dataset after execution.

User Guidance and Tooltips for Templates

Given a user is using the pre-built transformation templates, when they hover over any template name, then a tooltip should appear providing a brief description of what the template does to help the user make an informed selection.

Successful Execution of a Transformation template

Given a user applies a pre-built transformation template and initiates the transformation, when the process completes, then the resulting dataset should show the expected adjustments, such as correct data cleaning or reshaping as defined in the template.

Error Handling During Template Application

Given a user attempts to apply a pre-built transformation template on a dataset, when the dataset contains incompatible data types, then an appropriate error message should be displayed, detailing the nature of the issue and suggested corrective actions.

Performance Benchmarking of Pre-built Templates

Given multiple users are applying various pre-built transformation templates simultaneously, when measured, then the system should demonstrate optimal performance with transformation times not exceeding a specified threshold (e.g., 5 seconds for datasets under 1,000 records).

User Feedback Collection after Transformation

Given a user has successfully used a pre-built transformation template, when they complete the task, then they should be prompted with a feedback form to rate the usefulness of the template and report any issues encountered during the process.

Audit Trail for Data Transformations

User Story: As a compliance officer, I want an audit trail for data transformations so that I can ensure accountability and transparency in data handling procedures.
Description: The Audit Trail for Data Transformations feature enables users to track all changes made to datasets throughout the transformation process. This functionality is vital for compliance and transparency, allowing organizations to maintain a record of data handling activities. Users can review previous states of data, monitor who made changes, and when they occurred, fostering accountability and a deeper understanding of data lineage. By integrating this feature into the API, Datapy will empower users to maintain data integrity and support audit requirements.
Acceptance Criteria: User views the audit trail for a specific dataset after performing a transformation to confirm the changes made during the process.

Given a dataset has been transformed, when the user accesses the audit trail for that dataset, then all changes made, including previous states, timestamps, and user IDs should be displayed accurately.

User filters the audit trail by date range to view changes made during a specific period.

Given the audit trail is displayed, when the user applies a date range filter, then only the changes made within that specified date range should be shown.

User reviews the audit trail for a dataset that has received multiple transformations to ensure that all changes are logged correctly.

Given a dataset has undergone multiple transformations, when the user checks the audit trail, then all transformations should be listed sequentially with corresponding timestamps and identity of the user who made each change.

User exports the audit trail to a CSV file for external review and compliance purposes.

Given the audit trail is available, when the user selects the option to export, then a CSV file containing the audit details should be generated with all relevant information intact.

User accesses the audit trail after an unauthorized change attempt to verify the integrity of data handling.

Given an unauthorized change was attempted, when the user checks the audit trail, then the log must indicate the attempted action, the user ID of the individual who attempted the changes, and the status of the attempt.

User receives notifications for significant changes logged in the audit trail to maintain oversight of data handling activities.

Given the user is subscribed to notifications, when a significant change is logged in the audit trail, then the user should receive an alert detailing the type of change and the timestamp.

User Access Controls for Data Tools

User Story: As a data governance manager, I want user access controls for data tools so that I can secure sensitive data and regulate user activity within the platform.
Description: User Access Controls for Data Tools establishes permissions settings that enable administrators to regulate who can access and modify data transformation tools. This requirement is critical for ensuring data security and governance, especially as businesses scale. By allowing for granular control over user roles and permissions, this functionality enhances collaboration while protecting sensitive data from unauthorized access. The integration of user access controls ensures that only qualified personnel can perform critical data transformation tasks, maintaining the integrity of the analytics process.
Acceptance Criteria: As an administrator, I need to set permissions for users who can access the data transformation tools on Datapy, ensuring specific roles can be assigned to enhance data governance and security.

Given I am logged in as an administrator, when I create a new role with limited access, then the users assigned to that role can only access the specified data transformation tools and no others.

As a data analyst, I need to ensure that upon submitting changes to data transformation tools, only users with modify permissions can perform this action, preventing unauthorized alterations.

Given I am logged in as a regular user, when I attempt to modify a data transformation tool for which I do not have permissions, then I receive an error message indicating insufficient access rights.

As an operations manager, I want to review all access attempts to the data transformation tools to ensure that only authorized users are gaining access as per the defined permissions.

Given I am an operations manager, when I access the user logs, then I can see a record of all access attempts, including user roles and timestamps, allowing for audit and compliance checks.

As a product owner, I want to ensure that changes to user access controls can be reverted if necessary, maintaining data security in case an error has been made in permissions settings.

Given I am logged in as an administrator, when I change a user's permissions and then revert those changes, then the original permissions settings should be restored without error.

As an IT security officer, I want to ensure that access controls for the data transformation tools comply with industry standards, validating that user roles are properly enforced.

Given I am reviewing the system settings, when I check the user roles assigned to data transformation tools, then they should align with predefined security compliance standards documented by the organization.

As a collaboration-focused team member, I want the ability to see which users currently have access to the data transformation tools, facilitating better communication within the team regarding data responsibilities.

Given I am logged in as a team member, when I view the access settings for the data transformation tools, then I can see a list of all users assigned to each tool and their access levels.

Event-Driven Triggers

This feature allows users to set up triggers that automatically push data to and from Datapy based on specific events or thresholds. This automation streamlines data management processes and enhances responsiveness to changes in business metrics.

Requirements

Customizable Event Triggers

User Story: As a data analyst, I want to create customizable triggers for specific events so that I can automate data processes and respond quickly to changes in business metrics without manual intervention.
Description: This requirement involves the ability for users to create and customize event-driven triggers directly within Datapy. Users should be able to define specific events or thresholds based on their business metrics, allowing them to automate data transfer processes that align with their operational requirements. The implementation of this feature will enable businesses to respond promptly to shifts in their data, enhancing efficiency and accuracy. This capability will strengthen user engagement by giving them control over their data management workflows and ensuring seamless integration with other components of the platform.
Acceptance Criteria: User sets up a customizable event trigger based on an increase in sales data.

Given that the user has selected the sales data metric, when the user specifies a threshold increase of 10%, then the event trigger should be successfully created and a confirmation message should be displayed.

User receives notifications when a defined event threshold is met.

Given that a user has set up an event trigger for a 15% decrease in website traffic, when the traffic decreases by 15%, then the user should receive an automated email notification within 5 minutes of the occurrence.

User edits an existing event trigger to change the threshold value.

Given that the user has an existing event trigger for a 20% increase in lead generation, when the user edits the trigger to set the threshold to 25%, then the updated trigger settings should be saved and a success message should be displayed.

User can delete an existing event trigger.

Given that the user has previously created an event trigger for a reduction in customer complaints, when the user initiates the deletion process, then the trigger should be removed and a notification should confirm the deletion.

User views a list of all active event triggers in the dashboard.

Given that the user is logged into their Datapy account, when the user navigates to the event triggers section of the dashboard, then they should see all active triggers displayed with their respective thresholds and statuses.

User sets a multi-condition event trigger that combines different business metrics.

Given that the user wants to base the trigger on both sales performance and inventory levels, when the user sets a trigger for when sales drop by 10% while inventory drops below 50 units, then the trigger should be created successfully, and both conditions must be active for the event to fire.

User tests an event trigger to ensure it activates under the specified conditions.

Given that the user has created a test event trigger for an increase in email sign-ups, when they manually simulate an increase of 100 sign-ups, then the event trigger should activate, and the user should receive a confirmation notification of the activation.

Real-time Data Synchronization

User Story: As a business owner, I want real-time data synchronization to occur automatically based on triggers so that my team can access the latest metrics instantly and make informed decisions without delays.
Description: This requirement ensures that the event-driven triggers can facilitate real-time data synchronization between Datapy and external data sources. The triggers should automatically push and pull data in real-time based on defined events or thresholds, minimizing lag and ensuring that users are always working with the most current data. This functionality is critical for businesses that rely on timely insights for decision-making. By implementing this requirement, Datapy will enhance its competitive edge by ensuring that users have immediate access to updated metrics and analytics, making their operations more agile.
Acceptance Criteria: User triggers a data update when a specified sales threshold is met, and the data is automatically synchronized with Datapy without manual intervention.

Given the user has set a sales threshold for data synchronization, when the sales data reaches the specified threshold, then the system automatically pushes the updated sales data to Datapy in real-time.

A user updates inventory in an external system, leading to automatic data pull into Datapy for real-time inventory metrics.

Given the user has made changes to the inventory in the external system, when the updated inventory data is available, then Datapy retrieves the updated inventory data within 5 seconds without errors.

Upon achieving a specific customer engagement score, user receives an automatic notification that relevant data has been synchronized in Datapy.

Given a specified customer engagement score for data trigger is set, when the score reaches that threshold, then the user receives a notification confirming data synchronization in Datapy within 2 minutes.

User observes live dashboard metrics in Datapy reflecting real-time changes from an external marketing platform.

Given the external marketing platform is actively sending data, when there is a change in marketing metrics, then the dashboard in Datapy updates to reflect the changes within 10 seconds.

The system manages multiple triggers that push and pull data based on different defined events simultaneously without conflict.

Given that multiple data triggers are configured with different events, when those events occur, then all corresponding data updates are processed correctly without conflicts or data loss.

Users can view a data synchronization history log to track when the data was last updated between Datapy and the external source.

Given that a data synchronization log is implemented, when a user requests the synchronization history, then the system displays a log detailing the timestamp and data involved in the last 10 synchronization events.

In the event of a failure during data synchronization, the user receives an error message and troubleshooting guidance within Datapy.

Given a data synchronization failure occurs, when the failure is detected, then the user receives a clear error message along with suggested steps for resolution within 1 minute.

Notification System for Triggers

User Story: As a team leader, I want to receive alerts when triggers occur so that I can stay informed about critical changes in our data metrics and respond quickly to emerging issues.
Description: This requirement involves implementing a notification system that alerts users when an event-driven trigger is activated. Users should receive customizable notifications via email, SMS, or in-app messages, providing them with timely updates on significant data changes or actions taken based on the triggers. The notifications will enhance user engagement and ensure that stakeholders are informed promptly about vital business insights, allowing for quicker action and response to changing conditions. This feature will be instrumental in ensuring users stay informed and can adapt to shifts in data without constantly monitoring the platform.
Acceptance Criteria: User receives a notification when an event-driven trigger for sales data exceeds a predefined threshold.

Given the user has set a trigger for sales data exceeding $10,000, when the sales data is updated and exceeds the threshold, then the user should receive an email notification within 5 minutes.

User customizes notification settings for event-driven triggers via the Datapy platform.

Given the user accesses the notification settings, when the user selects preferred notification types (email, SMS, in-app), then the changes should be saved and reflected immediately in the user profile.

User receives an SMS notification for a critical event-triggered system alert.

Given the user has subscribed to receive SMS notifications for critical events, when a trigger for low inventory levels is activated, then the user should receive an SMS notification within 3 minutes.

User checks the notification history for past event-based alerts.

Given the user accesses the notification history page, when the user selects a date range, then the system should display all relevant event-triggered notifications within that range.

User enables or disables notifications for specific event-driven triggers in their account settings.

Given the user navigates to their account settings, when the user toggles the notification options for specific triggers, then the system should reflect the updated preferences immediately without error.

User receives in-app notifications for triggered events without delay.

Given the user is actively using the Datapy application, when an event-driven trigger is activated, then the in-app notification should appear within 2 minutes on the user’s dashboard.

User integrates the notification system with a third-party service for enhanced communication.

Given the user has configured a third-party service (e.g., Slack) for notifications, when an event-driven trigger is activated, then the relevant notification should appear in the integrated service within 1 minute.

Analytics Dashboard for Trigger Performance

User Story: As a data strategist, I want to analyze the performance of my event triggers on a dedicated dashboard so that I can optimize their effectiveness and ensure they contribute positively to our data management processes.
Description: This requirement entails developing an analytics dashboard that allows users to monitor the performance of their event-driven triggers. Users should be able to visualize trigger activations, success rates, and the impact of the triggers on their business metrics. This dashboard will provide insights and analytics that can help users refine their triggers over time for better performance and outcomes. By implementing this requirement, Datapy will empower users to analyze the effectiveness of their automation strategies and make data-driven adjustments as necessary, ultimately improving overall operational efficiency.
Acceptance Criteria: User navigates to the Analytics Dashboard to view trigger performance metrics during a business review meeting.

Given the user is logged into Datapy, when they access the Analytics Dashboard, then they should see real-time visualizations of trigger activations, success rates, and impact metrics displayed clearly on the dashboard.

User adjusts a trigger's parameters and wants to monitor the changes reflected on the Analytics Dashboard immediately.

Given the user modifies a trigger's settings, when the changes are saved, then the dashboard should refresh automatically to display the updated performance metrics within 30 seconds.

User wants to export the trigger performance data for monthly reporting purposes.

Given the user is viewing the Analytics Dashboard, when they select the export option, then they should receive a downloadable CSV file containing all relevant trigger performance data within 5 minutes.

User accesses the dashboard after setting up a new trigger to evaluate its initial performance.

Given the new trigger has been created and activated, when the user views the Analytics Dashboard, then there should be a clear indication of the initial success rate and activation count in less than 2 hours after activation.

User retrieves historical data to analyze long-term trends of trigger performance over the last 6 months.

Given the user is on the Analytics Dashboard, when they filter the data to view the last 6 months, then they should see accurate visualizations reflecting the performance trends over that time period within 1 minute.

User collaborates with team members to discuss trigger performance insights observed on the dashboard during a team meeting.

Given that team members have access to the Analytics Dashboard, when the user shares their screen, then all team members should be able to view the same dashboard metrics simultaneously with no latency.

User is using the dashboard to assess the effectiveness of a recently modified trigger in real-time.

Given the trigger was modified and activated within the last hour, when the user checks the dashboard, then they should see updated performance metrics reflecting the modifications without significant delay.

Comprehensive Documentation Portal

Accompanying the API is a well-organized documentation portal that provides detailed guides, examples, and troubleshooting advice. This resource empowers users to effectively utilize the API, ensuring they can maximize its potential for their custom analytics needs.

Requirements

User-friendly Navigation

User Story: As a new API user, I want a clear navigation system in the documentation portal so that I can quickly find the information I need without frustration.
Description: The documentation portal must feature an intuitive navigation system that allows users to easily find relevant content. This includes a structured menu with categories and subcategories, a search bar, and quick links to popular resources. The goal is to enhance user experience by minimizing the time spent searching for information. A user-friendly navigation system enables users, particularly those with limited technical expertise, to efficiently access API documentation, guides, examples, and troubleshooting advice, empowering them to better utilize the API for their analytics needs.
Acceptance Criteria: User seeks to navigate to specific API endpoint documentation quickly to implement a feature in their application.

Given the user is on the documentation portal, when they enter a keyword in the search bar related to the API endpoint, then relevant documentation should appear in the search results within 2 seconds.

A user wants to find the troubleshooting guide for a specific error they encountered while using the API.

Given the user is on the documentation portal, when they click on the 'Troubleshooting' category in the structured menu, then they should see all related articles listed clearly without any broken links.

A new user wants to understand the overall structure of the documentation portal and locate a popular API example document.

Given the user is on the documentation portal, when they access the main menu, then they should find a clearly labeled section for 'Popular Resources' containing the top 5 API example documents that are hyperlinked.

An experienced developer is attempting to use the documentation portal to review different categories of API guides.

Given the user has accessed the documentation portal, when they hover over the main menu, then a dropdown menu should display all categories and subcategories related to the API guides without any delay.

A user encounters difficulty using the search function of the documentation portal and requires immediate help.

Given the user is on the documentation portal, when they click on the 'Help' button, then they should be directed to a help section with FAQs and contact options for support promptly.

Search Functionality

User Story: As a developer, I want to be able to search the documentation portal for specific terms so that I can quickly find relevant examples and troubleshooting tips.
Description: Integrating a robust search functionality within the documentation portal is essential for users seeking specific information. Users should be able to enter keywords or phrases and receive immediate, relevant search results that direct them to the exact documentation they require. This feature should include filters and sorting options to refine results based on user preferences. By enabling users to efficiently locate specific information, this functionality will significantly enhance user satisfaction and decrease the learning curve associated with the API.
Acceptance Criteria: User searches for a specific API endpoint in the documentation portal.

Given a user is on the documentation portal, when they enter a relevant keyword related to an API endpoint, then they should see a list of search results that includes the exact documentation for that endpoint within 2 seconds.

User filters search results by category in the documentation portal.

Given a user has performed a search, when they select a specific category filter from the options provided, then the search results should update to display only the relevant documents within that category immediately.

User sorts search results by relevance or date in the documentation portal.

Given a user has initiated a search, when they choose to sort the results by 'most relevant' or 'most recent', then the results should reorder according to the selected sorting criteria without refreshing the page.

User accesses the documentation via a specific keyword and finds the help section.

Given a user types in 'troubleshooting' into the search box, when the search results appear, then the first result should be the 'Troubleshooting' section of the documentation, clearly marked and accessible.

User views a detailed example from the search results in the documentation portal.

Given a user searched for 'data visualization examples', when they click on a search result link, then they should be taken to a detailed documentation page with at least three examples clearly listed.

User receives no results for a non-existent API call.

Given a user searches for an API call that does not exist, when they submit the search, then a message should display indicating 'No results found' along with suggestions for related topics.

Interactive Code Examples

User Story: As an API user, I want to experiment with code examples in the documentation so that I can understand and test different API responses before implementing them in my project.
Description: The documentation portal should provide interactive code examples that allow users to test API calls directly within the browser. These examples should be functional, giving users the ability to input parameters and see real-time responses from the API. By enabling hands-on interaction with code, users can better understand how to implement the API in their own projects, resulting in increased confidence and efficiency in utilizing the API effectively for their analytics needs.
Acceptance Criteria: Interactive code example testing with various API parameters

Given a user is on the interactive documentation page, when they input valid API parameters into the code example box and click 'Test', then they should receive a valid response from the API that reflects the input parameters.

Error handling in interactive code examples

Given a user is testing the interactive code examples, when they enter invalid parameters into the code example box and click 'Test', then they should see a descriptive error message indicating the nature of the error.

Real-time response display for interactive examples

Given a user inputs API parameters in the interactive code example, when they click 'Test', then the response from the API should be displayed in real-time without any noticeable lag or delay.

Multiple programming languages support in interactive examples

Given a user selects a programming language from a dropdown menu in the documentation portal, when they view the corresponding interactive code example, then the example should reflect the correct syntax and structure for the selected language.

Tutorial guidance alongside interactive examples

Given a user is using the interactive code examples, when they click on a help icon, then they should see a pop-up tutorial that explains how to use the examples effectively and troubleshoot common issues.

Accessibility features in interactive documentation

Given a user accesses the interactive code examples, when they use keyboard navigation or screen reader tools, then all key elements of the interactive examples should be accessible and usable without a mouse.

Integration testing of interactive examples with live API

Given a user is testing the interactive code examples, when they execute an API call, then the system should successfully connect to the live API and return accurate data based on the specified parameters.

Feedback and Help Section

User Story: As a user, I want to submit feedback on the documentation so that I can inform the team of issues or suggest improvements for a better experience.
Description: A built-in feedback and help section should be established within the documentation portal, allowing users to report issues, ask questions, and provide suggestions. This section would include a form for users to submit inquiries or problems they encounter. Having a feedback mechanism enables continuous improvement of the documentation portal, facilitating engagement with users and ensuring that common pain points are addressed swiftly. This leads to enhanced user satisfaction and a better overall user experience within the platform.
Acceptance Criteria: User navigates to the Feedback and Help Section in the documentation portal after encountering an issue with API integration.

Given a user is on the documentation portal, when they click on the 'Feedback and Help' section, then the user should see a feedback form with fields for description, category, and contact information.

A user submits feedback regarding an unclear API call in the Help Section.

Given a user has filled out the feedback form and submits it, when they click the 'Submit' button, then they should receive a confirmation message indicating that their feedback has been received successfully.

An admin reviews submitted feedback from the documentation portal.

Given an admin is logged into the portal, when they access the submissions section, then they should be able to view a list of all feedback entries with user details and timestamps.

A user wants to report a bug they found in the API documentation.

Given the user is in the Help Section, when they select 'Report a Bug' from the feedback form options, then they should be presented with additional fields specific to bug reporting (e.g., steps to reproduce, expected vs. actual behavior).

A user seeks to provide a suggestion for improvement in the documentation.

Given the user is in the Feedback and Help Section, when they choose 'Suggestion' from the form options, then they should be required to enter a description but may leave contact information optional.

A user encounters an error message while submitting feedback.

Given the user fills out the feedback form incorrectly and clicks 'Submit', when they submit the form, then they should receive an error message highlighting which field needs correction.

A user reviews previously submitted feedback responses from the admin team.

Given a user is in the Feedback and Help Section, when they access their submission history, then they should see responses from the admin team regarding their inquiries or suggestions.

Version History Tracking

User Story: As an API user, I want to view the change log for the documentation so that I can determine if any updates require me to modify my implementation.
Description: The documentation portal must include a version history tracking feature that allows users to view updates and changes made to the API documentation. This feature should provide clear notes on what has changed with each version, enabling users to stay informed about new features, deprecated functionality, or important bug fixes. Such transparency fosters user trust and helps developers assess whether they need to adjust their applications in response to API updates, ultimately improving the integration process.
Acceptance Criteria: Version History Access for Users

Given a user navigates to the documentation portal, when they click on the 'Version History' section, then they should see a list of all API documentation versions with their release dates and changes summarized.

Detailed Change Notes Display

Given a user has selected a specific version in the 'Version History', when they view the detailed change notes for that version, then they should see clearly defined sections for new features, deprecated functionality, and bug fixes.

Notification of Updates on Homepage

Given a user accesses the documentation portal, when there has been a new version released, then the homepage should display a notification banner indicating the latest version and highlighting key changes.

User Feedback on Version History

Given a user is viewing the version history, when they encounter the change notes, then they should have the option to give feedback or report issues via a feedback form accessible from the version history page.

Search Functionality in Version History

Given a user accesses the version history document, when they utilize the search bar to find specific changes or terms, then relevant changes associated with their search should be displayed with the version details.

Export Version History to PDF

Given a user is on the version history page, when they select the 'Export to PDF' option, then the complete version history along with change notes should be downloaded as a PDF file.

Multilingual Support

User Story: As a non-English speaking user, I want the documentation to be available in my native language so that I can understand and utilize the API effectively without language barriers.
Description: The documentation portal should offer multilingual support to accommodate users from various linguistic backgrounds. This requirement involves translating key sections and tutorials of the documentation into multiple languages, making the API accessible to a global audience. By providing multilingual options, Datapy can expand its reach and usability, empowering non-English speaking users to effectively utilize the API for their analytics needs, thus broadening the potential user base significantly.
Acceptance Criteria: Multilingual Support for Non-English Users Accessing the API Documentation

Given a user navigates to the documentation portal in Spanish, When they access key sections and tutorials, Then all text should be accurately translated into Spanish without losing context or meaning.

Verification of Language Selection Functionality

Given a user selects a language option from a dropdown menu on the documentation portal, When they refresh the page, Then the selected language should persist, and all documentation should load in that language.

User Experience Testing with Multilingual Support

Given a user who is a native French speaker accesses the documentation portal, When they review troubleshooting advice, Then the content should be fully translated and easy to understand for a French-speaking audience.

Accessibility of Translated Content Across Devices

Given a user accessing the documentation portal from a mobile device in German, When they browse through the translated content, Then the layout and readability should be optimized for mobile viewing in German.

Accuracy of Technical Terminology in Translations

Given the API documentation has been translated into Chinese, When a technical term is referenced, Then the translation should reflect an accurate and commonly used term in the field.

Feedback Mechanism for Multilingual Users

Given a user is reading the documentation in Italian, When they encounter an unclear translation, Then they should have the option to submit feedback on the translation quality easily.

Completion of All Translations Before Launch

Given the multilingual documentation is scheduled for launch, When the documentation is reviewed, Then all key sections and tutorials must be fully translated into all target languages as planned.

Secure Data Transfer Protocols

To protect user data during transmission, this feature implements advanced security measures, including encryption and secure authentication methods. Users can trust that their data is protected, providing peace of mind while integrating critical business information.

Requirements

Advanced Encryption Standards

User Story: As a business owner, I want my data to be encrypted during transfer so that I can ensure its security and protect my company from data breaches.
Description: Implement advanced encryption protocols such as AES-256 during data transmission to ensure that all user data remains confidential and secure while being sent across networks. This feature enhances trust and compliance with data protection regulations, mitigating risks associated with data breaches. The integration of these standards into Datapy will provide users with robust security measures necessary for the protection of sensitive business information.
Acceptance Criteria: User initiates a data transfer process, selecting sensitive business information to upload to the Datapy platform while connected to an unsecured network.

Given the user is connected to an unsecured network, when they initiate the data transfer, then the data must be encrypted using AES-256 standards and remain inaccessible during transmission.

A user attempts to access a report containing sensitive data after it has been transmitted and stored in the Datapy platform.

Given the data has been transmitted and stored securely, when the user accesses the report, then the data should be decrypted only for authorized users and remain protected against unauthorized access.

IT administrators regularly audit the data transmission logs to ensure compliance with security protocols.

Given that the IT administrator accesses the data transmission logs, when evaluating logs, then all entries should indicate successful encryption with AES-256 and proper authentication measures prior to data transfer.

A user tries to share a data report with a colleague over a secure internal network.

Given the user is sharing data through a secure internal network, when the data is transmitted, then it should maintain AES-256 encryption and validate the recipient's identity before sharing.

A scenario in which data is transferred using the API between Datapy and a third-party application requiring sensitive data.

Given a request is made for sensitive data to be transferred via the API, when the data is sent, then it must utilize AES-256 encryption and require secure token authentication for the third-party application.

An audit process is performed to ensure encryption protocols are correctly implemented across all data transmission channels.

Given an audit of the encryption protocols is initiated, when the review is conducted, then all transmission channels must show compliance with AES-256 encryption standards without exceptions.

Secure Authentication Mechanism

User Story: As a system administrator, I want to enforce multi-factor authentication so that I can minimize the risk of unauthorized access to sensitive data.
Description: Introduce secure authentication mechanisms like multi-factor authentication (MFA) and OAuth to enhance user verification during the data transfer process. This security measure adds an additional layer of protection, ensuring that only authorized users can access sensitive data. By implementing this feature, Datapy will further safeguard user data and build stronger trust with customers, aligning with best practices for data security.
Acceptance Criteria: User Initiation of Data Transfer with MFA

Given a user initiates a data transfer, when they enter their username and password, then they must be prompted for a multi-factor authentication code to verify their identity before the transfer proceeds.

OAuth Authorization for Third-Party Applications

Given a user connects a third-party application to Datapy, when they select to authorize the application, then they must be redirected to an OAuth consent screen asking for permission to access specific data.

Security Logs for Authentication Attempts

Given an authentication attempt occurs, when the user enters their credentials, then a log entry must be recorded capturing the username, timestamp, successful or failed status, and the method of authentication used.

User Feedback on Authentication Process

Given a user completes the multi-factor authentication process, when they successfully log in, then they should receive a confirmation message indicating successful authentication along with the method used (e.g., MFA or OAuth).

Integration of Secure Protocols in Data Transfers

Given data is being transferred, when the transfer protocol is initiated, then the data must be encrypted using a secure protocol (e.g., TLS) during the transmission to ensure data protection.

Unauthorized Access Attempt Detection

Given an unauthorized user attempts to access data, when their credentials are submitted, then the system must block access and log the attempt for review.

Session Timeout Enforcement

Given a user is authenticated and active, when they become idle for a predetermined period, then the system should automatically log them out and require re-authentication using MFA or OAuth.

Comprehensive Audit Logging

User Story: As a compliance officer, I want to have a complete audit trail of data transfers so that I can ensure the company meets regulatory requirements.
Description: Develop a comprehensive audit logging feature that tracks and logs all data transfer activities, including timestamps, user IDs, and actions performed. This feature will not only enhance accountability and traceability but will also provide vital information for compliance audits and security assessments. By implementing this requirement, Datapy will strengthen its overall security posture and provide users with insights into their data handling processes.
Acceptance Criteria: Data transfer activities are logged accurately during user uploads and downloads.

Given a user performs a data transfer, when the transfer is completed, then an entry should be created in the audit log with the correct timestamp, user ID, and action performed.

Audit logs are accessible to authorized users for review and compliance checks.

Given an authorized user requests access to audit logs, when the request is made, then the system should return all relevant entries filtered by date and user ID.

Audit logs should ensure all logging activities are encrypted to maintain confidentiality.

Given audit log entries are created, when the log is generated, then all entries should be encrypted using established encryption standards before being stored.

System performance during data transfer should be tracked to ensure efficiency and compliance.

Given a user initiates a data transfer, when the transfer completes, then the duration of the transfer should be recorded in the audit log along with any errors that occurred.

Detection of unauthorized access attempts should trigger alert notifications.

Given an unauthorized user attempts to access protected data, when the system detects this attempt, then an alert should be generated and logged in the audit log for further investigation.

User-Friendly Security Interface

User Story: As a user, I want an easy-to-navigate security settings interface so that I can adjust my data transfer security options without technical assistance.
Description: Create a user-friendly interface that allows users to manage their secure data transfer settings easily. This feature will empower users to customize their security preferences, such as selecting encryption levels and enabling or disabling authentication methods. By providing an intuitive interface, Datapy will enhance user experience and satisfaction while ensuring that security measures are accessible and manageable for all users.
Acceptance Criteria: User accesses the security settings interface to customize their data transfer protocols.

Given the user is logged in, When they navigate to the security settings page, Then they should see options to select encryption levels and enable/disable authentication methods.

User selects a medium encryption level and enables two-factor authentication for their data transfers.

Given the user has selected a medium encryption level and enabled two-factor authentication, When they save their settings, Then the system should confirm the changes and immediately apply the selected settings.

User attempts to disable secure authentication methods after previously enabling them.

Given the user has enabled secure authentication methods, When they try to disable these methods, Then a confirmation prompt should appear to prevent accidental changes.

User views the status of their data transmission security settings in real-time.

Given the user is on the security settings page, When they check their current settings, Then they should see a live indicator reflecting the status of their encryption and authentication settings.

User encounters an error while trying to save their security settings.

Given the user has made changes to their security settings, When an error occurs during saving, Then an error message should be displayed explaining the issue and prompting for correction.

User wants to reset their security settings to default.

Given the user is on the security settings page, When they click the 'Reset to Default' button, Then all customized settings should revert to the manufacturer's preset values with a confirmation message displayed.

Data Transfer Alerts

User Story: As a user, I want to receive alerts for any suspicious data transfer activity so that I can take immediate action to protect my data.
Description: Implement a notification system that alerts users regarding data transfer activities, especially concerning any unusual or unauthorized access attempts. This proactive measure will help users quickly identify potential security threats and take necessary actions. By adding this feature, Datapy will improve real-time monitoring of data security, ultimately leading to enhanced protection of user information.
Acceptance Criteria: User receives an alert notification in real-time upon unauthorized access attempt to their data during a scheduled data transfer.

Given a user is logged into Datapy, When an unauthorized access is detected during a data transfer, Then the user receives an alert notification within 5 minutes of the event.

Users can customize the types of alerts they wish to receive regarding data transfers.

Given a user accesses the alert settings section, When they select their preferences for alert types, Then the system saves the selected preferences and applies them to future notifications.

Users receive a summary report of all data transfer activities, including alerts for suspicious activities on a weekly basis.

Given a week has passed since the last report was generated, When the report is generated, Then it includes a detailed summary of data transfer activities and any alerts for unauthorized access attempts within that timeframe.

The system ensures that alerts are sent through multiple communication channels as selected by the user.

Given a user has configured their alert preferences to include email and SMS, When an alert is triggered, Then the user should receive the notification via both channels simultaneously.

Users can view a historical log of all alerts related to data transfer activities for audit purposes.

Given a user accesses the historical logs section, When they query for data transfer alerts, Then the system displays a comprehensive log of past alerts with timestamps and details.

Alerts are clear and provide actionable insights to users regarding unauthorized access attempts.

Given an unauthorized access attempt occurs, When the alert is sent to the user, Then it should clearly state the nature of the attempt and suggested actions (e.g., changing password, reviewing recent accesses).

The user can set different alert thresholds for different types of data transfers based on sensitivity.

Given a user is configuring alert settings, When they specify different thresholds for different data types, Then the system should implement these thresholds and trigger alerts accordingly during data transfers.

Trend Watcher

An advanced alert system that continuously monitors key performance indicators (KPIs) and user-defined metrics in real time. Trend Watcher empowers users by sending instant notifications when significant changes or trends are detected, enabling timely intervention and proactive decision-making.

Requirements

Real-time KPI Monitoring

User Story: As a business analyst, I want to monitor KPIs in real-time so that I can quickly identify trends and make informed decisions to optimize our operational performance.
Description: This requirement involves the implementation of an advanced real-time monitoring system that continuously tracks user-defined Key Performance Indicators (KPIs) and business metrics. It is essential for spotting significant changes in metrics that could impact business operations. By providing immediate insights into deviations from set benchmarks, this function helps users take timely action to optimize performance. This feature will integrate seamlessly with Datapy's existing analytics infrastructure, ensuring a streamlined user experience while providing actionable alerts when trends or alerts are detected. The expected outcome is improved data-driven decision-making that enhances operational efficiency.
Acceptance Criteria: User receives an alert when a KPI surpasses the defined threshold for the first time during a reporting period.

Given the user has set a threshold for the selected KPI, when the KPI exceeds that threshold, then an instant notification is sent to the user via their chosen communication method (email, in-app alert).

User can customize which KPIs to monitor and set individual thresholds for alerts.

Given the user is on the Trend Watcher settings page, when they select KPIs and input corresponding threshold values, then those settings must be saved and reflected in the monitoring dashboard.

User can view a historical timeline of alerts triggered for each KPI.

Given the user accesses the KPI monitoring report, when they select a specific KPI, then a timeline displaying all alerts triggered for that KPI should be visible, with timestamps and thresholds breached.

System accurately detects and reports rapid fluctuations in KPI values.

Given a rapid change occurs in the KPI value (increases/decreases), when such a change exceeds a predetermined rate of fluctuation, then the system will generate a notification alerting the user of the volatility immediately.

User can deactivate notifications for specific KPIs without losing threshold data.

Given the user is on the notification settings page, when they choose to deactivate notifications for a selected KPI, then those notifications should stop while retaining the threshold data for future use.

user receives a summary of KPIs and alerts on a weekly basis.

Given the user opts in for a weekly report, when the week concludes, then an email summary containing all triggered alerts and current KPI statuses must be sent to the user.

Customizable Alert Settings

User Story: As a product manager, I want to customize my alert settings for KPI monitoring so that I only receive notifications that are relevant to my specific objectives and KPIs.
Description: This requirement focuses on allowing users to configure customizable alert settings for the Trend Watcher feature, enabling them to select which KPIs or metrics they want to monitor and under what conditions they should receive alerts. By supporting customization, this functionality ensures that users only receive relevant notifications based on their specific business needs and thresholds. This integration will enhance user experience by reducing alert fatigue, allowing users to focus on the data that matters most. The expected outcome is increased user confidence in the alert system, leading to better response actions and improved business outcomes.
Acceptance Criteria: User Configures Custom Alerts for Revenue KPI

Given the user has access to the Trend Watcher, when they navigate to alert settings, then they should be able to select 'Revenue' as a KPI to monitor and define a threshold for alerts.

User Receives Alert for Revenue KPI Exceeding Threshold

Given the user has set an alert for when the 'Revenue' KPI exceeds $10,000, when the revenue crosses that threshold, then the user should receive an instant notification alerting them of the change.

User Configures Multiple KPIs for Alerts

Given the user is on the alert settings page, when they add multiple KPIs including 'Customer Satisfaction Score' and 'Net Profit Margin', then all selected KPIs should be displayed in the alert summary for user confirmation.

User Defines Alert Conditions with Specific Time Frames

Given the user has added 'Website Traffic' as a KPI, when they specify to receive alerts only during business hours, then the system should restrict notifications to only those time frames.

User Edits Existing Alerts for KPIs

Given the user has already set an alert for 'Website Traffic', when they attempt to change the threshold from 1,000 to 2,000 visits, then the system should successfully update the alert and confirm the change with a message.

User Opts In to Daily Summary Notifications

Given the user prefers a summary instead of real-time alerts, when they select the 'Daily Summary' option for alerts, then the user should receive a consolidated report each day detailing all alert-triggered events.

User Disables Alerts for Specific KPIs Temporarily

Given the user wishes to pause alerts for 'Operating Expense' KPI during a seasonal downturn, when they toggle the alert to 'Off', then no alerts should be sent for that KPI until re-enabled by the user.

Trend Analysis Visualization

User Story: As a data analyst, I want to visualize trends in a graphical format so that I can easily identify patterns and anomalies for better strategic decision-making.
Description: This requirement outlines the necessity for a robust visualization tool that displays trends over time based on the collected data from the KPI alerts. By providing visual reports and graphs, users will be able to analyze historical data and identify patterns or anomalies in the trends monitored by the Trend Watcher. This feature aims to integrate with Datapy's existing dashboard functionality, allowing users to visualize metrics alongside other analytics. The expected outcome is a more in-depth understanding of performance, facilitating strategic planning and informed decision-making based on visual insights.
Acceptance Criteria: User receives notification of a significant KPI change while using the Trend Analysis Visualization feature.

Given that the user is viewing the Trend Analysis Visualization, when a defined KPI experiences a predefined significant change, then the user should receive an instant notification in the application alert section.

User interacts with a visual report to analyze historical data trends.

Given that the user has selected a visual report from the Trend Analysis Visualization, when they hover over a data point, then detailed information regarding that data point should be displayed immediately.

User customizes the Trend Analysis Visualization dashboard to display specific metrics.

Given that the user is on the Trend Analysis Visualization dashboard, when they select metrics from the customization menu, then the dashboard should update in real-time to reflect the selected metrics without requiring a page refresh.

User identifies patterns or anomalies within the visualized trend data.

Given that the user is analyzing the visual trend data, when an anomaly is detected in the displayed trends, then the system should highlight the anomaly clearly and provide contextual information about the deviation.

User saves a configured dashboard view for future access.

Given that the user has configured a specific layout and selected metrics in the Trend Analysis Visualization, when they click 'Save View,' then their configuration should be saved and retrievable the next time they access the feature.

User shares their visual reports with team members for collaborative analysis.

Given that the user has generated a visual report in the Trend Analysis Visualization, when they select the 'Share' option, then the report should be accessible by the specified team members via a shared link or notification.

Collaborative Notification System

User Story: As a team lead, I want to share KPI alerts with my team so that we can collaboratively respond to changes in our business performance.
Description: This requirement involves creating a collaborative notification system that allows users to share alerts and insights with team members within the platform. By facilitating team communication, users can quickly discuss the implications of alerts, coordinate actions, and engage in proactive problem-solving. This feature is crucial for cross-functional teams that depend on timely information to drive collective decision-making. The expected outcome is improved collaboration and increased responsiveness to real-time data changes.
Acceptance Criteria: User shares an alert generated by Trend Watcher with team members through the collaborative notification system within Datapy.

Given a user receives a notification from Trend Watcher, when they select the 'Share Alert' option, then they should be able to choose team members to share the alert with, and the selected members should receive the alert notification in real-time.

A team member receives a shared alert notification and accesses the collaborative discussion space to review it alongside teammates.

Given a user receives a shared alert notification, when they click on the notification, then they should be directed to a collaborative discussion space where all relevant team members can comment and respond to the alert.

Users can edit and customize alerts before sharing them with their team to ensure clarity and relevance.

Given a user is viewing an alert from Trend Watcher, when they choose to 'Edit Alert' before sharing, then they should be able to modify the alert content, and the changes should be reflected in the shared notification received by the team.

Team members can respond to the shared alert with comments and action items for better coordination.

Given a user is in the collaborative discussion space for a shared alert, when they add a comment or assign an action item, then all members in the discussion space should receive a notification about the new comment or action item.

Users can view a history log of shared alerts and their discussions for future reference.

Given a user accesses the collaborative notification system, when they select the 'Alert History' option, then they should be presented with a chronological log of all shared alerts and associated discussions.

Multi-channel Notification Delivery

User Story: As a user, I want to receive my KPI alerts via different channels so that I can choose the method that works best for me to stay informed about business critical metrics.
Description: This requirement addresses the need for multi-channel delivery of alerts, enabling notifications to be sent via various mediums such as email, SMS, and in-app messaging. By diversifying notification channels, users will have flexibility in how they receive critical updates. This integration with existing communication tools will ensure that stakeholders are kept informed regardless of their preferred communication method. The expected outcome is enhanced user engagement and quicker response times to important alerts.
Acceptance Criteria: Multi-channel alert delivery for KPI changes

Given a user has defined specific KPIs, When a KPI exceeds or falls below a predefined threshold, Then an alert is sent via email, SMS, and in-app notification simultaneously.

User preference settings for notification channels

Given a user accesses their notification settings, When they select their preferred channels for receiving alerts (email, SMS, in-app), Then the system will reflect these preferences and prioritize notifications accordingly.

Real-time delivery of alerts across channels

Given an alert is triggered for a significant trend change, When the alert is dispatched, Then all selected channels (email, SMS, in-app) must receive the notification within 2 minutes of the alert triggering.

Integration with existing communication tools

Given that a user has configured their existing communication tools, When an alert is triggered, Then notifications must be sent through the integrated communication tools (e.g., Slack, Teams) along with the primary channels.

User acknowledgment of alerts

Given a user receives an alert notification, When they acknowledge the alert through any channel, Then the system logs the acknowledgment and stops sending repeat notifications for that specific alert.

Customization of alert content

Given a user sets up an alert, When they customize the message included in the alert, Then the alert sent via any channel must include the user-defined message content alongside standard information.

Historical Data Storage for Alerts

User Story: As a business strategist, I want to access historical alert data so that I can analyze past trends and make data-driven decisions for future strategies.
Description: This requirement outlines the necessity for a system that archives historical data for all alerts generated by the Trend Watcher. Users should have access to past alert data, enabling them to analyze trends over historical timelines and understand the context of past performance. This functionality will be integral in driving retrospective analysis and ongoing strategy refinement. The expected outcome is enhanced analytical depth leading to more informed and strategic business decisions based on historical performance data.
Acceptance Criteria: Accessing Archived Alerts Data in Trend Watcher

Given that the user navigates to the 'Alerts History' section of Trend Watcher, when they select a specific date range, then they should see a list of all alerts generated within that range, including details such as timestamp, metric involved, and alert type.

Exporting Historical Alert Data

Given that the user is viewing the alerts history, when they click the 'Export to CSV' button, then a CSV file should be generated and downloaded containing all the visible alerts data within the selected date range.

Analyzing Historical Trends from Alerts

Given that the user has accessed the alerts history, when they analyze the data, then they should be able to graph historical alerts over time and identify patterns or trends effectively using provided visualization tools.

Receiving Notifications for Archived Alerts

Given that the user has previously set up alert criteria in Trend Watcher, when they look at the historic alerts, then the system should allow them to filter and view alerts that match their current criteria settings.

Understanding Context of Past Alerts

Given that the user is reviewing alert history, when they select an alert entry, then they should be presented with context information including related KPIs and any notes made by team members about the alert's significance.

Security and Data Access for Historical Alert Data

Given that multiple users have different access levels, when a user attempts to access the 'Alerts History' section, then they should only see data for alerts generated within their access permissions and roles.

Customizable Alert Triggers

This feature allows users to define specific thresholds and conditions for receiving alerts, tailoring the notification system to their unique business needs. By customizing alert triggers, users can ensure they are only notified about changes that matter most to them, enhancing focus and efficiency.

Requirements

Threshold Configuration

User Story: As a business analyst, I want to configure custom thresholds for alerts so that I only receive notifications for significant changes that affect my team's performance.
Description: The Threshold Configuration requirement allows users to set specific numerical or categorical thresholds that will initiate alerts. This functionality ensures that users can customize their alert parameters to align with their business goals. By establishing clear and actionable thresholds, users can efficiently monitor key metrics and receive notifications only when significant deviations occur, thus minimizing unnecessary distractions and optimizing response times. This capability integrates seamlessly with the existing alert system, ensuring that alerts are concise and relevant, ultimately enhancing user experience and operational efficiency.
Acceptance Criteria: User configures a threshold to receive alerts when sales revenue drops below $10,000 in a month.

Given that the user has set a threshold of $10,000, when the sales revenue for the month is reported below this amount, then an alert should be triggered and sent to the user's registered email.

User modifies an existing threshold for user sign-ups from 100 to 150 per month.

Given that the user has an existing threshold of 100 sign-ups, when the user updates this value to 150 and saves the changes, then the new threshold should be reflected in the system.

User opts in to receive notifications for any increase in website traffic beyond 500 visits per day.

Given that the user has set a threshold for website traffic at 500 visits, when the daily traffic exceeds this threshold, then an alert should be generated and displayed in the user's dashboard.

User checks the historical alerts triggered in the last month based on set thresholds.

Given that the user accesses the alerts history section, when the user selects the appropriate time frame, then all alerts triggered in that time frame should be displayed with relevant details (date, threshold value, metric).

User receives alerts only for selected metrics based on the customizable alert system.

Given that the user has configured alerts for specific metrics, when metrics are updated, then alerts should only be sent for metrics that exceed the defined thresholds set by the user.

Condition-Based Alerts

User Story: As a sales manager, I want to set conditions for alert notifications so that I can quickly react to changes that may impact my team's performance or sales targets.
Description: The Condition-Based Alerts requirement enables users to define various conditions under which alerts should be triggered. This could include events such as a drop in sales, inventory levels reaching a certain point, or shifts in customer engagement metrics. By implementing this requirement, users gain the ability to create highly specific alerts that are informed by the evolving needs of their business. This feature integrates with the analytics engine behind Datapy, ensuring that alerts reflect real-time data accurately and contextually. This tailored notification system enhances the responsiveness of teams and aids in proactive decision-making.
Acceptance Criteria: User sets up an alert for when sales drop below a predefined threshold over a specific period.

Given that the user has defined a sales threshold, when sales drop below that threshold, then an alert should be triggered and sent to the user via their preferred notification channel.

User customizes alert criteria for inventory levels, setting a specific minimum level that, when reached, should trigger an alert.

Given that the user has set a minimum inventory level, when the inventory level falls below that minimum, then an alert must be generated and logged in the system for review.

User implements an alert for changes in customer engagement metrics, such as a sudden drop in website traffic.

Given that the user has specified engagement metrics to monitor, when there is a significant drop in traffic by a defined percentage, then the system should notify the user immediately through the selected notification method.

User wants to receive alerts only during business hours, ensuring notifications do not interrupt after hours.

Given that the user has set parameters for business hours, when an alert is triggered outside these hours, then that alert should be queued for delivery at the start of the next business day instead of being sent immediately.

User tests the alert system to ensure that multiple conditions can be set for a single alert.

Given that the user has defined multiple conditions for a single alert, when all conditions are met simultaneously, then a single alert should be triggered without any delays or omissions in the alert reporting.

User modifies an existing alert condition and verifies the system recognizes the change.

Given that the user has updated an existing alert condition, when the condition is saved, then the system should reflect the updated conditions in the user’s alert configuration immediately and distribute alerts per the new settings.

Alert Frequency Control

User Story: As an operations manager, I want to control the frequency of alert notifications so that I can manage my attention and prioritize my focus during peak times.
Description: The Alert Frequency Control requirement allows users to adjust how often they wish to receive alerts based on the established triggers and conditions. This feature caters to users who may want to limit the volume of notifications during certain periods, such as during high-activity times or when they're already addressing other alerts. This customization option ensures users can balance being informed without being overwhelmed, thus improving overall productivity. It smoothly integrates into the existing alerting mechanism, further enhancing user control and personalization of their notifications.
Acceptance Criteria: User defines a high alert threshold for sales notifications during peak hours.

Given the user sets a high alert frequency of once per hour for sales notifications, when sales metrics reach the defined threshold, then the user receives one alert per hour.

User adjusts the alert frequency to receive no notifications during a specified downtime.

Given the user defines a downtime period for alerts, when sales metrics trigger alerts during this period, then no alerts should be sent to the user.

User receives a summary of alerts in a digest format instead of multiple individual notifications.

Given the user opts for summary alerts, when multiple triggers occur in a defined timeframe, then the user receives a single summary notification containing all relevant alerts.

User temporarily disables alerts during a specific time frame while in a meeting.

Given the user sets a 'Do Not Disturb' mode for alerts, when alerts are triggered during this mode, then alerts should be suppressed until the user ends the disturbance period.

User tests the new alert frequency setting to ensure it matches their preferences.

Given the user sets a custom frequency for alerts, when the user triggers an alert condition, then the system should deliver alerts according to the user-defined frequency.

Alert Channel Integration

User Story: As a project manager, I want to select how I receive alerts, whether through email or mobile notifications, so that I can stay updated on important changes without being tied exclusively to the platform.
Description: The Alert Channel Integration requirement provides the capability for users to choose their preferred channels for receiving alerts, such as email, SMS, or push notifications. This flexibility allows users to stay informed in the manner that best suits their workflow and preferences. By integrating various communication channels, Datapy enhances the reliability of notifications and ensures that critical alerts reach users in real-time, regardless of their current engagement with the platform. This promotes timely action on alerts and ultimately increases overall operational effectiveness.
Acceptance Criteria: User selects their preferred alert channels during the initial setup process of Datapy.

Given the user has access to the alert channel integration settings, when they select their preferred channels (email, SMS, push notifications), then these preferences should be saved and reflected in their profile settings without errors.

User receives alerts via the selected channel when a specified threshold is triggered.

Given that the user has set a threshold for analytics data and selected email as their alert channel, when the threshold is breached, then the user should receive an email notification within 5 minutes of the event occurring.

User changes their preferred alert channel from SMS to push notifications in the platform settings.

Given the user is in the alert channel settings interface, when they change their alert preference from SMS to push notifications, then the system should update the settings and notify the user of the successful change immediately.

User tests the alert notification system after setting up their preferred channels.

Given that the user has configured their alert channels, when they trigger a test alert, then all selected channels (email, SMS, push notifications) should receive the test alert within 2 minutes.

User attempts to set an unsupported alert channel.

Given the user is trying to set an unsupported alert channel, when they submit the new channel preference, then they should receive an error message indicating that the selected channel is not supported.

User views alert history to confirm which channels received alerts.

Given the user has triggered multiple alerts through the chosen channels, when they access the alert history functionality, then they should see a list showing the time, type of alert, and the channel that was used for each alert.

Alert History Log

User Story: As a data analyst, I want to access a history of alerts so that I can analyze past responses and adjust our business strategies based on previous alerts.
Description: The Alert History Log requirement introduces a comprehensive record of all alerts triggered, including timestamps, conditions met, and user responses. This feature not only provides users with a transparent overview of alert activity but also facilitates analysis and learning from past alerts. Understanding the history of alerts can enhance future decision-making and refine alert parameters over time. The integration of this feature will support data-driven strategies for improvement and efficiency within teams, significantly contributing to long-term analytics goals.
Acceptance Criteria: User views the alert history log to analyze past alerts triggered during a specific timeframe to enhance future decision-making.

Given a user accesses the Alert History Log, when they select a specific date range, then the log displays all alerts triggered within that period along with timestamps and conditions met.

User receives an alert notification based on a defined threshold and checks the Alert History Log to confirm the entry is recorded.

Given a user has set a threshold for a specific alert, when the condition is met and the alert is triggered, then the Alert History Log should contain an entry that includes the alert details and timestamp.

User reviews the alert history log to find specific alerts that have been responded to in the past.

Given a user opens the Alert History Log, when they filter for alerts by user response, then the log displays all relevant alerts with the response details included.

User wants to validate how many alerts have been successfully acknowledged over a month by checking the alert history log.

Given a user accesses the Alert History Log, when they filter alerts by acknowledgment status during the past month, then the log should display the count of acknowledged alerts accurately.

User seeks to analyze alert patterns by reviewing the history log over a period to identify common triggers and user responses.

Given a user accesses the Alert History Log, when they view the data visualization for alert patterns over a set period, then the log should provide clear insights into common triggers and responses.

A system administrator wants to ensure all set alerts are accurately reflected in the Alert History Log after a specific system update.

Given a system update occurs, when the administrator checks the Alert History Log, then all previously configured alerts should be present and correctly capture their respective timestamps and conditions.

User accesses the alert history log to export data for further analysis outside of Datapy.

Given a user is viewing the Alert History Log, when they select the export feature, then the alert data should successfully download in a CSV format without loss of information.

AI-Powered Insights Engine

Utilizing machine learning algorithms, this feature analyzes historical data patterns to identify potential future trends. The Insights Engine provides contextualized recommendations on how to respond to emerging trends, equipping users with actionable insights that drive strategic decision-making.

Requirements

Trend Detection Algorithm

User Story: As a business analyst, I want to receive alerts about emerging trends in my data so that I can adjust my strategies proactively and stay ahead of the competition.
Description: The Trend Detection Algorithm requirement entails the implementation of machine learning algorithms that analyze historical data patterns to identify potential future trends in business metrics. This feature will enable the AI-Powered Insights Engine to provide contextualized insights, empowering users to proactively adapt their strategies based on predicted market changes. The integration of this algorithm is crucial for enhancing the platform's predictive analytics capabilities, adding significant value by enabling data-driven decision-making and ensuring that users are prepared for emerging trends before they occur.
Acceptance Criteria: Trend Detection in Real-Time Dashboard

Given a user accesses the real-time dashboard, when they view the trends section, then the system should display the identified trends with corresponding confidence scores above 75%.

Alerts for Emerging Trends

Given a user sets criteria for emergent trends, when those trends are detected by the algorithm, then the system should send an automated alert to the user within one minute of detection.

Insights Accuracy Verification

Given a batch of historical data, when the algorithm processes this data, then the identified trends should match at least 85% with actual market outcomes over the past year.

User Interaction with Recommendations

Given a detected trend, when the user interacts with the recommendation feature, then the system should provide at least three actionable strategies for responding to the trend.

Scalability of Trend Detection

Given an increase in data input size by 50%, when the algorithm processes the new data, then it must complete processing within an acceptable time frame of 5 minutes.

Feedback Loop Integration

Given a user provides feedback on the trend prediction accuracy, when the feedback is submitted, then the system should incorporate this feedback into enhancing the algorithm's performance for future data analyses.

Usability of Insights Engine

Given a new user accesses the insights engine for the first time, when they follow the guided setup, then they should be able to generate a trend report within 10 minutes without external assistance.

Recommendations Engine

User Story: As a business owner, I want to receive customized recommendations based on my data trends so that I can make informed decisions that align with my business goals.
Description: The Recommendations Engine requirement focuses on developing a system that generates actionable recommendations based on the identified trends from the Trend Detection Algorithm. This engine should analyze the context of the trends, the specific data involved, and the business goals of the user. By delivering tailored recommendations, the feature will not only enhance strategic decision-making but also provide personalized insights that cater to different user needs. This capability is essential for promoting user engagement and maximizing the value derived from data interpretations.
Acceptance Criteria: User receives personalized recommendations based on identified trends after analyzing historical data.

Given a user has accessed the Recommendations Engine, when there are identified trends relevant to their data, then the system generates a list of at least five personalized recommendations that are contextualized to the user's business goals.

User explores the recommendations provided by the Recommendations Engine on a dashboard.

Given a user views the Recommendations Engine dashboard, when they select a specific trend from the list, then the system displays detailed insights and explanations for each recommendation related to that trend.

User provides feedback on the recommendations received from the Recommendations Engine.

Given a user receives recommendations, when they provide feedback selecting 'Helpful' or 'Not Helpful' on at least three of the suggestions, then the system stores this feedback for future improvement of recommendation accuracy.

User wants to update their business goals to receive more tailored recommendations.

Given a user is on the Recommendations Engine settings page, when they update their business goals and save the changes, then the system recalibrates the recommendations within one business day based on the new goals provided.

User interacts with the Recommendations Engine via a collaborative team environment.

Given multiple users are accessing the Recommendations Engine, when one user shares a recommendation with their team, then the shared recommendation is visible to all team members in real-time with the option to comment.

User is notified of recently generated recommendations based on real-time data analysis.

Given that new data is available, when the Recommendations Engine updates, then the user receives a notification of new recommendations within 30 minutes of the update being processed.

User Interface for Insights Display

User Story: As a non-technical user, I want an intuitive dashboard to view insights and trends so that I can easily make sense of my data and take timely action without needing technical support.
Description: The User Interface for Insights Display requirement focuses on creating a user-friendly dashboard that visualizes trends, insights, and recommendations derived from the AI-Powered Insights Engine. This dashboard should feature easy-to-read graphics, intuitive navigation, and customizable views that allow users to interact with their data effectively. Prioritizing usability ensures that even non-technical users can comprehend and leverage insights to drive decision-making, which is essential for enhancing user satisfaction and engagement with the platform.
Acceptance Criteria: Display of AI Insights on User Dashboard

Given a user accesses the insights dashboard, when the AI-Powered Insights Engine generates new trends, then the dashboard displays a notification indicating the availability of new insights within 5 seconds.

User Customization of Dashboard Views

Given a user is on the insights dashboard, when they select the customization options, then they should be able to add, remove, and rearrange at least three widgets on the dashboard, with changes saved in less than 3 seconds.

Interactivity of Insights Visualizations

Given a user is viewing trend graphics on the dashboard, when they hover over a data point, then they should see a tooltip with detailed information about that data point, appearing within 1 second.

Usability for Non-Technical Users

Given a non-technical user is utilizing the dashboard, when they navigate through different sections of the insights display, then they should successfully understand how to interpret at least 80% of the displayed visualizations without additional training or support.

Real-Time Data Synchronization

Given a user has the dashboard open, when new data is available from connected data sources, then the visuals on the dashboard should refresh automatically within 10 seconds to display the latest insights.

Responsive Design for Different Devices

Given a user accesses the dashboard from a tablet, when they view the insights, then the layout should adapt appropriately to fit the screen size without losing functionality or clarity.

Accessibility Compliance of the Dashboard

Given a user with visual impairments is using the dashboard, when they utilize the screen reader feature, then all components of the dashboard should be fully navigable and described clearly by the screen reader, complying with WCAG 2.1 AA standards.

Real-Time Data Synchronization

User Story: As a data manager, I want the insights to be generated in real time so that I can act swiftly based on the most up-to-date information available.
Description: The Real-Time Data Synchronization requirement ensures that the Insights Engine operates with the most current data available, allowing users to receive immediate analysis and recommendations. Implementing this feature is key to maintaining the relevance and accuracy of insights, thereby enhancing user trust and reliance on the platform. This capability also facilitates a proactive stance in business decision-making, as users can act on fresh data rather than outdated information, ensuring that strategies are based on the latest developments.
Acceptance Criteria: User needs to get updated insights from the AI-Powered Insights Engine without having to refresh the dashboard manually.

Given the user has opened the dashboard, when a significant data update occurs, then the Insights Engine should automatically refresh and display the latest insights without user intervention.

A user wants to ensure that historical data is correctly synchronized and reflected in real-time analytics for accurate trend analysis.

Given that historical data has been inputted into the system, when the user queries for insights, then the Insights Engine must return results based on the latest data, with no discrepancies from the original historical data.

Team members collaborate on decision-making based on the insights provided by the Insights Engine in real-time during a strategy meeting.

Given a live meeting setting, when team members review insights generated by the Insights Engine, then all participants should have access to the same real-time insights simultaneously without delays or inconsistencies in data display.

A user expects to see alerts when the data synchronization takes longer than expected, impacting the timeliness of analysis.

Given the user is using the platform, when data synchronization exceeds a defined threshold time, then the user should receive a notification alerting them about the delay and its potential impact on insights availability.

A user wants to configure the Insights Engine to synchronize data at specified intervals while maintaining real-time capabilities.

Given the user has set the synchronization preferences, when the specified interval elapses, then the system should synchronize data at that interval while also allowing for instantaneous updates from new incoming data.

A user requests a report generated by the Insights Engine that should include the latest data insights to aid their decision-making process.

Given the user has requested a report, when the report is generated, then it must include data that is up-to-date and reflects the most current insights from the Insights Engine with all relevant metrics included.

Collaborative Features

User Story: As a team leader, I want to easily share insights with my team so that we can collaboratively discuss and make strategic decisions based on our collective data analysis.
Description: The Collaborative Features requirement aims to integrate functionality that allows users to share insights, trends, and recommendations within teams seamlessly. This includes options for commenting, tagging teammates, and sharing dashboards or visualizations. Enabling collaboration promotes effective communication and collective problem-solving, ensuring that teams can align their strategies and decisions based on shared understanding and insights. This functionality is crucial for fostering a collaborative workplace culture and maximizing the impact of data-driven insights.
Acceptance Criteria: Team members need to share insights and comments on trends identified by the AI-Powered Insights Engine during a scheduled project review meeting.

Given that a user has access to a dashboard with insights from the AI-Powered Insights Engine, When the user selects an insight, Then the user should be able to add a comment and tag at least one teammate, and the comment should be visible to all tagged members.

A team leader wants to share a customized dashboard containing key performance indicators with their colleagues for better decision-making.

Given that a user has created a custom dashboard, When the user clicks the 'Share' button and selects teammates, Then the selected teammates should receive a notification about the shared dashboard with access rights to view it.

During a collaborative discussion, team members need to discuss specific insights from data visualizations for better understanding and decision-making.

Given that a user is viewing a data visualization, When the user clicks on a specific trend, Then the user should see an option to comment on that trend, and all comments should be stored and visible under that specific trend for future reference.

A team member wishes to review past comments and discussions related to shared dashboards to ensure all opinions are considered in decision-making.

Given that comments have been made on a shared dashboard, When the user navigates to that dashboard, Then the user should see a comments section displaying all past comments along with the names of the team members who made them.

A project manager needs to ensure that all comments on a shared dashboard are appropriately addressed before finalizing decisions.

Given that comments are present on a shared dashboard, When the project manager reviews the dashboard, Then the project manager should be able to mark comments as 'Resolved' and filter the comments view to show only unresolved comments.

A user wants to get an overview of all the dashboards shared within a team to analyze collaborative contributions.

Given that multiple dashboards have been shared within the team, When the user navigates to the 'Shared Dashboards' section, Then the user should see a list of all shared dashboards along with the names of the teammates who shared them and the date of sharing.

Feedback and Improvement Loop

User Story: As a user, I want to provide feedback on the insights I receive so that the platform can improve and better meet my expectations in future analyses.
Description: The Feedback and Improvement Loop requirement consists of establishing a mechanism for users to provide feedback on the insights and recommendations they receive from the AI-Powered Insights Engine. Collecting user input will facilitate continuous improvement of the algorithms and recommendation systems, allowing the platform to become more attuned to user needs over time. This functionality is vital for ensuring long-term user satisfaction and for continually enhancing the AI's accuracy and relevance in real-world applications.
Acceptance Criteria: User Submission of Feedback on AI Insights

Given a user has received insights from the AI-Powered Insights Engine, when the user submits feedback through the feedback form, then the feedback will be recorded in the system and available for analysis.

Feedback Influence on Algorithm Improvement

Given feedback has been submitted by users, when the system runs its improvement cycle, then the feedback should directly influence at least 10% of the insights generated in the next iteration.

User Visibility of Feedback Impact

Given users have submitted feedback, when they revisit the insights page, then they should see a confirmation message indicating how their feedback is shaping future recommendations.

Feedback Response Time Guarantee

Given a user submits feedback through the platform, when they check back for follow-up, then they should receive a response acknowledging their feedback within 48 hours.

Feedback Form Accessibility and Usability

Given a user is using the platform, when they want to provide feedback, then the feedback form should be easily accessible without more than three clicks and should be user-friendly.

Data Privacy for User Feedback

Given a user submits feedback, when the feedback is processed, then the user’s personal information must be anonymized and safeguarded in accordance with data privacy regulations.

Analysis of User Feedback Trends

Given a significant amount of feedback has been collected, when the system analyzes this data, then it should identify at least three trends in user recommendations to inform future updates.

Trend Summary Dashboard

A dedicated dashboard that aggregates all trend alerts, providing users with a comprehensive overview of significant changes at a glance. This feature enhances data visibility and allows users to quickly assess the situation and assess the overall impact on their business.

Requirements

Dynamic Trend Alerts

User Story: As a business analyst, I want to receive dynamic trend alerts so that I can quickly react to significant changes in my business metrics.
Description: This requirement involves creating a system that automatically identifies and highlights significant changes in data trends across various metrics in Datapy. It should utilize AI algorithms to detect anomalies or shifts from historical data patterns and notify users via alerts on their Trend Summary Dashboard. The primary benefit of this functionality is to ensure that users are immediately aware of important changes that could impact their business decisions, allowing them to stay ahead of potential challenges and opportunities. Integration into the existing data infrastructure should be seamless, ensuring real-time updates and comprehensive visibility into trending metrics.
Acceptance Criteria: User receives timely alerts for significant changes in key business metrics as they occur on the Trend Summary Dashboard, ensuring that they can quickly understand the implications of the changes.

Given the user is logged into the Datapy platform, when a significant change in data trends is detected, then an alert is displayed on the Trend Summary Dashboard within 5 minutes of detection.

The Trend Summary Dashboard effectively visualizes the trend alerts, allowing the user to easily comprehend the nature and impact of the changes on their operations.

Given the user accesses the Trend Summary Dashboard, when there are trend alerts present, then all alerts are displayed in a clear and organized manner with relevant visualization metrics (e.g., graphs, color codes) that indicate the severity of the trends.

Users are able to customize the parameters for which trend alerts they wish to receive notifications, ensuring relevance to their specific business needs.

Given the user has access to the Trend Summary Dashboard settings, when the user adjusts the alert parameters, then the system updates the alert configuration successfully, and only relevant alerts are displayed moving forward according to the new settings.

The AI algorithms accurately detect anomalies in historical data trends across various metrics, enabling proactive decision making.

Given the historical data is integrated and accessible, when the data patterns are analyzed, then the AI system identifies at least 90% of significant anomalies that deviate from established trends within a defined confidence interval (e.g., 95%).

Users can view a historical list of trend alerts to analyze past anomalies and responses, enhancing their decision-making capability.

Given the user accesses the historical alerts section, when the user inspects past trend alerts, then the system displays a complete, date-stamped list of alerts, including descriptions and resolutions for at least the past month.

Customizable Dashboard Widgets

User Story: As a user, I want to customize my dashboard widgets so that I can prioritize the data that is most important to my business.
Description: This requirement focuses on allowing users to customize the widgets on their Trend Summary Dashboard according to their specific needs and preferences. Users should be able to add, remove, or rearrange widgets that display trend data, making it easier to focus on the metrics that matter most to their operations. This customization enhances user experience by enabling a tailored view that aligns with business priorities, ultimately facilitating better decision-making and insight utilization.
Acceptance Criteria: User Customizes the Dashboard to Display Key Metrics for Marketing Performance.

Given the user has accessed the Trend Summary Dashboard, When the user selects the 'Add Widget' option, Then the user should see a list of available widgets to choose from. Also, when a widget is added, it should display relevant marketing metrics based on the user's selection.

User Rearranges Widgets on the Dashboard for Optimal View.

Given the user has multiple widgets on the Trend Summary Dashboard, When the user clicks and drags a widget to a new location, Then the dashboard should reflect the new arrangement immediately without any refresh required.

User Removes an Unwanted Widget from the Dashboard.

Given the user has a widget that is no longer needed, When the user clicks the 'Remove' button on the widget, Then the widget should be deleted from the dashboard, and a confirmation message should appear indicating successful removal.

User Saves Custom Dashboard Layout for Future Access.

Given the user has customized the Trend Summary Dashboard, When the user clicks on the 'Save Layout' option, Then the custom layout should be saved and loaded automatically the next time the user accesses the dashboard.

User Applies a Filter to Widgets to Display Specific Trend Data.

Given the user has various widgets displaying trend data, When the user sets a filter for 'Last 30 Days', Then all widgets should update to reflect only the data from the last 30 days accurately.

User Receives an Alert for Dashboard Layout Changes.

Given that the user is accessing their customized Trend Summary Dashboard, When the user saves changes to their layout, Then an alert should be displayed confirming that their changes have been successfully saved.

User Accesses Help for Dashboard Customization Features.

Given the user is on the Trend Summary Dashboard, When the user clicks on the 'Help' icon, Then the user should be redirected to a help section specifically outlining how to customize dashboard widgets.

Historical Trend Comparison Feature

User Story: As a manager, I want to compare current trends with historical data so that I can understand the context and significance of these trends over time.
Description: This requirement entails adding a feature that enables users to compare current trend data with historical data directly on the Trend Summary Dashboard. Users should be able to view side-by-side comparisons of trending metrics over specified time frames, which will provide context for the significance of current changes. By incorporating this feature, users can gauge the impact of specific trends more effectively, supporting informed strategic planning based on historical performance.
Acceptance Criteria: User compares current sales trend against the past three months' sales data on the Trend Summary Dashboard.

Given the user is on the Trend Summary Dashboard, when they select the 'Compare Historical Trends' button, then they should see a side-by-side comparison of current sales trends and the past three months' sales data, reflecting percentage changes.

User filters trend data by specific categories before comparison on the Trend Summary Dashboard.

Given the user is on the Trend Summary Dashboard, when they apply filters to select specific categories (e.g., product type, region), then only the relevant trend data for those categories should be displayed in the historical comparison view.

User views historical trend comparisons for at least five available time frames.

Given the user accesses the historical trend comparison feature, when they select a specific metric, then they should be able to compare this metric for at least five different time frames (e.g., week, month, quarter, last year).

User sees clear visual indicators highlighting significant changes in trends between current and historical data.

Given the user is comparing current trends with historical data, when the trends differ by more than 10%, then visual indicators (e.g., red arrows for declines and green for increases) should be automatically displayed beside the corresponding metrics.

User saves their historical trend comparison for future reference.

Given the user has created a comparison view on the Trend Summary Dashboard, when they click the 'Save Comparison' button, then they should be able to retrieve this comparison later from their dashboard's saved items section.

User accesses the Trend Summary Dashboard on a mobile device to view historical trend comparisons.

Given the user is accessing the Trend Summary Dashboard on a mobile device, when they navigate to the historical trend comparison section, then the layout should adapt responsively to display all relevant data clearly without loss of functionality.

Real-time Data Synchronization

User Story: As a team member, I want my dashboard metrics to be updated in real-time so that I can rely on accurate and timely data for decision-making.
Description: This requirement aims to ensure that the Trend Summary Dashboard reflects data in real-time, providing users with the most up-to-date information available. By implementing real-time data synchronization, users will receive instant updates on trend alerts, dashboard metrics, and any changes made in the data inputs. This capability is crucial for maintaining the accuracy and reliability of insights derived from the dashboard, enhancing the platform's usability as a central hub for analytics.
Acceptance Criteria: User logs into the Datapy platform and opens the Trend Summary Dashboard to monitor real-time changes in data metrics and trend alerts affecting their business operations.

Given the user is logged into the Datapy platform, when they open the Trend Summary Dashboard, then the dashboard should display the latest data metrics and trend alerts that have been updated within the last minute.

As a user interacts with the Trend Summary Dashboard, they need to see immediate updates without needing to refresh the page, which is critical for timely decision-making.

Given the user is actively viewing the Trend Summary Dashboard, when new data inputs or changes occur, then the dashboard should automatically refresh and reflect those changes within 5 seconds without user intervention.

A manager checks the Trend Summary Dashboard during a weekly review meeting to discuss significant business trends and possible actions based on the latest data.

Given the manager has opened the Trend Summary Dashboard, when new trend alerts are generated, then those alerts should appear prominently on the dashboard with associated timestamps to indicate recency and relevance to the discussion.

In the event of a system-wide data update, the user should be notified immediately through the dashboard to stay aware of any changing business conditions.

Given a system-wide data update occurs, when the Trend Summary Dashboard is open, then a notification should be displayed on the dashboard indicating the update, along with a summary of the affected metrics.

A user wants to share their insights with team members through the Trend Summary Dashboard, and needs real-time data visibility for effective collaboration.

Given a user is viewing the Trend Summary Dashboard while in a collaborative session, when they make a change to any dashboard filter, then all participants in the session should see the updated data instantly without delay.

An analytics team is reviewing previous analysis and needs to ensure that the insights generated are based on the latest available data from the Trend Summary Dashboard.

Given the analytics team is accessing the Trend Summary Dashboard for historical data, when relevant data is updated in the platform, then the dashboard should ensure that previously generated insights reflect this new data for accuracy.

Collaborative Insights Sharing

User Story: As a team leader, I want to share insights from my dashboard with my team so that we can collaborate effectively on our data-driven strategies.
Description: This requirement focuses on enabling users to share insights and key trends from the Trend Summary Dashboard with team members or stakeholders directly within the platform. Users should be able to create reports or share snapshots of their dashboards, enhancing collaboration and communication around data-driven insights. This functionality supports better teamwork by ensuring all relevant parties have access to the same information, fostering collective decision-making processes.
Acceptance Criteria: User shares a snapshot of the Trend Summary Dashboard with team members after identifying a significant drop in sales trends, ensuring everyone is informed and up-to-date.

Given the user is on the Trend Summary Dashboard, when they select the 'Share Snapshot' option, then a snapshot of the current dashboard should be created and shared via email or direct link to selected team members.

A user generates a report from the Trend Summary Dashboard highlighting key trends and sends it to stakeholders before a weekly meeting, allowing them to prepare questions and discussion points.

Given the user has selected key metrics on the dashboard, when they choose the 'Generate Report' feature, then the report should compile the selected metrics into a downloadable PDF format that can be shared via email.

Multiple team members are using the shared insights from the Trend Summary Dashboard during a collaborative discussion, ensuring everyone has access to the same visuals and data.

Given that a report has been shared with team members, when they access the shared report, then they should be able to view the report without error and have access to all visualizations included.

A user who is not directly involved in the data analysis accesses a shared dashboard snapshot to understand recent changes in market trends, ensuring clarity and alignment in discussions.

Given a user receives a shared link to a dashboard snapshot, when they click the link, then they should be able to view the snapshot without needing an account and understand the key trends at a glance.

After sharing insights during a meeting, team members provide feedback on the report generated from the Trend Summary Dashboard, ensuring continuous improvement in data accessibility.

Given that feedback is requested on a shared report, when team members submit their comments through the provided feedback form, then all feedback should be stored and linked to the respective report for future reference.

User Activity Tracking

User Story: As a product manager, I want to track user activity on the dashboard so that I can identify areas for improvement and better support our users.
Description: This requirement involves implementing functionality that tracks user interactions with the Trend Summary Dashboard, capturing metrics such as engagement levels, frequency of use, and feature utilization. This data will allow the team to understand user behavior, enhance future iterations of the dashboard, and tailor experiences that meet user needs better. Additionally, it can provide insight into training or support requirements for users who may struggle to take full advantage of the dashboard's capabilities.
Acceptance Criteria: User initiates a session on the Trend Summary Dashboard for the first time to navigate through various metrics and alerts.

Given the user is logged in, When the user opens the Trend Summary Dashboard for the first time, Then the system should record the timestamp, duration of session, and initial metrics accessed by the user.

A user accesses the Trend Summary Dashboard multiple times within a week to monitor ongoing trends and changes.

Given a user has accessed the Trend Summary Dashboard at least three times within a calendar week, When the session ends, Then the system should log the frequency of use, including timestamps and duration for each session.

A user interacts with different features on the Trend Summary Dashboard during a single session.

Given a user is actively using the Trend Summary Dashboard, When the user clicks on different features (e.g., trend alerts, visualizations, metrics), Then the system should track and log each feature accessed along with the corresponding engagement time for each feature.

The system generates a summary report of user interactions with the Trend Summary Dashboard over a given period.

Given the user activity data has been accumulated over one month, When an admin requests a user activity report, Then the system should accurately generate and display metrics including total active users, average session duration, and most accessed features within the dashboard.

A user provides feedback on their engagement with the Trend Summary Dashboard after using it for a specified period.

Given a user has interacted with the Trend Summary Dashboard for at least two weeks, When the user submits feedback regarding their experience, Then the system should prompt the user to rate their experience and capture any additional comments for analysis.

Collaboration Alerts

Facilitates team collaboration by allowing users to tag colleagues in alerts, fostering communication around trends and insights. This feature enhances teamwork by encouraging data-driven discussions and promoting collective action on important metrics, ensuring no critical trends are overlooked.

Requirements

Tagging for Collaboration Alerts

User Story: As a team member, I want to tag my colleagues in alerts so that we can discuss relevant data trends together and take timely action on important metrics.
Description: The Tagging for Collaboration Alerts requirement enables users to tag their colleagues in specific alerts related to data trends or insights. This feature is designed to enhance communication within teams by allowing users to notify their colleagues directly about important metrics that require attention. By tagging users, notifications are tailored to relevant team members, ensuring that critical insights are not overlooked and fostering a culture of collaborative decision-making. Implementation will include user interface elements for tagging, notification mechanisms, and user permissions to control who can be tagged.
Acceptance Criteria: User tags a colleague in a collaboration alert for an important sales trend during a team meeting, ensuring the colleague receives a notification to discuss the trend.

Given a user is viewing the collaboration alert, When the user tags a colleague, Then the tagged colleague should receive a notification about the alert.

A user attempts to tag a colleague who does not have permission to view the alert, preventing the tag from being sent.

Given a user tries to tag a colleague without permission, When the user submits the tag, Then an error message should inform the user that the colleague cannot be tagged.

A user tags multiple colleagues in a collaboration alert, focusing on different insights relevant to their jobs.

Given a user tags multiple colleagues in the collaboration alert, When the alert is sent out, Then all tagged colleagues should receive separate notifications about their respective insights.

A manager wants to review who has been tagged in collaboration alerts to ensure only relevant team members are notified.

Given the manager accesses the tagging history, When the manager views the alert history, Then they should see a list of colleagues tagged in each alert with timestamps.

A user needs to edit a collaboration alert after tagging colleagues to correct an insight or trend discussed.

Given a user wants to edit a collaboration alert after tagging, When the user changes the alert details and updates it, Then the previously tagged colleagues should receive a notification of the updated alert.

A user utilizes the tagging feature to see which collaborations have received the most responses or actions post-tagging.

Given the user views the collaboration alerts they created, When selecting a specific alert, Then they should be able to see the engagement metrics related to that alert indicating responses from tagged colleagues.

A user wants to ensure that tagging colleagues in alerts enhances teamwork rather than causing notification overload.

Given the user implements tagging in collaboration, When analyzing the team communication frequency, Then the user should notice a positive increase in collaboration metrics without a significant rise in irrelevant notifications.

Real-time Notifications

User Story: As a user, I want to receive real-time notifications when tagged in alerts so that I can respond quickly to important discussions.
Description: The Real-time Notifications requirement focuses on providing instant alerts to users when they are tagged in collaboration alerts or when critical metrics change. This feature ensures that users are promptly informed about discussions that involve them, allowing for quick response times and more dynamic collaboration. Notifications can be customized for each user, offering options such as email, mobile push notifications, or in-app alerts. This immediate feedback loop is crucial for maintaining momentum in decision-making processes and encourages active participation in ongoing analyses.
Acceptance Criteria: User is tagged in a collaboration alert by a colleague regarding a significant metric change in sales performance.

Given that the user has notifications enabled, when they are tagged in the alert, then they should receive a push notification on their mobile device within 1 minute.

A user has customized their notification preferences to only receive email alerts for collaboration tags.

Given the user has set their notification preferences, when they are tagged in a collaboration alert, then they should receive an email notification instead of an in-app alert.

A user is actively engaged in the Datapy platform and is tagged in multiple collaboration alerts regarding different metrics.

Given that the user is logged into the platform, when they are tagged in multiple alerts in a session, then they should see all alerts listed in the notification center with timestamps and clickable links.

An admin user wants to ensure that all team members receive critical notifications regarding system updates or outages.

Given that the admin triggers a critical alert, when the notification is sent out, then all team members are notified via email, push notification, and in-app alert within 5 minutes.

A user wishes to turn off notifications temporarily during a focus work period.

Given that the user has activated do-not-disturb mode, when they are tagged in a collaboration alert, then they should not receive any notifications until do-not-disturb mode is deactivated.

A user needs to verify their notification settings have been saved correctly after making changes.

Given that the user has updated their notification preferences, when they revisit the settings page, then the updated preferences should be displayed accurately without discrepancies.

A user experiencing a high volume of alerts wants to prioritize notifications regarding only key performance indicators (KPIs).

Given that the user customizes their notifications for KPIs, when they are tagged in an alert that is marked as a KPI, then that alert should be highlighted in both their email and in-app notifications, allowing for quick identification.

Discussion Thread Management

User Story: As a user, I want to manage discussion threads related to alerts so that I can easily refer back to important conversations and decisions.
Description: The Discussion Thread Management requirement allows users to track and manage conversations that arise from collaboration alerts. This feature includes functionalities for creating, viewing, and responding to threads associated with each alert, ensuring that all communication is organized and easily accessible. Users can reference past discussions to follow the evolution of insights and decisions, promoting accountability and transparency within teams. This functionality integrates seamlessly with the overall alert system, enhancing the team's ability to manage their workflow efficiently.
Acceptance Criteria: User creates a new discussion thread in response to a collaboration alert.

Given a collaboration alert is received, when the user clicks on 'Create Thread', then a new discussion thread should be initiated and linked to the alert, allowing the user to enter a message.

User views existing discussion threads linked to a specific collaboration alert.

Given a collaboration alert with existing threads, when the user opens the alert details, then all associated discussion threads should be displayed in chronological order with timestamps and participant names.

User responds to a discussion thread related to a collaboration alert.

Given a user is viewing a discussion thread, when they enter a response and click 'Send', then the response should be saved and visible in the thread for all participants to see.

User references past discussions to follow up on insights from collaboration alerts.

Given a user wants to review past discussions, when they access the discussion history for an alert, then they should be able to filter and sort threads by date and participants.

User receives notifications for new responses in discussion threads.

Given a user is part of a discussion thread, when a new message is posted in that thread, then the user should receive a notification alerting them of the new response.

User edits an existing response in a discussion thread.

Given a user has posted a response in a discussion thread, when they choose to edit that response, then the changes should be saved and reflected in the thread immediately.

User deletes a discussion thread linked to a collaboration alert.

Given a user has the necessary permissions, when they select the option to delete a discussion thread, then the thread should be permanently removed from the alert's discussion history.

Alert Visibility Settings

User Story: As a user, I want to set visibility preferences for my alerts so that I can control who can view sensitive discussions and insights.
Description: The Alert Visibility Settings requirement allows users to customize who can see their collaboration alerts. This feature empowers users to control the visibility of sensitive data or discussions, ensuring that only relevant team members are included in specific conversations. Users can select visibility options such as 'private' (only tagged individuals) or 'public' (all team members), thus enhancing the flexibility and security of team communications. This setting is essential for maintaining user trust and encouraging open discussions about data.
Acceptance Criteria: User selects 'private' visibility when creating an alert for a sensitive data trend, ensuring that only tagged individuals can view the alert.

Given a user is creating a collaboration alert, when they select the 'private' visibility option and tag specific colleagues, then only those tagged individuals should be able to see the alert.

User sets an alert to 'public' visibility and includes team members, allowing all team members to see the alert and participate in discussions.

Given a user is setting an alert to 'public' visibility, when they tag any team member, then all team members should be able to view and respond to the alert.

User receives an email notification for a private alert they are tagged in, enabling them to take action on the relevant data immediately.

Given that a collaboration alert is created with 'private' visibility and tagged to a user, when the alert is triggered, then the user should receive an email notification containing the alert details.

User attempts to view a 'private' alert they are not tagged in and receives a notification indicating that they do not have permission to view it.

Given that a collaboration alert is created with 'private' visibility, when a team member who is not tagged tries to access the alert, then they should receive a notification informing them that they do not have permission to view it.

User successfully edits the visibility of an existing alert from 'private' to 'public' and updates the tagged individuals in real-time.

Given that a user has created a 'private' alert, when they change the alert visibility to 'public' and update the tagging options, then all team members should be able to see the updated alert immediately.

Performance Analytics for Alerts

User Story: As a product manager, I want to see analytics on collaboration alert usage so that I can improve the feature based on user engagement and needs.
Description: The Performance Analytics for Alerts requirement focuses on providing users with analytics regarding the usage and response rates to collaboration alerts. This feature will analyze metrics such as the number of alerts sent, user engagement, and average response times. By providing insights on how alerts are utilized, product teams can iteratively improve the alert functionality and user experience. This analytical capability supports continuous improvement by identifying trends in user interactions and areas for enhancement.
Acceptance Criteria: User receives a collaboration alert tagged with their name regarding a critical sales trend detected in the data dashboard.

Given a user is tagged in a collaboration alert, when they access the alert, then the alert should display the relevant data insights and allow them to respond or comment directly within the alert interface.

Product team analyzes the usage statistics of collaboration alerts sent over a month to assess user engagement levels.

Given that collaboration alerts have been sent for a month, when the product team reviews the analytics, then they should see metrics such as total alerts sent, unique users engaged, and average response times displayed in a visual format on their analytics dashboard.

An admin wants to ensure that alerts are sent to the correct team members and track engagement.

Given an alert has been sent, when the admin reviews the alert logs, then they should see a record of which users were tagged, the time the alert was sent, and the current engagement status of each tagged user (opened, responded, or ignored).

Users are notified of upcoming trends by collaboration alerts generated based on real-time data interactions.

Given that a trend in user activity has been detected, when collaboration alerts are generated, then affected users should receive alerts within a specific time frame (e.g., within 30 minutes of trend detection) indicating the nature of the trend and actionable insights.

A user wants to understand their response patterns to alerts over time.

Given a user accesses their performance analytics within Datapy, when they review their alert response history, then they should see a breakdown of response times, the number of alerts acknowledged, and a percentage indicating engagement relative to total alerts received.

The product team seeks to identify areas for improvement in how alerts are being utilized by users.

Given the analytics dashboard has been updated with performance data, when the product team analyzes user feedback and response metrics, then specific recommendations for enhancements should be generated based on identified user pain points and engagement trends.

Historical Impact Analysis

Allows users to review historical trends and the actions taken in response to past alerts, helping to identify the effectiveness of decisions made. This retrospective feature fosters continuous improvement by allowing users to learn from past experiences and refine their future responses to emerging trends.

Requirements

Historical Data Retrieval

User Story: As a data analyst, I want to retrieve historical data trends so that I can analyze past decisions and their effectiveness in improving future responses.
Description: The Historical Data Retrieval requirement enables users to access and review past data trends related to their business metrics. This feature allows users to select specific time periods and extract data sets that reflect historical performance, enabling them to analyze outcomes and identify patterns over time. The implementation of this requirement will enhance decision-making as users can reference past actions taken in response to alerts and assess their effectiveness. By integrating this feature within the existing Datapy framework, users will have a comprehensive view of historical impacts that foster greater learning and adaptation.
Acceptance Criteria: As a user, I want to select a specific time range to retrieve historical data, enabling me to analyze performance trends over a defined period.

Given a user is logged into Datapy, when they select a time period from the date range picker and click on the 'Retrieve Data' button, then historical data sets related to the chosen time frame should be displayed correctly on the dashboard.

As a user, I need the ability to filter historical data by specific business metrics so that I can focus my analysis on relevant information.

Given a user has accessed the historical data retrieval feature, when they apply filters for particular metrics (e.g., sales revenue, operational costs), then the system should only display data sets related to those filtered metrics for the selected time range.

As a user, I want to export the retrieved historical data into a CSV file format for further analysis in external applications.

Given a user has successfully retrieved historical data, when they click on the 'Export' button, then a CSV file should be generated and downloaded automatically to their device containing the visible historical data.

As a user, I need to view the retrieved historical data in a clear and organized table format, enabling me to quickly assess trends and insights.

Given that historical data has been retrieved, then the data should be displayed in a table format with clearly labeled columns for each metric, sorted by date in descending order.

As a user, I want to see a graphical representation of the historical data trends so that I can easily identify patterns and make better decisions.

Given that historical data has been retrieved, when viewing the retrieved data, then a line graph should be generated displaying the trends of selected metrics over the specified time period.

As a user, I need to view a summary of key insights derived from the historical data, helping me to understand the performance at a glance.

Given that historical data has been retrieved, then a summary panel should display key performance indicators such as average values, peaks, and troughs related to the selected metrics for the specified time range.

Trend Impact Visualization

User Story: As a business manager, I want to visualize the impacts of past decisions on trends so that I can communicate insights to my team more effectively and guide future strategies.
Description: The Trend Impact Visualization requirement involves creating visual representations of historical trends and their impacts based on previous actions. This feature will transform complex data into easy-to-understand graphs and charts that illustrate the correlation between decisions made and their resultant impacts. Providing visual context will empower users to quickly comprehend their past choices and refine strategies for future actions. By integrating this into the Datapy platform, users can better articulate their findings and facilitate team discussions on results and strategies.
Acceptance Criteria: User accesses the Trend Impact Visualization tool to analyze the historical impact of decisions made over the past quarter.

Given a user has logged into the Datapy platform and navigated to the Historical Impact Analysis section, When the user selects the last quarter's data, Then the system displays a visual representation (graph/chart) of the trends and their impacts clearly, including tooltips with specific decision details.

User compares the impact of two different decisions taken in the last year using the visualization tool.

Given that two distinct decisions have been made in the last year, When the user selects both decisions for comparison, Then the system generates a comparative graph that shows the direct impacts of each decision side-by-side, highlighting differences in outcomes and effectiveness.

User utilizes the Trend Impact Visualization feature to facilitate a team discussion on past performance in a meeting.

Given the user is in a meeting with team members, When they share their screen to display the Trend Impact Visualization, Then all visual elements should load accurately, and team members should be able to interact with the visualizations in real-time (e.g., zooming in, filtering data), enhancing the discussion.

User reviews the historical data of a particular trend to identify patterns and inform future decisions.

Given the user has selected a specific trend in the visualization tool, When the user analyzes the visual data presented, Then they should be able to identify at least three distinct patterns or insights that can inform their strategic planning for the upcoming quarter.

User exports the visual impact analysis for review by upper management.

Given the user has finalized the analysis in the Trend Impact Visualization tool, When they select the export option, Then the system should generate a downloadable report in PDF format that accurately reflects the visualizations with all annotations and data sources included.

User checks the accuracy of data displayed in Trend Impact Visualization against raw data from the previous year.

Given the user has access to both the Trend Impact Visualization and the raw data sets, When they conduct a verification check, Then all data points in the visualization should accurately match the corresponding entries in the raw data set, with discrepancies being less than 2% accepted variance.

Action Effectiveness Review

User Story: As a team lead, I want to review the effectiveness of actions taken in response to past alerts so that I can determine what strategies work best and improve our response processes.
Description: The Action Effectiveness Review requirement focuses on allowing users to assess the effectiveness of specific actions taken in response to historical alerts. This feature provides users with tools to conduct a systematic review of decisions made, evaluating outcomes versus set objectives. It plays a vital role in driving continuous improvement by highlighting successful strategies and areas needing adjustment. The integration of this requirement within Datapy will streamline the analytic process, making retrospective evaluations actionable and insightful for future planning.
Acceptance Criteria: User Conducting a Review of Past Alerts and Actions Taken

Given a user logged into Datapy, when they select the 'Historical Impact Analysis' feature, then they should be able to access a list of past alerts along with the corresponding actions taken in response to those alerts.

Evaluating Effectiveness of Past Actions

Given a user performs a review of actions taken from past alerts, when they select a specific action, then they should be able to view detailed outcomes and how those outcomes align with objectives set for that action.

Generating a Report on Action Effectiveness

Given a user who has completed a review, when they choose to generate a report, then they should receive a compiled document summarizing the effectiveness of actions taken based on the historical analysis, including a visual representation of data.

Identifying Successful Strategies from Past Actions

Given a user reviewing historical data, when they filter the outcomes based on predefined successful criteria, then they should be able to identify and highlight the successful strategies used for specific alerts.

Adjusting Future Responses Based on Historical Data Review

Given the user has reviewed the past actions and outcomes, when they input their new strategies for future potential alerts, then the system should allow them to save those adjustments and link them to relevant historical data for future reference.

Collaborative Analysis of Historical Actions

Given multiple users access the Historical Impact Analysis feature, when one user modifies the review data or adds comments, then those changes should be reflected in real-time for all collaborating users to see.

User Feedback Integration

User Story: As a product user, I want to provide feedback on past data analyses so that I can contribute to the improvement of the analytical tools and our overall decision-making process.
Description: The User Feedback Integration requirement facilitates the collection and analysis of user feedback related to historical impact analyses. This feature allows users to provide insights on the effectiveness of specific responses and propose enhancements to future analytical capabilities. User feedback will be visually captured through integrated forms and analyzed using the same predictive analytics tools within the platform. The resulting insights will guide feature updates and enhancements, ensuring that the Datapy analytics experience stays relevant and user-centered.
Acceptance Criteria: User submits feedback on the effectiveness of past actions taken in response to alerts during a quarterly review meeting.

Given a user is logged into the Datapy platform, when they access the Historical Impact Analysis section, then they should see a feedback form with fields for rating the effectiveness of actions and providing additional comments.

An admin analyzes collected user feedback to identify trends in user suggestions for enhancing predictive analytics capabilities.

Given the user feedback has been collected, when an admin accesses the analytics dashboard, then they should be able to view a report summarizing user feedback with actionable insights highlighted.

A user updates their previous feedback based on new insights gained from recent historical analyses.

Given a user is logged into their account, when they choose to edit their previous feedback submission, then they should be able to modify their inputs and submit the updated feedback without errors.

Users review a summary of changes made to the platform based on previous user feedback during a biannual update session.

Given the users attend the biannual update session, when they review the summary report, then they should find clearly documented changes and enhancements that correlate directly with user feedback received.

User feedback is integrated into the predictive analytics tools, impacting future automated insights displayed.

Given that user feedback has been analyzed, when the predictive analytics tool generates new insights, then it should reflect changes that align with user-inputted suggestions for improvement.

Predictive Outcome Scenarios

User Story: As a decision-maker, I want to simulate predictive scenarios based on historical data so that I can understand potential outcomes and make more informed choices.
Description: The Predictive Outcome Scenarios requirement empowers users to create and test potential scenarios based on historical data and trends. Users will have the ability to input variables to predict how different actions may have influenced past results, providing valuable foresight on decision-making. This feature will enhance the predictive capabilities of the Datapy platform, allowing for a deeper understanding of cause-and-effect relationships, improving strategic planning and decision-making processes.
Acceptance Criteria: User inputs historical data related to sales trends and selects specific variables to analyze potential future outcomes based on previous marketing campaigns.

Given a user has historical sales data and variables, when they input this data and select the desired variables, then the system should display predicted outcomes and a comparative analysis with actual results from past actions.

A user wants to evaluate how a change in pricing strategy impacted sales performance over the last three years.

Given a user has access to three years of historical sales data and pricing changes, when they create a scenario predicting outcomes based on adjusted pricing, then the platform should visualize the predicted sales alongside existing historical performance data.

As a data analyst, I want to test multiple variables at once to see the potential impacts on customer retention rates based on historical trends.

Given the user has multiple variables (e.g., marketing spend, customer service interactions) to input, when they run the simulation, then the system should generate a report showing the predicted customer retention rates based on various combinations of those variables.

A business owner wants to understand the effectiveness of their last year's advertising campaign by simulating different budgets and their potential impact.

Given a user inputs different advertising budget scenarios, when they run the predictive model, then the system should provide a breakdown of how each budget scenario may have changed last year's metrics such as reach, engagement, and conversions.

Users are analyzing seasonal data trends to create a predictive scenario for the upcoming holiday season sales.

Given users have historical data for different seasons, when they adjust parameters relevant to the holiday season, then the prediction tool should forecast potential sales outcomes and highlight key trends to focus on.

A project manager needs to validate key decisions made in response to past alerts concerning operational efficiency.

Given historical alerts and decisions taken, when the user inputs specific parameters related to these decisions, then the system should visualize the effectiveness of the decisions on operational metrics over time.

Multi-Channel Notifications

Ensures that users receive alerts via their preferred communication channels, including email, SMS, or in-app notifications. By providing flexibility in delivery methods, this feature maximizes the chances of timely responses to significant trends, catering to diverse user preferences.

Requirements

Email Notification Integration

User Story: As a business analyst, I want to receive data alerts via email so that I can stay informed about important changes without having to log into the platform regularly.
Description: The Email Notification Integration requirement ensures that users can receive important alerts and updates related to their data and analytics directly in their email inbox. This will allow for a synchronous link between the platform and the user’s preferred email service, ensuring that key metrics and changes are communicated efficiently. The notifications should be customizable, allowing users to choose the frequency and types of alerts they wish to receive. The purpose is to enhance user engagement and responsiveness to significant data trends, ultimately driving improved decision-making and action.
Acceptance Criteria: Email Notification for Daily Data Update Summary

Given a user has subscribed to daily email notifications, When the daily data update occurs, Then the user receives an email report summarizing key metrics and changes from the last 24 hours.

Customizable Notification Settings

Given a user accesses their notification settings, When they choose the frequency of email alerts (e.g., immediate, daily, weekly), Then the system saves the preferences and applies them to future notifications.

Email Delivery Confirmation

Given an email notification is triggered by significant trend changes, When the email is sent, Then the user receives a delivery confirmation message within the app confirming the notification was dispatched.

Opt-Out of Specific Notification Types

Given a user is receiving multiple types of alerts (e.g., sales, performance, and operational alerts), When they opt out of sales notifications in their settings, Then they stop receiving email alerts related to sales metrics.

Include Personalization in Email Notifications

Given a user has customized their email alert preferences, When an email notification is sent, Then it should address the user by their first name and include personalized insights relevant to their business operations.

Email Notification for Urgent Alerts

Given a user is subscribed to urgent alerts, When an urgent alert is triggered, Then the user should receive an email within 5 minutes of the alert being generated.

Responsive Email Design

Given a user receives an email notification on their mobile device, When they open the email, Then the email should be fully responsive and easy to read, with calls to action clearly visible.

SMS Notification Functionality

User Story: As a manager, I want to receive SMS notifications for urgent data alerts so that I can act quickly on important business events while away from my desk.
Description: The SMS Notification Functionality requirement provides users with the ability to receive critical alerts and insights via text messages on their mobile phones. This feature aims to enhance on-the-go access to vital information, ensuring that users do not miss key alerts that could impact their business decisions. Users will be able to opt-in for SMS notifications based on their preferences and can set thresholds for different types of alerts. This promotes timely decision-making and action in response to data-driven insights, bolstering user engagement and responsiveness.
Acceptance Criteria: User opts in for SMS notifications through their account settings and defines threshold preferences for critical alerts.

Given that the user has an account in Datapy, When they opt into SMS notifications and set alert thresholds, Then the system should successfully save their preferences and confirm with a success message.

User receives an SMS notification when a critical business metric exceeds the predefined threshold.

Given that the user has set a threshold for alerts, When a critical metric exceeds that threshold, Then the user should receive an SMS notification to their registered mobile number within 5 minutes.

User changes their mobile number in their account settings and updates their SMS notification preferences.

Given that the user is on the notifications settings page, When they change their mobile number and save the changes, Then the system should send a confirmation SMS to the new number and update all notification preferences accordingly.

User receives multiple types of alerts (e.g., sales, inventory) and they want to ensure they are segmented correctly in SMS notifications.

Given that the user has opted into multiple alert types, When a sales and an inventory alert are triggered, Then the user should receive separate SMS notifications for each alert type clearly indicating the source of the alert.

User is not receiving SMS notifications despite having selected the option in their preferences.

Given that the user believes they have enabled SMS notifications, When they check their notification status in the account settings, Then the system should show accurate status and alert the user if their mobile number is not verified.

System handles cases where SMS messages fail to deliver to the user’s mobile phone.

Given that the user has opted in for SMS notifications, When the SMS delivery fails, Then the system should log the failure and send a follow-up in-app notification to alert the user of the SMS failure.

In-App Notification Center

User Story: As a team member, I want to be able to access my notifications within the app so that I can manage my tasks and updates in one place without missing important information.
Description: The In-App Notification Center requirement allows users to view and manage all notifications from within the Datapy platform. This center should serve as a hub for alerts related to metrics, updates, and collaborative features. Users can sort notifications by categories such as 'Urgent', 'Recent', or 'Past Activities'. This not only enhances user experience by centralizing information but also ensures that users are aware of changes without relying solely on external communications. The goal is to create a seamless integration of notifications within the platform, promoting better engagement and usage.
Acceptance Criteria: User accessing the In-App Notification Center to view notifications related to recent business metrics updates.

Given the user is logged into the Datapy platform, when they navigate to the In-App Notification Center, then they should see a list of notifications sorted by category (Urgent, Recent, Past Activities).

User receives a notification for an urgent metric alert within the In-App Notification Center.

Given the user has an urgent notification, when they view the In-App Notification Center, then the urgent notification should be highlighted and appear at the top of the notifications list.

User wants to filter notifications by category in the In-App Notification Center to focus on 'Past Activities'.

Given the user is in the In-App Notification Center, when they select the 'Past Activities' filter, then only notifications categorized as 'Past Activities' should be displayed in the list.

User marks a notification as read in the In-App Notification Center after reviewing its content.

Given the user has reviewed a notification, when they click the 'Mark as Read' button, then the notification should be visually indicated as read and moved to the appropriate category.

User navigates to the In-App Notification Center and wants to see the details of a specific notification.

Given the user is in the In-App Notification Center, when they click on a notification, then a detailed view of the notification should be displayed, showing all relevant information associated with it.

Multiple users collaborate and receive notifications related to updates from team members in the In-App Notification Center.

Given multiple users are working within the same project, when one user submits an update, then all other users should receive a notification about the update in their In-App Notification Center.

User interacts with the In-App Notification Center and wants to receive notifications in real-time as they happen.

Given the user has enabled real-time notifications, when a significant event occurs, then the user should see the relevant notification appear instantly in the In-App Notification Center.

User Preferences for Notification Types

User Story: As a user, I want to configure my notification preferences so that I only receive alerts that are relevant to my role and responsibilities.
Description: The User Preferences for Notification Types requirement enables users to select their desired communication channels for receiving alerts from Datapy. This feature should allow users to choose between email, SMS, and in-app notifications, as well as set preferences for the types of alerts they want to receive (e.g., performance metrics, collaboration updates). This ensures personalization and relevance of notifications, leading to increased user satisfaction and engagement with the platform. By tailoring notifications to user preferences, Datapy can enhance overall value and usability of its analytics offerings.
Acceptance Criteria: User selects preferred notification channels in their settings and saves the preferences.

Given that a user is logged into Datapy, when they navigate to the notification settings, then they can select and save their preferred channels (email, SMS, in-app) without errors.

User sets preferences for specific types of alerts they wish to receive.

Given that a user is in the notification settings, when they check the types of alerts (performance metrics, collaboration updates), then their selections should be saved accurately and reflect in the notification settings.

A user receives an alert through their preferred channel after setting their preferences.

Given that a user has selected their preferred notification channel as email, when a relevant alert is triggered, then the user should receive an email notification promptly as per their set preferences.

User updates their notification preferences and verifies the changes.

Given a user has previously set their notification preferences, when they change their preferences and save, then the application should confirm the updates and display the new preferences.

User encounters errors while selecting or saving notification preferences.

Given that a user tries to save invalid preferences (e.g., selecting multiple channels for the same alert type without specifying), then an appropriate error message should be displayed, guiding the user to correct their selections.

Users with specific alert types enabled receive alerts only for those types.

Given that a user has selected to receive only performance metric alerts, when a performance metric is triggered, then the corresponding user should receive the alert while other non-selected alert types should not be sent.

User deactivates specific notification channels and confirms deactivation.

Given that a user has previously activated a notification channel, when they deactivate it and save the changes, then no alerts should be received via that channel for any future notifications.

Real-Time Notification Synchronization

User Story: As a data officer, I want to receive real-time notifications regardless of my chosen channel so that I can stay up-to-date with critical changes as they happen.
Description: The Real-Time Notification Synchronization requirement ensures that alerts and notifications are sent and received in real-time across all selected channels, providing users with immediate updates regarding their analytics and business metrics. This synchronization must function seamlessly, regardless of whether the notifications are sent via email, SMS, or in-app. By ensuring timely delivery of critical updates, users can make informed decisions quickly, fostering a proactive approach to data management and analytics.
Acceptance Criteria: User receives a critical alert about a significant decline in sales metrics during a sales campaign via their chosen delivery method.

Given a user has selected Email as their preferred notification channel, When a critical sales alert is triggered, Then the user should receive the alert via Email within 1 minute of the alert being generated.

A user is monitoring customer support analytics and receives updates about ticket escalations through SMS as per their preferences.

Given a user is registered to receive SMS notifications, When a ticket escalation occurs, Then the user should receive an SMS notification within 2 minutes of the escalation happening.

A user balances multiple dashboards and needs to be updated in real-time when alerts occur in one of them; they prefer in-app notifications.

Given a user is actively using the Datapy app, When an alert is triggered in any of their selected dashboards, Then the user should receive an in-app notification immediately when the alert is triggered.

A user who relies on email notifications for their analytics receives an alert for a new trend detected in their data.

Given a user has selected Email notifications, When a new trend is detected, Then the user should receive an email notification within 1 minute of the trend being identified.

A user wishes to confirm the receipt of real-time notifications sent via all available channels.

Given a user has multiple channels enabled (Email, SMS, and in-app), When a notification is sent out, Then the user should confirm receipt of the notification through all selected channels within 5 minutes.

A user intends to switch their notification preferences from SMS to in-app notifications and confirm the change.

Given a user has changed their notification preference in settings, When the change is saved, Then all future notifications should be sent to the in-app system within 30 seconds of the requirement being met.

Custom Alert Scheduling

User Story: As a busy executive, I want to schedule my notifications to be delivered at specific times so that I can focus on my work without constant interruptions.
Description: The Custom Alert Scheduling requirement allows users to set specific times or frequency for when they would like to receive certain notifications. This feature aims to enhance the user experience by allowing for flexibility, ensuring that users can align alerts with their schedules or peak work hours. By providing users the option to customize alert delivery, Datapy can enhance user engagement while ensuring that critical metrics do not overwhelm users and are communicated at optimal times for attention.
Acceptance Criteria: User sets a custom alert for daily sales metrics at 9 AM and verifies the alert is received via the selected channel.

Given the user has selected 'Daily Sales Metrics' for notification, when they schedule the alert for 9 AM, then they should receive the alert via their preferred channel (email, SMS, or in-app) at the specified time.

User schedules a weekly summary report for every Monday at 8 AM and checks the notification history for receipt.

Given the user has opted for 'Weekly Summary Report' notifications, when they set the alert to trigger every Monday at 8 AM, then the system should log the alert in the notification history and the user should receive it as scheduled.

User modifies an existing alert to change the delivery frequency from daily to weekly and tests the new setting.

Given a user has an active daily alert, when they change the alert frequency to weekly and save the changes, then the system should update the alert frequency and confirm the new schedule.

User disables an active notification and verifies that no alerts are received thereafter.

Given the user has an active alert, when they disable the notification, then they should no longer receive alerts related to that notification until it is enabled again.

User attempts to set up multiple alerts for different metrics at different times and checks if all are successfully scheduled.

Given the user schedules alerts for 'Inventory Levels' at 10 AM and 'Customer Activity' at 3 PM, when they save the alerts, then both alerts should appear in the user’s alert list with the correct timings.

User reviews the user guide for custom alert scheduling and successfully applies the instructions to set up a new alert.

Given the user is unfamiliar with the feature, when they refer to the user guide for custom alert scheduling, then they should be able to follow the steps and successfully set up a new alert with all parameters correctly applied.

Shared Data Workspaces

Empowers teams to create dedicated zones within the Collaborative Insights Hub for specific projects or metrics. Each workspace facilitates focused data analysis and insight sharing, ensuring that all team members can easily collaborate on defined objectives, leading to more cohesive strategies.

Requirements

Workspace Creation

User Story: As a team leader, I want to create dedicated workspaces for each project so that my team can collaborate effectively on specific objectives without distractions from unrelated data.
Description: Allow users to create, name, and configure dedicated data workspaces within the Collaborative Insights Hub. Each workspace should support customizable settings, including privacy options, data sources, and user access rights. This feature enhances collaboration by enabling teams to tailor their work environments to specific projects or metrics, leading to improved focus and productivity.
Acceptance Criteria: Creating a new data workspace for a project analysis

Given a user is logged into Datapy, when they navigate to the Collaborative Insights Hub and select 'Create Workspace', then they should be able to name the workspace and configure its settings (privacy options, data sources, user access rights) successfully without errors.

Accessing and modifying workspace settings

Given a user has created a data workspace, when they access the workspace settings, then they should be able to modify the workspace name, data sources, and user access rights, and save these changes without any issues.

Sharing a workspace with team members

Given a user has an existing data workspace, when they click on 'Share Workspace', then they should be able to select team members, set their access rights, and send invitations successfully, receiving confirmation of the shared access.

Deleting a data workspace

Given a user is in a data workspace, when they select the option to 'Delete Workspace', then a confirmation prompt should appear, and upon confirming, the workspace should be removed from their list of workspaces and no longer accessible to users.

Viewing the list of existing workspaces

Given a user is logged into Datapy, when they navigate to the Collaborative Insights Hub, then they should see a list of all existing workspaces they have created or have access to, along with key details such as name, privacy status, and last updated date.

Setting privacy options for a workspace

Given a user is creating or editing a data workspace, when they select privacy options, then they should be able to set the workspace to 'Public' or 'Private', and this setting should be correctly reflected in the workspace details.

Integrating data sources into a workspace

Given a user is creating or modifying a workspace, when they select data sources from the available list, then they should be able to integrate multiple data sources seamlessly, and confirm the integration through a success message.

Data Sharing Permissions

User Story: As a project manager, I want to set permissions for team members in a workspace so that I can control who has access to sensitive project data and maintain its confidentiality.
Description: Implement a permissions management system that allows users to control who can view or edit data within each workspace. This feature is essential for maintaining data security and ensuring that sensitive information is only accessible to authorized team members. Administrators should have the ability to set role-based access controls, enhancing collaboration while safeguarding data integrity.
Acceptance Criteria: User roles within a workspace need to be defined and assigned based on project requirements, allowing for specific team member access to either view or edit data.

Given a user is assigned a role in a workspace, when the user attempts to access the data, then they should have permissions that align with their assigned role, allowing viewing or editing as defined.

An administrator should be able to set and modify permissions for users within each workspace, ensuring sensitive information is protected while enabling necessary collaboration.

Given an administrator is logged in, when they navigate to the permissions settings of a workspace, then they should be able to set and modify role-based access controls for each team member.

Users with view-only permissions should be able to see the data in the workspace but not make any changes to it, to ensure data integrity.

Given a user is assigned view-only permissions, when they access the workspace, then they should see the data but should be unable to edit or delete any information within that workspace.

A user attempts to access data in a workspace for which they do not have permission, ensuring that unauthorized access is effectively restricted.

Given a user tries to access a workspace for which they lack the necessary permissions, when they attempt to view or edit the data, then they should receive an access denied message and be unable to proceed.

When permissions are changed by an administrator, users' access should be updated in real-time to reflect those changes without delay.

Given an administrator modifies permissions for a user in a workspace, when the change is saved, then the permissions for that user should be updated immediately, allowing or restricting access accordingly.

The system should log all changes made to user permissions for audit purposes and accountability, allowing administrators to review changes if needed.

Given a permission change is made by an administrator, when the change is saved, then an audit log should record the change details, including the administrator's username, the timestamp, and the previous and new permissions.

Real-time Collaboration Tools

User Story: As a team member, I want to communicate in real-time with my colleagues within the workspace so that we can quickly share insights and resolve issues as they come up.
Description: Integrate real-time collaboration tools such as chat, file sharing, and notifications within each data workspace. This feature allows team members to communicate instantly and share updates on metrics or project progress, thus improving decision-making and responsiveness. The real-time aspect will encourage dynamic discussions and quick resolutions to issues as they arise.
Acceptance Criteria: When a team member enters a shared data workspace, they can initiate a chat with other team members to discuss project-related insights in real-time.

Given the team member is in a shared data workspace, when they click on the chat icon, then a chat window should open allowing them to send messages and receive responses in real-time.

A team member uploads a file to the shared data workspace to share insights with others, and notifications are sent to team members about the upload.

Given a team member uploads a file, when the upload is complete, then all team members in the workspace should receive a notification about the new file.

Team members work on a project and need to share updates immediately, ensuring everyone is aware of the latest metrics and progress simultaneously.

Given various team members are in a shared workspace, when a team member makes an update to a project metric, then all members should see the update reflected in real-time without needing to refresh the page.

A team leader holds a quick meeting within the workspace to address a project hurdle, using the real-time chat and file-sharing tools to enhance discussion.

Given the team leader initiates a chat, when they share a relevant file during the chat, then all team members in the chat should be able to download the file and discuss it immediately.

A team member wants to notify others about an important metric change to facilitate rapid response and discussion.

Given a team member identifies an important metric change, when they click on the 'Notify Team' button, then all members in the shared workspace should receive an instant notification about the change.

Workspace Templates

User Story: As a user, I want to access templates for common workspaces so that I can quickly set up projects without needing to configure everything from the ground up.
Description: Provide users with pre-defined templates for common project types or analyses that can be easily customized. This feature simplifies the setup process for new workspaces, allowing users to quickly launch projects based on best practice designs. It enhances user experience by eliminating the need to start from scratch, ensuring consistency in data presentation and analysis across workspaces.
Acceptance Criteria: User can choose from a variety of pre-defined templates when creating a new workspace for a marketing campaign.

Given the user is on the Workspace creation page, when they click on the template selection, then they should see a list of available templates specific to marketing campaigns.

Users can customize the chosen workspace template to fit their specific project requirements.

Given the user has selected a marketing campaign template, when they modify any aspects of the template (e.g., metrics, visuals), then those changes should be saved and reflected in the new workspace.

Users can preview a selected template before creating a new workspace.

Given the user is viewing the list of templates, when they select a preview option for a template, then a modal should display the layout and design of the template without creating a new workspace.

The system provides tooltips or help icons for each workspace template to assist users in understanding the intended use of each template.

Given the user is hovering over a template, then a tooltip should appear explaining what type of projects the template is best suited for and highlight its key features.

Multiple users can collaborate within the same workspace created from a template.

Given a workspace has been created using a predefined template, when additional users are invited to the workspace, then they should be able to access it and make simultaneous edits.

Users can save their customized workspace as a new template for future use.

Given the user has customized a workspace, when they save it as a new template, then they should have the option to give it a name and description for easy identification in the future.

Users receive confirmation when a workspace is successfully created from a template.

Given the user has completed the creation of a new workspace from a template, when the process is done, then a success message should appear indicating that the workspace has been successfully created and is now available for use.

Integration with External Tools

User Story: As a team member, I want to integrate our data workspace with Slack so that I can receive real-time notifications and updates without having to leave my primary workspace.
Description: Develop integration capabilities with popular external tools and platforms (e.g., Slack, Microsoft Teams, Google Drive) that teams commonly use. This feature will enhance the functionality of the data workspaces by allowing users to import/export data, notifications, and documents seamlessly, enabling a more cohesive workflow and improved productivity.
Acceptance Criteria: User successfully integrates Datapy with Slack for real-time notifications and updates regarding workspace activities.

Given the user has access to both Datapy and Slack, when the user initiates the integration process, then the system allows the user to complete the integration without errors, and alerts the user via Slack whenever new data is added to the workspace.

Team members access integrated Google Drive from a Datapy workspace to upload and share documents relevant to their projects.

Given a user is in an active workspace, when the user selects the option to import a document from Google Drive, then the system enables the user to successfully select and upload a document, which appears in the workspace without any loss of data integrity.

Users receive notifications in Microsoft Teams whenever a dashboard is updated in their Datapy workspace.

Given a user has linked their Microsoft Teams account, when a dashboard is updated in the workspace, then the user receives a notification in Teams that outlines the changes made to the dashboard.

Users seamlessly export project data from a Datapy workspace to Excel for reporting purposes.

Given a user is on the project workspace, when the user selects the export option, then the system generates an Excel file containing the current project data with all necessary formatting intact, allowing the user to download without errors.

Teams collaboratively analyze data within a dedicated workspace after integrating with an external analytics tool.

Given the team has access to the integration with an external analytics tool, when team members access the workspace, then they can collaboratively view and manipulate data from the external tool in real-time, with changes reflecting immediately for all team members.

Insight Tagging System

Allows users to tag insights with relevant keywords or categories, making it easier to search, filter, and retrieve important findings. This feature enhances the organization of shared knowledge, improving accessibility and ensuring that critical insights are never lost amidst large data sets.

Requirements

Keyword-Based Insight Tagging

User Story: As a business analyst, I want to tag insights with relevant keywords so that I can easily search, filter, and access important findings in the future.
Description: This requirement involves implementing a tagging system that enables users to assign relevant keywords or categories to insights generated within the Datapy platform. The primary functionality will allow users to create tags that can be associated with specific data findings, enhancing the organization and retrieval of insights. The implementation must facilitate the easy addition and modification of tags anytime an insight is created or updated. This capability will not only improve the accessibility of insights by allowing users to filter and search based on tags but also foster a more collaborative environment where team members can share knowledge in a structured manner. Enhanced searchability will ensure critical insights are never lost, thereby amplifying data-driven decision-making across the organization.
Acceptance Criteria: User tags an insight while creating a new report.

Given a user is creating a new report, when they add an insight, then they can select from existing tags or create a new tag to associate with that insight.

User edits an existing insight to include additional tags.

Given a user has an existing insight, when they edit the insight, then they can add or remove tags easily before saving their changes.

User searches for insights using tags.

Given a user is on the insights page, when they enter a tag into the search bar, then the system displays all insights associated with that tag in real-time.

User filters insights by multiple tags simultaneously.

Given a user is viewing insights, when they select multiple tags from the filter options, then the system only shows insights that match all selected tags.

User receives suggestions for tags based on existing insights.

Given a user is tagging a new insight, when they start typing a tag, then the system provides suggestions for relevant tags based on previously used tags.

System limits the number of tags that can be applied to an insight.

Given a user is tagging an insight, when they attempt to add more than five tags, then the system displays a warning message that the limit has been reached.

User deletes a tag that's no longer relevant to an insight.

Given a user is viewing an insight, when they choose to delete a tag, then that tag is removed from the insight and no longer appears in search results or filters.

Advanced Search Functionality

User Story: As a product manager, I want to use an advanced search option to filter insights by multiple criteria so that I can quickly find the most relevant information for my analysis.
Description: This requirement encompasses the development of an advanced search feature that allows users to search for insights not only by keyword tags but also by other criteria such as date ranges, insight type, and user contributions. This feature is crucial for users who need to quickly locate specific insights amidst a vast array of data. With advanced search capabilities, users can refine their searches to retrieve targeted information more efficiently. The implementation will include options for sorting and filtering search results, making it easier for users to find the most relevant insights based on their needs. This will ultimately streamline the decision-making process by providing quicker access to actionable data.
Acceptance Criteria: User searches for tagged insights using specific keywords related to a recent marketing campaign.

Given the user enters relevant keywords, when the search is executed, then the system returns a list of insights tagged with those keywords, sorted by date.

User wants to retrieve insights from the last quarter specifically categorized as 'sales performance'.

Given the user selects a date range covering the last quarter and the category 'sales performance', when the search is executed, then the system must return only insights that match those criteria.

Team lead requires to find insights contributed by a specific team member over a defined period.

Given the user selects a specific contributor and a date range, when the search is executed, then the system returns insights tagged with the selected contributor's name within the specified date range.

User seeks to find insights that are both tagged with 'customer feedback' and are from the past month.

Given the user selects the tag 'customer feedback' and a date range for the past month, when the search is executed, then the system must yield results that contain both filters accurately.

User needs to sort search results by relevance and date after performing an advanced search.

Given the user performs an advanced search, when the user selects sorting options for relevance and/or date, then the search results must rearrange accordingly to reflect the selected sorting options.

User must be able to filter search results to show only insights of a specific type, such as 'reports' or 'data visualizations'.

Given the user selects the insight type filter, when the advanced search is conducted, then the results must only display insights of the selected type, meeting user needs for specific data.

User wishes to see a summary of insight retrieval metrics after conducting searches.

Given the user completes a search, when the search results are displayed, then the system must show a summary of total insights retrieved and the filters applied to that search.

User Collaboration on Insights

User Story: As a team member, I want to leave comments on insights so that I can engage in discussions and share knowledge with my peers in the Datapy platform.
Description: This requirement focuses on integrating a collaboration feature where users can comment on and discuss tagged insights directly within the Datapy platform. By adding this functionality, users will be able to share their thoughts and provide feedback on insights, facilitating a richer discussion around data findings. This feature will enhance teamwork and knowledge sharing, as users can converse about specific insights directly beneath the corresponding tags, ensuring that valuable discussions do not become fragmented. Additionally, this collaboration tool will support notifications to alert users when comments are made on insights they are involved in, promoting engagement and continuous feedback.
Acceptance Criteria: User Collaboration on Tagged Insights

Given a user has tagged an insight with relevant keywords, when they add a comment to that insight, then the comment should be displayed immediately beneath the insight for other users to view.

Notification for Engagement on Insights

Given a user is involved in a tagged insight, when another user comments on that insight, then the involved user should receive a notification alerting them to the new comment.

Searching for Tagged Insights with Comments

Given a user is searching for insights by tags, when they retrieve the results, then the insights should display the number of comments alongside each tagged insight, indicating the level of engagement.

Filtering Insights by Tags and Comments

Given a user is filtering insights by tags, when they apply a filter, then the presented insights should only include those that have been tagged and have at least one comment, sorted by the most recent comment date.

Deleting Comments on Tagged Insights

Given a user has commented on a tagged insight, when they choose to delete their comment, then the comment should be removed from the display beneath the insight and no longer accessible to other users.

Viewing Insights with No Comments

Given a user is viewing a tagged insight that has no comments, when they access the insight, then they should see a message indicating that no comments are available, encouraging them to contribute.

Editing Comments on Tagged Insights

Given a user has posted a comment on a tagged insight, when they choose to edit their comment, then the edits should be saved and displayed immediately, reflecting the changes made.

Insight Tag Analytics Dashboard

User Story: As a data scientist, I want to view an analytics dashboard for tagged insights so that I can analyze trends and improve our data usage practices.
Description: This requirement entails creating a dedicated analytics dashboard that displays metrics related to tagged insights, such as the most frequently used tags, insights per tag, and engagement levels for comments on those insights. The dashboard will provide users with an overview of how insights are being utilized within the organization, highlighting patterns and trends in data usage. This feature is essential for evaluating the effectiveness of the tagging system and understanding user engagement. By providing visual representations of tagging trends, users will be able to make data-informed decisions to optimize their tagging practices.
Acceptance Criteria: User navigates to the Insight Tag Analytics Dashboard to analyze the usage of tags within their organization for a specific project over the past quarter.

Given a user is authenticated and on the Insight Tag Analytics Dashboard, when they select the project and time frame, then the dashboard should display a bar chart showing the most frequently used tags for that selection.

A user wants to view insights associated with a particular tag from the Insight Tag Analytics Dashboard to assess engagement.

Given a user has accessed the Insight Tag Analytics Dashboard, when they click on a specific tag in the tag list, then all insights associated with that tag should be listed along with their engagement metrics.

The management team needs to evaluate the overall engagement levels on insights tagged within the last month to identify areas for improvement.

Given the management team is on the Insight Tag Analytics Dashboard, when they filter insights by the last month's tags, then the dashboard should show a summary of engagement levels, including comments and likes, for those insights.

A user intends to track the effectiveness of tagging over time to explore trends and patterns in usage.

Given a user is viewing the Insight Tag Analytics Dashboard, when they select a time range from the dropdown, then the dashboard should display a line graph representing the tagging frequency trends over that time period.

A user needs to ensure that all tagged insights are properly categorized and easy to retrieve for a client meeting scheduled next week.

Given a user is on the Insight Tag Analytics Dashboard, when they search for a specific keyword related to insights, then only insights with matching tags should be displayed, confirming the functionality of the tagging system.

A product manager wishes to understand the correlation between popular tags and sales performance metrics to improve future tagging strategies.

Given a product manager accesses the Insight Tag Analytics Dashboard, when they view combined metrics of tagged insights and sales performance, then the dashboard should illustrate correlations through a scatter plot or comparable visuals for clear analysis.

Tagging Best Practices Guide

User Story: As a new user, I want to access a tagging best practices guide so that I can effectively use the tagging system to improve my data organization and retrieval.
Description: This requirement creates a comprehensive guide that will serve as a resource for users to understand best practices for tagging insights. This guide will cover aspects such as how to choose relevant keywords, the importance of consistency in tagging, and how tagging can enhance data retrieval and collaboration. Providing users with this guidance will ensure that the tagging system is used effectively, maximizing the value of the insights captured. The implementation will also include mechanisms for feedback on the guide, allowing for continuous improvement based on user experiences and suggestions.
Acceptance Criteria: Tagging Best Practices Guide Access and Navigation

Given that the user has access to the Tagging Best Practices Guide, when they navigate to the guide, then they should be able to view a clear table of contents that allows them to quickly access different sections of the guide.

Consistency in Keyword Tagging

Given the examples provided in the Tagging Best Practices Guide, when a user creates a tag for an insight, then the tag should match one of the recommended keywords or categories listed in the guide.

User Feedback Mechanism for the Guide

Given that the Tagging Best Practices Guide has been published, when a user finishes reading the guide, then they should be able to submit feedback through a designated mechanism that captures their suggestions or comments.

Enhancement in Data Retrieval Through Tagging

Given that multiple insights have been tagged following the guidelines in the Tagging Best Practices Guide, when the user searches for a keyword tag, then the relevant insights should be displayed with a minimum accuracy rate of 90%.

Training Session for Best Practices

Given the release of the Tagging Best Practices Guide, when a training session is scheduled, then users should receive an invitation at least one week in advance and the session should cover all key areas of the guide.

User Adoption Rate of Tags

Given that the Tagging Best Practices Guide is implemented, when users start tagging insights, then at least 75% of tagged insights should demonstrate adherence to the best practices outlined within the first two months of implementation.

Indexing of Important Tags

Given that insights have been tagged with relevant keywords, when a user accesses the tagging interface, then they should see a list of commonly used tags that allows for quick tagging based on the most frequently used keywords.

Comment and Discuss Threads

Enables team members to leave comments, feedback, and suggestions directly on data visualizations and insights within the hub. This real-time messaging capability fosters rich discussions around data, enabling better decision-making through collaborative input and diverse perspectives.

Requirements

Real-time Commenting

User Story: As a team member, I want to leave comments on data visualizations so that I can share my insights and collaborate more effectively with others on interpreting data.
Description: The Real-time Commenting requirement enables users to leave comments directly on data visualizations within the Datapy platform. This feature supports live discussions, allowing team members to engage in conversations about specific insights. By incorporating real-time feedback, users can collaboratively assess data interpretations and decisions, providing a dynamic approach to understanding business metrics. The benefit of this requirement lies in its ability to enhance collaboration, ensuring diverse perspectives are considered in decision-making processes. The functionality integrates seamlessly with existing data visualization tools, fostering a community-driven analysis environment that drives actionable insights.
Acceptance Criteria: Team members engage in a discussion about a sales report visualization, where they can comment on specific metrics directly within the Datapy platform.

Given a sales report visualization, when a team member types a comment and submits it, then the comment should appear in real-time for all other users viewing the report.

A project manager assesses a marketing metrics dashboard while receiving feedback from various team members through the comments feature.

Given the marketing metrics dashboard is open, when multiple team members submit comments simultaneously, then all comments should be displayed without any delays or data loss.

Users are reviewing customer analytics data and wish to add context to data visualizations by leaving comments to communicate insights or raise questions.

Given a customer analytics visualization, when a user adds a comment containing relevant tags and references, then those comments should be searchable and linked to the corresponding data point.

During a live presentation of trend analysis data, participants can interactively leave comments and insights to enhance the discussion.

Given the trend analysis data is projected in a meeting, when a participant submits a comment, then the comment should appear on all participants' screens instantly.

Users want to edit a previously submitted comment for clarity or to correct an error, ensuring ongoing discussions remain accurately documented.

Given a previously submitted comment, when a user selects the edit option and updates the comment, then the revised comment should replace the original comment and display a timestamp of the update.

Threaded Discussions

User Story: As a user, I want to participate in threaded discussions on insights so that I can keep track of related comments and suggestions in an organized manner.
Description: The Threaded Discussions requirement provides the ability to create organized comment threads for each visualization. This feature allows users to respond to specific comments, creating a structured conversation that enhances context and clarity around discussions. Threaded discussions enable users to follow conversations easily, making it simpler to track suggestions, feedback, and actionable items arising from the discussions. The requirement plays a crucial role in maintaining an organized communication space, ensuring that critical insights are not lost amidst general discussions. This enhances the team's ability to analyze and interpret data collaboratively, ultimately leading to informed common goals and objectives.
Acceptance Criteria: User adds a comment to a data visualization.

Given a user is viewing a data visualization, when they enter a comment and submit it, then the comment should be displayed immediately in the comment thread associated with that visualization.

User replies to an existing comment in a thread.

Given a user is viewing a comment thread, when they select a comment to reply to and submit their response, then the reply should be indented under the original comment and visible to all users in the thread.

User can view all comments related to a visualization.

Given a user is on the analytics page, when they select a specific visualization, then all comments related to that visualization should be displayed in chronological order in the comment section.

Users can delete their own comments.

Given a user has submitted a comment, when they select the delete option for their comment, then the comment should be removed from the comment thread with a confirmation message.

Notifications for new comments in a thread.

Given a user is actively following a visualization, when a new comment is added to the thread, then the user should receive a notification indicating a new comment has been made.

Users can edit their comments in a thread.

Given a user has posted a comment, when they select the edit option, modify the text, and submit, then the comment should be updated with the new text while maintaining the original timestamp.

Comment Notifications

User Story: As a user, I want to receive notifications for new comments on visuals I follow so that I can stay updated and engage in discussions at the right time.
Description: The Comment Notifications requirement automates alerts for users when new comments or replies are made on relevant data visualizations. Each user can customize their notification settings based on specific visualizations or threads they are interested in, ensuring they stay informed and engaged without being overwhelmed by irrelevant notifications. This feature enhances user engagement and accountability, prompting users to participate in discussions proactively. By promoting active involvement, this requirement supports the collaborative nature of Datapy and fosters thorough analysis of data insights among team members.
Acceptance Criteria: User receives a notification when a colleague comments on a visualization they are following.

Given a user has enabled notifications for a specific data visualization, when a new comment is added to that visualization, then the user should receive an alert via their chosen notification method.

User can customize their notification settings for multiple visualizations.

Given a user is accessing notification settings, when they select specific visualizations from a list, then their notification preferences should save correctly without errors, and the user should receive confirmation of the successful update.

User receives consolidated daily summaries of comments on visualizations they interact with.

Given a user has selected the option for daily comment summaries, when they log into Datapy the next day, then they should see a summary report of all comments and replies on the visualizations they interacted with the previous day.

User can mute notifications for a thread they are no longer interested in.

Given a user is viewing a comment thread, when they choose the option to mute notifications for that thread, then they should no longer receive alerts related to new comments or replies on that thread from that point forward.

Notification alerts are received in real-time without delay.

Given a user has notifications enabled, when a new comment or reply is added, then the user should receive the notification within 5 seconds without any manual refresh of the page.

User receives feedback alerts for comments directed at them.

Given a user has been mentioned in a comment, when that comment is made, then they should receive an immediate notification alerting them that they have been mentioned, regardless of their settings for other notifications.

Comment Moderation Tools

User Story: As a team lead, I want to moderate comments on data visualizations so that I can ensure discussions are appropriate and constructive.
Description: The Comment Moderation Tools requirement equips users with the ability to manage discussions effectively. Features such as editing, deleting, and reporting comments allow designated users like managers or project leads to maintain a healthy communication environment. Implementing this requirement ensures that discussions remain constructive, relevant, and free from spam or inappropriate content. By providing moderation capabilities, this feature enhances the overall quality of discussions surrounding data visualizations, allowing teams to focus on actionable insights derived from their collaborative efforts.
Acceptance Criteria: Team member wants to edit a comment they made on a data visualization after receiving feedback from colleagues to clarify their point.

Given a logged-in user is on the data visualization page, when they select a comment they authored, then they should see an edit option that allows them to modify the comment's content, and upon saving, the updated comment should reflect the changes made.

A manager wants to delete an inappropriate comment made on a data visualization to maintain a constructive discussion environment.

Given a logged-in user with moderator permissions is viewing the comments on a data visualization, when they select the delete option on a comment, then the comment should be removed from the thread and not visible to other users anymore.

A project lead wants to report a spam comment on a data visualization to the administration for further action.

Given a logged-in user with moderator permissions is viewing the comments, when they choose the report option on a specific comment, then an appropriate report form should appear, allowing them to submit details about the comment, and confirmation of the report submission should be displayed.

A user wants to view all comments related to a particular data visualization to gather insights from team discussions.

Given any logged-in user is on the data visualization page, when they click on the comments section, then they should see a list of all comments made, including the author's name, timestamp, and the option to expand each comment for more details.

A team member wants to see which comments have been edited to stay updated on the discussion.

Given a logged-in user is viewing comments on a data visualization, when they look at the comment list, then any edited comments should be visually marked to indicate they have been changed, along with the date and time of the last edit.

A user with no moderation privileges wants to provide feedback on a suggestion made in the comments section of a data visualization.

Given a logged-in user without moderator privileges, when they leave a comment on a data visualization, then the comment should be successfully submitted, visible to all users, and should not have options to edit, delete, or report.

A team member wants to ensure that all comments follow community guidelines for respectful and constructive communication.

Given the system is equipped with moderation tools, when a user posts a comment, then the system should automatically check for inappropriate language or content and alert the user if any guidelines are violated before the comment can be submitted.

Comment Tagging System

User Story: As a user, I want to tag my team members in comments so that I can ensure important insights are recognized and reviewed by the right people.
Description: The Comment Tagging System requirement allows users to tag relevant team members in their comments to draw their attention to specific discussions. This functionality supports environments where multiple stakeholders are involved, ensuring that critical perspectives are not overlooked. By enabling users to tag others, the requirement facilitates direct communication among team members while encouraging collaborative review and contributions to the analysis. The tagging system enhances engagement by allowing for tailored notifications to the tagged users, making discussions more participatory and focused on team efforts.
Acceptance Criteria: User tags a colleague in a comment on a data visualization to initiate a discussion about the insights presented.

Given a user is viewing a data visualization, when they enter a comment and use the 'tag' functionality to mention a colleague, then that colleague should receive a notification about the comment and be able to view it within the context of the visualization.

Multiple users engage in a comment thread where tagging has been utilized, requiring visibility and engagement among team members.

Given a user tags multiple colleagues in a comment, when those colleagues check their notifications, then they should see the comment, including the visualization’s context, and be able to respond or contribute to the discussion.

A user wants to ensure their comments reach the relevant stakeholders, hence they tag team members based on their expertise related to the data.

Given a user tags a colleague in a comment, when the tagged colleague receives a notification and views the comment, then the system should log the interaction and confirm the notification was successfully sent and opened by the tagged user.

Team members want to review their interactions and comments related to various data visualizations over time.

Given that a user accesses their comment history in the platform, when they view the comments they were tagged in, then all such comments should be displayed along with the respective data visualizations for comprehensive context.

Users need to maintain the relevance and focus of discussions by tagging only those directly involved or necessary for the conversation.

Given that a user attempts to tag a colleague in a comment, when the colleague is not part of the current project team or has exceeded engagement limits, then the system should alert the user and prevent tagging until conditions are met.

Comment Analytics Dashboard

User Story: As a project manager, I want to view analytics on comment engagement and trends so that I can assess how well my team collaborates on data insights.
Description: The Comment Analytics Dashboard requirement provides insights into discussion trends, engagement levels, and response patterns on data visualizations. By analyzing the comments made, this feature produces valuable metrics that help teams understand how effectively they are collaborating. The dashboard can display information such as the most discussed insights, the number of active participants, and overall engagement levels, allowing teams to fine-tune their communication strategies. This requirement is instrumental in promoting a data-driven environment even in discussions, empowering users to identify key topics and trends in collaborative efforts.
Acceptance Criteria: Team members are reviewing a data visualization on sales metrics and want to give feedback directly on the visualization within the Comment Analytics Dashboard.

Given a data visualization is displayed on the dashboard, when a team member comments on the visualization, then the comment should appear immediately beneath the visualization and be attributed to the user's name and timestamp.

The team wants to assess the engagement level on comments made about a specific data visualization to determine areas needing more discussion.

Given there are multiple comments on a data visualization, when a user accesses the Comment Analytics Dashboard, then the dashboard should display the total number of comments, the number of unique contributors, and a breakdown of engagement over time.

After several discussions on various data visualizations, the team aims to identify which insights generated the most comments to understand the topics of interest.

Given multiple data visualizations have comments attached, when the user filters the Comment Analytics Dashboard by 'Most Discussed', then the top three data visualizations with the highest number of comments should be displayed, along with the total comment counts for each.

In a weekly team review meeting, participants want to present analytics on team engagement in discussions to strategize for future communications.

Given the team meeting is scheduled, when the Comment Analytics Dashboard is generated for the date range of the previous week, then it should provide metrics like total comments, average response time, and active participant count in an easily presentable format.

The leadership team wants to monitor engagement trends over a period of time to evaluate the effectiveness of collaborative discussions in decision-making processes.

Given the Comment Analytics Dashboard is being used, when the user selects a custom date range, then it should update all metrics to reflect the total comments, engagement rates, and trends in discussion themes for that period.

Version Control Tracking

Keeps a history of changes made to shared reports and visualizations, allowing users to revert to previous versions if necessary. This feature promotes accountability and transparency in collaborative efforts, ensuring that teams can refine their analyses while maintaining a record of evolving insights.

Requirements

Version History Log

User Story: As a data analyst, I want to see a log of all changes made to reports so that I can track the evolution of analyses and ensure that our data interpretations align with the latest organizational objectives.
Description: The Version History Log requirement ensures that every modification made to reports and visualizations is recorded with a timestamp, user identification, and detail of changes. This functionality not only allows users to track the evolution of shared documents but also provides a means to compare different versions easily. By maintaining an accessible repository of changes, it supports accountability among team members who collaborate on data insights, fostering an environment of transparency and trust. The implementation will seamlessly integrate into the existing dashboard, allowing users to view, filter, and revert changes as necessary. This requirement enhances the collaborative experience while ensuring that valuable insights can be preserved and revisited at any time.
Acceptance Criteria: User accesses the Version History Log from the dashboard to review changes made to a shared report.

Given the user is logged in, When the user navigates to the Version History Log, Then the log should display a chronological list of all modifications with timestamps, user IDs, and change details.

User applies a filter to the Version History Log to view changes made by a specific team member.

Given the user is viewing the Version History Log, When the user selects a team member from the filter options, Then the log should update to display only the changes made by that team member.

User reverts a report to a previous version using the Version History Log.

Given the user is viewing the Version History Log, When the user selects a previous version and clicks 'Revert', Then the current report should reflect the selected version, and a confirmation message should be displayed.

User checks the details of a specific change in the Version History Log.

Given the user is viewing the Version History Log, When the user clicks on a specific change entry, Then a detailed view should appear showing the user ID, timestamp, and a detailed description of the modifications made.

User attempts to access the Version History Log without being logged in.

Given the user is not logged in, When the user attempts to access the Version History Log, Then the user should be redirected to the login page with an appropriate error message displayed.

User reviews the version history log for a specific report over a designated timeframe.

Given the user is viewing the Version History Log, When the user selects a date range to filter changes, Then the log should display only the modifications made within that specified timeframe.

User observes the user identification for all changes recorded in the Version History Log.

Given the user is viewing the Version History Log, Then each entry should include the user ID of the person who made the change, ensuring accountability.

Revert to Previous Version

User Story: As a project manager, I want the ability to revert to a previous version of a report so that I can mitigate any impacts caused by errors introduced in the latest updates.
Description: The Revert to Previous Version requirement allows users to restore any report or visualization to a previously saved state. This feature is crucial for users who may need to rollback adjustments that led to unintended results or errors in data analysis. By enabling easy navigation between versions, users can test new insights without the fear of permanently losing earlier analyses. This functionality will be integrated with a user-friendly interface, ensuring that all users, regardless of their technical expertise, can independently manage their reports. The implementation of this requirement will empower users to experiment with confidence, enhancing the overall usefulness of the platform for decision-making.
Acceptance Criteria: User wants to revert a report to a previous version after realizing that recent changes have led to inaccurate data analysis.

Given the user is viewing a report with version history, when they select a previous version from the version list and confirm the revert action, then the report should display the data and visualizations as they were at that previous version.

Team members need to ensure accountability by tracking the history of changes made to a report before reverting to an earlier version.

Given the user accesses the version history, when they view the details for a specific version, then they should see a timestamp and user information associated with each change made to the report.

A user wishes to test a new visualization that may not yield the required insights and wants to save the current state before experimenting.

Given the user is working on a report, when they click the 'Save Current Version' button, then the system should save the current report state and notify the user of successful saving.

Users want to ensure they can easily navigate to different report versions to compare data before and after changes.

Given the user is in the version history menu, when they click on any version to preview, then the system should open that version in a read-only format without affecting the current working version.

A user encounters an error while trying to revert to a previous version and needs feedback on what went wrong.

Given the user attempts to revert to a previous version but encounters an error, when the error occurs, then the system should display a clear and actionable error message indicating the reason for the failure and possible next steps to resolve the issue.

Users require confirmation before a report version is reverted to prevent unwanted changes.

Given the user selects a previous version to revert to, when they click 'Revert,' then a confirmation dialog should appear asking them to confirm the action before proceeding with the revert.

User Permissions for Version Control

User Story: As an administrator, I want to control who can access and modify version history so that I can maintain data integrity and manage collaborative permissions effectively.
Description: The User Permissions for Version Control requirement establishes a permission-based system to manage who can access and modify version history. This feature is fundamental in ensuring data security and integrity among users, particularly within collaborative and competitive environments. Administrators can assign roles that designate whether users can view, edit, or revert versions, preventing unauthorized changes that could compromise reporting accuracy. With this implementation, the platform will provide clearer accountability for report modifications, and will streamline the collaborative process by ensuring that only authorized personnel can make significant changes. This will foster a secure collaborative environment and promote trust among team members.
Acceptance Criteria: User Scenario for Viewing Version History by Role

Given a user with 'Viewer' permissions, when they access the version control section of a report, then they should see a list of previous versions without the ability to edit or revert those versions.

User Scenario for Editing Version History by Role

Given a user with 'Editor' permissions, when they view the version control section of a report, then they should have the option to edit and revert to previous versions, as well as the ability to save newly edited versions, reflecting their changes in the history.

Admin Scenario for Assigning User Roles

Given an administrator logged into the platform, when they attempt to assign roles to users in the version control settings, then they should be able to successfully assign and modify user permissions, which should reflect in the user permissions overview immediately after the action.

User Scenario for Attempting Unauthorized Changes

Given a user with 'Viewer' permissions who tries to revert changes in the version control section, when they attempt to execute this action, then they should receive an error message indicating insufficient permissions, and no changes should occur to the version history.

Audit Log Scenario for Tracking Changes in Version Control

Given any action taken within the version control feature, when a change (such as edit or revert) is made, then that action should be logged in the audit trail, including the user who took the action, the timestamp, and the type of action performed.

User Scenario for Notifications on Version Changes

Given a user who is part of a report with version control enabled, when a version is edited or reverted by another user, then they should receive a notification alerting them of this change regardless of whether they currently have access to the report.

Version Comparison Tool

User Story: As a business intelligence analyst, I want to compare different versions of a report so that I can understand the impact of changes and provide enhanced insights on data trends over time.
Description: The Version Comparison Tool provides users with the ability to visualize differences between versions of reports or visualizations side by side. This requirement enhances the analytical capabilities by allowing users to easily identify changes, trends, and the rationale behind decisions made regarding data presentation. By integrating this tool directly into the reporting interface, users will benefit from a streamlined analysis process, improving decision-making efficiencies by revealing how adjustments affect data interpretation. This comparison capability will ensure that users can make informed decisions based on the most relevant data, reinforcing the platform's value in supporting business strategies.
Acceptance Criteria: Version Comparison for Decision Making in Team Meetings

Given a user opens the Version Comparison Tool, when the user selects two different versions of a report, then the system should display the selected versions side by side with highlights on the differences, including added, removed, and modified elements.

Evaluating Changes for Stakeholder Review

Given a user accesses the Version Comparison Tool, when the user chooses two versions of a report to compare, then the system should generate a summary report of the changes made, including detailed metrics on how changes impact data interpretation.

Reverting to Previous Versions for Error Correction

Given a user is viewing the comparison of two report versions, when the user identifies an undesirable change, then the user should be able to revert to the previous version with a single click, maintaining the history of all versions.

Utilizing the Comparison Tool for Training Purposes

Given a new team member is being trained, when the trainer demonstrates the Version Comparison Tool, then the trainer should be able to show how to effectively navigate the tool, ensuring the new member can independently use it after training sessions.

Facilitating Collaborative Feedback Across Teams

Given two teams are collaborating on a project, when they utilize the Version Comparison Tool to review reports, then both teams should be able to leave comments on specific changes, fostering improved communication and project alignment.

Monitoring Changes for Regulatory Compliance

Given a user needs to track compliance-related changes in reports, when the user accesses the Version Comparison Tool, then the system should provide an audit log of all changes made, timestamped and attributed to specific users.

Enhancing Visual Presentation of Differences

Given a user is comparing two report versions, when the user selects a comparison view option, then the system should allow the user to toggle between a textual change log and a visual representation of changes, enhancing clarity and usability.

Notifications for Version Updates

User Story: As a team member, I want to receive notifications when a report I’m collaborating on is updated so that I can stay informed and make timely contributions based on the latest data.
Description: The Notifications for Version Updates requirement will alert users whenever a shared report or visualization is modified. This feature is essential for keeping team members informed about the latest changes, ensuring that everyone remains on the same page during collaborative projects. Notifications can be configured to be sent via email or in-app alerts, promoting constant communication and minimizing the risk of working on outdated versions. By implementing this functionality, user engagement and collaboration will be significantly enhanced, supporting a more synchronized workflow among team members who rely on real-time updates.
Acceptance Criteria: User receives an email notification when a shared report they are following is updated by a team member.

Given a user is subscribed to notifications for a specific report, when that report is modified, then the user should receive an email notification summarizing the changes within 5 minutes of the update.

User receives an in-app alert when a visualization they have been collaborating on is updated.

Given a user is actively viewing a shared visualization, when a change is made to that visualization, then an in-app alert should appear immediately notifying them of the update.

Users can configure their notification preferences for report updates in their account settings.

Given a user accesses their account settings, when they navigate to the notifications section, then they should be able to select and save preferences for receiving email alerts or in-app notifications for report updates.

Multiple users are notified simultaneously when a critical report is updated to ensure everyone is informed.

Given a critical report is updated, when the update occurs, then all subscribed users should receive their respective notifications (email and/or in-app) at the same time, within 5 minutes.

Users can view a history log of all notifications received for version updates.

Given a user navigates to their notification history, when they access this log, then they should see a chronological list of all notifications regarding report and visualization updates, including timestamps.

A user can opt-out of notifications for specific reports they are no longer following.

Given a user is subscribed to several reports, when they choose to opt-out of notifications for a specific report, then they should no longer receive notifications for that report and receive a confirmation message of this change.

Collaborative Brainstorming Sessions

Facilitates scheduled or ad-hoc sessions within the hub where team members can collectively discuss and brainstorm strategies based on shared data insights. This interactive feature enhances creativity and collaboration, turning data analysis into an engaging process that drives innovation.

Requirements

Interactive Data Sharing

User Story: As a data analyst, I want to share specific data insights during brainstorming sessions so that my team can make informed decisions based on real-time information and drive innovation effectively.
Description: The Interactive Data Sharing feature allows team members to seamlessly share selected data insights during collaborative brainstorming sessions. This functionality enables users to highlight specific data points, trends, or metrics directly from their dashboards, facilitating discussions that are rooted in real-time data. By integrating this feature within the session hub, users can ensure that all participants are on the same page, thereby enhancing the quality of discussions and brainstorming outcomes. The feature will also provide options for users to annotate or comment on the shared data, capturing contextual insights that can be referenced later. This is crucial for fostering a collaborative environment that encourages innovative thinking based on factual analysis.
Acceptance Criteria: Team members participate in a collaborative brainstorming session where they share insights from the Interactive Data Sharing feature.

Given a user is in a brainstorming session, when they select a data point from their dashboard and share it, then all participants should see the selected data point in real-time without delay.

Users want to annotate shared data points during the brainstorming session to provide additional context for their ideas.

Given a user shares a data point in a brainstorming session, when they add an annotation, then the annotation should be visible to all participants and saved for future reference.

A manager wants to ensure that all team discussions during the brainstorming session are based on the most recent data insights.

Given the user shares a specific metric in the session, when the data updates in the dashboard, then the shared metric should also reflect the most recent data automatically during the session.

Participants need to comment on shared data during the brainstorming session to encourage discussion and feedback.

Given a data point is shared in the session, when a participant adds a comment, then the comment should appear alongside the data point and be visible to all participants immediately.

Users need to access a history of data points and annotations shared during previous brainstorming sessions for context before future meetings.

Given the user accesses the brainstorming session hub, when they select the previous session, then they should see a history of shared data points and annotations from that session.

A facilitator wants to control the flow of sharing data insights during the session to maintain order and focus.

Given the user is designated as a facilitator, when they set rules for data sharing, then participants should only be able to share insights according to those predefined rules during the session.

Real-time Feedback Mechanism

User Story: As a team member, I want to provide real-time feedback on ideas during brainstorming sessions so that the discussion remains engaging and everyone can contribute their thoughts effectively.
Description: The Real-time Feedback Mechanism enables participants during collaborative brainstorming sessions to provide live feedback on ideas and suggestions put forth by their peers. This feature will allow team members to vote, comment, or express agreement/disagreement in real-time, creating an interactive and dynamic environment. It enhances engagement during discussions and ensures that all voices are heard, leading to more democratic decision-making. The collected feedback will be aggregated and displayed instantly, helping teams to prioritize ideas and concepts based on collective input. This capability is essential for optimizing session outcomes and fostering a culture of collaboration and inclusivity.
Acceptance Criteria: Users can provide real-time feedback during a collaborative brainstorming session to enhance engagement and decision-making.

Given a brainstorming session is active, when a participant submits feedback (vote, comment, or agree/disagree), then the feedback should be recorded instantly and displayed to all participants.

Team leaders can view aggregated feedback from brainstorming sessions to prioritize ideas and facilitate decision-making.

Given multiple feedback responses have been submitted, when the session concludes, then the aggregated feedback report should display a ranked list of ideas based on received votes and comments.

Participants are notified of new feedback activity in real-time to promote continuous engagement during the brainstorming session.

Given feedback is submitted by any participant, when a feedback action occurs, then all participants should receive a notification about the new feedback immediately.

The feedback feature supports diverse response types to ensure comprehensive input from participants.

Given a brainstorming session, when users provide feedback, then the system should allow for multiple response types: voting, commenting, and agreement/disagreement options.

Users can easily access past feedback from previous sessions to inform current brainstorming efforts.

Given a history of brainstorming sessions, when a user requests feedback records, then the system should display a searchable list of past sessions and their respective feedback data.

The real-time feedback feature maintains user anonymity to encourage honest and open participation.

Given the feedback is submitted, when a user votes or comments, then their identity should remain anonymous to other participants unless they choose to reveal it.

Participants can cancel or edit their feedback submissions during the brainstorming session to reflect their evolving thoughts.

Given a feedback submission has been made, when a participant decides to edit or cancel their feedback, then they should be able to do so easily with a single action before the session ends.

Session Recap and Action Items

User Story: As a session leader, I want a comprehensive recap of our brainstorming sessions so that I can ensure all action items are tracked and follow-up is efficient in our ongoing projects.
Description: The Session Recap and Action Items feature automatically compiles a summary of discussions and captured ideas from brainstorming sessions. At the end of each session, users will receive an overview of key points discussed, decisions made, and a list of action items assigned to team members. This summary will serve as a quick reference for participants to review the session's outcomes and ensure follow-up on actionable steps. Additionally, the recap can be integrated with other tools within Datapy to facilitate tracking progress and accountability, making it easier for users to monitor ongoing projects. Implementing this feature is vital for ensuring that collaborative efforts translate into tangible results and drive productivity.
Acceptance Criteria: Team members have participated in a collaborative brainstorming session aimed at enhancing marketing strategies using shared data insights.

Given the session has concluded, when the user accesses the session recap, then it should display a summary of key points discussed, decisions made, and action items assigned with responsible team members.

A user has completed a brainstorming session and is checking for follow-up items and accountability regarding decisions made.

Given the recap is reviewed, when the user looks for assigned action items, then all action items should be listed with deadlines and the respective assignees clearly marked.

A team member wants to ensure that the session recap can be shared with external stakeholders for transparency.

Given the session recap exists, when the user opts to share the recap, then the system should allow exporting the recap to PDF and sharing it via email to designated external stakeholders.

A user needs to track the progress of assigned action items from a brainstorming session.

Given action items have been assigned, when the user accesses the task tracking tool within Datapy, then it should reflect the status of each action item, indicating whether it is pending, in progress, or completed.

A follow-up session is scheduled to review the outcomes of the previous brainstorming session.

Given the follow-up session is scheduled, when the user prepares the agenda, then the system should automatically retrieve and include relevant details from the previous session's recap, ensuring continuity of discussion.

A user is incorporating feedback from the team about the clarity of the session recap.

Given team feedback has been collected, when the user reviews the session recap format, then it should show improvements such as organized bullet points, clear headings, and an easily digestible summary of discussions.

A user wants to access historical recaps of past brainstorming sessions.

Given multiple past sessions have recaps available, when the user searches for historical recaps, then a list of session recaps should be displayed by date with the option to view details for each, sorted in reverse chronological order.

User Roles and Permissions

User Story: As an administrator, I want to manage user roles and permissions in brainstorming sessions so that I can ensure data security and appropriate access levels for all participants.
Description: The User Roles and Permissions feature allows administrators to manage who can access and participate in collaborative brainstorming sessions. This functionality ensures that sensitive data is only shared among authorized users, enhancing security and compliance. Administrators can assign different roles with varying levels of permissions, such as viewer, contributor, or facilitator, providing flexibility to customize the session experience based on team needs. This feature is crucial for protecting intellectual property while promoting a collaborative environment that leverages diverse team skill sets appropriately.
Acceptance Criteria: User Role Assignment for Collaborative Brainstorming Sessions

Given an administrator is logged into the Datapy platform, when they navigate to the User Roles and Permissions section, then they should be able to successfully assign roles (viewer, contributor, facilitator) to team members for the collaborative brainstorming sessions.

Access Control Based on User Roles

Given a team member has been assigned a specific role (viewer, contributor, or facilitator), when they attempt to access the collaborative brainstorming session, then their access should be granted or denied according to their assigned role's permissions.

Modification of User Roles by Administrator

Given an administrator is in the User Roles and Permissions section, when they modify a user's assigned role during an active brainstorming session, then the changes should be reflected in real-time for that user within the session.

Audit Logger for User Role Changes

Given that a role has been changed for a user, when the administrator checks the audit log, then there should be a record of the role change including the user's previous role, new role, date, and the administrator who made the change.

Session Participation Restrictions

Given a user with viewer permissions tries to contribute during a brainstorming session, when they attempt to add content, then they should receive a notification indicating that they do not have permission to contribute.

Role-Based Notifications during Sessions

Given a user with facilitator permissions is conducting a brainstorming session, when a contributor submits a new idea, then the facilitator should receive a notification of the contribution in real-time.

User Role Review and Management

Given an administrator wants to review user roles, when they access the User Roles and Permissions section, then they should see a clear and organized list of all users along with their assigned roles and permissions before any session starts.

Integrated Task Assignment

User Story: As a participant, I want to assign tasks to my colleagues during brainstorming sessions so that we can directly translate discussions into actionable steps with clear ownership and timelines.
Description: The Integrated Task Assignment feature allows users to assign tasks directly from brainstorming sessions to specific team members. Within the session interface, users can transform ideas into actionable tasks with designated owners and due dates. This functionality streamlines the workflow by reducing the need to switch between applications for task management, helping teams to maintain focus and momentum on ideas generated during sessions. This feature is essential for promoting accountability and ensuring that brainstorming leads directly to actionable outcomes that drive progress.
Acceptance Criteria: User assigns a task to a team member during a collaborative brainstorming session to track a specific idea discussed.

Given a brainstorming session is open, when the user selects an idea and assigns it to a team member with a due date, then the task should be created in the task management system with the correct owner and due date.

A team member views the tasks assigned to them from the brainstorming session interface.

Given a team member accesses the task management interface, when they filter tasks by the 'Assigned To' field, then they should see all tasks assigned to them during the brainstorming sessions.

User modifies an existing task assigned during a brainstorming session.

Given a task assigned from a brainstorming session exists, when the user edits the task details (owner, due date), then the updates should be reflected in the task management system without errors.

User completes a task assigned during a brainstorming session and marks it as done.

Given a task assigned during a brainstorming session is marked as completed, when the user sets the task status to 'Done', then the task should no longer appear in the active tasks list but should be reflected in the completed tasks log.

Users receive notifications about tasks assigned during brainstorming sessions.

Given a task is assigned to a user from a brainstorming session, when the task is created, then the assigned user should receive a notification about the new task within the platform.

User deletes a task that was assigned during a brainstorming session.

Given a user wants to delete a task assigned from a brainstorming session, when they choose the delete option, then the task should be removed from the task management system and confirmation should be displayed.

Team members collaborate on updating progress for tasks assigned from brainstorming sessions.

Given a user wants to update progress on assigned tasks, when they modify the progress percentage for any task, then the updated progress should be reflected accurately in both the brainstorming session overview and task management interface.

Real-Time Notifications

Sends instant notifications to team members when new insights, comments, or changes are made in the Collaborative Insights Hub. This feature keeps everyone informed and engaged in the project, ensuring timely responses and minimizing communication delays.

Requirements

Instant Insight Notifications

User Story: As a team member, I want to receive instant notifications about new insights and comments so that I can stay engaged and respond quickly to changes.
Description: The Instant Insight Notifications feature will enable the platform to automatically send real-time alerts to team members whenever new insights, comments, or changes are made in the Collaborative Insights Hub. This functionality will enhance communication and collaboration among users, ensuring that all team members are immediately aware of critical updates, allowing for timely responses and engagement. By implementing this feature, Datapy will reduce communication delays, foster a more proactive team environment, and improve overall project management. Users can customize which notifications they wish to receive, further streamlining their workflow and ensuring that they focus on the most relevant updates.
Acceptance Criteria: Team members receive a notification when a new insight is added to the Collaborative Insights Hub.

Given a team member is subscribed to insights notifications, when a new insight is added, then they should receive an instant notification via their preferred communication channel.

Users can customize notification settings for different types of updates in the Collaborative Insights Hub.

Given a user is in their notification settings, when they select or deselect types of updates (insights, comments, changes), then their preferences should be saved and reflected in future alerts.

Team members receive a notification when a comment is added to an existing insight.

Given a team member is subscribed to comment notifications, when a comment is added to an existing insight they are following, then they should receive an instant notification via their preferred communication channel.

Users can toggle notification preferences on or off for the Collaborative Insights Hub.

Given a user is on the notification settings page, when they toggle the switch for notifications on or off, then the notifications should be enabled or disabled accordingly without errors.

The notification system functions consistently regardless of the user's activity within the Collaborative Insights Hub.

Given that a user is active in the Collaborative Insights Hub, when a new insight or comment is posted, then the notification should still be received in real time without any delays or failures.

Notifications contain accurate and relevant information about the insights or comments.

Given that a user receives a notification, when they open it, then it should contain the correct details about the insight or comment, including the author, timestamp, and content summary.

Team members exit the Collaborative Insights Hub and still receive notifications for new updates.

Given a user has exited the Collaborative Insights Hub, when new insights or comments are posted, then they should continue to receive notifications through their selected communication method.

Customizable Notification Preferences

User Story: As a user, I want to customize my notification preferences so that I only receive relevant updates and reduce notification overload.
Description: The Customizable Notification Preferences requirement allows users to personalize their notification settings based on their individual needs and preferences. This includes options to select which types of notifications they want to receive (e.g., insights, comments, changes) and the delivery method (e.g., email, in-app notifications, SMS). By offering this flexibility, users can better manage their attention and focus on the most pertinent updates that impact their work. This feature will integrate seamlessly within the existing notification system, providing users with an intuitive interface to adjust their settings, leading to improved user satisfaction and reduced notification fatigue.
Acceptance Criteria: User selects specific types of notifications to receive based on their role in the project.

Given a user is logged into the Datapy platform, when they access the notification preferences settings, then they should see checkboxes to select types of notifications such as insights, comments, and changes, and save their preferences successfully.

User opts for specific delivery methods for notifications.

Given a user is in the notification preferences section, when they choose their preferred notification delivery methods (e.g., email, in-app, SMS) and save the settings, then the settings should be updated successfully, reflecting their choices in the system.

User adjusts their notification preferences and updates are reflected in real-time.

Given a user modifies notification preferences, when they save their new settings, then all selected team members should immediately receive a notification corresponding to those preferences without delay.

User receives feedback confirming their notification preference changes.

Given a user saves changes to their notification settings, when the changes are applied, then a confirmation message should be displayed to the user indicating that their preferences have been successfully updated.

User tests the delivery of notifications through selected methods after updating preferences.

Given a user has selected their preferred methods for receiving notifications, when a new insight or comment occurs in the Collaborative Insights Hub, then they should receive the notification through their chosen delivery method (e.g., email, SMS, in-app).

User is able to revert notification preferences back to default settings.

Given a user wants to reset their notification preferences, when they choose to restore defaults within the notification settings, then all preferences should revert to the original factory settings successfully without any errors.

System records the history of notification preferences changes for the user.

Given a user changes their notification preferences multiple times, when they access their notification history, then they should see a log of all changes made to their preferences, including timestamps and modification details.

Notification Acknowledgment Tracking

User Story: As a project manager, I want to track which notifications have been acknowledged by team members so that I can assess engagement and follow up on critical updates.
Description: The Notification Acknowledgment Tracking feature will enable the platform to track and acknowledge when users have read or dismissed notifications. This functionality will provide insight into user engagement and responsiveness, allowing team leaders and managers to gauge how well team members are keeping up with project updates. The system will generate analytics on notification engagement, offering valuable data for understanding communication effectiveness within the team. This feature will enhance accountability and ensure users are aware of critical information that requires their attention.
Acceptance Criteria: User A receives a notification regarding new insights added to the Collaborative Insights Hub and accesses the platform to view the notification.

Given that User A has an active account and is logged into the platform, when User A views the notification, then the system must mark the notification as 'read' in the Notification Acknowledgment Tracking report.

Team Leader B wants to measure user engagement regarding notifications sent in the past week.

Given that notifications have been sent in the past week, when Team Leader B accesses the Notification Engagement Report, then the report should display the total sent notifications along with the count of those marked as 'read' and 'dismissed' by users.

User C dismisses a notification about a critical project update from the Collaborative Insights Hub.

Given that User C is logged into the platform, when User C dismisses the notification, then the system must log this dismissal in the Notification Acknowledgment Tracking report and update the dismissed count.

Team Leader D reviews the Notification Engagement Report to assess team response to project updates.

Given that the Notification Engagement Report is being accessed by Team Leader D, when the report displays, then it must include dates for notifications, user engagement status, and overall engagement percentage of all users.

User E logs in after missing several notifications and wants to catch up on project updates.

Given that User E logs into the platform, when they access the Notifications History section, then they should see a chronological list of all notifications including those marked as 'read' and 'dismissed', with timestamps.

A team manager wants to ensure that all critical notifications are acknowledged by the relevant team members.

Given a list of critical notifications, when the manager requests an acknowledgment tracking report, then the system must provide a report indicating who has read or dismissed each critical notification along with timestamps.

Notification History Log

User Story: As a team member, I want to access a history log of notifications so that I can find previous updates and understand the context of ongoing discussions.
Description: The Notification History Log requirement includes the implementation of a comprehensive log that captures all sent notifications within the platform. This log will allow users to review past notifications, ensuring that important messages are not missed. The feature will be accessible from the Collaborative Insights Hub, providing a historical context for discussions and decisions made based on previous insights. This functionality will enhance transparency and accountability, allowing team members to revisit past actions and discussions easily.
Acceptance Criteria: User accesses the Notification History Log from the Collaborative Insights Hub to review past notifications related to a specific project.

Given that the user is in the Collaborative Insights Hub, when they click on the Notification History Log, then they should see a complete list of notifications sorted by date and time, with the most recent notifications displayed at the top.

A team member receives a notification about a new comment on a shared project insight and wants to confirm it is logged in the Notification History Log.

Given that a notification is sent about a new comment, when the recipient accesses the Notification History Log, then the new comment notification should appear with relevant details such as date, time, and the user who made the comment.

A project manager wants to find all notifications sent regarding a specific decision made in the project to ensure all team members were informed.

Given that the project manager is using the Notification History Log, when they filter notifications by a specific date range and keywords related to the decision, then the log should accurately display all relevant notifications that meet the filter criteria.

A user queries the Notification History Log to check the record of actions taken after a certain notification was sent.

Given a user has accessed the Notification History Log, when they identify a specific notification, then they should be able to see a link to associated actions or discussions that occurred following that notification.

The platform's Notification History Log is meant to keep a secure and tamper-proof record of all notifications sent.

Given that notifications are logged in the Notification History Log, when the log is reviewed, then it must show all notifications without any deletions or alterations.

Users want to see how frequently notifications are sent within a certain timeframe for project analysis.

Given users access the Notification History Log, when they request a report of the number of notifications sent within a selected timeframe, then the system should generate an accurate count of notifications sent during that period.

Team Activity Overview Dashboard

User Story: As a team lead, I want to view an activity overview dashboard so that I can analyze team engagement with notifications and identify areas for improvement.
Description: The Team Activity Overview Dashboard will display key metrics related to team interactions with notifications, such as the number of notifications sent, acknowledged, and pending responses. This dashboard will provide team leads with actionable insights into how effectively team members are communicating and engaging with project updates. The visual representation of this data will help identify patterns and areas for improvement in team collaboration, facilitating better decision-making and enhancing overall project effectiveness.
Acceptance Criteria: Team leads access the Team Activity Overview Dashboard to review the latest metrics on notifications and team interactions in the Collaborative Insights Hub.

Given a team lead is logged into the Datapy platform, when they navigate to the Team Activity Overview Dashboard, then they should see real-time metrics including the number of notifications sent, acknowledged, and pending responses displayed visually on the dashboard.

A team member receives a notification about a new comment in the Collaborative Insights Hub and responds to that comment.

Given a team member has notifications enabled, when they receive a notification about a new comment, then the notification should be marked as acknowledged when the team member clicks on it, and the dashboard should update in real-time to reflect this change.

The Team Activity Overview Dashboard is analyzed by a team lead at the end of the week to identify areas for improvement in team communication.

Given the team lead views the dashboard after a week of activities, when they check the metrics, then they should be able to identify trends such as the number of unanswered notifications and areas with high engagement rates, enabling strategic decision-making.

A team member logs into Datapy after a week and checks the dashboard for updates on team interactions and notifications.

Given a team member wants to catch up on team interactions, when they access the Team Activity Overview Dashboard, then they should see an up-to-date summary of all notifications with clear indicators of acknowledged and pending responses.

A team lead configures the notification settings for their team to ensure all relevant notifications are sent in real-time.

Given the team lead goes to the notification settings, when they update the notification preferences, then all team members should receive notifications based on the new preferences with immediate effect.

During a team meeting, team members discuss the insights from the Team Activity Overview Dashboard to enhance team collaboration.

Given the Team Activity Overview Dashboard is displayed during the meeting, when team members review the metrics, then the discussion should focus on actionable areas for improvement and strategies to enhance communication based on the dashboard data.

Customizable Dashboards

Allows teams to create personalized dashboards that reflect their unique collaborative goals and metrics. Users can select the data visualizations and insights most relevant to their projects, enhancing focus and clarity in team discussions and strategy formulation.

Requirements

Dynamic Data Selection

User Story: As a business analyst, I want to select and customize the data visualizations on my dashboard so that I can focus on the metrics that are most relevant to my current project.
Description: The Dynamic Data Selection requirement allows users to seamlessly choose and modify the types of data visualizations they wish to display on their customizable dashboards. This feature must support multiple data sources and provide an intuitive interface for users to drag-and-drop various metrics onto their dashboards. The implementation should also ensure that changes can be made in real-time and that all visualizations auto-update with their respective data feeds. This enhances usability by allowing users to tailor their dashboard to their specific needs without needing technical expertise, ultimately making data-driven decision-making more efficient and effective.
Acceptance Criteria: Dynamic data customization by a marketing team during a weekly review meeting, allowing them to visualize their campaign performance through the dashboard's drag-and-drop functionality.

Given the user is in the dashboard customization mode, when they drag a metric from the data source panel and drop it onto the dashboard, then the metric should display in real-time without requiring a page refresh.

A finance team member updating financial metrics in the dashboard just before a presentation to upper management, ensuring all visual data is current and visually clear.

Given the user has selected financial metrics from multiple data sources, when they save changes made to the dashboard, then all respective visualizations should auto-update to reflect the most current data.

A product development team continuously monitoring key performance indicators (KPIs) during daily stand-ups, allowing them to adapt their metrics based on ongoing project changes.

Given the user has selected and added several KPIs to their dashboard, when they change a data source for any visualization, then the visualization should refresh to show updated data immediately.

An operations manager scheduling a quarterly review where they need to display various metrics from different departments to assess overall performance and optimize resource allocation.

Given the user has access to departmental data, when they create a new dashboard and drop multiple metrics onto the canvas, then the dashboard layout should support their arrangement without overlap and maintain clarity.

A sales team member wanting to quickly adjust their dashboard for an important client meeting, tailoring the displayed metrics to emphasize client-specific data and performance insights.

Given the user is in the dashboard, when they remove a previously added visualization and replace it with a client-specific metric, then the overall dashboard should reflect this change instantly.

User Role Permissions

User Story: As a team lead, I want to assign different permission levels for dashboard access so that my team members only see the data that's relevant to their roles and responsibilities.
Description: The User Role Permissions requirement provides a secure framework that allows administrators to set and manage user permissions for accessing and modifying dashboards. This feature is essential for ensuring that sensitive data is only accessible by authorized personnel and enhances collaboration by allowing team members to have tailored access based on their roles within the organization. The implementation of this requirement should include a user-friendly interface for assigning roles, as well as logging capabilities to track user changes and access. This will enable organizations to maintain control over their data and ensure compliance with internal policies.
Acceptance Criteria: Creating User Roles with Different Permissions

Given an administrator is logged into Datapy, when they access the user role management interface, then they should be able to create, edit, and delete user roles with customizable permissions for dashboard access and modifications.

Assigning User Roles to Team Members

Given an administrator has created user roles, when they assign different roles to team members for accessing dashboards, then each team member should only have access to the functionalities and data as defined by their assigned role.

Logging User Role Changes and Access

Given an administrator modifies user roles or permissions, when they perform these actions, then the system should log the changes including the user who made the change, the roles affected, and the date and time of the modification.

Access Restrictions Based on User Roles

Given a user is logged into Datapy, when they attempt to access a dashboard for which they do not have permission, then they should receive an appropriate error message stating they do not have access and be redirected to their home page.

User Role Management Interface Usability

Given an administrator is accessing the user role management interface, when they navigate the interface for more than 5 minutes, then they should be able to perform at least one role modification without requiring external help or documentation.

Customizable Dashboard Visibility Based on Roles

Given a team member logs into Datapy, when they view their dashboard, then they should only see data visualizations and metrics that are permitted by their user role as set by the administrator.

Testing User Role Permission Changes

Given an administrator changes a user's role or permissions, when the changes are saved, then the user should immediately experience the updated permissions the next time they log in, without any delay.

Collaborative Features

User Story: As a project manager, I want to collaborate with my team on dashboard metrics so that we can have informed discussions and strategy sessions without leaving the platform.
Description: The Collaborative Features requirement includes tools for real-time collaboration among team members directly within the customizable dashboard. This should enable users to add comments, tag teammates, and share insights or data visualizations, facilitating seamless communication. The goal of this feature is to enhance teamwork and make it easier for teams to discuss metrics without needing to switch platforms. The design should ensure that all changes and comments are visible to all collaborators in real-time, promoting transparency and fostering a collaborative environment.
Acceptance Criteria: Real-time commenting by team members on the customizable dashboard.

Given a user is viewing a customizable dashboard, when they enter a comment and submit it, then the comment should appear in real-time for all team members currently viewing the dashboard.

Tagging teammates in comments to notify them.

Given a user is submitting a comment, when they use the '@' symbol followed by the teammate's name, then the tagged teammate should receive a notification about the comment in the platform.

Sharing data visualizations from the dashboard during a team meeting.

Given a user is in a team meeting and viewing a customizable dashboard, when they select a data visualization and choose the 'Share' option, then that visualization should be shared with all meeting participants in real-time.

Visibility of comments and changes made by teammates in real time.

Given multiple team members are collaborating on a customizable dashboard, when one member makes a change or adds a comment, then all team members should see the change or comment instantly without refreshing the page.

Searching through previous comments for insights.

Given a user is viewing the customizable dashboard, when they use the search function to look for specific keywords in previous comments, then the system should return relevant comments that match the search criteria.

Checking for and managing user permissions for commenting and tagging.

Given an admin user is accessing the dashboard settings, when they review the user permissions, then they should see options to enable or disable commenting and tagging features for specific team members.

Data Visualization Templates

User Story: As a small business owner, I want to use predefined templates for my dashboard so that I can quickly display important business metrics without starting from scratch.
Description: The Data Visualization Templates requirement offers pre-built templates for commonly used dashboards within various industries. This feature reduces the time required for users to set up their dashboards by providing a selection of professional-grade templates tailored to specific business needs. Users should be able to choose a template that suits their goals, which can then be customized with their specific data. This enhances the user experience, particularly for those who may be less familiar with data analysis, by enabling them to get started quickly and effectively visualize important metrics from the outset.
Acceptance Criteria: User selects a data visualization template for a marketing dashboard and customizes it with their company data.

Given a user is on the customizable dashboards page, when they browse the list of data visualization templates, then they should be able to select a marketing dashboard template and edit it with their specific metrics.

A user without prior data analysis experience utilizes a pre-built template to create a financial summary dashboard.

Given a user with no prior data analysis experience is accessing the dashboard setup, when they select one of the financial summary templates, then they should be able to easily customize the template with their own data without requiring assistance.

A user saves their customized dashboard to their profile for future access.

Given a user has customized a dashboard using a selected template, when they click the save button, then the customized dashboard should be saved to their profile and accessible on subsequent logins.

Users from different teams collaborate on a dashboard using a shared template.

Given multiple users are working on a shared dashboard template, when one user makes changes to the dashboard, then all other users should see those changes in real-time without needing to refresh the page.

A user evaluates different templates based on industry relevance and usability.

Given a user reviews available data visualization templates, when they filter templates by industry and usability criteria, then the user should see a refined list of templates that match their business needs.

A user views a preview of a selected data visualization template before committing to customization.

Given a user is reviewing a template, when they click on the 'Preview' button, then a live preview of the selected template should appear, allowing them to see how their data would be represented visually before finalizing their choice.

A user utilizes help documentation while selecting and customizing a visual template.

Given a user is on the template selection page, when they click on the help icon, then they should be directed to comprehensive documentation that assists them in understanding how to choose and customize templates effectively.

Mobile Dashboard Access

User Story: As a sales representative, I want to access my performance dashboard on my mobile device so that I can stay informed and responsive while on the go.
Description: The Mobile Dashboard Access requirement ensures that users can access and modify their customizable dashboards from mobile devices. The design should be responsive, providing a streamlined and user-friendly interface that allows for the same level of customization and data interaction as on desktop. This feature is crucial for users who are often on the move and need to stay updated on their business metrics in real time. Implementation must ensure data security and integrity while providing a mobile experience that does not sacrifice functionality.
Acceptance Criteria: Mobile User Accessing Custom Dashboard on the Go

Given a user is logged into their Datapy account on a mobile device, when they navigate to their dashboard, then they should see the same layout and data visualizations as on the desktop version, ensuring it is responsive and user-friendly.

Real-Time Data Update on Mobile Dashboard

Given a user is viewing their dashboard on a mobile device, when new data is entered or existing data is modified in real-time, then the user should see the updates reflected in their dashboard within 5 seconds without needing to refresh the page.

Mobile Dashboard Customization Options

Given a user is accessing their dashboard from a mobile device, when they choose to customize their dashboard layout, then they should be able to select, resize, and organize widgets in the same way they can on the desktop version, maintaining full functionality.

Mobile Dashboard Security Authentication

Given a user wants to access their mobile dashboard, when they enter their login credentials, then the system should authenticate the user via multi-factor authentication to ensure data security and integrity.

Collaborative Sharing on Mobile Dashboard

Given a user is viewing their mobile dashboard, when they select the share option for a specific visualization, then they should be able to send a link to collaborate with other team members through email or messaging apps, ensuring user engagement.

Offline Access to Cached Dashboard Data on Mobile

Given a user has previously accessed their dashboard on a mobile device, when they lose internet connectivity, then they should still be able to view previously loaded data and visualizations, ensuring continued access.

Real-Time Feedback Collection

Empowers users to create and distribute surveys instantly, collecting customer feedback in real-time. This feature enhances the responsiveness of businesses, allowing them to gather insights promptly after customer interactions, ensuring decisions are data-driven and timely.

Requirements

Survey Creation Wizard

User Story: As a marketing manager, I want to create and customize surveys quickly so that I can gather timely feedback from customers after their interactions with our services.
Description: This requirement involves designing an interactive and intuitive survey creation wizard that allows users to build and customize surveys quickly. Features should include a drag-and-drop interface for adding questions, customizable templates, and the ability to embed multimedia elements. This functionality empowers users to create effective surveys without needing design or technical skills. Enhanced user experience ensures that feedback collection is seamless and engaging, resulting in higher response rates and better data quality.
Acceptance Criteria: User accesses the Survey Creation Wizard from the Datapy dashboard and starts building a new survey to gather customer feedback after a recent product launch.

Given the user is on the Survey Creation Wizard, when they drag and drop a question type into the survey, then the question should be added successfully with no errors.

The user selects a customizable template from the available options while creating a survey to ensure a consistent look and feel for brand identity.

Given the user has selected a template, when they preview the survey, then the template should load fully with all design elements intact and reflect the user's brand colors and logo appropriately.

As the user is building a survey, they choose to embed a video to enhance the question and provide context for respondents.

Given the user selects the multimedia element option to embed a video, when they upload a video file or input a video link, then the video should appear correctly in the survey preview with play functionality enabled.

The user completes the survey creation process and is ready to distribute the survey to customers immediately after an interaction.

Given the user has finalized the survey settings and clicked 'Publish,' when they confirm the distribution method (email, link, or social media), then the survey should be distributed without issues, and the user should receive a success notification.

The user wants to ensure their survey is engaging by previewing the survey before sending it out to customers.

Given the user selects the preview option from the Survey Creation Wizard, when they view the survey, then all elements (questions and multimedia) should display correctly, mirroring how it will appear to respondents.

As the user builds the survey, they wish to save their progress to return later without losing their work.

Given the user is in the middle of creating a survey, when they click the 'Save' button, then the survey should save successfully, and upon returning, all previously entered information should be intact and editable.

Real-Time Data Analytics

User Story: As a business analyst, I want to receive real-time analytics from survey responses so that I can make swift, data-driven decisions that improve customer satisfaction.
Description: This requirement focuses on incorporating real-time data analysis capabilities within the feedback collection feature. Users should receive instant insights through automatically generated reports that analyze survey responses, highlighting trends, sentiments, and key metrics. The ability to visualize data in real-time allows businesses to make informed decisions quickly, thereby increasing the value derived from customer feedback. Integrating AI-driven analytics will enhance the reporting capabilities.
Acceptance Criteria: Real-Time Data Analysis of Customer Feedback Post-Survey Submission

Given a customer has submitted a survey, when the user accesses the feedback dashboard, then they should see real-time updates that show trends, sentiments, and metrics derived from the feedback.

Automatic Generation of Reports Based on Survey Responses

Given multiple survey responses have been collected, when the user requests a report, then the system should automatically generate a comprehensive report that highlights key insights within 2 minutes.

Visualization of Survey Data in an Interactive Dashboard

Given survey responses have been analyzed, when the user navigates to the analytics dashboard, then they should be able to view real-time visualizations such as charts and graphs representing customer feedback metrics and sentiments.

AI-Driven Predictive Analytics Implementation for Customer Insights

Given the feedback data has been collected, when the user enables AI analytics, then the system should predict future trends based on historical feedback data with an accuracy rate of at least 85%.

User Notification upon Significant Feedback Trends Detection

Given that a significant trend has been identified in the survey responses, when the trend analysis is completed, then the user should receive an instant notification highlighting the trend's details.

Integration of Feedback Collection and Data Analytics Tools

Given the feedback collection feature is activated, when a new survey is created, then the analytics tools should automatically integrate with the survey data to provide immediate insights with no manual intervention required.

Multi-Channel Distribution

User Story: As a customer success leader, I want to distribute surveys through multiple channels so that I can maximize participation and obtain diverse customer insights efficiently.
Description: This requirement entails enabling users to distribute surveys across various channels, including email, social media, and embedded links on websites. This flexible distribution method broadens reach and increases the likelihood of achieving high response rates. Additionally, users should be able to track which channel generated the most responses, offering insights into customer engagement preferences. Incorporating this feature will enhance the overall effectiveness of the feedback collection process.
Acceptance Criteria: Distributing surveys via email and social media to gather customer feedback

Given the user is logged into Datapy, when they select the email and social media options for survey distribution, then the surveys should be sent successfully to the targeted audiences without any errors in delivery.

Tracking responses collected from different distribution channels

Given the surveys have been distributed across multiple channels, when the user navigates to the response analytics dashboard, then they should see a breakdown of responses by channel, including email, social media, and embedded links.

Creating a survey and distributing it through an embedded link on a website

Given the user has created a survey and generated an embedded link, when they insert the link into their website, then customers should be able to access and complete the survey directly from the website immediately upon clicking the link.

Collecting real-time feedback from customers after service interactions

Given a customer has completed a service interaction and received a survey request via email or social media, when they respond to the survey, then their feedback should be recorded and displayed in real-time on the user's feedback dashboard.

Ensuring user authentication for survey distribution channels

Given the user attempts to distribute a survey via any channel, when they are not authenticated, then the system should prompt them to log in before allowing survey distribution.

Giving users the ability to customize survey messages for different channels

Given the user is creating a survey, when they select the distribution channels, then they should be able to customize the message accompanying the survey for each selected channel individually.

Automated Follow-ups

User Story: As a project manager, I want to automate follow-ups for unresponsive survey recipients so that I can ensure higher response rates without manually tracking each survey recipient.
Description: This requirement focuses on the implementation of an automated follow-up feature that sends reminder emails or messages to users who have not responded to the survey within a specified timeframe. This mechanism can help boost participation rates and ensure that feedback is collected in a timely manner. Personalization options should be offered to encourage engagement, thus optimizing the survey completion rates and enhancing the quality of the collected data.
Acceptance Criteria: Scenario 1: User has not responded to the survey within the specified timeframe, and the automated follow-up mechanism is triggered to send a reminder email.

Given a user has received a survey but has not responded within 48 hours, when the automated follow-up is initiated, then the user receives a personalized reminder email regarding the survey.

Scenario 2: The system allows users to customize the message content for follow-up reminders based on survey context.

Given a survey creator is setting up a follow-up reminder, when they choose to customize the message, then the system should allow the user to edit the content and format of the reminder message before sending.

Scenario 3: Tracking the success rate of follow-up reminders on survey response rates over time.

Given that follow-up reminders have been sent, when the response rates are analyzed over a 30-day period, then there should be a measurable increase in response rates compared to a period without follow-ups.

Scenario 4: The system provides feedback on the effectiveness of different reminder strategies.

Given multiple follow-up strategies are implemented, when a user views the analytics dashboard, then they should see a comparison of response rates and feedback quality for each strategy.

Scenario 5: Automated follow-up feature should comply with data privacy regulations.

Given that the follow-up emails are sent to users, when they are sent, then all emails must comply with relevant data protection regulations (e.g., GDPR, CCPA) ensuring consent and opt-out options are clearly provided.

Scenario 6: User interface for setting up automated follow-ups is intuitive and user-friendly.

Given that a survey creator is using the interface to set up automated follow-ups, when they navigate through the setup process, then they should be able to set up follow-ups without requiring additional assistance or training.

Scenario 7: Notifications of automated reminders are logged in the system for tracking purposes.

Given that an automated reminder is sent out, when reviewing the system logs, then there should be a record of each reminder sent, including timestamp and user details, to facilitate accountability and auditing.

Customizable Reporting Dashboard

User Story: As a department head, I want to customize my reporting dashboard to focus on key metrics specific to my team's objectives so that I can monitor performance and adjust strategies as needed.
Description: This requirement involves developing a customizable reporting dashboard where users can view and analyze survey results in a format that best suits their needs. Users should be able to personalize their dashboard layouts with widgets for key metrics, trending topics, and response distributions. This customization will allow stakeholders to focus on relevant data and improve decision-making effectiveness based on feedback gathered.
Acceptance Criteria: User personalizes their dashboard to include specific metrics after collecting survey data from customers.

Given that a user is on the customizable reporting dashboard, when they select metrics to display, then the selected metrics should appear in the dashboard layout as specified by the user.

User wants to change the arrangement of widgets on their dashboard to prioritize key metrics.

Given that a user has multiple widgets on their dashboard, when they drag and drop a widget to a new location, then the widget should reposition as per the user's action and save this layout for future use.

User requires an overview of trending topics based on recent survey feedback.

Given that the surveys have been collected, when the user accesses their customizable dashboard, then the trending topics widget should accurately reflect the latest survey responses in real-time.

User needs to filter survey results by date range to analyze shifts in feedback over time.

Given the survey results are displayed on the dashboard, when the user applies a date range filter, then the dashboard should update to display only the survey responses that fall within the specified date range.

User wants to view response distributions in a visual format, such as graphs or charts.

Given that a user is on the customizable dashboard, when they select to visualize response distributions, then the system should offer multiple formats (e.g., pie chart, bar graph), and the selected format should display accurately according to the survey data.

User is collaborating with team members to refine dashboard metrics.

Given that a user has invited team members to view the dashboard, when team members access it, then all changes made by any user should be synchronized and visible in real-time to enhance collaborative decision-making.

User wants to export their personalized dashboard report for presentation.

Given that the user has customized their dashboard, when they select the export option, then the dashboard should generate a PDF or Excel report that maintains the layout and selections made by the user.

Sentiment Analysis Dashboard

Integrates advanced sentiment analysis tools that automatically categorize and interpret customer feedback. This feature helps users quickly grasp customer emotions and sentiments, enhancing understanding of qualitative data and aiding in strategic improvements.

Requirements

Customer Feedback Categorization

User Story: As a product manager, I want to categorize customer feedback automatically so that I can quickly identify trends and areas for improvement without manual analysis.
Description: Implement a robust algorithm that automatically categorizes customer feedback into predefined sentiment categories such as positive, negative, and neutral. This functionality will leverage natural language processing (NLP) techniques to analyze the text and provide insightful categorization. The benefit of this feature is to streamline the feedback analysis process, enabling users to quickly identify areas of improvement or strengths based on real customer experiences. Integration with Datapy allows users to visualize categorized data in the Sentiment Analysis Dashboard for improved decision-making and strategy formulation.
Acceptance Criteria: Customer feedback is submitted through a web form on the Datapy platform, where users enter comments about their experience with the product. Upon submission, the feedback should be automatically analyzed and categorized into predefined sentiment categories.

Given a user submits customer feedback in the designated web form, when the feedback is processed by the sentiment analysis algorithm, then the feedback must be categorized into one of the predefined categories: positive, negative, or neutral, and displayed accurately on the Sentiment Analysis Dashboard.

A user accesses the Sentiment Analysis Dashboard after feedback has been submitted over a week. The user expects to see a summary of sentiments that reflect the collective feedback received during that period.

Given that feedback has been submitted over the last week, when the user accesses the Sentiment Analysis Dashboard, then the dashboard should display a summary count of each sentiment category (positive, negative, neutral) reflecting the categorized feedback from the past week.

A product manager requires immediate insights from recent customer feedback to identify trends and areas for improvement. They request a real-time update on the sentiment analysis of the last three days of feedback.

Given new customer feedback has been submitted in the last three days, when the sentiment analysis algorithm processes the new feedback, then the categorized sentiments should update in real-time on the Sentiment Analysis Dashboard without requiring a manual refresh.

The development team needs to ensure that the sentiment analysis algorithm is functioning correctly with edge cases such as sarcasm or mixed sentiments in feedback.

Given customer feedback that includes sarcasm or mixed sentiments, when the feedback is processed by the sentiment analysis algorithm, then the algorithm must accurately categorize the sentiment without errors, ideally producing the correct category (positive, negative, neutral) based on the overall context.

A marketing analyst wants to generate a report based on the sentiment analysis results from the last month to prepare for a strategy meeting. They need the data exported in a readable format.

Given the sentiment analysis has processed feedback over the last month, when the marketing analyst requests an export, then the system must provide a downloadable report in a CSV format containing the categorized sentiments and their respective counts.

Customers provide feedback through both text and voice submissions via a mobile app, and the sentiment analysis should handle both formats effectively.

Given feedback is submitted through text and voice using the mobile app, when the feedback is processed by the sentiment analysis algorithm, then both types should be categorized accurately into the predefined sentiment categories and displayed on the dashboard accordingly.

Sentiment Visualization Tools

User Story: As a marketing analyst, I want to visualize customer sentiment trends so that I can better understand the effectiveness of our campaigns and make informed adjustments.
Description: Develop interactive visualization tools within the Sentiment Analysis Dashboard that allow users to visualize sentiment trends over time. This will include features like line graphs, bar charts, and heat maps representing how customer sentiments change. The visualization will enhance user understanding by providing clear insights into sentiment changes and patterns related to specific products, services, or campaigns. This functionality supports data-driven decision making by visually highlighting the impact of marketing strategies or product updates on customer sentiment.
Acceptance Criteria: Sentiment Trend Visualization for Recent Product Launch

Given a user navigates to the Sentiment Analysis Dashboard, when they select a specific product launch from the dashboard, then the system displays a line graph showing sentiment trends over the last six months for that product, including positive, neutral, and negative sentiment indicators by percentage.

Comparison of Sentiment Across Different Campaigns

Given a user accesses the Sentiment Analysis Dashboard, when they choose to compare sentiment for two different marketing campaigns, then the dashboard presents a bar chart that visually distinguishes the sentiment levels for each campaign, updating in real-time.

Heat Map Display for Daily Sentiment Changes

Given a user is on the Sentiment Analysis Dashboard, when they select the heat map visualization option for the last month's customer feedback, then the heat map accurately reflects daily changes in customer sentiment, color-coded to indicate levels of positivity and negativity.

Interactive Filtering by Customer Segment

Given a user is reviewing sentiment trends on the Sentiment Analysis Dashboard, when they apply filters to view sentiment data specific to a customer segment, then the visualizations update to display sentiment trends only for the selected segment, maintaining accuracy in representation.

Exporting Visualized Data Reports

Given a user has customized sentiment visualizations on the Sentiment Analysis Dashboard, when they choose to export the dashboard view, then the system generates a downloadable report containing the visual data in PDF format.

Real-Time Sentiment Updates from Feedback Sources

Given that the Sentiment Analysis Dashboard is open, when new customer feedback is received, then the visualizations update in real-time to include the latest sentiment assessments without requiring a page refresh.

Real-time Sentiment Updates

User Story: As a customer service manager, I want to see real-time updates of customer sentiment so that I can address any negative feedback immediately and improve customer satisfaction.
Description: Ensure that the sentiment analysis tools provide real-time updates as new customer feedback is collected. This requirement will involve building a system that synchronizes customer feedback data with the sentiment analysis algorithms continuously, allowing for immediate feedback on recent customer interactions. This capability is crucial for users to constantly monitor customer satisfaction and make timely improvements based on the most current data, effectively supporting agile decision making.
Acceptance Criteria: As a user of the Sentiment Analysis Dashboard, I want the system to update sentiment scores in real-time as new customer feedback is submitted so that I can have the most current understanding of customer emotions during the analysis.

Given that new customer feedback has been received, when the feedback is processed by the sentiment analysis tool, then the sentiment scores should be updated within 5 seconds on the dashboard.

As a product manager reviewing customer sentiments, I want to be notified immediately when a significant shift in sentiment occurs so I can address potential issue areas quickly.

Given that the sentiment score indicates a significant change (defined as a 20% drop), when this change occurs, then an alert notification should be sent to the product manager's email within 1 minute.

As a data analyst, I need to view a historical trend of the sentiment scores to analyze customer sentiment over time and facilitate strategic decisions.

Given that I access the trend analysis feature of the dashboard, when selecting the historical data range, then the sentiment scores for that range should be plotted accurately on the graph in under 3 seconds.

As a user using the Sentiment Analysis Dashboard, I want to ensure that sentiment analysis correctly categorizes feedback as positive, neutral, or negative based on customer keywords.

Given a set of customer feedback containing various keywords, when processed by the sentiment analysis tool, then the categorization of each feedback should be reflected accurately on the dashboard according to predefined sentiment criteria.

As a team member observing customer sentiment on the dashboard, I want to ensure that the visualization updates seamlessly without manual refresh, ensuring continuous access to the latest data.

Given that I am viewing the Sentiment Analysis Dashboard, when new feedback is submitted, then the visual representation of customer sentiment should update automatically without the need for any manual action on my part.

Sentiment Analysis Reporting

User Story: As a business executive, I want to receive regular sentiment analysis reports so that I can evaluate the overall customer satisfaction and make informed strategic decisions.
Description: Create a reporting feature that generates detailed sentiment analysis reports periodically, encapsulating the collected sentiment data, trends, and actionable insights. Users will be able to customize the frequency and parameters of these reports (daily, weekly, monthly) according to their needs. This will allow stakeholders to review performance and tailor strategies based on comprehensive insights derived from customer feedback, enhancing strategic planning and operational decisions.
Acceptance Criteria: As a user, I want to generate a sentiment analysis report on a weekly basis so that I can track customer feedback trends over time and make informed decisions.

Given I have accessed the Sentiment Analysis Reporting feature, When I select the option to generate a weekly report, Then the system should create a report summarizing the sentiment data collected in the past week with visualizations of trends.

As a stakeholder, I want to customize the parameters of the sentiment analysis report to focus on specific products or services so that I can tailor insights relevant to our strategic focus.

Given I have selected the option to customize the sentiment analysis report parameters, When I set specific filters for products or services, Then the report generated should reflect only the sentiment data related to those chosen parameters.

As a user, I want to receive notifications when a new sentiment analysis report is ready for review, so that I stay updated on customer feedback changes.

Given I have scheduled my sentiment analysis report, When the report is generated, Then I should receive an email notification indicating that the report is available for viewing.

As a product manager, I want to analyze the sentiment data compiled over a monthly report period to identify areas for improvement and capitalize on strengths.

Given I have chosen the monthly reporting option, When I view the report, Then I should see a clear summary of sentiment trends and actionable insights with recommendations for product enhancements.

As a user, I want to export the sentiment analysis report in multiple formats (PDF, CSV, Excel) to share insights with my team easily.

Given I have generated a sentiment analysis report, When I choose the export option, Then I should be given the choice to download the report in PDF, CSV, or Excel format, and the file should open correctly in the respective application.

Customizable Sentiment Thresholds

User Story: As a process improvement leader, I want to set thresholds for sentiment alerts so that I can take quick action on negative feedback before it impacts customer loyalty.
Description: Introduce customizable thresholds that allow users to define what constitutes a 'critical' sentiment level in customer feedback. Users can set their alerts based on these thresholds, which may trigger notifications when customer sentiment falls below a certain level. This feature empowers users to proactively respond to negative feedback before it escalates, ensuring swift action towards improving customer experience.
Acceptance Criteria: User customizes sentiment thresholds for critical feedback classification.

Given a user is on the Sentiment Analysis Dashboard, when they set a new threshold for critical sentiment at -0.5, then the system should save this setting and reflect it in the user interface.

User receives notifications for negative sentiment feedback.

Given a user has set a threshold for critical sentiment and a negative feedback score of -0.6 is registered, when this feedback is analyzed, then the user should receive a notification alerting them of the critical sentiment.

User modifies existing sentiment thresholds and verifies changes.

Given a user has previously set a threshold for critical sentiment, when they change the threshold to -0.3, then the updated threshold should be displayed correctly on the dashboard and the previous value should no longer be applicable.

User views historical data on sentiment trends influenced by threshold settings.

Given a user has adjusted their sentiment thresholds over time, when they access the historical data view, then they should see how customer sentiment trends correlate with adjustments in thresholds.

System defaults reset after a user has customized threshold settings.

Given a user has set custom sentiment thresholds, when the user opts to reset to default settings, then all custom thresholds should restore to the system's predefined defaults without affecting historical data.

User tests threshold impact on existing feedback data.

Given the user has set a critical sentiment threshold, when analyzing historical feedback data, then the dashboard should correctly categorize past feedback according to the new threshold indicating which feedback needs action.

Customizable Survey Templates

Offers users a diverse library of customizable survey templates tailored to different business needs. With this feature, users can easily design surveys that resonate with their target audience, ensuring better engagement and higher response rates.

Requirements

Survey Template Library

User Story: As a business owner, I want to access a library of customizable survey templates so that I can easily design surveys that resonate with my target audience, ensuring higher engagement and response rates.
Description: The system shall provide users with a diverse library of customizable survey templates designed for different business needs. This library will include pre-made formats that suit various industries and purposes, such as customer satisfaction, employee feedback, market research, and event planning. Users can access these templates to save time and ensure they leverage best practices in survey design, thereby increasing user engagement and response rates during data collection procedures. The integration of these templates will allow for seamless customization, where users can modify questions, styles, and formats to suit their specific requirements without needing extensive design skills, resulting in a more effective data collection process that informs business decisions.
Acceptance Criteria: User accesses the survey template library to create a new survey for customer feedback.

Given the user is logged into their Datapy account, when they navigate to the survey template library and select a customer satisfaction template, then the template should load with predefined questions and customizable elements visible for editing.

User customizes a selected survey template to fit a specific event planning need.

Given the user selects the 'Event Planning' template, when they modify at least three questions and change the visual style, then the updated survey should reflect the changes and remain saved in the user's account.

User searches for survey templates based on their business type.

Given the user is in the template library, when they use the search functionality with a keyword related to their industry, then the system should display relevant templates that match the search criteria.

User previews a survey template before finalizing it.

Given the user selects a template and clicks on 'Preview', when they view the survey, then all the questions and formatting should be displayed accurately as they would appear to end-users without any design errors.

User saves a customized survey template for future use.

Given the user has customized a survey template, when they click on 'Save As', then the system should prompt for a template name and save the survey under the user's account with no loss of changes made.

User shares a survey template with team members for collaboration.

Given the user is viewing a customized survey template, when they click on 'Share', then they should be able to enter team members' email addresses and send invitations with access to edit the survey.

User receives assistance while navigating the template library.

Given the user clicks on the 'Help' icon in the survey template library, when they view the help options, then they should see FAQs, video tutorials, and a contact support option to get further assistance.

Template Customization Features

User Story: As a marketing manager, I want to easily customize survey templates so that I can create visually appealing and relevant surveys tailored to my audience's preferences, increasing participation rates.
Description: The feature will allow users to personalize the survey templates by modifying questions, adding new fields, changing visual styles (like color schemes and fonts), and incorporating corporate branding elements. Customization will include drag-and-drop functionality and a user-friendly interface that enables users to preview changes in real-time. This requirement supports the personalization of data gathering efforts, which is crucial for user engagement and attaining actionable feedback. By empowering users to tailor templates to their needs, the platform will enhance the relevance and impact of data collected across various use cases, ensuring that insights derived are actionable and aligned with specific business objectives.
Acceptance Criteria: User Customization of Survey Templates in Real-Time

Given a user is logged into Datapy, when they access the customizable survey templates section, then they should be able to drag and drop elements to rearrange the layout, modify existing questions, and preview those changes in real-time without refreshing the page.

Incorporating Branding Elements into Survey Templates

Given a user is designing a survey template, when they upload their corporate logo and select a color scheme, then the logo should appear on the survey preview, and all elements should reflect the selected color scheme accurately, ensuring brand consistency.

Saving Customized Survey Templates

Given a user has completed customizing a survey template, when they click the save button, then the template should be saved successfully with all modifications intact, and the user should receive a confirmation message.

User Accessibility to Survey Template Library

Given a user is logged into their Datapy account, when they navigate to the survey template library, then they should be able to view and select from a diverse range of customizable templates, each with a clear description of its intended use.

User Feedback on Survey Template Usability

Given a user has utilized a survey template for data gathering, when they complete the survey process, then they should have the option to rate the usability of the template and provide feedback, which is then collected for analysis.

Adjusting Visual Styles of Survey Templates

Given a user is editing a survey template, when they change the font style and size from the customization options, then the changes should reflect immediately in the preview, and the final survey should display the updated visual styles as configured by the user.

Testing Survey Template Engagement Rates

Given a user has deployed a customized survey template, when they review the engagement metrics post-survey, then they should see an analytical report indicating response rates, question completion rates, and user engagement levels.

Analytics Integration

User Story: As a data analyst, I want to view detailed analytics from the surveys I distribute so that I can understand participant engagement trends and refine our survey strategies for better insights.
Description: The system should integrate robust analytics that tracks user responses, engagement rates, and completion times across different survey templates. This analytics feature will provide users with comprehensive reports that visualize response data and trends, enabling them to gauge the effectiveness of their surveys over time. Users will have access to insights such as average response rates, demographics of respondents, and highlights of key metrics that align with their business goals. By offering this analytics integration, the platform will empower users to make data-driven decisions, refine their future survey strategies, and enhance overall engagement with their customer base, thus driving improved business outcomes.
Acceptance Criteria: User utilizes the analytics integration after conducting a survey to assess response rates and engagement metrics.

Given the user has created a survey using a customizable template, When the user views the analytics dashboard, Then the dashboard displays metrics such as response rates, engagement rates, and completion times in real-time.

User needs to understand the demographic breakdown of survey respondents through the analytics reporting feature.

Given the user has completed a survey with responses collected, When the user accesses the demographic report, Then the report accurately shows the demographic information of respondents including age, gender, and location.

User wants to analyze trends over time for multiple surveys conducted using different templates.

Given the user has conducted multiple surveys over a specified time period, When the user selects the analytics report for trends analysis, Then the report visualizes trends in response rates and engagement metrics for each survey template over the selected time frame.

User desires to receive an overview of key performance metrics that align with their business goals.

Given the user is analyzing a completed survey, When the user views the summary report, Then the report highlights key metrics relevant to the user's predefined business goals, such as average response rate and overall satisfaction score.

User wants to validate if the analytics integration accurately records completion times for responses.

Given a user completes a survey, When the analytics integration logs the response, Then the recorded completion time should be accurate and match the time taken by the user to finish the survey.

User seeks to understand the effectiveness of their survey strategies through comprehensive reporting.

Given multiple survey results have been collected, When the user generates a comprehensive report, Then the report provides actionable insights and recommendations based on response data and engagement metrics.

User Collaboration Tools

User Story: As a project manager, I want to collaborate with my team on survey designs in real-time so that we can integrate diverse insights and approval processes without delays.
Description: The platform shall incorporate collaboration tools that enable multiple users to work on survey templates simultaneously. This will include features such as comment threads, version control, and shared access to templates. These collaborative functionalities will facilitate effective communication among team members, ensuring that feedback can be seamlessly integrated into the survey design process. By fostering a collaborative environment, businesses can leverage diverse perspectives and expertise when designing surveys, ultimately leading to more robust and effective data collection that reflects varied stakeholder inputs and improves the overall quality of insights produced.
Acceptance Criteria: Multiple users collaborating on a survey template in real-time can discuss and provide feedback via comment threads.

Given multiple users are accessing the same survey template, when they add comments in the thread, then all users should see the comments in real-time without refresh.

Users need to track changes made to survey templates and revert to previous versions if necessary.

Given a user is editing a survey template, when they access the version control feature, then they should see a list of all previous versions with the option to restore any version.

Teams require shared access to survey templates to update and modify collaboratively.

Given a survey template is shared with a group of users, when a user makes a change to the template, then all members should see the updates reflected immediately.

Users want to ensure that feedback from team discussions is integrated into the survey design.

Given a user is in the comment thread, when they mark a comment as resolved, then the comment should no longer be visible in the active discussion list but should be stored in a resolved comments archive.

Users need to manage permissions for team members accessing survey templates.

Given an admin user shares a survey template, when they set permissions, then team members should only have access as per the assigned rights (view, edit, comment).

Users collaborate on designing a survey template with changing member roles and responsibilities.

Given different users are assigned roles in editing a survey template, when a user’s role changes, then their access and permissions should be updated according to the new role.

Export and Sharing Options

User Story: As a customer relations officer, I want to easily share the surveys with customers through different channels so that I can enhance response rates and gather more comprehensive feedback.
Description: The requirement will provide users with various exporting options for distributing their surveys, including PDF, link, and email distribution formats. Users will have the capability to share survey templates directly via social media or export as documents for offline sharing. This functionality will enhance user experience by making it easier to disseminate surveys within their target markets, thus capturing a wider audience for feedback. By facilitating easy sharing and distribution, users can maximize participation rates and improve the robustness of the data they collect, further driving the enhancement of business metrics through user feedback.
Acceptance Criteria: User exports a survey template to a PDF format for offline distribution.

Given the user has created a survey template, When the user selects the export option and chooses PDF, Then the system generates a PDF document of the survey template that is accurately formatted and downloadable.

User shares a survey template via email directly through the platform.

Given the user has a survey template ready to share, When the user selects the email option and enters valid email addresses, Then the system sends an email containing a link to the survey template to the specified addresses without errors.

User shares a survey template on social media platforms.

Given the user is on the survey template page, When the user chooses a social media sharing option, Then the system generates a post with the correct survey link and a description that can be shared on the selected platform.

User exports a survey as a document for offline sharing.

Given the user has selected a survey template, When the user opts for document export, Then the system provides the user with options for formats (e.g., Word, PDF) and successfully generates the document with proper formatting for offline use.

User retrieves a link for the survey template to distribute it.

Given the user has finalized a survey template, When the user selects the 'Get Link' option, Then the system generates a unique, shareable link that directs respondents to the survey without any broken links.

User verifies the participant's response rate after sharing the survey template.

Given that several responses have been received after sharing the survey, When the user checks the analytics section, Then the system displays the correct response rate corresponding to the shared survey template.

User customizes the sharing options for a survey template before distribution.

Given the user is preparing to share a survey template, When the user accesses customization settings for distribution, Then the system allows the user to modify recipient settings and choose platforms accurately before finalizing the share.

Integrated Feedback Loop

Facilitates a seamless feedback loop where users can not only collect feedback but also respond directly to customers. By closing the loop, businesses can foster stronger relationships and demonstrate their commitment to customer satisfaction.

Requirements

Customer Feedback Collection

User Story: As a customer support manager, I want to collect customer feedback seamlessly so that I can understand their experiences and improve our services accordingly.
Description: This requirement enables users to design and implement various feedback collection mechanisms such as surveys, rating systems, and suggestion boxes within the Datapy platform. Users will benefit from customizable templates that allow for quick setup and deployment, ensuring they can gather valuable insights on customer sentiment and experiences. The integration of this feature will streamline how businesses monitor their service quality, identify areas for improvement, and understand customer preferences by systematically collecting data. Real-time analytics will support immediate interpretation of results, shaping responsive actions and prioritizing issues that affect customer satisfaction.
Acceptance Criteria: Design and Deploy Customer Survey

Given the user is logged into the Datapy platform, when they navigate to the feedback collection section, then they should be able to select a customizable survey template and successfully deploy it within 5 minutes.

Analyze Customer Feedback in Real-Time

Given that feedback has been collected through various instruments (surveys, ratings, suggestion boxes), when the user accesses the analytics dashboard, then they should see real-time visualizations of the feedback data within 2 seconds.

Respond to Customer Feedback

Given that feedback has been collected, when a user clicks on a specific feedback item, then they should be able to submit a direct response to the customer within 3 minutes.

Utilize Feedback for Service Improvement

Given that analysis of customer feedback has been completed, when a user identifies significant feedback trends, then they should be able to create an action plan based on these insights and export it as a report.

Customize Feedback Collection Templates

Given that the user is in the feedback settings, when they edit a feedback collection template, then they should be able to successfully save their changes and see a preview of the updated template.

User Training for Feedback Tools

Given that a user is new to the Datapy platform, when they access the integrated help resources, then they should receive a guided walkthrough of feedback collection tools within 10 minutes.

Monitor Customer Satisfaction Trends

Given that feedback has been collected over a period, when the user reviews the trend analytics, then they should be able to see changes in customer satisfaction metrics over time displayed in a usable format.

Response Management System

User Story: As a customer service representative, I want to have a streamlined management system for responding to customer feedback so that I can engage with customers promptly and foster stronger relationships.
Description: This requirement provides a robust framework for users to manage and respond to customer feedback efficiently. It will include features like automatic notifications for new feedback, a centralized dashboard for viewing comments, and predefined response templates for common inquiries. This integration helps businesses acknowledge customer input promptly, enhancing user engagement and trust. By streamlining the response process, organizations can close the feedback loop more effectively, ensuring customers feel heard and valued, which leads to improved retention and satisfaction rates.
Acceptance Criteria: Automatic Notifications for New Feedback

Given a user has configured their notification preferences, when new feedback is submitted, then the user should receive an immediate notification through their chosen communication channel (email or in-app).

Centralized Dashboard for Viewing Feedback

Given a user accesses the feedback management section, when they view the dashboard, then they should be able to see all customer feedback in one place, organized by date and priority, along with the status of their responses.

Predefined Response Templates for Common Inquiries

Given a user is responding to feedback, when they select a common inquiry type, then the predefined response template should auto-populate the response field, allowing for easy customization before sending.

Feedback Response Acknowledgment

Given a user submits a response to feedback, when the customer views their feedback status, then they should see an acknowledgment that their feedback has been received and is under review.

Tracking Response Times and Metrics

Given the feedback management system is operational, when a user views response metrics, then they should see real-time data on average response times and feedback resolution rates.

User Engagement and Satisfaction Metrics

Given a user accesses the analytics dashboard, when they review user satisfaction metrics, then they should find visualizations indicating changes in customer satisfaction scores after implementing feedback responses.

Feedback Analytics Dashboard

User Story: As a business analyst, I want a visual dashboard to analyze customer feedback data so that I can identify trends and insights to inform executive decision-making.
Description: This requirement outlines the development of an advanced analytics dashboard that summarizes collected feedback data visually through charts, graphs, and trend lines. The dashboard will provide key metrics such as Net Promoter Score (NPS), customer satisfaction trends, and response times. Users will be equipped with the tools to filter data based on various parameters such as time frames and customer demographics, allowing for a comprehensive analysis of feedback. This feature ensures that businesses can derive actionable insights from customer data, enabling them to make informed decisions that enhance the overall customer experience.
Acceptance Criteria: Displaying Key Feedback Metrics on the Dashboard

Given that the user is on the Feedback Analytics Dashboard, when they access the dashboard, then they should see visual representations of key metrics including Net Promoter Score (NPS), customer satisfaction trends, and average response times displayed on the dashboard.

Filtering Feedback Data by Time Frame

Given that the user is on the Feedback Analytics Dashboard, when they apply a filter for a specific time frame, then the displayed metrics should update to reflect only the feedback data collected within that time frame.

Filtering Feedback Data by Customer Demographics

Given that the user is on the Feedback Analytics Dashboard, when they select demographics such as age, location, or gender, then the dashboard should present updated metrics that correspond to the selected customer demographic group.

Exporting Feedback Analytics Reports

Given that the user has analyzed feedback data on the dashboard, when they choose to export the analytics report, then a downloadable file should be generated that includes all selected metrics and visualizations in a commonly used format (e.g., CSV, PDF).

Drilling Down into Specific Feedback Trends

Given that the user is viewing a specific trend on the Feedback Analytics Dashboard, when they click on that trend, then they should be able to see detailed underlying feedback data that contributed to that trend.

Real-time Data Synchronization

Given that the user is using the Feedback Analytics Dashboard, when new feedback is submitted, then the dashboard should automatically refresh to include the new feedback data, ensuring that the metrics presented are up-to-date.

Integration with Communication Channels

User Story: As a marketing manager, I want to respond to customer feedback through various communication platforms so that I can engage customers more effectively and enhance their satisfaction.
Description: This requirement facilitates the integration of Datapy's feedback loop with external communication platforms such as email, social media, and messaging apps. By allowing responses and follow-ups through multiple channels, businesses can reach their customers where they are most active. This integration enhances customer engagement, allows for personalized communication, and increases the chances of receiving timely feedback. The flexibility to engage with customers through their preferred channels demonstrates a commitment to customer satisfaction and enhances the overall user experience.
Acceptance Criteria: Integration with Email Platforms

Given a user initiates a feedback request through Datapy, when the user selects the email integration option, then the feedback request is successfully sent to the customer's email address and the response received is logged in Datapy.

Integration with Social Media Platforms

Given a user is monitoring customer feedback through Datapy, when the user clicks on a feedback item linked to a social media post, then the user can respond directly via the social media platform and the response is logged in the system.

Integration with Messaging Apps

Given a user collects customer feedback through Datapy, when the user chooses to respond via a specified messaging app, then the user’s response is sent and a timestamp is recorded in Datapy for tracking purposes.

Automatic Feedback Loop Closure

Given positive customer feedback is received through any integrated channel, when the user views the feedback summary, then the response shows as closed automatically without manual intervention.

Response Confirmation to Users

Given a user sends a response to a feedback request through any channel, when the response is sent successfully, then the customer receives a confirmation notification in their respective channel of communication.

Feedback Analytics Dashboard Update

Given customer responses from multiple channels, when the feedback is processed in Datapy, then the analytics dashboard reflects real-time updates of customer sentiment and response rates.

Customization of Communication Templates

Given the user is setting up a feedback loop, when the user customizes the communication templates, then the changes are saved and applied to all outgoing messages across the integrated channels.

Actionable Insights Generator

Translates collected feedback into actionable insights and recommendations, helping users prioritize areas for improvement. This feature saves time and boosts effectiveness by directing users' efforts toward the most impactful changes.

Requirements

Feedback Collection Integration

User Story: As a product manager, I want to collect feedback from multiple channels so that I can analyze customer sentiments from various sources and prioritize improvements effectively.
Description: This requirement focuses on integrating various feedback collection channels, such as surveys, social media, and direct user inputs, into the Datapy platform. The integration will ensure that all feedback data is centralized, allowing for a comprehensive analysis. The functionality enhances the platform's ability to gather diverse insights from users, enabling businesses to understand their customers’ needs better. The expected outcome is a streamlined process for accumulating feedback that seamlessly feeds into the actionable insights module, ensuring that no valuable data is overlooked.
Acceptance Criteria: User collects feedback through a survey that is integrated into the Datapy platform and submits their responses.

Given the survey is available on Datapy, when the user submits their feedback, then the feedback should be recorded in the centralized database without any data loss.

Feedback from various sources such as social media and direct user inputs is gathered into Datapy for analysis.

Given multiple feedback channels are connected, when feedback is submitted via any channel, then the data should be imported into Datapy within 5 minutes and reflect in the user’s dashboard as pending insights.

A user reviews feedback data from multiple channels within the Datapy platform.

Given the feedback is centralized, when the user accesses the feedback report, then all feedback should be easily accessible and sortable by channel, sentiment, or date for analysis.

User conducts a comprehensive analysis based on the gathered feedback.

Given the user has accessed the feedback data, when the user generates a report, then the actionable insights generator should provide at least 3 priority recommendations based on the analyzed data.

The platform synchronizes real-time feedback from various collection channels.

Given the system is operational, when feedback is submitted, then the system should refresh the insights dashboard in real-time without manual intervention or delay.

A team member collaborates on feedback analysis using the Datapy platform.

Given the user access is granted, when the team member shares a feedback report, then the shared report should maintain data integrity and show real-time updates to all users involved in the collaboration.

User receives notifications for new feedback collected across all channels.

Given feedback collection is active, when new feedback is submitted, then the user should receive a notification within 10 minutes about the new feedback for timely action.

Actionable Insights Dashboard

User Story: As a team leader, I want a dashboard that displays actionable insights so that I can quickly identify and prioritize areas for improvement in our processes.
Description: This requirement involves developing a dedicated dashboard that visualizes actionable insights generated by the platform. The dashboard will highlight key recommendations and areas for improvement based on the feedback data analyzed. It will feature customizable widgets that allow users to prioritize insights according to their specific business goals, making it easier for teams to navigate the insights and act strategically. This enhancement will empower users with a clear and concise view of necessary changes, ultimately leading to better decision-making and resource allocation.
Acceptance Criteria: Dashboard User Access and Permissions Setup

Given the user has logged into Datapy, when they navigate to the Actionable Insights Dashboard, then they should only see insights relevant to their assigned project and role permissions, ensuring data privacy and relevance.

Visual Representation of Actionable Insights

Given the user is on the Actionable Insights Dashboard, when they interact with the insights displayed, then all insights should be visually represented with clear graphs, charts, or widgets that effectively illustrate key recommendations and improvements.

Customizable Widget Functionality for Prioritization

Given the user is viewing the Actionable Insights Dashboard, when they use the customization options to prioritize insights based on their business goals, then the dashboard should successfully update to reflect these customized preferences.

Real-Time Data Synchronization and Refreshing

Given the user is actively using the Actionable Insights Dashboard, when new feedback data is collected, then the insights on the dashboard should automatically refresh to display the latest actionable recommendations within one minute.

Collaboration Features for Team Interaction

Given the user is on the Actionable Insights Dashboard, when they use the collaborative tools to comment on or share insights, then the comments should be successfully shared with relevant team members with notifications sent to their accounts.

Exporting Insights for Reporting Purposes

Given the user is reviewing the actionable insights on the dashboard, when they choose to export the insights for reporting, then the export should generate a comprehensive report in CSV or PDF format that includes all displayed insights and recommendations.

AI-Driven Recommendations Engine

User Story: As a business analyst, I want AI-generated recommendations based on user feedback so that I can implement high-impact changes that enhance our overall performance.
Description: This requirement entails creating an AI-driven recommendations engine that processes user feedback and provides tailored suggestions for improvement. By utilizing machine learning algorithms, the engine will analyze trends and patterns in feedback data to generate personalized insights reflecting users' specific business contexts. This feature will significantly enhance Datapy's value proposition by providing unique, data-driven recommendations that drive targeted actions and foster continuous improvement by enabling businesses to focus on their most promising opportunities.
Acceptance Criteria: User submits feedback through Datapy's interface and requests actionable insights.

Given that user feedback has been collected, when the user requests insights, then the system should analyze the feedback and return personalized recommendations within 5 seconds.

The system generates insights based on structured and unstructured feedback data from various sources.

Given that multiple feedback sources are integrated, when the AI-driven recommendations engine processes the data, then it should identify at least three actionable insights relevant to the user's context.

A user reviews generated insights and provides additional feedback on the effectiveness of the recommendations.

Given that insights have been generated, when the user rates the recommendations on a scale from 1 to 5, then the system should record the feedback and use it to improve future recommendations.

The user accesses the dashboard to view the generated insights and compares them with previous recommendations.

Given that the user is on the dashboard, when they view their actionable insights, then the dashboard should display a comparison of current insights with at least the last two sets of insights.

Multiple users collaborate on implementing the insights generated by the recommendations engine.

Given that multiple team members are working on insights, when they view the same recommendations, then each user should be able to add comments or notes to the insights for collaborative discussion.

The recommendations engine updates its algorithms based on user engagement with the insights over time.

Given that the user has interacted with the insights multiple times, when the engine evaluates its performance, then it should incorporate these interactions to refine its recommendation algorithms by at least 10% for better future accuracy.

Real-Time Insights Alerts

User Story: As a business operator, I want to receive real-time alerts for actionable insights so that I can address significant feedback as soon as it occurs and optimize our processes effectively.
Description: This requirement outlines the development of a system for real-time alerts based on actionable insights generated within the platform. Users will receive notifications directly when new insights are available or when feedback trends indicate the need for immediate action. This feature aims to ensure that users can react promptly to critical feedback or emerging patterns, rather than waiting for routine updates, ensuring that businesses are proactive in addressing potential issues and leveraging opportunities when they arise.
Acceptance Criteria: User receives an alert immediately when a new actionable insight is generated based on real-time feedback analysis.

Given the user is logged into Datapy, when new actionable insights are generated, then the user should receive a notification within 2 minutes of the insight being created.

User receives alerts for significant trends in feedback data indicating a need for immediate action.

Given the user has set up feedback trend thresholds, when a feedback trend crosses a predefined threshold, then an alert should be triggered and sent to the user in real-time.

User can customize the types of alerts they wish to receive based on specific metrics or insights.

Given the user accesses the alert settings, when the user selects specific metrics for alerts, then only selected metrics should generate notifications as per user preference.

User verifies the alert delivery system to ensure timely and accurate notifications are received.

Given that an actionable insight or threshold breach occurs, when the system generates an alert, then the alert should appear in the user's notification area without delay and should be accurate.

User has the ability to mute notifications for non-critical alerts to focus on high-priority insights.

Given the user is in the notification settings, when the user mutes specific non-critical alerts, then those alerts should not be displayed in the notification area until unmuted.

User can review a history log of past alerts to track previous actionable insights and trends.

Given that the user wants to view past alerts, when the user accesses the alerts history log, then they should see a chronological list of all notifications received alongside the associated insights.

Collaboration Tools for Insight Sharing

User Story: As a team member, I want to share actionable insights with my colleagues so that we can collaborate on necessary improvements and make informed decisions together.
Description: This requirement focuses on implementing collaboration tools within Datapy, allowing users to share actionable insights and recommendations easily with team members. Features will include commenting, tagging, and discussion threads directly associated with each insight. This functionality ensures that all team members are aligned and encourages collective decision-making based on the insights. It is crucial for fostering a data-driven culture within organizations, where team feedback is integral to improving business strategies.
Acceptance Criteria: User sharing insights in a team meeting via Datapy's collaboration tools.

Given a user has generated an actionable insight, when they use the sharing feature, then the insight is shared to all designated team members via notifications.

Team members providing feedback on shared insights using commenting and tagging features.

Given a team member views a shared insight, when they add a comment or tag another user, then the comment is saved and visible to all team members associated with the insight.

Conducting discussions around insights within shared threads to enhance collaborative decision-making.

Given a discussion thread exists for an actionable insight, when users reply to the thread, then all replies are timestamped and can be viewed by all team members.

Tracking engagement metrics for insights shared among team members.

Given multiple insights have been shared, when the user accesses the insights dashboard, then they can view the number of comments and tags associated with each insight.

Notifying users about new comments or tags on insights they are following.

Given a user follows an actionable insight, when a new comment or tag is added, then the user receives an automatic notification.

Integrating external resources within the insight-sharing feature for richer context.

Given a user shares an actionable insight, when they attach external resources (links/documents), then those resources are accessible from the insight view.

Encouraging team collaboration by using a voting feature on insights to prioritize improvements.

Given users can see shared insights, when they vote on an insight, then the voting results reflect how many users have prioritized that insight for action.

Multi-Channel Feedback Gathering

Enables users to collect feedback through various channels, including social media, email, and webinars. This feature widens the scope of feedback collection, ensuring diverse customer viewpoints are integrated into the analytics.

Requirements

Feedback Channel Integration

User Story: As a marketing manager, I want to collect customer feedback from email, social media, and webinars so that I can understand diverse customer viewpoints and improve our products accordingly.
Description: This requirement focuses on enabling the Datapy platform to integrate and collect feedback from various communication channels, including social media, email, and webinars. This integration allows users to gather diverse insights from customers, which can lead to more informed decision-making. By centralizing feedback from these multiple sources, users can effectively analyze trends and sentiments, leading to a more holistic understanding of customer experiences and expectations. This capability enhances customer engagement and satisfaction, ultimately improving overall business performance and responsiveness to market changes.
Acceptance Criteria: Integrating feedback from social media platforms like Facebook and Twitter into Datapy analytics.

Given the Datapy platform is connected to social media accounts, when a user collects feedback, then the platform should display the feedback in the dashboard in real-time, with timestamps and source attribution.

Collecting feedback from email campaigns sent through Datapy.

Given that email feedback forms are included in the email campaigns, when customers submit their feedback via the email link, then the responses should be aggregated in the Datapy dashboard and categorized by sentiment automatically.

Facilitating feedback collection during webinars hosted through the Datapy platform.

Given that a webinar is ongoing, when participants fill out the feedback survey after the webinar, then their responses should be collated in the feedback analytics section of the dashboard within 15 minutes of the webinar ending.

Comparing feedback collected from different channels to identify trends.

Given feedback has been gathered from social media, email, and webinars, when the user accesses the analytics dashboard, then they should see a comparative analysis section highlighting feedback trends and sentiment across all channels.

Enabling users to customize feedback questions based on the channel.

Given the feedback forms are created for each channel, when a user customizes questions, then those changes must reflect in the corresponding channel's feedback forms without affecting other channels.

Ensuring data privacy and compliance while collecting feedback.

Given that feedback is being collected, when data is processed, then the platform must adhere to GDPR and other relevant privacy regulations by anonymizing personal data where required.

Providing users with the ability to export feedback data for external analysis.

Given that the feedback has been collected from multiple sources, when the user opts to export data, then the platform should allow them to download the feedback data in CSV and Excel formats within seconds.

Real-Time Feedback Analysis

User Story: As a product manager, I want to analyze customer feedback in real-time so that I can instantly recognize trends and make quick decisions to enhance customer satisfaction.
Description: This requirement is essential for implementing a feature that processes feedback data in real-time, allowing users to visualize insights and trends as they are collected. The functionality will enable users to rapidly respond to customer feedback, addressing issues or opportunities promptly. By providing real-time analysis, Datapy will empower businesses to adapt their strategies quickly, increasing their agility in the market. The analysis should include sentiment analysis, categorization of feedback types, and actionable insights delivery, enabling users to make timely decisions based on customer input.
Acceptance Criteria: Real-time feedback analysis during a live webinar session.

Given the user initiates a live webinar with feedback collection enabled, when attendees submit feedback, then the system should display real-time sentiment analysis and categorization of the received feedback on the user's dashboard.

Monitoring feedback trends in a weekly report.

Given the user has collected feedback over a week, when the user accesses the insights dashboard, then the system should display trends and actionable insights based on the feedback data collected during that period.

Receiving notifications for urgent feedback responses.

Given the user has set thresholds for urgent feedback, when feedback is categorized as urgent, then the user should receive an immediate notification via email and dashboard alert.

Visualizing feedback categorization through an interface.

Given the user is on the feedback dashboard, when they choose to display feedback by category, then the system should visualize feedback data in pie charts for easy interpretation.

Aggregating feedback from multiple channels for a comprehensive view.

Given the user has connected various feedback channels, when they request a comprehensive report of all collected feedback, then the system should aggregate and display data from all channels with appropriate categorization and insights.

Allowing users to filter feedback by sentiment.

Given the user is reviewing feedback data, when they apply a sentiment filter, then the system should only display feedback that matches the selected sentiment (positive, negative, or neutral).

Customizable Feedback Dashboard

User Story: As a business analyst, I want to customize my feedback dashboard so that I can focus on the most relevant metrics for my analysis and presentations.
Description: This requirement specifies the creation of a customizable dashboard that allows users to visualize feedback data through charts, graphs, and reports. Users should be able to tailor their dashboard according to the metrics they find most relevant, providing a personalized view of customer feedback. This feature not only enhances user experience by allowing users to access the information they deem most crucial but also facilitates better data storytelling and insight sharing within teams. Custom report generation should also be considered to enable users to extract specific data sets for deeper analysis.
Acceptance Criteria: User Customization of Feedback Dashboard

Given a user is logged in to Datapy, When they access the customizable feedback dashboard, Then they should see an option to add, remove, or rearrange widgets displaying various feedback metrics.

Visual Representation of Feedback Data

Given the feedback dashboard is open, When the user selects feedback data metrics, Then a visual representation of the selected metrics (charts, graphs) should be displayed correctly and update in real-time.

Saving Dashboard Configurations

Given a user has customized their feedback dashboard, When they choose to save their configuration, Then the system should successfully store the configuration and apply it the next time the user accesses the dashboard.

Custom Report Generation for Feedback Data

Given a user is on the feedback dashboard, When they create a custom report by selecting specific data points, Then a downloadable report in the selected format (PDF, CSV) should be generated without errors.

User Accessibility of Feedback Metrics

Given the feedback dashboard is displayed and the user hovers over any metric, When they view the metric details, Then a tooltip should appear showing a brief description and the data source for that metric.

Collaboration Tools Integration

Given a user has customized their dashboard, When they use the collaboration tools to share their dashboard with team members, Then the invited team members should be able to view the customized dashboard and provide feedback as per the permissions set by the user.

Mobile Responsiveness of the Dashboard

Given a user accesses the feedback dashboard on a mobile device, When they open the dashboard, Then the layout should automatically adjust for optimal viewing and interaction on the mobile screen.

Automated Feedback Alerts

User Story: As a customer success manager, I want to receive automated alerts about negative feedback so that I can promptly address issues and maintain customer satisfaction.
Description: This requirement includes establishing a system of automated alerts that notify users when specific feedback triggers or thresholds are reached. For example, if negative feedback exceeds a certain percentage or if a significant trend emerges, the user will receive an instant alert. This functionality is critical for organizations that require immediate action in response to customer sentiments. By ensuring that users are promptly informed, businesses can make timely adjustments to their product or service offerings, effectively managing customer satisfaction and loyalty.
Acceptance Criteria: User receives an automated alert when negative feedback exceeds the defined threshold of 20% within a 24-hour period.

Given that the feedback analysis system is running, when negative feedback exceeds 20% in a 24-hour monitoring window, then the user should receive an automated email alert immediately.

A user should be notified about positive feedback reaching a set milestone of 100 responses within a week.

Given that the feedback collection is active, when the total number of positive feedback responses reaches 100 in one week, then the user should receive a notification via their preferred channel (email or SMS).

Users are alerted when there is an emerging trend of feedback indicating a drop in customer satisfaction over a predefined time frame.

Given that trends are analyzed daily, when customer satisfaction feedback indicates a continuous drop for three consecutive days, then the system should send an automated alert to the user.

The notification system should allow users to customize the alert thresholds and channels based on their preferences.

Given that the user is in the settings menu, when they adjust the feedback alert thresholds and select preferred notification channels, then the changes should save successfully and reflect in the alert management system.

Users receive alerts when feedback trends shift from neutral to negative, indicating possible customer concerns.

Given that feedback is collected and analyzed in real-time, when a significant portion of previously neutral feedback shifts to negative over 12 hours, then an alert must be sent to the user.

The system should provide a dashboard overview of alerts triggered over a specified time period.

Given that the user accesses the dashboard, when they view the alerts section, then the dashboard should display all alerts triggered in the last 30 days, with timestamps and summaries.

Feedback Aggregation Tool

User Story: As a data analyst, I want to aggregate feedback from multiple channels into one view so that I can easily analyze and report on customer sentiment.
Description: This requirement outlines the development of a feedback aggregation tool that consolidates data from all integrated channels into a single view. This tool will ensure that feedback from various platforms is captured and displayed in one cohesive interface, making it easier for users to analyze overall customer sentiment and engagement. The aggregation tool will also allow for filtering and sorting options so that users can categorize feedback based on various parameters like date, channel, or sentiment type. This streamlined view enhances users' ability to derive insights efficiently and inform decision-making processes.
Acceptance Criteria: User can access the feedback aggregation tool from the main dashboard after logging into the Datapy platform.

Given a user is logged into the Datapy platform, When the user navigates to the main dashboard, Then the user should see the 'Feedback Aggregation Tool' as an accessible option.

The feedback aggregation tool consolidates feedback from all integrated channels including social media, email, and webinars.

Given feedback has been collected from multiple channels, When the user opens the feedback aggregation tool, Then the tool should display all feedback entries from the integrated channels in a single view.

Users can filter and sort feedback based on date, channel, or sentiment type.

Given the feedback aggregation tool is open, When the user applies filters for date and sentiment type, Then the displayed feedback should only show entries that meet the specified filters.

User can visualize feedback using different formats (e.g., graphs, charts) to gain insights on customer sentiment.

Given the feedback is displayed in the aggregation tool, When the user selects the visualization options, Then the feedback data should be represented in the selected graph or chart format.

Feedback entries include metadata such as the source channel and the submission date for better tracking.

Given feedback has been aggregated, When the user views an individual feedback entry, Then the entry should show metadata including the source channel and submission date.

Users receive notifications when new feedback is gathered from any integrated channel.

Given new feedback has been collected, When the feedback aggregation tool checks for updates, Then the user should receive a notification about the new feedback entries.

Feedback History Tracker

Tracks historical customer feedback over time, allowing users to analyze trends and compare changes in sentiment. This feature empowers businesses to adjust strategies based on longitudinal data insights, refining their approach continuously.

Requirements

Feedback Data Integration

User Story: As a business analyst, I want to integrate customer feedback from multiple sources into one platform so that I can analyze sentiment trends more efficiently and provide actionable insights for strategy improvement.
Description: The Feedback Data Integration requirement encompasses the ability to seamlessly integrate various customer feedback sources, such as surveys, social media, and support tickets, into the Datapy platform. This integration will allow for a centralized repository of customer feedback, enhancing the ability to track, analyze, and visualize customer sentiment and trends over time. By consolidating feedback data, users can better identify patterns, improve their product offerings, and align their strategies with actual customer sentiment, ultimately leading to enhanced customer satisfaction and loyalty.
Acceptance Criteria: Integrating multiple feedback sources seamlessly into Datapy for analysis.

Given the user has multiple feedback sources (surveys, social media, support tickets), when the user integrates these sources into Datapy, then the platform should consolidate all feedback into a centralized repository without data loss.

Visualizing integrated customer feedback data in customizable dashboards.

Given the user has integrated feedback data, when the user accesses the customizable dashboard, then all feedback metrics should be visually represented and accurately reflect the latest data trends.

Ensuring historical feedback is accessible for trend analysis.

Given that the user has integrated feedback over time, when the user requests historical feedback data, then the platform should retrieve and display feedback trends over defined time frames accurately.

Updating the feedback repository with new data in real time.

Given the user has set up data sources for feedback, when new feedback is collected, then the integrated repository should automatically update in real time to reflect the most current sentiment.

Comparing customer sentiment before and after product changes based on feedback data.

Given the user has a set timeframe for feedback analysis, when the user compares sentiment data before and after a product change, then the platform should provide clear visual metrics highlighting any sentiment shifts.

Ensuring data integrity and accuracy during the integration process.

Given that various feedback sources are being integrated, when the data is transferred, then the system should validate the accuracy of the data against the original sources with a 99% accuracy rate.

Historical Feedback Visualization

User Story: As a product manager, I want to visualize historical customer feedback trends to understand how sentiment has changed over time, enabling me to make data-driven decisions for product enhancements.
Description: The Historical Feedback Visualization requirement includes the creation of advanced visual analytics tools that allow users to easily visualize historical customer feedback trends over time. This feature will utilize graphs, heat maps, and comparative charts to present data clearly and intuitively. Users will benefit from being able to quickly identify shifts in customer sentiment, correlate feedback with significant business events, and make informed decisions based on visual representations of data. This capability will enhance strategic planning and responsiveness to customer needs.
Acceptance Criteria: Customer Service Agent Analyzing Feedback Trends

Given that a customer service agent is logged into Datapy, when they navigate to the Historical Feedback Visualization section, then they should see a dashboard displaying a range of visual analytics including graphs, heat maps, and comparative charts representing customer sentiment over time.

Marketing Manager Reviewing Monthly Reports

Given that a marketing manager is reviewing monthly customer feedback reports, when they select a specific month on the comparative chart, then the system should highlight changes in sentiment corresponding to that month, displaying key feedback insights and trends.

Product Manager Correlating Feedback with Product Launches

Given that a product manager is examining historical feedback data, when they view the heat map alongside significant business events, then they should be able to see correlations between customer feedback trends and product launch dates clearly marked on the visualizations.

Business Analyst Identifying Trends Over Time

Given that a business analyst is analyzing data from different time periods, when they apply filters to the Historical Feedback Visualization, then the visualizations should dynamically update to reflect the selected time frame and show accurate trend data.

Executive Team Meeting to Discuss Customer Sentiment

Given that the executive team is meeting to discuss customer engagement, when they present the Historical Feedback Visualization, then they should be able to easily interpret sentiment trends and correlations, supported by clear visual representations of the data.

Sentiment Analysis Automation

User Story: As a customer success manager, I want to automatically analyze sentiment within customer feedback so that I can quickly identify areas of concern and improve our service offerings proactively.
Description: The Sentiment Analysis Automation requirement focuses on automating the process of sentiment analysis within the collected feedback data. This feature will leverage AI algorithms to evaluate customer comments and categorize them into positive, negative, or neutral sentiments. Automation will significantly reduce the manual effort needed to analyze large volumes of feedback while providing insights into overall customer satisfaction and potential areas that require attention. By streamlining this process, businesses can respond more proactively to customer needs and concerns.
Acceptance Criteria: Sentiment Analysis for Recent Customer Feedback

Given recent customer feedback data, When the sentiment analysis automation runs, Then 85% or more of the comments are accurately classified into positive, negative, or neutral categories according to predefined sentiment definitions.

Historical Sentiment Trends Report

Given historical customer feedback data tracked over the last year, When the user generates a sentiment trend report, Then the report displays sentiment changes over time with at least three distinct periods showing fluctuation in sentiment.

Real-Time Sentiment Updates

Given the feedback received in real-time, When new comments are analyzed, Then the sentiment analysis updates within 5 minutes, reflecting any changes in customer sentiment on the dashboard.

User Feedback on Sentiment Accuracy

Given the automated sentiment analysis results, When users review the categorization of 100 sample comments, Then at least 90% of users should agree with the provided sentiment classifications.

Integration with Business Metrics

Given the integration of sentiment analysis within Datapy, When users view business metrics on their dashboard, Then sentiment trends should be displayed alongside relevant performance metrics for contextual analysis.

Notification for Negative Sentiment Alerts

Given customer feedback containing negative sentiment, When such feedback is detected, Then alerts should be automatically sent to the designated team members within 10 minutes of analysis.

Customization of Sentiment Categories

Given the need for personalized feedback analysis, When an administrator modifies the sentiment classification criteria, Then the system should apply these changes successfully without data loss and update previous analysis accordingly.

Real-time Feedback Monitoring

User Story: As a customer experience manager, I want to receive real-time alerts about new customer feedback so that I can address issues as they arise and improve our customer service standards.
Description: The Real-time Feedback Monitoring requirement entails establishing a system for users to receive real-time notifications and updates regarding new customer feedback. This feature will keep stakeholders informed about customer sentiments as they arise, allowing for immediate responses to any urgent issues or emerging trends. By providing real-time insights, businesses can act quickly to address customer concerns, enhancing engagement and satisfaction. This capability aligns with the goal of fostering a customer-centric culture within the organization.
Acceptance Criteria: New customer feedback is submitted through the Datapy platform by a user during business hours.

Given a user is logged into the Datapy platform, when new customer feedback is submitted, then the user should receive a real-time notification within 5 seconds.

A user is monitoring customer feedback trends through the Feedback History Tracker feature.

Given a user accesses the Feedback History Tracker, when they filter feedback by date and sentiment, then the displayed data must reflect changes accurately over the specified time frame.

A user needs to respond to a newly submitted critical customer feedback.

Given a user receives a real-time notification of critical feedback, when they access the feedback directly from the notification, then they should have the option to respond within the same interface without delay.

Stakeholders receive regular updates on customer feedback over a week.

Given stakeholders are subscribed to feedback notifications, when new feedback is received, then stakeholders should receive a summary notification daily at a specified time that includes key sentiment trends.

A customer service representative accesses the feedback monitoring dashboard to address feedback.

Given the customer service representative is on the dashboard, when they click on a feedback entry, then the detailed view should load within 3 seconds with full context of customer sentiments and previous feedback.

A manager checks for any urgent customer feedback responses at the end of the day.

Given the manager logs into the system at the end of the day, when they check the feedback summary, then they should see a clear indication of all urgent response requirements labeled distinctly.

Customizable Feedback Reporting

User Story: As a business owner, I want to create customizable reports from customer feedback data so that I can present targeted insights to my stakeholders and drive strategic discussions effectively.
Description: The Customizable Feedback Reporting requirement empowers users to create tailored reports based on their specific needs and metrics of interest. This feature will allow users to select data points, visualization styles, and report formats that best serve their organizational goals. By offering customization, users can focus on the most relevant feedback data, share insights with stakeholders in a meaningful way, and make strategic decisions based on these targeted reports. This capability enhances user experience and encourages regular engagement with the feedback data.
Acceptance Criteria: User creates a new customizable feedback report based on selected metrics from customer feedback data for a monthly review meeting.

Given the user is logged into the Datapy platform, when they navigate to the 'Custom Reports' section and select at least three different data points along with a visualization style, Then the system should generate a report that accurately reflects the selected data points in the chosen format without errors.

A user wants to share a customized feedback report with team members through Datapy's collaboration tools.

Given the customized report is generated, when the user selects the 'Share' option and inputs the email addresses of team members, Then the system should send an email invitation with a link to the report, ensuring all recipients can access it successfully.

A manager wishes to adjust the visualization style of an existing feedback report to better highlight trends over time.

Given the user has an existing feedback report open, when they choose a different visualization style from the available options, Then the system should update the report dynamically and retain the previously selected data points.

A user assesses the effectiveness of feedback reporting on organizational decision-making over the past quarter.

Given the user has access to quarterly customized feedback reports, when analyzing trends in the sentiment data presented, Then the user should be able to identify at least two actionable insights derived from the reports.

An administrator reviews user interactions with the customizable feedback reporting feature to ensure usability improvements.

Given feedback reporting feature usage data is collected, when the administrator reviews the user activity logs, Then they should find that at least 75% of users engage with the customization options successfully within the first month of launch.

Dynamic Chart Customization

Empower users with the ability to create fully customizable charts tailored to their specific data needs. With a variety of chart types including bar, line, pie, and scatter plots, users can easily adjust colors, labels, and axes to enhance clarity and presentation impact. This feature enables businesses to present their data in a way that resonates with stakeholders, ensuring that critical insights are communicated effectively.

Requirements

Interactive Chart Builder

User Story: As a business analyst, I want to create customizable charts with specific color schemes and data labels so that I can present my findings in a way that is clear and impactful to my team and stakeholders.
Description: The Interactive Chart Builder allows users to create a wide range of customizable charts that can be tailored to their unique data requirements. Users can select from various chart types, such as bar, line, pie, and scatter plots, and modify colors, labels, and axes in real-time. This capability not only enhances data visualization but also makes it easier for stakeholders to understand complex business metrics. By integrating this feature into Datapy, users can quickly translate intricate data sets into visually appealing charts that facilitate better decision-making and communication within teams and with clients.
Acceptance Criteria: As a user, I want to create a new bar chart using the Interactive Chart Builder so that I can visualize sales data for the last quarter in a visually compelling way.

Given that I have selected the bar chart option, When I input my sales data and customize labels and colors, Then the chart should render accurately with the specified settings and data reflected correctly.

As a user, I want to modify an existing pie chart by changing its colors and labels so that it matches my company's branding guidelines.

Given that I have an existing pie chart, When I change the label text and adjust the color scheme, Then the chart should update in real-time to reflect the new labels and colors.

As a data analyst, I need to generate a scatter plot to analyze customer segmentation based on usage and feedback scores.

Given that I have selected the scatter plot option, When I input the relevant data for usage and feedback scores and click 'Generate', Then the scatter plot should be created with data points accurately plotted according to the provided dataset.

As a project manager, I want to share a customized line chart with my team to discuss project timelines and milestones.

Given that I have created a line chart with specific timelines and milestones, When I select the 'Share' option, Then the chart should be successfully shared with my team's workspace and accessible by all team members.

As a user, I want to delete a chart that I no longer need to maintain a clutter-free workspace.

Given that I have a chart displayed in my workspace, When I choose the 'Delete' option, Then the chart should be permanently removed from my workspace and should no longer appear in the chart list.

As a user, I want to export my customized charts in various formats such as PNG and PDF for presentation purposes.

Given that I have finalized my chart customization, When I select the 'Export' option and choose the file format, Then the chart should be exported correctly in the selected format without loss of detail or data accuracy.

As a new user, I want to understand how to use the Interactive Chart Builder so I can effectively create charts.

Given that I am a new user, When I access the Interactive Chart Builder for the first time, Then I should see an introductory tutorial that guides me through the features and functionalities available.

Real-time Data Updates

User Story: As a data manager, I want my charts to automatically update with any new data entered so that I can always have the most accurate information at hand for my presentations and reports.
Description: The Real-time Data Updates requirement ensures that users' charts dynamically refresh as new data is available, allowing for immediate insights and adjustments without manual intervention. This feature supports seamless integration with live data sources, automatically updating visuals to reflect the latest business metrics. This is crucial for making timely decisions based on the most current information, thus maximizing responsiveness and accuracy in reporting.
Acceptance Criteria: User updates charts during a live meeting where new sales data is coming in without lagging or performance issues.

Given that the user is in a live meeting, when new sales data is received, then the charts should update automatically within 2 seconds, reflecting the most current metrics without any manual intervention.

User integrates a third-party data source that updates in real-time and verifies that the data refreshes correctly on the chart.

Given that the user has connected a third-party data source, when data changes occur at the source, then the charts must synchronize and reflect the updated data accurately within 5 seconds.

User customizes a chart and expects real-time updates to maintain those customizations while data refreshes.

Given that the user has applied custom settings to a chart, when new data is loaded, then the chart must retain the customizations (colors, labels, axes) while updating the displayed data accordingly.

User runs a periodic report and expects the chart to display the latest data, seamlessly merging historical trends with real-time updates.

Given that the user initiates a periodic report calculation, when it executes, then the charts must show a combination of historical data and current updates without discrepancies or errors.

User tests multiple chart types to ensure they all refresh correctly when new live data is streamed.

Given that the user has created charts of various types (bar, line, pie, scatter), when new data is streamed, then all charts must refresh correctly and display the new data types as intended, in real-time.

User accesses the dashboard from different devices to verify consistent real-time updates and display performance.

Given that the user accesses the dashboard on a mobile device and a desktop, when data updates occur, then both devices must display the charts updated in real-time with consistent formatting and accuracy.

Export and Share Charts

User Story: As a marketing manager, I want to export the charts I've created to share them with my team via email, so that we can discuss our strategies based on the latest visual data insights.
Description: The Export and Share Charts feature enables users to easily share their customized charts with team members or stakeholders via various formats such as PDF, PNG, or directly through email. This functionality not only facilitates collaboration but also allows for the dissemination of insights outside the Datapy platform, improving accessibility and ensuring that key information reaches the right audiences efficiently. It enhances team communication and enables more informed decision-making across departments.
Acceptance Criteria: User needs to export a customized bar chart as a PDF to share with the marketing team for their weekly performance review.

Given the user has a customized bar chart open, when they select 'Export' and choose 'PDF', then the chart should be successfully exported and downloadable without errors.

A user wants to share a pie chart via email directly from the Datapy platform to three team members.

Given the user selects a pie chart and clicks 'Share', when they enter the team member's email addresses and press 'Send', then the email should be sent successfully with the chart attached.

A stakeholder requests to receive a scatter plot chart in PNG format to include in their presentation.

Given the user has a scatter plot chart displayed, when they select 'Export' and choose 'PNG', then the chart should be exported in high resolution and saved with the correct file extension.

A user modifies the customization of a line chart and wants to ensure the changes are preserved when exporting.

Given a line chart with customizations applied, when the user exports the chart, then the exported file should reflect all customization settings accurately (colors, labels, axes).

A team leader intends to share multiple charts simultaneously to facilitate a discussion among stakeholders.

Given the user selects multiple customized charts, when they click 'Export' and choose 'Bundle as PDF', then all selected charts should be compiled into a single PDF document without errors.

A user needs to confirm that an exported PNG chart maintains quality and clarity after being sent.

Given the user has exported a PNG chart, when they open the exported file, then the image should display without pixelation and retain the original chart details.

A user wishes to receive feedback on a shared chart from team members.

Given that a chart has been emailed to team members, when they open the chart and reply with feedback, then the original user should receive all replies tagged with the chart referenced.

Save and Load Chart Templates

User Story: As a project manager, I want to save my chart settings as templates so that I can quickly create consistent charts for my recurring reports without having to redo my work every time.
Description: The Save and Load Chart Templates requirement allows users to save their custom chart configurations as templates for future use. This feature adds efficiency, enabling users to quickly create new charts without having to start from scratch each time. Users can select from their saved templates when creating new charts, reducing repetitive effort and ensuring consistency in visual representation across reports and presentations, which is essential for maintaining branding and clarity.
Acceptance Criteria: User saves a custom chart configuration as a template after editing it with specific data sets, colors, labels, and axes adjustments.

Given a user has adjusted a chart to their satisfaction, when they select 'Save as Template' and provide a unique name, then the chart configuration is saved successfully in their templates list.

User loads a previously saved chart template to create a new chart for a different dataset.

Given a user has existing chart templates saved, when they select a template from the list and choose 'Load Template', then a new chart is created based on the selected template's configuration.

User attempts to save a chart template without providing a name or with a duplicate name.

Given a user has not provided a name or is trying to save a template with a name that already exists, when they hit 'Save', then they receive an error message indicating that a unique name is required.

User updates an existing chart template with new configurations and saves it.

Given a user has selected an existing template to edit, when they make changes and select 'Save', then the template is updated with the new configurations and reflects the changes in the templates list.

User wants to delete a saved chart template from their list.

Given a user is viewing their saved templates, when they select a template and choose 'Delete', then the template is removed from the list and no longer available for loading.

User shares a saved chart template with team members in the collaborative tools section.

Given a user selects a saved template to share, when they choose the 'Share' option and select team members, then the specified team members receive access to the shared template.

User integrates a loaded template to a report and checks for branding consistency.

Given a user has loaded a chart template into a report, when they view the chart within the report, then the branding elements such as colors and logos should reflect the company's branding guidelines.

Chart Annotations and Comments

User Story: As a team lead, I want to add annotations to my charts so that I can highlight specific trends or insights for my team discussions.
Description: The Chart Annotations and Comments feature allows users to add notes or comments directly onto charts. This functionality enables users to highlight key insights, changes, or important information that should be communicated alongside the visual representation of data. It fosters better understanding and discussion by providing context to the data being presented, enhancing collaboration among team members during review sessions or strategy meetings.
Acceptance Criteria: User adds annotations to a bar chart during a strategy meeting to highlight key sales figures.

Given the user is on the chart customization page, when the user selects a bar chart and enters annotations, then the annotations should be visible on the chart with the correct positioning and formatting.

Team member reviews a pie chart with comments added by another user to understand the context of the data presented.

Given a pie chart with existing comments, when the team member hovers over the chart, then the comments should display as tooltips associated with their respective segments.

User edits an existing annotation on a line chart to correct information during a review session.

Given a line chart with annotations, when the user selects an annotation to edit and saves the changes, then the updated annotation should reflect accurately on the chart without errors or loss of data.

User filters a scatter plot with annotations to display only specific data points based on user-defined criteria.

Given a scatter plot with annotations, when the user applies a filter, then only the relevant data points along with their annotations should be displayed, while others should be hidden.

User generates a report where all annotations and comments from various charts are compiled for presentation.

Given multiple charts with annotations and comments, when the user initiates the report generation, then the report should include all annotations and comments linked to their respective charts in a clear format.

User tries to add an annotation to a chart without selecting a specific data point first.

Given a chart is displayed on the screen, when the user attempts to add an annotation without selecting a data point, then an error message should be displayed indicating that a data point must be selected first before adding an annotation.

Interactive Data Drill-Downs

Allow users to explore their data at deeper levels with interactive drill-down capabilities. By clicking on data points, users can access detailed information and related datasets, transforming static visuals into dynamic storytelling tools. This feature enhances data comprehension and encourages more profound insights, helping users to uncover hidden patterns and make informed decisions.

Requirements

Dynamic Drill-Down Navigation

User Story: As a business analyst, I want to click on data points to view more detailed information so that I can uncover deeper insights and trends in our metrics.
Description: The Dynamic Drill-Down Navigation requirement enables users to navigate through various levels of their data by simply clicking on data points within interactive visualizations. This capability transforms static reports into engaging, dynamic storytelling tools that allow for a deeper understanding of the dataset. It should seamlessly integrate with the existing UI, providing a responsive experience that updates visuals in real-time as users explore different facets of the data. This feature is crucial for enhancing user engagement and comprehension, as it empowers users to independently uncover insights and make informed decisions based on granular data analysis.
Acceptance Criteria: User interacts with an interactive visualization chart displaying sales data and wants to drill down to view the sales figures for a specific product category over the last quarter.

Given that the user has clicked on the product category in the chart, when the drill-down action is initiated, then the interface must display the sales figures for that product category in a detailed table format with relevant metrics such as total sales, units sold, and average sale price within 2 seconds.

A user is analyzing customer data and wants to view engagement metrics by clicking on a bar representing a specific customer segment in a dashboard.

Given that the user has clicked on the bar representing a customer segment, when the user expects to see the engagement metrics, then the dashboard must update to show detailed engagement metrics such as click-through rates, conversion rates, and average interaction time within the same interface without page reloads.

While reviewing a financial report, a user identifies an anomaly in the data and wants to explore deeper insights by drilling down into the figures displayed on a visual representation.

Given that the user has identified an anomaly in the financial report visualization, when the user clicks on the data point representing the anomaly, then the system must provide a comprehensive view that includes a historical trend analysis and comparison with the previous month's figures, generated within 3 seconds.

A user is tracking marketing campaign performance and clicks on a trend line that represents a specific campaign period to discover additional details about the campaign's effectiveness.

Given that the user clicks on the trend line in the visualization, when the drill-down event occurs, then the system should display a breakdown of key performance indicators (KPIs) related to that campaign, such as ROI, customer acquisition cost, and total leads generated, in an organized format within 3 seconds.

An analyst uses the dashboard to review user behavior across multiple platforms and wishes to drill down to view specific metrics for mobile users by clicking on the mobile segment in a pie chart.

Given that the analyst is viewing the user behavior pie chart, when the analyst clicks on the mobile segment, then the dashboard must refresh to display detailed metrics for mobile users, including session duration, bounce rate, and user flow charts, seamlessly and within 2 seconds.

A business manager wants to use the drill-down feature to examine customer feedback scores by clicking on a specific score section within an interactive feedback dashboard.

Given that the business manager has clicked on a specific feedback score section, when the drill-down process is initiated, then relevant qualitative feedback and quantitative trends must be displayed in a clear manner, including timeframes and patterns, within 2 seconds of the user’s action.

Contextual Data Insights

User Story: As a data user, I want to view contextual information when drilling down into metrics so that I can better understand the implications of the data.
Description: The Contextual Data Insights requirement allows users to see supplementary information and related datasets when interacting with specific data points. This feature aims to enrich the user experience by providing context around the data being analyzed. When a user drills down into a particular metric, they should not only see detailed numbers but also visual insights and comparisons with relevant data segments, enabling them to make better-informed decisions with a holistic view of the data landscape. Integration with the platform's AI capabilities can further enhance the context provided, offering predictive insights and recommendations based on user interactions.
Acceptance Criteria: User drills down into a sales metric to view detailed data for a specific quarter.

Given that the user is on the sales dashboard, when they click on the Q3 sales figure, then they should see a detailed breakdown of sales by product category, along with visual graphs and comparisons to Q2.

User accesses contextual recommendations based on selected metric.

Given that the user has drilled down into the 'Customer Satisfaction' metric, when they view the related datasets, then they should be presented with AI-generated recommendations and insights for improving customer satisfaction based on historical data.

User examines employee performance data through a drill-down feature.

Given that the user clicks on the 'Top Performers' data point, when they drill down into the details, then they should see individual performance metrics and peer comparisons for the entire team, alongside visual indicators for performance trends.

User searches for anomalies in their financial data using drill-downs.

Given that the user has identified an unexpected dip in revenue, when they drill down into the revenue data points, then they should be able to view related financial metrics and historical data visuals highlighting potential reasons for the dip.

User collaborates with team members while analyzing drill-down results.

Given that multiple users have access to a dashboard, when one user initiates a drill-down on a critical metric, then all collaborating users should receive real-time updates and visual cues for the newly accessed data.

User utilizes drill-down capabilities to explore customer segmentation.

Given that the user is analyzing customer demographics, when they drill down into a specific demographic segment, then they should see supporting data fields including purchasing behavior, average spend, and retention rates compared against other segments.

Customizable Drill-Down Filters

User Story: As a marketing manager, I want to apply filters to my drill-down data so that I can focus on specific campaigns and performance metrics relevant to my initiatives.
Description: The Customizable Drill-Down Filters requirement gives users the ability to apply customized filters when exploring their data through drill-down actions. This functionality allows users to focus on specific segments or time periods, making data exploration more relevant and personalized. Users should have the flexibility to set these filters before or during their exploration to see only the information most pertinent to their goals. This feature promotes a tailored user experience that accommodates varying analytical needs and preferences, ultimately resulting in more effective analyses and insights.
Acceptance Criteria: User applies a date filter to view sales data for a specific month during a quarterly review meeting.

Given the user is on the drill-down report page, when they select a date range filter for the month of January 2025, then the displayed data should only reflect sales data from January 2025.

User wants to analyze customer feedback by segmenting it based on product categories.

Given the user has selected the Customer Feedback data drill-down, when they apply a filter for the 'Electronics' category, then all displayed feedback must pertain solely to products categorized under 'Electronics'.

User explores marketing campaign data and wants to compare results from different time periods.

Given the user is analyzing campaign performance and selects a date filter for 'Last Quarter', when the filter is applied, then only data from the previous quarter should be displayed in the report.

User attempts to apply multiple filters simultaneously to narrow down financial data regarding operational costs.

Given the user sets filters for both 'Operational Costs' and 'Q1 2025', when they hit apply, then the report should exhibit only those records that fall under operational costs for Q1 2025.

User is examining product sales data and wants to review specific regions separately.

Given the user is viewing the sales data overview, when they apply a filter for the 'North America' region, then the visualizations should update to show only sales data from North America.

User-Friendly Interface for Drill-Downs

User Story: As a first-time user, I want a simple and intuitive interface that guides me while I explore data so that I can easily learn how to uncover insights without frustration.
Description: The User-Friendly Interface for Drill-Downs requirement emphasizes creating an intuitive and engaging experience for users when accessing drill-down features. This means incorporating well-designed elements such as tooltips, hover effects, and animated transitions that guide users through their exploration. The interface should minimize the learning curve involved with interacting with complex datasets while maximizing the enjoyment and satisfaction of using the platform. This ensures users can quickly grasp how to leverage the drill-down features and promotes higher adoption rates of the analytical capabilities available.
Acceptance Criteria: User initiates the interactive drill-down by clicking on a data point in a dashboard visualization.

Given a user clicks on a data point in the dashboard, when the drill-down feature is activated, then a detailed view of that data point with relevant tooltips and information should be displayed within 2 seconds.

User interacts with the drill-down interface by hovering over various elements to receive additional insights.

Given a user hovers over any data point in the drill-down interface, when the hover effect is triggered, then a tooltip providing contextual information should appear without any delays or errors.

User navigates back from a drill-down view to the main dashboard.

Given a user is in the drill-down view, when they click the back button, then they should be seamlessly returned to the original dashboard without losing any previous settings or filters applied.

User customizes the drill-down experience by selecting different metrics to display.

Given the user is in the drill-down interface, when they attempt to customize the metrics displayed, then they should be able to successfully select and view different relevant datasets with real-time updates and without errors.

User experiences animated transitions when navigating between data views in the drill-down.

Given the user is navigating between different data views, when they interact with the drill-down feature, then smooth animated transitions should occur, enhancing the viewing experience without causing delays.

User receives guidance on how to use the drill-down features for the first time.

Given the user is accessing the drill-down feature for the first time, when the feature is loaded, then an interactive tutorial or tooltips should guide the user on how to use the functionalities effectively.

Real-Time Data Refresh

User Story: As a data-driven decision-maker, I want the drill-down data to update in real-time so that I am always working with the most current information when making strategic choices.
Description: The Real-Time Data Refresh requirement requires that the drill-down visualizations update in real-time as new data is entered or existing data is changed. This feature is essential for users who need the most current information while making decisions based on drill-down data. The system should continuously synchronize with the underlying data sources to ensure that what users see during their exploration reflects the most accurate and timely data possible. This capability enhances decision-making effectiveness and leverages the full power of Datapy's cloud-based architecture.
Acceptance Criteria: User accesses a dashboard with interactive data visualizations and clicks on a specific data point to drill down for more information.

Given the user is on the dashboard, when they click on a data point, then the drill-down visualization should update to display real-time data corresponding to the selected point, reflecting any changes made in the underlying data.

User inputs new data into the system while simultaneously monitoring a drill-down visualization to ensure it reflects changes instantly.

Given the user inputs new data into the system, when they return to the drill-down visualization, then the data should refresh automatically in less than 5 seconds without needing a manual refresh.

Two users are concurrently analyzing the same dataset through drill-down visualizations on different devices.

Given both users are accessing the same dataset, when one user updates the dataset, then the other user should see the updated data in their drill-down visualization in real-time without a delay.

User navigates back-and-forth between different data points in a drill-down visualization.

Given the user has navigated to a drill-down visualization, when they switch back to a previous data point, then the visualization should maintain its real-time data state without any previous data loading lag.

User receives an error message while trying to access the drill-down visualization due to server connectivity issues.

Given the server is experiencing connectivity issues, when the user attempts to interact with the drill-down feature, then a user-friendly error message should display, informing them that data is not available at this time and providing an option to retry.

User analyzes data trends over time using the drill-down feature during a team meeting.

Given the user is conducting a live presentation, when they demonstrate the drill-down feature, then the visualization should reflect real-time updates based on the most recent datasets, allowing for accurate trend analysis during the meeting.

User wants to customize the drill-down view based on specific metrics relevant to their analysis.

Given the user has access to customization features, when they adjust the metrics displayed in the drill-down view, then the visualization should reflect those changes immediately, updating the data in real-time to support their analysis.

Advanced Filtering Options

Introduce comprehensive filtering tools that enable users to slice and dice their data with ease. With the ability to apply multiple filters simultaneously, users can focus on specific segments of their datasets and tailor visualizations to their precise interests. This feature promotes efficient data exploration and tailored insights, catering to the diverse needs of different stakeholders.

Requirements

Multi-Filter Capability

User Story: As a data analyst, I want to apply multiple filters to my datasets simultaneously so that I can quickly drill down into specific segments and derive actionable insights without cumbersome processes.
Description: This requirement involves implementing a multi-filter capability that allows users to apply several filters across different data dimensions simultaneously. This feature enhances the user experience by providing a streamlined method for conducting deep dives into their datasets, enabling businesses to uncover insights that are highly relevant to specific segments or criteria. By integrating this feature into the existing Datapy platform, users can enjoy the flexibility to manipulate their data dynamically, ultimately improving decision-making processes and data exploration efficiency.
Acceptance Criteria: Applying Multiple Filters Across Different Data Dimensions

Given the user is logged into the Datapy platform, when they access a dataset and apply multiple filters simultaneously, then the dataset should reflect only the data that meets all the applied filter criteria without error.

Dynamic Updates to Visualizations After Filter Application

Given the user has applied multiple filters to a dataset, when they change any of the filter parameters, then the visualizations should automatically update to reflect the new filtered data in real-time without page refresh.

Saving and Reusing Filter Combinations

Given the user has applied and customized a set of filters, when they save these filter combinations, then they should be able to retrieve and apply those saved filters in future sessions seamlessly.

User-Friendly Interface for Filtering

Given the user is on the data exploration page, when they initiate the filtering process, then the interface should provide clear labeling, easy access to filter options, and a straightforward method to apply and remove filters.

Error Handling for Invalid Filter Combinations

Given the user has applied filters with conflicting criteria, when they attempt to apply these filters, then an error message should be displayed, informing the user and suggesting valid combinations.

Performance of Multi-Filter Application

Given a large dataset, when the user applies multiple filters, then the data processing time should not exceed 3 seconds for the results to be displayed on screen.

Mobile Responsiveness of Filtering Tools

Given the user accesses the Datapy platform via a mobile device, when they apply multiple filters, then the filtering tools should remain fully functional and visually coherent across all device sizes.

Dynamic Visualization Updates

User Story: As a business user, I want my data visualizations to update automatically when I change my filters so that I can see the impact of my selections instantly and make informed decisions more efficiently.
Description: This requirement specifies that the platform should support dynamic updates to visualizations as filters are applied or modified. Users should see real-time changes in their charts, graphs, and data displays without needing to refresh or navigate away from their filtered views. This functionality will significantly enhance user satisfaction and engagement by providing immediate feedback on their data manipulations, fostering a more interactive experience and intuitive understanding of data relationships and trends.
Acceptance Criteria: User applies a filter to visualize sales data by region, and the chart updates in real-time without refreshing.

Given a user applies a filter for the 'North Region', when the filter is activated, then the sales visualization updates to show only data for the 'North Region' without any delay.

A user modifies an existing filter to include a specific product category, and the data visualizations adjust accordingly.

Given a user modifies the filter to include 'Electronics', when the modification is made, then all relevant visualizations reflect sales data only for the 'Electronics' category within 2 seconds.

Multiple filters are applied by a user, and the visualization reflects the combined changes accurately and promptly.

Given a user applies filters for 'North Region' and 'Electronics', when both filters are selected, then all visualizations should update to show sales data that meet both filter conditions simultaneously, within 2 seconds.

A user removes a specific filter from their dashboard and observes the intended change in their visualizations immediately.

Given a user removes the filter for 'Electronics', when the filter is deactivated, then the visualization should revert to display total sales data without the 'Electronics' filter within 2 seconds.

In a collaborative environment, multiple users apply different filters simultaneously and expected results reflect in real-time for each.

Given multiple users apply different filters at the same time, when they make their selections, then each user’s visualization updates independently and accurately without impacting others’ views within 2 seconds.

Customizable Filter Options

User Story: As a power user, I want to create and save my own filter preferences so that I can quickly apply my most commonly used filters on my datasets, streamlining my analysis process.
Description: This requirement defines the need for customizable filter options that allow users to create and save their own specific filtering criteria. Users should be able to specify different filter parameters, conditions, and saved filter sets that can be easily accessed later. Providing personalized filter experiences will cater to diverse user needs and repeat tasks, improving users' workflow and enhancing overall productivity while using Datapy.
Acceptance Criteria: User creates a customized filter to view sales data for a specific region and time period in the Datapy analytics platform.

Given a user is logged into Datapy, when they navigate to the filtering options and specify the region and time period for sales data, then the data visualization should update to display only the filtered data according to the specified criteria.

User saves a customized filter to quickly access it later without re-entering filter criteria.

Given a user has applied a customized filter in Datapy, when they save this filter with a specific name, then the filter should be retrievable from the saved filters list at any later time.

User applies multiple filters simultaneously to enhance data segmentation in the analytics dashboards.

Given a user is in the Datapy dashboard, when they apply multiple complementary filters (e.g., product line, customer segment, and price range), then the dashboard should reflect only the dataset that meets all specified filter conditions simultaneously.

User modifies an existing saved filter to adjust the parameters for their data analysis needs.

Given a user is viewing the list of saved filters in Datapy, when they select an existing filter and update its parameters, then the changes should be saved and reflected when the filter is applied next time.

User removes a customized filter from their saved filters list after it is no longer needed.

Given a user is in the saved filters list, when they select a filter they wish to remove and confirm the deletion, then the filter should no longer appear in the saved filters list.

User utilizes the reset option to clear all applied filters and return to the default view.

Given a user has several filters applied in Datapy, when they click the reset filters button, then all applied filters should be cleared, and the default dataset should be displayed without any filters.

Filter Association with Metrics

User Story: As a business strategist, I want my applied filters to affect the relevant KPIs displayed, so that I can understand the broader impact of my data segmentation on my strategic goals.
Description: This requirement outlines the necessity for filters to be associative with specific metrics or KPIs that users select. When a user applies a filter, it should not only change the view of the dataset but also provide insights into how that filter interacts with selected performance indicators. This feature will ensure that users can understand the implications of their filtering choices and how they affect core business metrics, thus facilitating strategic data analysis and interpretation.
Acceptance Criteria: User applies multiple filters on a dataset to analyze sales performance by region and product category.

Given the user has selected a dataset, when they apply filters for region and product category, then the metrics displayed should update in real-time to reflect only the relevant data based on the applied filters.

User wants to see how applying a filter for the last quarter affects key performance indicators (KPIs).

Given a user selects a time filter for the last quarter, when the filter is applied, then the KPI metrics should adjust to display the data exclusively from the last quarter, providing clear insights into performance during that period.

User needs to remove a filter and observe the changes in the data.

Given the user has multiple filters applied, when they remove one filter, then all metrics should refresh to reflect the dataset without the removed filter, and this should occur within 2 seconds.

User wants to see correlation between filters when applied to certain segments.

Given that the user applies a filter for 'High Revenue' products while another filter for 'Region A,' when they view the metrics, then the results must show associations and alterations in performance KPIs related to that segment.

User accesses a dashboard with preset filters for quick insights.

Given that the dashboard includes predefined filters for 'Top 10 Products' and 'Last 6 Months,' when the user accesses the dashboard, then the visuals should immediately reflect the data according to these filters without requiring user intervention.

User assesses the impact of applying multiple filters one after the other on financial metrics.

Given the user applies a filter for 'Marketing Expenses' followed by 'Year 2024,' when both filters are active, then the financial metrics displayed should accurately represent the data filtered by both criteria, ensuring no loss of data accuracy or integrity.

User wishes to compare data before and after applying a filter.

Given the user clicks a 'Compare' option on the filter panel, when they apply the filter, then they should see a side-by-side comparison of metrics before and after the applied filter to understand the impact clearly.

User Training and Documentation

User Story: As a new user, I want access to training materials and documentation about the filtering features so that I can effectively use the tool to analyze my data and derive insights without feeling overwhelmed.
Description: This requirement stresses the importance of providing user training and detailed documentation on how to utilize the new filtering options effectively. Comprehensive training materials and resources should be developed to empower users to take full advantage of the advanced filtering tools. By investing in user education, the overall user experience can be enhanced, leading to better engagement and utilization of the platform's capabilities.
Acceptance Criteria: User accesses the training module for Advanced Filtering Options in Datapy.

Given a user is logged into Datapy, When they click on the 'Training' section, Then they should see a dedicated module for 'Advanced Filtering Options' with comprehensive guides and video tutorials.

A user applies multiple filters using the advanced filtering tools after completing the training.

Given a user has completed the training module, When they apply at least three different filters simultaneously on their dataset, Then the applied filters should accurately reflect in the visualizations without errors.

User downloads the documentation for Advanced Filtering Options to reference later.

Given a user is in the training module, When they click on the 'Download Documentation' button, Then the user should receive a PDF file containing detailed instructions on utilizing the advanced filtering options.

User provides feedback on the training materials after using them for a week.

Given a user has used the advanced filtering tools for a week, When they respond to a feedback survey, Then they should rate the training materials with an average score of 4 out of 5 or higher for clarity and usefulness.

User seeks help on advanced filtering during a live training session.

Given a live training session is in progress, When a user raises a question about applying filters, Then the trainer should provide a clear, step-by-step demonstration on how to effectively use the filtering tools.

Real-Time Collaboration Mode

Enable teams to collaborate in real-time on interactive visualizations, fostering enhanced teamwork and collective decision-making. Users can share live dashboards and charts, allowing team members to add comments and insights as data is updated. This feature bridges communication gaps and enhances engagement, ensuring that all members are aligned and informed during analytics discussions.

Requirements

Real-Time Dashboard Sharing

User Story: As a team member, I want to share my interactive dashboards in real-time so that my colleagues can see the latest updates and contribute to the discussion effectively.
Description: The Real-Time Dashboard Sharing requirement allows users to instantaneously share their interactive dashboards with team members. This feature enables seamless collaboration and ensures that all participants view the same data updates in real-time. By integrating this capability into the Datapy platform, users can enhance their discussions and decision-making, ensuring the insights derived from the data are accessible and understood by all stakeholders involved. As data updates occur, team members will see the changes simultaneously, promoting unified collaboration. This functionality is crucial for fast-paced environments where timely access to data can significantly influence the direction of conversations and outcomes. Additionally, the integration of this feature ensures that it aligns well with Datapy's core value of simplifying analytics for its users, ultimately driving more meaningful collaborations.
Acceptance Criteria: Real-Time Sharing of Dashboard Updates During Team Meetings

Given a user possesses an interactive dashboard, when they share the dashboard with team members during a meeting, then all team members should see the current data updated in real-time without refresh.

Commenting on Shared Dashboards

Given a user is viewing a shared dashboard, when they add a comment on a specific data point, then the comment should be visible to all other team members viewing the dashboard in real-time.

Simultaneous Dashboard Viewing by Multiple Users

Given multiple users are accessing the same dashboard at the same time, when one user changes a visualization or filter, then all other users should see the change reflected immediately.

Access Control for Dashboard Sharing

Given a user shares a dashboard, when a team member receives the share link, then they should have the appropriate access permissions to view and comment based on their role.

Notification of Data Updates

Given data within a shared dashboard is updated, when the data changes, then all users currently viewing the dashboard should receive a notification indicating that data has been updated.

Collaborative Annotation Feature

Given a user is viewing a shared dashboard, when they annotate a specific visualization, then that annotation should be saved and accessible to all team members in future sessions.

User Experience in Real-Time Collaboration Mode

Given a user engages in the real-time collaboration mode, when they navigate through the dashboard interface, then the user experience should remain smooth with no lag or performance issues.

Collaborative Commenting System

User Story: As a user, I want to leave comments on the dashboards and tag my teammates so that I can discuss specific data points and gather feedback right where it matters.
Description: The Collaborative Commenting System requirement enhances the Real-Time Collaboration Mode by allowing users to add and view comments directly on the visualizations and dashboards. This feature makes it easier for team members to provide insights or question data points as they review the analytics, ensuring that important discussions and notes are captured in context. The commenting system will support tagging individuals to prompt specific input, thereby increasing engagement and accountability among team members. By implementing this functionality, Datapy further solidifies its role as an empowering tool for collaborative decision-making, allowing for a more engaged user experience and driving deeper analysis of the displayed data.
Acceptance Criteria: Users can view and add comments to a dashboard during a team meeting, allowing for real-time discussion and insights on the data presented.

Given a shared dashboard is open, When a user adds a comment, Then the comment should appear instantly for all other users viewing the dashboard with a timestamp and the user's name.

Team members receive notifications when they are tagged in comments, ensuring that they are promptly informed of discussions that require their input.

Given a comment contains a user's tag, When the comment is submitted, Then the tagged user should receive a notification through the platform's notification system.

Users need to filter comments based on their relevance to specific data points on the dashboard, making it easier to access context-specific feedback.

Given comments are added to a dashboard, When a user applies a filter for a specific data point, Then only comments relevant to that data point should be displayed.

The commenting system should allow users to edit or delete their comments within a defined time frame to rectify any errors or update information.

Given a user has added a comment, When the user decides to edit or delete it within five minutes of posting, Then the user should have the option to successfully edit or delete the comment.

Users are analyzing sales data in a live dashboard and want to tag specific individuals for feedback on sales trends.

Given the sales dashboard is being viewed, When a user tags an individual in a comment, Then that individual should receive a direct notification and the comment should link to the relevant data point.

During a collaborative session, users want to ensure that comments related to specific visualizations are visible and can be expanded for detailed discussions.

Given multiple comments are attached to a visualization, When a user clicks on a comment, Then all comments related to that visualization should expand or be displayed, ensuring context is maintained.

Notification System for Updates

User Story: As a user, I want to receive notifications when my team updates dashboards so that I can stay informed and engaged with the latest data changes.
Description: The Notification System requirement enables users to receive alerts whenever significant changes are made to shared dashboards or visualizations. This feature ensures that all team members are promptly informed of updates, fostering a more agile response to changing data and promoting active participation in discussions. Notifications can be customized based on individual preferences, allowing team members to choose how they wish to be alerted—be it through email, in-app notifications, or SMS. Implementing such a system will ensure that users do not miss critical insights or discussions that could impact their decision-making processes, thus enhancing the overall effectiveness of the collaboration feature within Datapy.
Acceptance Criteria: User receives real-time notifications for updates made to shared dashboards when logged in to Datapy.

Given a user is logged into Datapy, when a significant change is made to a shared dashboard, then the user receives an in-app notification about the update.

User can customize notification preferences for updates on shared dashboards.

Given a user accesses the notification settings, when the user selects notification preferences (email, in-app, SMS), then the chosen preferences are saved and applied to future notifications.

Team members with different notification preferences receive alerts according to their selected method.

Given multiple team members have different notification settings, when a significant change occurs on a shared dashboard, then each member receives their alerts through their preferred method (email, in-app, SMS).

User can view a history of notifications received regarding dashboard updates.

Given a user accesses their notifications history, when they request to view past notifications, then the system displays a list of all received notifications related to shared dashboards with timestamps.

User receives a summary of key updates in daily or weekly digest notifications.

Given a user opts for a digest summary in their notification settings, when the specified time arrives (daily or weekly), then the user receives a summarized report of the significant changes in shared dashboards.

User can mute notifications temporarily for specific dashboards.

Given a user wants to mute notifications, when they select the mute option for a specific shared dashboard, then the user no longer receives notifications for that dashboard until muted again.

Admin can monitor notification delivery effectiveness for team members.

Given an admin accesses the notification management panel, when they review notification delivery statistics, then they can see metrics on the success rate of notifications sent to each team member.

Version Control for Dashboards

User Story: As a user, I want to be able to track changes and revert to earlier versions of my dashboards so that I can recover from mistakes and ensure data accuracy.
Description: The Version Control for Dashboards requirement allows users to track changes and revert to previous versions of their dashboards and visualizations. This feature is key for maintaining data integrity and providing a safety net in collaborative settings where multiple users interact with the same dashboards. By implementing version control, Datapy ensures that users can experiment and make changes without the fear of permanently losing valuable insights. This functionality promotes a culture of experimentation and risk-taking while empowering users to manage their analytics confidently and responsibly.
Acceptance Criteria: User tracks changes made to a dashboard by different team members using version control features.

Given a dashboard is being edited by multiple users, when a user accesses the version control history, then they should see a list of all changes, who made them, and the timestamp of each modification.

A user wants to revert to a previous version of a dashboard after making unsatisfactory changes.

Given a user has modified a dashboard and wants to revert to an earlier version, when they select a previous version from the version control history, then the dashboard should return to the selected state without any errors.

A team collaborates on a dashboard, and a new version is created after multiple edits.

Given a dashboard has been collaboratively edited by different users, when the final version is saved, then a new version should be created in the version control system reflecting all changes made since the last save.

A user needs to compare different versions of a dashboard to understand the evolution of data representation.

Given multiple versions of a dashboard exist in the version control system, when a user selects two versions for comparison, then they should be able to view side-by-side differences in metrics and visualizations.

A user utilizes the version control feature to ensure they are working on the latest version of a dashboard.

Given a user accesses a dashboard with version control enabled, when they open the dashboard, then they should be notified if a newer version is available before making further edits.

A user accidentally deletes a significant change and wants to restore it easily.

Given a user has deleted a critical change to a dashboard, when they access the version control history, then they should be able to restore their last saved version within three steps or less.

Multiple team members are informed about changes in the dashboard without confusion.

Given a dashboard has been modified, when a version change occurs, then all team members who have access to the dashboard should receive a notification summarizing the changes made and the person responsible for those changes.

Integration with Communication Tools

User Story: As a team member, I want to integrate Datapy with my communication tools so that I can easily share insights and engage with colleagues without leaving the platform I’m used to.
Description: The Integration with Communication Tools requirement allows seamless connections between Datapy and popular communication platforms like Slack, Microsoft Teams, and Zoom. This feature promotes a more connected workspace by allowing users to directly share dashboards, updates, and comments within their preferred communication channels. By providing integrations, Datapy enhances its collaborative capabilities, enabling users to engage with their team in real-time without switching contexts. This further aligns with Datapy's vision of simplifying data insights and fostering transparent communication among team members, making it easier to discuss and act upon analytical findings together.
Acceptance Criteria: Enable Slack Integration for Dashboards Sharing

Given a user is logged into Datapy, when the user shares a dashboard link via Slack, then the receiving Slack channel should display the dashboard link with an accompanying message that includes the dashboard title and a brief description.

Enable Microsoft Teams Notification for Data Updates

Given a user has integrated Datapy with Microsoft Teams, when new data is pushed to a connected dashboard, then a notification should be sent to the specified Teams channel with details of the update including the relevant metrics and timestamp.

Zoom Call with Shared Dashboard Screen

Given a user is in a Zoom meeting, when they share their screen displaying a Datapy dashboard, then all meeting participants should be able to interact with the dashboard and see real-time updates as they occur during the meeting.

Comment Functionality during Collaboration Sessions

Given users are collaborating in real-time on a Datapy dashboard, when a user adds a comment or insight, then all other users in the session should see this comment in real-time, ensuring immediate feedback and discussion.

Cross-Platform Communication for Insights Exchange

Given users are utilizing different communication tools (Slack, Teams, Zoom) but are collaborating on Datapy, when a user posts a dashboard link, then users in all communication tools should receive a notification and be able to access the corresponding Datapy dashboard seamlessly.

Historical Data Sharing via Communication Tools

Given a user wants to share a historical report from Datapy, when the user selects the report and shares it through any connected communication tool, then the shared message should include a link to the report and a summary of key insights.

Customizable User Permissions

User Story: As an administrator, I want to set permissions for my team members so that I can control who has access to sensitive information in our dashboards and ensure data protection.
Description: The Customizable User Permissions requirement enables administrators to define and manage access levels for different team members based on their roles. This feature ensures that sensitive data is only shared with authorized users and maintains data security within collaborative environments. By providing customizable permissions, Datapy empowers organizations to enforce security protocols while allowing flexibility in team collaboration. This functionality is essential for maintaining data privacy, especially in industries that work with sensitive information or adhere to regulatory compliance. It enhances user trust and promotes a culture of responsible data sharing.
Acceptance Criteria: As an admin, I want to assign different access levels to team members based on their roles within the organization, so that sensitive data is restricted to authorized users only.

Given that I am logged in as an administrator, when I select a user and assign a role, then the user should only be able to access data relevant to their permissions.

As a team member, I want to receive notification alerts when my access level changes, so I am always aware of my permissions and data accessibility.

Given that my access level is modified, when the change is saved, then I should receive an email notification indicating my new permissions.

As an admin, I need to be able to audit user permission changes, so I can track who accessed sensitive data at all times.

Given that I am viewing the user permissions audit log, when I filter the log by user or date, then I should see a complete history of permission changes and who made them.

As a project manager, I want to collaborate with my team in real-time while ensuring my sensitive data is protected, so I can discuss analytics without exposing confidential information.

Given that I have set customizable user permissions, when my team accesses a shared dashboard, then they should only see data that their permissions allow.

As a compliance officer, I want to ensure that the customizable user permissions feature complies with regulatory standards in our industry, so that we remain compliant with data protection laws.

Given that I review the permission settings, when I compare them with regulatory requirements, then I find that all permissions align with industry standards for data access and security.

As an admin, I want to quickly reset a user's permissions if they leave the company, so that sensitive data remains secure and unauthorized access is prevented.

Given that I select a user for permission reset, when I proceed with the action, then all their access should be revoked immediately and a notification should be sent to the admin team.

As a user, I want to request permission access to specific dashboards, so that I can work effectively while adhering to data security protocols.

Given that I submit a request for additional access, when the request is reviewed by an admin, then I should receive an update on whether my access has been granted or denied within 24 hours.

Narrative Visualization Builder

Transform data insights into compelling narratives with an easy-to-use visualization builder. Users can select from various storytelling templates that integrate visuals with narrative text, guiding viewers through the data's story. This feature helps to communicate complex information more clearly and persuasively, making data-driven presentations more impactful.

Requirements

Template Selection Options

User Story: As a data analyst, I want to select from various storytelling templates in the visualization builder so that I can present my data insights in a compelling manner that resonates with my audience.
Description: The Narrative Visualization Builder should include a wide variety of storytelling templates that users can select from to create their visual narratives. These templates must be customizable and cater to different types of data storytelling, allowing users to effectively choose styles that best represent their data's context and audience. The variety and adaptability of templates will enhance user engagement and provide diverse options for tailored presentations, ensuring that complex data can be communicated effectively in an easily digestible format.
Acceptance Criteria: User Interface for Template Selection

Given the user is on the Narrative Visualization Builder interface, when they navigate to the template selection section, then they can view at least 10 distinct storytelling templates available for selection.

Customization of Selected Templates

Given a user has selected a storytelling template, when they enter the customization mode, then they should be able to modify at least 5 elements of the template (e.g., colors, fonts, text content, images, layout).

Preview Feature for Selected Templates

Given a user has customized a storytelling template, when they click on the 'Preview' button, then they should see a live preview of how the final narrative will appear, including all customizations applied.

Template Categorization by Data Type

Given the user is on the template selection interface, when they filter templates by data type (e.g., financial, operational, marketing), then they should see relevant templates that cater specifically to the selected data type.

Search Functionality for Templates

Given the user is on the template selection page, when they enter a keyword related to the type of narrative they wish to create, then the system should display a list of templates that match the search criteria.

User Feedback after Selection

Given a user has selected a template and completed their narrative, when they submit their feedback, then they should receive a confirmation message thanking them for their input and informing them their feedback has been recorded.

Accessibility Features Integration

Given a user with accessibility needs, when they use the template selection interface, then they should be able to navigate, select, and customize templates using keyboard shortcuts and screen reader compatibility.

Interactive Drag-and-Drop Interface

User Story: As a business user, I want a drag-and-drop interface in the visualization builder so that I can easily create data narratives without needing technical skills or extensive training.
Description: The Narrative Visualization Builder must feature an interactive drag-and-drop interface that allows users to easily add, arrange, and manipulate visual elements and narrative texts. This functionality should simplify the design process for users with varying levels of technical expertise, enabling them to create customized visualizations without requiring programming skills. A well-designed interface can significantly enhance user satisfaction and productivity by reducing the time taken to build presentations and making the experience enjoyable.
Acceptance Criteria: User wants to create a new narrative visualization using various templates and visual elements that can be easily positioned and modified within the interface.

Given a user is on the Narrative Visualization Builder page, when they select a template and add visual elements, then they should be able to drag and drop the elements to rearrange them without any errors occurring in the application.

User with limited technical knowledge desires to create a compelling presentation using visual storytelling techniques without requiring assistance or prior experience.

Given a user without technical skills is using the drag-and-drop interface, when they interact with the elements, then they should successfully create a visualization that can be saved and shared with others without needing help.

User intends to modify an existing visualization by adjusting the size and position of visual elements and accompanying text to better suit their presentation needs.

Given a previously saved narrative visualization is opened, when the user drags and adjusts the size of a visual element, then the changes should be accurately reflected in real-time without loss of content or data integrity.

User wants to add multiple data visualizations and narrative sections to a single page within the visualization builder to enhance their story.

Given the user is in the narrative visualization builder, when they drag and drop additional visual elements and narrative texts onto the canvas, then all elements should be properly aligned and formatted with options for customization.

User is looking to utilize a preview feature to view how their narrative visualization will appear to the audience before finalizing and sharing it.

Given that the preview feature is accessed by the user, when they switch to preview mode, then the visualization should display accurately as it would in the final presentation mode, including all interactive elements.

User needs to create a visualization that integrates various types of data from different sources seamlessly using the drag-and-drop interface.

Given the user has multiple data sources integrated, when they drag and drop visuals related to different data types, then the interface should maintain data connections and representation without errors.

User experiences frustration when attempting to retrieve previously created visualizations and their associated narratives for future use.

Given the user is accessing their account, when they navigate to the saved visualizations section, then all previously created visualizations should be accessible and displayed with the correct titles and summaries.

Real-Time Data Synchronization

User Story: As a marketing manager, I want my data visualizations to update in real-time so that I can rely on the most recent information when making strategic decisions during presentations.
Description: The feature should enable real-time data synchronization with the underlying data sources, ensuring that visualizations reflect the most current data available. This functionality is crucial for businesses that operate in fast-paced environments where data changes frequently. By allowing users to create narratives based on the latest data, the Narrative Visualization Builder enhances the accuracy and relevance of the insights, empowering users to make timely decisions based on solid information.
Acceptance Criteria: Real-time data is synchronized when a user updates an underlying data source while working on the Narrative Visualization Builder.

Given the user updates a data source, when they refresh the Narrative Visualization Builder, then the visualization should reflect the updated data within 5 seconds.

Users generate a narrative visualization based on real-time data to present in an important business meeting.

Given the user has created a visualization using the Narrative Visualization Builder, when the user accesses the visualization during the meeting, then the data must reflect the most current information available at the time of accessing.

A user wants to ensure the accuracy of data displayed in the narrative visualization, specifically regarding sales metrics that vary throughout the day.

Given the user accesses the Narrative Visualization Builder, when they generate a narrative visualization containing sales data, then the sales metrics displayed must accurately reflect data pulled from the source within the last 10 minutes.

As team members collaborate on a shared narrative visualization, they want to ensure that all of them get the latest data and updates made by any team member.

Given multiple users are collaborating on the same narrative visualization, when one user updates the data, then all users should see the most recent changes in real-time without refreshing their views after an interval of 3 seconds.

A user creates a narrative visualization for marketing insights, ensuring any changes in customer behavior metrics are immediately reflected.

Given current metrics stored in the data source, when the user generates their visualization, then the presentation of metrics should automatically integrate any changes in data without needing manual intervention or refresh for the next 5 minutes.

During routine checks, a user wants to validate if the narrative visualizations reflect accurate data from the live source.

Given the user accesses the narrative visualization, when the user compares the visualization metrics with the live data source metrics, then discrepancies should not exceed 5% for accuracy to pass.

Export Options for Multiple Formats

User Story: As a project leader, I want to export my data narratives in several formats so that I can share them with team members and clients in the format that works best for them.
Description: The Narrative Visualization Builder should provide users with the capability to export their created visual narratives in multiple formats, including PDF, PPTX, and image files (PNG, JPEG). Supporting a range of export options will enable users to share their presentations seamlessly with different stakeholders, whether in meetings, reports, or online sharing platforms. This flexibility will enhance the usability of the feature and cater to various business needs.
Acceptance Criteria: User wants to export a narrative created in the Narrative Visualization Builder as a PDF for a client presentation.

Given the user has completed a narrative visualization, when they choose the export option and select PDF format, then the system should generate a PDF file of the narrative that maintains the layout and visuals as displayed in the builder.

A user needs to share their narrative visualization during a team meeting using PowerPoint.

Given the user has finalized a narrative visualization, when they select the export option for PPTX format, then the generated PowerPoint file should include all slides in the correct order and retain all text and visual elements from the narrative.

A user wants to save a narrative visualization as an image file to use in a report.

Given the narrative visualization is ready for export, when the user selects the export option for image formats, then the system should provide options for both PNG and JPEG, allowing the user to choose one, and the exported image should have a clear resolution suitable for print.

The marketing team needs to send their narrative visualizations to stakeholders via email.

Given the user has generated a narrative visualization, when they export it as a PDF, PPTX, or image file, then the system should provide a successful export notification, ensuring the file is ready for immediate sharing.

A user is creating a presentation and needs to align visualizations with their branding standards.

Given the user exports a narrative visualization, when the visual elements within the narrative adhere to the company's branding colors and fonts, then the exported file must reflect these standards without any discrepancies.

A user wants to validate the export functionality works across different devices.

Given the user accesses the Narrative Visualization Builder from a mobile device, when they export a narrative visualization, then the export options should be accessible and functional just as they are on a desktop, ensuring compatibility across devices.

An admin needs to check if users can export narrative visualizations successfully without errors.

Given the admin performs a load test by having multiple users export narrative visualizations simultaneously, then all exports should complete without error, maintaining performance standards over peak usage periods.

Integrated Collaboration Tools

User Story: As a team member, I want to collaborate with my colleagues within the visualization builder so that we can together create narratives that capture all our insights and ideas effectively.
Description: The Narrative Visualization Builder must incorporate integrated collaboration tools that allow multiple users to work on the same project simultaneously. This should include features such as commenting, version history, and user assignment to facilitate teamwork. Enabling real-time collaboration enhances communication and improves the overall quality of visual narratives created by teams, allowing for a streamlined process where feedback and ideas can be exchanged seamlessly.
Acceptance Criteria: Multiple users are collaborating on a data visualization project simultaneously, providing feedback and making real-time edits to the narrative and visual elements.

Given that multiple users are editing the same project, when changes are made by one user, then other users should see the changes reflected in real-time without refreshing the page.

A user assigns a specific task to another team member within the Narrative Visualization Builder, indicating what needs to be done and by when.

Given that a user assigns a task to another team member, when the assignee logs into the system, then they should see the assigned task in their notifications and be able to comment on it.

Users utilize the commenting feature to discuss various elements of the project without disrupting the ongoing work.

Given that a user leaves a comment on a visual or narrative element, when another user views that element, then they should be able to read the comments and reply to them in a threaded format.

Version history allows users to revert to and review previous iterations of the data visualization project, especially important before finalizing the presentation.

Given that a project has multiple saved versions, when users access the version history, then they should see a list of versions with timestamps and the option to revert to any previous version.

A team member wants to review changes made by others in the collaborative project to understand what has been modified since their last login.

Given that a user returns to a project, when they check the change log, then they should see a detailed summary of all changes made since their last access with the user responsible for each change.

The collaborative tools are integrated seamlessly into the Narrative Visualization Builder without interrupting the workflow for users.

Given that users are in the Narrative Visualization Builder, when they access the collaboration tools, then it should not hinder the project-building process and should be easily accessible from the main interface.

Users collaborate and finalize a narrative visualization project that requires input from various stakeholders before presentation.

Given that several users have worked on a narrative visualization, when they reach consensus on the final version, then they should be able to lock the project for editing while allowing commenting for feedback until presentation.

Automated Reporting Features

Streamline the creation of visual reports with automated templates that pull data directly from user-selected sources. Users can schedule regular reports, which automatically update with the latest data visualizations and insights, reducing manual effort and time consumption. This feature ensures that stakeholders receive timely, accurate visual reports that inform ongoing strategies.

Requirements

Dynamic Data Sources

User Story: As a business analyst, I want to connect automated reports to multiple data sources so that I can create comprehensive reports that reflect real-time insights without requiring extensive manual input.
Description: The Automated Reporting feature must support integration with multiple data sources, allowing users to select and utilize various databases, spreadsheets, and cloud services for their reports. This capability enables organizations to consolidate information from different areas of their operations, creating comprehensive and customized reports that reflect real-time data. Ensuring compatibility with popular business data sources will enhance user experience and streamline the reporting process. The system should provide a simple interface for users to set up and manage data connections without needing technical expertise, facilitating ease of use and reducing reliance on IT teams for routine reporting tasks.
Acceptance Criteria: User wants to create a visual report by selecting multiple data sources from their dashboard.

Given the user is logged into Datapy, when they navigate to the Automated Reporting section and select 'Add Source', then they should be able to choose from a list of supported data sources, such as databases, spreadsheets, and cloud services.

A user schedules a report to automatically pull data from selected sources at a specific time.

Given the user has configured a report with multiple data sources, when they set a schedule for that report, then the system should successfully save the schedule and execute the report generation at the specified time without any errors.

The user updates a data source connection in the Automated Reporting feature.

Given the user has an existing data source connection, when they navigate to the data source settings and update the connection details (e.g., URL, credentials), then the system should validate the updated connection and allow the user to save the changes successfully.

A user attempts to generate a report with incompatible data sources.

Given the user selects multiple data sources for a report, when at least one selected data source is incompatible, then the system should display a warning message and not allow the report to be generated until compatible sources are selected.

A user needs to visualize a report and verify if it reflects the real-time data accurately.

Given the user has scheduled an Automated Report with live data sources, when the user accesses the report after the scheduled generation time, then the report should display updated visualizations that accurately reflect the latest data available from the sources.

The user requires training on how to set up data connections without IT assistance.

Given the user navigates to the help section for Automated Reporting, when they view the training materials and instructional videos provided, then they should feel confident to set up a data connection on their own without further assistance.

Scheduled Report Generation

User Story: As a operations manager, I want to schedule reports to be generated automatically so that I can receive regular updates without having to request them, ensuring I stay informed about the latest metrics and trends.
Description: Users should have the ability to schedule automated reports to be generated at specific intervals (daily, weekly, monthly, or custom) to ensure stakeholders receive updated insights without delay. This requirement focuses on delivering timely data directly to users via email or within the platform. The scheduling feature must allow users to choose multiple formats for the reports (PDF, Excel, etc.) and customize the content based on audience needs. Enhanced scheduling will allow organizations to embed reporting into their workflow, improving overall operational efficiency and decision-making processes.
Acceptance Criteria: User Scheduling a Weekly Report for Team Meetings

Given a user is logged into Datapy, when they navigate to the scheduled reports section and select weekly frequency, then they should be able to set the report to generate every Monday at 9 AM and receive a confirmation message indicating successful scheduling.

User Selecting Multiple Formats for Scheduled Reports

Given a user is in the report scheduling interface, when they choose to schedule a report, then they should be able to select both PDF and Excel formats for the generated report and see both formats listed in the confirmation of their schedule.

User Customizing Report Content for Different Audiences

Given a user is creating a scheduled report for the marketing team, when they customize the report content to include specific metrics and insights relevant to marketing, then the saved customization should reflect only the chosen metrics when the report is generated.

User Receiving Scheduled Report via Email

Given a user has scheduled a report to be sent via email, when the report is generated at the scheduled time, then the user should receive an email containing the report in the selected format.

User Editing an Existing Scheduled Report

Given a user has an existing scheduled report, when they edit the scheduling parameters or format, then the modifications should be successfully saved and confirmed in the user's dashboard.

User Viewing Schedule Status for Reports

Given a user is on their dashboard, when they access the scheduled reports section, then they should see a clear indication of the status (active/inactive) for all their scheduled reports.

User Cancelling a Scheduled Report

Given a user wishes to stop receiving a scheduled report, when they select the option to cancel the report, then the system should successfully remove the report from the scheduled list and notify the user of the cancellation.

Customizable Report Templates

User Story: As a marketing manager, I want to customize my report templates so that I can align the presentations with our branding and make it easy for stakeholders to understand key insights quickly.
Description: The Automated Reporting feature should provide users with customizable templates that can be tailored to fit specific reporting needs and visual preferences. Users should be able to modify layouts, charts, colors, and styles to align with branding and communication standards. This flexibility allows for both standardization across the organization for consistency while also accommodating individual departments' reporting styles. The easy-to-use template design feature will save time in report creation, enhancing the user experience and ensuring consistency in data presentation.
Acceptance Criteria: User customization of report templates for a quarterly business review update.

Given a user is in the report creation section, when they select a customizable template, then they should be able to modify the layout, colors, and chart types, and save it successfully.

Team member accesses a customized report template for departmental metrics presentation.

Given a team member selects a predefined template, when they load the template, then the customizations (layouts, colors, styles) must appear accurately as saved.

User desires to standardize report formats across multiple departments while maintaining individual customization needs.

Given an administrator has created a base template, when a user creates a new report using this base template, then they should be able to apply individual modifications without altering the base template.

User intends to schedule automated reports using a customized template.

Given a user has created and saved a customizable report template, when they schedule a report, then the report should automatically apply the saved customizations for every scheduled run.

A marketing manager needs to visualize brand performance data using a tailored report template.

Given the marketing manager is in the reporting module, when they customize a template, then it must allow them to drag and drop charts and adjust the colors to match brand guidelines without errors.

User requires assistance in creating a report template that meets the company’s branding guidelines.

Given the user accesses the help section, when they request guidance on creating customizable report templates, then detailed instructions and examples must be provided.

Collaborative Report Sharing

User Story: As a team lead, I want to share my automated reports with team members so that we can collaborate effectively on insights and strategies based on real-time data.
Description: Automated Reports should include built-in collaborative features that enable users to share reports easily with team members and stakeholders through links or integrated communications within the platform. Users should be able to set permissions to control access and editing rights. This collaboration feature emphasizes teamwork and ensures that all relevant parties can view and discuss reports collectively, thus fostering a data-driven culture within the organization while maintaining data governance and security policies around sensitive information.
Acceptance Criteria: User shares an automated report with team members via a link during a weekly strategy meeting, ensuring that all participants have access to the latest insights for discussion.

Given the user has created an automated report, When the user generates a shareable link, Then the user can successfully send that link to team members for access.

A user receives a notification whenever a shared report has been updated or commented on, allowing them to stay informed about changes.

Given a report has been shared, When any updates or comments are made, Then the system sends notifications to all users who have access to the report.

User sets permissions for team members accessing a report to either view-only or edit rights and confirms the permissions are enforced.

Given a user is setting permissions for shared reports, When the user selects permission levels, Then the system enforces the designated access rights.

A user accesses a report shared with them and is restricted from editing the report due to set permissions, ensuring data integrity is maintained.

Given a report is shared with view-only permissions, When the user attempts to edit the report, Then the user receives an error message stating they lack the necessary permissions.

Team members collaboratively comment on a report and can view previous comments to facilitate discussions during meetings.

Given team members have access to a shared report, When they add comments, Then all comments are visible to every member with access to the report.

User integrates report sharing with the internal messaging system to enhance collaborative discussions on received reports.

Given a report is shared, When the user selects to share it via integrated messaging, Then the report link is successfully sent through the internal messaging system.

A user accesses a shared report from different devices, ensuring that the report's format and data display remain consistent.

Given a report is shared, When the user opens the report on different devices, Then the report retains its format and data display across all devices.

Visual Data Storytelling

User Story: As a data analyst, I want to enhance my automated reports with visual storytelling elements so that I can effectively communicate complex data insights to a broader audience.
Description: The Automated Reporting feature must support visual storytelling by allowing users to create engaging visual narratives within their reports. This includes adding annotations, commentary, and interactive elements that guide the reader through the insights presented in the reports. Providing users with options for visual enhancements like infographics, contextual graphics, and dynamic elements will help improve comprehension and retention of information. By prioritizing storytelling, the reports will not only present data but also convey its impact and narrative effectively to stakeholders at all levels.
Acceptance Criteria: Creating a visual report that incorporates various data sources and elements to tell a cohesive data story.

Given a user has selected multiple data sources for their report, when they add annotations and comments to different visual elements, then these elements should display correctly in the report with appropriate formatting and positioning.

A scheduled visual report that updates automatically with fresh data and storytelling elements for stakeholders.

Given a user has scheduled a report to run automatically, when the scheduled time arrives, then the report should pull the latest data and accurately reflect any annotations and dynamic elements that were previously configured.

Users sharing a visual report with stakeholders who may interact with the storytelling elements.

Given a user shares a report with stakeholders, when stakeholders view the report, then they should be able to interact with infographics and other dynamic elements without errors, and be able to see annotations and commentary in context with the data.

Editing an existing visual report to enhance the storytelling aspect with additional graphics and commentary.

Given a user opens a previously created report for editing, when they add new contextual graphics and commentary, then these elements should save correctly and update the report preview in real-time, maintaining the integrity of existing features.

User feedback on the usability of visual storytelling features in reports.

Given a user interacts with the visual storytelling features in a report, when they provide feedback through a dedicated feedback option, then the feedback should be recorded accurately and accessible for future review and improvements.

Utilizing dynamic elements within a visual report that responds to user interactions.

Given a user interacts with a dynamic visual element in the report, when they click or hover over this element, then it should respond appropriately by providing additional insights or changing its display to enhance comprehension.

360-Degree Dashboard Integration

Provide users with the ability to create panoramic dashboards that integrate multiple visualization types and data sources in one view. This comprehensive layout allows users to analyze various aspects of their business simultaneously, facilitating holistic analysis and strategic planning. Users can customize their 360-degree dashboards to focus on specific metrics, enhancing their ability to monitor performance effectively.

Requirements

Multisource Data Integration

User Story: As a business analyst, I want to integrate data from various sources into my dashboard so that I can analyze all relevant metrics in one place, leading to better insights and decisions.
Description: This requirement involves the capability to seamlessly integrate data from multiple sources into the dashboard. Users should be able to pull data from various platforms, databases, and APIs, allowing for a comprehensive overview of their business metrics. This integration will enhance the utility of the 360-degree dashboard, providing a singular platform for data analysis and fostering data-driven decision-making. It is essential for users to analyze disparate data sets in one view, making the dashboard a central hub for business insights.
Acceptance Criteria: Data Source Connection Validation

Given multiple data sources (APIs, databases), when a user attempts to connect to a data source, then the connection should be successfully established and verified within 5 seconds.

Data Import Functionality

Given that a data source is connected, when a user selects to import data from that source, then the data should be imported into the dashboard without errors and be visually represented in the chosen format within 10 seconds.

Error Handling for Invalid Data Sources

Given a user attempts to connect to an unavailable or invalid data source, when the connection fails, then an appropriate error message should be displayed, indicating the issue without crashing the application.

Real-time Data Synchronization

Given that data sources are actively connected, when data is updated in any source, then the dashboard should automatically refresh and reflect these updates in real-time without manual reloading.

Cross-Platform Data Integration

Given a user has multiple data sources from different platforms (e.g., CRM, ERP), when the user integrates these data sources, then all relevant data should be displayed cohesively in the 360-degree dashboard without loss of contextual information.

User Permission Management for Data Access

Given a user with specific role permissions, when accessing the data integration feature, then the user should only see and access data sources that they are authorized to use based on their role.

Custom Visualization Options

User Story: As a marketing manager, I want to customize the visualizations in my dashboard so that I can effectively communicate trends and insights to my team.
Description: This requirement focuses on providing users with the ability to create custom visualizations tailored to their specific business needs. Users should have access to various chart types, graphs, and other visualization tools that can be customized in terms of color, size, and data representation. This will empower users to present their data in the most effective manner suitable for their audience, which is vital for effective communication and decision-making processes. Enhanced visualization options will allow for greater flexibility in data interpretation.
Acceptance Criteria: User wants to create a custom bar chart visualization for sales data to track monthly performance across different product categories.

Given the user is on the dashboard creation page, when they select 'Bar Chart' from the visualization options, then they should be able to customize the chart by selecting data sources, adjusting colors, and setting the axis labels before saving.

A user desires to generate a colorful pie chart that represents their marketing spend percentages for various channels.

Given the user has chosen 'Pie Chart' as their preferred visualization, when they specify the data range and select colors for each segment, then the chart should accurately reflect the percentages and visual customizations made by the user.

The user wants to visualize sales trends using a line graph over the past year to identify seasonal patterns in their business.

Given the user has selected 'Line Graph' as their visualization type, when they input the required data range and specify the granularity (monthly or weekly), then the line graph should display the trend accurately with custom line properties like thickness and color.

A team member needs to create a comprehensive dashboard that integrates various visualizations to monitor KPIs simultaneously.

Given the user is on the dashboard interface, when they drag and drop multiple visualization types into the dashboard layout, then they should be able to arrange and resize these visualizations without losing data integrity or visual accuracy.

User requires a customizable area chart to showcase product growth over multiple quarters.

Given the user selects 'Area Chart' from the visualization menu, when they customize the areas, colors, and data points, then the final chart should visually represent growth accurately based on the data provided, with tooltips for additional clarity on each point.

The user wishes to implement filtering options within their dashboard to view data from specific timeframes or categories.

Given the user has built a dashboard with multiple visualizations, when they apply filters for date range or specific product categories, then all connected visualizations should update dynamically to reflect the filtered data accordingly.

A user needs to export their custom visualizations to a presentation format for a meeting with stakeholders.

Given the user has finalized a set of visualizations on their dashboard, when they choose the export option, then the system should allow them to download the visualizations in a PDF format while preserving all customizations and layout designs.

Real-Time Data Synchronization

User Story: As an operations manager, I want the dashboard to update in real time so that I can respond quickly to any changes in business metrics and make informed decisions immediately.
Description: This requirement entails enabling real-time synchronization of data within the 360-degree dashboard. Users should have the ability to view live updates of their data as it changes, ensuring they are always working with the most current information available. This is crucial for making timely decisions based on up-to-date analytics, especially in fast-paced business environments where data can change rapidly. This requirement is fundamental to the user experience and the overall utility of the product.
Acceptance Criteria: User accesses the 360-degree dashboard for the first time and expects to see real-time updates reflecting the latest data across integrated sources.

Given the user has connected valid data sources, when they load the 360-degree dashboard, then the dashboard displays current data updates within 5 seconds of changes occurring in the data sources.

A user is monitoring sales metrics throughout the day and refreshes the dashboard multiple times during a busy period to check for any live updates.

Given the user is actively refreshing the dashboard, when they refresh at any point within the day, then the dashboard should retrieve and display the most up-to-date information without requiring a full page reload.

A team uses the 360-degree dashboard during a strategy meeting to assess performance metrics relevant to a live product launch, requiring instant feedback on sales and customer engagement.

Given the team is in a live meeting, when any data point from the integrated sources changes, then the dashboard should visually indicate the update in real-time, allowing prompt discussion based on current data.

A user customizes their dashboard layout to include key performance indicators and wishes to see live updates reflected in those indicators throughout the day.

Given that the user has saved their customized layout, when real-time updates are generated, then the dashboard should automatically update the visualizations of those indicators without any manual intervention from the user.

A user on a mobile device opens the 360-degree dashboard to check for real-time inventory levels, expecting seamless functionality compared to the desktop version.

Given the user accesses the dashboard via a mobile device, when the dashboard loads, then it should synchronize and display real-time inventory updates just as effectively as on the desktop version.

A user receives alerts for specific metrics configured in their dashboard and wants to ensure these alerts reflect real-time data changes when they occur.

Given the user has set up alerts for particular data metrics, when the underlying data changes, then the user receives alerts within one minute, signaling the updates based on the current data.

Dashboard Sharing and Collaboration

User Story: As a team leader, I want to share my dashboard with my team so that we can collaborate on data analysis and align our strategies effectively.
Description: This requirement emphasizes the importance of collaboration within teams by allowing users to share their customized dashboards with colleagues. Users should be able to set permissions for who can view or edit the dashboard, enabling collaborative analysis and discussion. This feature will foster teamwork and ensure that everyone is aligned on business performance metrics. By facilitating easy access and communication, teams can work together more effectively, enhancing overall productivity.
Acceptance Criteria: User initiates the dashboard sharing process by clicking the 'Share' button on their customized dashboard.

Given that a user has a customized dashboard, when they click on the 'Share' button, then a sharing modal should appear allowing them to select users, set view/edit permissions, and send the invitation.

Team members receive a dashboard sharing invitation from a colleague.

Given that a dashboard has been shared with a team member, when they check their notifications, then they should see the sharing invitation with options to accept or decline, along with details of the dashboard.

A user accesses a shared dashboard with view-only permissions.

Given that a user has accepted a dashboard invitation with view-only permissions, when they open the dashboard, then they should be able to see all metrics and visualizations without the ability to edit or delete any elements.

A user modifies their permissions after initially sharing a dashboard.

Given that a user has previously shared a dashboard, when they access the sharing settings, then they should be able to change any user’s permissions from view-only to edit to allow collaborative editing.

A user attempts to share a dashboard with a non-existing team member.

Given that a user tries to share a dashboard with an email not associated with any existing team member, when they click 'Share,' then an error message should appear indicating that the user cannot be found.

A user collaborates in real-time on a shared dashboard with an assigned colleague.

Given that multiple users are viewing the same shared dashboard, when one user makes a change, then all other users should see the update reflected in real-time without the need for refreshing the page.

Automated Reporting Functionality

User Story: As a sales manager, I want to automate the generation of sales reports from my dashboard, so I can save time and ensure I always have the latest insights to share with my team.
Description: This requirement specifies the development of automated reporting features that allow users to schedule reports based on the data displayed on their dashboards. Users should have the ability to choose what data points to include in the report and how frequently it should be generated (daily, weekly, monthly). This function will save users time and ensure they receive updates on crucial business metrics without having to manually create reports. Automation will enhance efficiency and keep stakeholders informed of performance metrics regularly.
Acceptance Criteria: User schedules a weekly report that includes selected data points from their 360-degree dashboard.

Given the user is on the dashboard page, when they select data points and set the report frequency to 'weekly', then the report should be generated and emailed to the specified recipients every week at the chosen time.

A user wants to modify an existing automated report to change the data points included and the frequency from monthly to daily.

Given the user accesses the report settings, when they choose to edit the report, update the data points, and change the frequency to 'daily', then the modifications should be saved and take effect for the next reporting period.

A user checks the status of their scheduled reports to ensure they are set up correctly.

Given the user navigates to the scheduled reports section, when they view the list of scheduled reports, then all reports should display the correct data points, frequency, and scheduled times without errors.

User encounters an error while attempting to schedule an automated report.

Given the user inputs invalid data points or frequency settings, when they attempt to schedule the report, then an error message should be displayed indicating the invalid input and guiding them to correct it.

A user wishes to receive notifications when a scheduled report is successfully generated.

Given the user has opted in for notifications, when a report is generated, then the user should receive a notification confirming the successful generation along with a link to view the report.

A user wants to review the last five automated reports generated from their dashboard.

Given the user accesses the reporting section, when they click on 'View Last Reports', then a list of the last five generated reports should be displayed with timestamps and data points included.

A user needs to deactivate an automated report that is no longer needed.

Given the user is in the reports management section, when they select an active report and click 'Deactivate', then the report should be deactivated and removed from the scheduled reports list.

Multi-Factor Authentication

This feature adds an extra layer of security by requiring users to verify their identity through multiple methods, such as SMS codes, authentication apps, or biometric scans. By utilizing multi-factor authentication, businesses can ensure that even if passwords are compromised, unauthorized access is still prevented, significantly enhancing overall account security.

Requirements

Multi-Factor Authentication Setup

User Story: As a user, I want to set up Multi-Factor Authentication on my account so that I can enhance my account security and prevent unauthorized access to my sensitive data.
Description: This requirement involves implementing a user-friendly setup process for Multi-Factor Authentication (MFA) within the Datapy platform. It should allow users to easily configure their MFA preferences, whether that be through SMS, email verification, or authentication apps, ensuring that they can protect their accounts efficiently. The setup should guide users through the process with clear instructions and feedback at each step, making it accessible for users of all technical backgrounds. Successful implementation of this requirement enhances security and builds trust with users, ultimately reducing instances of unauthorized access and data breaches.
Acceptance Criteria: User successfully initiates the MFA setup process on their Datapy account for the first time.

Given the user is logged into their Datapy account, when they navigate to the security settings page and select 'Set Up Multi-Factor Authentication', then they should be guided through a step-by-step setup process with clear instructions for each MFA method.

User chooses SMS as their preferred multi-factor authentication method.

Given that the user is on the MFA setup page, when they select 'SMS' as their authentication method and enter their phone number, then an SMS verification code should be sent to that number for confirmation.

User successfully verifies their phone number during the MFA setup process.

Given that the user has received the SMS verification code, when they enter the code on the MFA setup page and submit it, then the system should confirm that the phone number is verified and MFA setup should be completed.

User experiences an error entering the verification code during MFA setup.

Given that the user enters an incorrect SMS verification code, when they submit the code, then an appropriate error message should be displayed indicating that the code is invalid and a prompt to resend the code should be provided.

User wants to change their multi-factor authentication method after it has been set up.

Given that the user has already completed the MFA setup, when they navigate back to the security settings and choose 'Change MFA Method', then they should be able to select a new method and be guided through the respective setup process again.

User disables Multi-Factor Authentication from their account settings.

Given that the user has MFA enabled, when they select the option to disable MFA from the security settings page, then they should be required to enter their password for confirmation, and once confirmed, MFA should be disabled successfully with a notification of the change.

Real-time Authentication Verification

User Story: As a user, I want to receive real-time feedback on my Multi-Factor Authentication attempts so that I can quickly understand if my login was successful or if I need to try again.
Description: This requirement stipulates the need for a real-time verification system that checks the authenticity of the second factor during the login process. It should provide instant feedback to users after they enter their SMS or app-generated codes, including success and failure notifications. This verification enhances user security by ensuring that only authorized users gain access and simultaneously provides a seamless user experience without significant delays. The system must also have fallback mechanisms to handle scenarios where the primary method of authentication fails, ensuring reliability and accessibility.
Acceptance Criteria: User attempts to log in to Datapy and is prompted to enter an authentication code received via SMS after entering their password.

Given the user has entered their correct password, when they enter the authentication code received via SMS, then the system should verify the code in real-time and allow access if valid or display an error message if invalid.

A user logs into Datapy using the authentication app to generate a verification code after entering their username and password.

Given the user has successfully input their username and password, when they enter the verification code generated by the authentication app, then the system should validate the code instantly and provide appropriate access or failure notifications without noticeable delay.

A user tries to log into Datapy but the primary method of authentication fails due to network issues.

Given the primary authentication method failed, when the user selects an alternative method (e.g., biometric or SMS), then the system should allow the user to proceed with the secondary authentication method and verify the identity in real-time.

User receives a notification about a failed login attempt due to incorrect verification code.

Given the user has attempted to log in but provided an incorrect verification code, when the system detects the failure, then it should send an instant notification to the user's registered email or phone number, indicating the failed attempt and suggesting corrective actions.

A new user registers on Datapy and sets up multi-factor authentication during the account creation process.

Given the new user completes the registration process, when they are prompted to set up multi-factor authentication, then they should be able to select their preferred authentication methods (SMS, app, biometric) and receive real-time confirmation setup is successful.

A user requests an authentication code via SMS after failing to log in multiple times.

Given the user has failed to log in three consecutive times, when they request a new authentication code via SMS, then the system should send the code immediately while implementing a cooldown period for subsequent requests to prevent abuse.

User logs into Datapy through a mobile application and uses biometric verification.

Given the user has previously registered their biometric data, when they attempt to log in via the mobile application, then the system should verify the biometric data in real-time and grant access if valid, providing a seamless user experience.

User Role Management with MFA

User Story: As an administrator, I want to enforce Multi-Factor Authentication for certain user roles so that I can ensure that our most sensitive data is protected from unauthorized access.
Description: This requirement involves the integration of Multi-Factor Authentication with user role management within Datapy. Administrators should have the ability to enforce MFA for specific user roles to ensure that sensitive accounts, such as those with administrative privileges, have additional security layers. The implementation should allow for flexibility and customization, giving organizations control over their security policies. This requirement is vital to minimizing risks associated with high-level access and ensuring compliance with organizational security standards.
Acceptance Criteria: Admin Dashboard Access with MFA for Sensitive Roles

Given an administrator attempts to access the admin dashboard, When the user role is marked as sensitive, Then they should be prompted for multi-factor authentication before gaining access.

User Role Configuration and MFA Enforcement

Given an administrator is configuring user roles, When they toggle the MFA enforcement for a specific role, Then the system should save this configuration and apply MFA during future logins for users in that role.

Unauthorized Access Attempt Notification

Given an attempt is made to access the system with an administrative account that fails MFA, When this occurs, Then the administrator should receive an immediate notification of the unauthorized access attempt.

User Experience with MFA Verification

Given a user with a sensitive role logs in, When they enter their credentials correctly, Then they should receive an MFA verification prompt via the selected method (SMS, app, or biometric).

Audit Log for MFA Activities

Given an administrator accesses the audit logs, When reviewing activities related to MFA events, Then all successful and failed MFA attempts must be accurately logged with timestamps and user details.

Fallback Mechanism for MFA Failure

Given a user receives a failed MFA attempt, When they cannot complete the MFA process, Then they should be able to utilize a fallback mechanism to regain access under strict security checks.

MFA Recovery Options

User Story: As a user, I want to have recovery options for my Multi-Factor Authentication so that if I lose access to my primary verification method, I can still access my account without difficulty.
Description: This requirement includes implementing robust recovery options for users who may lose access to their MFA methods. Users should be provided with alternative verification methods, such as backup codes, security questions, or email recovery options. It is essential to design this feature to ensure that recovery is secure yet user-friendly, allowing users to regain access to their accounts without compromising security. This not only enhances user satisfaction but also reduces frustration and abandonment rates during login.
Acceptance Criteria: User initiates the recovery process after losing access to their primary MFA method.

Given that the user has lost access to their primary MFA method, when they select 'Recover MFA Access', then they should be prompted to enter a backup code, answer a security question, or proceed with email recovery options. The user must successfully complete one of these methods to regain access.

User uses backup codes to regain access to their account.

Given that the user has generated backup codes during MFA setup, when they enter a valid backup code, then their account access should be restored, and the used backup code must be marked as used immediately.

User attempts to reset MFA settings and answers the security questions correctly.

Given the user does not have access to any primary or backup MFA methods, when they choose to reset their MFA settings and accurately answer the security questions, then they should receive a notification to set up MFA again without needing to contact support.

User successfully reverts to email recovery to access their account.

Given the user opts for email recovery, when they enter their registered email, then they should receive a recovery link via email, which allows them to set a new MFA method upon clicking the link while ensuring the link expires after a set time for security.

User attempts to recover access without complete recovery options.

Given a user who has not set up backup codes or security questions, when they try to recover access, then they should receive a clear message indicating the need to contact customer support for further assistance, ensuring guidance is available.

User utilizes the MFA recovery options successfully under network issues.

Given a user facing temporary network issues while using MFA recovery, when they try to recover their account via alternative verification, then they should receive a user-friendly error message that allows them to retry the process without losing their progress on recovery.

User wants to ensure their recovery options are updated post-account recovery.

Given that the user has regained access to their account, when they navigate to 'Security Settings', then they should be prompted to review and update their recovery options, enabling them to set up backup codes or answer security questions as needed.

Detailed Analytics for Authentication Events

User Story: As an administrator, I want access to detailed analytics of authentication events so that I can monitor security trends and ensure our systems are protected against potential threats.
Description: This requirement focuses on providing detailed analytics and reports regarding authentication events related to Multi-Factor Authentication. Administrators should be able to view logs and metrics, such as failed login attempts, successful logins, and MFA challenges, along with timestamps and user details. This information is crucial for identifying security threats and enhancing the overall security posture of the organization. Analyzing these patterns enables proactive measures to be taken against potential vulnerabilities or attacks.
Acceptance Criteria: Viewing Detailed Logs of Authentication Events

Given an administrator has logged into the Datapy platform, when they navigate to the authentication analytics section, then they should see a detailed log of authentication events, including timestamps, user IDs, and event types (failed login attempts, successful logins, MFA challenges).

Generating Reports on Failed Logins

Given an administrator is in the authentication analytics section, when they select to generate a report on failed login attempts over the last 30 days, then a downloadable report in CSV format should be created including user details, timestamps, and reasons for failure.

Analyzing Patterns of MFA Challenges

Given an administrator has access to the analytics dashboard, when they view the MFA challenges, then they should be able to see a visual representation of challenges over the last 30 days, categorized by success and failure rates, enabling pattern analysis.

Real-Time Notification of Suspicious Activity

Given an administrator has enabled alert settings in the platform, when there are three or more failed login attempts for a specific user within 15 minutes, then an immediate notification should be sent to the administrator via email.

Filtering Authentication Events by User

Given an administrator is in the authentication events log, when they use the filter option to search by a specific user ID, then the system should display only the authentication events related to that user, including successful and failed attempts along with timestamps.

Accessing Historical Data on Authentication Events

Given an administrator is viewing the analytics dashboard, when they select a date range filter for the last 90 days, then the dashboard should adjust to show only the authentication events within that selected date range with accurate metrics.

Dashboard Customization for Authentication Metrics

Given an administrator is on the analytics dashboard, when they click on the customization option, then they should be able to add or remove specific authentication metrics that they wish to track, such as total logins, failed logins, or MFA challenges in a personalized dashboard view.

Data Encryption Protocols

This feature implements robust encryption protocols for data in transit and at rest within the Datapy platform. By utilizing advanced encryption techniques, businesses can safeguard sensitive information from unauthorized access, ensuring compliance with regulations and instilling trust among users that their data is protected against breaches.

Requirements

End-to-End Encryption

User Story: As a compliance officer, I want to ensure that our data is encrypted both in transit and at rest so that I can meet regulatory requirements and protect sensitive information from breaches.
Description: Implement end-to-end encryption for data at rest and in transit within the Datapy platform. This feature will utilize advanced algorithms such as AES-256 for at-rest encryption and TLS 1.3 for data in transit to ensure that only authorized users can access sensitive information. By encrypting data throughout its lifecycle, we will enhance security compliance with industry regulations like GDPR and HIPAA, thereby fostering user trust and protecting against unauthorized breaches.
Acceptance Criteria: User uploads sensitive data to Datapy for analysis while ensuring it is securely encrypted and protected from unauthorized access.

Given a user uploads sensitive data, when the data is processed, then the data must be encrypted using AES-256 both at rest and in transit.

An organization accesses encrypted reports generated by Datapy and verifies the data security during sharing.

Given a user shares an encrypted report, when the recipient accesses the report, then the sensitive data must remain encrypted and accessible only to authorized users.

A user attempts to access data on Datapy from an unauthorized location or device, triggering a security protocol.

Given a user attempts to access encrypted data from an unauthorized location, when the security check occurs, then access to that data must be denied and logged in the system for compliance.

During regular security audits, compliance officers review the data encryption methods used by Datapy to verify adherence to industry regulations.

Given a compliance officer conducts an audit, when they assess the encryption methods, then the methods must align with regulations such as GDPR and HIPAA, demonstrating effective end-to-end encryption practices.

Users initiate real-time data transactions within the Datapy platform, requiring secure transmission against eavesdropping or data interception.

Given a user sends data in real-time, when the transaction occurs, then the data must be transmitted over an encrypted TLS 1.3 connection to secure against eavesdropping.

A user creates a new project in Datapy involving encrypted datasets while ensuring that encryption keys management is effective and secure.

Given a user sets up a new project, when the project utilizes encrypted datasets, then the encryption keys must be securely generated, stored, and managed according to best practices.

User-Controlled Encryption Keys

User Story: As a data manager, I want to control my own encryption keys so that I can enhance the security of sensitive information and comply with corporate policies.
Description: Develop a feature that allows users to create and manage their own encryption keys for added security. This functionality will enable users to define permissions, generate new keys, and rotate existing ones at their discretion. By allowing users to have control over their encryption keys, we enhance data ownership and provide an additional layer of security, ensuring that sensitive data remains accessible only to authorized personnel.
Acceptance Criteria: User creates a new encryption key for sensitive data management on the Datapy platform.

Given a registered user on the Datapy platform, when they access the encryption management feature and select 'Create New Key', then they must successfully generate a new encryption key and receive a confirmation message.

User rotates an existing encryption key for improved security on the Datapy platform.

Given a registered user has an existing encryption key, when they choose the 'Rotate Key' option, then the system must generate a new key, revoke access to the old key, and update the encryption status without data loss.

User sets permissions for an encryption key to control access within the Datapy platform.

Given a user has created an encryption key, when they assign permission levels to user roles, then the system must accurately restrict or allow access based on those defined permissions and notify users of their access rights.

User deletes an encryption key that is no longer needed on the Datapy platform.

Given a user selects an encryption key for deletion, when they confirm the deletion action, then the key must be permanently removed from the system, and the user should receive a confirmation of the successful deletion.

User retrieves a list of all created encryption keys and their statuses on the Datapy platform.

Given a user navigates to the encryption management dashboard, when they select 'View Encryption Keys', then they must see a complete and updated list of all encryption keys along with their status, including active/inactive indicators.

User receives notifications for key rotation reminders on the Datapy platform.

Given a user has an encryption key that requires rotation based on the configured schedule, when the rotation date approaches, then the user must receive a notification alerting them to perform the key rotation.

Audit Logging for Encryption Access

User Story: As an IT security analyst, I want to review logs of encryption key access so that I can identify unauthorized access and ensure compliance with security policies.
Description: Integrate audit logging that tracks all access and modifications to encrypted data. This feature will maintain a comprehensive log of who accessed which encryption keys, when, and what changes were made. By providing full transparency over data access, users can monitor for suspicious activities and ensure compliance with internal governance policies.
Acceptance Criteria: Audit Log Access Tracking for Sensitive Data Access

Given an encrypted data access event, when a user accesses the data, then an entry should be created in the audit log capturing the timestamp, user ID, and encryption key used.

Modification Tracking for Encryption Keys

Given an encryption key modification event, when a user updates the encryption key, then an entry should be logged in the audit log with details of the modification including the timestamp, user ID, and nature of the change.

Suspicious Activity Monitoring for Data Access

Given a specified period of data access history, when an admin reviews the audit log, then they should be able to filter and identify any unusual access patterns or unauthorized attempts to access encryption keys.

Compliance Check for Audit Logs

Given the request for compliance review, when an auditor accesses the audit logs, then they should find complete records for at least 12 months of all encryption key accesses and modifications.

User Notification for Unauthorized Access

Given an unauthorized access attempt is detected, when the system identifies this event, then it should generate an immediate notification to the system administrator with relevant details.

Audit Log Integrity Assurance

Given audit logs exist, when the system performs integrity checks, then it should confirm that all log entries remain unchanged and timestamps are accurate without any tampering.

Custom Report Generation from Audit Logs

Given access to the audit logging feature, when a user generates a report, then the system should allow filtering by date range, user ID, and type of access or modification to produce a detailed audit report.

Encryption Performance Metrics

User Story: As a system administrator, I want to understand the performance impact of encryption on system resources so that I can balance security needs with operational efficiency.
Description: Implement performance metrics that assess the impact of encryption on system performance. This requirement involves benchmarking data processing speeds and resource consumption with and without encryption. By providing insights into the trade-offs of enabling encryption, users can make informed decisions about operational efficiency versus data security.
Acceptance Criteria: Benchmarking data processing speeds with and without encryption during peak load times.

Given that the system has both encryption and non-encryption modes, when processing data under peak load conditions, then the processing speed should not degrade more than 15% with encryption enabled when compared to the non-encryption mode.

Measuring system resource consumption during data encryption operations.

Given that the encryption protocol is active, when performing a series of data operations, then CPU and memory usage should remain within 80% of average resource consumption observed during non-encrypted operations.

Gathering user feedback on the perceived impact of encryption on system performance.

Given that users have access to both encrypted and non-encrypted environments, when users are surveyed post-interaction, then at least 75% should report that the performance trade-offs are acceptable for the added security benefit.

Testing the latency introduced by the encryption process during data transfer.

Given that data is being transferred both encrypted and unencrypted, when measuring transfer times across multiple scenarios, then the average latency introduced by encryption should not exceed 200 milliseconds over a 1MB payload.

Evaluating the security incidents as a result of enabling encryption protocols.

Given that encryption is enabled on the platform, when reviewing security incident logs over a 90-day period, then the number of reported security incidents should decrease by at least 20% when compared to the previous 90 days without encryption enabled.

Multi-Layered Security Protocols

User Story: As a security officer, I want to ensure that there are multiple security layers in place so that I minimize the risk of data breaches and ensure secure access control.
Description: Introduce multi-layered security protocols that work alongside encryption measures. This feature combines encryption with additional security practices like two-factor authentication, access controls, and anomaly detection systems. By creating a layered security framework, we enhance data protection against various attack vectors and ensure that only authorized users can access sensitive information.
Acceptance Criteria: Implementation of Multi-Layered Security Protocols for new user onboarding process in Datapy.

Given a new user is onboarded in Datapy, when they complete the registration process and attempt to log in, then they must successfully verify their identity using two-factor authentication before gaining access to the platform.

Real-time monitoring of unauthorized access attempts within the Datapy platform.

Given the multi-layered security protocols are in place, when an unauthorized access attempt is detected, then the system should automatically trigger an alert and log the incident for review.

Testing the effectiveness of data encryption during data transfer within Datapy's analytics platform.

Given that a user is transferring sensitive data to and from the Datapy platform, when the data is in transit, then the system should utilize strong encryption protocols to secure the data, with verification that no unauthorized access occurs during the transfer.

User access control settings for sensitive modules within the Datapy platform.

Given a user has admin rights, when they configure access controls for sensitive modules, then only designated users specified by the admin should have access, and an audit log of these changes should be generated.

Effectiveness of anomaly detection systems under normal operation and high-load scenarios.

Given the anomaly detection system is active, when there is unusual activity within the platform, then the system must flag the activity for further investigation and temporarily restrict access if necessary.

Compliance check of multi-layered security protocols with industry regulations.

Given the multi-layered security protocols have been implemented, when a compliance audit is conducted, then the protocols must meet or exceed all specified regulations and standards for data protection and security.

Compliance Reporting Dashboard

User Story: As a compliance officer, I want a reporting dashboard that aggregates all encryption compliance metrics so that I can easily prepare for audits and demonstrate compliance adherence.
Description: Create a compliance reporting dashboard that aggregates encryption-related compliance metrics and audit logs into a user-friendly interface. This dashboard will allow businesses to easily visualize their compliance standing, manage encryption practices, and prepare for audits. By simplifying access to compliance data, we empower users with the tools they need to ensure ongoing regulation adherence.
Acceptance Criteria: User is a compliance officer who logs into the Datapy platform to access the compliance reporting dashboard to assess the effectiveness of encryption protocols in use.

Given the compliance officer is authenticated, when they navigate to the compliance reporting dashboard, then they should see an aggregated view of encryption compliance metrics and audit logs clearly displayed.

User wants to generate a compliance report for a regulatory body and needs to filter specific timeframes for the data shown on the dashboard.

Given the compliance officer is on the dashboard, when they apply a date filter to the compliance metrics, then the dashboard should update to reflect only the data within the selected timeframe accurately.

User requires quick insights into the compliance status of different encryption protocols implemented in their organization, including current compliance levels and any breaches.

Given the compliance officer is viewing the dashboard, when they look at the encryption compliance metrics, then they should see a visual representation (e.g., charts or graphs) that indicates the compliance levels and any recorded breaches clearly.

User needs to export the compliance metrics and audit logs for an upcoming audit while ensuring data integrity during the export process.

Given the compliance officer is on the dashboard, when they choose to export the compliance data, then the exported file should contain all relevant metrics and logs in a standardized format without any data loss or corruption.

User is a new user who requires a quick guide on how to interpret the compliance reporting dashboard metrics effectively.

Given the user is on the compliance reporting dashboard, when they hover over dashboard elements, then a tooltip or help guide should appear providing definitions and examples of the metrics displayed.

User wants to configure alerts for specific compliance thresholds related to encryption metrics that require immediate attention.

Given the compliance officer is on the dashboard settings page, when they set a threshold for alert messages related to compliance metrics, then the system should send notifications when those thresholds are breached.

Regular Security Audits

This feature facilitates systematic and thorough security checks on the Datapy platform, assessing for vulnerabilities and compliance with data protection regulations. Regular security audits provide users with peace of mind, ensuring that the platform remains secure against evolving threats and that necessary updates are proactively implemented.

Requirements

Automated Audit Scheduling

User Story: As a system administrator, I want to automate security audit scheduling so that I can ensure regular security checks without missing important deadlines.
Description: Develop a feature that automatically schedules security audits on a predefined cycle, allowing for regular checks without manual intervention. This functionality will include customizable scheduling options to accommodate various user preferences and compliance requirements. The benefit of this feature lies in its ability to ensure continuity in security practices, thus minimizing potential vulnerabilities through regular, consistent assessments. Integration with existing calendar and notification systems will enhance user awareness and compliance adherence, ensuring that audits are never overlooked and are seamlessly incorporated into the business routine.
Acceptance Criteria: Automated Audit Scheduling for Compliance Monitoring

Given a user has access to the Datapy platform, when they set up automated audit scheduling for security audits on a predefined cycle, then the system should automatically create and store a schedule that triggers an audit according to the set frequency without manual intervention.

Customizable Scheduling Options

Given a user accesses the automated audit scheduling feature, when they choose a schedule that deviates from the default (e.g., weekly, monthly), then the user should have the ability to select specific dates and times per their preferences and save those settings successfully.

Integration with Calendar and Notification Systems

Given the user has scheduled security audits, when the schedule is saved, then the system should automatically integrate with the user’s calendar (e.g., Google Calendar) and send notification reminders one week and one day before each audit is due.

User Interface for Scheduling Audits

Given a user navigates to the security audit scheduling section, when they view the interface, then the user should see an intuitive layout that allows for easy selection of scheduling options, with clear labels and tooltips for guidance.

Audit Execution Confirmation

Given an audit is scheduled and reaches its designated time, when the audit is executed, then the user should receive a confirmation notification detailing the completion of the audit and a summary of results.

Historical Audit Record Availability

Given a user has conducted security audits over time, when they access the audit logs, then the user should be able to view a historical record of past audits, including dates, findings, and actions taken for each audit.

Testing for Vulnerabilities and Compliance

Given a scheduled security audit has been executed, when the results are generated, then the platform should report on both security vulnerabilities and compliance adherence to data protection regulations, with actionable insights clearly outlined.

Vulnerability Reporting Dashboard

User Story: As a security officer, I want to view a vulnerability reporting dashboard so that I can quickly assess the security status and prioritize remediation efforts.
Description: Create a comprehensive dashboard that displays the results of security audits, highlighting identified vulnerabilities and their severity. This dashboard will provide users with clear insights on security posture, enabling them to prioritize remediation efforts effectively. The reporting dashboard will integrate visual representations of data, such as graphs and summary stats, to enhance understanding and facilitate quicker decision-making. This feature is critical for informing users of security risks in real-time and for tracking progress on mitigation efforts.
Acceptance Criteria: User accesses the Vulnerability Reporting Dashboard to review the latest security audit findings after receiving a notification about completed security checks.

Given the user is logged into the Datapy platform and has permissions to view the Vulnerability Reporting Dashboard, when they access the dashboard, then they should see the most recent security audit results, including all identified vulnerabilities and severity levels.

A user attempts to filter the vulnerabilities on the Vulnerability Reporting Dashboard by severity level to prioritize remediation efforts.

Given the user is on the Vulnerability Reporting Dashboard, when they select a severity filter option, then only vulnerabilities matching the selected severity level should be displayed on the dashboard.

A user reviews the graphical representations on the Vulnerability Reporting Dashboard to understand the overall security posture of the platform.

Given the user is viewing the Vulnerability Reporting Dashboard, when they look at the graphical representations, then they should see clear visual data on the number of vulnerabilities categorized by severity and resolution status.

The user receives an alert for newly identified vulnerabilities on the Vulnerability Reporting Dashboard after the completion of a security audit.

Given the user is viewing the Vulnerability Reporting Dashboard, when a new security audit is completed, then they should receive a notification highlighting any newly identified vulnerabilities since their last visit to the dashboard.

A user wants to track the remediation progress of vulnerabilities identified in previous audits using the Vulnerability Reporting Dashboard.

Given the user is on the Vulnerability Reporting Dashboard, when they review the historical data section, then they should see a comparison of previously identified vulnerabilities and their current remediation status.

An administrator checks the integration status of the Vulnerability Reporting Dashboard with external compliance tracking tools used by the organization.

Given the administrator has access to the settings of the Vulnerability Reporting Dashboard, when they navigate to the integration settings, then they should see successful connections to any external compliance tracking tools, along with real-time data synchronization status.

User Access Logs for Audits

User Story: As an auditor, I want detailed user access logs to ensure accountability and compliance with data security regulations during the audit process.
Description: Implement a feature that generates and stores detailed logs of user access and activities as part of the security audit trail. This capability will allow users to track who accessed data, when, and what actions were taken, thereby enhancing accountability and facilitating compliance with data protection regulations. It will also aid in detecting unauthorized access attempts and understanding user behavior patterns. The logs will need to be easily exportable for integration with other security tools and for compliance reporting purposes.
Acceptance Criteria: User Access Logging during Login and Activities

Given a user logs into the Datapy platform, when they perform any action, then a detailed log entry must be created capturing the user ID, timestamp, action performed, and any relevant data accessed or modified.

Exporting User Access Logs

Given that user access logs are generated, when a user selects the export functionality, then the logs must be exportable in a standard format (CSV or JSON) and include timestamps, user IDs, and action details without data loss.

Compliance Audit Review

Given that a compliance officer is conducting an audit, when accessing user access logs, then the officer should be able to filter logs by date range, user ID, and actions performed to review all relevant entries efficiently.

Unauthorized Access Detection

Given that the user access logs are continuously monitored, when an unauthorized access attempt is detected, then an alert should be generated and logged, capturing attempts including the user ID, timestamp, and action attempted.

User Behavior Pattern Analysis

Given the collected user access logs, when analyzing user activity patterns, then the platform should provide a report summarizing user actions over a specified period, highlighting anomalies or irregular usage patterns.

Integration with Third-party Security Tools

Given that user access logs are generated, when integrating with third-party security tools, then the logs must maintain a compatible format that allows seamless integration and transformation without errors.

Data Retention Policy for Logs

Given that user access logs are stored, when the retention policy is applied, then logs older than the specified timeframe must be automatically purged and securely deleted in compliance with data protection regulations.

Real-time Security Alerts

User Story: As a business owner, I want real-time security alerts so that I can act swiftly to potential threats to my business data and comply with security policies.
Description: Design a real-time alert system that notifies users of potential security breaches or vulnerabilities immediately after they are detected. This feature will leverage machine learning algorithms to assess user activities and flag any anomalies in real-time. By receiving timely alerts, users can take proactive steps to address potential threats before they escalate into more significant issues. Integration with mobile notifications and email will ensure that users are informed regardless of their current platform usage, thus enhancing responsiveness to security threats.
Acceptance Criteria: User receives a real-time security alert on their mobile device when a potential vulnerability is detected in their account activity during peak business hours.

Given that a user is logged into the Datapy platform and is actively using it during peak hours, When a potential security breach is detected, Then a real-time alert notification is sent to the user's registered mobile device and email address.

The real-time alert system functions effectively when unusual patterns of data access are identified by the machine learning algorithms.

Given that machine learning algorithms are monitoring user activity, When an unusual pattern is detected, Then the system triggers an alert to the user, detailing the nature of the anomaly and potential risks.

Users can customize their alert preferences to receive notifications only for critical security threats.

Given that a user accesses their notification settings, When the user selects their preferences for receiving alerts, Then the system saves these preferences and only sends alerts that match the selected criteria.

Alerts must reach users on both desktop and mobile platforms simultaneously to ensure immediate awareness of security issues.

Given that a user has enabled both desktop and mobile notifications, When a security alert is generated, Then the alert is simultaneously sent to both the user's desktop application and mobile application.

Users can view a history of real-time security alerts within the Datapy platform for reference and compliance.

Given that a user wants to review past security alerts, When the user navigates to the security alerts history section, Then they are presented with a chronological list of all security alerts that have been triggered, including timestamps and details.

The alert system is tested for response time to critical threats to ensure swift user notification.

Given that a critical security threat is simulated in the Datapy environment, When the alert system is activated, Then the user receives a notification within 5 seconds of the threat being detected.

Compliance Checklist Integration

User Story: As a compliance officer, I want a compliance checklist integrated into the security audit process so that I can ensure our practices meet regulatory requirements efficiently.
Description: Develop an integrated compliance checklist that aligns with industry standards and regulations relevant to data protection and security. This feature will guide users through necessary compliance requirements, providing prompts and documentation support throughout the security audit process. Users will benefit from this integration by gaining clarity on compliance demands, thus easing the burden of meeting regulatory expectations and improving audit readiness. The checklist will also be customizable to fit the specific needs and regulations of different industries.
Acceptance Criteria: User accesses the compliance checklist feature during a scheduled security audit to ensure all necessary data protection regulations are being addressed.

Given the user is logged into the Datapy platform,When the user navigates to the compliance checklist,Then the checklist is displayed with real-time prompts for all applicable data protection regulations.

User customizes the compliance checklist to fit the specific regulatory requirements for their industry.

Given a user is in the compliance checklist section,When they select the customization option,Then the user can modify the checklist items relevant to their industry's regulations and save the changes successfully.

User receives notifications regarding updates to compliance regulations affecting their checklist.

Given the user has previously set up the compliance checklist,When there are updates to applicable data protection regulations,Then the user receives an automated email notification detailing the changes to their checklist.

User conducts a self-audit using the compliance checklist and wants to generate a report of audit findings.

Given the user has completed the compliance checklist,When the user clicks on the generate report button,Then a downloadable report summarizing the audit findings is produced in PDF format.

Compliance checklist is assessed for completeness against the latest industry standards and regulations.

Given the compliance checklist is in use,When the system performs a periodic review,Then the checklist is updated automatically to include any new compliance requirements or standards without user intervention.

User collaborates with team members to complete the compliance checklist during a security audit.

Given multiple users are assigned to the compliance checklist,When a user makes an edit or update to the checklist,Then all assigned users receive real-time notifications of the changes made.

User Role Management

This feature allows administrators to define specific user roles and access levels within Datapy, ensuring that only authorized personnel can access sensitive data and functionalities. By implementing user role management, businesses can minimize the risk of inadvertent data exposure, thereby enhancing data governance and security.

Requirements

Role Definition and Management

User Story: As an administrator, I want to create and manage user roles so that I can ensure that only authorized personnel have access to sensitive data and functionalities.
Description: This requirement focuses on enabling administrators to create, define, and manage various user roles within Datapy. It incorporates the ability to grant specific permissions based on user roles, ensuring that users have appropriate access to sensitive data and functionalities. The primary benefits include enhanced security, improved data governance, and reduced risk of unauthorized access. This functionality will integrate seamlessly with the existing user interface, allowing for easy role assignment and management, helping businesses enforce their data policies effectively.
Acceptance Criteria: Administrators can create new user roles with defined permissions in Datapy.

Given an administrator is logged in, when they navigate to the user role management section and create a new role, then they should see the new role listed with no assigned permissions by default.

Administrators can assign permissions to user roles within Datapy.

Given an administrator has created a new user role, when they assign specific permissions to that role, then the role should reflect the updated permissions in the user role management section.

Administrators can edit existing user roles and their permissions in Datapy.

Given an administrator is managing user roles, when they select an existing role and change its permissions, then the changes should be saved and reflected accurately in the role's permissions list.

Administrators can delete user roles that are no longer needed in Datapy.

Given an administrator is viewing the list of user roles, when they choose to delete a role, then that role should be removed from the list and no longer visible to users with lower permissions.

Users are assigned roles and can access the functionalities permitted by their role.

Given a user is assigned a specific role, when they log in to Datapy, then they should only have access to the functionalities defined by their role's permissions.

Audit log captures changes made to user roles and permissions.

Given an administrator modifies a user role, when they save the changes, then an entry should be created in the audit log detailing the modification, including the role name and changed permissions.

Very high-level access controls are enforced for sensitive data.

Given a user attempts to access sensitive data, when their role does not have the required permissions, then they should receive an 'access denied' message instead of the data being displayed.

Access Level Configuration

User Story: As an administrator, I want to configure access levels for various user roles so that I can control who can view, edit, or delete data according to their responsibilities.
Description: This requirement details the ability for administrators to configure granular access levels for different user roles in Datapy. This includes permissions for viewing, editing, and deleting specific data sets and functionalities within the platform. By enabling precise control over access levels, businesses can tailor user experiences based on their roles, enhancing security and compliance. This capability will be designed to align with the overall role management feature, ensuring a cohesive user experience.
Acceptance Criteria: Administrator configures access levels for different user roles within the Datapy platform.

Given an administrator accesses the user role management section, When they configure access levels by selecting a user role and adjusting permissions for viewing, editing, and deleting data sets, Then the system should save these configurations and reflect the updated access levels in the user role management dashboard.

Users attempt to access data according to their assigned roles after the administrator has configured access levels.

Given a user with defined access levels in their role, When they log into Datapy and attempt to view, edit, or delete a data set they should only be able to perform actions that align with the permissions set by the administrator, and any unauthorized attempts should be denied with an appropriate error message.

Administrator reviews the audit log to ensure compliance with access level configurations.

Given the administrator wants to verify access compliance, When they access the audit log, Then the log should accurately display all user actions related to data viewing, editing, and deleting within the specified time frame, including timestamps and user roles for accountability.

The system validates that conflicts do not arise when multiple administrators set access levels concurrently.

Given two or more administrators are configuring access levels at the same time, When one administrator saves changes to a specific role, Then the other administrators should receive a notification that the access level has been modified, preventing any conflicting changes.

The administrator sends notifications to users regarding their access level changes.

Given the administrator has changed access levels for a user role, When these changes are saved, Then the system should automatically notify affected users via email about their updated permissions and access levels.

Users can request additional access through a formal process within the Datapy platform.

Given a user wants to request additional access, When they submit a request through the designated form, Then the request should be logged with necessary details, and the appropriate administrator should receive a notification to review the request.

The system allows for easy rollback of access level changes if needed.

Given an administrator wants to revert access level changes, When they access the version history of user role configurations, Then they should be able to select a previous configuration and reinstate it, effectively reverting to the desired access levels.

Audit Log Tracking

User Story: As an administrator, I want to have an audit log that tracks user role changes and access attempts so that I can monitor compliance and ensure data security.
Description: This requirement involves implementing an audit log feature that tracks user activities related to role management and access. The audit log will record actions such as role assignments, modifications, and access attempts, allowing administrators to monitor compliance and identify any unauthorized access attempts. This enhancement is crucial for maintaining security and accountability within the platform, fostering trust among users.
Acceptance Criteria: Audit log captures role assignments made by administrators for users within the Datapy platform.

Given an administrator assigns a role to a user, when the action is completed, then the audit log should reflect the role assignment with timestamp, admin ID, and user ID.

Audit log captures modifications made to user roles.

Given an administrator modifies a user's role attributes, when the modification is saved, then the audit log should capture the edited attributes along with the user's ID, timestamp, and admin ID.

Audit log records access attempts made by users to the role management feature.

Given a user attempts to access role management features, when the attempt is successful or denied, then the audit log should record the user's ID, action (access), timestamp, and result (success/denied).

Audit log allows administrators to filter logs by date ranges for compliance monitoring.

Given an administrator is viewing the audit log, when they apply a date range filter, then the log should only display entries within the specified range.

Audit log provides a summary of user access attempts for reporting purposes.

Given an administrator requests a summary report for user access attempts, when the report is generated, then it should include total attempts, successful access, and denied access counts.

Audit log maintains data integrity by ensuring that logs cannot be modified or deleted by users.

Given any user privileged or not, when they attempt to delete or modify an entry in the audit log, then the action should be denied and an attempt logged in the system with timestamp and user ID.

Role-Based Dashboard Customization

User Story: As a user, I want my dashboard to be customized based on my role so that I can easily access the metrics and insights relevant to my responsibilities.
Description: This requirement entails enabling users to customize their dashboard views based on their assigned roles. By providing tailored dashboards, users will be able to focus on metrics and information relevant to their specific responsibilities, improving efficiency and user satisfaction. This feature aligns with Datapy's goal of providing actionable insights and user-centric design, enhancing the overall user experience within the platform.
Acceptance Criteria: User with 'Manager' role accesses the dashboard and sees customized metrics related to team performance and project tracking.

Given a user with 'Manager' role, when they log into Datapy, then the dashboard should display metrics specific to team performance and project tracking, tailored to their responsibilities.

User with 'Sales' role logs into the platform and the dashboard reflects sales metrics such as revenue and conversion rates.

Given a user with 'Sales' role, when they access their dashboard, then the dashboard must present sales metrics including revenue and conversion rates specific to their functions.

An administrator modifies a user's role from 'Sales' to 'Finance' and verifies that the dashboard updates accordingly.

Given an administrator changes a user’s role to 'Finance', when the user logs back in, then the dashboard should reflect finance-related metrics instead of sales metrics.

Users can save customized layouts of their dashboards for future use based on their roles.

Given a user customizes their dashboard layout, when they save that layout, then it should be retrievable upon their next login without any need for further customization.

New users are assigned a default dashboard based on their assigned roles immediately upon account creation.

Given a new user is created with a specific role, when they log in for the first time, then they should see the default dashboard appropriate for their role.

Users with 'Admin' role have access to customize dashboards for all roles, ensuring oversight across departments.

Given a user with 'Admin' role, when they access dashboard settings, then they must be able to customize the dashboards available to all other roles in the system.

Group Role Management

User Story: As an administrator, I want to manage user roles in groups so that I can save time and ensure consistent access controls across all users in a team.
Description: This requirement focuses on the ability to manage user roles in groups, allowing administrators to assign or modify roles for multiple users simultaneously. This feature is essential for organizations with a large number of users, streamlining the role management process and ensuring consistency in access controls across teams. It will enhance administrative efficiency by significantly reducing the time and effort required to manage roles individually.
Acceptance Criteria: As an administrator of Datapy, I want to be able to assign roles to multiple users at once so that I can efficiently manage user access based on team requirements.

Given I am logged in as an administrator, when I select multiple users and assign them the 'Data Analyst' role, then all selected users should have their roles updated successfully without error.

As an administrator, I want to modify the roles of a user group, ensuring that changes are reflected immediately across the platform.

Given I am an administrator and I modify the role of the 'Marketing Team' group to 'Data Viewer', when I check the role of any member of the Marketing Team, then the role should be updated to 'Data Viewer' instantly.

As a compliance officer, I need to ensure that role changes are logged for security auditing purposes.

Given an administrator has modified user roles, when I check the audit log, then I should see an entry capturing the time, the administrator's ID, the previous role, and the new role for all changes made.

As an administrator managing a large user base, I want to have an overview of all roles assigned to users in a group for better role distribution analysis.

Given I access the User Role Management dashboard, when I view the 'Sales Team' group, then I should see a complete list of all users in that group along with their current roles presented in a clear format.

As an administrator, I want to implement batch role removal for a group of users to ensure access control is easily manageable.

Given I am logged in as an administrator and I select the 'Developers' group, when I initiate a batch role removal, then all users in that group should have their roles removed without any individual role assignment being required.

As a team leader, I want to receive notifications when my team's roles are changed to stay informed about access control.

Given I am a member of a team whose roles are being updated, when any administrator changes our roles, then I should receive a notification reflecting the changes made to our roles.

As an administrator, I want to ensure that I cannot assign or modify roles outside my authority level to maintain data security.

Given I try to assign a role to a user that exceeds my permissions, then the action should be denied with an appropriate error message indicating insufficient permissions.

Incident Response Toolkit

This feature equips users with a set of tools for identifying, analyzing, and mitigating potential security incidents. The toolkit includes predefined response plans and templates, enabling businesses to act quickly and effectively in the event of a security breach, minimizing potential damages and maintaining operational integrity.

Requirements

Incident Identification Dashboard

User Story: As a security analyst, I want a dashboard that consolidates security alerts so that I can quickly identify and prioritize incidents for effective response.
Description: The Incident Identification Dashboard provides users with a central interface to monitor real-time security alerts and incidents across the organization's network. It integrates seamlessly with existing data points and systems to aggregate and visualize potential threats, allowing teams to quickly identify and prioritize incidents based on severity. The dashboard employs advanced analytics and machine learning algorithms to reduce false positives, improving the efficiency of security monitoring. Its real-time capabilities enable proactive responses to threats, ensuring that potential breaches are addressed before they escalate.
Acceptance Criteria: User accesses the Incident Identification Dashboard for the first time to review current security alerts.

Given that the user has the necessary permissions, when they access the dashboard, then they should see a comprehensive overview of all active security alerts with relevant details such as severity level, timestamp, and affected systems.

Security analysts want to filter incidents by severity on the Incident Identification Dashboard.

Given that security analysts are viewing the dashboard, when they apply a filter for severity levels (e.g., high, medium, low), then the dashboard should update to display only the incidents that match the selected severity level.

A security team needs to prioritize response actions based on real-time threat data displayed on the dashboard.

Given that the dashboard displays various security alerts, when the security team assesses incidents by highest severity, then the dashboard should sort and display incidents in descending order of severity, enabling quick prioritization.

User interacts with the dashboard during a security drill, simulating a response to identified incidents.

Given that a security drill is in progress, when the user clicks on an incident, then detailed incident information should display, including suggested response actions based on predefined templates.

The organization applies machine learning algorithms to minimize false positives in incident detection.

Given that machine learning algorithms are implemented, when the dashboard generates security alerts, then the rate of false positives should decrease by at least 30% compared to previous data without machine learning integration.

Users need to receive immediate notifications for critical security incidents identified on the dashboard.

Given that a critical incident is detected, when the incident occurs, then all relevant users should receive an automated notification via email and/or in-app alerts within 5 minutes of detection.

A user wants to customize the dashboard layout to display the most critical metrics first.

Given that the user is on the dashboard, when they customize the layout by dragging and dropping widgets, then the layout should save their preferences, and they should see their customized layout upon the next login.

Predefined Response Templates

User Story: As a security manager, I want access to standardized response templates so that I can ensure our team follows best practices during a security incident response.
Description: Predefined Response Templates offer users a collection of structured plans and checklists tailored to various security incident scenarios. These templates assist teams in executing a well-coordinated response to incidents, greatly enhancing the speed and effectiveness of remediation actions. Each template will be customizable, allowing organizations to adapt to their unique workflows while ensuring that essential steps are not overlooked during an incident. The inclusion of these templates not only equips users with a guided approach to handling incidents but also ensures compliance with security policies and best practices.
Acceptance Criteria: User selects a predefined response template from the toolkit during a simulated phishing attack response drill.

Given the user is in the Incident Response Toolkit, When the user selects a predefined response template for a simulated phishing attack, Then the system should display the complete checklist associated with that template without errors.

A team member customizes a predefined response template to align with their organization's unique incident response workflows.

Given that the user opens a predefined response template, When the user modifies any part of the checklist, Then the system should allow the user to save the custom template successfully without losing any previous modifications.

The user engages with the predefined response templates during an actual security incident.

Given a security incident occurs and the user activates the response template, When the user follows the steps outlined in the template, Then the user should be able to complete the incident response process in under 30 minutes, ensuring all essential steps are followed.

A user navigates the Incident Response Toolkit to locate available predefined response templates.

Given the user is accessing the Incident Response Toolkit, When the user searches for response templates by keyword, Then the system should return all relevant templates that match the search term, displayed in a clear list format.

User accesses the incident response templates on a mobile device during an urgent incident response situation.

Given the user is using the mobile application, When the user accesses the predefined response templates, Then the layout should be user-friendly, with all critical information easily accessible and readable on a smaller screen.

Managers review the predefined response templates for compliance with organizational security policies.

Given that a manager is reviewing the predefined response templates, When they check the compliance of each template against a checklist of organizational requirements, Then all templates should meet at least 90% of the identified compliance requirements.

A user attempts to print a predefined response template for physical distribution during a training session.

Given the user is viewing a predefined response template, When the user selects the print option, Then the system should generate a formatted printout that includes all steps and instructions clearly laid out on standard paper size without cutting off any content.

Incident Reporting and Documentation

User Story: As a compliance officer, I want a platform to document security incidents so that I can ensure all incidents are recorded and analyzed for future prevention.
Description: The Incident Reporting and Documentation feature allows users to log and track the details of security incidents systematically. Users can capture critical information such as incident type, timeline, response actions, and outcomes. This functionality promotes accountability and knowledge sharing across teams, helping organizations learn from each incident. The gathered documentation will also support compliance efforts and provide insights for improving future security measures, enhancing the overall security posture of the organization.
Acceptance Criteria: User logs an incident through the Incident Reporting and Documentation feature after detecting a potential security breach during a regular security audit.

Given a logged-in user, when they input all required fields for the incident (incident type, timeline, response actions, outcomes), then the system should successfully save and display the incident with a unique identifier.

A team member retrieves and reviews the logged incidents to analyze trends and patterns for potential security vulnerabilities.

Given that there are multiple logged incidents, when a user accesses the incident reporting dashboard, then they should be able to filter incidents by type, date, and status, and download a report of the filtered incidents.

A compliance officer audits the documented security incidents to ensure all necessary information has been recorded for regulatory compliance.

Given a compliance officer is reviewing logged incidents, when they access an incident's detailed view, then all critical information (incident type, timeline, response actions, and outcomes) must be present and accurate to the original report.

A user receives notifications for incidents that require immediate action or review to ensure timely responses to potential breaches.

Given an incident is marked as requiring immediate attention, when the incident is logged, then the relevant users should receive a notification via email and in-app alert within 5 minutes of the incident being recorded.

A systems administrator updates or edits an existing incident report based on new findings or corrective actions taken since its initial logging.

Given a user with editing permissions, when they edit the response action or outcomes fields of a logged incident, then the system should save the changes and log the timestamp of the update.

Users collaborate on a logged incident to discuss response strategies and next steps through comments and attachments directly within the incident report.

Given a logged incident, when users add comments or attachments, then those contributions should be visible in chronological order within the incident report to all users with access.

A user searches for incidents in the Incident Reporting and Documentation feature to find historical data on past security breaches for analysis.

Given a user is on the incident reporting page, when they input keywords or use filters for the incident search, then the system should return relevant incident reports that match the search criteria within 3 seconds.

Collaboration Tools Integration

User Story: As a team lead, I want to integrate our communication tools with the incident response toolkit so that we can collaborate effectively during a security crisis.
Description: The Collaboration Tools Integration feature allows users to connect their existing communication platforms, such as Slack or Microsoft Teams, with the Incident Response Toolkit. This integration facilitates real-time communication and collaboration among team members during incident response activities. Users can share updates, escalate incidents, and distribute tasks efficiently, ensuring that all team members remain informed and work collectively towards resolving incidents. This feature enhances the responsiveness of the team and fosters a collaborative environment during high-pressure situations.
Acceptance Criteria: Team members are responding to a security incident and need to communicate rapidly using their integrated chat platform.

Given that a team member has identified a security incident, when they send an alert through the integrated communication platform (e.g., Slack), then all relevant team members should receive a notification within 5 seconds.

A user wants to escalate an incident during a high-pressure situation and needs to ensure the right team members are notified.

Given that an incident is being escalated by the incident response lead, when they select the 'Escalate' option in the toolkit, then an alert should be sent to the specified team members via the integrated communication platform, indicating the incident details and necessary actions within 10 seconds.

Users are collaborating on incident task assignments and need to ensure clarity on responsibilities.

Given that a user assigns tasks to team members during an incident response session, when they communicate these tasks via the integrated platform, then each assigned team member should receive a direct message with task details and deadlines within 5 seconds.

Team members need to discuss strategies for mitigating an ongoing security incident in real-time using the integrated platform.

Given that a team meeting is initiated through the integrated communication platform, when team members join the meeting, then they should be able to access shared incident data and response plans within the meeting interface.

After resolving an incident, the team needs to conduct a retrospective review to evaluate the response and improve future actions.

Given that an incident has been resolved, when a user initiates a retrospective review using the toolkit, then a summary report should be generated automatically capturing communication logs and task assignments linked to the incident within 5 minutes.

A user wishes to customize notification preferences for different types of incidents in the integrated platform.

Given that a user accesses the settings for the integrated communication toolkit, when they adjust their notification preferences for different types of incidents, then those preferences should be saved and applied immediately for all future incident notifications without needing to restart the application.

Post-Incident Analysis Module

User Story: As a security analyst, I want to analyze past incidents so that I can improve our incident response strategies and reduce recurrence of similar incidents.
Description: The Post-Incident Analysis Module enables teams to review incidents after resolution, assess the effectiveness of the response, and identify areas for improvement. By conducting thorough analyses, organizations can learn from past incidents to refine their incident response processes and enhance overall security measures. This module includes customizable reporting options, interactive dashboards, and the ability to generate actionable insights. Incorporating lessons learned into the organization’s security strategy significantly reduces future risks.
Acceptance Criteria: Users need to review the effectiveness of their incident responses by analyzing past incidents to improve their response strategy.

Given a resolved incident, when a user accesses the Post-Incident Analysis Module, then they should be able to generate a report detailing the incident, response actions taken, and a summary of outcomes.

The management team wants to visualize incident metrics over time to track the performance of the incident response team.

Given that multiple incidents have been documented, when a user navigates to the interactive dashboard, then they should see a visual representation of incident trends, categorized by type and response effectiveness, over a selectable time range.

Teams need to identify areas where they can improve their incident response process based on insights gathered from past incidents.

Given the Post-Incident Analysis Module, when a user reviews incidents from the last quarter, then they should be able to identify at least three actionable insights for improving security measures based on response effectiveness and incident outcomes.

Users want to ensure that the reporting options are customizable to fit the specific metrics and insights they wish to analyze post-incident.

Given the reporting feature in the Post-Incident Analysis Module, when a user attempts to customize a report, then they should be able to select specific incident metrics, time periods, and response actions to include in the generated report.

Security teams need to share their findings and improvements with other stakeholders within the organization effectively.

Given a completed post-incident report, when a user opts to share the report, then the system should allow the user to generate a shareable link or PDF document that includes all details and insights from the analysis module.

The incident response team needs to review the incident timeline to understand the sequence of events leading to the incident.

Given an incident has been logged, when a user accesses the incident overview, then they should be able to view a detailed timeline including timestamps for when incidents were detected, reported, and resolved.

Data Anomaly Detection

This feature utilizes AI-driven algorithms to monitor data interactions for unusual patterns or anomalies that may indicate unauthorized access or potential security threats. By alerting users to suspicious activity in real time, businesses can take swift action to address potential vulnerabilities before they escalate.

Requirements

Real-Time Anomaly Alerts

User Story: As a data analyst, I want to receive real-time alerts on data anomalies so that I can take immediate action to secure our data from potential threats.
Description: The Real-Time Anomaly Alerts requirement ensures that the Data Anomaly Detection feature provides immediate notifications to users when suspicious patterns or anomalies are detected in the data. This functionality is critical for allowing businesses to react swiftly to potential security threats, minimizing exposure and risk. The alerts will be customizable based on severity levels and user preferences, enhancing user experience and proactive response capabilities in maintaining data integrity and security.
Acceptance Criteria: User receives real-time anomaly alerts when suspicious data patterns are detected in the system.

Given a user is registered and has enabled real-time alert notifications, When an anomaly is detected, Then the user should receive an immediate notification via email and in-app alert.

Users can customize alert settings based on various severity levels for different types of anomalies.

Given a user has access to the alert settings, When they select severity levels for alerts, Then the system should save these preferences and apply them to future anomaly detections.

Users are able to view historical alerts to assess past anomalies and responses.

Given a user accesses the anomaly detection dashboard, When they navigate to the alerts history section, Then they should see a list of past alerts with details such as date, time, severity, and type of anomaly.

Users can acknowledge alerts to prevent repeated notifications for the same anomaly.

Given a user receives an alert for an anomaly, When they view the alert and acknowledge it, Then the system should mark the alert as acknowledged and prevent further notifications for that specific anomaly.

Critical anomalies trigger escalated notifications to a designated response team.

Given a critical anomaly is detected and configured for escalation, When the alert is generated, Then the system should send out notifications to the response team's designated communication channel (e.g., SMS, email, or app notification).

Users are notified of anomalies in real-time without any delay based on their preference settings.

Given a user has set their notification preferences, When an anomaly is detected, Then the system must deliver the alert according to the chosen mode of notification (immediate, delayed, or summary).

Anomaly Pattern Recognition

User Story: As a security manager, I want the system to recognize and categorize different anomaly patterns so that I can better understand the nature of potential threats and manage them effectively.
Description: The Anomaly Pattern Recognition requirement enables the Data Anomaly Detection feature to identify and categorize various types of anomalous behaviors in data. It will utilize advanced AI algorithms to differentiate between normal data fluctuation and potential threats. By providing visibility into historical patterns and trends, this capability helps users understand the context of anomalies, improving accuracy in threat assessment and responding accordingly, thereby enhancing operational security.
Acceptance Criteria: Data Anomaly Detection during Business Operations

Given that there is a set of historical data available, when the system is monitoring real-time data interactions, then it should identify at least 95% of anomalies that differ significantly from the established patterns.

Real-time Alert Notification to Users

Given that an anomaly has been detected, when the system generates an alert, then it should notify users within 5 seconds via the designated communication channel (email, SMS, in-app notification).

Visual Representation of Anomaly Trends

Given that anomalies have been detected over the past week, when users access the dashboard, then they should be able to see a graphical representation of anomaly trends that highlights peak incidents and patterns of occurrence.

User Interaction Log Analysis

Given that users interact with the data analytics platform, when the system analyzes user behavior, then it should successfully categorize and flag any unusual user engagement patterns from the last 30 days.

Assessment of Historical Anomalous Patterns

Given that historical anomaly data is available, when the system analyzes this historical data, then it should accurately categorize past anomalies by type with at least 90% accuracy against known incidents.

Threshold Settings for Alert Customization

Given that a user wants to set custom thresholds for anomaly detection, when the user adjusts these settings, then the system should allow for specific threshold values and accurately reflect these values in real-time monitoring.

Integration with Existing Security Protocols

Given that the anomaly detection system is operational, when a new security protocol is implemented, then it should seamlessly integrate with the anomaly detection feature without errors or data loss.

User Customization for Alerts

User Story: As a business owner, I want to customize alert settings for different data types so that I can prioritize what matters most for my operations and reduce distractions from non-critical alerts.
Description: The User Customization for Alerts requirement allows users to set their own rules and thresholds for anomaly detection alerts. This feature is vital as it empowers users to tailor the alerting system to their specific business needs and risk appetite. By providing flexibility in alert configurations, users can focus on the most pertinent issues, thereby optimizing response times to genuine threats while minimizing alert fatigue.
Acceptance Criteria: User sets a specific threshold for anomaly detection alerts based on historical data patterns observed over the past three months.

Given the user has access to the anomaly detection settings, when they submit a threshold value, then the system should allow saving the new threshold without errors and apply it for future anomaly detection.

User receives real-time notifications when an anomaly is detected that exceeds the user-defined threshold.

Given the user has configured their anomaly detection alerts, when an anomaly occurs that surpasses the threshold, then the user should receive an immediate alert via their chosen notification method (email or in-app).

User wants to create multiple alert configurations for different types of data streams depending on business operations.

Given the user is on the alert configuration page, when they create a new alert configuration for a specific data stream, then they should be able to save and manage multiple configurations without any overlapping issues in the alert system.

User desires to modify an existing alert configuration to adjust the threshold value at any time based on changing business needs.

Given the user selects an existing alert configuration, when they update the threshold value and save it, then the system should successfully update the configuration and trigger a confirmation message to the user.

Admin evaluates the effectiveness of user-defined alert configurations to minimize false positives.

Given the admin reviews the alert logs, when they analyze the frequency and accuracy of alerts triggered in the past month, then there should be a report generated that indicates the percentage of false positives against total alerts generated.

User accesses the help section for assistance on setting up anomaly detection alerts.

Given the user is on the help page, when they search for 'set up anomaly detection alerts', then relevant guides and FAQs should be displayed to assist the user clearly and effectively.

User aims to deactivate alerts while maintaining the settings for future use without loss of configuration.

Given the user navigates to the alert management section, when they select a 'deactivate' option for an alert, then the alert should be deactivated but remain in the system for later reactivation without losing previously defined settings.

Dashboard Visualization of Anomalies

User Story: As a user, I want to see visual representations of data anomalies on my dashboard so that I can easily analyze trends and make informed decisions based on the state of my data.
Description: The Dashboard Visualization of Anomalies requirement integrates a visual representation of detected anomalies within the Datapy interface. By utilizing graphs, charts, and heatmaps, users will have an intuitive overview of data irregularities, facilitating quicker insights into trends and patterns. Enhanced visualization enhances decision-making capabilities, allowing users to swiftly assess the state of their data and address issues promptly.
Acceptance Criteria: User views the anomalies dashboard to monitor real-time data irregularities and trends during a critical business decision-making meeting.

Given the user is logged into the Datapy platform, when they navigate to the anomalies dashboard, then they should see visual representations (graphs, charts, heatmaps) of any detected anomalies within the last 24 hours.

A user accesses the anomalies dashboard during a security audit to identify any historical anomalies that have occurred over the past month.

Given the user selects the past month view on the dashboard, when they review the visualizations, then they must be able to filter anomalies by severity and type, and view detailed descriptions of each anomaly.

An admin sets up alerts for anomalies on the dashboard to receive notifications whenever a critical anomaly is detected.

Given the admin user configures alert settings for critical anomalies, when an anomaly is detected, then the system should automatically send a notification to the admin via email and a notification within the Datapy platform.

A user wants to assess the overall state of the data by analyzing custom time ranges on the anomalies dashboard.

Given the user selects a custom date range for analysis, when they apply the filter, then the dashboard must display only the anomalies that occurred within the selected timeframe, with accurate, real-time visualizations.

During a team meeting, users collaborate to discuss anomalies displayed on the dashboard.

Given multiple users are viewing the anomalies dashboard, when they utilize the collaborative tools to comment on specific anomalies, then those comments should be visible in real-time to all users on the dashboard with timestamps.

The user accesses the anomalies dashboard from a mobile device to ensure functionality on different platforms.

Given the user opens the anomalies dashboard on a mobile device, when they view the dashboard, then the layout must be responsive and all visual elements should be clearly viewable and usable without any loss of functionality.

Audit Trail for Anomaly Detection,

User Story
Description
Acceptance Criteria: Anomaly detection audit trail view for authorized personnel.

Given a user with authorized access, when they navigate to the anomaly detection audit trail section, then they should see a complete historical record of all detected anomalies, including timestamps, user actions, and responses taken.

Alerts and notifications for detected anomalies.

Given an anomaly detection event has been triggered, when the event is logged, then an alert should be sent to all relevant stakeholders via email and in-app notifications, detailing the nature of the anomaly and recommended actions.

User ability to filter and search anomalies.

Given the user is on the anomaly detection audit trail page, when they apply filtering options or search for specific anomaly instances, then the displayed results should update to reflect the criteria inputs in real-time.

Automatic logging of user actions related to anomalies.

Given that a user takes action on a detected anomaly (e.g., marking it as reviewed or resolved), then this action should be automatically logged in the audit trail with the user's ID, a timestamp, and the action taken.

Exporting audit trail data.

Given an authorized user is viewing the anomaly detection audit trail, when they select the option to export data, then they should receive a download of the audit trail in a CSV or PDF format with all filtering applied.

Performance assessment of anomaly detection logging.

Given the anomaly detection system is operational, when multiple anomalies are detected over a short period, then the system should log each event without delays and maintain the integrity of the audit trail.

Access control and permissions for anomaly data.

Given an audit trail of anomalies, when a user without proper access attempts to view or modify the anomaly data, then they should receive an access denied message and no data should be presented.

Integration with Third-Party Security Tools

User Story: As a cybersecurity specialist, I want to integrate anomaly detection alerts with our existing security tools so that we can create a cohesive security framework and automate response actions to potential threats.
Description: The Integration with Third-Party Security Tools requirement facilitates seamless communication between the Data Anomaly Detection feature and external security systems. This will allow users to leverage existing robust security ecosystems, ensuring that detected anomalies can trigger automatic responses or additional security measures. Such integration enhances overall security efficacy and provides a comprehensive approach to data protection, making it easier to manage threat responses across multiple platforms.
Acceptance Criteria: Integration with a third-party security tool during a live security incident.

Given the Data Anomaly Detection feature is operational, when an anomaly is detected, then an alert should be sent to the integrated third-party security tool within 5 seconds.

Configuration of third-party security tool integration by the user.

Given the user accesses the integration settings, when the user inputs the required credentials and API keys for the third-party tool, then the system should save the settings successfully without errors.

Response to an anomaly alert from the third-party security tool.

Given an anomaly has been detected and an alert sent to the third-party security tool, when the tool triggers an automated response, then the response actions (like blocking an IP) should be logged in Datapy without delay.

User notifications for integration setup completion.

Given the user completes the integration setup for the third-party security tool, when the setup is successful, then a confirmation notification should be displayed to the user confirming successful integration.

Monitoring performance and response time of the integration.

Given the integration is active, when an anomaly occurs, then the system should track and report the time taken for the third-party security tool to respond, which should be within 10 seconds for 95% of anomalies detected.

Reporting and Analytics on Anomalies

User Story: As a compliance officer, I want to generate reports on data anomalies for regulatory purposes so that I can ensure our organization adheres to data security standards and identify areas for improvement.
Description: The Reporting and Analytics on Anomalies requirement provides users with comprehensive reports that summarize detected anomalies, their frequency, and patterns over time. This feature will empower businesses with insights for trend analysis and compliance reporting needs, enabling them to refine their data strategies and improve security measures based on historical data. Reports will be customizable, allowing users to extract relevant information tailored to their business context.
Acceptance Criteria: User generates a report to view detected anomalies over the past month.

Given the user is authenticated and on the reporting dashboard, When they select the 'Anomalies' report type and specify a date range for the past month, Then a comprehensive report summarizing all detected anomalies during that period must be generated and displayed clearly.

User customizes an anomaly report to include specific metrics and filters.

Given the user is on the anomaly report generation page, When they select desired metrics (e.g., frequency, type of anomaly) and apply filters (e.g., date range, severity), Then the report should update to reflect only the selected metrics and filter criteria without any error.

User accesses historical anomaly reports to analyze trends.

Given the user is on the historical reports section, When they select a specific report on anomalies from the past year, Then the report should accurately display historical data, trends, and patterns over time, allowing for effective comparison and analysis.

User receives a real-time alert about a security anomaly detected in the system.

Given the anomaly detection algorithm identifies a significant deviation from normal patterns, When the anomaly is detected, Then the system must send an immediate alert to the relevant users through the configured notification channels (e.g., email, in-app notifications).

User exports the anomaly report for compliance purposes.

Given the user has generated an anomaly report, When they select the 'Export' option and choose a format (e.g., PDF, CSV), Then the system should successfully create and download the report in the selected format without data loss or formatting errors.

User uses the insights from a previous anomaly report to make informed decisions.

Given the user has reviewed the previous anomaly report, When they reference insights from that report in strategic meetings, Then the insights must clearly inform and influence their discussions on improving security measures and data strategies.

Compliance Monitoring Dashboard

This feature provides users with a dedicated dashboard that continuously monitors compliance with data protection regulations and internal policies. It offers insights into compliance status, highlighting areas for improvement and ensuring that businesses remain aligned with necessary regulations, thereby reducing legal risks associated with data management.

Requirements

Real-time Compliance Alerts

User Story: As a compliance officer, I want to receive real-time alerts for compliance breaches so that I can promptly address issues and maintain data protection standards.
Description: This requirement involves implementing real-time alerts that notify users of any compliance breaches or potential risks to data protection regulations. The alerts will be triggered by specific rules defined by the regulatory framework or internal policies, ensuring businesses can take immediate action to address issues. This functionality will provide users with unparalleled visibility and control over compliance matters, enabling timely decision-making and reducing the risk of legal repercussions associated with data mishandling.
Acceptance Criteria: As a compliance officer, I want to receive real-time alerts on my Compliance Monitoring Dashboard whenever there is a compliance breach or risk detected by the system, so that I can take immediate action to mitigate any potential legal issues.

Given that I am logged into the Compliance Monitoring Dashboard, when a compliance rule is breached, then I should receive a real-time alert via email and an in-app notification with details of the breach.

As a user responsible for monitoring compliance, I want to see a history of all alerts generated on the dashboard, so that I can review past compliance issues and actions taken.

Given that I am viewing the Compliance Monitoring Dashboard, when I navigate to the alerts history section, then I should see a list of all past alerts along with timestamps and action status (resolved or unresolved).

As an administrator, I want to configure which compliance rules trigger alerts in the system, ensuring that the alerts are relevant to our specific regulatory requirements.

Given that I have administrative access, when I navigate to the alert configuration settings, then I should be able to select specific compliance rules from a list and save my configurations successfully.

As a compliance officer, I want to ensure that I am not inundated with false alerts, so I want to adjust the threshold settings for triggering compliance alerts based on risk levels.

Given that I am logged in as a compliance officer, when I access the threshold settings, then I should be able to set and save different alert thresholds for various compliance rules.

As a team member, I want to ensure that every alert triggered includes actionable recommendations, so I can understand the next steps required to address the compliance issue.

Given that an alert is triggered on the dashboard, when I click on the alert notification, then the details should include actionable recommendations specific to the compliance issue detected.

Customizable Compliance Metrics

User Story: As a business manager, I want to customize the compliance metrics displayed on my dashboard so that I can focus on the most relevant indicators for my company’s specific regulatory needs.
Description: This requirement allows users to customize the key performance indicators (KPIs) that they want to monitor on their compliance dashboard. Users can select, edit, and delete metrics based on their specific organizational policies or industry regulations, offering flexibility and personalization to meet diverse business needs. This feature empowers users to focus on the most relevant compliance aspects for their operations, enhancing the overall effectiveness of the compliance monitoring dashboard.
Acceptance Criteria: User Customization for Compliance Metrics Selection

Given a user is logged into the Compliance Monitoring Dashboard, When the user navigates to the customization settings, Then the user can view a list of available compliance metrics to select from.

Editing Compliance Metrics

Given a user has selected a compliance metric to monitor, When the user chooses to edit the metric details, Then the system allows them to modify the metric name and description, and saved changes are reflected on the dashboard.

Deleting Compliance Metrics

Given a user has selected compliance metrics to monitor, When the user opts to delete a metric, Then the metric is removed from the dashboard and the user is notified of successful deletion.

Viewing Customized Metrics on Dashboard

Given a user has customized their compliance metrics, When the user accesses the Compliance Monitoring Dashboard, Then the dashboard displays only the metrics that the user has selected for monitoring.

Setting Alerts for Non-Compliance

Given a user has customized compliance metrics, When a selected metric goes below the defined threshold, Then the user receives an automated alert notification.

Resetting Compliance Metrics to Default

Given a user has customized compliance metrics, When the user opts to reset metrics to the default settings, Then all customized metrics are reverted to the default state and the user receives confirmation of the action.

User Feedback on Compliance Metric Customization

Given a user has utilized the customizable metric feature, When the user submits feedback on the customization process, Then the feedback is recorded and can be reviewed by the product team for future improvements.

Audit Trail Reporting

User Story: As an auditor, I want to view an audit trail of compliance-related activities so that I can verify adherence to regulations and ensure accountability within the organization.
Description: This requirement entails the development of an audit trail reporting system that records all compliance-related activities and changes made by users. It will provide a comprehensive log of actions taken, data accessed, and reports generated, ensuring transparency and accountability. This functionality will be essential for organizations that need to demonstrate compliance during audits or investigations, giving them confidence that they have a robust and reliable record of all compliance efforts.
Acceptance Criteria: User accesses the audit trail reporting system to review user activities for compliance purposes.

Given a user with access rights, when they navigate to the audit trail reporting section, then they should see a list of all recorded compliance-related activities with timestamps, usernames, and action descriptions.

An admin generates a report of all access to sensitive data within a specified time frame.

Given an admin user, when they select a date range and click 'Generate Report', then they should receive a report detailing all user access to sensitive data, including user IDs, data accessed, and access timestamps.

A compliance officer reviews flagged activities in the audit trail for potential policy violations.

Given a compliance officer is in the audit trail reporting system, when they filter activities by 'flagged' status, then they should see a list of all activities marked for review along with details for each entry such as user ID and reason for flagging.

A user attempts to modify a report related to audit trail activities and track its changes.

Given a user with editing permissions, when they make changes to an audit report, then the audit trail should record the user's ID, the original content, and the modified content along with the timestamps of both actions.

An organization implements the audit trail reporting system to ensure compliance with data regulations.

Given the organization has deployed the audit trail reporting feature, when an external auditor requests compliance documentation, then the organization should be able to provide an accurate log of all compliance-related activities logged within the last 12 months.

Users access the reporting system to audit changes made to compliance policies.

Given a compliance officer accesses the audit trail, when they select the option to view changes made to compliance policies, then they should see a chronological list of all changes, including the user who made the change and the previous values.

Integration with Third-party Compliance Tools

User Story: As a systems administrator, I want to integrate Datapy with our existing compliance management tools so that we can maximize our current investments and streamline compliance monitoring processes.
Description: This requirement focuses on enabling Datapy to integrate with existing third-party compliance management tools that businesses may already be using. This integration allows for seamless data exchange and enhances the functionality of the compliance monitoring dashboard by providing enriched data and insights. It will help users leverage their current tools alongside Datapy, improving overall user satisfaction and effectiveness in managing compliance.
Acceptance Criteria: User connects Datapy to a third-party compliance tool for the first time and expects to see data integration status on the compliance monitoring dashboard.

Given that the user has valid credentials for the third-party tool, When the user initiates the connection, Then the integration should be established successfully, and the dashboard should display a connection success message.

A user wants to review the data exchanged between Datapy and a third-party compliance tool after integration is completed.

Given that the user is on the compliance monitoring dashboard, When they navigate to the data exchange report section, Then the user should see a comprehensive report of the last 30 days' data exchanged between Datapy and the third-party tool.

An administrator sets up alerts for compliance status changes dependent on data from a third-party tool.

Given that the user has configured alerts and linked a third-party tool, When the compliance status changes in the tool, Then Datapy should trigger a notification to the user as per the configured settings.

A user needs to check the compliance status of various business operations based on integrated data from multiple third-party tools.

Given that multiple third-party tools are integrated with Datapy, When the user accesses the compliance monitoring dashboard, Then they should see a consolidated view of compliance status across all operations based on the integrated data.

A user attempts to troubleshoot a failed integration attempt with a third-party compliance tool.

Given that the integration attempt has failed, When the user reviews the error logs, Then they should be provided with clear error messages and suggested steps to resolve the issue.

A user wants to disconnect a third-party compliance tool from their Datapy account when it's no longer needed.

Given that the user is on the compliance settings page, When they select the option to disconnect a third-party tool, Then the integration should be removed successfully, and the user should receive a confirmation message.

Compliance Training Resource Hub

User Story: As a team leader, I want access to compliance training materials so that my team can better understand data protection regulations and enhance our organizational compliance efforts.
Description: This requirement involves creating a dedicated hub within the compliance monitoring dashboard that offers training materials, guidelines, and resources pertaining to data protection regulations. Users can access articles, videos, and best practice guidelines, promoting a culture of compliance within the organization. This feature is critical for enhancing employee awareness and understanding of compliance issues, reducing the likelihood of accidental breaches.
Acceptance Criteria: User accessing the Compliance Training Resource Hub for the first time.

Given the user is logged into Datapy, When they navigate to the Compliance Monitoring Dashboard, Then they should see a dedicated section labeled 'Compliance Training Resource Hub'.

User searches for a specific compliance training material within the hub.

Given the user is in the Compliance Training Resource Hub, When they use the search function to enter a keyword, Then they should see relevant training materials and resources listed that match the keyword search.

User accesses a video resource from the Compliance Training Resource Hub.

Given the user is in the Compliance Training Resource Hub, When they click on a video resource, Then the video should play without any errors, and the user should be able to pause, rewind, and resume the video.

User downloads a guideline document from the Compliance Training Resource Hub.

Given the user has selected a guideline document from the hub, When they click on the download button, Then the document should download successfully and be accessible in the user’s downloads folder.

User shares a training resource with team members through the hub.

Given the user is viewing a training resource in the Compliance Training Resource Hub, When they utilize the share feature, Then the selected resource should be sent to the specified team members via email with a valid link.

User provides feedback on a training resource within the hub.

Given the user has accessed a training resource, When they submit feedback through the feedback form, Then the feedback should be recorded successfully and a confirmation message should be displayed.

Regulatory Updates Notifications

User Story: As a compliance manager, I want to receive notifications about regulatory updates so that I can adjust our compliance strategies and ensure ongoing adherence to the law.
Description: This requirement provides users with notifications regarding updates or changes in regulations that affect their compliance posture. Users will have the option to receive alerts via email or within the dashboard about key regulatory changes, ensuring they remain informed and can adapt their policies proactively. This feature is essential for keeping businesses aligned with the ever-changing regulatory landscape and preventing compliance lapses.
Acceptance Criteria: User receives email notifications for regulatory updates.

Given the user has opted into email notifications, When a regulatory update occurs, Then the user should receive an email alert with specifics of the update within 24 hours.

User views compliance changes on the dashboard.

Given the user is logged into the compliance monitoring dashboard, When a regulatory update occurs, Then the dashboard should display a notification banner with the details of the update.

User manages notification preferences for regulatory updates.

Given the user accesses the notification settings in their account, When they choose to modify their preferences, Then the system should allow them to enable or disable email and dashboard notifications for regulatory updates and save the changes successfully.

User receives no duplicate notifications for the same regulatory update.

Given the user has opted into both email and dashboard notifications, When a regulatory update occurs, Then the user should receive only one unique notification for that update across both platforms within a defined time window of 48 hours.

User can view a history of regulatory notifications.

Given the user accesses the notifications history section of the compliance dashboard, When they review the list, Then the system should display an accurate list of past regulatory updates received, including dates and types of updates.

User is alerted about critical regulatory changes.

Given the user has selected critical alerts in their notification preferences, When a critical regulatory change occurs, Then the user should receive an immediate alert via both email and dashboard notification.

User can customize notification frequency for regulatory updates.

Given the user accesses their notification settings, When they choose to set a frequency for receiving updates, Then the system should allow them to select options such as 'immediate', 'daily', or 'weekly' and save the settings successfully.

Press Articles

Unleashing Business Potential: Datapy Launches Enhanced Cloud Analytics Platform: FOR IMMEDIATE RELEASE

Contact:
Jane Smith
Public Relations Manager
Datapy Inc.
Phone: (555) 123-4567
Email: jane.smith@datapy.com
Website: www.datapy.com

Datapy Unleashes Business Potential with Enhanced Cloud Analytics Platform
Release Date: 2025-01-27

Datapy Inc., a leading provider of cloud-based analytics solutions, is excited to announce the launch of its enhanced analytics platform, designed specifically for small to medium-sized businesses (SMBs). This upgrade aims to empower businesses by transforming complex data into actionable insights effortlessly. With an intuitive interface, real-time synchronization, and AI-driven predictive analytics, Datapy enables users to make informed decisions quickly and efficiently.

In addition to the standard features expected from a powerful analytics platform, Datapy's latest upgrade includes customizable dashboards that allow users to visualize critical operational metrics clearly. With the increasing reliance on data-driven strategies, Datapy meets the growing demand for easy-to-use analytics tools that require minimal training and onboarding.

"We designed this platform with our users in mind, focusing on enhancing user experience and maximizing accessibility to data insights," said Mark Thompson, CEO of Datapy Inc. "Our goal is to empower businesses to leverage their data without the steep learning curve typically associated with analytics software."

The platform's new collaborative tools significantly improve team communication and foster a more data-driven culture within organizations. The system enables multiple users to access dashboards and reports simultaneously, promoting a collaborative approach to decision-making.

Features that stand out in the latest version of Datapy include:

Interactive Tutorial Sessions – Offering engaging sessions that guide users through core functionalities.

AI-Driven Walkthroughs – Personalized onboarding experiences tailored to user roles.

Real-Time Data Sync – Users can synchronize their data instantly, ensuring analytics reflect real-time information.

Collaboration Alerts – Tagging colleagues in alerts for improved teamwork.

Comprehensive Documentation Portal – A well-organized resource that provides guides and troubleshooting advice for users.

Feedback-Driven Improvements – Collecting user feedback to continuously enhance the onboarding experience.

Datapy’s mission is to simplify data analysis and make it accessible to everyone in an organization, regardless of their technical background. By removing barriers to data analysis, businesses can unlock their potential and operate more effectively in today’s data-driven world.

"We believe that every business, regardless of size, should have access to powerful analytics tools that drive growth and success. With this launch, we're taking a big step towards making that belief a reality," added Thompson.

In addition to the product launch, Datapy offers various resources for existing users, including a community support portal where users can find FAQs, forums, and user-contributed tutorials. These resources aim to assist users in getting the most out of their analytics experience.

For more information about how Datapy can empower your business with data-driven insights, please contact Jane Smith, Public Relations Manager, or visit us at www.datapy.com.

About Datapy

Datapy Inc. specializes in creating intuitive cloud-based analytics solutions tailored to the needs of small to medium-sized businesses. Our mission is to transform how organizations utilize data, helping them to make informed decisions and drive operational success. With a commitment to innovation and user empowerment, Datapy is at the forefront of the analytics revolution.

END

Datapy Empowers SMBs with AI-Driven Predictive Analytics: FOR IMMEDIATE RELEASE

Contact:
John Doe
Marketing Lead
Datapy Inc.
Phone: (555) 234-5678
Email: john.doe@datapy.com
Website: www.datapy.com

Datapy Empowers SMBs with AI-Driven Predictive Analytics
Release Date: 2025-01-27

Datapy Inc. is thrilled to announce the introduction of its AI-driven predictive analytics feature, designed to help small to medium-sized businesses (SMBs) forecast trends and improve decision-making processes. This pioneering advancement integrating artificial intelligence with data analytics aims to position SMBs for success in competitive markets.

With the new predictive analytics tools in Datapy, users can benefit from machine learning algorithms that analyze historical data patterns and make data-driven recommendations. These insights enable businesses to anticipate market shifts, customer behaviors, and operational needs, helping them stay ahead of the curve.

"In today’s fast-paced business environment, the ability to predict trends is paramount for maintaining a competitive edge. Our AI-driven predictive analytics changes the game for SMBs, allowing them to not only respond to market demands but to anticipate them," shared Emily Carter, COO of Datapy. "We are excited to provide tools that will help businesses make proactive decisions based on real insights."

Key features of this new predictive analytics module include:

Trend Watcher – Users receive instant notifications when significant shifts in KPIs are detected.

AI-Powered Insights Engine – Analyzes historical data to provide contextualized recommendations.

Historical Impact Analysis – Users can review the effectiveness of past decisions based on historical trends.

Customizable Alert Triggers – Users define the thresholds for alerts, tailoring notifications to meet their unique business needs.

Moreover, this update allows businesses to leverage trend summaries and insights within their custom dashboards, fostering an environment of continuous improvement and agility. By integrating predictive analytics into their operations, companies can make informed decisions and drive growth sustainably.

"The ability to generate insights from data without needing extensive analytics knowledge is incredibly powerful. Datapy is changing the game for how SMBs approach predictive analytics," continued Carter.

Datapy remains committed to ensuring businesses realize the utmost potential of their data. The company will hold an online webinar on February 10, 2025, to provide an in-depth demonstration of the new predictive analytics features and share best practices tailored for SMBs. Interested participants can register via the Datapy website.

For more information about Datapy’s latest features or to schedule a demo, please contact John Doe, Marketing Lead, or visit us at www.datapy.com.

About Datapy

Founded in [Year], Datapy Inc. has been committed to delivering powerful, user-friendly analytics tools for businesses. With an innovative suite of features designed to turn data into actionable insights, Datapy continues to empower organizations small and large to thrive in the data-driven world.

END

Revolutionizing Analytics: Datapy Introduces Custom Data Connectors: FOR IMMEDIATE RELEASE

Contact:
Alice Brown
Product Manager
Datapy Inc.
Phone: (555) 345-6789
Email: alice.brown@datapy.com
Website: www.datapy.com

Revolutionizing Analytics: Datapy Introduces Custom Data Connectors
Release Date: 2025-01-27

Datapy Inc. today announces the launch of Custom Data Connectors, a feature designed to enhance data integration and analytics capabilities for small to medium-sized businesses (SMBs). This feature allows users to create personalized data connectors that seamlessly connect their existing systems directly with the Datapy platform.

Custom Data Connectors eliminate data silos, enabling SMBs to enhance their analytics by integrating various data sources into one cohesive view. This feature serves as a game-changer for businesses that rely on multiple systems to manage their data.

"We understand that businesses operate through multiple systems, and connectivity is crucial for effective data analysis. Custom Data Connectors provide a practical solution that lets users tailor their integration process," stated Peter Allen, CTO of Datapy. "We are committed to making data accessible and ensuring it drives decision-making effectively."

The main advantages of Custom Data Connectors include:

Ease of Integration – Users can easily connect data sources without extensive coding knowledge.

Real-Time Data Sync – Ensures that data is synced in real-time for accurate and timely insights.

Flexible Customization – Tailor connectors to fit various business applications seamlessly.

Cross-Platform Compatibility – Works smoothly across multiple platforms to maximize technology investments.

With this announcement, Datapy emphasizes its commitment to equipping businesses with the tools needed to make data-informed decisions. By facilitating deeper connections within their data ecosystem, Datapy enhances its users’ ability to gain insights swiftly and efficiently.

"Custom Data Connectors simplify the integration process, empowering users to create a data landscape that fits their unique needs, thus maximizing their analytical capabilities," added Allen.

Datapy encourages users to explore the new feature and leverage its capabilities for better data management and analytics. For a detailed guide on creating data connections, users can access the Comprehensive Documentation Portal available through Datapy’s website or contact support.

For further information about Custom Data Connectors or to schedule a consultation, please reach out to Alice Brown, Product Manager, or visit us at www.datapy.com.

About Datapy

Datapy is dedicated to providing innovative cloud-based analytics solutions tailored to the needs of small and medium-sized businesses. By transforming complex data into actionable insights, Datapy enables organizations to thrive in a data-centric environment.

END

Collaborative Insights Hub: Datapy Launches New Feature for Real-Time Team Collaboration: FOR IMMEDIATE RELEASE

Contact:
Emily Johnson
Communications Director
Datapy Inc.
Phone: (555) 456-7890
Email: emily.johnson@datapy.com
Website: www.datapy.com

Collaborative Insights Hub: Datapy Launches New Feature for Real-Time Team Collaboration
Release Date: 2025-01-27

Datapy Inc. proudly announces the launch of its Collaborative Insights Hub, a groundbreaking feature that enhances teamwork and data analysis for small to medium-sized businesses (SMBs). This innovative tool enables teams to collaborate in real-time on data visualizations, share insights, and drive informed business decisions collectively.

The Collaborative Insights Hub allows users to view live dashboards and reports, providing a platform for enhanced collaboration and dialogue among team members. The feature includes functionalities for commenting, tagging colleagues in discussions, and version control, ensuring that every team member stays engaged and informed.

"We recognize that teamwork is critical to the success of any organization, especially when it comes to data-driven decision-making. The Collaborative Insights Hub enables teams to work together seamlessly, aligning their insights to drive optimal outcomes," said Sarah Miller, VP of Product Development at Datapy. "We are excited to offer our users a tool that emphasizes collaboration without compromising on the quality of analysis."

This new feature comes with various key functionalities, including:

Real-Time Collaboration Mode – Allows teams to work simultaneously on interactive visualizations.

Comment and Discuss Threads – Users can leave feedback directly on data visualizations, promoting engaged discussions.

Shared Data Workspaces – Dedicated zones for specific projects or metrics that streamline focused analysis.

Real-Time Notifications – Instant updates when changes are made, keeping all team members informed.

Datapy believes that the Collaborative Insights Hub will foster a culture of continuous learning and adaptability within organizations by uniting teams with data at the forefront of decision-making.

"With the Collaborative Insights Hub, organizations can turn data analysis into a collaborative endeavor that promotes innovation and strategic thinking," added Miller.

To learn more about the Collaborative Insights Hub and its features, Datapy invites users to register for an upcoming webinar on February 15, 2025. This session will showcase the hub’s capabilities and practical applications for teams working in various industry sectors.

For additional details about the hub or to schedule a demonstration, please contact Emily Johnson, Communications Director, or visit us at www.datapy.com.

About Datapy

Datapy Inc. provides robust and user-friendly analytics solutions designed for the unique needs of small to medium-sized businesses. By transforming data into strategic insights, Datapy drives operational success and innovation in an ever-evolving marketplace.

END

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Product Details

Personas

Tech-Savvy Entrepreneur

Growth-Focused Sales Director

Data-Driven Product Manager

Analytical Customer Support Lead

Innovative Business Strategist

Product Ideas

Product Features

Interactive Tutorial Sessions

Requirements

Interactive Onboarding

Progress Tracking System

Feedback Mechanism

Language Support

Gamification Elements

AI-Driven Walkthroughs

Requirements

Personalized Walkthrough Engine

Adaptive Learning Pathways

AI Feedback Mechanism

Multi-Role Support System

Interactive Tutorial Mode

Gamified Learning Modules

Requirements

Interactive Task Checklist

Reward System Integration

Progress Tracking Dashboard

In-Platform Tutorials and Tips

Social Sharing Capabilities

Feedback Mechanism

Contextual Help Tooltips

Requirements

Dynamic Tooltip Content

Tooltip Timing and Behaviour Configuration

Searchable Tooltip Index

Multilingual Support for Tooltips

User Feedback Mechanism

Contextual Video/Media Integration

Onboarding Checklist

Requirements

Interactive Steps Guide

Customizable Checklist Templates

Progress Tracking and Feedback

Integrated Help Resources

Completion Certification

Community Support Portal

Requirements

Integrated FAQ Section

User Forum Creation

User-Contributed Tutorial Repository

Community Engagement Metrics

Search Functionality Within the Portal

Community Guidelines and Moderation System

Notifications for Community Interactions

Feedback-Driven Improvements

Requirements

User Feedback Collection

Analytics Dashboard Integration

Automated Feedback Analysis

Feedback Loop Implementation

Onboarding Module Update Mechanism

Feedback Reminder Notifications

Real-Time Data Sync

Requirements

Real-Time Data Integration

User Access Management

Customizable Dashboard Widgets

Collaborative Analytics Tools

AI-Driven Forecasting Models

Mobile Application Access

Custom Data Connectors

Requirements

Dynamic Connector Configuration

Pre-built Connector Library

Connection Health Monitoring

User-Friendly Connection Wizard

Integration Testing Framework