InnoData Viz

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Product Details

Name

InnoData Viz

Tagline

Insights Made Simple

Marketing Manager

Name: Marketing Manager
Description: The Marketing Manager is a creative and analytical professional who plays a pivotal role in crafting and executing marketing strategies. They seek insights from data visualization to make informed decisions, optimize campaigns, and drive brand growth. With a focus on understanding consumer behavior and market trends, they engage with InnoData Viz to extract actionable visual insights that shape their marketing initiatives.
Demographics: Age: 28-45, Gender: Any, Education: Bachelor's or Master's in Marketing, Business, or a related field, Occupation: Marketing Manager, Income Level: Moderate to High
Background: The Marketing Manager has a background in marketing, with experience in crafting campaigns, analyzing consumer behavior, and driving brand awareness. They are tech-savvy, creative, and have a passion for harnessing data to inform marketing strategies and customer engagement initiatives.
Psychographics: They are driven by creativity and data-driven decision-making. They value innovative tools that provide actionable insights and foster creativity in marketing endeavors. Their motivation lies in optimizing marketing strategies, staying ahead of industry trends, and delivering impactful campaigns.
Needs: Access to comprehensive and real-time market data, intuitive data visualizations for campaign performance analysis, tools for customer segmentation and behavior analysis, collaboration and sharing capabilities for cross-functional teams
Pain: Lack of real-time data insights, difficulty in understanding complex datasets, inefficient collaboration in analyzing marketing campaign data, reliance on separate tools for data visualization and analysis
Channels: Digital marketing platforms, industry-specific forums and events, professional networking sites, marketing productivity tools
Usage: Frequent usage for campaign performance analysis, ad spend optimization, customer behavior analysis, market trend tracking, and annual marketing report preparation
Decision: Influenced by data accuracy and visualization ease, cost-effectiveness, integration with existing marketing tools, and team feedback on usability and collaboration features

Research Scientist

Name: Research Scientist
Description: The Research Scientist is an analytical and detail-oriented professional who delves into complex research data to uncover patterns and insights. They utilize InnoData Viz to visualize scientific data, analyze trends, and present findings in a clear and impactful manner. Their work revolves around exploring data to make groundbreaking discoveries and advancements in their field of research.
Demographics: Age: 25-55, Gender: Any, Education: Ph.D. in a scientific discipline (e.g., Chemistry, Biology, Physics), Occupation: Research Scientist, Income Level: Moderate to High
Background: The Research Scientist has an extensive academic background in their scientific discipline, with a focus on research methodologies, data analysis, and experimental design. They are driven by a passion for scientific exploration and breakthrough discoveries, seeking innovative tools to facilitate data visualization and interpretation.
Psychographics: They are motivated by the pursuit of knowledge, scientific advancement, and the desire to make meaningful contributions to their field. They value tools that simplify the visualization of complex research data and empower them to communicate their findings effectively.
Needs: Efficient data visualization for research findings, advanced analytical tools for pattern recognition, capability to export visuals for publication and presentations, support for cross-disciplinary collaboration and sharing
Pain: Inability to effectively communicate complex research data, limitations in data visualization, lack of advanced analytical capabilities, time-consuming data prep and analysis processes
Channels: Scientific research publications, academic conferences and forums, research collaboration platforms, scientific data analysis software communities
Usage: Regular usage for experimental data visualization, trend analysis, manuscript preparation, research proposal data exploration, and collaborative data review
Decision: Influenced by data security and privacy, visualization customization options, integration with scientific databases, and feedback from fellow researchers and collaborators

Financial Analyst

Name: Financial Analyst
Description: The Financial Analyst is a data-savvy and detail-oriented professional who relies on robust insights to drive financial planning and decision-making. They leverage InnoData Viz to analyze financial data, forecast trends, and present actionable visualizations to support business financial strategies and investment decisions.
Demographics: Age: 24-40, Gender: Any, Education: Bachelor's or Master's in Finance, Accounting, Economics, or a related field, Occupation: Financial Analyst, Income Level: Moderate to High
Background: The Financial Analyst possesses a background in finance, with experience in financial modeling, data analysis, and reporting. They are motivated by a quest for accurate financial insights and efficient decision-making, reflecting a passion for leveraging data to drive meaningful financial outcomes.
Psychographics: They are driven by precision, accuracy, and data-driven planning. They value tools that provide advanced financial analytics, facilitate data interpretation, and streamline the process of communicating financial insights to stakeholders.
Needs: Data visualization for trend analysis, forecasting tools for financial projections, integration with financial databases, real-time collaboration for financial reporting, ability to leverage complex financial models for scenario planning
Pain: Lack of real-time financial insights, limited forecasting capabilities, inefficiency in financial data collaboration, difficulty in communicating financial data insights effectively
Channels: Financial news and publications, finance and investment forums, professional finance communities, financial productivity tools
Usage: Frequent usage for financial trend analysis, investment portfolio performance evaluation, financial report preparation, and data-driven financial strategy formulation
Decision: Influenced by data accuracy and precision, visualization compatibility with financial reporting standards, integration with financial platforms, and feedback from finance team on usability and collaboration features

Product Ideas

Enhanced Collaborative Workspace: Create a feature that allows real-time collaborative workspace for users to work on visualizations together, enhancing team productivity and data insight sharing.
AI-Powered Data Recommendations: Implement machine learning algorithms to provide data recommendations based on user behavior and historical data usage, enabling users to make informed decisions and discover hidden insights more efficiently.
Custom Dashboard Templates: Introduce customizable dashboard templates that cater to specific user roles (e.g., Marketing Manager, Financial Analyst), allowing users to quickly generate tailored visualizations and insights to meet their individual needs.

Product Features

Real-time Collaboration

Empower users to collaborate in real-time, enabling seamless teamwork on visualizations and data insights while ensuring swift decision-making and enhanced productivity.

Requirements

Real-time Data Sync

User Story: As a business analyst, I want to collaborate on visualizations in real-time so that I can work with the most up-to-date data and insights, leading to informed decision-making and improved productivity.
Description: Enable real-time data synchronization to allow users to collaborate seamlessly on shared visualizations and insights. This feature will ensure that all users have access to the latest data updates, enhancing accuracy and decision-making.
Acceptance Criteria: User creates a new visualization and shares it with a team member

Given the user has created a new visualization, When the user shares the visualization with a team member, Then the shared visualization reflects real-time data updates for all users

Multiple users collaborate on a visualization in real-time

Given multiple users are editing a shared visualization, When a user makes changes to the visualization, Then all other users see the changes in real time

Data source is updated in real time

Given the data source is updated with new data, When the data source is synchronized in real time, Then the visualization reflects the updated data for all users

User accesses the latest version of a shared visualization

Given a user accesses a shared visualization, When the user opens the visualization, Then the visualization displays the most recent data available in real time

Interactive Commenting

User Story: As a team member, I want to provide real-time feedback on visualizations through interactive comments, so that I can engage in productive discussions and contribute to collaborative decision-making.
Description: Implement interactive commenting functionality to facilitate real-time feedback and discussion on visualizations. This feature will enable users to provide comments directly on visual elements, fostering collaboration and knowledge sharing.
Acceptance Criteria: User adds a comment to a visualization

Given the user is viewing a visualization, when they click on a specific data point, then a comment input field should appear, allowing the user to enter and submit a comment related to that data point.

User edits a comment on a visualization

Given the user has added a comment to a visualization, when they select their own comment, then an edit option should be available, allowing the user to revise and update the content of the comment.

Real-time visibility of comments

Given multiple users are collaborating on a visualization, when one user adds or edits a comment, then the change should be immediately visible to all other users viewing the same visualization.

Comment history and tracking

Given a visualization has multiple comments, when a user selects a comment, then the system should display the user's name, timestamp, and content of the comment, providing a clear history of interactions.

Version Control

User Story: As a data analyst, I want to maintain a history of changes to visualizations through version control, so that I can track and manage revisions for data integrity and compliance purposes.
Description: Introduce version control to track changes and revisions made to visualizations, ensuring data integrity and enabling users to revert to previous versions if needed. This capability will enhance data governance and control over the visualization creation process.
Acceptance Criteria: User selects a visualization for version control

Given a user has created a visualization, when the user selects the option to enable version control, then the system should create an initial version snapshot of the visualization and mark it as the current version.

User makes changes and saves new version

Given a user has an existing visualization with version control enabled, when the user makes changes to the visualization and saves the changes, then the system should create a new version snapshot and mark it as the current version.

User reverts to a previous version

Given a user has an existing visualization with multiple versions, when the user selects a previous version from the version history and confirms the action, then the system should replace the current version with the selected version and update the version history.

Data integrity during version control

Given a user has an existing visualization with version control enabled, when the user reverts to a previous version, then the data integrity of the visualization should be maintained, and the data used in the previous version should be accurately reflected in the reverted visualization.

Audit trail for version history

Given a user has an existing visualization with version control enabled, when the user accesses the version history, then the system should display a comprehensive audit trail of all changes made to the visualization, including user actions, timestamps, and comments.

Shared Annotation Tools

Introduce shared annotation tools for users to add comments and insights to visualizations, facilitating meaningful collaboration and knowledge sharing within the workspace.

Requirements

Collaborative Annotation Interface

User Story: As a business analyst, I want to be able to add comments and insights to visualizations so that I can collaborate with my team and share knowledge effectively.
Description: Implement a collaborative annotation interface that allows users to add comments and insights to visualizations. This feature will enable seamless collaboration and knowledge sharing within the workspace, enhancing the overall user experience and promoting data-driven decision-making.
Acceptance Criteria: User adds a comment to a visualization

Given that the user has a visualization open, when the user adds a comment using the shared annotation tool, then the comment is successfully saved and visible to other users.

User edits an existing comment on a visualization

Given that the user has a visualization open with existing comments, when the user edits a comment using the shared annotation tool, then the edited comment replaces the original comment and is visible to other users.

User views comments and insights on a visualization

Given that the user has a visualization open, when the user accesses the shared annotations, then all comments and insights are displayed clearly and can be interacted with.

User collaborates with other team members using the annotation tools

Given that the user is collaborating with other team members on a visualization, when multiple users add comments and insights, then all users can view, edit, and respond to each other's annotations in real-time.

Real-time Annotation Updates

User Story: As a data analyst, I want to see real-time updates for annotations so that I can engage in immediate discussions and address emerging insights effectively.
Description: Enable real-time updates for annotations added to visualizations, ensuring that users can see and respond to comments and insights immediately. This functionality will enhance the collaborative experience, enabling users to engage in meaningful discussions and address insights as they emerge.
Acceptance Criteria: User adds an annotation to a visualization and saves it

Given a user is logged into the system and has appropriate permissions, when the user adds an annotation to a visualization and saves it, then the annotation should appear immediately for all other users viewing the same visualization.

Multiple users simultaneously add annotations to the same visualization

Given multiple users are logged into the system and viewing the same visualization, when each user adds an annotation simultaneously, then all annotations should appear immediately for all other users viewing the same visualization.

User responds to an annotation

Given a user is viewing a visualization with annotations, when the user responds to an annotation, then the response should be visible to the original annotator and all other users viewing the same visualization.

Annotation history and version control

Given a user is viewing a visualization with annotations, when the user accesses the annotation history, then the system should display a clear version history and provide the ability to revert to previous annotations.

Annotation Notification System

User Story: As a decision-maker, I want to receive notifications for new annotations so that I can stay updated on incoming insights and contribute to discussions when needed.
Description: Develop a notification system to alert users when new annotations are added to visualizations. This system will ensure that users stay informed about incoming insights and can participate in discussions in a timely manner, enhancing overall collaboration and knowledge sharing.
Acceptance Criteria: User receives a real-time notification when a new annotation is added to a visualization in the workspace

Given the user is logged in and has permission to access the visualization, when a new annotation is added to the visualization, then the user receives a real-time notification with details of the new annotation.

User can view and respond to the new annotation from the notification

Given the user receives a notification for a new annotation, when the user clicks on the notification, then the user is taken to the visualization where the annotation was added and can view and respond to the new annotation.

User can customize notification settings for annotations

Given the user is logged in, when the user navigates to the notification settings, then the user can customize notification preferences for new annotations, including frequency, format, and delivery method.

Admin can manage annotation notification settings for the entire workspace

Given the admin has the appropriate permissions, when the admin accesses the workspace settings, then the admin can manage notification settings for annotations, including default preferences for all users.

Interactive Co-editing

Enable interactive co-editing of visualizations, allowing multiple users to contribute and modify visual data presentations simultaneously, fostering efficient teamwork and idea exchange.

Requirements

Real-time Data Synchronization

User Story: As a data analyst, I want real-time data synchronization while co-editing visualizations so that I can have the most current data to make informed decisions with my team.
Description: Enable real-time data synchronization to ensure that all users view the most up-to-date version of visualizations, promoting collaboration and accuracy in decision-making. This feature will allow changes made by one user to be immediately reflected for all other co-editors, minimizing discrepancies and enhancing the effectiveness of collaborative analysis.
Acceptance Criteria: As a user, I want to see real-time changes made by co-editors reflected in visualizations immediately.

Given that multiple users are co-editing a visualization, when one user makes a change to the visualization, then all other co-editors should see the change in real-time.

As a user, I want to ensure that the real-time data synchronization feature works seamlessly with large datasets.

Given a visualization with a large dataset, when changes are made to the dataset, then the real-time synchronization should update the visualization without significant delay.

As a user, I want to be able to track the history of changes made during a collaborative editing session.

Given that multiple users are co-editing a visualization, when changes are made, then a log of changes should be maintained, allowing users to track the history of edits.

User Presence Indicator

User Story: As a collaborative user, I want to see the online status of my co-editors so that I can coordinate better and avoid conflicting edits during real-time visualization collaboration.
Description: Implement a user presence indicator to display the online/offline status of co-editors, enabling better communication and coordination during collaborative visualization editing. This indicator will enhance real-time collaboration by providing visibility into the availability of team members and reducing potential conflicts during simultaneous editing.
Acceptance Criteria: As a user, I want to see the real-time status of co-editors to know who is online and available for collaboration.

When a user is online and actively editing a visualization, their presence indicator should show as 'online'. When a user is not actively editing a visualization, their presence indicator should show as 'offline'.

During a co-editing session, I want to be able to quickly identify the online status of my co-editors to avoid conflicting edits.

The presence indicators of co-editors should update in real time, reflecting their current online/offline status as they begin or stop editing a visualization.

I want to have confidence in the accuracy of the presence indicators when collaborating on visualizations with co-editors.

The presence indicator should update reliably and consistently, without delays or inaccuracies, to provide a trustworthy representation of co-editors' online status.

As a system administrator, I need to have control over the visibility of presence indicators for different user roles.

The system should provide configurable settings to control the visibility of presence indicators based on user roles, allowing administrators to customize the display of online/offline status for different user groups.

Version History and Rollback

User Story: As a data steward, I want the ability to review and revert to previous versions of visualizations to maintain data integrity and accuracy during real-time co-editing sessions.
Description: Introduce version history and rollback functionality to track changes made during co-editing sessions, enabling users to review and revert to previous versions of visualizations. This capability will enhance data governance and provide safeguards against unintended changes or errors during collaborative editing, ensuring data integrity and accuracy.
Acceptance Criteria: User accesses the version history of a visualization and views all available versions.

Given a visualization with multiple versions, when the user accesses the version history, then they should be able to view a chronological list of all available versions.

User selects a specific version for rollback and confirms the rollback action.

Given the version history of a visualization, when the user selects a specific version for rollback and confirms the action, then the visualization should revert to the selected version and the user should receive a confirmation message.

Multiple users co-edit a visualization simultaneously, each making changes and saving versions.

Given a visualization being co-edited by multiple users, when each user makes changes and saves versions, then the system should track and timestamp each user's changes, creating a version history that captures all modifications.

Version History Tracking

Implement version history tracking to enable users to review and revert to previous versions of visualizations, ensuring data integrity and facilitating collaborative improvement on shared projects.

Requirements

Version Control

User Story: As a business analyst, I want the ability to track and revert to previous versions of visualizations so that I can ensure data integrity and collaborate effectively on shared projects.
Description: Implement version control functionality to allow for tracking, reviewing, and reverting to previous versions of visualizations. This feature enhances data integrity and facilitates collaborative improvement on shared projects by providing a comprehensive history of changes.
Acceptance Criteria: User edits a visualization

Given that a user is editing a visualization, when the user makes changes and saves the visualization, then a new version is created with the updated changes.

User reviews version history

Given that a user wants to review version history, when the user opens the version history panel, then they can see a list of all previous versions with details such as date, time, and author.

User reverts to a previous version

Given that a user wants to revert to a previous version, when the user selects a specific version from the history and confirms the action, then the visualization reverts to the selected version.

Version Comparison

User Story: As a decision-maker, I want to compare different versions of visualizations to identify changes and improvements over time, so that I can make informed decisions based on historical data.
Description: Enable users to compare different versions of visualizations to identify changes and improvements over time. This capability provides valuable insights into the evolution of visualizations and supports informed decision-making based on historical changes.
Acceptance Criteria: User Reviews Version History

Given that a user has access to the platform, when they navigate to a shared project with version history tracking enabled, then they should be able to review and compare different versions of visualizations.

Identifying Changes in Versions

Given that a user is comparing different versions of visualizations, when they select two versions to compare, then the platform should highlight the changes and improvements between the two versions.

Reverting to Previous Versions

Given that a user is reviewing version history, when they choose to revert to a previous version of a visualization, then the platform should restore the selected previous version for further use.

Version Access Control

User Story: As an administrator, I want to control user privileges for viewing, editing, and reverting to previous versions of visualizations, so that I can ensure data security and compliance with organizational policies.
Description: Implement access control for version history, allowing administrators to manage user privileges for viewing, editing, and reverting to previous versions. This ensures data security and compliance with organizational policies regarding version management.
Acceptance Criteria: User views version history

Given a user has appropriate privileges, when they access the version history, then they should be able to view a list of previous versions with details such as timestamp, creator, and comments.

User reverts to a previous version

Given a user has edit privileges, when they select a previous version to revert to, then the visualization should be updated to the selected version while maintaining the integrity of the underlying data.

Administrator manages user privileges

Given an administrator is logged in, when they access the version access control settings, then they should be able to define user privileges for viewing, editing, and reverting to previous versions.

User attempts to edit a read-only version

Given a user has only view privileges, when they attempt to edit a read-only version, then they should receive a notification indicating their lack of edit privileges for the selected version.

Smart Data Insights

Leverage AI algorithms to automatically generate insightful data recommendations based on user behavior and historical data, enabling users to uncover trends and insights efficiently.

Requirements

AI-Driven Data Recommendations

User Story: As a business analyst, I want the platform to automatically generate data insights based on user behavior and historical data, so that I can uncover trends and anomalies efficiently and make informed decisions.
Description: Implement AI algorithms to analyze user behavior and historical data, providing automatic data insights that uncover trends and anomalies. This feature enhances the platform by enabling users to make informed decisions based on the AI-generated data recommendations, ultimately improving data-driven decision-making processes.
Acceptance Criteria: User receives AI-generated data recommendations upon accessing the platform dashboard

When the user logs in, the dashboard should display AI-generated data recommendations based on the user's behavior and historical data.

User interacts with the AI-generated data recommendations

When the user interacts with the AI-generated data recommendations, the system should accurately respond to user inputs and updates the recommendations dynamically.

User validates the accuracy of AI-generated data recommendations

When the user reviews the AI-generated data recommendations, the system should provide clear and meaningful insights that align with the user's expectations and facilitate informed decision-making.

Data administrator monitors AI algorithm performance

When the data administrator accesses the backend monitoring tools, the system should provide comprehensive metrics and analytics on the performance and accuracy of the AI algorithms in generating data recommendations.

User feedback on AI-generated data recommendations

When users provide feedback on the AI-generated data recommendations, the system should capture and analyze the feedback to continuously improve the relevance and accuracy of the recommendations.

Real-Time Data Visualization

User Story: As a data analyst, I want to visualize and analyze data in real time, so that I can gain immediate insights into changing data trends and patterns to make timely and informed decisions.
Description: Enable real-time data visualization capabilities within the platform, allowing users to visualize and analyze data as it is being updated. This feature empowers users to gain immediate insights into changing data trends and patterns, enhancing their ability to make timely and informed decisions.
Acceptance Criteria: User visualizes real-time data trends on the dashboard

Given that the user is logged in, when new data is updated in the system, then the dashboard should display the updated data in real-time.

User applies filters to real-time data visualization

Given that the user is on the real-time dashboard, when the user applies filters to the visualized data, then the dashboard should update to reflect the filtered data in real-time.

User accesses historical data alongside real-time visualization

Given that the user is on the real-time dashboard, when the user accesses historical data and real-time data together, then the dashboard should display both datasets in a synchronized manner.

User receives real-time alerts for data anomalies

Given that the user has set up alert preferences, when anomalous data is detected in real-time, then the user should receive immediate alerts with details of the anomaly.

Database Integration Enhancement

User Story: As a platform user, I want seamless integration with SQL and Excel, so that I can easily access and work with data from diverse sources, streamlining data preparation and analysis.
Description: Enhance integration with databases like SQL and Excel to improve data accessibility and usability for users. This enhancement streamlines data preparation and enhances the platform's usability, enabling users to seamlessly work with data from diverse sources.
Acceptance Criteria: As a user, I want to connect to an SQL database and import data into InnoData Viz.

Given a valid SQL database connection, when I select the import option, then the data from the SQL database is successfully imported into InnoData Viz.

As a business analyst, I want to seamlessly work with Excel data in InnoData Viz for data analysis and visualization.

Given an Excel file with valid data, when I upload the file to InnoData Viz, then the data is successfully imported and ready for analysis and visualization.

As a decision-maker, I want to effortlessly collaborate with my team in real time while preparing and analyzing datasets.

Given access to the real-time collaboration feature, when I make changes to a dataset, then my team members can see the changes in real time and collaborate effectively.

Personalized Data Suggestions

Provide users with personalized data suggestions tailored to their usage patterns and preferences, empowering them to make informed decisions and discover hidden insights with ease.

Requirements

Usage Pattern Analysis

User Story: As a business analyst, I want the system to analyze my usage patterns and provide personalized data suggestions so that I can easily discover relevant insights and make informed decisions based on my preferences.
Description: This requirement involves analyzing the usage patterns of users to provide personalized data suggestions. It will enable the system to understand user interactions, preferences, and behaviors, and use this information to recommend relevant and insightful data sets for the users.
Acceptance Criteria: As a new user registers on the platform, the system captures their initial data interaction and usage patterns.

Given a new user registers on the platform, When the system captures their initial data interaction and usage patterns, Then the system successfully records the user's initial data interaction and usage patterns.

A user with a history of analyzing sales data begins using the platform regularly, accessing various visualization tools and data sets related to sales.

Given a user with a history of analyzing sales data begins using the platform regularly, When the user accesses various visualization tools and data sets related to sales, Then the system provides personalized data suggestions tailored to the user's usage patterns and preferences.

A user repeatedly interacts with data sets related to marketing and advertising, showing a consistent preference for these types of data.

Given a user repeatedly interacts with data sets related to marketing and advertising, When the user continues to show a consistent preference for these types of data, Then the system recommends relevant and insightful marketing and advertising data sets to the user.

A user adopts a new data analysis approach, exploring data sets from different sources and experimenting with new visualization techniques.

Given a user adopts a new data analysis approach, When the user explores data sets from different sources and experiments with new visualization techniques, Then the system adapts the personalized data suggestions to align with the user's evolving usage patterns and preferences.

A user frequently requests data sets from specific databases and integrates them into their analysis projects.

Given a user frequently requests data sets from specific databases and integrates them into their analysis projects, When the user continues to request data sets from specific databases, Then the system optimizes the personalized data suggestions to prioritize data sets from the user's preferred databases.

Real-time Recommendation Engine

User Story: As a decision-maker, I want the system to provide real-time personalized data suggestions so that I can stay up-to-date with relevant insights and make timely, data-driven decisions.
Description: Develop a real-time recommendation engine that leverages AI to deliver personalized data suggestions. The engine will continuously analyze user interactions and data usage to dynamically generate relevant recommendations, enhancing the user experience and facilitating seamless discovery of valuable insights.
Acceptance Criteria: When a user logs into the platform, the real-time recommendation engine should analyze their recent data interactions and generate personalized data suggestions.

Given that a user logs into the platform, when the real-time recommendation engine analyzes their recent data interactions, then it should generate personalized data suggestions based on their usage patterns and preferences.

When a user interacts with a dataset, the real-time recommendation engine should dynamically update and suggest relevant data insights based on the user's actions.

Given that a user interacts with a dataset, when the real-time recommendation engine dynamically updates, then it should suggest relevant data insights based on the user's actions.

When the user receives a data suggestion, they should be able to easily explore and further analyze the recommended insights within the platform.

Given that the user receives a data suggestion, when they explore and further analyze the recommended insights, then they should be able to do so within the platform.

Integration with AI-driven Insights

User Story: As a data analyst, I want the system to integrate personalized data suggestions with AI-driven insights so that I can quickly identify trends and anomalies within the recommended data sets, leading to more efficient analysis and decision-making.
Description: Integrate the personalized data suggestions feature with AI-driven insights to ensure that the recommended data sets are enriched with intelligent highlighting of trends and anomalies. This integration will enhance the value of the suggestions by providing contextual intelligence and making it easier for users to identify significant patterns within the data.
Acceptance Criteria: User receives personalized data suggestion upon accessing the platform.

Given the user has accessed the platform, and their usage patterns and preferences are analyzed, when the system provides personalized data suggestions based on the analysis, then the acceptance criteria is met.

User interacts with a recommended data set enriched with AI-driven insights.

Given the user has selected a recommended data set, when the system highlights trends and anomalies within the data, then the acceptance criteria is met.

User perceives a noticeable improvement in identifying significant patterns within the data.

Given the user has leveraged the recommended data sets with AI-driven insights, when the user reports a heightened ease in identifying significant patterns, then the acceptance criteria is met.

Behavior-Driven Data Insights

Deliver data insights driven by user behavior, presenting timely and relevant recommendations that uncover hidden trends and actionable insights for swift decision-making.

Requirements

Behavior-Driven Data Insights UI Design

User Story: As a business analyst, I want to easily navigate and interact with behavior-driven data insights to uncover hidden trends and receive timely recommendations, so that I can make informed decisions and drive strategic initiatives.
Description: Design an intuitive and user-friendly interface for behavior-driven data insights, ensuring seamless navigation, interactive visualization, and easy access to AI-driven recommendations. The UI should support real-time collaboration and provide a compelling visual representation of complex datasets for swift decision-making and trend identification.
Acceptance Criteria: User navigates to the Behavior-Driven Data Insights dashboard

When the user navigates to the dashboard, they should see a clear and intuitive layout with easily accessible data visualization tools and AI-driven recommendations

User interacts with the data visualization features

When the user interacts with the data visualization features, they should be able to manipulate and customize the visual representations of the data, such as charts and graphs, in real time

User collaborates in real-time with team members

When the user collaborates in real time with team members, they should be able to view and interact with the same data insights simultaneously, enabling seamless collaboration and discussion

User receives AI-driven recommendations

When the user receives AI-driven recommendations, the recommendations should be timely, relevant, and actionable, providing valuable insights for swift decision-making

Behavior-Driven Data Insights AI Integration

User Story: As a decision-maker, I want behavior-driven data insights to intelligently highlight trends and anomalies based on user behavior, so that I can quickly identify actionable insights and make informed decisions.
Description: Integrate AI-driven recommendation engines with behavior-driven data insights to intelligently highlight trends, anomalies, and actionable insights based on user behavior and data patterns. The integration should leverage advanced machine learning algorithms to provide timely and relevant recommendations for swift decision-making.
Acceptance Criteria: User Behavior Tracking

Given a user interacts with the data insights platform, when their behavior is tracked and analyzed, then the AI integration provides timely and relevant recommendations based on the behavior patterns.

Data Anomalies Detection

Given data is ingested into the platform, when anomalies are detected in the user behavior and data patterns, then the AI integration intelligently highlights the anomalies and recommends actionable insights for decision-making.

Real-Time Data Analysis

Given real-time data is received by the platform, when the AI integration processes the data and uncovers trends or patterns, then it provides immediate actionable insights to the users for swift decision-making.

Behavior-Driven Data Insights Database Integration

User Story: As a data analyst, I want behavior-driven data insights to seamlessly integrate with SQL and Excel databases, so that I can efficiently prepare and analyze data for swift decision-making.
Description: Enable robust integration with databases like SQL and Excel for seamless data access and manipulation within behavior-driven data insights. The integration should support efficient data preparation and accelerated decision-making by leveraging existing data sources and structures.
Acceptance Criteria: User connects InnoData Viz to an SQL database to extract and analyze data for behavior-driven insights

Given a valid connection to an SQL database, When the user selects the desired data tables, Then the data is successfully extracted and available for analysis within InnoData Viz

User connects InnoData Viz to an Excel spreadsheet to import and visualize data for behavior-driven insights

Given a valid connection to an Excel spreadsheet, When the user imports the data, Then the data is displayed accurately and is ready for visualization within InnoData Viz

User creates a behavior-driven insight report based on SQL data in InnoData Viz

Given the extracted SQL data, When the user applies behavior-driven analysis tools, Then the insight report highlights relevant trends and anomalies for quick decision-making

User creates a behavior-driven insight report based on Excel data in InnoData Viz

Given the imported Excel data, When the user applies behavior-driven analysis tools, Then the insight report provides actionable recommendations and insights for strategic decision-making

Predictive Data Insights

Utilize predictive analytics to offer data recommendations that anticipate user needs, facilitating proactive decision-making and uncovering valuable insights before they surface.

Requirements

Predictive Data Model

User Story: As a business analyst, I want to utilize predictive data models to receive accurate recommendations and insights based on historical data, so that I can make proactive decisions and uncover valuable insights before they surface.
Description: The system should be able to create and train predictive data models using machine learning algorithms. This functionality will allow users to generate accurate predictions and recommendations based on historical data and patterns, enabling proactive decision-making and uncovering valuable insights before they surface. The predictive data models will integrate seamlessly with the existing data visualization tools, providing users with actionable insights and enhancing their decision-making capabilities.
Acceptance Criteria: Creating a Predictive Data Model

Given the user inputs historical data and selects machine learning algorithms, when the system trains the predictive data model, then it should successfully generate accurate predictions and recommendations.

Integration with Data Visualization Tools

Given a trained predictive data model and the existing data visualization tools, when the predictive data model seamlessly integrates with the data visualization tools, then users should be able to access actionable insights for enhanced decision-making.

Validation of Predictive Insights

Given generated predictions and recommendations, when users validate the accuracy and relevance of the insights, then they should consistently uncover valuable insights before they surface, facilitating proactive decision-making.

Real-time Predictive Insights

User Story: As a data analyst, I want to access real-time predictive insights that analyze incoming data streams, so that I can stay ahead of emerging patterns and make informed decisions based on up-to-the-minute predictive insights.
Description: Enable real-time predictive insights that continuously analyze incoming data streams, identifying trends and anomalies in real-time. This feature will empower users to stay ahead of emerging patterns and make informed decisions based on up-to-the-minute predictive insights. The real-time predictive insights will align with the platform's goal of providing actionable analytics and leveraging artificial intelligence to drive strategic decision-making.
Acceptance Criteria: Analyst receives real-time predictive insights on incoming data streams

Given the platform is receiving live data streams, when the predictive analytics engine identifies trends and anomalies in real-time, then the system displays the insights to the analyst in a timely manner.

Proactive decision-making based on real-time predictive insights

Given the analyst is using the platform, when the system provides proactive recommendations based on real-time predictive insights, then the analyst makes decisions leveraging the insights before the anomalies surface.

Verification of AI-driven real-time insights accuracy

Given the system has generated real-time predictive insights, when the analyst compares the AI-generated insights with actual emerging patterns, then the accuracy rate of the system's real-time predictions is verified. The accuracy rate must meet a predefined threshold.

Integration with External Data Sources

User Story: As a decision-maker, I want seamless integration with external data sources to enrich the predictive data models with diverse data inputs, so that I can leverage a wide range of data sources and enhance the accuracy and relevance of predictive insights for strategic decision-making.
Description: Implement seamless integration with external data sources, such as CRM systems and IoT devices, to enrich the predictive data models with a wide range of data inputs. This integration will enable users to leverage diverse data sources and enhance the accuracy and relevance of predictive insights. By integrating with external data sources, the platform will cater to a broader spectrum of user needs and ensure comprehensive predictive analytics capabilities.
Acceptance Criteria: User integrates CRM data into predictive models

Given a valid CRM data source, when the user selects the data integration option, then the platform successfully connects to the CRM system and imports the data into the predictive model.

User integrates IoT device data into predictive models

Given an IoT device with relevant data, when the user configures the IoT device connection, then the platform retrieves and processes the IoT data for use in predictive analytics.

User validates predictive insights accuracy with integrated data sources

Given the availability of integrated data from CRM and IoT devices, when the user generates predictive insights, then the platform provides accurate and relevant recommendations that demonstrate the impact of the integrated data sources on the predictive models.

AI-Enhanced Data Discovery

Enhance data discovery with AI-driven recommendations, enabling users to explore and analyze data with precision and efficiency, leading to actionable insights and informed decision-making.

Requirements

AI Model Integration

User Story: As a business analyst, I want AI-driven recommendations for data analysis so that I can efficiently explore and derive meaningful insights from complex datasets.
Description: Integrate AI models to assist in data discovery and provide users with smart recommendations for insightful analysis. This feature enables seamless utilization of AI-driven insights to enhance data exploration and decision-making, elevating the platform's capabilities in delivering actionable analytics.
Acceptance Criteria: User explores large dataset using AI-driven recommendations and identifies actionable insights

Given a large dataset and AI-driven recommendations, when the user explores the dataset and identifies actionable insights based on the AI recommendations, then the acceptance criteria is met.

User seamlessly integrates AI model with data discovery process

Given the option to integrate AI models with the data discovery process, when the user seamlessly integrates an AI model to obtain smart recommendations and insightful analysis, then the acceptance criteria is met.

User receives real-time AI-driven insights and anomaly detection during data exploration

Given real-time data exploration and AI-driven insights, when the user receives real-time AI-driven insights and anomaly detection during data exploration, then the acceptance criteria is met.

Collaborative Filtering

User Story: As a decision-maker, I want personalized data recommendations based on my interactions so that I can swiftly discover relevant insights for informed decision-making.
Description: Implement collaborative filtering algorithms to surface relevant datasets and insights based on user interactions and preferences. This functionality facilitates personalized data discovery, enhancing the user experience and promoting efficient exploration of relevant information.
Acceptance Criteria: User logs in and receives personalized data recommendations based on previous interactions.

Given the user has logged in and interacted with the platform, when they access the data discovery feature, then they should receive personalized dataset recommendations based on their previous interactions.

User interacts with the filtering options and receives relevant data suggestions.

Given the user interacts with the filtering options to refine their data search, when they apply the filters, then they should receive relevant data suggestions that match their preferences and selections.

Multiple users collaborate on a dataset and receive collective recommendations based on their interactions.

Given multiple users collaborate on a dataset, when they access the collaborative filtering feature, then they should receive collective recommendations based on the interactions of all users involved.

Anomaly Detection Framework

User Story: As a data analyst, I want to automatically detect anomalies in the data so that I can proactively address potential issues and leverage opportunities hidden within the datasets.
Description: Develop an anomaly detection framework to automatically identify and highlight unusual patterns and outliers within datasets. This capability empowers users to proactively identify potential issues or opportunities, facilitating proactive decision-making based on data anomalies.
Acceptance Criteria: User explores dataset with AI-driven recommendations

Given a dataset in the platform, when the user selects the AI-driven recommendations feature, then the system should provide relevant and insightful suggestions for data exploration based on the dataset's characteristics and patterns.

System identifies outliers in real-time data

Given a real-time data stream, when the anomaly detection framework is enabled, then the system should automatically detect and highlight data points that deviate significantly from the expected patterns, providing visual indicators for easy identification.

User proactively makes decisions based on anomaly detection

Given the highlighted anomalies in a dataset, when the user reviews the anomalies and takes proactive actions based on the identified outliers, then the system should facilitate seamless integration with decision-making tools, enabling the user to initiate strategic actions based on anomalous data.

Role-Based Dashboard Templates

Empower users to access pre-configured dashboard templates tailored to specific user roles, such as Marketing Manager, Financial Analyst, and Research Scientist, enabling quick and efficient generation of visualizations and insights to meet individual role-specific needs.

Requirements

Role-Based Dashboard Templates - User Roles

User Story: As a business user with a specific role, I want to access pre-configured dashboard templates tailored to my role so that I can efficiently generate visualizations and insights that meet my role-specific data analysis needs.
Description: This requirement involves creating pre-configured dashboard templates tailored to specific user roles, allowing users such as Marketing Managers, Financial Analysts, and Research Scientists to access role-specific visualizations and insights. The functionality will enable quick and efficient generation of visualizations to meet individual role-specific data analysis needs, enhancing user productivity and decision-making processes within the platform.
Acceptance Criteria: Marketing Manager Role Dashboard Template Creation

Given a Marketing Manager user role, when they access the dashboard template feature, then they should be able to generate visualizations and insights tailored to marketing data analysis needs.

Financial Analyst Role Dashboard Template Creation

Given a Financial Analyst user role, when they use the dashboard template feature, then they should be able to efficiently produce visualizations and insights aligned with financial data analysis requirements.

Research Scientist Role Dashboard Template Creation

Given a Research Scientist user role, when they utilize the dashboard template feature, then they should be able to generate visualizations and insights specific to research data analysis needs.

Role-Based Dashboard Templates - Drag-and-Drop Interface

User Story: As a user customizing a dashboard template, I want to have a drag-and-drop interface to modify visual elements so that I can personalize the dashboard to fit my role-specific requirements.
Description: This requirement involves implementing an intuitive drag-and-drop interface for users to customize and modify the pre-configured dashboard templates. Users should be able to easily rearrange and customize visual elements to match their specific preferences and analyze data in a way that suits their role-specific requirements, promoting a seamless and personalized user experience.
Acceptance Criteria: User drag-and-drops a visualization component onto the dashboard

Given the user has access to the dashboard and visualization components, when the user drags a visualization component onto the dashboard, then the component is successfully added to the dashboard.

User rearranges the order of visual elements on the dashboard

Given the user has access to the dashboard with visual elements, when the user rearranges the order of visual elements on the dashboard, then the new order is saved and reflected in the dashboard view.

User customizes visual element properties on the dashboard

Given the user has access to the dashboard with visual elements, when the user customizes the properties of a visual element, then the customized properties are applied to the visual element on the dashboard.

Role-Based Dashboard Templates - AI-Driven Insights

User Story: As a user analyzing data, I want AI-driven insights to highlight trends and anomalies relevant to my role so that I can make informed decisions based on the visualizations.
Description: This requirement involves incorporating AI-driven insights into the dashboard templates to intelligently highlight trends and anomalies relevant to the user's specific role. The AI-driven insights will enhance the user's ability to identify critical patterns and make informed decisions based on the role-specific data visualizations, improving the overall data analysis process within the platform.
Acceptance Criteria: Marketing Manager Role: AI-Driven Insights

When a Marketing Manager logs in, they should have access to AI-driven insights that intelligently highlight trends and anomalies relevant to marketing metrics and KPIs.

Financial Analyst Role: AI-Driven Insights

When a Financial Analyst logs in, they should have access to AI-driven insights that intelligently highlight trends and anomalies relevant to financial performance and key financial indicators.

Research Scientist Role: AI-Driven Insights

When a Research Scientist logs in, they should have access to AI-driven insights that intelligently highlight trends and anomalies relevant to research data and scientific metrics.

Customizable Widgets

Allow users to personalize and tailor dashboard widgets to their specific preferences, presenting relevant data and visualizations that align with their unique requirements, enhancing the relevance and usability of the dashboard templates.

Requirements

Widget Customization

User Story: As a business analyst, I want to be able to customize dashboard widgets so that I can tailor the visualizations and data displays to my specific analytical needs, enhancing the relevance and usability of the dashboard templates.
Description: Enable users to customize and personalize dashboard widgets to align with their specific preferences and data visualization needs. This feature enhances the usability and relevance of dashboard templates, allowing users to tailor visualizations and data displays to their unique requirements and analytical preferences. It provides a seamless way for users to interact and engage with their data, fostering a more personalized and intuitive data analytics experience within InnoData Viz.
Acceptance Criteria: User selects a widget for customization

Given that the user has access to the dashboard editing interface, when the user selects a specific widget for customization, then the widget should enter the customization mode with options to personalize and tailor its content and visualization.

User personalizes widget content and visualization

Given that the user has entered the customization mode of a widget, when the user personalizes the content and visualization by specifying relevant data, adjusting visual properties, and selecting suitable chart types, then the widget should reflect the user's customizations and update its display accordingly.

User saves customized widget as a template

Given that the user has personalized a widget to their preferences, when the user saves the customized widget as a template, then the template should be accessible for future use and should accurately reproduce the customized content and visualization when added to the dashboard.

Widget Sharing and Collaboration

User Story: As a team member, I want to be able to share my customized dashboard widgets with colleagues so that we can collaborate on data analysis and make informed decisions together.
Description: Facilitate the sharing and collaboration of customized dashboard widgets among team members, enabling seamless knowledge transfer and collaborative data analysis. This feature allows users to share their personalized dashboard setups and widget configurations with colleagues, fostering a collaborative and efficient analytical environment. It supports real-time collaboration and knowledge sharing, enhancing the teamwork and data-driven decision-making capabilities of the platform.
Acceptance Criteria: User shares a customized dashboard widget with a colleague

Given the user has customized a widget on their dashboard, when they choose to share the widget, then the colleague should receive an invitation to view the widget and be able to add it to their own dashboard.

Colleague adds a shared widget to their dashboard

Given the colleague has received an invitation to view a shared widget, when they accept the invitation, then the shared widget should be added to their dashboard without affecting the original configuration.

Real-time collaboration on shared widgets

Given multiple users have access to a shared widget, when one user makes changes to the widget, then the changes should be reflected in real-time for all other users viewing the widget.

Sharing feedback on shared widgets

Given a user is viewing a shared widget, when they provide feedback on the widget, then the original owner of the widget should be notified of the feedback and be able to view and respond to it.

Widget Version Control

User Story: As a data steward, I want to have version control for customized dashboard widgets so that I can track changes, maintain data consistency, and ensure reliable data visualizations across the platform.
Description: Implement version control for customized dashboard widgets, allowing users to track changes, revert to previous configurations, and maintain a history of widget setups. This functionality ensures data consistency and reliability by enabling users to manage and monitor the evolution of their dashboard visualizations. It provides a safeguard against accidental changes and supports data governance within the platform.
Acceptance Criteria: User customizes a dashboard widget

Given a dashboard widget customization interface, when the user adjusts the visual appearance and data content of the widget according to their preferences, then the changes should be saved and reflected in the dashboard view.

User reverts to a previous version of a customized widget

Given a history of widget versions, when the user selects a specific version to revert to, then the dashboard widget should successfully revert to the chosen version, displaying the previously saved configuration.

User views the change history of a customized widget

Given a dashboard widget with version history, when the user accesses the change history, then the system should display a chronological list of changes made to the widget, including details such as timestamps, user actions, and comments.

Drag-and-Drop Widget Customization

Enable intuitive drag-and-drop customization of dashboard widgets, allowing users to effortlessly modify and arrange visualizations to suit their individual preferences and presentation styles, enhancing the flexibility and usability of the dashboard templates.

Requirements

Drag-and-Drop Widget Customization UI

User Story: As a business analyst, I want to customize dashboard widgets through drag-and-drop functionality so that I can easily arrange visualizations to suit my preferences and create compelling presentations.
Description: Enable the creation of a user interface for intuitive drag-and-drop customization of dashboard widgets, allowing users to seamlessly modify and arrange visualizations to suit their individual preferences and presentation styles. This feature enhances the flexibility and usability of the dashboard templates, empowering users to tailor their data visualizations for maximum impact and insight.
Acceptance Criteria: User creates a new dashboard and adds a widget

Given a user has access to the dashboard customization interface, when they drag and drop a widget onto the dashboard, then the widget should be successfully added and displayed on the dashboard.

User modifies the position of a widget on the dashboard

Given a user has access to the dashboard customization interface, when they drag and drop a widget to a new position on the dashboard, then the widget should be repositioned as per the user's action.

User customizes the appearance of a widget

Given a user has access to the dashboard customization interface, when they modify the visual properties of a widget using the drag-and-drop interface, then the widget's appearance should be updated accordingly.

User saves the customized dashboard layout

Given a user has made changes to the dashboard layout, when they save the customized layout, then the changes should be persistently stored and reflected when the dashboard is accessed later.

Real-Time Preview of Customizations

User Story: As a data visualization designer, I want to preview my drag-and-drop customizations in real time so that I can instantly see the effects and make informed decisions about widget arrangements.
Description: Implement real-time preview functionality to allow users to instantly visualize the effects of their drag-and-drop customizations on dashboard widgets, providing immediate feedback on the modifications made. This feature enhances the user experience by enabling quick validation of customization choices, resulting in efficient and informed decision-making.
Acceptance Criteria: User customizes a dashboard widget using drag-and-drop

Given that the user has access to a dashboard widget customization interface, when the user drags and drops the visualization elements to modify the layout and appearance, then the changes are instantly reflected in a real-time preview within the same interface.

User rearranges multiple widgets on the dashboard

Given that the user has multiple widgets on the dashboard, when the user reorders and rearranges the widgets using drag-and-drop, then the dashboard layout is dynamically updated in real-time to reflect the new widget arrangement.

User compares different visualization options

Given a selection of visualization options, when the user drags and drops different visualizations onto the dashboard widget, then the user can compare and preview the visualizations in real-time to make an informed decision.

Customization History and Undo Functionality

User Story: As a dashboard designer, I want to have a history of customizations and the ability to undo changes made through drag-and-drop customization so that I can safely experiment with different widget arrangements and revert to previous states if needed.
Description: Develop a feature to track customization history and enable users to undo changes made through drag-and-drop customization, providing the ability to revert to previous widget arrangements. This functionality offers users a safety net when experimenting with customizations, ensuring they can explore different options without the fear of irreversible changes.
Acceptance Criteria: User modifies a dashboard widget using drag-and-drop customization

Given a dashboard widget, when the user drags and drops the widget elements to rearrange or modify the visualization, then the changes should be reflected in real-time and the customization history should be updated accordingly.

User attempts to undo a recent dashboard widget customization

Given a dashboard widget with recent modifications, when the user selects the 'Undo' option, then the widget should revert to its previous state as per the customization history, and the history log should be updated to reflect the change.

User attempts to undo changes beyond the recent modification

Given a dashboard widget with multiple customization changes, when the user attempts to undo changes beyond the most recent modification, then the system should revert the widget to the selected historical state as per the customization history, and the history log should be updated to reflect the change.

User attempts to redo a previously undone customization

Given a dashboard widget with undone changes, when the user selects the 'Redo' option, then the widget should revert to the state just before the undo action, and the history log should be updated to reflect the redo.

Interactive Filter Integration

Integrate interactive filters into the dashboard templates, empowering users to dynamically interact with and analyze data through customizable filters, facilitating personalized data visualization and exploration based on specific criteria and user preferences.

Requirements

Customizable Filter Options

User Story: As a business analyst, I want to be able to create personalized filter options so that I can analyze and visualize data based on my specific criteria and requirements.
Description: Implement a feature that allows users to create customized filter options based on specific criteria, enabling personalized data exploration and analysis. This functionality empowers users to tailor their data visualization experience and gain insights relevant to their unique needs and objectives.
Acceptance Criteria: User creates a new dashboard with custom filters

Given the user has access to the dashboard customization options, When the user adds a new filter widget to the dashboard, Then the user should be able to define custom filter options based on specific criteria and save the changes successfully.

User applies custom filters to visualize specific data

Given the user has created a customized filter, When the user applies the custom filter to the dashboard data, Then the dashboard should dynamically update to display the relevant data based on the custom filter options chosen by the user.

User modifies existing custom filter options

Given the user has previously created custom filter options, When the user accesses the settings for the custom filter, Then the user should be able to modify the criteria and conditions for the custom filter and save the changes successfully.

User shares a dashboard with custom filter settings

Given the user has configured custom filter options on a dashboard, When the user shares the dashboard with another user, Then the recipient should be able to view the dashboard with the same custom filter settings and interact with the data based on the shared filter options.

Real-Time Filter Interaction

User Story: As a data analyst, I want to interact with filters in real-time so that I can instantly visualize and analyze the impact of my filter choices on the data visualizations.
Description: Enable real-time interaction with filters on the dashboard, providing dynamic updates to the visualizations as users adjust filter settings. This feature enhances the user experience by allowing instant exploration of data variations and immediate visualization updates based on filter changes.
Acceptance Criteria: User interacts with filter dropdown menu to select specific criteria

When a user interacts with the filter dropdown menu, the dashboard visualizations update in real-time based on the selected filter criteria

User adjusts date range filter to analyze time-specific data

When a user adjusts the date range filter, the dashboard visualizations dynamically update to display data within the selected date range in real-time

User applies multiple filters simultaneously for in-depth analysis

When a user applies multiple filters simultaneously, the dashboard visualizations accurately and dynamically update to reflect the combined filter criteria in real-time

User resets all filters to default settings

When a user resets all filters to default settings, the dashboard visualizations reset to display the default data view without any filter criteria applied

Filter Integration with AI-Driven Insights

User Story: As a decision-maker, I want AI-driven insights to recommend filter settings so that I can quickly uncover meaningful data patterns and anomalies through filter-based visualizations.
Description: Integrate filter options with AI-driven insights to provide recommendations for filter settings based on data patterns and anomalies. This integration enhances the user experience by offering intelligent suggestions for filter selections, facilitating efficient and effective data exploration and analysis.
Acceptance Criteria: User applies filter settings and receives AI-driven insights recommendations

Given the user has applied filter settings on the dashboard, when the AI-driven insights algorithm analyzes the data, then the system should provide personalized recommendations for filter adjustments based on data patterns and anomalies.

User interacts with dynamic filters on the dashboard

Given the user interacts with the dynamic filters on the dashboard, when the filter settings are adjusted, then the visualizations and insights should dynamically update to reflect the new filter criteria, offering real-time data exploration.

User accesses AI-driven insights for filter recommendations

Given the user accesses the AI-driven insights panel, when the system detects data patterns and anomalies, then the AI algorithm should provide intelligent recommendations for filter settings, improving the efficiency and accuracy of data exploration.

User-Defined Data Sources

Allow users to define and integrate their own data sources into the dashboard templates, enabling access to personalized data sets for tailored visualizations, insights, and analysis specific to unique business requirements and individual preferences.

Requirements

Data Source Integration

User Story: As a business analyst, I want to integrate my own data sources into the platform so that I can create personalized visualizations and analysis specific to my business requirements, enabling me to make data-driven decisions.
Description: Enable users to define and integrate their own data sources into the platform, granting access to personalized datasets for tailored visualizations and analysis. This functionality will enhance the flexibility and customization of data visualization, allowing users to create insights specific to their unique business requirements and preferences. Data source integration will streamline the process of accessing and utilizing diverse data sources within the platform, empowering users to make informed decisions based on their personalized datasets.
Acceptance Criteria: User selects 'Add Data Source' option from the dashboard menu

When the user selects the 'Add Data Source' option, a modal window should appear with input fields for the data source name, connection details, and authentication credentials. The user should be able to input and save the data source information successfully.

User uploads a CSV file as a new data source

Given the user has defined a new data source as a CSV file, when the user uploads the CSV file, it should be successfully integrated and displayed as a data source in the platform. The user should be able to select and use the uploaded CSV file for visualization and analysis.

User connects the platform to an external SQL database

When the user establishes a connection to an external SQL database, the platform should verify and authenticate the connection. Upon successful connection, the user should be able to view and access the tables and data from the SQL database within the platform for visualization and analysis.

Custom Data Templates

User Story: As a decision-maker, I want to create custom data templates within the dashboard so that I can organize and visualize my integrated data sources according to my specific business needs, enabling me to generate actionable insights effectively.
Description: Provide users with the capability to create custom data templates within the dashboard, allowing for the organization and visualization of integrated data sources. This feature will enable users to structure and present their data in a manner that aligns with their unique business needs, improving the efficiency and usability of the platform for generating actionable insights.
Acceptance Criteria: User creates a new custom data template

Given the user is logged into the platform and has access to the dashboard, when the user selects the option to create a new custom data template, then a blank template interface with customizable fields is displayed for the user to define the structure of the template.

User defines the structure of the custom data template

Given the user is presented with the blank template interface, when the user defines and saves the structure of the template by adding, editing, and organizing data fields, then the template structure is saved for future use and visualization.

User integrates data sources into the custom data template

Given the user has a saved custom data template, when the user integrates their own data sources by connecting to databases, uploading files, or using other data import methods, then the integrated data sources are linked to the template for tailored visualizations and analysis.

Real-Time Data Refresh

User Story: As an analyst, I want the visual insights and analytics to be constantly updated with the latest information so that I can access and analyze real-time data, improving the accuracy and relevance of the insights.
Description: Implement real-time data refresh functionality to ensure that the visual insights and analytics are constantly updated with the latest information from integrated data sources. This feature will enable users to access and analyze real-time data, enhancing the accuracy and relevance of the insights derived from the platform.
Acceptance Criteria: Dashboard with User-Defined Data Sources

Given a user has defined and integrated their own data sources, When the dashboard is loaded, Then the personalized data sets should be accessible for tailored visualizations and analysis.

Real-Time Data Refresh Interval

Given the real-time data refresh is enabled, When the interval elapses, Then the visual insights and analytics should be updated with the latest information from integrated data sources.

Real-Time Data Accuracy

Given the real-time data refresh is enabled, When data is updated from integrated sources, Then the accuracy and relevance of the insights should be enhanced.

Press Articles

Introducing InnoData Viz: The Cloud-based Platform Revolutionizing Data Analytics: InnoData Viz is a cutting-edge cloud-based platform that empowers business analysts and decision-makers to effortlessly convert intricate datasets into compelling visual insights. With its intuitive drag-and-drop interface, real-time collaboration tools, and AI-driven insights, InnoData Viz democratizes data analytics for users of all skill levels. This revolutionary platform integrates seamlessly with databases like SQL and Excel, streamlining data preparation and accelerating strategic decision-making. Businesses can now harness actionable analytics at their fingertips, transforming into data-driven powerhouses.

Unveiling InnoData Viz: Empowering Data Analysts and Business Decision-Makers: InnoData Viz introduces a groundbreaking cloud-based platform that equips data analysts and business decision-makers with the tools to seamlessly transform complex datasets into compelling visual insights. Its intuitive interface and real-time collaboration features streamline data preparation and accelerate strategic decision-making. Additionally, AI-driven insights intelligently highlight trends and anomalies within the data, providing robust support for informed decision-making. With InnoData Viz, organizations can elevate their data analytics capabilities to drive impactful business strategies.

InnoData Viz: Democratizing Data Analytics for All Skill Levels: InnoData Viz revolutionizes the data analytics landscape by offering a cloud-based platform that makes data analytics accessible to users of all skill levels. Its intuitive interface and real-time collaboration tools allow for effortless transformation of complex datasets into actionable visual insights. Equipped with AI-driven insights and seamless integration with databases like SQL and Excel, InnoData Viz accelerates data preparation and strategic decision-making. This transformative platform empowers organizations to harness the power of data, driving informed and actionable decision-making.

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Product Details

Personas

Marketing Manager

Research Scientist

Financial Analyst

Product Ideas

Product Features

Real-time Collaboration

Requirements

Real-time Data Sync

Interactive Commenting

Version Control

Shared Annotation Tools

Requirements

Collaborative Annotation Interface

Real-time Annotation Updates

Annotation Notification System

Interactive Co-editing

Requirements

Real-time Data Synchronization

User Presence Indicator

Version History and Rollback

Version History Tracking

Requirements

Version Control

Version Comparison

Version Access Control

Smart Data Insights

Requirements

AI-Driven Data Recommendations

Real-Time Data Visualization

Database Integration Enhancement

Personalized Data Suggestions

Requirements

Usage Pattern Analysis

Real-time Recommendation Engine

Integration with AI-driven Insights

Behavior-Driven Data Insights

Requirements

Behavior-Driven Data Insights UI Design

Behavior-Driven Data Insights AI Integration

Behavior-Driven Data Insights Database Integration

Predictive Data Insights

Requirements

Predictive Data Model

Real-time Predictive Insights

Integration with External Data Sources

AI-Enhanced Data Discovery

Requirements

AI Model Integration

Collaborative Filtering

Anomaly Detection Framework

Role-Based Dashboard Templates

Requirements

Role-Based Dashboard Templates - User Roles

Role-Based Dashboard Templates - Drag-and-Drop Interface

Role-Based Dashboard Templates - AI-Driven Insights

Customizable Widgets

Requirements

Widget Customization

Widget Sharing and Collaboration

Widget Version Control

Drag-and-Drop Widget Customization

Requirements

Drag-and-Drop Widget Customization UI

Real-Time Preview of Customizations

Customization History and Undo Functionality

Interactive Filter Integration

Requirements

Customizable Filter Options

Real-Time Filter Interaction

Filter Integration with AI-Driven Insights

User-Defined Data Sources

Requirements

Data Source Integration

Custom Data Templates

Real-Time Data Refresh

Press Articles