Intent-Driven Dashboard Visualization Recommendations
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Solution Overview
Problem
Organizations face challenges in effectively utilizing their vast amounts of data to improve business practices due to the quantity and dissimilarity of data, leading to difficulties in creating coherent dashboards that convey meaningful insights.
Innovation Solution
The development of an intent-driven dashboard recommendation system that uses collection specifications associated with author intents to generate and rank collections of visualizations, allowing users to create dashboards that align with their analytical goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually create individual visualizations and compose dashboards, then dashboards can be created with selected visualizations, but the workflow becomes an afterthought rather than an active part of analytical thought process
Solution Approach 1:
The system performs preliminary action by automatically generating candidate visualizations and dashboard compositions before the user finalizes their analytical workflow. The AI assistant proactively creates visualization candidates based on the user's data and analytical intent, allowing users to review and select from pre-generated options rather than manually constructing each visualization from scratch.
2Adaptability or versatility
If users create a series of individual visualizations to comply with dashboard design goals, then dashboards can be assembled with relevant visualizations, but the process lacks analytically-driven guidance
Solution Approach 1:
The AI assistant serves as an intermediary between the user's analytical goals and the dashboard creation process. It translates high-level analytical intent into specific visualization recommendations and dashboard compositions, bridging the gap between user needs and technical implementation without requiring users to manually navigate complex authoring tools.
Solution Approach 2:
The system enables self-service by automatically generating visualization candidates and dashboard layouts based on user input. The AI assistant autonomously performs data analysis, selects appropriate visualization types, and proposes dashboard compositions, reducing the manual effort required while maintaining adaptability to user preferences.
3Quantity of substance
If organizations collect vast amounts of disparate data, then more business intelligence can be gathered, but it becomes difficult to effectively utilize available data to improve business practices
Solution Approach 1:
The system extracts meaningful insights from vast amounts of disparate data by using AI to automatically identify relevant patterns, relationships, and anomalies. The AI assistant selectively extracts key business intelligence from the data corpus and presents it through targeted visualization recommendations, filtering out noise and focusing on actionable insights.
Data Source
AI summary
A method for generating a collection of data visualization for dashboard composition that is performed at a computer system in communication with a display generation component, one or more processors, and memory storing one or more programs. The programs are configured to be executed by the processors. The programs include instructions for receiving a user input selecting one or more data fields (from a data source) displayed in a user interface via the display generation component. The programs also include instructions for generating a plurality of candidate data visualizations based on a type of analysis selected from a plurality of selectable types of analysis and the one or more user-selected data fields. The programs also include instructions for displaying, in the user interface via the display generation component, a subset of the plurality of candidate data visualizations as a collection that is associated with the type of analysis.