Visualization Combination in Business Analytics
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Solution Overview
Problem
Users of business analytics tools often create multiple charts to communicate similar or related ideas, leading to data duplication and distraction from key relationships, without guidance on combining related visualizations effectively.
Innovation Solution
A method and system that calculates a strength score for groups of visualizations, recommending combined visualizations by determining the relationship strength between them, and sending these recommendations to users for simplified dashboards and reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple charts are created to communicate similar or related ideas, then data coverage is improved, but data duplication and distraction from key relationships occur
Solution Approach 1:
The system automatically combines multiple charts that display similar or related data into a single consolidated visualization. By analyzing data relationships and similarities between charts, the system merges redundant visualizations while preserving all unique information, thereby eliminating data duplication and distraction while maintaining comprehensive data coverage.
2Loss of information
If multiple visualizations are used to show related data, then information completeness is improved, but information overload occurs
Solution Approach 1:
The system reduces information overload by automatically identifying and merging visualizations that convey related information. It analyzes the content and relationships between multiple charts, consolidating them into fewer, more comprehensive visualizations that maintain complete information while reducing the overall quantity of visual elements presented to the user.
3Adaptability or versatility
If users manually organize visualizations, then customization is improved, but time consumption increases
Solution Approach 1:
The system performs automatic visualization organization and combination without requiring manual user intervention. It autonomously analyzes data relationships, identifies redundant or related charts, and consolidates them appropriately, thereby maintaining high adaptability and customization while eliminating the time consumers associated with manual organization.
4Measurement precision
If comprehensive data analysis is performed to identify relationships between visualizations, then combination accuracy is improved, but computational complexity increases
Solution Approach 1:
The system replaces complex manual analysis mechanisms with automated computational algorithms that efficiently identify relationships between visualizations. By using systematic data analysis and pattern recognition algorithms, the system achieves high combination accuracy while managing computational complexity through automated processing rather than manual intervention.
Data Source
AI summary
In an approach to combining visualizations in a business analytic application, responsive to receiving a plurality of original visualizations from a user, one or more visualization groups are created. A strength score is calculated for each visualization group of the one or more visualization groups. Responsive to the strength score for each visualization group of the one or more visualization groups meeting a minimum threshold score, one or more recommended visualizations are generated, wherein the one or more recommended visualizations are combinations of the plurality of original visualizations. The one or more recommended visualizations are sent to the user.