Data Visualization Scoring Matrix for Objective Analytics Assessment
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Solution Overview
Problem
Existing data visualization assessments in data analytics environments are subjective and based on personal preferences, lacking objective criteria for evaluating the quality and complexity of visualizations.
Innovation Solution
A system operates as an expert advisor, using an analytics data visualization score matrix to evaluate data visualizations and provide scores and recommendations for improvement, ranging from beginner to advanced levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective assessment based on personal preferences is used to evaluate data visualizations, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent transforms the assessment from subjective to objective by changing the evaluation parameters from personal preferences to quantifiable metrics. The system uses multiple parameters including data accuracy (weighting 30-40%), visual clarity (20-30%), information completeness (20-30%), and design quality (10-20%) to objectively measure visualization quality, eliminating reliance on individual subjective judgments.
2Measurement precision
If an analytics data visualization score matrix with multiple parameters is used to evaluate data visualizations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the complex assessment task into distinct segments through the score matrix structure. Each parameter (data accuracy, visual clarity, information completeness, design quality) is segmented into separate evaluation criteria with specific weighting. This segmentation allows the complex assessment to be broken down into manageable, independently evaluable components that can be systematically processed.
Solution Approach 2:
The patent introduces an intermediary assessment system that mediates between the raw visualization and the final evaluation. The score matrix acts as an intermediary layer with predefined criteria and weighting, transforming the complex evaluation process into a structured intermediate representation that can be systematically analyzed and scored before producing the final assessment result.
Data Source
AI summary
Embodiments described herein are generally related to systems and methods for generating data visualization scores, for use with data analytics environments. In accordance with an embodiment, the system can operate in the manner of an expert system, or according to a series of processes or rules, to examine a data visualization of interest, compare a list of found elements with element types specified by an analytics data visualization score matrix, and generate, based on matching found elements with the analytics data visualization matrix, a data visualization score associated with the data visualization. In accordance with an embodiment, the system can operate as a data visualization advisor, during preparation of a data visualization, to provide a user with recommendations or score values indicative of a quality or complexity of their data visualization, which may be helpful in improving their data visualization, for example from a beginner-level to a more advanced-level.