Data Quality Certification via Objective and Subjective Metrics
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Solution Overview
Problem
Existing data quality certification mechanisms lack reliability, relying on trial and error or biased user reviews for evaluating data set quality, as they fail to incorporate objective and subjective metrics effectively.
Innovation Solution
A data certification system that evaluates data sets using objective metrics like origin, attestation, and freshness, combined with subjective reviews and analytical indicia derived from feedback loops, to generate a data quality rating and categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing data quality certification mechanisms use trial and error or biased user reviews, then the evaluation process is simple, but the reliability and accuracy of data quality assessment deteriorates
Solution Approach 1:
The patent segments the data quality evaluation into three distinct components: objective indicia (measurable data attributes), subjective indicia (user reviews and feedback), and analytical indicia (derived metrics from feedback loops). This segmentation allows each component to be evaluated independently and then integrated, improving reliability without overwhelming complexity
Solution Approach 2:
The patent creates a multi-functional evaluation system that simultaneously processes objective metrics, subjective user feedback, and analytical derived indicators. This universal approach consolidates multiple evaluation methods into a single comprehensive system, enhancing reliability while managing complexity through integration
2Measurement precision
If objective and subjective metrics are integrated effectively, then the accuracy of data quality rating improves, but the complexity of the evaluation process increases
Solution Approach 1:
The patent implements feedback loops where user reviews and analytical indicia continuously refine the objective metrics. This feedback mechanism improves measurement precision by constantly updating and validating the evaluation criteria against actual user experience and observed data patterns
Solution Approach 2:
The patent creates a composite evaluation framework that combines different types of indicia (objective, subjective, analytical) into a unified data quality rating. This composite approach leverages the strengths of each indicator type while mitigating their individual weaknesses, improving overall accuracy
Data Source
AI summary
Systems, apparatuses, methods, and computer program products are disclosed for categorizing a data set. An example method includes receiving first electronic information comprising objective indicia of quality associated with the data set and second electronic information comprising subjective indicia of quality associated with the data set. The example method further includes generating third electronic information comprising analytical indicia of quality associated with the data set based on the first electronic information and the second electronic information. The example method further includes generating a data quality rating for the data set based on the first electronic information, the second electronic information, and the third electronic information. Subsequently, the example method includes using a feedback loop to update the data quality rating for the data set where the feedback loop is based on an evaluation of the objective indicia of quality.