Reliability Scoring for Demographic Data Matching
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Solution Overview
Problem
Maintaining consistency and accuracy in demographic data across multiple sources is challenging due to data entry errors and inconsistencies, making it difficult to determine the most reliable record for a given entity.
Innovation Solution
A system that determines the most reliable demographic data by calculating a reliability score based on frequency, aging, and quality scores for each record, identifying the demographic field with the highest score as the most reliable and updating records accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple sources are used to collect demographic data, then data completeness and coverage are improved, but data consistency and reliability deteriorate due to entry errors and inconsistencies
Solution Approach 1:
The system implements feedback by calculating reliability scores for each demographic field entry and using these scores to identify and select the most reliable record. The reliability score mechanism provides continuous feedback on data quality, allowing the system to automatically correct inconsistencies by preferring higher-scoring entries across multiple sources.
Solution Approach 2:
The system transforms the raw demographic data into reliability scores through parameter changes. By introducing scoring parameters (frequency score, aging score, quality score) that quantify data reliability, the system converts unstructured inconsistent data into comparable metric values, enabling automatic selection of the most reliable entry.
2Productivity
If manual data entry is performed by multiple persons, then data collection capability is improved, but accuracy and precision deteriorate due to typographical errors
Solution Approach 1:
The system applies self-service by automatically detecting and correcting data errors without requiring manual intervention. The reliability score calculation and automatic selection of the most reliable record enable the system to self-correct typographical errors and inconsistencies, eliminating the need for manual data verification while maintaining high accuracy.
Solution Approach 2:
The reliability score acts as an intermediary between multiple data entries and the final selected value. Instead of directly comparing raw demographic strings, the system uses reliability scores as a mediating metric to objectively determine which entry is most accurate, resolving conflicts between multiple human-entered values.
3Quantity of substance
If multiple records exist for the same entity, then data coverage is improved, but difficulty in linking and confirming records increases
Solution Approach 1:
The system simplifies record linking by transforming multiple demographic fields into a single reliability score parameter. This parameter change converts a complex multi-field comparison problem into a simple single-value selection problem, where the record with the highest reliability score is automatically identified as the correct match.
Solution Approach 2:
The system replaces manual mechanical comparison of demographic fields with an automated computational scoring mechanism. Instead of manually examining and comparing multiple fields across records, the computational system automatically calculates reliability scores and identifies matches, substituting human cognitive effort with algorithmic processing.
Data Source
AI summary
A request for a most reliable demographic fields data for a selected demographic field associated with an entity may be received at an entity matching system. A plurality of records associated with the entity may be received at the entity matching system. Each of the plurality of records may include at least one demographic field. A demographic field corresponding to the selected demographic field may be identified from the at least one demographic field for each of the plurality of records. A reliability score may be determined for the corresponding demographic field for each of the plurality of records. The corresponding demographic field with the highest reliability score may be determined as the most reliable demographic fields data.