Fuzzy Matching Logic for Record Merging and Duplicate Prevention
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Solution Overview
Problem
Current information management systems, such as CRM systems, face challenges in tracking and ensuring the quality of information updates, particularly when employees move between companies, leading to issues with duplicate and inconsistent records.
Innovation Solution
The implementation of systems and methods that use fuzzy matching logic to integrate and merge records, ensuring compliance with information quality standards by receiving records from a feed, determining matches, forming composite records, and storing them if they meet quality criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional information management systems are used to track updates, then information can be stored, but duplicate and inconsistent records arise, reducing reliability
Solution Approach 1:
The system performs preliminary fuzzy matching of incoming records against existing records before merging. This preliminary action identifies potential duplicates and matches them with existing records, allowing the system to prevent duplicate record creation and maintain information quality proactively rather than reactively
Solution Approach 2:
The system calculates confidence scores based on match quality and uses this feedback to determine whether to merge records or create new ones. The confidence score mechanism provides continuous feedback on data quality, enabling the system to automatically adjust its merging behavior and maintain high reliability while preventing duplicates
2Productivity
If fuzzy matching logic is implemented to match records, then record integration improves, but system complexity increases
Solution Approach 1:
The fuzzy matching system is segmented into multiple independent matching components, each handling specific fields or record types separately. This segmentation allows the complex matching logic to be broken down into manageable units that can be processed in parallel, improving productivity while keeping individual complexity levels acceptable
Solution Approach 2:
The system uses configurable confidence score thresholds and weighting parameters to control matching behavior. By allowing parameter changes rather than hardcoding complex logic, the system achieves flexible record integration while maintaining simpler, more maintainable code structures that can be adjusted without rewriting entire matching algorithms
3Reliability
If records are merged to ensure completeness, then information quality improves, but processing time increases
Solution Approach 1:
The system performs preliminary filtering and matching actions before the actual merging process. By pre-identifying potential matches and pre-calculating confidence scores, the system reduces the time required for the main merging operation while ensuring complete and accurate record integration
Solution Approach 2:
The system merges only the necessary fields and record portions required for completeness rather than performing full record duplication. This partial action approach maintains data completeness for essential fields while significantly reducing processing time by avoiding unnecessary data copying and validation operations
Data Source
AI summary
A system and method of matching and merging records is disclosed herein. Embodiments comprise receiving, a plurality of records from a feed, wherein a record in the plurality of records from the feed may be either partial or complete. A processor executes fuzzy matching logic to determine whether one or more records in the plurality of records from the feed match an existing record. The processor then executes a merged of the one or more matching records with the existing record to form a merged composite record. Finally, the merged composite record is stored.