Adaptive Data Matching Service for Enterprise Repository Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computer systems lack effective mechanisms for adaptive data matching across multiple data repositories, leading to inconsistencies and inefficiencies in data integration, especially during corporate acquisitions or changes in business systems, which hampers accurate business decision-making and increases operational costs.
Innovation Solution
A system and method for adaptive matching of similar data in a data repository that learns from user interactions to determine and configure match criteria, normalizes data, tokenizes multi-word text, assigns weights to fields and tokens, and automatically merges similar records, allowing for intelligent data consolidation and reduced user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is maintained in independent computer systems for each corporate division, then data consistency problems are avoided, but the ability to identify and match similar data across divisions is lost
Solution Approach 1:
The patent introduces a data matching service as an intermediary component that operates between independent data repositories. This service uses machine learning models to identify and match similar data items across different corporate divisions without requiring direct integration or centralization of the underlying systems, thus maintaining data independence while enabling cross-division data matching and consistency.
Solution Approach 2:
The system implements feedback mechanisms where matching results and user corrections are fed back into the machine learning models to continuously improve matching accuracy. This allows the system to learn from previous matching outcomes and adapt to organizational data patterns, enhancing the ability to match similar data across independent systems while maintaining reliability through iterative improvement.
2Measurement precision
If manual data matching processes are used, then match accuracy can be maintained, but time consumption and operational costs increase
Solution Approach 1:
The patent implements self-service capabilities where the machine learning models automatically perform data matching without requiring manual intervention for each comparison. The system autonomously identifies similar data items, proposes matches, and can automatically consolidate duplicates based on configured criteria, significantly reducing the time required for data matching while maintaining high accuracy through intelligent algorithms.
Solution Approach 2:
The system replaces manual mechanical data matching processes with automated machine learning-based matching. Instead of relying on human operators to manually compare and match data items, the patent uses trained models that automatically analyze data patterns, compute similarity scores, and identify matches, thereby eliminating time-consuming manual labor while preserving or improving match accuracy.
3Productivity
If traditional data integration methods are used, then data consolidation can be achieved, but the system cannot adapt to new corporate systems or changes in business systems
Solution Approach 1:
The patent implements dynamic adaptability by using machine learning models that can be retrained and updated as new corporate systems are acquired or business systems change. The matching service dynamically adjusts to new data patterns, schemas, and formats without requiring fundamental system changes, allowing the organization to efficiently consolidate data from newly acquired companies or updated systems while maintaining productivity.
Solution Approach 2:
The data matching service is designed as a universal platform that can handle multiple data types, formats, and sources across different corporate divisions and acquired companies. The system provides multi-functional capabilities including automated matching, duplicate detection, data quality assessment, and integration with various data repositories, enabling efficient data consolidation across diverse systems while maintaining adaptability to organizational changes.
Data Source
AI summary
Adaptive matching of similar data in a data repository to determine if two or more data items are related in accordance with configurable criteria. Matches are adapted by learning and presenting appropriate match criteria based on previous user input. The system can merge the data items into one master data item, group similar items and perform further processing based on the result. The configurable match criteria presented to a user are adapted by the system based on previous interactions of the system with users. Matching is performed by selecting data items to match, removing frequently used strings, normalizing data, tokenizing multi-word data items, assigning weights to each token, calculating a score using the assigned weights, generating groups of similar records, assigning thresholds for match levels. Adapting choices of match criteria for a user based on past interaction allows for rapid match creation and match maintenance that optimizes data integrity across an enterprise.