Intelligence Engine Matching D-U-N-S and Single-Sourced Reference Files
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Solution Overview
Problem
Business data services struggle to provide meaningful answers to customer queries due to records lacking a unique business identifier or having individual and historical data views not matched in the reference file, leading to incomplete information retrieval.
Innovation Solution
A system and method that pre-assigns a D-U-N-S number to non-D-U-N-S numbered data, integrates it into a multi-sourced reference file, and uses an intelligence engine to match and cluster data entities, enhancing data availability and accuracy by leveraging external and consumer data sources, and improving matching processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system only searches multi-sourced reference files with D-U-N-S numbers, then data quality and reliability are improved, but data coverage and completeness deteriorate
Solution Approach 1:
The patent segments the reference files into two distinct types: multi-sourced reference files (with D-U-N-S numbers) and single-sourced reference files (without D-U-N-S numbers). This segmentation allows the system to maintain high data quality through multi-sourced files while expanding data coverage by incorporating single-sourced files, thereby resolving the contradiction between reliability and quantity.
Solution Approach 2:
The patent adds a new dimension to the data storage structure by creating a separate classification category for single-sourced reference files. This dimensional change allows the system to organize and retrieve data from both multi-sourced and single-sourced files without compromising the integrity of either category, thus improving both data quality and coverage simultaneously.
2Quantity of substance
If the system integrates single-sourced reference files into the matching process, then data availability is improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and categorizing single-sourced reference files before they are integrated into the matching process. The system pre-establishes the single-sourced reference file structure and prepares matching algorithms in advance, which reduces the complexity burden during actual query operations and makes the integration process more manageable.
Solution Approach 2:
The patent introduces an intermediary mechanism in the form of a hybrid matching system that mediates between multi-sourced and single-sourced reference files. This intermediary layer manages the complexity of integrating different data sources by providing a unified interface and coordinated matching process, thereby improving data availability without proportionally increasing system complexity.
3Measurement precision
If the system performs matching against both multi-sourced and single-sourced reference files, then match rate is improved, but processing time increases
Solution Approach 1:
The patent applies dynamics by implementing a flexible, adaptive matching process that can dynamically adjust the depth and scope of searching based on query characteristics. The system can switch between shallow matching (faster, lower precision) and deep matching (slower, higher precision) modes, thereby optimizing the balance between match rate and processing time for different scenarios.
Solution Approach 2:
The patent employs periodic action through a two-stage matching process: first performing a quick initial match against multi-sourced reference files, and then periodically or conditionally searching single-sourced reference files if the initial match fails. This periodic approach improves overall match rate while minimizing the time penalty by not always performing the complete search.
Data Source
AI summary
A system for providing enhanced matching for database queries. The system includes a data source; a data repository comprising a single-sourced reference file; a database comprising a multi-sourced reference file, the multi-sourced reference file having a first unique business identification number corresponding to a business entity; and an intelligence engine processing incoming data from the data source. The intelligence engine determines whether the incoming data matches the multi-sourced reference file and adds the data to the multi-sourced reference file when the data matches the multi-sourced reference file. The intelligence engine also determines whether the incoming data matches a single-sourced reference file contained within the data repository when the data does not match the multi-sourced reference file.