Data Quality Enrichment Integration System
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Solution Overview
Problem
Current systems lack turnkey integration and evaluation capabilities for data quality enrichment entities, leading to inefficiencies in filtering and correcting data quality issues, as well as comparing the performance of multiple data quality enrichment entities, resulting in high costs and limited options for companies.
Innovation Solution
A data quality enrichment integration and evaluation system that allows for the import of data into a database, validation using company-specific rules, and automatic assignment to appropriate data quality enrichment entities, enabling side-by-side comparison of enrichment results and geographic visualization of data quality issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If companies integrate with multiple data quality enrichment entities, then they can compare performance and select the best entity, but integration effort and cost increase significantly
Solution Approach 1:
The system segments the integration process into standardized components: a universal interface layer that handles communication with multiple data quality enrichment entities, and entity-specific adaptation layers that can be independently configured. This allows companies to integrate with multiple entities without creating complex custom integrations for each one.
Solution Approach 2:
The patent implements a universal integration interface that can communicate with multiple different data quality enrichment entities through a single standardized connection. This multi-functional interface handles various data formats and protocols, eliminating the need for separate custom integrations for each entity and reducing overall integration complexity.
2Reliability
If all data records are sent to data quality enrichment entities, then complete data coverage is achieved, but costs increase due to processing of junk records that cannot be corrected
Solution Approach 1:
The system performs preliminary validation and filtering of data records before submitting them to data quality enrichment entities. Junk records and obviously incorrect data are identified and excluded in advance, ensuring that only potentially correctable records are processed by external entities, thereby reducing unnecessary processing costs while maintaining data completeness for valid records.
3Adaptability or versatility
If custom programming is implemented for each data quality enrichment entity integration, then specific interface requirements are met, but ongoing maintenance burden increases
Solution Approach 1:
The patent introduces an intermediary integration layer that acts as a mediator between the company's database and multiple data quality enrichment entities. This intermediary handles all interface-specific programming and adaptation, providing a single point of maintenance that shields the core system from changes in individual entity interfaces. When an entity updates its interface, only the intermediary layer needs modification, not the entire integration system.
4Measurement precision
If companies evaluate multiple data quality enrichment entities, then they can verify quality claims and select the best provider, but time and resources required for evaluation increase
Solution Approach 1:
The system implements periodic evaluation cycles where data quality enrichment entities are assessed at regular intervals using standardized test datasets. This periodic approach allows companies to verify quality claims and compare entity performance systematically without continuous evaluation, reducing the time and resource investment required while maintaining measurement precision through structured assessment protocols.
Data Source
AI summary
Data quality enrichment integration and evaluation system that enables the import of data into a database and the turnkey integration with data enrichment entities. The data imported into the database may be validated using validation rules. Data with particular data quality problems may be sent to a particular bucket for to avoid processing or to obtain processing by a particular data quality enrichment entity. A bucket of data may be sent to be enriched by one or more data enrichment entities. The enrichment results may be compared between entities to enable the selection of a data enrichment entity. The enrichment results may also be drilled down into to provide geographic and other plots that show the quality of original data and quality of data enrichment provided by each data enrichment entity. Evaluation of the enrichment results side by side allows for the selection of a data enrichment entity.