Central Computing Component for Data Quality Management
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Solution Overview
Problem
Current data quality management systems struggle with integrating and consolidating data from multiple sources, especially in biological research, due to conflicting data values, ownership issues, and differing rules for conflict resolution, which can lead to inconsistent data interpretations and limited sample sizes.
Innovation Solution
A data quality management system with a central computing component that receives and evaluates data points from multiple repositories, determines quality scores, and transmits these scores to update assigned values, ensuring consistent data collections across repositories, while handling different data formats and metadata for improved data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple sources in biological research, then the quantity of data points increases, but data consistency and reliability deteriorate due to conflicting values and different evaluation rules
Solution Approach 1:
The patent introduces a central computing component as an intermediary that receives data from multiple distributed data repositories, evaluates data quality using standardized criteria, and returns consolidated data with quality scores. This mediator resolves conflicts between different data sources by applying uniform evaluation rules, thereby maintaining data consistency while preserving the quantity of data from multiple sources.
Solution Approach 2:
The central computing component serves multiple functions: it collects data from various repositories, evaluates data quality according to standardized criteria, assigns quality scores, and returns consolidated data. This multi-functional approach enables consistent data management across different biological research domains without requiring source-specific processing, thus improving reliability while maintaining data quantity.
2Measurement precision
If human experts manually assess biological data, then measurement precision improves through expert judgment, but productivity decreases due to time-consuming evaluation processes
Solution Approach 1:
The patent replaces manual expert assessment with an automated computing system that evaluates data quality using programmed criteria. The central computing component automatically receives data, applies quality assessment rules, assigns scores, and returns results without human intervention. This substitution maintains measurement precision through standardized evaluation algorithms while dramatically improving productivity by eliminating time-consuming manual processes.
Solution Approach 2:
The system enables self-service data quality assessment where the computing component autonomously evaluates data against predefined criteria without requiring expert intervention. The automated quality score assignment allows the system to self-regulate and maintain consistent standards, achieving both high measurement precision and improved productivity through autonomous operation.
3Reliability
If data is stored in centralized repositories, then data consistency improves through unified management, but loss of information increases due to confidentiality restrictions and ownership issues
Solution Approach 1:
The patent segments the data management system into distributed data repositories that maintain local data ownership and confidentiality, while a central computing component handles only the necessary quality evaluation and consolidation. This segmentation allows each repository to retain full data accessibility and ownership rights, preventing information loss, while still achieving data consistency through the centralized quality assessment mechanism.
Solution Approach 2:
The central computing component acts as an intermediary that processes data quality assessments without requiring direct access to or storage of the actual biological data. It receives data points, evaluates quality using standardized criteria, assigns scores, and returns consolidated results. This intermediary approach maintains data consistency through unified quality management while preserving full data accessibility and ownership at the distributed repositories, eliminating information loss.
4Reliability
If conflict resolution rules are standardized across repositories, then data consistency improves through uniform evaluation, but device complexity increases due to integration requirements
Solution Approach 1:
The patent extracts the conflict resolution and quality evaluation logic from the distributed data repositories and concentrates it in the central computing component. Each repository simply sends data points for evaluation, while the central component applies standardized quality criteria and resolves conflicts. This extraction reduces the complexity at individual repositories while maintaining data consistency through centralized standardized evaluation.
Solution Approach 2:
The central computing component provides universal data quality evaluation services to multiple repositories using standardized criteria. This multi-functional approach enables consistent data management across different biological research domains without requiring source-specific processing logic. The standardized evaluation framework simplifies integration by providing a single unified approach that works across all repositories, reducing overall system complexity while maintaining data consistency.
Data Source
AI summary
The subject matter presently claimed relates to a data quality management system and method whereby a first data point comprising a first obtained data and a first assigned value from is received from a first data repository, a first quality score as well as a first storable data of the first data point is determined and/or stored. A second data point comprising a second obtained data, which is similar to the first obtained data according to a predefined similarity measure, and a second assigned value is received from the second data repository, a second quality score as well as a second storable data is determined from the second data point and/or stored and a second transmittable data, determined from the second data point and/or the second quality score is transmitted to the first data repository, causing the first data repository to re-evaluate the first assigned value.