Crowd Sourcing Data Quality Error Resolution
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Solution Overview
Problem
Current methods fail to optimize human resources for resolving data quality errors in information assets, which are critical for business processes, due to inefficiencies in identifying and correcting issues such as spelling errors, missing data, and inconsistencies.
Innovation Solution
A system and method for information governance crowd sourcing, where data quality errors are identified and routed to crowds with the necessary performance level for correction, using a crowd hierarchy to select appropriate workers and compute wages for their work.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current manual methods are used to resolve data quality errors, then human resources can be deployed, but productivity and efficiency are insufficient
Solution Approach 1:
The system enables self-service by automatically detecting data quality errors through validation rules and routing them to appropriate crowd workers without manual intervention. The platform autonomously manages the entire workflow from error identification to resolution, eliminating the need for manual oversight while maintaining high productivity.
Solution Approach 2:
The patent introduces an intermediary platform that connects data quality issues with appropriate crowd workers. This intermediary system matches errors to workers based on performance levels and expertise, optimizing the resolution process and significantly improving productivity while reducing time loss.
2Manufacturing precision
If a crowd hierarchy is implemented to select workers, then result quality improves, but device complexity increases
Solution Approach 1:
The crowd hierarchy is segmented into multiple performance levels, with each level specialized for specific types of data quality tasks. This segmentation allows the system to match complex errors with high-performance workers while simpler errors are handled by lower-level workers, improving accuracy without requiring all workers to have equal expertise.
Solution Approach 2:
The system changes the parameter of worker selection by using performance levels and matching criteria rather than random assignment. By dynamically adjusting which workers are selected based on error complexity and worker capabilities, the system achieves high accuracy while keeping the matching logic manageable through automated algorithms.
3Productivity
If multiple crowds are used to correct errors, then productivity increases, but loss of information may occur through inconsistent corrections
Solution Approach 1:
The system implements feedback mechanisms where corrections made by crowd workers are validated against original data quality rules and reviewed by quality assurance processes. This feedback loop ensures that parallel corrections maintain consistency and do not introduce new errors, allowing high productivity without sacrificing data integrity.
Solution Approach 2:
The patent applies partial action by having different crowds work on different portions of data quality errors based on their expertise. Rather than having all crowds work on all errors, the system distributes tasks selectively, which maintains consistency while maximizing overall productivity through specialized handling.
Data Source
AI summary
A method, computer program product, and system for information governance crowd sourcing by, responsive to receiving a data quality exception identifying one or more data quality errors in a data store, identifying a performance level required to correct the data quality errors, selecting, from a crowd hierarchy, a first one or more crowds meeting the defined performance level, wherein the crowd hierarchy ranks the performance of one or more crowds, and routing, by operation of one or more computer processors, the one or more data quality errors to the selected crowds for correction.