Policy-Based Validation for Multi-Source Data Consistency
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Solution Overview
Problem
Managing and integrating data from multiple sources in a data engineering system is complex due to issues like data quality, compatibility, privacy, collaboration, and conflicts arising from different ownership and usage permissions, especially with longitudinal data, leading to inconsistencies and high costs for continuous monitoring and recalibration.
Innovation Solution
A computer-implemented method using policy-based validation and version control to ensure data consistency by detecting changes, validating against consistency rules, updating versions, and repairing inconsistencies, with policies tailored to specific enterprise needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from multiple sources are integrated manually with continuous monitoring and validation, then data consistency can be maintained, but the process becomes error-prone, tedious, and expensive
Solution Approach 1:
The system enables self-service through automated self-validation and self-repair mechanisms. The data engineering system automatically detects inconsistencies, validates them against policies, and repairs them without human intervention, allowing the system to maintain itself autonomously
Solution Approach 2:
The system implements continuous feedback loops where the update detector monitors data changes, the policy validator provides feedback on consistency violations, and the version controller adjusts data versions based on validation results, creating a closed-loop control system that automatically maintains data consistency
2Reliability
If automated validation and version control systems are implemented, then data consistency and reliability are improved, but system complexity increases
Solution Approach 1:
The system segments the data engineering process into distinct modular components: an update detector component for monitoring changes, a policy validator component for consistency checking, and a version controller component for version management. Each component has a specific function and can be independently configured and maintained
Solution Approach 2:
The system introduces intermediary components that mediate between data sources and the core system. The update detector acts as an intermediary that intercepts and monitors data changes before they propagate, while the policy validator serves as an intermediary layer that enforces consistency rules between different data sources
3Measurement precision
If continuous monitoring and recalibration of longitudinal data are performed manually, then data accuracy is maintained, but time and costs increase significantly
Solution Approach 1:
The system ensures continuous monitoring and validation of data consistency without interruption. The update detector continuously monitors for changes, and the policy validator continuously checks consistency, eliminating gaps in monitoring that would require manual re-validation
Solution Approach 2:
The system performs self-validation and self-repair of longitudinal data automatically. The version controller automatically manages version transitions and the policy validator automatically repairs inconsistencies, eliminating the need for manual recalibration and reducing both time and costs
Data Source
AI summary
A method for providing data consistency between data sources includes providing first data, detecting a change information for the first data, providing a first version information of the detected change information, validating the change information, wherein a policy information contains at least a consistency rule executed on a common information model to decide about a consistent state of the first data defining whether the change information can be applied to the first data; updating the first version information to a second version information while confirming and repairing the detected data inconsistency of the change information, and supplying the change information with the version information as a production projection to at least a client engineering application.