User Data Reconciliation Using Audit, Summary, and Final Records
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Solution Overview
Problem
Existing technologies have not efficiently managed to manage data conflicts and maintain data integrity across multiple platforms, particularly in environments with large volumes of changing data, leading to discrepancies and inefficiencies in data management.
Innovation Solution
A data management system utilizing audit, summary, and final records to resolve data conflicts, where the audit record logs initial data, the summary record dynamically updates and consolidates recent entries, and the final record stores the resolved data for downstream processing, with mechanisms for purging outdated data and maintaining data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is collected and stored from multiple platforms to build comprehensive user profiles, then data completeness and user profile accuracy are improved, but data conflicts and discrepancies increase
Solution Approach 1:
The system segments data management into three distinct record types: audit records (raw data from multiple platforms), summary records (consolidated and resolved data), and final records (processed data for downstream systems). This segmentation allows the system to maintain data completeness from multiple sources while resolving conflicts through structured processing layers.
Solution Approach 2:
The summary record acts as an intermediary between audit records (raw data from multiple platforms) and final records (processed data). It consolidates and resolves conflicts from multiple data sources before the data is finalized, thereby maintaining both completeness and consistency.
2Reliability
If all data entries are stored to maintain historical accuracy, then data integrity is improved, but storage costs and data volume increase
Solution Approach 1:
The system extracts and stores only the necessary historical data in audit records while maintaining a streamlined summary record structure. This allows the system to retain data integrity for reconciliation purposes while reducing overall storage requirements by not storing every single data entry in all formats.
Solution Approach 2:
The system discards redundant data entries after they have been processed and consolidated into summary and final records. Historical data is retained in audit records for reconciliation needs but is not duplicated across all record types, thereby reducing storage volume while maintaining integrity.
3Measurement precision
If data conflicts are resolved manually to ensure accuracy, then data quality is improved, but processing time and system complexity increase
Solution Approach 1:
The system performs self-service conflict resolution through automated rules that evaluate audit records and generate summary records without manual intervention. Conflict resolution is handled automatically based on predefined logic, maintaining data accuracy while significantly improving processing speed.
Solution Approach 2:
The system performs preliminary conflict resolution by processing audit records through summary records before data is finalized. This preliminary consolidation and conflict resolution happens automatically in advance, reducing the need for manual intervention and improving overall processing efficiency.
4Speed
If data is continuously updated across multiple platforms, then data freshness is improved, but data conflicts and reconciliation complexity increase
Solution Approach 1:
The system segments the data flow into distinct stages: audit records capture fresh data from multiple platforms, summary records perform conflict resolution, and final records store processed data. This segmentation isolates reconciliation complexity to the summary record processing stage, allowing continuous updates without propagating complexity throughout the entire system.
Data Source
AI summary
Various embodiments described herein support or provide operations for facilitating the reconciliation and storage of conflicting user data across multiple platforms. Specifically, an audit record that includes a plurality of data records of an attribute is identified. A consolidated data record of the attribute in a summary record is generated. A current value of the attribute is determined based on the consolidated data record of the attribute in the summary record. The current value of the attribute is stored in a final record for downstream processing.