Metadata-Driven Data Maintenance for ERP Systems
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Solution Overview
Problem
In Enterprise Resource Planning (ERP) systems, managing large volumes of data while ensuring compliance with legal regulations and maintaining system consistency is challenging due to the complexity of data sets and process variants, making it difficult to delete data at the appropriate time.
Innovation Solution
A metadata-driven approach is implemented to enforce data retention policies by identifying and analyzing data objects and their relationships, allowing for maintenance actions such as archiving or deleting data objects based on predefined criteria, ensuring timely removal of unnecessary data and compliance with regulations like GDPR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is retained in large volumes to support various processes and data sets, then system functionality and data availability are improved, but data volume and storage requirements increase
Solution Approach 1:
The patent segments data objects into different categories (active, archived, candidate for deletion) based on their relationship to active processes. This segmentation allows the system to manage large volumes of data by organizing them into manageable groups with different retention requirements, thereby maintaining system functionality while controlling overall data volume through selective archiving and deletion.
Solution Approach 2:
The system performs preliminary analysis of data objects against maintenance criteria before actual deletion or archiving occurs. By pre-identifying data objects that are no longer needed by active processes and pre-evaluating them against retention policies, the system can proactively reduce data volume while ensuring that data required for ongoing processes remains available.
2Quantity of substance
If data is deleted to reduce data volume and comply with regulations, then storage efficiency and regulatory compliance are improved, but system consistency and stability may be compromised
Solution Approach 1:
The system implements a feedback mechanism where data objects are continuously evaluated against maintenance criteria, and deletion decisions are based on real-time system state information. The system monitors process activity and adjusts deletion timing accordingly, ensuring data is only removed when it is safe to do so, thus maintaining system consistency while achieving data volume reduction.
Solution Approach 2:
Before deleting data objects, the system performs preliminary checks to ensure that no active processes depend on the data. By pre-evaluating data objects against maintenance criteria and verifying system state, the system ensures that deletion operations maintain system consistency and stability while still achieving the goal of reducing data volume.
3Ease of operation
If manual processes are used to manage data retention and deletion, then flexibility and control are improved, but time consumption and operational complexity increase
Solution Approach 1:
The system implements self-service automation where data objects automatically evaluate themselves against maintenance criteria and are automatically archived or deleted based on the results. This eliminates the need for manual intervention in data retention management, significantly reducing time consumption while maintaining flexibility through configurable maintenance criteria and policies.
Solution Approach 2:
The system allows flexible configuration of maintenance criteria parameters (such as retention periods, data types, and process relationships) without requiring manual management processes. By enabling parameter-based control, the system maintains operational flexibility while automating the actual data management tasks, thereby reducing time consumption.
4Measurement precision
If comprehensive data analysis is performed before deletion to ensure compliance and consistency, then data accuracy and regulatory compliance are improved, but processing time and computational resources increase
Solution Approach 1:
The system segments the data analysis process into distinct phases: initial screening against maintenance criteria, detailed evaluation of candidate objects, and final verification before deletion. This segmentation allows for efficient processing by applying different levels of analysis to different data objects based on their risk profile and importance, thereby maintaining data accuracy while reducing overall processing time.
Solution Approach 2:
The system applies partial analysis to data objects by performing comprehensive checks only on those that meet initial maintenance criteria thresholds. For low-risk data objects, simplified verification is sufficient, while high-risk objects receive more thorough analysis. This approach maintains data accuracy for critical objects while reducing processing time for the overall data set.
Data Source
AI summary
Techniques and solutions are provided for metadata-driven data maintenance. One or more data object queries are obtained from one or more data object frameworks. One or more sets of data objects are received based on the one or more data object queries. One or more data object nets are built based on the one or more sets of data objects and the one or more data object frameworks and respectively associated with one or more processes. The one or more data object nets and their associated processes are analyzed. Data object maintenance is performed on the data objects of the one or more data object nets based on the analysis of the one or more data object nets and their associated processes.