Master Data Inconsistency Evaluator for Database Transaction Integrity
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Solution Overview
Problem
Organizations face challenges with data inconsistencies in master data, such as duplicity and invalid data across database tables, which can halt transactions and affect business decisions, and existing solutions often fail to accurately identify the source of the issues, leading to temporary and potentially detrimental stop-gap arrangements.
Innovation Solution
The Master Data In-Consistency Evaluator (MICE) tool provides a framework to identify and remediate data inconsistencies by evaluating selection parameters across database tables, offering a data model-driven approach with event-based processing, allowing for flexible configuration and repair of inconsistencies, and persisting results for analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If direct database table operations are used to correct data inconsistencies, then the immediate issue can be resolved and business processes can continue, but the sanctity of the data is compromised and further issues may arise in applications consuming this master data
Solution Approach 1:
The patent introduces an intermediary evaluation system that acts as a mediator between data inconsistency detection and correction. This system evaluates potential corrections before applying them, ensuring that fixes do not compromise data sanctity while still enabling business process continuity. The intermediary layer prevents direct, potentially harmful database operations by validating corrections against multiple criteria before implementation.
2Reliability
If comprehensive data consistency checks are performed across all database tables, then data quality can be improved, but processing time and system resources are significantly increased
Solution Approach 1:
The patent segments the data consistency evaluation process into distinct, manageable components that can be executed independently and in parallel. The evaluation system divides master data tables into separate evaluation units, allowing simultaneous processing of multiple data segments. This segmentation reduces overall processing time while maintaining comprehensive coverage of data consistency checks.
Solution Approach 2:
The patent implements periodic evaluation where data consistency checks are performed at scheduled intervals rather than continuously. The system can evaluate data consistency on demand or at predetermined times, reducing unnecessary processing overhead while ensuring data quality is maintained. This periodic approach balances thorough evaluation with efficient resource utilization.
3Productivity
If multiple database tables are evaluated in parallel to speed up inconsistency detection, then processing efficiency is improved, but system complexity and coordination overhead increase
Solution Approach 1:
The patent segments the parallel evaluation process into independent evaluation units, each handling specific database tables or data segments. This segmentation allows parallel processing while maintaining manageable complexity through clear separation of evaluation responsibilities. Each segment can be processed independently, reducing coordination overhead compared to monolithic parallel processing.
Data Source
AI summary
Systems, methods, and computer products are described herein for identifying data inconsistencies within database tables associated with an application. A master data inconsistency evaluator receives data including at least one selection parameter within at least one database table. By the master data inconsistency evaluator evaluates the at least one selection parameter by comparing the at least one selection parameter with other database tables associated with the application to identify data inconsistencies. The master data inconsistency evaluator repairs the data inconsistencies to further facilitate an error free transaction.