Data Integrity Analysis Tool for Database Migration
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Solution Overview
Problem
Database integrity is compromised due to incomplete or inaccurate information, and data migration between systems requires efficient methods to purge irrelevant data to maintain system efficiency, which is challenging due to the complexity of data organization and cross-referencing between database tables.
Innovation Solution
A data integrity analysis tool that accesses a management object corresponding to database tables, generates code to evaluate content based on conditions, identifies entries that do not meet specified criteria, and presents these entries to users, facilitating data cleaning and migration by ensuring data compliance with new systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is migrated between systems, then system compatibility is improved, but data integrity may be compromised due to incomplete or inaccurate information
Solution Approach 1:
The system performs preliminary data quality assessment and validation checks before data migration occurs. It evaluates data completeness, accuracy, and compliance with target system requirements in advance, allowing corrective actions to be taken before migration to prevent data integrity issues in the target system.
Solution Approach 2:
The patent introduces an intermediary data validation and assessment layer between source and target systems. This intermediary component analyzes data quality metrics, identifies incomplete or inaccurate information, and facilitates proper data transformation and cleaning during the migration process to maintain integrity.
2Reliability
If data clean-up activities are performed, then data quality is improved, but processing time and complexity increase
Solution Approach 1:
The system performs data quality assessment and identifies cleanup requirements in advance before actual data migration or processing. By preliminarily evaluating data quality metrics and generating cleanup recommendations, it enables efficient batch processing rather than time-consuming iterative cleaning during migration.
Solution Approach 2:
The patent implements automated data quality assessment and cleanup recommendation generation that operates autonomously without requiring extensive manual intervention. The system self-evaluates data quality, identifies issues, and proposes corrective actions, reducing the time and human resources needed for data clean-up activities.
3Productivity
If irrelevant data is purged before migration, then system efficiency is improved, but risk of data loss increases
Solution Approach 1:
The system performs preliminary identification and classification of irrelevant data before migration, using defined criteria and validation rules. By preliminarily assessing which data should be purged versus retained, it creates a safety mechanism that prevents accidental loss of important data while efficiently removing truly irrelevant information.
Solution Approach 2:
The patent implements feedback mechanisms that track and report data purging actions, allowing verification that only appropriately identified irrelevant data is removed. The system provides feedback on data quality metrics before and after purging, enabling validation that system efficiency improvements do not compromise data safety.
Data Source
AI summary
A system for verifying data integrity in a database may include accessing a first file with a management object. The management object corresponds to a set of database tables located in a database. The system loads a data extract file including the management object, the data extract file containing fields related to content associated with the management object. The system accesses a second file with a condition, the condition being indicative of the content associated with the management object. The system generates code to evaluate the management object in the data extract file. The system evaluates the content associated with the management object in the data extract file using the generated code. The system identifies an entry in the data extract file that does not meet the condition, the entry representative of content associated with the management object, and then presents the entry to a user.