Data Validation Interface for Legacy System Migration
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Solution Overview
Problem
The process of data migration from one corporate platform to another, such as from SAP to Oracle, is lengthy and complex due to the need for multiple iterations of data validation, error reporting, and re-loading, which accounts for a significant portion of the project duration and effort in mergers and acquisitions, especially when ensuring data completeness and correctness across different systems.
Innovation Solution
A method and system for real-time data validation that includes detecting missing and duplicate values, orphaned records, and mapping records between legacy systems, with a user interface for displaying summary information and allowing users to edit and export data in a new format, reducing the need for multiple iterations and streamlining the data migration process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional multi-iteration data validation and re-loading process is used, then data completeness and correctness can be ensured, but the project duration and effort increase significantly
Solution Approach 1:
The patent applies preliminary action by performing data validation checks (detecting missing values, duplicates, orphaned records) and data mapping between legacy systems before the actual data migration process. This upfront validation and preparation work identifies and corrects issues in advance, preventing the need for multiple iterative cycles of loading, validating, and re-loading data, thereby significantly reducing project duration while maintaining data quality
Solution Approach 2:
The patent introduces an intermediary validation and mapping layer between the source legacy systems and the destination system. This intermediary process includes generating a user interface for validating data, mapping records between different data sources, and preparing data in the required format before migration, which streamlines the overall process and reduces the need for repeated iterations
2Reliability
If traditional data migration process with multiple iterations is used, then data quality can be validated, but the effort and complexity of the process increase
Solution Approach 1:
The patent reduces process complexity by performing all necessary validation activities (detecting missing values, duplicate values, orphaned records) and data mapping between legacy systems in advance, before the actual migration. This preliminary preparation consolidates the complexity into a single upfront phase rather than distributing it across multiple iterative cycles, making the overall process more manageable and less complex
Solution Approach 2:
The patent implements self-service through an automated validation process that autonomously detects data quality issues (missing values, duplicates, orphaned records) and performs mapping between legacy systems without requiring manual intervention in each iteration. The system automatically identifies and reports issues, enabling business analysts to validate data completeness independently without requiring IT involvement in each validation cycle
3Productivity
If real-time validation and mapping process is implemented, then project effort is reduced, but the initial processing time for validation increases
Solution Approach 1:
The patent performs validation and mapping operations as preliminary actions before data migration, consolidating the processing time into an upfront phase. While this initial validation processing takes time, it eliminates the need for multiple subsequent validation cycles during migration, thereby reducing overall project effort and making the actual migration phase faster and more efficient
Data Source
AI summary
A user interface may be designed to receive conflicting data from multiple data sources during a merger of legacy computer systems. The interface automatically validates the conflicting data against each other to generate a final data set for review. Summary information for each data set can be displayed in a single interface such that the summary data sets are individually selectable and provide an overview of the data merger process for each data set. By drilling down through the summary data, the user interface can display individual data sets where data conflicts can be resolved, missing data can be provided, and duplicate data can be eliminated. When the merger process is complete, the interface can translate the data set into a standardized format that can be exported to form generation modules to generate, for example, a welcome letter based on the translated data set.