Database Validation Engine for Disparate Data Integrity
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Solution Overview
Problem
Existing methods for validating data across disparate databases are inefficient and labor-intensive, requiring manual comparison and reporting, which can take days, months, or even years, especially when dealing with large datasets, and are prone to inconsistencies due to differences in data structures and ISO standards.
Innovation Solution
A system and method that uses a validation engine to automatically identify and compare data across multiple databases based on key name values and counts, generating reports on differences without manual intervention, allowing for timely corrective action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual comparison and reporting methods are used to validate data across disparate databases, then validation can be performed with simple tools, but the process takes days, months, or even years and is labor-intensive
Solution Approach 1:
The patent replaces manual mechanical comparison processes with an automated computer-based system that uses electronic data processing to identify and compare records across disparate databases. The system automatically retrieves data from multiple databases, compares records based on key fields, and generates validation reports without human intervention, reducing validation time from months to hours.
Solution Approach 2:
The validation system is designed to autonomously perform data retrieval, comparison, and report generation without requiring manual operation. The computer automatically executes validation queries, identifies matching records across databases, detects discrepancies, and produces validation reports, enabling the system to serve itself in the validation process.
2Extent of automation
If manual validation processes are used, then device complexity remains low, but the extent of automation is insufficient
Solution Approach 1:
The validation system is designed as a universal platform that can validate data across multiple different database types and structures simultaneously. It handles various data formats, database schemas, and comparison criteria through a single automated system, making the complexity worthwhile by providing broad automation capabilities across diverse validation scenarios.
3Adaptability or versatility
If data is stored in multiple disparate databases with different structures and ISO standards, then data can be accessed by different tools and programs, but inconsistencies and errors arise during validation
Solution Approach 1:
The validation system applies localized comparison criteria to different database regions, using key fields appropriate to each database type while maintaining overall validation consistency. It tailors the identification and comparison process to handle specific database structures, data formats, and ISO standards locally, ensuring reliable validation across heterogeneous environments.
4Productivity
If large datasets with billions of transaction records are validated manually, then resource requirements remain low, but productivity is severely limited
Solution Approach 1:
The validation system segments large datasets into manageable portions, processing validation in distributed batches across multiple computational units. It divides the billion-record validation task into smaller validation units that can be processed independently and efficiently, optimizing resource utilization while maintaining high throughput and enabling validation of massive datasets within practical timeframes.
Data Source
AI summary
Systems and methods are provided for validating data included in disparate databases. One exemplary method comprises identifying first data of a first database to second data of a second, different database, based on a key name value and a date common to the first and second data, and comparing a value the first data to a value of the second data. The exemplary method further includes generating a report when a difference between the value of the first data and the value of the second data exists, where the report is indicative of the difference in the values, and whereby a user associated with the validation command is permitted to take corrective action to avoid the difference in the values in one or more subsequent loads of data to the first database and/or the second database.