Configurable Data Validation Framework for High-Volume Migration
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Solution Overview
Problem
Existing data validation systems are inefficient, unreliable, and not scalable, particularly in high-volume data exchanges, as they rely on manual validation and are not adaptable to changes in data formats or consumers, leading to incomplete and error-prone data migration.
Innovation Solution
A configurable validation system that includes a specification configuration module, analytics module, and insights report module, allowing dynamic reconfiguration for different datasets without reprogramming, by using specification configurations to define expected file and data formats for automated validation and generating actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual validation is used by technicians, then validation can be performed with human expertise, but the process is not scalable and only about 10% of data can be validated
Solution Approach 1:
The patent replaces manual mechanical validation processes with an automated computer-based validation system. The system uses executable instructions to automatically scan, validate, and certify data files without human intervention, thereby eliminating the bottleneck of manual validation while maintaining or improving validation accuracy through consistent application of validation rules.
Solution Approach 2:
The validation system is designed to be self-configuring and self-validating. It automatically loads validation rules from configuration files, performs validation without requiring technician expertise, and generates certification reports autonomously. This self-service capability enables the system to handle large volumes of data independently.
2Reliability
If the validation system is reprogrammed for each dataset change, then validation can be tailored to specific data formats, but the process is not adaptable and requires continuous reprogramming
Solution Approach 1:
The validation system employs dynamic configuration through external rule files that can be modified without changing the core system code. The system loads validation rules, data formats, and certification criteria from configurable files, allowing it to adapt to different datasets and requirements dynamically. This dynamic approach maintains validation reliability while enabling flexibility.
Solution Approach 2:
The system changes its validation parameters by loading different configuration files rather than reprogramming. Validation rules, data formats, and certification criteria are stored as modifiable parameters in external files, allowing the system to adapt to different datasets by simply changing these parameters without affecting the underlying validation logic.
3Productivity
If automated validation is implemented, then validation throughput increases, but the system requires complex configuration and setup
Solution Approach 1:
The validation system is segmented into distinct modular components: file scanning module, validation rule loading module, data validation module, and certification reporting module. Each module handles a specific aspect of the validation process, making the overall complex system manageable through clear separation of concerns while maintaining high throughput.
Solution Approach 2:
The patent introduces configuration files as intermediaries between the user and the validation system. These files serve as a simplified interface that allows users to define validation rules and parameters without directly programming the system. The intermediary configuration layer abstracts the complexity from users while enabling automated high-throughput validation.
4Loss of time
If only 10% of data is validated manually, then validation time is reduced, but undetected errors remain in the data source
Solution Approach 1:
The validation system performs preliminary validation on 100% of data before certification or migration. By validating all data records upfront using automated rules, the system prevents undetected errors from propagating to downstream systems, thereby ensuring data quality without excessive time loss through efficient automated processing.
Data Source
AI summary
Systems, methods, and other embodiments are described that are associated with a configurable validation system and framework that may be used for data exchange or migration. The system provides a specification neutral design for validating a source dataset. The configurable validation system is configured to receive a specification configuration associated with a selected source dataset, wherein the specification configuration provides at least scanning parameters and formatting rules that are input to configure the configurable validation system to validate the selected source dataset. The configurable validation system is reconfigurable to scan and validate a different source dataset in response to receiving a different specification configuration without reprogramming the configurable validation system.