Data Quality Specification Module for Systematic Testing
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Solution Overview
Problem
Organizations face challenges in systematically identifying and quantifying data quality issues within their processing systems, which can impact their operations significantly.
Innovation Solution
A system and method for integrating data quality processing, involving a business rule creation module, data quality specification module, validation module, and result publication module, that creates and implements data quality specifications to test data against defined rules, maintaining data lineage and automating the workflow for efficient data quality assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data quality testing is performed manually without systematic integration, then flexibility in testing approaches is maintained, but data quality issues cannot be systematically identified and quantified across the organization
Solution Approach 1:
The system segments data quality management into distinct functional modules: business rule creation module for defining quality requirements, data quality specification module for creating test specifications, validation module for executing tests, and result publication module for reporting. This segmentation allows each module to specialize in specific tasks while maintaining overall system reliability without requiring complete reintegration of existing systems.
Solution Approach 2:
The patent introduces intermediary components including a data quality specification that acts as a bridge between business rules and validation tests, and a workflow engine that mediates between different system components. These intermediaries enable systematic data quality assessment by coordinating interactions between disparate systems without requiring direct integration of all underlying systems.
2Reliability
If comprehensive data quality testing is implemented across all systems, then data quality issues are systematically identified, but the time and resources required for testing increase significantly
Solution Approach 1:
The system performs preliminary actions by creating detailed data quality specifications that define test parameters, data sources, and validation criteria before actual testing begins. Business rules are established in advance, and test specifications are prepared beforehand, allowing the validation module to execute comprehensive tests efficiently without ad-hoc preparation during the testing phase.
Solution Approach 2:
The patent enables continuous data quality validation by integrating the validation module into existing data workflows, allowing testing to occur continuously as data flows through the system rather than through periodic batch testing. The workflow engine maintains continuous operation, and results are published continuously, eliminating idle time between testing cycles and maintaining uninterrupted data quality monitoring.
3Loss of information
If multiple systems are integrated for data quality processing, then traceability and data lineage are maintained, but the complexity of system integration and coordination increases
Solution Approach 1:
The system implements feedback mechanisms where the validation module continuously monitors data quality and provides results back to the result publication module, which then feeds information back to stakeholders and system operators. The workflow engine uses feedback from test results to adjust and refine data quality specifications, creating a closed-loop system that maintains traceability while managing integration complexity through iterative improvement.
Solution Approach 2:
The patent creates universal components that can operate across multiple systems and contexts. The data quality specification module uses standardized formats that can accommodate different data sources and validation requirements. The workflow engine provides universal coordination capabilities that work across diverse system architectures, reducing integration complexity by applying consistent multi-functional approaches rather than custom integrations for each system pair.
Data Source
AI summary
System and method for integrating systems to implement data quality processing. A business rule creation module is configured to create a business rule associated with a business term. A data quality specification module is configured to create a data quality specification based on the business rule. The data quality specification comprises (1) an identity of a column of a table stored in a database comprising data to be tested; (2) a test to perform on the data to be tested; and (3) reference data required to perform the test on the data. A validation module is configured to receive the data quality specification; retrieve data associated with the column from the database; and test the retrieved data in accordance with the test using the reference data. A result publication module is configured to return a result of the test to the data quality specification module.