Data Quality Management Engine for Multi-Hop Processing Error Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Data quality issues, such as errors and inconsistencies, often go undetected in output data generated by complex processing systems, leading to compliance and auditing challenges.
Innovation Solution
A data quality management engine identifies data feeds, defines data elements, and executes quality checks across multiple processes to detect error rates exceeding thresholds, triggering automated actions like stopping processing or remediating causes, and adjusting error thresholds based on outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data processing is performed through multiple processes, then data transformation and processing capability is improved, but data quality issues and errors increase
Solution Approach 1:
The patent implements automated feedback mechanisms where data quality checks continuously monitor processing outputs and trigger corrective actions. When errors are detected in data feeds, the system automatically generates feedback signals to stop processing, alert operators, or adjust parameters, creating a closed-loop system that maintains data quality despite complex multi-process transformations.
Solution Approach 2:
The system performs preliminary data quality checks before data enters the processing pipeline and continues monitoring throughout each process hop. By conducting quality validation in advance and at multiple intermediate stages, the system prevents errors from propagating through the entire processing chain, maintaining reliability while enabling complex transformations.
2Productivity
If automated data processing is implemented, then processing efficiency is improved, but error detection capability deteriorates
Solution Approach 1:
The data quality management system performs self-service by automatically executing quality checks, calculating error rates, and triggering corrective actions without requiring manual intervention. The system monitors its own processing outputs, detects errors, and executes predefined response protocols autonomously, maintaining both high processing efficiency and robust error detection capabilities.
Solution Approach 2:
Automated feedback mechanisms continuously monitor processing outputs and immediately trigger corrective actions when errors are detected. The system self-monitors its own performance, generating real-time feedback signals that enable automatic error correction, thereby maintaining both high productivity and strong error detection capability simultaneously.
3Reliability
If data quality checks are performed on all processes, then data integrity is improved, but processing time increases
Solution Approach 1:
The patent segments data quality checking into distinct phases: preliminary checks before processing, intermediate checks at each process hop, and final checks after processing. This segmentation allows the system to perform quality validation at specific critical points rather than continuously throughout all processing steps, maintaining data integrity while minimizing time overhead.
Solution Approach 2:
The system conducts preliminary data quality checks before data enters the processing pipeline and continues monitoring at key intermediate stages. By performing validation in advance and at strategic points rather than continuously, the system ensures data integrity is established early while reducing the cumulative time cost of repeated checks.
4Reliability
If real-time error detection is implemented, then compliance with regulatory standards is improved, but system complexity increases
Solution Approach 1:
The data quality management engine is designed as a universal system that performs multiple functions: monitoring data quality, calculating error rates, comparing against regulatory thresholds, and triggering automated corrective actions. By consolidating these functions into a single multi-functional platform, the system achieves compliance with regulatory standards while avoiding the complexity of multiple separate specialized systems.
Solution Approach 2:
The system performs self-service by automatically executing quality checks, calculating error rates, comparing results against regulatory thresholds, and triggering corrective actions without requiring external intervention. This automation reduces the need for complex manual monitoring and compliance verification processes, achieving regulatory compliance while keeping system complexity manageable through self-automated operations.
Data Source
AI summary
Systems and methods for data quality management are disclosed. According to one embodiment, a computer-implemented method may include: identifying, by a data quality management engine, a data feed from a data source; defining, by the data quality management engine, a data element in the data feed; identifying, by the data quality management engine, a plurality of processes in a multi-hop process involving the data element; executing, by the data quality management engine, a data quality check on each process of the plurality of processes; identifying, by the data quality management engine, an error rate with one of the plurality of processes; determining, by the data quality management engine, that the error rate exceeds an error rate threshold for the one of the plurality of processes; and executing, by the data quality management engine, an automated action in response to the error rate exceeding the error rate threshold.