Automated Audit Balance Control for Data Integrity
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Solution Overview
Problem
Existing data quality tools fail to comprehensively ensure the completeness, accuracy, and timeliness of data during and after movement between data stores in complex data processing environments, leading to issues like data corruption, missing data, and system failures.
Innovation Solution
The implementation of automated audit balance control (ABC) systems that inspect data at target stores, perform row count, checksum, and key value comparisons, apply business rules, and provide a user interface for creating and managing data validation procedures, ensuring data integrity and quality across data movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated audit balance control systems are implemented to comprehensively inspect data quality, then data integrity and quality assurance are improved, but system complexity and resource consumption increase
Solution Approach 1:
The ABC system is divided into multiple independent modules including data access module, data inspection module, and data quality determination module. Each module performs a specific function in the data validation process, allowing the system to maintain high reliability through specialized components while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary data inspection and validation checks before data is fully processed or used. By conducting audit balance controls and quality assessments in advance, the system prevents data integrity issues from propagating through the system, thereby maintaining reliability without requiring complete re-validation later.
2Reliability
If comprehensive data inspection and validation procedures are performed, then data quality assurance is improved, but processing time and productivity are reduced
Solution Approach 1:
The system performs targeted inspections on critical data attributes and high-risk data movements rather than exhaustive validation of all data. By focusing validation efforts on the most important aspects of data quality, the system maintains adequate quality assurance while reducing overall processing time compared to comprehensive validation of every data element.
Solution Approach 2:
The ABC system operates continuously in the background during data movements rather than performing batch validations. This allows data processing to continue while validation proceeds in parallel, maintaining productivity while ensuring data quality through ongoing inspection and monitoring.
3Measurement precision
If automated ABC systems are deployed to monitor all data movements, then data quality monitoring is improved, but resource consumption and system complexity increase
Solution Approach 1:
The system applies different levels of inspection and validation intensity to different data types, sources, and destinations based on their specific quality requirements. High-criticality data receives more rigorous validation while lower-criticality data receives streamlined checks, optimizing resource consumption while maintaining appropriate monitoring precision for each data context.
Solution Approach 2:
The system dynamically adjusts validation parameters such as inspection depth, sampling rates, and check intensity based on data characteristics, source reliability, and destination requirements. This allows the system to maintain high measurement precision when needed while reducing resource consumption during routine or low-risk data movements.
Data Source
AI summary
Systems and methods are described herein for performing automated audit balance control (ABC) procedures for data that has moved between data stores within an enterprise or other organization. The systems and methods inspect the data at a target data store and determine the quality of the movement of the data to the target data store based on the inspection. For example, the systems and methods can inspect row or record counts for the data in a data store, aggregate numeric sums within the data at the target data store, perform key data value comparisons between different locations that contained the data, perform full data comparisons between different locations that contained the data, perform checksum comparisons, apply complex business rules, and so on, when determining or verifying the completeness and accuracy of data as it arrives to a temporary or permanent location.