Automated Data Validation via Contextual Anomaly Detection
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Solution Overview
Problem
Current data validation processes are slow, expensive, and require substantial manual involvement, limiting scalability and accuracy, especially when dealing with inconsistencies, formatting errors, and anomalies in extracted data from various sources.
Innovation Solution
A fully-automated data validation and correction system utilizing a data manager that identifies anomalies using contextual information and validation rules, generates weighted lists of similar data elements, and automatically corrects errors, reducing the need for manual validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual validation processes are used to ensure data accuracy, then data quality can be maintained, but the process becomes slow and expensive with limited scalability
Solution Approach 1:
The patent replaces manual mechanical validation processes with an automated computer-based system that uses optical character recognition (OCR), pattern matching, and validation rules to detect and correct data anomalies, thereby maintaining data quality while dramatically increasing validation speed and scalability
Solution Approach 2:
The system performs self-validation by automatically comparing extracted data against validation rules, business logic, and reference data sources, enabling the system to identify and correct its own errors without requiring continuous manual intervention
2Measurement precision
If manual validation processes are used to identify and correct data anomalies, then data accuracy can be improved, but substantial manual involvement is required increasing costs
Solution Approach 1:
The patent substitutes manual inspection and correction activities with automated computational processes including OCR technology, pattern recognition algorithms, and rule-based validation systems that maintain high data accuracy while eliminating the need for substantial manual involvement
Solution Approach 2:
The system introduces an intermediary automated validation layer between data extraction and final data usage, which includes computer-implemented algorithms that act as mediators to detect, flag, and correct anomalies before human reviewers need to intervene
3Reliability
If traditional validation methods are used, then some data errors can be detected, but the process lacks scalability when dealing with large volumes of extracted data
Solution Approach 1:
The patent creates a universal automated validation system that can handle multiple types of data anomalies (formatting errors, content errors, OCR misrecognitions, business rule violations) across various data sources and formats, enabling the system to scale efficiently with increasing data volumes while maintaining consistent error detection capabilities
Data Source
AI summary
Techniques disclosed herein include systems and methods for data validation and correction. Such systems and methods can reduce costs, improve productivity, improve scalability, improve data quality, improve accuracy, and enhance data security. A data manager can execute such data validation and correction. The data manager identifies one or more anomalies from a given data set using both contextual information and validation rules, and then automatically corrects any identified anomalies or missing information. Identification of anomalies includes generating similar data elements, and correlating against contextual information and validation rules.