Data Quality Control Utility for Cross-Platform Record Validation
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Solution Overview
Problem
Cross-platform digital data movement systems face challenges with data errors, such as keystroke errors and packet loss, leading to incomplete datasets and processing issues, which require reprocessing and increased resource consumption.
Innovation Solution
A system that compares variable length input records to predetermined record types to apply rules determining data quality, ensuring data integrity by detecting anomalies early and reducing errors through customized validation and statistical measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is moved across cross-platform networks without validation, then processing speed is improved, but data errors increase leading to reprocessing
Solution Approach 1:
The patent applies preliminary validation rules to assess data quality before data is moved across the network. This prevents erroneous data from being transmitted, eliminating the need for reprocessing while maintaining high processing speed for valid data.
Solution Approach 2:
The system implements feedback mechanisms where validation results are returned to the data source systems. This allows corrective actions to be taken at the source, preventing error propagation across the network while maintaining overall system productivity.
2Reliability
If comprehensive data validation rules are applied to all records, then data quality is improved, but processing time increases
Solution Approach 1:
The patent applies validation rules selectively based on data type, format, and risk assessment. Not all data records undergo the same level of validation - critical data receives comprehensive validation while routine data receives streamlined validation, reducing overall processing time while maintaining data quality.
Solution Approach 2:
Different validation rules are applied to different fields and data types within the dataset. Each data element receives validation appropriate to its specific requirements, avoiding unnecessary validation overhead while ensuring data quality where it matters most.
3Loss of information
If data errors are detected and corrected, then data completeness is improved, but memory and processing usage increase
Solution Approach 1:
The patent extracts and validates only the critical data elements that are essential for data completeness. By focusing validation efforts on key fields rather than processing entire datasets, the system ensures data completeness while minimizing memory and processing usage.
Data Source
AI summary
A system includes an interface and one or more processors. The interface receives a dataset comprising a plurality of variable length input records, each input record comprising a plurality of fields. The one or more processors compare the input record to a plurality of predetermined record types to determine whether the input record matches one or more of the predetermined record types. Upon a determination that the input record matches one or more of the predetermined record types, the one or more processors determine one or more rules applicable to the input record. The one or more rules are determined based on the predetermined record types that match the input record. The one or more processors apply the one or more rules applicable the input record. The one or more rules determine the quality of the input record based on a structure of one or more of the fields of the input record and/or a value of one or more of the fields of the input record. The one or more processors determine a data quality of the dataset, the data quality determined based at least in part on the result of applying the one or more rules applicable to the input record.