Server Architecture for Data Quality Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data processing and storage systems face challenges in ensuring data quality, particularly when erroneous or outdated data is submitted by data furnishers, which can lead to inaccurate analyses and compliance issues with regulations like the Fair Credit Reporting Act.
Innovation Solution
A server architecture and method for data quality processing that includes modules for decrypting, formatting, analyzing, and reporting data quality indicators, allowing for automatic evaluation and improvement of data quality, while also generating reports and benchmarking analyses to ensure compliance and reduce disputes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data furnishers submit data to a large-scale data processing and storage system, then the quantity of data stored increases, but the data quality may deteriorate due to erroneous or outdated submissions
Solution Approach 1:
The system performs preliminary data quality assessment before data is loaded into the large-scale data store. The data quality assessment module evaluates data quality indicators and generates reports on incoming data sets, identifying potential errors or outdated information before they contaminate the main database, thus preventing data quality deterioration while allowing continuous data accumulation
Solution Approach 2:
The system implements feedback mechanisms where data quality assessment results are communicated back to data furnishers. The reporting module generates data quality reports that provide feedback to furnishers about the quality of their submitted data, enabling them to correct issues and improve future submissions, thereby maintaining data quality while the system continues to grow in data quantity
2Reliability
If comprehensive data quality assessment is performed on all incoming data, then data quality improves, but the processing time and system complexity increase
Solution Approach 1:
The data quality assessment system is segmented into multiple independent modules that can process different aspects of data quality in parallel. The assessment module evaluates multiple data quality indicators simultaneously rather than sequentially, reducing overall processing time while maintaining comprehensive data quality assessment
Solution Approach 2:
The system performs partial data quality assessment by focusing on critical data quality indicators that have the most significant impact on data reliability. Rather than exhaustively checking every possible data attribute, the system identifies and assesses key quality metrics, achieving effective data quality control with reduced processing time
3Reliability
If manual data quality review processes are used, then data quality can be maintained, but productivity and processing speed decrease
Solution Approach 1:
The data quality assessment module automatically evaluates incoming data sets without requiring manual review. The system self-assesses data quality indicators, generates quality reports, and provides feedback to data furnishers autonomously, maintaining data quality control while eliminating the time loss associated with manual processing
Solution Approach 2:
The system replaces manual mechanical review processes with automated electronic data quality assessment. Computer algorithms automatically evaluate data quality indicators, generate reports, and communicate feedback to furnishers, substituting human manual labor with automated computational processes that maintain quality control while significantly increasing processing speed and productivity
Data Source
AI summary
In one embodiment, a server architecture is disclosed that provides for processing and analyzing data received from data furnishers to evaluate quality of the provided data. The system may format the data received from the data furnishers into standardized form. Based on configuration information and rules for the data furnishers and the provided data, the system may analyze the data set to calculate one or more data quality indicators.