Data Readiness Analysis System Using Multi-Indicator Scoring
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Solution Overview
Problem
Current data quality evaluation methods lack comprehensive consideration for different aspects of data readiness and do not have standardized evaluation flows, which affects the effectiveness of subsequent data analytics in big data analysis.
Innovation Solution
A data readiness analysis system and method that includes a storage device, a field-data-description-file generating module, and various data readiness analysis modules to generate and calculate scores for consistency, completeness, accuracy, validity, and compaction indicators, determining data readiness across different categories and specific data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current data quality evaluation methods are used (expert analysis or software analysis), then data quality can be assessed, but the evaluation does not comprehensively consider different aspects of data readiness and lacks standardized evaluation flow
Solution Approach 1:
The patent segments data quality evaluation into five distinct modules: completeness evaluation, accuracy evaluation, validity evaluation, compaction evaluation, and consistency evaluation. Each module focuses on a specific aspect of data readiness, allowing comprehensive assessment while maintaining manageable complexity through modular design. The field data description file is also segmented into multiple fields (data source, data category, data format, etc.) that can be independently analyzed.
Solution Approach 2:
The evaluation system is designed to be universally applicable to different types of data (structured, unstructured, semi-structured) and different data sources. The standardized evaluation flow and multi-dimensional assessment framework can be applied across various domains and scenarios, making the system versatile rather than domain-specific.
2Measurement precision
If multiple data quality indicators are evaluated, then data readiness assessment becomes more comprehensive, but the evaluation process becomes more complex and time-consuming
Solution Approach 1:
The system performs preliminary actions by generating field data description files that organize and pre-process data metadata before the actual quality evaluation. This preliminary structuring of data information (including data source, category, format, and relationship descriptions) enables faster subsequent evaluation by having all necessary information readily available and organized.
Solution Approach 2:
The evaluation system automatically assesses multiple data quality indicators without requiring manual expert intervention for each metric. The automated computation of completeness, accuracy, validity, compaction, and consistency scores enables the system to serve itself, reducing time loss while maintaining comprehensive assessment.
3Reliability
If data quality evaluation considers multiple dimensions (completeness, accuracy, validity, compaction, consistency), then evaluation thoroughness improves, but the complexity of implementation increases
Solution Approach 1:
The patent divides the comprehensive evaluation into five independent but coordinated modules, each handling a specific dimension (completeness, accuracy, validity, compaction, consistency). This segmentation makes implementation easier by allowing each module to be developed, tested, and maintained independently while contributing to the overall thorough evaluation.
Solution Approach 2:
The system evaluates data quality by changing and analyzing multiple parameters simultaneously (completeness rate, accuracy rate, validity rate, compaction rate, consistency rate). Each parameter is computed independently based on specific criteria, and the combination of these parameter assessments provides thorough evaluation while keeping implementation manageable through parameter-based modularity.
Data Source
AI summary
A data analysis system is provided in the invention. The data analysis system includes a storage device, a field-data-description-file generating module, and a general data readiness analysis module. The storage device stores a plurality of raw data. The field-data-description-file generating module generates the field-data-description files corresponding to the raw data. The general data readiness analysis module obtains a score of the consistency indicator of the raw data according to the field-data-description files. The general data readiness analysis module obtains the data of the category which needs to be analyzed from the raw data according to the category of each field-data-description file. The general data analysis module obtains the score of the completeness indicator, the score of the accuracy indicator, the score of the validity indicator, and the score of the compaction indicator which all correspond to the data of the category which needs to be analyzed.