Data Quality Classifiers for Multi-Source Consistency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As the number of data sources increases, maintaining internal data consistency becomes more difficult, leading to issues such as operational errors, economic damage, and liability/credibility problems due to poor-quality data, with existing methods lacking a central definition for data validation and requiring high resource costs.
Innovation Solution
Implementing domain-specific classifiers that validate critical data points through tree traversal and static data analysis, generating a data quality report via a graphical user interface to ensure high-quality output, and providing a customizable tool for automating data management across various systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of data sources increases, then data coverage and information availability improve, but internal data consistency deteriorates
Solution Approach 1:
The patent introduces domain-specific classifiers as intermediary components between multiple data sources and the data warehouse. These classifiers act as mediators that validate, standardize, and normalize data from different sources before integration, ensuring consistency across the system while allowing expansion of data sources. The classifiers serve as a buffer layer that manages the complexity of multi-source integration.
Solution Approach 2:
The patent segments the data validation and classification process into domain-specific modules. Each domain (e.g., customer, product, order) has its own classifier that handles specific data types and validation rules. This segmentation allows independent management of different data sources and simplifies the overall consistency maintenance by dividing the complex validation task into manageable, specialized components.
2Adaptability or versatility
If traditional data validation methods are used without centralized definition, then flexibility in handling different data sources is maintained, but resource costs and complexity increase
Solution Approach 1:
The patent creates a universal classifier framework that can handle multiple data types and domains through a common architecture. The domain-specific classifiers follow a standardized interface and validation pattern, allowing the system to process diverse data sources uniformly. This universal approach reduces complexity by providing a single, reusable validation mechanism rather than separate custom validators for each data source.
Solution Approach 2:
The patent uses configurable parameters and metadata to define validation rules and classifier behavior. By changing parameters rather than restructuring the entire validation system, the framework adapts to different data sources and domains. This parameter-driven approach maintains flexibility while reducing complexity, as the same core validation logic can be adjusted through configuration rather than code modification.
3Reliability
If comprehensive data validation across all data points is performed, then data quality improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by implementing domain-specific classifiers that focus validation efforts on critical data points within each domain. Rather than uniformly validating all data points across the entire system, each classifier concentrates on the specific data types and validation rules relevant to its domain. This targeted approach maintains high data quality for critical information while reducing overall processing time by avoiding redundant validation of less critical data.
Solution Approach 2:
The patent implements partial validation by prioritizing validation of critical data points over comprehensive validation of all data. The domain-specific classifiers focus on essential validation checks for the most important data elements, accepting that not every single data point undergoes exhaustive validation. This partial action approach achieves sufficient data quality for business operations while significantly reducing processing overhead.
Data Source
AI summary
A computer-implemented method for managing data quality is provided. The method comprising determining, by a rule engine, a number of critical data points in a number of different software modules. A classifier is identified based on a data type of the critical data points, and the classifier is bound to the critical data points. The classifier scans the critical data points for anomality to verify an ability to correctly process the critical data points. A data quality report is generated based on the scan and displayed to an end user in a graphical user interface.