Dynamic Data Flow and Pattern Matching for Metadata Management
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Solution Overview
Problem
Organizations face challenges in managing data due to its generation and access by multiple individuals and groups, leading to data silos, inconsistent field definitions, and a lack of proper metadata management, which results in misleading KPIs and variances in data processing.
Innovation Solution
Implementing intelligent data definition and classification techniques that analyze data sources and usage information to identify and define metadata, building a corpus of fields and patterns, and using machine learning algorithms to classify data, thereby creating a knowledge repository that learns over time and provides recommendations for data definitions and classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data management by subject matter experts is used, then data definitions and classifications can be established, but the process is time-consuming and difficult to scale across multiple data sources
Solution Approach 1:
The system enables self-service data management by automatically analyzing data sources, extracting metadata, and generating data definitions and classifications without requiring manual intervention from subject matter experts. The intelligent data classification system performs self-directed analysis of data patterns, formats, and relationships to produce standardized data definitions across multiple data sources simultaneously.
Solution Approach 2:
The patent replaces the mechanical manual process of data definition and classification with an automated intelligent system. Instead of relying on human experts to manually examine and define data from each source, the system uses automated analysis algorithms to extract metadata, identify data patterns, and generate standardized definitions, thereby eliminating the time-consuming manual workflow while maintaining or improving accuracy.
2Adaptability or versatility
If multiple individuals and groups access data for different purposes, then data can be utilized across the organization, but data silos and inconsistent field definitions emerge
Solution Approach 1:
The system creates universal data definitions that serve multiple purposes and user groups simultaneously. By establishing standardized field definitions, data classifications, and metadata structures that can be applied across diverse data sources and use cases, the system enables a single set of definitions to support multiple analytical purposes, reporting requirements, and access patterns without creating inconsistencies or data silos.
Solution Approach 2:
The system transforms data from its raw, source-specific format into a standardized representation by changing key parameters such as field names, data types, and classification categories. This parameter transformation ensures that data from different sources with varying definitions is converted into a consistent format that maintains stability and uniformity across the organization while still serving multiple analytical purposes.
3Measurement precision
If comprehensive metadata analysis is performed across all data sources, then accurate data definitions can be created, but the complexity of managing and processing metadata increases
Solution Approach 1:
The system segments the complex metadata management task into distinct, manageable components: data source identification, metadata extraction, pattern recognition, classification assignment, and definition generation. By dividing the overall process into these separate functional modules, the system reduces the perceived and actual complexity of managing comprehensive metadata analysis while maintaining high accuracy in data definitions through systematic processing of each segment.
4Extent of automation
If automated data classification is implemented, then reliance on subject matter experts is reduced, but the initial setup and training of classification algorithms requires significant resources
Solution Approach 1:
The system performs preliminary analysis of data sources to automatically identify patterns, formats, and relationships before full-scale classification is implemented. By conducting initial metadata extraction and pattern recognition on sample data, the system pre-configures classification algorithms with learned patterns, thereby reducing the resources needed for subsequent full deployment while maintaining high automation levels and minimizing the need for extensive manual training data preparation.
Data Source
AI summary
Techniques are disclosed for data management in an information processing system. For example, a method comprises analyzing one or more data sources, wherein each of the one or more data sources comprise a set of metadata and usage information associated with the set of metadata. The method then determines at least one of data definitions and data classifications for the one or more sets of metadata across the one or more data sources, and stores the at least one of data definitions and data classifications for the one or more sets of metadata in a repository.