Semantic Glossary-Driven Business Conceptual Modeling
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Solution Overview
Problem
Traditional data management practices struggle to keep pace with modern business needs, leading to inefficiencies, data silos, and limited business team involvement in data initiatives.
Innovation Solution
The implementation of a hybrid data mesh approach, which empowers business teams to independently manage and govern their data through self-service platforms, business conceptual models, and semantic layers, bridging the gap between business concepts and technical implementations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized IT ownership and complex ETL pipelines are used, then data management control is maintained, but business team involvement is limited and data models fail to reflect business requirements
Solution Approach 1:
The patent implements self-service data modeling where business teams independently create and maintain their own data models using a user-friendly interface. Business users can define data entities, relationships, and attributes without requiring technical expertise or IT intervention, enabling them to directly reflect business requirements in the data model.
Solution Approach 2:
The patent introduces an intermediary layer between business teams and technical data infrastructure. This layer provides automated translation services that convert business-friendly data model definitions into technical implementations, eliminating the need for complex manual ETL pipelines while maintaining data management control.
2Measurement precision
If manual mapping of business terminology to underlying data sources is performed, then data accuracy is maintained, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical mapping processes with automated semantic matching algorithms. The system automatically maps business terminology to underlying data sources by understanding semantic relationships, eliminating time-consuming manual tasks while maintaining high accuracy through intelligent matching.
Solution Approach 2:
The patent implements feedback mechanisms where the system automatically validates and refines mappings based on business context and data characteristics. This iterative feedback process ensures accuracy while reducing the time required for initial mapping by leveraging learned patterns and relationships.
3Quantity of substance
If data volumes and complexity increase, then more comprehensive data coverage is achieved, but scalability and agility are hindered
Solution Approach 1:
The patent segments the data modeling process into independent, modular components that can be created and maintained separately. Each business team manages its own data model segment, allowing the system to scale horizontally by adding more segments without increasing overall complexity. This modular approach enables agility in handling increasing data volumes.
Data Source
AI summary
The embodiments herein are directed to a system and method for business data mapping which are characterized by the use of denormalized metadata. The system and method operate upon heterogeneous data sets in comparison to one or more business-specific glossaries. The returned data sets are characterized by adherence to business goals, and are dynamically available to an end-user with little to no specialized coding infrastructure.