Enterprise Data Model Schema for Incompatible Source Integration
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Solution Overview
Problem
Existing data management systems struggle to integrate and analyze data from different entities with incompatible data model schemas, leading to incompatibility issues and the inability to view and analyze data collectively.
Innovation Solution
A data management system that automatically integrates data from different entities by defining an enterprise data model schema, which encompasses all data types and provides uniform definitions and relationships, allowing for the creation of consolidated data views and collective analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from different entities with incompatible data models is integrated, then data collectivity and analysis capability are improved, but data model compatibility and system complexity increase
Solution Approach 1:
The patent introduces an intermediary layer (data integration platform or adapter layer) that sits between diverse data sources and the target system. This intermediary translates and harmonizes incompatible data models from different entities into a unified format, enabling data integration without requiring changes to the source systems. The intermediary handles schema mapping, data transformation, and protocol conversion, thus resolving the compatibility complexity while maintaining high adaptability.
Solution Approach 2:
The patent segments the data integration process into distinct modular components: data extraction modules, transformation modules, loading modules, and validation modules. Each component handles specific aspects of data integration independently, making the overall system more manageable and less complex. This segmentation allows different entities' data to be processed through standardized modular operations rather than requiring a monolithic complex integration system.
2Productivity
If a unified enterprise data model schema is implemented across all entities, then data access efficiency and analysis capability are improved, but system flexibility and adaptation to entity-specific models decrease
Solution Approach 1:
The patent implements a universal enterprise data model schema that serves multiple functions: it provides a standardized interface for data access across all entities, enables collective analysis, and simultaneously supports entity-specific extensions. The unified schema acts as a common denominator that maintains productivity while allowing optional entity-specific customizations through inheritance or extension mechanisms, thus achieving both efficiency and adaptability.
3Adaptability or versatility
If data from multiple entities is consolidated into a single view, then collective analysis capability is improved, but data security risks and compliance complexity increase
Solution Approach 1:
The patent applies local quality by implementing entity-specific security policies and access controls within the consolidated data view. Different entities can have different levels of data visibility, access permissions, and security requirements enforced locally within their respective data segments while maintaining a unified analytical framework. This allows collective analysis capability while preserving entity-specific security boundaries and reducing overall security risks.
Data Source
AI summary
Methods and systems are presented for collectively storing, managing, and analyzing data associated with different data sources. A data management system defines an enterprise data model schema based on different data model schemas associated with the different data sources. The data management system generates, for each data source, an enterprise data model instance based on the enterprise data model schema. Data is ingested from the different data sources, and then transformed and stored in a corresponding enterprise data model instance based on a mapping between a corresponding data model schema and the enterprise data model schema. Upon ingesting the data from the data sources, one or more consolidated data views are generated that combine at least portions of data from different enterprise data model instances. The data arranged according to the one or more consolidated data views is presented on a device and/or further analyzed to produce an analysis outcome.