Master Data Management Architecture for Enterprise Data Consistency
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Solution Overview
Problem
Current master data management solutions are limited by their technology specificity, centralized repository designs, and inability to efficiently share and manage master data across diverse systems, leading to redundancy, inconsistency, and suboptimal decision-making, especially in large enterprises with varying latency and availability requirements.
Innovation Solution
The Master Data and Information eXchange (MIX) architecture provides a technology-independent, adaptive, and extensible framework for unified master data management, incorporating a master data store, management parameter store, data integration component, data quality component, abstraction layer, management interface, inbound interface, and outbound interface to enable seamless data sharing and management across repositories and applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single centralized repository is used for master data management, then data consistency is improved, but deployment flexibility and adaptability to varying latency and availability requirements deteriorate
Solution Approach 1:
The patent segments the centralized repository into multiple distributed master data repositories across the enterprise. Each repository can be independently managed and deployed, allowing flexibility in meeting varying latency and availability requirements while maintaining data consistency through standardized data models and synchronization mechanisms.
Solution Approach 2:
The patent creates a universal framework that can be deployed in multiple configurations (centralized, distributed, hybrid) to serve different enterprise needs. The standardized data models and interface definitions enable the same framework to adapt to varying latency and availability requirements across different business units and systems.
2Reliability
If current technology-specific MDM solutions are implemented, then mastery over specific data types is improved, but ease of sharing and use across the broader enterprise deteriorates
Solution Approach 1:
The patent implements a technology-specific MDM solution with universal interface definitions and standardized data models that enable seamless sharing across the enterprise. The framework supports multiple data types (customer, product, supplier, employee, etc.) through a common architecture, allowing accurate management of specific data types while facilitating enterprise-wide data sharing and collaboration.
3Adaptability or versatility
If a complete technology stack is adopted, then functional completeness is improved, but device complexity and deployment cost increase
Solution Approach 1:
The patent segments the complete technology stack into modular, independently deployable components. Each module (data integration, data quality, master data management, synchronization) can be implemented separately using appropriate technologies, reducing overall system complexity while maintaining functional completeness. This modular approach allows selective adoption of components based on specific enterprise needs.
Solution Approach 2:
The patent creates a dynamic architecture where the technology stack can be adapted and configured based on specific enterprise requirements. The framework supports multiple technology options for each functional layer, allowing the system to evolve and adjust its complexity level dynamically rather than requiring a fixed, complete technology stack from the outset.
4Ease of operation
If local master data stores are maintained in each IT system, then operational independence is improved, but data consistency and synchronization across systems deteriorate
Solution Approach 1:
The patent introduces a master data management framework as an intermediary layer between local IT system stores and the enterprise-wide master data repositories. This intermediary enables operational independence for local systems while maintaining data consistency through standardized data models, validation rules, and automated synchronization mechanisms that mediate between local and enterprise data requirements.
Data Source
AI summary
Master data management and information exchange (MIX) architecture includes a technology agnostic framework that unifies sharing and management of master data across repositories, data formats and applications in an enterprise. The MIX architecture comprises at least one master data store which stores and updates master data for said enterprise; a management parameter store which assists in providing technology agnostic architecture for an adaptive extensible framework for unified master data; and, a unified interface for enabling master data discovery, sharing and use across the enterprise, wherein the unified interface comprises management interface, inbound interface and outbound interface. The MIX architecture might additionally include a data integration component, data quality component, and an abstraction layer. The present architecture enables extended MDM enterprise deployment and can be implemented as a stand alone solution or as an MDM veneer over existing applications without affecting overall system behavior and is technology independent.