CDMP-RMD Exam Dumps : Reference And Master Data Management Exam

The Master Data hub environment that serves as the system of record for Master Data is:

Consolidated Hub:

Central Repository: Acts as a central repository where master data is stored and managed.

Data Quality and Integration: Ensures data quality by integrating data from various source systems and providing a single source of truth.

System of Record: Maintains the most accurate and up-to-date information about master data entities.

Other Hub Types:

SOA (Service-Oriented Architecture): Focuses on providing a flexible architecture for integrating services but not specifically a master data hub.

Two-Speed Hub: A hybrid approach, but not solely a system of record.

Source Hub: May refer to original source systems, not a consolidated system of record.

Registry: Primarily maintains references to data stored in other systems but not a comprehensive system of record.

References:

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"

Reference and Master Data Management (RMDM) governance processes are designed to manage and ensure the accuracy, consistency, and quality of critical data assets across an organization. These processes focus on defining, maintaining, and governing the shared data entities and attributes that are essential for various business processes. One of the key aspects governed by RMDM is "Data change activity."

Reference and Master Data Definition:

Reference data is a subset of master data used to classify or categorize other data within an organization. It typically includes codes and descriptions.

Master data refers to the critical business information regarding the core entities around which business is conducted, such as customers, products, employees, and suppliers.

Data Change Activity:

This involves tracking and managing the changes made to master and reference data over time. The governance processes ensure that any changes to this data are properly authorized, recorded, and communicated to relevant stakeholders.

Managing data change activity includes monitoring modifications, updates, additions, and deletions of reference and master data.

Importance in Governance:

Effective governance of data change activity ensures that the integrity and quality of master data are maintained. It prevents unauthorized changes that could lead to data inconsistencies and inaccuracies.

It supports audit trails and compliance with regulatory requirements by providing transparency and accountability for data changes.

Registry style MDM involves maintaining a central registry that stores references to master data while allowing the actual data to remain in the source systems. This approach requires technologies that support data integration and real-time access without physically moving the data.

JSON Mapping:

JSON mapping is useful for data exchange but does not specifically support the registry style MDM approach.

ETL Toolset:

ETL (Extract, Transform, Load) tools are typically used for batch data processing and integration, which may not align with the real-time data access requirements of a registry style MDM.

Columnar Database:

Columnar databases are optimized for analytical queries but are not specifically designed for supporting registry style MDM.

Complex Queries:

While complex queries can be part of the data access strategy, they are not a comprehensive solution for registry style MDM.

Data Virtualization:

Data virtualization provides a unified view of data from multiple sources without physically moving the data. It supports real-time data access and integration, making it well-suited for registry style MDM.

It enables organizations to access and manage master data across different systems while maintaining a central registry for reference.