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

Reference data and master data serve different purposes within an organization, and storing them in separate repositories can be beneficial for managing them effectively.

Reference Data:

Reference data is used to classify or categorize other data. Examples include code tables, taxonomies, and standard lists like country codes or industry classifications.

It is often less volatile and has a higher degree of standardization.

Master Data:

Master data refers to the core business entities that are essential for operations, such as customers, products, employees, and suppliers.

It is often more dynamic and requires frequent updates to ensure accuracy and consistency across systems.

Separate Repositories:

Storing reference and master data in separate repositories allows for tailored management strategies, governance, and security measures suited to their specific needs.

This approach can improve performance, data quality, and accessibility by reducing complexity and focusing resources on maintaining each type of data appropriately.

The hub-and-spoke model is most often used for Master Data Management because it provides a central hub where master data is maintained, while the spokes represent different systems or applications that interact with the hub. This model allows for efficient management, synchronization, and distribution of master data across the enterprise, ensuring consistency and quality.

References:

DMBOK (Data Management Body of Knowledge), 2nd Edition, Chapter 11: Reference & Master Data Management.

The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling by Ralph Kimball and Margy Ross.

A 'Curation Zone' is a data architecture component used to semantically formalize source system content. This involves:

Data Curation: The process of organizing, integrating, and enriching raw data to make it meaningful and useful.

Semantic Formalization: Applying semantic models, ontologies, and metadata to standardize and contextualize the data.

Data Quality Enhancement: Ensuring the data meets quality standards through cleansing and validation processes.

Metadata Management: Capturing and managing metadata to provide context and meaning to the data.

The curation zone plays a critical role in transforming raw data into high-quality, semantically enriched information that can be effectively used for analysis, decision-making, and operational processes.

References:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.

"Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program" by John Ladley.