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.

An organization's master data can indeed be acquired from external third parties. Here’s how and why:

Third-Party Data Acquisition:

Enrichment: External data sources can be used to enrich an organization's master data, providing additional details and context.

Accuracy and Completeness: Acquiring data from reputable third-party sources can enhance the accuracy and completeness of master data.

Use Cases:

Market Data: Organizations may purchase market data to complement their internal customer or product data.

Reference Data: Common reference data, such as postal codes or industry classifications, are often obtained from external providers.

Integration:

Data Integration: Master data acquired from third parties needs to be integrated into the organization’s MDM system, ensuring it aligns with existing data standards and governance policies.

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)"

Data Architecture in MDM Solutions:The design of a Master Data Management (MDM) solution involves defining and managing relationships between data entities.

Types of Relationships:

Traceable relationships and/or lineage relationships:These are important for understanding data provenance and transformations but are more relevant to data governance and data lineage tracking.

Data Acquisition relationships:These pertain to how data is sourced and collected, rather than how master data entities are related.

Affiliation relationships and/or parent-child relationships:These are crucial in MDM as they define how entities are related in hierarchical and associative contexts, such as customer relationships, organizational hierarchies, and product categorizations.

Hub and spoke relationships:This refers to the architecture model for MDM systems rather than the type of data relationship.

Ontology relationships and/or epistemology relationships:These are more abstract and pertain to the nature and categorization of knowledge, not specifically to the functional relationships in MDM.

Conclusion:The correct answer is "Affiliation relationships and/or parent-child relationships" as these are essential for defining and managing master data relationships in an MDM solution.

References:

DMBOK Guide, sections on Data Architecture and Master Data Management.

CDMP Examination Study Materials.