DAMA Updated CDMP-RMD Exam Questions and Answers by ember

MDM matching algorithms benefit from various data characteristics but do not benefit from "Structural heterogeneity of data elements."

Matching Algorithms:These are used in MDM to identify and link data records that refer to the same entity across different systems.

Data Characteristics:

Distinctiveness:Helps in accurately matching records.

Common Data Points:Aids in the comparison process.

Comparability:Facilitates effective matching.

Validity:Ensures the data is accurate and reliable.

Structural Heterogeneity:Different structures can complicate the matching process, making it harder to align data.

References:

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

CDMP Study Guide

Entities such as Customer, Product, and Employee are typical examples of master data that need to be managed.

Master Data Entities:These are the key data objects around which business transactions are conducted.

Examples:

Customer:Central to sales and service operations.

Product:Essential for inventory and sales management.

Employee:Critical for HR and payroll systems.

References:

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

CDMP Study Guide

Reference data plays a crucial role in financial trading. It includes data such as financial instrument identifiers, market data, currency codes, and regulatory classifications. Despite the dynamic nature of financial trades, reference data provides the necessary static information to execute and settle transactions. Industry estimates suggest that approximately 70% of the data used in financial transactions is reference data, underscoring its importance in the financial sector.

References:

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

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

Industry publications and whitepapers on reference data management in financial services.

Taxonomic reference data involves categorizing and organizing data to enable structured access and retrieval. It facilitates content classification, allowing for efficient multi-level navigation, which is essential for Business Intelligence (BI) activities. By organizing data into a taxonomy, users can easily locate and analyze information, supporting better decision-making processes in BI.

References:

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

DAMA-DMBOK Functional Framework, Function: Reference & Master Data Management.