Dama CDMP-RMD Exam Dumps

Data integration for Master Data Management (MDM) and reference data is a critical process that ensures data consistency, accuracy, and availability across the enterprise. The goal is to enable seamless data flow and access for various business functions.

Timely Extraction and Distribution:

Data integration processes must be designed to extract and distribute data efficiently and in a timely manner to ensure that all parts of the organization have access to up-to-date information.

This involves implementing data pipelines and ETL (Extract, Transform, Load) processes that can handle large volumes of data and deliver it where needed without delays.

Root Analysis of Data Lineage:

While important for understanding data origins and transformations, root analysis of data lineage is typically part of data governance and auditing processes, not a primary focus during real-time integration.

Ad-Hoc Changes:

While controlled environments are important, integration processes should be flexible enough to accommodate necessary changes without compromising data integrity.

Single Value for the Same Concept:

Ensuring a single source of truth is essential but requires robust data governance and harmonization efforts rather than just focusing on integration.

Ignoring Minor Changes:

Ignoring changes can lead to data quality issues and discrepancies. Effective data integration should handle changes efficiently without causing disruptions.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Tracking the number of MDM subject areas and source system attributes refers to defining the scope and coverage of the subject areas and attributes involved in an MDM initiative. This process includes identifying all the data entities (subject areas) and the specific attributes (data elements) within those entities that need to be managed across the organization. By establishing a clear scope and coverage, organizations can ensure that all relevant data is accounted for and appropriately managed.

DAMA-DMBOK2 Guide: Chapter 10 -- Master and Reference Data Management

'Master Data Management and Data Governance' by Alex Berson, Larry Dubov

Reference data and master data are distinct in several key characteristics. Here's a detailed explanation:

Reference Data Characteristics:

Stability: Reference data is generally less volatile and changes less frequently compared to master data.

Complexity: It is less complex, often consisting of simple lists or codes (e.g., country codes, currency codes).

Size: Reference data sets are typically smaller in size than master data sets.

Master Data Characteristics:

Volatility: Master data can be more volatile, with frequent updates (e.g., customer addresses, product details).

Complexity: More complex structures and relationships, involving multiple attributes and entities.

Size: Larger in size due to the detailed information and numerous entities it encompasses.

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

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

Business entities are represented within an organization through various forms, primarily as data or records within information systems.

Technical Capabilities:

While technical capabilities support the management and usage of business entities, they are not the representation of the entities themselves.

Business Capabilities:

Business capabilities describe the functions and processes that an organization can perform, but they do not represent individual business entities.

Files:

Files can contain data or records, but they are not the direct representation of business entities.

Data/Records:

Business entities are captured and managed as data or records within databases and information systems.

These records contain the attributes and details necessary to uniquely identify and describe each business entity.

Domains:

Domains refer to specific areas of knowledge or activity but are not the direct representation of business entities.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Definitions and Context:

MDM Solution: This involves tools and processes to manage master data within an organization to ensure a single source of truth.

Onboarding Data Suppliers and Subscribers: This process involves integrating new data sources (suppliers) and distributing data to various applications or users (subscribers).

A key capability for onboarding is the flexibility in data format and transfer methods because different data suppliers may use various formats and protocols.

Ensuring flexibility allows the MDM system to easily adapt to different data sources and meet the needs of diverse data consumers, thereby facilitating quick and efficient onboarding.

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

The Open Group, 'TOGAF Series Guide: The Data Management Capability Assessment Model (DCAM)'.