Pass the DAMA CDMP CDMP-RMD Questions and answers with ExamsMirror

A key point of a Master Data Management (MDM) program is that it must adapt and evolve over time. The statement that an MDM program "can be effectively created and managed long-term using the same methodology" is not correct. MDM programs must continually evolve to address new data sources, changing business requirements, and advancements in technology. As data inventory grows and the data landscape changes, MDM methodologies and strategies need to be reassessed and updated to remain effective. This adaptability is crucial for maintaining data quality and relevance.

References:

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

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

Every process within an MDM framework includes a degree of governance. Here’s why:

Governance Definition:

Policies and Standards: Governance involves the establishment of policies, standards, and procedures to ensure data quality, consistency, and compliance.

Oversight: Provides oversight and accountability for data management practices.

MDM Processes:

Inherent Governance: All MDM processes, from data integration to data quality management, incorporate governance to ensure the integrity and reliability of master data.

Data Stewardship: Involves data stewards who oversee data governance activities, ensuring adherence to established standards and 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)"

Activities related to Reference & Master Data typically include managing the lifecycle, establishing governance policies, modeling data, and defining architectural approaches. However, evaluating and assessing data sources is generally not considered a core activity specific to Reference & Master Data management. Here's a detailed explanation:

Core Activities:

Manage the Lifecycle: Involves overseeing the entire lifecycle of master data, from creation to retirement.

Establish Governance Policies: Setting up policies and procedures to govern the management and use of master data.

Model Data: Creating data models that define the structure and relationships of master data entities.

Define Architectural Approach: Developing the architecture that supports master data management, including integration and data quality frameworks.

Excluded Activity:

Evaluate and Assess Data Sources: While this is an important activity in data management, it is more relevant to data acquisition and integration rather than the ongoing management of reference and master data.

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)"

Master Data relationships define how different master data entities are related to each other within an organization. These relationships are crucial for understanding and managing the dataeffectively. The types of master data relationships generally include hierarchies, groupings, and associations that help in organizing and categorizing the data.

Customer Household:

This refers to grouping individual customers into a single household entity. It is commonly used in consumer industries to understand the relationships and dynamics within a household.

Fixed-Level Hierarchy:

A hierarchy with a predetermined number of levels. Each level has a specific position and relationship to other levels, such as organizational hierarchies or product categorization.

Ragged-Level Hierarchy:

Similar to fixed-level hierarchies, but with varying levels of depth. It accommodates entities that may not fit neatly into a fixed-level structure, providing flexibility in the hierarchy.

Grouping based on common criteria:

This involves creating groups or segments of data based on shared attributes or criteria. For example, grouping products by category or customers by region.

Survivorship (NOT a relationship):

Survivorship pertains to the process of determining the most accurate and relevant data when multiple records exist for the same entity. It is a data quality and management process, not a type of relationship.

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).

Explanation:

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.

References:

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)".

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)"

Sharing reference and master data across an enterprise requires effective collaboration and communication among various stakeholders within the organization.

Staging Area:

A staging area can be used for intermediate data storage during processing but is not a requirement for sharing data.

Maintaining and Storing History Records:

Historical records are important for auditing and tracking changes but do not directly facilitate the sharing of current reference and master data.

Collaboration Between Multiple Parties Internal to the Organization:

Effective sharing of master and reference data requires collaboration among different departments and stakeholders to ensure data consistency, quality, and governance.

This includes establishing clear communication channels, defining roles and responsibilities, and ensuring alignment on data standards and practices.

Identification of Business Key and Surrogate Keys:

Keys are important for data integration and linking but do not by themselves ensure effective sharing of data.

Creation of Foreign Keys to Support Dimensions:

Foreign keys are used in relational databases to link tables but are not specifically required for the sharing of master data.

Marketing is crucial to the success of a Master Data Management (MDM) program for several reasons:

Encouraging Onboarding:

Master Data Onboarding: Effective marketing strategies can encourage different data sources to integrate their master data into the MDM system, ensuring comprehensive data coverage.

Promoting Benefits to Leadership:

Leadership Buy-in: Marketing the benefits of MDM to organizational leadership can secure necessary support and resources for the program. Highlighting efficiencies, cost savings, and improved decision-making can be persuasive.

Growing Subscriber Base:

User Engagement: Promoting the MDM program can help grow the base of subscribers and users who rely on the master data, ensuring the data is used effectively across the organization.

Ensuring Long-term Sustainability:

Sustainability: Continuous marketing helps maintain interest and investment in the MDM program, ensuring its long-term sustainability and relevance within the organization.

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)"

Planned control activities are not considered master data. Here’s why:

Master Data Examples:

Categories and Lists: Master data typically includes lists and categorizations that are used repeatedly across multiple business processes and systems.

Examples: Product categories, account codes, country codes, and currency codes, which are relatively stable and broadly used.

Planned Control Activities:

Process-Specific: Planned control activities pertain to specific actions and checks within business processes, often linked to operational or transactional data.

Not Repeated Data: They are not reused or referenced as a stable entity across different systems.

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)"

Authoritative listings of master data entities are essential for ensuring data accuracy and consistency across an organization.

Canonical Directories:

This term refers to standardized data models but is not typically used to describe authoritative listings of master data entities.

Entity Directories:

While this term could be used, it is not the most accurate or commonly used term for authoritative listings.

Reference Directories:

Reference directories are authoritative lists of master data entities such as companies, people, and products. They provide standardized, trusted, and verified data that organizations can rely on.

These directories ensure that everyone in the organization uses consistent and accurate data, supporting data quality and governance efforts.

Industry Directories:

These directories provide information specific to an industry but are not necessarily authoritative lists of master data entities.

Source Directories:

This term does not specifically refer to authoritative master data listings.