Free DAMA CDMP-RMD Practice Exam with Questions & Answers | Set: 2

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)"

The critical need of any Reference & Master Data effort is executive sponsorship. Executive sponsorship provides the necessary authority, visibility, and support for the MDM initiative. Key aspects include:

Strategic Alignment: Ensures that the MDM effort aligns with the organization's strategic goals and objectives.

Resource Allocation: Secures the required funding, personnel, and other resources needed for the MDM program.

Stakeholder Engagement: Facilitates engagement and commitment from key stakeholders across the organization.

Governance and Oversight: Provides governance and oversight to ensure the MDM program adheres to best practices and delivers value.

Without executive sponsorship, MDM initiatives often struggle to gain traction, secure necessary resources, and achieve long-term success.

References:

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

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

Master Data Management (MDM) involves various processes and technologies to ensure that master data is accurate, consistent, and trustworthy. The most comprehensive definition of MDM captures its multi-faceted nature, encompassing governance, technology, and organizational roles.

Governed Processes:

MDM involves establishing governance processes to define policies, standards, and procedures for managing master data.

These processes ensure that data is handled consistently and according to defined rules.

Role of People and Technologies:

Effective MDM requires the involvement of people, including data stewards, data owners, and governance committees, who are responsible for overseeing and managing master data.

Technologies, such as MDM software and tools, facilitate the implementation of governance processes, data integration, data quality management, and synchronization.

Key Objectives:

Master data should be understood by stakeholders, ensuring clarity and common understanding of data definitions and attributes.

Trust in master data is achieved through rigorous data quality and governance practices.

Data should be controlled, meaning that access, usage, and changes to the data are managed and monitored.

Master data must be fit-for-purpose, meeting the specific needs and requirements of the organization’s business processes.

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.

In any Master Data Management (MDM) effort, the most time and energy are typically spent on vetting business entities and data attributes through the Data Governance process. This step ensures that the data is accurate, consistent, and adheres to defined standards and policies. Itinvolves significant collaboration and decision-making among stakeholders to validate and approve the data elements to be managed.

References:

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

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

A registry in the context of Master Data Management (MDM) is a centralized index that maintains pointers to master data located in various systems of record. This type of architecture is commonly referred to as a "registry" model and allows organizations to create a unified view of their master data without consolidating the actual data into a single repository. The registry acts as a directory, providing metadata and linkage information to the actual data sources.

References:

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

"Master Data Management: Creating a Single Source of Truth" by David Loshin

For effective Master Data Management, an organization must agree on a single, standard definition of master data. Here's why:

Consistency:

Single Definition: A standardized definition ensures consistency across different departments and systems.

Avoids Confusion: Prevents discrepancies and misunderstandings regarding what constitutes master data.

Data Quality and Governance:

Unified Approach: A single definition supports unified data governance policies and data quality standards.

Data Integration: Facilitates easier data integration and interoperability across various systems and processes.

Business Efficiency:

Aligned Objectives: Ensures all parts of the organization are aligned in their understanding and use of master data, leading to more efficient operations and decision-making.

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)"

In Master Data Management (MDM), a classification scheme refers to a structured way of organizing data by using codes that represent a controlled set of values. These codes help in categorizing and standardizing data, making it easier to manage, search, and analyze.

References:

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

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

Registry style MDM involves maintaining a central registry that stores references to master data while allowing the actual data to remain in the source systems. This approach requires technologies that support data integration and real-time access without physically moving the data.

JSON Mapping:

JSON mapping is useful for data exchange but does not specifically support the registry style MDM approach.

ETL Toolset:

ETL (Extract, Transform, Load) tools are typically used for batch data processing and integration, which may not align with the real-time data access requirements of a registry style MDM.

Columnar Database:

Columnar databases are optimized for analytical queries but are not specifically designed for supporting registry style MDM.

Complex Queries:

While complex queries can be part of the data access strategy, they are not a comprehensive solution for registry style MDM.

Data Virtualization:

Data virtualization provides a unified view of data from multiple sources without physically moving the data. It supports real-time data access and integration, making it well-suited for registry style MDM.

It enables organizations to access and manage master data across different systems while maintaining a central registry for reference.

Master data represents the critical business information that is used across the organization. It provides context and structure for business transactions and analytical processes.

Data about Business Entities:

Master data typically includes key entities such as customers, products, suppliers, employees, and locations.

These entities are fundamental to business operations and provide the necessary context for transactions and analysis.

Providing Context for Business Transactions:

Master data provides the foundational information required to conduct business transactions.

For example, customer master data is used in sales transactions, while product master data is used in inventory management.

Supporting Business Analysis:

Master data is critical for business intelligence and analytics, providing a consistent and accurate view of the core business entities.

It enables effective reporting, analysis, and decision-making by ensuring that the data used in these processes is reliable and standardized.

Other Options:

A: Master data and reference data are distinct; reference data is used to categorize master data.

B: Master data is not necessarily mastered by business users but involves collaboration between IT and business stakeholders.

C: Provides visibility but also context for transactions and analysis.

E: Master data is about business entities, not technical entities.