Dama CDMP-RMD Practice Exam Questions & Answers

Master Data Management (MDM) lifecycle management encompasses the processes and practices involved in managing master data throughout its lifecycle, from creation to retirement. It ensures that master data remains accurate, consistent, and usable.

Reconciling and Consolidating Data:

This process involves merging data from multiple sources to create a single, unified view of each master data entity.

It ensures that duplicate records are identified and consolidated, maintaining data consistency.

Identifying Multiple Instances of the Same Entity:

This involves detecting and resolving duplicate records to ensure that each master data entity is uniquely represented.

Tools and algorithms are used to identify potential duplicates based on matching criteria.

Identifying Improperly Matched or Merged Instances of Data:

This step involves reviewing and correcting any errors that occurred during the matching or merging process.

Ensures that data integrity is maintained and that merged records accurately represent the underlying entities.

Maintaining Cross-Reference to Enable Information Integration:

Cross-references link related data entities across different systems, enabling seamless information integration.

This ensures that data can be consistently accessed and used across the organization.

Establishing Recovery and Backup Rules (NOT part of MDM Lifecycle Management):

While important for overall data management, recovery and backup rules pertain more to data protection and disaster recovery rather than the specific processes of MDM lifecycle management.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Ensuring high-quality reference data is critical for maintaining data accuracy, consistency, and reliability across an organization. The best way to achieve this is through robust data governance and stewardship practices.

Government Sources:

While government sources can be reliable, they are not the only sources of high-quality reference data. Relying solely on them may limit the comprehensiveness of reference data.

Drop-Down Menus:

Drop-down menus can help prevent invalid data entry but do not address the overall quality and governance of reference data.

Data Governance and Stewardship:

Implementing data governance and stewardship ensures that reference data is managed according to defined policies, standards, and procedures.

Data governance involves establishing a framework for decision-making, accountability, and control over data management processes.

Data stewardship assigns responsibility for data quality, ensuring that data is accurate, consistent, and fit for purpose.

Standard Reference Data (ISO):

Using standard reference data from organizations like ISO can enhance data quality, but it should be part of a broader governance strategy.

External Data Providers:

External data providers can offer high-quality reference data, but relying solely on them without proper governance can lead to inconsistencies and data quality issues.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

The registry style is the most difficult MDM style to implement data governance due to its reliance on maintaining a central registry of master data without consolidating data physically. This method makes it challenging to ensure consistent governance across disparate systems since data remains distributed and only loosely connected via the registry.

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

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

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 data effectively. 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.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Adhering to ISO standards for metadata is important for both in-house and commercial tools for the following reasons:

Standardization:

Uniformity: ISO standards ensure that metadata is uniformly described and managed across different tools and systems.

Interoperability: Facilitates interoperability between different tools and systems, enabling seamless data exchange and integration.

Guidance and Best Practices:

Structured Approach: Provides a structured approach for defining and managing metadata, ensuring consistency and reliability.

Compliance and Quality: Ensures compliance with internationally recognized best practices, enhancing data quality and governance.

ISO/IEC 11179: Information technology - Metadata registries (MDR)

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

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

Management of Reference and Master Data aims to mitigate the challenges of disparate data, which typically arise from:

Organic Growth:

Unplanned Expansion: Over time, organizations often develop new systems and applications organically, leading to isolated and redundant data stores.

Inconsistent Data: These disparate systems often result in inconsistent and unreliable data.

Isolated Systems:

Siloed Applications: Independent systems that do not communicate effectively with each other can lead to multiple versions of the same data.

Lack of Integration: Without proper integration, data consistency and quality suffer.

Mergers and Acquisitions:

Combining Systems: Mergers and acquisitions introduce the challenge of integrating different data systems and standards.

Data Redundancy: Newly acquired systems often come with their own data sets, leading to redundancy and conflicts.

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

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