DAMA CDMP-RMD Based on Real Exam Environment

Systems of record are primary suppliers of master data content to an MDM program.

Systems of Record:These are authoritative data sources that provide consistent and reliable master data.

Role in MDM:They supply accurate and up-to-date master data, ensuring that the MDM system has a solid foundation of information.

References:

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

CDMP Study Guide

Implementing Master Data Management (MDM) involves several challenges, but the disparity between data sources is often the most significant.

Disparity Between Sources:

Different systems and applications often store data in varied formats, structures, and standards, leading to inconsistencies and conflicts.

Data integration from disparate sources requires extensive data cleansing, normalization, and harmonization to create a single, unified view of master data entities.

Data Quality Issues:

Variability in data quality across sources can further complicate the integration process. Inconsistent or inaccurate data must be identified and corrected.

Defining Requirements for Master Data:

While defining requirements is crucial, it is typically a manageable step through collaboration with business and technical stakeholders.

DBA Cooperation:

Getting Database Administrators (DBAs) to share table structures can pose challenges, but it is not as critical as dealing with disparate data sources.

Complex Queries and Indexes:

While important for performance optimization, complex queries and indexing issues are more technical hurdles that can be resolved with appropriate database management practices.

Ownership is a crucial principle in managing Reference and Master Data. Here’s an in-depth look at why:

Organizational Ownership:

Unified Responsibility: Reference and Master Data are assets that span across various functions and departments within an organization.

Consistency and Accuracy: Ensuring that data ownership is attributed to the organization prevents silos and ensures data is consistently accurate and available across all departments.

Data Governance: Proper governance frameworks ensure that data is managed in a way that meets the organization’s needs and complies with relevant regulations and standards.

Avoiding Departmental Silos:

Cross-functional Use: Different departments use and rely on Reference and Master Data, so ownership by a single department can lead to conflicts and inconsistencies.

Holistic Management: Centralized ownership enables holistic data management practices, enhancing data quality and usability across 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)"

Reference data and master data serve different purposes within an organization, and storing them in separate repositories can be beneficial for managing them effectively.

Reference Data:

Reference data is used to classify or categorize other data. Examples include code tables, taxonomies, and standard lists like country codes or industry classifications.

It is often less volatile and has a higher degree of standardization.

Master Data:

Master data refers to the core business entities that are essential for operations, such as customers, products, employees, and suppliers.

It is often more dynamic and requires frequent updates to ensure accuracy and consistency across systems.

Separate Repositories:

Storing reference and master data in separate repositories allows for tailored management strategies, governance, and security measures suited to their specific needs.

This approach can improve performance, data quality, and accessibility by reducing complexity and focusing resources on maintaining each type of data appropriately.