Get CDMP-RMD Dumps : Verified Reference And Master Data Management Exam

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.

The format and allowable ranges of Master Data values are primarily dictated by business rules.

Business Rules:

Business rules define the constraints, formats, and permissible values for master data based on the organization’s operational and regulatory requirements.

These rules ensure that data conforms to the standards and requirements necessary for effective business operations.

Semantic Rules:

These rules pertain to the meaning and context of the data but do not directly dictate formats and ranges.

Processing Rules:

These rules focus on how data is processed but not on the allowable values or formats.

Engagement Rules:

These rules govern interactions and workflows rather than data formats and ranges.

Database Limitations:

While database limitations can impose constraints, they are typically secondary to the business rules that drive data requirements.

Managing master data elements can be performed at multiple levels within an organization. This includes third-party providers such as Dun & Bradstreet (D&B) which can supply enriched and standardized master data. At the enterprise level, organizations manage master data centrally to ensure consistency and quality across all systems and processes. Within application suites suchas ERP (Enterprise Resource Planning) systems, master data management ensures that data is consistent and accurate within and across different applications. Therefore, master data elements can be managed at all these points.

References:

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

"The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling" by Ralph Kimball

The centralized style is the easiest MDM style to implement data governance because it consolidates all master data into a single central repository. This centralization simplifies the application of data governance controls, ensuring consistent data quality, standards, and policies are applied across the organization.

References:

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

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

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)"