Online CDMP-RMD Questions Video

Data Governance plays a pivotal role in the development of an enterprise-wide Master Data Management (MDM) strategy. Here's how:

Role of Data Governance:

Policy Development: Data Governance establishes policies and standards for data management to ensure data quality, security, and compliance.

Data Stewardship: Assigns roles and responsibilities to manage and oversee data assets across the organization.

MDM Strategy Support:

Conceptual Data Model:

Producing and managing an enterprise conceptual data model helps align the organization's data architecture with its business processes.

It provides a unified view of data entities, their relationships, and how data flows through various systems, ensuring consistency and accuracy.

Alignment with Business Goals: Ensures that MDM efforts support business objectives by providing a clear framework for data usage and governance.

References:

Data Management Body of Knowledge (DMBOK), Chapter 3: Data Governance

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)"

Master Data Management (MDM) styles define how and where master data is managed within an organization. One of these styles is the "Coexistence" style, which allows data to be authored and maintained across different systems while ensuring consistency and synchronization.

Coexistence Style:

The coexistence style of MDM allows master data to be created and updated in multiple locations or systems within an organization.

It supports the integration and synchronization of data across these systems to maintain a single, consistent view of the data.

Key Features:

Data Authoring: Data can be authored and updated in various operational systems rather than being confined to a central hub.

Synchronization: Changes made in one system are synchronized across other systems to ensure data consistency and accuracy.

Flexibility: This style provides flexibility to organizations with complex and distributed IT environments, where different departments or units may use different systems.

Benefits:

Enhances data availability and accessibility across the organization.

Supports operational efficiency by allowing data updates to occur where the data is used.

Reduces the risk of data silos and inconsistencies by ensuring data synchronization.

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