Dama CDMP-RMD Exam Dumps

Tracking the effectiveness of a Reference and Master Data Management (RMDM) program requires monitoring various metrics that reflect the quality, usage, and governance of the data. The key metrics in this context are Data Quality and Data Consumption Trends, along with Access Control.

Data Quality:

Data quality metrics assess the accuracy, completeness, consistency, and reliability of the master and reference data.

Common data quality metrics include:

Accuracy: Correctness of data values.

Completeness: Presence of all required data values.

Consistency: Uniformity of data across different systems.

Timeliness: Up-to-date and current data.

Tracking data quality helps identify issues and areas for improvement, ensuring that the data remains fit for purpose.

Data Consumption Trends:

Monitoring data consumption trends involves analyzing how data is used across the organization.

This includes tracking the frequency and volume of data access, the number of users accessing the data, and the business processes that depend on the data.

Understanding consumption trends helps in identifying critical data assets, optimizing data delivery, and ensuring that the data meets the needs of its users.

Access Control:

Access control metrics track the security and governance of master and reference data.

This includes monitoring who has access to the data, how the data is accessed, and any unauthorized access attempts.

Ensuring proper access control is crucial for data security and compliance with regulatory requirements.

Value and Sustainability:

While important, these metrics focus more on the overall value and long-term viability of the RMDM program rather than specific operational effectiveness.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

A catalog where products are organized by category is an example of a taxonomy. Here's why:

Definition of Taxonomy:

Classification System: Taxonomy refers to the practice and science of classification. It involves organizing items into hierarchical categories based on their relationships and similarities.

Example: In the context of a product catalog, taxonomy is used to classify products into categories and subcategories, making it easier to browse and find specific items.

Application in Product Catalogs:

Categorization: Products are grouped into logical categories (e.g., Electronics, Clothing, Home Appliances) and subcategories (e.g., Smartphones, Laptops, Televisions).

Navigation and Search: Helps users navigate the catalog efficiently and find products quickly by narrowing down categories.

Data Management Body of Knowledge (DMBOK), Chapter 9: Data Architecture

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.

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

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

Entities such as Customer, Product, and Employee are typical examples of master data that need to be managed.

Master Data Entities: These are the key data objects around which business transactions are conducted.

Examples:

Customer: Central to sales and service operations.

Product: Essential for inventory and sales management.

Employee: Critical for HR and payroll systems.

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

CDMP Study Guide

Master data metadata provides crucial information about the master data, offering context and supporting its management and use within the organization.

Who, What, and Where Context:

Metadata provides descriptive information about the master data, including details about who created or modified the data, what the data represents, and where it is used.

This contextual information is essential for understanding the origins, purpose, and usage of the master data.

Includes a Sample of Content:

While metadata might include examples or samples of the data, this is not its primary purpose.

Improving Fit-for-Purpose Choices:

Metadata helps improve the application and governance of master data by providing context and supporting data management decisions.

Securing the Content:

Metadata itself is not primarily focused on security, though it can support data governance and access control processes.

Technical or Business Perspectives:

Metadata can encompass both technical and business perspectives, providing a holistic view of the data's context and usage.

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials