CDMP-RMD Exam Dumps - Reference And Master Data Management Exam

Definitions and Context:

Reference Data Dictionaries: These are authoritative resources that provide standardized definitions and classifications for data elements.

External Sources of Data: These are data sources that come from outside the organization and are used for various analytical and operational purposes.

Explanation:

Reference Data Dictionaries often contain listings and definitions for data that are used across different organizations and systems, ensuring consistency and interoperability.

They typically include external data sources, which need to be standardized and understood in the context of the organization’s own data environment.

References:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.

ISO/IEC 11179-3:2013, Information technology - Metadata registries (MDR) - Part 3: Registry metamodel and basic attributes.

Definitions and Context:

Master Data Management (MDM): MDM involves the processes and technologies for ensuring the uniformity, accuracy, stewardship, semantic consistency, and accountability of an organization’s official shared master data assets.

Data Quality Problems: These include issues such as duplicates, incomplete records, inaccurate data, and data inconsistencies.

Explanation:

Bringing order to your master data, through processes like MDM, aims to resolve data quality issues by standardizing, cleaning, and governing data across the organization.

Effective MDM practices can address and mitigate a significant proportion of data quality problems, as much as 60-80%, because master data is foundational and pervasive across various systems and business processes.

References:

DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition, Chapter 11: Master and Reference Data Management.

Gartner Research, "The Impact of Master Data Management on Data Quality."

Master Data Curation is a process aimed at improving the overall quality of data throughout the business. Here’s how:

Data Quality Improvement:

De-duplication: The process involves identifying and eliminating duplicate records to ensure a single, accurate version of each data entity.

Data Cleaning: Removes inaccuracies and inconsistencies, enhancing the reliability of the data.

Benefits of De-duplication:

Accuracy: Ensures that each entity (e.g., customer, product) is represented only once, improving data accuracy and reducing redundancy.

Operational Efficiency: Streamlines operations by eliminating duplicate records that can cause confusion and errors in business processes.

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)"

MDM (Master Data Management) is characterized by formal management with a high degree of diligence and collaboration. Here’s why:

Formal Management:

Structured Processes: MDM involves structured processes for managing master data, including data governance, data quality management, and data stewardship.

Policies and Standards: Establishes and enforces policies and standards to ensure data consistency, accuracy, and integrity.

Collaboration:

Cross-Functional Teams: Requires collaboration across different departments, including IT, business units, and data governance teams.

Stakeholder Involvement: Engages various stakeholders in the data management process, ensuring that master data meets the needs of the entire 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)"

In any Master Data Management (MDM) effort, the most time and energy are typically spent on vetting business entities and data attributes through the Data Governance process. This step ensures that the data is accurate, consistent, and adheres to defined standards and policies. Itinvolves significant collaboration and decision-making among stakeholders to validate and approve the data elements to be managed.

References:

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

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

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.

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.

Reference and Master Data Management (RMDM) governance processes are designed to manage and ensure the accuracy, consistency, and quality of critical data assets across an organization. These processes focus on defining, maintaining, and governing the shared data entities and attributes that are essential for various business processes. One of the key aspects governed by RMDM is "Data change activity."

Reference and Master Data Definition:

Reference data is a subset of master data used to classify or categorize other data within an organization. It typically includes codes and descriptions.

Master data refers to the critical business information regarding the core entities around which business is conducted, such as customers, products, employees, and suppliers.

Data Change Activity:

This involves tracking and managing the changes made to master and reference data over time. The governance processes ensure that any changes to this data are properly authorized, recorded, and communicated to relevant stakeholders.

Managing data change activity includes monitoring modifications, updates, additions, and deletions of reference and master data.

Importance in Governance:

Effective governance of data change activity ensures that the integrity and quality of master data are maintained. It prevents unauthorized changes that could lead to data inconsistencies and inaccuracies.

It supports audit trails and compliance with regulatory requirements by providing transparency and accountability for data changes.