CDMP-RMD Reference And Master Data Management Exam Questions and Answers

Marketing is crucial to the success of a Master Data Management (MDM) program for several reasons:

Encouraging Onboarding:

Master Data Onboarding: Effective marketing strategies can encourage different data sources to integrate their master data into the MDM system, ensuring comprehensive data coverage.

Promoting Benefits to Leadership:

Leadership Buy-in: Marketing the benefits of MDM to organizational leadership can secure necessary support and resources for the program. Highlighting efficiencies, cost savings, and improved decision-making can be persuasive.

Growing Subscriber Base:

User Engagement: Promoting the MDM program can help grow the base of subscribers and users who rely on the master data, ensuring the data is used effectively across the organization.

Ensuring Long-term Sustainability:

Sustainability: Continuous marketing helps maintain interest and investment in the MDM program, ensuring its long-term sustainability and relevance within 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)"

Authoritative listings of master data entities are essential for ensuring data accuracy and consistency across an organization.

Canonical Directories:

This term refers to standardized data models but is not typically used to describe authoritative listings of master data entities.

Entity Directories:

While this term could be used, it is not the most accurate or commonly used term for authoritative listings.

Reference Directories:

Reference directories are authoritative lists of master data entities such as companies, people, and products. They provide standardized, trusted, and verified data that organizations can rely on.

These directories ensure that everyone in the organization uses consistent and accurate data, supporting data quality and governance efforts.

Industry Directories:

These directories provide information specific to an industry but are not necessarily authoritative lists of master data entities.

Source Directories:

This term does not specifically refer to authoritative master data listings.

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

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)"

Reference data plays a crucial role in financial trading. It includes data such as financial instrument identifiers, market data, currency codes, and regulatory classifications. Despite the dynamic nature of financial trades, reference data provides the necessary static information to execute and settle transactions. Industry estimates suggest that approximately 70% of the data used in financial transactions is reference data, underscoring its importance in the financial sector.

References:

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

"The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling" by Ralph Kimball and Margy Ross.

Industry publications and whitepapers on reference data management in financial services.

Reference data can be structured in various ways depending on its granularity and complexity.

Simple List:

Reference data can be a simple list when it involves basic, discrete values such as country codes or product categories.

Cross-Reference:

When reference data needs to map values between different systems or standards, it can be structured as cross-references. For example, mapping old product codes to new ones.

Taxonomy:

For more complex hierarchical relationships, reference data can be structured as a taxonomy. This involves categorizing data into parent-child relationships, like an organizational hierarchy or biological classification.

Key processing steps for successful MDM typically include:

Data Acquisition: The process of gathering and importing data from various sources.

Data Sharing & Stewardship: Involves ensuring data is shared appropriately across the organization and that data stewards manage data quality and integrity.

Entity Resolution: Identifying and linking data records that refer to the same entity across different data sources.

Data Model Management: Creating and maintaining data models that define how data is structured and related within the MDM system.

Excluded Step - Data Indexing: While indexing is a critical database performance optimization technique, it is not a primary processing step specific to MDM. MDM focuses on consolidating, managing, and ensuring the quality of master data rather than indexing, which is more about search optimization within databases.

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)"

When implementing a Reference and Master Data Management (RMDM) program, it is crucial to utilize techniques that ensure consistency, accuracy, and reliability of data across various systems. Business key cross-references is one such technique. This technique involves creating a mapping between different identifiers (keys) used across systems to represent the same business entity. This mapping ensures that data can be accurately and consistently referenced, integrated, and analyzed across different systems.

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, which emphasizes the importance of business key cross-referencing in MDM.

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)"

The Reference Data Change Request Process typically involves the following sub-processes:

Receive Change Request:

Initiation: The process begins with the receipt of a change request, formally logged and acknowledged.

Identify Stakeholder:

Stakeholder Identification: Identifying all relevant stakeholders who need to be involved or informed about the change.

Identify Impact:

Impact Analysis: Assessing the potential impact of the requested change on existing systems, processes, and data.

Decide and Communicate:

Decision Making: Reviewing the change request, making a decision, and communicating the outcome to stakeholders.

Excluded Step - Monitor Database Change: While monitoring database changes is important for overall data management, it is not typically part of the specific change request process for reference data. This step pertains more to ongoing operational monitoring rather than the change request workflow.

References:

Data Management Body of Knowledge (DMBOK), Chapter 6: Data Development & Maintenance

DAMA International, "The DAMA Guide to the Data Management Body of Knowledge (DMBOK)"

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

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)"

An authoritative system where data consumers can obtain reliable data as an alternative to the system of record is known as a "Trusted System."

System of Record:

The system of record (SOR) is the authoritative data source for a particular data element or dataset. It ensures data integrity, accuracy, and consistency.

Trusted System:

A trusted system provides reliable data that consumers can use for transactions and analysis. It acts as a reference point and may serve as an alternative to the system of record.

It ensures that users have access to high-quality, consistent, and trustworthy data, which is essential for decision-making and operational processes.

Other Options:

System of Reference:Generally refers to a system used for lookup and reference purposes but not necessarily authoritative for transactions.

System of Origin:The original source of data before it is integrated into other systems.

Source System:Any system that contributes data to an enterprise system but is not specifically a trusted or authoritative source.

System of Use:The system where data is actively used and consumed for various business processes.

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

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.

Master Data Management (MDM) lifecycle management encompasses the processes and practices involved in managing master data throughout its lifecycle, from creation to retirement. It ensures that master data remains accurate, consistent, and usable.

Reconciling and Consolidating Data:

This process involves merging data from multiple sources to create a single, unified view of each master data entity.

It ensures that duplicate records are identified and consolidated, maintaining data consistency.

Identifying Multiple Instances of the Same Entity:

This involves detecting and resolving duplicate records to ensure that each master data entity is uniquely represented.

Tools and algorithms are used to identify potential duplicates based on matching criteria.

Identifying Improperly Matched or Merged Instances of Data:

This step involves reviewing and correcting any errors that occurred during the matching or merging process.

Ensures that data integrity is maintained and that merged records accurately represent the underlying entities.

Maintaining Cross-References to Enable Information Integration:

Cross-references link related data entities across different systems, enabling seamless information integration.

This ensures that data can be consistently accessed and used across the organization.

Establishing Recovery and Backup Rules (NOT part of MDM Lifecycle Management):

While important for overall data management, recovery and backup rules pertain more to data protection and disaster recovery rather than the specific processes of MDM lifecycle management.

Business entities are represented within an organization through various forms, primarily as data or records within information systems.

Technical Capabilities:

While technical capabilities support the management and usage of business entities, they are not the representation of the entities themselves.

Business Capabilities:

Business capabilities describe the functions and processes that an organization can perform, but they do not represent individual business entities.

Files:

Files can contain data or records, but they are not the direct representation of business entities.

Data/Records:

Business entities are captured and managed as data or records within databases and information systems.

These records contain the attributes and details necessary to uniquely identify and describe each business entity.

Domains:

Domains refer to specific areas of knowledge or activity but are not the direct representation of business entities.

Choosing unreliable sources for data can lead to significant data quality issues. This problem is often a symptom of underlying issues in data management practices.

Too Much Data:

While having excessive data can create challenges, it is not directly related to the reliability of data sources.

Immature Data Architecture:

An immature data architecture can contribute to various data issues, but it specifically relates to the overall design and infrastructure rather than the selection of data sources.

Weak Master Data Management (MDM):

MDM is crucial for ensuring data quality and consistency. Weak MDM practices can lead to poor data governance, lack of standardization, and the use of unreliable data sources.

Effective MDM involves establishing strong governance policies, data stewardship, and validation processes to ensure data is sourced from reliable and authoritative sources.

Too Little Data:

Insufficient data can be problematic but is not directly related to choosing unreliable data sources.

No Chance Controls:

This option is not a standard term in data management and does not directly address the issue of data source reliability.

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.

References:

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

CDMP Study Guide

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.

Data integration for Master Data Management (MDM) and reference data is a critical process that ensures data consistency, accuracy, and availability across the enterprise. The goal is to enable seamless data flow and access for various business functions.

Timely Extraction and Distribution:

Data integration processes must be designed to extract and distribute data efficiently and in a timely manner to ensure that all parts of the organization have access to up-to-date information.

This involves implementing data pipelines and ETL (Extract, Transform, Load) processes that can handle large volumes of data and deliver it where needed without delays.

Root Analysis of Data Lineage:

While important for understanding data origins and transformations, root analysis of data lineage is typically part of data governance and auditing processes, not a primary focus during real-time integration.

Ad-Hoc Changes:

While controlled environments are important, integration processes should be flexible enough to accommodate necessary changes without compromising data integrity.

Single Value for the Same Concept:

Ensuring a single source of truth is essential but requires robust data governance and harmonization efforts rather than just focusing on integration.

Ignoring Minor Changes:

Ignoring changes can lead to data quality issues and discrepancies. Effective data integration should handle changes efficiently without causing disruptions.

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.

ISO 8000 is an international standard focused on data quality and information exchange. Its primary purpose is to define and measure the quality of data, ensuring that it meets the requirements for completeness, accuracy, and consistency. The standard provides guidelines for data quality management, including requirements for data governance, data quality metrics, and procedures for improving data quality over time. ISO 8000 is not meant to replace ISO 9000, which is focused on quality management systems, but to complement it by addressing data quality specifically.

References:

ISO 8000: Overview and Benefits of ISO 8000, International Organization for Standardization (ISO)

DAMA-DMBOK2 Guide: Chapter 12 – Data Quality Management

Step by Step Comprehensive Detailed Explanation with all References:The 'consumer' of master data content received from an MDM platform is often referred to as a subscriber. Here’s why:

Role of Subscriber:

Data Consumption: A

subscriber is an entity (individual, department, or system) that consumes or uses the master data provided by the MDM platform. -Access and Utilization: Subscribers access the master data to support various business functions, ensuring they have consistent and accurate data.

MDM Context:

Data Distribution: In an MDM context, the MDM platform distributes master data to its subscribers, who rely on this data for operational and analytical purposes.

Stakeholders: Subscribers are key stakeholders in the MDM ecosystem, as they benefit directly from the standardized and high-quality data provided.

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)"

Deterministic matching, also known as exact matching, relies on predefined rules and algorithms to parse and standardize data, ensuring that records are compared based on exact or standardized values. This approach uses defined patterns and rules to determine whether two records represent the same entity by matching key attributes exactly. Deterministic matching is precise and unambiguous, making it a common approach for high-certainty matching tasks, although it can be less flexible than probabilistic methods that allow for variations in data.

References:

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

"Entity Resolution and Information Quality" by John R. Talburt

Probabilistic matching algorithms assign a score based on the weight and degree of match, assign weights to variables based on their discriminating power, and use individual attribute matching scores to create a match probability percentage. Additionally, after the matching process, some records typically require manual review and decisioning to ensure accuracy. Therefore, all provided characteristics describe the nature of probabilistic matching algorithms accurately.

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

Definitions and Context:

False Positive: This occurs when a match is incorrectly identified, meaning records are deemed to match when they should not.

True Positive: This is a correct identification of a match, meaning records that should match are correctly identified as matching.

False Negative: This occurs when a match is not identified when it should have been, meaning records that should match are not matched.

True Negative: This is a correct identification of no match, meaning records that should not match are correctly identified as not matching.

Anomaly: This is a generic term that could refer to any deviation from the norm and does not specifically address the context of matching records.

Explanation:

The question asks about a scenario where two records should have matched but did not. This is the classic definition of aFalse Negative.

In data matching processes, this is a critical error because it means that the system failed to recognize a true match, which can lead to fragmented and inconsistent data.

References:

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

ISO 8000-2:2012, Data Quality - Part 2: Vocabulary.