CDMP-RMD Questions and Answers

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

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.

Information Governance involves managing and controlling the data assets of an organization, addressing the 'what', 'how', and 'why'.

'What' pertains to Data Governance, which defines policies and procedures for data management.

'How' relates to Information Security, ensuring that data is protected and secure.

'Why' is about Compliance, ensuring that data management practices meet legal and regulatory requirements.

References:

DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 1: Data Governance.

"Information Governance: Concepts, Strategies, and Best Practices" by Robert F. Smallwood.

Master data and metadata are both used to aggregate data, but master data requires that the organization identifies or develops a trusted version of truth for each of its entities.

Trusted Version of Truth:

For effective master data management, an organization must establish a single, trusted version of truth for each master data entity (e.g., customer, product).

This involves harmonizing data from various sources, resolving duplicates, and ensuring consistency and accuracy.

Master Data:

Master data includes critical business information that provides context for business transactions and analysis. It must be consistent, accurate, and up-to-date to support operational and analytical processes.

Other Options:

Transaction Activity Data:Important for operational processes but not the focus for creating master data.

One Set of Data as Source and Target:Not sufficient for managing master data.

Specific Application Solutions:While useful, they do not ensure the creation of a trusted version of truth for master data.

Transaction Audit Data:Important for auditing but not central to master data creation.

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)"

A global identifier is used to link multiple references to the same entity across different systems or datasets. Here’s why:

Purpose of Global Identifier:

Unique Identification: Provides a unique identifier that can be used to recognize the same entity across disparate systems and datasets.

Consistency: Ensures that different references or records pointing to the same entity are consistently identified and managed.

Linking Equivalent References:

Equivalent References: Global identifiers link references that are equivalent, meaning they represent the same real-world entity even if the data is stored differently in various systems.

Entity Resolution: Helps in resolving different records to a single entity, ensuring data consistency and accuracy.

Example:

Customer Records: A customer might be listed in different systems (CRM, billing, support) with slightly different details. A global identifier links these records to recognize them as the same customer.

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)"

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)"

Metrics tied to Reference and Master Data Quality generally include:

Data Sharing Usage: Measures how often master data is accessed and used across the organization.

Rate of Change of Data Values: Tracks how frequently master data values are updated or modified.

Service Level Agreements (SLAs): Monitors adherence to agreed-upon service levels for data availability, accuracy, and timeliness.

Data Sharing Volume: Measures the volume of data shared between systems or departments.

Excluded Metric - Operational Functions: While operational functions are important, they are not typically considered metrics for data quality. Operational functions refer to the various tasks and processes performed by systems and personnel but do not directly measure data quality.

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)"

Understanding the Context:Location-related attributes are specific details that describe the physical or logical location of an entity. These attributes can include information such as geographical coordinates, address details, or logical identifiers used in software applications.

Categories of Data:

Reference Data:This is data that is used to define other data. It often includes code lists, taxonomies, and hierarchies. Examples are country codes or currency codes.

Metadata:This is data about data, providing context or additional information about other data. Examples include schema definitions or data dictionaries.

Application Suite Master Data:This refers to the master data used across an entire suite of applications but not necessarily enterprise-wide.

Application Master Data:This is master data specific to a single application or a closely related group of applications within a specific function.

Enterprise Master Data:This is master data that is used across the entire enterprise, supporting multiple functions and applications.

Application Master Data Identification:The question specifies that these location-related attributes are used exclusively by a group of financial applications. Thisexclusivity implies that the data is tailored for specific applications rather than being used across the entire enterprise or just for reference purposes.

Conclusion:Since the data is used specifically within a group of financial applications, it best fits the category of "Application Master Data" rather than enterprise-wide or reference data.

References:

DMBOK Guide: Data Management Body of Knowledge, specifically sections on Data Governance and Master Data Management.

Data Architecture in MDM Solutions:The design of a Master Data Management (MDM) solution involves defining and managing relationships between data entities.

Types of Relationships:

Traceable relationships and/or lineage relationships:These are important for understanding data provenance and transformations but are more relevant to data governance and data lineage tracking.

Data Acquisition relationships:These pertain to how data is sourced and collected, rather than how master data entities are related.

Affiliation relationships and/or parent-child relationships:These are crucial in MDM as they define how entities are related in hierarchical and associative contexts, such as customer relationships, organizational hierarchies, and product categorizations.

Hub and spoke relationships:This refers to the architecture model for MDM systems rather than the type of data relationship.

Ontology relationships and/or epistemology relationships:These are more abstract and pertain to the nature and categorization of knowledge, not specifically to the functional relationships in MDM.

Conclusion:The correct answer is "Affiliation relationships and/or parent-child relationships" as these are essential for defining and managing master data relationships in an MDM solution.

References:

DMBOK Guide, sections on Data Architecture and Master Data Management.

CDMP Examination Study Materials.

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.

Several factors can deter a company from developing a master data program, including the perceived value, commitment level, disruption, and data quality priorities.

Fail to See Value in Integrating Their Data:

If a company does not recognize the benefits of integrating and managing master data, it may not invest in an MDM program.

Lack of Commitment:

Developing an effective MDM program requires long-term commitment from leadership and stakeholders. Without this commitment, the program is unlikely to succeed.

The Process is Too Disruptive:

Implementing an MDM program can be disruptive to existing processes and systems. The perceived disruption can deter companies from pursuing it.

Data Quality is Not a Priority:

If a company does not prioritize data quality, it may not see the need for a robust MDM program. Poor data quality can undermine the effectiveness of business processes and decision-making.

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) Harmonization ensures that the data changes of one application are synchronized with all other applications that depend on that data.

MDM Harmonization Definition:This process involves aligning and reconciling data from different sources to ensure consistency and accuracy across the enterprise.

Synchronization:Ensuring that changes in one application are reflected across all dependent applications prevents data inconsistencies and maintains data integrity.

References:

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

CDMP Study Guide

Activities related to Reference & Master Data typically include managing the lifecycle, establishing governance policies, modeling data, and defining architectural approaches. However, evaluating and assessing data sources is generally not considered a core activity specific to Reference & Master Data management. Here's a detailed explanation:

Core Activities:

Manage the Lifecycle: Involves overseeing the entire lifecycle of master data, from creation to retirement.

Establish Governance Policies: Setting up policies and procedures to govern the management and use of master data.

Model Data: Creating data models that define the structure and relationships of master data entities.

Define Architectural Approach: Developing the architecture that supports master data management, including integration and data quality frameworks.

Excluded Activity:

Evaluate and Assess Data Sources: While this is an important activity in data management, it is more relevant to data acquisition and integration rather than the ongoing management of reference and master data.

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)"

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."

Sharing reference and master data across an enterprise requires effective collaboration and communication among various stakeholders within the organization.

Staging Area:

A staging area can be used for intermediate data storage during processing but is not a requirement for sharing data.

Maintaining and Storing History Records:

Historical records are important for auditing and tracking changes but do not directly facilitate the sharing of current reference and master data.

Collaboration Between Multiple Parties Internal to the Organization:

Effective sharing of master and reference data requires collaboration among different departments and stakeholders to ensure data consistency, quality, and governance.

This includes establishing clear communication channels, defining roles and responsibilities, and ensuring alignment on data standards and practices.

Identification of Business Key and Surrogate Keys:

Keys are important for data integration and linking but do not by themselves ensure effective sharing of data.

Creation of Foreign Keys to Support Dimensions:

Foreign keys are used in relational databases to link tables but are not specifically required for the sharing of master data.

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.

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.

References:

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

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

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.

For effective Master Data Management, an organization must agree on a single, standard definition of master data. Here's why:

Consistency:

Single Definition: A standardized definition ensures consistency across different departments and systems.

Avoids Confusion: Prevents discrepancies and misunderstandings regarding what constitutes master data.

Data Quality and Governance:

Unified Approach: A single definition supports unified data governance policies and data quality standards.

Data Integration: Facilitates easier data integration and interoperability across various systems and processes.

Business Efficiency:

Aligned Objectives: Ensures all parts of the organization are aligned in their understanding and use of master data, leading to more efficient operations and decision-making.

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 and Reference Data are forms of Data Architecture. Here’s why:

Data Architecture Definition:

Structure and Design: Data architecture involves the structure and design of data systems, including how data is organized, stored, and accessed.

Components: Encompasses various components, including data models, data management processes, and data governance frameworks.

Role of Master and Reference Data:

Core Components: Master and Reference Data are integral components of an organization’s data architecture, providing foundational data elements used across multiple systems and processes.

Organization and Integration: They play a critical role in organizing and integrating data, ensuring consistency and accuracy.

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)"

Tracking the number of MDM subject areas and source system attributes refers to defining the scope and coverage of the subject areas and attributes involved in an MDM initiative. This process includes identifying all the data entities (subject areas) and the specific attributes (data elements) within those entities that need to be managed across the organization. By establishing a clear scope and coverage, organizations can ensure that all relevant data is accounted for and appropriately managed.

References:

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

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

Every process within an MDM framework includes a degree of governance. Here’s why:

Governance Definition:

Policies and Standards: Governance involves the establishment of policies, standards, and procedures to ensure data quality, consistency, and compliance.

Oversight: Provides oversight and accountability for data management practices.

MDM Processes:

Inherent Governance: All MDM processes, from data integration to data quality management, incorporate governance to ensure the integrity and reliability of master data.

Data Stewardship: Involves data stewards who oversee data governance activities, ensuring adherence to established standards and policies.

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)"