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

A Canonical Data Model (CDM) is a design pattern used in data integration and exchange to create a standardized format for data that will be shared across different systems.

Canonical Data Model Definition:

A CDM provides a common, consistent, and agreed-upon representation of data across different systems within an organization.

It serves as an intermediary format to simplify data exchange and integration.

Standardization:

The primary purpose of a CDM is to standardize the format of data. This ensures that data from different sources can be integrated, understood, and used without needing extensive transformation or reformatting.

It addresses the problem of data heterogeneity, where different systems may have their own data formats and structures.

Data Integration:

By using a CDM, organizations can facilitate easier and more efficient data integration processes. Data from various systems can be mapped to the canonical model, enabling seamless data exchange and interoperability.

It simplifies the maintenance and management of data integration solutions by providing a single, unified data model.

Use Cases:

CDMs are commonly used in enterprise application integration (EAI), service-oriented architectures (SOA), and other environments where data needs to be exchanged between multiple systems.

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.

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.

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)"

Registry style MDM involves maintaining a central registry that stores references to master data while allowing the actual data to remain in the source systems. This approach requires technologies that support data integration and real-time access without physically moving the data.

JSON Mapping:

JSON mapping is useful for data exchange but does not specifically support the registry style MDM approach.

ETL Toolset:

ETL (Extract, Transform, Load) tools are typically used for batch data processing and integration, which may not align with the real-time data access requirements of a registry style MDM.

Columnar Database:

Columnar databases are optimized for analytical queries but are not specifically designed for supporting registry style MDM.

Complex Queries:

While complex queries can be part of the data access strategy, they are not a comprehensive solution for registry style MDM.

Data Virtualization:

Data virtualization provides a unified view of data from multiple sources without physically moving the data. It supports real-time data access and integration, making it well-suited for registry style MDM.

It enables organizations to access and manage master data across different systems while maintaining a central registry for reference.

A ragged hierarchy is an organizational structure where different branches of the hierarchy can have varying levels of depth. This means that not all branches have the same number of levels. In the given scenario, where a high-level manager has department managers with staff and non-managers without staff as direct reports, the hierarchy does not have a uniform depth across all branches. This kind of structure is best represented and maintained as a ragged hierarchy, which allows for flexibility in representing varying levels of managerial relationships and reporting structures.

References:

DAMA-DMBOK2 Guide: Chapter 7 – Data Architecture Management

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

The data-centric development lifecycle is best suited for Reference & Master Data efforts because it prioritizes data integrity, quality, and governance throughout the entire development process. This approach ensures that reference and master data are consistently managed, maintained, and leveraged across various systems and applications.

References:

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

DAMA International Guide to the Data Management Body of Knowledge (DAMA-DMBOK Guide), 2nd Edition.

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.

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.

International Classification of Diseases (ICD) codes are a type of industry reference data.

ICD Codes:

Developed by the World Health Organization (WHO), ICD codes are used globally to classify and code all diagnoses, symptoms, and procedures recorded in conjunction with hospital care.

They are essential for health care management, epidemiology, and clinical purposes.

Industry Reference Data:

Industry reference data pertains to standardized data used within a particular industry to ensure consistency, accuracy, and interoperability.

ICD codes fall into this category as they are standardized across the healthcare industry, facilitating uniformity in data reporting and analysis.

Other Options:

Geographic Reference Data:Includes data like country codes, region codes, and GPS coordinates.

Computational Reference Data:Used in computational processes and algorithms.

Internal Reference Data:Data used internally within an organization that is not standardized across industries.

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

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)"

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)"

Reference data and master data are distinct in several key characteristics. Here’s a detailed explanation:

Reference Data Characteristics:

Stability: Reference data is generally less volatile and changes less frequently compared to master data.

Complexity: It is less complex, often consisting of simple lists or codes (e.g., country codes, currency codes).

Size: Reference data sets are typically smaller in size than master data sets.

Master Data Characteristics:

Volatility: Master data can be more volatile, with frequent updates (e.g., customer addresses, product details).

Complexity: More complex structures and relationships, involving multiple attributes and entities.

Size: Larger in size due to the detailed information and numerous entities it encompasses.

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 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.

References:

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

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

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.

MDM programs often face challenges and can fail due to a combination of factors. Here’s a detailed explanation:

Emphasis on Technology:

Technology-Centric Approach: Overemphasis on technology solutions without addressing people and process components can lead to failure. Successful MDM programs require balanced attention to technology, people, and processes.

Positioning within IT:

IT Focus: Poor positioning of the MDM program within the IT organization can lead to it being seen as a purely technical initiative, missing the necessary business alignment and support.

Business Commitment and Engagement:

Lack of Engagement: Insufficient commitment and engagement from the business side can result in inadequate support, resources, and buy-in, leading to failure.

Program vs. Project:

Long-Term Perspective: Treating MDM as a one-time project rather than an ongoing program can limit its effectiveness. MDM requires continuous improvement and adaptation to evolving business needs.

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)"

Ensuring high-quality reference data is critical for maintaining data accuracy, consistency, and reliability across an organization. The best way to achieve this is through robust data governance and stewardship practices.

Government Sources:

While government sources can be reliable, they are not the only sources of high-quality reference data. Relying solely on them may limit the comprehensiveness of reference data.

Drop-Down Menus:

Drop-down menus can help prevent invalid data entry but do not address the overall quality and governance of reference data.

Data Governance and Stewardship:

Implementing data governance and stewardship ensures that reference data is managed according to defined policies, standards, and procedures.

Data governance involves establishing a framework for decision-making, accountability, and control over data management processes.

Data stewardship assigns responsibility for data quality, ensuring that data is accurate, consistent, and fit for purpose.

Standard Reference Data (ISO):

Using standard reference data from organizations like ISO can enhance data quality, but it should be part of a broader governance strategy.

External Data Providers:

External data providers can offer high-quality reference data, but relying solely on them without proper governance can lead to inconsistencies and data quality issues.

Adhering to ISO standards for metadata is important for both in-house and commercial tools for the following reasons:

Standardization:

Uniformity: ISO standards ensure that metadata is uniformly described and managed across different tools and systems.

Interoperability: Facilitates interoperability between different tools and systems, enabling seamless data exchange and integration.

Guidance and Best Practices:

Structured Approach: Provides a structured approach for defining and managing metadata, ensuring consistency and reliability.

Compliance and Quality: Ensures compliance with internationally recognized best practices, enhancing data quality and governance.

References:

ISO/IEC 11179: Information technology - Metadata registries (MDR)

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 matching algorithms benefit from various data characteristics but do not benefit from "Structural heterogeneity of data elements."

Matching Algorithms:These are used in MDM to identify and link data records that refer to the same entity across different systems.

Data Characteristics:

Distinctiveness:Helps in accurately matching records.

Common Data Points:Aids in the comparison process.

Comparability:Facilitates effective matching.

Validity:Ensures the data is accurate and reliable.

Structural Heterogeneity:Different structures can complicate the matching process, making it harder to align data.

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.

In the context of the Corporate Information Factory, understanding transactions involves integrating various types of data to get a comprehensive view. Master Data (core business entities), Reference Data (standardized information), and External Data (information sourced from outside the organization) are essential for providing context and enriching transactional data.

References:

DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 3: Data Architecture and Chapter 11: Reference and Master Data Management.

"Building the Data Warehouse" by W.H. Inmon, which introduces the Corporate Information Factory concept.

Master data represents the critical business information that is used across the organization. It provides context and structure for business transactions and analytical processes.

Data about Business Entities:

Master data typically includes key entities such as customers, products, suppliers, employees, and locations.

These entities are fundamental to business operations and provide the necessary context for transactions and analysis.

Providing Context for Business Transactions:

Master data provides the foundational information required to conduct business transactions.

For example, customer master data is used in sales transactions, while product master data is used in inventory management.

Supporting Business Analysis:

Master data is critical for business intelligence and analytics, providing a consistent and accurate view of the core business entities.

It enables effective reporting, analysis, and decision-making by ensuring that the data used in these processes is reliable and standardized.

Other Options:

A: Master data and reference data are distinct; reference data is used to categorize master data.

B: Master data is not necessarily mastered by business users but involves collaboration between IT and business stakeholders.

C: Provides visibility but also context for transactions and analysis.

E: Master data is about business entities, not technical entities.

An organization's master data can indeed be acquired from external third parties. Here’s how and why:

Third-Party Data Acquisition:

Enrichment: External data sources can be used to enrich an organization's master data, providing additional details and context.

Accuracy and Completeness: Acquiring data from reputable third-party sources can enhance the accuracy and completeness of master data.

Use Cases:

Market Data: Organizations may purchase market data to complement their internal customer or product data.

Reference Data: Common reference data, such as postal codes or industry classifications, are often obtained from external providers.

Integration:

Data Integration: Master data acquired from third parties needs to be integrated into the organization’s MDM system, ensuring it aligns with existing data standards and governance 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)"

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)"

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.

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.