DAMA CDMP-RMD Reference And Master Data Management Exam Exam Practice Test

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.

Data profiling is a crucial activity in mapping source system data for MDM efforts. Data profiling involves analyzing data from source systems to understand its structure, content, and quality. Key steps include:

Data Assessment: Evaluating the data to identify patterns, inconsistencies, and anomalies.

Data Quality Analysis: Measuring the quality of data in terms of accuracy, completeness, consistency, and uniqueness.

Metadata Extraction: Extracting metadata to understand data definitions, formats, and relationships.

Data Cleansing: Identifying and correcting data quality issues to ensure that the data is suitable for integration into the MDM system.

By performing data profiling, organizations can gain insights into the current state of their data, identify potential issues, and develop strategies for data integration and quality improvement.

References:

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

"Data Quality: The Accuracy Dimension" by Jack E. Olson.

Master Data Management (MDM) involves various processes and technologies to ensure that master data is accurate, consistent, and trustworthy. The most comprehensive definition of MDM captures its multi-faceted nature, encompassing governance, technology, and organizational roles.

Governed Processes:

MDM involves establishing governance processes to define policies, standards, and procedures for managing master data.

These processes ensure that data is handled consistently and according to defined rules.

Role of People and Technologies:

Effective MDM requires the involvement of people, including data stewards, data owners, and governance committees, who are responsible for overseeing and managing master data.

Technologies, such as MDM software and tools, facilitate the implementation of governance processes, data integration, data quality management, and synchronization.

Key Objectives:

Master data should be understood by stakeholders, ensuring clarity and common understanding of data definitions and attributes.

Trust in master data is achieved through rigorous data quality and governance practices.

Data should be controlled, meaning that access, usage, and changes to the data are managed and monitored.

Master data must be fit-for-purpose, meeting the specific needs and requirements of the organization’s business processes.

All organizations possess master data, even if it is not explicitly labeled as such. Here’s why:

Definition of Master Data:

Core Business Entities: Master data refers to the critical entities around which business transactions are conducted, such as customers, products, suppliers, and accounts.

Business Operations: Every organization maintains records of these entities to support business operations, decision-making, and reporting.

Implicit Existence:

Unlabeled Data: Organizations may not explicitly label this data as “Master Data,” but it exists within various systems, databases, and spreadsheets.

Examples: Customer lists, product catalogs, employee records, and financial accounts.

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)"

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.

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.

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.

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.

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.

Entity resolution is a critical step in the MDM process that identifies whether different data records refer to the same real-world entity. This ensures that each entity is uniquely represented within the master data repository.

Data Model Management:

Focuses on defining and maintaining data models that describe the structure, relationships, and constraints of the data.

Data Acquisition:

Involves gathering and bringing data into the MDM system but does not deal with resolving entities.

Entity Resolution:

This process involves matching and linking records from different sources that refer to the same entity. Techniques such as deterministic matching (based on exact matches) and probabilistic matching (based on similarity scores) are used.

Entity resolution helps in deduplication and ensuring a single, unified view of each entity within the MDM system.

Data Sharing & Stewardship:

Focuses on managing data access and ensuring that data is shared responsibly and accurately.

Data Validation, Standardization, and Enrichment:

Ensures data quality by validating, standardizing, and enriching data but does not directly address entity resolution.

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

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)"

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)"

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.

The registry style is the most difficult MDM style to implement data governance due to its reliance on maintaining a central registry of master data without consolidating data physically. This method makes it challenging to ensure consistent governance across disparate systems since data remains distributed and only loosely connected via the registry.

References:

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

Master Data Management: Creating a Single Source of Truth by David Loshin.

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.

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.

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)"

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.

Definitions and Context:

Data Profiling: This is the process of examining data from existing data sources and collecting statistics or informative summaries about that data.

Master Data Management (MDM): Establishing MDM involves processes and technologies for managing the non-transactional data entities of an organization.

Explanation:

Data profiling helps to understand the data's characteristics and quality by analyzing data values and comparing them to defined valid values.

This process is crucial in establishing a Master Data Management (MDM) system as it ensures the data adheres to the defined standards and is clean, accurate, and ready for integration into the MDM system.

References:

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

Kimball, R. & Caserta, J. (2004). The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data.

Master Data relationships define how different master data entities are related to each other within an organization. These relationships are crucial for understanding and managing the dataeffectively. The types of master data relationships generally include hierarchies, groupings, and associations that help in organizing and categorizing the data.

Customer Household:

This refers to grouping individual customers into a single household entity. It is commonly used in consumer industries to understand the relationships and dynamics within a household.

Fixed-Level Hierarchy:

A hierarchy with a predetermined number of levels. Each level has a specific position and relationship to other levels, such as organizational hierarchies or product categorization.

Ragged-Level Hierarchy:

Similar to fixed-level hierarchies, but with varying levels of depth. It accommodates entities that may not fit neatly into a fixed-level structure, providing flexibility in the hierarchy.

Grouping based on common criteria:

This involves creating groups or segments of data based on shared attributes or criteria. For example, grouping products by category or customers by region.

Survivorship (NOT a relationship):

Survivorship pertains to the process of determining the most accurate and relevant data when multiple records exist for the same entity. It is a data quality and management process, not a type of relationship.

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.

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)"

Measuring metadata documentation quality involves several metrics that collectively provide a comprehensive view of the quality and effectiveness of metadata management practices.

Random Survey based on Enterprise Definition of Quality:

Conducting surveys among data users to gather feedback on the perceived quality of metadata documentation. This helps in understanding user satisfaction and identifying areas for improvement.

Currency of Metadata in the Repository:

Ensuring that metadata is up-to-date and accurately reflects the current state of the data. This is crucial for maintaining the relevance and usefulness of metadata.

Collision Logic on Two Sources Measuring How Much They Match:

Comparing metadata from different sources to identify discrepancies and ensure consistency. This metric helps in assessing the alignment and accuracy of metadata across systems.

Percentage of Attributes that have Definitions:

Measuring the completeness of metadata by checking the percentage of attributes that have well-defined descriptions. This ensures that all data elements are clearly documented and understood.

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.

The Master Data hub environment that serves as the system of record for Master Data is:

Consolidated Hub:

Central Repository: Acts as a central repository where master data is stored and managed.

Data Quality and Integration: Ensures data quality by integrating data from various source systems and providing a single source of truth.

System of Record: Maintains the most accurate and up-to-date information about master data entities.

Other Hub Types:

SOA (Service-Oriented Architecture): Focuses on providing a flexible architecture for integrating services but not specifically a master data hub.

Two-Speed Hub: A hybrid approach, but not solely a system of record.

Source Hub: May refer to original source systems, not a consolidated system of record.

Registry: Primarily maintains references to data stored in other systems but not a comprehensive system of record.

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

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 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