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

Understanding Probabilistic Matching:Probabilistic matching algorithms are used in data matching processes to compare records and determine if they refer to the same entity. These algorithms use statistical techniques to calculate the likelihood of matches.

Possible Outcomes of Probabilistic Matching:

Likely Match:The algorithm determines that the records are probably referring to the same entity based on calculated probabilities.

Non-match:The algorithm determines that the records do not refer to the same entity.

Match:The algorithm determines with high confidence that the records refer to the same entity.

Non-Standard Outcome (D):The term "Underminable match" is not a standard term used in probabilistic matching outcomes. Typically, if the algorithm cannot determine a match or non-match, it might categorize it as "possible match" or leave it undecided but not as "underminable."

Conclusion:The term "Underminable match" does not fit into the standard categories of probabilistic matching outcomes.

References:

DMBOK Guide, specifically the sections on Data Quality and Data Matching Techniques.

Industry standards and documentation on probabilistic data matching algorithms.

Master Data, similar to a physical product, must meet certain requirements such as fitting consumers' needs, needing information about its characteristics, impacting business when unavailable, and having a useful lifespan. However, unlike physical products, Master Data does not deplete when pulled from inventory. Master Data remains available for use even after being accessed multiple times, as it is digital information that can be replicated and shared without loss.

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.

In a Consolidated style MDM (Master Data Management) approach, data from multiple source systems is integrated into a single consolidated repository. This consolidated repository acts as the authoritative source for master data, often referred to as the "system of record." The system of record maintains the most accurate, up-to-date, and comprehensive view of master data. Key traits of this approach include:

Centralization: All master data is centralized in one repository, which simplifies data management and governance.

Consistency: Ensures that all users and systems access the same consistent set of master data.

Data Quality: Enhances data quality through data cleansing, deduplication, and validation processes.

Single Source of Truth: Serves as the definitive source for master data, reducing discrepancies and inconsistencies across the organization.

References:

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

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

Implementing Master Data Management (MDM) involves several challenges, but the disparity between data sources is often the most significant.

Disparity Between Sources:

Different systems and applications often store data in varied formats, structures, and standards, leading to inconsistencies and conflicts.

Data integration from disparate sources requires extensive data cleansing, normalization, and harmonization to create a single, unified view of master data entities.

Data Quality Issues:

Variability in data quality across sources can further complicate the integration process. Inconsistent or inaccurate data must be identified and corrected.

Defining Requirements for Master Data:

While defining requirements is crucial, it is typically a manageable step through collaboration with business and technical stakeholders.

DBA Cooperation:

Getting Database Administrators (DBAs) to share table structures can pose challenges, but it is not as critical as dealing with disparate data sources.

Complex Queries and Indexes:

While important for performance optimization, complex queries and indexing issues are more technical hurdles that can be resolved with appropriate database management practices.

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.

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)"

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.

Division of Power in Data Governance:This approach distributes decision-making authority across different levels or areas within the organization.

Benefits:

Better alignment of decisions:By distributing power, decisions can be made that are better suited to the specific needs and contexts of different parts of the organization. This ensures that decisions about data management are relevant and effective for each particular area.

Avoids centralization issues:Centralized decision-making can often be disconnected from the needs of different departments or functions.

Improved responsiveness:

Decentralized governance can enable faster and more contextually appropriate responses to data management issues.

Other Options Analysis:

Spreads the blame for bad decisions:This is not a strategic benefit but rather a negative consequence.

Centralizing responsibility:This contradicts the concept of division of power.

Lower expense:While decentralization might lead to better decision-making, it doesn't inherently mean lower costs.

Facilitating a decision by committee model:This can lead to slower decision-making processes and isn't the primary benefit of a division of power.

Conclusion:The key benefit of a division of power approach in master data governance is the better alignment of decisions based on varying levels of organizational data sharing.

References:

DMBOK Guide, sections on Data Governance and Organizational Structures.

CDMP Examination Study Materials.

The three primary categories of components in a Master Data Management (MDM) framework are people, process, and technology. Here’s a detailed breakdown:

People:

Roles and Responsibilities: Involves defining roles such as data stewards, data owners, and data governance committees who are responsible for managing and overseeing master data.

Skills and Training: Ensuring that the individuals involved have the necessary skills and training to manage master data effectively.

Process:

Data Governance: Establishing policies, procedures, and standards for managing master data to ensure its accuracy, consistency, and reliability.

Data Lifecycle Management: Processes for creating, maintaining, and retiring master data.

Technology:

MDM Tools and Platforms: Utilizing technology solutions to support the management of master data, including data integration, data quality, and data management platforms.

Infrastructure: Ensuring the necessary technical infrastructure is in place to support MDM activities.

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.

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.

Master data refers to the critical data that is essential to the operations of a business. It typically includes entities such as customers, products, employees, suppliers, and other key business entities. The kinds of information treated as master data can vary widely between industries and even between companies within the same industry.

Industry-Specific Master Data:

Different industries have distinct core data entities critical to their operations. For example, in the healthcare industry, patient and provider data are crucial, whereas, in the retail industry, product and customer data are paramount.

Companies in regulated industries may have specific master data requirements mandated by regulatory bodies.

Company-Specific Master Data:

Within the same industry, different companies may prioritize different sets of master data based on their unique business processes, strategies, and operational needs.

Organizational size, structure, and business model can influence what is considered master data.

Customization and Flexibility:

Master data management (MDM) systems and practices are designed to be flexible to accommodate the unique needs of different organizations.

Customizing MDM allows companies to manage and maintain the integrity of the specific data entities that are critical to their success.

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.

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.

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 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)"

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.

MDM (Master Data Management) is a lifecycle management process that includes various activities to ensure the quality, consistency, and accessibility of master data across an organization. These activities include:

Provisioning of Access: Ensuring that trusted master data is accessible across applications through various methods such as direct reads, data services, or replication feeds.

Identifying Multiple Instances: Detecting and managing multiple representations of the same entity within and across data sources. This involves creating and maintaining identifiers and cross-references for integration.

Enforcing Use of Master Data: Ensuring that the organization consistently uses master data values in processes and applications.

Resolving Improper Matches: Identifying and resolving improperly matched or merged data instances to maintain data integrity.

The activity of "Ensuring effective and efficient retrieval and use of data and information by ETL logic" (C) is not specific to MDM. While ETL (Extract, Transform, Load) processes are crucial for data integration and warehousing, they are not a core activity unique to the MDM lifecycle.

References:

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

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

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)"

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.

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)"

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

In Master Data Management (MDM), a classification scheme refers to a structured way of organizing data by using codes that represent a controlled set of values. These codes help in categorizing and standardizing data, making it easier to manage, search, and analyze.

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.

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)"

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.

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.