Dama Reference And Master Data Management CDMP-RMD Exam Questions

Answer : B

Systems of record are primary suppliers of master data content to an MDM program.

Systems of Record: These are authoritative data sources that provide consistent and reliable master data.

Role in MDM: They supply accurate and up-to-date master data, ensuring that the MDM system has a solid foundation of information.

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

CDMP Study Guide

Answer : A

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.

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

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

Answer : E

Historical Perspective of Master Data: Maintaining historical data about master data objects is crucial for various reasons.

Reasons for Importance:

Provides an audit trail: Keeping historical data allows organizations to track changes and understand the evolution of data over time, which is essential for auditing purposes.

May be required in litigation cases: Historical data can serve as evidence in legal disputes, demonstrating the state of data at specific points in time.

Attributes about Master Data subjects evolve over time: As entities change, such as customers moving or changing names, maintaining historical data allows for accurate tracking of these changes.

Enables business analytics to determine the root cause of behavioral changes: Historical data can help in analyzing trends and identifying reasons for changes in business metrics or customer behavior.

Conclusion: All the provided reasons collectively highlight the importance of maintaining a historical perspective of master data.

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

CDMP Examination Study Materials.

Answer : A

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.

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.

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.

Answer : D

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.

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

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

Answer : D

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.

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

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

Answer : E

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.

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.