DAMA CDMP-RMD - Reference And Master Data Management Exam

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.

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.

Master Data Management (MDM) styles define how and where master data is managed within an organization. One of these styles is the "Coexistence" style, which allows data to be authored and maintained across different systems while ensuring consistency and synchronization.

Coexistence Style:

The coexistence style of MDM allows master data to be created and updated in multiple locations or systems within an organization.

It supports the integration and synchronization of data across these systems to maintain a single, consistent view of the data.

Key Features:

Data Authoring: Data can be authored and updated in various operational systems rather than being confined to a central hub.

Synchronization: Changes made in one system are synchronized across other systems to ensure data consistency and accuracy.

Flexibility: This style provides flexibility to organizations with complex and distributed IT environments, where different departments or units may use different systems.

Benefits:

Enhances data availability and accessibility across the organization.

Supports operational efficiency by allowing data updates to occur where the data is used.

Reduces the risk of data silos and inconsistencies by ensuring data synchronization.

Protecting and securing master data in a production environment can be achieved through various methods. Encryption ciphers, static masking, trust model technologies, and dynamic masking are all techniques used to safeguard data. However, usage agreements, while important for data governance and legal compliance, are not a technical method for securing data in the same way that the other options are. Usage agreements define the terms under which data can be accessed and used, but they do not directly protect the data itself.

References:

DAMA-DMBOK2 Guide: Chapter 11 – Data Security Management

"Data Masking: A Key Component of a Secure Data Management Strategy" by Anjali Kaushik

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)"

The Reference Data Change Request Process typically involves the following sub-processes:

Receive Change Request:

Initiation: The process begins with the receipt of a change request, formally logged and acknowledged.

Identify Stakeholder:

Stakeholder Identification: Identifying all relevant stakeholders who need to be involved or informed about the change.

Identify Impact:

Impact Analysis: Assessing the potential impact of the requested change on existing systems, processes, and data.

Decide and Communicate:

Decision Making: Reviewing the change request, making a decision, and communicating the outcome to stakeholders.

Excluded Step - Monitor Database Change: While monitoring database changes is important for overall data management, it is not typically part of the specific change request process for reference data. This step pertains more to ongoing operational monitoring rather than the change request workflow.

References:

Data Management Body of Knowledge (DMBOK), Chapter 6: Data Development & Maintenance

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

The ISO definition of Master Data quality focuses on the degree to which the data's characteristics meet the requirements of individual users. This implies that quality is subjective and depends on whether the data is suitable and adequate for its intended purpose, fulfilling the specific needs of its users.

References:

ISO 8000-8:2015 - Data quality — Part 8: Information and data quality: Concepts and measuring.

DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition), Chapter 13: Data Quality Management.

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.

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.

References:

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

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