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Master data and metadata are both used to aggregate data, but master data requires that the organization identifies or develops a trusted version of truth for each of its entities.

Trusted Version of Truth:

For effective master data management, an organization must establish a single, trusted version of truth for each master data entity (e.g., customer, product).

This involves harmonizing data from various sources, resolving duplicates, and ensuring consistency and accuracy.

Master Data:

Master data includes critical business information that provides context for business transactions and analysis. It must be consistent, accurate, and up-to-date to support operational and analytical processes.

Other Options:

Transaction Activity Data:Important for operational processes but not the focus for creating master data.

One Set of Data as Source and Target:Not sufficient for managing master data.

Specific Application Solutions:While useful, they do not ensure the creation of a trusted version of truth for master data.

Transaction Audit Data:Important for auditing but not central to master data creation.

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.

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

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)"

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

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.

CDMP Study Guide

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.

Deterministic matching is a method of record linkage that relies on exact matching criteria. This means that records are considered a match if certain key fields (e.g., name, Social Security Number) have exactly the same values. Exact string match is a straightforward example of deterministic matching, where the strings in specific fields must be identical for a match to be declared. Other methods like sorted neighborhood, regional frequency, editing distance, and phonetic matching are probabilistic or heuristic approaches that allow for some degree of variation or error in the data.

References:

DAMA-DMBOK2 Guide: Chapter 10 – Master and Reference Data Management

"Entity Resolution and Information Quality" by John R. Talburt

A registry in the context of Master Data Management (MDM) is a centralized index that maintains pointers to master data located in various systems of record. This type of architecture is commonly referred to as a "registry" model and allows organizations to create a unified view of their master data without consolidating the actual data into a single repository. The registry acts as a directory, providing metadata and linkage information to the actual data sources.

References:

DAMA-DMBOK2 Guide: Chapter 10 – Master and Reference Data Management

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