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.

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

Master data represents the critical business information that is used across the organization. It provides context and structure for business transactions and analytical processes.

Data about Business Entities:

Master data typically includes key entities such as customers, products, suppliers, employees, and locations.

These entities are fundamental to business operations and provide the necessary context for transactions and analysis.

Providing Context for Business Transactions:

Master data provides the foundational information required to conduct business transactions.

For example, customer master data is used in sales transactions, while product master data is used in inventory management.

Supporting Business Analysis:

Master data is critical for business intelligence and analytics, providing a consistent and accurate view of the core business entities.

It enables effective reporting, analysis, and decision-making by ensuring that the data used in these processes is reliable and standardized.

Other Options:

A: Master data and reference data are distinct; reference data is used to categorize master data.

B: Master data is not necessarily mastered by business users but involves collaboration between IT and business stakeholders.

C: Provides visibility but also context for transactions and analysis.

E: Master data is about business entities, not technical entities.

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)"

Ensuring high-quality reference data is critical for maintaining data accuracy, consistency, and reliability across an organization. The best way to achieve this is through robust data governance and stewardship practices.

Government Sources:

While government sources can be reliable, they are not the only sources of high-quality reference data. Relying solely on them may limit the comprehensiveness of reference data.

Drop-Down Menus:

Drop-down menus can help prevent invalid data entry but do not address the overall quality and governance of reference data.

Data Governance and Stewardship:

Implementing data governance and stewardship ensures that reference data is managed according to defined policies, standards, and procedures.

Data governance involves establishing a framework for decision-making, accountability, and control over data management processes.

Data stewardship assigns responsibility for data quality, ensuring that data is accurate, consistent, and fit for purpose.

Standard Reference Data (ISO):

Using standard reference data from organizations like ISO can enhance data quality, but it should be part of a broader governance strategy.

External Data Providers:

External data providers can offer high-quality reference data, but relying solely on them without proper governance can lead to inconsistencies and data quality issues.

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

Data Governance plays a pivotal role in the development of an enterprise-wide Master Data Management (MDM) strategy. Here's how:

Role of Data Governance:

Policy Development: Data Governance establishes policies and standards for data management to ensure data quality, security, and compliance.

Data Stewardship: Assigns roles and responsibilities to manage and oversee data assets across the organization.

MDM Strategy Support:

Conceptual Data Model:

Producing and managing an enterprise conceptual data model helps align the organization's data architecture with its business processes.

It provides a unified view of data entities, their relationships, and how data flows through various systems, ensuring consistency and accuracy.

Alignment with Business Goals: Ensures that MDM efforts support business objectives by providing a clear framework for data usage and governance.

References:

Data Management Body of Knowledge (DMBOK), Chapter 3: Data Governance

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