Marketing is crucial to the success of a Master Data Management (MDM) program for several

reasons:

Encouraging Onboarding:

Master Data Onboarding: Effective marketing strategies can encourage different data sources to

integrate their master data into the MDM system, ensuring comprehensive data coverage.

Promoting Benefits to Leadership:

Leadership Buy-in: Marketing the benefits of MDM to organizational leadership can secure necessary

support and resources for the program. Highlighting efficiencies, cost savings, and improved

decision-making can be persuasive.

Growing Subscriber Base:

User Engagement: Promoting the MDM program can help grow the base of subscribers and users

who rely on the master data, ensuring the data is used effectively across the organization.

Ensuring Long-term Sustainability:

Sustainability: Continuous marketing helps maintain interest and investment in the MDM program,

ensuring its long-term sustainability and relevance within the organization.

Reference:

Data Management Body of Knowledge (DMBOK), Chapter 7: Master Data Management

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

ISO 8000 is an international standard focused on data quality and information exchange. Its primary

purpose is to define and measure the quality of data, ensuring that it meets the requirements for

completeness, accuracy, and consistency. The standard provides guidelines for data quality

management, including requirements for data governance, data quality metrics, and procedures for

improving data quality over time. ISO 8000 is not meant to replace ISO 9000, which is focused on

quality management systems, but to complement it by addressing data quality specifically.

Reference:

ISO 8000: Overview and Benefits of ISO 8000, International Organization for Standardization (ISO)

DAMA-DMBOK2 Guide: Chapter 12 – Data Quality Management

Reference and Master Data Management (RMDM) governance processes are designed to manage

and ensure the accuracy, consistency, and quality of critical data assets across an organization. These

processes focus on defining, maintaining, and governing the shared data entities and attributes that

are essential for various business processes. One of the key aspects governed by RMDM is "Data

change activity."

Reference and Master Data Definition:

Reference data is a subset of master data used to classify or categorize other data within an

organization. It typically includes codes and descriptions.

Master data refers to the critical business information regarding the core entities around which

business is conducted, such as customers, products, employees, and suppliers.

Data Change Activity:

This involves tracking and managing the changes made to master and reference data over time. The

governance processes ensure that any changes to this data are properly authorized, recorded, and

communicated to relevant stakeholders.

Managing data change activity includes monitoring modifications, updates, additions, and deletions

of reference and master data.

Importance in Governance:

Effective governance of data change activity ensures that the integrity and quality of master data are

maintained. It prevents unauthorized changes that could lead to data inconsistencies and

inaccuracies.

It supports audit trails and compliance with regulatory requirements by providing transparency and

accountability for data changes.

Reference:

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

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-

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.

Reference:

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.

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.

Reference:

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

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.

Reference:

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.

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.

Reference:

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

Industry standards and documentation on probabilistic data matching algorithms.

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.

Reference:

DAMA-DMBOK (Data Management Body of Knowledge) Framework

CDMP (Certified Data Management Professional) Exam Study Materials

Ownership is a crucial principle in managing Reference and Master Data. Here’s an in-depth look at

why:

Organizational Ownership:

Unified Responsibility: Reference and Master Data are assets that span across various functions and

departments within an organization.

Consistency and Accuracy: Ensuring that data ownership is attributed to the organization prevents

silos and ensures data is consistently accurate and available across all departments.

Data Governance: Proper governance frameworks ensure that data is managed in a way that meets

the organization’s needs and complies with relevant regulations and standards.

Avoiding Departmental Silos:

Cross-functional Use: Different departments use and rely on Reference and Master Data, so

ownership by a single department can lead to conflicts and inconsistencies.

Holistic Management: Centralized ownership enables holistic data management practices, enhancing

data quality and usability across the organization.

Reference:

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) Harmonization ensures that the data changes of one application

are synchronized with all other applications that depend on that data.

MDM Harmonization Definition: This process involves aligning and reconciling data from different

sources to ensure consistency and accuracy across the enterprise.

Synchronization: Ensuring that changes in one application are reflected across all dependent

applications prevents data inconsistencies and maintains data integrity.

Reference:

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

CDMP Study Guide