The three primary categories of components in a Master Data Management (MDM) framework are people, process, and technology. Here's a detailed breakdown:
* People:
* Roles and Responsibilities: Involves defining roles such as data stewards, data owners, and data governance committees who are responsible for managing and overseeing master data.
* Skills and Training: Ensuring that the individuals involved have the necessary skills and training to manage master data effectively.
* Process:
* Data Governance: Establishing policies, procedures, and standards for managing master data to ensure its accuracy, consistency, and reliability.
* Data Lifecycle Management: Processes for creating, maintaining, and retiring master data.
* Technology:
* MDM Tools and Platforms: Utilizing technology solutions to support the management of master data, including data integration, data quality, and data management platforms.
* Infrastructure: Ensuring the necessary technical infrastructure is in place to support MDM
* activities.
* 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)"

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.
* 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)"

Several factors can deter a company from developing a master data program, including the perceived value, commitment level, disruption, and data quality priorities.
* Fail to See Value in Integrating Their Data:
* If a company does not recognize the benefits of integrating and managing master data, it may not invest in an MDM program.
* Lack of Commitment:
* Developing an effective MDM program requires long-term commitment from leadership and stakeholders. Without this commitment, the program is unlikely to succeed.
* The Process is Too Disruptive:
* Implementing an MDM program can be disruptive to existing processes and systems. The perceived disruption can deter companies from pursuing it.
* Data Quality is Not a Priority:
* If a company does not prioritize data quality, it may not see the need for a robust MDM program.
Poor data quality can undermine the effectiveness of business processes and decision-making.

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.
References:
* ISO 8000: Overview and Benefits of ISO 8000, International Organization for Standardization (ISO)
* DAMA-DMBOK2 Guide: Chapter 12 - Data Quality Management

MDM matching algorithms benefit from various data characteristics but do not benefit from "Structural heterogeneity of data elements."
* Matching Algorithms:These are used in MDM to identify and link data records that refer to the same entity across different systems.
* Data Characteristics:
* Distinctiveness:Helps in accurately matching records.
* Common Data Points:Aids in the comparison process.
* Comparability:Facilitates effective matching.
* Validity:Ensures the data is accurate and reliable.
* Structural Heterogeneity:Different structures can complicate the matching process, making it harder to align data.
References:
* DAMA-DMBOK: Data Management Body of Knowledge, 2nd Edition.
* CDMP Study Guide