Master Data Management (MDM)

Master Data Management (MDM)

MDM
Data Governance
Enterprise
Data Quality

Master Data Management (MDM) is a critical discipline in enterprise data governance that ensures consistency, accuracy, and control of an organization's core data assets. This post explores the fundamental concepts of MDM and its key components.

Introduction

Master Data Management represents a fundamental shift in how organizations handle their critical data assets. Unlike traditional data management approaches, MDM provides a comprehensive framework for ensuring data consistency, accuracy, and governance across the enterprise.

  • Declining data quality across organizations
  • Increasing scarcity of data modeling expertise
  • Over-reliance on programming solutions rather than data modeling
  • Loss of traditional database administration knowledge

During the past few years, there has been a steady decline in the quality of data and data modeling expertise is becoming increasingly rare. It is more common to hire a Java engineer than a UML data modeler.

— Pierre Bonnet

The Current Data Crisis

Modern enterprises face several critical challenges in data management:

  • Declining Data Quality: Organizations struggle with maintaining reliable data across systems
  • Loss of Expertise: Data modeling skills become increasingly rare
  • Over-reliance on programming solutions rather than data modeling
  • Loss of traditional database administration knowledge

The Business Model

There are three main kinds of repositories within an enterprise in our context. They are:

  1. Master Data Repositories ("Data")
  2. Business Rules Repositories ("Rules")
  3. Business Process Repository ("Process")

Master Data Repositories

Master data repositories serve as the single source of truth for critical business entities such as customers, products, employees, and locations. They ensure data consistency across the enterprise and eliminate redundancies and conflicts.

Business Rules Repositories

These repositories maintain the business logic, validation rules, and governance policies that ensure data quality and compliance. They define how data should be created, updated, and maintained throughout its lifecycle.

Business Process Repository

This component documents and manages the workflows and procedures that interact with master data. It ensures that data handling processes are standardized, efficient, and aligned with business objectives.

The Semantic Model

The semantic model provides a framework for understanding the relationships and meanings of data elements within the organization.

Three Dimensions of Data Governance

  1. Time Management: Data values can vary over time, including future-dated changes for regulatory compliance.
  2. Context Management: Data values may differ based on context (e.g., multi-language product names).
  3. Version Management: Data must be managed across different versions, maintaining traceability between versions.

Reference: Enterprise Data Governance: Reference & Master Data Management, Semantic Modeling by Pierre Bonnet (2013)