The ABCs of Master Data Management

ABCs of Master Data Management 300x195 photo (business intelligence guide)A:  What is it?

ABCs of Master Data Management 300x195 photo (business intelligence guide)A:  What is it?

At a basic level, “master data” is simply data that is used by two or more applications or systems in an organization.  Common examples found in most companies include Customer data, Employee data, Product data, Asset data, and Location data. “Management” of master data is needed in order to ensure that master data is exactly the same everywhere it is accessed in the organization.


Fundamental reality:  Master data exists in all companies, whether or not it is called master data, and whether or not it is managed in an organized manner.

Master Data Management (MDM) is a plan for creating and maintaining consistent, accurate, and appropriate lists of master data.  This is more complicated than one might think, because:

  1. MDM involves not only technology and tools but also processes. Implementation of MDM will require analysis of how and by whom data is collected and utilized in the company, and may necessitate changes in existing procedures.
  2. MDM is not an exact methodology, nor is it primarily a product implementation.  As usual, there are plenty of vendors that sell MDM tools and/or consultative “solutions.” And–also as usual–MDM capabilities/strategies have been added to some of the big data management tools.  But there is no magic wand or one-size-fits-all approach when it comes to MDM.
  3. MDM requires high-level support and enterprise-wide commitment.  The original investment and ongoing costs (in terms of effort as well as money) are daunting, and an MDM initiative will fall by the wayside if it is underfunded and poorly communicated.
  4. MDM is useless without appropriate data governance and data quality procedures. The entire data management infrastructure must be mature in order for MDM to be effectively implemented.

B:  Why does it matter?

Interest in MDM has been on the increase for the past few years.  Here’s why:



  • In many instances, master data is one of the most valuable assets held by an organization—in fact, a company may be acquired (or acquired at a high price) mainly for its data.  So taking care of data assets is good business.
  • Over time, strategies like Enterprise Data Warehousing (EDW) and Enterprise Resource Planning (ERP) have been successful in meeting some business needs—but in many cases these investments have not resulted in a unified view of information across the organization.  MDM is another approach to meeting that highly desirable goal.
  • The demands of regulatory compliance, the importance of consistent reporting, the rise of Software as a Service (SaaS) and Service Oriented Architecture (SOA), and the growing number of mergers and acquisitions have all made MDM more nearly a necessity than a luxury–especially among globalized companies.

MDM is not an exotic concept, and it is not directly (or at least obviously) connected with either revenue increases or cost savings.  It is, however, an important part of the foundation that makes business information reliable and usable.

C:  What’s next?

According to the promotional material for Gartner’s (@Gartner_inc) recent 2011 Master Data Management Summit, “MDM is not a single project, but an ongoing journey.”  Among the challenges on this journey (both for individual organizations implementing MDM and for the MDM industry itself) is integrating the MDM approach to data with traditional Business Intelligence.  For more on why this isn’t necessarily easy, check out the Information Management article  How MDM Changes BI Best Practices and Technology Transfer’s two-part look at Integrating Master Data Management and BI.