What is Master Data Management

by Jesse Spencer | August 19, 2019

First, let’s define “master data.” Master data is the data that sits at the center of all core business processes and applications. It’s high-confidence, high-integrity (master) data that’s relied on to produce favorable business outcomes.

As more decision making and planning activities leverage new algorithmic methods and processes (think Machine Learning), managing master data is more important than ever. You can imagine the problems and devastation caused by decisions based on bad or inconsistent data.

In fact, we can learn a lot from NASA’s loss of the Mars Orbiter in 1999. Billions of dollars in time, research, and equipment vanished because of an easily avoidable data consistency problem.

The problem? Different units of measurement were used by NASA and Lockheed Martin. According to NASA engineer Richard Cook, “The spacecraft had more or less hit the top of the atmosphere and burned up.” Apparently the thrusters were only putting out ¼ of the thrust they should have been.

In NASA’s case, units of measurement certainly was master data, but for some reason, it didn’t make their list.

What is master data management?

Master data management (MDM) is governing master data for consistency and reuse with the goal of reduced IT costs, assuring consistent positive business outcomes, and preserving business process integrity.

According to Gartner, Inc “Master data management (MDM) is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise’s official shared master data assets. Master data is the consistent and uniform set of identifiers and extended attributes that describes the core entities of the enterprise including customers, prospects, citizens, suppliers, sites, hierarchies and chart of accounts.”

MDM goes hand-in-hand with an organization’s enterprise data model which covers all (most!) of the organization’s data.

An Enterprise Data Model represents a single integrated definition of data, unbiased of any system or application. It is independent of “how” the data is physically sourced, stored, processed, or accessed. The model unites, formalizes, and represents the things important to an organization, as well as the rules governing them.

— Noreen Kendle, 2005

The importance of managing master data cannot be overstated. MDM increases agility, drives competitive differentiation, and underpins critical business processes.

What makes MDM difficult is the fact that many software applications are developed and deployed in silos. But adding even more difficulty is low desire, commitment, and ability in many organizations to create a common link between data and improving business outcomes.

Successful Master Data Management

Successfully developing and maintaining master data requires managing metadata, data modeling and mapping, and semantic reconciliation (recognizing that two objects are the same even when they’re described differently).

Not all master data needs to be managed. If you listed all data objects or attributes stored in all business processes and applications, the list would be immense.

Another way to determine the most important data to manage is looking at what metrics and data support the most important KPIs. Maintaining high data quality targets for these critical processes is important. 

DataQuality-Targets

A logical, organic approach to an MDM implementation is to adopt a single-subject-area approach. For example, a procurement outsourcing organization may use MDM for line-item invoice validations. Another common starting point is using MDM to create a 360-degree view of customers to support CRM objectives. This incremental effort builds foundational stepping points for future MDM growth.

MAsterDataManagement

Implementation styles are not mutually exclusive. Many organizations start with one style and later move to another. You might start with consolidation, and then move to coexistence based on changing needs.

There are many systems integrators and MDM vendors to choose from – all offering their flavor of MDM and tailoring the best approach for your organization’s goals and data. It is not uncommon to leverage multiple MDM products or vendors.

But don't make the mistake of thinking about MDM as a technology. Rather, it is a discipline focused on the processes required to govern master data. Technology is simply used to support the discipline.



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