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By Jessie Chimni

While master data management (MDM) may be a newer term to some IT departments, there is no disputing the value of better managing shared master data between departments, business functions and applications. While new MDM tools promise to create a consistent set of master data that can be leveraged across the enterprise for transactions, analysis and business intelligence, the potentially vast scope of executing against an all-encompassing vision of enterprise-wide master data may be daunting indeed. Here's an example of an evolutionary approach designed to show clear, incremental value.

The need for a unified data "hub" such as the newly announced SAP MDM as a means to more precisely use interrelated master data for better business intelligence cannot be disputed. Even for companies that have embraced a full suite of business applications, many IT initiatives such as service-oriented architecture (SOA), data warehousing, complex demand planning, enterprise resource planning (ERP) consolidation and industry standardization all point to the need for managing master data domains.

Every major technology analyst firm is working overtime to help articulate the value, cost and players in the MDM space. And while some are warning of so-called MDM hype, others such as Ventana Research show that MDM is indeed gaining momentum, with nearly half of the 230 participants surveyed in a May 2007 study, "MDM: Business and Technology Trends," indicating that an MDM project is either planned or under investigation in their organizations, and with 27 percent responding that a master data strategy is already underway.

At its most fundamental level, MDM enables companies to create a "reference book" for unifying master data across all enterprise applications and analysis. This master data can comprise almost anything — customer, product, asset, employee or financial data — and can be in the form of bar codes, part number schemes, vendors by SIC code, customer names or job roles. The key is in delivering a framework for consistent terminology and nomenclature so that apples-to-apples evaluations can be made in creating one true view of the data.

Or course, the idea itself isn't new, and it has existed as a core concept for solutions such as product information management (PIM) and customer data integration (CDI). The challenge is one that will sound very familiar to anyone who has ever survived an ERP implementation: creating and executing on an enterprise-wide vision is a road fraught with peril and one that may be hard to justify in the final analysis. Today, while the market may be marching forward and visionary IT teams may be eager to sign on to a solution that can make their lives easier in the long run, management is rightfully hesitant about embarking on a large-scale odyssey.

A strong MDM strategy touches so many parts of the enterprise that it may take years to define, evangelize and implement, but that doesn't mean that MDM needs to sit quietly on the sidelines until that time. Rather, an evolutionary approach to moving forward with MDM could in fact unlock the door to broader acceptance of an enterprise-wide MDM strategy.

An evolutionary approach means selecting a single project within a contained department or business function, with a readily identifiable business champion and well-defined success metrics. For example, trying to ramp up 50 or 80 manufacturing facilities in preparation for a global product launch might simply be too large and unwieldy a project for an MDM pilot. While visibility may be high and the resulting success could be a triumph for IT, there may be too many teams involved, and project slippages will be extremely visible, making the stakes rather high for an initial MDM project. On the other hand, applying a pilot MDM project to a program like spend analysis represents a more contained and risk-averse endeavor, with one clear champion — the chief procurement officer — and clearly quantifiable business value.

First, let's define spend analysis and why it's important.

Spend Analysis: Taking the Pain out of Procurement

A report by industry research firm Aberdeen noted that CFOs believe corporate procurement suffers from a lack of clear visibility into spend across various commodities, factors that hurt corporate competitiveness by preventing procurement from driving strategic initiatives such as supply base rationalization or commodity normalization. Spend analysis can be the remedy. Put simply: Spend analysis entails taking the existing information that companies track in the form of invoices, purchase orders and receipts and "cleansing, normalizing, categorizing and enriching the data," as marketing research firm Forrester Research defines the process. After doing so, companies will be better able to find sourcing opportunities that reduce cost, track non-compliance with purchasing policies and improve data management. With this approach, businesses could save 2-6 percent of their total spend by improving the way their procurement organizations secure products and services, according to both the Aberdeen Group and AMR Research.

The reasons why businesses fail to collect accurate, useful procurement information in the first place is not a lack of tools and applications to manage spend. The heart of the problem is data complexity, and that is exactly what an MDM strategy sets out to solve. For example:

• Different codes are often inadvertently used to describe the same supplier or item across divisions or even within the same division, making the broad set of data seem inaccurate when it is analyzed. For example, one plant might code HP as HP, while another might record it as Hewlett-Packard. Without a standard way to name a company, aggregate totals can be off, weakening a company's leverage with its suppliers.
• Item codes are used to define products, but these codes don't always connect an item to an industry-standard classification. This makes it difficult to aggregate similar types of data and combine spending across commodities, locations, suppliers and programs. If an item master record contains industry-standard classification, aggregation of spend at various levels of category will become possible.
• Relationships between suppliers aren't defined or are hard to decipher in a business application such as ERP. Understanding these relationships — such as the fact that Lab Safety Inc. is a subsidiary of W.W. Grainger — can help provide leverage when negotiating bulk deals or rationalizing per-vendor spend.
• Many crucial bits of information, such as the minority status of a supplier, might not exist within an ERP system. This information can help a company take advantage of tax breaks or be used for regulatory compliance. Without it, supplier rationalization initiatives can fall short of company-wide goals.

These pain points are leading the smartest procurement executives to initiate spend analysis initiatives, the key to which will be identifying, normalizing and connecting data. While the data may come from multiple catalogs or business applications, this is a very contained, finite set of data that can be readily cleansed by automated tools and subject matter experts. Once the initial cleansing and framework is in place, ongoing data management becomes simply a maintenance issue.