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Driving ROI with Master Data Management, Part 1: Build Your Business Case

ABSTRACT: MDM creates business value in three ways: it streamlines infrastructure, streamlines processes, and reduces risk.

If you break your glasses, life gets awkward. You see double, reach for things in the wrong place, and trip over your feet.

Along similar lines, business gets awkward if you have multiple versions of data. You see fragmented, conflicting views of the business. You choose the wrong view, make a bad decision, and take the wrong action. This results in project delays, unproductive employees, and angry customers—all of which impede business growth and damage the top or bottom line.

Master data management (MDM) makes business a little more graceful. MDM is a set of practices and tools that build a single source of truth with consistent, trusted records for key business entities. MDM tools match and merge data from various source systems to create standard attributes and terms that describe entities such as products, customers, and partners. These “golden records” strengthen data governance programs by helping reduce duplicates and resolve discrepancies. They enable enterprises to make smarter decisions, boosting efficiency and reducing risk.

Master data management (MDM) is a set of practices and tools that build a single source of truth with consistent, trusted records for key business entities.

But as with other aspects of data management, the value in MDM proves hard to measure. This blog, the first in a series on the return on investment (ROI) of MDM, helps data leaders assess the business case for an MDM initiative. The rest of the series breaks out as follows.

The second blog will describe how to drive ROI with your first project.

The third and final blog will describe how to iterate based on measured results and tackle new projects.

This topic matters because executives need evidence that investing in MDM will create near-term value in an uncertain economy. Lacking that evidence, they might defer their MDM investment and create bigger problems down the road.

MDM creates business value in three ways: it streamlines infrastructure, streamlines processes, and reduces risk. Let’s explore each of these value drivers and how data teams can build evidence for them, working from the bottom up in the following diagram.

Streamline infrastructure

Data teams can implement MDM in a few ways. They might index their master data in a central registry, have business users create and publish golden records to others, or maintain all their master data in a central hub. To varying degrees, each of these approaches can reduce the number of data copies. This in turn streamlines supporting infrastructure such as on-premises servers or cloud-based compute and services.

Data engineers should consult ITOps and CloudOps engineers as well as their finance manager to predict this direct cost benefit. The key question: how would an MDM initiative affect data volumes and outlays for IT infrastructure to support them? By answering this question with evidence-based forecasts, these stakeholders can help budget owners understand the upside of implementing MDM.

The key question: how will master data management affect 

data volumes and outlays for IT infrastructure?

Streamline process

MDM streamlines data-related processes by removing friction from team transactions and communication. It enables different functions to use the same names, attributes, and terminology to describe entities to one another. When a sales rep closes a deal in the customer relationship management (CRM) system, master data ensures the finance manager knows who the customer is and how to count that revenue. When a logistics partner opens a new warehouse, the supply chain manager can easily find and adjust that partner’s forecasts to assist production planning. In these and other ways, cross-functional teams collaborate with fewer errors and less confusion, accelerating operations.

Enterprises should estimate these indirect benefits of implementing MDM. The subject matter expert for a given process—perhaps the finance manager or supply chain manager in our examples above—should quantify the time currently lost to errors and confusion. In terms of cost, how would productivity and project execution time improve if they fixed these problems with MDM? In terms of revenue, how much capacity would they free up to support new projects? By building this business case with conservative estimates, data teams can give budget owners a more clear picture of MDM’s upside.

By quantifying the time currently lost to errors and confusion, 

data teams can assess the upside of master data management

Reduce risk

Business forecasts have a best-case and worst-case scenario. In between lies the risk that things go wrong. MDM improves the worst-case scenario because it reduces the risk that things go wrong in the form of bad business decisions. With master data in hand, managers have more accurate business views that increase the likelihood they’ll make the right decision and take the right action. Service representatives have more accurate records of customer interactions, so are more likely to appease an angry customer. More accurate financial records, meanwhile, make it less likely that compliance managers will run afoul of regulations.

It’s not easy to measure the dollar value of risk mitigation in situations like these. But finance and business managers can compile qualitative evidence of “averted disasters,” for example by examining notable bad data-driven decisions of the past. This evidence can give them the confidence to move that worst-case forecast just a little higher—another benefit of having a single version of the truth.

Resting your case

The Native American leader Chief Joseph observed in the 19th Century that “it does not require many words to speak the truth.” His observation holds true in our modern digital age, and in fact, summarizes the business case for master data. MDM streamlines both systems and processes and reduces the risk that businesses will misinterpret the truth. By building evidence of MDM’s business value in these areas, data and business leaders can make a clear-eyed “go-no go” decision about an MDM initiative in today’s economic climate. Our next blog will examine ways for data teams to ensure their first MDM project delivers the expected value.

Kevin Petrie

Kevin is the VP of Research at BARC US, where he writes and speaks about the intersection of AI, analytics, and data management. For nearly three decades Kevin has deciphered...

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