Five Ways to Transform Business Processes with MDM

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Change is a critical factor in MDM implementations, because business processes can and do change as a result of more mature data management practices within the enterprise. Change management must be handled with care. There are 5 techniques that facilitate change and smooth out some of the bumps along the way.

William McKnight, President, McKnight Consulting Group; [email protected] William will be speaking at the Master Data Management Summit & Data Governance Conference Europe 14-17 May 2018 in London, one the topics presented will be “Selecting from the Data Platforms to Create a Modern Data Architecture”.

Change management must always accompany the implementation of master data management (MDM) programs. In fact, it is a key ingredient and determining factor in the ultimate success (or failure) of MDM in an organization. Change is inherent in any information management project, but with MDM the changed is pronounced—particularly to end users. Companies are more and more data driven, so employees from analysts to marketing to sales professionals constantly use and manage data every day. In a company without MDM (or implementing MDM for the first time), these individual data management practices vary from pragmatic to sophisticated. Spreadsheets abound and key data elements are floated around as different users contribute or manipulate them to answer their business questions or manage their groups’ processes.

MDM is a catalyst to change all that and bring maturity and discipline to our data management practices. The benefits to the organization and the individual user are easy to state and quantify. When you get business users in the room and explain it all, you likely will get head nods and agreement. However, once the project is underway, there is change, roadblocks, and difficulty in adoption. Why? If everyone agreed MDM is the way to go, why is it difficult to adopt and adapt?

Imagine if all your employees drive their cars to work. In order to reduce commute times, traffic, and the environmental footprint, you require your employees to drive (or take the bus) to a central train terminal and ride the train to the office. Although most would see the greater good, there would be a lot of moaning and groaning. Most would have to alter their daily routine or would bemoan the loss of personal freedom—even if the train ride saved them the headaches of traffic, fuel costs, and even got them to the office in less time.

We tout MDM in much the same way—a centralized platform where data can be managed efficiently—saving effort, reducing redundant efforts, sharing data. However, users do give up some personal freedom. Maybe they lose their spreadsheet they have loving maintained for years—changing the layout and adding columns as they please. Maybe they have to change their routine to get a report out. Or, maybe toughest of all, they have to change their business process altogether. It is the latter that causes the greatest resistance in MDM implementations, so we will focus on business process change management.

Here are five ways to help ease the transition as business processes transform because of MDM.

Treat users’ data as the asset it is. In implementing MDM, don’t forget the two most important parts of any data system: the data itself and the people who need and use it. Handle with care and treat the data with the respect it deserves. Communicate to the business user that you understand their data, the process that derived or created it, and preserve the integrity the data has. One way is to carefully craft a business data definition for key data elements that communicates tot he user that “you get it.”

Alleviate data pain. Whatever way business users currently manage data, they are likely to experience pain and frustration with it.

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Yves Mulkers

Yves Mulkers is the founder of 7wData and a widely followed voice in the data and AI community. He curates the 7wData and AI Beat newsletters, reaching hundreds of thousands of data and AI professionals, and writes on data strategy, analytics, AI, and the evolving data ecosystem.