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Three master data management strategies to boost CRM-ERP success

'Bad data' circulating through a company's ERP environment will likely cause interfaces to fail. Learn how one business leader is cleaning things up.

If there's one thing that's clear to Gabriele Bauman, it's that providing the best possible customer experience...

means creating effective master data management strategies. And thanks to today's robust integration capabilities, that's exactly what she's been doing.

Bauman is vice president of global CRM delivery for London-based educational publisher and assessment provider Pearson, where she's been part of an effort to transform a complex, acquisition-fueled environment filled with numerous CRM and ERP applications into an integrated, finely oiled machine that taps a master data management system as the authoritative source of data.

"Do we really need ERP in every single country," Bauman asked during a well-attended presentation at Salesforce's mammoth Dreamforce 2018 conference in San Francisco this week. "Do we need all that data circulating multiple places at the same time? No."

Bauman and the rest of Pearson's IT team are wrapping up a four-year effort designed to make certain that Pearson's CRM and ERP environments, as well as its web, e-commerce, royalty management and telephony systems, are governed by the best master data management strategies possible.

The team has been taking stock of the company's various SAP, Oracle and Salesforce applications -- many of which are duplications that resulted from acquisitions -- in an effort to establish business process continuity across a global organization with 30,000 employees in 70 countries.

1. Align data

One of the most important master data management strategies is to align data, and that's been true for Pearson. Due to having so many systems, the company was finding a plethora of data discrepancies.

Indeed, how the various systems identified customers and products was creating chaos. What should have been a single customer record, for instance, might have been seen by Pearson's systems as multiple customers, because data points didn't match up. For instance, one system might have identified a customer that worked at the University of California, Berkeley as working at  "UC Berkeley," while another might simply have said "Berkeley." Or, one might have said the customer worked in the "biology" department, while the other said "bio."

Bauman said Pearson made this discovery while working to establish an Oracle master data management system as the final word, feeding reliable data to and from the various systems to which it's connected. Bauman's team found discrepancies in account and contact management, quote management and order management data. Whenever information on products or the customer didn't match up and align with legacy systems, difficulties followed.

What's more, having the "bad data," as Bauman called it, circulating through the company's ERP environment was causing interfaces to fail. There were also problems with downstream satellite apps that were too removed to be tested. For example, one of the company's ERP systems relied on a third-party reporting tool that was operating with missing attributes, and that led to further upstream data issues.

"Customer data and product data have to be uniform across systems," Bauman said. "The data has to be as clean as you can possibly imagine. If you think it's clean, clean it again."

2. Get stakeholders involved

Customer data and product data have to be uniform across systems.
Gabriele Baumanvice president of global CRM delivery, Pearson

For Bauman, another important master data management strategy was getting the kind of buy-in that only comes from making business users a part of the solution.

To get Pearson's data clean enough, Bauman and her team went business unit by business unit, country by country, through all of the systems and data pools, consolidating and cleaning. Along the way, she asked business unit and business process stakeholders to define how they wanted things to operate going forward. She didn't want IT staff making those decisions; she simply wanted them to build what was needed.

"You have to keep the business involved in all decisions," Bauman said. "Every executive stakeholder needs to be involved."

3. Establish authority and oversight

Once the data in Pearson's master data management system had been validated, Bauman's team started deploying the new architecture in stages, going live in the U.S. and Canada two months ago. The U.K. is next.

The master data management system has become what Bauman called the "system of authority," with interfaces between it and the company's Oracle ERP and Salesforce CRM environments running around the clock.

To ensure the ongoing quality of data, Pearson established customer and product governance teams with executive oversight, and the company is looking to squeeze more out of the data with a global business intelligence initiative that's just getting underway. Eventually, the company plans to augment that effort with the capabilities of the Salesforce Einstein AI platform.

In the meantime, the company is looking forward to being able to migrate additional Salesforce instances into the new architecture, with the ability to integrate them seamlessly with its various other systems.

It wasn't easy getting to that point, but Bauman made it clear the benefits were well worth the four-year effort. She strongly recommended attendees consider promoting similar efforts and using the most effective master data management strategies to increase the value of their own organizations' data.

"You can run your company in a much more streamlined way," Bauman said. "And executives can see the data they need to see."

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