In 2014, Jeff DeWolf of Tetra Pak put master data management (MDM) near the top of management’s priority list by treating it as a corporate asset. As the company’s director of global master data strategy, he framed master data within a strong business context. At the same time, he launched a data rejuvenation effort that completely overhauled the giant Swiss packaging company’s MDM model.
Tetra Pak already had a high level of maturity in its management of master data. It has an enterprise-level model and a single ERP instance. But the company was beginning to add new applications that had their own embedded data. Implementations were handled by a small group of individuals. Ownership was incomplete and scattered. Metrics varied. This led, for example, to problems with spare parts pricing, which in turn caused customers to complain. “The problem was we were not treating master data with respect,” DeWolf said.
The right framing
Between 2005 and 2013, the company had made many improvements by focusing on master data management rather than maintenance. But this was not enough to take the next giant leap forward. To do this, DeWolf changed the context of the program by framing it as a business imperative. “When I addressed the C-suite, I didn’t talk to them in terms of data, but in terms of customer realities,” said Dewolf.
For example, when the company developed a new product, it moved it to the ERP environment once it became commercially viable. Initially, there weren’t good checks and balances governing the use of data between the development and commercial environments. The item might have appeared to salespeople as available at a certain price. They would then proceed to quote the price to customers. But parts still in the design phase were constantly updated, leading to pricing changes.
“The result was that customers would keep getting new price confirmations in their inboxes, which customer representatives had to explain. In this case, the lack of clear data governance rules affected the end customer. We were exposing the customer to our own inefficiency,” DeWolf summed up. That got top management attention.
The three objectives
To put MDM back on the map, DeWolf set out three objectives:
- Automate the MDM workflow
- Remodel the MDM organization
- Change the kind of metrics used to measure the quality of the data
His first step was to transition the four master data domains (vendors, finance, customers, and materials) to a single system. To make this happen, he chose master data governance software—an off-the-shelf solution that allows users to centrally create, change, and distribute master data across the organization.
Next he turned to the way the group was organized. Prior to 2013, master data management was distributed among many regional master data teams. He wanted to centralize the expertise in a center of excellence (CoE) and chose Panama for its location, because Tetra Pak already had a global business services (GBS) operation there with some MDM staff. And there were several more in nearby Colombia who were willing to move. He ended up with 20 MDM experts (down from 50) who could work with global process owners around the world.
He says the role of the CoE will evolve overtime. Ultimately, he expects it to become a professional problem-solving operation. “The objective is to find areas of improvements, make suggestions, and feed them to the global process teams,” he notes. “We expect to have 70% of all manual MDM-related work automated by the end of this year.”
The COE has already made a huge impact. It reduced the number of MDM workflows by more than 85 percent, to just 400. Previously, each site had its own change-approval process. While some variations were minor, in the aggregate, the process was so unwieldy that it couldn’t be managed on a global level.
The next step is to reduce the 400 remaining workflows to four. Each will be supported by business logic that will allow customization within a context of standardization that would facilitate global process management.
The CoE is also going to be charged with very little data maintenance and focus more on proactively monitoring data quality and proposing solutions. That’s a role that didn’t exist before. The CoE staff will initiate cleanups based on materiality thresholds. It will then target areas with a potentially significant impact on the business, instead of waiting for problems to pop up.
Finally, DeWolf and his team of MDM experts are working on creating value metrics to replace traditional data-quality measures. For example, one metric DeWolf would like to introduce is linking data about the bills of materials accuracy to data about open sales orders. The idea is to measure the value (in terms of pending sales) that is at risk due to incorrect data. Erroneous bills of materials with open sales orders represent potential missed revenue. The idea here is to use business context to focus on what yields tangible business benefit. This is a non-traditional approach to master data quality.
There is an additional benefit to using value metrics: By translating data fields into value drivers, the MDM team moves the conversation with the business into a language that makes sense to them. If additional resources are needed to stop putting shipments at risk, this is a conversation that suddenly becomes possible.
Next on DeWolf’s agenda is master data governance. Right now, the process owners are also the governors of the data, but they don’t have the time to do it. He wants to give the process and organizational structure time to settle in before assembling a data governance council. That piece will happen in 2018-2019. At the same time, Tetra Pak is already pulling in new kinds of data as part of other big data projects, creating data lakes (big reservoirs of varied data types) and considering the possibility of eventually selling data.
Another future goal is to extend the MDM model to other data areas where DeWolf feels the company could benefit, like people, brand, and category data.
DeWolf feels strongly that the project will retain its momentum because it continues to have strong support from Tetra Pak’s senior leadership. “Senior management may not understand the details, but they certainly understand the importance of MDM, and I can get an audience when I need it.” An important factor in maintaining that strong support is presentation style. “Speak with a focus on customer accounts,” DeWolf advises.
For more on how data management can benefit your business, see How To Use The Right Data At The Right Time For Better Customer Relationships.
Source: Digitalistmag Big data technologies