Cleaning house: How Coca Cola have looked inward for the launch of their Big Data initiative
Speaking to Coca Cola’s Operational Excellence Manager, Global Business Services, Gerry Meegan, ahead of Data Analytics Nordics in February; PEX Editor Craig Sharp finds out about Coke’s regionally-structured data cleansing project.
Craig Sharp: Hi Gerry, thanks for joining us today.
Gerry Meegan: Thanks for having me Craig.
Craig Sharp: Can you begin by explaining "standardization of big data" to me?
Gerry Meegan: Of course, I’d begin by asking whether would you call it big data at all? What we’re looking at with standardization is our master data – all the information that we already have. In terms of what it is, it varies. Supplier data, employee data, marketing data. It crosses all the borders, its quality depends largely on how it’s used.
Trying to standardize that to make sure it’s clean and make sure it’s whole in terms of structure and quality, that’s been one of our first challenges – trying to achieve standardization of our master data.
Craig Sharp: On the topic of standardization, did you ever have issues with data fragmentation? Data spread over legacy systems or in multiple formats?
Gerry Meegan: Not noticeably, we’ve been running an SAP system since 1999 now so the data was largely whole. We have SAP across the globe, but the various regions may use it slightly differently and they have different codes, so it’s within the same system, so it’s unclean data within the same system.
Craig Sharp: I imagine we’re talking about a large amount of data here. When did you begin on this project?
Gerry Meegan: Well, my involvement began a couple of years ago. We set up a number of functions, people who would be custodians of that data, make sure it’s correct, up to date and regularly maintained. This is just for our region you understand? Not for the entirety of Coca Cola, although they do have their own projects in other regions.
Craig Sharp: What was the objective when the project began? Was there a particular catalyst?
Gerry Meegan: Well from a productivity perspective it was seen as an opportunity. Our objective was to standardize all of this information we had stored to improve efficiencies. We wanted to improve our processes intelligently and this was a good approach to that.
It’s not that it’s totally centralised across the organization, and each region have their own approach. Some will probably end up a few stages going and some might not. But basically if I can take one particular regional unit that has a couple of thousand employees, set up a master data team of maybe five or six people where all that data then is centralised. That’s where it’s sanitised and then entered going forward.
There would have been an exploratory initiative to see what the current state of our data and efficiencies were, and then from that current state work began to put a team together to design a future state.
Craig Sharp: What’s the current status of the project, how far along is it?
Gerry Meegan: Well for our region the initiative is just at completion, it’s just gone live.
Craig Sharp: And have there been any measurable outcomes?
Gerry Meegan: In terms of productivity - we’ve substantially improved productivity. But the key thing is that we have transitioned some work into central functions, and as we start to clean that data up it’ll deliver more over time.
A lot of big data, or certainly in terms of master data, is hard to quantify exactly what you’re going to deliver. You just know that by having information that’s accurate and clean it reduces operational risk and can be used for future projects across the internal organization and the external supply chain.
Craig Sharp: Congratulations on going live. What are your next goals or milestones?
Gerry Meegan: We’ve initiated a lot of various smaller projects around the organisation. One particular project at the moment is in relation to other types of data. The advantage of the master data is it highlights any organizational deficiencies so you can build an initiative in that area to fix it.