Q&A: Walter Mullikin Has Business on the Brain

CSC’s Walter Mullikin has a passion for data analytics. His latest work in that field — for food service giant Aramark — caught the attention of the editors at Consulting Magazine. In their July edition, they named him one of the top 25 consultants of 2007. Mullikin’s earlier projects for DuPont won him a 2004 DM Review Award and CSC’s 2002 Award for Technical Excellence. Mullikin discusses the inspirations, influences and philosophies behind his award-winning work at CSC.

csc.com: How did you end up where you are today?

WM: The trail goes back to the University of Pennsylvania where I earned my doctorate studying the vision centers of the brain. I had to automate the lab to complete my thesis, which got me into computers. It’s also where I got my start organizing data in more effective ways.

After building my own lab, I figured other people would need similar work done. Then personal computers came out, and I was excited. I thought, “Hey, I could do this really cheaply.” So I started building data acquisition systems driven by PCs that could perform research tasks, and then I co-founded a company and sold them all over the world.

Eventually, we got into automating the monitoring systems in operating rooms, which allowed doctors to see at a glance a complete picture of patient health. I came to CSC by happenstance. A neighbor said to me, “We could use someone like you at CSC.” I joined CSC because it was a deep company doing a lot of exciting things for a variety of businesses. I like to think that at CSC we’re trying to sense business the same way the human brain senses life.

csc.com: How did your brain research prepare you for the business world?

WM: It’s allowed me to see that businesses and brains are alike, both in their need for information and in the role they play in decision making. Current technologies are not able to match the brain’s ability in these areas. In fact, we are bad at basic sensory perception in business, and even worse at pulling together data from multiple sources and assembling it for complex thinking and decision making.

I care about this because when we start processing information in business, we have to know the most useful way to represent that information. There isn’t just one way to do it. The same information has a different meaning for every part of the business. In the end, all these meanings have to be integrated so that the different areas of the business can act as a single organism, sharing resources for optimum benefit.

csc.com: So your background helps you take a more scientific approach to business?

WM: Definitely. In science, we carefully measure our observations and the contexts in which they occur. This helps us understand why things happen and helps us build models that let us predict what will happen under a given set of conditions. These models help us see information in new ways and therefore apply it in new ways.

Most businesses try to do this, but their methods typically aren’t as structured and rigorous as they are in science. Companies gather data like squirrels preparing for winter, yet they don’t have the discipline to take it to the next level and mine and analyze the data to really improve the decision-making process.

My job is to help businesses address what they are doing to make the right decisions. I ask important questions, like how does the business sense its environment? What are its goals? What is it looking for? How is it structured?

Like everything in nature, form and function go together. If a business is structured poorly, it probably functions poorly. It has trouble sensing its market environment. It takes crude measurements. It doesn’t know what to do with the information it has. The resulting bad decisions are a foregone conclusion.

So what we do is clearly define the desired function and see how effective the current structure is at achieving it. We make observations about costs, profits, etc., and put those observations into contexts so that we can better understand and manipulate the business. Restructuring the business according to an accurate model gets you the results you want.

csc.com: Can you give some real world examples?

WM: Sure. When we started work at DuPont, it would take them up to 10 years to find a lead and develop it into a product. A big part of that time was spent looking through millions of compounds for leads. Improving DuPont’s ability to sort through and make sense of its research data would cut down that time substantially. But no models existed that we could follow for what we were trying to do — we had to create our own.

It came to me in my backyard one day how scientific data could come together in dimensional modeling. We had contracted to build a system for agricultural research, but the model we created could also be used for cancer research or heart research — you name it. We ended up creating a unified model for managing every kind of research data. And DuPont saved millions of dollars by shaving years off development time.

csc.com: What was significant about your work at Aramark?

WM: Aramark is a very large company that relies on partnerships with hundreds of food manufacturers. This means a lot of contracts have to be negotiated. But Aramark needed information to make the most of its partner community in the negotiation process. We set up algorithms that modeled best-case scenarios based on specific factors. By feeding the system data about contracts — including everything from rebates to shipping volume — Aramark was better able to understand its purchasing needs and negotiate contracts that met them. The end result was that Aramark saved millions of dollars by more accurately sensing its market environment and processing that data in its brain to make better decisions.