At a recent conference I attended, Gartner Research Vice President Michael Burkett told the following story. A company decided to undertake a "big data" analysis of its supply chain, but was stymied because its staff lacked the expertise required for the assignment. The company ended up having to hire an outside firm that supplied "data scientists" to carry out the project.
As more supply chain executives decide to undertake a big data analysis of their supply chains, they're likely to find themselves in that very same situation. Even though sophisticated computers and software can provide the necessary tools for conducting this type of analysis, users must have training in how to properly use them and the information that results. That's why a data scientist is required for the job. "You can't just throw terabytes into an off-the-shelf system and ask, 'what should I do?' Larry Snyder, an associate professor at Lehigh University, co-author of the book Fundamentals of Supply Chain Theory, and a member of the Supply Chain Quarterly editorial board, told me. "It takes data and decision-making experts to convert raw data into useable information, and ultimately into decisions."
Data scientists are knowledgeable in the fields of mathematics, statistics, modeling, and computer science. When solving a business problem, they use that knowledge to determine what data to examine and which analysis methods to apply. Then they test their theories by running experiments on sets of data.
Because supply chains involve multiple partners, each with its own information system, companies find themselves sifting through huge piles of data to find the "aha" insight they seek. That volume of information creates challenges for a supply chain team that's managing a big data project—another reason it's important to have a data scientist on staff, Michael Watson, an adjunct professor at Northwestern University and co-author of the book Supply Chain Network Design: Applying Optimization and Analytics to the Global Supply Chain, told me. "That person would be able to sort through the data and help the company determine what actions they should take or how they should build the data into their processes to run a better supply chain."
The ideal situation would be to have a full-time data scientist on staff. But supply chain managers aren't the only ones who want to mine big data these days; other functional areas also want to use big-data analysis to uncover hidden insights. As a result, data scientists are in hot demand and are commanding hefty salaries. So even if you do decide to bring in a data scientist, you may have to settle for engaging outside experts on a project-by-project basis.