Does your logistics department have a "data scientist" on staff? Probably not. But if you're planning to conduct a big data analysis to obtain insights into your distribution operations, you'd better be prepared to hire one ... at least on a temporary basis. "Data science skills are necessary because the supply chain team is sitting on a lot of unused but valuable data," says 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.
A data scientist is someone trained in the methods and techniques for extracting meaning from piles of information. Generally, he or she has a background in mathematics, statistics, and computer science. Although computers and software are powerful tools for facilitating analysis, a human expert is still needed to make decisions about what data to examine and how. "Data science is not a one-size-fits-all approach," says Larry Snyder, an associate professor at Lehigh University and co-author of the book Fundamentals of Supply Chain Theory. "So you can't just throw terabytes of data into an off-the-shelf system and ask it, 'What should I do?' It takes data and decision-making experts to convert raw data into useable information and ultimately, to make decisions."
Because so much raw information abounds in logistics, the discipline is considered to be particularly well suited to big data analysis. Logistics, by its nature, involves numerous data exchanges between multiple partners to make the supply chain flow, and there are piles of raw data sitting in all of those partners' systems. But it's not just traditional data systems that provide fodder for analysis. Big data analysis can encompass information gathered by sensors—say, on trucks or on packages in the warehouse.
The premise behind big data analysis is that if correlations can be made between all that raw data, users can gain a better understanding of why things happen and parlay those insights into process improvements. "Getting to root causes often requires analyzing data to understand correlations—what is related to what," says John Hagerty, a program director for big data at IBM.
Unfortunately, data analysis requires a particular set of skills that most logistics and supply chain managers do not have. "The supply chain team needs to have skills to drill into this data and then the ability to determine what action the company should take based on analysis of that data—the last part is where it is important to have a data scientist on staff," says Watson. "The person would be able to sort through the data and help the company determine what actions it should take or how it should build the data into its processes."
Given the boom in corporate interest in big data analysis, data scientists are in high demand right now. In fact, according to the job website Glassdoor.com, the median salary for a data scientist in the United States is currently $115,000.
That's why companies are turning to outside firms to hire data scientists on a project basis. At a recent conference I attended, Gartner analyst Michael Burkett told how one company had to contract with an outside agency to gain access to qualified data scientists to conduct a big data analysis of its supply chain.
Logistics managers can expect to find themselves in the same situation—that is, in need of outside expertise for their big data projects. That's why the first step for any manager planning such a project may be lining up an outside data scientist for the job.
