Guest Column: What Business Wants From Data Science

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Findings of a statewide survey of corporate technology needs

Karl D. Schubert, Ph.D.

Professor of Practice and Director of Innovation and Data Science Initiatives

College of Engineering and the Sam M. Walton College of Business

University of Arkansas

A LITTLE UNDER two years ago, I sent a letter to various Arkansas companies asking what kinds of tech grads they were looking to hire, and what particular skills these employees would need to have. They were provided a series of choices to check or not check, plus a place where they could amplify their thoughts about the survey choices they’d made.

The purpose of this survey was to help my UA colleagues and me as we worked to define the University’s data science curriculum to ensure that our future grads would be trained in solving real-world business problems, instead of starting their working lives armed with merely a surfeit of theory. In May 2019 I wrote—in this very newsletter—about the process of developing our curriculum. Now, as we begin the year in which our new classes will be rolled out en masse, I thought it would be useful to share some of the results of that business survey, to show just what corporate Arkansas is hoping to find in our coming tech grads.

First, a little information on the companies we queried. Although I’m based in Northwest Arkansas, home of such major companies as Walmart, Tyson Foods, and J.B. Hunt, our survey wasn’t limited to large companies or to any one geographic area. We covered the state, from Texarkana to Jonesboro, Eldorado to Fort Smith, and we surveyed small to mid-size companies as well. Start-ups were another focus, young companies with anywhere from half a dozen to a few dozen employees. This is a trend in Arkansas—start-ups that have cropped up to do various types of analytics and data science work for more established companies, not just here but around the country and even the world. Such start-ups reflect the need for specialized knowledge that so far isn’t widely available, and these up-and-coming companies tend to be looking farther ahead than some of the bigger companies.

This balance of large, medium, and small reflects the balance on our Data Science Advisory Council, which was the point of the Council itself—to be sure we covered all the bases. To that end, we began this process by surveying the Council members about the kinds of technology jobs their companies were trying to fill, and the different skills those positions would require. From that input came a list of 35 unique technology position titles (including levels within titles) and a listing of the common skill needs received from the Council members. Then we simply asked the question: Which of these position titles are you needing to fill, and which of these skills is required for each title?

Of course, it didn’t turn out as neatly as that. Some recipients didn’t use the survey form, they just wrote about their own internal company workings and needs and I had to integrate that into our overall findings. Other recipients, especially in the large companies, didn’t necessarily fill out the survey themselves, or only themselves; instead, they routed it through their company hierarchy to get more feedback—including position titles that weren’t on our list.

From our standpoint as educators charged with preparing future data science grads, understanding the required skills of today’s workplace was key, and I think our survey succeeded in eliciting much critical information. Here’s what we learned:

  • 36 position titles required “Evaluating the quality of data”;
  • 36 position titles required “Understanding and rigorously analyzing data using relevant software packages”;
  • 34 position titles required “Working in a team-based environment”;
  • 32 position titles required “Communicating findings in writing”;
  • 32 position titles required “Communicating findings via graphical and visualization techniques”;
  • 31 position titles required “Applying critical thinking skills to solve novel challenges”;
  • 31 position titles required “Data-cleansing, processing, and wrangling”;
  • 30 position titles required “Generalizing knowledge from one subject area to another using data science”;
  • 30 position titles required “Data science applied to business and economics in an organizational setting”;
  • 29 position titles required “Collecting data via research techniques”;
  • 29 position titles required “Relevant work or internship experience”;
  • 27 position titles required “Applying data science theories to understand the data and make predictions”;
  • 27 position titles required “Communicating findings via public speaking”;
  • 23 position titles required “Data privacy, security, and ethics”;
  • 21 position titles required “Management of databases”; and,
  • 17 position titles required “Project management skills and leading teams.”


I’M OFTEN ASKED if any of these survey findings surprised me. The answer is no, because I’ve worked in the business world. For many of these positions, I’ve either been one or managed one or hired one, so to me these survey results also reflected my own personal experience. If there was any element of surprise, it was that so many organizations recognized that these so-called “soft skills,” such as working in a team-based environment or communicating technological findings in writing, needed to be such a high priority in comparison to the purely technological skills. In my own business career, being able to do these deep, effective analyses, and then being able to explain them in a way that the non-technical people, or non-domain experts, or the people I worked for, could grasp it and use it—I felt that that was important. And I was pleasantly surprised that so many others saw it as important, too.

The curriculum that will debut at the UA Fayetteville system in September—and that we hope will eventually involve most of the 100+ two- and four-year colleges in Arkansas—owes much of its content to the businesspeople who took the time to give us their professional input. We used their knowledge and experience to build our actual courses, making sure that we teach the kinds of classes that either start with the desired skills or else create a set of problems and exercises that allow our students to build to those skills.

Our Advisory Council—that group of committed business and educational leaders from Arkansas and beyond—has been involved every step of the way, and we’ve used their feedback to validate that we haven’t missed anything. In our last Advisory Council meeting (October 2019), we took them through the learning objectives and outcomes for the first two years’ worth of courses, and got their feedback on that. In the next meeting, we’ll do the same thing for the second two years of courses.

So yes, the participating companies were an integral part of this revolutionary process, and their input continues to resonate as Arkansas attracts more and more knowledge-based companies to locate here. While I sometimes give new companies the full survey to take—I recently did that with ACHI and DXC, which Governor Hutchinson just announced was moving a whole bunch of new jobs to the state—I often find myself just running the results of that two-year-old survey by the new companies, just to make sure somebody from somewhere else isn’t going to throw us a curve ball. Sure enough, they generally look over the findings and say, “Yep, you got it. Those are exactly the skills we’re looking for.”

While I’m sure we’ll need to make changes and evolve as we go along, we feel good about the work we’ve done to put Arkansas at the forefront of data science education in the U.S. In that effort, we consulted educators and business leaders from around the country, and one such leader—Dr. Aric LaBarr, Associate Professor of Analytics, Institute for Advanced Analytics at North Carolina State—is a member of our Advisory Council. “I really appreciate how the University of Arkansas has created a bachelor’s level Data Science and Analytics education,” says Dr. LaBarr. “This space is filled with hundreds of graduate level degrees but only a handful of undergraduate level degrees. This unique offering provides a service that is not fully met in the U.S.

“Also, their approach to allowing many different flavors of the degree with concentrations through their ‘hub and spoke’ approach with many other departments is truly unique . . . which makes their degree truly interdisciplinary, just like the field of data science and analytics itself.”