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The Myth of the Data Scientist Shortage

September 26, 2016

By Thomas Davenport

Data scientists—people who can manage and analyze big, unstructured data—were once as scarce as vegetarian dogs. If your business wasn’t based in Silicon Valley or Boston, if you couldn’t offer massive stock options, and if you didn’t have a sexy business model, you were unlikely to be able to hire any. When I interviewed 35 of them in 2013 for an article in Harvard Business Review, my co-author (D.J. Patil, now a data scientist in the White House) and I wrote, “The shortage of data scientists is becoming a serious constraint in some sectors.” The most common educational background among the 35 data scientists I interviewed was a Ph.D. in experimental physics, and there aren’t a lot of those sitting around.

But now the world of data science has changed dramatically. There may not be a glut of data scientists, but they are much easier to find and hire than they used to be. If you’re based in Omaha, you’ve got a good shot at finding some good ones. If you can offer only a decent salary, you’ll probably be OK. And even the most traditional business can hire them these days. In short, there is no excuse for not building a data science capability.

Here are some common excuses companies use for not employing data scientists, and why they’re no longer valid:

  • “Universities just aren’t turning out data scientists.” Au contraire. There are more than 100 programs at U.S. universities alone that focus on analytics or data science. Some schools, like Northwestern University, New York University, and the University of California at Berkeley, have more than one degree program in data science. These programs are already churning out thousands of graduates.
  • There aren’t enough quantitative Ph.D.s to go around. First of all, you probably don’t need a Ph.D. data scientist. There are plenty of Master’s degree graduates out there who will have all the skills you need. Moreover, you’d be surprised how many unemployed or underemployed Ph.D.s there are in quantitative and scientific fields. There are programs that provide data science internships to Ph.D.s like the Insight Data Science Fellows program, and programs at vendors like SAS and Microsoft can ensure that the Ph.D.s have all the latest skills.
  • Data scientists don’t want to work in the hinterlands where my company is based. Think again. There are universities with programs in data science or analytics in Alabama, Kansas, Nebraska, South Dakota, West Virginia, and many other states far removed from the east or west coasts. At least some of the graduates of those programs are willing to work where they went to school.
  • Data scientists fresh out of school won’t understand my business. That may well be true. So train your own employees in data science. Cisco Systems, for example, worked with two universities to create distance learning education and certification programs in data science. More than 200 data scientists have been trained and certified, and are now based in a variety of different functions and business units at Cisco.
  • My company isn’t hiring anybody, but we still need data scientists. Again, you can retrain existing employees. You could take the Cisco approach and create a custom program. Or keep in mind that many of the university programs are online and can be taken part-time. If you make it known to your employees that your company needs and values data science skills—and that you might pay for some of the education—you will probably have some certified data scientists within a year.

Of course, it still may be difficult to find highly productive and effective data scientists, as with any sort of job. But there are now many potential candidates out there. No matter what your business is or where it’s based, chances are good that you can find someone to help with your difficult data problems.

Tom Davenport is a senior advisor, Deloitte Analytics, and distinguished professor, Babson College. He is also  a Fellow of the MIT Initiative on the Digital Economy.

This blog first appeared in the CIO Journal  on August 11, 2016, here.