The newest fantastic animal to inhabit the human imagination -- joining a long list that includes the dragon, Gorgon, Loch Ness monster, sphinx, unicorn and yeti -- is the data scientist. This mythical beastie has come to dominate the dreams of many of the otherwise sane people who run organizations. They see themselves locked in an epic struggle, coming up against a horde of data but armed with inadequate skills. As this pitched battle rages, the cry is heard: " Data scientists will save us!"
Feeding this vision are troubadours with PowerPoint presentations. They show up on the big data rubber-chicken circuit -- that surfeit of conferences ginned up to take advantage of the rapidly growing interest in high-end analytics -- to sing a narrative with three verses: There is ever more data, goes the first. There is potentially huge value in that expanding data set, runs the second. There is a rich and rapidly expanding tool set to assist in extracting value from that data, concludes the third. These are sung in a round over and over and over, but the air finishes on a very different note, with the sage on the stage saying something to this effect:
"And oh, by the way, you need really bright analytic geniuses/rocket scientists/quants/data scientists, who are very rare and very expensive. Despite this, you should buy our tools and get started anyway."
Naturally, outcomes-focused executives in the audience find that conclusion monumentally unsatisfying. But if data scientists are very rare, they decide, they will find them -- and recruit them at any price. (See " How to Nab a Data Scientist Job.")
At the IT Leadership Academy, we wanted to find out where this obsession with the mythical data scientist was heading. We interviewed over 100 executives charged with leading the charge to analytic competence in their organizations. It was generally agreed that data science and analytics is a multidisciplinary field, and it was widely conceded that it is virtually impossible to find all the necessary analytical skills resident in one human being. The non-hysterical in the bunch have rationally concluded that rather than stalk a mythological life form -- a data scientist with all the skills required -- they should adopt an "ensemble" approach to the deficit in analytical skills.
Here's how Scott Friesen, director for marketing analytics and customer insights at Ulta Beauty, explains this idea: "You have to create a portfolio of talent within a team. For example, you might have someone who is a great statistician but doesn't know database query mechanisms. So someone else on the team does the SQL pulls for the statistician, who hands off to the best communicator. That is who communicates the message to the business."
Glenn Wegryn, director emeritus of operations research at Procter & Gamble, skinned the analytical talent deficit in a very innovative way. As part of a multipronged talent strategy, he scoured the enterprise for employees who had analytical training but weren't employed in analytical jobs. This was a rich source of affordable raw quantitative skill. And that should not be surprising. Just about every student participating in the 6th Annual EEIC Engineering Capstone Design Showcase at Ohio State University demonstrated the raw skills necessary to create value with data.
So forget about the data scientist bogeyman. If you are eager to create value with data, go out and repurpose an engineer. They will love you for it.
Thornton A. May is author of The New Know: Innovation Powered by Analytics and executive director of the IT Leadership Academy at Florida State College in Jacksonville. You can contact him at firstname.lastname@example.org or follow him on Twitter ( @deanitla).
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