Data scientists are touted in analytics circles as the jet jockeys of information management. But as Big Data evolves, do the people at the very top of the org charts even recognize their importance? A recent survey seems to say maybe not.
Having just attended the @Gartner BI and Analytics summit in Dallas, data science is on my mind. Not so much because I just came from a conference center full of analytics junkies. But I did just come from exactly that, where after all, the must-see keynote was none other than Nate Silver, the rock star statistician who has single-handedly made analytics cooler than we ever thought they could be. But that’s not so much why I’m thinking about data science.
I’m thinking about it because over morning coffee I met a non-believer.
“I don’t need to find smarter analytics. I just need to find dumber competitors.”
Truth be known, if those are the choices, I’d look for smarter analytics. The dumb competitors might be dumb, but they can always go buy some smart analytics. And someone who knows how to use them.
This is exactly the person for whom the Supreme Being created the Data Scientist to walk among us. Data science is the filtration of the murky to make it clear, even to cynical business people who really just want the insights and don’t really care how it gets done. It’s been called the “it” career for the 21st century, and Data Scientists are said to be scarce enough that forward thinkers and Universities are already thinking about how to close the gap between demand and supply.
Universities can train them, but that may only be part of the answer. Some very smart people are thinking about the problem as well. Noted author, educator and adviser to business Tom Davenport believes that “data scientist” may be a misnomer, because one person cannot carry the load of discovery within data. He believes the approach should be an integrated team of experts who together provide the best potential for gleaning insights from the data muck. Emcien CEO Radhika Subramanian writes in “Data Scientist Scarcity” that automation is the answer to a short supply of data scientists, and she may well be right. Analytics are certainly not getting dumber nor systems getting slower, and these are the twin engines that drive automated discovery of insights within information. It is a very short leap–maybe one or two technology cycles–to get to a world where the man/machine balance in data science is more skewed toward the machine. And that is automation.
Interestingly, all the noise about career sexiness and security and the like may be just that if the corner office doesn’t make data science a priority for investment. At that same conference last week, Garner Research Fellow Ken McGee shared results of their latest CEO priorities survey. It was fascinating. The top two IT investment priorities to fuel business growth (sentiment is up this year!) are business analytics and enhanced business reporting. Clear that the corner office is very much tuned into all things data. But the same C-suite group places data science in the bottom third of priorities, that no-mans land of investment probability. Why the gap? I’ll guess Gartner will go find out, but one would think that either the senior executives think they are well steeped in people and technologies here, or they don’t have a good feel for how baseline data science is to their other objectives. I’ll bet on the second.
Whether the shortage is real or contrived, whether the corner office does or does not appreciate the need, and whether the answer lies in more bodies or more technology is yet to be determined. Clearly there is some education that needs to take place on just what is state-of-the-art for data science.
What isn’t at all in doubt is that on the market leader ladder relying on the false promise that there may be dumb competitors out there is only one rung above hope as a business strategy.