What makes a data scientist?

It's a whole new ballgame for traditional data analysts, as training focuses on deep knowledge of statistics and computer science.

Ask a dozen CMOs or CIOs what tops their list of strategic priorities and odds are exceedingly good that "big data" ranks either first or second. One of the greatest challenges, they'll tell you, is finding the talent they need to analyse and wring business value from the ever-increasing volume of complex data flooding their enterprises. What they need, they say, are good data scientists -- and lots of them.

In one of the most frequently cited reports on the topic, the McKinsey Global Institute estimates there will be a shortfall of 190,000 data scientists just in the US job market alone by 2018.

But how exactly do you become one of these in-demand big data specialists? Is it a matter of training, certification or both? Is it simply the next logical career step for a traditional business intelligence expert? Is a computer science degree required?

As it turns out, there is no one right answer, at least not at the moment. Instead, it's largely a scramble out there on the big data field.

"Big data is like a kids' soccer game. Everyone is running to the ball, but no one knows exactly what to do with it. It has created a huge competition for people," says Greg Meyers, CIO at Biogen Idec in Weston, Mass.

"It's a very fluid area," agrees Michael Rappa, executive director of the Institute for Advanced Analytics at North Carolina State University. "Depending on what industry you're in or what company you talk to, it's a different reality when you talk about big data."

While a single definition might be elusive, academic, career and business experts agree that there are certain fundamental tasks all data scientists need to perform and certain skills are required to perform them well. The main pillars of the discipline are data clustering, data correlation, data classification and anomaly detection.

Or, as Rob Bird, a data scientist and CTO at Red Lambda, a provider of predictive security analytics, puts it, "You make data simpler, find relationships, find the weird stuff, and then make predictions."

Data Science vs. Business Intelligence: What's the Difference?

The terms "data science" and "business intelligence" seem to be used a lot in connection with big data, but they're really very different disciplines. Experts say data science is all about predicting the future, while BI involves producing static reports.

"Traditional BI engineers are effectively reporting information as is, even if they're reporting trends and standard deviations away from the norm," says Andrew Dempsey, director of DVD BI and analytics at Netflix. "They aren't really discovering new nuggets of information. The data is what it is."

But with data science, there's an element of mystery. For example, Netflix looks at historical data "to identify why someone is more or less likely to churn because of their behaviour," Dempsey explains. "There's more uncertainty there because on an aggregate level, a lot of people may have similar viewing habits, but on an individual level, everyone is different."

Another key difference between the two disciplines has to do with the data itself.

First, there's the sheer volume of data. "With so much data, you need to assimilate it to look at the exceptions, rather than the reports," says Biogen Idec CIO Greg Meyers. The pharmaceutical manufacturer, he says, continually reviews data from signals throughout the manufacturing process to detect when events are out of tolerance levels. When an anomaly is detected, a different operating procedure is triggered. "It's all about trying to make sure the process of how we manufacture is as controlled as possible," Meyers says. "We've matured our analytics process by looking at data across batches so we look at trends to reduce the variability of certain things."

Another challenge is dealing with the variability of big data.

Josh Williams, a data scientist at Kontagent, notes that "in classic BI systems, you usually have highly structured data -- things like customer profiles. You come up with an analysis by correlating that data and running regressions on it."

In today's big data environment, in contrast, "you have a mess of complex data and you have no idea how the features you may be looking at -- the input factors -- relate to the output," Williams says. The upshot is that data science is "much more exploratory. It's easier to shoot yourself in the foot. You have to be much more rigourous. It's much more difficult to do the analysis, which is why there is so much more research around machine learning," he adds.

Stalking the elusive data scientist

It is 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 of executives from the IT Leadership Academy we spoke to 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

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