Role of a Data Scientist in a Big Data World

Recently I had the opportunity to join a panel of industry thought leaders in a timely session on DM Radio that focused on new roles in information management. So of course it didn’t take long before the conversation turned to the role of the data scientist. After all, Harvard Business Review calls this up-and-coming position, the “sexiest job of the 21st century.”

We can thank Big Data for this sudden popularity. Data scientists combine technical savvy and innate curiosity to analyze massive volumes of data and then deliver insights and answers that help solve real-world business problems. They’re part technologist, scientist, investigative researcher, business analyst, mathematician, statistician, economist and engineer.

It’s easy to understand the appeal of this new, high-ranking professional. Companies like Amazon, eBay, Google, Facebook, LinkedIn and Twitter all have become major household-name brands, thanks to the data scientists who mined, analyzed and processed Big Data to find patterns, preferences and behaviors that drove business.

These scientists all possess both a methodical approach and open mindset that enable them to explore and dig deep to find answers to questions that were critical to business success. In reality, that’s neither a common skill set nor one that is easy to create. That’s why a recent report from McKinsey Global Institute predicts demand for data scientists will exceed supply by 2018.

The impending shortage, coupled with reported six-figure salaries, is creating a ripple through existing IT ranks as programmers, application developers and engineers are aspiring to climb the corporate ladder by becoming data scientists. But, do they have what it takes?

Interestingly enough, some of the best candidates have emerged from the science community. Why? Because of their ability to create a theory or thesis, then work it, explore it and come at it from 100 different directions while applying the results to the business challenge. Too often, IT professionals and engineers are focused on building things in linear fashion based on a pre-defined set of criteria. Closed-box thinkers do not make good data scientists.

Mindset is just as important as skill set. Data scientists know how to search, experiment and test, which is quite different than the approach typically taken by someone who engineers or maintains systems. With that said, individuals who program in Java, Python and SQL—which many of our customers know well—have a skill that can be very applicable to the data scientist role.  If IT folks can add applied science methodologies to their skill sets, they’ll have a better shot at moving into this highly coveted area.

Likewise, applied scientists who learn SQL and other application development languages will improve their chances of migrating into this space as well. Of course, there will be endless opportunities for the technologists and scientists to collaborate, especially as the shortage of qualified data scientists deepens. 

As I mentioned on the DM radio broadcast, a teaming approach to the data science role can be warranted to help organizations get things moving. If you personally don’t have the data scientist “chops” yet, team up with others who balance the skills you lack. 

For those of you in the management ranks, the successful project starts with you. In order to answer tough questions with Big Data, you must start with the right questions. Don’t hire or promote team members to “see what you can find” or “get some data to back up our theories.” That’s backward. Start with the critical questions that need to be answered in order to move your organization forward.

Then, be sure to provide your data scientists with access to the required data and the power to find the answers. Oh yeah, and the freedom to think, analyze and think some more before making recommendations based on observations and research. And if they bring ideas that blow away your theory or gut feelings, stop and really pay attention before saying no.

So, do you and your team have what it takes? Drop me a line at with your ideas.

About the Author: Darin Bartik