In Search of the Data Scientist Unicorn

There seems to be a common thread in all my customer conversations lately—whether it’s during business meetings, casual lunches or following the recent big data and analytics press summit I moderated at Dell World. The people tasked with doing more with their data to solve business problems are struggling mightily to attract the right data analytics talent.

It seems that individuals possessing the ideal skills, mindset and intellectual curiosity are as rare as unicorns.

Image of a man with a unicorn head typing on a laptop computer

A large healthcare customer from southern California told me at Dell World that he had decided to hire someone who then could turn around and hire all the data scientists because the undertaking had proven to be so difficult, despite having ample budget and headcount.

Another customer, the dean of a business school in Texas, decided to build out his own data science curriculum to attract and train future generations of highly skilled analytical thinkers. Clearly these customers aren’t alone. According to a McKinsey study, by 2018, the U.S. alone may face a 50 to 60 percent gap between supply and demand of deep analytic talent.

This begs the question: How much worse will it get before it gets better? And, how do companies cope in the meantime? Luckily, many universities have stepped up their efforts to create one- and two-year graduate programs to help fill the gap. Today, many elite and top-tier schools have established analytics masters programs within their schools and institutes of business, engineering, computational science, computer science and applied mathematics.

In its list of top 20 big data masters programs, InformationWeek describes the course of study and prerequisites for both full- and part-time programs at Bentley, Carnegie-Mellon, Columbia, DePaul, Drexel, Harvard, North Carolina State, NYU and University of California, Berkeley among others. Programs at Arizona State, Fordham and the University of Maryland also are highlighted while Northwestern, Emory, and Duke were cited as gaining traction.

For some companies, the answer to closing the gap may be working with one or more of these universities on internship programs that marry academic theory with hands-on business experience. It’s important to realize that some masters programs will have a heavier technical slant, whereas others may be more business focused. Regardless, starting an internship may be one of the most effective ways to establish a pipeline of candidates.

Of course, this raises the next big question: How do companies attract the best and the brightest, even if yours is not the most analytics-savvy organization? First off, it’s important to walk the talk and be ready to embrace the endless questions and “what-ifs” that will come from a data science business function or team.

Data Informed addresses this in its guide, “How to Recruit Big Data Talent When You’re Not Google or Facebook.” It stands to reason that companies with strong reputations for using analytics to steer their businesses will be able to hire newly minted graduates from the head of the class. But that doesn’t mean everyone else is shut out.

Analytic thinkers, by nature, like a good challenge and depending on where the organization is in its data lifecycle, there may be endless opportunities to apply data insights in new and exciting ways. The key is for organizations to embrace the process and be willing to accept outcomes that may ignite critical changes in business direction, markets, customers, etc.

For those organizations not really in the game yet, the best place to start is with an internal task force. This group, which needs to comprise stakeholders across all major business functions, will prove instrumental in helping the company navigate cultural and organizational dynamics that accompany major shifts in business thinking. Moreover, the task force can assist in championing the analytics framework and ultimately a team of data scientists.

I know, customers today complain they can’t find ONE data scientist, so how the heck will they be able to build a team? The good news is there’s probably already some pretty smart data analysts within your company who can be mentored and nurtured by others with deeper analytics training.

The saying “it takes a village” applies to many things, including standing up a data science business function. It requires an open-minded culture and an appetite for knowledge. It also means creating a support structure with management oversight and shared curiosity, so data scientists have a sounding board with decision makers.

At Dell, our quest is to feed that curiosity with a blend of big data analytics tools and services that support companies on their journey to learn more while hopefully making it a bit easier to harness the ever-elusive data scientist unicorn.

How’s the chase going at your company? Connect with me on Twitter at @simpleisbetter to share your thoughts.

About the Author: Darin Bartik