Comb through the findings of any recent industry survey on the state of big data and big data analytics projects, and you’ll undoubtedly encounter commentary about the struggle to find a so-called data scientist. Despite being one of the most talked about and sought after roles in IT, and despite increasing desire among new and existing members of the IT workforce to develop into data scientists, there remains a dearth of individuals capable of filling this critical role.

It’s a problem that’s not going away any time soon. Given the growing C-level focus on deriving revenue-generating insights from data and information, the need for that elusive data scientist will only intensify in the year ahead. But the demand far outpaces supply, meaning that but for the fortunate, deep-pocketed few, hiring a data scientist from the outside won't be a viable option any time in the near future.

A Home Grown Solution

Now for the good news: For most organizations, there’s no need to go outside the company to hire a data scientist. You’ve probably got a potential data scientist sitting right under your nose. All you need to do is empower him or her to play the role. After all, the term “data scientist” is largely a marketing creation. Though academic programs aimed at training data scientists are starting to crop up, there’s still nothing that formally marks one person as qualified or unqualified for the role.

Data scientists are mathematicians, statisticians and data analysts applied in a new, business-centric way. They’re people who understand the business, understand its data and are adept at applying the latter in order to benefit the former. Just as importantly, they’re great communicators and collaborators, people who understand the inner workings of the organization and have the respect of its decision makers in such a manner that their findings and recommendations are accepted and used to drive change.

Why the Shortage is a Good Thing

In a way, the shortage of data scientists is a good thing. It will force companies to look within to find a data scientist, and in doing so, in the long-run -- and data analysis is all about the long-run -- they’re far more likely to wind up with a long-term employee who delivers real value and makes a real impact on the company, provided they are properly empowered.

That last point is a critical one. Finding a data scientist from within isn’t a matter of identifying someone with all of the aforementioned attributes and appointing them to the role. Finding someone who is proficient with data, understands the business, great communicator and well-respected by company leaders is the starting point. But from there, it’s incumbent upon the organization and its leaders to set this individual up for success if he or she is to truly have the desired impact. Here are a few steps critical to making that happen:

Change your mindset

As a company leader, the first step is changing your mindset about the need to go outside the company to hire a data scientist. Your mindset can’t be that you’re elevating someone to the role simply because you have no other options, otherwise you’ll never respect the work that person does and the learnings she uncovers. And if you’re not prepared to respect your data scientist enough to act on the findings of her analytical work, then you don’t need a data scientist to being with.

Instead, find the right team member to elevate, and do so with conviction. You’re not hiring her because you had no other choice, you’re hiring her because she is the person who knows everything there is to know about your company and its data, and because her work has the ability to change the way you do business for the better.

Provide them with self-service technologies

If you’re going to give someone the role, you’ve got to give that person the tools as well. Providing your newly anointed data scientist with access to self-service data technology enables him or her to better capitalize on the company’s big data investments.

Invest for success

Don't think of hiring a data scientist from within as a means to getting a data scientist on the cheap. Enabling a data scientist is not about saving pennies on operational costs. It’s about uncovering new ways to make more money. This is a classic case of having to spend more to make more. Allow the data scientist to assess the situation and outline the needs, and then invest as budget allows.

Exercise patience

Think of data as the sand on the beach and a data scientist as a metal detector. There’s a lot of sand to be explored and even the best metal detector might not find the treasure right away. Management needs to be patient and understand that data analysis is an ongoing process. Sometimes the ROI is immediate, sometimes not. But in either case, without the metal detector, there’s very little chance of finding that buried treasure at all.