#1: Think Iterative

If you're having trouble hiring data scientists, don't get all hung up about it. Everyone else is, too. Plus there's a lot of work that needs to be done in upfront data organization before you can move on to the task of organizing useful queries and reports, Bodkin said.

For that reason, the Think Big team treats big data projects as an iterative process. "Data science is extremely high value and as customers start doing more with big data, they start having increasing needs with data science," he said. "So, of course, we're hiring in the data science arena. But the foundation that allows that to be done is the engineering and architecture of big data platforms. Most customers start simply by getting big data into a form where they can access it at all, and do some basic analytics on it before they starting doing more sophisticated data science and predictive analytics."

For that reason, Bodkin worries more about whether a potential hire has the right programming background, knowledge of distributed systems, and data management background than whether he or she has data science experience.

Think Big currently has 80 employees, but it hopes to reach 100 by the end of this year. "Mentoring will be key. Realistically, you can't expect to lure people away from the big companies that are doing this, like Google or LinkedIn," Bodkin said.

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