Yes, maybe there’s a significant skills gap in the big data and analytics market. And with enterprises everywhere looking to reap the benefits of actionable large-scale business intelligence, we can’t wait forever for formalized education to bring the next wave of data scientists.

To get some perspective on this issue, I spoke with Deloitte Consulting’s Advanced Analytics & Modeling Global Human Capital Leader John Lucker for his tips on how to attract and train data scientists in the here and now.

“It’s taken years to find them and to train them,” Lucker says on the subject of his 150 person or so advanced analytics team.

The platonic ideal of a data scientist is someone with a background in statistical analysis, programming, business and the communications skills to turn the data into an actionable narrative. But Lucker says it’s a lot more productive to find somebody with a few of those skills on their resume and help train them up – assuming they want to learn. Lucker’s team came to Deloitte from past lives as mathematicians, economists, actuaries, behavioral psychologists – essentially, any line of work that requires the use of statistical analysis to draw some kind of pragmatic conclusion.

But the best prospective data scientists are the ones who thrive on new challenges, while still keeping their eyes on the prize of actionable insight. It’s about pragmatism – if your results turn up a super, super slight performance improvement in the customer’s business, you’re probably in the wrong business yourself. It’s about identifying and acting on trends, not analysis for analysis’ sake.

“We’re looking for pragmatism over perfection,” Lucker says.

The tradeoff is that Deloitte Consulting’s higher-level data scientists get to work on problems across verticals, working with Deloitte’s retail customers one day and healthcare customers the next.

Given that a lot of big data methodology is still in flux, Lucker says that the best way to train his team is to essentially allow them to train themselves: By applying a “standard” and an “experimental” methodology to every customer’s data set in parallel, Deloitte Consulting can come up with a set of results that it feels comfortable passing off to a client, while still pushing the envelope when it comes to how it processes huge data sets.

The final piece of the puzzle is the development of different skill sets amongst a team of data scientists. Some data scientists are better at the statistical analysis stuff. Some are better at coming up with and communicating that narrative. And even above just the analytics, Deloitte employs marketing, supply chain, implementation, and other specialists just to turn big data into a business plan – a crucial missing piece of the puzzle for many customers, who see data as data.

The bottom line is this. Vendors like IBM are helping universities develop their big data and analytics programs, but if Deloitte waited for the next generation of formally-trained data scientists, it could be ten years before the big data industry moves forward. No, if you’re looking at a big data strategy, it’s important to take your destiny into your own hands.