After the 2013 holiday season, retailers were notably concerned about lower shopping volumes. There was a silver lining, though. As one digital marketing agency headlined a November 19 blog post, "The Holiday Season's Greatest Gift Isn't Big Cash. It's Big Data."

Simply collecting Big Data does not unpack its potential value. People need to do that, and those people are hard to come by.

Wal-Mart already captures from customer transactions more than 2.5 petabytes (2.5 quadrillion bytes-a 16-digit figure) every hour. That is, every 60 minutes, Wal-Mart stuffs the equivalent of 20 million four-drawer filing cabinets with data. Fifteen out of 17 US sectors now have more data stored per company than the Library of Congress. A McKinsey Global Institute study finds that this single data category has "the potential to provide more than $800 billion in economic value to individual consumers over the next decade." Some gift! Provided companies can successfully unwrap and actually make use of it.

Simply collecting Big Data does not unpack its potential value. People need to do that, and those people are hard to come by. Just 3.4 % of CMOs surveyed by McKinsey in 2013 believe they currently have the right talent to fully leverage marketing analytics, and 98.8% describe finding that talent as "challenging." In consequence, while few dispute the potential value of Big Data, CMOs surveyed say they use marketing analytics just 29% of the time to make decisions, and a paltry 3% say that analytics contributes "very highly" to their company's performance.

Finding Talent: Look beyond the usual suspects

The University of California, Berkeley, recently announced an online Master of Data Science degree, and, in August, IBM unveiled a Big Data educational partnership with more than a thousand colleges and universities. Such academic forays into data science are noteworthy because they are exceptional. Over the long term, we're hopeful that these sorts of initiatives will prove to be the first of many and that they will at least begin to meet the demand for data science talent.

The most effective Big Data specialists are "translators" capable of bridging different business functions and effectively communicating between them.

For now, however, many companies are trying to fill their ranks with candidates holding degrees in computer science, industrial engineering, and statistics. But there are just not enough of them, so employers need to improvise. Major companies are starting to turn to disciplines as diverse as physics, philosophy, psychology, economics, and even biostatistics to find Big Data talent. They seek people with analytical minds and deep curiosity - critical talents for working with data in business. We know of companies, for example, that have successfully transformed computational fluid dynamics engineers and West Point-trained ordnance engineers into data scientists.

Find translators and bridge builders

Cracking the Big Data enigma requires more than number crunchers. To turn the fruits of Big Data insight into value in the marketplace calls for an extensive mix of expertise. Companies need to fill multiple roles with specific skillsets. Specialist competence is essential but not sufficient. Look for specialists who are also "translators," capable of bridging different business functions, comfortably and effectively communicating between them. "Campaign experts," for example, focus on turning data models into specific marketing campaigns.

You need people with multiple skillsets, but, realistically, it is very rare to find someone with all the skills you need. More feasible is finding people with at least two essential skills. Think of them as "two-sport data athletes," and look for such combinations as computer programming and finance, statistics and marketing, psychology and economics-just to name a few.

A good data scientist is a uniquely valuable professional whom other companies will inevitably covet.

The next aspect to consider is recruiting for the entire Big Data "value chain." This will ensure that you have the people you need to get the insights all the way to the front lines. In addition to the core data scientists, whose prime expertise is creating predictive models from data, the team should include "data hygienists," who give preliminary form to unstructured data by ensuring that it is clean and accurate. They hand off to "data explorers" who filter data to identify information actually capable of predictive analysis. "Business solution architects" further structure this filtered data, prioritizing material most likely to yield actionable business insights in the hands of the data scientists.

Does every team require separate people for each role? No. But each role represents an essential process, and so every role must be cast and played.

Address your retention issue

Having recruited a data scientist, a "translator," or a two-sport athlete, recognize that you have a uniquely valuable professional, who other companies will inevitably covet. Research competitive compensation levels. You will want to make a credible financial offer-including an upward career path. This latter part can be a problem, since most data analysts do not aspire to become managers, the traditional business career objective. Better get creative, then, and offer an alternative. One financial services company, for example, created roles designated "subject matter experts," which command the pay and prestige of a senior manager.

Recruiting for the entire Big Data "value chain" will ensure that you have the people you need to get the insights all the way to the front lines.

Often as important as money is providing an environment of intellectual challenge, collegiality, and extensive connection with those outside of the analytical team. Like many of us, analytical folk want to feel they're having a positive impact on the business rather than being held in a back room to collect and analyze data. Data teams need to be integrated into the company with a full appreciation of the value they create for the entire business. Seasoned analysts accept the reality that, most of the time, they are expected to work backwards from a marketing and sales decision to provide insight on its impact (or lack thereof). But if 80% of the analytical task is "decision-backward," it pays to engage your insight team by allocating at least 20% of the work to innovation, challenging and engaging analysts to create "data-forward" insights that lead decisions strategies rather than merely course-correct existing strategies.

As data increasingly drives businesses, getting-and keeping-your Big Data people behind the steering wheel will require innovative ways to recruit, retain, and inspire them.