Big data professionals also have to be intellectually flexible enough to quickly change their assumptions and approaches to problems, says Brian Hopkins, an analyst at Forrester Research. "You can't limit yourself to one schema but [need to be comfortable] operating in an environment with multiple schemas or even no schemas," he says.
That tends to be a different approach than most IT people are used to. "IT people coming out of a strong enterprise IT shop are going to perhaps be constrained a little bit in their ability to do things quickly and move fast and be agile," Hopkins says.
But once hiring managers find the right type of person, they're usually willing to retrain that person to fill a big data role. For example, Patil used to work at LinkedIn, where, he says, "we largely trained ourselves, because so much of this is open source." He thinks the same thing can happen at most companies. "You can make these people" -- if they have the right personality, he says.
IT workers who are flexible, willing to learn new tools and have a bit of an artist somewhere within can move into data architecture or even data visualization, says Sacheti.
In short, big data carries big potential for IT pros who would relish an opportunity to show their creativity.
Frequent Computerworld contributor Tam Harbert is a Washington, D.C.-based writer specializing in technology, business and public policy.
This version of this story was originally published in Computerworld's print edition. It was adapted from an article that appeared earlier on Computerworld.com.
Big Data Job Titles and Skills
Without conventional titles, or even standard qualifications, it's hard to know what makes someone suitable for a big data job. This listing, based on interviews with big data experts and recruiters, attempts to match up some of the most common titles with the skills required.
• Data scientists: The top dogs in big data. This role is probably closest to what a 2011 McKinsey report calls "deep analytical talent." Some companies are creating high-level management positions for data scientists. Many of these people have backgrounds in math or traditional statistics. Some have experience or degrees in artificial intelligence, natural language processing or data management.