The talent shortfall confronting enterprises seeking big data and analytics professionals makes finding and hiring qualified people extremely difficult. Compounding the challenge is the fact that data analytics is an emerging field, thus enterprise leaders aren’t yet sure who they should be hiring for analytics jobs. Beyond programming expertise and education, what traits and characteristics make for a good data scientist?
According to Talent Analytics, a company that uses analytics to quantify talent traits and predict performance in employees and potential hires, data analysts should be curious and creative—but not so much that they can’t finish a project.
“If somebody is too curious,” explains Greta Roberts, founder and CEO of Talent Analytics, “what happens is they continue to go to school, they continue to learn, and you get to a point of diminishing returns because they are too curious and won’t stop and deliver results.”
In its just-released study involving more than 300 participants, Talent Analytics and the International Institute for Analytics assembled a complex profile of data analysis professionals that spans education level and specialty, age, gender and job functions.
Perhaps most interestingly, the 2012 Analytics Professional Study also includes a psychometric profile outlining the characteristics and performance styles of analytics professionals.
In addition to curiosity and creativity, Roberts says, “authority” is a top positive trait in an analytics professional. “These are people who are believers: ‘I believe I know the right way to do this.’ They’re really disciplined in their approach,” she says.
Other characteristics that motivate analytics professionals include altruism and competitiveness, the study concludes.
Roberts says she got the idea for the survey from talking with clients looking for analytics professionals. “One day it occurred to me that analytics professionals aren’t using analytics to understand themselves. That’s kind of what we do, use analytics to understand humans,” she says.
Among the survey’s demographic findings:
• 57 percent of analytics professionals are under 40, while 17 percent are over 50
• 72 percent are male
• Only 16 percent have doctorate degrees, while 47 percent have master’s degrees and 36 percent no more than a bachelor’s degree
Roberts calls the low number of Ph.Ds “one of the surprises” of the study, simply because of the mismatch with demand. “When you speak with people or look at the want ads, people are looking for Ph.Ds,” she says.
The most common area of academic concentration among analytics professionals (no surprise here) is math and statistics, followed closely by business. Relatively few respondents to the survey had academic training in science, finance or economics.
That data analytics is a relatively young profession is underscored by survey results showing that 29 percent of respondents have been doing analytics for less than five years, and 60 percent have been analytics professionals for less than a decade.
More than half (52 percent) of the survey’s respondents were hired by their current employer no more than three years ago, and 49 percent have been in their current analytics profession for less than two years.
Roberts says analytics are important for getting beyond superficial insights into an analytics candidate’s qualities or an enterprise’s functional needs.
“Many thought leaders we talked to said, ‘Just show me somebody who’s curious and I can teach them the rest,’” she says. “But if you hire these creative people and have them to just one thing—like data acquisition, for example—they’ll get bored.”