Predicting the Performance of Analytics Talent
Talent research is redefining the way companies identify and predict the core capabilities that make a data scientist successful.
Competing With Data & Analytics
Fifty-five percent of big data analytics projects are abandoned.
This surprising finding comes from a recent survey of 300 IT professionals, conducted by a company called InfoChimps.
The most significant challenge with analytics projects, according to the survey? Finding talent. Most (80%) of the respondents said that the top two reasons analytics projects fail are that managers lack the right expertise in house to “connect the dots” around data to form appropriate insights, and that projects lack business context around data.
Greta Roberts, CEO of Talent Analytics Corp. says that part of the reason there is such a skills shortage with data scientists is that the current job description, often the one floated by Thomas Davenport and D.J. Patil, doesn’t quite hit the mark.
“It’s over-specified,” said Roberts. “There is a null set of people that fit the entire description. They’re unicorns; you can’t find them. Or there are a very limited number of people that fit the criteria.
“When you review data scientist hiring criteria you’ll find mutually exclusive requirements,” Roberts continues. “They want charismatic communicators that are able to effectively present findings. At the same time, they want people to sit and work with data all day. These are two different types of people. Our data shows companies in fact split up these roles.”
In the October 2012 issue of Harvard Business Review, Davenport and Patil popularized the idea that data scientists have “The Sexiest Job of the 21st Century.” These folks, they suggest, can do it all: make discoveries, write code, understand their technical limitations while fashioning new tools, conduct academic-style research and communicate effectively.
Roberts isn’t so much criticizing the work done by Davenport and Patil — both are leading researchers in the area of data analytics — as she is expanding upon their definition of a successful data scientist. As a faculty member at the International Institute for Analytics where Davenport is a co-founder and research director, Roberts’s team conducted research to determine if there is a common “fingerprint” among all data scientists. They looked for characteristics that are different from skills, experience or education — traits that govern motivation, indicate creativity and drive success.
Roberts’ research showed that there is a clear, measurable fingerprint.