Data Scientist Shortage? Try Team Building
A good data scientist is hard to find. So hard that enterprises should focus instead on creating small analytics teams that can glean insights from Big Data.
Data scientists are as sought after as a real Rolex at a thrift store – and just about as hard to find. In fact, enterprises may have to give up the search, said members of the International Institute for Analytics (IIA) faculty on a call discussing their annual predictions for analytics.
One of their eight predictions for 2013 was for a "continued shortage of data scientists," those elusive folks who can help enterprises turn Big Data into competitive advantage. Instead of focusing on finding the perfect scientist, enterprises will need to create small analytics teams, the experts advised.
"It's too hard to find all the requisite skills that comprise a data scientist in one person," said Tom Davenport, the IIA's research director and a visiting professor at the Harvard Business School. Analytics teams ideally would include skill sets in analytics, traditional data management and interpersonal communications, with healthy doses of creativity and a willingness to experiment, he said.
Because it's nearly impossible to find all of these qualities in a single person, companies are increasingly thinking about analytics teams, Davenport added. "I think it's really the only way to go to solve the availability problem."
While universities are beginning to respond to the demand with new degree programs oriented to Big Data and data science, not all of the skills sets are being addressed. IIA faculty member Ravi Kalakota, a partner at technology consultancy LiquidHub, mentioned a gap in management skills suited to data science projects.
"Classic project managers may not be able to understand how to make an agile data project work," Kalakota said. "I don't know where these skills will emerge, but they will be critical to make these projects work."
Definition of Data Scientist Still Evolving
A related IIA prediction is that while the mystique of the data scientist will persist in 2013, the lines between data scientists and other analytics professionals will blur. IIA faculty member and management consultant Robert Morison said he thinks professionals from both highly technical and less technical areas of the analytics profession will increasingly assume scientist-like roles.
Similarly, George Mathew, president and CEO of Alteryx, recently predicted the rise of "a new set of specialists who can bridge the gap between IT and business by evolving their core thinking and approach to analytics decision-making." Mathew told Enterprise Apps Today, “Alteryx calls them ‘data artisans’ and predicts an emerging generation of analysts that can provide answers to complex business questions in a short amount of time, using whatever data is required.”
Some experts believe folks well outside the traditional data management realm may become more involved in analytics-driven decision making as data analysis tools become easier to use. “Big Data is going to fuse more and more with traditional BI, providing insights to business people who can couple information from unstructured sources with structured ones. You won’t have to have a Ph.D., allowing the business analyst to start taking advantage of everything Big Data has to offer,” said Brad Peters, CEO and co-founder of Birst.
Yet data science will likely continue to involve a strong focus on statistics, said IIA faculty member Anne Milley, the senior director of Analytic Strategy, Product Marketing for SAS. "At the end of the day, if you look at the kinds of problems people are being asked to solve, it falls under statistics and other quantitative methods."
Despite all of the hype, Big Data analysis is still very much an emerging practice. "2012 was a breakout year with a tremendous number of pilots and lots of hype," Kalakota said. "But companies are having a hard time migrating pilots to production deployments."
"A Big Data shake-up or shake-out" is likely this year, Kalakota added in discussing another of the IIA's predictions, that 2013 will see a spate of mergers or acquisitions in the Big Data space and that many startups will fail.
Kalakota said two characteristics will put Big Data startups on acquisition wish lists: patents and customers. Companies that can simplify Big Data infrastructure also will be attractive acquisition targets, he said. "When you're dealing with terabytes and petabytes of data, it's a whole different game for IT."
Ann All has been writing about technology and business for 15 years. She is the editor of Enterprise Apps Today.