The Use of Data Analytics in Recruitment
March 11th, 2019
The EMC Data Science Survey involved responses from nearly 500 IT decision-makers from the U.S., Europe, India and China. Questions surrounded the definition of the evolving position of the data scientist as an IT specialist dealing with the exponential growth in data sources, but in different, deeper ways than other business intelligence professionals.
Eighty-three percent of respondents foresee an increase in the next five years in the number of data scientists needed because of the expansion of new technologies and expectations with unstructured data, according to the survey. In another question, 63 percent of respondents stated that the need for data scientists will somewhat or significantly outpace the current talent supply. There was a mixed response on where those new data scientists would come from, as 34 percent stated the data scientists would come from students in a computer science discipline, 27 percent stated data scientists would come from disciplines other than IT or computer science, while 12 percent believed they would consist of today’s BI professionals continuing their education.
Chuck Hollis, global marketing CTO at EMC, says organizations are in the midst of dealing with both the glut of data volumes and the uncertainty over the definition of the job title that would directly tackle that issue.
“Data scientists are kind of shaping up as the new rock stars for big data analytics. Organizations want to have a better understanding of who these people are, how they’re different, and how they get a bunch of them to work at their own organization,” says Hollis.
The role of data scientist touches on more elements of an organization’s data, and through a wider array of departments, than their existing BI counterparts, according to the survey. Data scientists focus more on data mining and advanced analytics tools than those in traditional BI, and are 11-percent more likely to make business decisions based on data and 19-percent more likely to parse data sets. Across an enterprise, the data scientist role brought data capabilities to a broader spread of departments, though BI professionals maintained a slight edge in connections with business management, marketing and sales.
Along with the growing shortage of data scientist workers, respondents pointed to obstacles in development and adoption of data science practices within their organizations. About one-third of organizations lack the skills and training for data science work, and another third lack the budget for that role in their departments. For the rest, respondents noted that organizations have the wrong organizational structure (14 percent), lack of tools or technology (10 percent), or insufficient executive support (9 percent).
Nearly one-half of respondents noted that skills and platform training would benefit departments seeking to increase their data science capabilities, and ongoing education at universities and coaching on specific unstructured data management issues also ranked high. Data scientists were 19 percent more likely than typical BI professionals to have a masters or professional degree, and another 9 percent of data scientists held a doctorate degree.
Hollis says it’s hard to predict who will take on those data scientist roles from current students because the needs of the position touch on numerous disciplines at universities with no set data science degrees.
“As a data scientist, you’re coming from somewhere else, you’re coming from computer science, from a hard science, or from psychology. And as we work with the universities, there is no data science curriculum to point to today,” Hollis says. “So it’s more of something you fall into as opposed to something you tell your mom you’re going to school for. The ones who are really good at it are the ones who are cutting across all those silos in academia.”
Article by Justin Kern for Information Management