Data Scientist In a Can?
Companies try to automate the data scientist function to deal with skills gap.
Competing With Data & Analytics
It’s gospel that companies everywhere want to hire data scientists, and can’t find them. In 2011, McKinsey projected a gap by 2018 of more than 140,000 unfilled big data jobs and 1.5 million related jobs in management and analysis. Not to be outdone, Gartner said that big data would create 1.9 million jobs in IT alone by 2015, of which two-thirds, or more than 1.2 million, would go unfilled.
At one time, there were expectations that a lack of telephone operators and chauffeurs would slow the spread of the telephone and the car. Instead, people learned to operate their own phones and cars, in part through development of easier-to-use technology. There are companies trying to do the same with analytics skills — to automate them so people and companies can “do analytics” without a PhD in a mathematically inclined field. Companies like Apigee and Nutonian are offering “data scientists in a can” — that is, analytics provided as a service, so companies can fill their need for data scientists without actually hiring some.
“For every organization, our pitch is, you don’t need them [data scientists]. We’re faster, more accurate, scale better — and we’re a lot cheaper,” says Scott Howser, senior vice president of products and marketing at Nutonian. Nutonian makes Eureqa, which it bills with the tagline “No PhD? No Problem.” Eureqa was initially developed when Michael Schmidt, Nutonian’s founder, was a graduate student at Cornell. His program then was called a “robotic scientist,” because it could derive scientific laws by analyzing experimental data. [The specifics are available on YouTube in an hour-long presentation by Cornell professor Hod Lipson, or see Michael Schmidt’s 15-minute TEDx talk on his “robotic scientist” for a shorter version.]
Many businesses and large organizations use Eureqa. A case study on its site about Kansas City Power & Light says the utility adopted Eureqa when its analysts needed to do heavy-duty modeling to predict energy demand during different kinds of weather.