Anant Jhingran is no fan of the term 'data scientist.'
Currently the vice president of Products at enterprise API management specialist Apigee, Jhingran was formerly vice president and CTO of Information Management at IBM, focusing on the development of Watson. Watson, of course, is the cognitive computing system that in 2011 famously defeated former Jeopardy champions Brad Rutter and Ken Jennings, winning a US$1 million grand prize.
"For the last few years I've been practicing what it really means to enable a large class of developers to build better and smarter apps," Jhingran says. "I've realized even more so that the data scientist has to go from being the nerd solving really hard problems to being the enabler of developers building apps."
"I've seen the transformation in myself," Jhingran adds. "I've gone from being the data geek focused on solving hard problems to seeing that my success is based on making other people successful."
Don't Call me a 'Data Scientist'
Jhingran says his discomfort with the term data scientist, which he first expressed in a blog post in 2011, just after giving a keynote at Hadoop Summit and a few months after Watson's big win on Jeopardy, is the result of a feeling that it sets people that do data science apart.
"It creates this aura that they're unapproachable," he says. "It also, in my mind, gives an easy way out for developers to say that data is very fickle and working with it is hard -- 'Let's not bother with it. Let's not build apps that learn and understand and change.' To both sides the term is a disservice."
This feeling is part and parcel of a major shift that Jhingran says he believes is happening in the field of data science today as the capability to use big data becomes more mainstream in the enterprise and a key competitive advantage for those organizations able to make use of operational analytics and analytics for business intelligence. That shift is that data scientists are no longer magicians operating behind a curtain; they are beginning to work hand-in-hand with developers to deliver business value to end users.
"All the successful companies that leverage analytics see massive top line or bottom line improvement, but they see it because they've made these things mainstream," he says. "It's really got to be at that level of importance to make this thing succeed. Obviously technology is important and the data scientist has to evolve with it. If we agree with the fact that big data is going mainstream, in my mind there is one entity that sits between the work of the data scientist and the end user, and that is the developer."