Back

 Industry News Details

 
Why do 87% of data science projects never make it into production? Posted on : Jul 19 - 2019

 “If your competitors are applying AI, and they’re finding insight that allow them to accelerate, they’re going to peel away really, really quickly,” says Deborah Leff, Global Leader and Industry CTO for Data Science and AI at IBM, on stage at Transform 2019.

On their panel, “What the heck does it even mean to ‘Do AI’? Leff and Chris Chapo, SVP of Data and Analytics at Gap, Inc., dug deep into the reason so many companies are still either kicking their heels or simply failing to get AI strategies off the ground, despite the fact that the inherent advantage large companies had over small companies is gone now, and the paradigm has changed completely. With AI, the fast companies are outperforming the slow companies, regardless of their size. And tiny, no-name companies are actually stealing market share from the giants.

But if this is a universal understanding, that AI empirically provides a competitive edge, why do only 13 percent of data science projects, or just one out of every ten, actually make it into production?

“One of the biggest [reasons] is sometimes people think, all I need to do is throw money at a problem or put a technology in, and success comes out the other end, and that just doesn’t happen,” Chapo says. “And we’re not doing it because we don’t have the right leadership support, to make sure we create the conditions for success.”

The other key player in the whodunit is data, Leff adds, which is a double edged sword — it’s what makes all of these analytics and capabilities possible, but most organizations are highly siloed, with owners who are simply not collaborating and leaders who are not facilitating communication.

“I’ve had data scientists look me in the face and say we could do that project, but we can’t get access to the data,” Leff says. “And I say, your management allows that to go on?” View More