The big data tsunami bears exciting new profit potential; it also brings with it some daunting challenges, thanks to the General Data Protection Regulation’s strict privacy rules, set to be enforced next year. Machine learning is the best weapon businesses have to maximize the bounty of big data and ward off the threats, according to Murthy Mathiprakasam (pictured), director of product marketing at Informatica Corp.

“Machine learning up until now has been seen as a nice-to-have, and I think very quickly it’s going to become a must-have,” Mathiprakasam said.

He spoke with John Furrier (@furrier), co-host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the recent BigData NYC event in New York. (* Disclosure below.)

The influx of data from multiple sources is simply too heavy for non-automated methods to handle, according to Mathiprakasam. Unlike many tools on the market, machine learning is able to scale out and grow with data. “There’s no other way, mathematically speaking, when the data’s growing 40 percent a year. Just throwing a bunch of tools at it doesn’t work,” he said.

Division of data labor

The consequences of failing on the data offense are grim enough; disruption from more data-savvy competitors looms around every corner. Failing to match them could put a company out of business. Now GDPR means neglecting data is considered to be more than lazy on the part of the enterprise; it could be criminal.

Companies can’t afford to hire an army of data scientists to guard a gazillion data points around the clock. The only feasible solution is an automated machine learning platform smart enough to handle the task, Mathiprakasam explained. And it’s important to distinguish between a scalable platform and a bunch of single-purpose tools.

“In a way, you can think of data management as a way of sort of cleaning stuff up. There’s people who have brooms and mops and all these different tools; well, we’re bringing a Roomba to the market,” he said.

Informatica’s “Roomba” — its big data management platform —  does not simply transfer data labor around. “You want to actually get the labor out of the equation so that the people are focused on business context, business strategy, and the data management is sort of doing the work for you,” Mathiprakasam concluded.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of BigData NYC 2017. (* Disclosure: Informatica Corp. sponsored this segment of theCUBE. Neither Informatica nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE