As much as companies would like silver bullet analytics to manifest insight from data, the only way out of the data jungle is through it, according to Arik Pelkey, senior director of product marketing at Pentaho Corp.

“It’s about solving the data problem before you solve the analytics problem,” he said.

Pelkey spoke to John Furrier (@furrier) and George Gilbert (@ggilbert41), co-hosts of theCUBE, SiliconANGLE Media’s mobile live streaming studio, at BigData SV 2017 in San Jose, CA. They discussed cutting the time data scientists spend mucking around in data so they can move on to model tuning and adding value. (*Disclosure below.)

Research shows preparation like feature tuning consumes between 70 and 80 percent of data scientists’ time, Pelkey said. To get to insight faster, the only route is through the data — specifically shortening that route with integration and streamlining.

Pentaho just announced a set of machine learning orchestration capabilities with this aim. The company facilitates “everything from ingesting new data sources through data preparation, feature engineering, which is where a lot of data scientists spend their time, through tuning their models,” Pelkey explained.

Internet of Things and machine learning

Streamlining data with machine learning can transform businesses. Pelkey gave the example of IMS, a company Pentaho works with that provides data and analytics to insurance companies.

“They put sensors in a car and then, using your mobile phone, can then track your driving behavior,” he said. The company adjusts premiums based on data and retroactively look at accidents.

“This is completely upending the insurance industry, which has always had a very fixed approach to pricing risk,” he said.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of BigData SV 2017. (*Disclosure: Some segments on SiliconANGLE Media’s theCUBE are sponsored. Sponsors have no editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE