Daily sales reports may be convenient, but they can’t keep the pace with real-time data streaming between organizations, according to Saket Saurabh (pictured), co-founder and chief executive officer of Nexla Inc.

“You cannot really take a daily sales report and feed that into your algorithm,” he said during Data Platforms 2017 in Litchfield Park, Arizona. An algorithm is only as accurate as the data it ingests — precise analytics and insights require very fine grain data, Saurabh added.

At the granular level, diverse data streaming between organizations is liable to change in form, volume, frequency, etc., he told George Gilbert (@ggilbert41) and Jeff Frick (@JeffFrick), co-hosts of theCUBE, SiliconANGLE Media’s mobile live streaming studio. (* Disclosure below.) 

Readying such messy data for business use cases and applications requires always-on reconnaissance impossible for humans, he stated.

Machine learning and DataOps are the most viable routes to understanding and using data steams efficiently, Saurabh explained. “If you think of maybe machine learning or advanced analytics as the application, then data operations is sort of an underlying infrastructure for it,” he said.

DataOps isn’t infrastructure in the sense that hardware or storage is; it is the layer on top, a system transporting data to various places in optimal form, he added.

24-hour dinger

Via machine learning, Nexla constantly monitors changes to incoming data so that adjustments can be made downstream if needed, Saurabh said, adding that this unbroken chain benefits business sectors from healthcare to eCommerce.

Traditionally, disruptions in data’s regularity could throw a pre-defined system out of wack; it might take days to discover missing or altered data, he stated.

The method combining DataOps and machine learning greatly outpaces traditional sales data reporting systems and does away with rigid standards (although sales reports still have a place in business for more rounded, long-term analysis), he continued.

“A lot of innovation is going into doing this at scale without necessarily having to pre-define something in a tight box that cannot be changed, because it’s extremely hard to control,” Saurabh concluded.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s independent editorial coverage of Data Platforms 2017. (* Disclosure: TheCUBE is a paid media partner for Data Platforms 2017. Neither Qubole Inc. nor other sponsors have editorial influence on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE