A solid data strategy has become central to all functions of business, but making data quality a high priority comes with a set of challenges many companies are not equipped to handle on their own. Through his work assisting business in organizing and streamlining data processes, Daniel Hernandez (pictured), vice president of offering management at IBM Analytics, has found most issues lie in data integration.

“It’s often a people process problem, not necessarily a technology problem,” Hernandez said.

In his firsthand experience, internal knowledge disparities between data science and governance teams are often to blame for poorly functioning data processes. That’s something that tools can’t fix, so IBM first addresses the workplace process issues before offering tools as a solution.

“If a client is purporting a data science problem, and it turns out they have a data management issue around data discovery … one of the key things I’ll do is bring in our CEO and his delegates … to help institute a practice inside of their own organization,” Hernandez said.

Hernandez spoke with Dave Vellante (@dvellante) and John Walls (@JohnWalls21), co-hosts of theCUBE, SiliconANGLE’s mobile livestreaming studio, during the IBM Data Science for All event in NYC. They discussed the ways in which developments in data awareness and regulation are affecting businesses and what they can do to successfully utilize data for maximum return. (* Disclosure below.)

Untangling the data lifecycle

While the chief data officer role is helping to bridge cultural gaps at many companies, Hernandez and his team at IBM continue to work through data discovery problems with their clients that arise from internal communication issues.

“Most of the time they have the data; they just don’t know where it is,” he said.

Much of Hernandez’s work is focused around helping businesses categorize their data in such a way that anyone with permission can locate it independently when they need it. In fact, a lot of his time in research and development is trying to solve the “discovery problem.”

“How do I discover data using semantics that I have? … And once I find it, how am I assured that what I get is what I should get so that I’m not subject to compliance related issues?” Hernandez asked.

IBM’s partnership with Hortonworks Inc. has helped it further support clients in growing their data science capabilities. “I want to enable self-service analytics throughout my enterprise, I want self-service analytics tools for everyone that has access to it, I want to make data available to them, but I want that data to be governed so that they can discover what’s in the lake and whatever I give them is what they should have access to,” Hernandez said, paraphrasing the clients he serves with Hortonworks.

Looking forward, Hernandez hopes to help more businesses become comfortable with the cloud. When he considers the future, he believes the customer ultimately wants to run data and analytic workloads in the public cloud.

“What I tell clients is that if you’re ready for certain workloads … and the capability exists in a public cloud, you should go there now, because you’re going to want to go there eventually anyway,” he concluded.

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the IBM Data Science for All event. (* Disclosure: TheCUBE is a paid media partner for the IBM Data Science for All event. Neither IBM, the event sponsor, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE