UPDATED 13:30 EDT / MAY 28 2020

BIG DATA

How AI enterprise, DataOps strategies help IBM prepare for crises like COVID-19

In the context of COVID-19, the world’s newly uncertain and volatile state increases the imperative to have a true strategy around data.

As the idea of a DataOps methodology has matured, a world where most enterprises are virtually working remote and managing worldwide demands in the midst of chaos shows just how crucial DataOps can be to survival in the midst of such an environment. That’s why IBM Corp. has embraced the idea.

“We’ve spent a lot of time while we were making IBM into an AI enterprise and infusing AI into our key business processes into essentially a DataOps pipeline that was very, very streamlined, which then allowed us to very quickly adapt to the COVID-19 situation,” said Inderpal Bhandari (pictured), global chief data officer of IBM.

Bhandari spoke with Dave Vellante, host of theCUBE, SiliconANGLE Media’s livestreaming studio, during the IBM DataOps in Action event. They discussed how IBM’s data strategy has transitioned into the cloud and AI, as well as how DataOps has helped with this strategy. (* Disclosure below.)

Becoming an AI enterprise through DataOps strategies

The journey of IBM’s data strategy has been centered around cloud data and AI, because IBM’s business strategy is also centered on those technologies, according to Bhandari. For a long while, people understood what AI meant for the consumer but not really for the enterprise — so IBM wanted its data strategy to be about becoming an AI enterprise so it could then showcase the possibilities to its clients. To do this, AI had to be infused into the workflow of the key business processes of IBM’s enterprise.

“If you think about that, workflow is very demanding,” he said. “You have to be able to deliver data and insights on time just when it’s needed. Otherwise, you can essentially slow down the whole workflow of a major process within an enterprise. But to be able to pull all that off, you need to have your own DataOps … very streamlined so that a lot of it is automated and you’re able to deliver those insights.”

As an example, one of the key business processes IBM tackled was its supply chain. As a global company, IBM’s supply chain is critical, and it has many suppliers all over the world. For each of these worldwide suppliers, there are many events or calamities that make it imperative to quickly understand the risk associated with any type of crisis with regard to IBM’s supply chain and make appropriate adjustments on the fly.

“That was one of the key applications that we built on our central data and AI plan,” Bhandari said. “And as part of our DataOps pipeline, that meant the ingestion of those several hundred sources of data had to be blazingly fast and also refreshed very, very quickly.”

IBM had to aggregate data from external sources and overlay that on top of its “map of interest” with regard to its supply chain sites and also where they were supposed to deliver. It also weaved in capabilities to track shipments so it knew exactly where things were in the chain. When COVID-19 emerged as a significant pandemic, IBM was able to quickly overlay the COVID-19 incidents on top of its sites of interest, as well as pick up what was being reported about those sites of interest, and maintain business continuity.

“It wouldn’t have been possible if you didn’t have the foundation of the DataOps pipeline, as well as that central data and AI platform in place to help you do that very, very quickly,” Bhandari said. “The overarching outcome metric that one focuses on is end-to-end cycle-time reduction. That had to do with the generation of metadata, which is data about data, and that’s usually a very time-consuming process. And we’ve reduced that by over 95% by using AI.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the IBM DataOps in Action event. (* Disclosure: TheCUBE is a paid media partner for the IBM DataOps in Action event. Neither IBM, the sponsor for theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

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