Data gravity: Centralizing data for generative AI and innovation – SiliconANGLE

With the exponential growth of data, the pace of innovation and evolving governance needs, the concept of data gravity becomes more pressing than ever.

Enterprises face the critical decision of whether to centralize their data or adopt an open format, particularly as generative artificial intelligence continues to advance. Choosing the right data gravity center is crucial in navigating this complex landscape, according to Tony Cyriac (pictured), managing director of data and AI – North America banking and capital markets at Accenture PLC.

“Everybody’s vying for what I call the center of gravity of data for the organization,” Cyriac said. “This is a landscape we’re working through. There’s a big question for organizations to decide and say, ‘What’s my center of gravity for data? Do I need one?’ Center of gravity doesn’t mean you put all the data there, but you at least need to know that there is a reasonable amount of context being held together across lines of businesses that you can give to each of these applications, and you can also get context back to add to this unified context.”

Cyriac spoke with theCUBE Research’s Dave Vellante and Sanjeev Mohan at the CDOIQ Symposium, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the importance of data gravity in this day and age. 

Through the data gravity lens

The data gravity ideology requires context to be connected as technologies continue improving and changing, as evidenced by gen AI. As a result, the center of gravity of data helps address the issue of whether information should be held in a single platform or open-table formats for enhanced productivity and efficiency, Cyriac pointed out. 

“Practically, I think you still have to load data together into a place for many of the use cases where latency and other things matter,” he noted. “I just come back to the simple concept that you got to pick your central gravity for data in some place. You got to have a way to have context that is connected and that is shareable and you can enrich it on a regular basis.  In terms of a cloud, you got to pick some primary cloud for your data. If you scatter it all over, it’s harder to stitch it together.”

Even though concepts keep on evolving, identifying the critical data is fundamental since it takes both structured and unstructured formats. This plays an instrumental role in opening the doors to gen AI use cases, according to Cyriac.

“What’s changed in the last year and a half is with this focus on unstructured data with many of the gen AI use cases demanding that now there’s a new light shown on that topic and there is untapped value there clearly,” he noted. “There’s a lot of focus into that and how do I integrate with the rest of the structured data work that has happened, and how do I get myself ready for more and more gen AI-powered use cases.”

Gen AI is revamping many sectors and financial services is not an exception since this cutting-edge technology triggers efficiency improvements and triggers digital avatar use cases. Nevertheless, the bottom line is making the data ready and accessible, Cyriac stated.

“Gen AI when applied the right way quickly gets you the answer … a complex question being answered in less than 30 seconds is possible and that’s really productive and valuable,” he said. “There are many such copilots if you can think about helping an RM, relationship manager, helping a wealth advisor or helping let’s say an AML investigator and so on and so forth, but it takes readiness of data as kind of the key to this and then obviously having the right expertise and the talent to do so.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of the CDOIQ Symposium: 

Photo: SiliconANGLE