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How big data and AI work together

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How is artificial intelligence – and its prominent discipline, machine learning – helping deliver better business insights from big data? Let’s examine some ways – and peek at what’s next for AI and big data analysis

Big-data-and-AI

Big data and AI

Big data and AI-Big data isn’t quite the term de rigueur that it was a few years ago, but that doesn’t mean it went anywhere. If anything, big data has just been getting bigger.

That once might have been considered a significant challenge. But now, it’s increasingly viewed as a desired state, specifically in organizations that are experimenting with and implementing machine learning and other AI disciplines.

“AI and ML are now giving us new opportunities to use the big data that we already had, as well as unleash a whole lot of new use cases with new data types,” says Glenn Gruber, senior digital strategist at Anexinet. “We now have much more usable data in the form of pictures, video, and voice [for example]. In the past, we may have tried to minimize the amount of this type of data that we captured because we couldn’t do quite so much with it, yet [it] would incur great costs to store it.”

How AI fits with big data

“The more data we put through the machine learning models, the better they get. It’s a virtuous cycle.”

There’s a reciprocal relationship between big data and AI: The latter depends heavily on the former for success, while also helping organizations unlock the potential in their data stores in ways that were previously cumbersome or impossible.

“Today, we want as much [data] as we can get – not only to drive better insight into business problems we’re trying to solve, but because the more data we put through the machine learning models, the better they get,” Gruber says. “It’s a virtuous cycle in that way.”

How AI uses big data

It’s not as if storage and other issues with big data and analytics have gone bye-bye. Gruber, for one, notes that the pairing of big data and AI creates new needs (or underscores existing ones) around infrastructure, data preparation, and governance, for example. But in some cases, AI and ML technologies might be a key part of how organizations address those operational complexities. (Again, there’s a cyclical relationship here.)

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Article Credit: The Enterprisers Project

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