The relationship between AI and big data is a two-way street, to be sure: Artificial intelligence success depends largely on high-quality data, and lots of it. Managing massive amounts of data and deriving value from it, meanwhile, increasingly depends upon technologies such as machine learning (ML) or natural language processing (NLP) to solve problems that would be too burdensome for humans to contend with on their own.
It’s a “virtuous cycle,” as Anexinet senior digital strategist Glenn Gruber told us recently. Whereas the “big” in big data once might have been seen more as a challenge than an opportunity, this is changing as organizations begin rolling out enterprise uses of machine learning and other AI disciplines. “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 explained.
Author: Kevin Casey
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