Big data and AI: 7 common misunderstandings | In the News

As organizations became engulfed in big data – high-volume, high-velocity, and/or high-variety information assets – the question quickly became how to effectively derive insight and business value from it.

The ‘garbage in, garbage out’ philosophy applies, as you need a sufficient amount of good data to drive meaningful value from your AI efforts,” Baritugo says. But how much data you need may vary. “Big data – where it means large data sets of both structured and unstructured data – feeds some applications of AI, [such as] when you need a lot of data to train AI, to analyze information to spot patterns and use probability to come up with answers to your questions,” explains Sarah Burnett, executive vice president and distinguished analyst at Everest Group. “Not all AI needs a lot of data.”

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