There’s no question that machine learning and predictive analytics have entered the public consciousness. A few things made this possible. For starters, compute power has gotten much faster and more economical, and data transfer speed and storage costs improved dramatically, all of which have enabled artificial intelligence (AI) algorithms to scale up to the highly glorified big data. But, enterprises should be wary of buzzwords and the cure-all promises of artificial intelligence before they jump in. Although business leaders may have a good sense of the outcomes they desire, many lack knowledge of what it takes to get there, like differences in data sources and types, and the nuances of different types of machine-learning models.
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