Are your AI projects stuck on your own data?
AI projects often stall because business data isn’t consistent or trustworthy. Here’s how to identify data gaps before they become an AI roadblock.
David Smith / Contributor
David Smith is the founder of Fractionalytics, a Miami practice that builds the data foundations underneath AI projects. He spent decades in data before that, most recently as Chief Data Officer at TheVentureCity, where he founded and led the data team. He now works with mid-market companies that have already started with AI and run into the wall underneath it: the pilot that works but can’t be connected to anything, the agent that averages a disagreement nobody knew was there, the number two teams report differently and both are right about.
His writing argues that most of this isn’t a technology problem. It ends in a decision about what a word means at that company, and nobody’s job description covers it.
He holds an MBA from Wharton and a degree in civil/environmental engineering, which turns out to be more useful than it sounds. You assess a site before you build on it, and you clean to the standard the intended use requires.
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