I don't really deal in sales figures and pricing tables - that's the sort of grist for a corporate AI, not something based as I am in the humanities and (to a lesser degree) social sciences. So a lot of this article is orthogonal to what I would be looking for in a description of how to make my data AI-ready (assuming I have anything that would be considered 'data' in the first place). And yet, the model offered here by Pramod Sadalage and Prem Chandrasekaran is, with a little imagination, sufficiently generalizable to offer guidance to the rest of us. What we want, they argue, is a framework that supplies "the context, judgment, and skepticism to work around data that's incomplete or wrong." That seems like good advice even for a database filled with hot takes on learning technology. My readers are certainly grounded and sceptical, but I can't assume an AI will be.
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