Writing
Notes from inside the work.
50 pieces on what actually goes wrong in data, analytics and AI engagements, and what we do about it. No numbers we cannot stand behind, and no piece that could have been written without doing the work.
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Four pieces that keep getting sent to clients, beside the three newest below.
Analytics and BITwo dashboards, one metric, two different answersTwo teams, two revenue figures, one warehouse. The tool is almost never at fault, and fixing it is less a modelling problem than an argument nobody wants to have.AntviaHow to tell whether you need a lakehouse or just a bigger databaseMost teams reach for a lakehouse after a dashboard times out. Here is the written test to run first, and the four reasons that genuinely justify the move.Applications and automationMost of the work you want to automate does not need a modelA model earns its place on three conditions. A great deal of what gets scoped as an AI project fails all three, and a form, a rule and a lookup would do it better.AI evaluationAn LLM judge is a measuring instrument and it needs calibratingModel-graded evaluation scales the way human review never will, which is exactly why nobody checks whether the grader is reading true.
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50 pieces
