Journal
Data automation and forecasting
When a forecast is useful: after reporting is trusted, the history can support a horizon, and the error is compared with a baseline.
Data automation is the work of connecting sources, naming definitions, and delivering the same view without reassembling it by hand. That is the first delivery. A model is a later step.
Predictive analytics is useful when three things are true: the history covers the horizon the decision uses, a baseline method already exists, and a named person will look at the error after the fact. Otherwise the honest deliverable is the report.
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An inquiry is enough. We will reply by email and start from the baseline, not from a promised return.