Forecasting with data-quality checks
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The situation this pattern fits
A team has forecasts that nobody quite trusts. The model may be fine, and the inputs may not be. Decisions get made on numbers that change when someone refreshes a spreadsheet.
What the work usually includes
- A documented pipeline from source tables to the forecast, with an owner for each input.
- Data-quality checks that fail the run when a series is late, duplicated, or out of range.
- A forecast the team can compare with what actually happened, on a schedule.
- A short note on what the model will not answer.
What a real write-up will add
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