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How does poor data quality affect forecasts and what should I fix first?
Poor data quality leads to wrong forecasts however good the model: duplicates inflate sales, missing days appear as zero demand, and inconsistent product codes split a history in two. Fix definitions, duplicates, gaps and product or customer identifiers first, since these cause the largest and most systematic errors.
A short checklist:
- Are dates and time zones consistent?
- Do product codes stay stable when items are renamed or replaced?
- Are returns, cancellations and discounts recorded?
- Are there gaps or duplicate records?
- Do totals match the finance system?
Reconciling with accounting totals is a powerful test. Also set up automated checks that run on every data refresh, so problems are found early, and assign an owner for each key data source.
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