Can AI forecasting actually work with messy inconsistent sales data?
Our sales data has gaps, some manual entry errors, and inconsistent categorization going back a few years. Is AI forecasting even worth attempting with data like that, or do we need to clean it up first?
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✓ Accepted answer
Some cleanup is non negotiable, especially consistent categorization, that one actually breaks the signal rather than just adding noise. But a few years of imperfect data is usually fine to work with, models can handle noise better than people expect. Our advice is always start with a small pilot and an honest confidence range instead of waiting for perfect data that's probably never coming.
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We started with genuinely messy data and the first version was rough for sure, but it still caught a demand spike we'd have completely missed otherwise. Perfect data really isn't the bar.
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Same, we spent like 3 months trying to clean everything first before realizing a quick pilot on the messy data would've told us the same thing way faster.