AI implementation
Less than you think — quality matters more than sheer volume.
You don’t need “big data”. You need clean, consistent data.
In short
For a useful forecasting model you generally need at least 12-24 months of relevant history (sales, demand, traffic), but raw volume matters less than data consistency and quality. Even with partial data, we can start with a simple model and improve it as more history accumulates.
- Recommended minimum: 12-24 months of relevant history
- Consistency and quality matter more than volume
- You can start with partial data, a simple model at first
- The model improves as new data comes in
What "quality data" means here
Consistent data means the same categories and units over time, without large gaps, with relevant events flagged (promotions, seasonality, external events). Ten months of clean data is worth more than five years of inconsistent data.
What we do if history is limited
We start with a simple model based on general trends and industry seasonality, and calibrate it progressively as more real data accumulates from your business.
- Initial simple model, based on available data plus industry benchmarks
- Periodic recalibration as new data arrives
- Transparency about the margin of error at every stage
Which data sources matter most
Historical sales, orders/requests, web or in-store traffic, seasonal data, and any relevant external event (campaigns, price changes) — all of these significantly improve prediction accuracy.