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Does my business need a data scientist or just a data analyst?
Most small and mid-sized businesses need an analyst, and the engineering to feed them, long before a data scientist. A data scientist adds value when you have a specific predictive problem, enough clean historical data, and a way to act on the predictions. Without these, the role tends to be underused.
Ask whether your questions are about what happened and why, or about predicting the future at scale. The first is analyst territory.
A data scientist makes sense for problems such as demand forecasting across many products, churn prediction or pricing optimisation, provided that:
- The data is trustworthy and has enough history
- Someone owns acting on the output
- The benefit plausibly exceeds the cost
Many firms use an outside specialist for such projects rather than a permanent hire.
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