Using AI as Your Data Analyst: A Practical Guide

You have data but not a data scientist. AI promises to fill that gap, and in many cases it can—if you know how to work with it effectively.

Start with clear questions, not data dumps. "Analyze this data" yields generic observations. "What factors correlate with customer churn in this dataset?" yields actionable insights. The specificity of your question determines the usefulness of the answer.

Iterative exploration works better than single queries. Start broad, drill into interesting findings, validate surprising results. AI is good at pattern recognition but can find spurious correlations. You're the judgment layer deciding what matters.

Code generation democratizes complex analysis. Describe the analysis you want in plain language and let AI generate Python or SQL. You don't need to understand every line, but you should be able to verify results make sense.