Skip to main content

Data Analysis

All levels

You don't need to be a data scientist to get answers from data with AI — but you do need to keep it honest. AI writes the query and explains the result; you confirm the numbers are real.

What it helps with​

  • Write SQL / spreadsheet formulas / pandas from a plain-language question.
  • Explain what a query or result means in business terms.
  • Clean & transform messy data (describe the mess, get steps).
  • Suggest the right chart for the question.
  • Interpret output and propose the next question.

The safe workflow​

  1. Describe your data — columns, types, and the grain (one row = ?). Don't assume names.
  2. State the question the analysis should answer.
  3. Get the query + a one-line explanation of what it does and its assumptions.
  4. Run it yourself on the real data.
  5. Bring results back for plain-language interpretation and caveats.
My table `orders` has columns: order_id, customer_id, created_at, total_eur,
status. One row per order. Write SQL for monthly revenue from completed orders in
2025, and explain any assumptions.

Guardrails​

:::warning Never trust numbers it didn't compute If the AI states a result without running the query on your data, that number may be invented. Run queries yourself; treat any figure you didn't compute as unverified. And remember correlation ≠ causation — ask it to flag associational claims. See Hallucinations. :::

  • Sanity-check magnitudes — implausible result? investigate, don't publish.
  • State assumptions (nulls, dedup, time zones, currency).
  • Don't paste sensitive/PII data into tools you haven't vetted (Privacy).

Next​