Iro AIIro AI

AI for data analysts

Use AI to write SQL, clean data, and explain results.

A data analyst's day is SQL, spreadsheets, cleanup, and explaining the answer to people who don't speak SQL. AI can write the query, fix the formula, and translate a result into plain English — if you give it the schema and check its work. Iro teaches the prompts and the sanity checks so the numbers you ship are right.

SQLFormulasData cleaningPlain-English explanationsChoosing chartsSanity checks
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The short version

AI is a strong pair for data analysts: describe the result and give it your schema, and it will write the SQL or the formula, fix a broken query, or explain a result in plain English for a non-technical audience. The catch is verification — it invents column names, misreads a join, or writes a query that runs but answers the wrong question — so you give it the exact schema and a sample row, then sanity-check the output before anyone acts on it.

  • Give AI your schema and a sample row; let it write or fix the SQL.
  • It's great at explaining a result in plain English and picking a chart.
  • A query that runs isn't a query that's right — always sanity-check the output.

What you'll be able to do

  • Turn a plain-English question into working SQL against your schema
  • Debug a query that errors or quietly returns the wrong numbers
  • Write and fix spreadsheet formulas without trial and error
  • Explain a result in plain English for a non-technical stakeholder
  • Choose the right chart and sanity-check a number before you ship it

Inside the path

A focused set of five-minute lessons — each one ends with a hands-on exercise, not a quiz you can guess.

  1. Describe the query, give the schema 6 min

    The prompt pattern that turns a question into correct SQL — table names, types, and a sample row.

  2. Debug a broken query 5 min

    Paste the query, the error or the wrong result, and your schema; get a fix you understand.

  3. Formulas without the fight 5 min

    Get the exact spreadsheet formula for a real column layout, with the edge cases handled.

  4. Explain it in plain English 5 min

    Turn a result into a clear takeaway for a stakeholder who doesn't read SQL.

  5. Sanity-check before you ship 5 min

    The quick tests that catch a query that runs but silently answers the wrong question.

Try a sample exercise

This is the kind of card you'd practice inside Iro — you do the thinking, then get feedback.

◆ Sample exercise · Prompt practice

You need a query for monthly active users over the last six months, but you're in an unfamiliar database. "Write me a SQL query for monthly active users" comes back built on table and column names that don't exist in your schema.

Your task: Pick the prompt that gets you a query you can actually run and trust.

  • "Write a SQL query for monthly active users."
  • "Write a PostgreSQL query for monthly active users over the last 6 months. Schema: events(user_id BIGINT, event_name TEXT, created_at TIMESTAMP). A user is 'active' in a month if they have at least one event. Sample row: (1024, 'app_open', '2026-03-14 09:02:00'). Return year-month and distinct active users, ordered by month. Explain the query and note how it handles months with zero activity and users in different time zones."
  • "Give me the best MAU query."
  • "How many active users do we have?"
See why the second prompt wins

The winning prompt names the dialect (PostgreSQL), hands over the exact schema with column types so the model can't invent table names, states the metric definition ("active" = at least one event), and includes a sample row that anchors the data types and date format. It also asks for an explanation and edge-case handling (empty months, time zones) — the details that turn a query that merely runs into one that's actually correct. The vague options force the model to guess your data, which is exactly how you get a confident, wrong answer. In Iro you'd write your own and get feedback on schema, sample rows, and the edge cases you left out.

Why AI is a great pair for data work — and where it lies

Analytics is a sweet spot for AI: the question is easy to say in words, and the answer is a small, testable artifact — a query, a formula, a chart choice. Describe what you want and hand over your schema, and a model like ChatGPT or Claude will write the SQL, fix a #REF! or a broken join, or explain a result to a non-technical stakeholder in plain English.

Where it lies is the details. It invents a column that isn't in your table, gets a join direction wrong, or writes a query that runs cleanly and answers a subtly different question than you asked. None of that means avoid AI — it means give it the real schema and a sample row so it isn't guessing, and sanity-check the output before a dashboard or a decision depends on it.

The prompt that gets correct SQL

The difference between useless and reliable is context. Instead of "write a query for active users," tell the model the database (Postgres, BigQuery, MySQL), the exact tables and column types, how you define the metric, and give it one sample row. Then ask it to explain the query and name the edge cases.

  • Schema: table and column names with types, so it can't invent them.
  • A sample row: anchors the data types and formats.
  • The definition: what "active," "churned," or "revenue" actually means here.
  • Explain + edge cases: nulls, duplicates, empty periods, and time zones — so you can read the logic and catch the silent bugs.

Data analyst AI questions

Can AI write SQL queries?

Yes, and it's one of its most reliable uses — if you give it the schema. Provide the database type, exact table and column names, how you define the metric, and a sample row, and it will write the query and explain it. Then sanity-check the result, because a query can run and still answer the wrong question.

Why did the AI query return the wrong numbers?

Usually a wrong join, a bad assumption about your data, or a metric defined differently than you meant. That's why you give it the exact schema and a sample row up front and test the output against a number you already know. A query that runs isn't proof it's right.

Can AI explain my results to non-technical stakeholders?

Yes — this is a standout use. Paste the result and ask for a plain-English takeaway for a specific audience, and it will translate the numbers into the 'so what.' You check that the interpretation matches the data before you send it.

Can AI help me choose the right chart?

It can. Describe your data and the comparison you want to show — trend over time, parts of a whole, distribution — and it will recommend a chart type and warn against misleading ones. The judgment call on what story to tell stays yours.

Is it safe to paste company data into AI?

Be careful with sensitive or personal data — check your company's policy and the tool's data settings. You usually get the same help by pasting the schema and a small, anonymized sample instead of the full dataset. Iro covers this in its AI-at-work lessons.

Related paths

AI for Excel & SheetsAI for financePrompt engineeringAll paths

More reading: How to use AI at work · How to spot AI hallucinations · The 7 prompt patterns that work everywhere.

Practice AI for data analysis.

Iro turns SQL, formulas, and result-explaining into five-minute exercises with feedback — so the next messy query or stakeholder ask is a rep you've already done.

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