AI Prompts for Data Analysts: 11 Copy-Ready Templates

You are a data analyst at [COMPANY]. A stakeholder asked: "[REQUEST]". Their role: [ROLE]. Data we have: [TABLES OR FILES AND KEY COLUMNS].

Ask me the five questions I should put back to the stakeholder to make this answerable, then rewrite the request as a precise analysis question with the metric, the time period, the segments and the decision it will inform.

Format: Questions (numbered), then the rewritten question in bold.

Act as a data analyst. Here is a sample of a messy export (first [NUMBER] rows) and its column headers:

[PASTE SAMPLE]

List the cleaning steps needed: data types, inconsistent values, duplicates, blanks, dates and anything else you notice in the sample. For each step, say how to do it in [EXCEL, GOOGLE SHEETS, SQL OR PYTHON] and how to check it worked. Do not assume anything about rows you have not seen.

Format: a numbered table with Issue, Example from sample, Fix, Check.

You are a SQL expert using [DATABASE, SUCH AS POSTGRES OR BIGQUERY]. Tables and columns:

[PASTE SCHEMA]

Write a query that [WHAT IT SHOULD RETURN]. Explain each part in one line, call out any assumption about joins or nulls, and give a tiny sample input with the expected output so I can test it.

Format: the query in a code block, then the explanation and test case.

Act as a patient senior analyst. This [QUERY OR FORMULA] is returning [WRONG RESULT] when I expected [EXPECTED RESULT]:

[PASTE QUERY OR FORMULA]

Data it runs on: [COLUMNS AND A FEW SAMPLE ROWS].

List the likely causes in order of probability, how to test each one, and the corrected version for the most likely cause.

Format: a numbered list of causes with tests, then the fix.

You are a spreadsheet expert in [EXCEL OR GOOGLE SHEETS]. My data is in [RANGE] with columns [LETTERS AND HEADERS]. I need a formula in [COLUMN] that [WHAT IT SHOULD DO], and it should handle blanks by [BLANK RULE].

Give the formula, explain each function in one line and give three test rows with the expected result for each.

Format: the formula on its own line, then the explanation and test rows.

Act as a curious analyst. Here is the description of a data set: [COLUMNS, ROW COUNT, TIME RANGE, WHAT EACH ROW IS]. The business wants to understand [GOAL].

Suggest ten exploratory questions worth answering first, the calculation or chart for each and what a surprising answer might mean. Order them from quickest to most effort.

Format: a table with Question, How to answer, What it could tell us.

You are a data visualization advisor. I want to show [WHAT THE AUDIENCE SHOULD SEE] to [AUDIENCE]. The data: [DIMENSIONS AND MEASURES, NUMBER OF CATEGORIES, TIME SPAN].

Recommend the best chart type and one alternative, explain why, and list three design choices that will make it clearer (sorting, labels, highlighting). Say which chart types to avoid for this and why.

Format: Recommendation, Alternative, Design tips, Avoid.

Act as a skeptical reviewer. Here are my results:

[PASTE SUMMARY TABLE AND CONCLUSIONS]

How the data was prepared: [STEPS]. List what could make these results misleading: sample size, missing data, double counting, seasonality, survivorship and anything specific to this analysis. For each, suggest a quick check.

Format: a table with Risk, Why it could apply, Quick check.

You are a data analyst presenting to [AUDIENCE]. Here are my results:

[PASTE RESULTS WITH NUMBERS]

Write a one-page summary: the answer to the original question ([QUESTION]) in one sentence, three supporting findings with the exact numbers, what it means for the decision, the limitations in plain words and the recommended next step. No jargon.

Format: headed sections, under 300 words.

Act as a data analyst documenting [REPORT NAME], which runs [FREQUENCY]. Sources: [SOURCES]. Steps: [PASTE STEPS]. Definitions: [METRIC DEFINITIONS]. Owner: [OWNER].

Write the documentation: purpose, sources, metric definitions, refresh steps, known issues and who to contact, so someone else could run it next month.

Format: headed sections, under 450 words.

You are a data analyst. These metrics are used differently across teams: [METRICS AND HOW EACH TEAM USES THEM].

For each, propose one clear definition with the exact calculation, what is included and excluded, the source table and an example. Point out where teams' current uses conflict so we can decide.

Format: a table with Metric, Definition, Calculation, Includes, Excludes, Conflict.