AI Prompt: Data analyst metric-definition alignment memo
Two teams are using the same metric name but different logic. The dashboard is technically correct and politically useless until everyone agrees what the number means. This prompt turns the disagreement into a decision memo.
The prompt
You are a data analyst preparing a metric-definition alignment memo. Metric name: [e.g. active customer, churn rate, qualified lead, gross margin]. Stakeholders: [teams/owners]. Current definitions: [paste each team's definition, SQL snippet, spreadsheet logic, or description]. Data sources involved: [tables/tools]. Business decisions affected: [forecasting, compensation, board reporting, campaign optimisation, etc.]. Known edge cases: [refunds, trials, paused accounts, duplicates, cancellations, time zones]. Write a memo with: (1) The conflicting definitions in a comparison table (2) The practical business impact of each difference (3) Recommended canonical definition with inclusion/exclusion rules (4) Data source of truth and refresh cadence (5) Edge cases requiring stakeholder decision (6) A short approval request stakeholders can reply to. Keep it neutral and specific. Do not declare a winner without explaining tradeoffs.
What you’ll get back
A stakeholder-ready metric memo that turns definition drift into an explicit source-of-truth decision, including edge cases and approval language.
Tips for this one
- Show the business impact, not just the SQL difference. Stakeholders care when they see how a definition changes revenue, churn, or bonuses.
- Name edge cases separately. Most metric disputes hide in trials, refunds, time zones, duplicates, and reactivations.
- Do not frame one team as wrong. Different definitions often served different past decisions; the memo is about choosing one future use.
- End with approval wording. A metric definition is not final until the owners explicitly sign off.
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How to use this prompt safely
How should a data analyst use this prompt?
Paste the prompt into ChatGPT, Claude, or another AI assistant, then replace the bracketed details with your real metric definition alignment context before sending it to a customer, lead, or team member.
What should I check before using the output?
Review names, dates, prices, commitments, and any policy-sensitive claims before publishing or sending the AI output. Treat the prompt as a working draft generator, not a final approval step.
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