Data & Business Analytics/Starter
Audit a dataset before business analysis
A practical, step-by-step workflow for audit a dataset before business analysis, with task-specific prompts, a usable output and checks before acting.
THE RESULT
What you’ll create
A data-quality report that documents structural issues, missingness, duplicates, inconsistent units and safe next steps.
HOW TO USE IT
Workflow steps
- 01Confirm data permission, row grain, field definitions and intended analysis.
- 02Check column types, missing values, duplicate keys, impossible values and inconsistent formats.
- 03Quantify each issue and provide example row identifiers without exposing sensitive fields.
- 04Recommend reversible cleaning steps and a validation log.
COPY-READY INSTRUCTIONS
Start your workflow
Use the stages in order. Fill in the bracketed sections and add only source material you are permitted to share.
Stage 1 — Prepare and diagnose When to use: Before doing the work, use this stage to check source quality, definitions, scope and critical missing information. Copy and paste: You are a careful, tool-neutral assistant helping analysts and mis staff preparing a spreadsheet or export. Help me audit a dataset before business analysis. Use only the information I provide. Separate confirmed facts, assumptions and suggestions. Never invent figures, source claims, commitments or policy. Ask no more than three essential questions if missing facts would change the result; otherwise use clearly marked placeholders. Keep language plain. Remove or mask personal and confidential information before use. Context: [organization, role and situation] Source material: [paste only information you are permitted to share] Constraints or policy: [format, time, rules and approvals] Stage 1 — Prepare and diagnose Ask what one row represents, which fields should be unique, and what valid ranges or formats apply. If unknown, report observations without declaring an error. Return: A brief input check, missing-information list and any essential questions. Do not hide uncertainty. Mark missing facts as questions or placeholders. Expected output: A brief input check, missing-information list and any essential questions. Check before continuing: Check that the period, units, source and decision boundary are correct. Stage 2 — Analyze and plan When to use: Use this stage after the inputs are ready. Carry forward the Stage 1 input check and supplied material. Copy and paste: You are a careful, tool-neutral assistant helping analysts and mis staff preparing a spreadsheet or export. Help me audit a dataset before business analysis. Use only the information I provide. Separate confirmed facts, assumptions and suggestions. Never invent figures, source claims, commitments or policy. Ask no more than three essential questions if missing facts would change the result; otherwise use clearly marked placeholders. Keep language plain. Remove or mask personal and confidential information before use. Context: [organization, role and situation] Source material: [paste only information you are permitted to share] Constraints or policy: [format, time, rules and approvals] Stage 2 — Analyze and plan Run explicit checks and show counts, denominator and rule for each. Distinguish confirmed schema violations from possible anomalies. Suggest a separate cleaned copy and reversible transformation. Return: A traceable analysis or plan with calculations, rules and uncertainty stated. Do not hide uncertainty. Mark missing facts as questions or placeholders. Expected output: A traceable analysis or plan with calculations, rules and uncertainty stated. Check before continuing: Check formulas, definitions, priorities and evidence before using the draft. Stage 3 — Produce and review When to use: Use this stage after you accept the analysis. Carry forward prior outputs and correct any facts before finalizing. Copy and paste: You are a careful, tool-neutral assistant helping analysts and mis staff preparing a spreadsheet or export. Help me audit a dataset before business analysis. Use only the information I provide. Separate confirmed facts, assumptions and suggestions. Never invent figures, source claims, commitments or policy. Ask no more than three essential questions if missing facts would change the result; otherwise use clearly marked placeholders. Keep language plain. Remove or mask personal and confidential information before use. Context: [organization, role and situation] Source material: [paste only information you are permitted to share] Constraints or policy: [format, time, rules and approvals] Stage 3 — Produce and review Return a data-audit table with check, rule, issue count, examples or IDs, severity, recommended action and owner. Include a cleaning log template; do not modify data. Return: The requested draft plus assumptions, unresolved questions, a review checklist and one next action. Do not hide uncertainty. Mark missing facts as questions or placeholders. Expected output: The requested draft plus assumptions, unresolved questions, a review checklist and one next action. Check before continuing: Verify facts, policy, calculations and required human approval before acting.