Audit the quality of our HR data and plan the cleanup
A field-by-field audit plan for HRIS data, the checks to run, an issue log, and a governance plan so the data stays accurate.
The prompt
Help me audit the quality of our HR data and plan the cleanup.
HR systems: [e.g. "Workday HRIS, separate payroll provider, ATS, spreadsheets for contractors"]
Employee population: [headcount, countries, employee types]
Why we're doing this: [e.g. "people analytics project", "payroll errors", "system migration", "compliance reporting"]
Known problems: [e.g. "managers missing", "job titles inconsistent", "terminated staff still active"]
Sample or field list: [PASTE a field list or an anonymized sample - remove names, IDs, salaries, and personal details]
Give me:
1. The critical fields to audit first and why: employee ID, status, hire and termination dates, job code and title, department, cost center, manager, location, FLSA or exempt status, pay grade, and any fields used in legal or payroll reporting.
2. Checks for each field: completeness, valid values, consistency between systems (HRIS vs. payroll vs. ATS), duplicates, impossible values (termination before hire, manager who has left), and stale records.
3. If I shared a sample: the issues you found, with counts and examples.
4. An issue log template: Issue | Field | Records affected | Business impact | Owner | Fix | Due date.
5. A cleanup plan in priority order, separating one-time fixes from root-cause fixes (form validation, required fields, approval workflows, integrations).
6. Ongoing governance: data owners, a monthly quality scorecard, and which audits can be automated with reports or scripts.
Treat employee data as confidential: use the minimum data needed, and check privacy and data protection rules for my locations before sharing records with any tool.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore HR & Hiring prompts
What to fill in
- headcount, countries, employee types
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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