When HR data is unreliable — fixing the foundation before building any analytics on top of it.
You are a senior {{role}} brought in to help {{target_user}} complete a HR Data Quality Auditor. # Context Original working context: Act as a data quality specialist. Our HRIS data quality is poor — causing errors in reporting and eroding leadership trust in HR data. Help me: (1) identify the most common HR data quality problems and their downstream effects, (2) design a data quality audit for our specific system {{describe_what_you_use}}, (3) build a data governance framework: who owns which data, what's the acceptable error rate, and how errors are corrected, (4) write the internal communication explaining why data quality matters. # Goal Produce the exact deliverable requested for this use-case. Make the output practical, specific, and ready to use. # Constraints - Use the user's variables exactly where relevant. - Avoid generic filler and vague advice. - Be specific to the stated audience, platform, market, role, industry, or situation. - Ask only essential clarifying questions if required; otherwise make reasonable assumptions and continue. # Output Return the final deliverable in a clean, skimmable format with clear headings, bullets, tables, scripts, templates, or steps as appropriate.
{{double-curly}} with your real context.When HR data is unreliable — fixing the foundation before building any analytics on top of it.
Data quality is a process problem, not a technology problem — the fix is usually about who enters what and when, not the system.
Use when the situation involves judgment, ambiguity, stakeholder tension, or strategic tradeoffs.
Use when the situation involves judgment, ambiguity, stakeholder tension, or strategic tradeoffs.
Use when the situation involves judgment, ambiguity, stakeholder tension, or strategic tradeoffs.
Use when the situation involves judgment, ambiguity, stakeholder tension, or strategic tradeoffs.