This project is based on a fully anonymized internal HRIS extract, adapted for portfolio use.
The dataset represents approximately 1,000 employee records across multiple departments, job families, locations, and employment statuses. It reflects the type of raw HRIS data commonly used by HR, Finance, and Total Rewards teams to support compensation analysis, workforce reporting, and compliance-driven decision-making.
At the time of analysis, the HRIS extract contained several structural and data integrity issues that introduced downstream risk, including:
- Duplicate employee records
- Inconsistent employment status and termination indicators
- Invalid or illogical date relationships (e.g., termination dates preceding hire dates)
- Inconsistent FTE classifications
- Fields required for workforce and compensation metrics that lacked standardization
These issues are common in operational HR environments where data accumulates across multiple processes, systems, and manual inputs over time.
Before the data could be safely used for compensation analytics, pay equity analysis, or workforce planning, it required systematic validation, standardization, and documentation to ensure accuracy, internal consistency, and audit readiness.
The objective of this project was to transform the raw HRIS extract into a trusted, business-ready dataset that leadership could rely on for compensation and workforce decisions without introducing analytical error or compliance exposure.
Established a validated, audit-ready employee master dataset
Standardized and reconciled core HRIS fields to create a single, trusted source for workforce and compensation analytics.
Identified and resolved structural data risks impacting pay analysis
Corrected duplicate records, invalid date logic, and inconsistent employment status indicators that would have distorted headcount, tenure, and turnover metrics.
Enabled defensible workforce KPIs for compensation and planning decisions
Delivered clean, reproducible metrics (headcount, FTE mix, average tenure, termination rate) suitable for leadership review and downstream pay analysis.
Reduced manual data preparation and reconciliation effort
Automated validation and transformation logic in Power Query materially reduced recurring reporting time and error risk.
Created a repeatable data quality framework for ongoing use
Documented validation rules and assumptions to support future compensation cycles, audits, and workforce analytics extensions.
Power Query workflow applying standardized joins and validation logic to produce a single, trusted employee master dataset used as the foundation for workforce and compensation analytics.
Power Query structure and applied validation steps documenting modular audit logic, including duplicate resolution, employment status normalization, and date consistency checks. This framework supports repeatable, auditable workforce and compensation analytics.