What Is Workforce Analytics?
Workforce analytics, sometimes called people analytics or HR analytics, is the application of statistical analysis and data-driven methods to workforce data to improve HR decision-making and organizational performance. It goes beyond basic HR reporting (which tells you what happened) to provide insights about why things happened, what is likely to happen, and what actions should be taken. The SHRM Body of Applied Skills and Knowledge (BASK) addresses workforce analytics as part of several competencies, including Business Acumen, Analytical Aptitude, and Critical Evaluation.
Levels of Workforce Analytics
Workforce analytics operates at four progressive levels, each building on the capabilities of the previous one.
Level 1: Operational Reporting
This is the foundation, consisting of standard reports on headcount, turnover, time to fill, benefits enrollment, training completions, and other basic HR metrics. Most HR departments have achieved this level, and it provides essential information for managing day-to-day HR operations. Our guide on HR metrics and KPIs covers the key metrics you should know.
Level 2: Advanced Reporting
Advanced reporting goes beyond simple metrics to include trend analysis, segmentation, benchmarking, and drill-down capabilities. Instead of reporting a 15% turnover rate, advanced reporting might show that turnover is 8% in established departments but 28% among employees with less than one year of tenure in the sales division, particularly among those hired through a specific recruiting channel.
Level 3: Strategic Analytics
Strategic analytics uses statistical methods to answer complex workforce questions. This level includes correlation analysis, regression analysis, segmentation analysis, and scenario modeling. Strategic analytics provides the evidence base for major HR decisions and investments.
Level 4: Predictive Analytics
Predictive analytics uses machine learning and advanced statistical models to forecast future workforce outcomes. Flight risk models predict which employees are likely to leave. Workforce demand models forecast hiring needs based on business projections. Understanding the concepts and applications is important for SHRM exam preparation, particularly at the SCP level.
Building a Workforce Analytics Capability
Developing effective workforce analytics requires investment in three areas: data, technology, and people.
Data Foundation
Analytics are only as good as the underlying data. Building a strong data foundation requires clean, accurate, and complete employee records in the HRIS, consistent data definitions and coding practices, integration of data from multiple systems, historical data retention, and data governance policies that ensure data quality and protect employee privacy.
Technology Platform
The technology requirements depend on the level of analytics being pursued. Basic operational reporting can be accomplished with spreadsheets and standard HRIS reports. Advanced reporting requires business intelligence tools. Strategic and predictive analytics require specialized analytics platforms or statistical software.
Analytical Talent
Perhaps the most critical requirement is having people with the skills to conduct meaningful analysis. Workforce analytics requires a combination of HR domain knowledge, quantitative skills, technical skills, and communication skills (translating analytical findings into actionable recommendations for non-technical audiences).
Practical Applications of Workforce Analytics
Turnover analysis. Beyond calculating turnover rates, analytics can identify the root causes of turnover by analyzing correlations between turnover and factors like compensation, manager quality, tenure, performance ratings, engagement scores, and career progression.
Recruiting optimization. Analytics can identify which recruiting sources produce the highest-quality hires, which stages of the hiring process have the highest dropout rates, and what candidate attributes predict success in specific roles.
Compensation analysis. Workforce analytics supports pay equity analysis, market competitiveness assessment, and the evaluation of pay-for-performance effectiveness.
Workforce planning. Analytics enables more accurate workforce demand forecasting by modeling the relationship between business metrics and staffing needs.
Learning and development. Analytics can measure the impact of training on performance and productivity, identify which learning methods produce the best results, and pinpoint skills gaps.
Test your knowledge of workforce analytics concepts with our diagnostic assessment.
Ethical Considerations in Workforce Analytics
As workforce analytics becomes more sophisticated, ethical considerations become more important.
- Privacy. Employee data must be collected, stored, and used in compliance with applicable privacy laws and organizational policies.
- Transparency. Organizations should be transparent about their use of workforce analytics.
- Bias. Analytical models can perpetuate or amplify existing biases if the underlying data reflects historical discrimination.
- Consent. Best practices suggest obtaining employee consent for analytics that go beyond routine business operations.
- Proportionality. The depth and intrusiveness of analytics should be proportional to the business need.
Start Practicing Today
Workforce analytics is a rapidly growing area of HR practice and an increasingly important topic on the SHRM certification exams.
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