HR Analytics

Concept, evolution, and scope of HR Analytics; People Analytics and Workforce Analytics; importance of data-driven HR management; HR analytics ecosystem; strategic role of HR analytics; HR data sources; workforce databases; HR information systems (HRIS); challenges in HR data collection; ethical and legal issues in HR analytics; data privacy and confidentiality.

Concept of HR metrics and KPIs; workforce demographics and diversity metrics; recruitment and selection metrics; employee turnover and retention measures; absenteeism and productivity metrics; training and development metrics; compensation and benefits analytics; employee engagement indicators; benchmarking HR performance; data quality and reliability.

Introduction to descriptive analytics; measures of central tendency and dispersion; frequency distributions; data visualisation techniques; charts, graphs, and dashboards; spreadsheet applications in HR analytics; correlation analysis; introduction to regression analysis; interpretation of analytical results for HR decision-making.

HR Analytics process and lifecycle; LAMP Framework (Logic, Analytics, Measures, Process); HCM:21 Framework; levels of HR analytics—descriptive, diagnostic, predictive, and prescriptive analytics; HR scorecard development; workforce planning analytics; performance analytics; employee engagement and retention analytics; analytics for talent management.

Predictive analytics in HR; employee attrition prediction; talent acquisition analytics; succession planning analytics; workforce productivity analysis; AI and machine learning applications in HR; HR dashboards and reporting systems; HR analytics for strategic decision-making; future trends in HR analytics; case studies on analytics-driven HR practices. Practical Exercises 1. Analyse employee demographic data and prepare an HR metrics report. 2. Calculate recruitment, turnover, absenteeism, and retention metrics using spreadsheet software. 3. Create HR dashboards using Excel or Google Sheets. 4. Perform basic correlation and regression analysis on employee performance data. 5. Develop an HR scorecard for a hypothetical organisation. 6. Design an employee attrition prediction model using sample datasets. 7. Prepare an HR analytics report and present workforce insights. 8. Analyse a real-world case on HR analytics implementation and strategic impact.