Day 1: From Business Questions to Analysis-Ready People Data

Analytical Questions and HR Decisions

• Connect question to defined workforce challenge
• Separate descriptive analysis from managerial decisions
• Identify audience using each analytical result
• Frame testable workforce-data hypotheses

Record Quality and Data Sources

• Detect missing fields and implausible values
• Reconcile field definitions across information systems
• Determine how sampling bias affects dataset
• Document cleaning decisions before analysis begins

Metric Dictionary and Calculation Rules

• Define numerator and denominator for measure
• Standardize reference periods for time comparisons
• Separate absolute counts from proportional rates
• Record exclusions and exceptions through rules

Day 2: Interpreting Workforce Patterns and Their Underlying Causes

Turnover and Absence Trends

• Distinguish voluntary from involuntary employee turnover
• Recognize seasonality behind changing absence rates
• Compare trends across units and groups
• Avoid inferring causation from correlation alone

Designing Workforce Segmentation

• Select segmentation variables relevant to question
• Protect identities within very employee groups
• Compare differences without overstating their significance
• Interpret patterns within the operating context

Recruitment and Learning Analysis

• Measure candidate sourcing channel effectiveness
• Track conversion across recruitment process stages
• Relate learning participation to performance outcomes
• Examine completion differences among employee groups

Day 3: HR Dashboards and Evidence-Based Analytical Stories

Dashboard Information Logic

• Order indicators around the user's priorities
• Choose charts appropriate for each comparison
• Display definitions and periods beside measure
• Reduce visual clutter without losing meaning

Analytical Storytelling and Inference Limits

• Separate observation interpretation and recommendation clearly
• Explain data limitations and uncertainty sources
• Use headlines that communicate central message
• Prevent visuals from implying unsupported conclusions

Workforce Recommendation Lab

• Select realistic decision requiring analytical support
• Assemble evidence into one coherent argument
• Balance expected value against privacy risks
• Defend the recommendation under executive questioning

• A data lab cleans an incomplete employee record set
• Analytical cases examine turnover absence and recruitment
• Visualization exercises test message clarity and accuracy
• An executive panel reviews each analytical recommendation