HR Analytics for People Leaders

• Introduction
• Chapter 1 The Business Case for HR Analytics
• Chapter 2 HR Data Foundations: Sources, Quality, and Governance
• Chapter 3 Core HR Metrics: Headcount, Turnover, Time-to-Fill, Cost-per-Hire
• Chapter 4 Workforce Segmentation and Cohort Design
• Chapter 5 Data Cleaning and Preparation in Excel
• Chapter 6 Querying HRIS and ATS Data with SQL
• Chapter 7 Dashboard Design for People Leaders in Excel
• Chapter 8 Executive Metrics: Linking People Data to Business KPIs
• Chapter 9 Diagnostic Analytics: Exploring Patterns and Root Causes
• Chapter 10 From Descriptive to Predictive: An Analytics Ladder
• Chapter 11 Turnover Analysis: Drivers, Segments, and Risk Scoring
• Chapter 12 Predicting Attrition with Logistic Regression in Excel
• Chapter 13 Workforce Planning: Forecasting Headcount and Hiring Needs
• Chapter 14 Time Series Forecasting for Hiring Demand in Excel
• Chapter 15 Skills and Capability Mapping: Taxonomies and Gap Analysis
• Chapter 16 Survey Analytics: Engagement, eNPS, and Culture Signals
• Chapter 17 DEI Analytics: Representation, Flow, and Outcomes
• Chapter 18 Compensation and Pay Equity Analysis
• Chapter 19 Talent Acquisition Funnel Analytics and Optimization
• Chapter 20 Learning and Development: Measuring Outcomes and ROI
• Chapter 21 Experimentation in HR: Pilots, A/B Tests, and Causal Inference
• Chapter 22 Communicating Insights: Storytelling, Visualization, and Data Narratives
• Chapter 23 Operating Rhythms: Embedding Insights into Decisions
• Chapter 24 Building a People Analytics Function: Roles, Tools, and Roadmaps
• Chapter 25 Ethics, Privacy, and Responsible Use of AI

People data has never been more abundant—or more essential to business decision-making. Yet many leaders still struggle to turn scattered reports into clear answers that guide action. HR Analytics for People Leaders is a practical, no-nonsense guide to moving from basic metrics to predictive modeling so you can inform strategy, anticipate risks, and earn executive confidence. The goal is simple: help you use workforce data to make better decisions that improve performance, experience, and outcomes.

This book starts with the building blocks: trustworthy data, shared definitions, and a focused set of metrics that matter. You will learn how to establish a data foundation across HRIS, ATS, learning, and survey systems; how to clean, shape, and join datasets; and how to create dashboards that leaders actually use. Along the way, we translate statistical ideas into plain language and show how they solve everyday HR problems—like identifying which roles face elevated attrition risk, where skills gaps threaten delivery, or how many hires you will need to hit next quarter’s plan.

The approach is hands-on. Each analytic concept is paired with step-by-step Excel and SQL workflows you can adapt inside your current tools—no expensive platforms required. You will build diagnostic analyses that explain why outcomes vary across cohorts, then advance to predictive models that estimate the likelihood of turnover or forecast hiring demand. Time series methods help you anticipate seasonality and headcount needs; classification techniques help you target retention interventions where they will matter most.

Analytics only creates value when it changes decisions. That is why this book emphasizes communication and influence as much as computation. You will practice designing stakeholder-ready visuals, framing insights in the language of business KPIs, and running brief, decision-focused readouts that lead to action. We cover operating rhythms—quarterly talent reviews, monthly workforce planning, and weekly hiring standups—so your metrics and models become part of how the organization runs.

Because people data is sensitive, ethics and governance thread through every chapter. We outline practical safeguards: data minimization, access controls, de-identification, and tested approaches to fairness and bias mitigation. You will learn how to evaluate models for disparate impact, how to be transparent about methods and limitations, and how to involve employees and leaders in responsible use of analytics.

Who is this book for? HR business partners, people managers, and analytics practitioners who want tools they can apply immediately—whether you are building your first dashboard or maturing a people analytics function. If you can use pivot tables and write basic SQL, you can follow the examples. If you are new to analytics, the early chapters will get you comfortable fast; if you are experienced, the later chapters on forecasting, experimentation, and operating models will deepen your practice.

By the end, you will have a repeatable playbook: define the question, assemble and prepare the data, choose the right method, build the analysis, pressure-test the findings, and drive a decision. You will know how to forecast attrition, size hiring pipelines, map skills to strategy, and measure the impact of learning and DEI initiatives. Most important, you will be ready to use analytics not as a reporting function, but as a strategic capability that helps your organization make better choices—consistently, ethically, and at speed.

From basic metrics to predictive modeling: turning workforce data into business decisions

• Introduction
• Chapter 1 The Business Case for HR Analytics
• Chapter 2 HR Data Foundations: Sources, Quality, and Governance
• Chapter 3 Core HR Metrics: Headcount, Turnover, Time-to-Fill, Cost-per-Hire
• Chapter 4 Workforce Segmentation and Cohort Design
• Chapter 5 Data Cleaning and Preparation in Excel
• Chapter 6 Querying HRIS and ATS Data with SQL
• Chapter 7 Dashboard Design for People Leaders in Excel
• Chapter 8 Executive Metrics: Linking People Data to Business KPIs
• Chapter 9 Diagnostic Analytics: Exploring Patterns and Root Causes
• Chapter 10 From Descriptive to Predictive: An Analytics Ladder
• Chapter 11 Turnover Analysis: Drivers, Segments, and Risk Scoring
• Chapter 12 Predicting Attrition with Logistic Regression in Excel
• Chapter 13 Workforce Planning: Forecasting Headcount and Hiring Needs
• Chapter 14 Time Series Forecasting for Hiring Demand in Excel
• Chapter 15 Skills and Capability Mapping: Taxonomies and Gap Analysis
• Chapter 16 Survey Analytics: Engagement, eNPS, and Culture Signals
• Chapter 17 DEI Analytics: Representation, Flow, and Outcomes
• Chapter 18 Compensation and Pay Equity Analysis
• Chapter 19 Talent Acquisition Funnel Analytics and Optimization
• Chapter 20 Learning and Development: Measuring Outcomes and ROI
• Chapter 21 Experimentation in HR: Pilots, A/B Tests, and Causal Inference
• Chapter 22 Communicating Insights: Storytelling, Visualization, and Data Narratives
• Chapter 23 Operating Rhythms: Embedding Insights into Decisions
• Chapter 24 Building a People Analytics Function: Roles, Tools, and Roadmaps
• Chapter 25 Ethics, Privacy, and Responsible Use of AI