Extension: People Analytics

“Without data, you’re just another person with an opinion.” - W. Edwards Deming

Why This Extension?

HR is becoming increasingly data-driven. People analytics transforms gut feelings into evidence-based decisions. Whether you’re in HR or managing people, understanding how to measure and improve human capital is essential.

Prerequisites: Phase 3B (People Operations)

Week 1: Foundations of People Analytics

Core Concepts

People Analytics: Using data to understand and improve human capital decisions. Also called HR analytics, workforce analytics, or talent analytics.

The Analytics Maturity Model: Most organizations are still at descriptive analytics (what happened). The goal is predictive (what will happen) and prescriptive (what should we do).

Data-Driven HR: Moving from “we think” to “we know” - using evidence rather than intuition for people decisions.

This Week’s Reading

📖 The Power of People by Guenole, Ferrar & Feinzig (Chapters 1-4)

• Why people analytics matters
• The seven pillars of people analytics
• Building a business case
• Common pitfalls to avoid

The Analytics Maturity Model

Key People Metrics

Reflection Questions

1. What people data does your organization currently track?
2. What decisions are made based on intuition that could be data-informed?
3. Where is your organization on the analytics maturity model?

Week 2: Measurement & Analysis

Core Concepts

Turnover Analysis: Not all turnover is equal. Regrettable vs. non-regrettable, voluntary vs. involuntary. Understanding patterns is key.

Engagement Analytics: Measuring engagement beyond the annual survey. Pulse surveys, sentiment analysis, behavioral indicators.

Performance Analytics: Understanding what drives high performance, not just measuring outcomes.

This Week’s Reading

📖 The Power of People by Guenole, Ferrar & Feinzig (Chapters 5-8)

• Measuring workforce performance
• Engagement and sentiment analysis
• Turnover and retention analytics
• Workforce planning analytics

Turnover Analysis Framework

Calculating Key Metrics

Application Exercise

Create a turnover analysis for your organization:

1. What’s your overall turnover rate?
2. Break it down by tenure, department, manager
3. What patterns emerge?
4. What hypotheses would you test?

Week 3: Predictive Analytics

Core Concepts

Flight Risk Modeling: Using patterns to predict which employees are likely to leave. Early warning enables intervention.

Talent Prediction: Predicting success in roles based on attributes. Moving beyond gut-feel hiring.

Statistical Thinking: Understanding correlation vs. causation, significance, and the limits of prediction.

This Week’s Reading

📖 Predictive HR Analytics by Edwards & Edwards (Selected chapters)

• Building predictive models
• Flight risk analysis
• Selection and prediction
• Interpreting results correctly

Flight Risk Indicators

The Predictive Process

1. Define the outcome to predict (turnover, performance)
2. Identify potential predictors (data available)
3. Build a model using historical data
4. Validate the model on new data
5. Deploy and monitor
6. Iterate and improve

Caution: Ethics & Privacy

Week 4: Implementation & Impact

Core Concepts

Storytelling with Data: Analytics is useless if it doesn’t drive action. Translation and communication are as important as analysis.

The Analytics Roadmap: Building capability over time. Start small, prove value, expand scope.

Change Management for Analytics: Getting buy-in from skeptical leaders and building trust in data.

This Week’s Reading

📖 The HR Scorecard by Becker, Huselid & Ulrich (Selected chapters)

• Measuring HR’s impact on business
• Building an HR scorecard
• Linking people metrics to business outcomes

Google re:Work Case Study

Google’s People Analytics has delivered breakthrough insights:

Building Your Analytics Practice

Capstone: People Analytics Project

Design a people analytics initiative:

1. Business Problem: What question needs answering?
2. Data Requirements: What data do you need?
3. Analysis Approach: How will you analyze it?
4. Communication Plan: How will you share insights?
5. Expected Impact: What decisions will improve?

Key Frameworks

Resources

Books

• ⭐⭐⭐ The Power of People (Essential - 8h)
• ⭐⭐ Predictive HR Analytics (Recommended - 7h)
• ⭐⭐ The HR Scorecard (Recommended - 9h)

Free Resources

• Google re:Work - rework.withgoogle.com
• LinkedIn Talent Blog - People analytics insights
• SHRM Analytics resources

Courses

• Coursera: People Analytics (Wharton)
• LinkedIn Learning: HR Analytics courses
• edX: Data Analytics for HR

AI Learning Integration

Analytics Problem Design Prompt

Help me design a people analytics project.

My organization has these challenges:
[describe 1-2 people challenges, e.g., high turnover, hiring quality, engagement issues]

Walk me through designing an analytics approach:
1. What specific question should we answer?
2. What data would we need?
3. What analysis method would work?
4. How would we measure success?
5. What are potential pitfalls?

Data Interpretation Prompt

Help me interpret some people analytics data.

Here's what we found:
- Turnover is 22% overall
- Turnover for employees with tenure < 1 year is 35%
- Turnover for employees whose manager has < 6 months experience is 40%
- Engagement scores dropped 8 points in department X
- High performers have 12% lower turnover than average

Ask me questions to help me:
1. Understand what's really happening
2. Identify root causes
3. Develop hypotheses to test
4. Propose interventions

Phase Assessment

Complete the following to demonstrate people analytics competency:

1. Quiz: People Analytics Concepts (30%)
2. Case Study: Analytics Challenge (70%)
   • Analyze workforce data
   • Identify insights and patterns
   • Recommend evidence-based interventions

I'm studying People Analytics (Phase EXT-PEOPLE-ANALYTICS of my MBA program).

Act as a Socratic tutor - don't give me direct answers. Instead, ask me questions to help me discover insights about these concepts: People Analytics, Data-Driven HR.

Start by asking what I already know about one of these topics, then guide me deeper with follow-up questions. Challenge my assumptions when appropriate.

After each of my responses, either:
1. Ask a deeper follow-up question
2. Point out a gap in my reasoning
3. Connect my answer to another concept

Let's begin.

Quiz me on People Analytics. Ask 10 questions covering: People Analytics, Data-Driven HR.

Rules:
- Mix question types (multiple choice, short answer, scenario-based)
- Start easier, get progressively harder
- After each answer, tell me if I'm right or wrong and explain why
- Keep a running score
- At the end, summarize what I know well vs. need to review

Ask the first question now.

Help me apply the main frameworks from this phase to a real situation in my life or work.

First, ask me to describe a recent challenge or decision I faced.

Then guide me through analyzing it using these frameworks:
- Which framework applies best?
- What would each framework reveal about the situation?
- What would I do differently knowing this?

Don't lecture - ask questions that help me discover the insights myself.

I want to practice case analysis for People Analytics.

Give me a short business scenario (2-3 paragraphs) involving People Analytics, Data-Driven HR.

Then ask me:
1. What's the core problem?
2. Which frameworks from People Analytics apply?
3. What biases might cloud judgment here?
4. What would you recommend?

After each answer, push back on my reasoning before moving to the next question.

I'm studying People Analytics and need to understand these concepts deeply: People Analytics, Data-Driven HR.

For each concept, ask me to explain it in simple terms (as if to a child).

If my explanation is unclear or wrong, don't correct me directly. Instead:
1. Ask clarifying questions
2. Give me a scenario that tests my understanding
3. Help me refine my explanation

The Feynman technique says if you can't explain it simply, you don't understand it well enough.