Formal vs. Real Organizations

Organizational charts show us the formal structure of a company — who reports to whom, how departments are divided. However, the actual way an organization works is often very different from what the chart suggests.

Informal networks, cross-departmental relationships, and actual communication patterns shape day-to-day reality. Data gives us the tools to understand this real organizational landscape, beyond the formal structure.

This insight is the foundation of HR Analytics: by systematically analyzing people-related data, we can make better business decisions that reflect how the organization actually works — not just how it is supposed to work on paper.

Digital Disruption: Data as the New Asset

In the digital economy, data has become more valuable than physical assets. Consider the most influential companies of our time:

• Uber — largest taxi company, owns no taxis
• Airbnb — largest accommodation provider, owns no real estate
• Facebook — most popular media owner, creates no content
• Alibaba — most valuable retailer, holds no inventory
• Netflix — largest movie house, owns no cinemas

These companies succeed because they harness data about people's behavior to make smarter decisions.

The main aim of HR Analytics: Making better business decisions by systematically analyzing people (human capital), who are key drivers of business outcomes.

Source: “Strategic HR Analytics course”, Dr. Jan de Leede & Luuk Collou MSc. University of Twente (NL)

Querying vs. Data Mining

Querying means searching for answers to questions you already have. You define what you want to know and retrieve the answer. It is limited to your existing assumptions.

Data Mining is different — it discovers patterns and relationships in data that you did not know to look for. Instead of asking a specific question, you let the data surface unexpected insights.

This distinction is critical: querying confirms what you already suspect, while data mining can reveal entirely new knowledge. In HR Analytics, data mining allows organizations to discover hidden drivers of performance, turnover, and engagement.

Source: “Strategic HR Analytics course”, Dr. Jan de Leede & Luuk Collou MSc. University of Twente (NL)

The Beer and Diapers Story

A famous example from Walmart illustrates the power of data mining: analysts discovered that young American men buying diapers on Friday afternoons also frequently buy beer — a correlation that no one would have discovered through traditional queries.

The insight was not obvious. There was no prior hypothesis that linked beer and diapers. Only by mining large transaction datasets did this pattern emerge.

The business response: Walmart relocated beer near the diapers section, resulting in a measurable increase in sales for both products.

This story shows that the most valuable insights in data are often the ones you didn't know to ask about.

Source: “Strategic HR Analytics course”, Dr. Jan de Leede & Luuk Collou MSc. University of Twente (NL)

Big Data Requires Big Theories (Huselid, 2016)

More data does not automatically mean better decisions. According to Mark Huselid, three principles are essential:

• More data is not always better. Without a clear purpose, more data creates noise, not insight.
• Behavioral science theory matters. Moving from raw data to actionable knowledge requires understanding how and why people behave as they do.
• The biggest challenge is not data infrastructure. Knowing what to measure and how to measure it is harder — and more important — than building the data pipeline itself.

This means HR Analytics is not primarily a technology challenge. It is a thinking challenge: before collecting data, you must have a clear theory about what drives the outcomes you care about.

Source: “Strategic HR Analytics course”, Dr. Jan de Leede & Luuk Collou MSc. University of Twente (NL)

Don't Start with the Data

One of the most common mistakes in HR Analytics is beginning with whatever data is available and asking "What can we find?" This approach rarely produces actionable insights.

Mark Huselid's principle is clear: "Don't start with the data — start with the question!"

The right order is:

• Define the business question clearly.
• Identify what information would answer that question.
• Collect and analyze the relevant data.
• Develop cause-effect understanding before thinking about statistics.

Analytics does not always mean statistics. The key is conceptual clarity about what drives organizational outcomes — data and statistics are tools to test that understanding.

Source: “Strategic HR Analytics course”, Dr. Jan de Leede & Luuk Collou MSc. University of Twente (NL)

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The Goal Framework: Understand → Explain → Predict

HR Analytics is not just about measuring — it follows a three-step goal framework:

• Understand: What did actually happen? What is happening right now? (descriptive analytics)
• Explain: Why did something happen? What caused this outcome? (diagnostic analytics)
• Predict: What will happen in the future? How can we prepare? (predictive analytics)

Most HR organizations today operate primarily at the "Understand" level — they measure and report. The real strategic value comes when organizations can explain why things happen and predict what will happen, enabling proactive, tailored decisions.

Source: “Strategic HR Analytics course”, Dr. Jan de Leede & Luuk Collou MSc. University of Twente (NL)

Hitting the Wall in HR Measurement

Many organizations adopt People Analytics methodologies and develop sophisticated HR measurement systems — but then hit a wall. Despite having good data and analytical tools, their results rarely lead to strategic change.

Why does this happen? The problem is that HR metrics are often not clearly connected to organizational strategy. HR teams measure what is easy to measure — headcount, turnover, absenteeism — rather than what drives business outcomes.

The lesson: HR metrics must contribute directly to organizational strategy. Analytics that doesn't connect to strategic decisions remains a reporting exercise, not a decision science.

Source: “Investing in People: Financial impacts of Human Resources Initiatives”, W. Cascio and J. Boudreau.

Decision Frameworks: Learning from Finance and Marketing

Finance and marketing have long used decision frameworks — logical chains linking investments to strategic outcomes. HR should do the same.

• Finance: Equity → Asset Productivity → Margin → Profits
• Marketing: Investments → Mix (4 P's) → Customer Response → Customer Value → Lifetime Profits
• HRM: Investments → Programs & Practices → Effectiveness → Organization & Talent → Sustainable Strategic Success

These frameworks also define three metric levels:

• Efficiency metrics: Link spending to programs and practices (most common in HR today)
• Effectiveness metrics: Link programs to their impact on the target group
• Impact metrics: Connect effects to overall organizational success (currently underdeveloped in HR)

Source: “Investing in People: Financial impacts of Human Resources Initiatives”, W. Cascio and J. Boudreau.

The LAMP Framework (Cascio & Boudreau)

The LAMP framework provides a structured approach to HR Analytics. Each letter represents a key question:

• Logic (L): Can we articulate a clear connection between HR investments and organizational effectiveness?
• Analytics (A): Can we create a design and analysis that will answer the questions we want to pose?
• Measures (M): Do we have — or can we build — indicators for the key components of our logical analysis?
• Process (P): Will the approach be compatible with the organization's values, culture, and readiness to act?

The LAMP framework emphasizes that HR Analytics is not just a technical exercise — it must start with clear logic and end with a process that the organization can actually use. Crucially, it starts with questions (Logic), not data.

Source: “Investing in People: Financial impacts of Human Resources Initiatives”, W. Cascio and J. Boudreau.

The HR Analytics Value Chain (Kathryn Dekas)

The HR Analytics Value Chain describes the progression from intuition to organizational impact:

• 1. Opinion: Decisions based on intuition or "gut feeling" — no data involved
• 2. Data: Raw, unstructured data that HR departments already possess (e.g., HRIS records)
• 3. Metrics: Regular indicators or dashboards that track trends over time
• 4. Analytics: Identifying relationships, trends, and patterns in the data
• 5. Insights: Meaningful findings that explain what is happening and why
• 6. Action: Business decisions — policy changes, process improvements, new programs — that drive organizational change

Real value emerges in the Analytics phase, where relationships and patterns are identified. Most HR teams stop at Metrics — the real opportunity is to go further.

Source: Kathryn Dekas, "People Analytics: Using Data to Drive HR Strategy and Action"
https://www.youtube.com/watch?v=l6ISTjupi5g

Behavioral & Performance Analytics

Beyond traditional HR records, modern organizations have access to an entirely new class of data:

• Email traffic metadata (who communicates with whom, frequency, response times)
• Calendar data (meeting patterns, cross-functional collaboration)
• Records on corporate intranets and collaborative platforms
• Browsing patterns on company systems
• Instant messaging records

Behavioral and Performance Analytics uses these data sources to understand which behaviors are effective for achieving organizational goals.

Key advantages: behavioral data is available almost immediately (unlike traditional HR indicators which require time), and it reveals patterns that would never surface from surveys or performance ratings alone.

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