The HR Numbers Game_ Why Being 'Good with People' Isn’t Enough

There’s a long-standing myth in HR: 
“If you’re good with people, you’ll be great in HR.” 

But here’s the truth in today’s data-driven world: 
Being great with people isn’t enough anymore. 

If you want to earn influence, drive decisions, and help shape business strategy, you need more than intuition. You need numbers—and you need to understand what those numbers really mean. 

The Real Language of Business? Statistics. 

A Story of Data-Driven Transformation

The modern HR leader is no longer just a culture builder—they are evidence-based advisors. And the foundation of evidence-based HR? Statistics. 

Yet many HR professionals shy away from it. We think statistics is for analysts. We think it’s too technical, too time-consuming, or too disconnected from our “people” work. 

That thinking is what keeps HR out of the boardroom. 

Let me be clear: 
You don’t need a PhD in statistics. 
But you do need to understand the basics—both descriptive and inferential. 

Descriptive Statistics: Know What’s Happening 

Descriptive statistics help you summarize and interpret raw data. 

Examples: 

  • Mean (average) performance score or salary 
  • Median years of service 
  • Mode of exit reasons 
  • Standard Deviation of engagement scores 
  • Trend lines in absenteeism or attrition 

These numbers help you answer: What is happening in my workforce? 

And when paired with data visualization tools—like bar graphs, histograms, scatterplots, and dashboards—you make insights clear, impactful, and actionable. 

Inferential Statistics: Go Beyond Description 

Descriptive stats are a good start—but inferential stats let you test assumptions, predict outcomes, and reveal deeper truths. 

Examples: 

  • Correlation: Are engagement scores linked to productivity? 
  • Regression: Can we predict turnover based on performance, tenure, or pay? 
  • Significance Testing (t-tests, ANOVA): Are differences in team performance meaningful—or just random? 

These tools help you move from observation to explanation and prediction—the heart of HR strategy. 

A Story of Data-Driven Transformation 

Ready to Become Fluent in the Language of Data

Take Jane, an HR Head in a company with 1,200 employees. She was warm, respected, and deeply connected to her team. But when the CEO asked for data, she would default to vague responses: 

“We feel morale is better.” 
“Training seems effective.” 
“Turnover looks okay.” 

Eventually, the CEO pulled in someone from Finance to interpret workforce data. Jane felt sidelined. 

Instead of stepping back, she enrolled in a statistics refresher. She learned how to use Excel for descriptive analysis, explored inferential methods like correlation and regression, and visualized her reports using Power BI. 

Soon, she was presenting insightful dashboards and backing up HR strategies with data. Her influence—and her confidence—soared. Today, she’s a trusted voice in the executive team. 

What changed? 
She learned the language of leadership: numbers. 

The Good News? You Can Do It Too. 

The Good News_ You Can Do It Too

Statistics is not just for analysts. It’s a vital tool for every HR professional—especially those who want to lead. 

That’s why I created the Basic Statistics for HR Guide — a practical, no-fluff resource that: 

  • Defines 50 essential statistics concepts every HR leader must know 
  • Explains where each concept fits in the HR workflow 
  • Shows how to use them in real HR scenarios—from survey analysis to performance trends 
  • Provides tips on data visualization and storytelling to make your reports executive-ready 
  • Includes sample language for meetings with analysts, IT, and the C-suite 

This isn’t just a glossary. It’s a confidence builder. A credibility booster. A language guide for data-informed leadership. 

Ready to Become Fluent in the Language of Data? 

The Real Language of Business_ Statistics

HR is no longer just about harmony and hiring. It’s about leading with insight, influencing with clarity, and communicating with evidence. 

Start with the basics. 
Download the Basic Statistics for HR Guide now: https://mailchi.mp/aseametrics/ez9j4c7q37
Drop your email and we’ll send it straight to your inbox. 

Let’s lead with numbers—without losing the heart. 

Because the future belongs to HR leaders who blend compassion with computation and empathy with evidence. 

Are you ready to transform your people and organization?

ASEAMETRICS provides innovative HR tools and data-driven insights to help you hire smarter, develop talent, and drive performance. Discover how our solutions can empower your organization to thrive. Contact us today and take the first step toward transforming your talent management.

For inquiries, email us at info@aseametrics.com or call us at (02) 8652 1967.

Liza Manalo-Mapagu

About the author

Liza Manalo-Mapagu is the CEO of ASEAMETRICS, a leading HR technology firm driving digital transformation to help people and organizations thrive in the evolving workplace. As one of the pillars of the industry,  she specializes in individual and organizational capability building, HR technology solutions, talent analytics, and talent management. A recognized thought leader in HR innovations and advocate for ethical AI in HR, Liza empowers businesses and HR leaders through innovative strategies that align people, organizations, and technology. She also serves as the Program Director of the Psychology Program at Asia Pacific College, shaping the future of HR through consulting, education, and leadership.

References
-Bersin, J. (2022). People Analytics: Reimagining Talent Strategy. Josh Bersin Academy.
-Fitz-enz, J. (2010). The New HR Analytics: Predicting the Economic Value of Your Company’s Human Capital Investments. AMACOM.
-SHRM (2023). People Analytics: A Guide. Society for Human Resource Management.
-Bassi, L., & McMurrer, D. (2016). HR Analytics Handbook. McBassi & Company.
-Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on Talent Analytics. Harvard Business Review.
-Watson, H. J. (2018). Data Visualization: How to Tell a Story with Data. Georgia Tech

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