Stop Saying “I’m Not a Numbers Person”, An HR Perspective

A personal story about identity, mindset, and what HR professionals owe to the profession, and to themselves.

I was a medical student. Dissections, diagrams, and Latin names for bones and muscles made up my world. Numbers belonged to others, like engineers and mathematicians. They seemed wired differently. From me, at least, that’s what I believed.

That belief followed me for years, quietly and persistently, like a shadow I learned to ignore. When I decided to pursue an MBA, I walked into a coaching class with that shadow still with me. Most of my classmates were engineers. Numbers came to them naturally and effortlessly. And there I was, the girl with a medical background, convinced I had drawn the short straw when it came to quantitative skills.

Then something changed. 

The coaching institute taught concepts in a new way. For the first time, I wasn’t just memorizing. I was learning to understand. What does this number mean? How do these numbers relate? Why does this formula work? Gradually, I began to see numbers differently, not as enemies but as a language I had never learned to speak properly.

I learned how to think about numbers.

The MBA experience deepened that understanding. My peers, patient, generous, genuinely kind people, sat with me and explained things. Matrices. Calculus. Concepts I had technically encountered in school but never truly grasped because nobody had shown me why they mattered. In real-world applications, they finally made sense. I am truly grateful to everyone who helped, who took the time to teach, and who never rolled their eyes.

That gratitude has stayed with me.

During that time, I learned something important that changed everything. In school, mathematics focuses on accuracy and speed. Getting the right answer fast is the goal. If you can’t do 254 plus 545 multiplied by 62 in your head, you feel lesser. But that perspective is wrong; it is fundamentally wrong.

Quantitative skills are not about mental math. They involve logic. The ability to understand how numbers interact, what they mean, and the insights they provide. That simple but radical shift in perspective felt like a turning point for me. 

I wasn’t bad with numbers; I had just measured myself with the wrong ruler. 

As my MBA progressed, something amusing happened. Classmates, many of whom were engineers, started coming to me for help with Excel formulas. I remember one afternoon clearly. I walked into a common area to meet a friend for a project session. She looked up, and said, “Oh, you’re here. We’ve been waiting for you. We were stuck and knew Divya would sort it out.”

Me, the girl from a medical background, who wasn’t supposed to be good with numbers. They were waiting for me.

Later, my first internship project immersed me more deeply in statistics. I had to evaluate the reliability and validity of training against actual work requirements. It was rigorous and required real statistical analysis, not just textbook exercises. I learned SPSS quickly, applied it meaningfully, and presented findings that stood up to scrutiny.

I did it. Then I did more. That project led to a Green Belt certification, not planned or because numbers were my strength. Necessity is a remarkable teacher, and I refused to use my background as an excuse.

In my years as an HR professional, working in various functions like compensation and benefits, and serving as a global head of HR and analytics, I held one belief firmly.

Data acumen is essential in HR. It always has been. 

For example, when discussing attrition, it’s not enough to say it has changed. Is that change significant? Statistically significant? A two percent change is not the same as a five percent change in every context. Simple tests, like proportion tests and pre-post analyses, can attach a p-value to that question and give an honest answer. In a preliminary attempt, I presented reports as an HR VP, using histograms to show spreads rather than just averages. I also used significance testing to add validity to discussions that previously relied on gut feelings. These analyses may not have been advanced, but their prior absence made them crucial.

I also witnessed the mistakes well-meaning HR professionals made when they didn’t think carefully about numbers. Once, a colleague calculated the proportion of high performers in the attrition pool and deduced that most leavers were average performers. That conclusion missed the point. You can’t look at a subset in isolation and draw meaningful conclusions. Context matters, and like-for-like comparisons matter. This isn’t advanced statistics; it is basic logic that every HR professional already possesses.

Was I a data scientist? No. Did I need to be? No, too.

What I needed, what any HR professional needs, is curiosity and the willingness to learn. Click the button you haven’t clicked before. Google the formula you don’t know. Try, fail, and try again. Google is a friend that never judges you for asking the same question twice.

When I reflect on my journey, I see layers of transformation. 

The first layer was survival, learning because a project required it. The second layer was reputation, being seen as capable by others, even before I recognized it in myself. The third, and perhaps most meaningful, was belonging. I felt welcomed into conversations I once thought were beyond me.

I recall discussing Markov analysis, a mathematical framework used to model workforce movements, with David Green, a respected voice in people analytics. It is not a casual topic and demands both clarity and numerical confidence. That conversation lasted thirty, perhaps forty minutes. We dove deep into the subject, and I held my own. Not because I had memorized formulas, but because I understood the logic, the application, and the why behind the method.

There was a time I wouldn’t have believed that was possible for me.

Around the same period, I was invited by a leading global Human Capital Management provider to consult on their analytics module. They wanted input on how HR practitioners actually think about data, the questions they ask, which tools genuinely serve them, and where gaps exist between what platforms offer and what the profession needs.

My inputs, in my view, were fairly basic, such as including medians as a metric for people data rather than relying solely on averages, and using log transformations to stabilise certain datasets. These were not advanced interventions, but they proved critical precisely because of their conspicuous absence.

I was in the room not as a student, not as someone who had scraped through statistics, but as a voice worth listening to

The girl from a medical background, who had carried a shadow about numbers for years. That shadow has faded.

I want to be honest: Even now, I don’t see myself as "good with numbers" in an absolute sense. I have brilliant friends and family. Good, bad, or average, these labels are all relative. But something in how I talk to myself has changed permanently. I know I will figure it out. I may not be the fastest or most fluent, but I will learn what needs learning and see it through.

Here is my message to every HR professional: Stop saying you are not good with numbers. 

That sentence is not humility; it is not honesty. It is a cage you have built around yourself and locked from the inside. Every time you say it, you make that cage smaller.

HR has an unfair reputation of being the function that doesn’t understand business - the people who deal with feelings, not facts. It struggles to justify its place at the table because it cannot speak the language of the boardroom.

We reinforce that reputation every time we shrug and say, “I’m just not a numbers person.”

You are logical and intelligent. You work with human behavior, the most complex factor in any organization. You can learn this. The tools are accessible, and the concepts, when taught well, are learnable. The only barrier between you and numerical fluency is the story you keep telling yourself. 

Change the story.