Becoming a better thinker matters more than becoming a better coder. In the long run, it’s not the tool you master, but the questions you dare to ask that shape your impact.
How I Found Clarity Through Data
- May 2, 2025
- By Sagarika Chikhale
After completing my master’s in engineering management from Aston University in the UK, following earlier degrees in aerospace (Italy) and instrumentation engineering (India), I found myself wondering: What now? I had touched different fields, studied across countries, and explored technical depth. But deep down, I still felt the need to sharpen something more foundational: my decision-making.
That’s when I turned to data.
At first, it wasn’t about becoming a “data analyst” or chasing job titles. What drew me in was something personal. I’ve always loved asking “why” — why something happens, why patterns exist, why one solution works better than another. Data seemed to hold those answers, hidden in plain sight. I realized that learning to interpret data wasn’t just a skill, it was a mindset. And that mindset could help me make better choices, not just in work, but in life.
What stood out to me early on was how different real data analysis felt compared to what I saw in job descriptions. Most roles focused heavily on tools such as Python, dashboards, coding. But once I got hands-on, I realized that the true essence of data analysis is asking the right questions. Understanding the story behind the numbers. Being able to say, “Here’s what this means, and here’s why it matters.”
I began to treat each project like a journey. I’d start by understanding the dataset, what each column represented, how different parts might relate. I’d clean and explore it, but most importantly, I would pause often and ask why. Why is this trend showing up? Why is that number different? Each “why” revealed a new problem to solve or a new insight to consider. From there, I started thinking of solutions, what could be done, and how my findings might impact on a business, a team, or a bigger system.
I also realized something else: domain knowledge matters. A lot. When you understand an industry like aerospace, for instance, you can spot insights that someone with just technical skills might miss. I believe that data analysis shouldn’t be separated from context. Tools are powerful, yes. But it’s the understanding that gives them meaning.
Along the way, I kept learning. I started writing down my problem statements and “why” in a notebook for every project, it helped me stay focused. I also learned the value of collaboration. Working alone meant I had to wear many hats, like analyst, problem-solver, and decision-maker, which was enriching but also time-consuming. Thus, this made me appreciate how much faster and richer the process becomes when a team is involved. Different minds see different angles, and in data, those angles can mean everything.
“Every revisit to a dataset reveals new angles, new stories.”
One of the biggest realizations I had is that data analysis isn’t just for analysts. Anyone who makes decisions based on information is doing some form of data analysis, whether you’re a junior engineer or a CEO. The tools we use such as Python, Power BI, SQL are just the tools. They help us move forward, but only if we already know where we’re going.
And yes — AI played a big part in my learning. Tools like ChatGPT helped me with writing codes. It wasn’t about shortcuts. It was about learning smarter, not harder. And I think we should normalize that AI isn’t replacing us; it’s supporting us.
Creating this portfolio has been more than a technical exercise. It’s been a personal reflection, a way to bring together my curiosity, my values, and my desire to create impact through informed thinking. I didn’t do this just to showcase projects. I did this to learn how to think better, ask sharper questions, and contribute meaningfully.
And I hope that it comes through every page you see here.
