My Most Valuable Lesson as an Aspiring Data Analyst

learning data analysis, I was overly obsessed with the tools and the glamor that might come with the title of being a data analyst.

My internship started, and I had one goal in mind: to develop my technical skills. I mean, everyone wants to make their LinkedIn profile decorated with skills and certifications.

What I didn’t expect, though, was that my most valuable lesson wouldn’t come from a tool or tutorial. It came from something much more human: collaboration.

Initially, I attempted to tackle everything on my own, viewing each task as a personal challenge. Little did I know, my productivity was somewhat limited because I spent longer hours trying to figure out solutions to problems each time I found myself stuck.

It wasn’t until I started seeking feedback and involving experienced professionals that things began to fall into place.

That’s when I realized that in data analysis, working with others is not optional; it’s more of a necessity.

As we progress, I aim to share my experience with collaboration and how it shaped me as an aspiring data analyst. Plus, why I believe it’s one of the most important (and underrated) skills every data analyst should focus on.

The Early Days of My Internship

As a young lad stepping into the field, I honestly just wanted to get my hands on real data. Up until then, most of my practice had been with sample datasets.

Now, I have my internship. I had the opportunity to work with data that mattered to an organization.

I was given a project to build a basic report using data on operational activities. The data wasn’t too messy, but it wasn’t clean either. It contained some inconsistent values, duplicate rows, and quite a few missing entries.

I handled it using Excel and Power Query, then cleaned up what I could, and built a dashboard that I thought looked decent. Honestly, I was proud of it.

Fast forward, it’s presentation time.

Before I move forward, here’s something: no one told me about the presentation aspect of data analysis.

As funny as that might sound, it’s true. I previously thought that I would work with the data, make meaning from it, then pass it off to the guys in administration or something like that.

Snap back to reality, I presented the dashboard, and my supervisor didn’t seem impressed. Not because the visuals were bad, in fact, he said it looked good.

The issue was that the dashboard didn’t communicate what the team actually needed to see.

Truthfully, I hadn’t spoken to anyone about what insights were useful to them, or what information would aid proper effectiveness in decision-making.

These are the fundamentals that matter in data analysis, and I was lacking in that aspect. I built it based on what I thought was important, not what they needed.

I hadn’t asked questions like:

• “Who is going to use this dashboard?”
• “What decisions will this help them make?”
• “Why does this information matter to them?”

That’s the power of collaboration, asking questions before the start of a project and seeking feedback on completion.

What Collaboration Taught Me

Over time, I began to notice that, despite my visuals being clean and my numbers accurate, people sometimes didn’t understand my reports.

I’d spend hours solving a problem that could’ve been avoided with a two-minute conversation. Take it or leave it, I believe data needs to be examined together and communicated in a way that brings others along for the ride.

The more I worked with people, the more I realized how critical collaboration is to the entire data analysis process. Looking back, those moments of working with others were when I grew the most.

One of the first times I sat down with a non-technical staff member, I was surprised by how differently they viewed the data.

I had spent many hours creating a chart to show monthly activity trends, but when I explained it, they said:

“Okay… but how do I know if we are doing better or worse than last quarter?”

I had a shift in mindset.

Instead of just building charts that look good, I started thinking from the perspective of a non-technical staff member. It’s like having extra eyes on a problem; it could help you see things differently.

Feedback

Before my internship, I’d build something, give it a couple of checks, and then jump right into another without asking for fresh takes on my analysis.

On the other hand, in a team setting, feedback is often part of the workflow.

Sometimes that meant revising a chart because it wasn’t clear, or realizing a KPI I thought was useful was irrelevant to the person reading the report.

Each round of feedback helped me refine both the visuals and the story the data was telling. It taught me that even in data analysis, creativity and revision go hand in hand.

And here’s the thing, feedback isn’t always about fixing mistakes. Sometimes it’s about uncovering opportunities you didn’t see on your own.

For many, seeking feedback is uncomfortable and can be a drag. Dont worry, you’re not alone. Central to this argument is the notion of this study that explains the sudden spike in the heart rates of individuals while receiving feedback.

The key lesson from this study shows that feedback isn’t criticism, but rather it’s collaboration in disguise.

It’s other people lending you their perspectives so your work can shine brighter. And trust me, the faster you invite it, the faster your skills grow.

I learned to stop waiting until my work was “perfect” before sharing it. Instead, I’d present early drafts, gather input, and improve along the way.

Collaboration builds more than just skills – it builds your network

Personally, networking in the data industry is highly underrated and not talked about enough. If there was one thing I didn’t realize before my internship, it’s how much collaboration naturally builds relationships.

When you work closely with people, maybe through asking questions, talking technical solutions over lunch, or even fixing a project together, you’re not just completing tasks; you’re creating connections.

I started to see how valuable this was when a developer I had collaborated with on a data pipeline issue sent me a course recommendation that turned out to be a game-changer for my SQL skills. It’s on YouTube, and I advise you to check it out.

From a technical perspective, collaboration expands your “toolbox” in ways self-study won’t do due diligence. Every time I worked with someone, I picked up something new (no matter how basic).

Now here is the best part: these relationships don’t just end when the internship does. The same people you collaborate with today can become your mentors, your referees for future jobs, or even your teammates again in another organization.

Collaboration is the bridge between your current skill set and your future opportunities.

Conclusion and takeaways

Looking back, my internship didn’t just teach me data skills; it taught me how to work with people. I understood that my real value is multiplied when I work with others, not just alongside them.

The truth is, no matter how good you are with Python, Tableau, or SQL, you’ll always go further and at an impressive pace when you tap into the knowledge and perspectives of the people around you.

If you’re starting in data analysis, keep in mind that your tools will get outdated, your tech stack will evolve, but your ability to work well with people will never lose its value.