Osiseh Irudunoghena

So because you build dashboards, you think you’re a Data Analyst? Let’s talk. 🧵

The goal isn’t to use every tool you know. The goal is to know why you’re using the tool you chose. And honestly, I think that’s part of becoming a better analyst.

The tools are part of the story. But they shouldn’t be the whole story. And maybe that’s the real lesson I took from sitting with my project today.

I want someone looking at my work to understand: “She had a problem, investigated it, made decisions about how to approach it, found something meaningful, and communicated what it means.”

Because ultimately, I don’t want my portfolio to say: “Osiseh knows Excel, SQL, Python and Power BI.” That’s useful, but it’s not the whole story.

Not just: “How did you get this number?” But: “Why did you calculate this?” “Why did you investigate this?” “Why did you choose this tool?” “Why does this finding matter to the business?”

You have to develop that judgement by actually working through problems. That’s why I’m becoming more interested in the reasoning behind the analysis.

And I think this is where the difference between learning tools and learning analytics starts to become clearer. Because nobody can simply hand you a formula that says: “Use this tool whenever you see this type of problem.”

And before you know it, you’ve built a project that uses six tools… but you’re not even sure why you used half of them.

And this made me think about personal projects too. Because when you’re building a portfolio, there’s pressure to show everything. You want your project to have SQL. You want Python. You want Power BI. You want DAX. You want Excel. You want GitHub. You want everything.

You didn’t start by saying: “I need to use SQL + Python + Power BI.” You started with: “I need to understand what’s happening.” The tools came after the question.

So you could end up with: SQL for the data preparation and investigation. Python for deeper analysis. Power BI for communicating the findings. And suddenly using three tools isn’t excessive. It actually makes sense. But here’s the important part:

But now you have to explain it to someone who wasn’t sitting beside you while you did all that work. That’s where Power BI, Tableau, Excel or another reporting tool might come in. Because finding an insight and communicating an insight are two different things.

And then there’s another part people sometimes forget. You’ve done all the analysis. You’ve cleaned the data. You’ve written the queries. You’ve investigated the patterns. You’ve found something important.

Automate some of the work. Now Python might become useful. Again, not because: “Real analysts use Python.” But because Python is useful for that particular stage of the investigation.