So you still use Excel? Good. Here’s why it isn’t going anywhere.

A case for the most underrated tool in data work and where it genuinely falls short.

Photo by Rubaitul Azad on Unsplash

There’s a quiet time that many people who work with data can recognize: someone asks how you handled a dataset, and immediately you pause before admitting, “I used Excel.” As if to be a real analyst, you only have to use Python or an expensive-sounding cloud pipeline.

That pause is completely uncalled for. Excel isn’t a stepping stone that we are supposed to outgrow but rather a tool that has outlasted dozens of competitors built to replace it, and it still runs on more work computers than anything else in data.

Where Excel still wins

Let’s have a look at the areas where Excel has held its own over time:

1. Financial modelling. Wall Street runs on Excel. DCF models, P&L statements, and forecasts are built in Excel, not out of habit, but because tracing a formula through cells and watching a model update on one changed assumption is genuinely useful. And let's try explaining a sensitivity analysis to a room full of investors using a Jupyter notebook. It becomes clear that only Excel can pull off the task here.

2. Ad-hoc analysis. When an answer is needed in 30 minutes, no one is going to spin up a database. Excel will get from question to answer faster than the database will. On top of that, it requires no environment setup and no dependencies, just open and go.

3. Cross-team communication. A Python script isn’t something you can hand to an accountant even when the accountant is proficient in Python. An Excel file, on the other hand, is a common language of business. Every person, right from the intern to the CFO, can open it, navigate it, and understand what they are looking at.

4. Small-to-medium datasets. Under a few hundred thousand rows, setting up SQL or BI tools will most likely cost more time than the problem is actually worth. If you’re bringing together three vendor reports for a Monday standup, Excel wins on speed every time.

Where it struggles

Honesty builds credibility, so here’s the other side of Excel;

• Version control is a known pain. Many people who interact with Excel can recognize this: “Hello, please find attached final_report_v3_REAL_final.xlsx.”
• Reproducibility is limited because the logic in Excel is scattered across cells with no clear narrative of what happened to the data.
• Scale is a hard ceiling at just over a million rows. Besides this, formulas get fragile long before you hit that ceiling.

Now, these are not reasons for one to abandon Excel. These are reasons to know when to reach for something else.

So why hasn’t Excel become obsolete?

Powerful tools arrive every few years, such as Python, R, Tableau, Power BI, and others, but Excel does not phase out. Why? It never does because nothing else can match its combination of zero setup, zero required expertise, universal readability, and complexity that scales with the learner.

Many analysts writing Python and SQL today started in Excel. It was where they learnt what a pivot table is, what the VLOOKUP does, and most importantly, how to structure data cleanly. Python didn’t replace Excel. Instead, it inherited the users of Excel after a certain point.

Excel isn’t the most powerful data tool out there. But its relevance, usefulness and worth cannot be ignored because it speaks a language the entire world of business understands, and it’s still where many people find and start their way into data-related work.

The takeaway

The tool doesn’t determine your skill. What determines your skill is the ability to know when to use it. If Excel gets the job done faster and clearer for the audience you’re serving, that’s the right call, not a shortcut.

So next time someone asks what tool you used, say Excel without the pause.

I go deeper into practical spreadsheet and data tools over on the blog:

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Hope I have cured that pause.