Why Excel Is Still the Core of the Analytics World — And Will Be for Years to Come

Every year, someone declares that Excel is dying. Every year, Excel proves them wrong.

I've been training data analysts for years. I've seen freshers come in convinced that learning Python is the only thing that matters. I've had students tell me they skipped Excel because "everyone uses Power BI now." And almost every single one of them — without exception — walks into their first job and is immediately asked to open a spreadsheet.

So let me say this plainly: Excel is not going anywhere. And if you're serious about a career in data analytics, understanding why Excel remains central — not just that it does — will change how you approach your learning.

The Numbers Don't Lie

Let's start with some context before I give you my take.

Microsoft Excel has over 750 million users worldwide. That's not a legacy number from the 90s — that's active users today. According to Microsoft's own data, Excel is used in virtually every industry across every company size — from a 3-person startup tracking sales in a sheet to a Fortune 500 finance team running multi-billion rupee P&L models.

A 2024 survey by DataCamp found that Excel still ranks as one of the top three tools used by data professionals globally — sitting comfortably alongside Python and SQL. Not replacing them. Alongside them. That distinction matters.

And here's the thing that really makes my point: Power BI — the tool everyone says is "replacing Excel" — directly imports Excel files and uses Excel's Power Query engine under the hood. You can't even get the most out of Power BI without understanding how Excel works first.

Why Excel Refuses to Die

I've thought about this a lot. And I think it comes down to three things: ubiquity, flexibility, and the human factor.

1. It's Already Everywhere

Walk into any company in India right now — any company, any size, any sector — and I guarantee you'll find Excel files being used for something critical. Sales tracking. HR headcount sheets. Finance reports. Inventory management. Customer data. Budget models.

These files exist. They've been built over years. They hold institutional knowledge. And somebody needs to open them, update them, analyse them, and present them. That somebody is the data analyst.

No company is going to throw away 10 years of Excel-based processes because Python exists. The transition to more advanced tools happens slowly, partially, and always alongside Excel — never instead of it.

2. It's the Universal Language of Business

Here's something I tell every student I train: Python is the language of machines. Excel is the language of business.

When a sales manager wants to understand why revenue dipped last month, they're not going to open a Jupyter Notebook. They're going to open a spreadsheet. When a CFO reviews quarterly numbers, they want a formatted Excel model — not a Python script.

Excel is the one tool that the data analyst, the finance team, the operations manager, the HR head, and the CEO all understand. It's the bridge between technical analysis and business communication. And that bridge is incredibly valuable.

3. The Skill Floor Is Low, the Ceiling Is Extremely High

This is something people underestimate about Excel. They think of it as a beginner tool — something you use before you "graduate" to real tools. That's completely wrong.

A truly skilled Excel analyst — one who knows dynamic arrays, Power Query, advanced DAX-like formulas, complex Pivot Table models, and data validation — is doing work that most Python beginners can't replicate with the same speed and communication-friendliness.

I've seen Excel models at large Indian corporates that would genuinely impress a senior data engineer. The tool has depth. Most people never get past the surface.

Excel in 2025–26 Is Not Your Father's Spreadsheet

Here's where it gets really interesting — and why anyone who dismissed Excel as "old technology" needs to look again.

Microsoft has invested heavily in making Excel a serious analytics platform. In the last two years alone:

  • Microsoft Copilot in Excel now lets you analyse data using plain English. You type "Show me which product category had the highest growth in Q3" and Copilot generates the formula, the Pivot Table, and the chart. Automatically.
  • Python in Excel — yes, you read that right — Microsoft has integrated Python directly into Excel cells. You can now run Pandas and Matplotlib code without ever leaving your spreadsheet. The two worlds are literally merging.
  • XLOOKUP, dynamic arrays, FILTER, SORT, UNIQUE — these relatively recent additions have fundamentally changed what's possible in a spreadsheet, removing limitations that used to push analysts toward Python for simple tasks.
  • Power Query inside Excel is a full ETL (Extract, Transform, Load) tool. You can connect to databases, APIs, websites, and cloud sources — all from within Excel — and transform messy data with a visual interface.

Excel isn't standing still while the world moves forward. It's running alongside everything else — and in many cases, pulling ahead in accessibility.

The Practical Reality of Indian Job Market

Let me bring this closer to home, because this blog is for people building careers in India.

Look at any data analyst job description on Naukri or LinkedIn today. Whether it's a startup in Bengaluru, an NBFC in Mumbai, a manufacturing company in Pune, or an IT services firm in Hyderabad — Advanced Excel is listed as a required skill in the vast majority of postings.

Not preferred. Required.

I've personally reviewed hundreds of job descriptions while building our courses at WeLevelUp. The pattern is consistent: Excel is expected at the base, Power BI or Tableau is a strong plus, and Python or SQL is a bonus or role-dependent. Start without Excel, and you're starting without the foundation.

The companies that are fully Python-first and Excel-free? They exist — mostly in mature tech product companies. And they'll still ask you to pull a quick number in a spreadsheet for a business review meeting.

So Where Does Excel Fit Alongside Python and Power BI?

I'm not saying Excel replaces Python or Power BI. I'm saying they serve different purposes — and a smart analyst knows when to use which.