The Excel Lie: Data Analysis, Financial Modeling, and the Reality of Business

There’s a lie that’s been circulating for some time now—a lie about Excel’s relevance in the modern world of business and industry. Social media is largely responsible for this distortion. Scroll through Excel content online, and you’ll be told—directly or indirectly—that the primary use of Excel is for data analysis.

Now, it’s not entirely false. There is certainly value in taking a set of data and producing summaries, charts, slicers, filters, and other flashy visualizations. These are the “sexy” features that make for great social media engagement. But behind this superficial appeal lie some dangerous assumptions.

The Data Set Presumption

First, there’s the assumption that a structured data set already exists. Most of the tutorials and influencer content begins with a CSV or Excel file already neatly prepared. But who created that data set? Where did it come from? In real business, datasets don’t appear by magic. They are the output of a process, not the beginning of one.

Second, because of this assumption, we’re fed the impression that the natural next step is to analyze that data set—hence the tidal wave of content on “data analysis with Excel.” That gives the misleading impression that this is Excel’s central purpose.

But That’s Not What Excel Is Mostly Used For

I have news for you. That’s not how Excel is deployed in industry. Not at all.

In fact, there are far better tools than Excel for raw data analysis—tools that are more robust, more distributable, and better suited to collaborative environments. Yes, Excel can be used for powerful reporting—but that’s not the point. The way it’s currently used in most organizations—emailing spreadsheets or sharing via network drives—is clunky and outdated.

And most importantly: Excel’s true power lies elsewhere.

Excel’s Real Job: Business Process

Look around your office. How many people are doing actual data analysis? How many spreadsheets are simply turning data into reports? And how many are part of a larger process—where spreadsheets are passed around departments, triggering actions, approvals, and decisions?

This is business process, not data analysis.

We used to send paper spreadsheets via internal post. Now, we just email Excel files. Functionally, we’re still working the same way. And that’s tragic—because Excel has, for over 30 years, had the built-in capability to support collaborative workflows far more effectively than this.

But you wouldn’t know that from social media.

Why Social Media Loves Data Analysis

It’s simple. Data analysis is easy to demonstrate. It’s easy to teach. It fits into short-form content formats. It’s sexy, visually appealing, and relatively non-threatening to beginners.

But in actual business environments, data analysis is rare—and in many cases, badly done.

Real Data Analysis: A Case Study

Let me tell you a story from my own career. It was my last job in industry, as a financial controller. The business was a small publishing house in London, 18 months old and already failing. Funded by Lloyd’s of London, the company produced high-end coffee-table books and sold them to major U.S. publishers.

The business was built on a simple claim: “This company cannot make a loss.”

That was the business model. It seemed airtight. The company owned copyrights to 50 titles. The sales director’s job was to secure large-volume, high-margin orders from U.S. publishers like Bantam Doubleday or Random House, and the books were printed in Italy.

The logic: no book would go to print unless the sale was large enough to cover print costs and overheads. Therefore, no losses.

The Challenge: Analyze What Went Wrong

I arrived as financial controller, and my job was to find out: why is the business failing?

We had a clean general ledger—every transaction was recorded properly. That was our data set. When I gave this data to self-proclaimed “data analysts”—even trainers and course creators—they all came back with the same answer:

“Sales are too low, cost of sales too high, overheads too high—therefore, the business made a loss.”

Brilliant. But that’s arithmetic, not analysis.

When asked how to fix it, the answers were equally uninspiring:

“Increase sales, reduce cost of sales, cut overheads.”

Really? Tell that to the printers, the staff, the landlords. Tell that to reality.

What They Missed

Here’s what the analysts missed:
They didn’t ask the one question that mattered:

“How exactly was this business model supposed to ensure it could never make a loss?”

The answer, if they’d asked, would have led them to this insight: No book should go to print unless the sales order was large enough to ensure a positive gross margin. This wasn’t just good practice—it was supposed to be a contractual rule of the business.

So what went wrong?

When I analyzed each of the 50 titles, I found that not a single one should have gone to print. Not one order was large enough to justify the cost of production. The entire business had violated its own model. That’s why it was failing—not because of generic arithmetic, but because of business process failure.

The Next Challenge: Financial Modeling and Control

Having diagnosed the issue, I was then told that shutting down the business was not an option—too many reputations (and unpaid printers) were at stake. My job was to prevent further damage.

What we needed was a financial model that would:

• Show the next 6–12 months of required sales.
• Flag when contracted sales were not yet in place.
• Indicate when deadlines had been missed—months before the consequences would appear in financial statements.

This is real-world modeling—not the theoretical “three-statement model” taught in most courses. The model needed to:

• Map required future cash flows.
• Track lead times for printers.
• Incorporate actual risk from unmet milestones.
• Alert management now about events that would unfold months later.

And Excel was the perfect tool for it.

The Fallacy of Alternative Tools

Many will say:

“This is too advanced. This should be done by an ERP, or with Anaplan, or Workday, or Power BI.”

But here’s my response:

Those tools will struggle to implement this level of agility and nuance. They may offer dashboards and workflow automation, but they’re rigid, slow to deploy, and ill-suited to rapid adaptation.

Excel, in the hands of someone who understands real business process, outperforms them in this domain.

The Final Takeaway

I have shown:

1. That the kind of “data analysis” being taught on social media is superficial and often useless in real life.
2. That real data analysis requires business understanding—not just number crunching.
3. That financial modeling isn’t about tweaking assumptions—it’s about building systems that mirror real business logic and flag risks before they become disasters.
4. That Excel, when used properly, is not just relevant—it’s indispensable.

This is not about visualizations. It’s not about three-statement models. It’s not about turning datasets into dashboards. It’s about solving real problems.

And if your Excel education hasn’t prepared you for that—then it hasn’t prepared you at all.