Why “Why?” is the most important question

When people hear the word why, they often brace themselves.

It can feel combative.
Invasive.
Judgmental.

As if the person asking is about to clutch their pearls and say, “I’m sorry… you want to do what with the data?”

But in data, analytics, and AI—especially when designing solutions in Microsoft Fabric and Power BI—“why” is not an attack. It’s the most important tool we have.

The Real Job of a Data Platform

There’s a common misconception that the purpose of a reporting platform is to answer questions like:

• What happened?
• How many?
• What’s the total?

Those answers are necessary—but they’re not the point.

The real purpose of your data and reporting platform is to help people understand, decide, and act. To support decisions you know you need to make today—and the ones you don’t know you’ll need tomorrow.

You can’t design for that without asking why.

“Can I Export This to Excel?”

Let’s talk about the most famous Power BI question of all time:

“Can I export this to Excel?”

Of course you can.
Power BI is a Microsoft tool—Excel is practically a sibling.

But here’s the thing: when I ask why, I’m not offended. I’m not disappointed. I’m not silently judging your life choices.

I’m curious.

Because there is always a reason.

The Many Reasons Behind “Excel”

When someone wants to export to Excel, it could be because:

• They’re simply more comfortable there
• Their team is resistant to change
• They need to merge this data with additional data
• They want to create a visualization that doesn’t exist in the report
• They need to print it
• They’ve “always done it that way”
• They’re asking for… a friend

Each of these reasons points to a different solution.

If I don’t ask why, I’m forced to design for the perceived requirement, not the actual need.

And that’s how we end up with technically correct solutions that still fail to deliver value.

Designing for the Questions You Don’t Know Yet

One of the most powerful outcomes of asking “why” is that it pushes us beyond today’s questions.

Not just:

• The questions you know you need to answer
• The questions you think you might need

But the questions you don’t even know you’ll need to ask yet.

This is where thoughtful data modeling and platform design quietly do the heavy lifting.

A Brief (non-why ask why) Side Quest: Star Schema and Intentional Design

The need/ask of tomorrow is one of the many reasons I’m such a strong advocate for dimensional modeling and star schema.

Not because they’re old.
Not because they’re academic.

But because they force intentionality.

When you take the time to define:

• Facts (what happened, at what grain, and why it matters)
• Dimensions (how the business actually wants to slice, filter, and reason about those facts)

You create a structure that is:

• Understandable to humans
• Predictable for analytics
• Scalable over time

In Fabric terms, this often shows up as well‑intentioned Gold views—not just “cleaned up tables,” but views that are purpose-built to serve a semantic model designed around a star schema.

Those Gold views aren’t for everyone to query directly.
They exist to serve the model, and the model exists to serve the user.

That chain of intent—from Bronze to Silver to Gold to semantic model—is much harder to achieve if you never stop to ask why the data is being used in the first place.

And when you do ask why, you give yourself the freedom to add new facts, new dimensions, and new business questions later—without rebuilding everything from scratch.

Why Asking “Why” Reduces Waste

Let’s be honest: there’s a lot of waste in analytics.

• Models that never quite get used
• Reports that look great but don’t drive action
• Dashboards that answer questions no one is asking
• People using a visualization or BI tool only to export the data and do their “real” analysis somewhere else

Asking why early—and often—helps surface these issues before they’re locked into the solution.

Why build a model and report that still doesn’t provide what is actually needed?

Why optimize visuals when the real problem is confidence, trust, or usability?

Why This Matters Even More in the Age of AI

As we move further into AI-enabled analytics, semantic models, and natural language querying, the importance of why increases—not decreases.

AI is very good at:

• Aggregating data
• Finding patterns
• Answering well-formed questions against a well-defined semantic model

What it cannot do on its own is:

• Understand organizational context
• Infer business intent
• Distinguish between an interesting question and a meaningful one
• Know which metrics actually drive decisions versus which are just “nice to know”

AI amplifies whatever structure and assumptions you give it.

If your model reflects unclear intent, unchallenged assumptions, or legacy “we’ve always done it this way” logic, AI will happily scale that confusion—faster and more confidently than ever before.

Asking why is how we:

• Clarify intent
• Define meaning
• Encode business logic on purpose into the semantic layer

That’s what makes AI useful instead of noisy.

Final Thought: “Why” Is an Act of Respect

When I ask why, I’m not questioning your intelligence or your intent.

I’m saying:

• Your time matters
• Your decisions matter
• Your outcomes matter

And if we take the time to understand the real need—not just the request—we can build solutions that last longer, scale further, and actually deliver value.

So yes.
You can export it to Excel. Yes, you can insert other request here.

But let’s talk about why first.