Why Good Analysis Starts with Good Questions?

When we think of data analysis, the first thing that usually comes to mind is discovering insights. While insights are undeniably valuable, they’re not actually the starting point. In fact, the most important part of the analysis process is asking the right questions—and that’s where many people struggle.

At Think By Data, I always start with a quick summary before diving into the subject. If you're looking for a short answer, you'll find it there, along with a mind map. For more details, please continue reading the rest of the article.

🗂️ Key Point	💡 Summary
🔍 Right questions = better insights	Good analysis starts with asking focused, purposeful questions.
⚠️ Vague questions waste time	General questions like “How can we increase sales?” lead nowhere.
✅ Make questions specific and actionable	Example: “Which campaign performed best last quarter and why?”
🧭 Always think: What will I do with the answer?	Avoid dead-end stats — tie your question to a decision or action.
🧪 Use hypotheses to guide exploration	E.g., “Will adding testimonials increase conversions?”
🎯 Align questions with business goals and targets	Don’t just report — ask if performance met expectations.
🔁 Tools like Google Analytics are for ‘what’, not ‘why’	Use the data to support, not replace, real reasoning.
💬 Ask stakeholders: ‘What will you do with this data?’	Forces clarity and gives your analysis direction.

What is the analysis process?

Analysis usually follows this pattern:

You ask a question (or multiple).
You inspect, investigate, and explore the data.
You uncover insights.

Sounds simple, right? But here’s the kicker: if you ask vague or misguided questions, everything that follows will be directionless. You might find yourself opening up your analytics platform, hoping for something to jump out at you—but instead, you end up lost in a sea of reports, unsure what you're even looking for. This is what we call “analysis paralysis.”

Ask questions => Data analysis => Insights

The Danger of Vague Questions

Let’s look at a common trap: vague diagnostic questions.

Why aren’t people subscribing to our newsletter?

It sounds like an important question, but tools like Google Analytics can’t tell you why something happens. They can show you patterns, behavior flows, or exit pages — but not the intent behind user actions.

"Why" questions like this can mislead your data analysis by making you focus on assumptions rather than evidence.

A better approach is to turn a guess into a testable question:

Will changing the button help increase newsletter subscriptions?

Now we’re talking. You can:

Identify the specific page where the newsletter button was added and other an UI button
Compare before-and-after conversion rates
Run A/B tests if you have enough traffic

Even if the results aren’t dramatic, you’ll learn something useful — and your analysis will be structured and purposeful.

Think Ahead: What Will You Do with the Answer?

There’s a magical question that can shine a light on your analysis path:

So what?

After every question, ask yourself: So what?

Let’s say someone asks, “How many new purchasers did we have last month?”
And you answer: “600.”
Then what?

If the question doesn't lead to a next step, it's not useful. Instead, pair numbers with context and intent. For example:

“Did we hit our goal of 1000 new purchasers this month?”
“Which channels contributed to the dip in new purchasers?”
“What content drove the most conversions last week?”

Every metric you report should tie back to a goal, hypothesis, or strategic decision. Otherwise, you're just doing data decoration — pretty charts with no purpose.

Specificity is Your Superpower

The more specific your question, the easier your analysis becomes. You’ll know:

What metric to look at?
What dimensions to segment by?
What tools or reports to use?
What decisions might follow?

For example, asking “What’s our top traffic source?” is a decent start. But even better would be:
“Which traffic sources contributed to our highest-converting customers over the past six months?”
That kind of question will lead you into cohort analysis, user journeys, and maybe even changes in budget allocation or content strategy.

Tie Your Questions to Business Goals

No analysis exists in a vacuum. Every question should connect back to a business target, whether it's sales, signups, retention, or something else.
Let’s say your email marketing team wants 1,000 new subscribers this month. Then a good question becomes:

Did we meet our email subscriber goal this month? If not, which channels underperformed?

From there, you can investigate further — maybe a particular landing page is underwhelming, or a campaign wasn’t promoted enough. The point is, you now have a measurable benchmark to guide your exploration.

Final Thought: Always Ask "Why Do You Need This?"

One of the most effective things I’ve learned is to challenge every request for data with a simple question:
“What are you going to do with this information?”

This not only filters out unnecessary tasks but also uncovers deeper insights about your stakeholders' goals. Sometimes, even they realize the number they’re asking for doesn’t really matter — what they’re after is a different insight altogether.
And if the number is useful, now you have more context to tailor your analysis and deliver value beyond the basic metric.