Questions Are More Important Than Answers 📊

The most underrated skill in problem-solving

Hello, data-driven and curious minds! Welcome to the 24th edition of DataPulse Weekly.

When it comes to problem-solving, whether with data or in general, one of the most underrated skills is the ability to ask the right questions.

I once had a teammate who always began with a question whenever we brainstormed a problem or explored a new idea. His questions weren't just direct; they were probing, peeling back layers like an onion with each follow-up. More often than not, our discussions would end by pinpointing the most critical aspects of the issue.

This approach was something I learned from him, and it's a skill every analyst or problem-solver should master—the art of asking the right questions.

Here are some ways to structure your thoughts around asking the right questions:

Simply Asking Why: Understanding the Purpose Behind Every Task

Most new analysts fall victim to this mistake: they focus on what’s being asked rather than why it’s being asked. While skipping the "why" might save a few hours each month, the long-term impact can’t be justified.

Good data analysis starts with curiosity. When analysts are genuinely interested in understanding a problem, they ask questions that dig deeper, rather than just skimming the surface. For example, consider a company experiencing a sudden drop in sales.

An analyst driven by curiosity wouldn't stop at:

→ What is the drop in sales?

They would keep probing:

→ Is this drop consistent across all products?

→ Is it affecting certain regions more than others?

→ Could recent changes in customer behavior be influencing this?

These deeper questions help uncover the real issues rather than just addressing the symptoms.

Keeping the End Objective in Mind

Knowing the end goal can guide which questions to ask. If the objective is clear, the path to the right questions becomes clearer.

For instance, if a business wants to increase customer retention, asking:

→ What’s our current churn rate?

might not be as useful as asking:

→ What are the common reasons customers are leaving?

The latter directly ties to actionable insights that can lead to meaningful changes.

Understanding Context: The Importance of Stakeholder Collaboration

Context is king in data analysis. Without understanding the context, data can easily be misinterpreted.

For example, an analyst noticing a spike in website traffic without knowing there was a major promotional campaign might wrongly attribute the spike to other factors, such as site improvements or organic growth.

Asking contextual questions like “What external events might have influenced this data?” ensures that analysis is grounded in reality, not just numbers.

Sometimes you need to ask these questions to yourself and then validate your hypotheses with stakeholders.

Avoiding Assumptions & Biases

Assumptions can be dangerous. They can lead analysts to ask leading questions that confirm their preconceptions rather than uncover the truth.

Imagine a product manager assuming that a drop in user engagement is due to poor product design and only asking design-related questions. In reality, the issue might be a marketing mismatch, where the product is attracting the wrong audience.

A better approach is to ask, “What are all possible reasons for the drop in engagement?” and then explore those possibilities without bias.

Iterative Questioning & 5 Whys Framework

Effective data analysis often requires an iterative approach. The first question might just open the door to more specific, deeper questions.

For example, if a company notices a decrease in customer satisfaction scores, an initial question might be:

→ What’s causing dissatisfaction?

As data comes in, further questions might include:

→ Is it related to customer service response times?

→ Is it due to product quality issues?

By continuously refining the questions, analysts can zero in on the root causes of problems. The 5 Whys framework is an excellent technique for this.

Additional Tips for Framing the Right Questions

Ask Open-Ended Questions: Instead of limiting inquiries to yes/no answers, ask questions that encourage exploration. For example, rather than asking “Did our latest campaign increase sales?” ask “How did different customer segments respond to our campaign?”

Be Specific but Flexible: Specific questions focus the analysis, but flexibility allows for the exploration of unexpected insights. Asking “What’s driving customer churn?” can be followed by more specific questions like “Is churn higher among a particular demographic?”

Use Hypotheses to Guide Questions: Develop hypotheses based on initial observations. Use data questions to test these hypotheses, but remain open to the data proving them wrong.

Conclusion

The art of asking the right questions is a skill that evolves with practice. It's about being curious, avoiding assumptions, understanding context, and continuously refining the questions as new information comes to light. By focusing on the right questions, analysts can uncover insights that lead to real solutions, rather than just scratching the surface or solving the wrong problems.

In the world of data, the questions you ask shape the answers you get. Make them count.

---

That wraps up our newsletter for today! If you found this valuable, please consider subscribing and sharing it with a friend—it motivates us to create more content.

Stay curious and connected!

Until next Tuesday!