The Question Behind the Question
A key technique for data analysis
About a year and a half ago, when I made the leap to start writing in public, this is one of the first articles I published. I’m pleased to see that it still comes up in conversation. I’m reposting it here to make it easier to find.
The Question Behind the Question
Why is it so important to ask the right questions? It’s about context, about getting to the heart of the problem rather than accepting inquiries at face value. All great data analysts can do this well.
To illustrate this point, let’s consider a common scenario that many data analysts encounter. Imagine you’re a data analyst. One day at work, a business stakeholder - let’s call him Joe - comes up and asks, “Quick question. Can you run a report for me? I need a spreadsheet with these exact rows and columns.”
How you, as the analyst, respond greatly impacts the quality of the work. Here’s a bad response:
You say: “Sure thing, Joe! How soon do you need it?”
Why is this response problematic? First, it reinforces the negative perception that the data team operates as a service desk, not as a thought partner. Second, it shows the analyst is not considering their other priorities before dropping everything for this unplanned work. Most importantly, it misses the opportunity to ask clarifying questions to solve the real problem, rather than fulfilling Joe’s vague request literally and blindly.
Here is a much better way to handle it:
You say: “Glad to help, Joe! Can you please describe the business problem you’re trying to solve?”
This response is better because it provides context. Perhaps Joe doesn’t really need that spreadsheet, in which case creating it would be wasted effort. The real need might involve a different spreadsheet, a past analysis on a similar topic, or access to a self-service tool. Asking clarifying questions helps ensure the request is handled appropriately. You could also ask, “Will this be used as part of a decision that needs to be made?” to document Joe’s intended action and - later, once the work is done - follow up with him to confirm the insights were actionable.
This approach is often referred to as the “question behind the question,” a technique all great analysts know. If you’re a new grad entering the analytics job market, start practicing this from day one. If you’re a data leader hiring new talent, evaluate candidates on whether they are the “service desk” or “thought partner” type of analyst. You undoubtedly want the latter.
Soon enough, our favorite AI co-pilots may replace the need for “service desk” analysts. We’ll all be better off with analysts whose work is elevated by their excellent critical thinking and human judgment skills.
In conclusion, asking the right questions is essential for delivering high-quality data analysis. Understanding the underlying problems behind requests allows you to deliver more insightful and impactful solutions. Whether you’re just starting your career or leading a data team, prioritizing this approach will set you apart and enhance your contributions.