Inside the Analyst’s Mind
Explore how data analysts approach problems by understanding key questions and examining a typical day in their workflow to see the implementation of these skills.
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Our last lesson taught us what data is, why it’s so important, and what data analysts do. Now, it’s time to go deeper. Imagine that we’re going behind the scenes, right into the mind of a data analyst. We’ll explore how they think by examining the kinds of questions they ask, then we’ll walk through a typical “day in the life” to see how they put their skills into practice.
How analysts think
Data is just raw information. It’s like a giant pile of LEGO bricks. We can have all the bricks in the world, but if we don’t have a plan or a question, we won’t build anything meaningful. This is where the analyst’s mindset comes in: they know how to ask the right questions to transform that ineffective pile of bricks into something meaningful. Asking smart questions is the first step to uncovering the insights hidden within data.
Data analysts ask different kinds of questions to reveal different layers of understanding. This helps them move beyond observing what happened to understanding why it happened, what will happen, and what should be done.
Descriptive analysis
When we use descriptive analysis, we’re acting like historians. Our main goal here is to summarize and describe what has already occurred. We’re essentially asking, “What happened?”
For example, imagine we run an online shoe store. A descriptive question might be: “How many pairs of running shoes did we sell last month?” or, “What was the average price of sneakers sold in the last quarter?” The analyst’s role here is to gather all the sales data and present these facts clearly, often using simple charts or summaries. We’re simply reporting on the past.
Descriptive analysis forms the foundation for all other types of analysis. We can’t figure out why or what will happen if we don’t first understand what happened!
Diagnostic analysis
With diagnostic analysis, we become detectives. Once we know “what happened,” we want to understand “why it happened.” This analysis involves digging deeper into the data to find the causes of trends, changes, or unusual events.
Diagnostic analysis often involves looking for “outliers” (data points very different from others) or sudden shifts in trends, which can be strong indicators of a root cause. Tools like “root cause analysis” are often applied here.
Let’s stick with our example of the online shoe store. If our descriptive ...