Making Data Visual

Chapter 2. From Questions to Tasks

All visualization begins with a question about data. An analyst wants to know something about a phenomenon in the world, or wants to share their knowledge about it with someone else. She believes the phenomenon they wish to examine is represented somehow in the data.

The challenge in this process is that the question the analyst wishes to address can seem far from the data. The analyst might be working on a broad goal: say, “Are high-salary employees more productive than less well-paid ones?” This leads to a process of making the question measurable. What does the analyst mean by high-salary, and productive? What visualization or set of visualizations would demonstrate the relationship between these variables?

The process of breaking down these questions into something that can actually be computed from the data is iterative, exploratory, and sometimes surprising. This chapter describes how to refine high-level questions into specific, data-driven tasks. The outcome of that process is a set of concise design requirements for a visualization tool that supports finding answers to those questions.

The general concept of refining questions into tasks appears across all of the sciences. In many fields, the process is called operationalization, and refers to the process of reducing a complex set of factors to a single metric. The field of visualization takes on that goal more broadly: rather than attempting to identify a single metric, the analyst ...