Making Data Visual

Chapter 1. Getting to an Effective Visualization

Choosing or designing a good visualization is rarely a straightforward process. It is tempting to believe that there is one beautiful visualization that will show all the critical aspects of a dataset. That the right visual representation will reveal hidden insights. That a perfect, simple, and elegant visualization—perhaps just a line chart or a well-chosen scatterplot—will show precisely what the important variable was and how it varied in precisely the way to illustrate a critical lesson.

This is often the impression that we, at least, are left with after reading data science case studies. But in our experience, this does not match the reality of visual data analysis. It takes hard work, and trial and error, to get to an insightful visualization. We start by thinking about what we want to know, and we refine fuzzy questions into actionable, concrete tasks. We clean, reshape, and restructure the data into forms that we can put into a visualization. We work around limitations in the data, and we try to understand what the user wants to learn. We have to consider which visual representations to use and what interaction mechanisms to support. Along the way, we find other variables that tell us more about the dataset and that help clarify our thinking. And no single visualization is ever quite able to show all of the important aspects of our data at once—there just are not enough visual encoding channels.

Designing effective visualizations ...