Upcoming Events

  • Book signing at the CHI 2018 conference, Tuesday, April 24, at 12:20 pm, at the Microsoft Booth
  • Miriah will be speaking at the OpenVis Conference, May 14, on "Designing Effective Visualizations."
  • Miriah and Danyel will be presenting a tutorial at the O'Reilly Strata London Conference, May 22. There will also be a "Meet the Experts" session and a book signing.


Making Data Visual

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 presents a paradox. On the one hand, visualizations are intended to help users learn about parts of their data that they don’t know about. On the other hand, the more we know about the users’ needs and the context of their data, the better we can design a visualization to serve them. The process described in this book embraces this paradox: it leverages the knowledge users have of their datasets, the context the data lives in, and the ways it was collected—including its likely flaws, challenges, and errors—in order to figure out the aspects of it that matter.

Put another way, this book is about the path from “I have some data…” to “We know this because of these clear, concise, and insightful visualizations.” We believe that creating effective visualizations is itself a process of exploration and discovery. A good visualization design requires a deep understanding of the problem, data, and users. 

- from Chapter 1

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