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Better Data Visualizations: Conversation with Jonathan Schwabish

Better Data Visualizations

Dr. Jonathan Schwabish is an economist, writer, teacher, and creator of policy-relevant data visualizations. He is considered a leading voice for clarity and accessibility in how researchers communicate their findings. His book Better Presentations: A Guide for Scholars, Researchers, and Wonks helps people improve the way they prepare, design, and deliver data-rich content and his edited book, Elevated the Debate: A Multilayered Approach to Communicating Your Research, helps people develop a strategic plan to communicating their work across multiple platforms and channels.

In this conversation, Jonathan talks about his newest book, Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks which details essential strategies to create more effective data visualizations.

Geetesh: Jon, your new book Better Data Visualizations certainly does not look like just another book. The content is so detailed and yet you have made the narrative sound so plain English. Tell us what motivated you to author this book and how was your experience writing and visualizing this one?

Jonathan: The last time we spoke, Geetesh, was after my first book, Better Presentations when I told you how Columbia University Press had reached out to me to write a book about data visualization. At the time, I was resistant to writing a book about data visualization because there were already many on the market (and more on the way), and I wasn’t sure how to cover the field in a way that was totally satisfactory to me.

Fast forward five years and Columbia reached back out to see if I was ready to write that follow-up book on data visualizations. Having changed jobs and focusing more of my time on data communication, I found myself with more to say and an idea for a book that would hopefully place it in a unique space in the field.

I wrote the first draft of the entire manuscript on two train rides between Washington, DC and New York City. But it would take another two-and-a-half years to fully develop it, reorganize it, expand it, and create and curate the more than 500 graphs that appear in the book.

So, writing it was really the easy part. I knew how I wanted to organize it and what I wanted to include, especially those sections that aren’t included in many other data visualization books such as chapters on qualitative data visualization, table design, and developing a style guide.

Like all books, the experience of writing is both thrilling and exhausting. It’s exciting to put something together that you hope people will be able to use to improve how they do their jobs or affect policy or change. It’s also exhausting—writing, collecting data, and making the graphs is a tall order. There was also a lot of work to get permissions for all of the other graphs I wanted to include in the book and I’m fortunate that so many people and organizations were willing to let me use their work. In the end, I think the book is a great mix of historical and modern data visualizations so the reader can see both the evolution and variation of the data visualization field.

Geetesh: Can you share some thoughts on how a reader should use this book? What approach will help them gain the most from Better Data Visualizations?

Jonathan: I have two main learning goals in this book, which also mirror the data visualization classes and workshops I teach. First, I want to give the reader the basics of data visualization best practices. This includes sections on how our visual processing network facilitates our understanding of information as well as five practical guidelines of data visualization that have served me well:

  1. First, show the data. Determine what data are important for your reader to see and highlight those points, lines, or bars.
  2. Second, reduce the clutter. Reduce or eliminate unnecessary visual elements that can distract your reader from your central message.
  3. Third, integrate the graphics and the text. Directly label your data, use active titles, and annotate your graph to help your reader better understand how to read the graph and what to get out of it.
  4. Fourth, avoid the spaghetti graph. Don’t feel like you always need to show your data in a single graph—sometimes, smaller, multiple graphs can be a better way.
  5. Fifth, start with gray. Whenever you make a graph, start with all-gray data elements. By doing so, you force yourself to be purposeful and strategic in your use of color, labels, and other elements.

The second goal of the book—and what takes up the bulk of the pages—is to show the reader more than eighty main different graphs, charts, and diagrams. The idea is to help readers understand there is more to the field of visualizing data than just bar charts, line charts, and pie charts. To help organize this ‘library’ of graph types, I split them across six different data categories: comparing categories, time, distribution, geospatial, relationship, and part-to-whole. There is also a chapter on visualizing qualitative data and good table design, both of which seems to have escaped many data visualization books to date.

Better Data Visualizations is not a book that necessarily needs to be read from cover-to-cover. I hope readers will use it as an essential part of their data visualization library, to dip in and out when they want to consider different ways to plot and communicate their data. I hope people will recognize that there is more to the world than just bar charts and to understand that the way we perceive and understand information is rooted in how our eyes and brains work.

It’s an exciting time to be working with data and there is a lot that people can do to communicate their work more clearly and more effectively to their target audiences, and I hope this book will help them along the way.

Introduction with Jon Schwabish
Introduction with Jon Schwabish

The views and opinions expressed in this blog post or content are those of the authors or the interviewees and do not necessarily reflect the official policy or position of any other agency, organization, employer, or company.