The best data visualizations books have (pardon the pun) a long shelf life.
While new tools emerge and old ones evolve, so many core best practices in data visualization remain evergreen — be they color selection, axis orientation, the appropriateness (or gratuitousness) of animation or interactivity in a given context, and far beyond.
“A lot of what changes is just software, [but] a lot of the concepts remain very constant,” said Alli Torban, a Washington, D.C.-based data visualization consultant. “Even a 10-year-old book still has a lot it can teach you.”
We asked four data visualization experts to select and discuss a few of their favorite data visualization books. Our experts include:
- Cole Nussbaumer Knaflic, CEO of storytelling with data, author of Storytelling with Data: A Data Visualization Guide for Business Professionals and Storytelling with Data: Let’s Practice!, and host of the SWD podcast.
- Alli Torban, an information design consultant and host of the Data Viz Today podcast.
- Randy Krum, founder of data visualization and infographics design firm InfoNewt, and author of Cool Infographics: Effective Communication with Data Visualization and Design.
- Tamara Munzner, a computer science professor at the University of British Columbia and author of Visualization Analysis and Design.
Many of the picks are written specifically for business contexts, but you’ll also find plenty of broadly relevant practical-application entries and, for those who wish to dive deep, an assortment of advanced theory textbooks.
Must-Read Data Visualization Books
- Better Data Visualizations by Jonathan Schwabish
- Good Charts Workbook by Scott Berinato
- How Charts Lie by Alberto Cairo
- Avoiding Data Pitfalls by Ben Jones
- Info We Trust by RJ Andrews
- The Big Book of Dashboards by Steve Wexler, Jeffrey Shaffer and Andy Cotgreave
- Data at Work by Jorge Camões
- Information Graphics by Sandra Rendgen
- Data Sketches by Nadieh Bremer and Shirley Wu
- Design for Information by Isabel Meirelles
Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks by Jonathan Schwabish
Cole Nussbaumer Knaflic: Published in early 2021 and including a stellar variety of visualizations (more than 500 examples), Better Data Visualizations reads as an enjoyable and educational encyclopedia of graphs.
The book is organized into three sections. It begins with a brief primer on data visualization best practices. Part two is the bulk of the book: chart types. Schwabish dives deep into different types of graphs that go well beyond the standards of lines and bars. He includes insightful discussion about why and when each works — and when they don’t — with many examples. The final section explores design and provides direction for developing and using a data visualization style guide.
While the subtitle suggests a more limited audience, this is an excellent resource for anyone who analyzes and communicates with data. I’m also a big fan of Schwabish’s related One Chart at a Time video series.
Good Charts Workbook: Tips, Tools, and Exercises for Making Better Data Visualizations by Scott Berinato
Alli Torban: A great book for a new practitioner, since it offers hands-on practice. It’s written in a style that almost feels like Berinato is a nice mentor or co-worker, and he’s just walking you through. It has quiz-like prompts, asking, for example, why a specific pie chart is hard to read, and what could make it better. Then there’s sketch space to draw what you’d do instead. The data sets he gives aren’t complex, so you can easily sketch them.
And there’s a discussion section where he doesn’t necessarily give answers, but explains how he’d think about it. In data viz, designers can sometimes think the way they do it is the [only] way, so I appreciate his measured approach: This is how I’d probably do it, and these are my considerations.
The Wall Street Journal Guide to Information Graphics: The Dos and Don’ts of Presenting Data, Facts, and Figures by Dona M. Wong
Alli Torban: It’s written from a journalist’s perspective — with principles to make good charts for the media. But it’s also very helpful for those who don’t necessarily have a data background. Wong’s clear examples address specific, common issues. For instance, there’s a section on good y-axis increments — what an awkward increment looks like, and what to do instead. There’s also a very helpful section on log scales. The book is pretty prescriptive; it’s in the subtitle: the dos and don’ts. So if you’re looking for a foothold, this is a great, example-rich starting point.
Also, she explains math concepts very clearly, such as how to calculate a percent change, which, if you’re creating a chart, those kinds of calculations are things you have to do. But if you don’t necessarily have a math background, it can be confusing and hard to find by Googling.
How Charts Lie: Getting Smarter about Visual Information by Alberto Cairo
Randy Krum: A must-read guide if you’re designing charts that will be published. Cairo is in data journalism, and he’s dedicated to ensuring data visualizations are done objectively and don’t skew or misrepresent the data.
He’s spoken for years about how some poor visualization choices in the news media have led to misunderstanding of data, whether that’s weather data — hurricane maps — and also election data. It’s a great treatise on how data can be visualized in misleading ways, and for practitioners, what to avoid so that you don’t get strongly criticized by visualizing something in a manner you didn’t intend to do.
Tamara Munzner: Cairo’s books are super accessible, very clearly written. His background as a journalist is obvious. He works very hard to get people to think about the use of visuals. For anyone intent on data literacy, on making charts that don’t mislead, he’s your go-to.
Avoiding Data Pitfalls: How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations by Ben Jones
Cole Nussbaumer Knaflic: I recommend Avoiding Data Pitfalls frequently. Conversational in tone and packed with relatable examples, this book details seven pitfalls that Jones has fallen into or seen others stumble over, in an effort to spare readers from making the same mistakes.
Each chapter focuses on a single peril, explaining the specific relevant dangers and illustrating through example, helping you learn how to recognize and avoid them. Though there is but a single chapter focused specifically on data visualization (pitfall No. 6: graphical gaffes), the book demonstrates throughout the types of questions and thought processes that anyone who visualizes data should be pondering when analyzing their data. Great for those early in their data analysis and visualization journey, the lessons outlined serve as an important reminder of issues that can be easily overlooked by more experienced analysts as well.
Info We Trust: How to Inspire the World with Data by RJ Andrews
Cole Nussbaumer Knaflic: Info We Trust is a beautiful book. It feels substantial. The colors are vibrant. The language is poetic. The content is inspiring.
Nearly every inch is filled with information: margins are packed with quotes from wide-ranging sources and other relevant tidbits. While I’ve spoken with others who have found this distracting, I thought it gave insight into the extensive research that went into the book and a peek at ancillary paths not taken. The text and margins are interspersed with hand-drawn images and graphs that help reinforce concepts. The chapters are relatively short in length but dense in ideas and abstracts, which provide a good balance.
Info We Trust is definitely not a how-to book, yet it is interlaced with practical advice. At one point, Andrews discusses sparking curiosity in your audience. He says — and I’m paraphrasing — that good stories leave space for the audience to forge connections. The book itself does this beautifully, making astute observations but allowing the reader room to connect and extrapolate to their own work.
Storytelling With Data: A Data Visualization Guide for Business Professionals and Storytelling With Data: Let’s Practice! by Cole Nussbaumer Knaflic
Cole Nussbaumer Knaflic: My books, Storytelling With Data and the follow-up Let’s Practice!, focus on making graphs that make sense in the business world and weaving them into compelling and action-inspiring stories. They codify and expand upon the lessons that we teach in our workshops, sharing practical tips and strategies that you can use in your next report or presentation.
The first book, Storytelling With Data, is meant to be read. The popularity it’s maintained since publication is likely in a large part due to its simplicity. It’s a relatively quick read that allows you to immediately appreciate the power of the lessons, demonstrated through real-world examples. Once you’ve read it cover to cover, it can serve as a great desktop resource to those making graphs and presentations.
Let’s Practice! invites you to do exactly that — practice. The same lessons that structure the first book guide this exercise-based book. Three sections of exercises comprise each chapter. In the first section, I pose a scenario for readers to work through on their own, but also detail my solution and the thought process that drove my decisions, sharing a good deal of content and many examples. This is followed by a section of unsolved exercises that are great for those who’d like additional independent practice or who teach others. In the final exercise section of each chapter, I break down strategies and outline how you can apply them to your next project.
Getting good at communicating with data takes practice, and this book will help you flourish. Everyone is invited to explore the interactive online companion resource, SWD community, too.
The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios by Steve Wexler, Jeffrey Shaffer and Andy Cotgreave
Alli Torban: [This is] the perfect book for business analysts, with countless examples of real dashboards, arranged by different scenarios: a sales dashboard for executives, rankings over time, complaint tracking, churn — things that you actually do in business contexts. Each chapter includes an example and explains why that dashboard works, and there are thoughtful discussions throughout each section on various practical and design considerations. Sometimes within a chapter, the co-authors disagree, so it’s really helpful to hear different sides.
I don’t do dashboards too often, but when I do, it’s convenient to be able to look up exactly what I need based on a given use case. There has been some dashboard backlash, and I agree, they’re not the most exciting thing, but people still need and use them. So you need resources for how to do them well.
Data at Work: Best Practices for Creating Effective Charts and Information Graphics in Microsoft Excel by Jorge Camões
Randy Krum: A great overview of visual perception and information theory, focused on communicating in a business environment. It goes through concepts like preattentive attributes, colors and how to choose the right chart for your data. Camões includes links for all his examples, which were built in Excel — the most common tool out there. So it doesn’t overwhelm people into thinking they need Tableau or Adobe Illustrator to build something beautiful.
You can download and explore his charts via the links, but the book is not an Excel how-to. It’s all about choosing the best design, communicating effectively and making your visualizations stand out, rather than look like every other Excel chart out there. He also writes about not simply relying on the default chart gallery.
The Big Picture: How to Use Data Visualization to Make Better Decisions — Faster by Steve Wexler
Randy Krum: Here, Wexler goes into some data-viz theory, but he really focuses on how to get organizations to adopt a data visualization mentality, how to effectively communicate data internally and how to get executives to start looking at charts that aren’t just bar charts.
I call them the big three: bar charts, line charts and pie charts. There are people who don’t like to go beyond those borders. They don’t want to see a Sankey diagram or a scatter plot. Wexler writes about how to move your organization into those other visual styles that will actually help your people understand the data better. Sometimes that means visualizing the same data in a few different ways to get better understanding.
Companies have so much data, and it keeps growing. They feel an imperative to use it to make better business decisions. Here, Wexler directly connects that to visualizations. If you want to make effective decisions with your data, people need to understand it.
The Back of the Napkin by Dan Roam
Randy Krum: A classic. Here, Roam teaches how to communicate visually by focusing on hand drawings. He dives into business communication using diagrams, charts, maps and visual explanations. His visual thinking codex is all about which charts and diagrams go best with which kinds of information.
[The point is:] Don’t be afraid to visualize. Even if you’re a bad artist, like I am, you can still get your point across — and it’ll come across better if you visualize it versus anything else. No matter your drawing or design skills, you will benefit from this book.
I do a lot of our work, whether it’s presentation or infographic design, that involves visual metaphors, using icons and illustrations to communicate concepts, rather than visualizing data. He goes into a lot of that here as well.
Cool Infographics: Effective Communication with Data Visualization and Design by Randy Krum
Randy Krum: I wrote this for people who use infographics, whether for internal communication, such as employee education or as a sales tool, or for public-facing, external marketing. It teaches you how to publish your designs online along with the SEO skills needed — how to set up a landing page and optimize for search. So many companies will design an infographic, throw it on their company blog or Facebook page, and then do nothing with it. You have to prepare before you publish.
Also, a full chapter is dedicated to infographic resumes, to help use the power of data visualization to market yourself. All the power of visualizing corporate data — letting readers quickly digest and retain information — applies to your personal information when applying for jobs as well. The chapter looks at the types of data that people visualize for resumes, plus questions like whether to separate the infographic from your text resume or integrate them together, and how to handle automated submission systems if you have an infographic resume.
Data Visualization History
Information Graphics by Sandra Rendgen
Alli Torban: A great coffee table book. It charts the evolution of visual communication design, with hundreds of graphics. The audience is people who are interested in information design, but it has general interest appeal. Anyone who picks it up will find something interesting.
The chapters are broken up by location, time, category and hierarchy, then every page has giant examples. I like to flip through when I need inspiration, to get a quick sense of how other people have solved similar design problems. So if I’m doing something with, say, time series, I’ll just flip through some dozens of pages of various time based-infographics and end up with a ton of ideas. Also, it’s just a really beautiful book.
Semiology of Graphics: Diagrams, Networks, Maps by Jacques Bretin
Tamara Munzner: Semiology was written in the 1960s, and Bretin’s insights are even more noteworthy given the technological constraints of the day. He pioneered methods for systematic data analysis that are now done with interactive computation, but he had to try them out as manual operations with paper and scissors and even built some mechanical devices.
The book contains amazing systematic diagrams. On the other hand, the English translation is terrible, and Bertin was admittedly idiosyncratic. A modern English speaking viz person would use very different terms than the ones used in the book. You wouldn’t say “implantation,” you’d say layout. But if you want to go deep and you’re interested in the history of the ideas, there is absolutely nothing like it.
In data viz, there’s visual encoding and there’s interaction. And we have much more deep theory about how to visually encode data than we do about interacting with it. So I think a lot of the ideas here, which are about visual encoding, do hold up.
Cartography: A Compendium of Design Thinking for Mapmakers by Kenneth Field
Cole Nussbaumer Knaflic: This book — written by a professional cartographer — isn’t solely about data visualization. Or is it? After all, a map can be thought of as a way to visualize data when the where matters. This is one of the most impressive books I’ve ever encountered. That’s not only due to its sheer heft — there are more than 500 pages packed with content — but also because of the thoughtful way Field structures and organizes so much information in a way that doesn’t overwhelm.
Each two-page spread focuses on a single aspect (for example, a map type or design principle). The text is formatted for easy scanning and paired with a variety of beautiful images. Topics are color-coded and alphabetized, so it’s easy to find items of interest or explore a concept that piques your curiosity. It turns out that much of the design rationale that goes into making a good map can direct efforts visualizing data in graphical form as well.
Data Sketches: A Journey of Imagination, Exploration, and Beautiful Data Visualizations by Nadieh Bremer and Shirley Wu
Tamara Munzner: Data Sketches has a special place in my heart. It’s a tour de force — sort of like a coffee table book meets tech, a great book if you know something about data viz and want to go to the next level. It’s an amazing resource for people who want to level up.
It chronicles the co-author’s long-distance collaboration, 12 visualization projects in total. It charts their process, going from how to even get the data, to ideating to sketching to coding. I don’t know of any other book that brings you along that whole road.
Visualization Analysis and Design by Tamara Munzner
Tamara Munzner: I wrote Visualization Analysis and Design to scratch my own itch: How do I show people that visualization is a systematic way of thinking, not just a grab bag of this technique and that technique? If you’re interested in diving deep and thinking systematically, it’s a good resource. It’s not just for people with a computer science background.
Admittedly, it has its dense moments, but I avoid math and jargon. I try to build bridges and present a big picture, but in a way that might make sense for a class textbook. But people definitely read it outside of classrooms — people in neighboring fields who want to know what this visualization thing is all about.
Alli Torban: One of my favorite chapters is “No Unjustified 3D.” Usually you’ll just hear, “Well, if you do 3D, then your marks get distorted and you can’t compare values as well.” But she goes further, explaining a number of different issues. And then she talks about cases where 3D actually works and provides examples. It’s a very reasoned consideration of all the decisions you have to make. The examples are more on the academic, scientific side, but it’s a very measured approach to thinking like a data-viz designer.
Interactive Visual Data Analysis by Christian Tominski and Heidrun Schumann
Tamara Munzner: This one is a bit scholarly and technical, focused on visual analytics, which is a particular sub-branch of visualization. It goes thoroughly back and forth between human-in-the-loop analysis and computational methods. This was the first really good visual analytics textbook out there. For those who do want to go deep into the interplay between computational and visual concepts, it’s great.
Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations by Isabel Meirelles
Randy Krum: This book explains how we visually perceive information, plus different ways available to visualize data — charts, but also maps, network diagrams, a lot of flowcharts, trees and other ways to visualize information that may not specifically be a table of data. It’s definitely for [data-viz] practitioners, not for, say, a product manager or someone just dealing with Excel charts. This is more advanced, like if you’re designing timelines of the history of your company.
It’s almost 10 years old now, but the concepts about structure — relational, hierarchical, spatial, temporal or textual structures — don’t change. Whether you use R or Tableau, or draw by hand, the ideas of communicating effectively in a certain structure are consistent and tool-agnostic.
Tamara Munzner: [This book is] a carefully curated walk through the world of visualization, specifically from a designerly viewpoint, but one that goes deep. It’s not just a pretty coffee table book; it really does explore the ideas.
The Elements of Graphing Data by William S. Cleveland
Tamara Munzner: For scientists and engineers who want to make scientific plots or charts for research papers, Elements of Graphing Data is astounding. Cleveland comes out of the statistics tradition. There was a legendary Bell Labs cohort, with John Tukey, who invented the term “exploratory data analysis.” Cleveland was his associate and very much an intellectual successor. This is where to go deep into, for instance, how exactly to make a statistical plot and all the issues you should think about. Recommended for people who come from very quantitative backgrounds.
Responses have been edited for length and clarity.