HELLO.

More than five years after the publication of Good Charts, everything has changed. And nothing has as well.

When I say everything has changed, I’m of course talking about, well, everything. The world has endured a pandemic, and many feel a great sense of unease. Information—data—was everywhere five years ago, a fact I mentioned in the introduction to Good Charts, but it has moved beyond that now. In the past half-decade, data and data visualization have grown exponentially. They’ve been used to great effect. They’re connected, for example, to creative breakthroughs in medicine and the creation of entirely new business sectors pouring massive value into the economy. They’ve been used to educate a grieving, locked-down public about the dangers of a virus. Data and visualization have transformed worlds big and small, from health care to fantasy sports. From agriculture to daily exercise. From sports to finance to education, and much more.

Of course, it hasn’t all been good. We’ve seen data and dataviz deployed to cover up corporate malfeasance and spark public debates over the meaning of facts and truth. But for the most part, we are more data visually literate than we were when I wrote Good Charts. And for the most part, visual discourse has improved things.

I looked for a suitable word to describe what’s happened and I couldn’t find one. So now I paw at descriptions of this sense that we’ve reached a level of supersaturation with information that is both awe-inspiring and also feels like a lowering presence. Data feels like a hyperstimulus. Information has become a kind of megacosm, a universe in itself that we created and now must inhabit.

When I say nothing has changed, I’m of course talking about our need to embrace, understand, and use data visualization to make things better. To help us ascend from the low place of the early 2020s. I’m talking about the need for broad datavisual literacy. The need to learn to positively wield data visualization’s power.

I’m optimistic about this. In the five years since Good Charts was published, I’ve spoken to and worked with thousands of people about the power of effective visuals and superior information design. Nearly all are open and eager to learn the skills that can help them get better at communicating visually.

And the overall quality of the data visualization I see is improving for many reasons, including a spate of excellent books and manuals from several tremendous authors, a thriving internet community, and new and improving tools that make it easier to generate good visual information. I’ve seen a kind of virtuous cycle emerge in which people who experience a good chart are inspired to want more like it and to make better ones themselves.

Most of all, I believe dataviz is getting better because of people’s recognition that they can do this. That pleases me, too, because I wrote Good Charts for precisely that reason. I wanted to show people that by learning a little, they can change a lot. I wanted to remove the intimidation many felt (and many still feel) about visualization, thinking of it as the domain of a few specialist masters when, in fact, it’s for everyone.

Early on when I was writing the book, a colleague said to me, skeptically, “Why should you be the person to write a book about this?” My answer surprised her: “Because I’m not an expert. Most people who have to do data visualizations aren’t either. Basically, I’m the reader.”

I don’t know if my colleague was convinced, but I do know that when I say to an audience now, “You don’t have to be a designer or a data scientist to make good visual communication—you just need a few simple strategies,” faces physically relax. A palpable wave of relief washes over the room. It’s amazing to see and feel.

I’ve learned so much from the audiences I’ve interacted with in person and, recently, on screen, over the past five years. This updated edition of Good Charts is my way to send out into the world what I’ve learned from them.

Speak to enough people for enough time and themes emerge. I’m updating and expanding this book to address the two most common questions I get after finishing a presentation or during a workshop:

• What tools do you use to make charts?
• How do I get buy-in to make people realize this is worth the time and investment?

You may suspect that the answers to these questions aren’t nearly as tidy as you want them to be, and that’s true. The tools environment is evolving rapidly; the information in the original edition of this book is frankly not good enough anymore. But more than a call for a list of potential software, the tools question gets at something more fundamental about the nature of dataviz work, which has been left to individuals, some of whom are unprepared for it, or plain uninterested in it. So, this edition will not only update the tools information but also explore how to put together teams to operationalize visualization—a step that will in some ways answer the tools question.

The second question about buy-in dovetails with this, for in the effort to make dataviz an operational team sport, you prove its value and get the buy-in that can sometimes feel elusive. An entirely new chapter called “A Return to Teamwork,” which is based on a successful Harvard Business Review article, will explore both questions and provide a framework for moving forward.

“Facts and Truth,” the chapter on chart manipulation (chapter 6) is also significantly updated. I still address that blurry line between persuasion and manipulation, but in a more holistic way to look at the nature of facts and truth, their similarities and differences, and to explore the relationship between visualizations and our emotions about them.

There are other, more prosaic reasons to update Good Charts. Time-series data ages, of course, so new charts with recent data replace older ones to keep things fresh. It also affords the opportunity to improve on charts I was never quite happy with in the first place. And I’ve included even more examples that didn’t exist five years ago. The pandemic, for example, was fertile ground for effective and innovative visualization.

This edition is updated and expanded, but it does not bulldoze the original. Remaining from the first edition are all the core frameworks and the design and decision-making principles that so many of you have told me have improved your ability to create good charts. Some of the handy reference material remains as well, including the glossary of chart types and their use cases. Just as with the world around us, in Good Charts everything has changed, and nothing has as well.

I hope this book will become a well-worn, dog-eared companion. (You will see this sentence again in the introduction, where it originally appeared.) I hope you mark up Good Charts, highlight passages, plant sticky notes, and sketch improvements to the charts I’ve created in ways that make it uniquely yours.

I’m thrilled when someone who saw me speak, or who read the book, tells me how they had learned to transform a visual to great effect, and how their data visualization changed something—an attitude, a strategy, their career. They sometimes tell me this as if it felt like a magic trick.

But there is no magic to it. As I say in the introduction to Good Charts, data visualization is neither art nor science; it’s the amalgam of the two. The beauty of the thing, the art, is found in its technical effectiveness, the science. Data visualization is a craft. Like cabinetmaking. Anyone can learn it. Well-crafted things take skill, and skill takes learning. I hope this book is part of what helps you learn, practice, and hone your craft.