Stephen Few gave a snappy name to his new book, Now You See It, and a cover that signals a gem — all black with a slice of sunset that highlights the “see.”
Inside, many charts are so beautiful — at least to a visual analysis fan — that they rival the waterfalls and trees of old Sierra Club coffee table books. It’s on paper so thick — and even smoother than Few’s first book, Show Me the Numbers — that you might feel like you’re flipping postcards.
While Show Me the Numbers was about how to present visualized data, he explains, Now You See It: Simple Visualization Techniques for Quantitative Analysis is about understanding them, which apparently includes how to make them.
Few’s a natural teacher. He reviews, again, the basis of visual analysis and how the brain is better wired for visualization than for row-and-column thinking. In each chapter, he takes the reader through by the hand from the most basic concepts of visual data analysis through complex ones. Toward the end, he points the student to the horizon to imagine a future state of visualization. I especially liked the interesting chapter “Prerequisites for Enlightening Analysis.”
My question after a …
Stephen Few gave a snappy name to his new book, Now You See It, and a cover that signals a gem — all black with a slice of sunset that highlights the “see.”
Inside, many charts are so beautiful — at least to a visual analysis fan — that they rival the waterfalls and trees of old Sierra Club coffee table books. It’s on paper so thick — and even smoother than Few’s first book, Show Me the Numbers — that you might feel like you’re flipping postcards.
While Show Me the Numbers was about how to present visualized data, he explains, Now You See It: Simple Visualization Techniques for Quantitative Analysis is about understanding them, which apparently includes how to make them.
Few’s a natural teacher. He reviews, again, the basis of visual analysis and how the brain is better wired for visualization than for row-and-column thinking. In each chapter, he takes the reader through by the hand from the most basic concepts of visual data analysis through complex ones. Toward the end, he points the student to the horizon to imagine a future state of visualization. I especially liked the interesting chapter “Prerequisites for Enlightening Analysis.”
My question after a while, though, was who Few’s “you” is. What kind of student does he imagine? If the student really requires the kind of dead-slow explanation found throughout the book, shouldn’t that student do some other kind of work instead? Besides, isn’t visualized data supposed to be easy?
I’m a mere beginner at visual data analysis, comfortable with Excel charts but not fully competent with Tableau. Even so, that out-of-the-box human brain function that visualization scholars call “pre-attentive cognition” seems to have guided me well through infographics in newspapers and books.
I can’t help but skim past some parts, admiring the charts as I go. In the chapter on part-to-whole and ranking analysis, for example, he spends four luxurious pages on what I thought would have been too basic for adults. That includes nearly one whole page on the meaning of trend lines: some go up, some go down, and some stay the same. After this, it’s hard to know when things turn interesting pages later.
Do the basic explanations bore me because I’ve seen at least parts of Few’s basic presentation too many times? Or did I learn enough from Show Me the Numbers, from Edward Tufte’s books and presentations, and from a stint with a Tufte-admiring market research boss?
Or could it be that, like so many other kinds of work, visual analysis is a lot easier to observe than to practice? Would I appreciate the explanations if I were up to my elbows every day in visual analysis? Hard to know.
Too basic for some, but the book could be just right for others. This book could be the bridge from the early adopters to mass adoption. Also, longtime analysts could appreciate the refresher — for that slap-on-your-own-forehead moment when reminded of basics.
Few conducts the book like a classroom, narrating with statements like, “We’ll look at the following techniques and best practices” as if coaxing freshmen through a long, hot afternoon session.
He does best when he illustrates a point with a story. The chapter on distribution analysis begins with Stephen Jay Gould — diagnosed with a kind of cancer with a median mortality of only eight months after discovery. Gould thought about that statistic and discovered that he was a prime candidate for longer survival. He went on to live another 20 years, and helped this chapter seem shorter.
But there’s got to be more to visual analysis than the simple charts in this book. I wish Now You See It skewed toward more sophisticated visual analysis, the kind I hear about but have not yet reached.
In fact, the phrase “now you see it” has another half, ignored here: “now you don’t.” The two halves suggest a cycle of knowing and then asking again — what Tableau calls the cycle of visual analysis. The Tableau white paper “Visual Analysis for Everyone: Understanding Data Exploration and Visualization” describes it — visually, of course — on page five. I suspect the subject deserves a book.
Let’s see if Few’s got that one up his sleeve for later.
