Thanks for taking the time to read my thoughts about Visual Business Intelligence. This blog provides me (and others on occasion) with a venue for ideas and opinions that are either too urgent to wait for a full-blown article or too limited in length, scope, or development to require the larger venue. For a selection of articles, white papers, and books, please visit my library.

 

The Inflated Role of Storytelling

July 14th, 2019

People increasingly claim that the best and perhaps only way to convince someone of something involves telling them a story. In his new book Ruined By Design—a book that I largely agree with and fully appreciate—designer Mike Monteiro says that “If you’re not persuading people, you’re not telling a good enough story.” Furthermore, “…while you should absolutely include the data in your approach, recognize that when you get to the point where you’re trying to persuade someone…, you need a story.” Really? Where’s the evidence for this claim? On what empirical research is it based? And what the hell is a story, anyway? Can you only persuade people by constructing a narrative—a presentation that has a beginning, middle, and end, with characters and plot, tension and resolution? In truth, stories are only one of several ways that we can persuade. In some cases, a simple photograph might do the trick. A gesture, such as a look of anger or a raised fist, sometimes works. A single sentence or a chart might do the job. Even a straightforward, unembellished presentation of the facts will sometimes work. The notion that stories are needed to convince people is itself a story—a myth—nothing more.

It reminds me of the silly notion that people only use 10% of their brains, which someone fabricated long ago from thin air and others have since quoted without ever checking the facts. This notion is absurd. If we used only 10% of our brains, the other 90% would wither and die. Stories are not the exclusive path to persuasion. Not everyone can be convinced in the same way and most people can be convinced in various ways, depending on the circumstances. While potentially powerful and useful, the role of stories is overblown.

One of the common errors that people sometimes make when promoting the power of stories is the notion that stories work because they appeal to emotions. For example, Monteiro wrote that “…people don’t make decisions based on data; they make them based on feelings.” This is the foundation for his rationale that stories are the only path to persuasion. Stories can certainly appeal to emotions, but stories can also present facts without any emotional content whatsoever. We all, no matter how rational, are subject to emotion, but not exclusively so. Stories structure information in narrative form and those narratives can appeal to emotions, to the rational mind, or both. In other words, saying that stories are powerful is not the same as saying that appeals to people’s feelings are powerful.

Don’t get me wrong, stories are great; they’re just not the panacea that many people now claim. The current emphasis on storytelling is a fad. In time, it will fade. In time, some of the people who promote stories to the exclusion of other forms of communication will look back with embarrassment. No matter what they claim, no one actually believes that only stories can convince people. No one exclusively uses stories to persuade. We all use multiple means and that’s as it should be. The sooner we get over this nonsense that only stories can persuade, the sooner we can get on to the real task of presenting truths that matter in all the ways that work.

Visual Business Intelligence Workshop in the United States

July 8th, 2019

For the first time since I retired from teaching data visualization workshops in 2018, Nick Desbarats of Practical Reporting Inc. will be teaching my courses publicly in the United States. Nick has been busy teaching the Visual Business Intelligence Workshop publicly elsewhere and privately in several countries but will now offer it to the general public once again in the United States where it originated 15 years ago and has since reached thousands.

The workshop will take place in Portland, Oregon from October 28-31, 2019. I’m particularly thrilled about Nick’s first U.S. location, because Portland is now my home, so I’ll be able to drop by to say hello. I hope to see some of you there.

The Data Loom Is Now Available!

May 16th, 2019

After a few months of waiting, my new book The Data Loom: Weaving Understanding by Thinking Critically and Scientifically with Data is now available. By clicking on the image below, you can order it for immediate delivery from Amazon.

Data, in and of itself, is not valuable. It only becomes valuable when we make sense of it. Unfortunately, most of us who are responsible for making sense of data have never been trained in two of the job’s most essentially thinking skillsets: critical thinking and scientific thinking. The Data Loom does something that no other book does—it covers the basic concepts and practices of both critical thinking and scientific thinking and does so in a way that is tailored to the needs of data sensemakers. If you’ve never been trained in these essential thinking skills, you owe it to yourself and your organization to read this book. This simple book will bring clarity and direction to your thinking.

Turn Up the Signal; Turn Off the Noise

April 21st, 2019

To thoroughly, accurately, and clearly inform, we must identify the intended signal and then boost it while eliminating as much noise as possible. This certainly applies to data visualization, which unfortunately lends itself to a great deal of noise if we’re not careful and skilled. The signal in a stream of content is the intended message, the information we want people to understand. Noise is everything that isn’t signal, with one exception: non-signal content that somehow manages to boost the signal without compromising it in any way is not noise. For example, if we add nonessential elements or attributes to a data visualization to draw the reader’s attention to the message, thus boosting it, without reducing or altering the message in any way, we haven’t introduced noise. No accurate item of data, in and of itself, always qualifies either as a signal or noise. It always depends on the circumstances.

In physics, the signal-to-noise ratio, which is where the concept originated, is an expression of odds: the ratio of the one possible outcome to another. When comparing signal to noise, we want the odds to dramatically favor the signal. Which odds qualify as favorable varies, depending on the situation. When communicating information to someone, a signal-to-noise ratio of 99 to 1 would usually be considered favorable. When hoping to get into a particular college, however, 3-to-1 odds might be considered favorable, but those odds would be dreadful in communication, for it would mean that 25% of the content was noise. Another ratio that is common in data communication, a probability ratio, is related to an odds ratio. Rather than comparing one outcome to other as we do with odds, however, a probability ratio compares a particular outcome to the total of all outcomes. For example, a probability ratio of 85 out of 100 (i.e., the outcome of interest will occur 85% of the time on average), is the mathematical equivalent of 85-to-15 odds. When Edward Tufte introduced the concept of the data-ink ratio back in the 1980s, he proposed a probability ratio rather than an odds ratio. He argued that the percentage of ink in a chart that displays data, when compared to the total ink, should be as close to 100% as possible.

Every choice that we make when creating a data visualization seeks to optimize the signal-to-noise ratio. We could argue that the signal-to-noise ratio is the most essential consideration in data visualization—the fundamental guide for all design decisions while creating a data visualization and the fundamental measure of success once it’s out there in the world.

It’s worth noting that particular content doesn’t qualify as noise simply because it’s inconvenient. Earlier, I said that a signal is the intended message, but let me qualify this further by pointing out that this assumes the message is truthful. In fact, the message itself is noise to the degree that it communicates misinformation, even if that misinformation is intentional. I’ve seen many examples of data visualizations that left out or misrepresented vital information because a clear understanding of the truth wasn’t the designer’s objective. I’ve also witnessed occasions when highly manipulated data replaced the actual data because it told a more convenient story—one that better supported an agenda. For example, a research paper that claims a strong relationship between two variables might refrain from revealing the actual data on which those claims were supposedly based in favor of a statistical model that replaced a great deal of volatility and uncertainty in the relationship, which could be seen in the actual data, with a perfectly smooth and seemingly certain portrayal of that relationship. On occasions when I’ve questioned researchers about this, I’ve been told that the volatility in the actual data was “just noise,” so they removed it. While they might argue that their smooth model illustrates the relationship in a simpler manner, I would argue that it over-simplifies the relationship if they only report the model without also revealing the actual data on which it was based. Seeing the actual data as well helps us keep in mind that statistical models are estimates, built on assumptions, which are never entirely true.

So, to recap, noise in communication, including data visualization, is content that isn’t part of and doesn’t support the intended message or content that isn’t truthful. Turn up the signal; turn off the noise.

Worthy of Your Attention

April 9th, 2019

I spend a great deal of time reading books. Many of them cover topics that are relevant to my work in data sensemaking and data visualization, and most of them are quite good, but only a few are extraordinary. The new book, How Attention Works: Finding Your Way in a World Full of Distraction, by Stefan van der Stigchel, definitely qualifies as extraordinary.

Stigchel is a professor in the Department of Experimental Psychology at Utrecht University in the Netherlands. Until recently, I taught annual data visualization workshops in Utrecht for several years. Had I known about Stigchel at the time, I would have definitely invited him out for a beer during one of my visits. His work is fascinating. This book focuses on a specific aspect of visual perception: visual attention—what it is, how it works, how it is limited, and how it has allowed the human species to progress beyond other species. It does so in a practical manner by explaining how an understanding of visual attention can improve all forms of information design.

I only know of one other author who has written practical works about visual perception with such clarity and insight: Colin Ware, Director of the Data Visualization Research Lab at the University of New Hampshire. It was from Ware’s two books—Visual Thinking for Design and Information Visualization: Perception for Design—that I learned much that I know about visual perception and its application to data visualization. Although Stigchel doesn’t address data visualization in particular, what he reveals about visual attention complements and, in some respects, extends what Ware covers in his books. Here’s an excerpt from the preface that will give you an idea of the book’s contents and intentions:

If you dig deeper into the subject of visual perception, you will quickly discover that we actually register very little of the visual world around us. We think that we see a detailed and stable world, but this is just an illusion created by the way in which our brains process visual information. This has important consequences for how we present information to others—especially attention architects.

Everyone whose job involves guiding people’s attention, like website designers, teachers, traffic engineers, and, of course, advertising agents, could be given the title of “attention architect.” Such individuals know that simply presenting a visual message is never enough. Attention architects need to be able to guide our attention to get the message across…Whoever can influence our attention has the power to allow information to reach us or, conversely, to ensure that we do not receive that information at all.

Everyone who visualizes data and presents the results to others is an attention architect…or should be. To visualize data effectively, you must learn how to draw people’s attention to those parts of the display that matter and to prevent the inclusion of anything that potentially distracts attention from the message. You can only do this to the degree that you understand how our brains manage visual attention, both consciously and unconsciously. Reading this book is a good start.