I recently spoke at an event in Melbourne, Australia that was sponsored by Innogence, a consultancy that exclusively supports SAP business intelligence implementations. My presentation was followed by two others: one of Innogence’s customers described his experience of turning around an SAP Business Objects (hereafter referred to simply as SAP) implementation that was failing, and an employee of SAP previewed coming attractions.

The fellow from SAP didn’t describe anything new in the realm of data visualization, but referred to Crystal Xcelsius (recently re-branded into two products, SAP Crystal’s Dashboard Design and Presentation Design) and Business Objects Explorer as SAP’s current datavis offerings. Both of these products demonstrate SAP’s lack of expertise in this area. What he featured most was a new in-memory technology called HANA, which promises to speed up access to data considerably, but at significant cost. While he was describing HANA, I couldn’t help but fear that it might at best enable a faster trip to nowhere.

Big, old, traditional BI companies are good at producing technologies that enhance the infrastructure of business intelligence—more and faster—but not the actual use of data in ways that lead to greater intelligence. Being big, focused primarily on technology from an engineering perspective, and devoutly sales driven makes it difficult for companies like SAP to develop useful tools for activities that support decision making: data exploration, sensemaking, and communication. To meet this challenge, they must shift their focus from technology to the humans who use it—our needs and abilities—and expand their perspective to embrace design. They must commit their efforts to what actually works, rather than silly, shiny features that fill their existing products with smoke and mirrors.

It will be hard for large organizations to turn their ships against the tide of tradition into unfamiliar waters. If it can be done at all, it will take time. Will SAP and other big vendors find their way into the analytics age? If so, will they do it in time, or will analytics become the exclusive realm of smaller and more agile vendors, leaving traditional BI companies in the back room to maintain the infrastructure (data collection, transformation, cleansing, warehousing, and production reporting)? Only time will tell, but I recently received a glimmer of hope that SAP just might discover the path forward. I had lunch three weeks ago with John Armitage, who has been tasked with improving the user experience of SAP’s information visualization offerings across the board. I’ve known John for a few years, and although I don’t know him well, I believe he has the perspective, experience, and skills that are needed to give this big ship a new bearing. John is a designer with years of experience working in the field of usability. He’s one of the guys inside the company who quietly thank me when I point out flaws in SAP’s data visualization capabilities and challenge them to do better.

If big, old, traditional BI companies are going to find their way from the back room to the living room and kitchen where people live, they’ll need people like John Armitage, and they’ll need to listen to them. John’s role can’t be token; it must help drive the organization.

Despite the acid that often drips from my tongue when I speak out against SAP’s contrived and clueless attempts so far to support data visualization, I’m not rooting for them to fail. That is, unless they continue to promote hollow promises about dysfunctional products, in which case they’ll deserve to fail and I’ll gladly bid them goodbye.

Take care,

Despite the many negative reviews that I’ve written over the years in this blog, nothing pleases me more than opportunities to praise something that’s exceptionally good. A new book by Jeff Johnson, Designing with the Mind in Mind: A Simple Guide to Understanding User Interface Design Rules, gives me just such an opportunity.

Even before opening it for the first time, I felt confident that this book would be worthwhile, based solely on the fact that Stuart Card wrote the foreword. Card is one of the smartest guys I know. His research in the fields of data visualization and human-computer interaction is some of the best. It always digs its roots deep into the soil of human perception and cognition, growing insights and best practices in design from an understanding of human needs and abilities. Jeff Johnson, who at one time worked with Card at Xerox, writes in this same spirit. This book teaches software design practices that, as the title suggests, directly address what science has revealed about the abilities and limitations of our brains.

Anyone who has a hand in developing software applications that people interact with should read this book. That includes those who build data visualization applications, such as custom analytical applications and performance monitoring dashboards. Computer technologies that are supposed to help people think will only work if they’re designed to interact hand in glove with human perception and cognition. This book distills the most important insights we’ve learned about how the human brain functions for the purpose of human-computer interaction, explains them simply, clearly, entertainingly, and in practical terms, then goes on to teach design principles that should be followed to build systems that people can interact with productively and enjoyably. Unlike the cookbook approach to guidelines of many design books (do this, don’t do that, and don’t ask why), Johnson explains enough about the inner workings of our brains to help us make sense of the guidelines.

User-interface design rules and guidelines are more like laws than like rote recipes. Just as a set of laws is best applied and interpreted by lawyers and judges who are well versed in the laws, a set of user-interface design guidelines is best applied and interpreted by people who understand the basis for the guidelines and have learned from experience in applying them. (page xii)

Only when we learn the “how” and “why” of rules can we apply them skillfully, especially when faced with novel situations. True to the lessons that he teaches about interacting with the human brain, Johnson writes in a manner that is always on message, understandable, and thoroughly enjoyable.

Even though I was already familiar with much of the material in this book—not surprising given my interest and extensive reading in the field—I was delighted by how much I learned. Several insights were new to me and a few things that I thought I knew well were expanded and corrected to some degree. For example, Johnson’s description of short-term and working memory, based on the latest research and conceptual models, revealed that my understanding was over-simplified and definitely outdated.

In the final sentence of the book’s foreword, Stuart Card wrote: “Above all, this is a book of profound insight into the human mind for practical people who want to get something done.” I wholeheartedly agree.

Take care,

One of the guiding mottos of my work in data visualization is “eloquence through simplicity”—eloquence of communication through simplicity of design. I share this goal with many designers, present and past. Leonardo da Vinci once wrote: “Simplicity is the ultimate sophistication.” Unfortunately, people sometimes misunderstand what we mean by simplicity, assuming that simplification is a pitched battle against complexity, striving to eliminate it. In fact, thoughtful simplification preserves useful complexity and makes it easier to manage by reducing it to its essence.

I was prompted to write on this topic while reading Donald Norman’s latest book, Living with Complexity. Norman is a brilliant designer, whose work has helped to shape me, but in the midst of many gems of insight in this book, some of his ideas about simplicity and complexity struck me as logically flawed, especially the following:

Simplicity is not the opposite of complexity: complexity is a fact of the world, whereas simplicity is in the mind. (p. 53)

Did I read correctly? Complexity is a fact of the world but simplicity is not? There are things in the world that are actually complex but none that are actually simple? Simplicity is in the mind (i.e., a matter of perception) but complexity is not? We might perceive something that is complex as simple, but never the opposite? Does this make sense? To me, it doesn’t. Nothing about simplicity and complexity requires Norman’s distinction.

Although simplicity and complexity are not in conflict with one another, they are indeed opposites in that they are two poles of a continuum—the more complex something seems the less simple it seems, and vice versa. They describe our perceptions of and interactions with things that we encounter in the world. All that we encounter falls somewhere along this continuum as experienced from a particular perspective and engaged for a particular purpose. Our understanding of something, whether simple or complex, is in our minds, a matter of perception; our interaction with something, whether simple or complex, is determined by our abilities.

The closest that we can come to declaring some things as simple or complex in and of themselves is rooted in the fact that things are composed of parts or units—the fewer the simpler. In this sense, the more units that combine to form what we perceive as a thing in the world (a product, process, system, etc.), the more complex it is. The idea that simplicity or complexity resides in the thing itself, something that can be objectively measured, and not in our perception of it, falls apart, however, because there exists no single correct way to draw the lines that break something into its parts. This is a matter of perception. Things that seem simplest are those that we perceive as a single conceptual unit. The more conceptual units that must be combined to form a whole, the more complex we perceive the thing to be. In other words, the only useful way to frame simplicity and complexity is as a continuum of perception, not as facts of the world.

Through experience and learning, things that we once perceived as complex become perceived as progressively simpler. We tame complexity by breaking something down into the simpler conceptual units of which it is composed, while working to understand how they relate to one another. Through experience (practice, practice, practice), we combine these simpler units into progressively larger chunks, which we learn to hold in our minds and memory as single conceptual units. These mental constructs are called “conceptual models” or “mental models.” As a cognitive psychologist, Norman has done a great deal to help the world of design understand the role of conceptual models.

When I preach the glories of simplicity, I’m not saying that complexity is bad or that it should be ignored or eliminated. Much of what we face in the world seems complex. Much that is valuable seems complex. Complexity can be a source of incredible enjoyment; grappling with it can be a delightful form of play. We seek to understand complexity, co-exist with it, and make use of it by taming it. We do so by representing it as simply as possible without sacrificing what’s essential and useful. We do so in part by removing all that is extraneous to the thing by paring it down to its essence in relation to a particular goal.

When working with information to understand and communicate it—the focus of my work—I strive to represent it simply by removing what’s not essential, but to never oversimplify. In so doing, I hope to make complexity manageable, refusing to let it become unnecessarily complicated.

By paring information down to its essence, relative to our goals, we can make a great deal of information manageable. This is especially true of dashboard displays: single screens of information that people monitor to maintain the situation awareness that enables them to do their jobs effectively. Most of the dashboards that exist in the world fail because, by including so much that is extraneous to the information that’s needed, relatively little information can be meaningfully displayed. Screen real estate is wasted by filling it with visual content that isn’t information, and the viewer’s attention is distracted by this fluff from the little information that’s actually there. When properly designed, however, perceived complexity can be tamed, making it possible for a dashboard to display a dense and rich collection of information. Airline pilots learn to manage a huge amount of information in cockpit displays, with practice, if the displays are well designed. This potential exists in dashboards and is made possible, not primarily by the wonders of technology, but by the effectiveness of the design.

Complexity is our friend. The more complexity we learn to manage, the greater our knowledge and abilities become. Complex information is definitely our friend. It’s time we learned to tame it.

Take care,

As a teacher, I want to spread the gospel of data visualization far and wide, but only in ways that I can do it well. It’s tempting to have my books translated into other languages, but if I don’t understand them, I can’t confirm the quality of the work, so I refrain. My data visualization courses could reach more people if I produced electronic versions of them, but I haven’t found a way to create rich learning experiences without interacting with my students directly, so I continue looking for new ways to bend technology and instructional methods to my needs. I could also extend my reach by allowing others to teach my courses, but to do so I would need to supervise their work, which is not how I want to spend my time. Anyone with a teaching mission faces this challenge.

Another way to reach more people with the content of my work, which I’ve enthusiastically embraced, involves collaboration with like-minded individuals in a way that requires no supervision, because there is a clear separation between their work and mine. I teach fundamental principles and practices in a generic manner so they can be applied by organizations of all types—businesses, schools, non-profit organizations, and government agencies. I try to make the concepts as easy to understand and broadly applicable as possible. It is often useful, however, to tailor these concepts for particular audiences, addressing their particular needs using data and examples that are relevant to them. If I were to do this myself, I’d have little time left to build the broader foundation of principles, practices, and skills that are so desperately needed. The truth is, others can adapt my work to meet the needs of specific audiences much better than I ever could, because they already know these specialized audiences well.

This vision is now being realized in the healthcare sector, thanks to the work of my friend and colleague, Katherine Rowell. I first met Kathy when she invited me to do some internal teaching and consulting for the healthcare data company QC Metrix, where she worked at the time. Since then, she has become a powerful advocate for the use of data visualization to improve the quality of healthcare, based on the greater insights and improved outcomes that good visual sensemaking and presentation practices can provide. Her specialized data visualization services are now being offered through Katherine S. Rowell & Associates.

Using my three books as the conceptual foundation of her work, she’s developing her own healthcare-specific versions of my courses. If you’re involved in healthcare, I encourage you to review her website and see what’s she’s doing. Perhaps sign up for her free newsletter while you’re at it. I found her most recent newsletter article, “Data Visualization Double Take,” about a misleading chart related to the importance of mammograms, interesting and insightful.

When I was invited by the World Health Organization (WHO) to teach and advise them next May, I asked Kathy if she’d join me for that week in Geneva. Even though I usually work alone, this is the kind of collaboration that excites me, because I know it will produce a better outcome for this important organization than I could ever produce on my own.

What Kathy Rowell in doing for healthcare, others could do elsewhere. For instance, I would warmly welcome someone with expertise in the visual analysis and presentation of education data who wanted to develop specialized data visualization services for educators. Helping particular audiences discover and apply the power of data visualization is fertile ground for exciting and meaningful work.

Take care,

Hans Rosling of GapMinder is one of my heroes. He has become an engaging and powerful teller of quantitative stories. He’s making a difference in the world. Even the most talented among us, however, sometimes slip up. Rosling’s recent video, produced by BBC Four, takes advantage of technology to place him behind a transparent bubble chart, making it possible for him to direct our attention to particular items with greater ease and clarity, without blocking our view—a worthy goal for statistical narrative. This approach suffers, however, from one significant flaw: in addition to Rosling, an entire room with bright lights, beams, and windows appears in the background as well, resulting in a great deal of distraction.

The production crew could have easily used a clean backdrop for the video, which would have removed all distractions and made it easy to focus on the data and Rosling’s narration. This problem perhaps never occurred to the technicians (although it should have), and I suspect that Rosling had no idea that all those windows and lamps with glaring lights would show up in the finished video. Attention to these details, however, makes the difference between fun and engaging data visualizations that tell stories effectively and those that feature novelty and entertainment over substance. To focus attention on the story, all distractions must be removed. As we venture into new opportunities that technology makes possible, we dare not forget the important lessons of the past. In the years since Edward Tufte began promoting the reduction of non-data ink in visual displays, research has confirmed the importance of this practice due to limitations in human perception, cognition, and memory. We can only focus on a small portion of the visual field at one time, we can only consciously attend to one task at a time, and we can only hold about three chunks of visual information at a time. There is no room for distraction of any kind. Anything that isn’t data must have a good reason to be there or it should be eliminated. As Antoine de Saint-Exupery once wrote, “Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away.” This is especially true when telling quantitative stories.

Take care,