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.


Details Regarding the Future of the Visual Business Intelligence Workshops

June 2nd, 2017

This blog entry was written by Nick Desbarats.

As Steve has recently announced, he’s decided to transition away from teaching in the coming months to focus on new projects. After more than 15 years in the field of data visualization and more than 30 in IT, hopefully, we can all find our way to forgiving him for shifting his focus. It seems futile to try to estimate the number of people and organizations that have learned how to understand and communicate their data from Steve, but to say he’s changed the day-to-day practice of data visualization for more people in more ways than anyone else doesn’t strike me as hyperbolic.

While anyone who’s attended one of Steve’s workshops will tell you that it was transformative, I was completely bowled over by the public workshop that I attended in Minneapolis in 2013. My data analysis and visualization work suddenly and unexpectedly collided with my longstanding interest in research findings in the fields of perception, cognition, neuroscience, decision-making, and design. I’d seen and done plenty of public speaking by that point, but the skill with which this potentially esoteric knowledge was explained and the accessible and engaging way in which it was delivered were astonishing. My eyes were opened to the importance of these crucial skills, the absence of which leads to bad decisions that cause untold suffering and waste around the globe every day.

Shortly after attending that workshop, I approached Steve and rather sheepishly asked if he’d ever considered adding a second instructor to teach his courses. Our subsequent conversations quickly veered away from data visualization and into a strikingly wide array of topics, touching on pedagogical research, evolutionary psychology, critical and statistical thinking, organized religion, artificial intelligence, and the nature of science, to name but a few. Seven months and the steepest learning curve I’ve ever experienced later, I began teaching Steve’s courses as on-site group workshops at organizations such as NASA, Bloomberg, and the Central Bank of Tanzania. Seeing lightbulbs go off above more than 1,000 workshop participants’ heads since then has been incredibly gratifying.

As Steve mentioned, I’ll soon begin teaching public workshops in addition to the private workshops and consulting engagements that I’ve been delivering via Perceptual Edge since 2014. I won’t say that I’ll try to fill Steve’s shoes since that would clearly be delusional, however, I will say that I’ll bring the same drive to increase data analysis and communication competence in the world to this work. Specifically, the following changes will occur on January 1st, 2018:

  • The Show Me the Numbers, Information Dashboard Design, and Now You See It courses will be offered via my new consultancy, Practical Reporting Inc., the website for which will be launched this summer and announced on this blog. The content of these courses will remain the same aside from some updating and minor tweaking, and Steve’s books will continue to be provided to all workshop participants.
  • Public workshops will continue to be offered in the U.S. and internationally. Public workshop locations, dates and registration links will be posted on the Practical Reporting website.
  • I’ll continue to deliver dashboard design consulting services and private, on-site training workshops for groups of 30 to 70 participants. These services will start being offered through Practical Reporting instead of Perceptual Edge.
  • I’ll continue to write about data visualization, dashboard design, and other topics, but will begin to do so on the Practical Reporting blog following its launch. I’ll also be soliciting feedback on sections of a new book on which I’m working that proposes a blueprint for organizing and designing whole data presentation systems that include dashboards, as well as other types of information displays, such as lookup displays and self-serve analysis displays.

Being mentored by Steve has been a unique and life-changing experience for which I will always be grateful, and his friendship is one that I’ll continue to hold dear. Teaching his workshops is an awesome responsibility, but it’s one that I relish. I hope you’ll join me as I take the torch from Steve and continue to teach his courses, courses that I found so transformative and insightful back when he first taught them to me.


Future Plans for the Visual Business Intelligence Workshops

June 1st, 2017

Since founding Perceptual Edge back in 2003, I’ve put in an enormous number of miles on airplanes. I’ve made most of these flights to teach workshops. After 14 years of this, I’m sure you’ll understand when I say that I’m weary of travel.

I’m leading up to some news. I will only be teaching three more of my Visual Business Intelligence Workshops. This work is no less important than when I began, but I’m now ready to focus my efforts differently and to spend more of my time close to home.

When I began this venture, data visualization was not well known, but that would soon change. I helped to build data visualization into the popular field of study and work that it is today, but with popularity has come an incursion of nonsense and bad practices. I trust that the tide will shift in time, but I’ve done my part as an educator and will now leave it to others to carry on this important work.

In September of this year I’ll teach my final U.S.-based Visual Business Intelligence Workshop in Portland, Oregon and in April of next year I’ll teach my final non-U.S.-based workshop in Stockholm, Sweden. The only other workshop that I’ll be teaching will take place in Sydney, Australia next week. If you find this disappointing, I have some good news. My workshops will continue, just not with my direct involvement. Nick Desbarats, who has been teaching my courses privately for the last few years, will begin teaching my courses in public workshops as well through his own company, Practical Reporting. Nick will describe his plans in this blog soon.

Now that little of my time will be dedicated to teaching and travel, I’ll be shifting the focus of my work to more research and writing. I have several projects that have been waiting in the wings for uninterrupted time to become available. I’m thrilled that this time is finally at hand.

Take care,


Lollipop Charts: “Who Loves You, Baby?”

May 17th, 2017

If you were around in the ‘70s, you probably remember the hard-edged, bald-headed TV police detective named Kojak. He had a signature phrase—“Who loves you, baby?”—and a signature behavior—sucking a lollipop. The juxtaposition of a tough police detective engaged in the childish act of sucking a lollipop was entertaining. The same could be said of lollipop charts, but data visualization isn’t a joke (or shouldn’t be). “Lollipop Chart” is just cute name for a malformed bar graph.

Bar graphs encode quantitative values in two ways: the length of the bar and the position of its end. So-called lollipop charts encode values in the same two ways: the length of the line, which functions as a thin bar, and the position of its bulbous end.

Lollipop Chart Example

A lollipop chart is malformed in that it’s length has been rendered harder to see by making it thin, and its end has been rendered imprecise and inaccurate, by making it large and round. The center of the circle at the end of the lollipop marks the value, but the location of the center is difficult to judge, making it imprecise compared to the straight edge of a bar, and half of the circle extends beyond the value that it represents, making it inaccurate.

What inspired this less effective version of a bar graph? I suspect that it’s the same thing that has inspired so many silly graphs: a desire for cuteness and novelty. Both of these qualities wear off quickly, however, and you’re just left with a poorly designed graph.

You might feel that this is “much ado about nothing.” After all, you might argue, lollipop charts are not nearly as bad as other dessert or candy charts, such as pies and donuts. This is true, but when did it become our objective to create new charts that aren’t all that bad, rather than those that do the best job possible? Have we run out of potentially new ways to visualize data effectively? Not at all. Data visualization is still a fledgling collection of visual representations, methods, practices, and technologies. Let’s focus our creativity and passion on developing new approaches that work as effectively as possible and stop wasting our time striving for good enough.

Take care,


What Is Data Visualization?

May 4th, 2017

Since I founded Perceptual Edge in 2003, data visualization has transitioned from an obscure area of interest to a popular field of endeavor. As with many fields that experience rapid growth, the meaning and practice of data visualization have become muddled. Everyone has their own idea of its purpose and how it should be done. For me, data visualization has remained fairly clear and consistent in meaning and purpose. Here’s a simple definition:

Data visualization is a collection of methods that use visual representations to explore, make sense of, and communicate quantitative data.

You might bristle at the fact that this definition narrows the scope of data visualization to quantitative data. It is certainly true that non-quantitative data may be visualized, but charts, diagrams, and illustrations of this type are not typically categorized as data visualizations. For example, neither a flow chart, nor an organization chart, nor an ER (entity relationship) diagram qualifies as a data visualization unless it includes quantitative information.

The immediate purpose of data visualization is to improve understanding. When data visualization is done in ways that do not improve understanding, it is done poorly. The ultimate purpose of data visualization, beyond understanding, is to enable better decisions and actions.

Understanding the meaning and purpose of data visualization isn’t difficult, but doing the work well requires skill, augmented by good technologies. Data visualization is primarily enabled by skills—the human part of the equation—and these skills are augmented by technologies. The human component is primary, but sadly it receives much less attention than the technological component. For this reason data visualization is usually done poorly. The path to effective data visualization begins with the development of relevant skills through learning and a great deal of practice. Tools are used during this process; they do not drive it.

Data visualization technologies only work when they are designed by people who understand how humans interact with data to make sense of it. This requires an understanding of human perception and cognition. It also requires an understanding of what we humans need from data. Interacting with data is not useful unless it leads to an understanding of things that matter. Few data visualization technology vendors have provided tools that work effectively because their knowledge of the domain is superficial and often erroneous. You can only design good data visualization tools if you’ve engaged in the practice of data visualization yourself at an expert level. Poor tools exist, in part, because vendors care primarily about sales, and most consumers of data visualization products lack the skills that are needed to differentiate useful from useless tools, so they clamor for silly, dysfunctional features. Vendors justify the development of dumb tools by arguing that it is their job to give consumers what they want. I understand their responsibility differently. As parents, we don’t give our children what they want when it conflicts with what they need. Vendors should be good providers.

Data visualization can contribute a great deal to the world, but only if it is done well. We’ll get there eventually. We’ll get there faster if we have a clear understanding of what data visualization is and what it’s for.

Take care,


We Never Think Alone: The Distribution of Human Knowledge

May 3rd, 2017

Only a small portion of the knowledge that humans have acquired resides in your head. Even the brightest of us is mostly ignorant. Despite this fact, we all suffer from the illusion that we know more than we actually do. We suffer from the “knowledge illusion,” in part, because we fail to draw accurate boundaries between the knowledge that we carry in our own heads and the knowledge that resides in the world around us and the minds of others. A wonderful new book by two cognitive scientists, Steven Sloman and Philip Fernback, titled The Knowledge Illusion: Why We Never Think Alone, describes the distributed nature of human knowledge and suggests how we can make better use of it.

The Knowledge Illusion

The following four excerpts from the book provide a sense of the authors’ argument:

The human mind is both genius and pathetic, brilliant and idiotic. People are capable of the most remarkable feats, achievements that defy the gods…And yet we are equally capable of the most remarkable demonstrations of hubris and foolhardiness. Each of us is error-prone, sometimes irrational, and often ignorant…And yet human society works amazingly well…

The secret of our success is that we live in a world in which knowledge is all around us. It is in the things we make, in our bodies and workspaces, and in other people. We live in a community of knowledge.

The human mind is not like a desktop computer, designed to hold reams of information. The mind is a flexible problem solver that evolved to extract only the most useful information to guide decisions in new situations. As a consequence, individuals store very little detailed information about the world in their heads. In that sense, people are like bees and society a beehive: Our intelligence resides not in individual brains but in the collective mind.

Being smart is about having the ability to extract deeper, more abstract information from the flood of data that comes into our senses…The mind is busy trying to choose actions by picking out the most useful stuff and leaving the rest behind. Remembering everything gets in the way of focusing on the deeper principles that allow us to recognize how a new situation resembles past situations and what kinds of actions will be effective.

In a world with rapidly increasing stores of information, it is critical that we learn how to find the best information (the signals) among the mounds of meaningless, erroneous, or irrelevant information (the noise) that surrounds us. Individually, we can only be experts in a few domains, so we must identify and rely on the best expertise in other domains. We don’t benefit from more knowledge; we benefit from valid and useful knowledge. One of the great challenges of our time is to find ways to identify, bring together, and encourage the best of what we know.

The power of crowdsourcing and the promise of collaborative platforms suggest that the place to look for real superintelligence is not in a futuristic machine that can outsmart human beings. The superintelligence that is changing the world is the community of knowledge. The great advances in technology aren’t to be found in creating machines with superhuman horsepower; instead, they’ll come from helping information flow smoothly through ever-bigger communities of knowledge and by making collaboration easier. Intelligent technology is not replacing people so much as connecting them.

 This book is well written and accessible. It provided me with many valuable insights. I’m confident that it will do the same for you.

Take care,