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.


Data Communicators – People Who Aren’t Interested and Don’t Care Are Not Your Audience

October 3rd, 2017

This week, I am enjoying the pleasure of my friend Alberto Cairo’s company. Alberto traveled to Portland, Oregon to speak for two events and I’m serving as innkeeper and chauffeur while he’s here. Last night an interesting topic arose over dinner. Several interesting topics, actually, but I’d like to share one in particular. Alberto and I both found ourselves bemoaning the assumption of too many data communicators that their audience isn’t interested in the data. This assumption leads to a great deal of poorly designed data displays.

The particular example that prompted our discussion was the assumption that people are unwilling to read brief instructions that explain how to interpret a chart. This assumption leads many data communicators to present data in ways that aren’t particularly informative out of concern that the better form of display would require a bit of instruction. What a travesty!

When we prepare data communications, we should almost always design them for people who are interested in the data. Dumbing the information down or adding entertaining effects that make the data difficult to interpret or comprehend is never justified.

Over the years I have had many debates with people who defend severe compromises in design effectiveness because they believe that their audience must, above and before all, be entertained. There is a place for entertainment. I incorporate a great deal of humor in my classes and lectures. I do so, however, in ways that don’t detract from the learning experience by compromising the content. Humor, used skillfully, can enhance the learning experience. Similarly, data can be displayed in visually engaging ways that enhance the degree to which the data informs, but this requires skill. Merely dressing up the data or adding meaningless and distracting visual effects requires no skill whatsoever, and it results in harm.

Personally, I have never assumed that my audience wasn’t interested in the data that I was presenting to them. I wouldn’t bother presenting data to people who weren’t interested and didn’t care. What would be the point? I match the content of my communications to the needs and interests of the audience. I don’t speak to audiences who lack needs and interests that I’m well-suited to address.

When we present information to people who are interested in it, we can focus on communicating as clearly, accurately, and fully as possible. If you have something to communicate that people care about, you are responsible for doing it well. If your audience isn’t interested in data that you’re communicating, perhaps you have the wrong audience.

Take care,


Data Is Not Beautiful

August 16th, 2017

Despite the rhetoric of recent years, data is neither beautiful nor ugly. Data is data; it merely describes what is and has no aesthetic dimension. The world that’s revealed in data can be breathtakingly beautiful or soul-crushingly ugly, but data itself is neither.

We can respond to data in ways that create beauty, justice, and wellbeing. We can do this, in part, both through data visualization and data art. Though data visualization and data art are constructed from the same raw materials (i.e., data), their methods differ. What does not differ, however, is their ultimate purpose to present or evoke meaning. When I visualize data, I do it to bring specific meanings to light or to make it possible for others to do that on their own. Similarly, when skilled data artists express data, they do it to evoke a meaningful experience. Even if the data artist’s meaning is less specific than mine as a data visualizer, the artist intends for the viewer to experience meaning and often emotion as well.

I appreciate good data art just as I appreciate good art of all types. What I cannot stomach is meaningless visual drivel that calls itself data art or, even worse, calls itself data visualization. I stridently object to the work of lazy, unskilled creators of meaningless, difficult to read, or misleading data displays. I’m referring to visualizations that fail to display data in ways that promote clear and true understanding. Many data visualizations that are labeled “beautiful” are anything but. Instead, they pander to the base interests of those who seek superficial, effortless pleasure rather than understanding, which always involves effort. There might be occasions when meaningless pleasure is useful, but not when data is being displayed. Data can potentially inform. We should never squander this potential.

Take care,


Something Going Up Is Not Always Good

August 7th, 2017

Even though our unique ability to deal with complexity propelled humans to the top of the evolutionary heap, we still crave simplistic (i.e., overly simple) explanations. I promote the value of simplicity in my work, but never simplicity that sacrifices truth. Simple things can and should be explained simply. Complex things can and should be explained as simply as possible, but never in a way that disregards or misrepresents their complexity.

When people hold simplistic assumptions about data, we should educate them, not accommodate their ignorance. One such assumption is that, in a time series, values going up are always good and values going down are always bad. I find it odd that people tend to interpret data in this manner, because no one interprets life in this manner. While we consider it good when our incomes go up or our health improves, we have no trouble recognizing that the cost of food going up or increases in suffering are bad. Why would we interpret data in this naive manner?

How do you deal with the commonplace exceptions to the “going up is good assumption,” such as the variance between actual and budgeted expenses? When considering expenses, being over budget is usually considered bad. Through the years of teaching data visualization courses, participants in my classes have often suggested that this assumption should be accommodated by reversing the quantitative scale, placing the negative values (i.e., under budget) above and the positive values (i.e., over budget) below. Is this an appropriate solution? Representing negative values as going up creates a new source of confusion, and does so unnecessarily.

Rather than accommodating ignorance by twisting data into awkward arrangements, why not correct the error instead? It is easy to explain that things going up aren’t always good in a way that everyone can understand. When specific cases of ignorance can be banished so quickly, easily, and permanently, why perpetuate it?

Data sensemaking and communication fundamentally seek to replace ignorance with understanding. Everything that we do in this venture should be done with this in mind. When we accommodate ignorance, we condone and encourage it. Doing so undermines the integrity of our work and the outcomes that we should be working hard to achieve.

Take care,


Confusing Expressions of Relative Proportions

July 17th, 2017

During elementary school we learn to reason quantitatively in fundamental ways. One of the concepts that we learn along the way is that of proportions. We are taught to express a value that is greater than another either in terms of multiplication (e.g., “The value of A is three times the value of B”), as a ratio (e.g., a 3 to 1 ratio), as a fraction in which the numerator is greater than the denominator, usually with a denominator of 1  (e.g., 3/1), or as a percentage that is greater than 100% (e.g., 300%). We are taught to express a value that is less than another either as a ratio (e.g., 1 to 3), as a fraction with a numerator that is less than the denominator, usually with a numerator of 1 (e.g., 1/3), or as a percentage that is less than 100% (e.g., 33%). In later years, however, some of us begin to express proportions in confusing and sometimes inaccurate ways.

Consider a case in which the value of A is $100 and the value of B is $300. To express the greater value of B proportionally as a percentage of A’s value, would it be accurate to say that B is 300% greater than A? No, it wouldn’t. B is only 200% greater than A (300% – 100% = 200%). It is correct, however, to say that “the value of B is 300% the value of A.” To avoid confusion for most audiences, it usually works better to express this proportional difference in terms of multiplication, such as “The value of B is three times the value of A.”

Confusion can also occur when we describe lesser proportions. Recently, while reading a book by a neuroscientist who has closely studied how humans reason quantitatively, I came across the unexpectedly confusing expression “a million times less.” As I understand it, you can reduce a value through multiplication only by multiplying it by a value that is less than one (e.g., a fraction such as 1/3 or a negative value such as -1). The author should have expressed the lesser proportion as “one millionth,” which is conceptually clear.

Consider the following results that encountered in Google News:

Mac Management Cost Headline

Notice the sentence attributed to Business Insider: “Macs are 3 times cheaper to own than Windows PCs…” Is the meaning of this proportion clear? It isn’t clear to me. It makes sense to say that something is three times greater, but not three times less. What the writer should have said was “Macs are one-third as expensive to own as Windows PCs,” or could have reversed the comparison by describing the greater proportion, as Computerworld did: “IBM says it is 3X more expensive to manage PCs than Macs.” When you describe a lesser proportion, express the difference either as a fraction with a numerator that is less than the denominator, usually with a numerator of 1 (e.g., 1/3rd the cost), as a percentage less than 100% (e.g., 33% of the cost), or as a ratio that begins with the smaller value (e.g., a cost ratio of 1 to 3).

People often struggle to understand proportions. We can prevent many of these misunderstandings by expressing proportions properly.

Take care,


Data Analysts Must Be Critical Thinkers

July 13th, 2017

During my many years of teaching, I have often been surprised to discover a lack of essential thinking and communication skills among the educated. Back when I was in graduate school in Berkeley studying religion from a social science perspective, I taught a religious studies course to undergraduate students at San Jose State University. When I first began grading my students’ assignments, I was astounded to discover how poorly many of my students expressed themselves in writing. There were delightful exceptions, of course, but several of my students struggled to construct a coherent sentence. Much of my time was spent correcting failures of communication rather than failures in grasping the course material. Many years later, when I taught data visualization in the MBA program at U.C. Berkeley’s Haas School of Business, I found that several of my students struggled to think conceptually, even though the concepts that I taught were quite simple. They were more comfortable following simple procedures (“Do this; don’t do that.”) without understanding why. In the 14 years since I founded Perceptual Edge, I’ve had countless opportunities—in my courses, on my blog, in my discussion forum, and when reviewing academic research—to observe people making arguments that are based on logical fallacies. These are people whose work either directly involves or indirectly supports data analysis. This horrifies me. This is one of the reasons why analytics initiatives frequently fail. No analytical technologies or technical skills will overcome a scarcity of sound reason.

Many of those tasked with data sensemaking—perhaps most—have never been trained in critical thinking, including basic logic. Can you analyze data if you don’t possess critical thinking skills? You cannot. How many of you took a critical thinking course in college? I’ll wager that relatively few of you did. Perhaps you later recognized this hole in your education and worked to fill the gap through self-study. Good for you if you did. Critical thinking does not come naturally; it requires study. Even though I received instruction in critical thinking during my school years, I’ve worked diligently since that time to supplement these skills. Many books on critical thinking line my bookshelves.

Good data analysts have developed a broad range of skills. Training in analytical technologies is of little use if you haven’t already learned to think critically. If you recognize this gap in your own skills, you needn’t despair, for you can still develop them now. A good place to start is the book Asking the Right Questions: A Guide to Critical Thinking, by M.N. Browne and S.M. Keeley.

Take care,