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.

 

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,

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Stephen Few’s Final U.S. Workshop

July 27th, 2017

This blog post was written by Bryan Pierce of Perceptual Edge.

It’s only a little over seven weeks until Stephen Few will be teaching the West Coast Visual Business Intelligence Workshop in Portland, Oregon on September 18-21, 2017. This will be the final Visual Business Intelligence Workshop that he will teach in the United States, and the second-to-last public workshop of this type that he will teach anywhere. We still have about 20 seats available each day, but we expect the workshop to sell out, so if you’d like to attend, I recommend that you register soon.

I hope to see you in Portland!

-Bryan

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,

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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,

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The Devaluation of Expertise

July 11th, 2017

Like it or not, we rely heavily on experts to function as a society. Expertise—high levels of knowledge and skill in particular realms—fuels human progress and continues to maintain it. For this reason, it is frightening to observe the ways in which expertise has been devalued in the modern world, nowhere more so than in America.

My most vivid and direct observations of this problem involve the ways that my own area of expertise—data visualization—has been diluted by the ease with which anyone with a modicum of experience can claim to be a data visualization expert today. Learn how to use a product such as Tableau or Power BI today, or Xcelsius a few years ago, and you’re suddenly a data visualization expert. Write a blog about data visualization and you certainly must be an expert. With the relative ease of publication today, you can even write a book about data visualization without ever developing more than a superficial understanding. This is nonsense, it is frustrating to those of us who have actually developed expertise, and it is downright harmful to people who accept advice from faux-experts.

My other direct observation of this phenomenon is the way in which the Internet has inclined people to believe that they are instant experts in anything that they can read about on the Web. Not only do some of the people with scant data visualization knowledge who write comments in response to this blog believe that they know more about it than I do, but many of us are inclined to instruct our medical doctors or our attorneys after an hour or two of Web browsing. We even have the temerity to call simple Web searches “research,” disrespecting those whose work involves actual research. The phrases, “I’m doing research on…” and “I’m an expert in…” used to mean more than they do today.

I’m not alone in my concern about this. I just finished reading a book by Tom Nichols entitled The Death of Expertise: The Campaign Against Established Knowledge and Why it Matters, which clearly describes this problem in great breadth and depth.

Death of Expertise

The book’s title is a bit of a misnomer, no doubt chosen to get our attention, for Nichols isn’t arguing that expertise is going away, but that its value is being devalued and ignored. Here’s an excerpt from the book’s jacket:

Thanks to technological advances and increasing levels of education, we have access to more information than ever before. Yet rather than ushering in a new era of enlightenment, the information age has helped fuel a surge in narcissistic and misguided intellectual egalitarianism that has crippled informed debates on any number of issues. Today, everyone knows everything: with only a quick trip through WebMD or Wikipedia, average citizens believe themselves to be on an equal intellectual footing with doctors and diplomats. All voices, even the most ridiculous, demand to be taken with equal seriousness, and any claim to the contrary is dismissed as undemocratic elitism.

As I mentioned earlier, this problem is perhaps most extreme in America. We have always prided ourselves on being self-made and resistant to intellectual elitism. It’s a deeply ingrained strain of the American myth. Nichols writes:

Americans have reached a point where ignorance, especially of anything related to public policy, is an actual virtue. To reject the advice of experts is to assert autonomy, a way for Americans to insulate their increasingly fragile egos from ever being told they’re wrong about anything. It is a new Declaration of Independence: no longer do we hold these truths to be self-evident, we hold all truths to be self-evident, even the ones that aren’t true. All things are knowable and every opinion on any subject is as good as any other…The foundational knowledge of the average American is now so low that it has crashed through the floor of “uninformed,” passed “misinformed” on the way down, and is now plummeting to “aggressively wrong.” People don’t just believe dumb things; they actively resist further learning rather than let go of those beliefs.

This isn’t all due to the Internet. Other factors are contributing to the devaluation of expertise as well, including our institutions of higher learning.

Higher education is supposed to cure us of the false belief that everyone is as smart as everyone else. Unfortunately, in the twenty-first century the effect of widespread college attendance is just the opposite: the great number of people who have been in or near a college think of themselves as the educated peers of even the most accomplished scholars and experts. College is no longer a time devoted to learning and personal maturation; instead, the stampede of young Americans into college and the consequent competition for their tuition dollars have produced a consumer-oriented experience in which students learn, above all else, that the customer is always right.

I observed during my own time of teaching at U.C. Berkeley that institutions of higher learning have become businesses that do what they must to compete for customers. Professors must please their students (customers) by providing them with an enjoyable experience if they wish to keep their jobs. Learning, however, is hard work.

Journalism also contributes to this problem when it focuses on giving readers what they want, making the news entertaining, rather than seeking to truthfully and thoroughly inform the public. The customer is not always right. The public can be easily entertained into a state of ignorance.

Experts sometimes get it wrong, but true experts still know a lot more about their fields of knowledge than the rest of us and they get it right a lot more often than we do. Occasional errors by experts are no excuse for turning our backs on knowledge.

Democracy cannot function when every citizen is an expert. Yes, it is unbridled ego for experts to believe they can run a democracy while ignoring its voters; it is also, however, ignorant narcissism for laypeople to believe that they can maintain a large and advanced nation without listening to the voices of those more educated and experienced than themselves.

Look where the devaluation of expertise has taken us in America. We now have a president who is the poster child of narcissistic ignorance whose only expertise is in being a media celebrity. This is a slap in the face of the expertise that built this nation and made it strong. America did not become a city on a hill for the world to see and emulate by celebrating ignorance. History has revealed more than once what happens when you place extraordinary power into the hands of a narcissistic bully. This has perhaps never been done, however, with someone who exhibits Trump’s degree of prideful ignorance.

What do we do? Nichols reminds us that “Most causes of ignorance can be overcome, if people are willing to learn.” Are we willing to learn? That doesn’t seem to be the case.

The creation of a vibrant intellectual and scientific culture in the West and in the United States required democracy and secular tolerance. Without such virtues, knowledge and progress fall prey to ideological, religious, and populist attacks. Nations that have given in to such temptations have suffered any number of terrible fates, including mass repression, cultural and material poverty, and defeat in war.

How can we get back on track? It might take a disaster of spectacular scale to turn the tide. I hope this isn’t the case, but no divine power will bail us out if we continue on our current course. We must do what humans have always done to thrive and advance. We must use our brains.

Take care,

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