You’ve probably noticed that my approach to writing, both in general and especially when reviewing software, is not typical. I write in the first person, referring to myself as “I,” rather than as some unidentified voice or indirectly as “the author.” I want you to feel as if I’m speaking to you and I want to take full responsibility for everything that I say.

When I talk about products, even when reviewing them negatively, I refer to them and their makers by name. This includes individuals, such as Andrew Cardno of Bis², the creator of this company’s new Super Graphics, which I reviewed in the edition of the Visual Business Intelligence Newsletter that was published today. I don’t do this to make it personal—it is personal, and I choose to acknowledge the fact. It’s too easy to hide behind indirect references, pretending that products are made by abstract entities called companies. Real people are behind the products. Real people are behind marketing campaigns. Real people are behind every decision that’s made in a company. I believe that if I know who these people are, I should acknowledge them, not only when speaking favorably but unfavorably as well. I believe that, however difficult and at times painful, this is an act of respect, much like looking a fellow directly in the eyes when talking to him.

Just as governments are improved by transparency, by holding politicians and other decision makers liable for their acts, the business intelligence industry likewise benefits when we put faces on the organizations and products that comprise it. We should take responsibility for our work and our decisions. When we screw up we shouldn’t hide behind obfuscations such as “mistakes were made.” When we disagree with one another, we should face one another directly and argue our positions. No matter what the cost, if we care about this industry, we should always state the truth as we know it, not what’s convenient or self-serving. When a company makes false claims, we shouldn’t excuse those claims as “marketing,” as if that somehow justifies deceit. Lies and products that don’t work are toxic. Given the sad track record of the business intelligence industry to deliver on its promise, it’s no wonder people are wary. Our industry will benefit from honesty, directness, a personal sense of responsibility, and, of course, from products, services, and opinions that are actually worthwhile. Not only worthwhile to ourselves but especially to those who rely on those products, services, and opinions. This is the “next generation BI” that I’d like to see—not new jargon to reanimate tired old ideas, but real solutions that help people make sense of information, present it clearly, and use it to make wise decisions.

Take care,

I try to read every new book that’s written about data visualization. This is possible because the field is still represented by a relatively small number of experts who take the time to write books. The most recent addition to my library is Howard Wainer’s new book titled Picturing the Uncertain World: How to Understand, Communicate, and Control Uncertainty through Graphical Display. Wainer is a longtime expert in statistical graphics who works as a research scientist for the National Board of Medical Examiners and as an adjunct professor of statistics at the Wharton School of the University of Pennsylvania.

Picturing the Uncertain World. Howard Wainer,
Princeton University Press, 2009.

The first thing I should say about this book is that you shouldn’t misunderstand the title as I did initially. Without giving it enough thought, I excitedly assumed that the book focused specifically on visual means to represent levels of uncertainty in graphs. People quite often ask me if there are better ways to do this than error bars, which is a great question that I haven’t taken the time to thoroughly investigate. I was hoping that Wainer had done this for me. It is probably no surprise to those of you who are statisticians, however, that Wainer is using the term “uncertainty” in the sense that all statistical information is uncertain—statistics after all is the “science of uncertainty”. The book isn’t specific in any sense; it addresses a broad, disparate set of issues pertaining to the graphical representation of statistical data.

Similar to Tufte’s books, this is a collection of Wainer’s ideas, stories, and examples, which meanders freely through the landscape of statistical graphics. Both Tufte and Wainer are exceptional thinkers and writers who sprinkle their books with marvelous insights, but neither write books to address a specific topic in a cohesive way. Picturing the Uncertain World is a compilation of articles that Waimer wrote over the course of 11 years (1996 through 2007), mostly for the statistics journal Chance.

Before commenting further on the content of Wainer’s book, I’d like to mention that, unlike many books in the field, at $29.95 this book is quite affordable. Princeton University Press resisted the temptation to which academic publishers often succumb, which is to price the book beyond the reach of all but university libraries. They accomplished this, however, by paying less attention to the book’s design and printing than a book about graphics deserves. Unlike my books and Tufte’s, Princeton University Press produced Wainer’s book on the cheap. One example of the difference is that Tufte and I both work hard to place figures as close as possible to the text that references them, which involves a lot of time and expense. This prevents the annoying experience of readers having to flip through pages to find the figure and then having to find their way back to the place they left off in the text, which undermines the learning experience.

Now, on to the book’s content. This book is for statisticians. It assumes a fairly high level of statistical fluency, yet I’m not sure that Wainer fully appreciates this fact. About two-thirds of the way into the book in a chapter on statistical error, after using statistical terms and concepts throughout, Wainer includes a section titled “A Brief Tutorial on Statistical Error.” It struck me as odd that he didn’t recognize the need for any tutorials until then. In this sole attempt to inform the uninitiated (that is, readers who either never took “an introductory statistics course” or took one but no longer remember the material), he exclusively covers the calculation of statistical error (hypothesis testing, Type 1 and Type 2 errors, the problem of multiplicity, the Bonferroni method, sensitivity analysis, resampling, multiple imputation, and standard error). Wainer’s rapid tour of statistical error left my head spinning. I felt as if I’d been stomped on by the “Bonferonni method,” which left me in need of some “sensitivity analysis.”

The inside flap of the book jacket concludes with these words: “We live in a world full of uncertainty, yet it is within our grasp to take its measure. Read Picturing the Uncertain World and learn how.” This is a bit misleading. Only those who already possess a fairly good understanding of statistics will find this book accessible. Those who don’t will be left gasping like fish on dry land. Statisticians will no doubt enjoy Wainer’s examples and insights, but others will need to take a course (or two or three) before learning much other than how uninformed they are about statistics.

Please don’t misunderstand me. I’m not saying don’t buy this book. I’m merely trying to define the audience that will find it most worthwhile. Although I struggled a bit, I found several sections fascinating and useful. More than the articles, the two parts of the book that spoke to me most compellingly were the “Introduction and Overview” in the beginning and the “Epilogue” at the end, where Wainer the man shines through with grace, warmth, humility, and passion. The fact that he cares deeply about his work and longs to leave the world better off than he found it is evident. I appreciate this and Wainer’s fine service to the world and the statistical graphics community in particular.

Take care,

I’ve written previously about my concern that infographics—the mixture of text and images to tell stories, explain concepts, describe processes, or provide instructions—have no real research to back up their claims of effectiveness. Visual communication is all the rage today, and rightfully so because it has great potential when used effectively, but much of what’s being sold by expensive consultants simply doesn’t work. This is a travesty; infographics could be used for good if we could only figure out when to use them and how to properly design them. Research is needed, but in the meantime organizations are spending bundles of money on silly posters that are rarely more effective than a simple written document.

Take the following example that XPLANE is currently exhibiting with pride. For a mere $24,000 paid for its design, plus the cost of printing and shipping, a company named Weatherford has placed a copy of this poster in the workspace of every one of its HR representatives worldwide.

According to the HR Manager at Weatherford who commissioned the work, this poster depicts the “Work Life Cycle of an employee in an organization and the role played by PeopleSoft HRMS system in managing talent and partnering with HR as an enabler.” Why do they need a poster? “We lacked a consolidated, high impact message that could capture and communicate with our people. We needed a clear, concise way to deliver our message to all levels, languages and cultures while remaining cost effective.” How does this poster communicate to all languages? They produced 12 different versions of it; one for each language group. In other words, the pictures didn’t solve the language problem. In fact, the pictures add no meaning to the poster whatsoever. The human figures walking, sitting, and standing in various settings, which resemble graphics common in old video games, are at best evocative of meanings that we already know from the text. The pictures are mere eye candy—empty calories.

Is this the best that infographics has to offer? Are infographics about decorating concepts and instructions with silly pictures to entertain people, thinking that only then will they actually read the words? If so, rather than paying $24,000 to have a graphic artist arrange images from his clipart library on a piece of paper to make a set of instructions look like a children’s game, why not just type up a list of instructions and put a picture of a kitten at the top of the page, or better yet, different kittens in various cute poses next to each section of text?

Infographics can be done well. Images can be used in ways that complement the text by elaborating, explaining, or clarifying it when words alone don’t do the trick. I’ve seen many examples in news publications such as the New York Times and Newsweek, which combine text, quantitative graphs, and sometimes diagrams and photos, to tell the story of a current event. These are quite different from the visual wasteland that’s pictured above. When they’re effective, what makes them so? This is what we people who produce infographics need to figure out, based on solid research.

Once again I’d like to ask you who are infographic experts to prove the worth of your methods. The fact that organizations are willing to pay for your services proves nothing. These are probably the same organizations that are spending big bucks on so-called data visualization software that allows them to put lighting effects on pie charts and then make them spin. After spending $24,000 of his company’s money on a poster, what Human Resources Manager is not going to argue its worth in an effort to combat cognitive dissonance? I’m issuing this challenge because I know there’s something worthwhile here, but it’s jumbled in with the crap. Start thinking critically about this stuff; question your methods, put them to the test, and eventually you’ll establish guidelines for separating the wheat from the chaff.

Until then, we’ll be papering our walls and cluttering our brains with the likes of the recent infographic below from GOOD magazine, which exhibits so many problems it’s hard to imagine where to begin critiquing it, so I’ll leave that to you. I’m going to go lie down now and cover my eyes with a warm compress.

Take care,

Few things are more annoying than the idiotic way that people usually communicate in the business world. What could be said quite clearly is often obscured or inflated so much that it loses all meaning—it is reduced to pure gobbledygook. I’m not only referring to marketing jargon, which ought to have a stake driven through its heart, but also to the way that most of us have learned to populate our emails, reports, and presentations with cryptic acronyms and hollow business-speak, resulting in little real substance and no real impact on our audience. If you’d like your voice to be heard, I recommend a book that I read this week: Why Business People Speak Like Idiots.

Written by three bright, witty, and plain-speaking former jargonoholics, this book is worthwhile, entertaining, and a quick read. I read it during a recent flight from Oakland to Boston. From the first page you’ll recognize the truth and importance of its message. Practicing what they preach, the book opens with these words: “Let’s face it: Business today is drowning in bullshit.” Yes, they could have said this more politely, more politically correctly, but they said it as they see it. What they said is true, and we all know it.

So how did it get this bad?

There are many reasons, all of which ensure that nobody makes any real promises or delivers any real meaning. The tide of political correctness has stranded us so that business idiots can’t speak frankly about anything. Fear of liability or even responsibility rules the day, and attorneys shape every document into a promise-free blob of text that deliberately says nothing. And then there are business schools, consultants, and gurus, all of whom make a living repackaging old concepts as something “new.”

Beyond that, there’s technology, which makes it all too convenient to automate the one part of business that should never be outsourced: our voice. Whether it’s using someone else’s jargon, a generic template, or even a speechwriter, too many business people give away their biggest chip in the influence game without a thought. The temptation is everywhere. We now have the option of deleting our personality from what we say and write.

(Why Business People Speak Like Idiots, Brian Fugere, Chelsea Hardaway and Jon Warshawsky, Free Press, 2005, pages 3 and 4)

The authors provide a clear set of instructions for cutting the nonsense from our words and allowing our real voices to find their rightful place in our professional lives. I recommend that you buy this book, read it, put it into practice, and then pass it on to your boss.

Take care,

Since “business analytics” has come into vogue, like all newly popular technologies, everyone is talking about it but few are defining what it is. In his inaugural article for the B-EYE-NETWORK, “Today’s ‘Analytic Applications’ — Misnamed and Mistargeted”, Merv Adrian argues that “analytic applications ought to be defined as those which use analysis to deliver business functionality.” He goes on to say that “the promise of the future is truly analytic business applications, software packages that execute automated business processes with and/or without human intervention, based on policies, rules and real-time analytic results.” Although Adrian never defines the terms “analysis” or “analytics” in his article, it is clear that he defines them differently than I do. As I define “analysis,” saying that a computer could do it “without human intervention” makes no sense.

I believe that data analysis is what we do to make sense of data. I could add a few more words around this basic definition to elaborate a bit, but essentially data analysis is the process of sense-making. It’s what we do with information to understand what it means. It’s the process that bridges the gap between information and knowledge. It’s what we do if we want to make informed decisions, based on evidence. Oh yeah, and when applied to business, it’s the heart and soul of business intelligence.

When software automates actions that are carried out in response to rules, it isn’t engaged in analytics or decision-making. Rather, it is simply enforcing a decision that has already been made, based on prior analysis-analysis that was done by a human. The automation of routine responses to specified conditions is a great use of technology, but let’s not confuse this with analysis. Doing so might give people the false impression that by automating such actions, they are addressing their organization’s vital need to understand its data.

I believe that Adrian’s vision of our analytical future involves something that computer applications have been doing for us all along. Software programs instruct computers to do particular things based on particular conditions. This is what all applications do and have always done. Business applications perform actions based on business rules. We shouldn’t confuse this with a process that involves the careful exploration and examination of information to make sense of it-that is, data analysis. The two are fundamentally different. The enforcement of rules is a procedural process that computers excel at performing. Thinking isn’t required.

Data analysis, on the other hand, requires thinking. The only computers that think are those that we read about in science fiction. Until this changes, if it ever does, data analysis will remain a human activity. Computers can support the process by giving us tools that support and augment our ability to think, but they can’t think for us.

Adrian apparently shares my opinion that the business intelligence industry has failed to deliver on its fundamental promise. But, just as he and I define “analysis” differently, we also understand the nature of this failure and its solution differently. If the business intelligence industry continues down its well-worn path of expecting technology to solve problems that are essentially human problems, without taking the time to understand human needs, abilities, and limitations, it will continue to fail in its primary mission.

Why do so few in the business intelligence industry understand this? Perhaps their analysis is faulty. Perhaps they’re coming at it from the wrong perspective.

Take care,