Infographics—It’s time to put them to the test

Visualizations of various types are used to support thinking and communication. I focus on their use for analyzing and presenting quantitative information, but they can also be used for other purposes, such as teaching concepts and procedures, and helping people understand processes and complex systems. With the publication of Visual Language: Global Communication for the 21^st Century in 1999, Robert Horn made a compelling case that visualization is a language, which is different from but often collaborates with verbal language. It is definitely true that, when trying to communicate certain information, “a picture is worth a thousand words.” As technologies such as television, video games, and the Internet fill our lives with increasing amounts of visual content, the potential of visualization is now taken for granted. The question remains, however, “Are we using this visual language effectively?”

I decided to address this topic today while looking at an “infographic” about the costs of the war in Iraq shown below, which was created by Good Magazine, based on the book Three Trillion Dollar War: The True Cost of the Iraq Conflict by Nobel Prize laureate Joseph E. Stiglitz and Linda J. Bilmes.

In Visual Language, Horn defined “infographics” (short for “information graphic”) as:

Moderately sized, meaningful combinations of words, images, and shapes that together constitute a complete communication unit. Visual and verbal elements are tightly integrated. Is as self-contained as possible on 1 or 2 pages or on a large screen. Usually contains more information than a concept diagram, although an information graphic may use any of the types of concept diagrams as its central visual element. Usually contains several blocks of text.

(Visual Language, Robert E. Horn, MacroVU, Inc,, Bainbridge Island, Washington, 1998, p. 61)

This form and use of visualization has become popular in the last few years. We now see frequent examples of infographics in major news publications. I’ve seen examples that work to communicate effectively, but more that, in my opinion, do not. What accounts for these differences in the effectiveness of infographics?

I believe that the Three Trillion Dollar War visualization, which tells a story that I care about and consider important, fails as an infographic. Aspects of its visual design discourage me from examining it. It’s hard to look at. Even if the aesthetics were more pleasing to the eye, I don’t think the graphics achieve their communication objectives. The story is adequately told by the text-the ten points that are described verbally to the right of the graphics. The graphics add no value or meaning that isn’t contained in the text. The pictures themselves don’t reveal anything we can’t learn more clearly from the text. Graphics should only be used when they communicate more effectively than words or words alone. Visual displays can do a great job of revealing relationships that might be difficult to communicate with words alone, but the relationships between the various costs that appear in this infographic are buried in visual clutter.

Until yesterday, I had never heard of Good Magazine. According to their website:

GOOD is a collaboration of individuals, businesses, and nonprofits pushing the world forward. Since 2006 we’ve been making a magazine, videos, and events for people who give a damn. This website is an ongoing exploration of what GOOD is and what it can be.

Based on what I’ve read, I like these guys and support what they’re trying to do. I want their work to succeed , but as an information visualization professional, I’m concerned that in this case at least their good intentions have been undermined by ineffective graphics.

My purpose here is not to critique this particular infographic, and certainly not to criticize the work of Good Magazine. Rather, I’m writing to raise concerns once again about the quality of infographics in general and the fact that it doesn’t seem to be improving. I believe infographics have great potential, but their effectiveness must be honed through empirical study. Infographics practitioners must become more introspective, more critical of their work, if they wish to give something useful to the world. Most of the infographics that I’ve seen are filled with what Tufte calls “chartjunk.”

Why are we still producing chartjunk? Jacques Bertin put us on the road to effective uses of visualization by introducing the basic vocabulary of visual communication. Tufte refined and extended this work, especially in regards to quantitative communication. Robert Horn synthesized much of what’s being done and demonstrated the existence of visual language. But today, rather than continuing in this critical scientific tradition, infographics reminds me of Web design in the early days: free expression with little regard for practices that have been proven to produce the desired outcomes. No one seems to be doing any work to determine what works and what doesn’t, and to understand why. Or, if they are, I’m not aware of it, and am rarely seeing the results.

In Visual Language, Horn wrote:

Basic scientific research is beginning to bear out the thesis of this book-that people find it easier and more effective to communicate by using combinations of words and images. Although visual language has yet to be subjected to a full battery of cognitive science or pragmatic tests, the few available studies support that conclusion…Because visual language is so effective, it is important that standards and criteria develop for its use. These criteria need to be based on principles that come from both cognitive science and design. Criteria for good practice will evolve both from the evidence of careful empirical studies that compare different visual methods of expressing a similar message and from the reflective judgments of practitioners. Out of such aesthetic factors come the models, the criteria, and the aesthetic factors that together make a message effective, efficient, and attractive. We have clearly entered a period of exciting dialogue and development of these ideas. (ibid., pp. 233 and 235)

I share Horn’s vision, but I’m not sure that during the last 10 years since he wrote these words, the hope and enthusiasm that he expressed in the final sentence applies to infographics. Just as statistical graphics have been subjected to empirical study, and continue to be, resulting in guiding principles that can be found in the works of Tufte, Cleveland, and more recently my own, infographics must do the same if we wish to apply them effectively.

I’m interested in your thoughts, especially if you’re an infographics practitioner. Are you aware of work that’s being done to put infographics on the track to effectiveness that it needs to mature and definitely deserves?

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Are visual analysis tools poised to become pervasive?

I spent most of last week at InfoVis 2008 in Columbus, Ohio. You might remember that I delivered the capstone presentation last year at InfoVis 2007, which also served as the keynote presentation for VAST 2007 (Visual Analytics Science and Technology). Last week the 2008 edition of this presentation was delivered by Christian Chabot, cofounder and CEO of Tableau Software. Chabot and I share the belief that visual analysis software is needed by a broad audience of people, not just those who have the term “analyst” in their titles. We also share the belief that with well-designed visual analysis tools like Tableau, visual analytics is poised to explode.

Participants in the conference consisted primarily of academics—professors and graduate students who spend their days inventing and refining visualization tools and techniques for making better sense of data. Chabot clearly wanted to challenge this audience to direct more of their efforts toward the practical needs of a broad audience of potential users.

Chabot identified four conditions that have set the stage for the current readiness of visual analytics to take off:

• Data explosion
• Technological advances
• General awareness
• Industry consolidation

The overwhelming amount of information that people now face has created a desperate need for tools that will help them make sense of it. Modern computer hardware and the Web have provided the infrastructure that is needed for people to interact with and share information effectively. Awareness of the visualization’s potential has reached a critical mass. Traditional business intelligence vendors, along with their tired, low-yield approaches to data analysis, have been bought up by large software corporations where they will languish, which has opened the door for better approaches to capture the attention of market. By rejecting the sins of traditional business intelligence vendors, refusing to compete for the hearts and wallets of customers through a litany of useless and ineffective pseudo-analytical features, software companies such as Tableau that are thoughtful, agile, design-oriented, and well-informed, have differentiated themselves from the pack and are now reaping the rewards of their commitment to give people analysis tools that really work. One result that we’re beginning to see is the gradual spread of data analysis tools to organizations of all sizes (from Google to the local bakery), and their proliferation throughout all parts of those organizations.

When the founders of Tableau Software were initially crafting their vision, they identified five core principles of visual analytics’ adoption:

• People adopt visual analytics primarily to help them see and understand complex data.
• People adopt visual analytics primarily to help them see and understand massive data.
• People adopt visual analytics primarily to help them see and understand new visual paradigms.
• People adopt visual analytics primarily to help them see and understand hidden insights.
• People adopt visual analytics primarily to help analysts save time.

Chabot is a Stanford MBA who worked for years after graduation as a high-end analyst—one of those guys that spend their days tackling complex analytical problems using complex analytical techniques. The other founders of Tableau, Chris Stolte, who earned his doctorate in computer science at Stanford by developing the prototype for Tableau’s eventual product, and Pat Hanrahan, the Stanford professor who supervised Stolte’s work, were immersed in the world of academic information visualization research. Their assumptions about what it would take to get people to adopt visual analytics made perfect sense, given their perspective at the time. As time passed, however, they kept their eyes open and learned that each of their assumptions turned out to be flawed.

Flawed Principle #1: People adopt visual analytics primarily to help them see and understand complex data.

Although sometimes complex, the data sets that people analyze are usually fairly simple. Chabot advised those of us in the information visualization community to start simple. Rather than focusing most of our attention on solving the complex, highly-specialized needs of a few, we can solve much more widespread problems that are just as important by making it easier for people to do the simple stuff that they must do over and over again each day, which are now unnecessarily onerous and time-consuming.

Flawed Principle #2: People adopt visual analytics primarily to help them see and understand massive data.

Although sometimes massive, the data sets that most people analyze are not particularly large. Chabot recommended that we start small, making it easy for people to work not just with huge corporate databases, but also with small files stored in Access and Excel.

Flawed Principle #3: People adopt visual analytics primarily to help them see and understand new visual paradigms.

Although there are times when new visual paradigms must be invented to solve peoples’ needs, most problems can be solved with proven visualizations, such as bar charts, line graphs, and scatterplots. Chabot suggested that we start proven by making it easier for people to use what we already know to work well in a seamless fashion.

Flawed Principle #4: People adopt visual analytics primarily to help them see and understand hidden insights.

While it is true that one of the great benefits of visual analytics is the discovery of previously hidden insights—those “Aha!” moments that we all crave—the primary reason, by far, that people want good visual analytics tools is more mundane, though no less useful: to save time. Chabot pointed out that we can design great tools that get out of the way, allowing people to become engaged in the act of thinking about data, rather than distracted by the mechanics of using the software.

Flawed Principle #5: People adopt visual analytics primarily to help analysts save time.

While analysts desperately need better tools to help them do their jobs, even greater benefit can be gained by providing tools that anyone can use, enabling everyone who must make sense of information to do their jobs and, as a consequence, freeing up analysts to spend their time solving the more complicated problems. With religious zeal, Chabot warned that we can no longer serve the needs of small groups with specialized needs, but should invite everyone to the table.

At the end of his presentation, Chabot reviewed his message and challenged us with these final facts:

• Millions of people need visual analytics technologies to help them understand information.
• The current state-of-the-art in business analytics (what most people rely on to do their jobs) is tragic.
• The primary barriers to visual analytics’ adoption are (1) awareness, (2) misperception, (3) ease of trial, (4) ease of deployment, (5) ease of use, and (6) ease of price.

What business intelligence vendors have still failed to do, a new breed of software company with roots in information visualization research, is poised to finally deliver. The world needs what we have to offer. To get it into the hands of those who need it, we must bridge the chasm that divides academic research and commercial software. Tableau and a few other ventures have done that. They’re inviting others to join them—not tomorrow, but now—because the time is ripe.

Take care,

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Bad software comes from bad business models

On several occasions I’ve taught the principles and practices of effective data visualization to people whose job it is to sell business intelligence software—sometimes for the entire sales team of a business intelligence vendor, but more often for mixed audiences that included a few salespeople among others. In such situations, I can always count on a particular issue to arise: “Yes, we know that much of what our products do and many of the features that we promote don’t work (silly eye candy and the like), but we include and promote them because they sell. We have no choice.” When I’m in the room with these folks whose livelihood is affected by this dilemma, empathy prompts me to explain how they can educate customers during the sales process to recognize the silly stuff for what it really is and thereby nudge customers in the direction of their own best interests. I’m beginning to realize, however, that this effort rarely, if ever, makes a difference. Some businesses are built on a model that will always favor immediate sales revenues over effective products, and nothing that I say to salespeople will change this.

Any business that measures its success by current sales revenues or profits without regard for the effectiveness of their products will go for the silly stuff every time. I could argue that this is a poor business model because it’s short-sighted and doomed to fail, eventually resulting in declining revenues, but what’s the point? Businesses built on this model lack the foresight to appreciate the greater intelligence of long-term planning around products and services that effectively address the real needs of people. I believe the root problem that belies such business practices is not strategic short-sightedness or a myopic focus on sales—these are symptoms of a deeper, more fundamental problem. I believe that it’s wrong to build a business on self-interest alone.

In the midst of the current presidential campaign, we’re reminded daily of how willingly and shamelessly politicians do whatever it takes to get elected. I’m embarrassed to live in a country that puts up with this. Yes, it’s true that most other countries are just as bad and many are worse, but that’s no excuse. We could be so much better. Our country could function so much more intelligently and morally. How did we come to expect so little of ourselves?

At least when politicians twist the truth and manipulate voters to get into office, however, they probably believe they’re doing it for the good of the country. Sarah Palin can say that she wasn’t really clueless when Katie Couric asked those questions that she couldn’t answer, she was just being “flippant.” Despite being a good Christian who was taught to value the truth, she probably believes that God makes exceptions when the stakes are this high—the ends trump the means. (Yeah, I know that Palin isn’t the only candidate twisting the truth, but her acts are so transparent, they’re especially insulting.) Whereas politicians rationalize their behavior based on the genuine belief that they’re better for the country, businesses that sell bad products are simply out for themselves. When I step back and think about those discussions that I’ve had with salespeople who promote software features that don’t work because that’s what it takes to sell their products, I’m affronted by the fundamental absurdity of this exchange. How did we come to find it acceptable to convince people to pay money for things that we know don’t work? How does “because this is what it takes to sell our product” excuse the fundamental wrongness of this end?

I’m in the business of helping people use information effectively. I don’t tolerate anything that undermines this end. I believe that business intelligence software vendors owe it to their customers to do business in this way as well. When I evaluate a a product as ineffective, I respect vendors that defend their software by making an honest attempt to show that it actually works. I don’t respect vendors that defend their efforts to sell software when they know it doesn’t work. Things that don’t work should not be sold—period. That’s good business.

Take care,

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Introducing Lazysoft – An engineer’s playground, but a data analyst’s nightmare

I’ll begin by admitting that this new business intelligence vendor’s name isn’t really “Lazysoft”, but it’s close. I took the liberty of transposing two of the letters in the name “Lyzasoft” to create a name that describes a fundamental problem with its software—it’s the product of laziness. Some software engineers no doubt had lots of fun developing this product, but Lyzasoft approached the task in the typically lazy manner of many software companies—they didn’t bother engaging the services of designers who actually understand data visualization or data analysis. What does the software supposedly do? Here’s what the press release states:

On September 22, 2008 Lyzasoft will introduce Lyza, a powerful desktop analytics solution that enables analysts to synthesize, explore and visualize data, then to publish compelling presentations and dashboards – all without the reliance on lengthy IT development cycles.

I won’t comment on Lyza’s ability to synthesize data, because this can’t be judged without actually putting the product to the test. I will say, however, that it fails miserably in its ability to help people explore, visualize and then present data. It allows you to create individual charts from a small gallery of chart types that appear to possess only primitive functionality. No real platform for data exploration or analysis is provided. Once you think that you understand the data after looking independently at a few charts, you can then place them into a Lyza’s presentation format, which is designed to look and function a lot like PowerPoint. Unfortunately, it falls prey to the worst of PowerPoint with a host of silly and distracting visual themes. What Lyzasoft calls a dashboard is actually nothing more than a series of PowerPoint-like slides.

Rather than giving this new product any attention beyond this brief warning to stay away from it, I’ll end with two screenshots from Lyzasoft’s promotional demo, which illustrate how little these folks know about data analysis and visualization.

Here’s a table, which, as you can see, fails in one of the most rudimentary ways: the numbers are left justified. The only other example of a table that appeared in the demo was different, but no better, for its numbers were centered. At no point in the demo were the numbers right-justified so the values in a column can be easily compared.

Next, you can see an example of what it looks like when you create presentation slides in Lyza. Notice the eye-catching background pattern and the unreadable title—a fitting backdrop for a bad pie chart.

I guess the advantage Lyzasoft is featuring here is the ability to create really bad slide presentations in a single tool, thereby skipping the labor-intensive step of copying and pasting graphs from Excel to Powerpoint. No doubt you’ll find this feature alone enough to make you want to rush out and pay $899 for a copy of Lyza.

I hope that people who buy data analysis and presentation software have learned enough about what’s actually useful to recognize a product like Lyza for what it is—someone’s attempt to make big bucks exploiting people’s need for a way to make sense of data without taking the time to learn how to do it, let alone build a product that supports the process.

Take care,

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Xcelsius Present – Fast Track to Nowhere

Recently, Business Objects released a new product named Xcelsius Present 2008. They are promoting this new version of Xcelsius as an application that will “make it easy for non-technical users to create interactive data presentations.” This product comes with ten pre-built analytical templates; novices supposedly can just match their data to a template and they’re ready to analyze. For instance, if you need to analyze sales data, you can use the sales data template. For compensation analysis, you can use the human resources template. Perhaps you can see how this sort of cookie-cutter approach, despite its appeal, might fall short. Let’s take a look at one of these templates to see what can happen when a business intelligence software company that focuses on superficial glitz rather than analytical substance makes “one size fits all” templates.

Business Objects calls this template “Unemployment Trends.” This title is misleading, however, for trends are discerned through time, but the template only accommodates a snapshot of data from a single point in the year 2007. The employment data comes from a 53 page PDF file filled with tables of numbers, prepared by the Bureau of Labor Statistics. What’s sad is that the original PDF file, with its many tables (one per state) is actually much more useful for data analysis than the analytical application that Business Objects has built. Even though tables can be used for analysis only in limited ways, the Xcelsius application, which claims to apply powerful data visualization, actually limits what can be done. It does this primarily by reducing what can be seen to only a few pieces of information at a time. The act of comparison is the essential task of quantitative data analysis, but with this application you can compare fewer values than you could with the original tables. The benefits of data visualization have been missed entirely.

The application provides a pie chart for comparing the America’s non-institutional population to the civilian labor force and two gauges for comparing employment and unemployment rates. Neither pie charts nor gauges of this type support effective comparisons, and in this case, because of fundamental design problems, comparisons between slices of the pie or the two gauges actually mislead.

The Gauges

Gauges designed like these are never very useful. They wastefully spread their girth across an extravagant amount of space to report a single number. Because they omit quantitative scales, we can’t compare the employment or unemployment rates based on the positions of the pointer. We don’t even know if they share a common scale. They could start and end at different values—we have no way of telling. Take a moment to see if you can figure out the values that are associated with each of the tick marks. Impossible, isn’t it? We’re forced to read the values printed as text, which we could do more quickly using the original tables.

Besides these problems, there’s another that isn’t obvious unless you review the original data set. Although it seems logical to compare employment and unemployment rates, this isn’t terribly useful because the two measures have been calculated using different methods. In the original report, the employment rate was based on the number of people working compared to those who could be working, including those who aren’t looking for work, including retirees and stay-at-home parents. The unemployment rate, on the other hand, is based on the number of people who are unemployed and looking for work compared to all those who want to work (both those employed and those looking for work). If the employment rate in the above screenshot were calculated the same way as the unemployment rate, it would be 95.4% instead of 63%. While the rates shown in the gauges might be useful, the different ways that they’ve been calculated should be explained so we can compare them appropriately.

The Pie Chart

If you’re familiar with my work, you probably already know that I’m not a fan of pie charts. They require us to compare the areas or angles of slices, but visual perception supports neither well. This particular pie chart, however, fails in an even more fundamental way. I’m not referring to the distracting flag image in the background or the simulated reflection of light on the pie, which almost makes it look like there’s a third, light blue slice. Rather, the problem has to do with two slices are “Non-Institutional Population” and “Civilian Labor Force.” “Non-Institutional Population” represents everyone who could be working, whether they want a job or not, while “Civilian Labor Force” represents those people who either have a job or are actively looking for one. In other words, the “Civilian Labor Force” is a subset of the “Non-Institutional Population”, not a separate segment that combines with it to make up some whole. The 39.77% that appears when I hover my mouse over the red pie slice is meaningless; the correct percentage doesn’t appear anywhere. The following pie chart is an example of one that actually makes sense.

Filter Controls

So far, I’ve focused on problem with the graphs, but the filtering controls exhibit fundamental problems as well. They allow us to select subsets of data such as women between the ages of 55 and 64 years old. Most filtering is done through the following scrolling list box:

To select a particular set of data, we must click the up or down arrows to scroll through various categories until we find what we want. For instance, to select “Black or African American Women,” we must scroll down to the “Black or African American” section and then select “Women.” If we tried to search for the “Women” section first, we’d fail to find it, because it doesn’t have its own section. We can only make a single selection from available options that combine multiple variables (sex, ethnicity, and age groups). This means we must select from 34 filtering options. Filtering shouldn’t be this difficult or limited. Ideally, separate filters should be available for sex, ethnicity, and age group, perhaps as three groups of check boxes that could be easily turned on or off, independently from one another. For instance, to view Black and Hispanic women of all ages, we would uncheck “Men” in the Sex filter, uncheck all but “Black or African American” and “Hispanic or Latino Ethnicity” in the Ethnicity filter, and leave everything checked in the Age Group filter. Unfortunately, the original tables that the Bureau of Labor Statistics provided in the PDF file don’t support this level of flexibility, but they support much better filtering control than the scrolling list box provides.

In addition to the scrolling list box, this application also allows us to filter by state. To do this, we must go to a separate screen in the application, which looks like this:

By clicking one of the states on the map, we can see employment data for that state—at least that’s the plan. Can you find Rhode Island on the map? Let me see if I can make it easier for you. In the image below, I’ve greatly magnified the New England portion of the map.

Rhode Island is the little blue spot that the cursor is pointing at. Even at this level of magnification, it’s almost impossible to see. Imagine how hard it is to click! While the map works alright for large states like California and Texas, it’s worthless for selecting small states like Rhode Island or Delaware. Furthermore, is a map really the best way for people to select at state? It works well enough for selecting familiar states like Florida, but anyone who’s geographically challenged like I am might not be able to pick out Indiana or New Hampshire. The state names appear in tooltips as you hover your mouse around the map, but this forces us to search around for the states with unfamiliar locations. A simple alphabetized list of states would probably work much better.

There’s one more problem with the map that isn’t obvious at first. When we click on a state, it doesn’t refresh the data in the pie chart and gauges as expected. We must also make a selection in the list box filter control (for instance, switching from Men to Women) for the data to update. This is such an obvious bug in the application, it’s hard to imagine how the folks at Business Objects could have missed it. Perhaps they no longer test their software before releasing it.

Basically, this application took a series of Bureau of Labor Statistics tables and transformed them into an unwieldy mess that actually undermines our understanding of employment data.

And Wait…There’s More

Several other analytical applications are packaged with and used as demos for Xcelsius Present, including one shown below for Compensation Analysis.

Apparently compensation analysis should be performed using a single pie chart along with a table that reiterates the same values. I encourage you to visit Business Objects’ website to try out the demos of these applications for yourself. I think you’ll find it enlightening, perhaps entertaining, and very, very sad. Be sure to let them know how much you appreciate Xcelsius Present when you’re done looking.

How Should We Respond?

Business Objects is a leading business intelligence vendor (based on sales), but its products consistently demonstrate that they don’t understand analytics and haven’t a clue about data visualization. A vendor that claims to be the best, which Business Objects unabashedly claims (just like every other major BI vendor), should be ashamed of selling such moronic products. Don’t reward them for irresponsible work—products that assume their customers are halfwits—by wasting your money on them. I’m not suggesting that if you use their products, you should necessarily abandon them. I’m suggesting that that stand up and let them know that you deserve better and don’t sit down until they start listening. They dress products up with a thin veneer of flash and no substance and rely on misdirection to sell them to you.

Why? In part because, when it comes to analytics, they must not know what they’re doing, but also because they believe this is what you want. “It’s not our fault, we’re just giving them what they asked for”, they reason. It’s time to let them know that they (and many of their competitors as well) are dead wrong. 

Take care,

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Dear IEEE and Other Academic Publishers — Perhaps It’s Time for a Change

Academic research cannot exist in a vacuum. The ideas it generates must be presented, discussed, promoted, improved, and applied. This requires venues for publication (papers, articles, and books) and interaction (conferences, for example). The venues that exist today don’t seem to be supporting academic researchers in the way that they need and deserve. In particular, my recent experience with IEEE, one of the foremost organizations which supports these needs for technical researchers, has shown that its publishing model needs repair.

IEEE is a venerable organization with a long history of serving the publishing and conference needs of several technical research communities. I’m grateful that IEEE has provided this important forum for the presentation and exchange ideas. I believe it’s time, however, for IEEE to take a fresh look at itself and how it’s doing. As a professional who has worked for many years in information technology, business intelligence, and now information visualization in particular, I reinvent myself again and again to remain effective. IEEE must do the same. I believe it’s long overdue.

As one of the only games in town, IEEE’s publishing services play a critical role in technical research. Because this work is important to the world, IEEE bears a responsibility to encourage researchers to produce their best work and to distribute it to all those who might benefit from or contribute to it. Information visualization (infovis) researchers aren’t getting the kind of support they need from IEEE, and I doubt that most are even aware of this, for they have probably never encountered a properly functioning publishing model. Infovis is too important to suffer from a publishing model that undermines its efforts, message, and development. Here are the fundamental problems that I’ve observed:

• The pool of potential contributors is being kept too narrow.
• Readership is too narrow.
• Resources necessary to produce high-quality publications are inadequate.
• The medium of publication doesn’t fit the nature of infovis, and therefore fails to present it effectively.
• Print is no longer financially viable for publications of this type.

I would like to describe these problems by telling the story of my recent experience with Visualization Viewpoints, a regular section of IEEE’s periodical Computer Graphics and Applications. I’m going to take my time with this and risk seeming long-winded, because I want to convey the issues and their implications thoughtfully and clearly.

I had the honor last October of delivering the capstone presentation at InfoVis 2007, one of IEEE’s conferences and perhaps the best ongoing infovis event in the world. Immediately after finishing my presentation, I was introduced to the editor of Visualization Viewpoints who invited me to write an article for the publication. I saw this as an opportunity to strengthen my connections with the infovis research community, which I was delighted to pursue. Once I received a more formal invitation along with instructions via email, here are the events that followed:

1. I make my living by writing, teaching, and speaking. I am fortunate in that I usually get paid well for my work. In addition to paid engagements, I also take on multiple projects each year without compensation, but I must be selective about how I invest my time. I assumed that I would not be paid for writing an article for Visualization Viewpoints, which was fine, but in lieu of compensation, I requested a complimentary subscription as a gesture of gratitude for writing an article and a way to make me feel welcome as a contributor. I have received this kind gesture from other publishers in the past, so it seemed a natural request that would be embraced without question. I was surprised when my request started a long series of emails between the editor of Visualization Viewpoints and a host of others within the organization, resulting in a bureaucratic process that never did result in an actual response. This would have cost IEEE nothing but postage. My subscription would not have increased the cost of printing or administration, nor would it have prevented a paid subscription that they would have received otherwise. A complimentary subscription for anyone who contributes an article to the publication would go a long way toward building goodwill and a sense of community. Is there really any question that someone who takes the time to write an article and go through the lengthy process of working with a publisher, deserves a gesture of thanks equaling the cost of postage? After many days of waiting, I was asked by the editor-in-chief of Computer Graphics and Applications—a friend of mine—if I would be willing to waive the complimentary subscription and proceed with the article nevertheless. Despite how absurd it seemed to me that IEEE would deny a request, I agreed to drop the matter.
    
2. I wrote and submitted the article ahead of schedule. Later, upon receiving feedback from two anonymous reviewers (peer reviews), I made appropriate revisions, once again in a timely matter. As someone who hasn’t gone through the formal peer review process since graduate school, I was surprised to discover that the reviewers remained anonymous. Something felt awkward about receiving anonymous feedback in a peer review process. Why would my peers need anonymity? Why shouldn’t I be able to know the qualifications of my reviewers, if not their actual identifies? Cloaking the process in anonymity seemed to indicate a level of discomfort with critique that I didn’t expect to find to this degree in academia. I think the need for anonymity should be questioned and evaluated, even if it arose from what seemed like good reasons in the past.)
    
3. I made timely revisions in response to feedback from a copyeditor, once again in a timely manner. The one controversy that arose during the editing process concerned the two affiliations that I listed after my name: Perceptual Edge (my consultancy) and U.C. Berkeley (my academic affiliation). I included them both because doing so reinforced the message of the article, which made a case for building better bridges between infovis research and the greater world out there, including the business world. I was told that only one affiliation could be listed. Why? Because this was the policy. I explained my reasons for wanting to include both, but only after several people at IEEE weighed in was my request finally granted. I persevered, not only because I wanted the article to be as meaningful and effective as possible, but also due to my resistance to policies that seem arbitrary, silly, or counter-productive.
    
4. Even though I completed all of my responsibilities in a timely fashion, IEEE didn’t begin work on the composition of the article (that is, formatting it for printing) until close to the publication date, which caused everyone involved to be stressed and rushed. During the composition process, layout problems were introduced that should never occur in an infovis publication. For example, figures (screen prints in this case) were rendered so small, they couldn’t be read. Having to point out the obvious and go through composition iterations to fix a problem like this is something an author should never encounter when dealing with an infovis publication. Anyone who cares about visual design would have objected to the problems that I pointed out. In an attempt to avoid the lengthy back and forth process that involved (1) getting a PDF from IEEE, (2) requesting necessary changes, then (3) waiting for a revised PDF to arrive, which in turn introduced new problems that started the process over again (and again and again), I offered several times to have my own compositor do the work, and do so within IEEE’s requirements, thus saving everyone a great deal of time and frustration. My offers were always declined. Something that complicated this process even more was the fact that I was never allowed to communicate directly with the compositor, but forced instead to work through a go-between, an arrangement that was inefficient and prone to error. Despite the last-minute rush and the frustrating process, the layout problems were all fixed on time.
    
5. The clincher occurred at the end. On the day before the publication was scheduled to go to the printer, without any advance warning, I was sent a “Transfer of Copyrights” form, and asked to sign it. I responded courteously, stating that I never give up my copyrights and explained why, but instead would happily grant all rights that IEEE needed to publish and distribute the article, without restriction. Several tense emails later, on the next day I was told that the article would be pulled from the publication because I wouldn’t give up my copyrights, despite the fact that I granted IEEE every right that was needed for publication and unrestricted distribution. In emails, I was accused of being ungrateful, disrespectful, and rude by withdrawing my article at the last minute. But I didn’t withdraw my article. I put a great deal of time and care into it, and couldn’t believe that IEEE would actually pull it from publication on the last day because I wasn’t willing to give up my rights as its author.

Following this fiasco, the Editor-in-Chief of Computer Graphics and Applications worked valiantly to forge a compromise between the keepers of copyrights at IEEE and me so the article could at least be published online. I agreed to sign IEEE’s transfer of copyrights form if they would simply state in it that they would not alter the article without my approval. IEEE refused to do anything that involved a change or addition to its form.

Much of the work that goes into publishing a periodical at IEEE is actually done, not by paid staff, but by volunteers who work hard without pay because they care. Even the Editor-in-Chief of Computer Graphics and Applications is a volunteer. I appreciate and respect the work of these volunteers. It’s a shame that their efforts are undermined by a dysfunctional bureaucracy that has little or no connection to infovis.

The frustrating experience that I’ve described—not just for me, but for everyone involved—brought several problems at IEEE to light. Let’s begin with the copyrights issue. I have worked with several publishers and I have never had to give up my rights as author. Most modern publishers know that they don’t need to strip authors of their rights in order to do their job. They’re happy to take over copyrights if the author doesn’t mind, but they also understand that these rights have been granted by law to authors for a reason—no one is in a better position to preserve the integrity of the written work than the person who wrote it. They understand how ludicrous it is to insist on rights that they don’t really need. All a publisher needs are the rights to publish and distribute the work. They don’t need rights to revise the work, translate the work, incorporate the work into other publications (in full or in part), or allow others to publish the work, without the author’s permission. In fact, it is in their interest as well as the author’s to preserve the integrity and credibility of the work by making sure that any action which could possibly place it in jeopardy is reviewed and approved by the author.

Despite several requests, at no time did I receive an explanation from those who manage IEEE’s copyrights policies for why they couldn’t publish my article unless I surrendered to them my rights as author. I was told over and over again that I was the first person who ever challenged this policy. If this is true, it saddens me. How did academic publishing get to this place where everyone surrenders their legal rights routinely and without question? At one point I was forwarded an assurance via email by one of the volunteers that IEEE would never alter an article without consulting the author, but this conflicts with the transfer of copyrights form which grants them this right. The person who offered this assurance admitted the contradiction between what IEEE claims it would never do and explicit statements to the contrary in its copyrights form, but he nevertheless discouraged any further attempts to get IEEE to fix the form as pointless. I believe this is dumb and harmful bureaucracy—resistance to change at its most absurd. And this from an institution that supports the needs of academia, society’s beacon of intelligence. The fact that other publishers handle copyrights as I requested and do so without any problems led me to believe that I have never received an explanation for IEEE’s copyrights policy because a reasonable explanation doesn’t exist. If anyone knows otherwise, I’m all ears.

The fact that members of IEEE’s staff have remained hidden behind a protective barrier of well-intentioned volunteers who have no authority to change policy is troubling. Any time people with power become isolated in this manner, they lose the ability to lead. Calcification sets in, strengthening the walls that separate them from those they supposedly serve and those on whom they rely to serve.

Apparently, IEEE as a publisher has been struggling to make ends meet. I have learned that its print periodicals are “literally fighting for their economic lives at the moment, and struggling to define their mission in this world of the Web and shrinking interest in professional societies as a source of information.” I’m not surprised. One response has been to reduce the budget, primarily by reducing staff. The result?—it is now more difficult to produce publications of high quality. For example, having a compositor who actually knows a thing or two about infovis would help Computer Graphics and Applications produce, with less effort, publications that actually incorporate the best practices that infovis teaches, but budget cuts have made this impossible. The world is changing, especially with the rise of the Internet, and publishers of all types are adapting their models to survive in this new world. When someone like me comes along who has worked with publishers that have remained successful despite these changes, it might have been useful for IEEE to consider my concerns and suggestions. When current models no longer work, one must view the situation from new perspectives. This requires, at the very least, opening one’s eyes. A publication that features visualization research in particular ought to know a thing or two about using its eyes.

One of my contacts at IEEE suggested that I perhaps found their practices unacceptable because I didn’t understand academic publishing. I do understand and appreciate the fact that academic publishing should be managed with the highest possible standards for content—peer reviews and all that. What I don’t understand is why academic authors should have fewer rights than other authors or be expected to tolerate any of the other problems that I’ve observed. One such problem is that the audience is but a fraction of its potential. Let’s consider this problem next.

Should an academic publication about information visualization only find its way to university libraries? I’m sure that a small percentage of subscriptions belong to non-academic institutions as well (for example, corporate research groups), along with a smattering of individuals, but why not shoot for broader distribution to everyone who has a keen interest in information visualization, including software professionals and data analysts? Several worthwhile changes would be required to encourage this:

1. The subscription price of $74 for six issues (rate for people who aren’t members of IEEE) would have to be reduced.
2. A few articles in each issue would need to address the needs or interests of more than a few academicians, preferably by offering practical solutions to prevalent real-world problems.
3. The language used in the articles should avoid esoteric terms and unnecessarily complicated explanations.

Not only would a broader audience benefit from these publications, these publications would benefit from a broader audience. I’m not just talking about greater revenues; I’m referring to a greater exchange of ideas. It’s too easy for people who rarely venture outside of academia to become isolated and parochial. Plenty of people outside of academia understand infovis and could contribute to its progress.

Rather than cutting the budget and reducing staff, which will only cause these publications to slide more speedily toward oblivion, perhaps it’s time to stop printing and mailing them in favor of electronic media only. Other than the fact that people don’t like change, what’s the loss? Electronic versions seem especially appropriate for infovis publications anyway, because infovis itself is a computer-based experience. Another benefit is the fact that articles will no longer need to be tightly restricted in length, which will give authors more opportunity to express their content meaningfully and will remove many of the challenges that compositors face, especially if individual articles are published as separate PDF files.

My experience with IEEE’s publishing wing has been frustrating, but mostly it has made me sad. I find it impossible to ignore anything that hinders progress in the field of information visualization. Many of the best innovations come from the intersection of disciplines, perspectives, and ideas. Because I have never worked with an academic publisher, I come from a different perspective—one that might actually be useful.

I’ve written this to encourage discussion about these issues, hoping that useful changes might still result from this experience. Rather than an attack on IEEE and other publishers who support academic research, this is a plea to do this better from someone who cares. I would love to hear from others who have had similar experiences, as well as those who can articulate contrasting views. Rather than letting it go to waste, I have published the article, which I originally wrote for Visualization Viewpoints—What Ordinary People Need Most from Information Visualization Today—in my monthly Visual Business Intelligence Newsletter. I hope you read it and find it useful.

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Winners Announced for MicroCharts’ Dashboard Competition

BonaVista Systems (the maker of the Excel add-in product MicroCharts), a subsidiary of XL Cubed, has announced the winners of its 2008 Excel Dashboard Competition. You might be surprised to discover how effectively a dashboard can be designed using Excel, when enriched with MicroCharts, which adds sparklines and bullet graphs to Excel’s library of charts. These dashboards were not designed for show or to wow people with superficial eye candy–they were designed for actual use. The folks at BonaVista Systems have resisted the temptation to support the flashy (but ineffective) displays that the market has come to expect from dashboards. I applaud their courage and integrity. I’m betting that they don’t have a lot of customers coming back to them disappointed that their dashboards don’t work and don’t get used.

Take care,

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Visual Thinking for Design

The field of information visualization is still relatively young. The milestones that mark its history can be listed and briefly described with the exhalation of a single deep breath. Only a few information visualization events today can be anticipated with guarantees of significance, including:

• IEEE’s annual InfoVis Conference, which always provides glimpses of a few new and promising innovations
• Edward Tufte’s seminar, if you’ve never been before, which will encourage you to strive for excellence
• A presentation by Hans Rosling, which will inspire you to use visualization to solve the world’s problems
• A new major release of software from Tableau or Spotfire, which will convince you that visualization can reach a broad audience when properly designed and commercially packaged

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Godin’s Silly Rules for Great Graphs

Seth Godin invited a reaction last week when he ventured into unfamiliar territory by publishing a blog post entitled “The three laws of great graphs.”

1. One story
2. No bar charts
3. Motion

He was apparently engaging in a bit of hyperbole—a statement that is intentionally exaggerated for the purpose of making a point—when he listed as one of his rules: “No bar charts.” When Jesus made use of hyperbole to warn that great wealth can lead to corruption—”It is easier for a camel to pass through the eye of a needle than for a rich man to enter into the kingdom of heaven”—I suspect most listeners understood that he was using an exaggerated analogy to make his point. Godin’s spare but sweeping statement, “No bar charts,” however, runs the risk of being taken literally by many who struggle with data presentation, and have an undiscerning appetite for simplistic rules.

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Nudge Me Tender, Nudge Me True

Richard H. Thaler and Cass R. Sunstein, both of the University of Chicago, have written a thought-provoking book full of practical suggestions for improving the decision-making process, …

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