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Federal CIO Dashboard: We Can and Should Do Better Than This
Over the past few months, the Obama Administration has worked to apply technology to our nation’s problems and opportunities. I applaud the efforts of our recently appointed Federal CIO, Vivek Kundra, to invest more wisely in technology and to make useful data more available both within government and to the public. While welcoming and encouraging these efforts, it is important that we critique their effectiveness as well and speak up when they could be significantly improved. It is in this spirit of patriotism that I would like to point out flaws in the new Federal IT Dashboard that is currently available in beta release. As someone who has designed a great many dashboards, I can say without reservation that the Federal IT Dashboard is about as useful in its current form as a typical business dashboard, and this isn’t a compliment. Others have written about the Federal IT Dashboard in articles and blogs with nothing but praise. Although it’s tempting to rain nothing but praise on a child who’s performed poorly in the past when he makes an effort to improve, it’s important to supplement that encouragement with instruction as well, if you really care. Kundra states on the website: “We tapped the brightest and most innovative minds from Federal agencies, Congress, independent oversight organizations, and the private sector as we built the IT Dashboard.” The project team apparently failed to tap anyone who has expertise in quantitative data analysis and presentation—data visualization in particular. On the dashboard’s website, Kundra invites suggestions. I think it’s time for us who have the expertise that appears to be lacking in the dashboard’s design to lend a hand.
When we initially access the Federal IT Dashboard, here’s what appears on the site’s home page:
The pie chart and its three companion bars on the right automatically morph every few seconds to display a few measures of a different government agency’s IT projects. Unfortunately for those of us who might actually like the time we spend on this page to produce something useful, neither the slices of the pie nor the segments of the bars are labeled, so we have no idea what we’re seeing. Perhaps the home page was meant to function only as an opening splash page of sorts and we must go elsewhere for actual information.
Let’s select the Investments tab at the top and hope for something useful.
Aha! Here we see the pie chart and bars from before, but this time the parts are labeled. Now we’re getting somewhere. Well, actually, we’re not getting anywhere without a great deal of unnecessary effort. Why are the charts three dimensional? Despite their unfortunate popularity, three dimensional displays of two-dimensional data are not only superfluous, they also undermine the simple task of graph perception and comprehension. As Edward Tufte would say, this is “chartjunk.” It breaks one of the basic rules of data presentation: “Do no harm.”
Those of us with expertise in quantitative data displays almost unanimously despise pie charts. The one thing they have going for them is the clear message that they’re displaying parts of a whole. It would help, however, if we could actually compare those parts by comparing the slices of the pie, but visual perception isn’t tuned to compare areas effectively. It is, however, highly tuned to compare the lengths of bars. Had the percentages of the projects that fall into the three categories of “normal,” “needs attention,” or “significant concerns” (see the legend at the bottom) been displayed as three separate bars with a common starting position and labels to the left, rather than pie charts, we could have easily compared these percentages. As it is, to make sense of the pie chart we must keep referring to the legend and then read the numbers that appear next to each slice, because the pie doesn’t do the job on its own.
We’re faced with a similar problem when we try to use the three stacked bars to understand “project costs,” “schedules,” and “CIO evaluations,” because we can’t effectively compare segments of a bar arranged end to end. Three separate horizontal bars for each set of measures (for example, “Costs”) arranged one above the other with a common starting point, on the other hand, would be easy to compare.
Even if the information were displayed using appropriate graphs, it would still be of little use because we derive meaning from quantitative information primarily through comparisons, but for any of these measures we can only compare values related to the three qualitative states of projects—”normal,” “needs attention,” and “significant concerns”. At any one moment we can only see either all agencies combined or a single agency, but never multiple individual agencies which prevents us from comparing them, and we can only see one point in time, which prevents us from comparing what’s going on now to the past to observe how things have changed.
If we wish to compare service groups and agencies, however, we can move to another page, which displays IT projects in the form of a treemap.
Using this treemap, we can roughly compare projects among different service groups by using the sizes of rectangles to compare one measure (total IT spending in this example) and the colors of rectangles to compare a second (% change in IT spending in this example). If the treemap were better designed, we could now get a fairly good overview of how projects among service groups compare, but a couple of problems make it tough going. In the treemap above, projects are organized into four service groups: “Services for Citizens,” “Management of Government Resources,” “Service Types and Components,” and a truncated category that begins with “Support Delive…”). Unfortunately, if we want to identify individual projects in these categories, we must hover with the mouse over each in turn to get the name to appear in a tooltip window.
If we drill down into a particular service group by clicking it, we can see projects in that service group organized by agencies (”Defense and National Security,” “Health,” etc.).
Based on this view, however, can you actually see the boundaries that separate one agency from another? For some reason, the borders that separate them have become partly obscured. Eventually we can drill down to a level in the hierarchy where a treemap is no longer the best way to view the projects because the number of them could be more easily compared using one or more bar graphs, but this option isn’t available. And finally, when we’ve drilled down to the lowest level—a single project—the treemap view is entirely useless, as you can see below. The unlabeled big gray rectangle tells us only that spending on this project—whatever it is—didn’t change much since the previous year. Perhaps it didn’t even exist in the previous year.
Below the treemap in the bottom left corner we have the ability to change the colors that are currently being used to display percentage change in IT spending ranging from -10% (blue) to +10% (yellow). This ability is useful for ad hoc data analysis when flexibility is needed to respond to unanticipated conditions , but on an analytical application like this, which has been designed to display a specific set of measures for a specific set of purposes, it would make more sense to select a color ramp that works well and resist complicating the dashboard with choices that aren’t necessary.
If we wish to see how spending on federal IT projects has changed over the years, we can proceed to the Analysis section of the dashboard and select Trends. The first of two displays that are available for viewing time-series data is an animated bubble chart, which attempts to use the method popularized by Hans Rosling of www.gapminder.org.
The strength of this approach is when it’s used to tell a story. When Rosling narrates what’s happening in the chart as the bubbles move around and change in value, pointing to what he wants us to see, the information comes alive. Animated bubble charts, however, as much less effective for exploring and making sense of data on our own. I doubt that Rosling uses this method to discover the stories, but only to tell them once they’re known. We can’t attend more than one bubble at once as they’re moving around, so we’re forced to run the animation over and over to try to get a sense of what’s going on. We can add trails to selected bubbles, which make it possible to review the full path these bubble have taken, but if trails are used for more than a few bubbles the chart will quickly become too cluttered. Essentially, what I’m pointing out is that this is not the best way to display this information for exploration and analysis. A simpler display such as one or more line graphs would do the job more effectively. Perhaps you’re concerned that a line graph couldn’t display two quantitative variables at once, such as “Total IT Spending” and “Percent Change in IT Spending,” which appear in this bubble chart. Assuming that two quantitative variables ought to be compared as they change through time, two line graphs—one for each variable—arranged one above the other, would handle this effectively. One of the fundamental problems with the bubble chart above, however, is that the two quantitative variables that appear in it really don’t need to be seen together. There is no correlation between total IT spending and percentage change in IT spending from year to year, so there’s no reason to complicate the display by viewing them together.
Even if this animated bubble chart were a good visualization choice in this case, several problems in its design would undermine its usefulness. When I first look at it, I was puzzled for awhile about what “03. % Change in IT Spending” meant. I couldn’t understand the significance of “03. %…” It took awhile to figure out that each variable that appears on the graph was numbered, beginning with “01.” and ending with “05.”, which was completely meaningless and confusing.
Unlike the intuitive use of colors to that we saw in the treemap, the rainbow of colors that appear in the bubble chart are ineffective. The order of the various hues as they change from red to blue is not intuitive. Take these colors and ask people to put them in order from high to low and you’ll get a variety of answers.
Also, the ability to switch the quantitative scales from linear to logarithmic certainly makes sense to people who have been trained in statistics, but is confusing to most of the folks who would use this dashboard. For this reason, I believe this feature should be removed. While it is appropriate to include such functionality in a general purpose data analysis tool, custom analytical applications like the Federal CIO Dashboard should eliminate features that aren’t commonly useful and are potentially confusing in an effort to keep the application simple. Even those who understand how to use a log scale don’t need it available on this dashboard, because few of them would be satisfied using this bubble chart, but would rather download the data and explore it using a better analytical tool.
For those who recognize the limitations and flaws of the bubble chart, an alternative in the form of a bar graph is available. For our entertainment pleasure, when switching between the two, the bubbles morph into bars before our eyes and line themselves up along the horizontal axis.
The bar chart version is just plain silly. None of the bars are labeled until you click on them one at a time to make labels such as “Education (Dept of)” and “Homeland Security (Dept of)” appear. Knowing only the identity of the selected bars (the others remain unlabeled) and watching the bars move around as spending changes through time is eye-catching but almost totally meaningless. Once again, simple line graphs for comparing changing values for the selected items would do the job much better.
Because I wanted to learn something more useful about federal IT spending, I decided to take advantage of the data feeds that are provided, but once again ran into a wall. Unfortunately, the information that can be downloaded is limited to a current year’s snapshot, which includes three variables—total spending, new/upgrades spending, and maintenance spending—broken into three time-based categories: last year’s actual spending, the current year’s enacted spending, and next year’s budgeted spending. Time series aren’t available nor is there a way to compare actual to plan. In other words, the comparisons that I would have found most meaningful couldn’t be made based on the information that’s available.
I want to encourage Vivek Kundra to complement his fine intentions with more effective designs. There’s no need to duplicate the mistakes that most businesses still make when working with information. Data analysis and presentation best practices are not a mystery and aren’t difficult to learn. Several of us who know and care about this are available to help. I suspect that others would be willing, as I am, to assist free of charge. America can do better than this. We have a great opportunity to use information technology to make the world a better place. Let’s not miss it.
Take care,
An Excellent Primer on Geo-spatial Analysis
In the past I’ve recommended two books on geo-spatial data analysis and presentation:Â Designing Better Maps by Cynthia Brewer and GIS Cartography: A Guide to Effective Map Design by Gretchen Peterson. Today I’d like to add a third to the list: The ESRI Guide to GIS Analysis, Volume 1: Geographic Patterns and Relationships by Andy Mitchell.
Although Mitchell’s book has been available since 1999, it was new to me when I recently purchased and read it. I was looking for a book that would serve as a good primer for folks who are just getting started with geo-spatial analysis, and this book does the job quite well. It assumes that you know little about geo-spatial analysis and lays out the basics clearly and simply. Mitchell outlines the books contents as follows:
In this book, we’ve identified the most common geographic analysis tasks people do every day in their jobs:
- Mapping where things are
- Mapping the most and least
- Mapping density
- Finding what’s inside
- Finding what’s nearby
- Mapping change
As someone who knows a great deal about data analysis and visualization in general but a limited amount about geo-spatial analysis in particular, I learned a great deal from this book. It was useful to have several gaps in my knowledge filled in as Mitchell took me on a superbly organized and simply expressed journey through the fundamentals. If you’re new to GIS and want a good primer to start the journey on the right foot, I highly recommend this book.
The Global BusinessObjects Network Has Pie On Its Face
This blog entry was written by Bryan Pierce of Perceptual Edge.
Here at Perceptual Edge, we like to show real-world examples of poor graph design to teach people what not to do, because knowing what to avoid and why it doesn’t work is an important step in the learning process. We often receive emails from people who follow this blog or have read Stephen’s books or articles who want to share examples that they’ve come across. The pie chart below is one such example:
This graph was produced by the Global BusinessObjects Network to promote the upcoming 2009 SAP BusinessObjects User Conference. It’s supposed to show what BusinessObjects products the attendees of last year’s conference used. Regular readers of Stephen’s work know that he dislikes pie charts because they don’t work as effectively as alternatives like bar graphs, so I won’t revisit the general problems with pie charts. (If you’re interested in more information about pie charts, Stephen wrote a full review detailing their significant problems and single, rarely-needed strength.) Unfortunately, the design of this graph is quite terrible, even by pie chart standards.
This graph is dysfunctional for two major reasons. First, only the large slices have been directly labeled. All of the small slices are labeled using a legend, but there are so many slices that it’s impossible to associate the colors in the legend with the slices in the pie because so many of the colors are so similar. Sure, people can just read the values from the legend and ignore the pie, but how is that better than a simple table?
The second major problem with this graph is this: Pie charts are designed to display part-to-whole data, with each slice representing one discrete part of the whole and all of the slices adding up to 100%. For instance, you could show the breakdown of sales by region for a company. In the graph above, however, the slices add up to significantly more than 100% because the categories aren’t mutually exclusive. For instance, 67% of attendees use BusinessObjects Web Intelligence, but many of those people use other BusinessObjects software, too, so they’re being counted several times. The end result is that the blue slice that represents BusinessObjects Web Intelligence has a value of 67%, but it only takes up about 15% of the space in the pie. The visual picture conveyed by the pie chart misrepresents the data.
Both of these problems could have been solved by using a horizontal bar graph. With a horizontal bar graph, all of the bars could be the same color and there would be no problem labeling all of the slices directly, which would address the first problem, and because bar graphs are more versatile than pie charts and can be used for more than just part-to-whole relationships, it wouldn’t be confusing when the bars added up to more than 100%, which solves the second problem.
At Perceptual Edge, we’ve seen plenty of graphs like this, and worse. But it always irks us when we see examples like this coming out of the Business Intelligence industry, an industry that should know better. In this case, the Global Business Objects Network puts on a large conference with dozens of educational courses on BI related subjects, including analytics and dashboards. How do they expect people to trust them with the sophisticated visualization training, when their simple graphs are so dysfunctional?
-Bryan
Xcelsius Developers Debate the Merits of “Flashy vs. Fewâ€
I just ran across an interesting and thoughtful blog post at EverythingXcelsius.com by Ryan Goodman, the founder of Centigon Solutions, titled “Ryan Goodman’s Take on ‘Flashy vs. Few.’” In it, Ryan responds to a lengthy discussion that has been brewing for awhile among Xcelsius developers about the flashy features of that product versus the best practices of dashboard design that I teach. If the tension between making dashboards flashy or making them effective interests you, I think you’ll find Ryan’s thoughts and the ensuing discussion worthwhile.
Here’s the URL:Â http://everythingxcelsius.com/2009/07/ryan-goodmans-take-on-flashy-vs-few.html
Take care,
Horizon Graphs Revisited
For the June/July issue of the Visual Business Intelligence Newsletter, I wrote an article titled “Time on the Horizon,” which featured a new time-series visualization called the horizon graph. Developed by Panopticon, horizon graphs can meaningfully display 50 or so full sets of time-series values on a single screen or page in a way that supports comparisons among them. The following example displays a year’s worth of stock prices for 50 separate stocks (one per row).
Though it takes some getting used to at first, once you’ve learned how to read it, it works quite well.
As it turns out, three information visualization researchers at the University of California, Berkeley became interested in horizon graphs and decided to put them to the test. To my delight, and no doubt that of the folks at Panopticon as well, the horizon graph performed well and some useful guidelines for its use were discovered.
I found this research inspiring, not only because of the results, but because it is such a fine example of the kind of constructive collaboration between software vendors and academic researchers that we desperately need. No, these researchers weren’t paid by Panopticon to confirm their work. In fact, neither I nor Panopticon knew about this until after the research was complete. This wasn’t the kind of collaboration that can sometimes get messy when money’s exchanged, but the kind that arises naturally from a group of researchers’ desire to apply their talents to something tangible-something that has a good chance of actually being used.
This research, done by Jeffrey Heer, Nicholas Kong, and Maneesh Agrawala, was well designed, well conducted, and written up beautifully in the paper “Sizing the Horizon: The Effects of Chart Size and Layering on the Graphical Perception of Time Series Visualizations”. It will be presented at CHI 2009 in April (CHI stands for Computer-Human Interaction) and has been nominated for the CHI Best Paper award.
Take care,
Software Support for Bullet Graphs—An Increasingly Popular Means of Display
This blog entry was written by Bryan Pierce of Perceptual Edge.
In January 2006, when Steve first introduced bullet graphs as a more effective alternative to circular gauges in his book, Information Dashboard Design, they were no more than a design concept that he created using Adobe Illustrator. There were no functional bullet graphs being used in the real world and any application of them would have required custom programming. They were a useful design that hadn’t been implemented.
Now, as we start 2009, it’s been three years since bullet graphs were first introduced. Over that time, they’ve become popular as an alternative to circular gauges as people have noticed their ability to provide more information in a smaller space, which is especially useful for dashboards. Ambitious designers have found tricks to implement bullet graphs in a variety of products, and some software vendors now include bullet graphs in the graph libraries that they provide. As of today, bullet graphs are available or can be created in the following products:
Products that support bullet graphs right out of the box:
MicroCharts (Excel add-in) by Bonavista Systems
CenterView by Corda
Visual:Acuity by Visual Engineering
DExperience by Developer Express
Although not provided as a standard graph type, bullet graphs can also be constructed with:
QlikView by QlikTech
MicroStrategy
Flex by Adobe
R
HTML/CSS courtesy of Matt Grams
Google Charts courtesy of Dealer Diagnostics
SVG courtesy of Chris Gerrard
This list has grown significantly in just the last year and I expect it to continue to grow as more people discover the merits of bullet graphs. If you currently use a product that can’t create bullet graphs, be sure to tell the vendor how useful they would be, and if you know of a product that I haven’t mentioned here, please share it by posting a comment.
-Bryan Pierce
About Face — Returning BI to Its Roots
In an article entitled “The Changing Face of Business Intelligence,” last month Dave Wells eloquently described how the business intelligence industry has strayed from its original vision and how it is now changing to recover what’s been lost. A longtime veteran of data warehousing and business intelligence, Wells is one of the leaders in the industry who have shaken free of the technology-centric perspective that holds the industry back.
Wells begins by reminding us of Howard Dresner’s original vision when he coined the term “business intelligence” (BI) in the early 1990s. Dresner defined BI as “a set of concepts and methodologies to improve decision making in business through use of facts and fact-based systems.” Over the years, the industry that took hold of Dresner’s visionary term (mostly data warehousing vendors at the time) buried the goal of decision making in an emphasis on technology. As Wells says: “The troubling thing is that all of the definitions are IT-centric” and “too much of today’s business analytics has little connection with real business analysis.”
He goes on to offer a new definition of business intelligence, which recaptures the essence of the original and enhances it to further clarify the goals. I don’t want to give too much away by quoting his definition here; you should read Wells’ words directly. I do want to include one more quote, however, which is central to Well’s vision of BI’s transformation:
It is analysts – the people who perform analysis – who find meaning in the data. These are the people who explore cause-effect relationships and who guide decision-making processes. It is they who will lead the charge to reshape decision making in business.
To recover the original vision, the business intelligence industry must shift from an emphasis on technology to an emphasis on the people who use the technology. Only then will it begin to fulfill its original promise.
(Note: While I consider Wells’ argument brilliant, I believe that some of the software products that he lists as examples of “next generation of analytics” don’t belong there. In fact, I believe that some of the products on the list exemplify little understanding of and support for data analysis. This difference of opinion suggests that our common vision must become informed by clear definitions of data analysis and analytics and clear criteria for assessing products’ ability to deliver. All in good time.)
Data Visualization and Analysis—BI’s Blind Spots
In September, I wrote a rather scathing review of a product called Lyza from a new business intelligence (BI) vendor named LyzaSoft. Part of my criticism was that LyzaSoft erroneously claimed that Lyza qualifies as data analysis and data visualization software. A month later, a good friend and respected colleague, Colin White, took issue with my opinion of Lyza. Thus began an email exchange between us and several other leaders in the field of BI. In this exchange, Colin noticed that we all seemed to use the terms “data analysis” and “data visualization” differently, so he asked each of us to define them. Here are the definitions that I contributed to the discussion:
Data analysis
Data sense-making. The process of discovering and understanding the meanings of data. (Not to be confused with preliminary steps taken to prepare data for the process of analysis.)
Data visualization
The use of visual representations to explore, make sense of, and communicate data. As such, data visualization is a core and usually essential means to perform data analysis, and then, once the meanings have been discovered and understood, to communicate those meanings to others.
On December 17th, Colin wrote about this in an article titled “Business Intelligence Data Analysis and Visualization: What’s in a Name?” Colin did a nice job of summarizing the discussion, but I believe that the conclusions that he reached miss the mark and are typical of most traditional BI professionals.
Here are Colin’s concluding opinions:
At a detailed level, two questions dominate the discussion:
- Are data transformation and integration different from data analysis? There are many examples of applications that retrieve data from multiple sources, restructure and aggregate it, and then load the results into a data warehouse. Similarly, data federation and data streaming technologies allow users not only to do dynamic in-motion data transformation and integration, but also data aggregation and summarization. These are all examples of processes that perform some level of data analysis. The ability to clearly delineate data transformation from data analysis is fast disappearing, and to say data transformation is completely different from data analysis makes no sense.
- Is data presented for presentation purposes only a form of data visualization? The mere fact that some of the comments got into semantic debates about what is data and what is information, and about whether a user is actually analyzing the results or not, suggests that a more pragmatic viewpoint is required. From my perspective, if data or information is presented to a user in a format that aids decision making, then that constitutes data visualization.
At a more macro level, it is important to define the role of a so-called expert or specialist. Our job is to help people understand and use new and evolving technologies and products for business benefit. As such, we need to use clear definitions and terminology that aids in this understanding. However, it is important that we accept that other people may have different definitions, and we need to find common ground. Defending our positions at all costs does not aid the industry. We also have to accept that business users may employ technology and use some terms in a completely different way, and it is important to adjust our positions and explanations accordingly. Unless we do that, business intelligence will continue to be usable only by the small subset of users that employ it today.
I’ll come back to Colin’s position in a moment, but first, I’d like to provide some context for what I’m going to argue. The BI industry has done a wonderful job of providing technologies that enable us to collect, cleanse, and store huge warehouses of data. We now have enormous reservoirs of data available to us, but most people are drowning in them, unable to do the only thing that really matters: actually use the information to achieve the understanding that’s needed to make good decisions. This is predominantly a human task.
The technologies that are needed to help us make sense of data must be built on a clear understanding of what people must do to understand data and the perceptual and cognitive processes involved in the effort. In other words, the solutions that are needed require a human focus, not the technology focus that has produced the tools that we use to collect, cleanse, and store data. I believe most of the people who have done great work to enable the BI achievements in building a solid data infrastructure are locked in a technology mindset from which they can’t escape and rarely even recognize that they should escape. Almost every vendor that is currently offering real solutions for data sense-making—a rather small group—has emerged from outside the BI industry. Some have been working for years as statistical analysis vendors and most others are spin-offs of information visualization research at universities. None of the major BI vendors seem to understand data analytics at all. I don’t think this is for lack of interest or effort, but because they are focused on technology, an engineering focus, rather than the human beings who use technology, a social science and design focus. I believe that the discussion that Colin, I, and others in the industry had about data analysis and data visualization illustrates this situation.
Contrary to LyzaSoft’s claim that businesspeople use the term data analysis for the entire end-to-end process of working with data (you can read their position in Colin’s article, which he refers to as “The Vendor’s Position”), I’ve found that the people who actually work in business and elsewhere to make sense of data know that the tasks of collecting, cleansing, aggregating, and storing data are different from data analysis. The former tasks precede and support the process of data analysis by making data accessible and reliable, but they aren’t data analysis itself. These folks would much rather have the IT department build a good data warehouse for them so they aren’t bothered by having to prepare the data and can spend their time actually analyzing it. This distinction between data preparation and data analysis is not just a matter of semantics. Until vendors understand this difference, they will continue to produce so-called data analysis products that don’t work. In contrast, vendors such as Tableau, Spotfire, Advisor Solutions, Panopticon, Visual I|O, and SAS—examples of those who haven’t emerged from within the BI industry—already get this.
Now that buyers of BI software are turning their focus to the actual use of data—to data sense-making and communication—it’s tempting and all too convenient for BI vendors such as LyzaSoft to call what they do “data analysis.” This murky use of the term not only renders it vague, confusing, and for all practical purposes useless, it also prolongs the state of affairs that has given rise to our current desire for data analytics: the fact that BI vendors have failed to provide useful tools for data sense-making and communication. These tools, which we desperately need to make better decisions, have always been the central, but failed, promise of business intelligence.
The opinion that Colin expresses in response to the second issue concerns me: ”From my perspective, if data or information is presented to a user in a format that aids decision making, then that constitutes data visualization.” I certainly agree that the goal is to achieve understanding and support decision making, but not every way of doing this is data visualization, and not everything that would like to call itself data visualization deserves the name. Information can be presented in various ways, just as it can be verbally communicated in various languages; each medium of data presentation (the spoken word, the written word, and visual representations of various types) has its strengths and weaknesses, its appropriate applications, and its rules for effective use. Saying that every presentation that aids decision making is data visualization is not a useful definition. In fact, it’s an example of what I warned against in our email discussion. Here’s what I said, as quoted in Colin’s article:
Confusion regarding terms such as data analysis and data visualization exists in the BI community because little effort has been made to sufficiently define them. Our industry tolerates a freewheeling, define-it-as-you-wish attitude toward these and other terms to the detriment of our customers. In the academic world, which I keep one foot in, a greater effort is made to define the terms to provide the shared meanings that are required to communicate, yet even in academia it gets a bit murky at times. I believe that terms are inadequately defined in the BI community in part because ours is an industry that has largely been defined for marketing purposes, rather than as a rational discipline. It serves the interests of software vendors to keep the terms vague.
I agree that we must be open to one another’s ideas and definitions, but I believe the goal of this openness, after thinking long and hard, is to narrow, not expand, our use of these terms. As it is today, these terms are barely useful because they are defined too loosely, broadly and inconsistently. Expanding the definitions will only add to the problem.
I’ll conclude this blog post as Colin ended his article, with the following question and invitation: “What do you think?”
2009 Visual Business Intelligence Workshops
Due to the popularity of my public workshops in 2008, beginning in 2009 I’ll be teaching my three-day Visual Business Intelligence Workshop in three U.S. locations. In addition to the east coast from June 2-4 in Boston and the west coast from September 29-October 1 in San Francisco, I’ve added a central location from April 21-23 in Austin. You can read about these workshops and register for courses by visiting the Workshops page.
The Mansion at Judges’ Hill in Austin
Also in 2009, for the fifth year in a row I’ll be teaching my workshop in Rome, and I’m currently in discussions about possible workshops in London, São Paulo, and Mumbai.
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 21st 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?