Information Dashboard Design

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Dashboards offer a unique and powerful solution to an organization's need for information, but they usually fall far short of their potential. Dashboards must be seen in historical context to understand and appreciate how and why they've come about, why they've become so popular, and why—despite many problems that undermine their value today—they offer benfits worth pursuing. To date, little serious attention has been given to their visual design. This book strives to fill this gap. However, confusion abounds, demanding a clear definition of dashboards before we can explore the visual design principles and practices that must be applied if they are to live up to their unique promise.

Problems with dashboards today
Dashboards in historical context
Current confusion about what dashboards are
A working definition of "dashboard"
A timely opportunity for dashboards

Above all else, this is a book about communication. It focuses exclusively on a particular medium of communication called a dashboard. In the fast-paced world of information technology (IT), terms are constantly changing. Just when you think you've wrapped your mind around the latest innovation, the technology landscape shifts beneath you and you must struggle to remain upright. This is certainly true of dashboards.

Live your life on the surface of these shifting sands, and you'll never get your balance. Look a little deeper, however, and you'll discover more stable ground: a bedrock of objectives, principles, and practices for information handling that remains relatively constant. Dashboards are unique in several exciting and useful ways, but despite the hype surrounding them, what they are and how they work as a means of delivering information are closely related to some long-familiar concepts and technologies. It's time to cut through the hype and learn the practical skills that can help you transform dashboards from yet another fad riding the waves of the technology buzz into the effective means to enlighten that they really can be.

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Dashboards can provide a unique and powerful means to present information, but they rarely live up to their potential. Most dashboards fail to communicate efficiently and effectively, not because of inadequate technology (at least not primarily), but because of poorly designed implementations. No matter how great the technology, a dashboard's success as a medium of communication is a product of design, a result of a display that speaks clearly and immediately. Dashboards can tap into the tremendous power of visual perception to communicate, but only if those who implement them understand visual perception and apply that understanding through design principles and practices that are aligned with the way people see and think. Software won't do this for you. It's up to you.

Unfortunately, most vendors that provide dashboard software have done little to encourage the effective use of this medium. They focus their marketing efforts on flash and dazzle that subvert the goals of clear communication. They fight to win our interest by maximizing sizzle, highlighting flashy display mechanisms that appeal to our desire to be entertained. Once implemented, however, these cute displays lose their spark in a matter of days and become just plain annoying. An effective dashboard is the product not of cute gauges, meters, and traffic lights (Figure 1-1), but rather of informed design: more science than art, more simplicity than dazzle. It is, above all else, about communication.

Figure 1-1: A typical flashy dashboard. Can't you just feel the engine revving?

This failure by software vendors to focus on what we actually need is hardly unique to dashboards. Most software suffers from the same shortcoming—despite all the hype about user-friendliness, it is difficult to use. This sad state is so common, and has been the case for so long, we've grown accustomed to the pain. On those occasions when this ugly truth breeches the surface of our consciousness, we usually blame the problem on ourselves rather than the software, framing it in terms of "computer illiteracy." If we could only adapt more to the computer and how it works, there wouldn't be a problem—or so we reason. In his insightful book entitled

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In many respects, "dashboard" is simply a new name for the Executive Information Systems (EISs) first developed in the 1980s. These implementations remained exclusively in the offices of executives and never numbered more than a few, so it is unlikely that you've ever actually seen one. I sat through a few vendor demos back in the 1980s but never did see an actual system in use. The usual purpose of an EIS was to display a handful of key financial measures through a simple interface that "even an executive could understand." Though limited in scope, the goal was visionary and worthwhile, but ahead of its time. Back then, before data warehousing and business intelligence had evolved the necessary data-handling methodologies and given shape to the necessary technologies, the vision simply wasn't practical; it couldn't be realized because the required information was incomplete, unreliable, and spread across too many disparate sources. Thus, in the same decade that the EIS arose, it also went into hibernation, preserving its vision in the shadows until the time was ripe… That is, until now.

During the 1990s, data warehousing, online analytical processing (OLAP), and eventually business intelligence worked as partners to tame the wild onslaught of the information age. The emphasis during those years was on collecting, correcting, integrating, storing, and accessing information in ways that sought to guarantee its accuracy, timeliness, and usefulness. From the early days of data warehousing on into the early years of this new millennium, the effort has largely focused on the technologies, and to a lesser degree the methodologies, needed to make information available and useful. The direct beneficiaries so far have mostly been folks who are highly proficient in the use of computers and able to use the available tools to navigate through large, often complex databases.

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Like many products that hit the high-tech scene with a splash, dashboards are veiled in marketing hype. Virtually every vendor in the BI space claims to sell dashboard software, but few clarify what dashboards actually are. I'm reminded of the early years of data warehousing, when—eager to learn about this new approach to data management—I asked my IBM account manager how IBM defined the term. His response was classic and refreshingly candid: "By data warehousing we at IBM mean whatever the customer thinks it means." I realize that this wasn't IBM's official definition, which I'm sure existed somewhere in their literature, but it was my blue-suited friend's way of saying that as a salesperson, it was useful to leave the term vague and flexible. As long as a product or service remains undefined or loosely defined, it is easy to claim that your company sells it.

Those rare software vendors that have taken the time to define the term in their marketing literature start with the specific features of their products as the core of the definition, rather than a generic description. As a result, vendor definitions tend to be self-validating lists of technologies and features. For example, Dr. Gregory L. Hovis, Director of Product Deployment for Snippets Software, Inc., asserts:

Able to universally connect to any XML or HTML data source, robust dashboard products intelligently gather and display data, providing business intelligence without interrupting work flow…An enterprise dashboard is characterized by a collection of intelligent agents (or gauges), each performing frequent bidirectional communication with data sources. Like a virtual staff of 24x7 analysts, each agent in the dashboard intelligently gathers, processes and presents data, generating alerts and revising actions as conditions change.

Gregory L. Hovis, "

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Several circumstances have recently combined to create a timely opportunity for dashboards to add value to the workplace, including technologies such as high-resolution graphics, emphasis on performance management and metrics, and a growing recognition of visual perception as a powerful channel for information acquisition and comprehension. Dashboards offer a unique solution to the problem of information overload—not a complete solution by any means, but one that helps a lot. As Dr. Hovis wrote in that same article in DM Direct:

The real value of dashboard products lies in their ability to replace hunt-and-peck data-gathering techniques with a tireless, adaptable, information-flow mechanism. Dashboards transform data repositories into consumable information.

Gregory L. Hovis, "Stop Searching for Information–Monitor it with Dashboard Technology," DM Direct, February 2002

Dashboards aren't all that different from some of the other means of presenting information, but when properly designed the single-screen display of integrated and finely tuned data can deliver insight in an especially powerful way.

Richard Brath and Michael Peters, "Dashboard Design: Why Design is Important," DM Direct, October 2004

Dashboards and visualization are cognitive tools that improve your "span of control" over a lot of business data. These tools help people visually identify trends, patterns and anomalies, reason about what they see and help guide them toward effective decisions. As such, these tools need to leverage people's visual capabilities. With the prevalence of scorecards, dashboards and other visualization tools now widely available for business users to review their data, the issue of visual information design is more important than ever.

The final sentiment that Brath and Peters expressed in this excerpt from their article underscores the purpose of this book. As data visualization becomes increasingly common as a means of business communication, it is imperative that expertise in data visualization be acquired. This expertise must be grounded in an understanding of visual perception, and of how this understanding can be effectively applied to the visual display of data—what works, what doesn't, and why. These skills are rarely found in the business world, not because they are difficult to learn, but because the need to learn them is seldom recognized. This is true in general, and especially with regard to dashboards. The challenge of presenting a large assortment of data on a single screen in a way that produces immediate insight is by no means trivial. Buckle up; you're in for a fun ride.

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Dashboards can be used to monitor many types of data and to support almost any set of objectives business deems important. There are many ways to categorize dashboards into various types. The way that relates most directly to a dashboard's visual design involves the role it plays, whether strategic, analytical, or operational. The design characteristics of the dashboard can be tailored to effectively support the needs of each of these roles. While certain differences such as these will affect design, there are also many commonalities that span all dashboards and invite a standard set of design practices.

Categorizing dashboards
Common threads in dashboard data
Non-quantitative dashboard data

Dashboards are used to support a broad spectrum of information needs, spanning the entire range of business efforts that might benefit from an immediate overview of what's going on. Dashboards can be tailored to specific purposes, and a single individual might benefit from multiple dashboards, each supporting a different aspect of that person's work. The various data and purposes that dashboards can be used to support are worth distinguishing, for they sometimes demand differences in visual design and functionality.

Dashboards can be categorized in several ways. No matter how limited and flawed the effort, doing so is useful because it helps us to examine the benefits and many uses of the medium. I'm one of those people who enjoys the process of classifying things, breaking them up into groups. It's an intellectual exercise that forces me to dig beneath the surface. I don't, however, assign undue worth to any one way of categorizing something, and I certainly don't ever want to give in to the arrogance of claiming that mine is the only way.

Taxonomies—a scientific term for systems of classification—are always based on one or more variables (that is, categories consisting of multiple potential values). For instance, based on the variable "platform," a dashboard taxonomy could consist of those that run in client/server mode and those that run in web browsers. The following table lists several variables that can be used to structure dashboard taxonomies, along with potential values for each. This list certainly isn't comprehensive; these are simply my attempts to express the variety and explore the potential of the dashboard medium.

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Dashboards can be categorized in several ways. No matter how limited and flawed the effort, doing so is useful because it helps us to examine the benefits and many uses of the medium. I'm one of those people who enjoys the process of classifying things, breaking them up into groups. It's an intellectual exercise that forces me to dig beneath the surface. I don't, however, assign undue worth to any one way of categorizing something, and I certainly don't ever want to give in to the arrogance of claiming that mine is the only way.

Taxonomies—a scientific term for systems of classification—are always based on one or more variables (that is, categories consisting of multiple potential values). For instance, based on the variable "platform," a dashboard taxonomy could consist of those that run in client/server mode and those that run in web browsers. The following table lists several variables that can be used to structure dashboard taxonomies, along with potential values for each. This list certainly isn't comprehensive; these are simply my attempts to express the variety and explore the potential of the dashboard medium.

Variable	Values
Role	Strategic Analytical Operational
Type of data	Quantitative Non-quantitative
Data domain	Sales Finance Marketing Manufacturing Human Resources
Type of measures	Balanced Scorecard (for example, KPIs)

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Dashboards are useful for all kinds of work. Whether you're a meteorologist monitoring the weather, an intelligence analyst monitoring potential terrorist chatter, a CEO monitoring the health and opportunities of a multi-billion dollar corporation, or a financial analyst monitoring the stock market, a well-designed dashboard could serve you well.

Despite these diverse applications, in almost all cases dashboards primarily display quantitative measures of what's currently going on. This type of data is common across almost all dashboards because they are used to monitor the critical information needed to do a job or meet one or more particular objectives, and most (but not all, as we'll see later) of the information that does this best is quantitative.

The following table lists several measures of "what's currently going on" that are typical in business.

Category	Measures
Sales	Bookings Billings Sales pipeline (anticipated sales) Number of orders Order amounts Selling prices
Marketing	Market share Campaign success Customer demographics
Finance	Revenues Expenses Profits
Technical Support	Number of support calls Resolved cases

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Preoccupation with superficial and functionally distracting visual characteristics of dashboards has led to a rash of visual design problems that undermine their usefulness. Thirteen visual design problems are frequently found in dashboards, including in the examples featured as exemplary by software vendors.

Exceeding the boundaries of a single screen
Supplying inadequate context for the data
Displaying excessive detail or precision
Choosing a deficient measure
Choosing inappropriate display media
Introducing meaningless variety
Using poorly designed display media
Encoding quantitative data inaccurately
Arranging the data poorly
Highlighting important data ineffectively or not at all
Cluttering the display with useless decoration
Misusing or overusing color
Designing an unattractive visual display

The fundamental challenge of dashboard design is the need to squeeze a great deal of information into a small amount of space, resulting in a display that is easily and immediately understandable. If this doesn't sound challenging, either you are an expert designer with extensive dashboard experience, or you are basking in the glow of naiveté. Attempt the task, and you will find that dashboards pose a unique data visualization challenge. And don't assume that you can look to your software vendor for help—if they have the necessary design talent, they're doing a great job of hiding it.

Sadly, it is easy to find many examples of the mistakes you should avoid by looking no further than the web sites of the software vendors themselves. Let's use some of these examples to examine design that doesn't work and learn why it doesn't.

In almost every case, I've chosen to use actual examples from vendor web sites to illustrate dashboard design mistakes. In doing so, I am not saying that the software that produced the example is bad—I'm not commenting on the quality of the software one way or another. What I am saying is that the design practice is bad. This results primarily from vendors' lack of expertise in or inattention to visual design. These vendors should know better, but they've chosen to focus their energies on other aspects of their products, often highlighting glitzy visual features that actually undermine effective communication. I hope that seeing their work used to illustrate poor dashboard design will serve as a wake-up call to start paying attention to the features that really matter.

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My insistence that a dashboard should confine its display to a single screen, with no need for scrolling or switching between multiple screens, might seem arbitrary and a bit finicky, but it is based on solid and practical rationale. After studying data visualization for a while, including visual perception, one discovers that something powerful happens when things are seen together, all within eye span. Likewise, something critical is lost when you lose sight of some data by scrolling or switching to another screen to see other data. Part of the problem is that we can hold only a few chunks of information at a time in short-term memory. Relying on the mind's eye to remember information that is no longer visible is a rocky venture.

One of the great benefits of a dashboard as a medium of communication is the simultaneity of vision that it offers: the ability to see everything that you need at once. This enables comparisons that lead to insights—those "Aha!" experiences that might not occur in any other way. Clearly, exceeding the boundaries of a single screen negates this benefit. Let's examine the two versions of this problem—fragmenting data into separate screens and requiring scrolling—independently.

Information that appears on dashboards is often fragmented in one of two ways:

Separated into discrete screens to which one must navigate
Separated into different instances of a single screen that are accessed through some form of interaction

Enabling users to navigate to discrete screens or different instances of a single screen to access additional information is not, in general, a bad practice. Allowing navigation to further detail or to a different set of information that achieves its purpose best by standing alone can be a powerful dashboard feature. However, when all the information should be seen at the same time to gain the desired insights, that fragmentation undermines the unique advantages of a dashboard. Fragmenting data that should be seen together is a mistake.

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Measures of what's currently going on in the business rarely do well as a solo act; they need a good supporting cast to succeed. For example, to state that quarter-to-date sales total $736,502 without any context means little. Compared to what? Is this good or bad? How good or bad? Are we on track? Are we doing better than we have in the past, or worse than we've forecasted? Supplying the right context for key measures makes the difference between numbers that just sit there on the screen and those that enlighten and inspire action.

The gauges in Figure 3-4 could easily have incorporated useful context, but they fall short of their potential. For instance, the center gauge tells us only that 7,822 units have sold this year to date, and that this number is good (indicated by the green arrow). A quantitative scale on a graph, such as the radial scales of tick marks on these gauges, is meant to provide an approximation of the measure, but it can only do so if the scale is labeled with numbers, which these gauges lack. If the numbers had been present, the positions of the arrows might have been meaningful, but here the presence of the tick marks along a radial axis suggests useful information that hasn't actually been included.

Figure 3-4: These dashboard gauges fail to provide adequate context to make the measures meaningful.

These gauges use up a great deal of space to tell us nothing whatsoever. The same information could have been communicated simply as text in much less space, without any loss of meaning:

YTD Units	7,822
October Units	869
Returns Rate	0.26%

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Dashboards almost always require fairly high-level information to support the viewer's need for a quick overview. Too much detail, or measures that are expressed too precisely (for example, $3,848,305.93 rather than $3,848,305, or perhaps even $3.8M), just slow viewers down without providing them any benefit. In a way, this problem is the opposite extreme of the one we examined in the previous section—too much information rather than too little.

The dashboard in Figure 3-6 illustrates this type of excess. Examine the two sections that I've enclosed in red rectangles. The lower-right section displays from 4 to 10 decimal digits for each measure, which might be useful in some contexts, but doubtfully in a dashboard. The highlighted section above displays time down to the level of seconds, which also seems like overkill in this context. With a dashboard, every unnecessary piece of information results in time wasted trying to filter out what's important, which is intolerable when time is of the essence.

Figure 3-6: This dashboard shows unnecessary detail, such as times expressed to the second and measures expressed to 10 decimal places.

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For a measure to be meaningful, we must know what is being measured and the units in which the measure is being expressed. A measure is deficient if it isn't the one that most clearly and efficiently communicates the meaning that the dashboard viewer should discern. It can be accurate, yet not the best choice for the intended message. For example, if the dashboard viewer only needs to know to what degree actual revenue differs from budgeted revenue, it would be more direct to simply express the variance as–9% (and perhaps display the variance of–$8,066 as well) rather than displaying the actual revenue amount of $76,934 and the budgeted revenue amount of $85,000 and leaving it to the viewer to calculate the difference. In this case, a percentage clearly focuses attention on the variance in a manner that is directly intelligible.

Figure 3-7 illustrates this point. While this graph displays actual and budgeted revenues separately, its purpose is to communicate the variance of actual revenues from the budget.

Figure 3-7: This graph illustrates the use of measures that fail to directly express the intended message.

The variance, however, could have been displayed more vividly by encoding budgeted revenue as a reference line of 0% and the variance as a line that meanders above and below budget (expressed in units of positive and negative percentages, as shown on the next page in Figure 3-8). The point here is to always think carefully about the message that most directly supports the viewer's needs, and then select the measure that most directly supports that message.

Figure 3-8: This graph is designed to emphasize deviation from a target, which it accomplishes in part by expressing the difference between budgeted and actual revenues using percentages.

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Choosing inappropriate display media is one of the most common design mistakes made, not just in dashboards, but in all forms of quantitative data presentation. For instance, using a graph when a table of numbers would work better, and vice versa, is a frequent mistake. Allow me to illustrate using several examples beginning with the pie chart in Figure 3-9.

Figure 3-9: This chart illustrates a common problem with pie charts.

This pie chart is part of a dashboard that displays breast cancer statistics. Look at it for a moment and see if anything seems odd.

Pie charts are designed specifically to present parts of a whole, and the whole should always add up to 100%. Here, the slice labeled "Breast 13.30%" looks like it represents around 40% of the pie—a far cry from 13.3%. Despite the meaning that a pie chart suggests, these slices are not parts of a whole; they represent the probability that a woman will develop a particular form of cancer (breast, lung, colon, and six types that aren't labeled). This misuse of a pie chart invites confusion.

The truth is, I never recommend the use of pie charts. The only thing they have going for them is the fact that everybody immediately knows when they see a pie chart that they are seeing parts of a whole (or ought to be). Beyond that, pie charts don't display quantitative data very effectively. As you'll see in Chapter 4, Tapping into the Power of Visual Perception, humans can't compare two-dimensional areas or angles very accurately—and these are the two means that pie charts use to encode quantitative data. Bar graphs are a much better way to display this information.

Refer to my book Show Me the Numbers: Designing Tables and Graphs to Enlighten (Oakland, CA: Analytics Press, 2004) for a thorough treatment of the types of graphs that work best for the most common quantitative messages communicated in business.

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The mistake of introducing meaningless variety into a dashboard design is closely tied to the one we just examined. I've found that people often hesitate to use the same type of display medium multiple times on a dashboard, out of what I assume is a sense that viewers will be bored by the sameness. Variety might be the spice of life, but if it is introduced on a dashboard for its own sake, the display suffers. You should always select the means of display that works best, even if that results in a dashboard that is filled with nothing but multiple instances of the same type of graph. If you are giving viewers the information that they desperately need to do their jobs, the data won't bore them just because it's all displayed in the same way. They will definitely get aggravated, however, if forced to work harder than necessary to get the information they need due to arbitrary variety in the display media. In fact, wherever appropriate, consistency in the means of display allows viewers to use the same perceptual strategy for interpreting the data, which saves time and energy.

Figure 3-18 illustrates variety gone amok. This visual jumble requires a shift in perceptual strategy for each display item on the dashboard, which means extra time and effort on the user's part.

Figure 3-18: This dashboard exhibits an unnecessary variety of display media.

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It isn't enough to choose the right medium to display the data and its message—you also must design the components of that medium to communicate clearly and efficiently, without distraction. Most graphs used in business today are poorly designed. The reason is simple: almost no one has been trained in the fundamental principles and practices of effective graph design. This content is thoroughly covered in my book Show Me the Numbers: Designing Tables and Graphs to Enlighten, so I won't repeat myself here. Instead, I'll simply illustrate the problem with a few examples.

In addition to the fact that a bar graph would have been a better choice to display this data (the division of revenue between six sales), Figure 3-19 exhibits several design problems. Look at it for a moment and see if you can identify aspects of its design that inhibit quick and easy interpretation.

Figure 3-19: This pie chart illustrates several design problems.

Here are the primary problems that I see:

A legend was used to label and assign values to the slices of the pie. This forces our eyes to bounce back and forth between the graph and the legend to glean meaning, which is a waste of time and effort when the slices could have been labeled directly.
The order of the slices and the corresponding labels appears random. Ordering them by size would have provided useful information that could have been assimilated instantly.
The bright colors of the pie slices produce sensory overkill. Bright colors ought to be reserved for specific data that should stand out from the rest.

The pie chart in Figure 3-20 also illustrates a problem with color choice.

Figure 3-20: This pie chart uses of colors for the slices that are too much alike to be clearly distinguished.

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Sometimes graphical representations of quantitative data are mistakenly designed in ways that display inaccurate values. In Figure 3-25, for instance, the quantitative scale along the vertical axis was improperly set for a graph that encodes data in the form of bars. The length of a bar represents its quantitative value. The bars in this graph that represent revenue and costs for the month of January suggest that revenue was about four times costs. An examination of the scale, however, reveals the error of this natural assumption: the revenue is actually less than double the costs. The problem is that the values begin at $500,000 rather than $0, as they always should in a bar graph.

Figure 3-25: This bar graph encodes the quantitative values as bars inaccurately, by failing to begin the scale at zero.

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Dashboards often need to present a large amount of information in a limited amount of space. If the information isn't organized well, with appropriate placement of information based on importance and desired viewing sequence, along with a visual design that segregates data into meaningful groups without fragmenting it into a confusing labyrinth, the result is a cluttered mess. Most examples of dashboards found on the Web are composed of a small amount of data to avoid the need for skilled visual design, but they still often manage to look cluttered and thrown together. The goal is not simply to make the dashboard look good, but to arrange the data in a manner that fits the way it's used. The most important data ought to be prominent. Data that require immediate attention ought to stand out. Data that should be compared ought to be arranged and visually designed to encourage comparisons.

The dashboard in Figure 3-26 illustrates some of the problems often associated with poor arrangement of data. Notice first of all that the most prominent position on this dashboard—the top left—is used to display the vendor's logo and navigational controls. What a waste of prime real estate! As you scan down the screen, the next information that you see is a gauge that presents the average order size. It's possible that average order size might be someone's primary interest, but it's unlikely that, of all the information that appears on this dashboard, this is the most important. As I'll discuss in Chapter 5, Eloquence Through Simplicity, the least prominent real estate on the screen is the lower-right corner. However, in this example the large amount of space taken up by the graphs that present "Computers Returns Across Models," as well as the larger font sizes used in this section, tends to draw attention to data that seems tangential to the rest. This dashboard lacks an appropriate visual sequence and balance based on the nature and importance of the data. Notice also that the bright red bands of color above each section of the display, where the titles appear in white, are far more eye-catching than is necessary to declare the meanings of the individual displays. This visually segments the space to an unnecessary degree. Lastly, note that the similarity of the line graphs that display order size and profit trends invites our eyes to compare them. This is probably a useful comparison, but the positional separation and side-by-side rather than over-under arrangement of the two graphs makes close comparison difficult. As this example illustrates, you can't just throw information onto the screen wherever you can make it fit and expect the dashboard to do its job effectively.

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When you look at a dashboard, your eyes should immediately be drawn to the information that is most important, even when it does not reside in the most visually prominent areas of the screen. In Chapter 5, Eloquence Through Simplicity, we'll examine several visual techniques that can be used to achieve this end. For now, we'll look at what happens when this isn't done at all, or isn't done well.

The problem with the dashboard in Figure 3-27 is that everything is visually prominent, and consequently nothing stands out. The logo and navigation controls (the buttons on the left) are prominent both as a result of their placement on the screen and the use of strong borders, but these aren't data and therefore shouldn't be emphasized. Then there are the graphs where the data reside: all the data are equally bold and colorful, leaving us with a wash of sameness and no clue where to focus. Everything that deserves space on a dashboard is important, but not equally so—the viewer's eyes should always be directed to the most crucial information first.

Figure 3-27: This dashboard fails to differentiate data by its importance, giving relatively equal prominence to everything on the screen.

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Another common problem on the dashboards that I find on vendor web sites is the abundance of useless decoration. They either hope that we will be drawn in by the artistry or assume that the decorative flourishes are necessary to entertain us. I assure you, however, that even people who enjoy the decoration upon first sight will grow weary of it in a few days.

The makers of the dashboard in Figure 3-28 did an exceptional job of making it look like an electronic control panel. If the purpose were to train people in the use of some real equipment by means of a simulation, this would be great, but that isn't the purpose of a dashboard. The graphics dedicated to this end are pure decoration, visual content that the viewer must process to get to the data.

Figure 3-28: This dashboard is trying to look like something that it is not, resulting in useless and distracting decoration.

I suspect that the dashboard in Figure 3-29 looked too plain to its designer, so she decided to make it look like a page in a spiral-bound book—cute, but a distracting waste of space.

Figure 3-29: This dashboard is another example of useless decoration—the designer tried to make the dashboard look like a page in a spiral-bound notebook.

Likewise, I'd guess that the designer of the dashboard in Figure 3-30—after creating a map, a bar graph, and a table that all display the same data—decided that he had to fill up the remaining space, so he went wild with an explosion of blue and gray circles. Blank space is better than meaningless decoration. Can you imagine yourself looking at this every day?

Figure 3-30: This dashboard is a vivid example of distracting ornamentation.

The last example, Figure 3-31, includes several elements of decoration that ought to be eliminated. To begin with, a visually ornate logo and title use up the most valuable real estate across the entire top of the dashboard. If a logo must be included for branding purposes, make it small and visually subtle, and place it somewhere out of the way. The background colors of gold and blue certainly draw our eyes to the data, but they do so in an unnecessarily heavy-handed manner. Also, the color gradients from dark to light provide visual interest that supports no real purpose and is therefore distracting. Lastly, the maps in the background of the three upper graphs, though visually muted, still distract from the data itself.

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We've already seen several examples of misused or overused color. The remaining point that I want to emphasize here is that color should not be used haphazardly.

Color choices should be made thoughtfully, with an understanding of how we perceive color and the significance of color differences. Some colors are hot and demand our attention, while others are cooler and less visible. When any color appears as a contrast relative to the norm, our eyes pay attention and our brains attempt to assign meaning to that contrast. When colors in two different sections of a dashboard are the same, we are tempted to relate them to one another. We merrily assume that we can use colors such as red, yellow, and green to assign important meanings to data, but in doing so we exclude the 10% of males and 1% of females who are color-blind. In Chapter 4, Tapping into the Power of Visual Perception, we'll learn a bit about color and how it can be used meaningfully and powerfully.

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Not being one to mince words for the sake of propriety, I'll state quite directly that some dashboards are just plain ugly. When we see them, we're inclined to avert our eyes—hardly the desired reaction to a screen that's supposed to be supplying us with important information. You might have assumed from my earlier warning against unnecessary decoration that I have no concern for dashboard aesthetics, but that's not the case. When a dashboard is unattractive—unpleasant to look at—the viewer is put in a frame of mind that is not conducive to its use. I'm not advocating that we add touches to make dashboards pretty, but rather that we attractively display the data itself, without adding anything that distracts from or obscures it. (We'll examine the aesthetics of dashboard design a bit in Chapter 7, Designing Dashboards for Usability.)

Figure 3-32 on the next page is a stellar example of unattractive dashboard design. It appears that the person who created this dashboard attempted to make it look nice, but he just didn't have the visual design skills needed to succeed. For instance, in an effort to fill up the space, some sections (such as the graph at the bottom right) were simply stretched. Also, although shades of gray can be used effectively as the background color of graphs, this particular shade is too dark. The image that appears under the title "Manufacturing" is clearly an attempt to redeem this dreary dashboard with a splash of decoration, but it only serves to distract from the data and isn't even particularly nice to look at. The guiding design principle of simplicity alone would have saved this dashboard from its current agony.

Figure 3-32: This is an example of a rather unattractive dashboard.

You don't need to be a graphic artist to design an attractive dashboard, but you do need to understand a few basic principles about visual perception. We'll examine these in the next chapter.

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Vision is by far our most powerful sense. Seeing and thinking are intimately connected. To display data effectively, we must understand a bit about visual perception, gleaning from the available body of scientific research those findings that can be applied directly to dashboard design: what works, what doesn't, and why.

Understanding the limits of short-term memory
Visually encoding data for rapid perception
Gestalt principles of visual perception

It isn't accidental that when we begin to understand something we say, "I see." Not "I hear" or "I smell," but "I see." Vision dominates our sensory landscape. As a sensophile, I cherish the rich abundance of sounds, smells, tastes, and textures that inhabit our world, but none of these provides the rich volume, bandwidth, and nuance of information that I perceive through vision. Approximately 70% of the sense receptors in our bodies are dedicated to vision, and I suspect that there is a strong correlation between the extensive brainpower and dominance of visual perception that have co-evolved in our species. How we see is closely tied to how we think.

I've learned about visual perception from many sources, but one stands out above the others in its application to information design: the book Information Visualization: Perception for Design by Colin Ware. Dr. Ware expresses the importance of studying visual perception beautifully:

Why should we be interested in visualization? Because the human visual system is a pattern seeker of enormous power and subtlety. The eye and the visual cortex of the brain form a massively parallel processor that provides the highest-bandwidth channel into human cognitive centers. At higher levels of processing, perception and cognition are closely interrelated…However, the visual system has its own rules. We can easily see patterns presented in certain ways, but if they are presented in other ways, they become invisible…The more general point is that when data is presented in certain ways, the patterns can be readily perceived. If we can understand how perception works, our knowledge can be translated into rules for displaying information. Following perception-based rules, we can present our data in such a way that the important and informative patterns stand out. If we disobey the rules, our data will be incomprehensible or misleading.

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In truth, we don't see with our eyes; we see with our brains. Our eyes are the sensory mechanisms through which light enters and is translated by neurons into electrical impulses that are passed on to and around in our brains, but our brains are where perception—the process of making sense of what our eyes register—actually occurs.

Our eyes do not register everything that is visible in the world around us, but only what lies within their span of perception. Only a portion of what our eyes sense becomes an object of focus. Only through focus does what we see become more than a vague sense. Only a fraction of what we focus on becomes the object of attention or conscious thought. Finally, only a little bit of what we attend to gets stored away for future use. Without these limits and filters, perception would overwhelm our brains.

Our memories store information starting the moment we see something, continuing as we consciously process the information, and finally accumulating over years in a permanent (or nearly so) storage area where information remains ready for use if ever needed again—that is, until access to that information eventually begins to atrophy.

Memory comes in three fundamental types:

Iconic memory (a.k.a. the visual sensory register)
Short-term memory (a.k.a. working memory)
Long-term memory

Iconic memory is a lot like the visual memory buffer of a computer: a place where images are briefly held until they can be moved to random access memory (RAM), where they reside while being processed by the CPU. Even though what goes on in iconic memory is preconscious, a certain type of processing—known as preattentive processing —occurs nonetheless. Certain attributes of what we see are recognized during preattentive processing at an extraordinarily high speed, which results in certain things standing out and particular sets of objects being grouped together, all without conscious thought. Preattentive processing plays a powerful role in visual perception, and we can intentionally design our dashboards to take advantage of this if we understand a bit about it.

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Preattentive processing, the early stage of visual perception that rapidly occurs below the level of consciousness, is tuned to detect a specific set of visual attributes. Attentive processing is sequential, and therefore much slower. The difference is easy to demonstrate. Take a moment to examine the four rows of numbers in Figure 4-1, and try to determine as quickly as you can the number of times the number 5 appears in the list.

Figure 4-1: How many fives are in this list? Note the slow speed at which we process visual stimuli that lack preattentive attributes.

How many did you find? The correct answer is six. Whether you got the answer right or not, the process took you a while because it involved attentive processing. The list of numbers did not exhibit any preattentive attributes that you could use to distinguish

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