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.

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

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.

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:

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:

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.

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.

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.

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.

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.

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.

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:

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.

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.

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.

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.

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.

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.

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.

Here are the primary problems that I see:

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

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.

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.

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.

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.

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.

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?

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.

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.

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.

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.

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 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.

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.

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 the fives from the other numbers. Now try it again, this time using the list of numbers in Figure 4-2.

Much easier this time, wasn't it? In this figure the fives could easily be distinguished from the other numbers, due to their differing color intensity (one of the preattentive attributes we'll discuss below): the fives are black while all the other numbers are gray, which causes them to stand out in clear contrast. Why couldn't we easily distinguish the fives in the first set of numbers (Figure 4-1) based purely on their unique shape? Because the complex shapes of the numbers are not attributes that we perceive preattentively. Simple shapes such as circles and squares are preattentively perceived, but the shapes of numbers are too elaborate.

In Information Visualization: Perception for Design

Back in 1912, the Gestalt School of Psychology began its fruitful efforts to understand how we perceive pattern, form, and organization in what we see. The German term "gestalt" simply means "pattern." These researchers recognized that we organize what we see in particular ways in an effort to make sense of it. Their work resulted in a collection of Gestalt principles of perception that reveal those visual characteristics that incline us to group objects together. These principles still stand today as accurate and useful descriptions of visual perception, and they offer several useful insights that we can apply directly in our dashboard designs to intentionally tie data together, separate data, or make some data stand out as distinct from the rest.

We'll examine the following six principles:

Two of the greatest challenges in dashboard design are to make the most important data stand out from the rest, and to arrange what is often a great deal of disparate information in a way that makes sense, gives it meaning, and supports its efficient perception. An understanding of the preattentive attributes of visual perception and the Gestalt principles provides a useful conceptual foundation for facing these challenges. It is much more helpful to understand how and why something works than to simply understand that something works. If you understand the how and why, when you're faced with new challenges you'll be able to determine whether or not the principles apply and how to adapt them to the new circumstances. If you've simply been told that something works in a specific situation, you'll be stuck when faced with conditions that are even slightly different.

As you proceed into the coming chapters, you'll have several opportunities to reinforce your grasp of visual perception by applying what you've learned to several real-world dashboard design problems.

The fundamental challenge of dashboard design involves squeezing a great deal of useful and often disparate information into a small amount of space, all the while preserving clarity. This certainly isn't the only challenge—others abound, such as selecting the right data in the first place— but it is the primary challenge that is particular to dashboards. Limited to a single screen to keep all the data within eye span, dashboard real estate is extremely valuable: you can't afford to waste an inch. Fitting everything in without sacrificing meaning doesn't require muscles, it requires finesse.

Unless you know what you're doing, you'll end up with a cluttered mess. Think for a moment about the cockpit of a commercial jet. Years of effort went into its design to ensure that despite the many things pilots must monitor, they can see everything that's going on at a glance. Every time I board a plane, I'm grateful that skilled designers worked hard to present this information effectively. Similar care is needed for the design of dashboards, but unlike aircraft cockpit design, few of those who create dashboards have actually studied the science of design. You can become an exception to this unfortunate and costly norm. It is unlikely that people will lose their lives if you fail, but businesses do occasionally crash and burn—and frequently lose money—due to failed communication of just this sort.

Henry David Thoreau once penned the same word three times in succession to emphasize an important quality of life that applies to design as well: "Simplify, simplify, simplify!" Though I often fail, I strive to live my life and to design all forms of communication according to Thoreau's sage advice to keep things simple. Eloquence in communication is often achieved through simplification. Too often we smear a thick layer of gaudy makeup over data in an effort to impress or entertain, rather than focusing on communicating the truth of the matter in the clearest possible way.

Edward R. Tufte introduced a concept in his 1983 classic The Visual Display of Quantitative Information that he calls the "data-ink ratio." When quantitative data is displayed in printed form, some of the ink that appears on the page presents data, and some presents visual content that is not data (a.k.a. non-data). Figure 5-2 shows two displays of quantitative data: one in the form of a table and the other in the form of a graph. Take a minute to examine them and try to differentiate the data ink from the non-data ink.

There isn't much non-data ink in either the table or the graph, because they were intentionally designed to keep it to a minimum. Figure 5-3 shows the same table and graph, this time with the non-data ink encoded as red.

Tufte defines the data-ink ratio in the following way:

A large share of ink on a graphic should present data-information, the ink changing as the data change. Data-ink is the non-erasable core of a graphic, the non-redundant ink arranged in response to variation in the numbers represented. Then,

Data-ink ratio

He then applies it as a principle of design: "Maximize the data-ink ratio, within reason. Every bit of ink on a graphic requires a reason. And nearly always that reason should be that the ink presents new information."

This principle applies perfectly to the design of dashboards, with one simple revision: because dashboards are always displayed on computer screens, I've changed the word "ink" to "pixels." Across the entire dashboard, non-data pixels—any pixels that are not used to display data, excluding a blank background—should be reduced to a reasonable minimum. Take a moment to examine the dashboard in Figure 5-4 on the next page and try to identify the non-data pixels that can be eliminated without sacrificing anything meaningful.

The best medium for displaying data will always be based on the nature of the information, the nature of the message, and the needs and preferences of the audience. A single dashboard generally displays a variety of data and requires a variety of display media, each matched to specific data. In the next section we'll pair specific data and messages with the graphic media that display them best, but let's begin here with a more fundamental question: "Should the information be encoded as text, graphics, or both?" The appropriateness of each medium for a given situation, either verbal language in written form (text) or visual language (graphics), isn't arbitrary.

Verbal language is processed serially, one word at a time. Some people are much faster readers than others—an ability that I envy—but everyone processes language serially. Especially when communicating quantitative information, the strength of written words and numbers compared to graphics is their precision. If your sole purpose is to precisely communicate current year-to-date expenses of $487,321, for example, nothing works better on a dashboard than a simple display like this:

Displaying individual values does not require graphics—indeed, their use would only retard communication. Let's continue to enhance this data to see if there is a point where switching from pure text to the addition of graphics adds clear value.

Sometimes just providing an individual number and label is appropriate, but often you want to say more. Let's enhance the data with a simple evaluative remark that this year-to-date expense figure is higher than it should be:

This certainly isn't the only way to communicate this evaluative information, but it is sufficient. As long as only measures in this condition are displayed in this fashion, even those who are color-blind will be able to recognize that we are calling attention to this expense amount (because we've boldfaced the number).

So far we've considered only the first, most fundamental step in selecting the best medium of display. Once you've chosen between text, graphics, or some combination of the two, you must then determine how to organize the text and/or what kinds of graphics to use. These choices are vital. A poorly chosen graph, for example, could completely obscure otherwise clear data. In this section, we'll focus specifically on the best choice of graphical display to use when you determine that a visual rather than a textual display is appropriate.

Most display media that work well on dashboards are probably familiar to you already. Quantitative graphs and several other types of charts that are commonly used in business reporting (for example, process flow and organization charts) work well on dashboards, provided their design is kept clear and simple.

This discussion focuses on dashboard display media that are used to present actual data. Other display media, such as command buttons, are sometimes needed, but they fall outside our scope of interest. Two fundamental principles have guided the selection of each display medium in this proposed library:

The library is divided into six categories:

The library of dashboard display media that I've proposed in this chapter is certainly not comprehensive, nor will it remain unchanged as time goes on. As new graphic inventions emerge that suit the purpose and design constraints of dashboards, this library will continue to grow, but I expect that it will do so slowly. Just because a vendor introduces a new visualization technique doesn't mean it belongs on a dashboard. Let's keep the vision true to form and effective for enlightening and efficient communication.

You can't just take information and throw it onto the dashboard any way you please. How the pieces are arranged in relation to one another can make the difference between a dashboard that works and one that ends up being ignored, even though the information they present is the same. Keep the following considerations in mind when you determine how to arrange data on the screen:

Differences in appearance always prompt us to search, whether consciously or unconsciously, for the significance of those differences. Anything that means the same thing or functions in the same way ought to look the same wherever it appears on a dashboard. Even something as subtle as arbitrarily using dark axis lines on one graph and light axis lines on another will lead viewers to suspect that this difference, which is in fact arbitrary, is significant.

It's important to maintain consistency not only in the visual appearance of the display media, but in your choice of display media as well. If two sections of data involve the same type of quantitative relationship (such as a time series) and are intended for similar use (for example, to compare a measure to a target measure for each month), you should use the same type of display for both (for example, a bar graph). Never vary the means of display for the sake of variety. Always select the medium that best communicates the data and its message, even if that means that your dashboard consists of the same type of graph throughout.

In 1988 Donald Norman, a cognitive scientist, wrote a wonderful book entitled The Design of Everyday Things (New York: Basic Books). It is a classic in the field of design that convincingly argues that the effectiveness of something's design should be judged by how well it works and how easy it is to use. In the years since its publication, designers have often accused Norman of ignoring the value of aesthetics. This frequent critique was one of his motives for writing the recent book entitled Emotional Design: Why We Love (or Hate) Everyday Things (New York: Basic Books, 2004).

In this book, Norman describes the psychological and physiological benefits of aesthetically pleasing design. If applied to dashboard design, Norman's point would argue that aesthetically pleasing dashboards are more enjoyable, which makes them more relaxing, which prepares the viewer for greater insight and creative response. This is not a departure from his earlier assertions in The Design of Everyday Things, but rather an extension asserting that aesthetics, when not in conflict with a product's usability, possess intrinsic qualities that also contribute to usability. This new book convincingly reframes the discussion about the importance of usability as a matter not of usability versus aesthetics but of usability versus anything that flagrantly undermines usability, which good, aesthetically pleasing design manages to avoid.

I love visual art. I appreciate beauty for its own sake. Moments of great beauty exalt me. Information design, however, is about communication: getting an intended message across in a way that results in useful understanding. Aesthetics are an important component of information design, but not in the same way that they are in art. If a dashboard is not designed in an aesthetically pleasing way, the unpleasant experience that results for the viewer undermines the dashboard's ability to communicate. On a dashboard, your aesthetic talent ought to be applied directly to the display of the data itself, not to meaningless and distracting ornamentation. The aesthetics of dashboard design should always express themselves simply, striving for the eloquence that emerges uniquely from simplicity.

As single-screen displays, dashboards do not always provide all the information needed to perform a job or to pursue a particular set of objectives. They can provide the initial overview that is needed for monitoring at a high level, but they might need to be supplemented with additional information for more comprehensive understanding and response. Dashboards should almost always be designed for interaction. The most common types of dashboard interaction are:

Whichever of these you intend, when your dashboard serves as a launch pad to additional, complementary information, be sure to keep the following principles in mind:

Enabling the viewer to access additional data (such as the details beneath the overview) via direct interaction is easy and intuitive, and it saves space on the dashboard by eliminating separate controls such as buttons. If you display a bar graph in which each bar represents the revenue of a different sales region, for example, it might be ideal to allow the viewer to click directly on a particular bar to see a graph that further subdivides that region's revenue according to the individual states that belong to the region. Likewise, if there are times when a viewer might want to know the precise value for a particular data point along a line graph, the ability to hover over that position and have the value pop up temporarily as text is ideal. Whatever mechanism you decide to build into the dashboard to initiate links to additional data, make sure that it is consistent wherever it appears, to avoid confusion.

No matter how well designed your final product turns out to be, it is always hard to dissuade people from predetermined notions of how it should look. Do your best to prevent those who will eventually use your completed dashboard from developing expectations about its look and feel apart from your input and expert advice. Present your users with a single prototype of the most effective design that you can create, and let that be the starting point for discussions about how it might be tweaked to better serve their needs. Don't present them with several alternative designs, because even though your users probably know what they need to accomplish, they don't know how the dashboard ought to be visually designed to achieve that result. You are the designer, so it is up to you to bring this expertise to the process.

You will never get everything right on the first try, no matter how skilled you are. You must put your design to the test. Only those who will actually use the dashboard are qualified to determine if it actually works and works well. Show it to them populated with real data, and observe them as they look it over and learn to make sense of the data. If you are introducing display media that are new to them, begin with simple instruction in how they work and explain why you chose those mechanisms rather than others that might be more familiar. If you've done your homework and your users really care about doing their jobs well rather than doing them in a particular way, usability testing will usually result in relatively minor additions and tweaks to refine the effectiveness of the dashboard, rather than major revisions. Although there are certainly exceptions when dealing with the foibles of human beings, good design usually results in a good reception.

Apart from executive dashboards, I suspect that no one type of dashboard is implemented more often than a sales dashboard. Sales activity is the life-giving heart of most businesses. Those in charge of sales need to keep their fingers on the pulse at all times, even when all is well. Sales strategies might need to change quickly when new opportunities, problems, or competitive pressures arise. A well-designed dashboard can be a powerful tool for a sales manager.

I began designing the sample sales dashboard by selecting the information that seemed most important for a sales manager to monitor. Each item that I selected is a measure of what's currently going on in sales. Here's the list:

For each of these items, I needed to make several decisions, including:

A Chief Information Officer must keep track of many facts regarding the performance of the company's information systems and activities, including projects that serve the company's information needs. I chose to include the following data in my sample dashboard:

This is a mixture of strategic and frequently updated operational information that a CIO might need. Examine Figure 8-11 closely and try to get a sense for how it might work in the real world.

Only one section of this dashboard—the upper-left corner—displays near real-time data. This section consists of a series of five alerts: one for each of the systems that the CIO might need to respond to immediately when a problem arises. If no red circles appear in this section, nothing critical is currently wrong with any of these systems. To better grab the CIO's attention, red alerts that appear in this section could blink until clicked, or even emit along with the blinks a sound that gradually increases in volume. The red alert objects could also serve as links to other screens that describe precisely what is wrong.

The rest of this dashboard provides the CIO with information that is more strategic in nature. Notice that a great deal of contextual information has been provided to complement the measures—especially comparisons to measures of acceptable performance. This is the kind of context that could help the CIO easily make sense of these measures.

There is a great deal of information on this dashboard, yet it doesn't seem cluttered. This is largely due to the fact that non-data pixels have been reduced to a minimum. For instance, white space alone has been used to separate the various sections of the display. A judicious use of color has also contributed to this effect. Besides gray-scale colors, the only other hues you see are a muted green for the name of each section and two intensities of red, which in every case serves as an alert. It is easy to scan the dashboard and quickly find everything that needs attention, because the red alert objects are unique, visually unlike anything else.

This sample dashboard was designed to monitor real-time operations so that a telesales supervisor can take necessary actions without delay. This isn't a dashboard that's likely to be looked at once a day, but one that will be kept available and examined throughout the day. It doesn't display as many measures as the examples you've seen so far in this chapter, because too many measures can be overwhelming when the dashboard is used to monitor real-time operations that require quick responses. Only the following six measures are included:

That's it—and that's plenty for a dashboard of this type.

Imagine that you're responsible for a team of around 25 telesales representatives and are using the dashboard in Figure 8-12 to keep on top of their activities throughout the day.

The primary metrics that you must vigilantly monitor are the length of time customers are waiting to connect with a sales representative, the length of time sales representatives are spending on calls, and the number of customers who are getting discouraged and hanging up while waiting to get through. Because of their importance, these three metrics are located in the upper-left corner of the dashboard and are extremely easy to read.

When problems arise, such as the lengthy hold times and excessively lengthy calls shown in this example, you must quickly determine the cause before taking action. This is when you would switch your focus to the performance of the individual sales representatives, which you can see on the right side of the dashboard. Individuals are ranked by performance, with those performing poorly at the top and a red rectangle highlighting those who are performing outside the acceptable range.

The last sample dashboard we'll look at is an example of one that supports analysis (Figure 8-13). Like all dashboards, it is used to monitor the information needed to do a job, but in this case that job happens to primarily involve analysis. Dashboards can provide a useful means for analysts to watch over their domains and spot conditions that warrant examination. Ideally, they can also serve as direct launch pads to the additional data and tools necessary to perform comprehensive analyses.

This particular scenario involves an analyst whose work supports the marketing efforts of the company's web site. She monitors customer behavior on the site to identify both problems that prevent customers from finding and purchasing what they want and opportunities to interest customers in additional products. To expose activities on the web site that could lead to insight if studied and understood, the following data appears on the dashboard:

The information that appears at the top of this dashboard provides an overview of the web site's performance through time and lists missed opportunities and ineffective marketing efforts. Notice that the time-series information regarding visitors to the site is segmented into three sections, each featuring a different interval of time. The intervals have been tailored to reveal greater detail for the recent past and increasingly less detail the farther back the data goes.

Much of the information on this dashboard has been selected and arranged to display a ranking relationship. This is common when a dashboard is used to feature exceptional conditions, both good and bad. Much of this ranked information is communicated in the form of text, with little graphical content. Given the purpose to inform the analyst of potential areas of interest with a brief explanation of why, text does the job nicely. The analyst must read each entry to decide if she'll investigate the matter, but graphical displays, which could be scanned faster, would not do the job as well. The fact that an item appears on one of these lists already implies its importance, so graphical devices such as alerts would add nothing.

To design dashboards that really work, you must always focus on the fundamental goal: communication. More than anything else, you must care that the people who use your dashboards can look at them and understand them—simply, clearly, and quickly. Dashboards designed for any other reason, no matter how impressive or entertaining, will become tiresome in a few days and will be discarded in a few weeks—and few things are more discouraging than having your hard work tossed aside as useless.

When I design something that makes people's lives better, helps them work smarter, or gives them what they need to succeed in something that is important to them, I am reminded that one of the great cornerstones of a life worth living is the joy of doing good work. This doesn't just happen; it is the result of effort that you make because you care. Your dashboards may not change the world in any big way, but anything you do well will change you to some degree for the better. Even if the business goals that you're helping someone achieve through a well-designed dashboard don't ultimately matter to you or are not intrinsically worthy of great effort, you're worth the effort, and that's enough. In fact, that's plenty.