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Elegance Through Simplicity
By keeping goals in mind and design simple, you can achieve elegant, easily understandable data presentations.
October 5, 2004
8 Min Read
Experts in any domain tend to lose awareness of the individual steps that they go through to produce their work. As their expertise grows and deepens over time, it becomes second nature, intuitive, and automatic. Ask them to break the process down for you and they might just stare blankly, genuinely befuddled. They know how to do it, but that knowledge has become so intimately integrated into their minds and bodies, they can no longer articulate the process. In this article, I'll try to save you some time and trouble by revealing the steps I take when designing data presentations.
This series of articles has examined the poor state of data presentation today, common mistakes you should avoid, how visual perception works and how you can apply that knowledge to the visual presentation of data, and the seven quantitative relationships that regularly need to be presented via tables and graphs (see Resources). Now let's examine the steps involved in designing effective data presentations. You'll be amazed at how simple they are; in fact, simplicity is the guiding principle of effective data presentation.
The Fundamental Challenges
The process of creating an effective data presentation begins with a clear understanding of the data and a firm grasp of your message. With this knowledge in hand, you must then face two fundamental challenges in your effort to present the data:
Determine the best medium of display. (Sentence, table, or graph? If a table, which kind? If a graph, which kind?)
Design the components of the medium you have chosen to display the data and your message clearly, without distraction.
The previous article in this series, "Eenie, Meenie, Minie, Moe" already addressed the first step in this process. Now we can proceed to the second step: what you should do to make each component of the table or graph that you've selected clearly encode and support the data, with the most important information shining brightest of all.
The Data-Ink Ratio
Edward Tufte, the expert whose work in the field of data presentation I cite and praise more often than any other, laid the conceptual foundation for visual clarity years ago. In his 1983 book, The Visual Display of Quantitative Data, he succinctly stated the goal: "Above all else show the data." He then proceeded to show us how. Tufte introduced a concept that he coined the "data-ink ratio":
"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 ÷ total ink used to print the graphic
= proportion of a graphic's ink devoted to the non-redundant display of data-information
= 1.0 — proportion of a graphic that can be erased without loss of data-information."
The data-ink ratio is the proportion of ink (or pixels, when displaying information on a screen) that's used to present actual data, without redundancy, compared to the total amount of ink (or pixels) used in the entire display, such as in a table or graph. The goal is to design a display that has the highest possible data-ink ratio (that is, as close to the total of 1.0 or 100% as possible), without eliminating something that is necessary for effective communication.
Figure 1: Example of a graph (taken from Business Objects' user documentation) with a low data-ink ratio.
To begin to understand and appreciate this concept, consider Figure 1. Everything that communicates actual data is data ink. In this example, the bars, the tick marks and numeric values along the vertical axis, the labels along the horizontal axis, the colors and labels that identify the four quarters, and the titles are all data. Despite the many data components, however, this graph falls far short of an optimal data-ink ratio. Several examples of non-data ink stand out as completely unnecessary. Can you identify them? In my opinion, the border around the legend, the border around the plot area, and the grid lines are all unnecessary data ink.
Based on the concept of the data-ink ratio, Tufte describes the goal of effective data display:
"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."
Now, let's learn how to achieve this goal.
Designing Clear Data Displays
The process of maximizing the data-ink ratio consists of two major steps, each of which can be further broken down into two minor steps:
Reduce the non-data ink.
Remove unnecessary non-data ink.
De-emphasize and regularize the remaining non-data ink.
Enhance the data ink.
Remove unnecessary data ink.
Emphasize the most important data ink.
You begin by identifying all of the non-data ink components of your display and asking the following question of each: "Will any data be lost or diminished if this component is removed?" For example, if the border around the legend is removed, will the data and its message suffer? If the answer is "No," then the component should go. Once you've completed this step, the only non-data ink remaining is that which somehow supports the data and its message in a necessary way. The writer Antoine de St. Exupery insightfully expresses a fundamental principle of elegant communication: "In anything at all, perfection is finally attained not when there is no longer anything to add, but when there is no longer anything to take away."
The most common examples of useful non-data ink in a business graph are the axes. Rather than encoding data, the axes define the space in which the data is displayed. In doing so, they play a useful support role. As such, they should be visually muted to the point where they're just visible enough to do their job, no longer competing with the data for attention. Also, if you always design non-data ink components to look the same wherever and whenever they appear, thus regularizing them, they'll never jump out demanding undue attention.
Now let's enhance the data ink, first by identifying and eliminating any that isn't necessary, which will automatically make what remains more accessible. Looking once again at Figure 1, do you see any data ink that isn't necessary? Four items jump out to me:
The cents portion of the revenue values along the vertical scale is useless and suggests a level of numeric precision that the graph doesn't support.
The repetition of the dollar sign for each revenue value is redundant.
The tick marks along the horizontal scale aren't needed to mark the positions of the three service lines (Accommodation, Food and Drinks, and Recreation).
The black borders around the bars add no value.
The cents, tick marks, and bar borders may be eliminated altogether, and the dollar signs may be replaced by simply adding the label "U.S. $" to the vertical axis.
Assuming that no one service line is more important than the others and that the four quarters are of equal significance to your message, none of these should stand out above the rest. If "Food and Drinks" needed to be the main focus of the graph, however, you could make it visually stand out above the other two service lines, such as by giving it a brighter color, but more on this a little later.
Figure 2: Example of a graph with a high data-ink ratio.
Given the equality of the three service lines and four quarters, the data that ought to be highlighted is probably the pattern of each service line's revenue as it moves through the four quarters and the comparative performance of the three service lines in any one quarter. Although bars can be used to accomplish this, I think that these features of the data can be highlighted best by encoding the values as lines of different colors, one for each service line, rather than as bars. Take a look at Figure 2 to see how well this works.
The three prominently displayed lines, without anything else to distract from them in the plot area of the graph, makes the key features of the data stand out clearly from the text data that surrounds them. Notice that the graph has been further simplified by eliminating the legend and labeling each service line immediately to the right of the line that represents it. Notice also that the weights of the three lines are the same and that no one line color stands out more prominently than the others, thus supporting the equal importance of the three data sets.
Visual Means to Highlight Data
In the original graph shown in Figure 1, the differing fonts and their sizes suggest distinctions in the data that don't really exist. For example, the huge font used for labeling the four quarters in the legend suggests that the quarters are somehow more important than the other data, but this clearly isn't the case. Making something bigger than the rest is a useful way to make it stand out as more important, so you must be careful to keep the sizes of similar components the same unless you really do want to emphasize them.
There are two fundamental means to visually highlight data:
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