Originally published August 8, 2006
This article begins a five-part series that will present the winning solutions to our 2006 Data Visualization Competition. Each article will feature one of the five business scenarios that participants were asked to respond to by developing a visual display to communicate a particular set of data for a particular purpose.
Here’s the first scenario as it was described to participants:
You have been asked by the Budget Manager of a large corporation to develop a visualization that will enable her to examine expense budget performance during the current year (it is now mid-November) across 12 departments. She needs to see more than aggregate measures of each department's performance for the year. She needs to see trends and specific performance problems that have occurred during the year clearly enough to recognize situations that demand more detailed analysis. She is primarily interested in budget variances to identify potential problems in the budget itself as well as problems in the management of the budget. Your solution need not consist only of a single graph, but should not exceed what you can fit on a single page.
In addition to this description, participants were given a specific set of data in an Excel file.
While judging the many solutions that were submitted for this scenario, in addition to clarity of communication and ease of use, I was looking for the following key characteristics:
The Winning Solution
Dylan Cotter of Spotfire submitted the winning solution for this scenario, which he created using Spotfire DXP, an exceptional tool for visual analysis.
Figure 1: The Winning Solution for Scenario 1, created by Dylan Cotter of Spotfire.
Here are some of the highlights that made Dylan’s solution stand out:
In addition to learning from Dylan’s wise design decisions, let’s put his solution under the microscope to see if it could be improved. Given the purpose of this display, a few design changes could improve its ability to communicate.
Solutions that Fell Short
Several of the solutions exhibited particular problems that are worth noting so you’ll know to avoid them. The first example (Figure 2) is actually quite good in most respects, but one aspect of its design in particular undermines its effectiveness. Time-series data ought to almost always be displayed horizontally, from left to right. Notice how the series of small graphs in the bottom left section displays months from bottom to top, forming a pattern that is much harder to read when looking for trends. Placing the months on the horizontal X-axis and connecting the data points in each graph with a line would solve this problem. While we’re improving this series of small graphs, I should also point out that, although it isn’t obvious, these graphs are in order of departmental performance, from best (Technical Support) to worst (Executive), but the sequence runs counter-intuitively from right to left. Arranging them from left to right would work better, unless your audience reads from right to left, which is not the case here.
The next example (Figure 3) also has several good qualities, but a couple of problems hinder its effectiveness. I’m mostly concerned with the graph on the right. Three-dimensional graphs are difficult to read. This is the only graph that displays monthly performance, but it is difficult to see trends and to interpret and compare the monthly values. In fact, something that ought to be obvious – the difference between positive and negative variances – is difficult to see. Which bars are extending upward from zero and which are heading downward into negative values? My other concern primarily involves the middle graph (“Adj Budget Variances per Month”). Nothing distinguishes the bar for November from the others, even though it represents only a half-month’s worth of data. This problem is minor compared to the month of December, however. The long bar extending downward for December suggests a large negative variance (that is, an extremely good value, well under budget), but there actually isn’t any data for the month of December. No bar whatsoever should appear for December.
The next example (Figure 4) falls far short in effectiveness compared to those we’ve already seen. This is a simple table that has been enhanced slightly by color-coding individual cells to indicate “Within Budget” (green) “Up to 4% Over” (yellow), or “> 5% Over Budget” (red). The primary problem with this display is that it communicates too little. We can’t see trends through time, nor can we see the relative values of each month or change from month to month, except in terms of these imprecise performance bins. And, in which bin do the values greater than 4% through 5% fit? There is a gap between two of the bins. Finally, by using the colors red and green to reveal important information, 10% of men and 1% of women – those who are color blind – are kept in the dark.
Even if a table were an adequate solution, this design forces us to work harder than necessary by centering the numbers and varying their levels of precision (the number of decimal digits), rather than making them easy to compare by displaying the same level of precision for every number (such as one decimal digit) and right aligning them, as numbers should always be aligned in tables.
I’ve saved the example that is least effective for last (Figure 5). This approach is certainly creative, but not in a good way. Can you figure out how to read it? Certainly not without instructions, and even with instruction, this method of encoding the data would remain too difficult to decipher. Conventional solutions, such as simple line graphs in this case, work quite effectively and require no training. Innovation isn’t necessary unless convention doesn’t do the job. When innovation is required, the test of effectiveness is that, once trained, can people easily see what they need to see and easily understand what it means.
We can learn from one another’s data visualization successes and failures. We all produce some of both. Next month, we’ll examine solutions to a scenario that asks for a display of checking account transactions and the resulting balance interspersed through the course of a single month.
Thanks again to everyone who participated in the Business Intelligence Network’s 2006 Data Visualization Competition. Through this unique form of collaboration, we can surely advance the state of effective data visualization!
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