Line plots clearly and immediately emphasize changes in some value over time. While a plotted symbol on its own, perhaps a circle or a rectangle, could represent each time period’s value, lines between them highlight for the reader variations from period to period. Several line plots show up in the law-firm research surveys and each of them elaborates on some element of the basic pattern.
Dykema Gossett MA 2016 [pg. 6] swabs down exceedingly thick lines (as if to make up for the paucity of 12 data points). The round symbols are visible, and overall the plot has nice labeling, unobtrusive colors, and bold labels. The year-over-year differences cannot be missed.
The Freshfields Bruckhaus Crisis 2013 [pg. 6] line plot offers much less separation between its four lines than does Dykema Gossett between its three lines. Individual values for periods, therefore, are harder to pick out. Unusually, the x-axis does not stand for years, months, or quarters, but for different intervals of time increasing to the right.
We might replace the repetitive “Within” of the x-axis labels with a less obtrusive “<” or, better, omit it entirely and we would snip the tick marks of the vertical lines. We are also struck by the amount of white space below the title and amid the legend.
A complex, data-rich line plot is found in DLA Piper RE 2017 [pg. 6]. It covers 13 years, but more dramatically it plots data against three y axes! Readers could struggle to make out that the green line represents Moody’s/RCA, the blue line represents the U.S. Consumer Confidence Index (CCI), while the black line represents the findings of DLA Piper on its measurement of confidence. Adding to the cognitive load, an odd blue coloring bathes the plot and seeps into the top of the plot itself.
Legal managers who create data-analysis graphs should strive to make those graphs effective communicators. Let’s pause for a teaching moment. I wrote a post about the 2016 ILTA/InsideLegal Technology Purchasing Survey and its question about areas of practice where respondents foresaw AI software penetrating.
The plot in the upper right portion of page 13 that summarizes the answers to that question could be improved in several ways.
The bar colors are nothing but distracting eye-candy, since the colors do not convey any additional information. If a couple of bars were colored to indicate something, that would be a different matter.
Second, it was good to add the percentages at the end of the bars, rather than force readers to look down at the horizontal axis and estimate them; however, if the graph states each bar’s percentage, the horizontal axis figures are unnecessary. Even more, the vertical grey lines can be banished.
Third, most people care less about an alphabetical ordering of the bars than they do about comparisons among the applications on percentages. It would have been more informative to order the bars in the conventional longest-at-the-top to shortest-at-the-bottom style.
As a kudo, it was good to put the application areas on the left rather than the bottom. Almost always there is more room on the left than in the narrower bands at the bottom.
A makeover using the same data cures these problems and displays a few other visualization improvements. The new plot removes the boundary lines around the plot, which gives a cleaner look. It also enlarges the font on the percentages relative to the font on the applications, since those figures are likely to be the ones that readers care most about and want most emphasized. Two final tweaks: the application names are on one line, and the axes have no “tick marks”, the tiny lines that mark the mid-point of an axis interval but that rarely add any value.