Storytelling with Data

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Storytelling with Data Page 17

by Cole Nussbaumer Knaflic


  Emphasize one line at a time

  One way to keep the spaghetti graph from becoming visually overwhelming is to use preattentive attributes to draw attention to a single line at a time. For example, we could focus our audience on the increase in the percentage of funders donating over time to health nonprofits. See Figure 9.22.

  Figure 9.22 Emphasize a single line

  Or we could use the same strategy to emphasize the decrease in the percentage of funders donating to education-related nonprofits. See Figure 9.23.

  Figure 9.23 Emphasize another single line

  In Figures 9.22 and 9.23, color, thickness of line, and added marks (the data marker and data label) act as visual cues to draw attention to where we want our audience to focus. This strategy can work well in a live presentation, where you explain the details of the graph once (as we’ve seen in the recent case studies), then cycle through the various data series in this manner, highlighting what is interesting or should be paid attention to with each and why. Note that we need either this voiceover or the addition of text to make it clear why we are highlighting the given data and provide the story for our audience.

  Separate spatially

  We can untangle the spaghetti graph by pulling the lines apart either vertically or horizontally. First, let’s look at a version where the lines are pulled apart vertically. See Figure 9.24.

  Figure 9.24 Pull the lines apart vertically

  In Figure 9.24, the same x-axis (year, shown at the top) is leveraged across all of the graphs. In this solution, I’ve created five separate graphs but organized them such that they appear to be a single visual. The y-axis within each graph isn’t shown; rather, the starting and ending point labels are meant to provide enough context so that the axis is unnecessary. Though they aren’t shown, it is important that the y-axis minimum and maximum are the same for each graph so the audience can compare the relative position of each line or point within the given space. If you were to shrink these down, they would look similar to what Edward Tufte calls “sparklines” (a very small line graph typically drawn without axis or coordinates to show the general shape of the data; Beautiful Evidence, 2006).

  This approach assumes that being able to see the trend for a given category (Health, Education, etc.) is more important than comparing the values across categories. If that isn’t the case, we can consider pulling the data apart horizontally, as illustrated in Figure 9.25.

  Figure 9.25 Pull the lines apart horizontally

  Whereas in Figure 9.24 we leveraged the x-axis (years) across the five categories, in Figure 9.25 we leverage the same y-axis (percent of funders) across the five categories. Here, the relative height of the various data series allows them to more easily be compared with each other. We can quickly see that the highest percentage of funders in 2015 donate to Health, a lower percentage to Education, an even lower percentage to Human Services, and so on.

  Combined approach

  Another option is to combine the approaches we’ve outlined so far. We can separate spatially and emphasize a single line at a time, while leaving the others there for comparison but pushing them to the background. As was the case with the prior approach, we can do this by separating the lines vertically (Figure 9.26) or horizontally (Figure 9.27).

  Figure 9.26 Combined approach, with vertical separation

  Having a number of small graphs together, as shown in Figure 9.27, is sometimes referred to as “small multiples.” As noted previously, it’s imperative here that the details of each graph (the x- and y-axis minimum and maximum) are the same so that the audience can quickly compare the highlighted series across the various graphs.

  Figure 9.27 Combined approach, with horizontal separation

  This approach, shown in Figures 9.26 and 9.27, can work well if the context of the full dataset is important but you want to be able to focus on a single line at a time. Because of the denseness of information, this combined approach may work better for a report or presentation that will be circulated rather than a live presentation, where it will be more challenging to direct your audience where you want them to look.

  As is frequently the case, there is not a single “right” answer. Rather, the solution that will work best will vary by situation. The meta-lesson is: if you find yourself facing a spaghetti graph, don’t stop there. Think about what information you want to most convey, what story you want to tell, and what changes to the visual could help you accomplish that effectively. Note that in some cases, this may mean showing less data altogether. Ask yourself: Do I need all categories? All years? When appropriate, reducing the amount of data shown can make the challenge of graphing data like that shown in this example easier as well.

  CASE STUDY 5: Alternatives to pies

  Recall the scenario we discussed in Chapter 1 about the summer learning program on science. To refresh your memory: you just completed a pilot summer program on science aimed at improving perceptions of the field among 2nd and 3rd grade elementary children. You conducted a survey going into the program and at the end of the program, and want to use this data as evidence of the success of the pilot program in your request for future funding. Figure 9.28 shows a first attempt at graphing this data.

  Figure 9.28 Original visual

  The survey data demonstrates that, on the basis of improved sentiment toward science, the pilot program was a great success. Going into the program, the biggest segment of students (40%, the green slice in Figure 9.28, left) felt just “OK” about science—perhaps they hadn’t made up their minds one way or the other. However, after the program (Figure 9.28, right), we see the 40% in green shrinks down to 14%. “Bored” (blue) and “Not great” (red) went up a percentage point each, but the majority of the change was in a positive direction. After the program, nearly 70% of kids (purple plus teal segments) expressed some level of interest toward science.

  Figure 9.28 does this story a great disservice. I shared my less-than-favorable view on pie charts in Chapter 2, so I hope this judgment is not met with surprise. Yes, you can get to the story from Figure 9.28, but you have to work for it and overcome the annoyance of trying to compare segments across two pies. As we’ve discussed, we want to limit or eliminate the work your audience has to do to get at the information, and we certainly don’t want to annoy them. We can avoid such challenges by choosing a different type of visual.

  Let’s take a look at four alternatives for displaying this data—show the numbers directly, simple bar graph, stacked horizontal bar graph, and slopegraph—and discuss some considerations with each.

  Alternative #1: show the numbers directly

  If the improvement in positive sentiment is the main message we want to impart to our audience, we can consider making that the only thing we communicate. See Figure 9.29.

  Figure 9.29 Show the numbers directly

  Too often, we think we have to include all of the data and overlook the simplicity and power of communicating with just one or two numbers directly, as demonstrated in Figure 9.29. That said, if you feel you need to show more, look to one of the following alternatives.

  Alternative #2: simple bar graph

  When you want to compare two things, you should generally put those two things as close together as possible and align them along a common baseline to make this comparison easy. The simple bar graph does this by aligning the Before and After survey responses with a consistent baseline at the bottom of the graph. See Figure 9.30.

  Figure 9.30 Simple bar graph

  I am partial to this view for this specific example because the layout makes it possible to put the text boxes right next to the data points they describe (note that other data is there for context but is slightly pushed to the background through the use of lighter colors). Also, by having Before and After as the primary classification, I’m able to limit the visual to two colors—grey and blue—whereas three colors will be used in the following alternatives.

  Alternative #3: 100% stacked horizontal bar graph

 
When the part-to-whole concept is important (something you don’t get with either Alternative #1 or #2), the stacked 100% horizontal bar graph achieves this. See Figure 9.31. Here, you get a consistent baseline to use for comparison at the left and at the right of the graph. This allows the audience to easily compare both the negative segments at the left and the positive segments at the right across the two bars and, because of this, is a useful way to visualize survey data in general.

  Figure 9.31 100% stacked horizontal bar graph

  In Figure 9.31, I chose to retain the x-axis labels rather than put data labels on the bars directly. I tend to do it this way when leveraging 100% stacked bars so that you can use the scale at the top to read either from left to right or from right to left. In this case, it allows us to attribute numbers to the change from Before to After on the negative end of the scale (“Bored” and “Not great”) or from right to left, doing the same for the positive end of the scale (“Kind of interested” and “Excited”). In the simple bar graph shown previously (Figure 9.30), I chose to omit the axis and label the bars directly. This illustrates how different views of your data may lead you to different design choices. Always think about how you want your audience to use the graph and make your design choices accordingly: different choices will make sense in different situations.

  Alternative #4: slopegraph

  The final alternative I’ll present here is a slopegraph. As was the case with the simple bar chart, you don’t get a clear sense of there being a whole and thus pieces-of-a-whole in this view (in the way that you do with the initial pie or with the 100% horizontal stacked bar). Also, if it is important to have your categories ordered in a certain way, a slopegraph won’t always be ideal since the various categories are placed according to the respective data values. In Figure 9.32 on the right-hand side, you do get the positive end of the scale at the top, but note that “Bored” and “Not great” at the bottom are switched relative to how they’d appear in an ordinal scale because of the values that correspond with these points. If you need to dictate the category order, use the simple bar graph or the 100% stacked bar graph, where you can control this.

  Figure 9.32 Slopegraph

  With the slopegraph in Figure 9.32, you can easily see the visual percentage change from Before to After for each category via the slope of the respective line. It’s easy to see quickly that the category that increased the most was “Excited” (due to the steep slope) and the category that decreased markedly was “OK.” The slopegraph also provides clear visual ordering of categories from greatest to least (via their respective points in space from top to bottom on the left and right sides of the graph).

  Any of these alternatives might be the best choice given the specific situation, how you want your audience to interact with the information, and what point or points of emphasis you want to make. The big lesson here is that you have a number of alternatives to pies that can be more effective for getting your point across.

  In closing

  In this chapter, we discussed considerations and solutions for tackling several common challenges faced when communicating visually with data. Inevitably, you’ll face data visualization challenges that I have not addressed. There is as much to be learned from the critical thinking that goes into solving some of these scenarios as there is from the “answer” itself. As we’ve discussed, when it comes to data visualization, rarely is there a single correct path or solution.

  Even more examples

  For more case studies like the ones we’ve considered here, check out my blog at storytellingwithdata.com, where you’ll find a number of before-and-after examples leveraging the lessons that we’ve learned.

  When you find yourself in a situation where you are unsure how to proceed, I nearly always recommend the same strategy: pause to consider your audience. What do you need them to know or do? What story do you aim to tell them? Often, by answering these questions, a good path for how to present your data will become clear. If one doesn’t, try several views and seek feedback.

  My challenge to you is to consider how you can apply all of the lessons we’ve learned and your critical thinking skills to the various and varied data visualization challenges you face. The responsibility—and the opportunity—to tell a story with data is yours.

  chapter 10

  final thoughts

  Data visualization—and communicating with data in general—sits at the intersection of science and art. There is certainly some science to it: best practices and guidelines to follow, as we’ve discussed throughout this book. But there is also an artistic component. This is one of the reasons this area is so much fun. It is inherently diverse. Different people will approach things in varying ways and come up with distinct solutions to the same data visualization challenge. As we’ve discussed, there is no single “right” answer. Rather, there are often multiple potential paths for communicating effectively with data. Apply the lessons we’ve covered in this book to forge your path, with the goal of using your artistic license to make the information easier for your audience to understand.

  You have learned a great deal over the course of this book that sets you up for success when it comes to communicating effectively with data. In this final chapter, we’ll discuss some tips on where to go from here and strategies for upskilling storytelling with data competency in your team and organization. Finally, we will end with a recap of the main lessons we’ve covered and send you off eager and ready to tell stories with data.

  Where to go from here

  Reading about effective storytelling with data is one thing. But how do you translate what we’ve learned to practical application? The simple way to get good at this is to do it: practice, practice, and practice some more. Look for opportunities in your work to apply the lessons we’ve learned. Note that it doesn’t have to be all or nothing—one way to make progress is through incremental improvements to existing or ongoing work. Consider also when you can leverage the entire storytelling with data process that we’ve covered from start to finish.

  Now I want to overhaul our entire monthly report!

  You likely see graphs differently than you did at the onset of our journey together. Rethinking the way you visualize data is a great thing. But don’t let overambitious goals overwhelm and hinder progress. Consider what incremental improvements you can make as you work toward storytelling with data nirvana. For example, if you’re considering overhauling your regular reports, an interim step could be to start thinking of the report as the appendix. Leave the data there for reference, but push it to the back so it doesn’t distract from the main message. Insert a few slides or a cover note up front and use this to pull out the interesting stories, leveraging the storytelling with data lessons we’ve covered. This way you can more easily focus your audience on the important stories and resulting actions.

  For some specific, concrete steps on where to go from here, I’ll outline five final tips: learn your tools well, iterate and seek feedback, allow ample time for this part of the process, seek inspiration from others, and—last but not least—have some fun while you’re at it! Let’s discuss each of these.

  Tip #1: learn your tools well

  For the most part, I’ve intentionally avoided discussion on tools because the lessons we’ve covered are fundamental and can be applied to varying degrees in any tool (for example, Excel or Tableau). Try not to let your tools be a limiting factor when it comes to communicating effectively with data. Pick one and get to know it as best you can. When you’re first starting out, a course to become familiar with the basics may be helpful. In my experience, however, the best way to learn a tool is to use it. When you can’t figure out how to do something, don’t give up. Continue to play with the program and search Google for solutions. Any frustration you encounter will be worth it when you can bend your tool to your will!

  You don’t need fancy tools in order to visualize data well. The examples we’ve looked at in this book were all created with Microsoft Excel, which
I find is the most pervasive when it comes to business analytics.

  While I use mainly Excel for visualizing data, this isn’t your only option. There are a plethora of tools out there. The following is a very quick rundown of some of the popular ones currently used for creating data visualizations like the ones we’ve examined:

  Google spreadsheets are free, online, and sharable, allowing multiple people to edit (as of this writing, there remain graph formatting constraints that make it challenging to apply some of the lessons we’ve covered when it comes to decluttering and drawing attention where you want it).

  Tableau is a popular out-of-the-box data visualization solution that can be great for exploratory analysis because it allows you to quickly create multiple views and nice-looking graphs from your data. It can be leveraged for the explanatory via the Story Points feature. It is expensive, though a free Tableau Public option is available if uploading your data to a public server isn’t an issue.

 

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