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Storytelling with Data

Page 16

by Cole Nussbaumer Knaflic


  That said, sometimes there are considerations outside of the ideal scenario for communicating with data that must be taken into account, such as your company or client’s brand and corresponding standard template. This was the challenge I faced in one consulting project.

  I didn’t recognize this immediately. It was only after I had completed my initial revamp of the client’s original visual that I realized it just didn’t quite fit with the look and feel of the work products I’d seen from the client group. Their template was bold and in your face with a mottled, black background spiked with bright, heavily saturated colors. In comparison, my visual felt rather meek. Figure 9.2 shows a generalized version of my initial makeover of a visual displaying employee survey feedback.

  Figure 9.2 Initial makeover on white background

  In an endeavor to create something more in sync with the client’s brand, I remade my own makeover, leveraging the same dark background I’d seen used in some of the other examples shared. In doing so, I had to reverse my normal thought process. With a white background, the further a color is from white, the more it will stand out (so grey stands out less, whereas black stands out very much). With a black background, the same is true, but black becomes the baseline (so grey stands out less, and white stands out very much). I also realized some colors that are typically verboten with a white background (for example, yellow) are incredibly attention grabbing against black (I didn’t use yellow in this particular example but did in some others).

  Figure 9.3 depicts how my “more in line with the client’s brand” version of the visual looked.

  Figure 9.3 Remake on dark background

  While the content is exactly the same, note how different Figure 9.3 feels compared to Figure 9.2. This is a good illustration of how color can impact the overall tone of a visualization.

  CASE STUDY 2: Leveraging animation in the visuals you present

  One conundrum commonly faced when communicating with data is when a single view of the data is used for both presentation and report. When presenting content in a live setting, you want to be able to walk your audience through the story, focusing on just the relevant part of the visual. However, the version that gets circulated to your audience—as a pre-read or takeaway, or for those who weren’t able to attend the meeting—needs to be able to stand on its own without you, the presenter, there to walk the audience through it.

  Too often, we use the exact same content and visuals for both purposes. This typically renders the content too detailed for the live presentation (particularly if it is being projected on the big screen) and sometimes not detailed enough for the circulated content. This gives rise to the slideument—part presentation, part document, and not exactly meeting the needs of either—which we touched upon briefly in Chapter 1. In the following, we’ll look at a strategy for leveraging animation coupled with an annotated line graph to meet both the presentation and circulation needs.

  Let’s assume that you work for a company that makes online social games. You are interested in telling the story around how active users for a given game—let’s call it Moonville—have grown over time.

  You could use Figure 9.4 to talk about growth since the launch of the game in late 2013.

  Figure 9.4 Original graph

  The challenge, however, is that when you put this much data in front of your audience, you lose control over their attention. You might be talking about one part of the data while they are focusing somewhere else entirely. Perhaps you want to tell the story chronologically, but your audience may jump immediately to the sharp increase in 2015 and wonder what drove that. When they do so, they stop listening to you.

  Alternatively, you can leverage animation to walk your audience through your visual as you tell the corresponding points of the story. For example, I could start with a blank graph. This forces the audience to look at the graph details with you, rather than jump straight to the data and start trying to interpret it. You can use this approach to build anticipation within your audience that will help you to retain their attention. From there, I subsequently show or highlight only the data that is relevant to the specific point I am making, forcing the audience’s attention to be exactly where I want it as I am speaking.

  I might say—and show—the following progression:

  Today, I’m going to talk you through a success story: the increase in Moonville users over time. First, let me set up what we are looking at. On the vertical y-axis of this graph, we’re going to plot active users. This is defined as the number of unique users in the past 30 days. We’ll look at how this has changed over time, from the launch in late 2013 to today, shown along the horizontal x-axis. (Figure 9.5)

  Figure 9.5

  We launched Moonville in September 2013. By the end of that first month, we had just over 5,000 active users, denoted by the big blue dot at the bottom left of the graph. (Figure 9.6)

  Figure 9.6

  Early feedback on the game was mixed. In spite of this—and our practically complete lack of marketing—the number of active users nearly doubled in the first four months, to almost 11,000 active users by the end of December. (Figure 9.7)

  Figure 9.7

  In early 2014, the number of active users increased along a steeper trajectory. This was primarily the result of the friends and family promotions we ran during this time to increase awareness of the game. (Figure 9.8)

  Figure 9.8

  Growth was pretty flat over the rest of 2014 as we halted all marketing efforts and focused on quality improvements to the game. (Figure 9.9)

  Figure 9.9

  Uptake this year, on the other hand, has been incredible, surpassing our expectations. The revamped and improved game has gone viral. The partnerships we’ve forged with social media channels have proven successful for continuing to increase our active user base. At recent growth rates, we anticipate we’ll surpass 100,000 active users in June! (Figure 9.10)

  Figure 9.10

  For the more detailed version that you circulate as a follow up or for those who missed your (stellar) presentation, you can leverage a version that annotates the salient points of the story on the line graph directly, as shown in Figure 9.11.

  Figure 9.11

  This is one strategy for creating a visual (or, in this case, set of visuals) that meets both the needs of your live presentation and the circulated version. Note that with this approach, it is imperative that you know your story well to be able to narrate without relying on your visuals (something you should always aim for regardless).

  If you’re leveraging presentation software, you can set up all of the above on a single slide and use animation for the live presentation, having each image appear and disappear as needed to form the desired progression. Put the final annotated version on top so it’s all that shows on the printed version of the slide. If you do this, you can use the exact same deck for the presentation and the communication that you circulate. Alternatively, you can put each graph on a separate slide and flip through them; in this case, you’d only want to circulate the final annotated version.

  CASE STUDY 3: Logic in order

  There should be logic in the order in which you display information.

  The above statement probably goes without saying. Yet, like so many things that seem logical when we read them or hear them or say them out loud, too often we don’t put them into practice. This is one such example.

  While I would say my introductory sentence is universally true, I’ll focus here on a very specific example to illustrate the concept: leveraging order for categorical data in a horizontal bar chart.

  First, let’s set the context. Let’s say you work at a company that sells a product that has various features. You’ve recently surveyed your users to understand whether they are using each of the features and how satisfied they’ve been with them and want to put that data to use. The initial graph you create might look something like Figure 9.12.

  Figure 9.12 User satisfaction, original graph

&
nbsp; This is a real example, and Figure 9.12 shows the actual graph that was created for this purpose, with the exception that I’ve replaced the descriptive feature names with Feature A, Feature B, and so on. There is an order here—if we stare at the data for a bit, we find that it is arranged in decreasing order of the “Very satisfied” group plus the “Completely satisfied” group (the teal and dark teal segments on the right side of the graph). This may suggest that is where we should pay attention. But from a color standpoint, my eyes are drawn first to the bold black “Have not used” segment. And if we pause to think about what the data shows, it would perhaps be the areas of dissatisfaction that would be of most interest.

  Part of the challenge here is that the story—the “so what”—of this visual is missing. We could tell a number of different stories and focus on a number of different aspects of this data. Let’s look at a couple of ways to do this, with an eye towards leveraging order.

  First, we could think about highlighting the positive story: where our users are most satisfied. See Figure 9.13.

  Figure 9.13 Highlight the positive story

  In Figure 9.13, I’ve ordered the data clearly by putting “Completely satisfied” plus “Very satisfied” in descending order—the same as in the original graph—but I’ve made it much more obvious here through other visual cues (namely, color, but also the positioning of the segments as the first series in the graph, so the audience’s attention hits it first as they scan from left to right). I’ve also used words to help explain why your attention is drawn to where it is via the action title at the top, which calls out what you should be seeing in the visual.

  We can leverage these same tactics—order, color, placement, and words—to highlight a different story within this data: where users are least satisfied. See Figure 9.14.

  Figure 9.14 Highlight dissatisfaction

  Or perhaps the real story here is in the unused features, which could be highlighted as shown in Figure 9.15.

  Figure 9.15 Focus on unused features

  Note that in Figure 9.15, you can still get to the differing levels of satisfaction (or lack thereof) within each bar, but they’ve been pushed back to a second-order comparison due to the color choices I’ve made, while the relative rank ordering of the “Have not used” segment is the clear primary comparison on which my audience is meant to focus.

  If we want to tell one of the above stories, we can leverage order, color, position, and words as I’ve shown to draw our audience’s attention to where we want them to pay it in the data. If we want to tell all three stories, however, I’d recommend a slightly different approach.

  It isn’t very nice to get your audience familiar with the data only to completely rearrange it. Doing so creates a mental tax—the same sort of unnecessary cognitive burden that we discussed in Chapter 3 that we want to avoid. Let’s create a base visual and preserve the same order so our audience only has to familiarize themselves with the detail once—highlighting the different stories one at a time through strategic use of color.

  Figure 9.16 depicts our base visual, without anything highlighted. If I were presenting this to an audience, I’d use this version to walk them through what they are looking at: survey responses to the question, “How satisfied have you been with each of these features?”—ranging from the positive “Completely satisfied” at the right to “Not satisfied at all” and, finally, “Have not used” at the far left (leveraging the natural association of positive at the right and negative at the left). Then I’d pause to tell each of the stories in succession.

  Figure 9.16 Set up the graph

  First comes a visual similar to what we started with in the last series that highlights where users are the most satisfied. In this version, I’ve leveraged different shades of blue to draw attention not only to the proportion of users who are satisfied but specifically to Features A and B within those segments that rank highest, tying these bars visually to the text that illustrates my point. See Figure 9.17.

  Figure 9.17 Satisfaction

  This is followed by a focus on the other end of the spectrum to where users are least satisfied, again calling out and highlighting specific points of interest. See Figure 9.18.

  Figure 9.18 Dissatisfaction

  Note how it isn’t as easy to see the relative rank ordering of the features highlighted in Figure 9.18 as it was when they were put in descending order (Figure 9.14) because they aren’t aligned along a common baseline to either the left or the right. We can still relatively quickly see the primary areas of dissatisfaction (Features J and N) since they are so much bigger than the other categories and because of the color emphasis. I’ve added a callout box to highlight this through text as well.

  Finally, preserving the same order, we can draw our audience’s attention to the unused features. See Figure 9.19.

  Figure 9.19 Unused features

  In Figure 9.19, it is easier to see the rank ordering (even though the categories aren’t monotonically increasing from top to bottom) because of the alignment to a consistent baseline at the left of the graph. Here, we want our audience to focus mainly on the very bottom feature in the graph—Feature O. Since we’re trying to preserve the established order and can’t do this by putting it at the top (where the audience would encounter it first), the bold color and callout box help draw attention to the bottom of the graph.

  The preceding views show the progression I’d use in a live presentation. The sparing and strategic use of color lets me direct my audience’s attention to one component of the data at a time. If you are creating a written document to be shared directly with your audience, you might compress all of these views into a single, comprehensive visual, as shown in Figure 9.20.

  Figure 9.20 Comprehensive visual

  When I process Figure 9.20, my eyes do a number of zigzagging “z’s” across the page. First, I see the bold “Features” in the graph title. Then I’m drawn to the dark blue bars—which I follow across to the dark blue text box that tells me what’s interesting about what I’m looking at (you’ll note my text here is mostly descriptive, mainly due to the anonymity of the example; ideally this space would be used to provide greater insight). Next, I hit the orange text box, read it, and glance back leftward to see the evidence in the graph that supports it. Finally, I see the teal bar emphasized at the bottom and look across to see the text that describes it. Strategic use of color sets the various series apart from one another while also making it clear where the audience should look for the specific evidence of what is being described in the text.

  Note that with Figure 9.20 it is harder for your audience to form other conclusions with the data, since attention is drawn so strongly to the particular points I want to highlight. But as we’ve discussed repeatedly, once you’ve reached the point of needing to communicate, there should be a specific story or point that you want to highlight, rather than let your audience draw their own conclusions. Figure 9.20 is too dense for a live presentation but could work well for the document that will be circulated.

  I’ve mentioned this previously but would feel remiss not to point out that in some cases there is intrinsic order in the data you want to show (ordinal categories). For example, instead of features, if the categories were age ranges (0–9, 10–19, 20–29, etc.), you should keep those categories in numerical order. This provides an important construct for the audience to use as they interpret the information. Then use the other methods of drawing attention (through color, position, callout boxes with text) to direct the audience’s attention to where you want them to pay it.

  Bottom line: there should be logic in the order of the data you show.

  CASE STUDY 4: Strategies for avoiding the spaghetti graph

  While I very much enjoy food, I have a distaste for any chart type that has food in its title. My hatred of pie charts is well documented. Donuts are even worse. Here is another to add to the list: the spaghetti graph.

  If you aren’t sure if you’ve seen a spaghetti graph before
, I’ll bet that you have. A spaghetti graph is a line graph where the lines overlap a lot, making it difficult to focus on a single series at a time. They look something like Figure 9.21.

  Figure 9.21 The spaghetti graph

  Graphs like Figure 9.21 are known as spaghetti graphs because they look like someone took a handful of uncooked spaghetti noodles and threw them on the ground. And they are about as informative as those haphazard noodles would be as well …

  which is to say …

  not at all.

  Note how difficult it is to concentrate on a single line within that mess, due to all of the crisscrossing and because so much is competing for your attention.

  There are a few strategies for taking the would-be-spaghetti graph and creating more visual sense of the data. I’ll cover three such strategies and show them applied in a couple of different ways to the data graphed in Figure 9.21, which shows types of nonprofits supported by funders in a given area. First, we’ll look at an approach you should be familiar with by now: using preattentive attributes to emphasize a single line at a time. After that, we’ll look at a couple of views that separate the lines spatially. Then finally, we’ll look at a combined approach that leverages elements of these first two strategies.

 

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