Friday, May 25, 2012

telling multiple stories (part 1)

When it comes to data visualization, I often emphasize the importance of identifying the single most important story you want to tell and crafting a visual to support this. But how should you approach the visualization challenge when there are multiple stories you want to tell with the same data? How can we do that in a way that holds our audience's attention and is effective?

I conducted a workshop at Google recently, where this challenge was presented. The group (and I apologize for the intentional vagueness that I will have to use throughout this example due to the sensitive nature of the content) had recently given an informational presentation internally where they wanted to show a visual and then multiple different takeaways that the audience should know from each visual.

Here is one such example:


In the above, the details have been hidden/generalized to preserve the confidentiality of the information. But generally, we can see there are multiple comparisons trying to be made: first, how the metric of interest (graphed along y-axis) varies across different categories; second, how the metric varies by country.

I have two main issues with this visual: 1) the comparisons are difficult to make visually, and 2) I'm left to try to determine what's important on my own - there are no visual cues or descriptions to help me out
and tell a story with the data that is shown. Where should I focus? What is interesting about it? Why should I care?

Here's a recap of the lessons we can apply here and a peak into my thought process as I consider and play with the data:
  • Determine the best chart type: I initially thought that a horizontal bar chart might be easier to read, but a quick check showed this was not the case: it was harder to orient the words in a way that was legible. The vertical bar chart also fits on the page/screen better. So I elected to keep the basic chart the same.
  • Determine the primary comparison you want the audience to make: In the initial visual, this wasn't clear - because roughly equal visual attention was drawn to each (to category through spacial separation, to country by color), neither comparison was very easy to make visually. In my makeover, I decided category would be the primary comparison I wanted my audience to make. Note that country is still there, but less attention is drawn to it, so this becomes a second-order priority visual comparison; I know it will take my audience a little more work to do and I've decided I'm ok with that.
  • Order the categories in a way that makes sense: I knew I wanted to order the categories from greatest to least - this provides an overall framework to the visual that makes for easier interpretation than haphazard category order. (Note that I could have also chosen to order from least to greatest, depending on how I wanted to frame the story to be told: keep in mind that, all else equal, most people's attention will go to the top left side of your visual first, so you generally want to put the most important parts there.)
  • Reduce visual clutter: The black background underemphasizes my data and overemphasizes things that aren't so interesting like gridlines. I stripped out everything I didn't think needed to be there and pushed some of the remaining things that need to be there but don't need to draw a lot of attention to the background by making them grey or smaller. As part of my clutter reduction, I also made sure that I was using color with a distinct purpose (getting rid of red titles, etc.).
  • Tell a story: Finally, I wanted to use words to describe what is interesting about the visual and give my audience a sense of where they should pay attention and why. Note how the comments are connected to the data they describe through both proximity and color linkage.
Now that I've described what I did, here's the unveiling of the madeover visual:



In this example, we looked at how you can tell multiple stories with the same data in a single visual. Stay tuned for the next post, where we'll continue this topic and look at an example that illustrates the benefit of repetition and how the strategic use of preattentive attributes can help you to draw attention and build a story around different parts of a robust visual.

4 comments:

  1. Great post, will be sharing with my coworkers (they all know a graph geek) :-D

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  2. Thank you for the useful post. Look forward to read 'Part 2' :)

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  3. Very curious, what software did you use to create the graph?

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  4. Thanks all, for your comments!

    Tony, the graph was created in Excel. The end product is a combination of graph and text boxes for some of the labeling (e.g. category names) and comments.

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