Tuesday, September 4, 2012

a few words go a long way

Part of my day job is internal consulting to our analytics team. One of our interns is getting ready to present findings from his summer project and asked for help visualizing results. This is a part of my job that I really enjoy - helping make the "so what", the "why this is important or interesting" part of an analysis we've undertaken visually clear.

As with many of my work-related examples, I have to keep the details confidential and generalize the situation a bit. In this case, we conducted a study where there was a baseline group receiving no treatment, and then several possible categories of treatment received by other groups. We were looking to understand the difference in impact these various treatments would have on a given outcome.

Here was the original data viz (slightly generalized from the original form):

My initial feedback looked something like the following:
  • Nice use of preattentive attribute (color) to draw your audience's attention to where you want them to focus.
  • The graph needs a title. The legend should be closer to the data it's describing.
  • If baseline is what the audience is meant to compare to, put that first and make that clear - think of adding a summary stat on the right side of the bars that is "increase vs. baseline" or similar.
  • I'm not sure the grey bars are adding value? If they represent 100% minus Outcome observed, stack them on the green bars to add to 100% and make that clear.

After discussing live, I spent a little time with the visual and ended up here:


In addition to incorporating the feedback outlined above, I also separated the Baseline visually and added a subtitle to the treatment groups to try to make it clear that each treatment is meant to be compared against the baseline (reinforcing this via the summary stat on the right).

Note that we aren't done at this point - the story still needs to be put around this data. In this case, the story could be something like "Treatment A results in highest increase over baseline" and a recommendation for rolling this treatment out more broadly. But note how some relatively minor formatting changes and the addition of a few words makes the information easier to consume.

The Excel file for the latter version is downloadable here.

4 comments:

  1. Is it "favorable outcome observed" vs. "unfavorable outcome observed", or really "any outcome observed" vs. "we have no idea what happened to that patient"?

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  2. If the gray bars are 100% minus the green bars, they are redundant. Remove them, and stretch the green bars to fill the space, with higher resolution.

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  3. Mostly commenting because it's rare to have three guys named Jon posting.... I disagree with Jon P on this case; it's important to show how often the result did not happen to put the whole thing in context. For instance, if you did it the way you suggested, the chart when "outcomes observed" were closer to 70% would look about the same.

    I agree with the premise, though. My first experience with this was a long time ago when I was handed a bar chart of survey responses, with the title being "Q.17" and the responses being 1, 2, 3, 4, 5. No other explanation. It made a lasting impression on me.

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  4. as Jon i don't like 100% bars ... I propose something like this:
    http://www.prodomosua.eu/images/taglio.gif

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