Monday, March 26, 2012

visualizing survey data

Having recently wrapped up our annual employee survey, we are in heavy survey-analyzing mode in my day job at the moment. This may sound strange, but I enjoy this immensely. I love working with masses of numbers and comments that can seem at the onset overwhelming and cumbersome and teasing a story out of it, with clear insights and areas to act upon and impact.

Since survey data is abundant in many organizations and good ways to show it are not always clear, I thought it might be useful to share a couple of genericized examples of how I've been visualizing some of this data.

The following examples are based on survey data collected on a likert scale that are grouped into three categories (in decreasing order of agreement): favorable, neutral, and unfavorable. Note that these are two different examples - they are not based on the same source data.

Example 1: summarizing responses
The following visual shows the breakdown of survey responses by % favorable, % neutral, and % unfavorable. In this example, related survey items are grouped into categories. The theme score represents the average across the various items in a given category. The story I ended up wanting to tell here was focused on % unfavorable, so I've organized the items within each category in order of descending % unfavorable.



Example 2: comparing to peer groups
The following visual shows the theme % favorable for the group of interest (blue markers) against the range of % favorable scores across the same categories for a peer group (grey bars). As in the last example, the theme scores are averages across a group of related items, but this same approach could be used to show specific survey items as well.


The visuals alone don't get you the whole way there. The context that you can bring as you analyze the data and pull in related information is where the story gets created. Data in a vacuum is difficult to interpret: it's the context that will help bring it to life and help your audience make sense of it. Some things to consider along these lines:
  • Do subgroups within the data you're summarizing feel the same? Are there any interesting outliers worth mentioning?
  • Are there any useful comparisons to other groups that could aid in the interpretation of the results? 
  • Is there qualitative data (for example, open text comments) that can be pulled in to help bring the data to life?
  • Have any specific actions been taken that are impacting the results? If so, describe them and the impact they have on what the data shows.
For example, here's a (highly genericized) version of the final "story" I formed around the peer group comparison visual above:


I tried to connect the story to the data visualization via the colored number markers, but am on the fence on whether I like this approach. There's no question that this leads to a pretty packed visual. Maybe too packed?

In case you're interested in taking a closer look, here is the excel file (examples 1 and 2) and here is the power point file (story). Let me know what you think!

Monday, March 19, 2012

lessons from GMN

GMN is the Grant Managers' Network. This afternoon, I had the distinct pleasure of speaking at their annual conference in San Antonio.

My topic was storytelling with data, presented in two 90-minute sessions to about 200 super engaged grant managers. The first session was an overview of data visualization best practices. The second was a hands-on workshop focused on applying the lessons covered in the first session to real examples (submitted by participants) to practice employing those freshly learned skills for showing philanthropic impact through storytelling with data.

With this post, I thought I'd recap the lessons I covered today and show them applied to one of the visual makeovers that we focused on during the workshop session.

First, the lessons:

  1. Choose the right type of display: leverage bar charts whenever possible due to ease of interpretation, lines are for continuous data only, avoid pies
  2. Eliminate the clutter: get rid of the stuff that doesn't need to be there, de-emphasize the necessary but non-message impacting stuff
  3. Focus attention where you want it: leverage pre-attentive attributes (color, size, thickness, enclosure, placement on page) to draw your audience's attention to the important parts and create a visual hierarchy of information
  4. Think like a designer: include affordances that make it clear to your audience how they should interact with your visual, make the visual accessible by favoring simple over complex, take the time to make your visual aesthetically pleasing to gain your audience's attention and patience
  5. Tell a story: don't use graphs to show data, use graphs to reinforce your story; make your story explicit with words

Now, let's take a look at these lessons applied to one of the participant-submitted visual. Here's the visual:

There is a lot of information here. Here's a glimpse into my thought process as I look at it and start to figure out how I want to approach turning it into a story:
  • I'm unsure at first how the data in the graph and two tables are related. The first thing I did was some math to try to better understand how the numbers relate to each other (to learn/verify things, for example Adoptions + Transfers + RTO = Live Release).
  • Upon closer inspection, it seems the overall story is around positive (live release) and negative (euthanasia) outcomes; I want to make these two sides of the story more immediately visually clear.
  • There are some unfamiliar things that I want to clarify: the acronym RTO and the fiscal year dates (so I know whether/how to compare Jan to FYTD). A quick email exchange with the submitter later, I learned that the former is "Released to Owner" and the latter is Oct-Sep.
  • Depending ont he story to be told, there may be too many comparisons. The way it's set up now makes me want to compare Jan figures with fiscal YTD figures, which probably doesn't make sense.
  • I crave an action title: the top-of-page space is precious because it's the first thing an audience encounters. I should be used to tell the audience what they need to know and orient them to the information that will follow.

After sketching out some things on paper and iterating a few times in Excel, here is my makeover of the visual:


Let's take a brief look at the changes I made, according to the 5 lessons I outlined at the beginning of this post:
  1. Choose the right type of display: This isn't necessarily the right display, but it's one approach. I combined the data into a single visual and made life and death visually opposing (note that death is both red and in the negative direction to reinforce visually that it is a poor outcome).
  2. Eliminate the clutter: I reduced the visual comparisons by focusing on FYTD and eliminating the January comparison. I pushed the axis and axis labels to the background by making them small and grey so they wouldn't compete for attention with the more important parts of my visual.
  3. Focus attention where you want it: I made use of pre-attentive attributes: bold, color, space, and size of text to draw the audience's attention to the important parts.
  4. Think like a designer: In this case, this was mostly about paying attention to detail: making sure things were aligned, leveraging white space, and trying to make the visual as easy to understand as possible.
  5. Tell a story: I added a story in words to make the message clear.

I also did another version with a very slight twist to show how relatively small changes can completely reframe the overall message:


What do you think? If you attended one of the sessions, I welcome feedback on what worked well and what could have been better. (Other comments welcome, too.)

Stay tuned - I'll post about the other makeovers in the coming weeks. If you're a first time visitor to my blog, you can sign up for email updates in the upper left. I'll also point you to a couple popular posts: how to do it in excel and no more excuses for bad simple charts: here's a template. Happy storytelling with data!

12/5/13 update: To download the spreadsheet with the visual above, click here.

Monday, March 12, 2012

infographics...

A graphic from MetaLayer's landing page
...have been all the rage for a while now. I remember when I first heard the term "infographic". It struck me. I like the concept of mashing together data and pictures and this word seemed to capture that idea so well. But the infographic rage that has followed what has quickly become a buzz word (and lost a bit of its appeal for me in the process) has been disappointing. Too often, sexy or eye catching is valued over the actual communication of data. I find this sad. There are some good infographics out there, but the majority are eye candy at best (and misleading through false representation of data at worst).

So I find it interesting that there seems to be a race of sorts to become "the" online place for making/collecting/sharing infographics: Visual.ly launched in 2011 (related blog post), I recently heard about another start up in this space, MetaLayer, that has been in the press lately (article). And I'm sure there are others.

I have a strong (not super positive) perspective here. But I'm curious whether others have found value in these sites. What's your take on infographics and the sites that are setting out to store and share them? Am I too quick to shun them? Leave a comment with your thoughts.

Wednesday, March 7, 2012

birthday data viz

I celebrated my birthday last weekend. Those who know me are familiar with a few of my passions: good wine, good food, and good data viz. The first two were enjoyed during a wonderfully relaxing weekend trip to Calistoga. It turns out that my team at work had the latter covered. Today at our team meeting, they presented me with the following:

The birthday card: note the effective use of preattentive attributes.
A laminated poster: they know me well. :-)
A big thanks to my amazing team for the creative birthday wishes!

3/12 update: turns out my current team doesn't have a monopoly on the data viz card business. I received the following card from a friend - as he points out, amusing due to my "love" of pie charts.