Friday, August 3, 2012

data viz challenge

A participant from a past workshop recently reached out to me hoping for help visualizing some data she wants to communicate about affiliate community foundations to members of those organizations and her board. Rather than keep all of the fun for myself, I thought I would put this challenge out to you.

Here's the description of the problem faced:

Basically, we serve as a host or parent organization for several smaller, local foundations throughout our service area. What we would like to show is how the money-in (gifts) compare to the total assets and money-out (grants) in a given year, and also allow the audience to compare their foundation to the other affiliates.

The data is contained in several separate tables (attached), and includes not only the values in $ but also the number of gifts, funds and grants. This information is not critical to the story, but if there's a way to include it (possibly by merging a table and graph, as I saw on your site) there are some specific cases where having this information will be useful. I can see where it will be hard to include it though, without being cluttered.

I'm open to whatever suggestions you have. I attached a pencil drawing of what I was picturing, but I'm not dead set on it being a certain way. I thought the format would be good because it allows you to visualize the relative size of the incoming to the outgoing for a given year, but also see how it changes over time, and how it compares to the other organizations, fairly easily. If there's a way to include the data labels in the bars or something, that would be good.

One major obstacle may be that the values vary so much (from something like $28 to almost $5 million). I don't know if this will make it impossible to do visually. It will be fine to drop the cents off the values.

[8/7 update: see comment below for additional context.]

Here's the sketch provided to start to get the creative juices flowing:

You can access the full data here.

Your challenge is this: come up with a straightforward way to visualize this data that will allow for the main desired comparisons outlined above by Wednesday, August 15th. When complete, you can leave a comment with a link to your visual, or email it to me directly (cole.nussbaumer@gmail.com) along with any comments you'd like me to post with it, and I'll put it into Dropbox and create a comment for you with the link.

What will you win? A chance to help a philanthropic organization better communicate with data. Eternal notoriety. Oh, and I'll invite the creator of my favorite to write a guest blog post. Ready... set... GO!

Wednesday, July 11, 2012

input please: competing visualizations

As part of my day job, I consult internally on data visualization. I was recently working on some mock visualizations for a project being undertaken by my team. Since I can't talk about the details here, I've created another scenario to use to explain some generalized versions of the visuals and show the iteration process that I went through to get to what I thought was clearly the best visual.

Challenge: of the four versions of the visual I put together, the project manager favors the one I like the least.

So I thought I'd try to leverage the wisdom of the crowd (you, in this case) and see where that takes us. I won't bias you by telling you which visual the PM favors and which I do (though if you've been reading for a while or have attended one of my workshops, it will likely be clear). Rather, I'll simply describe a scenario and then show you the made up data and the four mock ups that I put together. Then I'll let you be the judge.

What I'd like to know from you is: which is your favorite and why?

Here's the scenario*: you are going to administer a personality test and your challenge is to visualize the results to give back to the individual in a way that is straightforward to understand. There are five different personality profiles, each of which has four measures that are expressed on a scale with possible values 0, low, medium, or high.
*Note that this scenario is totally made up (to protect some confidential stuff), but should provide you the sort of context you'll need to evaluate the effectiveness of the visuals.

Here is the made up data that I used for my mock ups:


Here are the four mock up visuals I put together based on the above data:








Which do you like best? Why? Leave a comment with your thoughts!

Thursday, July 5, 2012

we are what we eat

As those who know me are aware, some of my biggest passions arise in the realms of data visualization and food. Every so often, there is an intersection of the two seemingly unrelated subjects. I recently came across one example, a project called "The Eatery: A Massive Health Experiment".

Part of the project is an app: you take pictures of the food you eat, and it records data to show patterns about your eating habits back to you. There's an interesting crowd sourcing component, where in addition to rating how healthy the various dishes you're eating are, others (friends or strangers) can rate the healthiness of what you're eating as well. The concept is interesting: that by being more informed about what you are eating and how it fits together, you can be more aware of unhealthy patterns and change habits to improve health. Here's a video with more details:



The data collected isn't yours alone, but also contributes to a growing database, from which the folks behind the project are starting to pull observations and trends from analysis and visualization of the data: currently the data is over 7 million food ratings of half a million foods by Eatery users from over 50 countries over a span of 5 months. While I'm not a huge fan of the cartoony infographics, they do contain some interesting factoids, and I love the time-based visual on the relative healthiness with which people eat across geographies. I've put a screenshot of it below; you can view the interactive version of it as well as the infographics here.



Collecting individual data for better decision making seems to be an area of growing interest. Are you aware of other mechanisms for doing so? What data do you (or would you like to) collect about yourself? What do you do with it?