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!

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?

drawing attention with data labels

I am a firm believer that data is inherently interesting. When you find the right story to tell with the data, that is. Graphing applications, unfortunately, don't know our data or what stories to tell with it. So, while it's easy to put your data into a chart and feel like you're done, this is a disservice to both your mission and your data.

The lessons in this post are two-fold. We're going to focus on an anonymized example from a recent workshop I conducted and discuss teasing the story out of the data and producing a visual that better tells this story, using data labels to help draw our audience's attention to where we want it.

Here is the visual we'll begin with:

Ticket Volume Over Time

I'm going to implore you to resist the urge to scroll immediately downward and instead concentrate on the visual above for a moment. What story could we tell with this data?

It takes some time staring at the data in its current format, but if we take the time to do so, one thing to note is that the volume processed vs. received are close to one another during the first part of the year, but we start to see separation as we continue through the latter part of the year, with volume processed lagging the volume of tickets received. That's the beginning of a story.

When plotting multiple series over time, bars tend to quickly get visually overwhelming. Lines can often show trends over time in an easier to consume fashion, so let's start by seeing what this same data looks like in a line graph:

Note that in addition to changing the chart type, I've done a couple other things to make the visual above easier to interact with. The series are labeled directly, eliminating the work of going back and forth between a legend and the data to understand what I'm looking at. I also pushed the axis lines and labels to the background by making them small and grey, so they are there for reference, but don't compete visually for attention with my data.

In the above, I eliminated the data labels altogether. But bear with me a moment while I add them back:

Labeling every data point creates a cluttered visual, one of the issues with the original graph. But I think we can use them here in a way that will add value. The data labels act like added marks (a preattentive attribute) that draw our attention. The problem with labeling every point in this case, though, is that our attention isn't drawn anywhere except to the lines that were already drawing it in the first place. But take a look at what happens as we play with which points we label:

In this case, we're drawn more to the right side of the graph because of the additional visual pull of the data labels. But this still looks a little cluttered to me, so I'm going to remove a couple more labels:

Bingo. I'm drawn to the part of the graph where the lines really start to diverge: forcing my audience fo focus visually on the part of the graphic that really tells the story. Now that I've got a visual that can be used to tell the story I want to tell, it's time to put the words down on this page to actually tell that story. This is when we need to take the context we know about the situation that the audience needs to know and figure out how to make our story compelling.

I don't actually know the context here, but I can use the data to start to make observations that will lead to questions that would help me figure it out. When I look at the data, I find it interesting that the incoming ticket volume was higher at some points earlier in the year, and yet we were able to keep up with it then, whereas in the latter part of the year we are falling short. This suggests that something changed. Perhaps there was attrition from the team that processes the tickets. Or perhaps a process or systems change took place that meant the sort of tickets coming in during the latter part of the year were more difficult and took longer to resolve than earlier in the year. Whatever the context is, we need to explain it. 

The final visual could look something like this:

Note that this isn't the only story we could have focused on to make a compelling argument for the conclusion: we need more resources. We could have instead focused on the growing ticket backlog over time, for example. One reason that I didn't do that here was lack of data: since I only had the data in the chart starting in January, I didn't have any indication of whether a backlog existed prior that would be important to consider. 

In almost every case, there are multiple different stories you could tell or ways to tell the story (or show it visually) that will get the message across. But the compelling stories don't suddenly materialize when we plot our data for the first time. Rather, it takes spending time on this piece to ensure you aren't just showing data for the sake of showing data, but rather that it's for a specific purpose, with a fleshed out story that your visual reinforces. That's storytelling with data.

If you're interested in the Excel file for the above progression, click here.