Beer Delivery Visualization

Time to tackle some of the more important parts of life. I saw this delivery schedule in a cooler at a DC Whole Foods. Bell’s Beer is from Kalamazoo and we take pretty big pride in our hometown beer. So we (I mean, I) get really geeked when Bell’s shows up during travels. But this delivery schedule? It’s not working for this data visualizationist (I just made that term up – how does it sound?).

chart of delivery for various Bell's beers

I couldn’t put my finger on precisely what bothered me so much about it, so I took a snapshot and mulled it over for a few weeks. Here is the revised chart I settled on.

Revised chart of Bell's Beer delivery

The revision still isn’t sitting right with me, but I’ll tell you what important changes I did make:

1. I reversed the order of the listings. Previously, the beers that are delivered year-round were closest to the headers listing the months. That meant that the ones that were the most narrowly distributed were furthest away from the month listings, making them the hardest to decipher. In the revised version, I also just put the month listings across the bottom of the chart, too. Why not?

2. The beers are in a very slightly different order, now depicted by the length of months they are on the market. Just adding a little more logic.

3. The labels over the bars make for less seek-and-find. Previously, the viewer would have to locate the desired beer label, trace the bar to the right, and then simultaneously locate the months across the top, traveling that information down the graph, to find where the paths crossed, just to determine if Favorite Beer was in stock. Too much. This way, we remove one element of difficulty in decoding the chart. Still, I’m unhappy with how I had to abbreviate the top two beers to make them fit in the label.

4. White background. The textured orange background (intended to be beer) was too busy and conflicted with the colors used to differentiate each bar. I’m not totally in love with my new color scheme, but it’s a step up.

If I had access to the beer label icons, I would have still placed them along the left side of the chart. That visual cue is important for the viewer to quickly identify the beer in question.

Proper Placement of Chart Legends

In my dissertation study (probably the most boring four words to start any blog post), I saw a lot of evaluation reports that grouped all tables and graphs into the appendix. Tables, graphs, and other graphics really must be placed right next to the narrative describing them. Why? When we flip back and forth between pages, we impair working memory’s ability to make sense of the associated words and images. Truly, the ideal situation for our brains is extremely close placement. Whenever we have to seek-and-find to match up content, we hurt cognition. That’s why I was super excited to see Jeff Johnson talking about charts and their legends in his book, Designing with the Mind in Mind.

Jeff also pointed out how it is hard for people to distinguish legend colors when produced in default mode (so two lessons here). This is what the typical chart looks like in Excel, with some modifications I made to clean it up:

The blues and purples do get hard to distinguish, don’t they? So Jeff recommends enlarging the legend colors so they are both more easily distinguished and closer to the actual lines they are associated with, reducing the need for an actual eye movement between the line and it’s corresponding legend entry. You can’t really do this in Excel, though. So I faked it, by inserting a square, matching the color, and putting it right over the line in the original legend. See here:

It’s an improvement, yes. However, there’s still a lot of searching to match up each line to it’s legend entry. So I got this idea from Storytelling with Data to get rid of the legend entry altogether. In the example below, I’ve inserted text boxes with the correct word from the legend, so it is totally obvious which line goes with what and the need for the search-and-find in the legend is removed. Cognition supported. Oh yeah.

What Exactly is Happening in this 3D Chart?

When giving advice about chart styles, one of my friends likes to say “save 3D for the movies.” She’s right – research shows that 3D charts actually slow reader comprehension. I’ve long advocated for the use of ChartTamer as a friendly way to restrict data visualization to that which will actually support reader cognition. In a recent webinar I heard Cole Nussbaumer at Storytelling with Data give another compelling reason why we shouldn’t graph in 3D – Excel doesn’t use the front line or the back line of the 3D bar when aligning with the y axis – it graphs the midpoint of the bar. What??? Yes, she said Excel uses the middle of the 3D rendering as the determiner of the data point. I didn’t believe it. How could anything so antithetical to cognition actually survive in this sink-or-swim society? So I tried it myself:

Here is a 3D column chart of some fake data about customer satisfaction with prices in various departments of a natural health food store, before and after their physical move to a new location:

Now I know I’m breaking some serious graphing rules here, but stick with me. I super enlarged the y axis and honed in on one range of it so that we could really see where the gridlines line up with the tops of the columns.

See the blue bar in Produce? The actual data point for that blue bar is 85. Neither the front line, the back line, nor the midpoint of the top of the column are at 85. See the blue bar for Beauty? The actual data point there is 83. Again, none of the possible points of measurement on the 3D column are accurately expressing 83. Maybe more like 82.75, but definitely clearly absolutely not reaching the gridline marking 83.

I selected the exact same raw data table and created a similar column chart, just in 2D:

This time the blue bar in Produce is exactly at 85 and the blue bar in Beauty accurately represents the data point of 83.

I wasn’t able to reproduce Cole’s assertion that Excel uses the midpoint of the 3D column. But it is pretty clear that the audience misinterpretation often cited in research that results from the use of 3D charting is due to more than the complexity of analyzing in the third dimension – it is also because the columns simply aren’t accurately visualized.

My 2011 Personal Annual Report

Releasing the Evaluation Report Layout Checklist

So I made this lovely checklist of graphic design best practices as a product of my dissertation (Standing Rule: If you want to know the details of my dissertation, you’ll have to buy me a drink). It included input from a panel of graphic designers including Peter Brakeman, Christy Kloote, Chris Metzner, and Kevin Brady.

I’ve been having such a great time travelling around the country, giving workshops on the checklist and using graphic design to improve the way we communicate in evaluation. But I’ve gotten overwhelmed with requests for the checklist, so I’ve decided to make it freely available:

Enjoy! And do post comments on your use of the checklist. (Well, your nice comments anyway.)

The Year of Data Visualization and Reporting

On the plane, heading back from the American Evaluation Association’s annual conference in Anaheim. Long plane rides such a great opportunity for reflection. What’s on my mind? The overwhelming success of the Data Visualization and Reporting Topical Interest Group. We had so much support in getting this group launched and we were so embraced by the rest of the conference attendees. Highlights:

1. Slide Clinic – The night before the regular sessions began, we held an open clinic where attendees could bring their conference session slideshows for some quick diagnosis and triage. I recall a few years back, when I sat through a workshop given by someone who attended the clinic. Helping her choose legible fonts – improving her communication of her otherwise very insightful charts – was damned rewarding.

2. Ignite the Night – We held our required annual business meeting as an Ignite session. You know about Ignite, yes? Fast-paced, 5-minute talks where the slides auto-advance every 15 seconds, whether or not the speaker is ready. Never has that much fun been had without alcohol at a TIG business meeting, I’m sure of it. Video will be posted soon.

3. Data Visualization and Reporting-sponsored Conference Sessions – Audience size at our sponsored sessions was a clear indication that evaluators are becoming increasingly interested in good communication and reporting. We had the good problem of overcrowded rooms at all of our sessions – beyond standing room only, sitting on any open floor spot, spilling out into the hallway. For our first presence at the conference, we sure made ourselves known.

As founding chair of the TIG, I stepped aside after our business meeting, turning things over to the new chair, Amy Germuth. This year’s total rock star debut will keep feeding my soul until next year, people, in Minneapolis.

Juice Analytics

Zach Gemignani, of Juice Analytics fame, gave the keynote at the AEA/CDC Summer Institute yesterday. I had followed their 30 Days to Context Connection list earlier last year, so I was super excited to witness the fun in person. His keynote speech focused on the 10 steps to becoming a Data Vizard. Yep, vizard.

Good tips in there, too. One was to follow the leaders – meaning, check out the awesome folks who have cut down some of the hard work out there on data visualization. Though I thought his list was a little slim (okay, he only had 45 minutes), he did point out the range of leaders out there, from Stephen Few to Jonathan Harris (Side note: Why only white men getting to lead the field of data viz?)

My favorite tip was to think like a designer. He said there’s a thin overlap of folks who are both data junkies and designers (that’s me). But those more on the data junkie side can make tiny adjustments to normal presentations that will help make a bigger impact. For example, choose one color for emphasis and use it to actually emphasize, not decorate. My hack job of his slide, illustrating this idea, is below.

Another tip was about choosing the right chart. For help on that task, check out Juice Analytics’ chart chooser. It’ll guide you through your data needs and let you download a chart template for Excel that is designed for clarity and beauty. Cool!

Evaluation Report Layout Checklist

A graphic designer, I am not. A laborer of long words and awkward sentences structures, I am. That’s why I became super fascinated by the world of report layout and formatting. Maybe the geekiest hobby, I hear you. But so important!

I’ve detailed the importance of good communication elsewhere on this blog. For evaluators in particular, the packaging and presentation of our content are often dealbreakers. Indeed, at times our choices in font and line length actually impede our clients’ ability to comprehend our findings. Yikes! Not our goal!

After reading a bazillion books and getting input from a panel of graphic design experts (Kevin Brady, Peter Brakeman, Christy Ennis Kloote, and Chris Metzner), I’ve compiled a checklist of graphic design good practice specifically for written evaluation reports.









Want a copy? Send me an email.

But be warned, I’m about to use the checklist on roughly 90 evaluation reports as part of my dissertation. Surely in there I’ll find good reason to make a tweak or two. I’ll post the revised version then. But in the meantime, go forth and make good work!

Remember This

Data visualization (or information visualization or infographics) isn’t just a sweet way to display your evaluation findings. It is a critical pathway to helping clients actually remember what you said. Blame the brain.

Visual processing of information is the dominant method among all the senses – it is called the Pictorial Superiority Effect. There are like 10 kajillion sensory receptors in the eye. And this has served us well, evolutionarily. The ability to pick up slight differences in motion, color, and shape have saved us from being dinner for the lurking tiger or waking the snoozing python. While we don’t have to be quite as perceptive these days (unless you’ve recently driven in downtown Chicago), the biological functions are still there. This preattentive functioning works without intentional effort, as our eyes scan the grassy horizon or the latest evaluation report. Evaluators should be making better use of the preattentive function with data visualization. Clients will be much more likely to have their attention caught if the heights of two bars on a graph are different or if an image is included in a page of otherwise gray text.

But once we have caught a client’s preattention with an infographic, we need to help the client use their working memory to process the information. Working memory is like a sieve (how many times have you forgotten what you went into a room looking for?). Evaluators will need to do as much as possible to reduce the cognitive load when trying to guide the processing of our findings. This can be accomplished through clean, clear, undistracting graphics. The graphic should do the mental calculations for the viewer.

Then to encode the information into long-term memory, it needs a bit more of our assistance. By combining the graphic with verbal explanation, more connections are created in the brain, more schemas are activated, and better recall occurs. Verbal communication alone results in about 10% retention after 72 hours. Combining verbal and visual increases the retention rate to 75%. Using graphic visualization to emphasize information speeds the acquisition of that information and reduces opportunity for misinterpretation. These end results are precisely what we want to encourage among clients listening to or reading our evaluation findings. It is another step we evaluators can take responsiblity for in trying to ensure that our findings are used. While comprehension, retention, and recall may not (yet) predict use of our results, it sure is a step in the right direction. And a pretty one, at that.

I’m going to talk more about data visualization and the use of graphic design in evaluation at this year’s American Evaluation Conference. Check me out.

I’m also working to organize a new Topical Interest Group on data visualization and reporting. If you are an AEA member and want to join, contact me or come to the informational meeting at the conference this year on Friday night, 6:05-6:25 PM in the Goliad Room.

In the meantime, here’s what I’ve been reading on this topic:

Brain Rules by John Medina

Visual Language for Designers by Connie Malamed

Design Elements: A Graphic Style Manual by Timothy Samara

Data Visualization and Reporting TIG

Evaluation use is a hot topic, but no one is looking at the role of graphic design.

Guidance on graphic design of evaluation reports in the literature of our field is sparse. Typically, discussion of use of evaluation findings (or lack thereof) focuses on types of use (i.e., conceptual, process, etc) and factors affecting use (i.e., relevance, timing, etc.) but graphic design is notably absent. Texts on the topic of communicating or reporting evaluation findings are also limited in this regard. They tend to limit their discussion to knowing one’s audience and formats of reporting (i.e., brochures, newsletters, oral presentations). Some texts acknowledge the role of graphic design in reporting, but give it a cursory address, such as suggesting that one hire a graphic designer, or “use white space” with no direction on how to make that happen. A couple of evaluators have advocated for the “methods-oriented report” that emphasizes results over the traditional layout, but these have been short on the details of how to enact their recommendations in a word-processing program. Only a few texts have attempted to give guidance on graphic design, such as providing direction on how to create charts or structure a report. However, the resources are all dated. In fact, if one takes into consideration contemporary teachings on graphic design principles, the evaluation texts have the potential to be miseducating.

In my last post, I said I’d develop a checklist for the use of graphic design in reporting. Yep, its coming. In the meantime, I am working on the proposal for a new TIG (Topical Interest Group) within the American Evaluation Association. Its time to bring consideration of data visualization and communication to the mainstream in evaluation. If you’re interested in being a member, send me an email.

And in that meantime, check out how data visualization is developing in other fields. (A shoutout to Humphrey Costello for sending me the links to these blogs, none of which are written by AEA members.)

Andrew Gelman’s blog, which makes statistical translation look easy.

Nathan Yau’s blog, which also has a link to the periodic table of swearing, FYI.

Shawn Allen’s blog, which is designed for a course but we can peek anyway.

Let’s bring this same caliber of work into evaluation. Let’s be interesting. Join me.