Makeover Monday: Not Saying “Groin”

Posted on Leave a commentPosted in Makeover Monday, Viz Review

Sometimes I take things too seriously. This week’s Makeover Monday is a great example. It comes from a fivethirtyeight article about how the sports media refers to male professional athletes getting kicked in the groin, referencing a particular example from this year’s NBA playoffs. After Steven Adams was unceremoniously jumbled in the porker by Draymond Green, hundreds of journalisms gleefully took to the internet to write about it. The article, written by Kyle Wagner, takes a lighthearted look at the differences between the styles used by various sports media outlets, noting the proliferation of the word “groin” and surprising avoidance of the word “penis.” Wagner suggests that this is of some concern, possibly implying that journalists avoid terms the public will react to in disgust especially in an internet-medium where traffic is king. I have a hard time leaving it at that. Outside of having fun using phrases like “jumbled in the porker,” what I see when I look at this data set is a study in the evolution of language. The viz in Wagner’s article is heavy in text and settles for telling the story that “Groin” is used more than other terms. It is largely a crosstab, useful […]

Makeover Monday: The Next to Die

Posted on Leave a commentPosted in Makeover Monday, Viz Review

Andy and Andy have been posting a series of relatively morbid Makeover Monday topics recently, perhaps none as somber as The Next to Die – an exploration of death penalty executions across the United States since 1976. It’s possible that this topic is on my mind because we’re currently in an incredible political season in America, but I’ve found myself thinking more and more lately about the concept of communicating facts and their importance when making a persuasive argument. “The Next to Die” repeatedly makes claims to its own impartiality, reflecting upon the lack of opinion it portrays about the morality or efficacy of the death penalty. However it is clearly a politically-driven document, and its motivations are transparent the second you read it. Any critique of the visualizations presented within “The Next to Die” must consider the intended argument and the language used in addition to the visual design of the pictures portrayed within. The Map This map is still difficult for me to understand. While i appreciate the effort to normalize the size of states to avoid the intrinsic bias in over-representing larger geographic areas, I find the use of color confusing. The brightness of the red coloring indicates […]

Makeover Monday – Breaking Convention to Capture Attention

Posted on Leave a commentPosted in Makeover Monday

In the most recent episode of Tableau on Tableau, Dustin Smith and I briefly mentioned what we refer to as the “Andy Cotgreave” method – which is reflected in a lot of the British Andy’s visualization work. I brought up Andy’s tendency to defy convention in the interest of making visuals which will start conversations and draw his audience in. There are pros and cons to this approach. On one hand, and as more comprehensively described by Robert Kosara, a data graphic designed purely for presentation purposes can be more memorable if it contains creative or pictographic elements. This can make an audience remember and care about your point even without a complete understanding of the data. Alternatively, breaking visual best-practices can distract from the real conversation if you’re in a room full of data viz experts, and it can create real barriers to entry for your audience. If they can’t quickly understand your view, it doesn’t matter how great it looks. Perhaps this topic is what made me take the approach I took for this weeks’ Makeover Monday exercise. First, take a look at the source viz, published on I looked at this viz and read the article, and these […]

Makeover Monday – Throwback Edition: U.S. Tuition Increases

Posted on Leave a commentPosted in Makeover Monday

I have a new employee! As many of you may know I manage a small team of technical pre-sales consultants at Tableau and one of my challenges is finding a way to train / “ramp-up” new people. Tableau has numerous resources to teach our new hires the methodologies and technical skills relevant to their job experience, and we even send every new hire to Seattle to spend 2 weeks meeting their coworkers, getting ingrained in the culture, and understanding our history and our mission. But one area where we haven’t invested as much as I think we should is in teaching visualization skills. Odd for a data visualization company, right? Well, there are a lot of things you have to know about when you’re a Tableau technical expert. Server implementation techniques. Analytical math. Hardware sizing and scoping. Being able to create a beautiful viz is understandably lower on the list, especially when you consider the fact that businesses usually have their own requirements for data viz design and they aren’t always in line with design best practice. But I think being able to create competent data-oriented stories is an important skill to have for any technical consultant, and in our […]

Makeover Monday: Data journalism isn’t just about design, it’s about honesty

Posted on Leave a commentPosted in Makeover Monday, Viz Review

After a bit of a hiatus, I decided to try my hand at another of Andy and Andy’s Makeover Monday challenges. This week’s view comes from UK business insider. The headline: American women work way more than their European counterparts. I think the problem with this viz isn’t the chart type. A stacked bar is actually a fine way to represent distribution for a data set like this one which buckets “hours worked” into 5 categories. You can clearly see that the percentage of people who work more than 40 hours are higher in the US versus the other countries shown, and higher than the average. But here lies the problem: not very many countries are shown! The author handpicks Japan, France, Germany, Italy, and the UK as countries for comparison, but I see no reason why those countries should be a representative sample of all of Europe, as the title of the article indicates. As a journalist, I think it’s one’s responsibility to make sure that your claims are backed by data, and not just the part of the data that you think supports your argument. When I looked at the data I saw something different. Sure, there were […]

Makeover Monday: Viz-Off

Posted on 1 CommentPosted in Makeover Monday, Viz Review

This week Wilson and I decided to both try our hand with the Makeover Monday challenge and compare notes afterward. We were sitting in Tableau’s New York office and decided to both start around the same time, cutting off our efforts after an hour. Even though it wasn’t intended to be a competitive exercise we are always trying to one up each other a bit so it was fun to trash talk as we created our respective vizzes. He didn’t tell me this until afterward, but Wilson cheated and started an hour before talking to me about it. This week’s viz comes from Andy Cotgreave before he was famous. Andy published this viz based on Guardian data from 2001-2007: What works: This is a very straightforward approach, with appropriate views to easily interpret the data. The major details stand out and it passes the “5-second test” of knowing exactly what the viz is about very quickly. It’s also easy to rank/sort the information dynamically. What needs some work: It isn’t very engaging. If the point of a dashboard like this is to quickly analyze and come away with facts, this viz nails it. But it’s not going to compel a […]

Makeover Thursday: Football Salaries

Posted on Leave a commentPosted in Makeover Monday

I was a little busy earlier this week but I finally got to Andy Kriebel’s Makeover Monday challenge. Here’s the original article, from the Mail Online. There are a few visualizations in the piece, but I’m focusing on the first one. This one: What works: Ugh. Not much. The color consistency helps the viewer draw parallels between the levels of different leagues, even though the names of the leagues have changed. A lot of people hate donut charts but I kind of like the idea that you can throw a nice big label in the middle of a pie chart for better use of space. It’s not terribly hard to make the important comparison (1985 Division 1 wages vs 2015 Premier League wages) because the difference is so vast. What doesn’t work: It’s pretty hard to tell how different the numbers are, and virtually impossible with the lower leagues. The colors are too similar to quickly know which shade of blue corresponds to which league. It’s hard to tell what the “Avg Income” refers to. Most importantly: Even if you are a fan of pie charts, a pie chart makes absolutely no sense here! Pie charts are for comparing percentages […]

Makeover Monday: Police Violence

Posted on Leave a commentPosted in Makeover Monday

In this week’s Makeover Monday Challenge, Andy found a data set on The article and corresponding visualizations discuss police killings in America’s 60 largest police departments. It is an interesting article and the charts presented aren’t poor by any means. But I think they can be made  better by thinking about the story the article wants to tell. First of all, the visualizations compare police killing rates to a national average, but you have to search through the chart to find the bar that represents an average. This can be made better with a simple reference line. Second, the article calls out which police departments disproportionately kill black citizens. To do this, the same data is presented again in crosstab form with an icon calling out cities where 100% of deaths were black. I decided that consolidating those points onto a single chart would make more sense. Finally, there is comparison to violent crime rates in each town. This is an interesting point but confused by the use of a dual-axis chart with different axis ranges and unit types. A best practice with correlative analysis is a scatter plot. If your question is “how much does number A effect […]

Makeover Monday: Americans’ Savings

Posted on Leave a commentPosted in Makeover Monday

This week’s Makeover Monday challenge comes from an article entitled 62% of Americans Have Under $1000 in Savings, Survey Finds. Go ahead and take a look before reading further. You’re back? Great. Welcome. There are obviously some problems with the charts provided as part of the article. We can debate the efficacy of pie/donut charts to clearly tell a story, but the thing that struck me most was that there are 4 charts that all tell the same story. I appreciate that the author rethought whether her initial viz, or subsequent iterations, would be right for each progressive breakdown of the data. I think that in some cases, especially the male/female comparison later in the article, might call for a slightly different look at the data. However, a great way to make the story simple and clear, and allow for comparisons across the many dimensions explored in the article, lies in a more consistent approach. For my version of the viz, I used a stacked bar similar to the second viz in the article. Each rectangle in my stacked bar represents one of the savings amount categories represented in the article’s stacked bar as well as in the donut chart, […]

Makeover Monday: Technology Purchase Intent

Posted on Leave a commentPosted in Makeover Monday

Andy Cotgreave and Andy Kreibel recently kicked off a series on the latter Andy’s blog entitled Makeover Monday. In it, they select a data visualization from the web and attempt to transform it into a more meaningful analysis. Andy C. informed me about the exercise last week and I couldn’t wait to try my hand at it myself. I am nowhere near the data viz expert that either of these alphabetically superior men are, but it’s quite intriguing to see how my approach to a data set will compare to a couple of experts for whom I have a great deal of respect. The prompt for this week’s exercise is the following viz: Are Consumers Bored With Technology? published in Business Insider two weeks ago. My approach was to try to tell the same story highlighted in the article, but use better data visualization practices to make the conclusion more clear. For example: I like the cute icons the author used – it makes the viz more memorable. However, I thought a more straightforward approach would help the discrepancies in the data stand out more. The result is a decidedly less infographic-y approach. I also limited myself to an hour […]