Christopher and I have both challenged ourselves to be part of the elusive 100% club that makes up the Tableau community’s social project: Makeover Monday. I’ve personally read through the attestations from several of my peers within the community on how their involvement has positively influenced their work and shaped them on their data artist journey.
To add further depth to the social project, Christopher and I thought it would be a good idea to take a look at each other’s Makeover Monday work and give our general impressions and opinions. As I stated it during our recent phone call, the idea was to first take on the role of an end user or interactor. Walk through findings and perceptions. Then dive in from the perspective of a Tableau developer (or better said: fellow data artist) and see some of the technical components and how the visualization was made.
Now that the background is out of the way, it’s time for me to dive in to the viz. The first thing I immediately appreciate is that this seems like the viz is set up for data discovery. There’s a little bit of guidance in terms of what the subject matter is (and I happen to know since I vizzed it too), but other than that – it’s really up to the end user to explore the data points and understand what’s going on. I fully appreciate that all data points are plotted because there’s a lot of good data to ingest.
My eyes were immediately drawn to the smaller (more zoomed in) Above $300k scatterplot. I liked how quickly I could see the spread of gender within these jobs. I was struck with the thought of “Really? Only 2 female occupations above $300k?” And I could immediately answer that by determining what they are. After some additional investigation, my mind pondered to the idea that really most of these jobs in this section were very specialized and had few employed individuals. I found myself justifying (if I can honestly say that as a feminist) that maybe the data for the 26 female Neurosurgeons was skewed a bit downward because the male number of the same occupation was 142 and we ARE talking averages.
Moving on, the next striking part within the viz was the massive number of female office workers, dwarfing any other occupation gender combo represented. That data point caused me to trail back the jobs from largest number of individuals back toward 0,0. I found myself nodding at the titles and thinking “I’m not really surprised by that # of individuals.” Reinforcing some of the general gender norms I’ve grown up on.
Last I took a look at the Below $70k section. It seems to me that it is filled with tons of female occupations and doing some investigation, comparing the occupations between male and female seemed to always lead to female workers being paid less.
Now for the technical take: I dove right into this workbook. The first thing I wanted to know was about the arrows. I’m a very structured developer and so I wanted to see what the arrows were made from. They turned out to be floating images. Doing some additional digging, I realized that they were the arrows that come bundled with Tableau (shapes folder). I chuckled to myself thinking that if I were going to do the same thing I probably would have made a sheet with the shape set to a calculated field or perhaps MIN(Number of Records). Great reminder to myself: keep things simple.
Next: I had suspected that the large scatterplot had clustering applied to it. So I went into the main sheet and validated. This led me to see that the created clusters were actually used as filters for the two mini-scatterplots (also floated!) in the viz. The last sheet to look at: the gender legend sheet. Let me say this: I appreciated having this immensely. (Mini-tangent) I tend to get confused with the gender shapes and having them reinforced with the legend and in some of the smaller vizzes with pink and blue was helpful to my mind. Using double encoding on a data point (pink = female, plus shape = female) adds integrity and trust to my visual inspection process.
Summing it all up. Things I really liked and appreciated: double encoding, built for data discovery, no enforced or projected point-of-view, subtle chunking of the data. What’s still lingering on my mind? If we had demographics dropped on the clusters and added narrative language to describe them, what would they be called? Would Cluster 1 (dark blue, high income, low # of individuals) be best described as: Australian men have the market cornered on high-paying, highly specialized medical jobs. And oh by the way, good luck marrying one because there’s only like 50 on the continent?
All-in-all a great start to Makeover Monday 2017. And what’s more interesting, the giant juxtaposition between my approach and Christopher’s.
I encourage you to check out the full viz on Christopher’s Tableau Public.