You know that joke about a man trying to solve a problem while the woman just wants him to listen?
I’m seeing a lot of discussion about the representation of women in #IronViz. Setting aside the socio-cultural context for under-representation, as a woman who has participated – and not participated! – in the competition, part of me just wants someone to…ask…
So, since the hashtag is #WeAreDataPeople, not #WeAreQuantitativeDataPeople, I’ve decided to start us off with a dataset of n=1. Please feel free to steal the questions and add your own answers. Maybe with enough interviews we can cycle back around to quantifying some of this into a Tableau dashboard (insert autoethnoGRAPHy pun) and making some recommendations and changes.
1. Tell me about yourself. What is your professional background? When did you participate in Iron Viz?
I participated in two Iron Viz feeder competitions in 2016 (entry 1 and entry 2). I was working as an Assessment Research Analyst when I participated; I am now a Data Visualization & Analysis Librarian. I have a master’s degree in information and library science and I studied data visualization in grad school. I got an English degree as an undergrad and I tend to see my data work as an extension of that degree – interpreting and making sense of content/information – rather than deviating from it. As a result, I’m pretty interested in data ethics, critical theory, data and visualization literacy, and data humanism.
2. Is Tableau a part of your job/professional identity?
Definitely. I don’t necessarily work with Tableau every day, but I work with data collection and analysis every day. The Tableau work tends to come in waves – I’ll not open the program at all for a few weeks, then I’ll have a project where I’m in Tableau all day every day until the final product is done.
3. How did you find out about Iron Viz?
I remember finding out about the Iron Viz competitions in grad school and was eager to participate, but I think I had just missed the last feeder window that year. I made a mental note to look it up the next year and remember being absolutely furious when I saw the results of the Iron Viz Food competition – not for anything related to the contest, just because I had followed Tableau in like six different ways (through work and through my personal social media accounts) and still managed to miss the announcement. I followed them in a few more places (I remember grumpily adding separate subscriptions for the Tableau Public blog and the Tableau Desktop blog on my RSS feed) and finally managed to see the announcements in advance of the second contest that year. I was really pleased when they finally offered a mailing list announcement just for Iron Viz announcements.
4. Did you have any reservations about participating in Iron Viz?
Not about the competition itself – I wasn’t nervous about submitting my work for feedback or competing against others. I remember thinking that I was more excited about the second place prize (at the time, $500) than the first place prize, but that was probably the broke grad student talking. I think it also gave me a blanket of plausible deniability in the off-chance I didn’t win 😉
After the theme of the first competition was announced (Politics) I was definitely nervous, though. My viz was certainly not coming from a neutral perspective, and if there was a chance it would go viral (as the occasional winning/highly regarded Tableau viz does), I was worried about the possibility of backlash (e.g., getting doxxed or harassed online). I didn’t let it stop me from submitting, but I still don’t have my name associated with Twitter or my blog in part because that content- and anxiety – is still there.
5. Talk me through your favorite submission to Iron Viz. What did you like about it? Why?
My favorite (out of two :P) is my Politics entry. The original one is here, the revision is here, and the blog post (which several preambles and justifications) is here. I enjoy exploring subjectivity and rhetoric through visualization, and for this project I wanted to focus how we create and interpret meaning and messages in a dataset.
I quantified deeply subjective categories (“consistency”) into a single value, mapped it on a grid, and then hid the value such that the only way to find the right answer was to click around until you found it. The idea was that this would force the user to make meaning out of the slight differences they could see in the shifting central visualization (the blurring/distorting of candidate faces) and, presumably, reflect on the gap between their own understanding of the candidates and “reality” (as interpreted/visualized by the author). Because I am also a librarian, I wanted the supporting information (the “raw data” of the candidate statements and the coding of each organization) to be available below so that they could drill down into individual statements to see how much they agreed/disagreed with my breakdown.
I think Tableau can lead people to think about data and analysis in specific ways and I was excited to show how might be useful in other contexts (like digital humanities) and start a conversation about squishier concepts, like how we interpret data and our emotional responses to data.
I’ve thought a lot about changes I could have made to make this (admittedly, highly conceptual) project. It could definitely be clearer—and cleaner—but it still remains the project I’m proudest of (…so far) and the only one where I felt like values were intentionally and deliberately encoded into the project. So…that’s the one!
6. What else do you remember about participating in Iron Viz?
How much time it took! I took at least a day and a half off work for my first entry, and I know I ditched some weekend plans last minute for the second. I had joined twitter shortly before I entered, so the other thing that stands out was that I wasn’t particularly well known in the community and it felt like I received proportional attention for an absolute newbie (that is: very little).
At a job interview shortly after my Politics entry, I was asked about “the hardest visualization I’d ever done” and, as a follow up, “what success looked like” for that visualization. I remember admitting that in terms of initial goals for that project -winning the contest (…slash $500…), starting a conversation in the Tableau community – it was spectacularly unsuccessful. However, I found that I was able to use that experience and thought process to start other conversations, and that I learned a lot about the kinds of projects and conversations I want to put energy behind going forward.
I’d still love to win IronViz and meet those initial goals, but I think I’m now waiting for it to more naturally intersect with those interests.
7. Which Iron Viz competitions did you participate in, and why?
Iron Viz Politics and Mobile.
I outlined some of my thought process for the Politics entry in my blog post, but mostly I wanted to do something different than the political analysis I felt like I was drowning in at the time. It felt like a way to create space for nuanced and more personally-meaningful insights than election projections and (presumably) objective analyses.
With Iron Viz Mobile, it was more that I had an idea I didn’t know how to fully execute and solving it continued to be interesting all the way through. Still does, actually – I’ve continued to tweak aspects of this viz as new features are added and as I learn more about Tableau. It’s still not completely where I originally wanted it for the competition (…yes, the deadline was over a year ago), but I like to set ambitious goals 🙂
In both cases, I felt like I was able to find a way in to the contest theme that felt unique and worth the time it would take to put together an entry I could feel proud of.
8. What competitions did you not participate in, and why?
Iron Viz Geospatial, Safari, and Silver Screen.
For the Geospatial and the Silver Screen contests, it was all about timing. Both competitions were announced just before week-long conferences, and there was no way I’d find the time to put together a competitive entry on top of preparing my own presentations. I was pretty agonized about missing the Geospatial contest – I’d just started working with our Maps librarian about how to tackle GIS questions with Tableau and it seemed like such such a perfect opportunity to work out some of the issues. By the time it got to Silver Screen, though, I’d already started to accept that it wasn’t going to happen again this year.
The Safari contest just felt too referential for me (…I instantly set the bar at Jonni Walker). If I’d known it would be the only one I’d be able to enter, I might have pushed myself to come up with a more original idea. But, since it also changed the format (2nd place was now Best Rookie over Crowd Favorite), I only had one thing to aim at (the #1 spot) and it really didn’t seem like I could top the stuff that already existed—much less what I could imagine would be coming.
9. Do you participate in any other (non Iron Viz) Tableau community events?
More in person events than online ones. I’ve presented at local TUG events several times (on campus and in the area), and have been invited to speak on a few panels and to corporate user groups. I’ve also taught Tableau in libraries-related spheres (at in-class instruction sessions, one on one consultations, and conference workshops) but those are less reflective of the “Tableau Community”.
In the online community, I participated in one Makeover Monday last year. I love the idea, but I’m not exactly hurting for sample datasets at work. I do direct students there all the time, though. I’ve seen Workout Wednesday, but I don’t tend to be invested in figuring something out until I see a need for it in my own work. I learned how to do polygon maps in Tableau before line graphs. I memorized the calculations for Sankey Diagrams and Network Diagrams before I’d even heard about LOD expressions. I follow Twitter discussions, but I tend to tweet very occasionally – and most typically about libraries-related conferences or events.
10. Do you have any suggestions for improving representation in Iron Viz?
So many! See Part 2, here!