While I’ve been using Tableau and sharing visualizations publicly for some time, I never seriously thought to throw my hat in the ring for Iron Viz until last year.  But by the time I convinced myself to go for it, found data, and got to work, the clock ran out and I didn’t enter.  So I promised myself that no matter what, I will make sure I participate proudly in 2021 – and I’ve managed to keep that promise!

If you don’t know what “Iron Viz” is, here’s a brief answer:  it’s like Iron Chef, except it’s for data visualization.

Here’s a longer answer:  it’s a data visualization competition that is typically one of the highlights of Tableau’s annual conference.  Normally, there are a number of ‘feeder’ competitions to qualify for the world championship.  But this year, similar to 2020, there is just one global qualifier round.  The authors of the top 3 visualizations, scored by a panel of judges on the basis of analysis, storytelling, and design, will advance to the main competition that will take place virtually in November 2021.  You can read all the particulars here.

On a related note, a few months back, one of the Iron Quest project themes was “Passion Projects”, which was all about visualizing something you personally are interested in.  While I really wanted to participate that round – since I could very happily create something related to trad music – I had to prioritize some things and just wasn’t able to carve the time out for it then.  I was bummed, but I moved on.

So when Tableau announced a month ago that this year’s theme was Data + Joy: viz what you love, I thought, how awesome – this is my second chance to do something cool with trad music on Tableau!!  And I should be able to make time for it… score!

My approach to the data

I initially came up with a long list of ideas around what I could potentially investigate.  Some of this included:

  • Trad sessions around the world (or within a particular country) – but sessions are not documented well, from my experience.  I would still be trying to consolidate that information right now, no joke.
  • Deep dive into specific albums and artists – however, I’m not so sure this would be interesting to a non-trad-loving audience.
  • The Irish Traditional Music Archive (ITMA) in Dublin has a ton of info/data – I might attempt to tackle something around this in the future – spoiler alert!
  • Log my non-digital music collection as well and combine it with my digital to see what other insights I can find – this would be labor intensive and given the timeframe, not feasible.

Ultimately, I decided to stick to the areas where I could either find ready-made data or collect it for the project relatively quickly and easily, and at the same time have enough information to be able to craft a coherent narrative with some interesting points or call-outs.

So that left me with:

  1. My digital music collection – It was mostly ready-made and the data prep wasn’t too crazy.  I ended up using a lot of groups and sets in Tableau.
  2. Tune Pal search data – I am thankful that Bryan, the leader at Tune Pal, was willing to share this with me once again.  He provided a few CSV files that required some prep, but all was manageable.  He shared some of this data with me a couple years ago and I was keen to get it refreshed since I was VERY curious what impact, if any, the pandemic had on searches.
  3. Festival data – This was the messiest part, but worth taming.  While I did try doing some clever things using Alteryx to clean up the data, I ended up doing it the old fashioned way:  in Excel using copy, paste, modify and customize, to suit my needs. I had to do a fair bit of tinkering to get the mapping functionality to work the way I wanted it to. In hindsight, it’s likely because of the way I structured the data set. A note for future mapping endeavors!

The project did grow arms and legs along the way, but after lots of trial-and-error, I eventually settled on the parts of my story I wanted to keep and those to cut out.  Having a few initial chats with some members of the Tableau Community before I was too settled into any specific idea really helped shape my thinking as well, particularly because they were being introduced to my topic with no prior knowledge or perceptions about it.

It’s a tricky balance between being thorough but not be TMI (too much information).  The biggest challenge for me was:  because I’m passionate about and close to the topic of trad music – I’ve been at it most of my life! – it was easy to slip into what I might consider to be the TMI-zone.  It was hard to resist, but I think I did my best with it, so I’ll leave it at that.

The time commitment

While my TUNES entry doesn’t have any particularly fancy charts, it is one of the most time-consuming visualizations I’ve published to Tableau Public so far, for several reasons.

Firstly, tracking down (or creating) the right data and cleaning it can definitely take time.  And then for whatever reason you decide not to go with it, move to something else, and start over.  This was a big time-drain for me, but looking back, it was a necessary part of the process.  No pain, no gain.

Secondly, I wanted to learn something new along the way, which required a bit of research and reflection on what it was that I wanted to dig into a bit more.  I call it the selfish bit:  What’s in it for me (the WIIFM)?  What am I going to get out of doing this?  Personally, I like the WIIFM to be something I can apply at work later on. But that’s just me.

Thirdly, since I loved the topic, naturally I spent more time with it.

And lastly, life is just plain busy, isn’t it?  For the month of June, I have been filling in all its cracks – or shall we say windows of opportunity – with working on my viz and not other things I would normally be doing.  I know too well that I’m not alone on this last point.

I must mention that I am extremely fortunate that my husband fully supported my participation in IV.  Even with all my early-morning starts and late-night finishes to make time, he was still on the hook for all-things-family more than usual during the month of June.  He’s glad to have me back now!

New learning

I’d like to share some of my new learning points during the creation of this visualization:

1) Working with and parsing XML data. Thanks Rob Carroll for the tutorial here.  This was a rabbit hole that I went down for awhile when trying to get my iTunes library (that iTunes will export for you in XML format) into a table format.  But as they say, often times the simplest solution is the right one.  Turns out all you need to do in iTunes is highlight and copy your library within iTunes itself and paste it into Excel – job done.  But hey, next time I have XML data I’ll know what to do, right?!

2) I got a small taste of reverse geo-coding.  While in the end I didn’t quite get the result I was looking for, I am certainly better equipped to tackle this in future if and when the requirement comes up.

3) I’m completely sold on a new approach to working with layout containers.  Thanks Curtis Harris for the tutorial here.  I had watched this video in the past but to be honest it didn’t totally sink in at the time.  I did, however, have a bit of an ah-ha moment this time around after watching it a several times and following along in the context of building my Iron Viz entry. Floating containers might be my new thing!

4) I finally had a proper dive into Figma.  It seems like a lot of folks in the Tableau Community are talking about and using Figma these days so I wanted to see what all the fuss is about.  I’ve seen so many vizzes thinking ‘how on earth did they do that in Tableau?’ and it often turns out that some of it was done in another application like Figma or Illustrator and then brought into Tableau as an image.  I found the tool to be great for ensuring text is sized and aligned very precisely and it has a nice selection of fonts.  Beyond that, I can see some great use cases on the horizon for it on some work-related items, like dashboard mock-ups, and a few other things outside of Tableau.

I’ll share some of the resources on this topic that I found really useful:

► Her Data Learns: Dashboard Design and Illustrator with Judit Bekker

► Her Data Learns: Figma with Ghafar Shah

► Design Secrets for a Non-Designer: Modern Business Dashboard with Chantilly Jaggernauth

In closing

I’d like to congratulate everyone who participated this year, particularly all the other first-timers! I enjoyed the support and buzz shown from the Tableau Community during the month – mostly over Twitter – which helped me personally to keep going when I really wondered if I’d ever finish. It was great to be reminded that I wasn’t alone.

I want to give a big shout and thank you to the folks who were so generous with their time to help provide feedback on my visualization:  Sarah Bartlett, Michelle Frayman, Klaus Schulte, Autumn Battani, and Zach Bowders – and of course my husband and my sister-in-law, both of whom are natural eagle-eyes.  Cheers to all of you!

I’m proud of my submission and I couldn’t agree more with Tableau’s line about entering the competition:

Win or learn – you can’t lose.


Thanks for reading.

Here is my Iron Viz submission below, click the image to see the full version on Tableau Public.

Click the image for the full version

Photo credits:

Thumbnail image by Денис Марчук from Pixabay

Checklist photo by Glenn Carstens-Peters on Unsplash

Clock photo by Mpho Mojapelo on Unsplash

Pencil photo by Tim Mossholder on Unsplash