Searching for My Inner Artist

Inspiration at Tableau Conference 2018

TC18 left me inspired, empowered, wanting to do everything, and also a bit tired.  Like many others I wanted to rush to complete a blog as soon as I left New Orleans.  However, as often happens, we start out with great intentions but life intervenes.  I started writing in fits and spurts, finding it difficult to get an opportunity to sit down and get my thoughts out.  At the very least though, I knew what my blog topic would be.

On the last day of the conference, I had the great fortune of attending Giorgia Lupi’s talk on Data Humanism.  A month or so before the conference I had seen a wave of people in the Tableau Community tweet about her Observe, Collect, Draw book and start creating wonderful artistic imagery, outside the realm of our normal day to day data viz.  They weren’t just creating charts or reports – they were exploring their own creative ability.

My Data Viz Background

I have to admit that the artistic aspect of data visualization does not come naturally to me, and it’s the piece that I want to improve upon the most.  I’ve never been artistic.  My ability to draw by hand didn’t progress much beyond elementary school.  I can be creative poetically and occasionally musically but visual creativity has always been hard for me.

I did not come to Tableau for its artistic capabilities.  I began my career in business analysis type positions where I used a ton of Excel and looked at a lot of numbers.  Most of my professional Tableau use has been more on the data analysis side.

Ask a question > find an answer > use the visualization as a means of finding the answer.

In the data viz community, especially in professional environments, visualizations that don’t provide quick and obvious impact are sometimes scoffed at and considered “data viz as art”.

Seriously, You Should Watch This

While I had begun to appreciate the artistic side of data visualization from connecting to the Tableau community on Twitter, Giorgia’s talk at TC18 was the type of experience that would change the mind of any data viz purist.

If you want an example of the experience, read about her work with Kaki King called “Bruises: The Data We Don’t See” in this Medium article (link) then watch the video they created here:

I’ve never been that moved by data visualization before.  Bar and line charts can’t convey that type of emotion (don’t @ me).  The feeling that her worked evoked in me was encapsulated in her definition of data humanism – “using data to become more human instead of more efficient.“   That sentiment had not crossed my mind before that point.

Additionally, her Dear Data Project with Stefanie Posavec showed me that inspiration for artistic data viz can come from anywhere and does not need to follow standard conventions. My top takeaway quote from her session was, “it’s much more interesting to see how we can expand definitions of data viz instead of constricting them”.

Find Inspiration Everywhere

As I was procrastinating from writing this blog by perusing Twitter, I found that Tableau Zen Master Neil Richards had already included a great tribute to Giorgia’s talk at TC in his post TC recap.  While I highly recommend reading it (link), it also took me to the recording of his TC talk “Design Driven Data”.  Unfortunately, I had been unable to see it live, but I’m so glad I watched it later.

In it, Neil discusses his own non-artistic background and how he’s been able to use Tableau to explore his creative ability.  Instead of the typical approach of data driving the design of a visualization, he implores his audience to find data that fits an original design that you create.  Doing so may lead to better results, and possibly have more impact than a traditional data driven approach.  His response to those that say that “data art isn’t proper visualization”?  “Good data art still visualizes the data.”

After watching both Giorgia and Neil espouse the beauty of design and captivation in data visualization, and in admiration of Neil’s ability to find inspiration anywhere, I was ready to create my own piece of design driven data art.


While I was searching for inspiration, I noticed a dreamcatcher at the Airbnb in Austin I was staying in the week after TC.  I appreciated it’s design and colors, and most importantly I believed that I could create something like it in PowerPoint.  I used Kevin Flerlage’s method of creating polygon-like vizzes without polygons (link) to get to work.

Before I had any data, I created a Dreamcatcher design with some random colors in PowerPoint.  Each piece of the dreamcatcher is an individual shape.  I saved individual png images of each of the 32 shapes so that I could use them later in Tableau.

Dreamcatcher Shape

Embracing the design driven data approach, I now had my design but did not yet have data to go with it.  I focused on the 32 shape image files I now had.  The only two things I could think of that have the number 32 are the number of teams in the National Football League and the number of teams in the World Cup.  As I’m a big fan of the NFL and know much more about American Football than Everywhere Else Football, I decided to use NFL data.

For the color, I utilized this fantastic list of 100 different color palettes from Canva (link).  It was both a blessing and a curse as I tried out many, many, different color combinations before deciding on one I liked.

Now that I had the design part down, my traditional data viz and analysis background kicked in.  I needed to find some data that worked with my design.

Data Behind The Design

While Kevin’s blog shows a method for using size to differentiate each shape, since I have 32 shapes (each representing one of the 32 NFL teams) and they are in very specific positions, making each a different size would cause them to overlap each other.  Instead, I decided to find some binary, yes/no type data, where a shape would appear if its value in my dataset was “yes” and would disappear if its value was “no”.

There are many sites that have NFL statistics.  On, I saw they had data on each team’s Passing Play Ratio – the percentage of a team’s plays that are passes versus those that are runs.  I decided to use this for my binary data.  More than or equal to 50% passing equals “yes”, below 50% passing equals “no”.

To decide how to position each team’s shape, I started at the very top.  The shape at position 12 o’clock is the top ranked team for that year.  The teams then decrease in rank clockwise around the circle.

Dreamcatcher - original

Over a period of 16 years (starting at the top left and working to the bottom right, if a team passed 50% or more of the time, their shape appeared on that season’s dreamcatcher.  If they passed less than 50%, their shape is hidden.  The viz shows that passing is becoming more common (less shapes disappearing over time).  In fact, over the last 4 seasons, only the 2018 Seattle Seahawks have run more than they’ve passed – and the season’s not yet over.  I’ve included viz in tooltips to show more traditional data on each team’s Passing Play Ratio.

Honestly, the hardest part of creating this viz, more so than creating the shapes in PowerPoint, or gathering my data, was creating the legend to go along with it.  An often overlooked and under-appreciated aspect of artistic data representation is the legend that explains what you’re seeing.  This is the piece that differentiates data-based art from non-data art.  Since my viz is built off of NFL data, I need to inform the viewers of what they are seeing and how to read it.  I’ve also leveraged viz in tooltips in my legend.  My intention in doing so was to save space and maintain the height of the original Dreamcatcher viz.

Dreamcatcher with legend

And here is my final piece (Tableau Public link).  This is not something I would have considered creating before TC.  Having done it though, I’m eager to see what other types of artistic data viz I can create.  Not everything I create is or ever will be the most artistic or most visually appealing, but at this point I feel like my artistic exploration is more important than the outcome.