I loved studying Economics; it's a social science, where it studies the behaviour of society, individuals and 'economies' as a whole, but then it also attempts to model, quantify and segment the vast amount of variables and factors which are always in flux. This allowed us as students to choose which we preferred, and which school of thought we wanted to subscribe to.
In some ways, I hated this about economics - it could be explained in such layman terms, and concepts could be discussed with great vigour. But then someone could equally refer to a mathematical model, or a quantification of impact, or use Shapely values to place worth on something to take the discussion in a different, arguably more scientific direction. I liked my economics simple, understandable and as a trigger for thoughts, debate and discussion - in a way which could engage as many people as possible.
Tonight, Chris Love launched his #TableauKISS movement - taking Tableau back to it's basics to highlight what can be achieved without edge cases, hacks or advanced charts, or charts which aren't native to Tableau.
On Friday, I was able to demonstrate Tableau to a room full of Excel and Powerpoint junkies - I was promised 30 minutes to talk through it but ended up being given 7 - and in that 7 minutes, the energy and excitement that I was able to generate was something which I didn't expect.
Did I create a Marimekko chart? A Sankey chart? A chord diagram? No - four charts; a bar chart, a line chart, a scatter plot and a map. Put them all on a dashboard, add some filters and suddenly I'd created something that the room had never thought would be possible.
Trying to display everything I knew in that demo, or attempting to replicate a chord diagram, a Sankey chart or any other incredibly amazing chart type that those who have mastered Tableau and *just get it* do - It's cool, yes - but more often than not, the message can be clearer and the information can be portrayed in a simpler way than getting lost in calculations and hacks.
The beauty of Tableau is that it allows anyone, everyone to pick up some data and begin creating something amazing in a quick, informative and beautiful way - that's it. And pushing Tableau to it's limit is simply awe-inspiring - see Josh Milligan's games, Noah Salvaterra's 3D Donut chart (I love this one) and the recent gorgeous Beatles viz by Adam McCann to just name 3 incredible visualisations. But with no coding knowledge, virtually no software or database experience and a passion for numbers in sport - I'd never have been able to do this.
This notion has struck me often over the past week, especially following Chris' post about his love/hate relationship with Tableau edge-cases - And it took me back to my four months as a Data School padawan.
If I haven't mentioned it enough, the Data School was an incredible experience where we spend virtually every day from June until November using Tableau and Alteryx, learning more and honing our data visualisation skills.
But I remember a few weeks after we learnt *that* much Tableau in *that* short a space of time, that I found myself trying to apply everything I'd ever learnt in Tableau on every week-long project. And it always came back to the same thing - this works, but was it the right choice? What was good? What could have been better?
Some of the best Tableau visualisations are just absolutely simple, with a clear message - and when I was mulling over this idea, a particularly poignant quote from i, Robot came to me.
This was the one key thing for me, and perhaps a turning point - Asking the right questions. When you learn a whole load of tips, tricks and lessons in using Tableau, understanding data visualisation and telling stories through data... it's incredibly easy to try and apply all the tips and tricks at once to show that you *can* do it all. And I definitely have fallen foul of chart junk before being shaken to reality.
But as the weeks went by, I felt that I - and also the others around me at the Data School - found myself thinking two or three steps ahead. What is the key message I want this visualisation to portray? Should it be exploratory or explanatory? What will add to the visualisation, and what will take it away?
This preset of questions allowed me to have a repository of ideas to pull from - and the weirdest part? Most of them were remarkably simple chart types. Bars, lines, scatter plots, maps. Things that Tableau almost gifts to the users on a plate. I appreciated that learning Tableau every day meant that I had covered an unimaginable amount of content - but once I started to take my toolbox, take a step back and actually think, plan and start at the base of the problem I wanted to solve or question I wanted to answer.. The solution largely lay in a really simple idea.
I'm always learning - and I don't ever want to stop, but understanding that complex visualisations are just that, and appreciating the time that they take to create puts things into perspective. Personally, I'm a big fan of the #TableauKISS movement, and I think it's a great idea that I'm hoping to contribute to as often as possible in the next few weeks.