Defining the #TableauShuffle

I've now used Tableau almost every day since June, and even now I'm impressed at how much thinking it does for me - And in particular, I'm talking about the speed at which queries are run, and how this allows the user to iterate quickly* (*quickly here coming with a caveat of the structure, size and nature of the data you are connecting to Tableau..) "at the speed of thought" - Meaning you're able to answer questions quickly, easily and actually, pretty effectively.

How good are you at Powerpoint? 
I excel. 

Was that a Microsoft Office joke?
Word.

When asked where Tableau falls in the Powerpoint/Excel spectrum (a remarkably common question), I launch into a anecdote (obviously) of when I was intern during my year in industry working in business development. I'd work on a 2-3 month project, collating qualitative and quantitative data before presenting it in a 3-4 hour meeting to the project leaders/clients. My worst nightmare? Being stopped on slide 36 of 60 and being asked to change the colour of a chart, or to pivot it, or to add another chart underneath it to show something in a different way. Argh! Such a small pain, but meaning another month attached before it would disappear off my plate.

This story has seemed to resonate with quite a few analysts I've met since I started with the Information Lab - The iteration process, people not trusting the data they're seeing & the painful cost of time. Wouldn't it be nice if there was a simpler way?

Enter Tableau - And more importantly, the #TableauShuffle.

I have to credit Craig Bloodworth with the coinage of this term which I have used endlessly since he challenge us (#DS1) to think about what's going to happen to the visualisation before we drag and drop a field onto columns or rows. This is where I began to understand the value of actually understanding the tool.

So what is the Tableau Shuffle?

(apart from a super cool move which should be added to the range of dance moves that Jewel Loree came up with for #TC15) (Jewel, if you're reading this - if this ever gets repeated, it would make my life if the Tableau Shuffle was added to #WatchMeViz!)

Though, perhaps thie drag and drop move works quite well...

The Tableau Shuffle is the iterative process of dragging and dropping dimensions and measures to see what happens - It's one of the best parts of Tableau. Indeed, I didn't know that double-clicking a field would drop it on rows and columns until I started at the Data School. And it was a few weeks before I discovered right click and drag - But I digress...

For example...
I used the TransferMarkt dataset I uploaded a few weeks ago (the first viz I made for this is here) to do this (click the image to bigger it);

Here, I'm looking at the age curve of players who start a match across the entire dataset - As we can see, there's a clear bell curve. Now I see this, I like it, I understand it - But I want to know how does this compare to the number of sub appearances?

Nice. But what about by league?

Yup. That's it.

This is what I love about the Tableau Shuffle. You can drag something on, dislike it, take it off - Drag something else on, change the chart type.

*Everyday I'm shuf-shufflin*

And it just works.

A step further to this was the last challenge I set for the 2nd Cohort of the Data School (#DS2) in the Data School Gym (where we flex our viz muscles of course) - The premise was using this button 

which turns off automatic updates... And then building a dashboard! It's harder than you think, and makes you think about the composition of a view while you're building it.

I'd love to know you guys' thoughts on the Tableau Shuffle - And any experiences you've had where you would have loved to use such a technique.