Vizzing the Eurovision

Click for an interactive version

If you’re reading this you’re either:

  • an attendee at the Tableau Conference in the Hague
  • a fan of the Eurovision Song Contest

Everyone of you should be at least one of those!

In the keynote, Tableau EMEA VP, James Eiloart showed a dashboard investigating an important issue of the day: how Austria beat everyone else to win the 2014 Eurovision Song Contest. The graphic above shows the story in a more focussed way. What’s going on?

The red dots show how the TV audience across Europe voted for each entry. The orange ones show how the judges scored them. They are sorted left-to-right by the score given by each country (ie the ones who gave them 12 points are at the left and the null points countries are at the right).

What’s super-clear to me is that while the all the countries’ judges gave both countries high scores, the TV audience scores for the Netherlands really drops off. That’s the reason. I’ve highlighted that below.

tv scores


There are some other interesting things that can be found in the scoring data. For example, which countries saw the TV audience and the judges disagreeing? Have a look at the chart below. Poland and Azerbaijan saw the biggest differences but there are a few others.

judge v tv


In fact, if you look at how individual countries voted, you can see this in even more stark detail. Check out the UK – the TV Audience loved Poland, but the judges hated them:

tv judge UK

Getting the data

How did I get the data for this? It’s all available on the Eurovision site. It’s not in an easy to gather form (data never is!). So I used I set up a crawler and you can use it too. (note – there are a few gotchas in the Tables being scraped as they are inconsistently formatted on the Eurovision site. did it’s best guess but I did have to do some manual fixing once I’d downloaded it).

Getting the workbook

Feel free to download the workbook if you want. It’s a bit flaky – it was designed primarily for one slide in the Keynote.

Data storytelling: one size does not fit all

If you haven’t listened to the latest episode of Datastories you should. Enrico and Moritz discuss data storytelling with Robert Kosara and Alberto Cairo. It’s a great discussion and worthy of 80 minutes of your time. Of the group, Moritz seems the most unsure/cynical about what storytelling is. He made these clear in the podcast, and also on his “Look ma, no story” post. There are two reasons he is missing the point. In this post I’ll explain why.

“Yes, but what if…?”

For each example of how data stories could work, Moritz suggested a use case where a data story isn’t necessary. We go round in these circles all the time in data visualisation and it appears we may end up in the same vortex with data storytelling. Here’s 2 examples of the circles we experience in other areas of data viz:

“Law” 1: Thou must not use pie charts

This Law of Data Viz says, “Pie charts are bad and should therefore never be used”. But there are perfectly legitimate times to use them (FlowingData has some good examples). But there’s lots of anger against them (even I did a session entitled Pie Chart Are Evil in my days before chilling out about it)

“Law” 2: Bars must point upwards

Bars MUST NOT point down. Ever. Ok?

The Law of Data Viz says, “Bars should point upwards.” That law has been invoked recently with the gun deaths in Florida debate. Of course – the author took inspiration from an amazing example where the law was broken because it was appropriate to evoke a particular emotion.

Bars CAN point down

And the roundabout goes on and on. Yes pie. No pie. Bars up. Bars down. The answer is: IT DEPENDS.

It depends on your audience, on your data, on your objective.

The same applies to storytelling and yet Moritz seems to base his criticism on the fact that for every example of where a story works, he came up with a superficially similar use case where it won’t. In their podcast they touch upon the Martketwatch treemap.

No need for a story?
No need for a story?

Moritz explains that a financial expert doesn’t need a story based around that chart. And he uses that example in his blog post as his criticism of data storytelling. But – hello? – of course the data expert doesn’t need a story about it.

It doesn’t negate the fact that other people might need a story about it. Alberto did make this point in the podcast, fortunately, An executive wanting a financial market report might benefit from some annotation. And a separate audience, for example newspaper readers, would love a story – possibly even an anthropomorphized one – about, say, the battle between Google, Samsung and Apple.

Arguing over validity based on wildly different use cases doesn’t get us anywhere.



Alberto’s trinity of annotation, narration and story is perfect and addresses another problem I’ve struggled with around data storytelling: worrying about the definition of story.

Moritz falls into this trap in the podcast and Robert has done in the past, too (“Stories don’t tell themselves“). If we have an arrangement of data/charts/annotations in front of us, is it a vignette, anecdote, story, annotation, or narrative?

Frankly I don’t care.

We shouldn’t fret about taking words that evolved to describe verbal and written methods of communication and make sure we get the term perfectly aligned to something in data visualisation. We can argue all day about whether something is a story or an annotation. In fact: I am sure we will debate it for years. Whatever word you choose, I hope we all agree that there’s a difference between a basic unannotated bar chart, and an annotated, sequentially arranged chart/charts.

If you say annotation and I say story, well, hey, that’s just fine.

Moritz told us to “make worlds, not stories”, which is even more of a semantic challenge; I don’t know what that even means.

I do suspect storytelling will be over used by marketing departments and the media to cover all manner of things. This will be a shame and dangerous in the long run. See the back lash against big data as an example

It depends…

The longer I spend in this field, the more I realise the only answer to a question about what’s the right term, or the right thing to do is “It depends….” What I heard in the Datastories podcast was a little too much rigidity from Moritz. There are, at best, guidelines about what’s right and wrong, and any work you produce should be measured by its effectiveness, not it’s conformance to a specific term.