Why do data visualisations matter?


It seems like an obvious challenge, to avoid saying ‘a picture paints a thousand words’, when writing a blog about data visualisations. So I’ll fail early and get it out of the way. A picture paints a thousand words.

The phrase is a slight simplification, of course. Martin Luther King’s I Have A Dream speech — from its famous refrains (“I have a dream” and “let freedom ring”) to its urgency (“the tranquilizing drug of gradualism”) — could never be fully encapsulated in a painting. A picture can paint a thousand words, but it depends on the picture and depends on the words.

When dealing with data, there’s a tendency for words to slip into the dry and dreary. The statistics can add weight to an argument, but it can be hard to make that weight pack a punch. Transform the data into a visual, though, and it can turn into a hefty fist to throw.

There’s something simple — almost primitive — about seeing one thing and another, larger thing, and gaining information from it. Words and data are somewhat abstract, not directly related to any of our human senses. But showing them visually can make differences incredibly clear, like the previous New York Times front page or the below graphic about Premier League injuries. How long does it take you to notice the extent of Crystal Palace’s injury crisis?

This concept of turning figures into visual clarity gets turned up to another level when and if they’re able to be turned into objects that we’re familiar with. Every election, election maps are widely used and for a reason: it’s our votes superimposed onto our nation, a shape we’ve seen since childhood. 

And it doesn’t just have to be about votes.

There’s a clear equivalent when it comes to football, where anything plotted onto a pitch is effective. Pitch plots like pass maps or shot maps seem to not only distill the information for your brain to understand, but also trigger memories. You know what a shot from that part of the box looks like, and you know the types of forwards who get them. Just by plotting the data, you can see a season’s worth of information within a moment whilst building up a three-dimensional mental image of a player.  

Heatmaps are a particular marvel that deserves more appreciation than their ubiquity gives them. When there’s too much data to digest by plotting each plot individually, you plot them as clouds (for want of a better word) instead. The visual gets simplified, concentrating the message down to its most straightforward form. 


In the example above, the amount of data points in the touch map makes it slightly difficult to tell which zone has the most crosses. The heat map makes it abundantly clear.

It’s also a great example of two of the big things that data visualisations do well. They make the differences in the data stark, which is what we saw in the New York Times and The Athletic examples, but they also show something that would be tough to put into words. It might not take a thousand words to say it, but this particular picture paints the story better and quicker than a hundred would be able to.

Ultimately, the beauty of data visualisations is that they’re another form of communication. It simply makes sense to draw from any source you’re able to in order to best illustrate your point. Languages famously do this, and not simply for names of foreign objects: English took ‘ennui’ from French, ‘wanderlust’ from German, and (fittingly) ‘loot’ from Hindi. 

Each way of communicating has its own advantages, and adding data visualisations into your repertoire means you have a broader range of advantages to draw from.