Data Visualization: the good, the bad and the ugly (0)
By: Mina Pêcheux
Tags: Data Visualization, Data, Dataviz, Tips And Tricks, Puzzle

Data Visualization: the good, the bad and the ugly (0)

During my internships and various projects, I’ve gradually learnt that working with data, and especially doing data visualisation, can be tricky: knowing how to properly show your data so that the others understand it and get your point is necessary but it’s complicated!

Some people think graphs aren’t that important and what really matters is the facts and figures you’re talking about. I do believe the growing trend of adding data visualisations courses to engineering formations is a proof of the opposite…

The importance of data visualisation

Nowadays, we are flooded with data: it’s everywhere, all the time and often filtered by so many intermediaries it’s difficult to go back to the original content. Every time you search for something, there are so many links, websites and other sources to sort through!

We are constantly flooded with information of all sorts!

That’s why it’s worth actually observing the data you play with and dive in it deep enough to be sure you get its message. Of course, while extracting the core idea of a dataset is a daunting task, it’s essential if you wish to use it in the right way. And the way you represent it is, I think, a good indicator of whether or not you understood it correctly.

(Because, yes, what is NOT hard is misrepresenting and, therefore, misinterpreting data…!)

After reading this really nice article on 16 data visualisations examples, I took a peek at the e-book featured at the end and discovered a very nice, well-knitted dossier on data visualisation that taught me a lot. What is great about this short report (only 50 pages, with graphics and plots) is that it goes back to the fundamentals and lists the different types of data, the different relationships you can establish between your elements and the different common representations. So you really put together the basic items and re-learn the glossary.

Moreover, it has a lot of examples: too me, it’s the best way to truly get how visual representation can bring out the soul of your data or crush it completely.

A little puzzle game for you :)

Therefore, I’ve decided I would try and give my take on the topic through a basic puzzle game for the reader: the Good, the Bad and the Ugly of Data Visualisation (or GBUDV for short, but that’s weird to say).

For this little series, I designed some fictitious datasets and represented them in several ways to show how fitting or misleading the visualisations can be. Foreach dataset, I made:

the Good visualisation , which shows the information contained in the data as accurately as possible

, which shows the information contained in the data as accurately as possible the Bad visualisation , which kind of gives you the data but is not easily interpretable (you actually have to think about it to get it!)

, which kind of gives you the data but is not easily interpretable (you actually have to think about it to get it!) the Ugly visualisation, which intentionally plays around with the implicit rules of data visualisation and with our biases to deceive the reader

And since a picture is worth a thousand words: