What data visualisation has taught me so farby@eyeofdata
6,123 reads
6,123 reads

What data visualisation has taught me so far

by AnaJanuary 22nd, 2018
Read on Terminal Reader
Read this story w/o Javascript
tldt arrow

Too Long; Didn't Read

Since I dived into data visualisation I’ve learned so much that sometimes it’s difficult to explain every little detail. So I thought I’d start by writing about the fundamentals of making a good and accurate visualisation I’ve gathered so far. I’m writing this for everyone that might be interested in this topic and, as me, is still in the beginning.

Coin Mentioned

Mention Thumbnail
featured image - What data visualisation has taught me so far
Ana HackerNoon profile picture

Since I dived into data visualisation I’ve learned so much that sometimes it’s difficult to explain every little detail. So I thought I’d start by writing about the fundamentals of making a good and accurate visualisation I’ve gathered so far. I’m writing this for everyone that might be interested in this topic and, as me, is still in the beginning.

Most of the fundamentals of data visualisation I learned from the books I mentioned in my previous post.

There are many different definitions of what data visualisation is. In his book “Data Visualisation: a successful design process”, Andy Kirk defines Data Visualisation as:

a multidisciplinary recipe of art, science, math, technology, and many other interesting ingredients.”

I know every person has its own take on this topic but, to this day, this was the definition that really got stuck with me.

Getting the definition right is the easy part. Creating something meaningful that inspires other people and addresses the information accurately is the hard part. There have been many times where I created visualisations and infographics that when looking back, I would have changed certain aspects and some I even chose not to use.

But I think the most important thing I’ve learned so far is that your work never actually goes to waste— even if a design you created doesn’t work for the purpose that you created it, it doesn’t matter — sometimes you’ll end up re-using some of those designs in the future, or maybe they will be of use as inspiration for some other work.

If you have the time, make sure you read Andy Kirk’s book “Data Visualisation: A Handbook for Data Driven Design” as well as Cole Nussbaumer Knaflic’s “Storytelling with Data”.

two of my reference books

As Nadieh Bremer explains in her interview in the SuperDataScience podcast, the most important step is the question. You have to have a specific question about the data that you want answered both with your analysis and your visualisation. This is the first step.

In the beginning, I wasn’t used to paying much attention to the details in a graph, plot or figure. There are tiny details that we don’t notice until they are missing. Consider the images below, which were created for BinaryEdge’s 2016 Internet Security Exposure Report.

bad vs. good visualisation

In my opinion this is a good example of what I was mentioning above — the details make all the difference. You can see that although both figures are based on the same data, the way they are presented is significantly different.

One of the most basic components the graph, figure, infographic or visualisation you’re designing should have a title (and maybe a subtitle) to a graph, figure, infographic or whatever you’re designing. This is the first piece of information that the reader will be drawn to. Looking back, I realise now that I should’ve added a subtitle that added a bit more context to the image, such as “number of IP addresses with the 10 most common SSH banners”.

Then, it’s fundamental to lead the reader through your work. For instance, maybe you noticed that the image on the right has a small label on top indicating that we’re looking at SSH banners (already said in the title) and that the numbers are a count of the IP addresses with those banners. Of course, the level of detail in which you describe your design is highly dependable on how the information is going to reach your audience, so you should add as many guidelines as necessary that they can understand your work without you.

Independently of the tool you use to create your design, make sure it is precise. Having precision in design makes a huge difference in the final result of your work. Don’t forget that your design will end up representing you, so you want to be sure that you’re precise and methodical with your work. For example, in the image below, the area of each circle is proportional to the data associated with it.

notice how the markers indicate both location and quantity

Note: When using the area, be aware that the difference between the squares isn’t as easy to distinguish visually as it would be if, for instance, length was used (and this is one thing that could’ve been done differently in this example).

Another important point you want to be careful with when creating data visualisations, infographics or even presentations is to make them engaging. This means, removing everything that is not relevant but making sure that the key information is kept. Of course that having a beautiful design is very important but, for example, if you have something in your design that is only there to make it pretty, maybe it shouldn’t be there at all as it is taking not only space but also the reader’s attention.

There are also some extra details that are worth paying attention to.

  • The composition of your design is key — don’t just throw stuff in a blank page, make sure it is arranged as best as possible.
  • Choosing a good font makes all the difference — avoid the use of fonts such as Times New Roman or Comic Sans as they look a bit careless, transmitting the wrong idea to your audience and choose fonts that are easy to read.
  • Colour shouldn’t be used only to embellish your work — it should have a functional purpose. For instance, it can be used to accentuate a certain detail. In the image above, the colour saturation was used make it easier for the user to differentiate between the higher and lower numbers.

Finally, I have to mention storytelling. Storytelling is all about communicating efficiently with your audience, give them context to what they’re seeing/ reading and put yourself in their shoes. You have to consider the audience’s profile (who you’re targeting) and what is the most relevant and interesting information for them. In a future blogpost I intend to talk more in detail on this topic and show you some examples

I created this small reference card for guidance (yours and mine)

All of these concepts are fundamental to keep in mind when creating a data visualisation piece or any other form of disseminating information. Some of them are easy to understand but more difficult to put into practice. I speak for myself of course when I say that sometimes I am so deeply focused on the data and how to represent it that I forget to pay attention to all this tiny details. Only later, when I go back and analyse my design again is when I realise that a few key “ingredients” are missing.

This is only a small summary of the key ideas I took from the amazing books I mentioned above.