Why We Care About Data Visualisation (And So Should You)

In today's world, several factors help bring out the social relevance in data visualisation. Maybe you are not familiar with the term, but I’m sure you know what I’m talking about because data visualisations are everywhere! Just think about all the graphics you saw in the newspaper this morning or all the maps you’ve seen on how the COVID-19 is spreading around the world. But why should we care about it?


We are visual creatures

Let’s start at the beginning: Data visualization is the process of creating a visual representation of data. We humans are visual creatures, it is way easier for most of us to understand the information in a graphical form than read it from a table full of numbers. This has become especially relevant due to the increase in accessibility to big data. Data visualisations have become a way to make sense of all that information in a short period of time.

At Bit, we know that Data visualisation can be a powerful communication tool. Reason why we constantly use it. So much so, that we have recently created our own data visualisation style! Since we are a research and prototyping study, logically, the first step was to research the subject. Here are some of our suggested data visualisation books that will bring your graphic game to another level:

The Visual Display of Quantitative Information by Edward Tufte  The Functional Art by Alberto Cairo Data Visualization: a successful design process
It is probably the most important book ever written on data visualisation. Tufte exposes why the design of statistical graphics is a universal matter, and explains how to communicate information through the simultaneous presentation of words, numbers, and pictures. In an age when the volume of information available is multiplying tenfold every few months, the importance of visual data representation is growing at a similar rate. Cairo explains why data visualisation requires a balance between aesthetics and functionality. By translating numbers into graphic forms, we ‘re allowing the reader to recognize the stories that the numbers contain. The book explores how data visualisation should be understood as the fusion of art and science that enables audiences to discover key trends and insights from data.

Best practices 101

Now that you know about the books that helped us develop some basic rules on how to present our data most efficiently, we should proceed to telling you that data visualisation is not to be approached as an automated design task, we should think critically about the meaning, the content and the audience of these digital artifacts. If you want to use the data visualisation’s power for good (as we do) you have to gain good design skills to execute systems for effective visual communication. In the data context, the designer must understand how to create and edit the visualization to facilitate the understanding of the audience. The designer should also be aware of the importance of topics such as the manipulation of data. Communicating through data visualisation is not a simple task, doing it well, and mindfully requires skills and practice.

Since we did the hard work already, here you have some tips to keep in mind next time you design a data visualisation:

  • Pick the right chart for your data (here we have a tip for you)
  • Pick the right chart for your data (here we have a tip for you)
  • Be clear about the message: Effective data visualizations should put meaning in complicated datasets so that their message is simple and concise.
  • Include a descriptive headline
  • Label the axes with what they represent and in which unit
  • Make sure the contrast between background, text, and plots are high enough
  • Use color, size, position, and fill to visually encode dimensions of your data
  • Remove borders, grids, and other unnecessary lines (what Edward Tufte calls the data-ink ratio)
  • Format in the (Bit) style with brand fonts and colors. Here you have our example!

Data visualisation is not just choosing some colors and one chart or another, in fact, data visualisation design is perhaps a widely misunderstood talent. Good design is not just the color selection or the chart aesthetics, that is only one part of it. Hopefully now you have learned and acquired the tools to implement awesome visual representations!

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