The benefits of effective data visualisation can be substantial. My concern is that good practice is often just equated with use of the latest tools. Flashy visual gimmicks are usually a distraction from our ultimate aim.
Q: What should our ultimate aim be when creating a data visualisation?
A: An efficient presentation which effectively communicates its intended message.
So the purpose of data visualisation is communication. With this in mind, let’s consider the purpose of communication and cut that back to basics.
Q: How do we effectively communicate our intended message?
A: By clearly stating and presenting the message in a way that your audience can understand.
This isn’t rocket science, but when we consider what constitutes effective communication, it is certainly not the tools we use that come to mind. Whether we are communicating verbally, by phone, by e-mail or using a carrier pigeon, it is the form and content of the message that determines whether it will be clearly understood by its intended audience.
While we need to understand how to use our tools, it is not the tools that determine the quality of our message.
Carrier pigeon (not actual size)
Back to data visualisation and the same applies. In order to communicate effectively with data, we need to know our tools but, most importantly, we need to know how to communicate our message.
Writers like Edward Tufte, Stephen Few and Alberto Cairo have provided great insight into the cognitive aspect of data visualisation. Their ideas aren't well-known outside the field and even amongst data visualisation practitioners, they are often neglected. I’d like to change that.
I’m fascinated by the psychological aspect of data visualisation. If we are going to communicate our message effectively then we need to understand how our audience will perceive and comprehend the information that we present to them.
Data visualisation is the front line between data and decision making
We should be aiming to make the transition of information from page/screen into our readers’ brains as seamless as possible. To do this well, we all need to know the science and psychology behind effective data visualisation. Some examples:
Tools like Excel, Tableau, Qlik, Cognos or Power BI and languages like D3, R or WebGL will allow you to create a vast array of different visualisations but they will never be able to tell you how to most effectively communicate your message.
Data visualisation tools and languages are constantly evolving and it is obviously important to know your tools and to be aware of technical developments within the field. However, this will always be secondary to knowing the value of, and methods behind, effective communication and so I’d encourage everyone to find out as much as they can about the theory behind their craft.
If you’d like to know more then please get in touch!