I think everyone would agree that data visualization is all about making information accessible. But after some discussions at the see conference #4, I’m not sure whether everyone agrees about the target audience of data visualizations: A common conception is that data visualizations should be understandable by everyone because – in contrast to a multi-page scientific text – it is their purpose to be easily readable. It is my feeling that this thinking limits the potential of data visualization as a tool. Let me explore this with an example:
The above visualization of a brain showing where and when neurons fire is certainly readable to everyone who knows what a brain looks like, but in no way does it convey the meaning of the data to anyone but experts on brain science. It doesn’t even have labels that would clear things up. It is a tool to explore and think about data visually, not the end result of the research. Interestingly, nobody argues that because it is “scientific”.
But some people seem to think, that a visualization coming from a designer should be immediately graspable because it is “beautiful” and “designed” and therefore the design fails if they don’t get it. We think so ourselves sometimes, always wanting to create simple things that even our mothers can use. Simple is great, of course, it’s what motivates us! It’s just that if we are designing an expert tool, it may very well require expert knowledge. If we are clear about our target audience and their abilities, we will be able to produce more powerful tools by building on their knowledge and expanding it, instead of restricting ourselves thinking we have to keep things simple.
So, who is using the tool and how are they using it. Is it about consuming data, or working on data. Is it about creating knowledge, or taking it up? Are we building a tool for consumers or a tool for experts? Fill in your own thinking here …
Data visualization is an important way to think about data. Even though it is often used to “just” visualize some numbers to make people think about a topic, we should not neglect it’s power as a tool to work with data and create relevant knowledge.