Three questions for: Daniel Gross, catalogtree
Data visualisation is supposed to help us understand better – or even at all – what raw data means. Therefore the challenge is to translate the context of certain data into reliable and readable information. Hence, three disciplines, as discussed in the article “Erst Info… Graphics Second” in form 255, have to go hand in hand: science, journalism and design. Also Catalogtree, a design studio based in Arnhem, provided us with several infographics for this article. We asked Daniel Gross from Catalogtree three questions about their work.
1. Good data is the premise for good information design. What is your experience: is it hard to acquire good data?
It depends on what you regard as good data. Trustworthy data can be rare and expensive, but “good” data might also be cheap and massive. Once you got the data, it is even more difficult to decide if your data is a fit to tell a story worth telling. Data in itself is not information. Information comes after taking certain editorial steps in order to tell the story with data. Data with no story behind them will remain noise until the end – no matter how sophisticated your skills might be.
2. How do you proceed when you have received the data? Do you get additional help from non-designers like scientists or journalists to make sure your information graphics are correct regarding the content?
Before we get the data, the data has to be collected. The data was collected because someone has asked a question worth answering. That said it might be an even better approach not to wait for the data but to talk to the people who are asking the questions. Methods to transform data into design are as numerous as the types of data itself. Nevertheless, the method of transforming data to design should always fit. If you have a Top 10 of most-read books you do not start with a pie chart. And if you have millions of data points you do not start to draw by hand.
Experts do not need visualisation to be able to read their data. A musician can hear the music when he reads the score. The rest of us is at the mercy of graphics and audio. When we work for newspapers or magazines, the data is checked and will be checked again after we transformed it into a graphic. When the data is getting more complex, we start a close dialogue with the researchers, looking for ways to reveal the hidden stories within. This is often a type of “research by design” because our visualizations sometimes seem to ill-fit the stories they want to tell. Not that any of us want to distort the truth, but every visualisation is just one way of looking at a subject. The trick is to be clear and straightforward about what the scope of the story is but also what it is you are uncertain about. In the end, the aim of visualising data is to give access to people who are not experts on a certain subject.
3. What do you think distinguishes your design from others? What is your philosophy?
Sometimes we get the impression that data loves us better than we love data. Self-organisation of content is an important tool to us. Instead of telling each word or data point where to go and what to look like exactly, we devise a set of rules by which content should behave. Form = Behaviour. We believe this way, a design can be more than the sum of its parts. It is exciting when a design has some “swarming behaviour” and becomes, much like a flock of birds, a new organism in itself.
Data visualisation demands this approach of self-organisation because graphic devises such as position, colour and size have a quantitative meaning first. Data visualisation as a term is therefore almost a tautology: many infographics are just a visual version of a data set. But we try to let the content of books and websites “self-organise” itselves as well. To us, design is losing control in a controlled way, it is putting your hands in the air on a roller coaster ride and hope you don't derail.