Heart Internet recently published an article on how to build better data visualizations, featuring commentary and thoughts from data viz practitioners including Nadieh Bremer, Shirley Wu, Mike Brondbjerg and myself. If you like what you read in the preview below, just follow the link to read the whole thing!
By Oliver OLIVER LINDBERG
The field of data visualisations has expanded rapidly in recent years. All kinds of exciting projects, such as Polygraph, are pushing the boundaries. Some are very insightful, while others can be classed as more expressive data art. However, if you’re new to data visualisations, it all can feel a little overwhelming.
Where do you start? And what kind of principles should you consider? To help you create better data visualisations, we talked to some leading experts and asked them for their tips and advice on best practices. Here’s what they had to say.
Focus on the idea, not the tool
UX designer and strategist Catherine Madden recommends, rather than getting hung up on the tools you need to use and/or learn, you should focus on the idea: “If possible, worry about execution only once you have a great concept and outline.”
Data visualisation designer Nadieh Bremer agrees. Even when you explore the tools, don’t trust that the default of charting tools is the best. “These have often been thought up by developers, who can do amazing things, but they typically don’t know any visualisation best practices.”
(As an aside, Heart Internet is proud to announce that we’ll be sponsoring Pixel Pioneers Bristol on 22 June. This one-day conference on web design and development will feature Nadieh Bremer giving a talk on how to create better data visualisations.)
Follow graphic design principles
Designer and creative coder Mike Brondbjerg argues that your designs should work at the service of the data if you want to create insightful visualisation projects and make them clear and accessible.
“Follow principles of good graphic design,” he suggests. “Make sure everything in your graphic supports what you trying to communicate, doesn’t distract, confuse or obfuscate the data. Get rid of everything that is superfluous. And if possible always make your data and process available to the viewer for further analysis and understanding.”