Hannah: With that in mind, what factors do you think make a good data tool?
Mike: For me, I think from an editorial perspective, it’s making it user-centred – telling users something that’s relevant to them, and putting them in the picture.
It’s also important to have one that’s iterative as you use it – the user’s information changes the story, they can influence it and see the visual workings. They should also be able to have a non-linear experience – being able explore the data and choose the comparisons themselves is important.
“Users should also be able to have a non-linear experience – being able explore the data and make the comparisons themselves is important”
It’s also got to be usable, clear and obvious how you can do the things you want to do. UX and visual language is vital to make something absolutely frictionless to use. All that design ethos goes into making a great data tool.
Joe: We’re often tasked with conveying complex information in accessible ways. For me, I feel we’ve done a good job when the user isn’t conscious of, or daunted by the complexity of the information they’re absorbing. There’s a great quote by a French aviator named Antoine de Saint-Exupéry that I read in the autobiography of Yvon Chouinard – the founder of Patagonia – that really chimes with me: “Perfection is finally attained not when there is no longer anything to add but when there is no longer anything to take away, when a body has been stripped down to its nakedness.”
Hannah: What do you think of the quality of data visualisations out there at the moment?
Joe: It’s a mixed bag. We see some exceptionally good ones on a regular basis from certain outlets and individuals, but there’s a lot of mediocrity as well. The best ones are just immediately intuitive and accessible, not just beautiful. As far as possible they should make sense to non-technical audiences and not need ‘how to use’ explainers to be understood. Often it’s a lack of good editorial context or poor UX that can let them down.
Mike: I think there was a huge explosion of data visualisations around the Covid-19 pandemic because it was one of those weird situations where data was like water in a desert… it became a matter of survival. People were desperately trying to find elegant ways to show trends. I think the quality of data tools increased over that time.
Hannah: What can organisations that need more people to act on the data they hold learn from this editorial and interactive approach?
Mike: You need some great statisticians to do the analysis, and to work out what the trends and clusters are. The BBC Class Calculator was a very curated experience. If we’d just made a series of bar graphs, it would have been of little interest. It needs the story packaged up to present the findings. If you have interesting data, you need to get some analysis done and you need to work out where the stories lie within the analysis.
“If we’d just made a series of bar graphs, it would have been of little interest”
Don’t give people a complete sandpit of data, but allow them to explore within the analysis. People should be able to play around and find their own stories within it. Allowing people to form their own conclusions. The story should emerge in front of them.
Hannah: Lastly, and top tips for anyone planning on taking this approach?
Mike: If it’d been left to my editorial team to create the Class Calculator, it wouldn’t have worked – we’d never have been able to get to the bottom of it. I needed Professor Mike Savage and the statisticians’ understanding of the concepts, and Joe and the Applied Works team’s understanding of the visual design and interaction. You need a marriage of editorial expertise, UX expertise and an understanding of the data.
”You need a marriage of editorial expertise, UX expertise and an understanding of the data”
Data is useless unless you get to the analysis stage – that’s when you can start telling stories. As soon as you can show meaning from the data, that’s when it’s powerful.