We’re moderately obsessed with the world of data visualisaton here at Labs for a number of reasons: the ability to generate fresh insight from extraordinarily complex data sets, the ability to trigger radical reappraisal of familiar problems, the ability to put consumers in control of the vast quantities of personal data they generate every day. Not to mention the extraordinary fusion of technology and creativity it represents.
We firmly believe that data visualisation has a wealth of exciting commercial applications, from communicating in new ways to developing new tools, apps and utilities for clients and consumers alike. So we’ve grown slightly frustrated by the rise of visualisations that are moderately pretty but add little in terms of real insight, utility or illumination.
We’re also, as we may have mentioned, big fans of Manuel Lima here at Labs. So we were intrigued to see that he has authored an “Information Visualisation Manifesto”, a provocative (but characteristically generous and nuanced) take on the future of data visualisation which tackles head on the thorny questions at the heart of this ever-expanding field:
- Art versus Science
- Intrigue versus Immediacy
- Aesthetics versus apprehension.
Manuel comes down firmly on the side of clarity of communication versus visualisation for visualisation’s sake, citing the discipline’s roots in the desire “to facilitate understanding and aid cognition” and a growing frustration with the “eye candy” approach to the craft. Many of his principles are rooted in this utilitarian approach, reading almost like a Bauhaus manifesto (and none the worse for that):
- Form follows Function
- Do not glorify Aesthetics
- Look for relevancy
- Aspire for Knowledge
It’s a bold, purist and punchy vision yet also acknowledges the power of narrative and the role of intrigue. Indeed the question of narrative seems to lie at the heart of this Manifesto; the need to pose a specific question of the data and to weave coherent themes and stories from it. These themes then drive the aesthetic approach. As Manuel puts it:
“Form doesn’t follow data. Data is incongruent by nature. Form follows a purpose, and in the case of Information Visualisation, Form follows Revelation”
This is perhaps the key distinction between Information Visualisation as defined here and what Manuel suggests we start thinking of as “Information Art”. Within this approach, artists will freely allow form to follow data, using the random-ness this creates to add texture and interest. Take, for example, Aaron Koblin’s desire to embrace the random-ness of a data set and indeed the richness and texture added to his famous Radiohead video by “interrupting the data”:
“I think it really gives character, because I think it’s really that kind of intricacy and detail that builds character and in a sense it’s the errors and flaws that make art”.
As ever, then, we need to return to objectives, to ask what we are trying to achieve:
- Do we want to educate around an issue, making complex questions simple?
- To shift perceptions and provoke a response?
- To offer a fresh perspective on an infrastructure question for our clients?
- To offer our consumers better comprehension and control of their behaviours?
Simply put, are we going to offer something that is either very, very useful or very, very beautiful? Either way, greater clarity of intent and greater discipline throughout the industry can only be an advantage in building credibility and engagement. Building that credibiltiy is vital if data viz is going to become not just an entertaining diversion but a vital tool for navigating a world generating more and richer data by the second.
If what we are building is neither very beautiful nor very useful, to Manuel’s final point “Avoid Gratuitous visualisations”: “Simply conveying data in a visual form, without shedding light on the portrayed subject, or even making it more complex, can only be considered a failure”.
Or as William Morris put it: “Have nothing in your house that you do not know to be useful, or believe to be beautiful”.