2nd September 09
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”.Both approaches are undoubtedly valid. Within any medium there will be times when we seek immediacy and times when we are prepared to be intrigued and to explore. There will be times when we want to understand our world better and times when we want to turn perceptions of it on its head. I can think of few practical applications of, say, the “Synchronous Objects” visualisation series but it mashes up art forms and messes with my mind in a truly delightful way.
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”.
27th August 09
Author: Jim Carroll, Chairman, BBH London
I recently attended an excellent Made by Many event hosted at BBH which featured a re-presentation by Manuel Lima of his 2009 TED talk on data visualisation. Manuel is the curator of visualcomplexity.com and is an eloquent, modest, charming pioneer in this fascinating field.
As a novice myself, I could not help wondering why we are all so immediately and instinctively attracted to the best of data visualisation.To start with, I’m sure there is some fundamental truth that for most of us data become meaningful only when we can see scale, change, patterns and relationships. Seeing is understanding.
It’s also very reassuring to discover that complex, seemingly chaotic data sets and networks can be expressed as elegant, colourful, ordered maps and models. Perhaps there’s something akin to what the Enlightenment scientists felt as every new discovery revealed the endless beauty of nature.
Indeed the best examples of data visualisation have their own aesthetic beauty. (I felt a nostalgic pang as I recalled time spent with spirograph in my bedroom as a child.)
8th July 09
Posted in design
Mulling over the various excellent posts springing up on why there isn’t more great work in the digital space it struck me that one area rarely discussed is the fundamentally different definitions of what constitutes “great”.
Traditional agencies are instinctively drawn to disruptive work-work that stops the consumer in their tracks and forces them to pay attention. Digital specialists on the other hand are focussed on a smooth and seamless user experience. Ideas that disrupt this experience risk increasing bounce rates from a site for designers working to the 10 second stay-or-go “rule” . This tension between disruption and usability is so profound it’s hardly surprising that we struggle to find a common understanding of what great looks like, much less deliver it.
Traditional agencies in the digital space (and indeed traditional digital agencies) are easily seduced by the power of Flash and the wonders of animation; we want attention and spectacle but what happens next? Why should the user stay, what are we asking them to do and where should they go next? The campaign microsite is perhaps the prime expression of this tendency-as Iain Tate puts it, impressively punchily, in Campaign:
“No one cares about your bloody microsite. In 2009 the flashy high production value microsite is finally starting to feel irrelevant. Sites that seem to do everything, but deliver nothing.”
12th May 09
Posted in Uncategorized
As you may just have heard (we’ve been a tad over-excited…) data visualisation maestro Aaron Koblin came into to talk to us yesterday. He kicked off with a showcase of his work, from his exquisite grad school visualisations of flight paths (see post below) to his latest embryonic projects for Google labs. Along the way he showcased extraordinary visualisations of the ebb and flow of information in cities and around the globe, experiments in crowdsourced sound design and perhaps his most famous project, the Radiohead “House of Cards” promo.
In showcasing his extraordinary portfolio he touched on a number of powerful and provocative themes which we followed up on in our interview. Themes around the power of social context to make data compelling, the power of data visualisation to embrace the complexity of our lives today and the tension between the human and the machine present in crowd-sourcing engines. He also shared his key learnings from life at the front line of data visualisation:
Looking at everyday things in new ways completely changes your perspective: there is no ”mundane” data when you set it in context.
Use multiple visualisation techniques: there’s more than one way of seeing things
Stay true to the data, not the “real world” : There is a random-ness to data-it will make patterns you never anticipated. Respect the random-ness.
You don’t have to use all the data : sometimes seeing patterns is about what you leave out
Set the data free: open-source and let other people play with your data
Following his talk, very graciously agreed to be interviewed by Labs about our (and your) burning questions around data visualisation. It was a fascinating conversation for us and we hope for you. So over to Aaron….and many thanks to those who submitted questions for him.
Why do you think the world has suddenly gone crazy for data visualisation? 18 months ago it was a struggle to get anyone interested in data and now it’s the new rock and roll…
I guess it’s really the times that we live in, now you have tools like Twitter and Facebook and things that are widely used not just by the nerds but by everybody. Popular culture has also just all of a sudden embraced the power of storytelling through data and the relevance of all the data to their lives. All kinds of things have happened that simply weren’t possible before – the author you look up to, the musician, etc. they’re sharing all kinds of things – you can be intimately living their lives along with them and you see all different types of applications.
Do you think it’s partly about the explosion in the amount of data currently available, the data trail we leave behind us now or the fact that companies have more data than they can process so they end up giving it away?
I think ultimately the biggest change is that the data is now relevant to people’s lives. Before most of the data was about infrastructure at best and a lot of it was locked away or presented in aggregate form. When you’re presented with a huge lump sum number that has no context it’s just not interesting, but now when you get these granular stories, things that are saying at this specific point in time here’s the way that things changed, just by giving it that context and social relevance it becomes interesting.
11th May 09If data visualisation is the new rock and roll, Elvis has (just) left the building. Aaron Koblin played to an enthralled audience of BBH-ers this afternoon, blew our minds and incredibly kindly agreed to be interviewed by Labs afterwards.
Our interview to follow soon, but to whet your appetite, a quick download of our (and your) key questions for the rock star of the data visualisation world.Balancing immediacy and intrigue: A frequent criticism of data visualisation is that while often extremely beautiful, sometimes it doesn’t make the information contained any clearer-it can sometimes even seem to obfuscate in the name of art. Should great data visualisation simplify or should it embrace complexity and reward exploration? Should it be reductive or expansive in intent?Where left brain meets right brain: When embarking on a project, which comes first, the data or the technique? How critical a role does software play? Do the themes and memes recurring in data visualisation reflect the artists’ preoccupations or the data sets available?Proliferation versus privacy: One of the key enablers of data visualisation is the phenomenal explosion in the amounts of data we now generate everywhere we go. We live in a golden age of open-ness around personal data but will we reach a tipping point where we reclaim our personal privacies? Or will we opt in to share anonymised data for the common good?The power of synesthesia: Some of the most compelling data visualisation projects are those which express one medium-almost one sense- by means of another. Visually representing dance or music, aurally representing data sets-what is it we find so compelling about this “synesthetic” effect?
Crowd-sourcing versus the wisdom of the crowd: Koblin’s recent work experiments with crowd-sourcing but suggests an ambivalence about the process. While a central theme of data visualisation is the wisdom of the crowd, how does it skew the data if the crowd knows it’s being watched? Is the unconscious wisdom of the crowd purer and more compelling or is conscious collaboration of the masses the future? How important is the role of the curator in that process?
Answers – or at least compelling and considered answers – on a blogpost near you shortly….