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”.

Incongruity making art: Aaron Koblin's "House of Cards" promo for Radiohead

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”.

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.)

Like spirograph, but better: Email map by Christopher Baker

To some extent however this elegance, which makes data visualisation so immediately compelling, also represents a challenge. It’s possible that the translation of data, networks and relationships into visual beauty becomes an end in itself and the field becomes a category of fine art.

No harm in that perhaps.

But as a strategist one wants not just to see data, but to hear its story. And it can seem that for some visualisations the aesthetic overpowers the story. I spent many hours when younger staring at data tables, yearning for them to reveal a narrative. It is the prospect of bringing articulacy to hitherto cold, laconic facts that should be at the heart of the excitement around data visualisation.

The more compelling projects from Manuel’s archive did indeed seem to reveal some insightful truth about the relationships that they considered. Enron’s email patterns, the map of Segolene Royal’s supporters, the plotting of visitor eye traces in Barcelona, all looked extremely useful.

Enron Communication Graph, by Kitware Inc.

With this last instance in particular,  one can start to imagine how understanding the dynamic patterns of tourist traffic around the city and its most photographed areas might enable the development of all kinds of helpful tools and services for both tourist and city.

Tracing the Visitor's Eye by Fabien Girardin

Manuel himself talked about ‘turning tools of curiosity into tools of functionality’. In this respect he quoted Chaomei Chen: ‘A taxonomy of information visualization is needed so that designers can select appropriate techniques to meet given requirements.’ And clearly this desire to enable greater utility is driving Manuel’s own research into the different methods and models of visual representation.

As a pioneer in his field, Manuel discussed the opportunities emerging in interactive data maps and he described a Californian experiment in which it should be possible physically to interact with a huge data set distributed about a six storey building.  Blimey. I think I’ll leave that to the true data connoisseurs …

Finally, as a grey haired strategist, I found myself considering how the paucity of visual representation techniques had impacted the way we tackled problems in the past. I think we knew fundamentally that most events were precipitated by complex systemic pressures and relationships. But our limited power to disentangle the many elements in one system reduced us to characterising most strategic problems in rather monochrome ways.

So, this is progress indeed. Data visualisation has radically improved our understanding of these complexities. The real question is: what will we do with that understanding?

"House of Cards" promo www.aaronkoblin.com

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.

Visualisation of SMS messages in Amsterdam www.aaronkoblin.com

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. 

Perhaps the big difference between what you do and the bar chart or the single number that it really embraces complexity rather than trying to reduce everything-ur lives are complex and this gives you a deeper understanding of that, not simpler, but richer

 I think what’s really nice is when you can have that kind of simplicity but then allow for investigation.  Not necessarily forcing it into this sterile reality, but being able to present a story clearly and convincingly and simply but then allow for justification where you can say this is why, it’s fine to give a summary number but then be able to say this is why the number exists.

So do you think data visualisation should be about immediacy or intrigue? Should it be “I see that and I get it” or “I see that and I don’t get it so now I’m going to play with it”?

Definitely it boils down to your purpose. I think there certainly is a place for scientific visualisation. There still is a necessity for that type  of clarity and objectivity but there’s also a place for design and I would argue for art, that to be able to use data to tell stories and to tell the right types of story requires different kinds of techniques and different means. Often times I think the whole medium-is-the-message sense of actually using the system to think about the system can be valuable and fun and productive

What do you think is so fascinating about seeing one medium like music or dance portrayed through another?

I think that’s also something that’s really picking up because of digital culture. Now that everything has become digital it’s so easy to run it through a completely different process. You can make it,  just tweak the algorithm and sound becomes image and image becomes motion. It’s kind of a natural process, it makes a lot of sense, especially for people like myself who are visual thinkers and learners. I think translating a lot of these concepts and numbers and pure abstractions into something tangible, something to be seen and experienced and interacted with means the world, because for me it makes a completely different kind of sense. A lot of times that kind of experiment can reveal the underlying structure and point out the way that it makes sense.  

 You talked letting the data do the talking and really embracing the random-ness of the data; do you think that’s what makes data visualisation so compelling as art, because art is very seldom truly random?

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. If you look at creative practice – like with the Sheep Market project for instance, if every person drew a perfect sheep they would all be the same and it would be a horrible project. It’s actually seeing the ways that people fail, the different intricacies and character that comes from the individual that adds a lot. You see that in all data visualisation, it’s the little variations that gives the character  and makes it interesting.

On the crowdsourcing sheep project, you talked about it potentially being a very fragmented and alienating experience  but you’ve drawn it into a coherent whole-there’s a certain ambivalence there 

I think it was the juxtaposition of those two qualities that makes it interesting for me. On the one hand you look at this huge grid that looks very much like a matrix of computer created content, but then juxtaposing it with each individual, looking down at the fine level you see there are actual little people in there. I’ve always been interested in microscopes, I bought a few microscopes and I have a family friend, Gary Greenberg, who makes these amazing oblique-lighting microscopes. Basically microscopes that produce images that are more like beautiful photographs than back-lit medical tools. He used to let me play with them and it was amazing fun. This notion that there’s a device that can completely transform the way you see something is really inspiring. You can look at the whole thing but you can also drill down and it’s a totally different world.

The Sheep Market www.aaronkoblin.com

The wisdom of the crowd versus crowd-sourcing is a fascinating topic. The wisdom of the crowd seems to kick in when the crowd doesn’t know it’s being watched, whereas with crowd sourcing it can sometimes just feel like mass-sourcing. Do you think the future of crowd-sourcing is genuinely collaborative, with the crowd consciously working together and making things better? 

I think you already see that happening with all the Wiki projects which are really inspiring. To some extent I think it’s because the motivation is different, it’s not really about money. Money really complicates things. With the sheep the people who I had paid two cents felt totally ripped off and were really mad at me but the people who knew what the project was were asking if they could give me free sheep! So they had a completely different perspective on the situation which was interesting to note. I think the weird thing about crowdsourcing is that to some extent it feels like the inevitable evolution of capitalism which is just something to think about.

Which is weird, because on the other hand you could argue it’s socialism in action….

Right, it’s both – it boils down largely to the approach of working with the crowd. There probably needs to be a better disambiguation for actions involving the masses. There’s wiki-style collaboration, the kind of thing you see with open source projects, people working together to make something massive. On the opposite end of the spectrum is the current incarnation of the Mechanical Turk, where you have individuals being harvested for isolated menial tasks, and somewhere more towards the middle you find things like CrowdSpring – more of a massive sifting of the crowds.

There are some themes which seems to recur in your work, such as the energy of cities or some themes about the flow of information. Is that about themes that interest you or is it about the data sets that are readily available?

 I wouldn’t yet say readily available, it’s still really tough to get at some of that data, but I think I am generally interested in all kinds of data that have anything to do with our lives and revealing the way that we live and build systems. So to me it’s something I’ve always been drawn to and part of it obviously is because I grew up in this computer and game culture and I’ve always been interested in algorithms and the way things work and the process behind things. There were a lot of films growing up about information and visualising information that inspired me a lot.

What tends to come first for you, the data set or the visualisation technique?

Based on the projects that I’ve done it’s usually either that I’m presented with an awesome dataset or it’s that there’s a data set I’d really like to create. I guess that’s answering it by saying both. So with the mechanical turk projects it was more about being interested in that tool and wanting to create data that would reflect the tool.

Does the software you use have a big impact on the way you work? You talked about the impact of the Processing tool?

I think it definitely has. The nice thing about Processing is that it’s an open source tool so it’s constantly being added to and growing and because it’s open source it makes it much easier not to be heavily influenced by it. I think because you can modify it to an extent that’s much more thorough than if you were using a closed source tool. In the sense that there’s all kind of things people have written that you can use but also in the sense that if something doesn’t work the way you want it to you can rip it apart and make it different and make it work the way you want.

We are living in a golden age of data availability right now. Does it bother you either that there is such a rich data trail available about our lives or that people may start withholding data on that basis?

I think that we will see people change, at least in terms of personal data, I think we’ll see people change their interest in sharing as much as they are. But I think that will probably come in the form of not necessarily less data acquisition just less public data sharing. I think what we’ll probably see is better disambiguation between aggregated public data and individual public data where I’ll be willing to opt in to something to share my information but not with my name on it and I think that that will end up being really valuable for all kinds of social studies and applications. But I think that will also potentially be less damaging to individuals as we see more of that. I feel bad for the younger generation that’s growing up right now.  A lot of the stories that they’re bonding to their existence, will leave trails that will last with them for the rest of their lives. Forgetting is a wonderful ability, and one that technology is not currently adapted to.

Yahoo! search burst globe, www.aaronkoblin.com