Data Art

After various discussions, one with my tutor Jane Brake and one with superfly superstar Sam Jeffers, I’ve begun trying to further formalise my understanding of the implications of data. Specifically the data that generated by my digital artworks. Most of my practise so far has been fairly ‘happy go lucky’ in a lot of ways. Mostly I’ve been interested in creating things purely for the sake of creating them – and I’m more than happy to stand by that point of view. Even if one’s creative output doesn’t broach a political subject, or doesn’t directly evoke an intense emotionally reponse in the audience, it does not intrinsically diminish its value. However, what I’ve finally realised, is that better understanding of some of the constructs that I’m working with – the Web, the network effect, data, and people – will allow me to produce “better” work. At the very least, it can’t hurt!

I was talking to Sam – who plays in post-rock band Fridge – at mine and my sisters joint birthday party, at our parents house in Yorkshire. Unfortunately I was a little drunk, and so our conversation is now obscurred by a thin blanket of imparement, but I remember thinking it was great at the time. Inspite of the drunkeness I have retained some of what we talked about. One key point that came up is that a lot of my work utilises the network effect. Its silly really, I knew my work was using it, indeed using it was the point, but I hadn’t really thought about the network effect as an individual entity. More of a by-product. Pretty simply the network effect refers to how one good ‘user’ can have a positive effect on the system as a whole. I think it’s normally used in economics and business, it’s also a really good analagy for the so called ‘Web 2.0’ – something I wrote about a lot in my dissertation.

We also talked about the value of data and the power that it holds. Facebook, Digg, Delicious, MySpace; all gathering unimagineable quantites of data about their users. Amazon gathering precise statistics of how people use their services, in order to better tailor them toward people making purchases. For the big web players, it’s a data game and a numbers games. The data is the value, but its only valuable in numbers, but the numbers are there.

And what about thinking laterally about data. Below the surface there are often hidden depths. A little like Deep Thought concluding that “the answer” is 42; it makes sense, but only if you know what the question is. Data often seems to be relatively useless but if you ask it the right questions then it will come into focus. Sam mentioned a paper (that I think he wrote) whilst studying at Harvard. By specifying some, apparently strange, criteria the paper defined a way that you could identify the poorest economies in the world by using Google Earth.

I can’t remember what most of the criteria actually were, but they included things as seemingly random as ‘if the capital city’s governmental district has a high density of small blue patches in it’ and ‘if the capital city is obscurred by clouds’. The first suggests a poor economy because it signifies that high ranking government figures have swimmingpools, possibly indicative of corruption which is more commonplace in poor nations. The second point is because the satellite or plane that captures images for Google Earth will likely not return to recapture cloud obscurred pictures over the poorest nations.

It’s just very interesting to get underneath the ‘bonnet’ of data and to try and look at it in unusual ways. More on that later.