While I wait for the video from my TEDx talk to be edited (I'm told it'll be ready in a couple of weeks) I thought I'd post my own notes from the day. This text is basically the framework I talked around on the day - so it's likely to be a bit different from what I say on the video (this is code for "on the day I was very nervous and lost my way a bit"). The talk was about some of my own work to analyse and represent large sets of data as well as the process of data representation.

Introduction

Hello, thanks for this great opportunity to talk to you today - My name is Paul May, and I’m a student at the Interactive Telecommunications Program here at NYU.

My goal with this talk is to continue a conversation that’s happening at ITP right now. That conversation is about the ways in which information/data about our day to day activities is gathered and stored. It’s also about the ways in which personal data and other large data sets - census data, seismic data, political polling data, are mapped into other forms - represented as graphics or sound or sculpture.

We think this conversation is timely. So much of our lives now are spent transacting and interacting with data. If you open a newspaper or website and it’s highly likely you’ll find a slickly produced infographic. Television news has become a mash-up of live footage and data-driven graphics. When we interact with our families on Facebook, we’re interacting with pieces of data about them - data has become a cultural artifact and something we exchange in return for goods and services.

My Projects - Moving Away to Gain Perspective

So, when I came to ITP I think I did what a lot of people, including many Irish artists like Joyce and Beckett, do when they leave their home - they look back on where they’re from with a new sense of detachment. Moving away has been an opportunity for me to think about home in a new way. Some of my work in data representation takes data related to Ireland, and tries to make sense of it, to tell stories with it. In doing this work I learned a huge amount.

My first project, called Irish Data - started out as an attempt to examine the cause’s of Ireland’s recent problems. Ireland, as you may have heard, has been experiencing an acute financial and social crisis. I wanted to examine data relevant to this period of time, to see what I could discover.

I found relevant data incredibly hard to come by, and where data was available it was often in outmoded formats, structured in ways that could almost have designed to make the data difficult to use. The project became much less about representation of data, and much more about trying to grapple with the messiness of data. I took this messy data, cleaned it up, created my own repository and made it available through an API - then finished by making some very basic graphics based on this data. This is just one example of data taking me in directions I hadn’t anticipated.

In another project called From Over Here I wanted to examine how Ireland is perceived from the perspective of my new home here in New York. I suppose like most people I have a thoroughly unscientific idea in my head as to how my place of origin is perceived. I used data pulled from the New York Times to show the amount of coverage of Ireland, and the types/themes of articles. I built this data into a physical form so I could walk around it, manipulate it, find stories. I found that coverage of Ireland is reasonably narrow, focusing on some not-so-positive topics - with our recent financial woes getting more coverage than any topic.

Lessons Learned

In doing these early projects, I learned a huge amount - firstly, as somebody with only basic programming skills, I found that the technical barrier to working with data is incredibly low. Each of us probably has access to a computer capable of accessing, storing and analysing huge quantities of data. Tools like Python, Processing and R - supported by extensive online resources and tutorials - make it easy for us to get started with our own data representation projects.

On the flip side of this reality are the aggregators of data, governments and corporations - who have never had greater access to valuable data about citizens and customers, or greater access to the tools required to manipulate this data. I suppose doing these projects made me acutely aware that we are in a position of incredible weakness when it comes to asking for this data; anything we’re given is essentially afforded to us, and we have practically no insight into the processes by which data is manipulated before it gets to us - we can’t see behind the curtain. We are, I think - too kind to governments and companies who hold data, and either don’t make it available, or make it available in limited/messy ways.

The Value of the Journey

The most important thing I learned in these few projects was that it’s possible to draw a huge amount of personal, resonant insight from large sets of data. It was possible for me to engage with data about Ireland’s financial crisis and for that to be an emotional and informative experience. Before doing this work I wouldn’t have associated emotion with data.

I also realised that, while the end products of the work are aesthetically pleasing - it was the process of engaging with data (finding it, gathering it, sifting through it) that helped me to learn as much as I did.

I think this is an important point; In recent weeks there has been a lot of discussion about the merits of creative representations of data versus more utilitarian ones; slick graphics versus cool charts. While this debate is really important - it feels as though we are arguing over the shape and colour of the tip of the iceberg.

When I look at a photograph in a newspaper, I don’t think that it represents the sum of an entire situation - I understand that it’s an imperfect product taken from one point of view. I’ve learned to question its aesthetic composition, the way it was cropped, and I understand that it captures a moment in time that might have been decades or centuries coming. It doesn’t matter how “good” the photograph is - it’s a product of the information and processes that came before it, representing one possible outcome.

Doing these projects made me realise that data representations are very similar; the underlying story of how that data was gathered, by whom - then how it was treated before it got to me is crucial. The things that are highlighted or omitted are really important, and what I make - my graphic or product - is just one outcome. Unfortunately, we see very little of this noise when we’re presented with infographics online and in print. They are clean and tidy, generated, clockwork - inevitable outcomes. My proposal today is that where we are presented with an infographic that says nothing about the process by which is was constructed, we should treat that graphic as a purely aesthetic product.

Thankfully, we have an opportunity to work through these issues for ourselves, to take up the tools and data behind these products - to tell our own stories and draw our own conclusions, and in doing so we become more informed consumers of infographics and representations of data.

Closing Thoughts

I’m really glad that I took some time to do these simple projects and I think this stuff really matters. Every day we write the most intimate diaries - where we go, what we do, who we meet along the way, what we buy, who we love. We write these diaries in the data stored in devices, communicated over networks - stored by governments and companies. In years to come I think it’ll be very useful for us to have the skills and insight needed to gather this data on our own terms, to re-combine and interpret it; to tell our own stories.

So, maybe after you leave here you might ask yourself a few simple questions: What you are most passionate about or what question most intrigues you? What data exists that is related to this passion or question? How will you get this data, how will you treat or change it, and then finally - how will you represent it in a form that is meaningful to you?

The possibilities are really intriguing, and I am sure you will learn a huge amount - so I leave these as open questions for you, as a starting point of a journey - and most of all I really thank you for your time today.