Thank you.
Thank you.
I've learned so much already in the last couple of days at PopTech, and already some of the
things that I'm learning are offering me a little bit of clarity.
For example, Moran Surf yesterday told us that there are actually two voices in our
brains that are helping us to make decisions.
In this case, I have me on the stage standing here right now, and then I have the me that
decided that this slide would be a good way to start off a presentation.
Big data.
I mean, I think all of us have probably seen presentations already about big data.
Big data in business, big data in science, big data in pizza delivery, whatever those
things are.
But I think one of the things that we haven't heard very much about is what is the subjective
experience like of living in this world of big data?
What is it like to be us living in this ever more complicated world?
I do a lot of traveling, and so maybe this is an example with some bias, but I think
if we imagine the experience of being in an airport, we might start to understand this
data experience.
First of all, because there are a lot of systems that are transparent to us that are happening
around us.
Our baggage is moving on carousels.
There are security agents who are hurting us through lineups and so on and so on and
so on.
Second of all, there's a loss of control there.
I think it's one of the only places that we voluntarily give up control in our lives
in an airport.
We're put into lineups.
We're kind of directed.
We're put onto these planes and these kind of data packets that are then sorted into
another airport and offloaded and so on and so on and so on.
Maybe more importantly, though, is this idea that we're part of a system that we can't
possibly imagine the magnitude of.
Right now, as we speak, there are more than a million people in the air.
The graphic that you see behind us is this respiring system of airplanes landing and taking
off at 15-minute intervals.
There are thousands of airplanes in the air right now.
So I really think that this idea of being caught in a system which is very complicated
and too complicated for us to understand really mirrors this experience of big data.
The American novelist David Foster Wallace was very prescient about this.
He was asked by his editor when he was writing Infinite Jest about why he put so many footnotes
in the book.
The footnotes in that book are really incredible and they sometimes the footnotes have footnotes
and occasionally those footnotes have footnotes as well.
He said that one of the things he wanted to do was to mimic the information flood and
data triage that he expected to be an even bigger part of life 15 years hence, Infinite
Jest was written 16 years ago.
I've been really excited about this idea of big data since I had a conversation about
three years ago with this man, Lashlow Beribashi.
We were working on a project for Wired Magazine in the UK where I was the editor of a somewhat
unfortunately named section called InfoPorn.
I worked with Dr. Beribashi on a project to help show some of the results from a project
that he was working on about human mobility and we had the really good luck of working
with this data set which is one of the largest data sets of cell phone usage from an unnamed
European country.
The country has a name, I'm just not allowed to tell you what it is.
The first thing we see is this graph which is not a very exciting graph.
What this is, this is a segment about 50,000 of those people and as the graph gets taller
on the left those people talk a lot on their phones and as it gets shorter on the right
they don't talk very much at all and the people on the left they talk 84 hours a week
on their phone and the people on the right are not really talking at all.
On the left hand side of this graph I took a whole bunch of these sections and stacked
them up so that we could just see the richness of this data.
There's a lot of data and for each person in that data set we were able to see their
calling history over time, piece it together, see exactly how they were calling and who
they were calling but maybe more interestingly is the thing that happened on the other side
of the page for which I built these little cubes which I call mobility maps and so what
we're seeing here is we're seeing a cube that shows a single person's travel over about
four days.
So this person is clearly a commuter, they travel back and forth from one location to
another but in that data set we were able to produce these mobility maps for everybody
in that data set, tens of thousands, hundreds of thousands of people and see how their lives
can be represented in this really simple form and one of the things that surprised me and
that surprised the Barabashi group was that there was a lot of predictiveness in this
data and so I started getting this idea that we could look at the data trails we were leaving
behind and we could start to construct things from them.
So the next few projects that I worked on carried through that idea.
This is a project called Just Landed, how many of you are on Twitter?
Probably most people are on Twitter.
So you've read those tweets, right there, somebody says, I just landed in Hawaii, we're
stuck on the runway for a second or for 20 minutes, this is really irritating, right?
There are these rich white people tragedy quotes, right?
I thought it would be really interesting to take those kind of really self-serving things,
these kind of thinly veiled show-offs and put them together into a map, maybe we could
recreate a human mobility system by seeing how people are showing off about their travel
around the world.
Maybe a little less meanly, I also put together this project called Good Morning which looks
at everybody saying good morning to each other on Twitter.
So here's everybody in the world, in 2009, so more than three years ago, all saying
good morning, the green people are getting up early and saying good morning, the orange
people a little bit later and the red people really late, when we look at the United States
you really see the red on the west coast and the green on the east coast.
And we carried these ideas, I've been carrying these ideas throughout my work ever since
then.
At times we built this project working together with a statistician named Mark Hansen and
with the rest of the really talented team at the R&D group, at the times we built this
project called Cascade.
And what Cascade does is it looks at conversations about times content on Twitter and we're able
to recreate every single conversation that happened about every piece of times content
in real time.
So we're looking at a story which is a couple of years old here, but it's an interesting
one for a couple of reasons, so just explain what we're seeing on the left-hand side is
the very birth of this conversation and then now we're about 24 hours into the conversation.
As degrees of separation get above, we're going from one person to the other who tweets
to the other person, tweets to the other person, but really what we see is we see the architecture
of discussion, we see something that we've never seen before, so exciting, this was one
of my favorite projects to work on, I felt like an archaeologist exposing things for
the first time that we've never seen before.
But something I always sat really uneasily with me about this project and with the other
work that I showed you, and that is that largely this work depends on opportunism, we're dependent
on taking people's data without telling them about it.
And even though Twitter is overtly public, that I don't think makes me feel a lot better.
I was speaking at a conference a couple of years ago and somebody said this for the first
time, I think the first time that I heard it, data is the new oil.
And people, they clapped, they were excited about data being the new oil, I think they
were thinking about this, right?
Whereas I was thinking about this, but in the context of this, we didn't do very well
with oil, and to suggest that data can be the new oil, I find frankly terrifying.
But maybe there is a piece of this analogy that works for us, because oil is composed
of all these tiny microorganisms, these prehistoric microorganisms that have been compressed into
this sort of valuable resource.
Data consists of fragments of our lives, the valuable data that we're talking about consists
of fragments of our lives that are being compressed into this valuable resource.
Now I don't really trust, maybe it's the Canadian in me, but I'm not sure that I trust corporations
to take charge of this type of resource.
And I think I'm really interested in how we can do a better job with data than we did
with oil.
So last, we mentioned before in an interview last month, had some really interesting things
to say about data and its value to us.
So he says, if we want to do all these great things with data, we have to come to a social
consensus, because this data is valuable and it's owned by all of us collectively.
So how do we come to a social consensus to make sure that that data can be used for good
and not necessarily only for profit?
Well, three things I think, data ownership.
We have to get people used to the idea of owning their own data.
It is your data, you should own that data, and that's not the way it works right now.
So at the times we put together this project called Open Paths, which allows you to store
your location data securely and share it if you wish with resources.
It's the if you wish that's important there with researchers.
So you can share this if you'd like to.
So please download the app, start recording your location data.
It's really fun to explore, and then share that data if you wish.
And the second thing that we need to really be talking about is data and ethics, because
I think ethics have been almost all lacking from this conversation.
And it's really important that as consumers of data services, we start to make decisions
based on ethics.
And then finally, let's get back to the first thing that I talked about, which is this subjective
experience of living in a data world.
I'm really, really, really convinced that the only way we can reach this consensus that
we're talking about is by sharing with people and exposing to people what is happening in
this data world.
And that's, I think, where the role of data art comes in.
I come here today because I'm excited about data, but also because I'm terrified.
I'm terrified that we are having progress without culture in the world of data.
And as we've seen with these field industries before, progress without culture does not
work.
And there's a lot of powerful people in this room, and if I can leave you with one thing,
let's try to bring culture into our discussion with data, and let's try to not make the same
mistakes with this new resource that we have with the last ones.
Thank you.
