<Curators Notes>

Linda Doyle_
Director of CONNECT/CTVR, Professor of Engineering & the Arts Trinity College Dublin

It appears I am not self-optimised. I am suboptimal. It turns out that I don’t know myself. I am not quantified. I have realised this since working on LIFELOGGING. It seems that you need to know yourself in numbers, that you need to measure every part of your life, so that you can then analyse it all and take action to be optimal. And so, it would appear, I am not counting or measuring enough.

I come from a research centre called CONNECT. My life is about wireless connectivity. So while CONNECT does not focus on the specific kinds of applications that are part of this show, we are about the infrastructure that allows people to lifelog. You need to be connected to quantify yourself. The data needs to be sensed and then stored, locally or in the cloud (wherever the cloud is), and then finally, analysed. The lifelogging world is a subsection of a world that is sometimes called the Internet-of-Things, or IoT. To try and quantify this world, people talk about there being fifty billion connected devices by 2020. Fifty billion is actually not all that much. It is roughly seven devices per person. Though, of course, most of those devices will be in the Global North, so we can’t really divide fifty billion by the seven billion that populate the earth.

So while the phone as one device, one amazing complex and multifaceted sensor, actually enables diverse lifelogging opportunities, many newer lifelogging appliances are helping push up the total number of devices well beyond seven per person. And when all of these diverse devices are increasingly sensing, chatting, and logging activity, they put more and more pressure on the networks. So in CONNECT we wonder about how the networks of the future will bear up under such strain and how we can make sure that at every moment of every day and in every space you can stay connected. Because, after all, you need to be connected to measure and count and optimise yourself?

When you lifelog you might start just by doing it for yourself: tracking your weight, how many steps you took, your blood pressure, your mood, and much more. And then of course it can be hard to lifelog in a vacuum, so there is an overwhelming need to connect, share and compare. Ultimately, how can you become self-optimised in a vacuum? Don’t you need to know who you are worse, better, slower, faster, fatter, thinner than? The thing is that once you move beyond the vacuum or the private bubble, the endless measurements begin to accrue more value.

The question is value for whom? Value for you? To self-optimise? Because, no doubt, an optimised self is a happy self?

Or value for others?

Data is now a currency that is in common and flourishing use by us all. When you use Google you appear to get a free search service. OK, sometimes you need to watch some ads, but the main way in which you pay is through the currency of privacy. You slowly, or some cases in a mad rush, reveal things about yourself: your likes, your dislikes, your biases… and the machine that is Google or Facebook or the latest new app monetises all of this.

I use Twitter, but here again, like my lack of initiative in the lifelogging domain, I am a Twitter stalker and not that active a user. I recently sold my Twitter feed. I used a service called Datacoup — in fact I heard of this service through my fellow LIFELOGGING curator, Nicholas Felton. The straplines on the Datacoup site include ‘unlock the value of your personal data’ or ‘reclaim your personal data’. Datacoup claim that data brokers in the US account for a $15 billion industry while they have ‘zero relationship with the customers whose data they harvest and sell’. They talk about ‘getting people compensated for the asset they produce’.

It turns out that my Twitter feed is worth 35c a month. I appear to be suboptimal on Twitter as well.

The point, however, is not how suboptimal I am. The point is that data is an asset, an emerging currency. Data is king. The world of IoT is about collecting data by the bucket load and using it initially for what you thought you wanted to use it for (e.g. measuring your levels of activity) but ultimately the data gets used for something you never envisaged, like determining whether you should get health insurance, when you should be offered a deal on a new pair of runners, or whether you are more likely to be hit by a car. And the world of data analytics is about finding patterns that you, as a human, might not even know exist, patterns that can be monetised further in some way for someone, most likely not you. We have not got to the point yet where we can say that the ten data points for how fast you ran in the last week is equivalent to two data points of how much chocolate I ate yesterday — though clearly it is possible to say my Twitter feed is worth 35c — but we are in moving in a direction of where this understanding is crucial and essential, a world in which data is a means of exchange. And while sites like Datacoup exist, we are mainly moving in this direction without paying enough attention.

So do I remain suboptimal? What should I optimise (other than the underlying networks that allow everyone to connect). And when I dump data, is it now the equivalent to flushing cash down the toilet?

We need to know our data, whether by lifelogging ourselves, or whether our lives are logged by others.