I went to my first Quantified Self Meetup last week in London. I’d been signed up and getting the emails for the Boston Meetup group for a while, but never felt compelled to go. Though I track a lot of things like my exercise, financial information, and nutrition, I never really identified with the movement and the more edgy “self-hacking” aspects of the trend. But at the insistence of Joshua Kauffman, I went and checked out the London chapter, and I’m very glad I did.

The format is refreshingly brief, to the point, and inclusive, all to the organizer Adriana Lukas’ credit. The 10-minute “show and tell” presentations that are followed by ample time for group discussion allow for thoughtful interaction, and make use of the wealth of opinions, backgrounds, and ideas in the room. And the highly-encouraged pub time following allows for ample mingling and sharing of interests and we’ve all got something good to talk about after the ideas are swimming from the presentations before. Kudos to good structure!

Even in just one presentation, there were so many things that resonated with my  research interests in how people think about personal data. Case in point: The first presentation of the night after Adriana’s introduction was given by a man in a snug, sporty zip up that showed off his muscular physique. A self-described immortalist, Stuart Calimport presented his Memome project, detailing how over the last 1.5 years he has tracked more than 11,000 thoughts that came to his mind and classified them as either healthy or unhealthy. With just an Excel spreadsheet and word clouds, he quantified his preoccupation with “optimization.” Stuart also looked at his overall idea generation by matching raw output of ideas to a day’s activity with his Fitbit and Fitocracy data, and discovered that he generated more ideas on low-exercise, high-calorie intake days (i.e., at conferences), and he wants to find better ways of optimizing high impact idea generation in healthier ways. The project made Stuart aware of the disconnect between his thoughts and his values, and he explained how he believes it is important to resolve those conflicts in frequency to improve health and overall well being.

Slide from Stuart Calimport’s Memome presentation at Quantified Self London 25 October, 2012.

When pushed further by the audience to explain the negative aspects of the experience or “dark journey,” he said that we have only so many hours in the day and to use them up thinking unhealthy thoughts isn’t how he wants to spend his time. Responding to the relatively simple analysis of the data, the group offered suggestions for looking at runs in the data to show statistical patterns of healthy or unhealthy thoughts, or suggested adding timestamps to his thoughts to facilitate more granular correlation other tracked behaviors (as Self Quantifiers are want to do). But for Stuart, the act of keeping track of thought patterns was sufficient, and contributed to larger self improvement project that included becoming vegetarian and improving his muscle:fat ratio. All this, in service of his larger goals of optimizing mutual health and well being in himself and others.

Just this one story brought out so many of the themes that interest me about this community. While I was sitting the room, my initial reaction was that this is just the kind of extreme example of self tracking that leads the popular press to describe these efforts in terms of navel gazing and narcissism. What good does it do to try to keep track of ideas? But upon further reflection, Stuart’s project generated a lot of interesting questions for me: What are the limitations of analysis of qualitative content, and what’s the utility of tracking something so abstract as ideas and memetic concepts? What data is or isn’t important for self quantifiers, and to what end? How do self quantifiers use and interpret their data to weave narratives about themselves? What motivates self quantifiers to track the things that they do? And most importantly, what is the relationship of personal data to the conception of the self?

My initial reaction to Stuart’s talk also brought forward my own hesitation with identifying as a self quantifier. In what ways is my own DailyBurn calorie and exercise tracking different from his Memome project, and what about those differences makes me feel like just a hobbyist? Where is the line between what Alicia Morga described as the two camps of “Oprah Winfrey school of self-improvement than the Silicon Valley data geeks.” These are important questions as I think about myself in relation to this community as I become more active both as a participant and as an observer.

The Quantified Self community collectively represents a group of people who’ve given a lot of thought to what active and passive tracking means to them, how they value their data and conceive of their ownership of it, and what their data means for their self awareness and definition. It’s a group with rich stories to tell, and for those reasons I’m leaning towards focusing my masters research this year on Quantified Self attitudes, behaviors, and conceptions about personal data. I’m betting that among stories like Stuart’s, there are just as many threads to tug on that get at what personal data means to us today and will mean to us in the future.

On a personal note, when I started putting the pieces together thinking about what I had observed at the Meetup, and began to see all the ways this group intersects with all the things I’ve been thinking about when it comes to describing personal data writ large, I’ve gotten really excited and energized about my work, and that’s always an optimal thing.

[This post also appears on #wethedata.]

It’s been a wild year. From living in Chongqing, to getting married in the US and moving to the UK, we’ve been living an itinerant but momentous life this year. We’ve arrived in Oxford, gotten set up with broadband, and now we’re hitting the ground running and getting back to work.

So, what am I doing here at Oxford? I’m reading (as the Brits say) for my MSc in the Social Science of the Internet at the Oxford Internet Institute [and yes, at the end of this, I will be a Master of the Internet, and I ask that you please refer to me as such]. I’ve been working in tech and the internet in some capacity for the last few years, from enterprise IT to online video, so this feels like a pretty natural next step. The main difference now is one of focus: I’m studying personal data. I’ve been thinking about personal data for a while now, and I wanted a chance to study it more rigorously, and with the institutional backing to do so.

I’m also here to learn. I’m not a social scientist by training. I fashioned myself a comparative media scholar by way of English and Film Studies in undergrad, and emerged as a business analyst by teaching myself how to read financial statements. Now I’m here to explore how I might be an anthropologist, or an economist, or a legal scholar of how society understands (or perhaps doesn’t) personal data.

But I’m discovering that in getting trained in broad social science methods, I’m also here to meta-study the social scientists themselves. As much as Facebook and other platforms are using personal data to improve products and build business models, the social scientists are just as, if not more eager, to make use of the wealth of social data that’s being collected. While the latter may have IRB sanctions and methodological ethics training, the former doesn’t necessarily, so I’m interested to explore some of the similarities and differences across these two different sets of users of data. The promise of Big Data looms large right now for both, but those messages haven’t necessarily been translated to consumers except in fear-mongering, sensationalized ways (see Target pregnancy story). Bridging that gap is hopefully where I fit in.

One of my biggest motivations for studying personal data as opposed to privacy is because I believe we as a society don’t have practical frameworks for understanding the day to day decisions we make about our relationships to firms that use our data in exchange for their “free” services. It’s not just about the potential for breaches, security, protecting ourselves from identity theft. We’re in an important moment right now: we are still in the process of exploring and delineating these new norms and expectations are around data use, information flows and contexts (like many, I’m heavily influenced by Nissenbaum). I want to help shape and describe those norms, those expectations as they develop. My project is one of user awareness, savviness. It’s not one of conservatism, it’s one of conscientiousness.

And I’m in good company in thinking about how we think about data. Projects like #wethedata are trying to raise awareness and show the potential for different relationships to data. The World Economic Forum has been describing data as a new asset class. These are all steps in the right direction. But there are also some metaphors being used to liken data to oil, to be mined and refined by large corporations (i.e. Big Data). This metaphor disempowers and dehumanizes the individuals in the equation. People aren’t natural resources. I want to contribute to this dialog, and figure out better metaphors for talking about our data that could ultimately shape policy, consumer behavior and decisions, and even have an impact on the very economic model of the internet (“free” services and content in exchange for data). There won’t be market demand for changing practices around personal data if users don’t have a concrete grasp of how data is used today and could be potentially used tomorrow.

We are bringing more and more of our lives online, and in doing so, we are generating more data, leaving more crumbs, but we haven’t necessarily seen the benefits of our own quantification on a personal level. The existence of this data isn’t necessarily good or bad, but how we use it, who can use it, and where it goes in the future is all up for discussion right now, and we need a savvier user to have a say in that discussion.

The Intention Economy: When Customers Take Charge by Doc Searls
My rating: 3 of 5 stars

I was really excited to read The Intention Economy because it is one of the first efforts I’ve seen to extend popular privacy concerns into the realm of the economics of personal data and user empowerment. I’ve been pursuing these ideas in talking about the nature of our transactional relationships with internet services and companies by “paying with our data,” and we’ve seen early signs of this in the World Economic Forum’s discussion of personal data as a new asset class.

Searls’s strongest explanation comes in describing the underlying problem in the existing internet economy that gives data use power to companies: it’s all based on those boilerplate, impossible-to-parse terms of service that operate as “contracts of adhesion.” Contracts of adhesion is an old idea, introduced by Friedrich Kessler to describe the heavily biased positioning of bargaining power with the company. These contracts exist to allow for mass production and consumption at scale, but they get in the way of principles of “freedom of contract”. So how do we put users on a more level [contractual] playing field with companies?

Searls proposes that control over the terms of data use need to lie with the user, and that fourth party data brokers (also called data lockers) could negotiate and manage our terms and our data on our behalf to share with the services that need access to our data. Terms might include things like “If we cease our relationship, you can keep my data but not associate any PII with that data.” While customer relationship management (CRM) platforms have been around for more than ten years to help companies manage interactions with and data about customers, Searls suggests that we’ve been missing the mirrored pair on the customer side of the relationship, which he’s deemed vendor relationship management (VRM).

While I’m excited about shifting the dynamic of predominant economic models as they relate to data, Searls’ book falls short in a couple of key ways:

• It’s too optimistic. Searls doesn’t address or even acknowledge the of the limitations and challenges (most obviously, inertia) that would prevent The Intention Economy from realization. It’s too easy to punch holes.
• Searls puts too much focus on and faith in the tools. He says, “We didn’t need a car, a copier, a radio, or a smartphone until we saw one and said to ourselves, ‘I need that.’…SO, THEN Customer liberation requires necessity-mothering inventions.” I tend to disagree. While developing tools to enable these markets is important, making customers aware of the problem, and making them aware of an alternative model, is a bigger challenge, first. One audience member in Searls’s book talk asked: “Customers are lazy. What’s going to make them want to do anything about this?” Sure, having tools that make this as easy and seemless as possible is going to make adoption go a lot more smoothly, but there won’t be a market if enough consumers don’t see this as a clear problem. The popular media’s coverage of privacy concerns over the last year has certainly raised the level of discourse, but it’s mostly been focused on fear mongering. We haven’t seen much in terms of alternative models for moving forward, and that’s what conscientious consumers should pick up from this book.
• Searls doesn’t address the elephant in the room, which is: why should we trust fourth parties with our data anymore than we trust Facebook or Target? Might it just be the case that the fourth party managing our data for us will just end up being one of the companies that already has a lot of our data already? Will Facebook become our data broker, like it’s already our identity manager (given all the startups using Facebook for signup and login)? That possibility seems more likely to me, given the concentration of users already captured on these platforms. It’s harder to imagine one of the current data locker startups rising to the top to come between us and Facebook (unless of course they got bought by Facebook). But even for Facebook to become our data broker, the dominant economy of the internet will have to shift way from advertising to create something that looks a little like Searls’s Intention Economy. He doesn’t make this argument, though, since he’s so invested in those VRM startups.

Aside from this more glaring problems, Searls’s writing is full of awkward, imperfect metaphors like calf-cows relationships (“The World Wide Web has become a World Wide Ranch, where we serve as calves to Web sites’ cows, which feed us milk and cookies.”) and Chinese walls between advertisers and consumers (“If advertisers would peek over on our side of the Chinese wall, they would see two icebergs toward which TV’s Titanic is headed, and both promise less tolerance for advertising.”).

All that being said, The Intention Economy is bold, and it’s hard to cover all the bases when your arguments are so radical. But it’s also hard to convince others to rally around the cause if there are too many questions left unanswered.

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