Data is gathered by outsiders every time we buy a product, search the Internet, play with our smartphones, stream a movie on Netflix, go to the doctor … the list is endless. What if we could tap into all that personal data for our own use? This, says Gary Wolf (@agaricus), contributing editor at Wired magazine, is what the Quantified Self (QS) is all about.
QS, which started as a collaboration between Wolf and Kevin Kelly, has grown to include a multitude of forward thinkers who work on QS projects and gather for “Show & Tell” sessions to discuss and present ideas.
Likening QS to the personal computer in the 1980s, Wolf explains in the following interview how the concept is reaching the mainstream and what needs to change for it to become ubiquitous.
How did Quantified Self get started?
Gary Wolf: We started the Quantified Self as a way to investigate some things coming out of the tech scene that could affect our lives in a big way.
Let me tell you how the Quantified Self looks to me, with the caveat that although I’m the co-founder and play an active role, it has grown far beyond the point where any one person’s view can be accurate and complete. We are a collaboration among users and tool-makers of self-tracking systems, devoted to exploring the personal meaning of personal data — that is, self-knowledge through numbers.
Numbers play a key role in science and management. But we tend to think of data as a tool that others — advertisers, marketers, academics, and bureaucrats — use to understand or manipulate us. We’re interested in how the new tools of tracking and data analysis can give us knowledge about ourselves.
I see a parallel to what happened with computing in the ’80s. Computers were understood as tools of management. A few people saw things differently: they argued that computers were for personal expression and communication. That notion seemed very strange — why use a computer to connect with another person when you could call them on the phone or talk to them face to face? But it turned out that the personal uses of computers were not just an important use, but the most important use.
Our collaboration in exploring the personal use of personal data takes several forms. There are open “Show & Tell” meetings in more than 20 cities around the world — Helsinki is our latest addition. We also have a blog, and we recently received some generous funding from the Robert Wood Johnson Foundation to produce an online user’s guide to self-tracking tools. The biggest thing in our immediate future is our first Quantified Self conference. This will be a relatively small meeting at the end of May, in which users and tool-makers from around the world will gather to share practices, methods, and questions.
How do you see QS making its way into the mainstream?
Gary Wolf: QS is becoming mainstream so quickly that it is taking our breath away. This has been a funny experience because Kevin Kelly and I have learned through experience that the adoption of new cultural practices associated with technology usually takes longer than our intuitions suggest.
Although we both travel and talk to people, the view of the future from inside the tech culture tends to be foreshortened. In this case, though, three forces are driving QS from outside. The first two of these are obvious: fitness trends and the health care crisis. The third is a bit more obscure, but important: the rise of big institutional systems to track individual behavior. We know we are being tracked, and that others are gathering more information about us than we have about ourselves. So, part of what is happening in the world of QS is a response to this sense that powerful tools of understanding are available. We want access to them for our own purposes.
What components — hardware, usability, technological advancements — need to improve before QS will be widely adopted?
Gary Wolf: The most important thing missing is the widespread understanding that personal data has personal meaning. That your data is not for your boss, your teacher, or your doctor — it’s for you. This is a cultural shift. Again, it is analogous to the shift in how we came to understand computers. Everybody who makes a useful QS invention is contributing to this shift.
When it comes to specific inventions, there are many opportunities for making things more useful at this stage. We are seeing new products and companies focused on data collection through sensors and interfaces; on data analysis through aggregation and multi-sensor inputs; on data-driven social/community, mainly through web services; and on scientific discovery through platforms for experimentation. Many participants in QS do more than one of these things. Of course, there is some imitation and mutual influence — sometimes I feel like I’m seeing the same slide decks, just shuffled in a different order. But this is always going to happen in a lively community of inventors and advanced users.
How do you think QS will affect health care?
Gary Wolf: We know that health care — or let me say “the health care industry” — is a train wreck. The problems are well known: immense costs, questionable efficacy, brutal inequity, and perverse incentives.
We see QS as part of the new system that will emerge from this wreckage. Every institution that has a stake in the current system — medical professionals, hospital corporations, pharmaceutical companies — will be picking their way through the ruined landscape of health care, repurposing its valuable components, and hooking them up into the new practices that emerge. We could get all technical and talk about “capitation” (this means payment of a set fee “per head” to health care companies, rather than fee-for-service). Capitation will change care and make it very important to take individual knowledge, desire, and behavior more seriously.
But even without getting into the details of how the health care industry works, we can see that we are not being well served by conventional medicine. You take a drug, for instance, but have no good data about whether it works and what side effects it causes. Health care is going to undergo the most important critical advance in perhaps a century when QS data becomes the yardstick by which its success is judged.
In what other ways do you see QS affecting our daily lives in the future?
Gary Wolf: I wouldn’t pretend to be able to give a complete summary or prediction, so let me just throw out a few interesting and colorful examples I’ve seen. This are all real projects presented at various QS Show & Tell meetings, not prototypes:
- Facial tracking to improve happiness.
- Cognition tracking to evaluate effects of simple dietary changes on brain function.
- Food composition tracking to determine ideal protein/carb meal ratios for athletic performance.
- Concentration tracking to determine effects of coffee on productivity.
- Proximity tracking to assist in evaluation of mood swings.
- Mapping of asthma incidents and correlation with humidity, pollen count, and temperature.
- Energy use tracking to find opportunities for savings.
- Gas mileage tracking to figure out if driving style matters.
Again, I want to emphasize that these are not prototype or lab experiments. These are individual users who are exploring an aspect of their own behavior and performance, often for deeply personal reasons.
This interview was edited and condensed.
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