to work their magic on your life in the near future if you let them. In the case of the H(app)athon Project, global Happiness Indicator metrics also provide the framework of a positive vision for the future not dependent solely on influence or wealth.
Need help defining your own vision? Check in with your data and see what revelations you have to offer yourself.
5
QUANTIFIED SELF
Wearable computing devices are projected to explode in popularity over the next year and, with a wave of new gadgets set to hit the consumer market, could soon become the norm for most people within five years. ABI Research forecasts the wearable computing device market will grow to 485 million annual device shipments by 2018. 1
ABI RESEARCH
W E ALL CURRENTLY have wearable computing devices in the form of our smartphones. Slap a piece of Velcro on your iPhone and wear it on a headband and you’re good to go. In terms of history, if you wore an abacus on a necklace back in the day, you’d also technically be part of the wearable computing movement.
In a similar fashion, quantified self as a practice has been happening since time began. When Eve asked Adam, “Does this fig leaf make me look fat?” she was comparing herself to a previous measurement. If you’ve used pencil and paper to figure out your finances, that form of self-tracking also fits the bill (pun intended).
Quantified self (QS) is a term coined by Kevin Kelly and Gary Wolf of Wired in 2007. It refers to the idea of self-tracking, or “life-logging,” as well as the organization by the same name that helpscoordinate hundreds of global meet-up groups around the world. According to the group’s website, the community offers “a place for people interested in self-tracking to gather, share knowledge and experiences, and discover resources.” Wolf wrote a defining piece about the notion of QS in the New York Times Magazine in 2010. In “The Data-Driven Life,” Wolf describes how improving efficiency is not the primary goal for self-trackers, as efficiency for an activity requires having a predetermined goal. Trackers pursue insights based on data collected in real time, where more questions may develop as part of an overall self-tracking process. 2
This notion of collecting data with an unknown goal strikes most non-trackers as odd. In a world that typically rewards productivity above all, how could someone spend so much time measuring his or her actions with no set goal in mind? As with data scientists, self-trackers look for patterns in their actions to form insights versus approaching the data with hypotheses that could color the outcome of their findings.
Measuring your actions without a set goal in mind is hard. We’re trained to think that all of our actions must have a defined purpose resulting in improved productivity. I remember years ago working in a high-end café and getting admonished by my manager because she felt I was moving too slowly. She taught me how to look around the café and quickly assess multiple tasks that needed to be done based on walking clockwise around the room. The lesson stuck with me. To this day I still use this technique in my own kitchen, although the only patrons I need to take care of are my kids getting ready for school.
The downside of this type of harried productivity, however, comes in the toll it can take on your psyche. It’s very difficult not to gauge your success as defined by others, and the plethora of self-help guides touting increased productivity only adds to the stress.
We’re coming into a time, however, when the aggregation of our data will help us automatically become more productive. Analyzing patterns and offering recommendations based on behavior provide a huge increase in productivity and value via personalized algorithms (predictive computer equations based on past actions). Stephen Wolfram, a complexity theorist and CEO of Wolfram/Alpha, notes in an interview with MIT Technology Review that he
Need help defining your own vision? Check in with your data and see what revelations you have to offer yourself.
5
QUANTIFIED SELF
Wearable computing devices are projected to explode in popularity over the next year and, with a wave of new gadgets set to hit the consumer market, could soon become the norm for most people within five years. ABI Research forecasts the wearable computing device market will grow to 485 million annual device shipments by 2018. 1
ABI RESEARCH
W E ALL CURRENTLY have wearable computing devices in the form of our smartphones. Slap a piece of Velcro on your iPhone and wear it on a headband and you’re good to go. In terms of history, if you wore an abacus on a necklace back in the day, you’d also technically be part of the wearable computing movement.
In a similar fashion, quantified self as a practice has been happening since time began. When Eve asked Adam, “Does this fig leaf make me look fat?” she was comparing herself to a previous measurement. If you’ve used pencil and paper to figure out your finances, that form of self-tracking also fits the bill (pun intended).
Quantified self (QS) is a term coined by Kevin Kelly and Gary Wolf of Wired in 2007. It refers to the idea of self-tracking, or “life-logging,” as well as the organization by the same name that helpscoordinate hundreds of global meet-up groups around the world. According to the group’s website, the community offers “a place for people interested in self-tracking to gather, share knowledge and experiences, and discover resources.” Wolf wrote a defining piece about the notion of QS in the New York Times Magazine in 2010. In “The Data-Driven Life,” Wolf describes how improving efficiency is not the primary goal for self-trackers, as efficiency for an activity requires having a predetermined goal. Trackers pursue insights based on data collected in real time, where more questions may develop as part of an overall self-tracking process. 2
This notion of collecting data with an unknown goal strikes most non-trackers as odd. In a world that typically rewards productivity above all, how could someone spend so much time measuring his or her actions with no set goal in mind? As with data scientists, self-trackers look for patterns in their actions to form insights versus approaching the data with hypotheses that could color the outcome of their findings.
Measuring your actions without a set goal in mind is hard. We’re trained to think that all of our actions must have a defined purpose resulting in improved productivity. I remember years ago working in a high-end café and getting admonished by my manager because she felt I was moving too slowly. She taught me how to look around the café and quickly assess multiple tasks that needed to be done based on walking clockwise around the room. The lesson stuck with me. To this day I still use this technique in my own kitchen, although the only patrons I need to take care of are my kids getting ready for school.
The downside of this type of harried productivity, however, comes in the toll it can take on your psyche. It’s very difficult not to gauge your success as defined by others, and the plethora of self-help guides touting increased productivity only adds to the stress.
We’re coming into a time, however, when the aggregation of our data will help us automatically become more productive. Analyzing patterns and offering recommendations based on behavior provide a huge increase in productivity and value via personalized algorithms (predictive computer equations based on past actions). Stephen Wolfram, a complexity theorist and CEO of Wolfram/Alpha, notes in an interview with MIT Technology Review that he
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