Free The Naked Future by Patrick Tucker Page A

the outcome of similar cases . . . A physician could have intuitions about a patient, but supplementing that
intuition with instantly available statistics
will likely result in fewer mistakes” (emphasis added).
    Though Stephen Wolfram is the seminal self-quantifier, he believes that personal data collection has to become both a great deal easier and more immediately rewarding before it becomes mainstream. The technology itself is no longer the problem. What’s lacking is expertise. We imagine computerized data to be neat and clean. But getting our data into a usable form isn’t as simple as just pressing the Returnkey.
    â€œThere’s all sorts of plumbing that has to be done,” says Wolfram, meaning that service providers need to make these services easier to use for everyone, not just computer geniuses. “If everyone used one vendor’s equipment, that would be a start. But that won’t happen. It will stay a complex, multiproduct, multivendor environment. This data [is] in computers; [it’s] in pedometers; [it’s] in lots of different places. First step is to make it possible to upload to somewhere. Maybe upload it to a cloud that’s shared but some people are too paranoid to do that. I don’t think there’s anything
technically
difficult about this. It just requires all sorts of
work
.”
    In the past couple of years, some enterprising start-ups have sprung up to relieve the amount of work involved in keeping track of signals, physical states, and so on. The United Kingdom’s Tictrac isa platform that allows you to take your data from different devices and sites and create a snapshot of yourself in the present. I spoke with founder Martin Blinder while the company was still beta testing. It’s since opened to the public and has been steadily gaining users. He knows that Tictrac will only succeed if it can offer personal analytics in a way that’s intuitive and user-friendly. It needs to be able to take your data and present you with a future prediction you couldn’t have reached yourself, and do it in a way that’s perfectly understandable at any given moment.
    â€œWe want to offer a breakdown of the food you’ve eaten in the last month, type of food and calories, and whatnot. But we also want you to be able to see analytics across different data sets, so you can pull in your calendar so you can see that you spend about twelve hours a week on business lunches—and then cross that with weight and find a correlation between the two,” he told me.
    The average Tictrac user has as many as ten thousand different points of data that users bring to the practice of lifestyle management. Those points include everything from Facebook posts to e-mails to GPS or fitness logs of physical activity from such devices as Fitbit, Nike+, and Blipcare. Some of his more exceptional users have twenty thousand data points. And the exceptional is quickly becoming the average. As a measure for how much personal data there is today versus ten years ago, only two digital data streams that Tictrac users build into the graphs were around prior to 2003: e-mail and calendar.
    Perhaps the most encouraging aspect of the Tictrac platform is that the number of data sources and possible insights is limited only by how much data can easily be collected through an API. With enough streaming data, it’s possible to see how all of your life areas interact, how overbooking appointments affects your exercise levels, how your communications with one person changes your drinking and sleeping or monthly expenses, even how what you eat influences your electricity usage. These sorts of data streams will grow as we integrate more sensing and broadcasting capability into more objects and our environment.
    Devices like the Q Sensor from Affectiva, which looks like a strange wristwatch, can measure your level of interest and engagement in a given activity based on