A few years ago, the notion of the “quantified self” was the domain of a relatively small group of hackers, engineers, and computer enthusiasts. Now, under its many names—lifelogging, self-tracking, fitness monitoring—it’s become one of the fastest growing segments of the technology industry, from Fitbits to the Apple Watch. Its tools are small computers that live in everyday devices: bracelets, phones, televisions, light bulbs. And its promise is a world where where we make better choices based on insights provided by the computation of large data sets. But to get to that point means confronting a future that many find disconcerting: homes and bodies integrated with machines that will track our movements, our heart rates, and our feelings.
This hour, we set out to understand and interrogate this phenomenon. Can “the self” actually be quantified? Should it be?
How Low Can the Philadelphia 76ers Go?
I felt as if I were looking in on a strange social-science experiment: Throw together a group of marginal, overmatched professional athletes and give them a shot at their lifelong dream. The results were both inspiring and heartbreaking. The Sixers dove headlong for loose balls. They habitually fell far behind at the start of games — 15 points, 20 points, more — but kept fighting long after most veteran teams would have quit. They went on long scoring runs, then fell just short of winning. They are one of the youngest teams in the history of the league, almost all of them in their early 20s. When management scheduled family events around the holidays, few of the players brought their wives, girlfriends or children. They brought their mothers.
.. While a basketball box score records the number of times a player steals the ball from the opponents, advanced analytics will also track how many plays he disrupts by deflecting passes or even just getting an offensive player to change directions. The aim is to use large amounts of this kind of data to get a truer picture of a player’s real worth.
Drill Down to Ask Why, Part 1
Suppose that you work for an airline as a fare planner.
.. Imagine five ways in which the fare planner might ask why. I’ll arrange these in order of increasing breadth and complexity:
- Give me more detail. Run the same yield report, but break down the high-level routes by dates, time of day, aircraft type, fare class and other attributes of the original yield calculation.
- Give me a comparison. Run the same yield report, but this time compare to a previous time period or to competitive yield data if it is available.
- Let me search for other factors. Jump to nonyield databases, such as a weather database, a holiday/special events database, a marketing promotions database or a competitive pricing database to see if any of these exogenous factors could have played a role.
- Tell me what explains the variance. Perform a data mining analysis, perhaps using decision trees, examining hundreds of marketplace conditions to see which of these conditions correlates most strongly with the drop in yield (explaining the variance in data mining terminology).
- Search the Web for information about the problem. Google or Yahoo! the Web for “airline yield 2008 versus 2007.”
Just provide me with a system that captures all the relationships in the data.
I was recently in a meeting with a disgruntled business user who glared at the DW manager and said “Just provide me with a system that captures all the relationships in the data.” If only that one sentence were sufficient for the poor guy to design a data warehouse.