I'm a father of two young children, an Enterprise Architect at Intuit (the makers of Mint.com, TurboTax and many other great products) and obsessed with data. I have always been interested in basing my decisions around hard fact, even though I rarely had hard facts to base them on.
I started really thinking critically about this when evaluating the switch to compact florescent bulbs. At the time, I had stumbled upon a deal for Insteon home automation light switches that I could not pass up. These switches act just like a normal switch with two exceptions. First, you can control them remotely via a smartphone, computer a dedicated remote or even another switch. The second and perhaps more important feature is that when these switches are flipped, they send out a signal saying so. The home automation package I was using (Indigo) logs these events. Some very quick data integration would result in knowing when and how long each device controlled by one of these switches was on or off.
First, I immediately wanted to know which lights were lit most often so I could prioritize my personal CF replacement program. As I was researching which CF bulbs to purchase I learned that there is a multitude of factors that effect bulb life. Everything from ambient temperature, power cycles, cycle duration, cycle duration in relation to ambient temperature, individual component quality, fixture quality and even power quality. I read and have since learned that that dirty power kills CF bulbs and their delicate integrated ballasts. Turning a CF bulb off before it is warmed up can shorten life. I also read an interesting article on how low quality CF bulbs actually produce resonant feedback (due to their running at 10,000-20,000Hz) that can be measured at the power substation. Cheap bulbs are actually affecting the power quality of neighbors and was enough of a concern with the EE interviewed in the article that he was evaluating it's long term effect on the power grid.
All of these factors made me realize that there was benefit in not only assessing which lights I use most often, but also ongoing metrics around bulb life. Simply adding some quick data about when you change a bulb and the make and model gives us correlated data for cycle count, cycle duration, total duration across the life of the bulb. Consumers would be interested in which bulbs have the longest life in real world conditions. Bulb manufacturers would love to have field samples of millions of bulbs as well. Power companies might spot power quality issues in the last mile where they don't necessarily have monitoring equipment. This data becomes valuable.
I added a watt meter to my breaker panel and now I can measure the actual amount of power that 11w CF bulb is drawing over it's life. Does it live up right out of the box? Does it increase it's power draw over time? The same folks would be interested in this data. More data, more value.
Seeing how this very simple data increased value when correlated was my original inspiration. I quickly turned the lens on myself and how I might find value other aspects of my life. I began measuring my biometric data. Once I discovered the Quantified Self movement, I quickly added measurements for productivity, food and water intake, location, caffeine, pharmaceuticals and supplements, sleep and much more.
Since 2009 I have been collecting data about myself and my surroundings. The things I measure have waxed and waned. I have learned a little and can't wait to learn more.