Data Log 2/21: A Day Observing My Data

In this day and age, it is very difficult to go through one’s daily routine in a networked economy without generating digital data in some way. Indeed, you almost have to go out of your way to consciously avoid certain activities, tasks, and behaviors that are routinely tracked and captured in digital form.

The following activity log illustrates both the frequency and breadth of the data that I generated in a single day.

  • 7AM: Wake up. My smartwatch senses that I am moving and provides a summary of my sleep.
  • 7:30AM: Turn on cable TV while eating breakfast, and select and save programs to watch later.
  • 9AM: Go on the internet and read news articles, check and send email, and update my calendar.
  • 11AM: Walk to MIT campus. My smartwatch provides a summary of my walk.
  • 1PM: Listen to music on my smartphone. I receive recommendations based on my listening habits.
  • 2PM: Order grocery delivery online and receive automatic email notification.
  • 2:15PM: Purchase books on Amazon and receive automatic email notification.
  • 2:30PM: Play guitar and record my playing on an iPad app, which sends the information to the Cloud.
  • 6:30PM: Exercise on treadmill, which tracks my workout (along with my smartwatch).
  • 7PM: Use CharlieCard to board the T and travel from Cambridge to Boston.
  • 7:15PM: Visit store and use smartwatch to purchase items.
  • 7:30PM: Visit convenience store and use credit card to purchase items.
  • 8PM Use Uber app on my smartphone to request a pickup to go home.
  • 10:30PM: Change thermostat setting in my apartment.
  • 11PM: Watch TV and update my saved programs.

The resulting activity log is interesting in several regards. First, since it captures only the activity that can be tracked digitally, it can result in an inaccurate portrayal of how one’s time is spent (or more generally, how a system functions or behaves). In my case, for example, I spent more than five hours during the day reading and studying, yet that activity was not captured digitally.

Second, the data that is generated can be categorized in many different ways: location tracking, motion detection, and transactional, for example. Data can also be captured for different purposes: health, entertainment, operational efficiency, convenience, surveillance.

A less obvious attribute of the data log above, however, is the degree of awareness associated with the capturing of data in each activity. Some behaviors (such as requesting an Uber ride) are active and require more consciousness and explicit consent about the data that is being tracked. Other activities (such as walking around in areas that have IP surveillance cameras) are more passive and subconscious with regard to data, and consent is usually implicit. In all cases, however, vast amounts of data are being captured digitally.