Over the course of a Saturday in Cambridge, I create the following data simply by living and conducting my regular activities:
- How long I sleep and what time I wake up, based on when I set and turn my alarm on the previous night, and what time I finally stop the alarm in the morning
- News and public information I consume, given that I usually start my day reading the latest on Twitter, LinkedIn, and Apple News.
- Which friends I check up on and engage with, based on whose Instagram pictures and Stories I view and ‘like’, as well as WhatsApp and Slack messages I read and respond to
- Based on my email reading, sorting, and responses:
- Topics and senders I think are important by marking them with ‘star’ in Gmail
- Topics and senders I think are unimportant by immediately putting them in ‘Trash’ in my Gmail
- Topics and senders I am deprioritizing / procrastinating on by reading the message and then later marking as “unread” in Gmail
- Where I tend to spend my time, based on the location of my iPhone
- My exercise, since my phone must have tracked my 22-story walk up my apartment stairs when the elevator was down
- My spending patterns, after buying items and food throughout the day
- My eating patterns, after logging food consumption / calories in my MyFitnessPal app
- My television preferences, based on Netflix viewing in the evening
- My musical preferences, based on Pandora playlist listening, song ratings, and time spent listening throughout the day
- My attachment to my phone / anticipation of information from my regular checking of the lock-screen to see if a new text message appeared, or checking Instagram, Facebook, Slack, and Email to see if new messages appeared throughout the day
- My transportation movements and price sensitivity via Uber and Lyft checking, requests, and tips
- My movements around the MIT campus, via student ID card swipes and iPhone location tracking