Evolving the Ecosystem of Personal Behavioral Data
Jason Wiese, Sauvik Das, Jason I. Hong, and John Zimmerman
Human-Computer Interaction
April 2017
Everyday, people generate lots of personal data. Driven by the increasing use of online services and widespread adoption of smartphones (owned by 68% of U.S. residents (Anderson, 2015)), personal data takes many forms including: communications and social interactions (e.g. email, SMS, Skype, Facebook), plans and coordination (e.g. calendars, TripIt, Basecamp, online to-do lists), consumption of entertainment (e.g. YouTube, iTunes, Netflix), personal finance and purchases (e.g. banking, Amazon, Zappos, eBay), activities (e.g. step counts, bike rides, check-ins), and even healthcare (e.g. doctor visits, medications, vital signs). Collectively, this data can provide a highly detailed description of an individual. Personal data affords the opportunity for many new kinds of applications that might improve people’s lives through deep personalization, tools to manage personal wellbeing, and services that support identity construction. However, developers currently encounter challenges when attempting to work with personal data due to its fragmentation across applications and services. This paper evaluates the landscape of personal data including the systemic forces that created current fragmented collections of data and the process required for integrating data from across services into an application. It details challenges the fragmented ecosystem imposes. Finally, it contributes Phenom, an experimental system that addresses these challenges, making it easier to develop applications that access personal data and providing users with greater control over how their data gets used.
