Big Data and personal data for behavioral analysis and behavioral change
In a broader article on Big Data and privacy, the New York Times writes about the work of Alex Pentland, a computational social scientist, director of the Human Dynamics Lab at the M.I.T., and academic adviser to the World Economic Forum’s initiatives on Big Data and personal data.
His M.I.T. team, writes the New York Times, is also working on living lab projects. One that began recently, the Mobile Territorial Lab, is in the region around Trento, Italy, in cooperation with Telecom Italia and Telefónica, the Spanish mobile carrier. About 100 young families with young children are participating. The goal is to study how much and what kind of information they share on smartphones with one another, and with social and medical services, and their privacy concerns.
The Mobile Territorial Lab (MTL) aims at creating an experimental environment to push forward the research on human-behavior analysis and interaction studies of people while in mobility. MTL has been created by Telecom Italia SKIL Lab, in cooperation with Telefonica I+D, the Human Dynamics group at MIT Media Lab, the Institute for Data Driven Design (IDA³) and Fondazione Bruno Kessler, and with contributions from Telecom Italia Future Center.
The data presents a valuable and unique source for investigating personal needs, community roles, phone usage patterns, etc. and for providing benefits to people in terms of personal, economic and social benefits.
MTL aims at exploiting smartphones’ sensing capabilities to unobtrusively and cost-effectively access to previously inaccessible sources of data related to daily social behavior (location, physical proximity of other devices; communication data (phone calls and SMS), movement patterns, and so on. The Mobile Territorial Lab (MTL) in Trentino aims at fostering mobile phone related research activities with real people on a very responsive territory. This include the involvement of a significant number of committed users with the goal of having a continuous and active user base to interact with and cutting down the experimentation setup costs. Not only.
A continued and active user base equipped with smartphones, enabling users to access (from everywhere) online services and to collect personal or contextual information from the integrated sensors, represents a valuable and unique sample for investigating new paradigms in the management of personal data.