Every day, 2.5 quintillion bytes of data are created. This data comes from digital pictures, videos, posts to social media sites, intelligent sensors, purchase transaction records, cell phone GPS signals to name a few. This is Big Data. There is a great interest both in the commercial and in the research communities around Big Data. It has been predicted that “analyzing Big Data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus”, according to research by MGI and McKinsey’s Business Technology Office. But very few people seem to look at how Big Data can be used for solving social problems.
Most of the work in fact is not in this direction. Why this? What can be done in the international research community to make sure that some of the most brilliant ideas do have an impact also for social issues? Big Data is clearly of interest to marketers and enterprises a like who wish to offer their customers better services and better quality products. Ultimately their goal is to sell their products/services. This is good, but how about digging into Big Data to help people in need? Preventing / predicting natural catastrophes, helping offering services “targeting” to people and structures in social need?
One motivation of our Lab is to encourage the international research community to work on Big Data problems that have a potential positive social impact for mankind.
The Frankfurt Big Data Lab and ODBMS.org cooperate with the Center for Entrepreneurship & Technology (CET) at UC Berkeley to enable the creation of project proposals for Big Data for the Common Good.
Creating opportunities for societal impact using Big Data
Frankfurt, Germany /Berkeley, Calif. – March 31, 2015– The Frankfurt Big Data Lab and ODBMS.org aim to support the creation of Collider project proposals for Big Data for the Common Good to be submitted to the Center for Entrepreneurship & Technology (CET) at UC Berkeley, and if selected implemented at UC Berkeley within the CET Collider context. more …