Swiss statistical systems could be enhanced by big data

Using anonymized policyholder data from Swiss insurance company La Mobilière, EPFL scientists were able to predict a number of socio-economic indicators in 170 Swiss towns. This innovative approach could help increase the granularity and applicability of official statistics. A huge volume of digital data has been harvested, stored and shared in the last few years - from sources such as social media, geolocation systems and aerial images from drones and satellites - giving researchers many new ways to study information and decrypt our world. In Switzerland, the Federal Statistical Office (FSO) has taken an interest in the big data revolution and the possibilities it offers to generate predictive statistics for the benefit of society. Conventional methods such as censuses and surveys remain the benchmark for generating socio-economic indicators at the municipal, cantonal and national levels. But these methods can now be supplemented with secondary, mostly pre-existing data, from sources such as cell-phone subscriptions and credit cards. According to the FSO's  2017  Data Innovation Strategy ,  "The goal of data innovation is to enhance the quality, scope and cost-efficiency of statistical products and to reduce the response burden on households and businesses." Anonymized data Against this backdrop, a team of scientists at EPFL's Laboratory on Human-Environment Relations in Urban Systems ( HERUS ) conducted a ground-breaking study on novel uses for the data held by insurance companies.