Monitoring national development plans and the 2030 Agenda on Sustainable Development requires high quality data, broken down by socio-economic groupings and with a strong spatial dimension. This is difficult even for the most developed statistical systems. It is increasingly recognised that traditional statistical approaches have to be complemented by exploiting new, innovative data sources to “[…] increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts”.

As an example and a category of new data sources, Big Data - defined as traces of human actions picked up by digital devices - has come to be seen as one solution to fill data gaps and provide more efficient, timely and detailed data. Big Data is becoming increasingly relevant as it represents a huge change to the world of official statistics.

Many efforts are underway to focus on how different types of big data (e.g., telecom data, social media, sensors, etc.) can be used to fill specific data gaps. Recent examples include the experimentation with big data sources such as electricity consumption, night time lights (from satellite imagery) and call detail records as proxies to estimate poverty levels.

PARIS21 specific activities include:

  1. Advocacy at the global level
  2. Fostering change at country level through national/regional events
  3. Developing inventory of innovations (PISTA) and piloting some of them in countries

Additional Resources

Discussion Paper: Access to new data sources for statistics: Business models and incentives for the corporate sector

new data sources

Read this PARIS21 Discussion Paper

Discussion Paper: Public-Private Partnerships for Statistics: Lessons Learned, Future Steps

ppp statistics

Read this PARIS21 Discussion Paper