PARIS21's Innovation Work on New Data Sources
Monitoring national development plans and the 2030 Agenda on Sustainable Development requires high quality data, disaggregated by socio-economic groupings and with a strong spatial dimension -- a task that can be difficult even for the most developed statistical systems. It is increasingly recognised that traditional statistical approaches must 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 noted in SDG 17.18. PARIS21 is supporting NSOs to find innovative approaches to benefit from new data sources and to thrive in the new data ecosystem.
In the past years, we have witnessed the rise of new data sources for the potential production of official statistics, ranging from administrative data and online survey data to citizens' digital footprints. As they are gradually proved to be able to fill data gaps and provide more efficient, timely and detailed data, new data sources are becoming increasingly relevant as they represent a major shift in the world of official statistics.
The COVID-19 pandemic has impacted national statistical offices in low-income countries (LICs) and lower-middle-income countries (LMICs) disproportionately, as 9 out of 10 NSOs in LICs and LMICs saw their ability to meet international reporting requirements affected. Using new data sources to complement traditional surveys can help these countries collect and produce data to facilitate evidence-based policy making.
Based on the nature of the data sources and their potential application, the new data sources for NSOs can be categorised as:
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New data sources within the NSS, such as administrative data from government agencies
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New data sources from non-government data providers, including:
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Private data providers
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Not-for-profit data sources such as academia, NGOs and citizen-generated data sources
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Many efforts have been underway to help NSOs use different types of new data sources to fill specific data gaps. Recent examples include 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. Several NSOs have been able to establish partnerships with private data providers to complement traditional activities.
In line with our Capacity Development 4.0 framework, PARIS21 has developed several workstreams to help NSOs enhance their capacity in utilising new data sources. Globally, PARIS21 is actively engaging in the UN working group on big data. Additionally, our work in citizen-generated data (CGD) proposes a working definition of CGD and an innovative approach for NSOs to use CGD for reporting purposes. At the country level, we piloted our CGD project in the Philippines. We are also supporting DANE Colombia to use data science skills to produce high-resolution population mapping to produce data on poverty and health. Our ongoing data science programme aims to help low capacity countries benefit from data science in a sustainable way.
Additional Resources
Discussion Paper: Access to new data sources for statistics: Business models and incentives for the corporate sector
Discussion Paper: Public-Private Partnerships for Statistics: Lessons Learned, Future Steps
