资源环境科技发展态势分析平台

In September 2015, the world committed to the 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDGs), which have broad and interconnected social, economic, environmental, and governance aspects. The SDGs constitute a universal call to action with countries setting forth 169 specific, time-bound, and quantifiable targets for 2030 to leave no one behind (United Nations 2015).

Monitoring the 17 SDGs and the concomitant 169 targets have created new and huge demands on national statistical systems (NSSs). NSSs will have to report on more than 230 indicators in the official SDG indicators framework, with many of these indicators requiring disaggregation by location, sex, age, income, and other relevant dimensions.

Key points

• The Sustainable Development Agenda aims to leave no one behind. From a data collection perspective, it entails enhancing current systems to gather adequate information about different population groups, especially the small and vulnerable segments of society. Monitoring the SDGs requires better, more granular data to be available faster. To meet this challenge, national statistical systems should capitalize on new data sources, particularly big data.
• The readiness of national statistical systems to harness big data and other innovative data sources depends on several factors, including hardware and software requirements for storing, examining, and visualizing big data. Official statisticians need to strengthen their skills in analyzing unstructured, unfiltered, and complex collages of data points collected for distinct purposes and which may not have clear target populations.
• Using big data is not just about the actual data, but also about the data ecosystem, i.e., the need for partnerships, frameworks, and communication strategies. Harnessing big data requires research to determine what does and does not work, capacity development in national statistical systems, and stronger partnerships and frameworks to sustain more timely, granular, and meaningful statistics.