This report showcases around 140 big data approaches to potentially assist traditional statistical methods in capturing and analysing data to support the calculation of SDG indicators and the achievement of SDG targets. The presented approaches also aim to replace costly occasional surveys of traditional statistics with cheaper real-time information.
The structure of the report is as follows: First, the SDGs are introduced with a focus on current challenges regarding lacking data as well as methodologies. Then an overview of big data, IoT and AI is given with a focus on categorization, opportunities and challenges.
The main section is dedicated to describing, classifying and linking the aforementioned approaches to suitable SDG indicators and targets. Benefits, risks and potential recommendations for pilot projects are discussed per big data category. This is followed by a summary of the key findings, an analysis and the conclusion.
The JUDS report titled, ‘The Frontiers of Data Interoperability for Sustainable Development,’ which was released in November 2017, presents five principles for promoting the interoperability of data: using and reusing existing standards; not overlooking metadata; using common classifications wherever possible; publishing data in machine-readable formats; and ensuring that data standards are user-driven. It also makes recommendations for structuring the work of the Collaborative on SDG Data Interoperability, a partnership that was established between the UN Statistics Division (UNSD) and the Global Partnership for Sustainable Development Data (GPSDD) in follow-up to the first UN World Data Forum (WDF), in January 2017.
The JUDS report emphasizes that the Collaborative can play a critical role in advancing the agenda for interoperability, by providing coordination among data communities and the broader development community. JUDS is a collaboration between the NGO Development Initiatives and the Publish What You Fund campaign, and it was created in response to a WDF call to modernize statistical methods and promote data interoperability.