The Sustainable Development Goals (SDGs) suffer from a lack of national data needed for effective monitoring and implementation. Almost half of the SDG indicators are not regularly produced, and available datasets are often out-of-date. New monitoring approaches using big data are advancing rapidly and can complement official statistics to help fill critical data gaps. However, there is poor information-sharing on the latest innovations and research collaborations across different thematic areas, and limited evaluation of strengths and weaknesses for supporting national monitoring. This paper provides a systematic review of the academic literature over the past 5 years relating to the use of big data to support monitoring of the SDGs. It reviews the state-of-the-art research using big data and advanced analytics to produce new datasets, the alignment of these datasets with the official SDG indicators, the main types and sources of big data used, and the analytical methods applied. We developed a set of evaluation criteria and applied it to highlight some of the strengths and limitations of these datasets derived from big data. We find that recent research has developed a considerable range of new datasets that could contribute to monitoring 15 goals, 51 targets, and 69 indicators. Dominant focal areas of research include land and biodiversity, health, water, cities and settlements, and poverty. Satellite and Earth Observation data were the primary sources used, most commonly applied with machine learning methods and cloud computing. However, several challenges remain, including ensuring the relevance of new datasets for monitoring SDG indicators, cost and accessibility considerations, sustainability aspects, and linking global datasets to nationally owned monitoring processes.
- Big data
- Earth observation
- Machine learning
- Sustainable Development Goals (SDGs)