Given the pressing need to fill data gaps to support monitoring the Sustainable Development Goals (SDGs), it will be vital to harness all promising innovations across the data ecosystem. It is therefore important to ensure that emerging global datasets derived from big data can be used for SDG monitoring, are developed alongside national capabilities and information systems, and do not aim to replace conventional methods.
In recent years, the potential for “big data” to support SDG monitoring has incited both enthusiasm and debate relating to its benefits and risks. Big data offers many benefits by increasing coverage, as well as efficiencies due to cost reductions and increased timeliness. However, these benefits come with a number of challenges and risks, including methodological problems, skills and capability gaps, sustainability and access issues, and data privacy and security concerns. Despite these challenges, there is a general consensus that the statistical community can greatly benefit from big data.
This has been demonstrated during the COVID-19 pandemic, which has disrupted traditional data collection efforts, placed additional pressures on NSOs, and resulted in new partnerships to leverage alternative data sources to bridge data gaps.