To implement a smart city agenda, city leadership must draw on expertise from diverse actors. Guidance and clarity are needed on a wide range of issues, including the collection and management of data, data privacy, technology procurement, collaboration with the private sector, and increasing public participation in smart city projects. Local governments can reconcile these issues and fill existing knowledge and resource gaps through multi-stakeholder collaborations with universities, national public organizations, and private companies.

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With more than two-thirds of the Sustainable Development Goals (SDGs) off-track, high-quality, timely data to measure and monitor progress is more important than ever before. And to collect and produce this data, strong national statistical systems are needed. However, the SDGs present a complicated monitoring challenge for national governments. Traditional data sources, such as official censuses and surveys, are often outdated and/or lacking data, which creates gaps in SDG reporting. As such, non-traditional data sources, including big data, citizen science, and Earth observation (EO) are becoming increasingly important to complement official statistics. Adopting these innovative data sources will improve SDG monitoring, reporting, and progress, and will lead to better informed, data-driven decision making.

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“You need to be ahead of what your user requires,” stated Macdonald G. Obudho, Director general of KNBS during SDSN TReNDS and Open Data Watch’s UN World Data Forum Event. “If you do not know what your users require, you risk producing data that does not satisfy their needs.”

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With the rise of ChatGPT and a slew of other AI advancements, discussions around AI have been ubiquitous in the news, popular culture, and across the global development community. While there are a number of ethical challenges that AI brings into question, the technology offers great potential for positive societal impact in the field of sustainable development. In particular, AI combined with traditional data sources allows for significantly more granular, quality, lower-cost, less resource-intensive, and timely data than ever before to improve decision-making and planning.

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