Technical and organizational innovations such as Open Data, Internet of Things and Big Data have fueled renewed interest in policy analytics in the public sector. This revamped version of policy analysis continues the long-standing tradition of applying statistical
modeling to better understand policy effects and decision making, but also incorporates other computational approaches such as artificial intelligence (AI) and computer simulation.
Although much attention has been given to the development of capabilities for data analysis, there is much less attention to understanding the role of data management in a context of AI in government.
In this paper, we argue that data management capabilities are foundational to data analysis of any kind, but even more important in the present AI context. This is so because without proper data management, simply acquiring data or systems will not produce desired outcomes.
We also argue that realizing the potential of AI for social good relies on investments specifically focused on this social outcome, investments in the processes of building trust in government data, and ensuring the data are ready and suitable for use, for both immediate and future uses.