Reliable cross-section and longitudinal data at national and regional level are crucial for monitoring the evolution of a society. However, data now available have many new features that allow for much more than to just monitor large aggregates’ evolution. Administrative data now collected has a degree of granularity that allows for causal analysis of policy measures. As a result, administrative data can support research, political decisions, and an increased public awareness of public spending. Unstructured big data, such as digital traces, provide even more information that could be put to good use. These new data is fraught with risks and challenges, but many of them are solvable. New statistical computational methods may be needed, but we already have many tools that can overcome most of the challenges and difficulties. We need political will and cooperation among the various agents. In this vein, this chapter discusses challenges and progress in the use of new data sources for policy causal research in social sciences, with a focus on economics. Its underlying concerns are the challenges and benefits of causal analysis for the effectiveness of policies. A first section lists some characteristics of the new available data and considers basic ethical perspectives. A second section discusses a few computational statistical issues on the light of recent experiences. A third section discusses the unforeseeable evolution of big data and raises a note of hope. A final section briefly concludes.