The Path from Pilot to Product: Operationalizing Synthetic Data Decision-Making for Federal Statistics

Thomas M. Menino Convention & Exhibition Center 

Applied synthetic data research is having a translational moment. In the last few years, federal statistical agencies have produced numerous pilot synthetic data products such as those discussed in this session, both at individual agencies and through coordinated efforts like those of the National Secure Data Service. As synthetic data matures, and statistical agencies seek to transition these pilots into production, they will encounter a number of methodological and operational challenges. Barriers to adoption frequently stem from difficulties mapping technical methods onto specific synthetic data generation and evaluation problems, working within computational and personnel constraints, and even addressing whether synthetic data is the most appropriate privacy-enhancing technology for a given scenario. Drawing from my work as both as synthetic data open-source software developer and as a contributor to many of the projects within this session, I'll discuss some cross-agency lessons learned about how agency staff can most successfully navigate the frequently hidden decision-making burden that must be overcome to make high-value synthetic data products a reality.