This paper details the anticipated impact of synthetic "big" data on learning analytics (LA) infrastructures, with a particular focus on data governance, the acceleration of service development, and the benchmarking of predictive models. By reviewing two cases, one at the sector-wide level (the Jisc learning analytics architecture) and the other at the institutional level (the UvAInform learning analytics project at the University of Amsterdam), we explore the need for an on-demand tool for generating a wide range of synthetic data. We argue that the application of synthetic data will not only accelerate the creation of complex and layered learning analytics infrastructure, but will also help to address the ethical and privacy risks involved during service development.