Exploring Synthetic Data, 2021-2022

In the UK, administrative data has helped us gain a picture of public service users and their needs. But administrative data contain personal and sensitive information which is important to protect so that individuals can never be identified. However, the approvals and governance processes associated with accessing administrative data are extremely time consuming which can threaten the timeliness of research. The other time-consuming part of any research study using administrative data is understanding the structure of the data, and developing data cleaning and analysis plans. The time taken conducting final analyses is often comparatively short. Research timelines could be substantially reduced if there was a way to do these preliminary tasks, in parallel to applying for access to the real data on which final analyses would be conducted. This is where synthetic (or artificial) data could help. Synthetic data are artificially generated data designed to mimic real datasets, without containing personally identifiable information. Synthetic data also has a lot of potential for capacity building. It can be difficult to recruit researchers who have experience in using administrative data, in part because it is often impossible to grant access to these datasets to MSc students. If we could train students using synthetic data, we could really enhance the training we can provide to the next generation of data scientists. Synthetic data can: • Facilitate easier access to data for those who are generating hypotheses and developing tools • Prepare and train researchers for the practical challenges of working with national clinical datasets • Be used as pilot data (instead of real data) to strengthen research applications Being able to explore the datasets, understand what is available, and test code on the data can help streamline the research process, and enable researchers to make informed decisions and plan their research thoroughly, in a low-risk setting. Although the idea of synthetic data was introduced around 30 years ago, it is still not widely used, and synthetic versions of administrative datasets are not routinely available. One of the main barriers to wider use of synthetic administrative data is uncertainty about the level of fidelity that is required in the data. However, terminology describing synthetic data varies, which makes it difficult to communicate within the research field, and with data providers and members of the public. In an article published in the International Journal of Population Data Science (IJPDS), we provide a comprehensive overview of the main synthetic data generation methods in the context of UK administrative data research. We discuss the benefits and challenges, and propose simplified terms that would help data holders and data users familiarise themselves with the concepts of synthetic data. Using a consistent terminology should promote collaboration and engagement and allow effective communication of the benefits of synthetic data, to help build further acceptance and trust. Our workshop highlighted the clear value of synthetic data for a range of purposes. It also showed that the first step to demonstrating the value of synthetic data would be to facilitate the rollout of a number of low fidelity datasets for training and to consolidate and validate data methods for synthetic data. Demonstrating value with low fidelity datasets, which are not resource-intensive to generate, will pave the way for high fidelity datasets. For example, more research is needed to understand how best to replicate the longitudinal and high-dimensional nature of administrative datasets.