The Good, the Bad, the Clunky: Improving the Use of Administrative Data for Research

INTRODUCTION: Administrative data arising via the operation of public service delivery systems hold great benefits for citizens and society by enabling new research questions to be addressed, providing they can be made available in a safe, socially acceptable way. In recognition of this potential, the UK Administrative Data Research Network was established in 2013 to enable new research for public benefit. However, there are considerable challenges to be overcome for effective data use, and many of these are common to administrative data enterprises in general. Using this network as a practical case study, we set out to explore the issues and propose how to share the ‘good’, suggest solutions to the ‘bad’, and improve the ‘clunky’ issues, to lead to improvements in administrative data use. METHODS: A qualitative survey representing the data use pathway was carried out across the network, followed by a workshop to discuss the summarised findings and make further suggestions. This led to a set of recommendations to inform the development of an action plan for implementation. RESULTS: The survey respondents (N=27) and workshop participants (N=95) comprised multi disciplinary staff from across the network. The responses were summarised by consensus of three researchers and grouped into six areas: A) Data acquisition pathway; B) Approval processes; C) Controls on access & disclosure; D) Data and metadata; E) Researcher support; and F) Data reuse & retention, leading to an embedded set of 18 recommendations. Key developments promoted by this study were the development of themed research partnerships to progress data acquisition, and a policy of data retention and reuse for research. CONCLUSIONS: The network has broken new ground in using administrative data for research. This study informed the development of an evidence-based action plan to address many challenges in the effective use of administrative data. It represents a practical worked example, and the learning is widely relevant to enterprises working with administrative data across the world.