Big Data and Policing
RUSI conducted research into the potential use of big data analytics by UK policing, providing insights into current limitations and priorities for expanding these capabilities.
The project explored the potential use of big data analytics by UK policing, and provided new insights into the limitations of the current use of data and the police’s priorities for expanding these capabilities. Primary research was conducted in the form of interviews with 25 serving police officers and staff, as well as experts from the technology sector and academia.
The project also involved convening a half-day workshop in London, bringing together representatives from five police forces as well as the Home Office, College of Policing and a number of academics. The workshop provided an opportunity to validate findings, generating an informed discussion on future technological developments, as well as ethical and legal considerations around the police’s use of data.
Aims and objectives
In recent years, big data technology has revolutionised many domains, including the retail, healthcare and transportation sectors. However, the use of big data technology for policing has so far been limited, particularly in the UK, despite the fact that the police is collecting vast amounts of digital data daily. There is a lack of research exploring the potential uses of big data analytics for UK policing, and this project was designed to enhance the evidence base in this area. Notably, the project identified specific ways in which big data analytics could enable UK police forces to make better use of the information they collect, allowing officers to act more efficiently and effectively.
Drawing directly from policing expertise and experience, the project offered an overview of the current state of police technology use in England and Wales, before exploring how big data could support policing at the operational, strategic and command levels. The project explored the range of limitations and challenges encountered in this area, and identified practical solutions.
Supported by
Unisys
This project was sponsored by Unisys.
Project output
Access the key publication for this project.
