When it comes to providing services for the most vulnerable people in society, the goal is better prevention and early intervention, rather than later reaction.
Through the Catalyst Project, the University of Essex helped Essex County Council (ECC) and Suffolk County Council (SCC) use cutting-edge scientific methods to predict risk, evaluate services and better target valuable resources.
Funded by the Higher Education Funding Council for England (HEFCE) in 2015 and monitored by the Office for Students (OfS), the Catalyst Project involved the University’s cross-disciplinary expertise in data analytics, big data and evaluation and supported both county councils, as well as other local partners such as Essex Police, to assess risks for vulnerable members of the community and provide evaluation techniques to fully understand the impact of council initiatives.
Through the Catalyst Project, our Essex researchers were able to apply their knowledge and skills to “real life” problems presented by the public service partners. This proved to be an invaluable opportunity for all and enabled the University to show how academic research could contribute and have impact on public life.
A wide range of training events and seminars on predictive analytics, evaluation and analytical software were delivered during the Catalyst Project, which provided a foundation for the University’s Business and Local Government Data Research Centre’s training programs and data science programs for the public sector.
Although the Catalyst Project ended in 2020, a continued close working relationship on data analysis between local authorities and the University has been guaranteed through their collaboration in multiple initiatives, such as the Essex Centre for Data Analytics (ECDA) – an innovative partnership between the University, ECC, and Essex Police to use data analytics as a means of understanding, predicting and subsequently reducing risk.
The relationship nurtured by the Catalyst Project between these local authorities and the University has provided an excellent example of how to organise data science collaboration that will serve as a reference in the future and a precedent of success.