The Urban Big Data Centre (UBDC) and the Business and Local Government Data Research Centre (BLGDRC) jointly established the Social Analytics Strategic Network (SASNet) - a network focused on capacity building for social analytics of emerging diverse forms of data, including big data.
By social analytics, we refer to methods drawn from the data sciences - as well as social sciences - for analysing, visualising, simulating, modelling, and interpreting complex and novel data. These methods are used for knowledge discovery and understanding of complex social systems.
Aims and Objectives
The core work of the SASNet project, which ran from January 2016 to the end of September 2017, was to deliver training courses and workshops as well as advanced quantitative training in urban informatics for urban analysts and planners.
In particular, the project had three core objectives:
- Build a strategic network with key users, which focuses on big data and analytics needed to work with such data
- Introduce researchers to novel sources of data and related methods for their research, and for public and business organisations to develop an internal strategy around analytics that improves their decision-making
- Consolidate a body of knowledge that draws on the strength of existing ESRC centres and enriches the data infrastructure of the centres by using data for real-world teaching and workforce development.
We delivered a cohesive capacity-building programme focused on big data analysis and applications for business process management as well as urban planning and informatics. SASNet strengthened our ties with government, industry, and civil society organisations, helping us understand the potential uses of big data and the skills needed to realise these. This work also generated further insights and activity in the areas of big data, predictive analytics, automation, AI and smart urban futures.
Key findings from the project were:
- Significant differences exist between the level of the technical skills needed for many private sector activities in the transport and smart cities sector, and in the skill levels and ability of the public sector and local governments to use or adapt emerging forms of data and technology. Training in the government, SME and voluntary sectors in the transport and smart cities space can be helpful in the efficient and fair deployment of such technologies.
- There is a need for advanced training among postgraduate students, particularly in the areas of information management, automation, machine learning, and transport and urban modelling and simulations.
- While there is a common understanding that new analytics and modelling can help improve the productivity of operations, lack of training and general support to the middle management in SMEs may limit their capability to support these changes.
- As trends in AI, automation and big data continue unabated, it is essential to examine the theoretical and conceptual underpinnings of these developments for the governance of smart and intelligent futures from a social science perspective.
UBDC and BLGDRC jointly organised a set of user-focused seminars, webinars, lectures and masterclasses presented by academic and non-academic speakers. The objectives were to improve organisations' understanding of techniques for the analysis of a variety of forms of data, to share best practice around analytics, and to develop collaborative links.
The SASNet training programme took the form of workshops and computer lab sessions arranged into three tracks:
- Fundamentals of Data-Driven Solutions Including Real-time Analytics (led by UBDC)
- Real-time analytics and business process management (led by BLGDRC)
- Urban Informatics and urban policy and planning processes (led by UBDC)
At UBDC, the experience improved our understanding of how the public sector uses social analytics and has led to the development of a variety of collaborations including with Glasgow City Council, Transport Scotland and NHS-Scotland.
The SASNet Fellowship Programme was open to UK and international residents. It enabled academic and non-academic (business, industry and public policy) visitors to join the research community at either Centre. Fellows participated in the SASNet training and seminar series or brought a novel source of data or specialist technical method to the Centres.