Towards data centric engineering

The Institute is a national centre to promote advanced research and translational work in the application of data science. Formed in 2015, our collaboration with the Institute has brought the world’s leading engineering brains together to ensure that humanity reaps the maximum benefit from ‘Big data’ and ‘the Internet of Things’.

Our involvement in Data-centric Engineering spans the full range of global infrastructure but its impact has been proven this year in a major, multi-party initiative aimed at protecting London’s economy and infrastructure during the Covid-19 pandemic. The Data-centric Engineering partnership was already working on an ambitious collaboration with the GLA (Greater London Authority) and TfL (Transport for London), working on the capital’s air quality, with additional support from Microsoft.

During 2020, working alongside a team of researchers from the universities of Warwick, Cambridge, and University College London, the team repurposed their existing models, infrastructure, and machine learning algorithms from the air quality work. We used similar techniques to understand how and when levels of ‘busyness’ were changing over the lockdown period. Microsoft were a key partner, providing Azure Cloud and AI services, and expertise, to the project.

Theo Damoulas, Deputy Programme Director for the Data-centric Engineering programme, is the Turing Institute’s lead on this project and Associate Professor in Data Science at the University of Warwick. Theo said, “The data, algorithms, and outputs from our research have the potential to act as an early warning system to trigger different interventions and more targeted policies. They can shed light into how the transmission of the virus is driven by human mobility, social interaction and social distancing across the city. We are delighted to be collaborating with our university partners, the GLA, TfL and others to provide valuable real-time insights to support planning for London’s managed emergence from the pandemic.”

Data is at the heart of London’s Covid-19 response and it is vital that data experts, public authorities and businesses pull together for the response to succeed. The London Data Commission is proud to contribute to the city’s efforts in dealing with the crisis and, in due time, the recovery plans that will allow London to flourish in the future.

Jasmine Whitbread, Chief Executive, London First

The Foundation’s involvement in data-centric engineering centres around the impact of developments in big data on the safety and performance of engineering assets and infrastructure, such as energy, transportation and shipping. With the Alan Turing Institute, we are looking to improve safety through advanced mathematics, computer science, algorithms.

Within the next five to 10 years we are going to witness step changes in sensor technology, data-driven intelligent systems, computer science and algorithms for data analysis, impacting all aspects of the business life-cycle - from design to manufacturing, maintenance to decommissioning.

Big data is going to bridge the gap from monitoring 'what is' to predicting 'what if'. The Foundation intends to become a major supporter of international research in the field.

Prof Richard Clegg, Managing Director of the Lloyd’s Register Foundation

The Data-centric Engineering programme has worked with some of the world’s leading academic institutions and over 30 major industrial partners from across the engineering sector. We’ve established new partnerships with BP and Sensat, the Oil and Gas Technology Centre and the charity of Guy’s and St Thomas’ Hospital. The programme has also seen the launch of a journal, Data-Centric Engineering.

The partnership has worked in four key areas:

1. Technology road-mapping: collaborating with the research community to forecast technology developments in data-centric engineering and plan and co-ordinate efforts.
2. Design for data: recognising that embedded sensors, intelligent systems and data management will form part of engineering design requirements.
3. Codes and standards: as more data is generated, collected, transmitted, stored and manipulated by engineering systems, there is a need for assurance of the quality, traceability, security and integrity of that data.
4. Data analytics: developing algorithms and mathematical models for data analysis, helping make informed decisions to enhance the safety, reliability and performance of assets and infrastructure.