Connecting Data – Case Studies: Where are the success stories?

Connecting data: driving productivity and innovation, says that harnessing the power of data analytics – big data – and linking key datasets reliably in real time has immense potential to drive innovation and enhance UK productivity, which is currently lagging 17% behind the average across the G7 economies.

However, good practice is currently not widespread or consistent enough across and between each sector of the economy, and faster progress needs to be made to ensure rigorous performance and resilience. Here we present the case studies from the report.

The following provides examples where the use of big data and data analytics has brought benefits. These benefits are more broadly applicable across all sectors. The examples are based on detailed case studies that will be published separately from this report.

Transport - Modelling the UK’s rail network: ORBIS is Network Rail’s £330 million seven-year programme to create a detailed digital model of the UK’s rail network in order to improve the efficiency, cost-effectiveness and safety of the organisation’s asset management capability. Digital solutions are being developed to collect, join and exploit accurate asset data on the rail infrastructure. Geographical data allows an interoperable spatial model of the railway to be created containing information about how the assets are used, and their capability and performance. Successful implementation resulted partly from the involvement of on-the-ground maintenance staff in developing interfaces for collecting data using tablets and smartphones.


Transport - Operation of buses in Helsinki: A Helsinki-based bus company (Helsingin Bussiliikenne Oy) is using a data warehouse to combine data from its fleet of 400 buses, in order to improve cost-effectiveness, reduce carbon emissions and improve the service for users. The sensors monitor and analyse fuel usage and other data for each driver, route and vehicle, allowing mechanical problems to be identified, helping to improve individual drivers’ performance and increasing understanding of why accidents have occurred. Other cities are interested in applying this to their own bus services.


Built environment - Construction of Crossrail: Data analytics were used to improve the performance of infrastructure delivery during the construction of Crossrail. Existing buildings were at risk of damage from settlement and ground movements during tunnelling works, so instrumentation was put in place to monitor this. The application of novel data analytics techniques to data obtained from monitoring equipment, based on a collaboration between engineers and specialist data scientists, helped project managers to manage risk and reduce costs. Part of the success lay in effective visualisation of the data in a dashboard, allowing project managers to interpret and act quickly on settlement data.


Healthcare - An app for treating burns injuries: The Mersey Burns Application (app) for use on smartphones was designed to improve the assessment and resuscitation of patients following a burn injury, before the patient reaches a specialist unit. The app allows clinicians to shade the burn pattern onto the screen and generate detailed fluids protocols as well as send an email to a receiving burns unit. The greatest challenge was to develop a robust process that satisfied the regulator, MHRA. The application is the first UK healthcare app carrying a CE mark from MHRA.


Energy - Optimising performance in the energy industry: A tool has been created by Wood Group Intetech that helps oil and gas operators optimise performance. A global database of well failure data has been assembled, overcoming operators’ reluctance to share data by keeping sources confidential. The data allows operators to benchmark their performance with other operators, and identify where changes in operations or maintenance regimes are beneficial. This example demonstrates how sharing commercially sensitive data, albeit in an anonymous form, can bring benefits to the companies that participate.


Manufacturing - Monitoring the condition of industrial equipment: Finning is an international company that sells and rents Caterpillar equipment to a diverse range of industries and also provides customer support. It has developed a new service that allows customers to optimise their asset management through the proactive, holistic use of condition-based monitoring data, thus minimising customers’ owning and operating costs. Factors such as operator performance, site conditions and equipment history are reviewed in addition to component health, and possible future problems flagged up. The development of a dashboard for presenting information aids its communication, along with a bespoke customer portal,MyFinning, where regular reports at asset, site and fleet level can be accessed.


Learning from other sectors - Performance development for Williams F1 racing cars: Big data collected from F1 cars is vital for maximising performance. Effective use of data goes beyond aerodynamics to design, manufacturing and race engineering. Performance development teams rely on real-time data from multiple data sources including sensors, video-feed, on-car telemetry and simulations. Stringent F1 regulations limit the time and resource that teams are allowed to use to develop the car, both on track and in the factory. This puts a premium on extracting the maximum amount of useful information from the minimum amount of testing, requiring carefully designed experiments and efficient processes. A consistent method is needed to evaluate whether a new part or change increases performance according to the agreed development direction.


Learning from other sectors - Weather forecasting by the Met Office: Data used in numerical prediction models is obtained from myriad sources, including observation stations, radiosondes, aircraft and satellites. The challenge is to bring data into prediction models that vary considerably in timeliness, granularity and quality, and to process and store huge volumes of data. Weather forecasting benefits greatly from the agreement between national meteorological services worldwide to exchange data freely between them. The usefulness of meteorological data is broadened by using it in combination with other data sources, for example, combining wind data with information about leaf drop to predict when ‘leaves on the line’ are going to be a problem for the rail operator.