Towards trusted data sharing: guidance and case studies

Case study 2

CityVerve Manchester:

a platform of platforms for smart city data sharing

CityVerve Manchester is a smart city demonstrator. It uses Internet of Things (IoT) technologies to help Manchester City Council improve the way it designs and delivers services for the people who visit, live or work in Manchester. Projects focus on four themes: culture and public realm; health and social care; energy and the environment; and travel and transport. The ‘platform of platforms’ allows the city and other organisations to access multiple sources of data, with controls placed on how data is accessed and used. It enables the application of data analytics across domains and sectors, and creates the potential for the city and other organisations to develop business models around data and smart services.

Summary: the eight dimensions of data sharing

The opportunity:

Data from multiple sources is brought together to improve city services to benefit the city of Manchester and its inhabitants. A similar solution could potentially be applied to other smart cities.

The data and its use:

IoT data plus other sources. It has multiple uses across four themes: health and social care; energy and environment; travel and transport; and culture and public realm.

The business model and value creation:

Primarily, CityVerve is a demonstration of technology; however, the technology provides the tools and methods for future business models around services for the city and citizens. There is potential to monetise data via the platform.

The model for data sharing and the partnership:

Technology provides a common platform for finding and accessing data by the city authorities and third parties. A public and private sector partnership is led by Manchester City Council with Cisco as the main technology partner.

People with the right skills and expertise:

Technology expertise comes from primary technology partners and SMEs, alongside academic expertise in analytics and domains of application. City partners have domain expertise.

Constraints on how data is shared and used:

GDPR, regulations on sharing healthcare data and commercial sensitivity are all constraints to be considered.

The data architectures and technologies:

The ’platform of platforms’ provides a single way of accessing and connecting data from different sources. It sits above individual ‘data hubs’.

Governance / oversight / enabling trust:

Trust is enabled by security and by controls on access to data that accommodate the requirements of various data controllers. A collaboration agreement between R&D partners ensures they are all equally connected to the project. A data sharing ‘workpackage’ will be an output to the project.


CityVerve Manchester is a £10 million project funded by the Department for Digital, Culture, Media and Sport and Innovate UK, which ran between July 2016 and July 2018. The consortium of 21 organisations was led by Manchester City Council. Cisco was the lead industry partner, and worked alongside a number of other technology companies. Manchester Science Partnerships was the business engagement lead, and the University of Manchester was the project evaluator, among other roles.

Data-sharing initiatives already existed in Greater Manchester before the project was initiated, providing existing partnerships and a strong basis upon which to build the project. Two important components are Datawell, an innovative digital technology project that allows doctors and care providers to share patient and client information, and GM-Connect, a data-sharing authority launched in 2016 by the Greater Manchester Combined Authority, that aims to help break down the barriers that prevent public services sharing information. [30, 31]

CityVerve built on earlier smart city projects by improving the way in which smart applications are linked together, thus helping to break down silos between applications. The project developed a ‘platform of platforms’, a unifying layer that acts as a secure ‘catalogue’ and precludes the need to collect together different datasets from multiple platforms in one repository. Instead, the data is held on a series of ‘federated’ platforms. The ability to use data generated in multiple ways and apply analytics across domains and sectors was a key aspect of the project.

The project investigated a number of use cases in the areas of health and social care, energy and environment, travel and transport, and culture and public realm. The data collected was driven by the use cases, but further value was generated if that data was used by another use case or if other sources of data were used. Data from other smart city initiatives such as Triangulum was also exploited, as were other existing sources. [32]

For example, researchers involved in use cases on chronic obstructive pulmonary disease aimed to collect data about individuals’ symptoms and experience alongside environmental data such as weather data, with the aim of providing individuals with personalised feedback to help them to manage their condition. Under the energy and environment theme, sensors were retrofitted into buildings and linked to the building management system to analyse energy use and occupancy. Multiple energy-usage data sets across an area were brought together for a study on local area energy management. Cross-domain data analytics was applied by the University of Manchester, drawing on both analytics and domain expertise from Manchester Informatics, the university’s institute for digital research.

Individual data hubs were deployed by the various tech companies involved in the project. For example, the BT hub collected transport data. Another organisation, Asset Mapping, carried out a project to identify, monitor and visualise all city assets on a map, using a scalable database.

Other UK smart city projects include MK Smart (case study), Bristol Is Open and Future City Glasgow. [33, 34]

“New business models and forms of partnership are emerging whereby third parties are offering services to cities, taking on the risk and sharing profits with the city”

Business model

The main purpose of CityVerve Manchester was to demonstrate technology that could be transferred to other smart cities. Companies that were involved were interested in creating a demonstrator of reusable technology that they could then find markets for elsewhere. The focus of the project was therefore less about specifically developing business models.

However, the technology provides the tools and methods for new business models to become a reality in the future. It creates the potential for the city or other organisations to generate new business models around data or smart services. For example, new business models and forms of partnership are emerging whereby third parties are offering services to cities, taking on the risk and sharing profits with the city. Companies involved in developing individual data hubs will also be developing their own business models. In addition, the data has value for research organisations.

“Researchers involved in a project on chronic obstructive pulmonary disease collected data about individuals’ physical activity alongside air quality and weather data, to provide individuals with personalised nudges to encourage healthier behaviours”

Technical and data curation arrangements

Cisco’s platform of platforms sits above individual platforms or ‘data hubs’ to provide a single way of accessing and connecting data from different sources. The project addressed the challenge of building a technology that can scale - potentially incorporating an unlimited number of data hubs - and that can be deployed and managed easily and securely.

The platform incorporates ways to enable policies about data access and use to be enforced, for example if the data is commercially sensitive or personal. Individual data controllers specify the rules for data access and use, and the platform will be able to accommodate their various requirements. It will, however, be challenging to enforce policies around linkage and analysis of data. The platform will allow data controllers to restrict access to data if it is not being used in the agreed way.

The project explored capabilities such as data provenance tracking and auditing who has accessed data. A further capability that was explored was local data processing – where data is analysed close to sensors and actuators - allowing local autonomy and precluding the need to transfer data to and from the cloud. Three aspects of security were addressed: authentication, integrity and confidentiality.

The platform connects data from many different sources, but does not itself copy the data. A central challenge is how to retain control of data if it is copied and used outside the platform, which is common to many other situations beyond CityVerve. For example, it has implications for implementing the ‘right to forget’, one of the principles of the GDPR.

HyperCat has been mandated as the standard that underpins the ‘platform of platforms’ and the catalogue, which contains descriptions of what data exists and where it resides. The standard has been enhanced within the CityVerve project to address additional challenges, such as the introduction of a bi-directional capability that allows data to be written or modified as well as read - therefore enabling control of what is happening ‘in the field’ - and the ability to define real-time data streams of data.

A means of monetising data is being developed, so that if access to data is requested, it is possible to assign a commercial model to the request in an automated way. In future, it is real-time data generated by sensors that will generate most value, rather than static datasets held in databases. An ecosystem could potentially develop that includes data providers and those who sell solutions based on data.

Legal and commercial arrangements

Partners signed a collaboration agreement that ensured they were equally connected the project. The agreement addressed intellectual property, liability and governance of the project, data and other assets within the project. A specific part of the agreement was that there was no requirement for the partners to bring the solution to market together. Each partner has a different exploitation plan; private sector partners may be looking for future business; public sector partners are interested in the project’s legacy and citizen benefits. Big corporates are looking for branding and at developing new business models.

A data sharing work package within the project covered technical, ethical and social issues, including privacy. It was led by BT, and involved representatives from several interested organisations.

Sensitive data was subject to bespoke governance arrangements. For example, the chronic obstructive pulmonary disease data from the health study was not shared more widely.

“The project has changed the usual relationship between a public authority and private sector supplier from a customer/vendor relationship, towards a more collaborate partnership”

CityVerve Manchester – a smart city dashboard for combining and analysing multi-source data streams

Gledson, A., Ba Dhafari, T., Paton, N. and Keane, J., School of Computer Science, University of Manchester. Presented at Smart-City 2018 Conference, 28th June 2018

Outcomes and lessons learned

  • The ability to connect many different sources of data and break silos results in use cases and solutions that are very different from what is achieved in individual silos. The technology has the potential to be applied to any sector.

  • The project’s agile approach means that many aspects, such as the legal agreements and technologies, are being developed over the course of the project. SMEs have demonstrated innovation and creativity, and have introduced ideas that have enhanced the project.

  • The project has changed the usual relationship between a public authority and private sector supplier from a customer and vendor relationship towards a more collaborative partnership with the participation of SMEs. It requires the sharing of risk and different ways of working. It is vital for ensuring that rapidly-evolving technology can be developed and applied in an optimal way.