Data as Infrastructure for Smart Cities
2: Tech London Advocates Smart Cities
This book describes how smart cities can be designed with data at their heart, moving from a broad vision to a consistent city-wide collaborative configuration of activities. The authors present a comprehensive framework of techniques to help decision makers in cities analyse their business strategies, design data infrastructures to support these activities, understand stakeholders' expectations, and translate this analysis into a competitive strategy for creating a smart city data infrastructure. Readers can take advantage of unprecedented insights into how cities and infrastructures function and be ready to overcome complex challenges. The framework presented in this book has guided the design of several urban platforms in the European Union and the design of the City Data Strategy of the Mayor of London, UK.
Inspec keywords: information management; government data processing; critical infrastructures; knowledge management; smart cities; business data processing; government policies
Other keywords: smart cities; city data management; business models; urban intelligence; services innovation; data infrastructures; reference architecture framework; business strategies
Subjects: Economic, social and political aspects of computing; Business and administrative computing; Information services and centres; General and management topics
- Book DOI: 10.1049/PBPC023E
- Chapter DOI: 10.1049/PBPC023E
- ISBN: 9781785615993
- e-ISBN: 9781785616006
- Page count: 313
- Format: PDF
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Front Matter
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1 Introduction
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This book takes smart cities design from a broad vision to a city-wide collaborative and consistent configuration of activities. Its purpose is to improve the quality of cross-domain city service management through improving the selection and definition of data infrastructures, speeding decision-making process, readying the city about exploiting data and digital services and informing implementation and data infrastructures and service operations.
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Part I Smart cities and data infrastructures
2 The evolution of urban intelligence
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Studies on the future of technologies applied in urban spaces along with the notion of global city wired society and network society have triggered the creation of a new field of activity which comprised professionals working at the intersection of “people, place and technology”. Digital technologies offer opportunities to help cities achieve sustainable development. This new field of activity has examined issues such as cultural, spatial and socio-economic repercussions and relationship of information and communication technologies (ICTs) and cities, local innovation systems and electronic applications (e.g. e-government, GovTech start-ups and incubators), and the movement of digital to cyber to wired to intelligent to smart cities. The article provides a historical background on the role that technology innovation has played in re-shaping cities and economies, and how it impacted the development of cities using the knowledge framework.
3 Smart cities
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The unprecedented speed in urbanization along with the current economic crisis and the ageing infrastructure of cities has stretched cities to the limit which are suffering to provide basic urban services. Cities of today with high density of population will have several challenges not experienced so far and will need to be organized in a strategic way to enable a sustainable economic growth and guarantee a certain prosperity. Scholars have outlined the growing demand for a more sustainable, efficient and liveable model in urban development. The concept of smart cities has emerged due to increasing interest in researches on the innovative socio-technical and socio-economic aspects of urban development, the physical-digital infrastructure to improve the quality of urban services and the mitigation of the effects of environmental change. The literature on the smart city is fairly new and some publications have theorized and described the usage of this concept. The article considers topics such as physical-digital integration, Internet of Things, closed-system approaches, open-systems approach and barriers in city data management.
4 The management of city data
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Cities find it tremendously difficult to specialize in all the competencies involved in designing, building and maintaining data infrastructures. However, cities have neither the incentive nor the means of bringing external partners round to the necessary supply chain networks. The consequence of not doing so is that complexity has often overtaken data infrastructures development. Often, it has become longer than a simple ICT project, and as a consequence, more expensive and difficult to design and maintain. A platform-centric approach for the provision of city data in smart cities enables pooling of multiple organizations' knowledge bases - especially cross-sectorial domains - that are more valuable in combination than in isolation. Building an ecosystem of stakeholders who complements the capabilities of a data infrastructure can potentially bring insights about specialized domains, different application markets, lower costs and shorten the time to market for the development of new services. We give an overview of the theory of platforms and their main characteristics. In an effort to move beyond this confusion, a growing literature on open data and data platforms has emerged, though what practical guidance it offers to governments is often unclear. Examining the prevailing strategy of data catalogues or platforms design is an essential starting point in understanding why a new approach is needed to integrating both technology and non-technology components more effectively into data management and business models.
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Part II The link between data infrastructures and business strategies
5 Services innovation and business models
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Rather than acting solely as implementers of initiatives, government initiatives must take social influence into account while maximizing the efforts of other stakeholders who are working towards the achievement of the same goal: to facilitate deep knowledge discovery and the creation of new valuable integrated services through the exploitation of rich, interoperable and engaging cross-domain city. Data infrastructures can provide many functions that transcend space (and time), break down the barriers to information access and enhance communication and collaboration. Thus, it enables people to have access to information that will enable them to innovate, to work better, to commute more efficiently in between places, enable governments to get insights on the urban services being provided anywhere and anytime they want. To put these principles into practice, our business models framework combines city data offering with a business model thinking to renew and extend common innovation and competitive strategies, and address intra- and inter-firm issues such as organizational change, value network design and innovation management. From a practical perspective, the main purpose of our framework is to allow governments to create, deliver and capture value through data infrastructures which are designed on the basis of social influence and not authority.
6 The business models framework
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Understanding of smart cities and their business models are important in studying how one can best provide value to its stakeholders. Our approach provided through a data infrastructure proposes to improve innovation, development, user engagement and stakeholders'collaboration of smart cities services. Service innovation is directly related to the business models that support these services. We argue that a one-size-fits-all approach to city data management transformation and simplistic approaches to engage stakeholders of city data are unlikely to work. Rather, we focus on the enabling processes and activities by which innovative use of technology and data in the context use of a city's inhabitants, alongside governance strategies supported by a strong value network of partners, can help deliver the various visions of data strategies for cities in more efficient, aligned and effective ways. Embracing the technology and non-technology components of data infrastructures will ensure that standards are adhered to, interoperability is guaranteed, smart governance is in place, a strong value network of partners are built, and feedback is facilitated. The use of our framework will help transform current data management practices and facilitate the creation of a data marketplace within both the public and private sectors facilitating the exploitation of city data in smart cities.
7 The reference architecture framework
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The requirements and goals of the platform for smart cities highlight that the supply chain must enable a data environment in which all stakeholders involved are able to co-exist and compete among them. Therefore, the closed-loop supply chain (CLSC) model is designed and managed to explicitly consider activities along both the forward and the reverse flow chain. Data can be produced, re-used, re-manufactured or even disposed. The interesting point here is to facilitate the `knowledge' created in smart cities to be infused back onto the system, and users are able to be part of the data processing and improve the platform through collaborative networking. The value chain analysis explicitly recognizes the interdependencies and profits cost efficiencies from the exploitation of linkages between value activities of a city. For instance, changes in the standard formats (one value activity) may significantly influence the activities involved in operations and outbound logistics (another value activity). These activities must be well co-ordinated if the change in standards is to be accomplished. Establishing new data standards without the platform having means to support it will affect the reliability of the platform. Another issue to consider is a service (an activity value) not be real-time available-to-promise/capable-to-promise and fulfilment or users' needs. Time-sensitive processes in a city, such as fraudulent operation, traffic flows, infrastructure monitoring, emergencies or tragedies, need high data/service availability in order to detect any problem before it actually happens. Value chain analysis and exploration provides a powerful tool for strategic thinking for creating a smart city with a sustainable platform.
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Part III Applied data infrastructures design
8 Introduction
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In the previous chapters, we have described the SMARTify approach to design data infrastructures for smart cities. SMARTify is a systematic business-model-driven framework, which guides the design of large and highly interconnected data infrastructures that are provided and supported by multiple stakeholders. The framework can be used to model, elicit and reason about the requirements of the service, technology, organization, value, and governance aspects of smart cities. One of the key objectives of the method we have developed is supporting the analysis and definition of business models, requirements and the design of a reference architecture for the data infrastructure. In the article, we provide examples of the applicability of our frameworks. The nature of decisions made when designing data infrastructures using our frameworks will fundamentally vary from cities to cities. This means that some business models'components or components of the closed loop value chain may not become relevant to decision makers, while others will find necessary to incorporate all SMARTify activities rigorously to both design new data infrastructure and take current infrastructures forward. We expect that different data infrastructure projects will use SMARTify in different ways simply because their problems and goals are different. As a result, the validation of the usefulness of the framework is subject to the context in which our approach is being applied.
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Part IV Assessment and evolution of data infrastructure design
9 The dynamics and evolution of business models
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In the article we discuss the relevance of service innovation in smart cities. We also discuss trends and drivers that stimulate the development of data infrastructure and define our core concepts. Our focus is on the design of data infrastructures for smart cities and their underlying business models. At the moment, there is no common framework for data infrastructure design nor is there a common framework for designing a common reference architecture for data infrastructure and their business models. We have tried to develop the first framework part of the SMARTify approach. We discuss the theoretical foundation for our business model approach, defining the business model concept as well as the core concepts and specifying the relevant concepts in the business models domains. We look at the interdependencies within as well as between the domains and discuss the core concepts from a design perspective, as well as the CDIs (critical design issues). We also explain in detail the viability and feasibility of business models by looking into CSFs (critical success factors).
10 Applied data infrastructures assessment
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The assessment of the business models viability is performed by systematically clustering the critical design issues (CDIs) and using them to assess and balance the requirement specifications elicited in the business models analysis. In the case any requirement specification negatively impacts a critical design issue, a requirements trade-off analysis must be carried out. For instance, consider the simplistic example in which a data infrastructure must support the federation of data from external data sources and at the same time satisfy pre-defined CDIs, such as Target Users (service), User engagement (service), Interoperability (technology), and Broaden Partnership (organization). On the one hand, increased content targets engage users with the data platform as well as increase the partnership with external data providers (contributing partners). On the other hand, federating data from other datasets significantly compromises data interoperability and requires the implementation of several mechanisms to mitigate semantic mismatch. Based on such arguments, this requirement should not be satisfied at the moment and revisited at a later stage when circumstances change. The article provides examples of how to trade off requirements against the CDIs, and how to validate business models against the pre-defined critical success factors (CSFs).
11 Conclusion
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This book concentrates on the definition of data infrastructures and their business models and components. The method employs a business model-driven approach to support the elicitation and modelling of requirements and data strategies, and a closed-loop supply chain model to serve as a reference architecture model for data infrastructures. By using critical design issues and factors, the positive and negative contributions that may occur among the requirements and specific design needs can be easily identified, as well as the final contributions of the data infrastructure to the realisation of smart cities. Our framework facilitates the requirements of elicitation process from business models analysis, and the detection of requirements mismatches across the five domains of the business models. It offers templates for requirements balancing and refinement which can be used to determine the trade-offs to be made during the design of such large interconnected systems. The dynamic business models' approach enables decision-makers to evaluate the evolution of the business models and how external factors may impact the several stages of the development process of digital strategies of cities. The closed-loop supply chain model can give government and their value network of partners the ability to better collaborate on the basis of a common and accurate reference architecture. As such, cities are equipped with methodologies that facilitate the design and delivery of sustainable, agile, evolvable and cost-effective data management solutions for smart cities.
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Back Matter
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