Conclusion

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Author(s): Larissa Suzuki  and  Anthony Finkelstein
Source: Data as Infrastructure for Smart Cities,2018
Publication date December 2018

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.

Chapter Contents:

  • 11 Conclusion

Inspec keywords: Big Data; government policies; standards; sustainable development; systems analysis; smart cities; supply chain management; business data processing

Other keywords: requirements elicitation; closed-loop supply chain model; critical design issues; requirements balancing; sustainable data management; business model-driven approach; data strategies; reference architecture model; business models analysis; government; large interconnected systems; data infrastructures; digital strategies; dynamic business models; cost-effective data management solutions

Subjects: Data handling techniques; Economic, social and political aspects of computing; Business and administrative computing

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