Big data analytics, resource orchestration, and digital sustainability: A case study of smart city development

Highlights

• Data orchestration for data applications in a successful smart city were analyzed.

• A framework identifying three phases of smart city development was developed.

• Each phase develops a different capability and tackles specific data issues.

• Data orchestration involves sourcing, processing, and utilizing various data sources.

• Each phase requires a different approach: integration, co-creation and revolution.

Abstract

Smart cities are expected to improve the efficiency and effectiveness of urban management, including public services, public security, and environmental protection, and to ultimately achieve Sustainable Development Goal (SDG) 11 for making cities inclusive, safe, resilient, and sustainable. Big data have been identified as a key enabler in the development of smart cities. However, our understanding of how different data sources should be managed and integrated remains limited. By analyzing data applications in the development of a sustainable smart city, this case study identified three phases of development, each requiring a different approach to orchestrating diverse data sources. A framework identifying the phases, data-related issues, data orchestration and its interaction with other resources, focal capabilities, and development approaches is developed. This study benefits both researchers and practitioners by making theoretical contributions and by offering practical insights in the fields of smart cities and big data.

Introduction

As the urban population surges, cities worldwide are increasingly challenged to improve their efficiency and effectiveness in managing urban issues such as public services, public security, transportation, natural resource utilization, environmental protection for sustainable development, and disaster management (Albino, Berardi, & Dangelico, 2015; Kuk & Janssen, 2011; Wang, Medaglia, & Zheng, 2018). These issues call for transformation in the existing pattern of city management (Liu & Zheng, 2018). The concept of “smart cities” responds to this need by focusing on innovative solutions for urban issues through the adoption of information and communication technologies (ICTs) (Chong, Habib, Evangelopoulos, & Park, 2018; Gil-Garcia, Pardo, & Nam, 2016).

Building on the capabilities of ICTs, smart cities not only aim to satisfy citizens' needs and demands at the local level but also to advance city development by facilitating service integration and citizen interactivity (Belanche-Gracia, Casaló-Ariño, & Pérez-Rueda, 2015; Nograšek & Vintar, 2014). Many cities and metropolises worldwide have embarked on smart city development, including Amsterdam (Capra, 2016; Zygiaris, 2013), Barcelona (Bakici, Almirall, & Wareham, 2013; Gascó-Hernandez, 2018), Milan (Gascó, Trivellato, & Cavenago, 2016), and Seoul (Lee, Hancock, & Hu, 2014). These smart cities harness ICTs for urban transformation, such as smartcards for public transportation and recreation (Belanche-Gracia et al., 2015), smart grids for energy management and sustainability (Albino et al., 2015; Giest, 2020), and artificial intelligence applications for public healthcare (Pee, Pan, & Cui, 2019). In the United States, California has undergone an ICT-driven transformation to improve traffic flow and upgrade aging water, sewer and electric infrastructure. The city also launched the “Smart Capital” project to enhance business, local government, and community through the use of Internet resources to reduce digital divide (Nam & Pardo, 2011; Pee, Kankanhalli, & On Show, 2010). Although the specified objective of different smart city initiatives varies (Kar, Ilavarasan, Gupta, Janssen, & Kothari, 2019), they share a common core of improving the living environment and the quality of life for citizens (Axelsson & Granath, 2018).

Big data have been identified as a key in the development of sustainable smart cities (Matheus, Janssen, & Maheshwari, 2020) by offering “the potential for cities to obtain valuable insights from a large amount of data through various sources” (Hashem et al., 2016, p. 748). Effective utilization of data is expected to enhance public services (Al Nuaimi, Al Neyadi, Mohamed, & Al-Jaroodi, 2015), reduce costs of management, and optimize resource consumption (Batty, 2013). Nevertheless, our understanding of how different data sources should be managed and integrated to support the development of smart cities remains limited, as Hashem et al. (2016, p. 756) stated: “the vision of the smart city is to integrate such a large amount of data from multiple sources; data integration within the smart city is one of the important challenges to be addressed.” Managing data from different sources is more than a technical challenge of ensuring data quality – it also demands attention to socio-technical issues such as political power and privacy. For example, the privilege of access to data by different individuals or political groups in different powers or positions must be taken into consideration and addressed carefully (Al Nuaimi et al., 2015). Accordingly, the research question addressed in this study is: How can different data sources be orchestrated to facilitate the development of a smart city?

To address this question, this case study analyzed data management in Wuhu, a prefecture-level city in China, with the resource orchestration perspective as the initial theoretical lens. Wuhu is one of the earliest smart cities in China and has since developed various data applications for e-government services, urban management, community development, and tourism. The variety of data utilized to make the city smarter makes it an exemplary case for understanding the management of different data sources. The theoretical lens of resource orchestration provides a useful conceptual basis for peering into the black box of various data applications to examine the different types of data involved as well as how they can be organized to create value (Cui, Pan, Newell, & Cui, 2017; Sirmon, Hitt, Ireland, & Gilbert, 2011).

The paper is structured as follows: The next section provides an overview of the conceptual background. This is followed by an explanation of the research design, including case selection, data collection, and data analysis approach (Section 3). Section 4 details the case in terms of the three phases of development, while Section 5 identifies the data orchestration processes. Section 6 discusses the contributions for research and practice, and Section 7 concludes the study.