The phenomenon of digitalization has led to the emergence of a new term—big data. Big data refers to the vast volumes of digital data characterized by its volume, velocity, variety, veracity, and value. The accumulation of enormous amounts of digital data has encouraged academics to develop appropriate technologies and algorithms to manage and analyze these data in order to leverage the embedded relationships within the data to support decision-making. This approach has revolutionized the organizational strategies of most business areas by digitally transforming business operations and decision-making processes.

A “smart city” is a new concept that depends primarily on digitization and big data analysis. The aim of a smart city is to tackle the challenges of ever-increasing urbanization by utilizing atypical approaches. The utilization of big data analysis in smart cities has been investigated thoroughly in the literature from various aspects, such as those related to recommended technologies and the domains of applications. A smart city is a compound system with multi-domain attributes in which the citizens represent key participants in decision-making. However, harnessing big data analysis to support decision-making in the smart city context is rarely approached in academia. The infrequency of this type of research was sufficient to motivate this interesting research. Two research questions drive this thesis: RQ1: What are the challenges of utilizing big data analytics (BDA) to enable decision-making in smart cities? RQ2: What are the design principles of the BDA framework in the context of smart cities? 

To address these research questions, numerous research methods were applied, including a systematic literature review, design science research, use case, and case study. In addition, internationally acknowledged information systems databases were searched to collect quality scholarly articles and conference proceedings: ACM Digital Library, IEEE, SCOPUS, Springer Link, INSPEC, INSPEC, and Web of Science. A freely published dataset for experimental purposes on Yelp (www.yelp.com) was used for the use case experiment. Lastly, the case study was based on data from a national Egyptian digital transformation project called Nafeza.

The research findings revealed the need to introduce an inventive framework for exploiting big data analysis in smart city applications. The main contribution of this research is the proposal of a novel framework for utilizing big data analytics in smart cities. The proposed framework, the Smart Cities Data Analytics Panel (SCDAP), is a domain-independent big data analysis framework. It compiles the relevant design principles mentioned in the literature, particularly those that are distinctive to smart cities. The design principles of SCDAP are founded on the literature review, use case, and case study methodologies and are the main contribution of this research.

As the four papers that formed the foundation of this thesis combine theoretical and practical research, the contributions of this research can be of direct benefit to academic researchers in this field and practitioners of smart city projects.