# Data-Driven Decisions in Smart Cities: A Digital Transformation Case Study  
[PERSON]  
1 Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology,  
971 87 Luleal, Sweden; [EMAIL_ADDRESS] (A.A.E.); [PERSON] (A.S.)  
1  
[PERSON]  
1 Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology,  
971 87 Luleal, Sweden; [EMAIL_ADDRESS] (A.A.E.); [PERSON] (A.S.)  
1  
[PERSON]  
1 Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology,  
971 87 Luleal, Sweden; [EMAIL_ADDRESS] (A.A.E.); [PERSON] (A.S.)  
1  
Footnote 10:  
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Although the relation between SCs, BDA, and decision-making has been studied thoroughly in the literature [7; 8; 9; 10], research articles that address the tangible experiences from actual case studies are still required. Tackling this scarcity of case study research is the motive of the present research, which entails addressing the interrelationship between the three principal pillars: SCs, BDA, and decision-making. Filling this gap will shed light on the challenges and design principles that should be considered when designing a BDA artifact in the domain of smart cities. In addition, it helps to reveal how BDA can contribute to the decision-making process.  
Although the human brain is a powerful organ capable of processing reasonable amounts of information, it suffers from a tendency to rely on previous experience and intuition (cognitive bias) [15; 16]. This is one of the traditional problems that influence the efficiency of the decisions taken by decision-makers. A modern trend to overcome this problem is the use of BDA to support decision-makers. BDA contributes to providing decision-makers with latent relationships and facts that cannot be deduced easily by the human mind. This helps the decision-makers make effective decisions based on realistic analytical foundations. In this article, we investigate how the exploitation of BDA, as a data-driven decision-making enabler, influences the efficiency of decisions. Research beneficiaries include strategic decision-makers such as mayors, municipal councils, planners, and citizens.  
The research question that drives this article is how BDA, as a data-driven decision-making enabler, influences decision-making in smart cities. To answer the research question, we opt to develop ensemble software artifacts in the form of flexible monitoring dashboards. In this context, we follow the design science research approach proposed in [17]. Ensemble artifacts are defined as \"_the material and organizational features that are socially recognized as bundles of hardware and software_\" [17; 18]. The authors in [19] presented a good example of developing ensemble artifacts in the field of unit load device (ULD) management and planning. In light of the outcome results of the design step, we investigate the impact of utilizing BDA on the efficiency of decision-making by developing and implementing a decision-support tool for an actual national digital transportation project in Egypt.  
The rest of this paper is structured as follows: the related work follows this Introduction. Section 3 is a comprehensive section presenting the methodology, material, and methods. Analysis of results is presented in Section 4. Section 5 is the conclusion, followed by a discussion of limitations and future research directions in Section 6.