# Data-Driven Decisions in Smart Cities: A Digital Transformation Case Study
## 4 Analysis of Results
Remodeling the problem instance into a class of problems enables the conceptual migration of this problem case study into a generalized category. Researchers outlined the outcomes from the IT and organization perspectives to formalize learning. These summarized results were formulated as design principles (DPs), and further considered as improvements to the theory that contributed to the original design. This stage drew on one principle: (1) generalized outcomes. Although ADR generally assumes that reflection and learning occur continuously during BIE cycles, the conceptual formalization of learning is an activity of its own. Design research relies on the importance of design principles for the design of artifacts. According to [PERSON] et al. [17], the design principles of the artifact should be practice-inspired and theory-ingrained. In their paper, they also explained that design principles contribute to the literature in a specific domain; we, accordingly, posit that the design principles of BDA in SC will contribute to the literature of data-driven decisions, and to the literature on smart cities. In design research, the design principles could be thought of as the input to the artifact design, or could also be thought of as the output of the artifact in terms of design-generated knowledge. That has been reflected in a number of articles theorizing about design research. For example, [35] described the various types of IT artifacts at varying abstraction levels of knowledge contribution based upon the maturities of the problem area and the solution domain. Accordingly, a specific design research project could generate novel artifacts on one or more of three levels, as follows:  
* Level one: specific instantiations in the form of products and processes;
* Level two: general contributions in the form of nascent design theory (e.g., constructs, design principles, models, and technological rules;
* Level three: well-developed mid-range design theories about the phenomena under study. Lastly, design principles also contribute to the evaluation of the IT artifact as a possible output of design research [36]. In this subsection, we present the concluded design principles collectively as follows:  
* DP1 Data-Agnostic  
This principle is twofold. The first part is concerned with the resilience of the analytics module to digest additional data sources (e.g., historical data and data from OGANs). On the other side, it is concerned with the design of the dashboards with the highest level of abstraction and flexibility to integrate with a diversity of OGANs. The value of this principle emerged during the design of the data cubes and dashboards (BIE Cycle 1) to accommodate the diversity of OGANs and additional data sources that might be required. * DP2 Flexible Dashboards  
This principle is concerned with the flexible design of the dashboards with the highest level of integrated information content satisfying the diversity of consumers' requirements. The value of this principle emerged during the development of dashboards and interaction with the users; the more information the system provided to users, the more the complexity of their requests increased (BIE Cycle 2). Therefore, it was necessary to consider the development of dashboards and databases in a flexible manner that allows these requirements to be met in an acceptable time. * DP3 Direct Consumption without Intermediaries  
This principle pertains to the data analytics module's ability to present complex analytical outcomes to non-specialist consumers in meaningful business terms (BIE Cycle 2, 3). * DP4 Analytics to Insights  
This principle is concerned with turning the outcomes of data analytics into actionable business insights that influence data-driven decisions. In this context, actions might be either traditional human actions or machine actions in the form of embedded analytics (stage 3 of the ADR process--reflection and learning).