We present an expanded framework for the use of business analytics in projects. To the commonly used descriptive, predictive, and prescriptive analytics, we add comparative analytics, wherein we compare the performance of systems under different interventions. This framework provides a conceptual roadmap for the implementation of business analytics projects. We then demonstrate this framework using recent operations research literature on analytics in healthcare, summarizing papers focusing on one of these aspects. Next, we discuss queue mining as an example of theory and practice illustrative of these aspects. We conclude there is room for further work by operations researchers and management scientists within business analytics projects generally and the healthcare industry more specifically. We argue future work should consider both theory and practice, especially within prescriptive analytics projects, where analysis through the lens of operations research and management science is imperative. We provide some thoughts on the current and future state of operations research and management science in business analytics.
KEYWORDSbusiness analytics, service operations, healthcare analytics, queue mining
FRAMEWORK FOR BUSINESS ANALYTICSRecent advancements in computing and analytics suggest that both the practice and theory of service operations have been dramatically changed and will continue to shift in the near and more distant future. The availability of data and efficient tools for visualization and analysis opens several possibilities for research and practice in service operations. Cohen (2018) presents a modern view of the impact of big data on service operations. Business analytics in service operation is very broad as it includes, for example, revenue management for services, shared economy, and post-purchase services.To improve our understanding of the different paths toward implementing analytics, we surveyed the literature on analytics in services. As this is a vast literature, we only discuss the literature on business analytics in healthcare operations, with a focus on work related to queueing in this sector. Importantly, the trends and approaches discussed are applicable to business analytics in many services, as well as in other sectors.A common theme of analytics is that it uses quantitative analytical tools, such as statistics, econometrics, computer science, and operations research, to analyze specific problems. Moreover, these analytic tools are used to analyze and model data. Finally, business analytics uses analytics to support decision making in businesses. Similar to the decision-centric definition of analytics in Rose (2016), we offer the following concise definition:• Business analytics: Usage of data-driven analytical methodologies to support decisions.
Defining descriptive, predictive, comparative, and prescriptive analyticsQuantifiable performance measures of different business objectives for different business units are often required to support business decisions. Therefore, every bus...