In this article, we present an experiential perspective on how a big data analytics course was designed and delivered to students at a major Midwestern university. In reference to the MSIS 2006 Model Curriculum, we designed this course as a level 2 course, with prerequisites in databases, computer programming, statistics, and data mining. Students in the class were mostly seniors or at the graduate level, and had a strong technical and quantitative background. We include details of concepts covered in the course, as well as summaries of four major sample course assignments used. Some of the concepts covered include large-scale data collection and management using the Hadoop ecosystem, stream mining, visual analytics, and social network analytics. Besides Hadoop, the course also introduced various IBM and Teradata big data tools. We show how the course modules align with the intended learning goals and course objectives. A post-course survey indicated that the structure and organization of the course helped students clearly and concisely assimilate the course content.
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In recent times, business analytics and big data have gained momentum both in industry practice and academic research. The objective of this paper is to provide both a research and teaching introduction to business analytics in the context of both current and prospective perspective of the business analytics domain. It begins by providing a quick overview of the three types of analytics. To assist the future analytics professionals, we identify, group and discuss nine different participants of the analytics industry into clusters. We then include a brief description of some current research projects under way in our team. We also note some research opportunities in Big Data analytics. The paper also concludes with a discussion of teaching opportunities in analytics.
Implementing electronic health record (EHR) systems in the next few years is on the agenda for many healthcare organizations. Before investing in an EHR system, however, decision makers need to identify and measure the benefits of such systems. We propose using a system dynamics (SD) approach to measuring the benefits of EHR systems. Using an SD approach, it is possible to map complex relationships among clinical processes in hospitals into a model by which one can dynamically measure the effect of any changes in the parameters over time. Simulation of EHR implementations using an SD model produces useful data on the benefits of EHRs that are hard to obtain through empirical data collection methods. The results of an SD model can then be transformed into economic values to estimate financial performance. This paper presents an example of an SD model and its application in EHR decision making.
This paper reports on a panel discussion held at AMCIS 2014 and subsequent panel member research and findings. We focus on curriculum design, program development, and sustainability in business analytics (BA) in higher education. We address some of the burning questions the IS community has asked concerning the various stages of BA program building, and we elaborate challenges that institutions face in constructing successful and competitive analytics programs. Furthermore, given that the panelists have achieved outstanding accomplishments in academic and industrial leadership, we share our experiences and vision of a "dream" analytics program. We hope that our community will continue a dialog that encourages and engages faculty members and administrators to reflect on challenges and opportunities to build dream programs that meet industry needs.
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