Build Your Dream (not just Big) Analytics Program

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

Digital Service Innovation in Plant and Mechanical Engineering: Exploring Contextual Factors in the Innovation Process

In recent years, the transformation from pure product businesses to data-based service innovation in various industries has intensified. This paper extends previous studies on „servitization“, i.e. the transition from product manufacturer to service provider, focusing on digital service innovations. We develop an integrative model to examine the technological, organizational, and environmental context as prominent components of the initiation, adoption, and routinization of digital service innovations. Drawing on the Technology-Organization-Environment (TOE) framework, different factors are identified and then validated by conducting ten expert interviews regarding their relevance to the innovation process of digital services in the plant and mechanical engineering industry. The results strongly suggest that the general TOE framework needs to be revisited and extended to be used in this specific context. The extended TOE framework can serve as a basis for studying contextual factors in digital service innovations and guide managerial decision-making

Impact of MBA Programs’ Business Analytics Breadth on Salary and Job Placement: The Role of University Ranking

Although many business schools have started to offer business analytics programs and courses for their MBA students, they lack understanding about how these efforts translate into job market gains for their graduates and whether all business schools have a level playing field. To bridge this gap, we use signaling theory to investigate the impacts that the business analytics breadth (BAB) level and university ranking of MBA programs have on graduates’ future employment success in terms of salary and job placement. We collected and analyzed data on business analytics-relevant courses that the top 89 business schools in the United States according to Bloomberg (bloomberg.com) offered. Our findings show the vital role of university ranking in determining the efficacy of BAB to produce job market gains for students: university ranking moderated the effect of business analytics offerings on post-graduation salary and job placement. These findings provide interesting insights for researchers and business schools interested in understanding the return on investment in business analytics programs

Business Analytics Education: A Latent Semantic Analysis of Skills, Knowledge and Abilities Required for Business versus Non-Business Graduates

Market demand for business analytics (BA) professionals has been skyrocketing in recent years, but challenges arise in developing BA programs. In this study, we seek to uncover the key components of skills, knowledge, and abilities (SKAs) that employers require for this emerging profession by business degree and non-business degree (e.g., computer science, engineering, statistics, mathematics). We used Latent Semantic Analysis (LSA), a text mining technique, to analyze text data of BA position advertisement on LinkedIn, and adopted the educational framework of Bloom’s taxonomy as a sensitizing lens to interpret our results. Our analysis reveals differences in the SKAs for different education: Business graduates are expected to have SKAs in mathematics and BI technologies, while CESM graduates are desired to have business strategy and market knowledge. KSAs are also identified for BA positions that are open to any other academic degrees. Implications on BA program and curriculum design are discussed

A Business Analytics Maturity Perspective on the Gap between Business Schools and Presumed Industry Needs

Business analytics is a fast-growing job market for business school graduates. Hence, researchers have made many calls to enhance business analytics training in business schools to meet the growing market demand for analytics-savvy employees. A growing set of business analytics courses have begun to address these calls. In this paper, we examine the maturity of business analytics offerings in business schools in the United States by analyzing current business analytics-related course offerings of the top 104 business schools (363 courses) and 20 unranked business schools (51 courses) in the United States. We analyze these data by examining the types of courses offered and rank the schools based on their maturity levels in terms of business analytics offerings. Our findings indicate that, to the extent that these schools reflect what is happening across the nation, business schools still have a long way to go before they reach higher levels of business analytics maturity and that they are not yet in an ideal position to serve the presumed industry needs. We offer actionable recommendations

Teaching Tip: A Scalable Hybrid Introductory Analytics Course

We report on the design and development of an introductory analytics course delivered to almost 10,000 undergraduate business students to date. One novel aspect of the course is its orientation to add analytics capabilities to a business student’s toolbox, resulting in significant design and implementation implications. We anchored the course on three fundamental principles: maximizing learning, operating at scale, and a consistent experience for all learners. To enable a rigorous and valuable learning experience, the underlying course curriculum is based on the modified CRISP-DM (CRoss Industry Standard Process for Data Mining) framework. Bloom’s taxonomy is applied to the course assessments to evaluate the depth of learning. The course is delivered in a hybrid mode, arguably the best combination of online and face-to-face delivery modes. In a naturally occurring experimental setting, the COVID-19 pandemic accelerated the evolution of the course and generated additional reinforcing lessons. We explore those lessons and suggest directions for further research

Teaching Big Data Management – An Active Learning Approach for Higher Education

Since big data analytics has become an imperative for business success in the digital economy, universities face the challenge to train data scientists and data engineers on various technological and managerial skills. In addition to traditional lectures, active learning formats ensure a practice-oriented education enabling students to handle novel big data technologies. In this paper, we present a big data management syllabus for master students in the field of big data analytics, which includes various hands-on and action learning elements. The course encompasses seven lectures and nine tutorials and takes place at Chemnitz University of Technology. It covers a broad range of big data applications and facilitates knowledge on various cognitive levels. The paper gives an overview of the course content and assigns learning objectives to lectures and tutorials using Krathwohl’s revised taxonomy. Finally, we present the feedback, which we have received by the students over the years

Managing the Innovation Process: Infusing Data Analytics into the Undergraduate Business Curriculum (Lessons Learned and Next Steps)

The designing of a new, potentially disruptive, curricular program, is not without challenges; however, it can be rewarding for students, faculty, and employers and serve as a template for other academics to follow. To be effective, the new data analytics program should be driven by business input and academic leadership that incorporates innovation theory and practice concepts. Similar to many innovative projects, our journey began with a business problem, i.e., the explosion of data from a plethora of sources, the realization that data transformed into information and intelligence can generate business value, and the recognition that there are currently too few graduates with the necessary skillset to make this happen in the foreseeable future. The approach developed here may provide other universities with a path toward an information systems curriculum that is more in tune with the emerging big data world