Invited Paper: The Transition from MIS Departments to Analytics Departments

This paper takes a look backward while simultaneously looking to the future for MIS departments that are making the transition to Analytics departments. MIS has a long past of providing a base of skills supporting organizations. We examine this history as well as how the blending of MIS with business translator and modeling skills has led to the development of analytics programs and concentrations. While the transition to analytics has taken place in many MIS departments at least partially, the question is how long analytics will remain a focus and when will the next major shift occur

Unraveling the Skillsets of Data Scientists: Text Mining Analysis of Dutch University Master Programs in Data Science and Artificial Intelligence

The growing demand for data scientists in the global labor market and the Netherlands has led to a rise in data science and artificial intelligence (AI) master programs offered by universities. However, there is still a lack of clarity regarding the specific skillsets of data scientists. This study aims to address this issue by employing Correlated Topic Modeling (CTM) to analyse the content of 41 master programs offered by seven Dutch universities. We assess the differences and similarities in the core skills taught by these programs, determine the subject-specific and general nature of the skills, and provide a comparison between the different types of universities offering these programs. Our findings reveal that research, data processing, statistics and ethics are the predominant skills taught in Dutch data science and AI master programs, with general universities emphasizing research skills and technical universities focusing more on IT and electronic skills. This study contributes to a better understanding of the diverse skillsets of data scientists, which is essential for employers, universities, and prospective students

Teaching Social Media Analytics: An Assessment Based on Natural Disaster Postings

Unstructured data in social media is as part of the “big data” spectrum. Unstructured data in Social media can provide useful insights into social phenomena and citizen opinions, both of which are critical to government policy and businesses decisions. Teachers of business intelligence and analytics commonly use quantitative data from sales, marketing, finance and manufacturing to demonstrate various analytics concepts in a business context. However, researchers have seldom used social media data to analyze social behavior and communication. In this study we aim to demonstrate an assessment structure for teaching social media analytics concepts with the goal of analyzing and interpreting social media content. We base this assessment on forum postings regarding two recent events: the Christchurch earthquake in New Zealand, and the Japanese earthquake and tsunami. The aim of the assessment is to discover social insights. We base the assessment structure on Cooper’s Analytics Framework to cover such concepts as term frequency (TF), term frequency–inverse document frequency (TFIDF), data visualization, sentiments and opinions analysis, the Nearest Neighbor K-NN classification algorithm, and Information Diffusion theory. We review how the students performed on the assignment that used this assessment, and we make recommendations for future studies

A systematic review on business analytics

Purpose: Business analytics, a buzzword of the recent decade, has been applied by thousands of enterprises to help generate more values and enhance their business performance. However, many aspects of business analytics remain unclear. This study explores different perspectives on the definition of business analytics and its relation with business intelligence Moreover, we illustrate the applications of business analytics in both business areas and industry sectors and shed light on the education in business analytics. Ultimately, to facilitate future research, we summarize several research techniques used in the literature reviewed. Design/methodology/approach: We set well-established selection criteria to select relevant literature from two widely recognized databases: Web of Science and Scopus. Based on the bibliometric information of the papers selected, we did a bibliometric analysis. Afterward, we reviewed the literature and coded relevant sections in an inductive way using MAXQDA. Then we compared and synthesized the coded information. Findings: There are mainly four findings. Firstly, according to the bibliometric analysis, literature about business analytics is growing exponentially. Secondly, business analytics is a system enabled by machine learning techniques aiming at promoting the efficiency and performance of an organization by supporting the decision-making process. Thirdly, the application of business analytics is comprehensive, not only in specific areas of a company but also in different industry sectors. Finally, business analytics is interdisciplinary, and the successful training should involve technical, analytical, and business skills. Originality/value: This systematic review, as a synthesis of the current research on business analytics, can serve as a quick guide for new researchers and practitioners in the field, while experienced scholars can also benefit from this work, taking it as a practical reference.Peer Reviewe

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

Integrating Data Cleansing With Popular Culture: A Novel SQL Character Data Tutorial

Big data and data science have experienced unprecedented growth in recent years.  The big data market continues to exhibit strong momentum as countless businesses transform into data-driven companies. From salary surges to incredible growth in the number of positions, data science is one of the hottest areas in the job market. Significant demand and limited supply of professionals with data competencies has greatly affected the hiring market and this demand/supply imbalance will likely continue in the future. A major key in supplying the market with qualified big data professionals, is bridging the gap from traditional Information Systems (IS) learning outcomes to those outcomes requisite in this emerging field. The purpose of this paper is to share an SQL Character Data Tutorial.  Utilizing the 5E Instructional Model, this tutorial helps students (a) become familiar with SQL code, (b) learn when and how to use SQL string functions, (c) understand and apply the concept of data cleansing, (d) gain problem solving skills in the context of typical string manipulations, and (e) gain an understanding of typical needs related to string queries. The tutorial utilizes common, recognizable quotes from popular culture to engage students in the learning process and enhance understanding. This tutorial should prove helpful to educators who seek to provide a rigorous, practical, and relevant big data experience in their courses

Development of an Introductory MBA Course in Business Analytics Using Data-Driven Decision-Making (DDDM) Model

Business Analytics has become an important field of study in the MBA curriculum. Over the last decade, many MBA programs have added Business Analytics (BA) courses into their curriculum. Unlike most other disciplinary area courses that are somewhat similar across MBA programs, BA courses tend to vary significantly in content and structure depending on the faculty who teaches it. Thus, very little systematic guidance is available to faculty who are considering developing and teaching a BA course in the MBA program. One important pedagogical concern is making MBA students understand the importance of Business Analytics in making data driven decisions. This paper presents a conceptual model of the datadriven decision-making process that was the foundational guide for the methodical development of a BA course and its implementation. Some implications of this model for development of BA courses for MBA programs are also presented

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

Contents and Skills of Data Mining Courses in Analytics Programs

Data Mining (DM) is one of the most offered courses in data analytics education. However, the design and delivery of DM courses present a number of challenges and issues that stem from the DM’s interdisciplinary nature and the industry expectations to generate a broader range of skills from the analytics programs. In this research, we identified and compared frequencies of the contents and skills of DM course syllabi in various data analytics programs. We also identified and systemized DM contents and skills in the analytics job market and compared them with the contents and skills from DM syllabi. Based on these analyses and comparisons, we developed four different templates of the DM contents and skills for a DM course at various levels of the analytics education that include: specialized graduate analytics program (MS), general graduate program (MBA), specialized undergraduate analytics program (BS), and general undergraduate program (BSBA). These templates may be specifically useful for educators to design new or improve existing DM courses in data analytics curricula

Data Analytics for Effective Decision-Making in Crises - Identifying Relevant Data Analytics Competencies for Automotive Procurement Departments

Crises become the norm for organizations, as recent years have shown. Especially the automotive industry is still facing disruptive changes such as e-mobility, connected cars or autonomous driving. Disrupted supply chains, related production downtimes and associated financial losses are consequences. Procurement departments are the interface between internal and external stakeholders in supply chains, and therefore, the central authority for managing crises. In such situations, effective decision-making is essential. Positive effects of data analytics on decision-making were part of numerous research endeavors, as well as related data analytics competencies. We conducted semi-structured interviews with experienced experts about relevant data analytics competencies in procurement departments. We present an overview specifically for procurement departments and derive implications of these competencies on decision-making. As a result, we apply our findings to existing research from a theoretical perspective and support procurement leaders and their departments in facing current and future challenges from a practical perspective