Introduction to Enterprise Architecture and Business Processes Analysis Minitrack

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15 Years of Enterprise Architecting at HICSS: Revisiting the Critical Problems

The Enterprise Architecture (EA) minitrack has been a mainstay of HICSS for the past 15 years. The methodology, tools, and processes of enterprise architecting have evolved during that period. In 2005, Kaisler and Armour identified some critical challenges in modeling, management, and maintenance for EA that needed attention to ensure a viable technical discipline. Over 15 years, we have accepted 93 papers for presentation. Reviewing these papers and drawing up on our experience over the past 15 years, we conclude that some progress has been made, some challenges remain to be addressed, and some new challenges have emerged. This paper revises existing challenges and identifies additional challenges to be addressed in the next 10 years

Introduction to Big Data and Analytics: Concepts, Methods, Techniques, and Application Minitrack

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Data Science Roles and the Types of Data Science Programs

A growing field, data science (and, by extension, analytics) integrates concepts across a range of domains, such as computer science, information systems, and statistics. While the number of data science programs continues to increase, few discussions have examined how we should define this emerging educational field. With this in mind, during the 23rd Americas Conference on Information Systems (AMCIS’17), a panel discussion explored emerging questions regarding data science and analytics education. This paper reports on that panel discussion, which focused on questions such as what a data science degree is and what a data science program’s learning objectives are. The panel also debated if there should be different types of data science-related programs (such as an applied data science program or a business analytics program) and, if so, should there be a common core across the different variations of programs. Information system educators who can gain a better understanding of current trends in data science/analytics education and other information system researchers who are interested in how data science/analytics might impact the broader field of information systems and management education should find interest in this report

Big Data Redux: New Issues and Challenges Moving Forward

As of the time of this writing, our HICSS-46 proceedings article has enjoyed over 520 Google Scholar citations. We have published several HICSS proceedings, articles and a book on this subject, but none of them have generated this level of interest. In an effort to update our findings six years later, and to understand what is driving this interest, we have downloaded the first 500 citations to our article and the corresponding citing article, when available. We conducted an in-depth literature review of the articles published in top journals and leading conference proceedings, along with articles with a high volume of citations. This paper provides a brief summary of the key concepts in our original paper and reports on the key aspects of interest we found in our review, and also updates our original paper with new directions for future practice and research in big data and analytics

Big Data and Analytics: Issues and Challenges for the Past and Next Ten Years

In this paper we continue the minitrack series of papers recognizing issues and challenges identified in the field of Big Data and Analytics, from the past and going forward. As this field has evolved, it has begun to encompass other analytical regimes, notably AI/ML systems. In this paper we focus on two areas: continuing main issues for which some progress has been made and new and emerging issues which we believe form the basis for near-term and future research in Big Data and Analytics. The Bottom Line: Big Data and Analytics is healthy, is growing in scope and evolving in capability, and is finding applicability in more problem domains than ever before