Track 5

Track description

The rapid advancements in computing power, sensors, storage, and connectivity technologies have a massive impact on our society and revolutionize the way we live, act, and work together. Cars drive autonomously, smart home systems recognize and adapt to individual user preferences, and medical assistance systems support doctors in diagnosing hard-to-find diseases. Likewise, business environments become increasingly digitized, and the ubiquitous use of IT has become an indispensable anchor for many organizations. This situation favors the collection of vast amounts of data generated at high frequency from multiple sources and heterogeneous systems. These data sources constitute a valuable resource to establish data-driven business processes and fact-based decision making. 

To exploit these potentials and turn data into value, methods and tools of modern data analysis and data management are required that are often subsumed under the umbrella terms data science & business analytics. This includes multiple approaches from a variety of disciplines, such as statistics, artificial intelligence, natural language processing, process mining, visual analytics, business intelligence, data quality management, data governance, and many more. 

Against this background, we welcome diverse information systems research contributions related to data science and business analytics. These range exemplarily from the generation, collection, and representation of (big) data, the development of innovative theories, methods, and procedures to solve business and societal problems, the design of analytical artifacts, to the adoption and integration of these approaches in companies. Research papers on the development of new statistical and machine learning techniques are welcome, as long as a link to solving concrete business or societal problems is demonstrated. We encourage the submission of relevant and original contributions exploiting the methodological breadth of the research area.

Patrick Zschech

Friedrich-Alexander-Universität Erlangen-Nürnberg, patrick.zschech@fau.de

Patrick Zschech is the holder of the Junior Professorship for Intelligent Information Systems at Friedrich-Alexander-Universität Erlangen-Nürnberg. He previously completed his doctorate in information systems at Technische Universität Dresden. The focus of his research is on business analytics, machine learning, and artificial intelligence, with a particular interest in the design, analysis, and use of intelligent information systems. Patrick’s results have been published in leading IS journals such as Decision Support Systems, Business & Information Systems Engineering, and Electronic Markets, and have been presented at international conferences such as ICIS, ECIS and WI.

Barbara Dinter

Technische Universität Chemnitz, barbara.dinter@wirtschaft.tu-chemnitz.de

Barbara Dinter is Professor and Chair of Business Information Systems at Chemnitz University of Technology, Germany. She holds a Ph.D. from the Technische Universität München, Germany, where she previously earned a master’s degree in computer science. Barbara worked for several years at University of St. Gallen, Switzerland as a Post-Doc and project manager. In her role as IT consultant, Barbara Dinter has worked with a variety of organizations. Her current research includes Business Analytics, Business Intelligence, data management as well as data driven innovation and digital manufacturing. She has chaired many times ECIS, HICSS, AMCIS and WI (mini) tracks and has published in renowned journals such as Decision Support Systems, Journal of Database Management und Journal of Decision Systems and at conferences like ICIS, ECIS and WI.

Tobias Brandt

Westfälische Wilhelms-Universität Münster, tobias.brandt@ercis.uni-muenster.de

Tobias Brandt is Professor of Digital Innovation and the Public Sector at Westfälische Wilhelms-Universität Münster. His work focuses on digital innovation at the interface between the private and public sectors of the economy and society in general. His research on smart city technologies and urban analytics has been published in leading IS and OR journals (e.g., JMIS, MSOM, JOM, EJIS, EJOR), while his work on organizational data analytics capabilities and data-centric services has been published in practice-oriented journals such as Harvard Business Review. Tobias is also the co-founder of the urban data science startup Geospin, in which he remains actively involved.

Christoph M. Flath

Julius-Maximilians-Universität Würzburg, christoph.flath@uni-wuerzburg.de

Christoph M. Flath holds the Chair of Information Systems and Business Analytics at the University of Würzburg. Previously, he studied industrial engineering and management at the Karlsruhe Institute of Technology and earned his doctorate in information systems. His research combines big data analytics with mathematical optimization approaches for the design and evaluation of data-driven decision support systems. He regularly publishes his research in leading journals in the areas of operations management, information systems, and operations research (e.g., POM, EJOR, JIT, JOM, EJOR, TranSci).