The growing availability and diversity of data is fundamentally changing not only companies, but also our society. Increasingly powerful technologies make it possible to collect, store and analyze huge amounts of data in real time. Whether AI-controlled logistics systems that optimize supply chains, predictive maintenance in Industry 4.0 or personalized recommendations in e-commerce – data-based applications are increasingly shaping operational and social reality. At the same time, data-driven systems are influencing our daily lives, from digital health applications to smart cities in which urban living spaces are designed more efficiently. Companies are faced with the particular challenge of analyzing and controlling complex and dynamic operational processes in a data-driven manner. Different data sources, often with heterogeneous formats and qualities, make a uniform view of business processes difficult. In addition, there are high demands on the integration, interpretation and use of data in order to create added value.
In order to exploit this potential and create added value with data, methods and tools of modern data analysis and data management are required, which are often summarized under the collective term Data Science & Business Analytics (DS & BA). This includes a variety of approaches from different 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 the entire diversity of business informatics-related research efforts in the areas of Data Science & Business Analytics (DS & BA) in our track. These range, for example, from the generation, collection and representation of (big) data, the development of innovative theories, methods and procedures for solving business and social 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 methods are welcome, as long as they are related to the solution of a business or social problem. We encourage authors to submit relevant and original contributions that exploit the methodological breadth of the research field.
Possible topics include:
- Innovations and emerging trends in DS & BA
- Business value and monetization of DS & BA
- Introduction, establishment, maturity and use of DS & BA
- DS & BA for societal benefit, individual and societal empowerment and digital responsibility
- Explainable artificial intelligence and interpretable machine learning
- Data protection, data quality and data governance
- Opportunities and challenges in data sharing and open data
- Digital manufacturing and the Internet of Things
- Operational, real-time or event-driven business analytics
- Process mining and the benefits of robotic process automation
- Visual analytics and the analysis of unstructured data (e.g. text, images, audio, video) to address organizational and/or societal challenges
- Prescriptive analytics and operations research
- Data work and professions in the field of data science
Track chairs
AEs
- Marie-Louise Arlt, Universität Bayreuth
- Henning Baars, Universität Stuttgart
- Ivo Blohm, Universität St. Gallen
- Simon Emde, Universität Jena
- Andreas Fink, Helmut Schmidt Universität Hamburg
- Maximilian Förster, Universität Ulm
- Burkhardt Funk, Leuphana Universität Lüneburg
- Karoline Glaser, Technische Universität Dresden
- Kai Heinrich, Otto-von-Guericke-Universität Magdeburg
- Charlotte Köhler, Europa Universität Viandria
- Niklas Kühl, Universität Bayreuth
- Alexander Mädche, Karlsruher Institut für Technologie
- Milad Mirbabaie, Otto-Friedrich-Universität Bamberg
- Frederik Möller, Universität Braunschweig
- Oliver Müller, Universität Paderborn
- Roland Müller, Hochschule für Wirtschaft und Recht Berlin
- Dimitri Petrik, Universität Stuttgart
- Nicolas Pröllochs, Justus-Liebig-Universität Gießen
- Christian Schieder, Ostbayerische Technische Hochschule (OTH) Amberg-Weiden
- Guido Schryen, Universität Paderborn
- Philip Stahmann, TU Dortmund
- Sven Weinzierl, Friedrich-Alexander-Universität Erlangen-Nürnberg
- Lin Xie, University of Twente
- Sandra Zilker, Friedrich-Alexander-Universität
- Thomas Setzer, Katholische Universität Eichstätt-Ingolstadt
- Alona Zharova, Humboldt-Universität zu Berlin
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