Description
In the era of digitization, data has become a very important resource for competition. To generate value from these constantly growing amounts of data and to create innovative services and business models based on the data, organizations need to rely on well-trained data scientists and analysts. The required skill set for such experts is complex and challenges higher education in the information systems discipline. Despite some first and promising efforts, there is still a lack of novel teaching approaches for data driven innovation. In this paper we design a morphological box providing a solution space for teaching data driven innovation at universities. For the systematization we analyze the submissions of an academic analytics contest and combine our findings with the existing knowledge base. Furthermore, we present our learnings from two teaching cases and reflect our experiences when applying them in class.
Recommended Citation
Dinter, Barbara; Kollwitz, Christoph; and Fritzsche, Albrecht, "Teaching Data Driven Innovation – Facing a Challenge for Higher Education" (2017). AMCIS 2017 Proceedings. 38.
https://aisel.aisnet.org/amcis2017/ISEducation/Presentations/38
Teaching Data Driven Innovation – Facing a Challenge for Higher Education
In the era of digitization, data has become a very important resource for competition. To generate value from these constantly growing amounts of data and to create innovative services and business models based on the data, organizations need to rely on well-trained data scientists and analysts. The required skill set for such experts is complex and challenges higher education in the information systems discipline. Despite some first and promising efforts, there is still a lack of novel teaching approaches for data driven innovation. In this paper we design a morphological box providing a solution space for teaching data driven innovation at universities. For the systematization we analyze the submissions of an academic analytics contest and combine our findings with the existing knowledge base. Furthermore, we present our learnings from two teaching cases and reflect our experiences when applying them in class.