Description

In most modern enterprises, Business Intelligence & Analytics (BI&A) Systems represent a core enabler of decision-making in that they are supplying up-to-date and accurate information about all relevant aspects of a company’s planning and operations: from stock levels to sales volumes, from process cycle times to key indicators of corporate performance. Modern BI&A systems leverage beyond reporting and dashboards also advanced analytical functions. Thus, today they also play a major role in enabling data-driven products and services. The aim of this course is to introduce theoretical foundations, concepts, tools, and current practice of BI&A Systems.

The course is complemented with an engineering capstone project, where students work in a team with real-world use cases and data in order to create running Business intelligence & Analytics system prototypes. Before the capstone project is conducted, exercises (virtual sessions) are offered to prepare students for the capstone project.

Learning objectives

• Explore key capabilities of state-of-the-art Business Intelligence & Analytics Systems
• Learn how to successfully implement and run Business Intelligence & Analytics Systems from multiple perspectives, e.g. architecture, data management, consumption, analytics
• Get hands-on experience by working with Business Intelligence & Analytics Systems with real-world use cases and data 

Prerequisites

This course is limited to a capacity of 50 places. The capacity limitation is due to the attractive format of the accompanying engineering capstone project. Strong analytic abilities and profound skills in SQL as wells as Python and/or R are required. Students have to apply with their CV and transcript of records. The capstone project is a group assignment in a compressed four-day’s hackathon format. Students are allocated to different use cases mostly based on their preferences. The language of instruction is English. Further organizational details of the lecture and the capstone project will be presented in the kick-off session in the first week of lectures via MS Teams. More informatio (e.g. schedule for lecture and exercises) will be available via the ILIAS course of this lecture.

Bibliography

• Arnott, D., Pervan, G., 2014. A critical analysis of decision support systems research revisited: The rise of design science. J. Inf. Technol. 29, 269–293.
• Chen, H., Chiang, R., Storey, V., Storey, 2012. Business Intelligence and Analytics: From Big Data to Big Impact. MIS Q. 36, 1165–1188. https://doi.org/10.2307/41703503
• Power, D.J., 2008. Decision Support Systems: A Historical Overview, in: Handbook on Decision Support Systems 1. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 121–140. https://doi.org/10.1007/978-3-540-48713-5_7
• Sharma, R., Mithas, S., Kankanhalli, A., 2014. Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. Eur. J. Inf. Syst. 23, 433–441. https://doi.org/10.1057/ejis.2014.17
• Turban, E., Aronson, J.E., Liang, T.-P., Sharda, R., 2008. Decision Support and Business Intelligence Systems, 8th ed. Pearson Prentice Hall.
• Vercellis, C., 2009. Business Intelligence: Data Mining and Optimization for Decision Making, Business Intelligence: Data Mining and Optimization for Decision Making. John Wiley and Sons. https://doi.org/10.1002/9780470753866
• Watson, H.J., 2014. Tutorial: Big Data Analytics: Concepts, Technologies, and Applications, Communications of the Association for Information Systems

Further literature will be made available in the lecture.

If you have questions regarding the lecture, please contact Miguel Angel Meza Martinez. If you have questions regarding the capstone project and exercises, please contact Sven Michalczyk.