Business Intelligence Systems


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 BI&A prototypes. Before the capstone project is conducted, exercises are offered to prepare students for the capstone project.

The first organizational lecture will be held onsite and not via Zoom as announced before. 3G-rules apply!

Learning objectives

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


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 analytical 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. All organizational details and the underlying registration process of the lecture and the capstone project will be presented in the first lecture. The teaching language is English.


  • 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.

  • 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.

  • 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.

  • 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.

  • 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.