Business Intelligence & Analytics Systems
With the exponential growth of data and technological advancements in analytics, organizations have recognized the value of using data to drive their business decisions. In order to enable employees across all facets of the business to become more data-driven in their decision-making, organizations employ a variety of business intelligence & analytics (BI&A) systems. Examples include BI&A systems for data provisioning (e.g., data warehouses), information generation (e.g., process mining platforms), and information presentation and distribution (e.g., dashboards). This course focuses on the fundamental concepts and core components of BI&A systems as well as their role in data-driven decision making within organizations. It is not a technical course, but rather takes a managerial perspective on the design, use, and impact of BI&A systems. In the exercise, students will work on real-world BI&A case studies and get hands-on experience with state-of-the-art BI&A tools.
Learning Objectives
After successful participation in this course, students will be able to:
- Explain what business intelligence & analytics (BI&A) systems are and how they enable data-driven decision making in organizations
- Differentiate between BI&A systems for data provisioning, information generation, and information presentation and distribution
- Explain the theoretical and conceptual foundations guiding the design, implementation, and management of BI&A systems
- Identify key challenges with different types of BI&A systems and develop strategies for addressing these challenges
In addition, students will gain hands-on experience with state-of-the-art BI&A tools.
Course Details
Course number (Stud.IP) | 35002 |
ECTS | 5 |
Hours per week (SWS) | 4 (Lecture 2 + Exercise 2) |
Module applicability | Wirtschaftsinformatik / Information Systems (Master Wirtschaftsinformatik & Master Business Administration: Wirtschaftsinformatik Vertiefung) |
Examination | Portfolio: Group work and presentations during the course (40%); final exam (60%) |
Requirements | Basic skills in data analysis and/or programming (e.g., Python, R) are recommended. |
Language | English |