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This entry is from Summer semester 2018 and might be obsolete. No current equivalent could be found.

Business Intelligence
(dt. Business Intelligence)

Level, degree of commitment Specialization module, compulsory elective module
Forms of teaching and learning,
Lecture, recitation class,
180 hours (attendance: 50 hours preparation and follow-up: 65 hours exam preparation: 65 hours)
Credit points,
formal requirements
6 CP
Course requirement(s): Written examination
Examination type: Essay (2-3 pages)
The grading is done with 0 to 15 points according to the examination regulations for the degree program B.Sc. Business Administration.
One semester,
each winter semester
Person in charge of the module's outline Prof. Dr. Paul Alpar


Today, almost all business processes are supported by computer systems, so that large amounts of detailed data are generated in companies. The goal of business intelligence is to structure this data appropriately and make it available to decision makers in the form of standardized reports or complex analysis results. With such information, managers can both monitor the fulfillment of predefined goals and receive impetus for new business opportunities. Selected methods and tools are presented in the lecture, which participants can then try out and learn for themselves in the exercise.

Qualification Goals

Students will be able to analyze data from a database or data warehouse using widely available software tools to solve business problems. This includes, for example, the determination of key figures for the management and control of financial, marketing, sales, procurement or production processes.




Module imported from B.Sc. Business Administration.

When studying M.Sc. Computer Science, this module can be attended in the study area Minor subject Business Administration.

Recommended Reading

  • Gluchowski, P.: Management Support Systeme und Business Intelligence: computergestützte Informationssysteme für Fach- und Führungskräfte, 2. Auflage, Springer, Berlin 2008.
  • Alpar, P.; Niedereichholz, J.: Data Mining im praktischen Einsatz, Vieweg, Wiesbaden 2000.

Please note:

This page describes a module according to the latest valid module guide in Summer semester 2018. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

  • Winter 2016/17
  • Summer 2018
  • Winter 2018/19
  • Winter 2019/20
  • Winter 2020/21 (no corresponding element)
  • Summer 2021 (no corresponding element)
  • Winter 2021/22 (no corresponding element)
  • Winter 2022/23 (no corresponding element)
  • Winter 2023/24 (no corresponding element)

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.