Stud.IP Uni Oldenburg
University of Oldenburg
29.02.2024 06:41:43
inf604 - Business Intelligence I (Complete module description)
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Module label Business Intelligence I
Module abbreviation inf604
Credit points 6.0 KP
Workload 180 h
Institute directory Department of Computing Science
Applicability of the module
  • Master Applied Economics and Data Science (Master) > Data Science
  • Master of Education Programme (Vocational and Business Education) Computing Science (Master of Education) > Akzentsetzungsbereich
  • Master's Programme Business Informatics (Master) > Akzentsetzungsmodule Bereich Wirtschaftsinformatik
  • Master's Programme Business Informatics (Master) > Akzentsetzungsmodule der Informatik
  • Master's Programme Computing Science (Master) > Angewandte Informatik
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Embedded Brain Computer Interaction
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Human-Computer Interaction
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Systems Engineering
Responsible persons
  • Marx Gómez, Jorge (authorised to take exams)
  • Lehrenden, Die im Modul (authorised to take exams)
No participant requirement
Skills to be acquired in this module
Objective of the module/skills:
Current module provides basics of business intelligence with focus on enterprises and strong emphasis on data warehousing technologies. Students of the course are provided with knowledge, which reflects current research and development in a data analytic domain.
Professional competence
The students:
  • name and recognize the role of business intelligence as past of daily business process
  • being able to analyse advantages and disadvantages of different approaches and methods of the data analytics and being able to apply them in simple case studies
  • obtain theoretical knowledge about data collection and modelling processes, including most applicable approaches and best practices
Methodological competence
The students:
  • being able to execute typical tasks of business intelligence, and also being able to deepen knowledge on different approaches and methods
  • gain a hans on experience and being able to understand advantages and disadvantages of different methods and being able to use obtained knowledge in most efficient ways
Social competence
The students:
  • build solutions based on case studies given to the group, for example solving the issue of a factless fact table
  • discuss solutions on a technical level
  • present obtained case studies solutions as part of the exercises

The students:
  • critically review provided data and information
Module contents
Data warehouse technology together with business intelligence are increasingly being used by business in order to get better decision support and enrich ongoing rocesses with data-rich decisions. Data warehouse technology enables an integration of data from heterogeneous sources, whether business intelligence builds data rocessing on top of it. For instance, business intelligence allows to build reporting on very large volumes of data (including historical) coming primary from data warehouse.
As past of the current module following contents are taught:
  • Definition and scope of business intelligence.
  • Procedures and objectives of data warehousing.
  • Process of extracting, transforming and loading (ETL) of data.
  • Phases of data modelling, data capturing and reporting in conjunction with a plausible case studies/scenarios.
  • Prospects for further and evolving topics for business intelligence (e.g. Adaptive Business Intelligence, In-MemoryComputing. etc.)
  • Introduction to Data Mining.
  • Case studies based practical exercises and assessments in order to impart practical knowledge.
Recommended reading
  • Marx Gómez, Rautenstrauch, Cissek (2008): Einführung in die Business Intelligence mit SAP NetWeaver 7.0.
  • Marx Gómez, Rautenstrauch, Cissek, Grahlher (2006): Einführung in SAP Business Information Warehouse, Springer, Heidelberg.
  • Moss, Atre (2006): Business Intelligence Roadmap, Addison-Wesley, Boston.
  • Loshin (2003): Business Intelligence, Kaufmann, Amsterdam.
  • Müller, Lenz (2013): Business Intelligence.
  • Sabherwal, Becerra-Fernandez (2010): Business Intelligence: Practices, Technologies, and Management
Languages of instruction German, English
Duration (semesters) 1 Semester
Module frequency annual
Module capacity unlimited
Teaching/Learning method 1VL + 1Ü
Previous knowledge none
Type of course Comment SWS Frequency Workload of compulsory attendance
Lecture 2 WiSe 28
Exercises 2 WiSe 28
Total module attendance time 56 h
Examination Examination times Type of examination
Final exam of module
At the end of the lecture period
Written exam max. 120 minutes