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Business Intelligence

Business Intelligence (BI) is an integrated, company-specific, systematic approach to automating and improving high-volume operational and managerial decisions. BI promotes a shift to fact-based decision making which is important for a company in order to increase productivity and to survive competition. Today it becomes one of the most important management instruments.

Once focus of the module is the state-of-the-art in data warehousing technologies. In particularly, the students willunderstand the role of data in BI and learn how data is processed for gaining information from data. Furthermore we will extensively deal with data analysis (like OLAP) and especially with data mining problems, process mining and text mining. Data mining goes a step beyound the use of statistical techniques and machine learning techniques, and detects without an exact query formulation previously undiscovered connections within the data.

Process mining is a process management technique, which allows the analysis of business processes based on event logs. The object of text mining is to find important information and relationships in large amounts of text.

ECTS
Type
Level

6
Core Course
Advanced

Should be visited:
FT: 3rd
PT: 3rd or 5th
Academic Module Coordinator
Prof. Dr. Stella Gatziu Grivas
Lecturers
Prof. Andreas Reber
Pre-requisites

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Overall hours
Contact hours: 60 h
Self-Study: 120 h
Outline Content

This course will include topics like:

  • Introduction to Business Intelligence (Concepts, Mechanisms, Main Application Areas)
  • Building the Data Warehouse: Preparing data for gaining information
  • Data and Text Mining
  • Reporting and Dashboarding Concepts
  • Analytical Systems (Balanced Scorecard, Planning systems, Consolidation Systems, Budgeting Systems, Risk Management Systems)
  • Enterprise Performance Management
  • The BI Project: Development of BI solutions and Operation
  • The BI Competence Center: BI Management and Organisation
Teaching and Learning Methods
The module is taught through plenary lectures and plenary/group workshops, including exercises, practical training with software tools, case studies, and presentations. Plenary sessions will be accompanied by assignments to be done as homework or during lecture. The results of the assignment have to be presented in class. In addition, students undertake guided independent study throughout. Class participation is actively encouraged. In the plenary lectures students may be required to orally present results of guided self study or repetition of previous lecture.
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