Business Intelligence Systems

Business intelligence (BI) is an umbrella term that encompasses various forms of computer-based decision support. The two core elements of BI are data and tools to store, manage, and analyze that data. Thus, BI advocates a data-driven approach toward aiding decision-making. The concrete form of support can be manifold and includes systems to query databases, tools for visualizing information and offering business insight, and algorithms for data summarization and knowledge discovery.

The module aims at providing a broad overview of BI approaches and at familiarizing students with specific practical skills related to data management, querying, and analysis. Specific learning objectives include, but are not limited to:

  • Students fully understand the difference between operational and analytic information systems
  • Students are familiar with the data warehouse (DWH) principle. They understand the multi-tier architecture of DWHs and their role in corporate decision support.
  • Students are aware of common front-end technologies to inform managerial decisions in the form of reporting, dashboards, and OLAP. They understand the main focus of alternative approaches and are able to recommend a suitable approach for different groups of business users.
  • Student possess theoretical and practical skills in the field of data mining. They grasp the four branches of data mining and understand popular data mining methods.
  • Students are familiar with recent trends in the scope of big data and understand how corresponding developments necessitate new tools for data storage and processing. They are also possess a basic understanding of these tools
Specific practical learning objectives are twofold:
  • Students posses a solid understanding of SQL and are able to formulate queries for reporting and multidimensional data analysis purposes
  • Students are acquainted with algorithmic data analysis and master graphical data mining workbenches.  
To facilitate students developing corresponding practical skills, the the module includes practical computer exercises. In particular, we will use RapidMiner in exercises related to algorithmic data analysis.

Language: The module is normally taught in German. It can also be offered in English upon student request. All materials including slides, exercises, etc. will be offered in English.

Semester: WiSe 2020/21