Students can explain business intelligence architecture. They can outline the ETL process and describe data integration in a data warehouse. They can explain the fundamentals of MapReduce, big data analysis and visualization. They can carry out visualizations using business intelligence tools.

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The purpose of business intelligence is to collect, analyze, and represent information, which leads to improved decision-making by executives. Big data is primarily concerned with data growth (data volume, data speed, data diversity). Both topics are strongly related and make up central elements of data management.

• Business intelligence architecture
• ETL process, data warehouse
• MapReduce, big data analysis and visualization
• Using business intelligence tools

• Dorschel J. (eds.) (2015): Praxishandbuch Big Data: Wirtschaft - Recht - Technik. Springer Gabler.
  
• Ferrari A./ Russo M. (2018): Datenanalyze mit Microsoft Power BI und Power Pivot für Excel. dpunkt.verlag.
  
• Freiknecht J./ Papp S. (2018): Big Data in der Praxis: Lösungen mit Hadoop, Spark, HBase und Hive: Daten speichern, aufbereiten, visualisieren. 2nd revised and extended edition. Hanser Verlag.
  
• Grossmann W./ Rinderle-Ma S. (2015): Fundamentals of Business Intelligence: Data-Centric Systems and Applications. Springer.
  
• Mayer-Schönberger V./ Cukier K. (2014): Big Data: A Revolution That Will Transform How We Live, Work, and Think. Nachdruck. Eamon Dolan.

Lecture, supervised group work dealing with the case study, using a business intelligence software

Written exam (70%), assessment of participation during group work and business intelligence software tasks (30%)