Advanced Business Intelligence and Analytics

• Enabling students to design and implement a Business Intelligence and Business Analytics infrastructure so as to support management decision
• by structuring customers‘ requirements, analyzing data source quality and identifying appropriate data structures and algorithms
• they will become able to design an appropriate infrastructure. They plan the staging of raw data to analytical data and assess the applicability of classical and modern techniques delivered by common BI/BA platforms.
• Based on these skills they will be able to build up an appropriate decision support infrastructure to improve decision processes and to maximize enterprise profits.

1. Classification of decision support
2. Methodology Reference models for BI/BA infrastructure development
3. Data Preparation for classical and advanced analytics
4. Data structures for management support (Data vault, Multi Dimensional, No-SQL)
5. Applicability of advanced algorithms

• Flipped classroom
• Exercises + team work
• hands-on-workshop on ETL tools

Learning Material Provided by Lecturer

Software tools for

• multidimensional modeling
• data transformation
• report generation
• data Mining

• Giles, J.: Elephant in the Fridge. Guided steps to data vault success through building business-centered models. Technics Publications, 2019
• Hultgren, H.: Modeling the Agile Data Warehouse with Data Vault. Brighton Hamilton, 2012.
• Kimball R.: The Data Warehouse Lifecycle Toolkit. John Wiley & Sons. 2008
• Linstedt, D.; Olschimke, M.: Building a scalable data warehouse with data vault 2.0. Amsterdam, Netherlands: Morgan Kaufmann, 2016.
• further sources to follow