The implementation of a data warehouse system in SAP HANA including the design phase on a conceptual level including the analysis of analog data.

Students will understand the concept, tools and limitations of in-memory-based business intelligence, which enables analytics far beyond traditional data warehousing. They will also understand how methods of artificial intelligence interact and enhance analytics. Furthermore, students will learn to analyse analogue data and to merge it with formatted data from commercial information systems, such as ERP. The analogue data used will be voice clips from a “helpdesk support”, which are analysed in speech recognition, assigned to topics talked about and the sentiment of the talk. This enables to analyse customer feelings about the company products, which are related to traditional analysis of formatted data, in this case customer interactions.

Formatted data analysis, however, will not be based on traditional data warehousing based on aggregate “cubes”, but will utilize in-memory computing to analyse individual records which enable a much more in-depth analysis.

Students will also learn how to conceptually plan such a data warehouse with particular reference to unformatted and analogue data sources and their analysis.

According to the examination regulation full attendance is intended for a PI.

The entire course will emulate a real-world warehouse implementation project from its early planning stages to final use. The system used will be SAP HANA as well as tools for speech recognition and analysis.

Assessment will be based on:

- Case study in data modelling for data warehouses

- Implementation of the case study in SAP HANA implemented in class.

- Implementation of another case study in homework.

all criteria in individual assessment and accounting for 33,3% of the overall assessment each.

Grading scale:

(1) Excellent: 90% - 100%

(2) Good: 80% - <90%

(3) Satisfactory: 70% - <80%

(4) Sufficient: 60% - <70%

(5) Fail: <60%