335137a Vorlesung Business Intelligence

• Lecture: 15 weeks, 1,5 hours = 22 hours
• Preparation: 15 weeks, 3 hours = 45 hours

Participants will learn the key aspects and principles of business intelligence, data warehousing and data mining know. Specifically, the following points are covered:

• Betriebliche Informationssysteme
• Das Problem der Vertikalen Integration
• Das Data-Warehouse-Konzept
• Data-Warehouse-Referenzarchitektur
• Strukturen eines Data Marts
• Online-Analytical-Processing

• Dimensional Fact Modelling
• Star Schema
• Der ETL-Prozess
• Basic Data Flows
• Lookup-Transformations
• Calculation-Transformations
• Mapping Source - Destination

• Galaxy Schema
• Slowly Changing Dimensions
• Modellierung von KPIs
• Modellierung von Hierarchien
• Modellierung von berechneten Kennzahlen

The lecuture will introduce the concept of Business Intelligence to the audience. For informed decision-making in companies an integrated view of all decision-relevant information is essential. Business Intelligence (BI) refers to strategies, methods and technologies to gain from heterogeneous distributed data and mission-critical knowledge about the status, potentials and prospects of a company. The associated functions are performed by different tools, seamless interaction is the key feature of a BI landscape. Data warehouse systems and their processes are central concepts for the integration of data and ensuring high data quality. Building on offer BI systems such as Management dashboards or OLAP-based BI services capabilities to analyze operational metrics and processes. Data mining methods also provide insights into previously unknown relationships in business data.

Gluchowski, P.; Gabriel, R.; Dittmar, C. (2008): Management Support Systeme und Business Intelligence. Computergestützte Informationssysteme für Fach- und Führungskräfte, Springer, 2008

Kimball, R.; Ross, M. (2013): The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, Wiley, 2013.

Inmon, W.H.; Linstedt, D. (2014): Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault, Morgan Kaufmann; 1 edition, 2014.

Material available on Moodle