Einsatz von Business-Intelligence-Systemen zur Risikoidentifikation

Abstract

The environment in which companies operate is constantly changing and therefore becoming more demanding. Because of that, it is essential for companies to identify risks early. Risk management approaches have only focused on analysing historical data, but only this is no longer enough to secure the existence of a company. This paper should demonstrate how business-intelligence-systems can support companies in identifying risks early. This paper analyses which data visualization methods can support decision-making processes within companies. The theoretical part of my thesis explains the fundamentals of risk management according to ISO 31000, typical risk types and the risk management process. After that, the term business intelligence will be defined, and it will be distinguished from related disciplines like data analytics and artificial intelligence. Then, the key functions of business intelligence systems like data visualization, reporting, dashboards and ad-hoc-analyses will be presented. Additional to this, the common visualization types such as bar charts, line charts and pie charts, integration options for various data sources using ETL-processes and various licensing models will be presented. For the practical part of my thesis a prototype in the form of a dashboard will be developed using the software “Tableau Desktop”. The dashboard should demonstrate, how delivery delays can be identified in an early stage and to visualize these delays using an integrated early warning mechanism (traffic light system). The key performance indicators in this dashboard are delivery punctuality in %, average delivery delay in days and resulting costs. For this dashboard an Excel-database was created with 300 fictious delivery data records. An evaluation of the dashboard shows that visualizations supported by business intelligence can make risks more transparent and show them all at once. Especially the intuitive user guidance provided by the traffic light system and the interactive filter functions will be pointed out. There are limitations due to the use of static and artificial data and the lack of testing within a company. In the last part of my thesis, two companies will be analyzed, which have already implemented business intelligence systems to identify risks early. These two companies are “Harrah’s Entertainment Inc.” and “Continental Airlines”. The result of these analyses is that business intelligence tools are an effective tool to identify risks early especially when there are systematic data analyses and suitable visualizations.

Date of Award	2025
Original language	German (Austria)
Supervisor	Harald Staska (Supervisor)