KÄLBER / In E.ON's Group Risk Management it was assumed that there would be information relevant to risk assessment available in the mass of text documents published daily on the Internet, but this is not accessible to standard analysis models. We wanted to test this hypothesis. Our goal was to fill in as far as possible the "blank spots on the map of usable data" by using the Global Risk Radar technology.

KÄLBER / Using semantic analysis, the Global Risk Radar structures the unstructured part of mass data in E.ON's business context. In this way, opinion leaders who are scattered among the masses of text documents on the Internet become measurable, so that we can use them for risk assessment. In addition, machine-trained algorithms can, of course, also take on some completely different tasks. In addition to risk management, in the financial area planning, forecasting or model-based management approaches are also possible. All processes that are based to a large extent on data and rules can also be partially or fully automated using machine learning combined with robotics.

KÄLBER / We will only be able to fully exploit the potential of the new technologies once we have specific job profiles and skills. On the one hand we need Data Scientists with expert digital knowledge, and on the other hand business experts who can assess the relevance of this technology for specific applications. The latter are particularly critical for success as they provide the link between highly complex technology and business requirements. This particular mix of skills is currently especially difficult to find in the job market.