The increasing digitalization, combined with the generation of extensive amounts of data – also referred to as Big Data. Big Data presents new challenges to the traditional risk management implemented so far on the one hand, but on the other hand it also opens up potential for a broader practical application, especially with regard to risk identification and assessment, which has not been possible to this extent so far.
In processes such as machine learning – a subdomain of artificial intelligence – IT systems develop patterns and regularities for risks on the basis of these existing data sets and with the aid of algorithms, and make corresponding forecasts which are continuously adjusted (Beierle & Kern-Isberne 2003, p. 99f.). This enables more comprehensive early warning systems to be implemented and thus promotes the proactive handling of risks. In addition to accessibility, the validation of data plays a decisive role in this context.
Furthermore, the higher data availability is accompanied by improved transparency, which opens up new potentials for visualization (also called mapping). Thus, it is possible to visualize e.g. movements of persons or objects and to predict future changes in the process due to occurring risks by means of corresponding data sets.
The improvement in risk identification has also resulted in the development of automated analysis and evaluation procedures. The large volumes of data and the large number of sources of data supply require the use of algorithms and complex evaluation procedures in order to draw conclusions about the future development of risks (predictive analytics) and to be able to forecast the company’s optimum response to future risks (prescriptive analytics) (Krause & Pellens 2018, p. 51). As a result, strategies for dealing with risks can be decided at an earlier stage and precautionary measures can be initiated that are tailored to the specific situation. In addition, a better cost-benefit assessment can be made for individual measures, e.g. through better quantification of resources.
In our paper about New Applications for Risk Management through the Use of Big Data – Two Case Studies, we describe two practical examples of the use of Big Data in risk management. Firstly, the containment of epidemics through risk analysis using Big Data. Secondly, the reduction of the risk of pollution from ship hulls through the evaluation of satellite data on global sea conditions, as well as data sets on sea temperatures, cloud cover, biological factors, salinity, acidity and other minerals.
Extract from Schröder et al. 2019, Neue Anwendungsmöglichkeiten für das Risikomanagement durch den Einsatz von Big Data – Zwei Fallbeipspiele, in: Schröder, Meike; Wegner, Kirsten (Hrsg.) Logistik im Wandel der Zeit, Springer: Wiesbaden, pp. 121-136.