The success of operational and managerial decisions depends on the reliability of the information provided to decision makers by the respective business analytics applications. Thus, in this research-in-progress paper, we explain how the mathematical foundations of the Algebra of Random Varia-bles (AoRV) can be used to extend the capability of business analytics applications to process and report unreliable data. First, we present the theoretical foundations of the AoRV in a concise way that is tailored to business analytics. Second, we present and discuss two example cases, in which we evaluate an application of the AoRV to real-world business analytics scenarios. Initial results from this first design-and-evaluate feedback loop show that the additional reliability information provided by the AoRV is of high value for decision makers, since it allows to predict how uncertain-ties in complex business analytics scenarios will interact. As the next step of this research project, we plan to test the potential of the AoRV to extend business analytics applications through another evaluation loop in a fully natural setting.
Beese, Jannis and Bodner, Martin, "Calculating with Unreliable Data in Business Analytics Applications" (2018). Research-in-Progress Papers. 55.