From its early roots, the analytics service Demand Analysis was designed as predictive analytics service. Its goal was to anticipate order entries for 3 months in advance. This helped to optimize the spending of media budgets as well as sales commissions based on data-driven analysis instead of experience and gut-feeling. The service used sophisticated analysis methods (e.g., machine learning and neural networks). Challenge Developing an analytical model to predict the future order entry posed two major challenges. First, data that could be used from AUDI systems was not always complete. Second, the effects that elevated previous order entries were not always known and were thus not correctly interpreted by the analytical model. confronted with how to manage all the data sources data if they act within privacy and legal guidelines. Con- available at AUDI. Tobias had the task to illustrate the sequently, he sent Table 6 to Hortensic. most important data sources depending on their hidden Presenting Table 6 Tobias explained: "Car data is by far business value, capturing the eventual case that an analysis the most valuable data source we have right now. However, it with this