If a data warehouse already exists, it most likely holds valuable input for process mining. However, many organizations do not have a good data warehouse. The warehouse may contain only a subset of the information needed for end to end process mining, e.g., only data related to customers is stored. Moreover, if a data warehouse is present, it does not need to be process oriented. For example, the typical warehouse data used for Online Analytical Processing (OLAP) does not provide much process-related information. OLAP tools are excellent for viewing multidimensional data from different angles, drilling down, and for creating all kinds of reports. However, OLAP tools do not require the storage of business events and their ordering. The data sets used by the mainstream data mining approaches described in Chap. 3 also do not store such information. For example, a decision tree learner can be applied to any table consisting of rows (instances) and columns (variables). As will be shown in the next section, process mining requires information on relevant events and their order.