  ERP systems contain huge amounts of data related to the actual execution of
business processes. These systems have a particular way of recording activities
which results in an unclear display of business processes in event logs.
Several works have been conducted on ERP systems, most of them focusing on the
development of new algorithms for the automatic discovery of business
processes. We focused on addressing issues like, how can organizations with ERP
systems apply process mining for analyzing their business processes in order to
improve them. The data handling aspect of ERP systems contrasts with those of
BPMS or workflow based systems, whose systematical storage of events
facilitates the application of process mining techniques. CRISP-DM has emerged
as the de facto standard for developing data mining and knowledge discovery
projects. Successful data mining requires three families of analytical
capabilities namely reporting, classification and forecasting. A data miner
uses more than one analytical method to get the best results. The objective of
this paper is to improve the usability and understandability of process mining
techniques, by implementing CRISP-DM methodology for their application in ERP
contexts, detailed in terms of specific implementation tools and step by step
coordination. Our study confirms that data discovery from ERP system improves
strategic and operational decision making.
