This chapter introduces a framework for organizational data analysis suited for data-driven and hypotheses-driven problems. It shows why knowledge discovery and hypothesis verification are complementary approaches and how they can be chained together. It presents a methodology for organizational data analysis including a comprehensive processing scheme. Employing a plug-in metaphor, data analysis process engineering is introduced as a way to set up data analysis processes based on taxonomies of tasks that have to be performed during data analysis and on the idea of re-using experience from past data analysis projects. The framework aims at increasing the benefits of data mining and other data analysis approaches, by allowing a wider range of business problems to be tackled and by providing the users with structured guidance for planning and running analyses.