A profound analysis of all relevant business data in a company is necessary for optimizing business processes effectively. Current analyses typically run either on business process execution data or on operational business data. Correlations among the separate data sets have to be found manually under big effort. However, to achieve a more informative analysis and to fully optimize a company's business, an efficient consolidation of all major data sources is indispensable. Recent matching algorithms are insufficient for this task since they are restricted either to schema or to process matching. We present a new matching framework to (semi-)automatically combine process data models and operational data models for performing such a profound business analysis. We describe the algorithms and basic matching rules underlying this approach as well as an experimental study that shows the achieved high recall and precision.