Traditional activity-centric process modeling languages treat data as simple black boxes acting as input or output for activities. Many alternate and emerging process modeling paradigms, such as case handling and artifact-centric process modeling, give data a more central role. This is achieved by introducing lifecycles and states for data objects, which is beneficial when modeling data- or
knowledge-intensive processes. We assume that traditional activity-centric process modeling languages lack the capabilities to adequately capture the complexity
of such processes. To verify this assumption, we conducted a survey among Business Process Management experts. The survey results allow us to identify the problems of contemporary modeling languages in regard to the modeling of business data. To this end, survey respondents rated the data modeling capabilities of a variety of business process modeling tools and notations. Overall, the paper
confirms the need of data-awareness in process modeling notations in general
