Data-centric processes have numerous advantages compared to classic activity-centric processes, including better integration of data and enhanced flexibility. Several approaches for data-centric process management have been proposed, focusing on the specification and modeling of data-centric processes. Yet, these approaches do not provide a corresponding support for a comprehensive runtime, severely hampering their use in practice. Data-centric processes consists of objects with lifecycle processes, which interact with one another and need to be coordinated to reach a business goal. From this definition follow several implications that make conception and implementation of a working runtime challenging. Objects must acquire data in a flexibly and robust fashion. The interacting objects may be in complex relationships, such as transitive one-to-many and many to many relationships. Additionally, object lifecycles run concurrently to each other. This thesis discusses these challenges and provides solution for the enactment of object lifecycle processes and their coordination. The overall goal of this thesis is to provide concepts and approaches for runtime of data-centric process management systems. This requires the design of operational semantics for object lifecycle processes that take the challenges into account, providing flexible and robust data acquisition. Furthermore, an approach to coordinating object lifecycles is presented, consisting of concepts of the relational process structure, semantic relationships, and coordination processes as well as their operational semantics. Together, these concepts can provide sophisticated coordination to concurrently running object lifecycles in complex relationships. Both, object lifecycles and coordination are implemented in the PHILharmonicFlows object-aware process management system, demonstrating the practical applicability and usefulness of the operational semantics for object lifecycle processes and object coordination.