Continuous pharmaceutical manufacturing (C-PM) is a complex operation requiring high degrees of process control, understanding and experience. To achieve this level of control, process parameters are continuously monitored and adjusted by using in-process control methods. Currently, the measurement of uncertainties and their propagation throughout the process are not well understood. Evaluation of these at all testing points in the system will improve process understanding and identify routes for improvement. Presently, there are no common standards for the storage of (meta)data of instruments or the handling of such large heterogenous data. While control software tools attempt to capture the information required to regulate the processes ad-hoc, they do not facilitate capturing analytical results from process-related instruments. Thus, there is a need for improved data storage, governance and access to the myriad of information about the ongoing manufacturing process to enhance the quality and efficiency of medicines manufacturing and emphasising that quality data is at the heart of these developments. The PhD will develop novel methods for the propagation of information and uncertainty delivered through multistage process control in an end-to-end pharmaceutical process as well as management and semantic modelling of the process (meta)data.