With the emergence of Industry 4.0 and Big Data initiatives, there is a renewed interest in leveraging the vast amounts of data collected in (bio)chemical processes to improve their operations. The objective of this article is to provide a perspective of the current status of Big-Data-based process control methodologies and the most effective path to further embed these methodologies in the control of (bio)chemical processes. Therefore, this article provides an overview of operational requirements, the availability and the nature of data, and the role of the control structure hierarchy in (bio)chemical processes and how they constrain this endeavor. The current state of the seemingly competing methodologies of statistical process monitoring and (engineering) process control is examined together with hybrid methodologies that are attempting to combine tools and techniques that belong to either camp. The technical and economic considerations of a deeper integration between the two approaches is then explored, and a path forward is proposed.