A holistic approach for future bioprocessing must be centered around connectivity of data and people across unit operations and scales. Data is already generated in abundance, but many of the current systems create data silos. To get to a scalable approach, data needs to be digitally available, integrated between source systems, connected across the entire process, and contextualized such that knowledge can be accumulated across the product life cycle. All these aspects dictate dedicated life cycle management of data and models, emphasizing model scalability and evolution.
The following examples illustrate how such a common foundation for data aggregation and contextualization lays the ground for exponential productivity improvement beyond the productivity gained from automating data integration.