Structuring bioprocess data for better decision-making

Bioprocessing teams generate massive amounts of data across R&D, scale-up, and production stages, but much of it goes unused due to poor structure, inconsistent formatting, or missing context. In this session, we explore what it means to have “clean,” actionable data and how better data hygiene enables faster decision-making and smoother downstream use of tools like AI, modeling, and statistical process control.

We’ll cover real examples of common pitfalls (like unit inconsistencies or missing metadata) and share best practices to future-proof your data so it can be reused across teams and time.