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Bioprocess data is messy by nature - multiple sources, different units, inconsistent metadata, and endless Excel sheets. In this session, BioRaptor CTO & Co-Founder Yaron David walks through how bioprocess teams can structure their data to enable cross-run comparisons, uncover hidden contributing factors, and future-proof their analytics for machine learning and AI.
Topics & timestamps
02:18 — How bioprocess data becomes knowledge
04:12 — Why identical-looking runs behave differently, and why small inconsistencies or missing context create blind spots during analysis.
06:25 — An example of a single run analysis walking through offline data, online data, setpoints, and target measurements to understand one fermentation batch.
11:31 — Identifying hidden correlations across parameters. A real example showing how airflow, agitation, and concentration interact in a way that isn’t obvious without proper structure.
15:24 — What happens when you have 10, 20, or 100+ runs, and why manual aggregation becomes impossible.
17:00 — What “AI-ready data” actually means
20:23 — What a properly structured batch record looks like. A walkthrough of a recommended template for capturing run-level parameters and batch-level data cleanly.
27:22 — How BioRaptor brings all data sources and enables automated multi-run comparison
Watch the recording
Don’t feel like watching the recording? You can read our blog on the topic.
READY TO STRUCTURE YOUR BIOPROCESS DATA THE RIGHT WAY?
BioRaptor collects, harmonizes, and analyzes all your bioprocess data - online, offline, and contextual - in one place, giving you instant visibility into trends, variability, and optimization levers.
