Data Management for the Life Sciences

S3 versioning protects against accidents. It does not give datasets the schemas, lineage, or workflow checks that real governance requires. This is the end-to-end pattern we run with biotech teams...

21 CFR Part 11 was written for paper-era systems and applied to every electronic record since. This is a practical, vendor-neutral checklist for getting S3-backed pharma R&D records ready for a Part...

S3 is an excellent object store and an inadequate catalog. Teams that build past one bucket eventually need a versioned, searchable, governed layer on top. These are the ten capabilities we look for...

Choosing a scientific data platform in 2026 starts on AWS for almost every biotech R&D team. The harder question is what sits on top of S3, because that decision shapes how scientists find data, how...

Your AI agent can generate. It can even analyze. But can it actually learn? Most can't — and they all fail for the same reason. The answer starts with understanding why AI already works brilliantly...

Transform hours of tedious manual data analysis into minutes with connected scientific data and AI

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I recently had the privilege of speaking at the 2025 Nextflow Summit, which allowed me to explore one of the most persistent challenges in life sciences data—metadata—and how Quilt uses the...

It’s tantalizing to talk about generative AI but how can you make AI practical for your enterprise? While working with companies in the life sciences with cross-functional teams of wet and dry...