When Legal and M&A Teams Rebuild What R&D Already Built

By Whitney Smith, Scientific, Sales and Product Expertise in Life Science Software and Services

There's a particular kind of frustration that shows up on deal teams and in legal departments at pharma and biotech companies, usually a few weeks into due diligence, a licensing evaluation, or a patent dispute. Someone on the legal or M&A side needs a clear, defensible picture of a research program: what was tested, when, by whom, under what conditions, with what results. It sounds like it should be a data pull. It usually turns into a project.

Paralegals request files instrument by instrument. IT gets pulled in to track down formats from lab systems nobody outside R&D has opened in years, while outside counsel bills hours reconciling inconsistent naming and missing context. Weeks later, the team has something usable, a rebuilt, reorganized, re-classified version of research data that, somewhere else in the same company, already existed in structured form.

R&D and informatics teams have spent years building the infrastructure to make their own research data findable, traceable, and usable, integrating instruments, lab systems, and electronic notebooks into something coherent. It works, and scientists rely on it daily. But that infrastructure was built to answer scientific questions, in a scientific format. Legal, M&A, and business development teams aren't asking different questions about different data, they're asking different questions about the same data, and need an entirely different view of it: a chain of custody instead of an experiment log, a timeline instead of a results table. Treated as a new data problem, that difference triggers a new classification scheme, a new manual review, a new project, when it's really a presentation problem, not a data problem.

This isn't a rare edge case, it resurfaces every time R&D-generated data needs to leave R&D's world: a due diligence deadline, an IP dispute reaching back years into research history, a licensing evaluation, a regulatory data request, or the departure of scientists whose institutional knowledge about how a dataset was organized leaves with them. Each time, the cost lands somewhere: compressed deal timelines, outside counsel hours, IT staff pulled off other priorities, legal teams working from data they can't fully vouch for under time pressure.

The nuance worth naming here: this isn't really a legal problem or an IT problem. It's a structural gap between two teams who need the same underlying facts, structured two different ways, with no shared path between them. Fixing it doesn't mean building a second data infrastructure for legal and business teams to maintain in parallel, that just adds a third silo to the two that already don't talk to each other. 

The more efficient approach is to re-purpose the infrastructure R&D already built, the one designed from the ground up to make scientific data findable, traceable, and verifiable. There's no need to limit that framework to R&D's use case; it's just been hard to do otherwise, until recently.

Today, there are platforms and services built for exactly this kind of problem, designed to extend research data governance to the teams who need it for legal, deal, and compliance purposes without forcing them to rebuild it from scratch. If deal timelines, discovery requests, or documentation gaps around research data are a recurring headache at your organization, it's worth a conversation.

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