Laboratories are in the midst of a major shift in how they handle data. For years, data management was treated as an afterthought—something relegated to disparate systems, spreadsheets, and siloed instruments. But that’s no longer sustainable. Today, three interlocking trends are defining how organizations approach lab informatics: breaking down data silos, adopting cloud-hosted platforms, and embracing FAIR data governance. These developments are not isolated. Together, they represent a symbiotic evolution toward a more holistic, digital-first laboratory—one that is positioned to take full advantage of artificial intelligence.
1. Breaking down data silos
For decades, labs operated as islands of information. Instruments generated data in proprietary formats, lab software systems didn’t talk to one another, and sharing results required tedious manual effort. These silos limited collaboration, reduced efficiency, and kept valuable insights locked away. And for many labs, this situation is still their reality.
The problem is not unique to science—knowledge workers across industries spend nearly 12 hours per week chasing down siloed information, according to a 2022 article by VentureBeat. Another 2022 survey by Aspen Technology found that about half of pharmaceutical companies say silos hinder cross-functional collaboration. For labs handling high-value data like clinical trial results or genomic sequences, the stakes are even higher: if datasets cannot be easily combined, important discoveries may be delayed or missed entirely.
Instrument manufacturers often reinforce the issue by keeping labs in “walled gardens” of lab software that only work with their own products. While this ensures smooth internal communication, it discourages interoperability. Fortunately, companies such as Ganymede and TetraScience are addressing this, offering cloud-based data lakes and other features that consolidate data from various equipment.
Garrett Peterson, chief strategy officer of Yahara Software points out, the push to break down silos is also part of a broader “lab-in-the-loop” vision, where workflows across LIMS, QMS, ERP, instruments, and human-driven steps are integrated. “Done well, this enables hyper-acceleration of discovery through rapid, top-to-bottom process iteration,” he says. Even without full automation, labs can bootstrap with existing systems and layer in agents or AI to operate within their current toolchain. This incremental approach allows organizations to see tangible benefits while minimizing risk.
2. Cloud SaaS platforms
If desiloing is the “why,” then cloud-hosted platforms are often the “how.” Software-as-a-service (SaaS) solutions have become integral to the way labs centralize and interact with their data. By migrating to the cloud, labs gain scalability, accessibility, and reduced IT overhead. Labs no longer need to maintain expensive on-premises servers or patch local installations.
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Cloud SaaS also enables collaboration in ways that on-premises systems cannot. A principal investigator can access results from home, a collaborator across the country can analyze shared datasets in real time, and IT teams can roll out security updates instantly. This flexibility has been especially important in recent years, as remote and hybrid work models have become more common in research organizations.
Still, cloud solutions aren’t a silver bullet. Some OEMs replicate lock-in by creating proprietary cloud ecosystems in which only their instruments can exist. Labs must weigh whether a given SaaS platform fosters openness or just relocates the walled garden. Peterson notes that many labs are navigating this balance between custom, high-fit solutions—which may carry higher costs—and waiting for generalized SaaS offerings to mature. Both paths aim at the same goal: freer data flow and faster iteration.
When chosen wisely, cloud SaaS platforms provide the backbone for modern lab informatics—and a launchpad for AI.
3. FAIR data governance
Desiloing and cloud adoption solve the problems of access and storage, but there’s another hurdle: making data usable. That’s where FAIR principles—Findable, Accessible, Interoperable, and Reusable—come in.
FAIR is no longer aspirational. Major institutions like the National Institutes of Health have formally adopted FAIR principles as guiding policy, and Google Trends shows steady growth for interest in “FAIR data” as a search query.
FAIR data is significant because it is a prerequisite to actionable data. This is especially important for labs in highly regulated or collaborative environments. In drug development, for example, data needs to move seamlessly between preclinical, clinical, and regulatory teams. Without FAIR structuring, that data often requires manual reformatting, introducing delays and errors. With FAIR, it can flow smoothly through each stage of the pipeline.
The synergy of these trends
Taken individually, each of these trends offers tangible benefits. But together, they are transformative. Cloud platforms provide the infrastructure to centralize data. That centralization enables desiloing. Once data is consolidated, FAIR principles ensure it is structured for maximum utility. With these pieces in place, labs reach a tipping point: they are primed to become more data-driven, streamlined, and AI-ready. Instead of relying on generic models trained on external datasets, labs can fine-tune AI systems on their own unique data, yielding insights that are specific, accurate, and actionable.
Key takeaways
The path toward a fully digital, AI-enabled laboratory isn’t built in a day. It requires careful steps: dismantling silos, embracing the cloud, and structuring data according to FAIR standards. These aren’t isolated initiatives but interconnected trends that reinforce one another.
Labs that act now to align with these shifts will be best positioned to thrive in the coming decade. The labs of tomorrow will not simply generate data; they will harness it—fluidly, intelligently, and with a digital backbone designed for innovation.
-Note: This article was produced with the assistance of generative AI.
