How biotech can leverage scientific enterprise SaaS

Early-stage companies in drug discovery face a relentless set of competing priorities. Raising funding, assembling the initial team, building lab operations from the ground up, generating proof-of-concept scientific data – to name a few – but all of which demand resources and attention.

Against that backdrop, it’s understandable that establishing a data management framework might fall to the bottom of the list. But before dismissing data management as out of scope or beyond your bandwidth, reconsider whether it’s merely an operational concern that can be treated as an afterthought. Could it be, in contrast, a key strategic pillar, where setting a solid data foundation puts a new company on the path to long-term success?

The answer matters, because the way a company manages experimental data and results shapes how effectively it can make decisions, pivot or scale its operations, collaborate across teams, support partnering discussions, and generate value from its science – particularly in this age of AI, where the future usefulness of data depends not only on its availability, but on its quality, context, and ability to be understood long after it was first generated.

The data model: Setting a strong foundation

Storing results in spreadsheets or via disparate file systems, analysing data with bespoke scripts, and sharing findings by email, team chats, or PowerPoint slides may work tactically in the very earliest stages, but fast becomes fragile as experiments, assets, staff, collaborators, and decision points multiply. If your organisation, or a company you are considering investing in, manages experimental data in this way — or you are not sure how consistently data is being captured, annotated, analysed, and reused — it may be time to take a closer look.

The alternative to this kind of fragmented approach is to adopt an enterprise data model: a structured, central framework that captures all experimental data generated by a company, standardises their analysis, and connects results to the context needed for understanding and reuse. A strong data model makes it possible to drill down into the context behind any result. For example, which sample was used in an experiment, how it was made and prepared, how and when the raw data was generated, which instrument and consumables were used, and how the raw data was analysed. Overarching context such as this is essential if results are to be trusted, compared, reproduced, and compound their value over time.

A strategic asset, not an administrative burden

The easiest way to implement such an enterprise data model is to adopt a software system that has it built-in, ready for use. Such a system serves multiple stakeholders across an organisation. Scientists use it to capture, analyse, and interpret experimental data. Group leads use it to aggregate results and decide the next steps for a project. Senior leaders use it to understand progress, risk, strategic opportunities, and the value that has been created. Data scientists – and increasingly, AI agents – depend on data that is accurate, high-quality, fully annotated, and FAIR at source so it can be mined, connected, and used to create additional value. They find it in such a system.

Seen in this light, data management and infrastructure is not just an administrative layer or a technical detail. It is a competitive advantage and a strategic value-adding component of a company’s infrastructure in and of itself.¹ Larger organisations leverage enterprise software with built-in data models to support their research operations. The question for younger companies is whether delaying that investment – relying instead on short-term workarounds and cheaper point solutions – may prove to be a false economy, given the rising development and maintenance costs of such a fragmented approach. Instead of simply asking, “Can we afford this software now?”, early-stage companies should also ask, “Can we afford to limit the value of our data?” The answer to the latter, of course, is no. But what if an enterprise-grade system seems out of reach on a start-up budget?

Enterprise-grade software, without complex infrastructure: SaaS delivery

The answer, we believe, is that it doesn’t have to be out of reach: biotechs and start-ups should be able to leverage gold-standard digital and software systems from inception, at a price point attainable for their stage of growth.

Critically, cost should not be the sole consideration – after all, as argued above, a data platform is an investment in a biotech’s future. Instead, key questions to ask include:

• How quickly can we get rolling? For biotechs, time is especially critical. Seek solutions with simple, one-click installation (and upgrades) and with e-training options to speed up user onboarding.
• How scalable is deployment? For many biotechs, a software-as-a-service (SaaS) model with a shared, cloud-hosted environment is a simple, scalable, and cost-effective option to start. However, as a company matures, its assay needs grow more complex and teams expand, so it’s worth thinking ahead, by picking a vendor that’s proven in scaling. Does your provider offer more advanced capabilities, making it straightforward, down the line, to add new workflows or manage and interrogate accumulated data? Will the solution be able to handle increasing data volumes and user traffic?
• How much will we need to build? Seemingly cheaper solutions can end up costing more, by requiring extensive in-house configuration, customisation, and resources. Vibe-coded apps cannot replace a well-developed, enterprise-level system.² Therefore, a better option will have key capabilities, including automated analysis workflows for the most standard assays, preconfigured out-of-the-box.
• Does this solution offer enterprise-level value? By enterprise value, we mean scalability, but also industry-wide adoption, compliance features, and scientific depth. Is the software you are selecting part of a robust, enterprise-ready platform? Does it enforce best-practice analysis and data management? When and if the day comes when your assets are acquired, will the acquiring company easily be able to integrate your data model?

These days, venture capital for start-ups is more competitive than ever. In this climate, biotechs need to leverage every possible scientific and technological approach they can, to hit commercial and therapeutic milestones. This includes AI and the data that powers it. By considering how to build a solid data foundation from the beginning – and by asking the questions above – smaller start-ups or biotechs can make sure they are ready to go; ready to use their data as a launchpad for innovation.

References

(1) Hardy, K.; Heyse, S. FAIR Data Policies Can Benefit Biotech Startups. Nat Biotechnol2023, 41 (8), 1060–1061. https://doi.org/10.1038/s41587-023-01892-8.

(2) Keary, T. The SaaSpocalypse Maybe Ending, But SaaS Will Never Be The Same Again. Forbes. https://www.forbes.com/sites/timkeary/2026/06/30/the-saaspocalypse-maybe-ending-but-saas-will-never-be-the-same-again/ (accessed 2026-08-27).

About the authors

Mark Brewer is head of Genedata Screener in the UK, with over 20 years’ experience in drug discovery and biopharma. A former computational chemist, he has held research leadership and scientific account management roles and now helps major biopharma and biotech organisations digitalise research workflows. He holds a DPhil from the University of Oxford and completed postdoctoral research at the University of Utah and Lawrence Berkeley National Laboratory.

Ada Yee is a science and technology manager at Genedata, supporting scientific communications and engagement. Previously, she has been a scientific editor and earned her PhD in Neuroscience from Stanford University.