Omics technologies have transformed life sciences, generating unprecedented volumes of biological data. Yet for many organisations, the challenge is no longer access to data—it is knowing how to use it.

 

Dan Ryder, CEO of Bridge Informatics, believes the issue lies in a fundamental disconnect. “Companies are starting to realise they need to become data companies first,” he says—not instead of biology, but to make it actionable at scale.

 

Bridge Informatics operates at this intersection, helping organisations move from fragmented datasets to structured, usable assets. While the industry has invested heavily in analytics and AI, Ryder argues the real bottleneck is infrastructure. Without well-organised, traceable and scalable data systems, even the most advanced tools struggle to deliver meaningful insights.

 

This challenge is becoming more visible in today’s constrained funding environment. As biotech companies face pressure to demonstrate value, the focus is shifting toward efficiency and outcomes. For Ryder, that means connecting data generation directly to decision-making—whether validating targets, prioritising programmes or supporting clinical strategy.

 

He also challenges the idea that omics-driven research is merely a “fishing expedition.” In complex biology, hypothesis generation is essential. High-dimensional data can reveal patterns and mechanisms that traditional approaches miss. The key is ensuring that exploration leads somewhere useful.

 

That principle extends to AI. While often seen as a solution, Ryder cautions that models alone are not enough. Without strong data foundations and regulatory-ready systems, AI risks adding complexity rather than clarity.

 

Ultimately, Ryder sees progress depending on greater integration—between biology and data science, and across the wider ecosystem. Collaboration, shared frameworks and better infrastructure will be critical to unlocking the full potential of data-driven drug discovery.

 

For Bridge Informatics, the goal is clear: helping organisations turn data into decisions that move science forward.