The Most Valuable Oncology Dataset May Be the One You Already Own

Every pharmaceutical company is investing heavily in data. More sequencing data. More screening data. More biomarker data. More real-world evidence. More AI initiatives. More cloud infrastructure.

Yet despite unprecedented investment, many of the most consequential decisions in oncology development are still made without the benefit of the full knowledge that already exists within the organization.

The problem isn’t a lack of data. The problem is that data alone does not create intelligence. And increasingly, it is not data volume that limits progress. It is decision latency.


The Hidden Cost of Decision Latency
Every oncology organization faces a common challenge. Before a critical decision can be made—whether selecting a biomarker strategy, prioritizing a lead asset, choosing a translational model, or designing the next experiment—scientists must assemble evidence from a growing collection of disconnected sources. The information may exist across:

  • High-throughput screening platforms
  • Omics datasets
  • Pharmacology reports
  • Animal study outcomes
  • Clinical observations
  • Publications
  • Internal presentations
  • Vendor reports
  • Historical development programs

Individually, these assets are valuable. Collectively, they represent years of institutional knowledge and often tens of millions of dollars of scientific investment. Yet much of this knowledge remains fragmented across departments, repositories, teams, vendors, and individual experts.

As a result, organizations often spend weeks—or months—answering questions whose components are already known somewhere within the enterprise. Questions like:

  • Which models have historically been most predictive for this mechanism?
  • Which biomarkers have demonstrated signal in related programs?
  • What lessons were learned from previous failures?
  • Which patient populations appear most likely to benefit?
  • What evidence already exists that could influence our next experiment?

The cost is rarely measured…. But it is substantial.

Every week spent gathering information is a week not spent advancing a program.

Every experiment launched without full context introduces avoidable risk.

Every decision made with incomplete information increases the probability of expensive downstream consequences.


The Search Problem Is Becoming a Decision Problem
Historically, organizations have attempted to solve this challenge through data warehouses, data lakes, knowledge management systems, and enterprise search tools.

These investments have improved access to information. But accessibility is not the same as intelligence. Scientists do not simply need more documents. They need a faster path to understanding. They need to connect findings across disparate datasets, evaluate competing hypotheses, identify hidden relationships, and understand the implications of those findings for the next decision.

The challenge is no longer finding information. The challenge is converting information into actionable insight quickly enough to influence outcomes.


The Emergence of Agentic Scientific Exploration
Recent advances in agentic AI systems introduce a fundamentally different approach. Rather than requiring scientists to manually assemble context from dozens of systems, agentic technologies can help researchers explore complex scientific questions using natural language.

Instead of searching for documents, scientists can explore relationships. Instead of retrieving information, they can investigate hypotheses. Instead of spending weeks gathering evidence, they can synthesize years of accumulated organizational knowledge in minutes.

This shift is not merely about productivity. It is about changing the quality and speed of decision-making itself. Organizations that reduce decision latency gain a powerful advantage. They can:

  • Reduce redundant experimentation
  • Accelerate translational learning
  • Preserve institutional knowledge
  • Increase confidence in development strategy
  • Identify opportunities that might otherwise remain hidden
  • Most importantly, they can make better decisions faster.


The Next Competitive Advantage
For years, life sciences organizations have focused on building data assets. The next competitive advantage may come from transforming those data assets into decision assets. The organizations that succeed will not necessarily be those with the largest datasets.

They will be those that can most effectively connect, explore, and learn from the knowledge they already possess. In an era when AI is making information increasingly accessible, the differentiator will not be who has the most data. It will be who can turn knowledge into decisions most effectively.

The future of oncology research is not simply about generating more data. It is about ensuring that every decision benefits from the full context of everything an organization already knows. Because ultimately, the organizations that compress weeks of analysis into minutes of insight will not simply operate more efficiently.

They will discover faster.

Develop smarter.

And bring better therapies to patients sooner.

About Certis Oncology
Certis Oncology is an AI-enabled translational science company dedicated to realizing the promise of precision oncology. At the core of the company’s platform is CertisAI™, a patented machine-learning system designed to model the relationships between drug chemistry and tumor biology. By integrating computational modeling with functional testing in clinically relevant patient-derived tumor models, Certis generates Oncology Intelligence®—highly predictive therapeutic response data that helps pharmaceutical companies select better drug candidates, design smarter development strategies, and improve the probability of clinical success.

Founded in 2016, Certis operates a CLIA-certified, AAALAC- and OLAW-accredited laboratory in San Diego’s Sorrento Valley life sciences hub.

MEDIA CONTACTS
Certis Oncology Solutions
Kristein King
kking@certisoncology.com
573-818-4528