How federated analytics enables decision-ready evidence in pharma

At a glance

• Drug development costs continue to rise. 
• Clinical trials are becoming more complex. 
• Real-world evidence (RWE) is now influencing decisions across R&D, clinical development, regulatory strategy, market access, medical affairs, and commercialization. 
• Decision-makers increasingly require evidence that is timely, credible, transparent, and relevant. 
• Healthcare data remains fragmented across countries, organizations, systems, and modalities. 
• IT and data science teams are under pressure to support scalable analytics while meeting privacy, governance, interoperability, and auditability requirements. 

Life sciences organizations have access to more healthcare data than ever before, yet many still struggle to generate evidence that is timely, transparent, auditable, and relevant to specific decisions. As evidence requirements expand across the product lifecycle, organizations must transform fragmented, governed healthcare data into trusted, decision-ready evidence while meeting growing expectations around privacy, governance, interoperability, and auditability. 

Why pharma needs a new evidence operating model 

Today’s organizations often focus on: 

But those questions miss the operational challenge now facing the industry. Life sciences organizations have access to more healthcare data than ever before, yet data abundance is not the same as evidence readiness. Larger datasets do not automatically produce better decisions. Decision-ready evidence depends on context, quality, provenance, governance, and analytical rigor.  

As evidence requirements expand across the product lifecycle, organizations are shifting from collecting data to supporting decisions. That shift requires an operating model capable of applying common analytical methods across distributed, governed data environments. One-off evidence workflows are difficult to scale, often increasing timelines, duplication, costs, and inconsistencies while limiting opportunities for reuse. 

Core takeaway 

Pharma teams may start by asking how to access more data, but the bigger challenge is how to generate reliable, comparable evidence from data that is fragmented across countries, partners, and healthcare systems. As data movement becomes harder to scale, organizations need approaches that can support consistent evidence generation without requiring every dataset to be centralized. 

The three tensions reshaping evidence generation 

For pharma teams, the challenge is not simply finding more data. It is turning fragmented, governed data into evidence that can support decisions across research, clinical development, regulatory strategy, medical affairs, and commercialization. Three tensions are making that harder. 

More data is available, but making it usable for evidence is harder 

Healthcare data continues to expand across systems, countries, partners, and modalities, creating promising new opportunities for evidence generation. But for pharma teams, access alone is not enough. Data must be findable, permissioned, harmonized, governed, and analyzed consistently before it can support a regulatory, clinical, medical, or commercial decision. 

Evidence needs are becoming more specific, but the right data is still fragmented 

Organizations increasingly need evidence tailored to specific research, regulatory, clinical, and commercial decisions. Yet the data required to support those decisions often remains distributed across multiple organizations, healthcare systems, and geographies. 

Global evidence needs are growing, but data access and governance rules are becoming more complex 

As evidence generation becomes more global, organizations must navigate country-specific access rules, privacy requirements, and data-governance expectations while still producing evidence that is transparent, traceable, and trusted. It is not enough to access distributed data; teams also need confidence in how that data was permissioned, analyzed, and transformed into evidence. 

Together, these tensions make it harder for pharma teams to generate evidence that is timely, consistent, and decision-ready. Accessing more data is not enough if each study requires custom data preparation, separate governance processes, or new analytical workflows. Organizations need a more scalable way to apply common methods across distributed, governed data environments while preserving trust, transparency, and local control. 

Core takeaway 

Pharma teams need an evidence model that can scale across fragmented, governed data environments without recreating the process for every study. The opportunity is to make evidence generation more consistent, reusable, and decision-ready while preserving trust, transparency, and local control.

Federated analytics as the bridge between data access and evidence generation 

Federated analytics offers a different way to work with healthcare data that cannot always be moved, pooled, or centralized. Instead of bringing every dataset into one repository, organizations can apply common analytical methods across governed data environments and compare results across countries, partners, and systems. 

This makes federated analytics especially relevant for global studies, multi-country data networks, multi-modal evidence generation, and settings where privacy, data sovereignty, or national governance requirements limit how data can be shared or moved. 

In simple terms, analytics travel to the data rather than the other way around. Organizations can answer common research questions across distributed datasets while allowing local partners to retain appropriate control over their data. Researchers can then combine those insights to generate credible, decision-ready evidence across countries, populations, and care settings. 

Because healthcare data is inherently distributed across care settings, systems, organizations, and countries, federated analytics gives pharma teams a practical way to generate trusted evidence across diverse, governed real-world data environments. 

Core takeaway 

Federated analytics gives pharma teams a more scalable way to generate evidence from data that remains distributed across countries, partners, and healthcare systems. By applying common methods across governed data sources, teams can produce results that are easier to compare, trust, and use.

A framework for building fit-for-purpose evidence 

A modern evidence operating model starts with the decision first, then identifies the data, methods, and governance needed to support it. Rather than starting with the data that happens to be available, organizations start with the decision they need to support and build the evidence strategy around that objective. 

Building fit-for-purpose evidence requires six interconnected capabilities: 

Evidence design

Start with the decision, population, geography, and evidentiary requirements. 

Bespoke data network assembly

Assemble the right mix of internal, partner, and external data sources. 

Research-ready harmonization

Transform fragmented data into consistent, usable evidence assets. 

Flexible governance and access 

Align access models with privacy, regulatory, and operational requirements. 

Cross-region interoperability 

Support evidence generation across countries and healthcare systems. 

Lifecycle reuse 

Preserve data assets, methods, governance models, and workflows so future teams can build on what already exists. 

Federated analytics acts as the enabling layer across this framework, allowing common data assets, methods, governance models, and analytical workflows to be reused across teams and decisions. Instead of rebuilding the process for every study, organizations can create a stronger evidence foundation that saves time, reduces duplicated effort, lowers cost, and improves consistency across the product lifecycle. 

Core takeaway 

Leading organizations are moving beyond one-off evidence projects and building reusable evidence foundations that save time, reduce duplicated effort, lower cost, and help teams generate more consistent evidence across the product lifecycle.  

Three questions every leader should ask

• Are we designing evidence around the decisions we need to support, or around the datasets we happen to have?
• Can we generate comparable evidence across countries, partners, and data types without starting from scratch each time?
• Are we building reusable evidence assets that save time and improve consistency, or rebuilding the process for every study?

Putting federated, fit-for-purpose evidence into practice 

Designing a federated evidence operating model is one thing. Operationalizing it across geographies, data partners, and healthcare systems is another. 

BC Platforms helps organizations put this model into practice by combining global, multi-modal data access, federated and hybrid research models, trusted research environments, data harmonization, and reusable evidence foundations. Together, these capabilities help life sciences teams generate consistent, decision-ready evidence from healthcare data that remains distributed across countries, partners, and systems. 

The value extends across the organization. Evidence, clinical, market access, data science, IT, and digital teams all benefit when trusted data assets, methods, governance models, and workflows can be reused — shortening timelines, reducing duplicated effort, improving consistency, and strengthening readiness for AI-enabled evidence generation. 

The next five years: evidence generation becomes federated by design 

Evidence generation is evolving rapidly. AI-enabled research workflows, multi-modal data integration, and increasing use of real-world evidence in regulatory decision-making are raising expectations around transparency, reproducibility, and scale. At the same time, organizations are moving toward continuous evidence generation and reusable evidence ecosystems that support decisions across the product lifecycle. 

As these trends converge, federated analytics will become a core capability for global, privacy-preserving evidence generation. Rather than relying on data centralization, organizations will increasingly bring analytics to governed data environments. 

Evidence generation will become: 

• More global 
• More iterative 
• More automated 
• More decision-driven 
• More federated, with analytics moving to governed data environments instead of relying on data centralization 

From fragmented data to decision-ready evidence 

More data can create more opportunity, but only if organizations can turn fragmented, governed data into trusted evidence that is consistent, reusable, and ready to support decisions across teams and markets. 

Federated analytics, combined with reusable evidence foundations, gives organizations a more scalable way to do that — helping teams generate evidence faster, reduce duplicated work, improve consistency, and create more long-term value from their evidence investments.