Data Fabrics in Pharma: Connecting Data for Better Decisions

A data fabric is a design approach for connecting data across many systems (on-prem and cloud, structured and unstructured). Data fabrics provide a unified way to discover, access, govern, and deliver that data using metadata and automation.

A helpful way to think about it:

• The data stays where it is (often for good reasons: validation, governance, ownership, performance).
• The fabric provides an “integration layer” that makes the data findable and usable across workflows.
• Access and policy are handled consistently through metadata and controls.

The difference isn’t in architecture, but in the content of the connected silos. A clinical data fabric (CDF) might unify everything from trial protocols to EHR data. More broadly, a “biopharma”, “drug development” or “life science” data fabric might include:

• A chemical compound registry
• Results from in vitro assays
• Results from non-clinical studies
• CMC records
• Human trial documents and findings
• Regulatory submissions
• Global value dossiers
• A link to web sources such as PubMed

That’s only a partial list. There is no official set of sources that make a data fabric a “clinical” or “discovery” data fabric. Still, whenever these sources contain proprietary data, it’s even more critical that the rules governing their security and management remain intact. Data fabrics respect those controls.

The value of a CDF lies in enabling secure, cross-study discovery without disrupting validated systems. Teams can retrieve evidence across trials, programs, and documents while maintaining access controls and traceability.

For many organizations, the CDF becomes the first high-impact step toward a broader data fabric in pharma.

A well-designed data fabric in pharma does more than connect systems. It enables faster, more reliable decision-making across the development lifecycle.

Key benefits of data fabric in life sciences include:

• Faster cross-system search without duplicating validated data
• Better use of unstructured content, including study reports and regulatory documents
• Stronger governance, preserving source-system permissions and audit trails
• Reduced duplication, avoiding unnecessary data movement
• Improved AI readiness, supported by structured metadata and permissions-aware retrieval

In regulated environments, these capabilities translate into shorter cycle times, improved evidence reuse, and more traceable outputs.

In a data-fabric setup, indexing means creating a searchable representation of connected content so it can be discovered and retrieved through a single point of entry, without needing to copy everything into a new system.

Modern indexing often includes:

• Metadata indexing: titles, authors, dates, study IDs, compounds, endpoints, source systems, and access controls
• Content indexing: extracting text (and sometimes tables/structure) from documents like PDFs and Word files
• Semantic indexing: representing meaning (often with embeddings) so “conceptual” matches work, not just keyword matches
• Entity/relationship indexing: tagging key biomedical entities (drug, target, AE, population, endpoint) and how they relate, useful for exploration and graph-style queries.

A data lake is a centralized repository that stores large volumes of raw data, structured and unstructured. Data is typically copied or ingested into the lake. While lakes can support analytics at scale, they may introduce duplication, governance challenges, and version-control complexity if not carefully managed.

A data mesh is an organizational and governance model, rather than a specific technology. It promotes domain ownership of data, treating data as a product and distributing responsibility across teams. A mesh focuses on the operating model and accountability more than on integration architecture.

A data fabric is an architectural approach that connects data across systems without requiring everything to be moved into a single repository. It relies on metadata, indexing, and governance controls to enable unified discovery, access, and policy enforcement across distributed environments.

In practice:

• A data lake centralizes storage
• A data mesh decentralizes ownership
• A data fabric connects and unifies access

These approaches are not mutually exclusive. A data fabric in pharma can connect existing lakes and operate within a mesh-style governance model, helping organizations improve discovery and compliance without rebuilding their entire data estate.

Data fabric and federated computing are sometimes mentioned together because both aim to reduce unnecessary data movement. However, they solve different problems.

Federated computing (often discussed as federated learning in life sciences) is a computational approach. Instead of moving data into a central environment, algorithms are sent to where the data resides. The model trains locally, and only model parameters or aggregated results are shared centrally. This approach is commonly used when privacy, data residency, or institutional boundaries prevent raw data from being shared.

Federated computing is primarily about:

• Distributed analytics or model training
• Preserving privacy across institutions
• Minimizing cross-border or cross-organization data transfer

A data fabric, by contrast, is an architectural approach for unified discovery, access, and governance across distributed systems. It focuses on metadata, indexing, and permissions-aware retrieval so users can find and use data across silos without centralizing everything.

A data fabric is primarily about:

• Cross-system search and discovery
• Consistent access control and policy enforcement
• Connecting structured and unstructured content
• Supporting traceable analytics and AI workflows

In practice:

• Federated computing distributes computation
• A data fabric unifies access and visibility

The two approaches are not mutually exclusive. A life sciences organization might use a data fabric to index and discover data across trials, institutions, and repositories, while using federated computing to train models on sensitive patient-level data that cannot leave its source environment.

In short, federated computing protects how models learn from distributed data. A data fabric improves how people and systems discover, access, and govern that data across the enterprise.

A data fabric provides governed, discoverable, high-quality, well-described data and metadata, which is exactly what generative AI needs to retrieve the right context, stay compliant, and produce reliable outputs.

Generative AI can help in three practical ways:

A common best practice is retrieval-augmented generation (RAG): retrieve relevant passages from an external index, then generate an answer grounded in those passages. This typically uses a dense/semantic index as an external “memory” for the model.

GenAI can pull structured fields (e.g., PK parameters, dosing, populations, endpoints, adverse events) out of narrative documents and turn them into analysis-ready tables, especially when extraction follows a defined schema and preserves relationships among fields.

In regulated settings, speed only helps if results can be verified. A strong pattern is “answers with receipts”: show the supporting source passages and maintain traceability from source → extraction/summarization → output.

Start with a small number of high-value workflows (e.g., trial feasibility, safety signal review, evidence synthesis) and connect only what’s needed first.

When possible, connect systems in place and avoid duplicating data into yet another lake or warehouse, especially where validation and data ownership matter.

Use active metadata to drive search, governance, lineage, and automation. Permissions-aware retrieval should be part of the design, not an afterthought.

Indexes become unreliable when they drift. For high-change sources, near-real-time indexing helps keep answers current.

Use RAG-style grounding, require citations, and design review checkpoints. This reduces hallucination risk and helps with QC and validation.

Clinical and development environments increasingly emphasize risk-based quality approaches and responsible use of modern technologies, which makes auditability and controlled workflows especially important.

Applying FAIR-style thinking (Findable, Accessible, Interoperable, Reusable) helps the fabric support not just one project, but cross-program learning and reuse.

These are common, high-impact patterns where data fabrics and reference-backed GenAI tend to pay off.

Trial enrollment often stalls because critical signals are spread across structured records and unstructured notes/criteria text, making matching slow and inconsistent.

A data fabric in pharma can connect patient records, genomics data, and trial information (without replicating everything), enabling faster search, relationship discovery, and more transparent matching rationale.

Some programs need to connect genomics formats (e.g., VCF/BAM), literature, images, and clinical context. A fabric supports cross-source search and harmonization, while AI-based extraction and entity recognition can enrich what’s retrievable, even beyond existing ontologies.

Teams frequently need rapid answers like: What’s been tested before? What signals appeared? Where are the relevant reports? A fabric makes it possible to retrieve evidence across many repositories and summarize it with traceable references, without manually hunting through systems.

Many workflows require turning narrative documents into structured datasets (e.g., endpoints, arms, doses, populations, outcomes). A fabric supplies the retrieval foundation; GenAI supplies the extraction layer, especially when prompts/workflows are schema-driven, and QC is built in.

Regulatory work is fundamentally evidence assembly. A fabric enables fast retrieval across relevant sources, and grounded GenAI can accelerate drafting and review, so long as outputs remain reference-backed and reviewable.

A drug-development-ready implementation needs: broad connectivity, governed indexing, strong retrieval, grounded GenAI, traceability, and deployment options that fit regulated environments.

Certara.AI is positioned around those requirements:

• Flexible data fabric for real-time access across multiple sources, supporting simultaneous search and analysis.
• Real-time indexing and connectivity framework, helping keep content current and searchable.
• Reference-first trust features, including workflows designed for review/validation with references to support verification.
• Model-agnostic approach, supporting tailored models or preferred model choices depending on governance and infrastructure needs.
• Security and deployment options aligned with enterprise and regulated requirements (including ISO27001 and deployment flexibility).

In practical terms, this means the same best-practice blueprint described above, connect → index → retrieve → extract/summarize with references → QC, can be implemented in a single environment designed for drug-development workflows and traceable outputs.