FAIR Data in Biopharma: An Excelra Perspective

Turning Data Into a Strategic Asset for AI-Driven R&D

Biopharma R&D is generating data at unprecedented volume, velocity, and variety. Without the right frameworks, this data can overwhelm teams, stall research, and slow regulatory approvals.

The FAIR principles—Findable, Accessible, Interoperable, and Reusable—offer a blueprint for transforming raw data into a strategic advantage. But FAIR isn’t a switch—it’s a journey requiring cultural change, technical modernization, and measurable ROI.

Drawing on two decades of leadership in scientific informatics and data stewardship, Excelra shares how life sciences organizations can accelerate FAIR adoption at speed and scale.

In this whitepaper, you’ll discover how to:

• Align with regulatory mandates (EU Data Act, FDA RWE, EMA IDMP, PMDA Digital Strategy) to cut re-submission cycles by up to 25%.
• Build AI/ML-ready datasets with consistent identifiers, metadata, and governance to boost model performance.
• Manage real-world data from wearables, diagnostics, and eSource platforms—turning complexity into leverage.
• Quantify the hidden costs of “dark data” and how FAIR unlocks millions in saved resources.
• Operationalize FAIR through Excelra’s three-pillar value framework:
  1. Data Enrichment & Ontology Harmonization
  2. Metadata Automation & Provenance Capture
  3. Semantic Integration Layer with graph-based analytics
• Explore a case study in OMICS data FAIRification that improved findability by 40%, cut duplicate experiments by 25%, and fueled 15+ publications.
• Understand how FAIR benefits diverse R&D roles—from translational scientists to regulatory leads—by streamlining compliance, improving reuse, and fostering collaboration.

Why FAIR Matters Now

• Regulatory Tailwinds – FAIR is being written directly into digital submission frameworks.
• AI/ML Readiness – FAIRified corpora sharpen predictive models and reduce validation overhead.
• Digital Health Convergence – FAIR unlocks value from real-world and patient-centric data streams.
• Cost of Inaction – Poorly annotated data lakes waste $7–11M annually in duplicated work.

Bottom line: In 2025, FAIR isn’t just competitive differentiation—it’s operational survival in an AI-first research landscape.

Download the whitepaper to explore Excelra’s FAIR journey—and learn how to future-proof your R&D data ecosystem.