Ten Years Of FAIR: How Far We've Come, And How Far We Still Need To Go

By Giovanni Nisato, Ph.D.

As the life sciences industry grapples with the demands of AI and the need to integrate real-world evidence into increasingly complex pipelines, the FAIR data principles — Findable, Accessible, Interoperable and Reusable — are beginning to prove their value. Nearly a decade after being introduced, the Pistoia Alliance conducted new research to assess the technical progress and business value of FAIR across areas including drug discovery, clinical development and chemistry manufacturing and controls (CMC) functions.

The findings show that FAIR has shifted from theory to implementation in many organizations and is increasingly seen as the foundation for a scalable, trustworthy, data-driven ecosystem, including the deployment of AI technologies. Yet, the journey toward FAIR is far from complete. While many companies are out of the starting blocks, FAIR maturity varies widely and several companies are only just starting the journey. Organizations face challenges spanning culture, leadership, infrastructure, and skills to implement FAIR data principles.

Here we explore the current FAIR landscape, the business outcomes now emerging and three imperatives for advancing FAIR maturity.

FAIR In Action: What The Research Shows

A key finding from the research is that the application of FAIR principles has expanded beyond R&D into clinical development, regulatory workflows, and CMC functions, among others. Organizations that began implementing FAIR five or more years ago are now reporting tangible business benefits, clustered into four key themes:

• Trusted data: Improved data integrity and quality through better metadata. One organization noted metadata accelerated clinical trial setup and enabled faster partner identification, including CROs.
• Cost savings: Increased productivity and reduced data duplication. One respondent cited lower storage costs by eliminating redundant data copies.
• Speed: Faster time to market, decision making, pipeline cycle time, and throughput. Several organizations reported reducing the time to locate and aggregate relevant data from weeks to just hours or even minutes.
• Effectiveness: Unlocking insights that were previously out of reach. One respondent mentioned new insights generated from in vivo pharmacology and clinical studies, such as mechanics of pathways and endpoints.

The research also found FAIR’s value extends beyond technical and business benefit to human impact. One respondent explained FAIR had optimized resource allocation, enabling teams to manage a larger pipeline with improved job satisfaction due to reduced manual burden. Another observed that intuitive dashboards now allow senior leaders to access and query data without technical skills.

Mapping The Maturity Curve

While the business benefits of FAIR are becoming clear, many organizations still struggle to scale initiatives beyond pilot projects. This is often not due to lack of technology infrastructure. Instead, the challenge lies in driving cultural and process change within organizations. Until recently, progress was difficult to benchmark, as assessments were mostly informal and internal, and often focused too much on technology rather than FAIR as a multifaceted challenge. Even as third-party consultancies have begun conducting FAIR assessments, there’s still a lack of consistency in evaluations as many different methodologies exist and they often produce conflicting results.

To help organizations understand their current level of maturity and learn how to advance their FAIR initiatives, the Alliance developed the FAIR Maturity Matrix. The Matrix is a publicly accessible framework that enables consistent evaluation of progress, both within and across companies. Reflecting the complexity of FAIR, the matrix spans seven dimensions: Data, Infrastructure, Knowledge, Roles, Processes, Strategy, Leadership. Each is evaluated on a scale from Level 0 (‘life is unFAIR’) to Level 5 (‘FAIRest of them all’).

So far, no organization has reached level 5. Achieving this top level requires seamless cross-organizational interoperability, which remains aspirational given the complexity of aligning infrastructure, standards and governance across diverse systems, and regulatory environments.

What’s Next For FAIR? Three Imperatives For Progress

1. Embed change management

FAIR is as much a cultural transformation as it is a technical one. While initiatives are launched from the top-down, success depends on organization-wide engagement. Even with strong leadership, cultural change remains one of the biggest barriers to progress. Change management should be embedded from the outset to ensure that individual business functions understand the value of FAIR and are aligned with technical efforts.

FAIR champions, whether internal advocates or external experts, can sustain momentum and bridge functional divides. Leadership support is especially critical, consistently cited in the research as a key success factor. Executive-level sponsorship is key to breaking down many cultural and technical silos and secure the investment needed to scale FAIR initiatives effectively.

2. Treat FAIR as a digital evolution, with revolution potential

FAIR isn’t a one-time project. It’s an evolving capability that underpins the broader digital transformation as organizations shift toward becoming data-centric. FAIR must adapt alongside shifting regulations, business strategy and technologies like AI.

Organizations should take an iterative approach, embedding FAIR through use cases and gradual change. Pistoia’s survey found it can take two to five years to realize the full benefits of FAIR, especially in complex settings like clinical trials. Progress may be slow, but consistent change lays the groundwork for more transformative outcomes over time. Therefore, tracking and communicating small wins is important to demonstrate early value to secure investment for future FAIR initiatives.

3. Standardize and scale

To embed FAIR at scale, the industry needs shared standards, ROI frameworks, FAIR champions and assessors (both internal and external) consistently using tools such as the Maturity Matrix. These mechanisms are essential for benchmarking progress and aligning FAIR efforts across departments and industry partners. Additionally, shared standards and frameworks pave the way for cross-organizational interoperability and collaboration, a hallmark of the highest FAIR maturity level. Without common standards and evaluation methods, progress risks remaining fragmented. Establishing a unified approach is key to advancing FAIR beyond individual organizations and unlocking its full value across the life sciences ecosystem.

Building A FAIRer Future

FAIR is not an end point, it’s a foundation for trustworthy, efficient, and intelligent science. In a fragmented ecosystem where data is scattered across systems, functions and companies, no single organization can define common standards alone. Collaboration is an effective strategy for enabling interoperability and aligning FAIR efforts across the industry. Companies are embracing FAIR because it helps them achieve better outcomes, from more trustworthy data and reduced duplication to faster development timelines. Investing in FAIR today will help organizations remain competitive and compliant in an increasingly data- and AI-driven future.

The Pistoia Alliance will continue to evolve the FAIR Maturity Matrix, support best practice sharing, and explore new models for assessment and benchmarking. Organizations are invited to join this next phase, whether as contributors, assessors, or adopters. By working together, we can move beyond siloed progress and unlock the cross-organizational collaboration that true FAIR maturity requires.

About The Author:

Giovanni Nisato, Ph.D., is an expert in collaborative innovation management across organisations at the international level. He has over 20 years of experience in the industrial and public-private deep tech sectors. As a Project Manager with the Pistoia Alliance, he has facilitated the FAIR Community of Experts since 2022. Its aim is to co-create and maintain pre-competitive resources to foster the implementation of FAIR data principles in the life sciences ecosystem for the benefit of pharmaceutical companies, CROs, technology providers, and ultimately, patients.