This is an article from the Pistoia Alliance FAIR for Pharma Community 

Giovanni Nisato, Project Manager, Pistoia Alliance

Picture a data junkyard. Assets everywhere. Potentially valuable. Almost impossible to find anything specific, and even if you do, you are not sure whether it is reliable, what terms apply to it, or how to integrate it with anything else.

Now picture an online store. Every product is catalogued. Searchable by multiple criteria. Described with structured information. Access conditions are clear. You can browse, filter, compare, and order in minutes.

Most pharma organizations sit somewhere on that spectrum. The question is: where are you now, and what does the journey to a better data experience look like in practice?

That is the question the FAIR Maturity Matrix was built to answer.

Who should read this 

This is for data strategy leads, CDOs, R&D informatics leaders, data governance teams, and anyone tasked with making FAIR a reality inside a pharma or life science organization — especially those who have struggled to communicate progress, or to compare their situation with peers.

What this article covers

Three things: why a sector-wide FAIR maturity model was needed, what the FAIR Maturity Matrix actually is and how it works, and how to use it as a practical tool for your organization.

1. The problem: no shared language for where you are

Organizations have been implementing FAIR data principles for years. But assessing, comparing, and communicating progress has been consistently difficult, not because organizations are not making progress, but because there was no agreed-upon reference point to describe it.

What does “we are working on FAIR” mean? Is it one team adding metadata to a pilot dataset? A program-level governance policy? An enterprise infrastructure for persistent identifiers? All of these are “working on FAIR” — and they represent vastly different levels of organizational capability, investment, and maturity.

This ambiguity has real consequences. Leadership cannot allocate resources effectively without understanding where they are. Partners cannot collaborate without a shared baseline. Regulatory and funding bodies cannot assess commitments without a common reference.

The Pistoia Alliance FAIR Community of Experts started addressing this in February 2023: building a cross-sector, organizational-level maturity model that practitioners from across pharma and life sciences could agree on — descriptive enough to be accurate, simple enough to be usable.

The result is the FAIR Maturity Matrix (FAIRMM).

2. What the FAIR Maturity Matrix is 

The FAIRMM is a structured framework with two axes: six maturity levels and seven dimensions. Together they give a 42-cell picture of an organization’s FAIR implementation at any point in time.

Six levels — from junkyard to online store

The six levels map a plausible FAIR implementation journey, from the starting point to a fully interoperable data ecosystem:

• L0 — Life is unFAIR (Junkyard) — Data exists but is application-centric and largely inaccessible. No systematic management. Data treated as a tactical, not strategic, asset.
• L1 — Started the FAIR journey (Flea market) — Initial steps taken: humans can begin to locate data more efficiently, some basic metadata exists. Data is starting to be recognized as a strategic asset.
• L2 — Getting FAIR (Street market) — Machine findability and interpretation begin to become possible. Data is being decoupled from applications. Access protocols and licensing start taking shape.
• L3 — Pretty FAIR (Local market) — Findability is largely achieved within the organization. Processes for FAIR are formalized, including training, documentation, and workflow integration. Data can be reused at departmental level.
• L4 — Really FAIR (Hypermarket) — Data exchange across functions enables AI development and insight generation. Regulatory compliance and auditing are facilitated. Interoperability enables dataset integration at scale.
• L5 — FAIRest of them all (Online store) — Cross-organizational data exchange and augmentation. AI trusted actionability significantly increases. Secondary reuse of data, real-world evidence mining, and cross-industry collaboration become possible.

One key point the FAIRMM makes explicit: you do not need to target Level 5. The appropriate maturity level depends on your organization’s goals, business context, and use cases. A Level 3 organization with well-governed, findable, and reusable data within its own functions may be exactly where it needs to be.

Seven dimensions — the full picture of FAIR implementation

Progress along the maturity axis is assessed across seven dimensions, which together capture everything an organization needs to implement FAIR effectively:

1. FAIR Data — the quality and FAIRness of the data assets themselves
2. FAIR Leadership — executive commitment, governance, and resource allocation
3. FAIR Strategy — how FAIR is embedded in organizational strategy and planning
4. FAIR Personas — the roles and capabilities in place to implement and champion FAIR
5. FAIR Processes — the workflows, policies, and operational practices that make FAIR happen
6. FAIR Knowledge — the training, awareness, and expertise available across the organization
7. FAIR Tools and Infrastructures — the technical systems supporting FAIR implementation

The matrix is designed to be read both horizontally (how do all dimensions look at your current level?) and vertically (what does improvement in one dimension look like across levels?). This dual reading gives practical direction: not just “where are we?” but “what specifically do we need to change?”

3. Using the FAIRMM as a practical tool

The FAIRMM is descriptive, not prescriptive. It enables multiple stakeholders to look at the same organization and reach similar conclusions about its current FAIR state — a shared reference point for conversations that have historically been difficult to have.

Four concrete benefits, directly from how organizations have used it:

• Enterprise alignment — a common language across business, IT, governance, and data teams to drive consistent adoption and avoid parallel, disconnected initiatives.
• Measurable progress tracking — trackable KPIs for leadership: what is our current maturity level, which dimensions are lagging, and how has that changed over time?
• Clear baseline and targets — an evidence-based view of the current state and a realistic end-state that matches organizational goals and resources.
• Prioritised investment roadmap — converting identified gaps into a sequenced improvement plan, which strengthens the funding case and enables cost/benefit analysis.

The FAIRMM was recognized with the Bio-IT World 2025 Innovative Practices Lighthouse Award — an acknowledgement from the broader life science data community of its practical value.

It is free, openly published, and available now.

What you can do

Three steps to start using the FAIRMM this quarter:

1. Run a self-assessment. Pick one business unit or data domain. Walk through the seven dimensions for each maturity level and identify where the description best matches your current reality. Even a rough first pass will surface the most significant gaps.
2. Use it to align stakeholders. Share the matrix with leadership, IT, and governance teams and run a structured conversation: do we agree on where we are? Where do we want to be in 12–18 months? What dimensions are most limiting our current goals?
3. Map it to a use case. Connect your FAIRMM position to a concrete business pain point: a cross-trial analysis that takes too long, an AI project blocked by data quality, a regulatory query that requires weeks of data archaeology. FAIR improvement in the relevant dimensions is the path to resolving it.

Where to learn more

The FAIR Maturity Matrix — free, publicly available, and actively maintained by the Pistoia Alliance FAIR Community of Experts

The full FAIRMM documentation, including all dimensions, levels, personas, and business value connections.