Is your lab’s data truly working for you, or is it just sitting in silos, buried in spreadsheets, and locked away on individual workstations? As scientific projects grow more complex and collaborative, unmanaged data can slow progress, hinder reproducibility, and create compliance risks.

That’s where the FAIR principles come in. By following them, labs can turn fragmented datasets into valuable, shareable assets. But translating those principles into action isn’t always straightforward—every lab is unique, so there are no “one size fits all” packages that labs can use to achieve FAIR data. That’s why the Pistoia Alliance developed the FAIR Data Maturity Matrix, a practical framework to help labs evaluate where they stand and how to move forward.

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What does FAIR mean, and why does it matter?

FAIR stands for Findable, Accessible, Interoperable, and Reusable. These principles aim to make data easier to locate, understand, share, and reuse by both people and machines. For lab managers, implementing FAIR practices means fewer bottlenecks, more efficient collaboration, and higher confidence in data quality. FAIR data can accelerate research, simplify regulatory compliance, and improve reproducibility across projects and teams. As labs face increasing data volume and complexity, adopting FAIR principles offers a structured way to manage information sustainably and strategically. FAIR data is also an important component of lab digitalization and AI readiness, making it a worthwhile strategic investment.

What is the FAIR Data Maturity Matrix?

Developed by the Pistoia Alliance and released in 2024, the FAIR Maturity Matrix offers a consistent and easy-to-use framework for organizations to benchmark their own place in the FAIR data journey and next steps avenues for further implementation.

The matrix consists of “Dimensions,” which are likened to rows in a spreadsheet, and “Levels,” which equate to columns. At each intersection of rows and columns is a cell describing what the Dimension may look like at the accompanying Level of maturity.

The Dimensions of FAIR maturity

The Dimensions of the Matrix represent the various perspectives by which the status of an organization’s FAIRness can be seen. fixed, non-hierarchical, and independent—meaning each one can progress to a different Level regardless of the others. Here’s a breakdown of what each dimension represents as described by the Pistoia Alliance:

• Data: The various data that a lab generates—experiment results, inventory data, equipment statuses, and more—as well as its metadata and the data products or tooling associated with the data, such as dashboards, LIMS, and AI models.
• Leadership: The leaders in the organization who would be responsible for supporting and strategizing FAIR implementation. According to the Pistoia Alliance, leadership owns the vision and can ensure that the proper resources are allocated to the process.
• Strategy: As the Pistoia Alliance says, “Strategies are frameworks for making decisions related to FAIR implementation, from business case to capability-building to running operations.” This dimension also covers identifying metrics for success, which departments must be involved, and qualifying any cultural changes that may be required.
• Roles: The specific jobs required to implement FAIR principles, including identifying who will be accountable for various aspects of the implementation, what tasks they’ll handle, and ongoing maintenance after initial implementation.
• Processes: The workflows and standard operating procedures bridging the other Dimensions, particularly the Data, Roles, Knowledge, and Tools/Infrastructures Dimensions. This Dimension is like the glue joining all the others.
• Knowledge: The “factual, conceptual, [and] procedural knowledge required for FAIR implementation at various stages.” Associated with subject-matter experts, this Dimension connects to the Roles Dimension quite intuitively.
• Tools and Infrastructures: The hardware and software needed to implement and run a FAIR data solution. It is, of course, closely related to the Data Dimension. The Pistoia Alliance also describes this Dimension as containing the tools or infrastructure that specifically help achieve each element of FAIR.

The Levels of FAIR maturity

Unlike the Dimensions, the Levels are mutable, increasing as an organization’s FAIR implementation becomes more comprehensive and robust. Here’s an outline of each level, complete with questions that you can weigh against each of the Dimensions and help determine your lab’s FAIRness status.

1. Level 0: “Life is unFAIR”
   • No awareness of FAIR; data is siloed, inconsistently managed, and undocumented
   • Am I here? Questions to consider:
     • Is data stored in a consistent or searchable way?
     • Can others use your lab’s data without help?
     • Have FAIR principles been discussed by your team or leadership?
2. Level 1: “Started the FAIR journey”
   • Initial conversations about FAIR have started, possibly initiated by you, the lab manager. Individual supporters for FAIR data transformation may exist, but there is no tooling or strategy in place.
   • Am I here? Questions to consider:
     • Has anyone in the lab taken the initiative to explore or drive the change for FAIR practices?
     • Are they isolated efforts to document or standardize data/metadata?
     • Has your lab begun evaluating existing data for FAIR-related improvements?
3. Level 2: “Getting FAIR”
   • Data is being cataloged with unique identifiers and standardized metadata for findability. There is general interest in building on this foundation to make all the lab’s data more useful and actionable.
   • Am I here? Questions to consider:
     • Are data and metadata uniquely identified and easily accessible?
     • Are there any FAIR pilot projects or official advocates?
     • Are any training materials or standards emerging?
4. Level 3: “Pretty FAIR”
   • Data findability has largely been achieved with consistent data labeling, access protocols, and accessibility in place. Multiple departments are now able to make use of the data, and processes for FAIR are established, complete with training and documentation.
   • Am I here? Questions to consider:
     • Is FAIR budgeting included in new project plans?
     • Are metadata and identifiers standardized across teams?
     • Are FAIR principles included in documentation and onboarding to ensure long-term FAIR feasibility through staff turnover?
5. Level 4: “Really FAIR”
   • FAIR data is “prevalent across departments and divisions” and FAIR tools allow for automated data exploration and use. New cross-functional use cases become possible. FAIR implementation aligns with the broader data governance and risk management processes of the organization, and a comprehensive FAIR strategy is in place.
   • Am I here? Questions to consider:
     • Are data and metadata interoperable across the organization?
     • Is FAIR embedded in governance and risk processes?
     • Do teams outside the lab contribute FAIR data as well?
6. Level 5: “FAIRest of them all”
   • FAIR data principles are in use across all domains and core functions of the organization, not just the lab. Data is FAIR across its entire lifecycle, and infrastructure is in place for easy access and interoperability, allowing CROs, regulatory bodies, and other organizations to access it as well when needed.
   • Am I here? Questions to consider:
     • Is your lab’s data interoperable with data from external partners, like CROs or regulatory bodies?
     • Has FAIR become standard practice in project planning, training, and compliance across all teams?
     • Can digital tooling like dashboards and AI models automatically access and use your data without manual steps?

Example of a FAIR data maturity matrix

The FAIR Data Maturity Matrix can be a helpful resource for mapping a path to more interoperable lab data practices. Labs may already be excelling in some areas—such as data infrastructure or leadership support—while lagging in others, like assigning roles or formalizing processes. The matrix helps teams pinpoint those weak spots and identify specific, actionable steps to strengthen them. The Matrix is a tool you can revisit regularly to track your lab’s progress, prioritize next steps, and ensure that FAIR implementation continues to mature alongside your broader data strategy.