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Version: 1.0 Date: 2024-03 04-19

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Why a FAIR

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organisational maturity model? Problem statement

Organizations can be at very different stages in implementing FAIR data principles at a given time. This variance presents challenges, among others, for their leadership to assess, qualify, measure and manage progress towards FAIR implementation. Benchmarking across organizations or even within one department can be very hard. Organizations must spend significant time clarifying situations, defining possible actions for desired outcomes, and road mapping, which . This further complicates the identification of stakeholders, resources needed, and as well as internal and external partners required to implement FAIR data principles to produce desired outcomes.

While there are multiple FAIR data maturity models and metrics, there is no simple, agreed-upon, sector-wide maturity assessment model for implementing the FAIR data principle at the organizational level in the life science sector.

FAIR: What do we mean with Findable, Accessible, Interoperable and Reusable ?

We refer to the FAIR data principles.

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"Reusable" refers to the suitability of data for use in different contexts and by different stakeholders, both humans and machines. Reusability involves providing clear and comprehensive documentation, including metadata, about the data's content, structure, and usage permissions. This documentation should be easily understandable and accessible to facilitate the effective reuse of the data by others. Additionally, data should be formatted in a standardized and interoperable manner, allowing it to be integrated with other datasets and analyzed using various tools and methods. By making data reusable, researchers can efficiently leverage existing datasets for new analyses and investigations, accelerating scientific progress and innovation. Moreover, clear usage licenses and permissions should accompany the data to specify how it can be reused, ensuring legal and ethical compliance.

Who worked on this model? FAIR implementation project, Best Practice Working group

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In February 2023, the Best Practice Working group Best Practices of the Pistoia Alliance’s FAIR implementation project started creating a cross-sector, organizational-level maturity model for FAIR implementation stages in the life sciences. This model intends for is intended to assist decision makers to evaluate in evaluating the stage of a given organization (or department) in terms of FAIR maturity, assess the options to achieve higher maturity levels and identify relevant resources that may be required to do so.

Guiding Questions for the creation of the FAIR maturity model

Here are some examples of the guiding questions used when drafting the model:

How do we establish a common shared understanding of the stage at which a given organization finds itself along a plausible FAIR implementation journey?
Where and how do we initiate a FAIR implementation journey?
What key road mapping stages can we recognise as a sector-wide group, i.e., the observable situations, based on the group experience?
What hurdles and benefits can we harvest along the way?
Which components, tools, and resources exist that we can refer to regarding FAIR implementation maturity?
What could one do at a given stage to improve and move to the next one?
How can we simplify and streamline communication to align different stakeholders?

Who is the FAIR maturity model for?

Stakeholders and intended user groups for the FAIR Maturity Matrix include:

Leadership and Managers of life-science organizations: those responsible for resource allocation and budgeting, even if they need to gain expertise in FAIR data principles.
FAIR Data Experts: individuals well-versed in the intricacies of FAIR data principles who play a crucial role in guiding and executing the implementation process.
Implementation Partners: This encompasses Contract Research Organizations (CROs), service providers, and consulting firms and academic partners, which contribute essential support and expertise.
National Regulatory Authorities and Funding Agencies: Involving regulatory bodies to ensure alignment with compliance standards and regulations, fostering a comprehensive and compliant FAIR data implementation.

Intended use of the FAIR maturity model

The Pistoia FAIR maturity model intends to provide an actionable tool for (self-)assessment of an organization's implementation of the FAIR data principles.

The model is descriptive rather than prescriptive. It should enable multiple stakeholders to reach similar conclusions based on observations of a specific organization at a given time. There are different data models, and the reader can also refer to the EDMC’s DCAM (, DAMA’s DMBoK) and NIST’s Research Data Framework (RDaF).

The structure of this first model is a matrix. We see each matrix element in relation to its nearest neighbors for the “maturity axis” and with all the elements in the same level, or the “dimensions axis”. That should, in turn, indicate concrete directions for improvement.

Should an organization aim at the highest possible maturity level? Not necessarily. That depends on the organization's goals, business, and use cases.

Building a FAIR maturity model - guidelines

The aim is to provide an actionable (self) assessment tool. The model should capture as much of the required complexity as possible without too much detail, striking a balance between the inherent complexities and details and still extracting general features to describe the maturity qualitatively but with sufficient accuracy to enable stakeholder alignment.

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As self-consistent and actionable as possible, the interpretation of the content should be in the context of lower and higher maturity levels and different dimensions. The structure of the first model is a matrix. We see each matrix element in relation not only to its nearest neighbors for the “maturity axis” but with all the elements in the same level, or the “dimensions axis”. That would, in turn, indicate concrete directions for improvement.

As much as possible, we use commonly understood and ideally referenced terminology. We provide examples of relevant practices, cases or implementations as much as possible. Where possible, we use references to known definitions.

The intention is to provide an initial instrument to the FAIR community. It is unlikely the first version will comply with the FAIR data principles. This model is and will likely never be perfect, but it could hopefully be “good enough” to enable better and more effective implementation of FAIR. It should be updated and improved in subsequent iterations.