FAIR personas

Date Version: 2026-04-10
License: Pistoia Alliance CC by 4.0

Table of Contents

• Purpose, who are FAIR personas for?
• Context
• Process
• Structure of a FAIR persona
• Selected Sources for FAIR Personas
• List of FAIR Personas
• Contributors to the FAIR Personas

Purpose, who are FAIR personas for?

If you are involved in a FAIR transformation journey, this section is for you. Its purpose is to provide a catalogue of personas, or archetypal actors, present in an organization undergoing FAIR transformation. This is a key element of the “FAIR Roles" and "FAIR Leadership” dimension of the FAIR Maturity Matrix framework. FAIR personas also play a role in the currently emerging FAIR Business Framework.

In any cultural change journey, it is essential to identify and take into account the perspectives of different stakeholders, the realities of their situations, and what might motivate them to change. Reducing their “pains” and increasing their “gains” can be powerful motivators, but what exactly are those? To shift the conversation, one must raise awareness, cultivate empathy, and enable agency for transformation.

FAIR personas represent stakeholders in this transformation. In the best-case scenarios, they are drivers and beneficiaries of the transformation, not merely observers or, worse, passive passengers. What are their job descriptions? What are their key responsibilities? What pain points do they experience, particularly when data is not yet sufficiently FAIR? What do they stand to gain as data becomes increasingly FAIR? What is the value proposition of FAIR data for them?

By describing archetypal personas, we aim to provide actors involved in the transformation with perspective as well as actionable insights, enabling more people to understand what is in it for them. We hope you recognize yourself in one or more of these personas, and that this framework helps you support your colleagues in envisioning a more desirable and mature FAIR future for your organization.

Context

The first versions of the FAIR Maturity Matrix (FAIR MM V1.0 and V1.1) identified a series of FAIR “roles.” These roles - for example, “Data Scientist” - were listed in the lexicon section with only a brief description.

During discussions with the Pistoia Alliance FAIR-for-Pharma Community of Experts, starting in May 2025, it became clear that a more structured approach was needed. The term “role” was considered insufficiently descriptive. Instead, the term “persona” was adopted. A given persona may be embodied by an individual within an organization. However, particularly in the early stages of a FAIR transformation journey, it is common for one individual to embody multiple FAIR personas simultaneously.

As the discussions progressed, we had to balance two objectives: reflecting the diversity and complexity of organizations while limiting unnecessary proliferation of categories. A significant number of distinct personas were identified. This does not imply that all of them need to be present at the outset of a FAIR journey. On the contrary, organizations should start from their current state, identifying relevant stakeholders and clarifying what may reasonably be expected from those participating in the transformation.

Beginning with FAIR MM V1.2 and subsequent versions, we aim to map and present personas as they manifest across different levels of FAIR maturity.

Process

The first version of the FAIR personas was developed between May 2024 and February 2025. The work involved more than twenty experts from pharmaceutical companies, data and technology solution providers, consulting organizations, and research universities. The process evolved from structured meeting notes to a consolidated Excel table, which subsequently served as the basis for generating individual Markdown documents. These documents form the foundation of this publication.

The starting point was the list of roles identified in the FAIR Maturity Matrix (FAIR MM) V1.1 lexicon. This initial mapping pointed to at least nine distinct personas. Additional role descriptions were identified from supplementary sources, as referenced below.

The list of personas was further enriched based on expert observations, practical experience, and identified needs. Over time, the scope of each persona description evolved into a structured template defining the information required to consistently characterize a FAIR persona. The personas can play different roles with respect to data.

This exercise required balancing consolidation and differentiation: merging personas where overlap justified simplification, while preserving distinctions that reflect the diversity and complexity of real-world organizations. This tension is constructive and likely to remain an ongoing discussion as the framework evolves.

Each persona underwent peer review by at least two independent experts; in most cases, four or more contributors provided input and validation.

Finally, we determined that it was necessary to introduce a non-human persona in the form of AI agents. Defining an AI agentic persona presents conceptual and methodological challenges; therefore, we introduced a high-level abstraction. We anticipate that, in future iterations, certain human personas may increasingly operate in collaboration with AI agents performing comparable or complementary roles.

Structure of a FAIR persona

The Value Proposition Canvas (VPC) was an inspiration for creation of the template of the FAIR personas. The rationale is that during a FAIR transformation journey, it is important to "sell" what FAIR can bring to different stakeholders in a way they can relate to. The VPC is a frame used routinely in innovation management practice to identify the jobs of a users as well as their pains (issues, or downsides), potential gains (or upsides), of a user and matching them to a given product or service features. This process can surface the benefits, or value proposition for the user.

The data structure of a FAIR persona, while still evolving, was normalized into the following fields:

Label of the {persona}: e.g. Business_Leader

This is the {persona} label, or identifier, which is used in FAIRMM and other frameworks

Synonyms of {persona}

The same persona can be identified differently in various organizations, this table lists known synonyms.

Description of role of {persona}.

Description of the role, or "job description" of the persona

FAIR persona related to {persona}

• List of other labelled FAIR personas which interact with this specific one
• The nature of their relation may also specified.

Tasks of {persona}

• Description of the 'tasks' or 'jobs' of the persona.
• This may partialy overalap with the "role" description, but is usually more specific.
• If you are familiar with the Value Proposition Canvas, this is may be referred to as the "customer jobs"

Pains for {persona}** - Downside

• description of issues experienced by the persona in a low level FAIR maturity organisation, typically L0 - L1. In the VPC: "customer pains".

Gains for {persona} - Upside

• Description of what could the persona gain in a higher level FAIR maturity organisation (e.g. L2 - L4). In the VPC: "customer gains".

Description of FAIR-benefits for {persona}

• Description of the benefits the persona could experience in a higher maturity FAIR environments
• This this is in essence a value proposition combining the selected FAIR data principles which are both "pain relievers" and "gains creators"

List of how specific FAIR data principles add value to the persona

• The relevant FAIR data principles are listed with a rationale on how their implementation may help the persona

Selected Sources for FAIR Personas

The work was informed by multiple important resources which are listed below.

RDM kit:

https://rdmkit.elixir-europe.org/your_role
The RDM kit identifies six "roles": Data Steward, Policy Maker, Principal Investigator, Research Software Engineer, Researcher and Trainer. It provides extensive guidance not just on their profile identification but on how they can play a role in a FAIR organisation.

GO-FAIR

https://www.gofair.us/data-stewardship
The GO-FAIR Foundation was instrumental in introducing and expanding on the role of "Data Stewards". Their training programs are largely focused on Data Stewards as personas that are able to deal with very complex situations and help, in very concrete technical terms, the organisations to evolve towards higher levels of FAIR maturity.

FAIR Toolkit https://fairtoolkit.pistoiaalliance.org/methods/data-stewardship/

The FAIR toolkit has a "method" document on Data Stewardship. The document identifies : Scientists, Data Scientists, Policy Makers, Funders among others.

DAMA DMBOK https://dama.org/learning-resources/dama-data-management-body-of-knowledge-dmbok/

The DMBOK has many roles related to data management that are clearly delineated.

Value Proposition Canvas (VPC)

https://www.strategyzer.com/library/the-value-proposition-canvas
The Value Proposition Canvas (VPC) was an inspiration for the template creation of the FAIR personas.

List of FAIR Personas

Contributors to the FAIR Personas

We are very grateful to all the active members of the FAIR-for-Pharma Community of experts for their contributions to this work and especially to:

FAIR-for-Pharma Community facilitation, FAIR Maturity Matrix editing and coordination: Giovanni Nisato, Pistoia Alliance, 0000-0002-5824-0061