FAIR business value frame

Version 1.0.0 2026-04
Pistoia Alliance
CC By 4.0

"Not everything that can be counted counts, and not everything that counts can be counted."
/ Attributed to A. Einstein /

Summary

The FAIR Business Value Framework provides a structured, community-driven approach to define, measure, and communicate the business value of FAIR data in pharmaceutical and life sciences organizations.

Developed through a combination of secondary research, primary industry engagement, and expert-driven synthesis, the framework translates FAIR principles into actionable business drivers, measurable metrics, and financial outcomes.

At its core, the framework enables organizations to: - Link FAIR data practices to strategic business objectives - Quantify value across Trust, Speed, Cost, and Effectiveness - Support AI-driven transformation and data-centric decision-making - Build credible, transparent business cases for FAIR investments

The framework is implemented as a structured data product, with a hierarchical model and associated parameters, and can be operationalized through tools such as the FAIR Business Value Calculator.

Purpose, who is FAIR Business Value Frame for?

The FAIR Business Value Framework is designed for:

Executive stakeholders

• Chief Data Officers, Digital and AI leaders
• R&D and business transformation leaders
• Strategy and investment decision-makers

It supports understanding why FAIR matters for business performance, assessing return on investment and strategic impact, and prioritizing data and AI initiatives.

Data and FAIR practitioners

• Data stewards, architects, and platform teams
• FAIR implementation leads
• Analytics and AI teams

It supports: translating FAIR principles into measurable outcomes, aligning technical initiatives with business value, and defining metrics, baselines, and targets.

Industry and community stakeholders

• Pistoia Alliance members and partners
• Life-science organisations
• Service proving organisaitons
• Research institutes

It provides: A shared language for FAIR value, a reference model for benchmarking and alignment on business value creation.

Context

The pharmaceutical industry is undergoing a profound transformation driven by data, digitalization, and artificial intelligence. In this context, FAIR data principles are increasingly recognized as a critical enabler of data reuse, interoperability, and AI readiness.

However, a persistent challenge remains:

How can organizations quantify and communicate the business value of FAIR data?

Early approaches, such as financial ROI models FAIR-ROI-Method-V1.0, provided a valuable starting point by linking FAIR to: - efficiency gains
- cost reductions
- time savings
- improvements in R&D productivity

While these models introduced rigor through DCF and decision-tree methodologies, they also revealed limitations: - difficulty in capturing qualitative and strategic value - complexity and limited usability for non-financial stakeholders - insufficient alignment with modern data and AI-driven operating models

In parallel, industry demand evolved toward: - enterprise-level value articulation - alignment with AI and data strategy - need for transparent, standardized metrics for business value creation

The FAIR Business Value Framework was developed to address these gaps.

How to use the FAIR business value frame

The FAIR Business Value Framework is designed to be used in two complementary ways:

1. Qualitative exploration and alignment
2. Quantitative modeling and financial assessment

Both dimensions are essential. The framework delivers value even without any calculation, and its full impact emerges when qualitative insight and quantitative rigor are combined.

This simple flow captures how the framework supports awareness creation and translations to action.

Explore qualitatively → Identify value → Measure quantitatively → Inform decisions

The framework was designed with a flexible and modular mindset. It can be deployed at the level of a research lab or of an entire organisation. It can be used "top down", starting from the higher level drivers, or "bottom up" by modelling a very specific case from one or more of the deeper level drivers.

There are currently 38 calculation formulas, which are parametrized. The user can use any of all of them. That provide a vast amount of modelling options.

1. Qualitative use: identifying and framing value

At its core, the framework provides a structured narrative of potential value creation.

Each level of the framework, namely Strategic Drivers (Level 0) Business Value Areas (Level 1) and Quantitative Drivers (Level 2) is associated with a descriptive narrative text providing contextual explanations. Level 3 is associated with elements that can be calculated,

1.1 Expanding awareness of value

A primary use of the framework is to identify value drivers that may not have been previously considered and expose hidden or implicit benefits of FAIR data. In practice, users may discover value areas they had not initially anticipated. The framework may also act as a checklist of value creation opportunities

1.2 Supporting qualitative business case development

Even in the absence of numerical data, the framework enables a structured articulation of why FAIR matters and alignment of FAIR initiatives with business priorities**. This is particularly important in early-stage of FAIR Maturity journeys discussions, where data may be incomplete and benefits are not yet measurable. (see: fairmm.pistoiaalliance.org)

1.3 Identifying non-quantifiable value

Not all value can easily translated into metrics. Examples include: increased employee satisfaction and engagement, improved collaboration across teams, enhanced trust in data and decisions, better organizational learning and knowledge reuse. For example, when a data scientist spends less time cleaning data, the benefit is not only time saved, but also improved focus, motivation, and innovation capacity.

Such effects are difficult to quantify directly yet strategically important for organizational performance

1.4 Enabling structured conversations

The framework supports: thought-provoking discussions, reframing of existing assumptions and alignment across stakeholders.

By exploring a given business driver and the associated parameters needed to measure it teams are encouraged to question current practices , identify gaps in data and processes, and define what “good” looks like .

1.5 Providing a neutral and shared language

The framework establishes: a common vocabulary across roles and organizations as well as a neutral ground for discussing value.

This helps to surface implicit assumptions, align technical and business perspectives, and reduce ambiguity in decision-making.

2. Quantitative use: measuring and modeling value

Building on the qualitative layer, the framework enables quantitative assessment of FAIR impact.

2.1 Selecting drivers

Based on their FAIR transformation project, users can select the relevant Level 2 drivers , identify associated parameters, and focus on areas where data is available or can be estimated.

2.2 Defining baseline and target states

For each selected driver: - define current performance (baseline)
- estimate improvements enabled by FAIR

This step often reveals missing metrics and gaps in measurement capabilities.

2.3 Performing calculations

Using the defined parameters, the framework supports the calculation of: cost savings, productivity gains and value creation. Importantly, aggregation into financial metrics such as: ROI, NPV, and payback period is provided,

This is implemented through the FAIR Business Value Calculator.

2.4 Scenario modeling and sensitivity analysis

The framework enables the comparison of different assumptions, exploration of scenarios, and assessment of uncertainty and risk. This supports more robust business cases and more informed decision-making.

3. Combined use: from insight to decision

The full value of the framework emerges when qualitative and quantitative uses are combined.

• The qualitative layer: expands understanding , identifies value, and aligns stakeholders

• The quantitative layer: checks assumptions , provides impact metrics, and supports investment decisions

Together, they enable: a comprehensive and transparent approach to articulating the business value of FAIR data

4. A tool for transformation, not only calculation

The FAIR Business Value Framework should not be seen only as a calculation tool. It is: - a thinking framework - a conversation enabler - a decision-support system

It supports organizations in moving from intuition and implicit value to explicit articulation of value. In combination with FAIR maturity matrix, this can support the transition from isolated initiatives to aligned, value-driven transformation

Structure of the FAIR business value frame

The FAIR Business Value Framework is structured as a three-layer hierarchical model, supported by a set of parameters and definitions.

Level 0 — Strategic Business Drivers

Four top-level drivers represent the primary dimensions of business value:

• Trust — confidence in data quality, integrity, and compliance
• Speed — acceleration of data access, analysis, and decision-making
• Cost — efficiency gains and reduction of operational overhead
• Effectiveness — improvement in outcomes and business impact

Level 1 — Business Value Areas

Each strategic driver is decomposed into: - 12 qualitative business value areas

These represent: - key domains of value creation - aligned with real enterprise challenges - derived from industry input and validation

Level 2 — Business Value Drivers (Quantitative)

Each value area is further decomposed into: - 38 quantitative drivers

Each driver: - is defined through explicit formulas - includes input parameters - supports transparent calculation of value

Parameter Layer

The framework includes: - over 100 parameters - enabling: - customization to organizational context - scenario modeling - alignment with real-world data

Data Product Perspective

The FAIR Business Value Framework is not only a conceptual model but a:

Structured data product

It consists of: - atomic definitions (in .md format) - formalized relationships between drivers - reusable components for implementation

The FAIR Business Value Calculator represents one operational instantiation of this data product.

Process of creation of the FAIR business value framework

The FAIR Business Value Framework was developed through a multi-phase, evidence-based methodology, combining research, industry engagement, and expert synthesis.

Phase 0 — Initial starting point: ROI methodology

The work was initially informed by: the Ontoforce FAIR ROI model detailed in FAIR-ROI-Method-V1.0.

This model applied decision-tree and discounted cash flow methods, it defined ROI as ΔNPV (FAIR vs baseline). It focused on:efficiency gains, time reduction cost savings

While rigorous, it was perceived by some testerrs as complex and rigid, difficult to generalize and insufficient to capture broader business value.

This led to a deliberate shift in approach.

Phase 1 — Secondary research

The first step consisted of reviewing existing literature, analyzing ROI models and industry benchmarks, identifying known value levers in pharma R&D and data management

This provided conceptual grounding and initial hypotheses on value drivers

Phase 2 — Primary research and voice of customer

A comprehensive industry engagement was conducted including * interviews with business leaders - FAIR Business Survey across Pistoia Alliance members - workshops and expert discussions

This phase captured: real-world expectations and experiences, perceived value of FAIR. challenges and pain points.

Phase 3 — Thematic analysis and coding

A structured analysis was performed: - qualitative data was coded and clustered - recurring themes were identified - terminology was aligned across stakeholders

This process led to: identification of core business drivers, consolidation of value areas, and emergence of a shared vocabulary.

Phase 4 — Framework design

Based on the analysis: a hierarchical model was defined( Strategic Drivers → Value Areas → Granular and Quantitative Drivers) and a dual structure was introduced with two axes: - Qualitative (conceptual) - Quantitative (measurable)

This marked the transition from “calculating ROI” to starting “modelling business value”.

Phase 5 — Formalization and translation

The framework was: encoded in structured formats (XLS, then .md) translated into: - formulas - parameters - computational logic

At this stage, a 4 → 12 → 38 structure was finalized and initial calculator prototypes were developed.

Phase 6 — Data product realization

The outcome of the process is:

A structured, community-derived data product

Key characteristics: - modular and extensible - transparent and auditable - reusable across organizations

The calculator is packaged in one html executable enabling simulation and financial modeling. It requires no internet/ data exchange to operate to reduce security risks and enable IPR control for users.

Phase 7 — Current - Validation and iteration

The framework continues to evolve through expert review, user testing and real-world application.

Milestones

Starting point 2023

• The ROI of pharma project calculator methodology by Ontoforce.
• see: FAIR-ROI-Method-V1.0
• Feedback from users: very comprehensive and quantitative, but also rigid and complex to to use. Not clear how now modalities of pharma are taken into account. Not clear how to use the qualitative added value of FAIR.

Milestone - March 2024 - Pistoia Alliance Workshop - London

• Start of the FAIR business value journey: sketching the needs, assets available and mapping the start of jouney and committing to the first actions

Milestone - June 2024

• Collected and analysed secondary references
• Identified drafted business drivers for FAIR in pharma companies
• Started the FAIR business survey of Pistoia Alliance member and 1-1 guided interviews of business leaders

Milestone - November 2024 - Pistoia Alliance Workshop - Philadelphia

• 12 business leaders interviewed

• Confirmed the top levels business drivers
• Collected over 50 potential metrics and FAIR business drivers from the participants
• Decision to have a Qualitative and Quantitative framework

Milestone - May 2025

• Completed all intervies in Jan 2025
• Performed thematic and coding analysis, created working documents for the expert group
• Public version of the FAIR BUSINESS SURVEY REPORT

Milestone - June 2025

• First visualisation of the FAIR business frame as a value"tree"
• 3 levels of business drivers defined. Skecthed a higher level model connectin FAIR Maturity, Personas and Business value.
• Started coding using XLS; starting the collective translation process and drafting
• Starting idenfitying the core parameters of the calculator

Milestone - November 2025 -Pistoia Alliance Workshop - Boston

• Draft completed of the FAIR business value driver completed in XLS in October 2025
• First computation translation of 4 Strategic Drivers → 12 Business Value Areas → 38 Business Value Drivers
• First demonstration of a web-based functional FAIR business value calculator

Milestone - March 2026

• Atomic definition in .md format of 4 Strategic Drivers, 12 Business Value Areas, 38 Business Value Drivers and over 100 parameters.
• Stand-alone calculator html application realized for testing
• Content and functional review started by FAIR community experts

Milestone - April 2026

• Documentation, first public presentation webpage

Next steps - foreseen

• May 2026: Completion of the internal review - Users tests and feedback collection
• June 2026: public version of the FAIR Business Value Framework calculator

Source - Use Cases

Contributors to the FAIR business value framework version 1.0

• The FAIR business calculator is coded by Campana-Schott as contribution to FAIR community of experts. Special thanks to Anuj Uppal 0009-0009-3610-6409 and the development team and Campana-Schott.

• We are very grateful to all the active members of the FAIR-for-Pharma Community of experts for their contributions to this work from 2024-05 to 2026-04

• FAIR-for-Pharma Community facilitation, FAIR Maturity Matrix V1.2 editing and coordination: Giovanni Nisato, Pistoia Alliance,0000-0002-5824-0061.