Development Process and Milestones
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 testers 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 the 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, at first in XLS, then in sets of .md documents, and 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 asset.
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 use. Not clear how new 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
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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 interviews 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. Sketched a higher level model connecting FAIR Maturity, Personas and Business value.
- Started coding using XLS; starting the collective translation process and drafting.
- Started identifying 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.
Milestone - May / June 2026
- Completion of the internal content and functional review by the FAIR community of experts.
- Alpha-tester package released, with parameter definitions and worked reference cases.
Milestone - July 2026
- Alpha-test feedback collected and triaged; usability, transparency and calculation defects corrected.
- Beta package released to Pistoia Alliance members: the calculator and the framework visualisations delivered as a single self-contained HTML file that runs offline.
- Framework and calculator presented in the public FAIR Forward webinar.
Milestone - August 2026
- Public documentation site prepared for release through the FAIR Community of Experts documentation hub.
Next steps - foreseen
- Q3 2026: public release of the FAIR Business Value Framework documentation and calculator.
- Following the release: domain-expert work on metrics for AI-ready data, and continued review and iteration of the quantitative drivers with the community.