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Delivering Data Driven Value

Resource Tag: Delivering Data Driven Value

IDMP Ontology Information Sheet

The IDMP Ontology is an expert-built, vendor-neutral framework that gives medicinal product data a shared and consistent digital language across systems, companies, and borders. It is the pharmaceutical industry’s directly deployable implementation layer for the five ISO IDMP standards, owned and governed by the Pistoia Alliance as a neutral not-for-profit.

Rather than each organization interpreting the ISO standards on its own, the IDMP Ontology provides a shared structure that keeps product data consistent, connected, and interoperable for internal data management, regulatory alignment, cross-system integration, and AI-ready data.

This information sheet is a plain-language introduction for regulators, industry teams, and anyone new to the ontology.

  • What the IDMP Ontology is and what makes an ontology different from a spreadsheet or database schema.
  • The problems it solves, like harmonizing multiple names and IDs for the same product, and unifying interpretations of the ISO standards.
  • How it’s governed: Pistoia Alliance stewardship, a vendor-neutral Steering Committee, and quarterly maintenance releases.
  • How it’s used today, from EMA PMS mapping and multi-market submissions to partner data exchange and AI readiness.
  • How to get started and access it: three implementation levels and the public, member, and sponsor access tiers.

Download the information sheet to learn more.

Pharma General Ontology (PGO)-Terminology: Building a Semantic Backbone for FAIR and AI-Ready Knowledge Graphs

This talk was presented at Bio-IT World 2026 (Knowledge Graphs session, Boston) by Giovanni Nisato, PhD — Project Manager, Pistoia Alliance.

As life science innovation becomes increasingly data-driven, semantic interoperability is essential to scaling FAIR data and trustworthy AI. Yet the pharma ecosystem still lacks a shared reference vocabulary. Today, the same concept (compound, molecule, substance, drug) is used inconsistently across domains and organizations, deep domain ontologies proliferate in disconnected silos, and there is no cross-pharma lingua franca. Teams spend enormous effort on semantic reconciliation before any real work begins, and interoperability stays aspirational.

The Pharma General Ontology (PGO)-Terminology project is a Pistoia Alliance–led, industry-governed initiative that tackles this challenge head-on. It is building a community-governed reference set of core pharmaceutical concepts. Think if it as a shared “lingua franca” to serve as a semantic backbone for interoperable knowledge graphs, cross-domain data integration, and AI-ready foundations across the pharma ecosystem.

In this talk, Giovanni Nisato introduces PGO-Terminology’s scope, design principles, current stage, and long-term vision.

What you’ll learn:

  • The problem: Why limited semantic interoperability creates costly “semantic silos” that slow the path to data-centricity across R&D, manufacturing, clinical, regulatory, and commercial domains.
  • What is PGO-Terminology? A community-governed, cross-domain reference set of core concepts, published open access. It is not a replacement for MedDRA, SNOMED, or ChEBI, not a top-down mandate, and not (at this stage) a full ontology.
  • How it’s built: An industry governance model with clear definition rules: reuse existing definitions from known sources, keep them human-readable across domains, and give every concept a publicly resolvable URL/IRI.
  • Where the project stands: Phase 1 delivered 17 core Research & Early Development concepts at controlled-vocabulary level. Phase 2 is expanding beyond R&D, moving toward machine-readable output, and mappings to external ontologies.
  • Why it matters for AI: Knowledge graphs are only as good as their underlying semantics. Features of the PGO-Terminology help to reduce hallucination risk in RAG and LLM pipelines.
  • The long-term vision: To deliver a machine-readable core terminology that is discoverable on resources such as EMBL-EBI OLS and BioPortal, to promote data assets tagged with PGO URIs, and to facilitate cross-company, CRO, partner, and regulator data exchange without manual reconciliation.

PGO-Terminology is developed openly on GitHub under permissive licenses (CC BY 4.0 / MIT)

Learn more on the PGO-Terminology project webpage.

Pistoia Alliance Ontology Design Principles

The Pistoia Alliance Ontology Design Principles establishes a comprehensive governance framework for developing, maintaining, and releasing high-quality ontologies and semantic artefacts across the life sciences. It defines best practices covering ontology architecture, metadata standards, term reuse, modularity, quality control, versioning, documentation, and long-term governance, while promoting interoperability through the adoption of FAIR principles and established standards such as the Basic Formal Ontology (BFO). Designed for ontology developers, project teams, and governance bodies, the guidance provides a consistent foundation for creating sustainable, reusable semantic resources that support collaboration across the Pistoia Alliance ecosystem.

FAIR Forward in Life Sciences

 
What does it take to move FAIR data adoption forward across an entire ecosystem?

On April 2026, 36 pharma and life sciences professionals gathered at the Royal Society of Medicine in London for the FAIR Forward Workshop, held alongside the Pistoia Alliance Annual Conference. The participants spanned industry, academia, and service providers and came prepared: a pre-workshop survey mapped their views on FAIR maturity, actors in the ecosystems, and what they see as constraints and enablers.
 
On the day, four groups worked through a structured landscape-mapping exercise, placing thematic elements on a shared board across effort and time-horizon axes. The result: 28 clusters capturing what it would take the lifesceince ecosystem to move it forward in terms of FAIR data implementation.
 
In this webinar, Pistoia Alliance FAIR Steering Group members,  who also participated on the day, will present key findings and what they mean for the field.

FAIR Forward 2026 Ecosystem Landscape Map

On 15 April 2026, 36 participants from across the pharmaceutical and life sciences ecosystem gathered at the Royal Society of Medicine in London for the FAIR Forward Workshop, held as part of the Pistoia Alliance Annual Conference. Working in four pre-assigned groups (BALDER, INANNA, OSIRIS, PERSEPHONE), participants independently identified the actors, constraints, and enabling assets shaping FAIR data adoption in pharma R&D, then collectively placed their outputs on a shared board. This interactive map is the primary output of that exercise, produced by the Pistoia Alliance FAIR Community of Experts.

The map organises 28 thematic clusters on two axes: time horizon (left to right, from near-term to long-term) and effort required (bottom to top, from low to high). Three zones emerged from the collective synthesis: a low-hanging fruit zone in the lower left, where resources and tools are available today at low effort; a collaborative potential zone in the centre, where progress requires coordinated community action; and a hard-to-do zone in the upper right, where change depends on regulatory, legislative, and systemic conditions that no single actor controls. Each hexagonal cluster is interactive: click to expand and explore the elements it contains. Use pinch-to-zoom or the on-screen controls to navigate.

This is not a roadmap. It is a landscape: a shared, multi-perspective snapshot of where the ecosystem stands and what it will take to move it forward. Different actors will read it differently. A pharma company assessing its FAIR programme may focus on the capability and culture clusters. A technology provider may see entry points in the low-hanging fruit zone. A standards body or regulator may find the hard-to-do zone most relevant. We invite you to explore it from your own vantage point, identify where your organisation sits today, and consider what role you might play in moving the landscape forward.

Building the Future of IDMP-O: Community, Adoption, and What’s Next

The IDMP Ontology has entered a new phase defined by long-term stewardship, governed quarterly releases, and a growing community of implementers. Join us for this Community of Interest webinar to hear directly from the team shaping IDMP-O’s future: where it stands today, how it’s being used in practice, and how you can get involved. As medicines data requirements tighten globally, the case for a shared digital language for IDMP has never been stronger.
 

Agenda
  1. Welcome and introduction
  2. The next phase of IDMP-O: vision and funding
    An overview of the new stewardship model, governance structure, and what the long-term commitment means for adopters.
  3. Building the IDMP-O community
    How the Community of Interest works, how to participate, and why peer engagement is central to the ontology’s evolution.
  4. Current use and refactoring strategy
    Real-world applications of IDMP-O and the roadmap for simplifying implementation and streamlining adoption.
  5. Demonstration: IDMP-O supporting EMA PMS use cases
    A live look at how IDMP-O supports the EMA’s Product Management System.
  6. Open discussion and Q&A
    Bring your questions. Hear how your peers are navigating implementation.
Speakers
  • Aditya Tyagi, Pistoia Alliance
  • Heiner Oberkampf, CEO & Co-Founder, Accurids
  • Sheila Elz, Senior RIM Manager, Boehringer Ingelheim
  • Raphael Sergent, Head of QA & Pharma Solution Lead, Accurids

FAIR-Community of Experts Documentation Hub

The FAIR Community of Experts (FAIR CoE) Documentation Hub is a collaborative knowledge base created by the Pistoia Alliance to support the implementation of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles across the life sciences industry. It brings together best practices, frameworks, tools, maturity models, case studies, and community-driven resources designed to help organizations improve data quality, interoperability, AI-readiness, and long-term research value. The hub showcases outputs from the FAIR for Pharma community, including the FAIR Toolkit, FAIR Maturity Matrix, implementation guidance, and working group initiatives, while providing an open platform for sharing standards, methodologies, and practical approaches that enable more effective data-driven research and collaboration in pharmaceutical and healthcare R&D.

Demo video: IVP Assay Repository 

Watch this short demonstration of the Pistoia Alliance’s IVP Assay Repository, a platform designed to standardize how in vitro pharmacology assays are captured, shared and reused across the life sciences industry. The video walks through key features, including how to search, filter and explore registered assays, and how standardized data fields and controlled vocabularies support improved data quality, reduced duplication and greater interoperability. Discover how the repository is enabling more collaborative, transparent and efficient research across organizations and with regulatory partners.

To learn more about the IVP project, visit the project webpage.

FAIR Maturity Matrix Introduction – Japanese Subtitles

FAIR refers to findability, accessibility, interoperability, and reusability. These principles are foundational to enable data-centric organizations and value creation. Implementing the FAIR data principles in a life sciences organization involves transformational journeys that tend to be complex. At any given time, organizations may be experiencing different stages of their respective FAIR implementation journey, which makes it challenging to perform benchmarks and assess progress. So while there are multiple FAIR data maturity models, there was no maturity assessment model at the organizational level. 

To address this gap in 2023 we collaboratively designed such an organizational model with the Pistoia Alliance Community of Experts.

FAIRは、**Findability（発見可能性）、Accessibility（アクセス可能性）、Interoperability（相互運用性）、Reusability（再利用可能性）**を指します。これらの原則は、データ中心型組織を実現し、価値創出を促進するための基盤となるものです。ライフサイエンス分野の組織においてFAIRデータ原則を実装することは、一般に複雑になりがちな変革のプロセスを伴います。

どの時点においても、各組織はそれぞれのFAIR実装の取り組みにおいて異なる段階にある可能性があり、そのためベンチマークの実施や進捗の評価が難しくなります。したがって、FAIRデータの成熟度モデルはいくつか存在するものの、組織レベルでの成熟度評価モデルは存在していませんでした。

このギャップに対応するため、2023年に私たちはPistoia AllianceのCommunity of Expertsと協力し、組織レベルのモデルを共同で設計しました。

Unlocking the ROI of FAIR Data in Pharma and Life Sciences – Japanese Subtitles

The video explains how applying the **FAIR data principles—Findable, Accessible, Interoperable, and Reusable—can unlock measurable return on investment (ROI) for pharmaceutical and life sciences organizations by making data easier to discover, integrate, and reuse across teams and systems. It highlights how FAIR data enables better collaboration, accelerates research and analytics (including AI and machine learning), and improves efficiency across the R&D lifecycle, ultimately helping organizations reduce costs, speed up innovation, and extract more long-term value from their scientific data assets.

この動画では、FAIR原則（Findable、Accessible、Interoperable、Reusable）を適用することで、製薬およびライフサイエンス分野の組織がデータをより容易に発見・統合・再利用できるようになり、データから得られるROI（投資対効果）を高められることを説明しています。FAIRデータは、チームやシステム間の連携を促進し、研究や分析（AIや機械学習を含む）を加速させ、研究開発プロセス全体の効率を向上させることで、イノベーションの促進と科学データの価値最大化に貢献します。

Open-Source Instrument Data Converter

Life Science laboratories today rely on a diverse ecosystem of instruments, producing data in proprietary or customized formats and creating a patchwork of datasets that cannot easily flow between systems or support lab automation. Each organization is forced to engineer their own one-off data conversion tools to resolve the bottleneck, slowing down progress and burdening both researchers and IT teams.
 
To address these long‑standing barriers, the Pistoia Alliance is exploring the development of a community-driven repository of open‑source instrument data converters — reusable scripts designed to transform data to standard open formats and thus reduce duplicated effort across the industry.
 
This interactive roundtable brings together members of the community to discuss challenges and opportunities in achieving interoperability across instruments and software platforms. 

For further information, please get in touch with Nathalie Batoux.

Automating Batch Tracking Across Supply Chain and Regulatory Using the IDMP Ontology

Fragmented batch, material, and product version data across MES, ERP, and RIM systems continues to drive manual reconciliation, inefficiencies, and compliance risk across supply chain and regulatory functions. This webinar presents a practical IDMP Ontology (IDMP-O)–based integration use case that demonstrates how a shared semantic backbone can automate batch-level data management and align product information across CMC and Regulatory domains.
 
The session will showcase how IDMP-O enables consistent batch tracking, end-to-end genealogy, and traceability across product versions and clinical studies. We will highlight recent progress, including the establishment of core semantics, expansion of batch-tracking capabilities to meet broader regulatory requirements, development of competency questions and reference architectures, and early collaboration with pharmaceutical companies to validate and test the approach.
 
Attendees will learn how this approach delivers a single, queryable view of batch genealogy, supports faster and more reliable impact assessments and change control, and improves compliance readiness and data quality across regulatory submissions. This webinar is relevant for professionals working at the intersection of supply chain, CMC, regulatory affairs, and data standards seeking scalable, standards-based solutions for IDMP implementation.
 

Speakers
  • Aditya Tyagi, Pistoia Alliance
  • Gerd Kleemann, Amgen
  • Toby Broom, CrownPoint
  • Elisa Kendall, EDMA