FAIR Data Registry

Create a single, unified reference for all data across federated systems

The FAIR Data Registry is the foundational platform for large-scale pharma enterprises to achieve true data interoperability.

Identify any asset with Globally Unique, Persistent and Resolvable IDs

Connect data from every system, silo, and department.

Unify it all into a single, queryable Knowledge Graph.

The Challenge

Your Enterprise Data is Disconnected. Your Business Isn't

In large enterprises, simple, high-value questions often become complex, manual projects because the data lives in disconnected silos.

Answering simple business questions takes weeks

Manually exporting data from ERPs, CRMs, and LIMS into spreadsheets just to answer a single, cross-departmental question is slow, inefficient, and often delivers insights too late.

AI and analytics projects constantly stall.

Valuable data remains locked in silos with inconsistent identifiers, making it impossible to build the reliable, unified datasets required for modern AI and analytics initiatives.

Data loses its context every time a system changes.

When applications are updated or replaced, the links between historical data are broken, making it nearly impossible to maintain a consistent, long-term view of an asset's lifecycle.

The Strategic Foundation for Data-Driven Leaders

“Partnering with ACCCURIDS to introduce a graph-based Browser for Reference and Master Data has brought substantial value to our users facilitating easy access, discoverability, and connectivity of critical data in Regulatory Science, R&D. Especially the platform's responsiveness and user-friendly interface are appreciated by our user community.”

Ivana Vasiljevic

Data Governance Lead for Regulatory Science – Bayer CropSciences

The Solution

Unique IDs forming the connective tissue across business applications

The FAIR Data Registry solves data fragmentation at its core. It removes ambiguity by registering all important data objects with clear semantic context so that data can be automatically connected across systems forming a virtual linked data network.

Makes All Enterprise Data Findable & Accessible

Creates a Future-Proof,
AI-Ready Data Foundation

Eliminates Data Silos & Manual Data Hunts

Capabilities

How to Implement FAIR Data at Scale

Globally Unique Identification (GUPRIs)

A permanent identity for your data that survives system changes

The Registry allows you to generate and govern GUPRIs i A GUPRI (Globally Unique, Persistent, and Resolvable Identifier) is a permanent, clickable web link that acts as a unique ID for any piece of data (like a substance, sample, or product), ensuring it can always be found and identified, regardless of which system it is in. for any asset, from physical equipment with QR codes to abstract data objects like projects or regulatory submissions. This ensures every critical asset has a single, unambiguous identifier for its entire lifecycle.

The Enterprise Knowledge Graph

A flexible, connected view of your data that mirrors your business

Once identified, assets are linked in a flexible Knowledge Graph. This allows you to model and query the complex, real-world relationships between your assets (e.g., this sample was analyzed on that instrument as part of this project) that rigid, table-based systems cannot handle.

Instant Answers & Application Integration

Find the data you need, when you need it, how you need it

The unified data network is accessible through a powerful search engine, an AI-powered chat for natural language questions, or via API for integration into your existing business applications. This turns your connected data from a passive archive into an active, queryable asset.

Enterprise-Wide Data Quality & Governance

Trustworthy, reliable data for your most critical decisions

The Registry is not just for connection, but for control. Define, monitor, and enforce data quality rules across all your connected domains. Our platform enables a federated governance process, allowing business experts to own the quality and accuracy of their data directly.

Features

Six components for a
shared Data Language

Ontology

Reuse of standard industry ontologies through ACCURIDS Hub and creation of own ontologies for your specific business needs

Reference and Master Data

Manage reference and master data aligned to ontology definitions in a collaborative environment with lean governance processes

Data Registry

Govern the identity of data objects managed in many different systems through a centralized registration process

Graph

Connect data from different sources in knowledge graphs to enable question answering

Quality

Define and monitor data quality rules for different types of data objects

AI

Use an AI-powered chat to answer business questions in natural language with facts from the knowledge graph

Frequently asked questions

Can't find your answer?

What is a "FAIR Data Registry"?

Its a system the manages Globally Unique Persistent Identifeirs for all your important data objects across the enterprise. The FAIR Data Registry does not copy data but provides a logical, unified, persistent "Data Identity Resolver". By centralizing the governance oversight of which systems manage with data objects, we decouple the identity of data from the systems where they are currently manged. This creates a reliable virtual network of interlinked data - independent of system and application changes and from your existing, fragmented systems. It is a central source of truth built on a Knowledge Graph core that is purpose-built to make your data Findable, Accessible, Interoperable, and Reusable (FAIR) across the enterprise. This transforms your siloed information into a lasting, strategic, and AI-ready asset.

How does the ACCURIDS platform technically make our data FAIR?

Our platform embeds FAIR principles into its core architecture:
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- Findable: It assigns a persistent, unique ID to every unified "Golden Record" of data, such as a substance or product.
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- Accessible: The unified "Product Data Backbone" can be directly and easily accessed by business users and applications , with flexible deployment options to meet your enterprise security needs.

- Interoperable: We use the official IDMP Ontology as a common language, automatically transforming differently structured data into a single, uniform format that ensures true interoperability.

- Reusable: By connecting data in a Knowledge Graph, we create a rich semantic network that makes the data immediately actionable and reliable for reuse in analytics, automation, and AI models.

What is the key technology that enables this level of data interoperability?

The core of our platform is the Knowledge Graph. Unlike traditional databases that store data in rigid tables, a knowledge graph is designed to map the complex relationships between data points. This creates a semantic network that understands, for example, which substance belongs to which product and which was used in which clinical trial. This contextual understanding is the key to unlocking true interoperability and surfacing hidden insights for AI and advanced analytics.

Is the FAIR Data Registry a separate product from your IDMP solution?

No, they are two sides of the same coin. Achieving IDMP compliance is the powerful, business-critical entry point that creates the FAIR data foundation. The trusted "Product Data Backbone" built to solve the immediate IDMP requirement is the same strategic, enterprise-wide asset that delivers FAIR data for all other use cases. It allows you to solve a pressing compliance need while simultaneously building a long-term, strategic data asset for the future.

How does creating a FAIR Data Registry prepare our organization for AI?

AI and machine learning models are only as good as the data they are trained on. Most AI initiatives struggle because data is fragmented, inconsistent, and lacks the necessary context to be useful.
Our platform directly solves this by creating a foundational, AI-ready asset. By standardizing and unifying your data into "Golden Records" and then connecting them in a Knowledge Graph, we build a rich semantic context around your information. This process unlocks the immediate, reliable AI value from your standardized data, allowing your data science teams to focus on building powerful models instead of manually cleaning data.

Can't find your answer?