FAIR and AI-Ready Data

In the life sciences industry, data is the lifeblood of innovation at scale. The ability to leverage data effectively determines how fast we can bring novel therapies to patients.

And when discussing the topic of data, two terms often surface in digital transformation discussions: FAIR data and AI-ready data. While they sound similar, they serve different purposes, and understanding the distinction is critical for organizations aiming to harness the power of artificial intelligence (AI).

So, what is FAIR Data?

FAIR stands for Findable, Accessible, Interoperable, and Reusable. In a nutshell, it aims to ensure that data can be easily shared and reused. FAIR data means:

• Findable: Data is indexed and searchable with persistent identifiers (e.g., metadata standards).

• Accessible: Data can be retrieved using standardized protocols, respecting security and privacy.

• Interoperable: Data uses common vocabularies and formats to integrate across systems.

• Reusable: Data is well-documented, with clear usage licenses and provenance.

FAIR is about data governance, stewardship, and ensuring its long-term value through reusability across multiple use cases and contexts, which we may not even think of today.

And… what is AI-Ready Data?

AI-ready data goes a step further. It refers to data that is optimized for machine learning, advanced analytics, and other AI technologies. And while FAIR principles make data shareable and reusable, AI-ready data ensures it is fit for use in the AI context. This includes additional considerations, such as:

• High quality and completeness: Minimal missing values, consistent units, and validated entries.

• Labeled and contextualized: For supervised learning, data must be annotated with correct labels.

• Normalized and harmonized: Eliminating redundancies and aligning with model requirements.

• Ethically and legally compliant: Addressing bias, privacy, and regulatory constraints.

FAIR data is the foundation and ensures data can be found and understood, but AI-ready data is the structure built on top of it to enable effective use of the AI algorithms and responsible and ethical AI usage.

Always consider FAIR + AI-Ready!

FAIR principles alone do not guarantee that data is suitable for AI. AI-ready data begins with FAIR, but it requires additional steps like cleaning, feature engineering, and bias mitigation.

A simple checklist for Making Data AI-Ready could start with

• FAIR Compliance

  • Persistent identifiers and rich metadata

  • Standardized vocabularies and formats

  • Clear usage rights and provenance and data lineage

• Data Quality & Completeness

  • Validate entries, remove duplicates

  • Handle missing values and inconsistent units

• Normalization & Harmonization

  • Align terminology (e.g., MedDRA, IDMP)

  • Standardize formats across sources

• Labeling & Contextualization

  • Annotate for machine learning (e.g., NLP tags for labeling content)

  • Add domain-specific context for algorithms

• Bias & Compliance Checks

  • Detect and mitigate bias in datasets

  • Ensure privacy and regulatory compliance (GDPR, FDA, EMA)

• Technical Readiness

  • Convert unstructured text into structured, machine-readable formats

  • Optimize for model input (tokenization, feature engineering)

Remember, organizations that stop at FAIR risk missing the full potential of AI-driven innovations.