Free Assessment · 4–6 minutes
How AI-ready is
How AI-ready is
your data foundation?
Score your organisation across 9 dimensions of data interoperability. Discover your maturity level and get a personalised set of next steps.
0 of 18 questions answered
1 — Not started
2 — Early / ad hoc
3 — Partial
4 — Established
5 — Optimised
1
Foundational interoperability
Can your systems exchange data technically?
Source systems can exchange data without manual intervention
API-based integration exists across core platforms
2
Structural interoperability
Is data consistently formatted so it can be parsed across systems?
Data formats are standardised across source systems (FHIR, HL7, CDISC, or equivalent)
A FHIR-compliant or equivalent integration layer exists as the canonical data contract
3
Semantic interoperability
Does a data point mean the same thing across all systems?
Terminology is mapped to a controlled vocabulary (SNOMED, LOINC, RxNorm, or equivalent)
A centralised terminology service exists — every pipeline draws from the same controlled vocabulary
4
Organisational interoperability
Are governance, policy, consent, and trust frameworks in place?
Data sharing agreements exist across internal departments and external partners
Consent management and audit trails are built into the platform — not managed manually
5
Entity resolution and identity layer
Can your platform consistently identify the same entity across all source systems?
A Master Patient Index, Customer MDM, or Asset MDM exists as an enforced join layer
Duplicate and fragmented records are detected and resolved before reaching the AI layer
6
Data architecture — medallion layers
Is raw data separated from curated, AI-ready data? Is lineage preserved?
Raw data is separated from normalised and curated data in distinct layers (bronze / silver / gold)
Full data lineage is traceable from source record to AI training dataset
7
Governance and compliance posture
Is compliance treated as an architectural constraint?
Access controls, audit logging, and data classification are enforced at the platform level
Regulatory requirements (HIPAA, BCBS 239, GDPR, ONC HTI-1, or equivalent) are reflected in architecture decisions
8
AI model integrity
Is the data feeding your AI models coherent, resolved, and semantically consistent?
AI training datasets are validated against source data fidelity before models go into production
Model outputs can be traced back to the source data and decisions that shaped the training dataset
9
Organisational AI readiness
Does your leadership team have the structured judgment to make the right AI decisions?
There is a clear owner for data interoperability with both technical authority and organisational influence
AI initiatives are sequenced around data readiness — high-value domains prioritised first
Your score by dimension
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Maria Hussain Wright-Noor — Data Interoperability and AI Readiness Consultant — Trustworthy, not just possible.