AI-Ready Data Infrastructure
Starting with SDMX
The first step towards AI in official statistics: published data that is standardised, machine-readable and available through a documented API.
EnStatX helps National Statistical Offices convert existing published statistics into SDMX, the international standard for statistical data and metadata exchange, and release them through a REST API. A phased pilot on one statistical domain builds the foundation and the internal capacity before any organisation-wide rollout.
Aligning methodologies with global standards
Why SDMX comes before AI
Many statistical offices still publish in static tables, PDF reports and portal-specific formats. People can read them. Machines cannot, at least not reliably.
At the same time, users increasingly ask general-purpose AI assistants instead of opening an official publication. Without a structured, verifiable official source, those systems can return figures that sound confident but are wrong, and the statistical office carries the reputational risk.
SDMX fixes the foundation. Every figure carries its dimensions, codes, units and methodological notes, including series breaks. AI tools and data pipelines built on that structure can trace each answer back to the official source.
There is a practical benefit too: automated, machine-to-machine access reduces the recurring workload of ad hoc data requests, freeing subject-matter and IT staff for analytical work.
A Phased SDMX Pilot
Start small, prove the value, then scale. This fast-track entry point fits the EnStatX Transformation Framework and can begin before a full modernisation programme.
Readiness Assessment & Scoping
We review how your statistics are produced, stored and published today, and agree the pilot domain, architecture and resources before anything is built.
- Audit of the data workflow, databases, metadata practices and current dissemination channels
- Selection of open-source SDMX tools for registry, dissemination and API services
- Staff skills assessment and a targeted training plan
- Confirmation of legal basis and an open data licence for SDMX outputs
Pilot on One Statistical Domain
An end-to-end SDMX pipeline for one domain, typically the Labour Force Survey, from structure design to a live, documented API.
- Data Structure Definitions, concept schemes and codelists aligned with ILO and international standards, reusing existing global structures where they fit
- Methodological breaks, observation status and revisions encoded as machine-readable metadata
- Versioned SDMX REST API with SDMX-CSV, SDMX-JSON and SDMX-ML output and OpenAPI documentation
- Data dictionaries and discovery endpoints for AI tools, data pipelines and researchers
- Hands-on training and handover to statisticians and IT staff
Organisation-Wide Scale-Up
The pilot ends with an evaluation and a costed roadmap. Rolling out to other domains is planned and financed separately, once the pilot has proven itself.
- Pilot evaluation with performance benchmarks and lessons learned
- Costed technical roadmap for further domains such as prices, national accounts or SDG indicators
- Reuse of shared infrastructure and cross-domain codelists
- Governance model for maintaining SDMX artefacts over time
What Your Office Gets
Validated SDMX structures
Data Structure Definitions, dataflows, concept schemes and codelists for the pilot domain.
A live, documented API
An SDMX REST API serving data and structural metadata, with OpenAPI documentation, validation and monitoring.
A trained team
Workshops, operating procedures and manuals, so your staff can maintain the structures and the API themselves.
A costed roadmap
A scale-up plan with cost estimates and governance for extending SDMX to other statistical domains.
A data foundation that AI can trust
SDMX is a technical standard, but getting it right depends on statistical judgement: which dimensions matter, how codes are harmonised, and how methodological changes are recorded.
Methodology, not just format conversion
Designing structures and harmonising codelists is statistical work. We bring 20+ years of official statistics experience, so the SDMX model reflects how the data is actually produced, including series breaks and definition changes.
A low-risk starting point
The pilot uses published, already-approved aggregate statistics only. Confidential microdata stays out of scope, and production systems are not disrupted.
Built for AI from day one
Structures, metadata and documentation are designed so AI assistants and automated pipelines can find and query the data reliably. Our own Labour Market Data Assistant shows what becomes possible on top of structured official data.
Open source, no lock-in
We prioritise open, non-proprietary SDMX tools and transfer the skills to your team. Structures, scripts and procedures become permanent institutional assets, with no recurring licence costs.
Ready to make your statistics AI-ready?
Whether you are starting with a single domain or planning organisation-wide SDMX dissemination, let us discuss your context.