Our Data & AI Government operating model is based on five principles:
Governance is applied from the beginning of the data lifecycle to prevent quality, security, and compliance incidents; it is not a subsequent phase or a bureaucratic “gate.” This enables impact control prior to production, comprehensive traceability, and reduced time-to-value in analytical and AI use cases.
We define a matrix of domains and roles with explicit functions and responsibilities; permissions are implemented on the platform and dynamically reassigned in the event of organisational changes, avoiding bottlenecks and promoting government federation.
A Metadata Lake unifies business, technical, operational, security, and regulatory metadata; from there, we orchestrate active metadata to automate actions (e.g., apply policies, trigger flows, update descriptions or permissions).
Workflows and notifications align policies and procedures with demand management, eliminating friction between areas and ensuring evidence of compliance.
Adoption is incremental to capture early ROI, with bidirectional integration into data platforms and CI/CD toolchains to accelerate DataOps, Data Mesh/Fabric, and Lakehouse.
Result:
A government proactive and preventative which adds value to the business, reduces costs through synergies and promotes governed self-service y secure sharing data and models.