Platform
Power BI Automation.
Semantic models and automation built on the platform layer — not bolted on top of it.
Problem
The problem
Power BI estates go wrong in a specific way: dashboards proliferate, semantic models drift, and nobody can answer “where does this number come from” six months later. The fix is not another dashboard. It is a disciplined semantic layer that lives on the platform, with automation that keeps it aligned to the data underneath.
Delivery
What the practice delivers
We build Power BI semantic models on the Cloudbuilder Data Platform, with automation for refresh, lineage, and the reporting estate itself. The same practice designs the data pipelines and the semantic layer: there is no translation gap between what the data means and how it surfaces in a report.
Self-service enablement where it fits; disciplined semantic modelling where it has to.
Track record
In practice
Power BI work has shipped alongside data platform work in regulated financial services, at European-scale leisure operations, and into public-sector analytics. The reporting layer is treated as platform output, not as a separate product with its own data pipeline underneath.
The semantic-modelling approachRead moreHide
Semantic models are designed against the business domain, not as a thin wrapper over the underlying tables. Measures, hierarchies, and calculation groups encode the business logic once; reports consume from the same model rather than redefining the same logic per dashboard. The model is versioned alongside the platform layer it sits on.
Where the engagement calls for self-service, the semantic model is the controlled surface that self-service users build against — not the underlying tables. That keeps the freedom-to-explore separate from the discipline-of-truth.
What gets automated, what stays manualRead moreHide
Refresh schedules, dataset deployment, lineage propagation, and access-policy synchronisation are automated through the same CI/CD pipelines that govern the data platform. A change to a source table’s schema flows to the semantic model through the deployment process, not through a manual edit to the production report estate.
What stays manual: the modelling decision itself — what the business measure means, where it lives in the hierarchy, how it relates to other measures. Modelling is judgement work; deployment is mechanism work. The split keeps each in its appropriate register.