I design Microsoft Fabric environments built around clear structure: workspaces that reflect how your organisation operates, boundaries that control who owns what, and a foundation that holds as the platform grows - whether I am designing from the ground up or bringing order to what already exists.
Governance determines who owns data, how it is defined, and what happens when the numbers behind a decision are questioned. I work with organisations to make those decisions and build them into the architecture, so governance becomes a structural property of the environment, not a policy that sits above it.
AI produces confident answers from whatever data it is given. If your data foundation lacks clear ownership, consistent definitions, and governed models, AI will scale those problems rather than solve them. I assess and design the foundation that AI requires to be trusted.
Most reporting environments expand dataset by dataset, team by team. Metrics diverge, datasets are duplicated across teams, and Fabric capacity costs climb with every new report built on its own copy of the data. I work with organisations to design a reporting architecture where data is shared, definitions are governed, and the numbers behind every decision are consistent.
Most organisations do not discover they have an architecture problem until they are already deep inside one. By that point the signs are hard to miss. Delivery timelines keep expanding, platform costs grow faster than value, teams rebuilding data that should already exist, and an environment that was supposed to support decision making but now has become difficult to trust and manage.
Architecture is the set of decisions made before the platform is built. Those decisions explain how workspaces reflect business domains, how data ownership is assigned, how semantic models are governed across departments, and what the environment needs to look like before AI can be trusted to work with it. Architectural decisions do not become easier to make once the platform is in use, but significantly more expensive if the right foundation was never put in place.
This is the level I work at: the architectural and governance decisions that determine whether a data platform remains manageable, trusted, and fit for decision-making as the organisation grows.
24 March 2026
Most Fabric environments start well. The structural problems appear later — when ownership is unclear, workspaces have grown without a domain plan, and governance exists on paper but not in the platform.
Read Article20 March 2026
When no one owns a dataset, everyone uses it differently. This is how semantic drift starts — and why fixing it later costs significantly more than designing ownership in from the start.
Read ArticleDataBrief is a Nordic Dynamics product that tracks and curates the most relevant Microsoft Fabric developments for enterprise data and analytics teams.
Visit DataBriefIf you are working through a serious architecture or governance challenge, I am open to talking.