Why Fabric Data Models Drift – And Why DAX Alone Can’t Fix Broken Analytics

Most organizations over‑trust their centralized data models because they look consistent, fast, and professionally built. Abstraction layers in Fabric, BI tools, and semantic models hide important assumptions: how customers are defined, which events count, and what “active”, “churned”, or “qualified” really mean. When those assumptions stop matching how teams work on the ground, the model becomes a historical opinion presented as current fact—leading leaders to optimize for the wrong signals while believing they are “data‑driven”.

Every data model encodes human decisions: which sources to trust, which edge cases to ignore, which trade‑offs to accept. Business logic is never neutral; it is embedded in joins, filters, measures, and transformations. When analysts and engineers are disconnected from product, sales, finance, or operations, these opinions drift. The model keeps calculating perfectly, but what it represents becomes less and less aligned with how value is actually created and measured in the organization.

Data engines like Fabric, Power BI, or any DAX‑based system execute logic with perfect reliability—even when that logic is outdated, incomplete, or just wrong. Dashboards can be beautifully designed, fast, and consistent across teams, while still misrepresenting reality because the underlying definitions no longer make sense. Accuracy in computation is not the same as correctness in meaning. No amount of DAX heroics can fix a model whose assumptions are broken, misaligned, or never clearly documented in the first place.

A core theme of this episode is ownership: who actually owns your key metrics, and who has the authority to change their definitions when the business changes? Many teams run on metrics nobody really owns—analytics builds them, business uses them, and nobody is formally responsible for their truthfulness. We discuss why metric and model ownership must be explicit, cross‑functional, and tied to real business outcomes, not just to the analytics or data team. Without this, every new initiative adds more tables, more measures, and more drift.

Adding more data, more events, and more integrations does not automatically create better decisions. In many cases, each new source increases ambiguity because teams can’t see which numbers matter or what they actually mean. Local knowledge—held by people close to customers, operations, and processes—often outperforms centralized models that have lost connection to context. We talk about when simplifying metrics helps, when it hides critical nuance, and how to design data models that remain explainable to non‑technical stakeholders.

WHO THIS EPISODE IS FOR

ABOUT THE HOST

Mirko Peters is a Microsoft 365 expert, architect, and host of m365.fm. He works with organizations from small businesses to large enterprises on Microsoft 365 architecture, security, AI integration, governance design, and system architecture. His work focuses on designing context‑driven systems that reduce complexity, enable autonomous execution, and create scalable performance across modern enterprises.