Why Fabric Data Models Drift — and Why DAX Can’t Save Them

• The Myth of the “Single Source of Truth”
  • Why most teams over-trust their data models
  • How abstraction layers can hide assumptions and errors
  • The danger of treating derived metrics as facts
• Data Models Are Opinions
  • Every model reflects decisions made by humans
  • Business logic is embedded, not neutral
  • Analysts and engineers encode trade-offs—often implicitly
• Execution vs. Understanding
  • Data engines execute logic perfectly, even when the logic is wrong
  • Accuracy in computation does not equal correctness in meaning
  • Why dashboards can look “right” while still misleading teams
• Ownership and Accountability
  • Who actually owns metrics and definitions?
  • Problems caused by disconnected analytics and business teams
  • The need for shared responsibility across roles
• Context Is More Important Than Scale
  • More data does not automatically mean better decisions
  • Local knowledge often outperforms centralized abstraction
  • When simplifying data creates more confusion than clarity

Notable Insights

• Treating analytics outputs as facts removes healthy skepticism.
• Data platforms don’t create truth—they enforce consistency.
• Metrics without narrative and context are easy to misuse.
• Trust in data should be earned through transparency, not tooling.

Practical Takeaways

• Question how metrics are defined, not just how they’re calculated
• Document assumptions inside data models
• Encourage teams to challenge dashboards and reports
• Prioritize understanding over automation

Who This Episode Is For

• Data analysts and analytics engineers
• Product managers and business leaders
• Anyone working with dashboards, KPIs, or metrics
• Teams building or maintaining modern data stacks

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