Post by Michał Kulikowski

A full data architecture can be implemented pretty quickly today. Modern platforms make it possible to stand up Bronze / Silver / Gold layers, dashboards, and automated pipelines in weeks, not months. The first real obstacle often isn’t technology. It’s data governance. You start with: - Getting data in - Building dashboards - Showing value fast At that stage, governance feels optional: KPI definitions live in people’s heads - ownership is implicit QA happens only when something breaks This works — for a while. Then complexity grows - post POCs: The same metric produces different results Questions about “which number is correct” become common Changes create uncertainty instead of confidence Data architecture without governance scales confusion, not clarity :( Governance isn’t exciting, but it’s foundational: - Clear KPI definitions - Explicit ownership - Data lineage - Consistent quality checks - Predictable access models This is a necessary investment. Build fast if needed, but introduce governance earlier than feels necessary. Pipelines can be refactored. Trust in data is much harder to rebuild. Best practices are best practices, not mandatory rules — but skipping them always comes with a delayed cost. And then there’s data strategy. Do you actually have one? #Fabric #MicroStrategy #DataGovernance