# Data Governance  Image ref: [Substack post by John Wernfeldt](https://jw12727830.substack.com/p/most-executives-say-data-governance) The objective of data governance is to **enable effective decision making** by establishing clarity of what matters (or what is broken) and who is accountable for it, rather than to building extensive documentation or broad theoretical frameworks. - Data Governance is how decisions about data actually get made when things break: It defines **who decides**. # The four steps to data governance ## Focus on real business decisions Governance should directly help teams make faster, more confident decisions by stabilizing and standardizing the core metrics and data that actually matter, rather than trying to govern everything at once. - Focus on business outcomes first. ## Assign clear ownership Each key metric or decision area needs one accountable owner (not a committee). This ensures that definitions, fixes, and conflicts are resolved quickly and clearly, rather than being stuck in shared responsibility.   - This is the metric. - This is how it is calculated. - This is when it changes. - This is who is accountable when it breaks. ## Clarity before execution Introducing data governance begins with freezing changes/updates of KPIs and metrics and surfacing reality: Document what is broken and who is responsible before adding structure. - Document and track broken metrics. - Ownership for fixes has to be explicit. - Escalation paths must exist. ## Prove value quickly Demonstrate governance value within days through tangible outcomes (trusted dashboards, removal of manual workarounds, and AI use-cases that run on governed metrics) rather than producing complete policy documents or frameworks. # Enabling AI, analytics, and trust  Image ref: [LinkedIn post by John Wernfeldt](https://www.linkedin.com/posts/john-wernfeldt-82894b58_frankly-everyone-wants-the-top-layer-ai-activity-7417848684274515968-8kG8/?utm_source=share&utm_medium=member_ios&rcm=ACoAAAC_pm0BBEsyWiigMhjQcWtNBihOeQKYnZw)