Metric Governance

Nothing erodes trust in a data platform faster than two dashboards disagreeing about revenue. Metrics are where semantic inconsistency becomes visible to executives, because a metric is a definition compressed into a single number — filters, grain, time window, currency handling, exclusions — and every BI tool, notebook, and export is an opportunity to recompute it slightly differently. Metric governance applies the MDDE pattern to this specific, high-stakes domain: the metric's definition lives once, as structured metadata — its formula, grain, filters, and ownership — and every place the metric appears is generated or validated from that definition. Consumption surfaces such as exposure views carry the computed metric so downstream tools inherit rather than reimplement it, and checks flag any artifact that computes a governed metric its own way. Defined in business-friendly metadata, the definition is also something a controller can read and sign off on — which matters, because a metric definition is ultimately a business decision. The result is that "which number is right?" becomes a resolvable question with an owner, a version history, and a single answer — semantic drift contained at the point where it costs the most credibility.