What practice helps monitor a dashboard for data quality and notify anomalies?

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Automated monitoring alerts let you continuously watch a dashboard for data quality issues and anomalies, and immediately notify the right people so issues can be investigated and fixed quickly. By defining rules or thresholds for key metrics, data freshness, and data completeness, the system can detect when values stray from expectations, when a data source fails to refresh, or when there’s an unusual spike or drop. When an alert fires, it delivers a notification through preferred channels (email, chat, on-call system, etc.) and can be configured to escalate if the issue isn’t acknowledged, ensuring rapid attention.

This approach is superior because it automates detection and communication, turning monitoring into an active process rather than a passive one. In contrast, inquiring about structure changes focuses on schema, not ongoing data quality; reaching out to users daily is a manual, reactive communication method that doesn’t scale or quickly flag issues; rebuilding the dashboard addresses symptoms after a problem but doesn’t provide ongoing, proactive monitoring.