About this demo
Trusting your data isn’t a gut check—it’s knowing the right validations are in place, visibility is always-on, and the right people are alerted the moment something breaks. In this walkthrough, you’ll see how Atlan’s Data Quality Studio brings data quality into the flow of work by combining active metadata, end-to-end lineage, and native support for Snowflake and Databricks—so teams can confidently use data for analytics and AI without guessing what’s safe. Learn how to start where it matters most using Reporting Center insights that reveal coverage gaps across your most-used tables and business-critical dashboards, then trace lineage upstream to pinpoint the highest-impact candidates for checks. See how Atlan AI recommends meaningful rules from metadata context, how stewards and consumers can apply pre-built rules without SQL, and how power users can extend coverage with custom SQL checks. Finally, explore how results become actionable trust signals everywhere—at the column level, in search and asset discovery, across the Data Product Marketplace, and directly in lineage for faster impact and root-cause analysis—plus real-time notifications to Slack or Teams and failed-row SQL to accelerate remediation.