Used For

• +**Your analysts waste 3 hours finding the right customer table** → AI search finds it in 30 seconds with automatic descriptions
• +**Schema changes break 5 downstream reports and nobody knows which ones** → Visual maps show every connection before you make changes
• +**New hires ask the same data questions for weeks** → Self-serve search in Slack handles 50% of requests automatically
• +Sets up data quality monitors without coding - alerts when row counts drop or data goes stale
• +Auto-generates documentation for tables and columns so you're not writing descriptions manually

Best For

• >Your data team spends half their day answering "where is this data?" questions instead of analyzing
• >Data pipelines break every weekend because nobody knows what connects to what
• >You're drowning in spreadsheets, databases, and reports with zero documentation

Not For

• -Teams under 10 people — you don't have enough data chaos to justify a catalog
• -Companies wanting plug-and-play simplicity — you'll spend time setting up monitors and connecting tools
• -Anyone on a tight budget — no free tier and it's built for multi-tool data stacks

Pairs With

• *dbt (monitors your data transformation jobs and tracks success rates)
• *Looker (shows which dashboards will break when you change table schemas)
• *Slack (where the AI bot answers data questions so people stop pinging you)
• *Snowflake (connects to scan and document your warehouse tables automatically)
• *Tableau (previews reports and tracks data lineage from warehouse to dashboard)

The Catch

• !Takes 1-2 hours to set up monitoring and lineage properly, not the "15 minutes" the docs claim
• !AI can run SQL queries on your data (admin can disable this, but it's on by default)
• !Owned by Atlassian now, so expect enterprise pricing and feature bloat creep

Bottom Line

Turns your data mess into a searchable directory so people stop asking "where's the customer data?" every five minutes.