Product demo. 
What is data observability? 
What is a data mesh--and how not to mesh it up 
The ULTIMATE Guide To Data Lineage 
The Monte Carlo difference
Automatic impact analysis
Enhanced team collaboration
Proactive data consumer communications
“It gets us out of the business of emailing users to let them know that there’s a problem with a table. Monte Carlo [alerts] give that to us automatically, so the analyst that’s using a table will know there’s a problem and we can focus on resolving it.”
“We are now able to reach out proactively when those reports are behind or the data is lagging…we’d much rather be the ones pointing out when something isn’t right with the data.”
“Previously, our developers didn’t have visibility or feel ownership of these business problems. They shipped and QAed. They perform fixes based on alerting now.”
The Undisputed Leader
Warn data consumers
Proactively notify data consumers when there are issues with their data.
- Send customizable warnings to relevant business intelligence dashboards and catalog pages.
- Route alerts to your collaboration tool of choice Microsoft Teams, Slack, email, and more.
- Surface data product health status and SLIs with automated dashboards.
Break down silos
Get everyone on the same page.
- Assign data source owners and find frequent table users from surfaced metadata.
- Review documentation from past incidents.
- Trace the impact of changes with field-level lineage.
- Prevent PRs that break assets downstream.
Manage Tickets Your Way
Ensure loose ends are tied with efficient incident management.
- Track incidents by severity, status, owner, and schema within Monte Carlo’s intuitive incident feed.
- Generate automatic descriptions to populate new or link to pre-existing tickets in JIRA, ServiceNow, and other ticket systems.
