Leverage machine learning to proactively identify data and data pipeline anomalies so you can fix bad data before it impacts your organization.
Ready to fix bad data before it impacts your organization?
Reliable Data & AI Delivered
Legacy data quality approaches, such as unit testing, cannot scale with the variety, velocity, and velocity of data and AI in Azure Synapse Analytics and Microsoft Fabric.
Rather than trying to anticipate all the ways data can break, data observability provides full visibility into your data, systems, and code so you can quickly detect and resolve anomalous behavior.
Find it
Automated machine learning monitors. No code, no guesswork, no oversights.
- Scale anomaly detection across your pipelines automatically.
- Deploy deep quality monitors with +50 metrics.
- Build custom rules for unique business logic.Â
- Ensure consistency across tables and databases.
Mitigate it
Transform your incident response from reactive scramble to proactive service.Â
- Enhance focus with automated impact analysis.
- Get actionable alerts to the right team.
- Track incident tickets, severity, and status.
- Display data product SLAs and health status.
Fix it
+1,000 incidents are resolved in Monte Carlo every day.
- Understand where incidents originated with cross-system data lineage.
- Zero in on bad source data with automated segmentation analysis.
- Discover system failures with metadata monitoring and incident correlation.
- Surface bad queries and faulty logic with code change insights.
Prove it
Build trust by displaying reliability levels and response times.
- Show how data reliability levels have changed.
- Communicate the current health of key assets.
- Measure your team’s operational response.
- Drill into insights at the data product and table levels.
