Trusted by the data teams at
Reduce time and resources spent on data quality.
Monte Carlo streamlines your data quality operations by consolidating all incident management and root cause analysis workflows in one place.
Make data reliable and trustworthy.
Data observability uses lineage, logs, statistical correlations, and data sampling to understand the health of your data at any stage in its life cycle.
Mitigate the risk and impact of data quality issues.
Understand the upstream and downstream dependencies so you can pinpoint the root of the issue – fast.
Resolve data incidents faster.
Faster time-to-resolution is enabled with automated lineage, impact radius assessment, workflow tools, and the context your teams need—all in one platform.
Understand data dependencies – automatically.
Within 24 hours of deployment, Monte Carlo equips your team with automated, up-to-date field-level lineage to map upstream and downstream data dependencies.
Automatic impact assessment
As soon as your data team is notified of an incident, they can immediately triage by understanding who and which reports were impacted. Need the details? Just scroll or click.
Automatically conduct root cause analysis.
Monte Carlo equips your team with a suite of automated root cause analysis tooling, including field-level lineage, query run history, query logs, affected reports, and more.
Operationalize data incident management at scale.
Incidents are only root caused and resolved when everyone on the data team is on the same page. Use Monte Carlo to set up severity levels, assign owners, and keep your stakeholders up-to-date about asset heath for their critical reports.
“The self-service capabilities of data observability helped build back trust in data, as users were seeing us in action: going from a red alert to a blue ‘work-in-progress’ to ‘resolved’ in green. They knew who was accountable, they knew the teams were working on it, and everything became crystal clear.”
Gopi Krishnamurthy Former Director of Engineering