Platform
Data Observability

Data observability: automate data quality coverage across your environment

The data estate has changed. Your data quality strategy needs to change too. Scale coverage, eliminate maintenance, and identify gaps automatically with AI-powered monitoring, automated scaling, and agentic recommendations across data and AI systems.

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Leader in Data Observability for 13th consecutive quarters

G2 Grid® for Data Observability Software

Automated monitoring reduces data downtime by 80% year over year

Monitoring and recommendation agents scale coverage without manual work

End-to-end data quality coverage from ingestion to consumption

Field-level lineage and root cause context make alerts actionable

Features

Monitor what matters—and scale coverage in seconds

Your biggest hurdle for reliability? Scale. Stop wasting time on manual workflows. Create and deploy new monitors in seconds, discover the best monitors for each table, and autoscale coverage with your environment.

Get instant coverage out of the box for common issues like freshness, volume, and schema powered by AI.

Anomaly detection illustration in Monte Carlo's data + AI observability platform

Understand what matters at-a-glance—and how to improve it
Leverage AI monitor creation to define and deploy the right coverage faster
Work your way—with SQL, inside our codeless UI, or with our observability agents

Monitor across tables, systems, environments and beyond
Get everyone on the same page about where the issues are—and how to solve them
Eliminate syncing issues to keep products performing so the data your agents retrieve is as reliable as the dashboards your analysts read

AI-powered data quality rules and templates and automatic data profiling
Monitoring for structured and unstructured data

Monitoring a Data Product

Detection is where it starts.
Resolution is where it counts.

Monitoring surfaces the issue. Four capabilities carry it the rest of the way — routing it to an owner, tracing its blast radius, isolating the cause, and optimizing what it costs you.

Route Alerting & Communication Every alert reaches the person who can fix it. Granular routing by domain, automated lineage grouping to collapse related incidents into one, and root-cause context attached to the notification. Explore alerting Trace Lineage & Impact Follow an incident upstream to its source and downstream to everything it touches. Field-level lineage maps tables to the dashboards, models, and agents that depend on them. Explore lineage Resolve Root Cause Analysis Find out why it broke. Correlate data changes, system failures, and code changes to isolate the cause, instead of working through hypotheses by hand. Explore root cause analysis Optimize Performance Tune what monitoring surfaces. Identify slow queries and resource-heavy assets, cross-referenced with downstream importance so effort goes where it actually matters. Explore performance

What our customers say

Monte Carlo is the only solution battle-tested in 100s of production environments

"Monte Carlo helps you pinpoint any bottlenecks in your pipeline and streamline the effort to reduce technical debt where it’s needed most.   Monte Carlo helps you maintain a high performance of your pipeline, quickly address emerging issues, and reduce cost consumption."

"Monte Carlo gives upstream and downstream lineage for a 40,000-foot view of the entire data ecosystem — and this helps stakeholders across the company truly run."

"In the past, we might have to backfill six months of data to when the issue started. But if we can detect an issue in a day, then a one-day backfill can be a lot cheaper. There are lots of benefits, like engineering time, that we’re seeing from data observability."

"We have 10% of the incidents we had a year ago."

"Data observability has become a necessity, not a luxury, for us. As the business has become more and more data-driven, nothing is worse than allowing leadership to make a decision based upon data that you don’t have trust in. That has tremendous costs and repercussions."

"Data trust is a human emotion, which isn’t always entirely rational. But, if you can give the analysts access to Monte Carlo where they can see the health of all the data objects they are using, you can sell them on the data’s quality."