Product demo. 
What is data observability? 
What is a data mesh--and how not to mesh it up 
The ULTIMATE Guide To Data Lineage Data trust for your company
Monte Carlo’s data observability platform uses automated monitoring to ensure your data is reliable at every stage of the data pipeline.
Mitigate risk and impact of data quality issues at your company.
Data breaks – call it a fact of life. Poor data quality erodes stakeholder trust, data team resources, and company’s revenue. So, what’s a data team at your company do?
Enter data observability: the key to reliable data.
-
Monte Carlo connects to your existing data stack in minutes, monitoring and alerting to freshness, volume, and schema changes out-of-the-box.
-
Monte Carlo equips data teams with rich context about data incidents, including end-to-end field-level lineage for rapid triaging, trouble-shooting, impact analysis, and resolution.
-
Monte Carlo generates insights to help you understand what data matters most to your business, where you can cut costs, and how data quality has improved over time.
Schedule a meeting with your company’s dedicated expert to learn how Monte Carlo can help your company achieve better data quality.
Trusted by industry leaders at
The Undisputed Leader
-
“Monte Carlo is a tool that is easy to implement, use, and drive value through for users across our organization. In our case, we started seeing value during the first week …and have continued to generate more value since!”
-
“Monte Carlo performed best in Customer Experience with an A- grade, notably in TCO/ROI, receiving an A due to its articulation of strategic values and tools to help customers calculate costs. In Usability, intelligence and user experience are strengths.”
-
“The platform’s diverse compilation of source connections, low-code user interface, ML monitoring capabilities, strong data pipeline support, and data catalog make this an extremely well-rounded offering out the gate.”
What do our customers have to say?
“We wanted something that would effectively get this off the ground and running without us having to put in that effort. The schema, volume, and freshness checks that Monte Carlo offers delivers on that.”
Ed Kent Principal Developer
Monte Carlo Delivers
-
Freshness seeks to understand how up-to-date your data tables are, as well as the cadence at which your tables are updated. Freshness is particularly important when it comes to decision making; after all, stale data is basically synonymous with wasted time and money.
-
Monitoring data volume can help identify missing data, duplicate data and other issues. If 200 million rows suddenly turns into 5 million, you should know.
-
Is your data in an acceptable range? Quality gives you visibility into null values, duplicate data, and other specific issues based on what you should expect from your data.
-
Were any changes made to the organization of your data? Monitoring who makes changes to these tables and when is foundational to understanding the health of your data ecosystem.
-
When data breaks, the first question is always “where?” Data lineage provides the answer by telling you which downstream assets were impacted, which upstream sources are contributing to the issue and which colleagues need to be looped in.
Monte Carlo is named G2’s Best Software Awards as one of the fastest growing products in 2024!
