Software Spotlight: Monte Carlo
Company Snapshot
Founded: 2019
Employees: 75
Funding: $236M
Valuation: $1.6B
Stage: Series D
Locations: San Francisco, remote
Company Overview
Monte Carlo is a data observability solution.
Tell Me More
Monte Carlo is an end-to-end Data Observability solution that monitors and alerts employees about data issues across all data warehouses, data lakes, ETL, and business intelligence. The platform uses machine learning to learn about a company’s data and proactively identifies and alerts employees.
By automatically and immediately identifying the root cause of an issue, teams can easily collaborate and resolve problems faster. Monte Carlo also provides automatic, field-level lineage and centralized data cataloguing that allows teams to better understand the accessibility, location, health, and ownership of their data assets, as well as adhere to strict data governance requirements.
Monte Carlo is a differentiated bet on the data space, sitting a bit earlier in the data lifecycle than analytics, but behind ingestion. Think of it like this: Snowflake can hold all of your data, Databricks can help you analyze it, and Monte Carlo makes sure that all the data flowing into your repositories is correct.
Market Opportunity
So how do you think about managing the health of such large datasets? “Data observability” is the blanket term for understanding the health and the state of data in your system. Essentially, data observability covers an umbrella of activities and technologies that, when combined, allow you to identify, troubleshoot, and resolve data issues in near real-time.
To make data observability useful, it needs to do all of these activities:
Monitoring—a dashboard that provides an operational view of your pipeline or system
Alerting—both for expected events and anomalies
Tracking—ability to set and track specific events
Comparisons—monitoring over time, with alerts for anomalies
Analysis—automated issue detection that adapts to your pipeline and data health
Logging—a record of an event in a standardized format for faster resolution
SLA Tracking—the ability to measure data quality and pipeline metadata against pre-defined standards
In software engineering, these concepts exist up and down the stack. In DevOps, infrastructure monitoring alerts engineers when a problem needs to be fixed. Public companies like New Relic, DataDog, Dynatrace, and more help software engineers identify core issues in complex software systems. Upstream, in AI and ML observability, observability enables machine learning engineers to monitor how their production models perform in ever-changing environments.
Like Infrastructure Monitoring and ML Monitoring, Data Observability is working to monitor dataset health management and alert data scientists with at-risk data. These three monitoring solutions work together and provide engineers with the information they need to monitor their modern data stack.
Today, the top 5 largest Application Performance Management companies have a combined enterprise value greater than $50B. While it’s still early, it is estimated that Data Observability will eclipse the Application Performance Management market as a whole, which Emergen Research estimates will grow at a steady compound annual growth rate to reach $15.4 billion by 2028.
Why I like the company
Cloud continues to penetrate the enterprise, and businesses across all industries are re-platforming to cloud infrastructure for better data-driven decisions. Compared to public markets, infrastructure grows quicker than application SaaS — in fact, 7 of the top 10 fastest growing cloud businesses with $500M+ in revenue are infrastructure-related.
Monte Carlo fits this mold perfectly. Between Q3’20-Q3’21, the company scaled its ARR by 8x. Comparing Monte Carlo to Datadog, an application observability company, there is still ~30x room for enterprise value expansion. Monte Carlo has positioned itself well in the market sitting between ingestion and analytics, making it an attractive product and growth-stage investment.
The importance of data within an organization cannot be understated. As datasets become bigger and data systems become more complex, data observability will be a critical tool for realizing maximum business value.
Since closing their series C in August 2021, Monte Carlo more than doubled revenue every quarter and achieved 100% customer retention in 2021. Over the past six months, Monte Carlo has brought on new customers, including JetBlue, Affirm, CNN, MasterClass, Auth0, and SoFi, with hundreds of customers paying for and driving value from the platform.
Lastly, the team is composed of experienced growth-stage leaders who have seen companies like Gainsight, Barracuda, Rubrik, and more scale.
Monte Carlo’s Hiring Corner
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Reminder
As always, you can find the abbreviated list of companies we’ve already talked about here. Below are links to the previous posts