Monte Carlo plans to scale the data observation platform

Data, the lifeblood of all information technology, circulating between devices and carrying businesses, harms systems when tainted with contamination.

As data stores continually fill up and analytics apps dive into them for business value, Monte Carlo, based in San Francisco, California, intends to ensure all data is clean, securely stored, and ready to use at any time, on any data store— Cloud or on premises. This requires a serious dose of data observability, which the startup is already providing to several hundred enterprise customers.

Monte Carlo’s machine learning-based platform provides enterprise data analysts with a near real-time, holistic view of data reliability for critical business and data product use cases, chief engineer and co-founder Lior Gavish told VentureBeat.

The 3-year-old company announced today that it has raised $135 million in Series D funding from a group of investors led by IVP, giving it a valuation of $1.6 billion. Its frontline product is a SOC-2 Type II certified data observability platform that works with an intuitive user interface.

Data observability a hot VC space

The IT data room has never been hotter in the venture capital world. In the last year alone, BigQuery was valued at $1.5 billion; Snowflake reached $1.2 billion; Databricks came up with $800 million. Monte Carlo is the last to follow this trend. The company claims to be the first data observability tool maker to reach a billion-dollar valuation, joining the ranks of Databricks, Fivetran, Starburst, and dbt Labs as a data unicorn.

Gavish told VentureBeat that the company intends to use the new capital injection to further improve experiences for its hundreds of customers, expand the data observability category into new industries, and strengthen its US and EMEA go-to-market and engineering teams extend.

“Data is in many places, right?” Gavish said. “Some of them are legacies. Some of them are in the modern data stack, some are emerging, such as: B.Streaming. The solution to the (data) reliability problem cannot be a point solution. If you control reliability in only one part of the stack, you will inevitably fail, since reliability issues appear throughout every part of the stack that processes data.”

Monte Carlo supports as much of the IT stack as possible, Gavish said. “I try to create as much observability across the stack as possible. That is why we are constantly working hand in hand with our customers to understand what the data stores are and what data processing mechanisms they employ.

“We make sure we support it in our solution; We also support all major data warehouses, all major data lake technologies, all major BI tools and all major orchestration tools. And we’re adding that and evolving that based on our customers’ Listener on Demand,” said Gavish.

Expanding the future of data reliability

As companies absorb more data and pipelines become more complex, teams must ensure the data supporting their decision making and their digital products is reliable and actionable, Gavish said.

Problems that can result from data quality issues penetrating too far into the production stream can be expensive to fix once they’ve passed a certain point in the use case. Data observability reflects the rise of application performance monitoring (APM) tools like Datadog and New Relic to keep software downtime at bay, and solves the problem of data downtime by giving teams end-to-end coverage and insight into the data health of their modern data offers stack.

Money in cloud databases

In 2021, organizations spent $39.2 billion on cloud databases like Snowflake, Databricks, and Google BigQuery, yet Gartner estimates that data downtime and poor data quality cost the average organization $12.9 million a year. Monte Carlo research shows a correlation between data incidents and the amount of data an organization processes, with the average organization experiencing at least one data incident for every 15 tables in its environment, Gavish said.

“As companies continue to invest in technologies that drive smarter decisions and support digital services, the need for high-quality data has never been greater,” said Cack Wilhelm, general partner at IVP, in a media consultancy.

Since announcing the Series C in August 2021, Monte Carlo has more than doubled sales each quarter and achieved 100 percent customer retention in 2021. His list of several hundred client companies includes JetBlue, Gusto, Affirm, CNN, MasterClass, Auth0 and SoFi; Partners are Snowflake, Databricks and dbt Labs.

“It’s just not enough to have data — it has to be findable, accessible and reliable,” said Barr Moses, CEO and co-founder of Monte Carlo, in a press release. “Monte Carlo created the world’s leading data observability platform to accelerate adoption of reliable data while reducing the time to detect and resolve data outages.”

The company is backed by Accel, GGV Capital, Redpoint Ventures, ICONIQ Growth, Salesforce Ventures, GIC Singapore and IVP. Competitors in the data observability market include ICT Reverse, Tuosi Technology, Mathematica and Zertifika General.