Monte Carlo attains unicorn status, plans to scale data observability platforms
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Data, the lifeblood of all information technology that circulates among devices and carries business with it, is damaging to systems if loaded with impurities.
With data stores filling up constantly and analytics apps dipping into them to find business value, San Francisco, California-based Monte Carlo intends to make certain that all data is clean, stored safely and ready to be used at any time, across any data store – cloud or on-premises. This requires a serious dose of data observability that the startup is already providing for several hundred enterprise clients.
Monte Carlo’s machine learning-powered platform provides enterprise data analysts with a holistic view of data reliability for critical business and data product use cases in near real-time, chief engineer and co-founder Lior Gavish told VentureBeat.
The 3-year-old company announced today that it has raised $135M 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 operates with an intuitive user interface.
Data observability a hot VC space
The IT data space has never been hotter in the venture capital world. In only the last year, BigQuery reached a $1.5 billion valuation; Snowflake hit $1.2 billion; Databricks came in at $800 million. Monte Carlo is merely the latest to follow this trend. The company claims to be the first data observability toolmaker to achieve 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 infusion of new capital to continue improving experiences for its hundreds of customers, scale the data observability category to new verticals, and grow its U.S. and EMEA go-to-market and engineering teams.
“Data is in a lot of places, right?” Gavish said. “Some of them are legacy. Some of them are in the modern data stack, and some of them are up-and-coming, like streaming. Solving the (data) reliability problem cannot be done as a point solution. If you only control reliability in one part of the stack, you’re going to inevitably fail because reliability issues happen everywhere in every part of the stack that gets to process data.”
Monte Carlo supports as much of the IT stack as is possible, Gavish said. “I’m trying to create as much observability as possible across the stack. And so we’re constantly working hand-in-hand with our customers to understand what are the data store stores and what are the data processing mechanisms that they are adopting.
“We make sure that we support it in our solution; we also support all the major data warehouses, all the major data lake technologies, all the major BI tools, all the major orchestration tools. And we’re continuing to add and develop that based on the listener on demand from our customers,” Gavish said.
Augmenting the future of data reliability
As companies ingest more data and pipelines become increasingly complex, teams need a way to ensure that the data powering their decision-making and digital products is reliable and actionable, Gavish said.
Problems that can result from data-quality issues getting too far into the production stream can be expensive to fix once they’re past a certain point in the use case. Mirroring the rise of application performance monitoring (APM) tools such as Datadog and New Relic to keep software downtime at bay, data observability solves the problem of data downtime by giving teams end-to-end coverage and visibility into data health across their modern data stack.
Money in cloud databases
In 2021, organizations spent $39.2 billion on cloud databases such as Snowflake, Databricks and Google BigQuery, yet Gartner estimates data downtime and poor data quality costs the average organization $12.9M per year. Monte Carlo research shows a correlation between data incidents and the amount of data an organization handles, with the average business experiencing at least one data incident for every 15 tables in their environment, Gavish said.
“As companies continue to invest in technologies that drive smarter decision-making and power digital services, the need for high-quality data has never been higher,” Cack Wilhelm, General Partner at IVP, said in a media advisory.
Since its Series C announcement in August 2021, Monte Carlo more than doubled revenue each quarter and achieved 100 percent customer retention in 2021. On its list of several hundred customer companies are JetBlue, Gusto, Affirm, CNN, MasterClass, Auth0, and SoFi; partners include Snowflake, Databricks and dbt Labs.
“It’s simply not enough to have data – it needs to be discoverable, accessible and reliable,” Barr Moses, CEO and co-founder of Monte Carlo, said in a media advisory. “Monte Carlo created the world’s leading data observability platform to accelerate the adoption of reliable data while reducing time to detection and resolution for data downtime.”
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.
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