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Monte Carlo, a data observability startup based in San Francisco, California, today announced that it raised $60 million in funding from Iconiq Growth with participation from Salesforce Ventures, Accel, GGV Capital, and Redpoint Ventures. It brings the company’s total raised to date to $101 million, and CEO Barr Moses says it’ll be put toward bringing partners onboard, growing the company’s workforce, and expanding to new markets including Asia-Pacific, Europe, the Middle East, and Africa.
According to a recent report from Gartner, data teams spend millions of dollars per year and 40% of their time tackling poor data quality. Data systems are becoming more complex, distributed, and decentralized, widening the gap for data downtime — i.e., periods of time when data is missing, erroneous, or otherwise inaccurate.
Monte Carlo’s platform seeks to address these challenges by providing visibility across data pipelines and products. The company’s AI-powered platform provides engineers and other stakeholders with a view of their company’s data health and reliability for business use cases.
Moses, former VP of customer operations at Gainsight, cofounded Monte Carlo in 2019 with Lior Gavish, an ex-SVP of engineering at Barracuda Networks. Both were struck by what they perceived as an ease-of-use problem when it came to tools for identifying and resolving infrastructure issues: While these tools were widely available, they didn’t offer a simple way to guarantee the validity of data flowing through pipelines.
“[I]t’s obvious the data market has never been hotter, yet with 1 in 5 companies losing a customer to bad data and data teams spending a [substantial portion of their time] on data quality issues, data teams still can’t trust their data,” Moses told VentureBeat via email. “This ‘good pipelines, bad data’ problem magnifies the need for reliable, accurate data, and the industry’s enthusiasm for a solution like Monte Carlo that will bridge this gap and restore trust in data.”
According to Moses, Monte Carlo’s platform was built with a “security-first” approach that leveraged Gavish’s experience at Barracuda, where he was responsible for developing the technology behind the company’s zero-day phishing detection service. Monte Carlo employs machine learning to automate data validation and monitoring that relies on time-intensive threshold setting. The platform’s algorithms take a historical snapshot of data assets to prevent “bad data” from corrupting otherwise good pipelines, Moses says.
“[W]e take stock of [a customer’s] data assets and use machine learning to determine which ones are most ‘critical’ — i.e., which ones are most widely used, how many people are using them, and how they are using them,” she explained. “We also use various AI anomaly detection techniques to benchmark historical data, metadata, and patterns in the customer’s environment, and then identify substantial deviations from these benchmarks that could indicate the presence of bad data.”
Along the same vein, Monte Carlo recently released Incident IQ, an AI-powered capability that conducts root cause analysis for data issues at each stage of the data life cycle. AI models identify patterns in query logs, trigger investigative follow-up results, and look for upstream dependency changes to pinpoint what caused issues to occur. Moses claims that Incident IQ can reduce the amount of incidents enterprises experience by 90%.
“One recent use case is from a real estate unicorn. For this company, revenue basis points vary significantly between ZIP codes. One time, there was a specific ZIP code that had a square foot issue … Incident IQ helped the team root cause the issue through automatic root cause analysis, statistical analysis, and incident management workflows. The team later found out that if they hadn’t caught the issue, it would have cost them $16 million for an entire year,” Moses said.
Over the past year, Monte Carlo says that its customer base, which now includes thousands of brands across ecommerce, retail, fintech, and insurtech markets, increased by 10 times. Intuit, Affirm, Fox, Vimeo, and Zalora are among the notable additions to the roster. Revenue has continued to double every quarter since Monte Carlo’s last funding round in February 2021 as the company formed partnerships with Snowflake, Looker, and PagerDuty, and Monte Carlo’s workforce now numbers 50 employees across the U.S., Canada, the U.K., South America, and Israel.
“Having just raised our series A in September 2020 and series B in February, we weren’t looking for new funding — this was an opportunistic raise,” Moses said. “The pandemic has made data reliability a priority for most businesses. With companies moving online because of the pandemic, building data platforms is an urgent priority for most data-driven businesses. This increased investment in data platforms and democratization of data across the organization relies on end-to-end data observability at all stages of the data pipeline, which Monte Carlo provides.”
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