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Monte Carlo raises $60M to advance data observability

Vendor Monte Carlo brought in new funding as it builds out new capabilities to bring more data trust and visibility to both data warehouses and data lakes.

Data observability vendor Monte Carlo said Tuesday it raised $60 million in a Series C round of funding.

The new funding round is the second time that the San Francisco-based vendor has raised money in 2021, following Monte Carlo's $25 million Series B round in February.

Total funding to date for Monte Carlo now stands at $101 million. The vendor said it will use the new funding to expand its go-to-market efforts and to build out new capabilities for its platform.

Monte Carlo last updated its Data Observability Platform on July 14, with the introduction of the Incident IQ capability that enables users to better understand data pipeline problems such as outages or poor performance. The overall goal of Monte Carlo's platform is help organizations improve data quality with better visibility into data.

Why data observability and persistent data quality matter

The need for improved data quality across organizations is acute, said Paige Bartley, senior research analyst of data, AI and analytics at S&P Global Market Intelligence.

She said that historically, data quality has sometimes been treated as a project with a deadline or a destination. But the problem with the deadline or destination approaches is that data is constantly changing. So periodic assessment and remediation of data quality issues can't keep pace with changes in the modern enterprise data environment.

"Today's organizations are frequently seeking data quality visibility and monitoring approaches that are more persistent, iterative and automated," Bartley said, "so that anomalous situations in the data environment can be detected early and fixed before they create turbulence in downstream dependencies."

Why Monte Carlo is raising more money

Barr Moses, CEO and co-founder of Monte Carlo, noted the market for her firm's data observability technology has accelerated in 2021.

Today's organizations are frequently seeking data quality visibility and monitoring approaches that are more persistent, iterative and automated, so that anomalous situations in the data environment can be detected early.
Paige BartleySenior research analyst of data, AI and analytics, S&P Global Market Intelligence

Moses noted that Monte Carlo has doubled its revenue every quarter for the last year, bringing in a number of well-known companies as Monte Carlo customers, including video sharing service Vimeo, financial services firm Affirm and broadcaster Fox.

"We are planning on using the funding to help grow the category and the product, and make our customers happier," she said.

Plans include improve data observability for data lakes

Lior Gavish, CTO and co-founder of Monte Carlo, explained that to date, much of the vendor's focus has been on data observability for data warehouses. A big push for Monte Carlo in 2021 will be enabling enhanced data observability for data lakes.

"Our objective, which I believe we will accomplish before the end of the year, is to have parity across our data warehouse and data lake capabilities," Gavish said.

The core capabilities of data observability for Monte Carlo include: data freshness, schema visibility and data lineage. Gavish said the scale of data, as well as the tendency to have more unstructured data in a data lake than in a data warehouse, are among the key challenges of bringing full data observability to data lakes.

Monte Carlo Data Observability Platform screenshot
Monte Carlo's Data Observability Platform provides a dashboard for users to track data lineage across different data pipelines.

Gavish noted that data lakes are usually an amalgamation of several different technologies, including a cloud object storage service such as Amazon S3 and a metadata storage catalog like Apache Hive or AWS Glue, and different ways to transform or query data, including Apache Spark, Presto and Amazon Athena.

"The challenge is how do you properly map all of the different components and pull observability information from all of them to work effectively," Gavish said.

The new funding round included participation from ICONIQ Growth, Salesforce Ventures, Accel, GGV Capital and Redpoint Ventures.

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