A startup called Monte Carlo Inc. is looking to extend its lead in an emerging category for data observability tools after announcing a $60 million funding round today.

ICONIQ Growth led the Series C round, with participation from Salesforce Ventures and existing backers Accel, GGV Capital and Redpoint Ventures, bringing Monte Carlo’s total amount raised to $101 million to date.

Monte Carlo is a fast-growing startup that’s focused on ensuring data reliability. The company explains that it’s trying to tackle a very big problem, namely helping enterprises deal with “poor-quality” data. It cites a 2021 report from the research firm Gartner Inc. that highlights how data teams spend millions of dollars per year and devote as much as 40% of their time to tackling unreliable data.

Barr Moses, co-founder and chief executive of Monte Carlo, told SiliconANGLE that poor-quality data is an issue that has arisen from the sheer volume of information that companies use. Nowadays, she explained, companies use anywhere from dozens to hundreds of internal and external data sources to produce analytics and machine learning models. The problem is that any one of these data sources can change in unexpected ways that can affect the reliability of that data.

“For example, an engineering team might make a change to the company’s website, thereby modifying output of a data set that is key to marketing analytics,” Moses said. “As a result, key marketing metrics may be wrong, leading the company to make poor decisions about ad campaigns, sales targets and other important, revenue-driving projects.”

Another issue Moses highlighted is that data pipelines have become more complex, with multiple stages of processing and dependencies among various data assets. Companies don’t have enough visibility into these dependencies, but a single change made to one data set can have unintended consequences that affect the correctness of dependent data assets.

“Something as simple as a change of units in one system can seriously impact the correctness of another system, as in the case of the Mars Climate Orbiter,” Moses said, referring to the NASA space probe that crashed as a result of a data entry error that brought it too close to the planet.

Unreliable data leads to what Moses calls “data downtime,” which she refers to as the periods of time when data is missing, inaccurate or otherwise erroneous. Companies can pay a very steep price for data downtime in terms of time, money and trust. “We estimate that data teams spend 40% of their time, around 120 hours a week, firefighting data downtime, and that is time that could otherwise be spent building data products or otherwise innovating,” Moses said.

Monte Carlo is hoping to eliminate all of this data downtime through it new data observability platform. It’s based on the same principles that power application observability tools such as those from Datadog Inc. and Cisco Systems Inc.’s AppDynamics, only it applies them to data pipelines instead.

Specifically, Monte Carlo’s observability platform connects to a company’s data stack in order to observe the various data sources it relies on. It uses machine learning algorithms to understand what the normal behavior of these data streams looks like, and monitors them for any issues that may suddenly arise, whether that’s in a data warehouse, data lake, business intelligence tool or something else. If an issue with any data source appears, it can quickly assess its impact and notify teams of the problem.

“By automatically and immediately identifying the root cause of an issue, teams can easily collaborate and resolve problems faster,” Moses said.

It’s an interesting idea that has already gotten traction prior to today’s funding round. Monte Carlo said its revenue has grown more than 800% in the last year, off an undisclosed base, with new customers that include Intuit Inc., PagerDuty Inc. and Vimeo Inc. adopting its platform recently. The company also has partnerships in place with the largest cloud data warehouse firm, Snowflake Inc., and application observability firms PagerDuty and Looker Inc.

Analyst Holger Mueller of Constellation Research Inc. told SiliconANGLE that with compute platforms increasingly moving out of the data center and into the cloud, it’s becoming more difficult for enterprises to monitor them. That trend gave birth to the rise of DevOps, and then the application observability trend, he said.

“It is interesting to see the dynamic in this space extend to data, with many startups looking for a piece of the pie including Monte Carlo,” Mueller added. “But the key to winning in this space will be to move beyond observability and into manageability, as automation is the real goal of enterprises running their next-generation applications.”

Monte Carlo said it will use its freshly filled pockets to invest in its product offering to support more use cases, help make customers more successful and expand into new markets.

Images: Monte Carlo