Self-styled data reliability firm Monte Carlo Inc. announced today that it has closed on a $135 million late-stage funding round led by IVP that brings its total amount raised to date to $236 million.

Accel, GGV Capital, Redpoint Ventures, ICONIQ Growth and GIC Singapore also participated in the Series D round, which values the startup at $1.6 billion.

Monte Carlo is a startup that’s trying to tackle enterprises’ problems around data reliability. In an interview with SiliconANGLE last year, Monte Carlo co-founder and Chief Executive Barr Moses said the struggle with poor-quality data is a common issue among enterprises that has arisen because of the sheer volume of information they accumulate.

The problem is that enterprises typically use anything from a dozen to hundreds of internal and external data sources for purposes such as data analytics and machine learning. Those data sources have a nasty habit of changing in unexpected ways that can impact the reliability of that information.

For instance, if an engineering team alters the company’s website somehow, they may inadvertently modify the output of a key data set used by its marketing teams. That could result in those teams using inaccurate marketing metrics.

Unfortunately, that’s just one example. In addition, the data pipelines on which enterprises rely have become incredibly complex, with multiple stages of processing and dependencies among various data assets. Most enterprises don’t have enough visibility into these dependencies, and a single change to one data set can impact various dependent data assets.

Monte Carlo has created “data observability” software that helps companies to overcome these problems. Its platform is based on the same principles that guide application observability tools such as Datadog and AppDynamics, applied to data pipelines instead of app metrics.

What Monte Carlo’s platform does is connect to a customer’s data stack to observe the various data sources on which they depend. It then applies machine learning algorithms to try to understand the normal behavior of these data streams, so it can identify if anything changes. If an issue appears, it can assess the impact and notify the right people to go and fix it.

Monte Carlo cites research from Gartner Inc. that shows how the impact of data downtime and poor-quality data costs organizations an average of $12.9 million per year.

Such an alarming statistic helps to explain Monte Carlo’s growth over the past couple of years. The company said today it has expanded its headcount from 20 people to 120 in the last 20 months. And since closing on its Series C round of funding in August 2021, the company claims to have doubled its revenue in every quarter, with sales up more than 800% year-over-year, though it didn’t disclose absolute numbers. In the last six months it has added multiple new enterprise customers, including JetBlue Airways Corp., Affirm Inc., The Cable News Network, Auth0 Inc. and MasterClass parent company Yanka Industries Inc.

“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,” Barr said in a statement today. “Our customer traction and roster of great partners like Snowflake, Databricks and dbt Labs highlights the continued growth and maturation of the data observability category, as well as the industry confidence in Monte Carlo’s approach.”

Monte Carlo said it will use the funds from today’s round to continue improving experiences for its customers, scale up the data observability category into new vertical industries and grow its U.S. and Europe-based go-to-market and engineering teams.

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