The Story of Monte Carlo: Building the Future of Data Reliability

Every startup story has a pivotal moment where opportunity meets preparation. For Monte Carlo, that moment came when CEO Barr Moses, fresh from her experience at Gainsight, started cold-calling potential customers to validate different startup ideas. One particular pain point kept surfacing in these conversations – the persistent challenge of unreliable data and its impact on business operations.

From Military Intelligence to Data Innovation

Barr’s journey to founding Monte Carlo was shaped by her early experiences. “I actually worked with data even then,” she recalls of her time in the Israeli Air Force, where at age 18 she led a team analyzing operational data. “Very early on sort of was exposed to both the challenges of delivering accurate, reliable data and also of leading teams from a very young age.”

This foundation in data operations and team leadership would prove crucial in Monte Carlo’s development. But first, Barr would gain valuable experience in category creation at Gainsight, helping pioneer the customer success category.

Finding the Right Problem

After leaving Gainsight, Barr took an unconventional approach to starting her next venture. Rather than immediately building a product, she tested multiple ideas simultaneously. “I actually worked on a couple of different ideas and kind of tested out different ideas in parallel. And the idea was to see which idea has traction,” she explains.

The methodology was simple but effective: cold calling potential customers to validate different problems. While many ideas fell flat, one struck a chord. “The idea of, hey, the data is wrong, what can I do about this? Or why am I always the last person to hear about this? Why am I hearing from downstream consumers that the data is wrong?” These questions consistently resonated with potential customers.

Building in Stealth

Unlike many modern startups that launch with splashy marketing campaigns, Monte Carlo took a deliberately low-key approach to building their company. “We actually took us a long time to get our first website,” Barr reveals. “I think it was maybe a couple of years into the company’s existence. We already had the product, we had customers, we had the full thing where we didn’t have a website.”

This wasn’t about being secretive – it was about focus. As Barr explains, “The only thing that matters at Monte Carlo now and forever is getting as many customers as possible and making customers as happy as possible. And every single person at Monte Carlo should be working towards one of those two goals.”

Creating a Category

The emergence of data observability as a category wasn’t planned in a traditional sense. Instead, it evolved from customer language and needs. The company drew inspiration from established practices in software engineering, applying concepts from APM solutions like Datadog and New Relic to data teams.

“We didn’t invent this pain has been around forever for a really long time,” Barr notes. “But the idea that there’s actually a solution to this is something novel.”

Looking to the Future

As Monte Carlo continues to grow, their vision extends beyond just fixing data reliability issues. “Our mission at Monte Carlo is to help accelerate the adoption of data by reducing what we call data downtime,” Barr explains. This mission is becoming increasingly critical as organizations become more data-dependent.

The company’s future is tied to the broader evolution of data infrastructure. With the rise of companies like Databricks and Snowflake, each generating between one and two million in revenue, the need for data reliability solutions is only growing. As Barr observes, “It’s kind of like Monday time now for data teams. Organizations really want to use that data, and not having confidence in it makes it extremely hard to do that.”

For Monte Carlo, this represents an opportunity to not just lead a category, but to fundamentally change how organizations think about and rely on their data. The journey from startup to category leader wasn’t about following a traditional playbook – it was about staying relentlessly focused on solving customer problems and building solutions that work. As data continues to become more critical to business operations, Monte Carlo’s bet on data reliability appears increasingly prescient.