Black and white headshot of Barr Moses, Co-Founder of Monte Carlo, against a colorful graphic design background
This post is part of Accel’s Secrets to Scaling series, where leaders from across our portfolio share their learnings and advice with the next generation of entrepreneurs and exceptional teams everywhere.

In 2018, Barr Moses was ready for a career change. She wanted to launch a company, but there was one problem: Barr wasn’t sure what the company should do. She had plenty of ideas, so instead of launching just one startup – she tried three. Barr’s plan was to work at them all and pick the one with the best customer feedback. A few years later, the winner was clear. There were hundreds of technical leaders with a common concern around data reliability, and Monte Carlo was the solution they’d been hoping for. 

Building a Solution for Data Downtime

Barr was no stranger to the struggles with data quality. Her entire career had been filled with broken data products (dashboards, reports, you name it) and data problems (wasted time, lost trust, and the dreaded early morning fire drills). She was perfectly poised to fix this problem she referred to as “data downtime.” 

“There was such an emotional response when we discussed problems with data reliability. It was clear there was a big problem to be solved.”

In just a few years, Monte Carlo has grown to be the leading data reliability company and the creator of the industry's first end-to-end Data Observability platform. We sat down with Barr to discuss her background, her passion for customers, and advice for founders.

Let’s start by talking about your background growing up in Israel and joining the Israeli Air Force. Can you share a bit about that experience? 

That’s right. I was born and raised in Israel and drafted into the Air Force. At a very young age, I had a lot of responsibilities. I was commander of a team focused on data analysis for operational units. That’s when I first started to understand the difficulties of dealing with massive amounts of data, and the importance of team diligence to make sure the data is actually accurate. 

In addition to supporting the training that went into this function, I really felt responsible for the team's mental and physical health. There were about 100 of us, all around 18 years old. Motivation and camaraderie among a shared mission was really important, and I learned how to develop it from scratch.

Where did your journey take you from there?

I moved to the Bay Area to study math and statistics at Stanford University. I actually thought that I was going to go into academia because my dad is a physics professor. I was really sure about it. But I worked in the Statistics Department for a bit and realized that while academia is amazing, it's probably not for me. So, I joined Bain as a management consultant. I was mostly working with Fortune 500 organizations, doing data analysis and strategy work for them. 

Then, I joined a company called Gainsight, which was creating the customer success category and I was so excited to work with amazing people on hard problems. We wanted to become really data-driven as a company. We were using data in our boardroom and using data to make decisions and leading with data in executive meetings. 

Our customers were using our data too, and I remember noticing a strong shift in emphasis toward applying data to customer success.Before that, we had been quite transactional like a lot of software companies. Really, it was because of data we could become more customer-focused. 

“We could forecast churn and come up with signals and solutions to prevent that. Because we were able to understand [customers], we could become more relationship-based.”

But I learned the data was wrong a lot. And when I tried to investigate if our data teams had a solution I found nothing. I thought I was crazy. What was going on? I could not believe there was no solution for this. So we hacked one together. 

What compelled you to start your own company?

I knew I needed a change but I really didn’t know what that would look like. So I left Gainsight and I actually took some time to recharge and figure out what I wanted to do. I went on a silent meditation retreat, and then honestly just watched Netflix for a full week straight... long before it was socially acceptable. 

One of the options I was debating was starting my own company. I decided to meet with about 50 founders in one month to learn all about their process, what they did and what it was like. I think around this time I met Steve Loughlin and the team at Accel. He told me I should go for it. I remember thinking to myself: wow, this investor believes in me even more than I believe in myself, and that was really powerful. 

After our conversation, I found myself even more motivated to explore options, and I spoke to hundreds of leaders like CTOs, and Heads of Data, VPs of Engineering. I asked them: “what's top of mind for you?” or “what is bothering you these days?” Then, based on that feedback, I decided to try launching three companies at once in completely different industries and see which idea worked. 

One of my company ideas was a tool that would detect issues in your data, and give teams the tools to resolve and even prevent them – in other words, data observability. Again and again, I heard stories of people waking up sweating at night because they were not sure that their numbers were right or if they made a mistake and they had to present it to the board. 

There was such an emotional response when we discussed problems with data reliability. It was clear there was a big problem to be solved. Then I would pitch them my idea to apply principles from DevOps to data and make sure it was accurate. Their reaction was so strong that I knew it would stick. It’s what I was looking for. I ran with it.

One of the biggest challenges for founders is time management and ensuring that you're focused on the right problems, at the right time. As Monte Carlo has grown, how do you prioritize what your team is focused on?

Your team can be doing 100 different things, but the things that matter the most are the ones that have customer impact. That’s how we prioritize.” 

When you join Monte Carlo, there’s something called a Week One Ship. This means that within your first week at the company, you're shipping something to production and making an impact on customers right away. That's how we set the tone that customers are what matters – the only thing that matters. 

And so as a company, we're very, very focused on two things: getting as many customers as possible and making customers as happy as possible. That's it. There are no other OKRs. There is no other hidden mission. And I think in the end great companies are built thanks to great customers. If we can help them avoid the next data disaster or make their lives easier, we know we’ve made a big difference. I'm obsessed about that.

You successfully raised Monte Carlo’s Series A, Series B, and Series C in two years. What advice would you give to early-stage startups when it comes to funding and team building?

For founders raising capital, my advice would be two things: look for investors who have deep experience in your industry, and show them you are building a strong customer base. We're very lucky to have amazing partners with us. As investors in companies like Segment, Snowflake, and Datadog, our board is full of people who have seen the movie before and can guide us through that lens. 

During our actual fundraising process, we also focused on emphasizing our happy customers. We let our investors actually talk to our customers before they invest, and that's what got them excited. 

Reflecting on the journey of Monte Carlo so far, is there anything you wish you knew at the beginning of your journey, that you would share with others?

“Don’t listen to advice – listen to your customers. That's where the answers are.” 

Step into your power. Listen to your gut, listen to your data. Advice is actually pretty useless, you could find five people that say something and five others that tell you the opposite. So be present and listen to the market.