Lessons From Female Founders: How Barr Moses is Pioneering Data ObservabilityMay 20, 2022
Barr Moses is a pioneer for data observability. As the CEO and Co-Founder of Monte Carlo, Barr has made it her mission to ensure teams can trust data. From graduating with a Stanford B.Sc. in Mathematical and Computational Science to serving in the Israeli Air Force as a commander of an intelligence data analyst unit and to being the VP of Customer Success at Gainsight, Barr's experiences gave her the base to understand the impact of data has on everyone. In addition, by focusing on customers' pain points and building relationships with them, Barr led Monte Carlo to earn Unicorn and Unicorn Plus status.
On May 18th, Advancing Women in Tech (AWIT) and AWS Startups hosted Lessons from Women Led-Startups: Data Observability Builds Trust at Scale. Nancy Wang, the founder of AWIT and Manager of AWS Data Protection and Governance, led a Fireside Chat with Barr Moses. As part of our conversation series with women leaders in tech, Barr shared how she developed the first Data Observability Platform by building inclusive teams, making customers happy, and creating data solutions for the industry.
Nancy Wang: What first got you interested in data analytics?
Barr Moses: I moved to the Bay Area 13-14 years ago, and what data meant ten years ago, five years ago, or today is not the same. I studied math and statistics, and my dad is a physics professor, so I thought I was going to go into academia. I worked in Stanford's statistics department for a bit, and then I realized it wasn't working for me. I shifted gears and turned towards the industry.
Throughout my career, I have been really fortunate to work with companies on their journey to become "data-driven." Prior to Monte Carlo, I joined Gainsight and helped to create the Customer Success category. Part of that was about helping organizations make decisions about their customers based on data. Throughout my life, I have used data differently, but there has been this common theme that led me to start Monte Carlo - it was really hard to answer some of the fundamental questions in regards to data. While it has become a lot easier today to process data, to store data, to transform data, it is still really hard to actually have trust in the data.
Nancy Wang: How did you come up with the name Monte Carlo?
Barr Moses: When I worked in the statistics department at Stanford, I worked a lot with Monte Carlo simulations. "Monte Carlo" is a way to predict an outcome when you don't have a closed model for it. When it came to company naming, I didn't have a lot of time to choose a name. I was flipping through stats books in my office and came up with Monte Carlo.
Nancy Wang: At Amazon, we have the principle of working backward on the customer. The way that you just described how you saw a problem with this lack of trust around data, what problems downstream does that create for customers?
Barr Moses: One of our very first values was customer impact. When we wrote our values for the company, I thought adding that would be cliche and inauthentic. I was talking to my team, and they thought there is nothing that matters more than our customers. Everything should reflect our customers, including our product, our team, and our brand. There is really nothing else that matters.
Before starting the company, I had several hundred conversations with folks in data. I asked the question, "what is keeping you up at night these days?" This problem that we later identified as "data downtime," which is when data is wrong or inaccurate, came up again and again. Everyone was saying, "there's one thing that I'm responsible for, which is the data, and it's really hard when folks can't trust it."
Let's say I'm about to present my report to my CMO, and 15 minutes before my meeting I am finding out that my report is wrong. Or, on a Friday at 5pm, I get an email saying that all of the reports look wrong. That was a shared pain from folks everywhere across all industries. The implications of data downtime are that data teams aren't aware until it moves downstream to analysts or even CMOS. The first problem we set out to solve was to make data teams be the first to know about data problems.
Nancy Wang: When you speak about this pain, there are typically different buyer personas within a company. Where did you find that your message most resonated? Who did you start that conversation with?
Barr Moses: The type of roles and people that are in data has changed. There is this rise of new roles, data engineering being one of them, with folks of different backgrounds. Today we see companies double and triple the sizes of their data teams and make significant investments in data and hiring in these roles. Data downtime affects the data engineers, the analysts, the business team, and the data product manager. More products are now being powered by data. The importance of making sure that data is accurate is more important than ever. The focus for us is data analysts and data engineering, but it touches so many people.
Nancy Wang: At what stage of development or maturity do you advise companies to start thinking about data quality as a problem they should solve?
Barr Moses: We put together a maturity model in our early stages to help companies identify where they were in their data quality journey. If you're actually using the data and the data is driving something, you need to make sure it's accurate. Once a problem is identified, it is crucial that there is an automated way to analyze the data.
Nancy Wang: In 2019, I feel like not a lot of people were talking about incorrect data, stale data, or redundant data. What mental model did you have entering an existing market rather than creating a net-new one?
Barr Moses: I have been fortunate to be a part of the story that has created the customer success category.
In the early days, you are trying to prove that there's a real value, that someone would see that value and pay for the value it offers. I remember turning to our engineering counterparts, and they had all of these solutions to guarantee their software had structure, but data teams had no similar tools. They had to do the work manually to validate the data numbers. Now all of these people need to be able to trust the data.
When starting the company, we knew that the category of data observability would exist. We listened to our customers as they began to tell us that data observability was important, and now there are categories within that because of their needs. We took all of the great conversations that we had and created our five data observability pillars to build our product around them. The most rewarding part is to see what our customers are doing with the data observability category.
Nancy Wang: You talk a lot about customers and how you engage with them. Can you walk us back through some of the early days of Monte Carlo and how you built that trust?
Barr Moses: My co-founder Lior is a CTO as well. We started the company with just the two of us and had a good sense of what we wanted to do and build. We found customers that told us about the pain points they had and the impact it had on their organizations. We worked hand in hand with them and didn't charge them in the very early days.
Our first design partner explained, "this is a great product and a great service. We want to pay. We are actually seeing a lot of value." That's how we started selling, with a customer-led approach. We gained trust by putting ourselves in the shoes of our customers. We focused on how to minimize onboarding time and how to show value extremely quickly. Our implementation today allows for customers to be up and running in 30 minutes, and within 24 hours, they are able to see our product's value.
Nancy Wang: One metric that really caught my eye about Monte Carlo is that in 2021 your renewal rate was 100%. Can you walk us through some top customer use cases and maybe some lighthouse customers that you have had?
Barr Moses: We would not exist without the customers that are still taking a chance on us every day, and we earn the right to work with our customers every day. The companies we work with include companies in gaming, media, eCommerce, and more. The JetBlue team adopted Monte Carlo as a solution to move from where they were manually looking at the data to having a proactive notification and approach to data observability.
The data reliability life cycle has three core principles:
• Protection is being the first to know about data issues.
• Resolution is knowing how to resolve a problem as soon as you know about it.
• Prevention addresses how to prevent data breakage even with strong communication and processes in place.
Many of our customers report to us that nearly 70% of their data downtime incidents are reduced by thinking about it at the operations stage. Fox makes their decisions based on data around the number of users, devices, bandwidth, and content. Their executives get daily reports based on data to make decisions.
Nancy Wang: Can you walk us through what is your recruiting strategy for building inclusive teams?
Barr Moses: We have tried lots of different things, and I can't tell you that we're perfect, but building inclusive teams is something that we work on and think about a lot. We recently did a survey that had a question that asked if employees saw career and professional opportunities for themselves. 100% of women answered "yes." It is really important to create not only a space where folks feel comfortable but also see opportunities for themselves in the future.
Nancy Wang: In 2021, Pitchbook's data shows that less than 2% of venture capitalist funding was given to women founders. As you and your co-founder have successfully raised over $100 million in venture funding, how did that process go for you as a woman founder? What advice can you share with aspiring founders?
Barr Moses: On our journey, in particular, we have focused on the customer from day one. We have focused on building a company with strong fundamentals. There are only two things that matter, and Monte Carlo, making customers as happy as possible and getting as many customers as possible. If you're not looking at those two things, nothing else matters. Happy customers are what makes our world go round.
In a lot of the fundraisers we have done, investors have spoken to dozens of our customers without even talking to us first. To a certain degree, our customers helped to fundraise for us. What has worked for us is solving a problem that people care about, showing value quickly, and building trust. That helped to build a brand that people really wanted to be a part of and has helped us in so many ways, whether it is company building, recruiting or fundraising.
Nancy Wang: I notice that you write often. Every time I go to LinkedIn, I feel like I see a new blog from you. What inspires you to write, and how do you have the discipline to write so frequently?
Barr Moses: The first blog I wrote took me more than a month. English is my second language, so writing is really hard for me, which is why I enjoy math and stats so much. The thing that helps me is understanding that as a startup, the worst thing is that nobody cares. That pushes me to make sure we put ourselves out there. I have an amazing team that helps me and gives me inspiration. Most of the inspiration for the content comes from our customers' needs. It is a way to give back to our customers and community.
Nancy Wang: Advancing Women in Tech works to amplify the stories and impact of women technical founders. What are some ways we can make more room for women founders in B2B infrastructure companies?
Barr Moses: I wasn't sure if I actually wanted to start a company. I took some time off, and then I decided to start three companies in parallel because I wanted to get conviction surrounding the idea and the market. It was really helpful for me to start a few and see what product market fit could look like. I would say if you want to start a company, just do it. There's not much other advice I would give.
RBG was one of the women I looked at as an example. She is a good example of someone who changed the system from within. What I can do and what we can do at Monte Carlo is to create more examples of that. It's about getting in there every day, showing up, and building the best company that we can.
If we can build a company that leaves a mark on the industry, that can go further than anything I could ever say. For us, it's about building a great company, being proud of our journey, and looking back knowing we made a difference.
This interview has been edited and condensed.