That's quite the ride too. Congrats on the first company and the success with that. You guys have, Monte Carlo is blowing up quite a bit. You guys came into my radar. I think right as you were launching. It's been cool to watch. Backing up a bit. We hear the term data observability being thrown out all the time now. In general, it's just the hotness. What is data observability?

It's a great question. It's the one that we worked on quite a bit in the early days of Monte Carlo to even define what it means or what to do about it. The way we define it is as follows. What we've been seeing is that a lot of companies out there are making investments in data. They want to leverage their data to grow faster, to save costs, to build new products and new markets. As they do that, they essentially start treating data as a product. Whether it's used internally for analytics, for decision-making by various stakeholders in the company, or it could be building machine learning models, they're making automated decisions for the company, or it could be data stores that are actually a product that are part of a digital experience, or sold to partners, or otherwise used. As those things are happening, it became increasingly important to productize these things, to make them ready for people to consume with little hand-holding from the data engineer or the data scientist to build the first thing in the first place. When you productize things, and that's something we've seen in other domains as well, one of them is software engineering, when you want to move from a pet project you've been coding up in your free time to a service or a product that other people are using, you start thinking about things that were important before. One of the biggest things that you'd be thinking about is reliability and trust. How do you make sure that the thing that you're delivering to your customers is reliable and works for them when they need it? How do you really manage DSLAs around it? Nothing is 100% reliable, but how do you make sure that it's reliable enough to be trusted and to be useful to the people that are consuming it? Over the years in software engineering and security in other domains as well, emerged this idea of observability. The idea that you're not building black boxes, but rather you're creating a visibility into how the system works, and through that visibility you're able to understand how healthy the system is and manage that proactively. Not only that, you're able to do it as a team. A very important concept is in observability is you don't have to call the person that built it every time it breaks, but rather as a team you're able to know that issues are happening, to resolve them, to address them, and even to prevent them from happening in the first place. That's observability in general. Now double clicking into data observability. We view data observability as the ability to really measure the reliability and health of data products. And data products in a lot of ways are similar to software products, but they're also very unique and different. Different stack, different types of challenges, different types of reliability issues that can happen. And so we've coined the term data observability to capture that. The set of methodologies, the set of tools that teams need in order to deliver reliable and trustworthy data

products.