The Exchanges

Q Whenever you think about the moment the company was started, what was like the story, the two years before that, what was going on?

A I had been working in data for a very long time. I, you know, my, my core skillset over the course of my career has been quantitative. I had practiced that in a bunch of different ways. Like I had been at Deloitte Consulting and did large IT projects and data stuff as a part of that. And Went to the startup side of the house and, uh, ran data team at Squarespace and very nascent data team. And then I, I joined an analytics tech vendor called RJ metrics. And I was an early user at Squarespace of this company, RJ metrics. And, um, we were supposed to be what Looker became. Uh, the thing that RJ was never, and I don't think would have ever been able to, uh, anticipate was The cloud, the rise of the cloud for data specifically. And, uh, so I remember experiencing this in, in 2014 or 20 15. I got to use a product called Amazon Redshift for the first time. And as a, as a long time data practitioner, I was just very immediately within writing a couple of queries. I was like, oh, this is going to change everything about my profession.

AI assessment note: “I joined an analytics tech vendor called RJ metrics... I got to use a product called Amazon Redshift”

Q I mean, we could probably spend hours talking about just those few months in 22, but what were the most important things that you figured out?

A We had to have a very clear, explainable answer to the question, why should I use the commercial product and not the open source product? And it had to be digestible by someone with a C in their title. And for a long time, we actually didn't know if we were going to be a PLG business or a sales-led business. Our first, let's call it Ten million in ARR was like very PLG oriented. And so it felt like maybe that would just continue to be true in data. That's like almost never true. When you sell to software engineering, sometimes you can go PLG for a lot deeper in the journey and, you know, Stripe and Twilio and others have, have done that very successfully. But data for a variety of reasons that are probably too boring to get into ends up needing to be sales led and it, and most of the dollars come from the enterprise. And so we couldn't just build a broad set of tools that developers, that practitioners really loved. We had to really focus our efforts and build cohesive stories that we could explain to, to senior data leaders. The big unlock for us was as people use dbt for longer, the complexity of their code went up and up and up, and it became really a problem for the most sophisticated users. And oftentimes they were at the largest companies. And so there was a real opportunity for us To step in and solve that. And so we, for the first time started talking about complexity a…

AI assessment note: “solving complexity is the biggest differentiator for dbt cloud”

Q What did the first version look like? And did you already have team members who were technical enough to build it or who built the thing?

A Drew did. So recently out of undergrad was learning how to do the analytics work to like keep some consulting clients happy so that he could pay his own salary to half of his time and probably nights and weekends. And he was hacking to, to build the initial versions of this thing. And we had played with in the run-up to starting the company we had played with Prototype versions of it. I wanted to start a business having an understanding of like how I was going to help clients on day one. And so I was trying to get my tool stack all ready and all this stuff. So we had a version that had probably two weeks of engineering time put into it on day one. And fortunately, like when you build command line tools, you can get a lot of value out of them. You don't have to build a user interface. You don't have to build login. So you can get a lot of value out of a small amount of effort.

AI assessment note: “Drew did... when you build command line tools, you can get a lot of value”

Q For those that haven't thought that much about starting as a consultancy, Oftentimes because investors say the number one risk is you're just going to be a consultancy, but what's some of the goodness that other people might not know about in just starting as a consultancy versus trying to sort of start a more traditional venture-backed software company?

A VC is wonderful. If you are really going to build a company that can potentially IPO someday, and maybe it does, or maybe it doesn't, but like, if it has the potential, then, then VC can be really wonderful. As an entrepreneur, there are a ton of really, really good outcomes that can happen well below an exit event. It is wonderful to just be a capitalist where you build a thing that people want. They pay you money for it. Maybe you hire some people. You have a P and L every month. You watch the cash balance go up and down and hopefully it goes up and you can maybe, you know, you can build a consulting business and sell it for two to three times revenues. Or you can just like continue to operate it because you love what you do. But fundamentally, like you get all of these choices that are literally not on the table. If you're a venture backed company, there's not a quote unquote venture scale exit potential in a consulting business, but there is a really satisfying founder scale potential in, in those businesses. And that, that can be like really just fun to, to operate. Even when in 2019, we had a thousand companies using dbt, And continued, you know, three X year over year growth rate. I still didn't want to take venture, but it was so clear we were doing the product and the community a disservice by under investing in it. We had three engineers supporting a thousand companie…

AI assessment note: “you get all of these choices that are literally not on the table”

Q Is there anything else kind of, when you think about the topic of understanding that your key point is there's no paint by numbers to get to product market fit, but are there certain things that someone can do that will increase the chance?

A I think it can be very challenging when every single customer call can be, oh, everything is terrible or, oh my God, maybe we have a billion dollar company here. This emotional rollercoaster in the early days can be so significant if what you're trying to do is create a billion dollar company. And sometimes it's actually this nice psychological hack to lower your standards. And instead of being so attached to the outcome is like, I'm building a company. The outcome is like, I'm solving a problem in the world. I don't know what size company that's going to be, but if I'm attached to the problem that I'm solving and I'm super curious about the problem and so obsessed with it that I couldn't imagine not spending all my time on this, then I think you increase your chances of actually being financially successful. The success of the company has to be downstream of all of that stuff, but you, you can't constantly be focused on that, or I think it's self-defeating.

AI assessment note: “if I'm attached to the problem... you increase your chances of actually being financially successful.”

Q How did you find them and what did you pitch them on you helping them with?

A So this is 2016 and this is, there's two market trends happening in VC that really helped us out. One of them was direct to consumer and the other one was SAS. I had worked, I had been an exec at three SAS businesses to that point and I had worked at my last company at RJ Metrics with With a bunch of direct to consumer e-commerce companies. So I like knew both of those business models like quite well and the data behind them. We were very explicitly focused on series A, B, C companies that needed to understand their, their data. And we knew the playbook. We knew the technology that we had to use and we can deliver like it really an unbelievable amount of value very quickly and with not a lot of consulting dollars. So the, so the play was like, You could go hire a full-time data person, but they're not going to have the experience that we have, or you could hire us. And I think our annualized rate was, I don't know, something like 60 K a year. So it was like kind of half of an entry-level data analyst. Uh, we were just like, look, we will produce more value for you than if you hire internally.

AI assessment note: “So the play was like, You could go hire a full-time data person”