How Collibra CEO Felix Maele grew Collibra to $210M revenue and 1K customers in 2025.

this is the top where I interview entrepreneurs who are number one or number two in their industry in terms of Revenue or customer base you'll learn how much revenue they're making what their marketing funnel looks like and how many customers they have I'm now at $20,000 per talk 5 and6 million he help on global domination we just broke our 100,000 unit sold Mark and I'm your host Nathan Latka this is episode 683 coming up tomorrow morning I talked to Nico who breaks down how to raise your first round of funding with his Healthcare API and Redux founder again his name is Nico good morning everybody my guest this morning is Felix vanal he is the CEO and one of the founders of a company called CRA which he took from idea to founding to more than eight years of record growth and Industry leadership we'll talk about it he's responsible for the company's Global business strategy now prior to co-founding khra Felix served as a researcher at the semantics technology and applications research laboratory uh at a university over in Brussels where he focused on on ontology focused Crawlers for the semantic web and semantic data integration he holds a masters in computer science and software engineering from that University and a master in general management from the vurk business school Felix are you ready to take us to the top yes I am thanks for did I did I get all those words correct you got them right I did my best I did my best sounds complex good so tell us what we want to dig more into your story but tell us what kber does and what's the business model how does it make money sure sure so basically we're a software company SAS company and we help typically larger organizations to better find understand and control their data ultimately um yeah and tell tell us a story about one of the customers that use you so we get a real example yeah sure so we really got started in financial services so every bank today needs data governance which is what we do what how how it's called Uh which basically uh the reason that they need that is for Regulatory Compliance so every Bank needs to be able to show to The Regulators that they understand uh what data they have how it flows through the organization so ultimately if they report to Regulators they have full confidence uh that the numbers are correct uh that's really where we started but right now it's it's really across lots of different Industries um driven by big data analytics iot uh Ai and so forth and what year you launch the company in we launched it in 2008 okay 2008 and bootstrapped or have you raised capital uh we raised Capital um so we actually started out of school my first company the first thing I ever did um and we raised a seed round of about 800,000 Euros which at the time was a pretty big deal especially in Belgium which didn't really know doesn't really know Venture Capital as we as we know here in the US um but then we started to bootstrap it until profitability uh and we were profitable tripling every year and that's uh when we we raise our first bigger round our series B round of about 20 million uh and then we were in kind of full growth mode what year was the series B series B was 2040 okay so all in you've raised about 20, 400,000 something like that uh in December just last December we did a $50 million rounds series rounds all we did about seven seven um 75 million got it so 75 million in total and you said this is your first company out of college how old are you today I am 32 32 awesome this is so you've been focused on this for about a decade right yes it takes a while sometimes it does hey the focus is an important thing so so the risk in this chat we have is that I lose the audience because they don't understand uh kind of the data side of what I describe because you you are very I mean you're an engineer right so semantic web things like this people may not understand is there a way you can dumb this down so that and I'm not calling my audience dumb but even for me right can you dumb this down for me absolutely and I I I love to use a bit of an Al the analogy let's say you have a library and Library you have book books and you have these index cards and these index cards tell you where to find the book who the author is who's last rented it uh it's very similar to organizations with data right so have you have lots of databases that store the data these are the books of a library what we do we are the index cards we help uh people users find um where the data is uh what it means how they can use it uh what the quality is these these types of things and and ultimately what we want to get is to get to is that we call it the Amazon ification of data where ultimately users just shop for data like they are on Amazon they they they put it in their data basket so to speak and then they goes to approval work workflows to actually get access to it and so they can run the analytics interesting so it's I mean is this almost like a search engine for data sets it's it's in a way yes it helps them find the data so that's in a way the search engine part y uh but then you need to understand it there's quality there there's really the governance because now it's chaos everybody wants to do data and everybody does data and it's a lot of chaos and we help um companies control it more so people can actually find it and get value out of it and where are you today in terms of customers how many customers you have paying you right so we have uh close to 200 customers and I imagine if you raise 75 million bucks and you have 200 customers this is very much an Enterprise play with really high kind of arpus what's your arpo at on average yes it varies but right now it's about 200 to 250k annually or monthly Ann annually got it yeah monthly would be a would be a nice business huh that would be nice got it so if I if I divide that out by 12 you have the average customer paying you somewhere around 20 grand uh per month now are these contracts do you pull them all forward are they all annual plans where the cash is up front uh annual plans with cash front yes okay so no MTI no monthly yeah never got it so I'm Felix I just want to get a broad sense of Revenue if I take 200 customers times the number you just gave me that puts you at somewhere around 4.1 million bucks in Mr or somewhere around call it 10 million in ARR is that about right or sorry sorry not 10 not 10 sorry 50 50 million in ARR um a bit less we started actually with a Perpetual model so some of these customers are still on Perpetual model what what is what's that model look like for for people listening right now that might be starting they might want to use that model yeah so it's it's a license fee basically the customer pays you one big license fee up front and then they own a software and then they pay uh maintenance fee yearly which is typically 20% of the initial fee I see and the difference between the typical SAS m is that they pay the same amount every year I see so the recurring part is is less than if we if we throw up a range then could we say somewhere between 30 and 50 million bucks in ARR yeah that covers it yeah yeah yeah got it okay good let's go back into the story so so you graduate from school you you you were an engineer right you got your engineering degree yes correct how did you get this idea um it's uh I was doing research on semantic technology at the time and I was doing another master in software engineering uh and I was in Argentina studying and I was thinking a long time okay what do I want to do next I basically had four option in my mind I could continue do a PhD I could go into banking could go into Consulting um or I could actually start my own thing and the last thing was the only thing that really uh sounded exciting to me and I finally I started reading these books in Silicon Valley I think in from Silicon Valley did these biographies like yeah I think they had a great book at the time it's called Founders at work um it's fantastic book it's a bit old now but it talks about the founders of Twitter and so and so on before that existed and in all my I think naivity I thought well when they can do it I can at least try right why why shouldn't I and kind of that's how we got started and you were at this point what 22 23 yeah 22 years old 23 and you said we tell me about the founding team so we started uh four Founders oh wow yeah four Founders uh the three others were doing uh their PHD at the lab and then while we got started I did like mini a mini MBA the the general management during that time we kind of wrote the first business plan and then as I graduated we raised 800,000 Euros uh seeed round and started the company that's a lot of Founders to start the company with how do you have the tough conversation around Equity or did you just split it 25 each we just split it 25 each no that's a lazy way to do it you just you said boom I don't want to debate about this let's just be fair we're all going to do the exact same amount of work everyone gets 25% yes okay so let me ask you this because this this happens and no one...