Cofounder, Collibra
Fireside Chat: Felix Van de Maele (Co-Founder & CEO, Collibra) with Matt Turck (Partner, FirstMark)
We welcomed Felix Van de Maele, Co-Founder & CEO of Collibra, for a fireside chat with FirstMark's Matt Turck in February 2022. They explored the rise of the modern data stack, how to position and differentiate in a crowded market, the concept of a data mesh, and much more.
Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, co-hosted by FirstMark's Matt Turck (Partner) and Jack Cohen (VP, Platform).
About Felix Van de maele
In 2022, Felix Van de Maele, co-founder and CEO of Collibra, discussed the company’s focus on data governance and intelligence. He described governance as a policy-setting exercise centered on trust, involving business glossaries and workflows to ensure data is created, managed, and used reliably. Van de Maele stated that Collibra connects to various systems to capture metadata without moving data, comparing the approach to managing a library’s index cards. He expressed support for the data mesh concept, calling it “governance for architects” and arguing that decentralization makes governance more important)Skip. He noted that the company had over 600 enterprise customers, including Adobe, Heineken, and Bank of America, and highlighted momentum with cloud partners such as Google, Amazon, and Snowflake. Van de Maele said that the complexity of finding trusted data has increased as data becomes more mission-critical, and that economic pressures are pushing organizations to take data more seriously. He described Collibra’s acquisition related to data quality and observability as an opportunity to use AI/ML for automation and monitoring of real-time data pipelines. He also mentioned the introduction of a data marketplace and a product called Collibra Protect for access governance and data masking. Van de Maele stated that the company’s mission is to deliver trusted data for every user and use case, and that scaling data as a business function is a key priority.
Transcript (29 segments)
I
Interviewer0:10
And we're going to talk about what that means. You most recently raised a 250 million dollar Series G at a post-money valuation of 5.25 billion. Quick rise in valuation, very impressive. I'd love to start with a round of definition, starting maybe with data governance, and we'll go into others. What's the sort of two-minute version of what data governance actually means?
F
Felix Van de maele0:42
Yeah, absolutely. Diving right in, what does data governance mean? Ultimately, governance is all about trust. How do we make sure we can trust the data, we can trust how the data is being created, we can trust how it's used? Typically, they start with a business glossary. How do we make sure we have one shared common language so we can actually understand each other? If I tell you we have 5% churn, you probably want to know exactly what that means, how we calculate that. Agreeing on definitions is absolutely important. It creates a shared language that we can understand and agree on. Then the second step is about stewardship. How do we assign roles and responsibilities, the organizational aspect? Who is the data steward for our customer data? If I have a problem, who do I go to? Who's responsible to solve it? Third is around policy management, defining your policies: who can access the data, what do we do with sensitive data, what do we do with privacy data? How do we think about that? You need a workflow engine, like a business process engine, to help people work together effectively. So in practice, when you hear organizations implement data governance, that's typically what it looks like.
I
Interviewer2:23
Okay, so that's data governance. What is metadata management?
F
Felix Van de maele2:29
I think it's a very technical term. It's not a new term. We've been talking about metadata and metadata repositories for 30 years. So clearly, metadata management is really the technical method: the data, the tables, schemas, columns that you manage. But I think what has happened is that the level of complexity, the level of fragmentation, the level of distribution has only increased, and so it's only become more difficult for people to actually find the right data, understand that they can use it, make sure they understand what it means. We call it a system of engagement, a system of records for data. Just like ServiceNow is a great analogy: 15 years ago, every company was investing in IT, it became chaos, the CIO came in and said we need control, and the foundation of IT governance was your configuration management database, your CMDB. Then it evolved into IT service management, how to automate all these IT workflows. That's how I think about metadata management today. The chief data officer, chief analytics officer comes in, sees chaos, needs more control. Initial reaction: data governance. We need to understand what data we have, where it is, who has access to it. So we need to build that metadata management foundation, we call it the metadata graph. And on top of that, you provide these educational and approachable capabilities for everyone.
I
Interviewer4:10
What's your data catalog? And then the next one will be: what is data quality, what does that actually mean?
F
Felix Van de maele4:20
A data catalog is really how you inventory your data. How do you know what data you have in the cloud, on-premise? What are your tables, schemas, databases, columns? What do they mean? I think of it almost like an Amazonification of data. A data catalog is often the same experience but for data sets. How do you allow a user, a business analyst, to shop for data? It doesn't really matter where the data resides, on-prem or in the cloud, traditional databases or the new kind. And data quality: we've seen a renaissance in data quality as part of this modern data stack, where you have all these data pipelines. You need to understand what's happening in your data ecosystem, your data stack. So you need to start monitoring, observing, ensuring you understand the quality of the data as it flows through all of your systems. That's why data quality and data observability have become so important. You have production machine learning models; if something breaks, it's a real-time issue that requires real-time resolution.
I
Interviewer5:41
Great. Let's talk about that concept of data intelligence cloud. I'd love to do a little bit of a deep dive into the Collibra platform. What is it? What does it do with all this?
F
Felix Van de maele6:11
That's our evolution as a company. We started from governance, started adding capabilities: data catalog, data lineage (how does data flow in your organization), data privacy, and most recently data quality. The way we think of it, data intelligence is really an organization's ability to understand its entire data landscape, trust that the data is used in the right way, and then automate these workflows. These are the three big components. One is around governance, lineage, and catalog. It's all about how we make sure we understand what data we have. Second is around quality and observability, understanding what's happening with that data through the whole architecture. And third is around privacy and security. All these products combined on that one metadata graph make up the data intelligence cloud.
I
Interviewer7:15
At its core, when a company rolls out Collibra, you have a series of connectors into all the various repositories, whether on-prem or cloud. Do you move the data, or do you just collect the metadata? How does that work?
F
Felix Van de maele7:35
Exactly, we don't move any data. We have connectors that tie into all of your data systems, on-prem and in the cloud. We capture all of that metadata: tables, schemas, columns, files, and so forth. That's how we build that metadata graph. But we're not in the business of moving or storing any of the data, just the metadata. Like a library: you have the index cards, that's what we manage. The books, the data itself, stays where it is. Data stewards are typically the librarians responsible to steward the data, make sure we have great definitions, understand where it comes from, and ensure it's being treated correctly. If I'm a business analyst creating a Tableau report, or a data engineer or data scientist creating an ML model, my first step is always: where do I find the right data? I'm in marketing, I want to do customer churn analysis or build a customer churn model. I need customer data. I'm sure we have lots of different copies, but where can I find the right customer data that includes all of our customers, not just European customers? How do I make sure I'm using that data correctly because it's very sensitive data? How do I make sure legal signs off on this? Do I have to manually do this? How do we capture the fact that legal has signed off? This whole coordination effort is what we help with.
I
Interviewer9:11
An ideal customer for Collibra: is that a large enterprise where there's a lot of complexity, or is that a smaller, faster-growing startup? Who's best?
F
Felix Van de maele9:21
I'd say the bigger the complexity, the bigger the chaos, the more value we can add. A small company has similar problems as large companies, just on a different scale. What we've done really well, seeing where we came from after the financial crisis, we started working with all the large banks. We've been very successful in being able to cope with the complexity of the largest companies in the world. We also have a lot of high-growth companies that have a lot of data and complexity around data. You'd be surprised at some of those digitally native, data-first companies. You would think they have it all figured out, but they have the same problems.
I
Interviewer10:10
How do you particularly fit in with some of the key trends we've covered in this event over the last few months and years? The rise of the modern data stack. How does that fit? Do you sit on top of the data warehouse? Is the data warehouse just one of the many sources? How does that fit?
F
Felix Van de maele10:29
We are not part of this supply chain in the sense that we don't move the data, we don't store the data, we don't change the data. To your point, we sit above, but not just the storage or the data warehouse, but really across that entire supply chain. One of the value propositions we think of ourselves as: we handle every user, every use case, across every source, all the way from the source to the reports in Tableau, Looker, Power BI, and everything in between. These are these pipelines, and how do we make sure we can trust what's happening there? Then privacy and security: everything you're building is compliant with regulations and security concerns. So to your point, we definitely sit on top across that entire supply chain.
I
Interviewer11:26
The other big trend that people talk about a lot is this concept of data mesh. Where does governance fit on top of this, and how do you build for that world of decentralization?
F
Felix Van de maele11:38
Absolutely, and we're big fans of data mesh. If you think about it, data mesh is really all about governance. It's how do you do all of that right? It's almost like governance for architects, if you can call it that. It's very much an organizational construct around decentralization. I think that's absolutely the right approach. We've been hearing that promise for the last 25 years, and it's never solved all of our problems, and it never will. We have to embrace the fact that data will be diverse, different, decentralized. So governance only becomes more important. If you think about some of the key principles in data mesh, this kind of domain orientation where you organize across domains, it's absolutely the right way to do governance. We talk about federated governance versus centralized governance. If you think about data as a product, that's again tying it back to metadata, the usability around data. Over the last 10 years, we've been way too focused on just storing more data. When I last spoke with DJ Patil, he had a great quote: we need less collecting of data and more connecting of data. We have a lot of data, that's typically not a problem. The problem is: where is it coming from? What is the quality? How is it being used? How are we allowed to use it? So that usability and data as a product is a really important component. So we're big fans, and I think it's absolutely the right way that data has to evolve, the way we organize ourselves around data.
I
Interviewer13:30
One question from the group here live, which perfectly anticipates where I was going to go next, which is around competition. How do you compete with hyperscaler native solutions on that front? And more broadly, because what you do is so incredibly mission-critical to any company that wants to deploy data, how do you position, how do you differentiate, and how do you win?
F
Felix Van de maele13:55
We think of that ecosystem. Some missed the boat to the clouds, and I think they're going to struggle there to provide that experience. And then finally, to the question of the hyperscalers: they're great at managing within their ecosystem. If you're purely technical metadata within BigQuery, Snowflake, or Databricks, and so on, but then how do you tie to the business? That's not something they do. How do you tie to your organizational model, your policies, your quality observability? And most importantly, how do you bridge across that entire data supply chain, talking about the modern data stack from source to ingestion to storage to consumption? It's not all going to be in one place, but you want to provide that broad experience. We call it that system of engagement across your entire data function. That's a differentiation, and I think it's a strong one.
I
Interviewer15:10
Let's go back to the beginning in particular. How you nailed the initial product-market fit, which is this sort of elusive starting point that so many entrepreneurs look for.
F
Felix Van de maele15:21
It's a great question. We started in 2008, just when the financial crisis happened. We had to fight for four or five years to find product-market fit. Actually, the financial crisis helped us. That's where we found product-market fit in the financial services industry, around compliance and governance related to data governance. All the large banks had to comply with a lot of new regulations after the financial crisis. They basically had to prove to the regulators that they were in control of their data. Like, 'You give me a report that shows a number, explain to me where that number comes from.' When we started, I think there was one chief data officer. Now there are thousands of chief data officers doing what we do. It's an exciting place to be.
I
Interviewer16:15
And how do you navigate a roadmap over such a long period of time? In 2008, the world was in a certain state, largely pre-cloud, pre-big data, and certainly pre the resurgence of machine learning and AI. Today we're in a completely different world. How do you build a product and platform, make sure the older parts are not completely antiquated while you build the new stuff?
F
Felix Van de maele16:47
It's not easy. Start with our vision: we ultimately believed that data was important, and that clearly has been shown to be true and accepted by everyone now. I think the future is about the cloud. We started this cloud transition architecturally and from a business model perspective. It seems obvious now, but four years ago, most data products were still on-prem, like Hadoop and Tableau. Doing cloud data wasn't as obvious. A lot of our customers still manage their data on-prem, and we of course supported that. But because we only capture the metadata, we built an architecture that is able to do hybrid, which is important. Ultimately, the experience needs to be seamless. That's the bar nowadays. But there were lots of fights. The biggest fight was at the very beginning when we started, going back to product-market fit. We wanted to do semantic data integration, we tried for two years, had zero customers. So that was a tough lesson.
I
Interviewer18:10
In building the team in particular, there's always this really interesting tension between promoting people from within, especially the people that were earning the company, and bringing in experienced management that have seen the next level of scale. What's your philosophy on that?
F
Felix Van de maele18:30
It's hard. I use this quote that I've stolen from somewhere: in the beginning, you're like a pirate ship. The only thing that matters is to preserve cash and sell, build product, or sell products. Over time, you need to build more like a navy ship, where it needs to be more structured, with ownership, repeatable processes. Put a pirate on a navy ship, that's not going to work. Put a navy captain on a pirate ship, that's not going to work either. But that's what you're trying to do. You just learn every year is different, and you have to explain what you're doing, why you're doing it, why it's important. I would recommend to all founder CEOs: building a leadership team is probably one of the most important jobs you have. I remember the first leadership team I built. I thought these are all amazing people, and they all are amazing people. I thought that was going to be your leadership team for the future. Two or three years later, it was a different leadership team. So just get used to that really quickly when you're growing and changing really quickly. That's one of the harder things to manage as a founder CEO.
I
Interviewer19:45
And a little bit to that point, maybe as a last question: how do you personally, as a founder and CEO, at the stage where you're at right now, which is effectively pre-IPO, soon IPO hopefully, where you need to be this super efficient manager? You said you hadn't realized that it was your first job, that you have never done this before. At a very personal level, do you have mentors? Do you read all the time? How does one learn the job on the job?
F
Felix Van de maele20:36
It's a cliché: surround yourself with great people. The reason you need to build a great leadership team is exactly for that reason. Find great investors, great board members, mentors, and surround yourself. But I think it's also about doing it right. I often hear, 'I'm an entrepreneur, I don't want to be a manager.' But if you don't want to be a manager, then it's not going to work. You have to think of it like this: I have a product background, a software engineer. Initially, you build a product. Now you build a company. You have to think about communication just like you have APIs and SLAs in a product. You need to do the same thing on the company level. You have to modularize, you have components, you have to organize. There are actually a lot of analogies. It's not that super complex, but you have to want to do it. You have to be super humble and always want to learn. Having that growth mindset is super important. And then just surround yourself with great people that you can learn from.
I
Interviewer21:48
Great. One last question from the group, since we're just talking about this: how do you surround yourself with great people? Any lessons learned in making a distributed team work well together and make sure that everybody learns and finds mentors and all the things?
F
Felix Van de maele22:20
I don't think I have any silver bullets or secrets to share. But I hired a few new executives as part of the executive team without having seen them in person ever. You just have to get over this very uncomfortable idea that you would hire someone to lead a company without ever seeing them. But I think it worked out really well. Keep investing in bringing the team together while still being distributed. That continues to be super important, building that trust. And on the mentors: it's hard, but it's easy as well. The pool in which you can fish gets bigger as well. You're not constrained anymore to your local area.
I
Interviewer23:10
Great. All right, well thank you so much for joining us tonight and sharing all of this, including the journey, which is always fascinating. Congratulations on everything you guys have done. Thank you. I realize you are just getting started. I am looking forward to seeing all the success compounding over the next few years. You've clearly built a very important company. So thanks again, appreciate it. And thanks to everyone who joined us tonight. We'll see you guys next time.