The Exchanges

Q Great. And then data quality. What, what does it matter? What is it?

A Again, data quality. We've seen a renaissance and data quality, a lot of, uh, kind of new data quality, data observability, uh, startups. Originally it kind of, it came from how do we make sure that our marketing database that we send email mailings to the addresses are correct. That's where data quality came from, 20 years ago. Today, obviously very different. Again, as part of this kind of modern data stack where all you have all these data pipelines, you need to understand, uh, what's happening in your data and your data, um, 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 to all of your systems. And that's why I think that quality data observability has become so important. Uh, you have kind of in production machine learning models. If something breaks, it's a real time kind of issue that requires real time resolution.

AI assessment note: “you need to start monitoring Observing, ensuring you understand the quality of the data”

Q dive, uh, into, uh, the Colibra platform. What is it? What does it do with this? All the components that we just talked about are sort of like merged into like one, one platform. So I, if I'm, uh, you know, a, a, a company and I want to make sure that my data is under control, I, uh, work with Colibra and then what do I have access to?

A Yeah, so, so that's kind of, in our evolution as a company, we started from governance, sort of adding capabilities, the data catalog, data lineage, again, how does data flow to your organization, data privacy, and most recently data quality. And the way we think of it is as like data intelligence, that's really an organization's kind of ability to understand its entire data landscape, trust that the data is used in the right way, and then automate these workflows. And so these are really the three big components, almost three big categories of everything about data intelligence. One is around governance, lineage, and, and catalog. It's all about, it's all about how do we make sure we understand what data we have. Second is around quality and observability, understanding, uh, what's happening with that data kind of through the, through the whole architecture. And a third is around privacy and security, right? How do we make sure we, we, we, we are treating PI data, sensitive data in the, in, in the right way. So these are the three big categories. So these are the, I went through all the kind of the products that we have. Combined on that one metadata graph makes our data intelligence cloud.

AI assessment note: “Combined on that one metadata graph makes our data intelligence cloud.”

Q equilibrium in particular sort of fit in the, you know, some of the key trends that, uh, we've covered in this event, uh, over the last few months and years. So, so the rise of the modern data stack, what, what, what does that fit? Do you sit on top of the data warehouse? Is the data warehouse just like one of the many sources? Uh, how does that fit?

A Like we, 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 kind of sit above, but, but not just the, the, the storage, the data warehouse, for example, but really across that entire, that entire supply chain, right? One of the value propositions that we think of ourselves as, uh, we kind of handle every user, every use case across every source, all the way from the source to all the way to the reports, Tableau, Looker, Power BI, and, and everything in between. And these are kind of the three categories that we think of a data intelligence run around kind of governance lineage and, and, and catalog understanding what data you have across that entire data, more than data sec, uh, uh, quality and observability to make sure, okay, what happens through these pipelines and how do we make sure we can trust what's happening there? And then privacy and security that everything you're building is kind of compliant to kind of regulations and security, uh, security concerns. And so we, to your point, I think we, we definitely sit on top, uh, across that entire supply chain, if you will.

AI assessment note: “we definitely sit on top, across that entire supply chain, if you will.”

Q Okay. So that's data governance. Uh, what is metadata management?

A I think it's a very technical term. It's not a new term. We've been talking about metadata, metadata repositories for 30 years. So today, metadata management is really the, the, the technical metadata, the tables, key them, uh, schemas, columns that, that you manage, right? But I think what has kind of 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, um, make sure they understand what it means. And so it's only become harder for people to actually consume and produce data. Uh, we believe kind of a new approach is, is required where, um, where the metadata management kind of foundation You really built a kind of a, almost what we call a system of engagement of system of record for data.

AI assessment note: “metadata management is really the, the, the technical metadata, the tables, key them, uh, schemas, columns”

Q And in that analogy, who are the librarians?

A Great question. So that's typically kind of the data stewards, right? Uh, and then you have different personas. The data stewards typically are the librarians that are responsible to make sure, uh, kind of to steward the data, right? To make sure we have great definitions. We understand where it comes from to make sure data is being treated correctly. If I'm a business analyst, I need to create a tableau report, or if I'm a data engineer, a data scientist, I need to create an ML model. Typically my first step is always, okay, where do I find the right data? Like I'm in marketing. I want to do a customer chart analysis or want to build a customer chart model. I need customer data. Okay. I'm sure we have lots of different copies, but where can I find the right customer data that, that, that includes all of our customers, not just European customers. How do I make sure I'm using that data correctly? Because it's obviously very sensitive data. How do I make sure that legal kind of, uh, signs off on this? Do I have to manually do this? How do we capture the fact that legal has signed to this whole coordination efforts as something that we then kind of facilitate and ultimate. Uh, and so of course the data stewards are a key kind of persona user. Business analyst, data engineer, data privacy manager, uh, data scientist. These are kind of the key users of the, of the platform.

AI assessment note: “So that's typically kind of the data stewards, right?”

Q And who's an ideal customer for Calibra? Is that a large enterprise where there's a lot of complexities at a smaller, faster growing startup? Who's, who's best?

A I'd say the, the, the bigger the complexity, the bigger the chaos, the more value we can add. And I think a small company has the similar problems, uh, as large companies just on a different scale. What we've done really well, again, Seeing where we came from after the financial crisis started to work with all the large banks. We we've been very successful in being able to kind of cope with the complexity of the largest companies in the world. Uh, we also have a lot of kind of High growth companies, um, that have a lot of data to have a lot of complexity around data. Uh, it would be, you'd be surprised that some of those digitally native companies, very data first companies, you would think they have all of the data in order as definitely far from the truth. Um, and so, um, but mostly large companies, I would say, because that's where we can help the most.

AI assessment note: “mostly large companies, I would say, because that's where we can help the most.”

Q 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?

A Yeah, absolutely. And we're, we're big fans of data mesh. And if you think about it, data measure is really all about governance. It's really, how do you do all of that, right? It's almost like governance for architects, if you can call it that. It's, um, because it's very much an organizational construct around decentralization. I think that's absolutely the right approach. We've seen it clearly work well within engineering. The only way to scale is to decentralize. Uh, if you look at all of, all of these kind of Data repositories, data warehouses, they all argue that just move all of your data in one place, and it's going to solve all of your problems. We've been, we've been hearing that promise for the last 25 years, and it's never solved all of our problems, and it never will. Like, we have to embrace the fact that data will be diverse, different, and kind of decentralized, and so governance only becomes more important. And if you think about some of the key principles in data mesh, this kind of domain orientation, right, where you organize across domains, It's absolutely the right way to do governance. We talk about federated governance, not centralized governance. Um, if you think about data as a product, I think that's again, tying it back to metadata, thinking of almost like the, the usability around data. And we, we've over the last 10 years, like I said, we've been wa…

AI assessment note: “We talk about federated governance, not centralized governance.”

Q How do you, how do you scale the team in, in, in particular, this, or is this really interesting tension between promoting people from. Within, especially the people that were early in the company, and then like bringing, uh, you know, experience management that I've seen the next level of scale you're, you're in. What, what's your philosophy on that?

A It's, it's hard. Um, I use this quote that I've, I've stolen from somewhere that in the beginning you're like a pirate ship. The only thing that matters is kind of preserve cash and sell like build product or sell product. Over time you built more like a, you need to build more like a, like a Navy ship where it needs to be more structured, again, ownership, but more repeatable, more processes. And again, put it, put a pirate on Navy ship. That's not going to work. Put a, put a Navy captain on a pirate ship. That's not going to work either, but that's kind of what you're going through. And so you have to get it. That's a massive kind of change management exercise. Are you going to make mistakes? You're going to bring in people too early. You're going to, you're going to, um, you're going to lose people too soon. Um, and unfortunately I think it's part of the journey. Uh, and I've just learned every year is kind of different, and you have to kind of explain what you're doing, why you're doing, why is it important? But I would recommend all of kind of father CEOs, building a leadership team. It's probably one of the most important jobs that you have. And, uh, and I remember the first leadership team I've built, I thought these are all amazing people and they all are, all are amazing people. And I thought that was going to be your leadership team for the next foreseeable future. An…

AI assessment note: “put a pirate on Navy ship. That's not going to work”