The Exchanges

Q And maybe, ah, just to walk back in history. So, 20 years ago, you were in the world of data warehouses, and that was, I guess, a specific use case that corresponded to just expensive product and small amounts of data. Do you want to talk about this and how the world has evolved?

A Yeah, you bet. And, you know, as you just saw a moment ago, um, we have, we've seen now two database companies come up and talk about how they're looking to reinvent parts of the data stack What's going on right now across the entire industry is that the entire analytics stack, the entire data stack is being reinvented from the ground up, and I'd even say pretty much the entire infrastructure world is being reinvented from the ground up, but we're going through a period of incredible innovation where the opportunities being created by advances in hardware, advances in memory, and the move to the cloud, Create just dramatically different palette than what we had 10 or 20 years ago, right? If you look at the fundamental constraints that the territories the world were designing to, They're completely different now, right? They were largely building around a, um, single rack design, I guess you'd call it, um, with disk being a fundamental constraint. Everything had to be locked down to disk. If you're a database engineer, it's all about locking the transaction to disk before you can do anything else. And nowadays, you know, companies are doing in-memory designs where they're locking it down to memory across multiple machines and geographic applications and so on. Because dramatically different performance characteristics, dramatically different opportunities, the expectation worldw…

AI assessment note: “dramatically different palette than what we had 10 or 20 years ago”

Q So, maybe to unpack some of this, and again, maybe to anchor this whole conversation, uh, perhaps for the less technical part of the audience, what's, what's a good example of a data integration problem for, from a, from a user perspective? I think I've heard you talk about just a simple example of a name and how the name would be different. Do you want to maybe explain that?

A Sure, you bet. I'll, I'll give a really common scenario that most of our customers have. As a matter of fact, almost every customer Uh, has this problem somewhere in their, their business. Um, what companies are trying to do is to do more targeted marketing, better support by understanding everything that they know about their customers. They call it a 360 degree view of their customers. And so if you think about all the touch points that you as a company have with your customers, you have things that they've done on your website, Before they've ever become a customer. You have all the things that happened along the way in your sales system. They have the transactions that they've done with you in your finance system. They have your support requests. You've got any other, um, uh, things that they might do online that you'd have visibility to on Facebook or Twitter and so on. All of these different touch points that you have, which tend to live in literally dozens of different systems, In order to really figure out what you know about your customer, you want to bring that all together. Most companies actually haven't taken this step, but when you start doing it, what you realize is that all of those different systems have slightly different and slowly drifting apart versions of who you are as a customer, right? You'll have, there'll be a, you know, Mike Toucan in this one. There…

AI assessment note: “Mike Toucan in this one. There'll be a Michael Toucan in that one.”

Q Great. Presumably an important part of the first smile is governance of data, meaning being able to know where the data comes from, and who has access to it. Is that, is that a big driver, presumably, these days?

A It is. It's, so, it's one of the first problems that everyone has, is where is all my data? And, and what is my data, right? And how is this piece different than that one, and how they relate to each other, and who's been using it, and, and what happened to it along the way? If I change this, what else gets changed? We call that whole problem a data catalog, and we think there are a couple of really important aspects of a data catalog that, you know, what we're, we have built and are continuing to build. The first one is, it should be your single source of truth around what all your data is, right? The, you know, we just saw a really interesting study from IDC saying that, um, Data analysts and data engineers tend to waste around one to three hours per day on duplicating work that someone else has already done. Because they don't know what's, that's already been done. They don't know that data sets exist. They don't, they can't verify where it came from and so on. And that's the data catalog problem, right? And so it should be a single source of truth. It should be like a Google searchable thing or probably better, better analogy is like an Amazon searchable thing because you can search for it and then get a little, you know, sort of selector window on the side saying drill down by a number of different attributes. And then you can see reviews and, you know, is this certified o…

AI assessment note: “It is. It's, so, it's one of the first problems that everyone has”

Q know, in very concrete terms, like having a great team, everybody wants a great team, ah, but when you show up, you know, does that mean you need to just, ah, level up the existing team and get that VP of sales that has taken a company from 50 to a hundred, and then something in marketing, do you need to just completely change the cast of, of characters involved?

A Ah, in this case I did, but it's not a, it's not any kind of, sort of, You know, unique playbook. You need to look at each person individually and say, are they the right person to take us through the next step? And by and large, I'm, I'm not generally, for me personally, a, a huge experiential hirer, right? If you look at the, the people that we brought in that did successfully take the company from, you know, fifty million to 200 and growing, They were all what I'd call up and coming profiles, right? Everyone needs to do a job once for the first time, right? I was a first time CEO just from my previous job before that, and if no one had taken a bet on me, then I wouldn't be where I am now. So, you have to look for, have they seen scale before? Have they been successful, and are they great leaders? Do they hire well? Do they have a clear understanding of strategy, and can they execute? So that the same concept that I was talking about at the company level bring down to the person level, and most importantly, are they, you know, team players that will, um, you know, work in a team environment like, uh, the one I'm trying to build. And so if you look at the, the people that we ultimately hired, Um, I think one of them had done that role before in a, in a company of our size and, and grown through it, and the other ones were, I was making a bet, they'd seen pieces of it in maybe …

AI assessment note: “Ah, in this case I did, but it's not a, it's not any kind of”

Q started as an open source company, and then I've sort of doubled down on open source. Is that one of the key differentiating factors? Because you have a, as you alluded to, a highly competitive, um, you know, segment with Informatica, IBM, all those, um, companies, and then Tipco, and then, uh, Some startups. And so is that, is that the, is open source one of the key differentiating factors?

A Open source has been an important part of where we've come from. Um, it, it's, I'd say equally important to open source has been the fact that we're open, right? And openness isn't just that, um, good chunk of the word open core is, is one of the other, um, presenters pointed out in their model. Um, so a lot of the open, this code base is, is open itself, but it's also a philosophy about, um, we're extensible, right? We have APIs, we have extensibility in a lot of different ways, um, that mean that you, you, you can plug us in and, and build us into a bigger end to end solution, which sounds obvious and a no brainer, but amazingly, um, most of the companies that are competitors of ours don't take that approach and they have a very, very closed and proprietary kind of model. They don't, um, try to look for innovations around the rest of the ecosystem. They don't allow you to extend the solution themselves. Um, so that openness, not just the open source aspect of it, that openness as a general philosophy has been really important to us.

AI assessment note: “equally important to open source has been the fact that we're open”

Q right? We, we see a lot of, uh, early-stage companies that use open source essentially as an accelerant, um, to gain market share by saying, hey, you can take a bet on us, uh, all of it is open, um, and by the way, it's most of the time free, um, so it's a great marketing strategy, but you're now a public company. How does, how does that scale up?

A What we find is that, um, still today, uh, well over half of our, uh, sales opportunities come from people that are trying it out for free, right? Um, and, you know, there's open source as you move into the cloud world. It's a little less, um, important that it's open source. It's more important that it's free and easily triable and so on. But it's, the concept, the conceptual model works the same. People want to try it out before they're willing to make a commitment. Um, and so for us, um, that's been a really important part of how we worked, um, uh, you know, how we built the business, and, you know, we, we find that, um, in almost every company that we talk to, there are people using the free version of talent, which is great, which means they're already comfortable with the tool, they already know what we do, um, and, you know, all we then need to do is say, hey, You know, here's the extra value that you have, you buy the commercial version. There's a whole bunch of simple benefits that we can do around reliability and security and so on. And, you know, if you're really running it for something production ready, it's something worth considering. And so that's been a really important part of the, um, initial go to market up to here. Again, in the cloud world, it's, it's more about trial ability. So I think there's a, a very similar go to market aspect, but I, I firmly believ…

AI assessment note: “well over half of our, uh, sales opportunities come from people that are trying it out”

Q Now, digging into the more technical aspect of this, how, how do you guys do that? How are you able to be that integration layer for such a complex environment?

A One of the most important things that we've designed for that's critical is this concept of Um, enormous change and enormous innovation that's happening. And let me, let me spend a moment and frame that up and then talk about what we've done differently. I talked a little bit about how the world is being reinvented and how we're seeing all these changes at every level of the stack. Think about what that means for all of us in the data world, right? We're now in this period that's been going on for probably five years and probably will go on for another 10 years where What you're designing to isn't a fixed point anymore. Remember back 10 years ago, right? We were in this kind of magical situation. It didn't feel that way at the time, but boy, now it sure does, where the most important Decision that you made, ah, strategically, technology, strategy-wise, was what kind of database are you going to run on? What is your analytical database going to be? Am I a Teradata shop? Am I an Oracle shop? Am I an IBM shop? Are you going to take a risk on this new SQL server thing? But what am I going to do? Right? Once you made that choice, and you spent a year sweating over that thing, you were good for 10 years, right? That was a very, very stable decision. Look now and ask yourself, how many of you think you're going to be using your existing solution for 10 years? I mean, I don't think you…

AI assessment note: “let me spend a moment and frame that up and then talk about what we've done”