In this episode, Michael and Sarah talk with Billy Bosworth, CEO of Dremio – a cloud data lake engine that operationalizes data lake storage and speeds up analytics processes.
Billy discusses the state of modern data infrastructure, what led to the founding of Dremio, how they’re approaching selling in the cloud data lake market, and the one thing he wishes prospects knew.
Show Notes
On the evolution of data infrastructure
“I would classify it as a spiral […] At every turn, even though you see some of the familiar territory, you are in a slightly different coordinate. Things advance, things change. Things become more sophisticated as technology changes with it.” [03:12]
“But the notion of being able to get a handle on your data such that you understand it thoroughly, you understand the quality of it, you understand what it really is about – the metadata around the data, so to speak – this has been a problem for 30 years and it hasn't gone away.” [04:20]
On the explosion of data and the need for cloud data lakes
“What we're seeing now is the awakening to a new architecture that says, what if I could leave my data in a cloud storage layer? That would allow me to separate my data from my computer [...] and my actual data now becomes its own first-class tier. And at Dremio, we’re sitting in between the data consumers and the cloud storage layer and making that access possible.” [10:30]
“By freeing the data in independent formats, you have the capability to fundamentally change your workflow. We're proposing that you can eliminate a very significant part of the complexity of the data movement chain. And in doing that, you can move much faster.” [14:09]
On the challenges of selling in the cloud data lake market
“Many problems when it comes to new paradigms and paradigm shifts – they are human problems masquerading as technology problems. […] Organizations develop very rigid muscles over time, they do things a certain way, and there often are competing initiatives.” [17:28]
“I would say helping people understand what's happening and giving them the tools to understand the paradigm shift is really important. And that has nothing to do with our product, per say. That happens any time there is an advancement in technology. And so at Dremio, the way we contribute to that is we do several things in the open source community. Education, education, education – that's what you have to really do to help your customers quickly adopt this technology and move forward.” [18:42]
On how Dremio uses data internally
“We're seeing data being used in just simply every aspect of the company. Whether it's from the classic sales and marketing [tools], the more modern sort of Martech engines, or inside the product itself for adoption and efficiency and how the product works. You don't get away from it. It's inherent to every single part of your DNA. [21:01]
On characterizing the maturity of cloud data lakes
“In this stage, it’s both late and early. […] If you think about the cell phone market, we hit a point where we made the decision that we were going to use cell phones. But as we all know, that went through its own evolution, right? It went through analog to digital, and then to the smaller flip phone concept, and then all of a sudden came along this idea of a smartphone. And that was a pretty big fundamental change in one sense, but at its core, it was still a cell phone. So that's where we are with data lakes: They’ve been with us for quite some time now, we get it, but when you step back and look at what's possible with these modern architectures, it's mind-blowing.” [24:18]
Full Transcript
Michael Pollack [00:00:09] Hello everyone, and welcome to Selling in the Cloud: a podcast about the business of cloud sales and marketing, brought to you by Intricately, the authoritative source product for adoption usage and spend data for sales and marketing teams. I'm Michael Pollack and I'm here with Sarah Brown. And we are your co-hosts.
Sarah E. Brown [00:00:38] Michael, today on our show, we're really excited to talk to Billy Bosworth, the CEO of Dremio – which is the cloud data lake engine for big data queries.
Michael Pollack [00:00:47] I'm really excited to talk to Billy. Billy, thank you for joining us. I actually have some context on your background, but for our audience, it'd be great if you could share a little bit about your background, which I know has spanned numerous businesses across the cloud. Just give us some background on you, some of the technologies you've been involved in, and what led to Dremio and how this all kind of came together.
Billy Bosworth [00:01:11] Happy to, Michael. Thanks for having me on today. If you look for one common thread in my nearly 30 year career now – I can't believe I'm that old, but I guess it happens to everybody – the one common thing has been data.
From my very first job as a developer, writing applications and learning about the backend relational database and how to design what we call the "schemas", where the data sits and learning about how those databases work and how to make them fast – and moving along from there into the product management world, where I worked for companies who built tools for data experts, for people who were data developers and database administrators, then moving from there to a company where we were the actual database, at DataStax. And we had a database called Apache Cassandra, which was a very large scale-out, very high throughput database. And that gave me a new sense of being on the actual database side of the equation. And then from there coming to Dremio, where looking at the analytics world and some of the big transformations that are happening here catalyzed by the cloud – which I'm sure we'll talk about a lot today – became a very exciting opportunity.
And so really all along the journey, whether I was writing the applications or designing the backend databases, or doing the administration or building the tools, or building the actual database and now querying the data – data has been the common thread.
Michael Pollack [00:02:41] And just to follow up on that, when you think about where you were at the beginning of your career, just thinking about the state of data infrastructure then and the evolution to today, and I'd say both the depth and complexity and breadth and perhaps even the intricacies of the data. And a lot of our audience is in this space. So you don't need to be too rudimentary. But can you just put into context what it's been like to have a front row seat in that evolution and watching that kind of unfold and evolve?
Billy Bosworth [00:03:12] I would classify it as a spiral.
And I wanted to say my first reaction was to be a pendulum, watching over 30 years, how many times paradigms go from one extreme to the other, whether it's the idea of decentralizing everything and then back to centralizing everything or whether it's relational versus non-relational. But I don't think it's a pendulum. I do think it's a spiral because at every turn, even though you see some of the familiar territory, you are in a slightly different coordinate; like things advance, things change. Things become more sophisticated as technology changes with it. And so in some ways, the paradigms that I learned in college 30 years ago are the same ones we have today. Basics on how to query data. The good ol' SQL language is still with us today. I don't know how many tens of millions of people know how to write SQL. And that is still a fundamental core. But there's new things as well around technologies like Spark and using programmatic languages like Python and R for machine learning. So these have been additive to the story.
But the notion of being able to get a handle on your data such that you understand it thoroughly, you understand the quality of it, you understand what it really is about – the metadata around the data, so to speak. This has been a problem for 30 years and it hasn't gone away. We've tried to get better and better at it. We went through phases of different kinds of data warehouses and then we went through the Hadoop cycle of the world. And now we're moving to the world of cloud data lakes, which has some pretty significant transformative properties to it. But I would just say watching the oscillations over the years of the centralizing and the decentralizing and learning how people are architecting their data and challenging themselves to continually get faster access to intelligible data, to turn data into information, you know, that journey feels as fresh as it was 30 years ago in some ways, even though the technologies have changed significantly on how we do that over that time.
Michael Pollack [00:05:18] It reminds me of that quote about how the more things change, the more they stay the same kind of thing. And it's interesting when you look at the evolution in this space, how, you know, if you think about the early days of computing and you look at mainframes to microcomputers, to personal computers, to wireless devices, now moving back to the cloud and the intelligence increasingly being centralized in these large data centers, it is ironic how we've come so far forward. But you're right that it is spherical in that it kind of moves in patterns. It kind of comes back. But at the end of the day, you're right that the core challenges are still ultimately how do I make use of my data so I can determine what to do?
Billy Bosworth [00:05:56] That's right. And it even in the centrality aspect, let's just use that as one example to bring home the point. One could argue, wow, it feels like we're returning to centralized data centers again, in one sense, in a very theoretical sense, it's like now I have my "cloud data center". And that may sound familiar to somebody who's been doing this for quite some time and who grew up with perhaps one data center that was their main data center. But let's look at underneath the covers and how different that actually is. Think about the resiliency that's built into that one logical cloud data center. Think about how distributed it actually is under the covers so that the availability that we get without even thinking about it anymore, it's just unheard of back in the original days of centralizing all of your information. And so that's the spiral aspect. On the one hand, you could say, well, this looks a lot like the old centralized days, but on another hand, you want to say no, it's entirely different. I mean, the amount of engineering and resiliency and technology underneath it is just radically different from what we knew 20 years ago. And so that's the part that makes it very exciting and it makes it fresh every day. Every day you wake up, it feels like you're learning something new.
Sarah E. Brown [00:07:03] For those who are listening, can you tell us a little bit about what cloud data lakes are all about? And for those who are unfamiliar with the space, what are some challenges in selling to them? And how is Dremio approaching selling in this space?
Billy Bosworth [00:07:16] OK, so we all know the story of the exponential, almost inconceivable increase in the amount of data. I don't think we need to rehash that. I think anybody listening to this show is more than familiar with how amazing of an increase that has been over the years.
So the question has constantly been, what do we do with it? Now that we've got it, how valuable is it? How much of it do we actually need in order to make sense of it? How are we going to access it? And maybe, fundamentally, where are we going to put it? Where are we going to store all this data?
And as you saw, the evolution of capturing all of the so-called data exhaust – all the log files, the clickstream data, all the time, series data, the geo locations, everything that makes our modern technology world what it is – we saw a shift happening around roughly, call it 2010 sort of time frame, where it started to feel like Hadoop was going to be the answer to that. We're going to be able to put this on commodity machines, very distributed, very inexpensive, and that was going to be the answer. We're going to be able to store everything. We're going to be able to access everything. Well, again, a yes and no: we got there in some ways, but in some ways it fell short. And I think in arguably today, people would say the complexity of managing those clusters simply got to be too great. And there was another problem. And the other problem was that the compute was not separated from the storage. And so you would buy these blades and these blades would have the we call it a JBOD, right? Just a bunch of desks and they would be attached to the compute. And so that created a bit of an administrative hassle as well.
Along comes the cloud. And in a cloud environment, we're starting to see not only the ability to separate these things out, but to do so from a practical perspective – infinitely scalable, you're not going to need scale limits of any practical matter – nearly free when compared to your classic models of paying for your storage. You look at cloud storage. When I say cloud storage, I mean things like Amazon's S3, Microsoft ADLS – things that were built to be cloud storage. And then finally, unbelievably easy, unbelievably easy to administer. So you combine infinite scale, extraordinarily low cost, easy administration. Guess what? You're going to get a default BitBucket. You're going to get a dumping ground. You're going to get a place where everybody in the organization says, you know what? That's the easiest thing to do is just shove my data in there. And so data has started accumulating in these cloud data storage layers and it's gotten bigger and bigger and bigger and more systems inside. The companies are putting their data there. But now we're back to our fundamental problem: what do I do with it? How do I query it? How do I access it? The old paradigm has said that you go through a series of jobs and scripts and data movement, data copying to put that in a data warehouse. And so a company like Snowflake bears witness to just what a lucrative economic model that is. They're doing phenomenally well because they made it very easy.
But in parallel to that, what we're seeing now is the awakening to a new architecture that says, but what if I could leave the data there? What if I could just leave it in the cloud storage layer? What if I could store it in open formats instead of a proprietary system? Well, that would allow me to separate my data from my compute, not just my storage, but my actual data now becomes its own first class tier. And at Dremio, and companies like us, this is the problem we're solving. We're sitting in between the data consumers and the cloud storage layer and making that access possible. Direct access to your full data set in need of cloud storage stored in open formats. And that opens up a whole array of possibilities for your architecture, because now you can use best-of-breed services, you could use a Spark service, your streaming service, all kinds of services that are available in the cloud directly against that same data set. And you eliminate copies and you eliminate movement. So that's the major shift we're seeing happen today. None of it is even possible. None of it is even conceivable without cloud storage becoming the disrupting force that has made all this possible.
Michael Pollack [00:11:41] So just a question on that point, if I were to unpack a couple of things you said there, the technical innovation is effectively this access to infinite storage, right? That storage costs have moved to near zero. There's increasingly somewhat of, I'd say almost of a business model innovation of separating out purchasing of compute from purchasing of storage. And that kind of unlocks a slightly different business model innovation that I guess happened here. But I'm curious then, for Dremio, the core challenge is really focused on here is it's really for customers to be able to operationalize these almost near infinite data stores. Is that the core problem of, "I've got this giant bucket of data and I'm, I don't know, I've got 10 or 15 different kinds of hoses to try and connect things into it." The compelling argument from the Dremios of the world is, throw away your hoses, throw away your conversion tools. Effectively, it's going to be stored in this giant bucket that you can easily access and easily interrogate, interact and build software from. Am I characterizing that fairly?
Billy Bosworth [00:12:48] I think you're very close. You're certainly in the right neighborhood and I think you're even on the right street. I would probably just add just a little bit of maybe a couple of clarifying points to that and a couple of examples. I mentioned stuff earlier. One of the things that made Snowflake so successful was the innovation on the business model, as you said. Snowflake separated the billing of your compute from the billing of your storage. That's a big step forward. You want to make sure we give appropriate credit where it's due. And I think that is one of the significant business model advancements that we've seen in the past 5-10 years.
But now, step back and look at that and say, "how do I get to that storage, because that's where my data lives?" Well, to do that, I have to go through their Snowflake interface. That's just how a data warehouse works. You put your data in the data warehouse. What we're saying is, because theoretically, the correction I'm making or maybe the slight clarification I'm making is, conceivably, you could take all the data that we talked about that lives in the data lake storage, and you could conceivably put it all in something like a cloud data warehouse, and then you start clearing away. The problem is you have lots of other services that you want to also access that data, but the data is bound up inextricably with that vendor's proprietary software.
And so by freeing the data, not the storage, but the data itself in independent formats, now you have the capability to fundamentally change your workflow. You can simplify it, you no longer have to take that additional step of copying and moving that data to yet another location before you can start doing analytics on it. We're proposing that you can eliminate a very significant part of the complexity of the data movement chain. And in doing that, you can move much faster because one of the challenges data teams have today, they'll tell you, is they've got a never-ending backlog of requests from the data consumers. Data consumers are people like the Tableau users, the PowerBI users, the Excel users – they all want more and more data. They want more access to it. But the more you put it through a series of copying and moving events, the harder it is to make those quick changes and give them access to the data. So that would be the way that modern architectures are evolving. Let's not take the extra step of moving yet one more time. Let's eliminate that final last mile and just say the growing BitBucket is cloud data lake storage. Let's let that be the canonical source. Let's let that be the place where the data lives as its own independent here. Then everybody can access it right from there and you can do it through services like Dremio, as an example.
Michael Pollack [00:15:27] Interesting. So then the way to think about it is Dremio is obviously a lot of complicated technology that's most likely on a site. Most people don't see it, but really it's about providing very simple thing conceptually, which is your unified source of truth for data that can be accessed, I'd say almost agnostically in the sense of it's not proprietarily tied to anything and all your teams can reference this one place. So it's fair to say that, again, a huge amount of technology to ironically deliver something that seems like it should be pretty simple.
Billy Bosworth [00:15:58] Which is the goal of all technology, right? Wasn't it [Arthur C. Clarke] who said, "Any sufficiently advanced technology is indistinguishable from magic?
Michael Pollack [00:16:06] Exactly.
Billy Bosworth [00:16:07] You want it to be so easy to use that it does become indistinguishable and that that is exactly where we want to be. Now, you're on the porch. Now we're on the porch having a drink together.
Michael Pollack [00:16:19] Sweet. We made it, we made it.
Billy Bosworth [00:16:21] And by the way, there's lots of other really interesting and cool technology being developed that continue to add more and more functionality to that data lake layer to do things that previously you would have had to do in a data warehouse, like transactions, like time travel with your data, like cross-table transactions, things like that that are now made possible through – and this is a really, really exciting part – a lot of it's through open innovation. There's a project from Apache called Apache Iceberg and Apache Iceberg helps you to do table management and transactions and things right on your data lake itself. And that's a good example of the open formats that I was telling you about, the open standards. So there's some really exciting innovation going on at many different levels around data lakes.
Sarah E. Brown [00:17:05] So you've mentioned your target market and how you think about your value that you provide to them. What are challenges in selling to your target market? And I'm curious how you think about data as you're building out Dremio's go-to-market programs.
Billy Bosworth [00:17:17] When we think about the challenges and to whom we're selling and what that looks like – you know, interestingly, this is one of those things that hasn't changed. I told you, the pendulum swings back and forth. Many problems when it comes to new paradigms and paradigm shifts; they are human problems masquerading as technology problems. And what I mean by that is that organizations develop very rigid muscles over time and they do things a certain way and there often are competing initiatives inside of organizations.
Billy Bosworth [00:17:52] And there's a lot of challenges around budgets and even job security and job protection and things of that nature. And so it's amazing to me, one thing that hasn't changed since I came out of college in 1992 is the fact that there are still so many things where people get in the way of seeing the project getting done as efficiently as possible. And that gets solved through very clear communication, very clear goals and objective settings inside of companies, and a real corporate DNA that says we are going to evolve and change and move very, very fast. I mean, this is one of the reasons why native tech companies are so formidable in the market. They don't have a lot of that old scar tissue. They can move very, very fast with brand new paradigms without having to worry about 50, 60 years of legacy technology inside the organization. So I would say helping people understand what's happening and giving them the tools to understand the paradigm shift is really important. And that has nothing to do with our product, per say. That happens any time there is an advancement in technology. And so at Dremio, the way we contribute to that is we do several things in the open source community. We do a hosted industry event called Subsurface that is not a Dremio event. It's an industry event. Education, education, education – that's what you have to really do to help your customers to quickly adopt this technology and to move forward.
Regarding your second point on what do we do with data internally: quite a lot, because I would say any modern organization is really thinking about being data driven. And what does that mean for a vendor? Well, for a vendor it means layering on top of the classic sales and marketing model, which is already very data intensive. Just think about all your systems like Marketo and HubSpot and all the infinite number of tools in your Martech stack, which I'm sure you're quite familiar with, Sarah. It's a dizzying array of technologies, all designed to leverage technology to tell you more about your customers and about their behaviors and their prospects. And one big one is intent data. That's all the rage nowadays, right, is not "What is Sarah doing right now?" but "What is Sarah gonna do tomorrow? What is Sarah gonna do next week?" I don't figure that out. I can't figure that out. You know, short of a crystal ball, I need data. I got to figure out what kind of models to build around that. So everybody's trying to do that.
And then the other approach is they're also layering in product-led growth. And product-led growth requires a lot of real time data that you must gather and make actionable while the users are actually actively using your product. Because you want to be able to guide them in their journey to make them as successful as possible in as short a time as possible. And then finally, when Michael was speaking about earlier, deep in the product itself, we use data to make that workflow better. So if I see metaphorically, if I see inside the product that you're querying a certain data block numerous times, well, maybe I want to just go ahead and set up some advanced indexing on that on your behalf, without you even knowing it. I can't do that without all the data. So we're seeing data being used in just simply every aspect of the company, whether it's from the classic sales and marketing, the more modern sort of marketing Martech engines inside the product itself for adoption and even for efficiency and how the product works. You don't get away from it. It's inherent to every single part of your DNA.
Sarah E. Brown [00:21:21] You know, it's interesting thinking about marketing data. Are there certain things that you can see if a customer has already invested in certain technologies? You mentioned a certain stage of their cloud native status or evolution of these companies to be ready for Dremio. How do you know when they've reached that stage?
Billy Bosworth [00:21:37] For us, it's actually quite easy. It's when they have already made the decision that those cloud data lakes are going to be that default BitBucket that I talked about. Once you've made that choice, you've crossed the Rubicon. Now it's time to figure out, what do I do with that data? If you're in a position where you're still trying to figure out, do I want to do that, you've got a ways to go in your education and in your journey. So for us, that's what we look for. We look for companies that have already made that decision. And then depending on where they're in, their journey is less relevant. If you could just be getting ready to start that movement, you could be deep into your migration. You could have been there for two or three years. That part's OK. And so that's one of the things we look for. Incidentally, for what it's worth on the vendor side, all those things we talked about are all dangerously close to also being creepy. And I think we all know that today, that we want to be very conscientious about how we use people's data. I do. I mean, I have three kids that are now entering young adulthood, and I've been lecturing them for ten years on how to think about their data, and what does that mean. What does a digital signature look like? And, you know, for a long time, I think that they thought I was just grumpy old dad. And then a movie like The Social Dilemma comes out and I'm like, vindicated, you know, like I've been telling you guys this for a long time.
So we as a company also have to be very good stewards of people's data. And I'm a strong believer in that. And if you do it right, people will willingly allow you. They'll invite you into their data world so that you can make their lives better, but should never force your way in. And you should certainly never do it without people's permission.
Michael Pollack [00:23:14] You know, you touched on an interesting point, going back for a second. When thinking about the role of education in the market and educating your customers about data lakes being a solution to potentially a challenge they might not even know they have, or it's a challenge masquerading as something else, and a data lake potentially is the solution. It's interesting, we collect data about digital infrastructure, about businesses, about publicly facing infrastructure. We spend a lot of time educating our customers on: here's all the ways you could use this data that you might not have even known existed. I'm curious to hear from you, about, at this time, data lakes are kind of like, a known thing. Or you think we're kind of in the first inning still of educating people. Obviously in the Fortune 500, businesses with lots of data, lots of resources have already started to make the leap. But from the standpoint of, I don't know, not quite the hype curve or something crazy like that, are we in your opinion, are we kind of at the beginning of this or at the middle? Where would you characterize the maturity of the marketplace right now?
Billy Bosworth [00:24:15] You have such good questions because the answers are both yes and no. And in this stage, it's both late and early – maybe an analogy would help.
Billy Bosworth [00:24:23] If you think about the cell phone market, we hit a point where we made the decision that we were going to use cell phones. And I'm old enough to remember my first analog phone that I probably shouldn't have been holding onto to my head, but I was – you know, the battery lasted about 20 minutes, it's a big, heavy brick. But we all made the decision we're going to do cell phones. But as we all know, that went through its own evolution, right? It went through the analog to the digital, and then it went to the smaller and smaller sort of flip phone kind of concept and then all of a sudden came along this idea of a smartphone. And that was a pretty big fundamental change in one sense, but in another sense, it was a cell phone, right? That's where we are with data lakes. You know, data lakes, the concept is like a cell phone. Been with us for quite some time now, over a decade – which in our world is an eternity, right? anything over that long, people feel like it's already old. But what's happening now with the modern architecture? That's the smartphone.
Billy Bosworth [00:25:25] So we know the data lakes are there. We kind of get it, they're data lakes, but oh my goodness, when you step back and look at what's possible with these modern architectures, it's mind blowing. And so we're early in that sense, but we're late in the you know, the basic concept of a data lake. Hopefully that makes some sense.
Michael Pollack [00:25:44] That completely makes sense. And so a follow up to that: you know, it's interesting when you look at, I'm not sure if we're in the second or the third or the fourth wave of technology, as we kind of think about modern technology; but, you know, in earlier waves, it was proprietary decisions around infrastructure, right. If you think about the largest database provider kind of historically in history, an incumbent like Oracle, in many respects what seems to be happening is a move away from proprietary formats to true open source formats, right. The containerization of your data: take it with you, do what you want with it. Do you think we're – and similar to kind of beginning and end, and where do you think we are – are we just at the beginning of some of that disruption, like for a business that's an incumbent, whether that's an IBM or an Oracle, and it's trying to kind of reinvent a little bit for the cloud age. How do you characterize them in all of this? And I would just love to hear your point of view on them as it relates to Dremio in the market space as a whole.
Billy Bosworth [00:26:44] I think the industry is learning very rapidly how to balance the trade-offs between open and proprietary. And it is a constant question that you want to ask yourself. Let's first start with the customer lens. Let's say you're a classic archetype enterprise that's been around for 50 years and you're trying to figure out your future, and maybe you feel a little stung by the Oracle bills that you've gotten in the past and maybe you feel like those maintenance bills sort of boxed you into a corner. And so now you get this vision of freedom painted to you in open source. And now, never again, never again will you be beholden to a vendor. And then you go try and do it. And this is really hard. "Turns out I don't have 3,000 PhDs like Google does, this might be a little harder to implement than I thought. And the maintenance on this thing of just keeping this all together might be a little harder than I thought." And I think the first wave was the euphoria of open source equals free equals no bills, equals no vendors, equals no lock-in. I'm going to go do all that. It was also cool. I mean, I'm a nerd at heart, graduated with a computer science degree. So I say this with all good intentions with my fellow nerds: but we really get into having nerd badges, right? We like to have that nerd badge of honor that I know how to do this. Back in my day, it was Unix Admins. You know, they loved the fact that their world was so complex that nobody else could do it. They sort of walked around with that badge of honor. Open source got to be like that for a little bit. You know, you were cool if you were doing open source – as a developer, you wanted that on your resume. And I think to some extent, you still do. But from a customer perspective, as you're trying to run your business, what you've got to figure out is the right balance. And to figure out the right balance, you've got to balance time to market. You've got to balance your ability to be flexible. You've got to balance your own internal resources. What can you do? I was also in a very weird dichotomy. I was a college football player and I was a high school coach. And one of the best things I learned when I was coaching was when I drew up this really good play on the board. And this old coach looked at me and said, "You've got the kids who can run that play?" And I said, "What do you mean?" He's like, "Can your athletes do that?" I said, "Probably not." He's like, "Well you probably need a new play, you know, this isn't the NFL." And the same thing with businesses. You've got to say, "Can I actually execute what I'm talking about with my open source strategies?" And so here's how you solve it: You start looking at what things you want to make sure stay open and flexible, and what things are you willing to say, "You know what? I'm going to pay a vendor for that. And it's OK because I see my long term strategy where I'm not going to get locked in." For us, obviously, this is a very internal belief, but I think it does apply even to somebody thinking about this rationally, externally. It seems to me your data is one of the key things that you would want to keep in the most open, flexible format possible, because at the end of the day, that is the crown jewel. And so I think you are going to constantly walk a balancing act of open versus proprietary around all those factors that I mentioned. Time to market, ease of use, flexibility. What can you actually accomplish? So there's no really easy answer for that one.
Sarah E. Brown [00:29:58] Billy, thank you so much for sharing all of your insights today. Would love to hear, just as we wrap, what's one thing that you wish your prospects knew if you have a chance to speak directly to them right now, that they don't maybe know today?
Billy Bosworth [00:30:10] In general or about Dremio?
Sarah E. Brown [00:30:12] I'll let you decide.
Billy Bosworth [00:30:15] I think about Dremio is how much we actually really want to educate the market on what's happening. Of course, we want customers. Of course, we want to have a big successful company where we're taking care of people. But we also deeply believe in these things. We discussed on this podcast today about, you know, architecting your data for a future that gives you maximum flexibility and freedom and openness. And we want you to be educated on that. And if you can get help, get that education from us, fantastic. If not, get it from somewhere, but get that education, educate yourself on what's happening. Be aware of that stuff, because once you do, you will think about everything differently. Like once you see what's possible, going back to my analogy, once you realize you've got a smartphone in your hands, not a flip phone, everything changes. So get educated. That's what we want people to do.
Sarah E. Brown [00:31:07] Fantastic. Well, thank you so much for joining us on this episode of Selling the Cloud. Really enjoyed having you with us on the show, Billy.
Billy Bosworth [00:31:13] Thank you. Have a great week.
Michael Pollack [00:31:15] That's it for us then. This episode may be over, but we can continue the conversation on Twitter with the hashtag #SellingInTheCloud. On Twitter, I'm @MRPollack.
Sarah E. Brown [00:31:24] And I'm @SEBMarketing.
Michael Pollack [00:31:26] Thank you to everyone for joining for this episode of Selling in the Cloud, brought to you by Intricately, the authoritative source of digital product adoption, usage, and spend data for cloud sales and marketing teams. If you like the show, head on over to iTunes or wherever you listen to podcasts, and please give us a review. We appreciate it. Until next time!
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