Hex: Making Data Shareable (with Barry McCardel)

Welcome to Contrary Research Radio, the podcast that dives deep into the world's most exciting private tech companies. I'm Kyle Harrison, general partner at Contrary. Today we're talking to Barry McCardel, the CEO and co-founder of Hex. Data is, as they say, the new oil. Over the last few decades, it's evolved from a pretty run-of-the-mill byproduct of a company's operations and has become a pretty valuable commodity. The stuff that business insights are made out of. The global market for data analytics was valued at $241 billion in 2021, and it's expected to grow to $656 billion by 2030. Data, just like oil, can be a pretty messy business. Getting your arms wrapped around it is no easy task. That's where data scientists come in. The average job growth across all roles is expected to grow by about 5%. For data science, it's expected to grow by 36% from 2022 to 2031. As much as the analysis that these data scientists are doing is at the cutting edge, the tools that they're using are very much not. These platforms aren't talking to each other. They're spread across a bunch of different tools, and they're built on the backs of chart screenshots in Google Docs and downloading CSVs. The problems only get more complicated as more cooks start to stick their fingers into the data kitchen pie. You're seeing more non-technical people try their hand at data analysis, whether it's product managers, finance, marketing, you name it. More people are starting to get their hands dirty with data dashboards and SQL scripts. So that's where Hex comes in. Hex was founded in 2019 by Barry McCardel, Caitlin Colgrove, and Glen Takahashi. Hex is a collaborative platform that's meant to enable technical and non-technical users to be able to analyze, visualize, and share data. It's making code-based workflows more accessible and integrates with a range of different products across the data stack, all revolving around this one mission: helping teams ask and answer data questions by working together. Hex wants to do for data science what Figma did for graphic design. To help us understand what Hex is and how its business works, we're joined by CEO and co-founder Barry McCardel. All right, Barry, thank you so much for taking the time to do this. I'm excited.

Hey, I'm excited to be here. Thanks for having me.

Yeah, this will be super fun. Listen, I got to tell you, I have been excited about this chat. As I was kind of thinking about some of the things I wanted to talk about, one of the things that I realized— I don't know, I had put this together, but I think like in general, some of the smartest people that I know have come from Palantir. You're a sharp guy yourself, but like in particular, I feel like a lot of like really talented like data infrastructure and data scientists have come from Palantir. And I found myself like reflecting, like, why is that? What are the commonalities? Feels like Palantir is obviously solving like really complicated problems, have really complex data problems to solve. But I thought that would be like a really good place to start, like maybe walking through a little bit of your background, but also just like the kind of data lineage of like, what did you learn and why do you think it kind of landed you where you are? I thought that'd be a fun place to start.

Yeah, happy to. So I spent almost 5 years there. I had started my career in management consulting and I was doing like a lot of data stuff in like Excel. I was always the person in my projects who was like wanting to build the model. And I was like running these illicit access databases, like under this, with this like purloined PC under the desk at one of my clients. And like, I was loving that. And a friend of mine from college who was at Palantir was like, you should just come do it like for real at a place where like, this is all we do. So yeah, there were a ton of smart people at Palantir. I always felt like I was the dumbest person there because like some true geniuses there. Yeah. And I learned a ton. I think there's like the specifics in data, but even just like the culture there, I think was like very entrepreneurial, very first principles, very intellectually rigorous. I learned a lot. I made a lot of mistakes. I look back with a lot of fondness for it being a place where they would throw like a bunch of 25-year-olds in on like, this is like really hard unsolved problem and just kind of be like, you'll figure it out. And sometimes we did, and that was pretty great. But I think like one interesting thing in retrospect that I certainly did not appreciate at the time was we were really at kind of this forefront of change in data. You kind of had these two trends in the middle of the 2010s when I was there, which was like, big data, quote unquote. You know, at the time there was like this data lake trend and it was like, you know, anything bigger than what was in Excel was considered big data. And we were really at the forefront of sort of like how you actually make that type of data storage and scale useful. And then, you know, we also, I think, one of the things, my biggest takeaways and something we've carried forward in some ways into what we're doing now is this idea of making data really powerful for people who aren't necessarily technical. Like at Palantir, our power users, our champions, our heroes were soldiers or oil rig workers or people on the floor at an aerospace manufacturer. Like these are the people that we were trying to empower with data. And these are people who are not writing a lot of Python code. Like these are not people who even are necessarily like spreadsheet literate. That's not a part of their job. They're smart, but they're really smart people. They're trying to solve a task and they're analytically minded, how do you bring the power of all this data and processing that we were able to bring to bear and make it into something that's useful for them to solve their mission and their task? And so that was, that was really cool. I look back with great admiration for that, and I have a lot of peers and friends from Palantir who have gone on to start other really cool things. I think it was a great breeding ground for that.

So, well, and correct me if I'm wrong, that was where you and your two co-founders all met, was working at Palantir.

Is that right?

That's right.

Yeah. So Glenn was my intern in 2014, so we've been working together for almost 10 years. And Caitlin, I started working with a few years after that. And yeah, I'd worked really closely with both of them and came to respect them. And, you know, we were kind of reluctant founders. We were not the type of people who were like, oh, we want to be founders. We just need to go find an idea. It was kind of like this obvious idea that we all got excited about. And I reached out to them and we started talking and it just— I don't think I would have started the company unless I had been able to work with them on it.

Yeah. And it feels like— so to speak to that Palantir experience you all had and that sort of— you've talked about this a bunch before, kind of enabling the non-technical person, the person has a job to solve. And it also feels like there is this evolution that you've seen within sort of data science more broadly, where it used to be very much this like ivory tower of R&D teams that were very divorced from like the business problems to solve and things like that. Do you feel like Palantir was part of the evolution away from that as a company, or was it something you sort of saw independent and thought, this is something that we want to help empower? Or how did you think about your place in that change?

Yeah, I mean, I think that was actually a really big part of the ethos at Palantir was this idea of like again, like bringing the data to the people actually trying to make the decision and do the thing. Sometimes we would come into a customer, we had this very forward deployed engineer model where we would go and work really closely with a customer, like unusually closely, like badge access to their offices. And whenever we came into a customer and there was like, yeah, we want you guys to go work with like the data science team and they sit on the 13th floor in this other building. Or they sit in the Innovation Center. And the Innovation Center, it's different than our other offices. It's got beanbag chairs and Post-it notes, and that's how you know the innovation's happening there. And then we're like, yeah, but where are the people making the decisions and actually doing the thing? And they're like, oh, they're on the other side of the campus, but they don't talk. You wanna go over there. And we're like, why do we wanna go over there? We wanna go over there. We wanna go talk to the people doing the thing. And I think that was not always popular with, but yeah, I mean, that ethos of wanting to, to bring this power to the people making decisions. And today our primary users at Hex are typically data scientists, data analysts, but I think we see this very tangible difference of people who are in those roles who are really connected to the business being like the types of people we think usually are able to do the most valuable work in Hex versus folks who are doing like pure R&D or, you know, sort of being in that ivory tower.

It also is an interesting, and maybe this speaks like how you've built the product, but there is this component of like those people that have that technical capability to dig into data and sort of solve these problems. It's sort of easier to target as well, right? When you're trying to find these people, to your point, like what part of campus are they on? Even today, it's like finding out what team they're in or how it works or whatever. How do you now, and I wanna talk a little bit more about the product and how, what you do exactly, but first when you started thinking about who is our ideal target user and then maybe it expands from that. How did you begin to think about targeting those people?

Yeah, so Hex, our move is we're, we're excited to go work with those data users. Like our core heroes, champions are— it's a little bit different than Palantir. Palantir, we were very like solutionized. It was like, here is a solution for sort of a, you know, someone doing a very specialized task. I'd say Hex is more general purpose in that we are focused on being a tool that any, anyone, any data practitioner who's doing this type of data science exploratory analytics work, anyone who's asking and answering questions of data and wants to be able to, to make those results more impactful, we want Hex to be really valuable for them. Now I think what's interesting is we, we first come into companies targeting those really high-end data practitioners, right? Like data scientists, data analysts, we change their workflow, we change their lives. But then what I think the biggest impact and affect is not just making those people more efficient, but it's making their work more useful for the rest of the organization. Like the traditional way a lot of those people are able to like share their work is like, I'm going to write a SQL query and download the results to a CSV and then paste it into a Jupyter Notebook. And then I'm going to, you know, work in this Python thing. And then I'm going to like export that to a CSV and then put it in like a dashboarding tool. And then I'll screenshot a chart and put it in a PDF. Of a doc that I'm going to send in an email, and that's how I'm going to communicate my work. And like, that's insane. It's like, it's crazy. Like, you see the workflows people do and it's like, wait, what are you doing? And I mean, I used to do this. Like, I know what they're doing. Like, it sucks. And it's really hard and it's brutal because like that, like even if you get that chart out and it's like the person looking at it is like, oh, good chart. But what if, what if we shift this thing a month? Like going and trying to redo that whole magic trick is like very difficult. And so I think with Hex, the idea is like, can we give these data practitioners a tool that they can adopt and they can use that makes it, integrates their workflow so that all feels much more seamless, and then makes it easy to share and collaborate with others in the org, their stakeholders, and even make it just so, you know, if they want to change something, they can just go ahead and do that and interact with it in a more direct way. And that, I think, you know, if you think about the sort of data team ivory tower, This ability to connect the data practitioners with the stakeholders and the folks in the business they're trying to inform more directly and help them come together in one place, I think is a big change in how sort of insights and data work can help influence decisions better. That's something we're really excited about.

Yeah. Well, it's also, it feels like if you unpack that a little bit, there are these sort of like two challenges you guys undertook, one being this sort of increasing the efficiency with which people— and you guys have this great, I think, chart and, uh, somewhere on your website where you, you gotta visualize this, like all the different tools and back and forth and stuff.

Yeah.

So just streamlining that is like a huge, I'm sure like a huge value add. The collaboration function, and we've talked about this a little bit before. Um, you know, it's easy to kind of think about the analogy. Um, like Figma is an easy analogy to think about where it's like collaboration was such a huge piece of the, of the unlock. It wasn't just like, yes, it was nice that it was in one place. There was a bunch of tools. The, you know, building in prototyping, right? All that stuff was great, but collaboration and allowing multiple different stakeholders to be comfortable going into one surface area, that's pretty powerful. Why not? Like, why didn't you guys just stop at the, like, let's just make these sort of data people's lives easier? Why was collaboration such a necessary additional component to solving the problem?

Yeah, it's a great question. Well, I think, I think it's that question of how do you help data teams have maximum impact? Like, and you know, Kyle, maybe the simplest answer is like, that's the pain point I was feeling right before I started Hex. It was like this, this moment I left Palantir, I went to a healthcare startup and we had a great data team and we were doing all this cool work and we had a pretty modern data stack. But then like most of the actual like interesting stuff was happening in like random SQL scratchpads and Jupyter Notebooks and scripts. And screenshots of charts and PDFs of decks that were sent in emails or whatever. And it's like I actually started this journey as a buyer. Like I was looking for something like Hex. I was like, surely someone has solved this. And then we tried a couple of products. They weren't quite right. You know, there are people like, hey, if you cobble together these 5 open source things and you get all the versions right and you have someone maintaining it and Jupiter and Mercury are in alignment, like then, you know, maybe that'll solve it. And it's like that didn't work. And so that pain of that collaboration and both within the team itself, like, you know, the Figma analogy is like two designers trying to work together. In Hex, it's, you know, you and I might be data practitioners, but then that, how do you connect to stakeholders and make them part of the data process and make the insights and outcomes of that more useful? That's the part that felt super unsolved. And you brought up Figma, like, you know, Figma, I don't, I'd heard a stat somewhere. It's like, you know, It's like 40% of their paid seats are designers or something like that, right? And part of that is because they bring these other people into the design process in a way they weren't previously invited. You know, the old school design workflow that I used to live a lot was like, you're in Sketch or you're in Illustrator, and it's like the same thing as I was describing with data, right? It's like, I like do the thing in Photoshop and then I bring it into Illustrator and then I export it to a PDF and I send it in an email and then you're looking at it and you're like putting stickies in it and comments and then you like send it back and it's like, version 5 final final, and there's a file on Dropbox. And it was insane. It was like, looking back, it's like, we worked that way. And I think data is like still there in a lot of ways. And that's the thing that we felt like, hey, we can solve that. We can bring some insight to that. We can make this better and make this more useful for people.

Yeah. Well, it's also, it's really interesting to think about, to your point, like data and where it is and how people use it. Cause design, it's sort of, you think about collaboration and design and the joke is that like, you you wouldn't have two designers in there, both trying to drag a shape in different directions or whatever. Right. Like I'm thinking about, you know, this is a throwback, but I remember watching NCIS with my dad and there's this episode. Yeah, yeah, exactly. Two idiots on a keyboard. Right. It's like, they're fighting.

The number of times I've sent that clip around.

That's right. It is the quintessential, like idiot collaboration image. Right. It's like basically these, for people who don't know, it's like these two people, they're like getting hacked or whatever. And they're trying to like fight the firewall. And there's two people on one keyboard, both going at it, just like trying to, and it's like, it's so, I think it's so funny.

I think there's a woman, there's a woman trying to solve it. She's on the keyboard and then of course a guy walks up and he's like, oh, let me help. And then they're both typing on the same keyboard. It's, it's comical.

And exactly.

I always figured the writers had to have such a good time with that. Right.

Like 100%.

And then I believe the scene resolves with like the older guy coming in and just unplugging it.

Unplugs it. Yeah.

Like problem solved.

Chef's kiss. It's so good. Yeah, exactly. That is like the definition of, of like collaboration, right? It's like if you can get two people on a keyboard, you've solved it. Like it's, but it's so, so I was thinking about that and cause I was thinking about like within data, it's like, yeah, with design it's so obvious. Like you're not both dragging a shape in different directions, whatever. Within data, it feels like it's kind of similar. Like you're probably not, you may not have two people getting in to like analyze a specific problem or whatever. It's like one person gets in to do that work, you've made their lives easier. And then the output, the sort of collaboration function comes in when people are trying to get in, observe things, make comments, reactions, maybe make decisions. Yeah. Yeah. Tell me more about that. Like when you think about, so, so you fast forward 5, 6 years from now, somebody else is quoting the Hex statistic the same way as you just quoted Figma, right? It's like, okay, there's a ton of people using Hex who are not data scientists. Who are they, what are they doing? Like, what is that interaction like?

Yeah. Well, I, I, you know, you sometimes we get called like Figma for data. I look, I'm like waiting for the day when there's a Hex for something else. Like, I'm not saying, you know, you've made it, right?

That's right.

I think the collaboration patterns and data, there are good analogies there. Like I, you know, how often you having like two designers both playing chopsticks in there? Like very often, but I'm in Figma all the time with our team. Like leaving feedback, we can be in there together. Like there is a kind of magic to that, kind of like I can be there with you and we can be talking about a thing together. And Hex has the same, you know, real-time multiplayer comments, a lot of that same affect like brought to data. I think data does have this then sort of additional, like data analytics workflows then have this sort of additional step often, which is like I want to publish this in a way that's sort of like fixed that other people can use. And like, you think of like dashboards, reports, like I want to publish this version of this thing in a way that actually then is going to be the thing that's going to be used and consumed and live. And so there's almost these kind of two collaboration patterns. There's like you and I actually collaborating and working together on an artifact, and then there's like the— I kind of call it like the editor-editor loop. And then there's like the editor-consumer loop, which is like a little more one-directional. And that of course happens in things like Figma too. Like there's stakeholders that have, you know, viewer seats in Figma that aren't necessarily editing or creating themselves. But in data, I think that's like a big part of the affect is like, you know, you have, your company might have its collection of dashboards or something. I think what's interesting to me is this longer term opportunity to really democratize a lot of those analytics workflows. And that's something that's been interesting to us for a long time. I was talking about at Palantir, like it was, you know, we were able to go work with people who aren't necessarily data practitioners. I think everything happening in AI right now, I think opens up some very obvious but also very nuanced and difficult opportunities around how to potentially open up these analytics workflows to a broader set of people. Like, is it important that you know Python, the whole pandas syntax anymore? I don't think it is. Is it important that you know, like intricately how to do certain types of things in SQL? I'm not sure that is the like skill there's, but I also don't think that means that like the robots are just going to go take away all the jobs or, nor necessarily that, okay, we don't need a data team anymore because everyone in the company can be a data scientist. Like, I don't think that's it, but I do think you're going to see much broader access and democratization of being able to get in and ask and answer questions. So you're asking me this question of like a few years from now, what do I hope the stat is? You know, I would hope that there's something not dissimilar of like, remember when only a small number of people could do these things? Or remember when there was, you know, to ask another question of the data team, you had to open a ticket and they had to send you something? Like, I think there's going to be a paradigm shift there that we're really excited to be a part of.

It's interesting too, because it feels like the kind of current, you know, AI boom, if you will, that we're in, whatever, that people are getting excited about this functionality. It's like, what's interesting is that on the one hand, some people think it is this, like, it's a matter of hours before 90% of the jobs are replaced, right? It's like, and then you've got the people who are just like, listen, this is glorified autocomplete. Like we're not, you know, it's, we haven't shaken the, the, we haven't even started, right? We haven't even got outta the driveway. Like there's these sort of different perspectives. And then where you guys sit, it's really interesting cuz it kind of goes back again to this like technical versus non-technical user and who you're enabling and stuff. And it definitely feels like there is the sort of technical folks or experts in SQL or whatever, the sort of statisticians. And then you have these folks— I've heard you talk about this idea of like the sort of role of a data analyst is, is like a very naturally curious role, right? It's this person whose job it is to understand business context, have a hypothesis. Totally. How do you like thread that needle between those two and who you're serving today as you think about like, well, who is AI going to impact? The most and how do we make sure that we are well positioned for that?

My thinking on this, I think the alarmist version is like, well, the AIs can just write SQL and Python now. So why do we need the people who write SQL and Python? And I think that's really reductive because I think the people who write SQL and Python are also doing a lot of other things like thinking. And I think if you think about that workflow for a data user, it is, it's like a naturally creative role. You're forming a hypothesis, you're following a line of inquiry. You could even say you're making beautiful art with a nice visualization. And I think that's a great use of human time and ingenuity. I also think data workflows are full of a lot of tedium and bullshit, like fixing dependencies and casting column types and hunting down a missing parenthesis. Code somewhere. And like, that's the stuff that data teams really don't want to be spending their time doing. And there's a lot of munging and a lot of plumbing. And, you know, you go talk to data practitioners and they're like, I'm a glorified data plumber. And that's not how they want to be spending their time. It's not like they got into data to do that, right? They got into data to do like the creative, interesting stuff. And I think AI has the prospect of freeing us up to do that. There's an interesting story from the 1800s. In 1865, there was an economist, William Jevons, and he had this observation that as the technology for using coal to create power got more and more efficient, like as, you know, steam engines got more efficient, our net consumption of coal actually went up, not down. Which, looking back, you can see how that was counterintuitive, because it's like, wait, we're getting more efficient at burning coal. Why are we burning more coal? Well, it's because as it gets more efficient to use that coal to generate power, we kept finding new things to do with it. Like, very unfortunately for our atmosphere and our carbon footprint.

Yeah, pros and cons.

We didn't know, but, you know, now we're trying to fix it now. But like, you know, we kept finding new things to do for it, with it, right? Like, it was like, oh, we can power a train, but now this is getting efficient enough. Like, let's just put a generator down the hall and use this to power this loom. Or whatever, and the billion other things we've come up with to do with wattage since then. So that's what's called a rebound effect in economics. It's like as the cost of doing something becomes lower, actually often the net consumption of it will go up because people are finding new utility for it. And I think that's what we're going to see with a lot of things with AI. And I think it's what we're going to see with AI and data, which is this idea of As you make some of these data tasks more efficient, yes, humans will spend less like hours on each of those tasks, but I think we'll get a lot more out of them. And that's because I think there's this boundless demand for insight. Like in retrospect with the coal thing, it's like it turns out there was a boundless demand for energy. Like, and I think we'll see this with data where there's, there's this boundless demand for insight where there's not some finite number of questions that companies have that they want to get answered. It's not like the number of data analysts or data scientists an organization have are like, that's the exact supply-demand matching of the number of humans we need to answer the number of questions we have. It's more often, it's just like, I don't know, that's the number of folks that the CFO would let us hire, right?

Yeah.

And so if you take these things and you make them more efficient and easier and more accessible, I think we're going to get more, not less. I think we're actually not— you're going to have these data teams have a ton more leverage and a ton more impact and a ton more centrality. In how organizations understand their world and make decisions. That's really exciting. And I think the idea of the, like, it's going to take our jobs is like very reductive. And also like we can look at these examples from the past and kind of see reasons that it could actually be the opposite.

Yeah, I really like that. I think also like maybe taking that analogy a little bit further, like it feels like, you know, if we'd been, if those sort of like steam engines or whatever had actually gotten less efficient, you start to think like, well, this is really expensive. Like either we need to stop doing this thing because it's really expensive and complicated, or we need to step back, fix the engine, make it more efficient so we can keep doing more of what we want to do. And I feel like, you know, maybe AI is still very early where it's like we're just getting started with that sort of energy source, if you will. You step back into the modern data stack and that's a place where I think there's actually people are, it's more established and people are more looking for how to improve the efficiency than just being blown away by what it is. Right. And it's like, I feel like also just like that phrase, like the modern data stack, it's like the top 10 baby names, you know, blog posts, but for like blog post titles, like the modern data stack is everybody wants to talk about it. But I feel like, and I think we've talked about this a little bit before, but it's like, it feels like believing that the data stack can have 40 different independent categories that all will have massive companies and multiple winners and all this stuff, probably a pretty zerpy phenomenon, right? It's like, it's very much like there's a lot of, of, of waste in that, in that pipeline, right? If we're constantly switching tools to do this, then this, then this, then this, it's very similar to this sort of data science stack that you're talking about where things are going back and forth and there you're losing things or there's inefficiencies, whatever. So it feels like right now we're at a place where the data stack is different from AI, where it's not just this like new unbounded thing to play around with, it's like, no, like let's, let's sort of tighten this thing up and hone it down. And you have stuff like Datadog, right? And all their earnings, they have these, these awesome charts where they show over the years how they've sort of eaten more and more of their own space, right? It feels like that's going to happen with the data stack. And so I'm curious, like, oh, for sure. How do you guys think about that? Where do you sit in that? What do you want to own today? And like, how does that change over time?

Yeah, it's a great question. And you're mentioning Datadog, but there's a lot of other categories I think where we're starting to see that consolidation, like the gravitational pull start to outweigh the energy pushing out from, you know, as the interest rate force becomes back to earth. I think in data, as in a lot of places, there's not like 40 categories, there might be like 6. And, um, it's okay to have like a smaller initial category or an initial niche, but you have to be able to determine like where a lot of those are like tributaries into a bigger river. Um, for us, we had an opportunity to go and build something that really spoke to the workflows that we and a lot of other people were struggling with that were unsolved around. We can call it like the data science or exploratory analytics workflows that there was nothing really that was doing a great job, we thought, on doing that, nothing that was doing a great job on modernizing that, nothing that we thought had a complete enough picture on that. And for us, that's a great starting point. Like, we've gone, I think, built something that we're very proud of. It feels very differentiated from other tools. We get great feedback from, you know, hundreds and hundreds of customers we have who use it. I do think there's going to be more consolidation, and I think you'll— you might see some degree of vertical consolidation, but within some spaces, we'll also see an amount of horizontal consolidation. And for us, we kind of already do that where there's sort of, you know, these standalone like SQL editor tools and there's like Python notebook tool and then there's like a kind of Python notebook into a report kind of tool. And, you know, you can use Hex and go pretty end to end on that. You can use it just to write SQL if you want. You can use it just to build these sort of more appy report type things. You can use it just as kind of a Jupyter Notebook replacement. But what we find is like being able to bring those workflows together allows someone to really go end to end. We're able to actually replace multiple tools usually when we come into a new customer. And that can take us to a lot of new places over time. I think the thing that's interesting to me right now is you're right that there's sort of this gravitational force exerting of, you know, higher interest rates. Like there was a kind of Cambrian explosion over the last few years in the data stack, as in other spaces. I think AI is going to also then be this new sort of wave of dynamism. I know, you know, it's very hypey right now, but there's also a lot of things when you look in data, even just outside of Hex's purview, where it's like, I can't imagine we're going to be doing it the old way in 5 years, like writing data pipelines and ETL, like You know, how is it not going to be the case that you're going to be able to just point like source data? I want it to look like this and I want it to be refreshed every 30 minutes and I want it to spend no more than this many hundreds of dollars a month in warehouse credits to do it. Like, go. Like, how is it not going to work? Like, that's very obvious to me that like that stuff will work and like there's just going to be a change to how those workflows do. And some of that will likely benefit incumbents. Some of that will challenge them. There might be sort of new categories that pop up around new ideas for how that works. But I think will come back together. And so we'll see, but I think the consolidation wave is going to come and it's going to happen. You're seeing it at kind of every layer of the stack and we're really interested in how we can be part of— for us, it's, you know, helping customers get more out of our product and provide more value across workflows in our product. Yeah.

And then, so walk me through how that translates into like some of the customers you have and whether— and I don't know how much— I'm sure you have case studies and things like that you talk about, but maybe a customer that sort of personifies that where it's like, here is sort of the perfect customer for us. What did that look like? What were they doing? How did we fit in? And maybe a little bit, I don't know, you don't have to guess as to how this particular customer plays out, but in general, like you get into an ideal customer, what do you expect that expansion to look like over time? Will you do more? Is it just taking on more and more of their processes or teams or what does that look like?

Yeah, we, we've got some great customers who love Hex. As the founder, I look at our product and I only see all the problems. But it's fun to talk to customers and they remind you we've done a few things, a few things pretty decently. My team reminds me all the time we should celebrate that more. They are right.

Well, it's also— this is— yeah, this is maybe— and I can't remember. I know you just had a kid. I think, I think it was your first kid, right?

Yeah, that's right. That's right.

So I've got 3 kids. And so maybe I'm not saying that this is like a bad parenting thing or whatever, but sometimes you feel a little bit the same with your kids where you're like, All I see is all the things that they've shattered and the screaming and the messes. And when people compliment your kid, you're like, but you don't know them. Like they're crazy. Like that's how you can feel sometimes, but you do love them. So I get it. Like I get how you feel. Yeah.

That's my relationship with our products. That's right. I think especially cause it's the product I always wish I had. So I'm constantly just wanting more from it. But, um, you're asking a question about sort of ideal customers and how they come in. Um, there's a customer we landed recently. I am forgetting if we have logo rights, so I'm going to just I'll just bleep them out for a moment. Sure, sure. They're an autonomy and mobility company, and they brought us in to do sort of this thing that they're— they have a legacy dashboard tool, you know, it's a very popular solution for sort of dashboarding. And they brought us in alongside it to start because, you know, they were doing actually most of their interesting data work outside of dashboarding. Like, it's kind of also funny, like BI is like really just reporting and dashboarding. And like, that is all at once very important and also like not the most interesting thing that data people are doing. Sure. Hex caters to that. So we, you know, they brought us in to do that exploratory data science collaborative sort of analytics work that their data team was really spending a lot of time on. And we landed with like some maybe less than 20 seats initially. And now there's dozens and dozens. I mean, we may be over 100 soon in that account because it just kind of spreads because people share it and then someone else picks it up and starts using it. And what you'll see often is like you'll have the first adoptees will be like the data scientists who, for whom it's like an immediate replacement for sort of this legacy, like local notebook workflow that they were doing. And like a Jupyter or Python notebook. But then you start seeing like software engineers and PMs and marketers who start using it because like they know some SQL and like Hex is great for, like you can write a whole notebook just in SQL and visual cells and like it's really fun. And so there's this sort of spread we see in our expansion rates are pretty good, I think because of that. And then over time we start to see customers kind of questioning like, hey, do we need these other tools? And we don't go in being like, hey, get rid of Tableau, replace it with Hex. I think that's like a really bad idea. Like Tableau is really good at being Tableau. They've been doing it for 20 years. And like, you can't out-Tableau Tableau. There's like companies who have tried to do that and it hasn't worked well. We kind of were like, hey, there's like a different way we think some of these workflows should work. There's actually a different population, different audience that we want to really excel for. And I don't mind if a customer has a legacy dashboarding tool sitting around that is doing some things well, that's fine. But we are really excited when we feel like we unlock a lot of new capabilities and a lot of new frontiers of creativity for users who are doing like the really important work that we want to support. So that's our focus and how we're thinking about it.

How do you think about the— because I think pricing turns into like a complex thing when you're trying to like, you know, you're trying to bring a lot of people in, you're trying to make it—

Pricing's complex.

It's tough, turns out. Yeah, it turns out. But I was thinking about this because the other day I was chatting with a company and it's like they're more built around like the number of integrations that they enable, right? And it's kind of the same thing where like, you don't want to disincentivize people to add more integrations because the more integrations you have, the more powerful your product gets. I would imagine for you guys, even like a seat-based thing, right? It's like when, when it's an activity that is not, you know, usually something that one of these collaborators might usually have a seat to, they might hesitate to say, well, I don't, I don't want to cause us more to cost more or whatever. How do you guys think about that of like trying to balance driving more usage while also making sure that you are. Capturing the value that you're creating?

It's a great question. Pricing stuff, you know, we're an app, we're an application layer business, so we're a seat-based business. That's what makes sense for, you know, 90-some percent of apps, I think. You know, we're more like a Figma than a, you know, a database or whatever, you know, where they all charge by consumption. And in an app-based business, you get this kind of like highs and lows sawtooth effect where you'll see, you know, a lot of seats get added and then they get purged. And every seat-based business will see this where it's like, well, people get seats and then someone will go in and be like, oh, these people need these seats. And it incentivizes a lot of app companies, I think, to kind of go for these dark patterns of like, well, make it really easy to become an editor and then like make it really hard to disable an editor. And like, you can play those games. I always feel best when I, when We are trying to just be right by our customers, and I want to earn every seat. I want it to be hard to decommission an editor, not because we hide the button, but because people are getting value out of the thing, and people will scream if they get a seat taken away. And I want to be able to stand behind every bill we send out, where it's like, hey, you don't want to feel like you're kind of ripping people off. And again, everyone, every Every SaaS business has their own version of these dark patterns, and I think for us, we want to earn that. And so when we see like a customer who will go in and like purge a bunch of seats, or, you know, very unpleasantly in the last year, you've seen situations where there's been like layoffs and stuff like that. We always just try to bias toward being like really generous. If we get a customer reaching out like, hey, my bill is unexpectedly high, these editors got added, I don't want them. We just try to bias toward being like really generous there and being like, oh, no problem. Cool. We'll rebate you that because we don't feel like we earned that. Now, as the product evolves and becomes more charismatic for more types of people and more types of workflows, we want to go earn the right to have those people become devoted users. And I wanted that to be our lever to go capture a lot more value.

I think that's a pretty good way. I mean, it's like a very good way to earn trust too, where people feel like— like I remember one time I was at a store with my brother and we were talking, we talked to some clerk and I was like, hey, do you guys have this thing? And he was like, we do have this, but it's probably not exactly what you're looking for based on what you're describing. This store across town does have that. And we were walking away. I said to my brother, I was like, I was like, that's dumb. That guy should be trying to trick me into buying the thing. And he's like, and my brother is like, well, aren't you going to be way more likely to come back here knowing that they're not trying to screw you, whatever you're trying to buy? Like he's trying to actually help you. Yeah. Like you guys are definitely creating that value.

That's how you want to feel. And I think even just, you know, we have a sales team and even when I was saying earlier about like some, you know, a customer will come and ask you like, oh, is it good for this? And I don't think it's good for anyone to try to wedge yourself in for a thing that you're not gonna be great for. And I have literally pointed people to other products in the past being like, hey, for what you're asking for, I think you're gonna want this. Like, it's interesting to us. Like, someday I wanna come back to you and ask you about this thing, but you should go use that thing right now. And like, I don't know. I, I'm like, you, you, you do this long enough, you just kind of be out in the world long enough. You, you see enough cases where kind of karma. You know, comes around. Um, that's the right thing to do.

Yeah. I like that. Um, maybe a final question for you, cuz I think like what is really interesting about where you sit is that just the sort of foundation level, what you do already today, there's so many people that could benefit from it. And I think about different companies, right? And some of them, they do incredible just like being just distribution monsters, right? They take something that they're really good at and they try and get it in the hands of as many people as possible. I think about something like Zapier, right? And it's like, there's not a— it's not a massive evolution. The product today versus 5, 6 years ago, not super different, but like it's just constantly getting into the hands of more and more people. Then you think about something like a Databricks and it's like, man, like there's so many things, it's so expansive. They're trying to do a ton of different things and that's how they've kind of grown. When you think about the opportunity you guys have, you know, fast forward 3, 5 years from now, where do you think you are? Is it sort of taking what you're doing well today and getting it in the hands of as many people as possible? Or is there this much more expansive vision for what this kind of allows you to be capable of?

A little of both. I think you have to do one before the other. Like Databricks, you know, for a long time was like hosted Spark. Like that was the story, right? It was like, you're using Spark, trying to scale it yourself. You want to jump out of a window? Hey, we'll help you. Like, like, and now, you know, when you go to them, It's very, you know, the story is much more than hosted Spark. In fact, that's, you probably have to hunt around to find the like, that is like the primary offering, but that's how they got into a lot of places and how they earned the right to do more. So I think today my focus is on, hey, we've got a set of things we're excited to go bring out into the world and improve. We want to get that in as many places as possible. And then yeah, over time there's a lot of other types of things that people do with data. There's a lot of other types of workflows people want to achieve. There's a lot of other types of users that have a desire to participate in these things. We have a lot of things we think about, but I also am very— I've learned the hard way over the years doing Hex and before that, that like focus is just such an important thing. Like the moment, the times I'm least proud of are the times where we lost focus on like trying to do a few things like really well. So yeah, I think, I think in the short term we feel like there's a lot of room for us to really do a great job in what's in front of us. And then there's a lot of other things we can do over time. We want to earn the right to do those.

Yeah, totally. Yeah, I love that. Well, Barry, thank you so much for taking the time to do this. I had a blast getting to chat through the details.

This is great. Thank you so much. And I look forward to chatting again soon.