 Hey everyone, welcome to this CUBE Conversation featuring Hex. I'm your host, Lisa Martin. Today, excited to be joined by Barry McCartle, the CEO and co-founder at Hex. Barry, great to have you. Thanks so much for joining us today. Thanks for having me, it's great to be here. Tell us a little bit about you, your background, about Hex, which I know is built to enable businesses to do more with data together. But give us that backstory. Yeah, absolutely. I have personally been a builder and user of data tools my entire career. I've worked with every different type of data tool, done, worked with many, many different data teams. And most notably my co-founders and I spent many years working together to build data products at Palantir. And Hex in many ways is the product we always wish we had. We had sort of always felt this pain point around a lot of fragmentation and a lot of pain for organizations that actually want to make value of all the data they're bringing together. They're spending millions of millions of dollars on data storage, ETL, compute, hiring out, hold data teams. They want to get a lot of value from that, but then the fragmentation of the actual tools that people are using every day and the lack of collaboration really winds up holding these folks back. So we started Hex based on that personal pain. And I always like to say Hex was just the product I always wish I had. And it's exciting now to see a lot of other folks that are finding value in that and apparently had all the same pain points that we had. Excellent, the product you always had and now you have it because you created it. Talk a little bit about some of the products under the offering. I see one of them is called Magic. So I thought that would be great to talk about that from an AI standpoint, but just give us kind of a lay down of the actual technologies. Yeah, so you can think of Hex as the sort of integrated workspace for data analytics. And you can go all the way from just asking like a quick query, like I'm just gonna write some SQL and get the results set back and maybe build a chart all the way up through building a really complex sort of notebook that's doing a deep dive analysis or building out a model around something. And then you can take that and actually publish it as an interactive report or dashboard that anyone can use. And so you can really kind of go end to end. And this is in contrast to a lot of existing tooling where you have to jump between a bunch of different things to do anything. And all of this is cloud based. It's collaborative first. So it works like a modern productivity tool that you'd expect, like think of a Google Doc or Figma or Notion, these are collaborative first. They're built for teams and Hex is built in much the same way. And it allows data teams and the folks that they're interfacing with to all come together around asking and answering questions of data and building knowledge. And then the magic features you mentioned are our AI powered augmentation features. And they're just built right into the product right into the workflow. A lot of people are excited about AI. A lot of people are using things like chat GPT. Our philosophy with magic is let's bring, you know these really world-class state of the art, LLM capabilities right into the product where people are already doing things. So you don't have to tab over to chat GPT to paste a bunch of things in and leak all your data out or something. It works right in the product and is really integrated. So if you're a data scientist or data analyst you can ask our magic features to help you generate, edit, explain, debug code right where you're working and it really helps people work more efficiently, more effectively and focus on the things humans are really great at which is the creative aspect of work. You know, it's forming hypotheses, it's exploring ideas, not like tracing down a missing parenthesis somewhere in a query or, you know trying to look up the syntax or something you're trying to do. Which is unfortunately how a lot of data practitioners spend their day. So those features have been super well received and bringing AI into more parts of workflows and big focus of ours. So really a massive improvement in productivity, efficiency, collaboration as you talked about. You know, we can't, every conversation we have we talk about data, the data explosion is real, we're living it, there's the proliferation of connected devices, some of the projections even for next year 2024, 2025 are crazy with the volumes of data, the number of connected devices. Every business has to be a data business. I always think whether it's my grocery store or an auto dealer, they have to be. So with that context, describe some of the hard problems Hex is trying to solve for, for this data analyst, this data scientist. Yeah, well you're right on this. I think these organizations are bringing a lot of data together there. They know inherently and it's been a thing that people have talked about for a decade now of like every business is gonna become a data business. Data is the new oil, right? It's all these things people say. And people have responded to that by kind of hoarding data, right? They've deployed data warehouses and data leaks and brought all this data together. And I think a lot of organizations are now in this point where it's kind of like, all right, so what, are we getting value from this? Is this actually useful for us day to day to make better decisions? And there's a bunch of things sort of at that usage and collaboration layer that are really holding people back. So I mentioned that fragmentation. I think that's a big problem. I think it makes individuals less productive. You have to jump between one tool to write some SQL and another tool to work with something in Python. And then you're jumping over to this other place to build it into a dashboard. And then a lot of things are honestly just winding up back in spreadsheets. And that holds individuals back, but it also makes a collaboration really hard. Like things are living in a bunch of different places. And you wind up with this sort of siloing or balkanization of workflows and users based on sort of arbitrary technicality levels. You'll see like the business analysts live over here over here, the data analysts live over here, the data scientists live here, the ML engineers live over here. We think that's kind of insane. We think that's pretty broken. And again, with Hex, we've tried to build this really integrated set of workflows that bring everyone together around this. And we see also, again, this opportunity now to bring AI into these workflows and allow people to even give deeper insight out of this. I think AI can allow you to go. And one of our customers I was talking to, one of the users there recently was talking about our magic AI features. He said that they're trying things that they weren't trying before. Like they are more ambitious in the types of projects that they are taking on because they have this assistant that can help them do more complex and sophisticated things. And that is awesome for me to hear because that is sort of the ultimate ambition I think we've always had of like, how can you help these organizations do more with data? How can you empower individuals to do more interesting and deeper and more impactful work? Like that's pretty awesome. And I think that's sort of our ultimate vision how more organizations can become really, really data-driven in the future. I love that. We talk a lot about key benefits of solutions and ambition isn't one that I normally hear, but it's so important because, we talk about unlocking the value of data or freeing up data analysts, data scientists to be able to really focus on key projects that the business analysts do their work. But the ambition, that's a cultural impact that you guys are having. It sounds like also from a business and a technical perspective, that collaboration is facilitated. But what you guys are really enabling organizations to do is unlock the value of cultural change, which is not an easy thing to do. It's not an easy thing to do. And I think a lot of companies, their culture around data is this sort of thing where you have a small group of like these high priests that are the really high-end data scientists and engineers and these folks are great. I come from that cloth. But I think organizations ultimately get very limited in that those are the only folks in an organization who can take on really interesting data challenges. And I think there's a very interesting opportunity we have to sort of allow more people to be participants in these data workflows. And I don't mean that everyone in an organization is gonna learn Python overnight and become a hardcore data scientist. I mean, that actually would not be a good use of everyone's time. In some ways, bringing everyone together around these things even just means making it easier for the data teams to collaborate with their stakeholders, making it easier to actually share an insight. One thing we see at a lot of customers is, the data team will come up with something really interesting and they'll develop it in like a Python notebook or a script and they'll wind up sharing via a screenshot of a chart and a PDF of a deck that's sent in an email that's lost in someone's inbox. And then the stakeholder maybe opens it up and looks at the chart and they go, oh, I wonder what it would look like if it was this instead and they email the data practitioner back and then it takes another week to get the new screenshot of the chart and the PDF of the deck in the email. And I think if you just think about that lag time and that feedback cycle, just culturally, of course you're gonna have trouble making an organization feel really data driven. Of course that stakeholder's gonna have a lot of trouble incorporating data insights into their decisions. And with Hex, you sort of collapse that all down and you go, hey, actually the data practitioner can quickly hit publish, share someone on something. They're able to go and just look at it and actually adjust a parameter or tweak it and see it in a different way or comment right there to the data practitioner. Actually have a conversation and context with the data. The data practitioner could publish a new version of it and quickly update it. That type of tightening that feedback loop is awesome. And you're mentioning the cultural shift. I think that's a big part of that. How can you help connect those insights directly to the decisions that people are trying to make every day? At some level that is the sort of fundamental mission we're trying to accomplish. Yeah, it's connecting those insights that's critical for organizations to really be able to use the data for the value that it can deliver because of course it's all about the customer experience regardless of the business. And every customer has the expectation that the experience is going to be tailored, it's going to be spot on, it's going to be personalized, relevant, et cetera. Walk me through the how, you mentioned data fragmentation for data teams, limitation of BI tools. Walk me through the how of, how HEX is actually solving these issues that the data practitioners are facing. Well, I think it starts with our philosophy from a product perspective of really trying to build an integrated platform with a low floor and a high ceiling. And what I mean by that is a product that is accessible for folks to walk up and just start using it. And most traditional data science tooling, like step one to using it is like learn computers. Like you'd have to like install a local Python environment, figure out how to use a notebook, you'd have to write all the syntax and pandas and good luck trying to have a SQL query in there. First you have to figure out how to roll a database connection and I could go on. It's really gross. And like a lot of people get through those steps but like a lot more people don't. And so a product that is sort of much more welcoming and inviting and intuitive for a bigger population of people or even those folks who are using data tools today, just find HEX to be much more accessible and easier to use much lower friction than overhead. So I think that's a sort of low floor. Like you want to just have the product feel really easy to just get started in. And then the high ceiling is not limiting people arbitrarily. And there's a lot of tools over time that just sort of been like pure no code tools. And we have a bunch of no code features in HEX. It's great for that lowering the floor effect. But the problem is that people pop out on that pretty quick. And then that's that tool fragmentation thing. Then you have another tool that's the next level of technicality. Another tool that's the next level of technicality. And we think that we can have a product that can actually bring people together no matter what they're doing project-wise. And that's not even just different personas but sometimes an individual user actually has different tasks they might be trying to do that might call for different capabilities or languages or compute environments. They shouldn't have to jump around between different things to do that. So I think that's one really big thing from an approach perspective that we took on early philosophically. I think also just like building for modern software assumptions, honestly, like having something be based in the cloud, having something be collaborative first, having something that starts to incorporate the latest advancements in the AI directly into the workflow. We think bringing these sort of really cutting edge software capabilities to these data workflows that can only have kind of languished. You look at a lot of data tools out there that people use every day. They've been around for decades in some cases literally decades. And we think taking a fresh approach to these things that takes advantage of the latest whether it's modern compute environments or collaboration features or AI is a big part of our value problem. So in that value prop, you talked about some of the key unique elements. When you're in customer conversations or conversations with prospects, you're comparing and contrasting traditional data tools that like you said have been around for decades versus Hex. What are like the top couple of differentiators that come to mind that customers go, I get it? I think the one thing that has really always stood out for folks is when you're working in a Hex project, you can mix, it's almost like mixed media it's like you can mix different languages and different paradigms together. So you might start your project with an SQL query to pull some data from a database. And then you may actually just insert some Python because you wanted that and take it and project it or transform it differently. And then you can add a no-code chart to take that data and then visualize it. And then you may point and click in that chart to drill down to a filtered data frame that then you can work again within SQL or Python. And on and on you can mix and match these things however you want. And that is might sound obvious but was the type of thing that is effectively impossible to do in most of their tools and is actually slightly hard to do technically. Like you have to get some things really right in terms of how you're doing compute, how you're thinking about your UI frameworks, even how you make that obvious and intuitive for users there's a lot there. And so we, that's something that I think when people start using the product it just immediately clicks. And I think the big aha moment for a lot of folks come when they hit share, they hit publish and they share their work with someone else in a way that they're able to just use and interact with. And again, that might sound obvious but that is not the way data tools are historically built. And so that sort of integrated seamless workflow where I think that like it just works is the effect that a lot of people get and that really stands out and I think has always been something that when people start using the product it's very obvious how it differentiates some other things they might have used in the past. It just works, I love that. So where can the audience go to learn more about HEX because I know they're gonna want to? Our website is hex.tech. You can check out a demo, you can sign up for a trial, you can even check out our careers page we're hiring. So, but we'd love to see you in the product and love feedback or thoughts from folks who get a chance to keep their eyes. Excellent, hex.tech. Barry McCartle, thank you so much for coming on theCUBE as part of this CUBE conversation and sharing with us about HEX, what you guys are doing and why and the impact that you're making. We really appreciate your time. It's a pleasure, thank you. 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