 Hey everyone, welcome to theCUBE's presentation of the AWS Startup Showcase, Analytics and Cost Optimization. This is season three, episode two of the ongoing series that covers exciting startups from the AWS ecosystem. I'm your host, Lisa Martin, today excited to be joined by CUBE alum, Barry McCartle, the CEO and co-founder at Hex. Barry's here to talk about living in the AI era as a data practitioner. Barry, great to have you back on theCUBE. Thanks for joining me. Great to be here, Lisa, thank you. Talk a little bit about Hex that the audience understands its mission, vision, gaps in the market that you guys saw back in 2019. Absolutely, so Hex is a collaborative data platform. It allows everyone in an organization to come together and ask and answer questions and data, collaborate more effectively and then ultimately build knowledge so that they can make better decisions. We started the company, we had been builders and users of data tools our entire careers and it experienced a lot of the pain that came from having to jump between a lot of different fragmented solutions and making it very hard to work as an individual but also collaborate as a team. And so we built Hex to be the sort of end-to-end platform where you can do everything from a quick query and a visualization just to answer a question all the way up through building really sophisticated reports, dashboards and data apps and everything in between. And so Hex really solves a lot of that fragmentation. It makes these workflows a lot more accessible. It's built to be collaborative first. And now we're on the leading edge of incorporating large language models and AI into the product in an integrated way that brings those power of those models directly into the workflow for our users. And users, as we talked about in the CUBE conversation typically data analysts, data scientists. So with respect to AI, how do you think it's going to transform their daily lives of those data practitioners? It's a great question and it's something we think a lot about. And a lot of people are wondering, our vision for this we like to say is very pro-human. We are bullish on humans. And I think folks sometimes picture this world of AI as like, you know, we'll replace all the data practitioners with some black box AI that stakeholders will type a question into and just get answers. And I really don't think that's the ultimate destination or role here. We see AI as an amazing opportunity to help everyone be more creative, more insightful, do better work. You know, data work is fundamentally creative. I think, you know, you don't often don't think of data practitioners as creatives, but if you look at kind of what they do, it's like you're forming a hypothesis. You're testing an idea. You're, you know, telling a story. You're even sometimes building beautiful art with, you know, visualizations and charts. And I think that is a art, I think it is a science and I think it is a great use of human time. Data work is also full of a lot of TDM and drudgery. You're tracking down a missing dependency or you, you know, messed up a query formatting thing or you refactor a bunch of code. And like, you know, those are the necessary day-to-day tasks, but they're not what anyone really wants to be spending all their time on. And I think AI is this amazing opportunity to sort of liberate people from a lot of that, that TDM, that sort of grunt work, and focus more on the things that are most fulfilling to us as humans, which is engaging with ideas, being creative and telling stories. And that's really, I think the ultimate potential of AI for data practitioners. And that's certainly the vision we're building on and executing on right now. I think that's such an important message, Barry, to get out there because there's a lot of, you need to turn on the local news or the national news. And there's a lot of negativity about AI. There's a lot of fear. I think there's a lot of misinformation, but there's also not a lot of awareness for the potential and the impact. We talked about freeing the practitioners, the data practitioners to be creative, to be able to be scientific. So it's such an important message to get across. Walk us through how HEX is approaching the transformation of leveraging AI so that these data practitioners can really transform the way that they work, be more collaborative, be more productive. Yeah, well, I agree. By the way, with what you were saying, there's a lot of fear, a lot of angst. And for good reason, right? Like I think our media over the last century is rich with stories and sci-fi about AI gone wrong. And I do think there are potentials for that, but I am an optimist. And I think at HEX, we are optimists. And as I said, we're sort of pro-human in this. And the way we've approached this is trying to bring these features into the workflows that people are using every day to make them feel more confident, more ambitious, more efficient. We, our first version of our magic features that we built into HEX that are these AI-powered assistant features. You know, they live right where you're doing your work. You're editing some code, you can quickly invoke it and ask it to help you edit something or generate new code or explain or debug something for you. You can ask it to automate tasks that you're trying to do as you're working through your project. These are things that help people save time, sure. But I think one of the examples I was talking with a customer user about recently was, they were saying that it helped them be more ambitious. Like they were able to take on the types of projects and things that they weren't really thinking about doing before. They're able to approach things in a much bigger way than they might have thought that they could before. They're able to take on questions and try to answer questions that maybe felt unknowable previously. That is really cool. And I think that is an example of how AI can actually augment and accelerate human insight and not replace it. And so we're all in on that concept. I don't think I would want to live in a future and I certainly wouldn't want to build a product that tried to advance the future where data folks aren't able to do those fundamentally creative human tasks. We want to build wonderful tools for those people augmented by AI, not replacing with AI. Right. I love that you said ambition that a customer articulated to you. That must have been, I can imagine a surprise when we talk about customer benefits. We don't usually talk about ambition but I think what that says to me is that the customer has confidence in its technology giving them the ability to do so much more, be free to do other projects but also to be exploratory. And that's an important, that's not a soft benefit. I think that's a pretty hardcore important benefit that organizations can get. What about for the non-technical users? We talked about data scientists, data analysts. What about the business analysts and the business folks as well? Yeah. We've always been really, really interested in this population of folks. We call them analytically technical. You think about all the millions and millions of people every day who work with data but don't have data in their job title, right? Like these folks don't write code. Maybe they can write a little SQL but this is not a big part of their day-to-day workflow. They're certainly not writing hardcore Python all day. I think this is a really important population. And a lot of those folks are doing some work in Excel, maybe clicking around in the dashboard. We're really, really interested in ways to help them be more creative and confident as you said to help them ask and answer questions. I think there's some nuance there where I don't think it's now everyone's a data scientist. I actually think that high-end data practitioners, folks who are really ensconced in things like statistical methods and data modeling, they're always going to have a really, really big role in organizations. I think more and more their role can be partnering and enabling a bigger population of people. I think those folks will have more leverage and more impact because of this. And we see this already. I've been a data practitioner my whole career but I don't write a lot of code day-to-day anymore. And even when I have questions about our business, I go into Hex. I immediately reach for the magic generate feature to start writing queries for me. And it helps me both work quicker but there's questions I have that I'm like, man, I don't know how I would go about answering that right now. I'm not spending my day writing queries anymore. And sort of as someone who's become a business user over time for better or worse, I find these really useful. I find they give me more confidence and more leverage and more ability to go and investigate things myself. And I hope we can help advance a future where every curious person, every analytically capable person in an organization has that same power. Curiosity is such an important trait, I think, for when organizations are hiring. And like I said, it's not soft. It might be called a soft skill, but it's really not. But- It's tough to quantify the sales. Yes, yes. It has to be like, we'll make your people 20% more curious, which will contribute to your bottom line in this way. But I find myself when I talk about this stuff, even with customer prospects, talking about things like creativity and curiosity and insight. And it feels soft, but I think that if you kind of back up and you think about it, isn't that what we hire humans to do? Like, I don't know. I think certainly a lot of humans do wrote tasks all day, but those are the least fulfilling parts of our day. Like, think about the things that excite you. Think about the things that when you wake up every morning, are you excited to dig into this stuff in front of you, or are you dreading it? I think it correlates really, really strongly with the amount of curiosity and creativity you're gonna be able to apply to those tasks. And my biggest ambition and hope for this AI-enabled future, both for what we're doing and for society at large, is it helps humans spend more of our time on those things. I do think there's value there. It might be hard to quantify, but I think it's very valuable. Absolutely, I agree. One of the things when you and I spoke before, we were talking about this explosion of tools that are impacting the data practitioners that are leading to fragmentation. Talk about some of that and how hex is different. Yeah, well, people, you know, if you look within the average organization when I talk to our customers, prospects, they've got maybe dozens of different data tools floating around to do things. And even an individual user, individual data practitioner, they might jump between four different separate interfaces to do one workflow. They might write a query in one place, might drop that data down into a CSV and to push it into a Jupyter notebook, Python notebook type thing, they're doing some processing there and then they get some data that's worth using and back to a CSV in the spreadsheet and push that up into a dashboarding tool and then they publish that. And then, you know, someone wants to go and pivot the data in a different way and so they download it into a CSV again and do a pivot chart in a spreadsheet. And, you know, that gets the job done, but it's not very efficient. It's not very collaborative. None of these things are really built for the cloud and cloud data scale. And I think they wind up really holding people back. And so we always felt like that was something that was sort of obviously broken and we were approached with Hex trying to build sort of more integrated end-to-end workflows having to jump around less. And again, I think that's really valuable for individuals. You save time, you're more efficient. You can sort of trace back the lineage and reproduce your work all the way from the end through the beginning, which is I think an underrated thing about data work. But it also makes collaboration much easier. You have all these things in one place. You're not hunting down which version of it in which tools connected to which thing. Teams can work together much more efficiently and effectively. So I wouldn't say with Hex, we're trying to be everything to everyone. And certainly I don't think we're, you know, in the future, every organization only has one data tool and it's Hex. I think there will always be different things that suit themselves well to different tasks. But I think for most analytics things that people are tackling in a given day, we can do much better. And that's sort of a big interest area for us at Hex. But definitely consolidation and really helping organizations deal with the fragmentation that's been around for a while. Yeah, absolutely. And I do think we've seen this sort of Cambrian explosion in the last few years. I think some of that is because you saw a big shift further down the stack. So the advent of cloud data warehouses. So Redshift is really the first big one on AWS. And that really enabled, you know, a different pattern within organizations of how they were bringing data together. And then great, that adoption started. And then there were sort of opportunities to build new generations of data tools on top of that. And so you have seen this sort of proliferation of things. And I do think as things go, we're going to be in a more consolidated recycle. And we certainly think that one of the value pops of Hex being able to bring people together, bringing workflows together, being able to actually save our customers money to, by the way, by, you know, instead of four tools, maybe you have two or one is pretty compelling right now. So we're definitely seeing that in the market. Talk about AI as a problem solver to help an aid getting rid of the fragmentation and really making things much more consolidated and collaborative. Yeah, well, you know, I think there's a real benefit there when I talk about, we talk about bringing different levels of technicality and different types of people together. If you can use AI to sort of augment and accelerate, you know, we talk sometimes with Hex so having this like low floor, high ceiling, if you can sort of augment and make it even easier for people to access and participate in these workflows, or even things like as a stakeholder, being able to more quickly access the insights that teams have produced. I think it can be a big accelerant for this. And I think we can help sort of level that playing field and remove the necessity of having a bunch of different fragmented tools. At the same time, I think, you know, AI is definitely at a crescendo hype cycle-wise and we're investing a lot. We are big believers in it. I do think that it's easy to underrate these days though, the value of just having nice, intuitive, well-built user interfaces that are old school product engineering, not necessarily AI. And I think incorporating AI in those interfaces and products in a really seamless way is a big unlock because you want to bring that sort of consolidatory effect together. Can AI be a fabric between the different tools that organizations have and that as a facilitator of unlocking data value? I think we'll see some of that. I would think of it, there's a couple of dimensions that I'm really curious to see unfold. And some of these are things that are like in our remit and others are just things I'm curious to see how they happen. One thing is like, how do software products talk to each other? There's like an interesting thing to think about here. Like the last 10 years has been very dominated by APIs. Like, you know, almost every product has an API. It's how that is definitionally how software talks to each other. What does that look like in the future? There's a world where APIs are more important in a certain AI driven future where you can, but there's also a world where that sort of actually starts to change where products might start publishing API definitions that are actually really useful specifically tailored for API or for, excuse me, for AIs to be able to interpret and understand and work with. And it may change the way we think about how software products can work together where you as a human may not even need to go and write the integration between two APIs. You may be able to point one product at another product and say, Hey, just go talk to this thing. It may go show up to it and say, Hey, okay. Here's, I can kind of see how this thing works from the spec that's been published and can just start programming against it. That might sound a little sci-fi, but I think that's actually like really reasonable. In the data space, there is a lot of integration. There's integration between source systems and storage. There's integration between storage and transformation tools. There's integration between all those things and application layer visualization and analysis tools like HEX. And so I think there's going to be a lot of interesting things that'll come of AI in terms of how products talk to each other. I also think that it will make it easier to be able to search and work across a bunch of different types of data and data stores. That is the type of thing that's very hard today. If you have a bunch of unstructured data in one place and a bunch of structured data in another place, that's difficult. Most enterprises actually have many, many versions of those sort of different storage heuristics. They may have multiple data lakes and multiple data warehouses. I think AI can play a big role in how you can search for insight across all of those different things. So there's a ton of potential here. And I think it is a very, very exciting time to be a builder and explorer in this space because there's a ton of potential and we're really just on the tip of the iceberg right now. Exactly, I agree. Last question for you. You mentioned some customer feedback that I loved about customers being more ambitious and I added more confidence. Is there a customer story that HEX has that you think really just shines a light on the clear value prop that you're delivering? Yeah, I mean, there's one I'm very proud of. We have a large financial services company that I caught up with their head of data about a year into them using HEX. And I was asking about how their team was finding it and he said something to me that I will probably always remember, which is we are better partners for the business because of HEX. And that really stood out to me because at the end of the day that is why organizations invest in data storage and data infrastructure and data teams is because they want to help make better data-driven decisions and the data teams, I think one challenge that a lot of data teams have is understanding their purpose in place and that fundamentally they are there to serve and provide insight and help inform decisions for the business. And that is something that I think every organization and every data team aspires to but is really hard in practice and the HEX product is really built around trying to accelerate that. So hearing that story, they have hundreds of data practitioners using HEX every day. They have thousands of stakeholders within the York who consume insights that are published via HEX. And that affect that outcome of being better partners for the business I thought was something that I'll always stick with me and I shared it with our team as sort of an example like this is the thing that we're really here to help accomplish and felt really great. So I hope to have many, many more stories like that as we go along. I have a feeling you will Barry. It's been so great having you back on theCUBE as part of the AWS startup showcase analytics and cost optimization. Talking about HEX, what you're enabling organizations to unlock getting rid of the fragmentation, collaboration, ambition, confidence. We really appreciate your time. Great being here. Thanks for having me. Our pleasure. We want to thank you for watching and remind you to keep it right here for more action on theCUBE. You're a leader in high tech coverage.