 Welcome back everyone to theCUBE's live coverage here in San Francisco for Google Next 2023. I'm John Furrier, your host, Rob Streche is with me. He's leading the CUBE analyst team. We have CUBE team coverage. Lisa Martin is here. Dustin Kirkland, contributing analyst. And our applause are out getting all the data for you and sharing that we're here live. It's been a third day. It's been a long, long couple of days where I have lots to unpack, been fun. A lot of great content. It's all about the data. AI has been all about data from day one. Now you start to see the jewels kind of flow out in the marketplace or the flowers starting to grow in the model garden that's coming from Google. Out of great cash here that goes back into the DNA of the data days in the CUBE. We've got Adam Wilson, Senior Vice President, General Manager of Alteryx, Analytics Cloud. Great to see you. Great to be here. Thanks for having me back again. We normally see you at trifecta, which we covered in depth, not bottom, Alteryx. First of all, great to see you. So trifecta joined Alteryx. Now you're running Alteryx Cloud. Take us through what you're doing right now. The changes with trifecta, Alteryx, and what your role is. Yeah, yeah. So about a year and a half ago, Alteryx acquired trifecta, and really trifecta's become the platform foundation for the entire Alteryx Analytics Cloud. So now everything that you're trying to do around analytics automation, around data quality, around prep and blend, around location intelligence, machine learning, AI, all of that now plugs into the trifecta infrastructure and gives everybody an integrated experience that allows them to complete their end-to-end journey. So it's exciting for us, coming from a startup environment, to really be part of a much bigger platform play, and to be able to tap into the 8,000 logos that use Alteryx today, and really the almost half a million users that are raving Alteryx fans, and we get to be a big part of bringing Cloud to all of them. And great success IPO for Alteryx. We've been around for the big data movement. You know, the big data, we go back to 2010 when we started theCUBE 13 years ago. It's been a great run to see the historical role. And we've been kind of waiting for that AI moment that's happening now, but there's been a lot of iterations around the data, the new data industry and old school analytics to kind of like cloud-based next generation cloud, data planes, control planes. Yeah, what's the big thing people should look at right now that I want to get a sense of where they are with data, where the market is, how they tie in their either legacy or new projects with data? What's the current state of the position of data? Yeah, yeah, yeah. Well, I think it's really interesting when we were chatting a little bit about this before the show, you know, there's a lot of discussion about what's going on at the infrastructure level, you know, GPUs and everything else that is going on there. And then there's also a lot of discussion about what's happening, you know, with the modeling and with the algorithms. But at the end of the day, if your data quality is bad, then your AI is probably worthless. And so I think everyone can't lose sight of the importance of data now more than ever before. And hopefully this creates a burning platform for organizations to start thinking about, like, not only how do I stitch my data together and harmonize it in the right ways that are going to allow me to, in the generative moment, like start to really tailor or customize and extend these foundation models, but how do I really kind of re-approach, you know, data quality in a meaningful way that's going to ensure that I'm getting, you know, good analytics and good predictions and good models and things that are coming out the other side. And I think you just can't lose sight of that piece in the middle, which is where a ton of heavy lifting needs to occur. And ultimately where I think a lot of this is going to be won or lost. That's a great point. I want to ask you the next question, which is, okay, in the past six months, so much has happened, you go back to last Thanksgiving, you know, Thanksgiving table last year was, you know, if you were a nerd, you're talking about open AI, if you were, and you got it in January. A couple months later, but look at just what's happened in the past six months. We'd like to say Google Cloud, AWS, Microsoft. You kind of starting to see what's happening, what's out in the landscape for the market. There's going to be a lot of change. And so the operational impact will be huge for businesses. I mean, literally we think, and we've been saying in research on theCUBE, that the operations resets that need to go on while they're just trying to do digital transformation. So you got this innovator's opportunity and dilemma where, okay, I know I got to get to AI, it's going to come from the organic, it's going to come from the top, but I'm also in the middle of digital transformation anyway. Now I got AI, this is kind of causing some confusion. How would you clear that up as you look at the landscape, you guys, with your SaaS platform, you guys are in the cloud? Yeah, for sure. What's those conversations like? How should customers be thinking about that middle of transformation? And now I got the new architecture, I may have to rethink ops. Well, I think the good news is that a lot of that new infrastructure has built for some of the scale and agility that is going to leverage or be a sort of a necessary ingredient to what people are going to try to do with AI and ML. And so in a lot of the technology companies like Altrux are really embracing that and we're trying to provide a responsible approach to it too that addresses some of the concerns that come out of the Chief Information Security Officer or that come along with some of the governance questions that people have. I think there's a lot of excitement, there's a lot of energy, but there's also probably now emerging among the large enterprise is an equal amount of concern about things like vicarious liability and what is this going to mean if we start automating decisions now based on a lot of these algorithms and how do we do that responsibly? And so I think our approach has been to really take what's going on sort of in the wild and start to say like, hey for years we've connected to sort of all of the enterprise data. We know how to help extend and tailor a lot of what's going on in these foundation models. We can do it because of our split plane architecture where we have a control plane and we have a data plane. We can let you do that in your VPC so that it is secure and protected and then we can make that available to your users so that now you can start to democratize kind of even more fully with confidence. And I think that's always been part of our ethos and this is just a continuation of that same thing. It's funny, it's almost like everything's going enterprise not consumer. The data is an enterprise challenge. And I think that's the interesting thing because they have so many products here and I think one of the things and again what we were talking about before we got on is that that connective tissue between I land the data somewhere, I get it somewhere, I have to either ELT or ETL it to do that to make it usable. And that piece is a big part of what's been missing but you've been working with Google for a while on that like through a Google prep perspective which I found very interesting because you don't have a lot of partners that come on who actually have a co-branded product with them and you had some announcements as well too. Yeah well and so really the first time in the history the relationship that was established with Google around Google data prep was the first time in the history of Google Cloud that they'd ever put their brand on a third party technology. So we were quite honored to be selected as that solution. Now you have thousands of users, thousands of logos that are leveraging the technology. And a lot of it was I think a real desire to figure out like how do we, given all the infrastructure, given the databases, the engines, the APIs, all the great things that Google does, how do we create simple, powerful experiences that help people who are not necessarily structured programmers start building data products on their own and really starting to think about letting the people who know the data best do more of this work. And so we've now been in market with Google for almost six years with this product and today or yesterday at the show we announced an amplification of that relationship around Looker Data Studio. And so now there'll actually be a freemium version of Designer Cloud, which is the ultra solution for prepping and blending data that is going to be made available to Looker Data Studio customers so that they can connect to a broader variety of sources, things like Excel, which is very, very prevalent, but also that will allow them to structure, shape, blend, cleanse, transform data so that they can build reports and dashboards easier than ever before. And this is going to be something that will get into the hands of millions of monthly active users and we see this as completely transformational to what's going to be going on on the Looker side of the business. Yeah, and I think what it does is, and I'm saying that I've done some research on the data product side and data product management, what's coming in data engineering. We have kind of a talk track around actually the data developer and how the data developer is kind of a hybrid between the data engineer and data product manager and really building data. And I think this is where your product set seems to really hit, is how do you engineer the data, how do you build the data into a data product like you were saying. So it's usable as is. And I think that's where it's super interesting because I think a lot of people are saying, well, I just take all the data and I read it into my LLM and that's not to your point, that's garbage in, garbage out. We all know that from the OCS 101. I think, are you seeing that really the people and the processes are coming along and you're running into more data product managers and data engineering teams, is that like the persona that you're talking to a lot like every day? Yeah, absolutely. And I think what you're seeing from us is a lot of the heritage was going to the line of business, going to the end users who are data driven, data savvy professionals, but not necessarily structured programmers and we got started there. But then very quickly you realize that collaboration that needs to go on across technical and non-technical users. And so, Altrix as a platform has really evolved to be not just sort of no code, low code, but really code friendly. And making sure that that collaboration is facilitated because building out those data products now, most organizations see as the frontier for competition. They're going to compete on that as much as the goods and services that they make and provide. You know, Adam, I wrote up the comment earlier about enterprises being kind of complicated and consumers easier and everyone's consumerizing products. With the iPhone for this, if you think about what AI is doing, the demos they show here, the duet, it's like multiple apps talking first party data to each other. There's a complicated enterprise data architecture that needs to be in place to handle the addressability of the data and to manage the compliance and the legal aspects of it. So that used to be a blocker. But if you can build that in and let the data be viewed by the janitor and the CEO and the CFO, without any kind of data set changes, that's a game changer. And that's kind of where the market's going. So how do you guys fit into that kind of scenario? Because we're going to see more of that. Apps having native hooks into data. I guess called by code. Code just calls data and says, give me the data. I don't really want to get in the weeds on policy. It's already been set up or enabled. That's right. Well, and I think this is where the idea of a trusted platform becomes really, really important, right? Because you're going to go back to the technology providers that have been working with you for, in some cases, decades. And you're going to be saying like, listen, we're trying to figure out how to balance the excitement and the innovation that's happening right now with, again, doing it in a way that's kind of responsible and governed. Given that you're already the people helping me figure out my data challenges and figure out how to govern this stuff carefully, can you help me sort of jump into this in a way that I can feel sort of good about the direction that we're headed? And so a lot of these conversations in the last six months have just exploded within the customer base. They're coming back and saying, let's partner on this, because you're already touching all of the internal data and assets that we want to sort of bring to bear as we start thinking about LLMs and as we start thinking about Jenner. And I think as the proof in the pudding, as they say, shows can be visually looked at, oh, wow, that came from a net new use case because we changed some data things under the covers through UlcerX and others, and it's like, okay, cool. So that's going to be, I think, going to move the needle on the customer side. I have to ask you while you're here, because, again, in this market, when it's changed, it always got the advantage. You see some things, you guys have a good architecture. What's your secret sauce? I mean, you know, you can create market, you can get a good business model. Deputy's doing great. Technology, what's under the hood? What's the secret sauce of the UlcerX cloud? Now you've got the mashup with trifac to UlcerX. What's going on that you could share without giving away the secret recipe? Yeah, no, no, no, it's a great question. I mean, I think first and foremost, the thing that impressed me the most when I hit the door and we came over was just the raving fans that truly see what UlcerX has done for them is transformative to their careers. So again, these are people who forever were being told be more data-driven and they're like, great, I will be if you can give me the data in a form that I can use it. Don't just tell me to be more data-driven and then you tell me I have to wait six months for somebody to add a table to a data warehouse, right? So really that sense of empowerment and that sense of change to me, like that rich feedback and that interaction and that community that's really been built up around UlcerX is just mind-blowing. I went to a conference when I first started it and I was talking to one of the community members and as he was gesturing, I noticed that his fingernails were painted and I was like, you got to tell me of what's going on here. He said, these are the icons for my favorite tools inside of UlcerX. I've painted them on my fingernails. So it literally is like raving. Catch who's your next and look at the jackets. Raving, raving fans, which I think is amazing. But if you kind of move beyond just the greatness of the community and what that's allowed UlcerX to do, you know, I would say when you think about cloud, I think one of the things that we really see as differentiating is the fact that we are an independent platform that interoperate. You know, we're not tied to a specific security model. We're not tied to a specific storage architecture. We're not tied to a specific execution environment. We provide interoperability across all of these environments and we do it in a way that increasingly for large enterprises takes into consideration the fact that they want SAS-like agility and characteristics, but they want to make sure that they always can control their data and they know what's happening with their data at any time. And so that split plane architecture that allows us to have the authoring and the design time and a lot of the exploration going on kind of in the cloud where we maintain that and can upgrade that literally every week. And at the same time, their data assets are staying in their world and it controlled and governed by them. That is an architecture and doing that across clouds and across infrastructure and across engines a very complex problem to solve. But it ensures that we're a decision that customers make that guarantee their other decisions are changeable. And that is a very powerful place to occupy in their architecture. For the folks watching out there that aren't altruist customers, when do they know and they need you? What's the symptom or is it, what's the use case or what, I mean, I'm probably different vectors in there from a client, it was just a standpoint, but they have a lot of pain, they need growth, there's a more enablement, what's the core reason to call you guys up and engage with altruists. Yeah, I mean the simplest example, just to show you how he quickly and easily it starts is I'm doing things in a spreadsheet and I need to automate them. Like that alone, as much as we can talk about big data, as much as we can talk about big data, the reality is that the world runs on files and there's more small data than big data, right? And so what we find is that the minute they start bumping into that as a challenge then we can help automate those analytics for them. Certainly then the value gets increased and enhanced dramatically the minute you start now talking about, well, what if the diversity explodes? What if the complexity explodes? What if I need to collaborate? What if I need to start doing more of this in a metadata driven way, like I mean all those things, once IT gets involved and says no, no, no, now we need to govern it, like scaling it up. Those are all the things that eventually, as organizations mature, we get to and those become key differentiators for us. But in the beginning, it's often very, very simple situations where it's like, let me take an existing business process that often is running on files and let me try to automate that in a governable data-driven way where I can start to take some of the repetitive, complex tasks out of what we're doing and in a moment like right now where everybody's in more with less, more with less, being able to make the machines do that work and to do that automation is incredibly productive and saves a lot of money. Adam, thanks so much for coming. Great to see you again. Yeah, yeah. And the final minute we have left, give it a pitch, what's the pitch to the customers? Why alter it? Yeah, yeah, well I guess the one thing I would say is that as they're thinking about the data challenges that they face right now, now more than ever before, being able to go in and build these data products in a self-service way, but in a way that really provides governance is crucial. And I think that as we move into now a generative moment and as we start to see what's happening there, Altrix has introduced a lot of capabilities under the umbrella of Aden, which is our brand for AI, ML and generative that's allowing us to go tackle the other two thirds as well. So if you think about a third of the work is doing it, a third of the work is packaging it and a third of the work is communicating it, a lot of what's happening with generative is allowing us to now automate the process of actually generating the insights and making sure that that can be sent out to all the key stakeholders so you can spend more time getting on with the actual execution than spending time actually sort of mired in the data. Yeah, looking at the customers, looking at the front of the business, Adam, great to have you on, congratulations on the trifect connection with Altrix and good to see you on theCUBE. Adam Wilson, SVP in General Manager, Altrix Cloud. I'm John Furrier, Rob Stretcher. We'll be back with more in the closeout to show day three of three days of live CUBE coverage. We'll be right back, stay with us. All right.