 Hello, and welcome back everyone to theCUBE's live coverage of Teradata Possible. I'm your host, Rebecca Knight, along with my co-host and analyst, Rob Streche. We are joined by Eduardo Castner. He is the chief data officer in GM for data and AI for the software and digital platform division at Microsoft. That's a long title, sir. Welcome Eduardo. And Daniel Sperling, he is the SVP product engineering at Teradata. Thank you so much. Thank you, great to be here. So, before the cameras are rolling, I can feel you got your buddies. But I heard that you were frenemies, so tell us a little bit about how the two of you met and how you started working together. Yeah, Teradata has been working with Microsoft for many, many years. We've had products on Azure Cloud. We've also launched some new products on Azure Cloud, but it wasn't until, like, I was pushing the envelope with some of the team members at Microsoft, and they're like, come in and talk with us. So, I went up to Redmond, spent a little time at Redmond, and as I walked in, I sat down and some guy popped up on the screen, and that guy was Eduardo. Yeah, yeah, it was awesome because we had this meeting which we started with, is it going to be a meet and greet? And it was literally just a meet and greet. And I started saying, Dan, it's a pleasure to meet you, but I don't want to meet you or greet you. I can't believe we're doing this. It's mind-blowing. We've had this relationship for years. We've launched products. We're together driving impact for customers. Why are we calling this a meet and greet? It makes no sense. How about you let me show you a demo? Yeah, and I mean, he knows, I walked in with a full agenda. I was like, okay, here's the stuff we want, here's some ideas. And he's like, okay, I'm glad you have that stuff. Hold on. I'm like, hold on. And he's like, what's going on with this guy? And I'm saying, hold on. So, I called one of my team members who's an incredible machine learning specialist and an AI expert. And I said, Alex, roll, roll the video. And so, literally, he showed a demo of a system where you ask a question, plain English. Show me the products, how they sold, the relationship, visualize it, et cetera, whatever. Immediately, it shows you the query. Then it shows you the result of the query and the visualization. And it was all on Teradata's infrastructure. And then Dan said, why are we sitting here? Like, let's go, we've got a word to do. And the thing about it was, Alex built, was built around using a lot of the open stuff that was online, the APIs that we have published online, the systems that we have online. And he built it, like you were talking about, built it against Teradata, but he built it on all the open stuff. And I think that for me, looking at that and saying, wait a minute, if he did this all on everything that's open, imagine if we opened up our internal engineering and Microsoft's engineering together and then went and built something. And that was where we really kind of spawned what we are launching here, which is our Ask.ai co-pilot for Teradata. Plus, I was a little improper and I said, hey Dan, we build this over the weekend. So imagine what happens if we put your engineering team and my engineering team to do this in a month. And initially we said a month. I don't know if we both believed it or not, but we said, is this for real? And we were like, yeah, let's just put a month. Let's build it in a month. And the reality of what was possible was still unknown. It was still so early, certainly for Ask.ai Teradata, it was still so early that we were saying like we were caveatting it, right? Okay, yeah, we'll do a month, but we will do it in a rapid prototype, rapid POC, and then move it very quickly as we see the value, as we see the opportunity. And we kept unblocking hurdle after hurdle after hurdle and kept working together and doing more and led to a formal announcement of a product on Teradata, something that we did, like you said, in a very, very rapid time. It was literally six weeks. But there's some stuff that's really worth to go deeper into it. See, everybody that's talking about Generative AI has some five real concerns, which of course, Teradata also had, and three misconceptions. Now, that Teradata didn't have. The misconceptions are you can do everything with GenAI. So that's it. Shiny new object, that's all you need. That's not true. You need an application around it. You need other types of AI and you need a whole bunch of really good handling and managing of the data. The second thing is that because you can do a sprint fast, you can release it to production fast. And that is not true. So this is the five things that we had to address really well. It was privacy, security and compliance, performance, cost management, and the most important one that the system is reliable. That the questions that you ask of it are actually what you're expecting to hear back. And so we actually addressed each one of those in a sequential way through the six weeks. And we met all the requirements of the full Teradata production development cycle, which was incredible. And hence we were now announcing ask.ai. Yeah, yeah. And, I mean, Microsoft basically kicked all of this GenAI off, I mean, open AI did, but with Microsoft, really, how do you see that Microsoft really just got out in front of the market on that? How did that really get to market first? And I think you're right. I think having, you know, I call them SLMs, you know, segmented language models. So hey, very specific. How does that all play together with what you guys are producing now? It's a really, I mean, once you know the answer to that, it's a really long answer. So I'll give you the very short version. Like the super short version. We've been at this for some people say 14 years, at least building the data centers with the GPUs and getting ready because we were doing cognitive search, we were doing cognitive vision, we were doing cognitive speech, we were doing synthesis and summarization, we were doing sentiment analysis. Way before this became next shiny object and GenAI and et cetera. So all of a sudden, when open AI needs a place to train the models, nobody else had the data centers and the chips and you can't just go build them in six months. I mean, we all know there's a shortage of it, but even if you had them, even if you had all the chips, you can't just set those data centers up in a minute. And so we had, by any reason that you want vision and look and inspiration and correct execution, we had 14 years before this. So when you got to this moment, it was a perfect combination of having the space, having the other components needed, and then here comes natural language, right? With GenAI and with open AI, summarization as the component to unlock the key to all this. And that is the reason why we got in front, like you said, but more importantly than in front, we got in front in what I care or dare say as an enterprise great solution because of what I just said, privacy, security, compliance, performance management, cost management. It's all things that maybe you don't talk about and they may not be sexy, but if you want to release something to production, you can't live without them. So the fact that Microsoft is so far out in front of the competition, how has this fueled the partnership with Teradata? I mean, as Jacqueline Woods was just on here saying, we're really at an inflection point. So you've been doing this for a long time now, but maybe sort of the rest of the business world has caught up and a lot of them are saying, okay, so where do we go next? I think at the macro, Teradata and Microsoft have worked together for many, many, many years. Like even before cloud, even before Azure, Teradata launched a product on Windows. And so we've looked at the partnership as something that we enabled, but I think that we lost focus collectively, we lost focus on how do we be better together? Part of it was candidly, you were talking a little bit of the frenemies in the beginning. Part of it was the fact that we have in some would say competing products, but the reality is our products serve different spaces. And I think that when we actually started saying, wait a minute, we can build solutions together that serve like the entirety of the customer markets that we're going after is when we started being a little bit more open, both of us being more open on what do we build together? What's the future together? And as we've had more and more success, it has snowballed further and further. So I think the overarching partnership has just grown and we've built a lot of trust. We've got a lot that we're working on right now that we cannot wait to talk about in the future because we're seeing that this opportunity that we're talking about here with our launch of Vantage Colleg on Azure that kind of snowballed into our launch of a generative AI solution is snowballing into many more things. And so the partnership is absolutely just taken off and growing and we're continuing to be excited about what we're going to keep doing together. Yeah, but take a moment to think about if we released Ask AI and this break record speed and incredible innovation, it showcases two things. It's all about people. And Dan's team is world class. And the fair data team that is building their product is incredibly agile. So when my team, which this is the way we operate, we were testing the waters. We were like, does it resonate? Do we have a partner on the other side that we can build something incredible and super fast speed and drive tremendous innovation? All those sound fun, sound like good words, buzz words. All of a sudden you meet another team that's running at your same speed and you achieve it. And then they're not buzz words anymore. And so I think that it was really the incredible opportunity of finding two teams that are running at that breakneck speed and want to drive value and demonstrating it on a very short term. You make it sound like Kismet that these two teams that were both so innovative, so motivated, so engaged in solving these problems, working together and then coming together. Is it possible for other companies to do it? I mean, it sounds so almost magical. We're certainly not in this space. We've got this one locked out, yeah, that's not. Don't even try. Yeah, exactly, exactly. What do you think of the Kismet? Yeah, but it starts with the attitude that Dan brings to the table, the attitude that I bring of let's talk about what we can't achieve less and let's talk a lot more about what we can achieve. And so it is that attitude that unlocks because, of course, the more people that jump into this type of behavior in this era, because a lot of people are saying what could be possible, what is next? Well, what is next is incredible teams collaborating versus before it was a lot of it, you got to go my way or their way. It's my silo, your silo, maybe we'll meet. Exactly, and maybe there's an interface somewhere in the middle, right? And we can mock up something so the customers can get some back door, but this is not that. This is a true open collaboration that is incredibly fast and incredibly transparent. And building on that, I think that we both, you've seen Microsoft transition, Teradata has an entirely new executive leadership team. I came in about three years ago as a part of that change. We've seen two companies that are working on shifting their culture to say the industry is moving at a pace that is hard for anybody to keep up with. Our culture has to embrace conscious and appropriate risk taking, embrace conscious and intentional, like trying something out. And if it fails, then you learn very quickly and you move on. Like both of our cultures are moving in that direction. So when we came together, we brought similar mindsets together around, wait, we're going to try this out. And if it doesn't work, we're not going to beat each other up. We're going to say like, okay, we just learned something and what's the next thing we're going to do? And what else can we do? But you know, the question that you asked also seemed to me as in, can other technologies do the same thing? I'd like to change that question for a second and say, why not, you know, airlines and manufacturing and energy and government? And yes, and when they work in this innovative way and this deep collaborative way, then we all benefit tremendously by products, right? And by services that are differentiated than the ones we have today. So yeah, I truly believe so. Trust me, we all wish that the government worked better and better together. Let's put it that way. But I think that part of it is, it's about the customers, right? And you kind of hit on that. And they're always concerned about security and quality of data and being able to get at things. I mean, again, having the ask.ai and the co-pilot like experience for that. So I don't have to have a data analyst. What are you hearing from customers and why are they excited about this? Well, I would just correct one part. And I'm sorry I said correct, but I mean it. One part, I have experience through my team and with my team about 180 deployments across tech companies. And I think that's the biggest number you're going to hear from almost anybody in the industry. And I have yet to see that this technology replaces. I have always seen that this technology augments in the sense that everybody starts with, okay, so could I do with less of X, people, investments, costs, and as soon as they kick it off, they're like, wait, I can do triple. So it shifts the mentality from I was in here to reduce, but now I can actually achieve what I needed and the growth that I required. So it unlocks a completely different chip that goes from cost savings and reduction to increase in capacity, growth, and expansion. Productivity, yeah, makes sense. I mean, I wish I would say that to you as a I guess or a research, I have 180 examples, this being just one of them. So it is very tangible. And I think that that's super exciting, but I missed the question. So why are the customers excited about this partnership? What's really got them excited about it? I think the answer was in your question. There's very few companies that can offer the platform for data, right? The platform for analytics that Teradata can provide. There's very few companies that can provide the platform for cloud that Azure provides. Meeting security, meeting compliance, meeting performance, meeting location and requirements. I could keep going. But it is an incredible combination of two companies that actually check all the boxes and have been checking the boxes with a ton of our customers across all segments for years now. And so it is that combination of two very strong engineering innovative companies that achieve it. And you couple that with that Teradata has been in the AI space for over a decade. We made acquisitions that really got us in over a decade ago. We refined those. We're not just going out and now trying to go buy an AI company to get into the AI game. We have played deep in the space. And with that focus, we have said we will enable customers to be able to build the most complex, massive at scale AI solutions. It's really been machine learning focused, right? We will label them to build that. But the reality is up until we really started as a company going focused and all in on cloud, it took companies a lot. You had to be, you had to have almost a PhD in Teradata. You had to implement this giant system. You had to know what you had to try it. You know, it was a lot of work. The partnership between Microsoft and Teradata allows customers to gain exactly what Juwardo has said, access to that massively scalable AI engine, but do so in a world that is all about simplicity and rapid access to innovation, rapid access to trying something out, rapid access to POCs as well as to production level, production grade services. But in a more easy to consume, more rapidly consumable way. So really the partnership for us and showing that we are truly focused, like you were talking about, focused on innovation together, is around giving customers massive scale, but in an easy to consume way in a way that lets them experiment and try without having massive cost barriers or massive implementation barriers. So we're almost out of time, but there's a lot of people who are watching or listening to this just wondering, okay, what do I do? Tell me, Chief Data Officer, tell me, SVP of Engineering, what did I do? What's your one piece of advice? My one piece of advice, and I bet we're going to have the same, start now. Like do not go and look at, like you were talking about the difficulty with government. Don't treat it like government, don't treat it like healthcare, don't treat it like some unaccomplishable thing. Instead, take off one little bite and say, what would a proof of concept be? Leveraging solutions today. And then learn and grow. It's exactly what we did. It's grab one scenario, push it all the way to production and set a date that's super aggressive. So say I'm going to achieve this one scenario, I know you won 15 or this one could have been better or that one, no regrets. Just grab one, push it all the way to production and once you do that, you will understand what this technology can do, even more important what it cannot do. And then you dispel all the concerns that you have about the long list of, these are all the things that would stop me. Well, dispel them with one important scenario. And after that, trust me, the 15, 20, 30, 60, they'll just fall in place. Eduardo Daniel, thank you so much for coming on theCUBE. Thank you, Ashley. Thank you, appreciate it. I'm Rebecca Knight for Robstretch A. Stay tuned for more of theCUBE's live coverage of Teradata Possible.