 Welcome back, everyone, to theCUBE's live coverage of Teradata Possible here in Orlando, Florida. I'm your host, Rebecca Knight, along with my co-host and analyst, Rob Stratche. We have two illustrious guests joining us for this session. We have Jacqueline Woods. She is the CMO Chief Marketing Officer at Teradata. Welcome, Jacqueline. Thank you for having me. Welcome back to theCUBE, I should say. Thank you. And Vedad Akhan, he is the VP Data Science, Data Science and AI at Teradata. Thank you so much for coming on theCUBE. Thank you, Rebecca. So Jacqueline, you already did a great preview with Rob, so we already got a little bit of a sneak peek of what was going to be happening here at Teradata Possible. Why don't you start by telling our viewers a little bit about the Teradata approach to helping customers with AI, which is really the talk of the show. Thank you. I think it really starts with our why. And our why is that we believe that when empowered with better information, people thrive. And so the question is, how do you get to that information? And we know that AI is not just all the rage, but it's certainly the zeitgeist or topic of the day. But I think, as you know, it's something that's been around for more than half a century. But the question is, where do we go from here? Because clearly we are at an inflection point. So if you look at all the things that have happened in history, whether it's electricity, whether it was the car, whether it was the internet, whether it was mobile technology, that is where we are when it comes to AI, machine learning, and deep data and analytics. And that is Teradata's history. That is our heritage. We've been in it. We didn't just get to this party. We've been doing this for a long time and it is fundamental. And I would argue that we probably have one of the best products in the industry and that's called ClearScape Analytics. Yeah, so I think it's one of these things that AI is a journey. And it's kind of a destination too, but there's a path to get there. How is it really that you're keeping the AI solution secure? Because I think when people, it's one of the last thing we do a lot of different places with a lot of so-called data companies and security almost never comes up. How are you looking at it from a security perspective because like you said, for over 40 years now it's you've been keeping people's data secure some of the most financially sensitive data. Largest customers, governments, exactly. So how about, Ben Dot, how is it keeping it secure? Well, trust is very important in AI. As AI practitioner of more than two decades, I have done a lot of use cases and solved tough business problems. And as practitioners, we have to be very responsible in approaching how we use data for AI and ML models. And Teradata has strong governance in place. We have model ops that will monitor the performance and make sure that the data is well-governed and protected. That's how we approach it. And especially in the gen AI evolution, it is important more than ever to build that trusted AI ecosystem. Yeah, I mean it makes total sense because when you look at, you've been had the most sensitive data from the largest companies and I look at it and not everybody's going to go, nope, in fact, I don't think anybody should go build chat GPT all over again as a company. But if I wanted to go and have a model that services my HR department and helps me with all of my data or my retail and I want to have next best recommendation for my retail shoppers when they're in a particular market, that would seem like a focus on how you can keep it secure because that's what you've been doing for so long. Is that really the approach? And I think that's what we do today. And the one thing that I would say is I don't think many companies can say for the last two decades, we have been listed as one of the most ethical companies. So that's something that we've had for over 20 years. Our ethos as a company is to be not just trusted but also to be responsible. When we talk about AI, we usually say, we don't say responsible AI because as you know, when we spoke, I say people are the people who are responsible and can you trust the output that the responsible people are building? And I think that's critical. I do think that people are in some ways reticent about where can we go with AI and generative AI? And it's going to be incredibly important that leaders are the ones that are focused on governance, that are focused on compliance and that are ensuring the integrity of their systems. Our technology does what it's supposed to do in terms of ensuring that we have the integrity in our systems and to make sure that you can build an environment that's compliant and govern the way that you want it to be governed. So really the conversation, there's so much excitement on one hand about the potential for generative AI in organizations but yet as you said, there's a lot of concerns. I mean, you put up some very compelling statistics about concerns about security, privacy, governance. How do you talk with customers and partners about and balance these two things? Do you want to take that? Well, we have flexible open and connected ecosystem with our partners and that gives the options and possibilities to enable our customers to use the proven technologies or their preferred technologies and tools. And that's an amazing capability that we're bringing to the table to be able to give that open and connected ecosystem with proven partners and that's how we approach that. And one of the things that we say is that there is unlikely to be the company that becomes the gen AI company and that's the company that provides everything. I almost think that's nearly impossible when you think of all the use cases that can be thought about. How do you change work? How do you improve productivity? How will you use generative AI across your enterprise? And so thinking about it and understanding that you are a component, we have a platform that is what I would call an engine to enable AI. We have a platform that enables LLMs. We have a product that's in private preview now that's called ask.ai where you can kind of create your own environment that helps you build your own LLMs. All of those things are things that we do right now. But there's going to be, we're working with Microsoft, we're working with AWS, we're working with Google, we're working with open AI. This is, it's going to take a village to really bring these things to life in a way that's going to help businesses. Where do you see the future of AI within the enterprise going? Well, one of the biggest premises of GenAI is to democratize data, meaning that anyone in enterprises will be able to access the data and at any time and create visualizations. So that's why we approach this from a practitioner perspective and that's something I really appreciate at Teradata that starting from our product organizations we always approach it in a way that what will be the impactful use case for our clients. And that's where I see the immediate impact to democratize data for any stakeholder, business stakeholder in the organizations. Of course, we have to make sure that it is accurate and reliable. That's very critical. Yeah, no, that makes total sense and I think that again it's, you've been doing it for so long that people have built reporting, they have scripts on top and as they move to cloud, move that data cloud, it becomes easier for them to then pick up the other tooling around that to reuse that. You have a semantic layer built in, you have another number of other things. How do you see the role of data quality? Because to me it's, as was said on the stage during one of the keynotes, garbage in, garbage out kind of thing. How does data quality play a role and how does Teradata play with that? So you use what I would use as an AI practitioner, garbage in, garbage out. So, we talked about today on the stage about data harmonization and integration. AI, especially gen AI, require lots of quality data. Therefore, data harmonization and integration is the most important factor to enable value. And for example, at American Airlines, we integrated and harmonized data from 28 different business departments. Now, that number may seem small to anyone, but I do have airline experiences and employee as well as a consultant. I know how much data is generated in a large airline and integrating that and harmonizing that to drive business critical decision making is a huge differentiator. That's why we at Teradata has been ready for AI and ready for tomorrow as well. So Jacqueline, you listed off a bunch of the challenges that leaders face when they're trying to manage the data for AI. And you've also talked about the importance of use cases and how critical they are within the organization, within the enterprise, to point out, say, this is how we're using this. These are the business critical decisions we're making based on this. Can you walk us through a couple of examples that Teradata has helped customers overcome these challenges and what's most exciting to you? Yeah, what's really most exciting to me, Vada gave a couple of examples about American Airlines. I'll give you a couple ways to think about it, more from customer experience and thinking about what is it that people are actually trying to do. Everyone talks about being stickier. Everyone talks about how do you deepen customer engagement? How do you have more loyalty? How do you have more share of wallet? And we gave an example this afternoon where we actually showed the engine of Teradata actually helping with generating someone in the e-commerce site thinking about, well, if we've offered you this or you bought this product as an example, it was those boots. But what are the other things that would surround that purchase and how do you do a better job of cross-selling and up-selling? Now, when I think about that model as an example, I want personalization, but I want it within context. I happen to be allergic to wool. It just makes me itch. I'm sorry, some people think it's all fancy. I don't, but so I really, when something's recommended to me from a recommendation engine, I actually want them to understand that and have that information stored. There's certain information that I don't care if it's stored as long as it helps me better, have a better experience and more contextualized. So we will, our product will kind of gather that type of information. What other things have you purchased? What things go with, what it is that you want for this particular outdoor experience as an example, that's what we shared earlier today. Those are some of the things that actually give you a more contextualized, more personalized experience. So you're not just kind of, we've all been on websites where things are just popping up. We've all had chatbots where they call it a virtual assistant that's not assisting. And so the only way that you actually have a virtual assistant that really is assisting is when you have this deeper, more contextualized knowledge and for sure, once you have that better purchase experience it's faster, you feel better about the product and what do you do? You come back. They remember, I really like Kashmir. They remember, exactly, exactly. I want to add to that actually, to expand that we have clear scape analytics experience that is open to everyone today. And you don't have to be an AI practitioner or a data scientist to log in and use that because we have more than 80 real-world use cases, business use cases that are impactful right away with very clear instructions, business problem definition, the data and why it is valuable for businesses. Any data scientist, account executives, business stakeholders, developers, IT professionals, anyone can easily follow and practice how those AI and machine learning solutions are working in real-world cases. So I would recommend everyone to visit the clear scape analytics experience page, log in and explore all those 80 plus use cases. It's critical because I think the skill sets in data teams is, data teams are pretty small within these, even in these largest organizations a lot of times you need that and a lot of times, I think to that point they don't need necessarily AI, what they're really looking for is analytics and I think people sometimes get wrapped around the axle on the new words and things like that. I 100% agree with that. I mean, artificial intelligence is about picking up patterns and learning and trying to do something faster than you did before, trying to model something that you might have a hypothesis on. So I think that when you think fundamentally I start with what is the business problem that someone is trying to solve and it could just be I want to sell more golf balls. That could be the business, it could be that simple and should that be the problem then what is it that I need to know and how do I need to formulate my hypothesis on how to do that. So those are some of the things that I think that they're just simple problems but you can use data and analytics to help solve them. Yeah, no, I think that that is key. It's the simplicity of it has to be there and I think one of the other things as we get towards the end here that's really super important to the customers I've been talking to here and out at different user group meetings is really scalability and cost and I mean, Teradata has been again around, has a real positive legacy of being able to scale and do it cost effectively. How are you doing that now with cloud and AI in mind? Of course, again, to also connect it with your previous comment, scalability is very important and you know, as Jacqueline said, sometimes just doing descriptive analytics in terms of what happened in the past and why did it happen will provide a lot of value and we do have massive parallel process architecture for our clearscape analytics in database. Plus, we also have the predictive side machine learning algorithms that also run on parallel processing architecture. So that makes it very scalable. Sometimes, not sometimes, almost always when you have a very large data set for prediction, when you divide it into segments you have to run tens of thousands, sometimes millions of models. To be able to do that in parallel processing will immediately bring you results so that business shareholders do not have to wait for a long time and you can do what if scenario analysis. Now on top of that, every time you do a what if and run, you have to reuse the features that are going into those machine learning models and if you keep recreating them, it is going to cost a lot of extra cost basically. So we do have enterprise feature store so you can create those features only once and then however many iterations you have to go through, you can just reuse them. That's how we create minimum data movement, run the models in the database, use enterprise feature store, reuse them, do not incur extra cost and scalability because of massive parallel processing architecture. So you're not reinventing the wheel or not reinventing the wheel. Absolutely, but this is very unique. This is a very unique capability. It is unique and the couple last comments that I want to make on this topic because it's critically important. We grew up when we started, we were physically in a server so you had to optimize in a certain space that was a physical space. If you're born on the cloud, you can always fell over to more CPU, more this. You're not. It feels like infinite, right? It's infinite. You're not kind of born to optimize and optimize costs. That's how we were raised. So we have a cloud like product but our cloud like product is still optimized in terms of we want to use the minimum amount of resource to get the maximum amount of output. That's critical. The other thing that we have that really no one else has is workload management. So this ability to understand when you're optimizing your workloads, large versus small, et cetera, we do that better than anyone. All of those things keep your costs down. So we have the best price performance in the market today. Excellent. Great note to end on. Jacqueline Vida, thank you so much for coming on theCUBE. Thank you. Thank you for having us. Thank you. I'm Rebecca Knight for Rob Stretche. Stay tuned for more of theCUBE's live coverage of Teradata Possible.