 Hello everyone, welcome back to theCUBE's live coverage here in Las Vegas, I'm John Furrier, Dave Vellante. Your CUBE hosts, we're here for Sass Explore, their event where they're bringing AI to the table, bringing data, democratizing data, rolling out pragmatic AI, but also headroom for the future. You've got generative AI, a lot of great stuff here. You've got two great guests, we've got Jack Thompson, head of US Financial Services for Sass and Parish Patel, expert infrastructure engineer with Discover Financial Services. Gentlemen, large scale data is not something you guys are strangers to. You know all about it, we've done theCUBE before at Discover, a lot of data, Financial Services, Primo Prime Vertical that's leveraging AI, they're cutting edge anything they want, so welcome to theCUBE. Yes, thank you so much for having us, we're excited to be here. So obviously the billion dollar investment in Verticals, that's the top story that was coming into this event, highlighted here with all the demos and the positioning of this platform, developers on both sides of the equation, developers of data scientists, developers and analysts, kind of a developer theme built in. This is the theme here. Yeah, so Sass has always been great for developers as Parish knows and has experienced for years. We also see the value in investing in these vertical solutions for very complex problems like fraud, risk management, something that we've been speaking to our customers about all year is real time balance sheet management, asset liability management. So these things are emerging, and you spoke about AI as the AI hype cycle extends and as we see the reality of what AI can bring to the table, it's also important that you have governance scale and control and that's what Sass is bringing to the table. Parish has got a lot of background with the development side though, so maybe you can speak about that. Oh absolutely, means you started with lots of data, right, big data, volume of data. For financials that's very, very common theme, right? We do not deal with gigabytes, we deal with multi, multiple gigabytes. Also, Sass is the tool that at least what we have used at least in my experience, what I've seen is Sass easily scales in the processing of the data, whether it is megabytes of data, gigabytes of data, hundreds of gigabytes of data. Easily scalability is the main feature that we can expect from this tool. Take fraud detection for a moment if we can. So you go back, I don't know, 10, 12 years, it was like days, sometimes months, you know, you'd have to check your statement, it was really on you, and then it became much better, much more real-time, but a lot of false positives, and now that's getting better and better and better. Does gen AI change that, or is we on a sort of just a natural progression with AI? Was there any change with the AI heard around the world? Has there some kind of change? I mean, the thing about fraud is the second that you catch up to it, it evolves, right? And so during COVID, we saw this massive influx of synthetic identity fraud, and now with the emergence of AI, we've already seen automation of these fraud methodologies and new methodologies to create fraud utilizing this technology. So we have to stay ahead of that, and we have to be very conscious of it, right? So you got to fight the criminals with their same tools, and I think that's where AI comes in from the fraud perspective. It's so, follow-up question on the billion dollar, you know, investment. How do you adjudicate where that money goes, and how much goes to financial services, for instance? How do you and your colleagues sort of vie for that investment? Well, that's a great question. I think the guy you just talked to could have given you a better answer than me, but I would say that from a SaaS perspective, right, 44% of our 3.2 billion dollars in revenue is coming from financial services. We've just started to release our enterprise decisioning framework. Fraud is a huge part of that, and we're releasing new versions of our AML and fraud solutions at the end of the year. So I would say, you know, when it comes to fraud and enterprise risk management, those are at the top of the list so far as our solutions, and that's across not just financial services, but government, healthcare, et cetera. How are you making money with AI? What's the bottom line? Are you in the money business? You don't expect me to give you those answers, but basically on fraud, I can say, you know, depending on the tool of choice, whichever tool we use, whether it is one vendor against another vendor, the development cycle has been reduced with the introduction of AI, right? If the development cycle is reduced, we can have more visibility into the fraud patterns and certain such scenarios, and that is where SaaS also helps us, and it is a pattern where if you catch fraud, you lose less money, and that is how you prevent losing money. That is one way of increasing your revenue. Drops it right to the bottom line. But there are many, many more, right? There are many more, but you don't expect me to give you all those answers. One of the themes in the keynote, I wrote this down, make sure I don't get it wrong, was making sure that you get down, take down the barriers for companies to interact with their data. You mentioned scale, we've talked to a lot of people, whether it's observability data or any data, they don't even use a lot of it. They don't get to it, so that's one problem. So I want to get to that after I bring up this new role of platform engineering in the cloud. So you've got platform engineers becoming the new, I'd say alpha position in terms of setting up the data. And now you've got developers coming in, platform engineering is setting the table. So you've got scale, platform engineering, and now using the data. All those things are now top of mind of all the top conversations. How do you react to that? Right, right. See, scalability of data, right? That has always been our scalability of data, performance, visibility, monitoring, that have always been some challenging topics in terms of how industry views that thing, right? There is no single tool right now, but with the current recent offerings, we do see it as offering usage of multiple tools within their tool, right? As well as observability, monitoring, the performance tuning and all those things. It does offer those things. So let me be more specific. So in the security world, we saw shift left. You have people getting in, the developers getting guardrails and dealing with security. Data is kind of going down the same road. So what I saw today in the keynote was the same kind of vision, letting the developers get access to the data. Yeah, right. Okay, that is like a huge concept. And I think it's not new per se, but the scale and viability is new. So as financial services, how do you guys see that playing out? Because if I'm a developer, I'm going to be slinging APIs, slinging LLMs against LLMs and putting AI into applications. Well, I mean, everything that you're saying is great. And those are all the benefits of the cloud. And that's kind of the promise of what these financial services institutions have been going towards for years. But the reality is, you have to be very distinct in how you're creating governance structures around all the things you're talking about, right? So you talk about data engineers and platform engineers taking over and having an alpha position or power position, right? They have to understand the business context of the data that they're servicing so that you can actually get use out of it. And that's where we're uniquely positioned because we've always lived between a business application and the IT developer world. So it's very important that as you're building out this infrastructure of the future, that you're connecting those dots because we have seen so many false starts where people just load everything into a mesh or a lake and then you can't do anything with it, right? So we're trying to make sure that we tone that down a lot for financial services. And what's the best practices to get value out of the data in your opinion? What do you guys see? Because that's the fear. I don't want a data swamp, okay? I want a data layer access to horizontally scalable data in low latency. Yeah, yeah, yeah. What happens is people, there should be certain vision, right? People want to move to the cloud, but there are no strategies, no reasoning. First of all, it should have a reason. They should have a strategy, what you're doing. Lift and shift, you're doing refactoring and those effects. And then when you're giving access, right? Access should be given to the right people at the right level, right? And accessibility should be very, very simple. It cannot be, you're jumping through a lot of hoops as well as cloud. Sometimes it makes it easier. You've got IM roads, you've got certain security inbuilt. And then with the recent offerings with Kubernetes, many of the roles are predefined. So you are already managing the security, you are giving access to the right people and the right amount of access. That is very critical. You cannot be an obstacle for the data access, but you cannot also leave data open to anyone and everyone. So security is important. I would say from my macro perspective, I'm not a practitioner, right? But we've seen success when people think about the governance model first, as opposed to just building something and then trying to figure that out at the end, right? You have a point about just shoving it into a lake. I mean, you sort of have these two emerging models where one is very, I'll call it DBMS centric. Right. Okay, and that's not you. And then I would say maybe, can I say catalog slash governance centric, which kind of is you, right? But you're Lake agnostic, right? I mean, your data platform, data store agnostic, I guess I should say. So how do you see that playing out with data so distributed? You know, the cloud, yeah, still growing faster, but we're kind of reaching an equilibrium now. You're not just throwing everything into the cloud because many, for many reasons. So how do you see that all evolving and what role does SAS play? Well, I think that's a great question. So look, we've always been data agnostic, right? When there was relational databases, we were agnostic to that. When the Hadoop craze came around, we were agnostic to whatever flavor you wanted to pick out of that as well. So in the cloud it's the same thing, right? You're going to be storing data in multiple clouds, maybe in multiple software as a service solutions, multiple different cloud databases, right? SAS wants to be able to aggregate and pull all that data together for really analytical data prep. So before you get something into an operational decision, we are still the best vendor to get all that data ready for true analysis. You can store it wherever you want to, but when it comes time to make a decision, we're the vendor you can trust. So that's where we see ourselves. And one thing I'd like to add, right? SAS is not new to this. In the sense that it has been years I've been working with SAS, right? We have a flat file, we have Excel file, we have mainframe file, we have Unix file. SAS is the tool which makes that integration and getting that data very easy into the platform. And then you can do the analytics. Similar trend with the new one. So I'm not surprised that SAS is doing it. Any type of vendor, wherever your data is, SAS will be able to bring it up into one platform and then you can do the analytics. So it is known for that. It is not something new that they have to develop. They have already been doing that and I've been using that for a long time. Yeah. What's the cloud story with that discussion? Because SAS has been there, done that. Now you get cloud, next-gen cloud happening. You got applications with ecosystems. What's the cloud modernization story with this area? How would you describe where we are right now and what needs to happen next? For sure. I've seen, I'm talking from experience with so many clients I've worked with in my consulting career. People want to adapt and move to cloud, right? But I'm presenting later on in the day where I'm saying. Here, you're presenting here. Yeah, yeah, yeah. Okay. 430 today. What I'm saying is you need a strategy. First of all, you need a reason to move to cloud. It is just because everyone is moving, it's not a thing to do, right? You need a reason. You need either business reason, technical reasons. You must evaluate what the security concerns would be. Secondly, you need a strategy. And after that, once you move the data, you don't need data sources. You need processing power. You need security. Everything should be taken into consideration, right? And then, all these cloud providers, right? They are services. If you do not use them properly, your bills are going to rise higher and higher, right? So you have to be very, very careful. Water sources. Yeah, we know. So what? You've experienced that. Who hasn't? Yeah. So people, means I've read somewhere that cloud providers make more money by people not using their services than people using their services. So that is the main mantra. That you should be aware of what resources people are using and how much are they using. Do you have a control? Do you have insight? Do you have monitoring or that? That is the main critical portion of people want to move to a different area, especially the cloud. What's the use case you're presenting on today? Oh, the use case is migration steps from moving some of our on-premise the red processing to SaaS container-based platform. My final question on the cloud modernization story, which is the topic of this talk here, is working with other people. API for us, Gen 1 Cloud. Foundation models now could be that next integration point between companies. How do you look at how you work with other vendors like Snowflake or Databricks. You got other providers. You're in the cloud. Everything's there, but you have on-premise too. How do you look at the whole multi-bender partner or supplier equation? Oh, wow. The nightmare, is it working? Like, what's your view? Yeah, yeah. There are always, see when you have got to choose between multiple vendors and multiple data sources, there are always some cost consideration as well as some technical consideration as well as user experience. So like you said, right? What do you give to the analyst? What do you give to the data centers? You always need to look what the user experience is and you have to balance it with the cost as well as technical. You will not use a row-based data warehouse, right, for analytics. It is rather column-based. So those considerations should be taken into... And I think, we've talked about this, right? This is where I think we're a huge partner for financial services institutions as they move to the cloud, right? Most institutions have a large SaaS analytics platform. They've built an open source platform. They've built data lakes on-premise. They're moving those, right? We can join your decisioning platforms together because we embrace open source. You talked about Snowflake and the data mesh and the data lakes that are out there. We've got Snowflake here. We can push down our analytical services into your data to reduce data movement and we can really help organizations to actually achieve those business outcomes that the cloud promises, right? And I like how you said, you're not new to this. We're not, right? So there's a lot of seed funding. There's a lot of new companies that are coming into the space. But these things are not new. We know how to get operational decisions into flows within a business process. So that's not going away. Can I bring your LLM specificity, your industry specific AI models into those data stores as well? So yeah, that's something that we're waiting into the waters of, right? So we have these solutions. We're investing in them heavily. But because we have that domain knowledge and we have the knowledge of analytics and artificial intelligence, I see us having a big impact in that area. So yes. That might be the right model, you think. You're still investigating, but it seems like it would be. Yeah, I mean, we're going to plug those into solutions. That's the vision, but, you know, again, Brian couldn't talk to that. Move that to the data, right? We'll look at Brian later. Yeah. He's not going to get rid of us. We're like a bone right now. If he just wants, we're going to continue the conversation. Excellent. The complexity you bring up is interesting because the complexity of existing stuff is there. Yes. A.K. Brownfield. And, you know, the old enterprise formula was solve complexity by adding more complexity. Okay. But that's now not the cloud way. The cloud way is abstract away or bolt on, bolt on maybe in the short term AI, but now you've got simplicity with the developers. So I guess my final question, it would be a discover. How are you experiencing SaaS from a customer standpoint, looking at the future, knowing you've got known things you can knock down now low hanging fruit. What's the future look like for you? What are you going to do next? And people who aren't yet customers or customers from SaaS, what could they do now? What advice would you give them today and going forward with SaaS? With SaaS, there are interesting things coming up right now with the recent offerings. Everything has to be installed on the Kubernetes platform. So that gives you a lot of things. You do not have to do manual things that you were doing earlier, whereas monitoring, orchestration, any of the container stuff, any of the resource utilization. So that is the right trend of going to Kubernetes platform and using IAC DAC for installation deployments. And people always have to evaluate tools and see which one fits them the best. It cannot be a decision where everyone is using this. I am going to use it. It is always going to be evaluate the tool, evaluate your budget, the reasoning for going using a tool and the strategy for that. Kubernetes is getting boring, means it's working. We'll be a coupon in Chicago in a couple, in a month. Interesting times now in financials. So what's your final word for the customers watching in the financial area? What's the innovation angle? Where's the compliance falling? What's your final word? No, I mean, look, it's been a very interesting year to say the least. So we've been investing for decades in the basic principles of enterprise risk management in a deregulated environment over the past administrations. That's kind of lost its favor and investment within organizations, but all of a sudden it's exploded and good thing that we've invested in these solutions because we're right there with it. You talked about the emergence of fraud as well, but on the platform side, you said the cloud is not complex so we're not going to rebuild things. I've actually observed customers rebuilding a lot of decisioning processes and failing and doubling back. And I really think that where we're at with Viya, and I've tried not to use the tagline from the software because I want to talk about a customer approach, but where we're at with Viya now is the best of both worlds. We can help people to get code into a decision. We can help people to leverage the baseline IP that they have in SaaS and in their open source world and we can get them to the next level. So I think we're a great partner for organizations and now that cost pressure will be coming downward because of the regulatory environment and the interest rate environment, things have to work and we're a good partner for that. Well certainly a lot of refactoring, replatforming going on with the cloud, great opportunity and the tailwind is AI. So Jack, thanks for coming on theCUBE. Thanks for spending the time. Thanks a lot. Thank you. Good luck on your talk later today. Thank you. All right, theCUBE live on the floor here in Las Vegas for SaaS Explorer. I'm John Furrier with Dave Vellante. We'll be right back with our next guest after this short break.