 Welcome back everyone to theCUBE's live coverage here in Las Vegas on the show floor at SAS Explorer. Getting all the content, all the coverage from the news, the executive conversations. I'm John Furrier with Dave Vellante, our hosts here at theCUBE. We're constructing the civil noise. Next guest is J.F. Church, who's the CIO of SAS here on theCUBE to tell us how they're doing. Thanks for coming on. Grateful to be here guys, thank you so much. So digital transformation's happening. Okay, the cloud, next gen's here. Software's open source industry. AI just drops on everyone's lap from the past year. It's been around for a while. People have been doing machine learning. But now foundation models are here. Large data sets being reused, re-trained. A lot of innovations just flowering out. It's been an exciting time. And a lot of people are trying to figure out how do you let chaos reign? And then how do you reign it in? So, you know, it's a CIO. You're in the middle of it. Both as a CIO and SAS, a leader with a lot of data chops. Out there and with great news. So, take us through, what's on your mind? Ooh, well, I think first of all, the role of the CIO today is changed so much from what it's been over the last, call it five or 10 years, right? I think boards are looking for digital dividends now from the investments that CIOs are taking. I think that there's a lot out there about, obviously AI, the investment of AI, how to use that to best scale up human productivity and the decision-making process. And then I think a lot of companies now are turning towards how do we become more resilient? And how do you use data and AI to achieve that? So that's, honestly, that's a lot of what I do with every day and where I have to kind of engage with my board and my boss and my executive team. That resiliency push. Free pandemic, I mean, a lot of CIOs would tell me, yeah, we kind of had our DR plan was what our resilient business resiliency plan was. We learned fast that that wasn't enough and we rethought that. Did you experience that at SAS or with your colleagues in the industry? How did the sort of post-isolation economy, how was that different than sort of pre-COVID? Well, I mean, I think you're touching on something right now. It's incredibly uncertain times, right? We started off with COVID, running into financial geopolitical climate. I mean, you name it, we've faced it, it feels like the last couple of years. And so I think in the CIO communities that I participate in, we keep asking, what does it mean to be resilient? SAS is very curious by nature, it's in our DNA, and so we kicked off a study at the end of last year and end of this year to kind of ask CIOs, so executive leaders from large companies around the world and all major industries, what does it mean? And out of that, it was really interesting. There were a couple of things that were found. Number one, nine out of 10 as you would expect, lean into data, right? When they're trying to solve for uncertain times. Number two is 97% of those surveyed said, yes, absolutely, I got to have my business resilient and it's a CIO responsibility, but only 47% said, yeah, I'm actually resilient. So there's this massive gap between perception and reality when it comes to being resilient. So we took that a step further and said, okay, what does it mean to be resilient? And we came up with five rules that we tried to take out all the CIOs through. And there's an assessment out there now that those that are watching might be interested in going and taking to see how resilient are you as compared to your peers. Those five came back to a couple of things, curiosity, right? Curious, he's got to be in your DNA. Number two is you've got to have a data culture, data first mindset. Number three is you've got to have speed and agility in how you run your business. Number four, you've got to drive innovation. And then last but not least, you got to drive responsible innovation. So thinking about the ethical use of data and how you use data-led insights into drive innovation. Now, I understand it correctly. You have a dual role. You're sort of an internal service provider to SaaS employees, but also you're an operator of the SaaS cloud. Is that correct? That is correct. Okay, so how do you divide your time there? Sales guys must be dragging you out all the time to talk to customers. How do you sort of balance your time? Yeah, I mean like saying earlier, right? The CIO role is very different than it was in days of old, right? So I still carry the burden to operate. I still carry the burden to have cost containment as we continue to grow. But I also have a commercial responsibility to the company. So thinking about the role of the operator in our cloud. So everywhere we choose to distribute our analytical property to our customers and maybe the cloud is my domain, if you will, every day. It's fun. I get to embrace how they're using our technology. We get to innovate together. And then I also get to bring that voice to the operator back to our R&D organization and talk to them about, well, we should improve this or that. The other thing, because I run IT for our shop, I get to see how others in the market do it. And if I see somebody that's out there that's doing something really interesting, maybe I'll bring that back and say, hey, we should consider something like that for ourselves as well. So I think it's a great voice that kind of continues on every day in our R&D roadmap. And again, it's really fun for me to get out and participate in the industries that we serve. We've obviously, over the years, we've seen the shadow IT, we saw shadow big data, we saw shadow cloud. Are we not seeing as much shadow AI because it's such a dangerous weapon or are we seeing the same old story? Same old story. There's shadow AI everywhere. I hate to say it. I mean, different CIOs take different vectors at how do they solve for this. My philosophy has always been, look, I'm a service provider. I'm going to enable my partners internally to do what they need to do to run their business. I want to embrace what they're doing for their business to make them successful. And of course, as you would expect from a company like SAS, we're going to empower AI in every one of those divisions, right? So in those cases, absolutely, there's shadow AI going around. I like to think I've got enough guardrails and hopefully safety nuts around it to protect us. Because typically what's happened is that it's the Wild West and then you're called to clean it up. The board in this corner office is, all right, we got to get control here. Through your experiences over the years, do you feel like, sounds like you feel like you have a better handle on that than maybe some previous cycles? Well, I think part of it, SAS. I mean, at the end of the day, I'm providing AI to some AI, the best AI experts in the world, right? So they have a good mindset around how to do it safely and securely. I still have to bring enough, I'll say border patrol to the table, right? To make sure that it's working well. And the other thing that's really interesting is generative of being on such a hype cycle right now. It's on top of mind for everybody. We've done a good job putting out our guidelines, our principles for how to do that responsibly at SAS. And so while we push a policy, I try to, and again, kind of create the capability to deliver it. And then we just excite the curiosity that is at SAS. Jay, I want to ask you because you're on both sides of the equation at SAS as an employee and a CIO, you kind of have to, you have peers out there that don't have that luxury of sitting in with all the brain trust at SAS talking about data, protecting it, managing it, making developers develop on it, app factory. I mean, basically putting data as a programmable, useful, now intellectual property-based system. It's pretty compelling. AI's going to be at the heart of that. Absolutely. So one of the things that I think about all the time is can your business operate at the speed of data, right? And so how do I as a CIO and responsibility to provide that service to the company make sure that we have good data hygiene, we create and present it in a way that can be consumable, that we can then drive the artificial intelligence aspirations of the division partners that we have. So my next question is, okay, let chaos reign and reign in the chaos. That's Andy Groves' famous quote from Intel. And that's how innovation happens. And so we want to be stoking some innovations, but you also want to put guardrails. I hate to use the word guardrail. Dave knows I hate that word because it means like constrict, slow down. But it protects. Good guardrails, balls bounce around, then everything's happy. How do you not put a wet blanket on the innovation fire? Because right now it's highly frothy, enthusiastic, confidence is getting there day by day. Compliance could slow things down. Legal could slow things down. Operational dogma could slow things down. CIOs could get their hands in a lot of that. One's out in the world. How do you get around that? That's right. So a couple of things. One, not all CISOs report to the CIO, I have that opportunity. So I've got our chief information security officer at night table every day talking through how we enforce and how we protect. Number two is SAS is the most trusted brand in AI and analytics in the world. It took us 47 years to acquire that. It can take me one day to reach to lose it. So that is always top of mind. Now we try to create secure enclaves for innovation to occur. So we want to make sure again, as you're saying, I'm not a wet blanket against an idea or creative spark that happens anywhere in the company. So we allow them to do their jobs in the areas of exploration. And then as that innovation matures, we do start to apply a little bit of policy and standardization around it. And of course there's always those last checks before we would ever take it to market. Does CISO reports to you? He does. And that's a unique experience, right? We know a lot of the peers that I speak with, they kind of sit shoulder to shoulder or sometimes that CISO sits outside and maybe even a legal organization, we wanted to bring it close to the technology, right? There's a responsibility of securing the technology, but also as you were saying, not stopping innovation because we know the technology is the heartbeat of our business. How do you, what do you think the best way is to manage the sort of inconsistencies and wide margin of error that you see with large language models like ChatGPT? How are you advising your firm and what are your colleagues doing there? So I think we have to appreciate what large language models can do for us. Amazing. Absolutely and incredibly transformative. I think that the workforce productivity side and all the internal science experiments that all of us CIOs are doing right now is fascinating. We want to continue to encourage those experiments. We need to do it with safety in mind. We need to understand that an eloquent answer doesn't necessarily mean that it's right and then we need to figure out again how we use that as a force multiplier for our workforce. I think what we're trying to do at SAS is take that a step further and think about how we imply the data science, the math, the things that have made SAS so successful for so long, combining it with large language models and bring that forward. At the show earlier today, we were really hyped up. We talked a lot about what we're doing around digital twins as an example and synthetic data. I think those are feeders into this and I think SAS is really, really poised to take advantage of this incredible excitement that is the adoption curve of General AI. So when you had this world of PC, PC software, it was pretty constrained and then we networked it and it got to the client server and then the cloud and mobile. I mean, it dramatically reduced the cost associated with building software and now you're seeing Gen AI come in. How does it compare in terms of the productivity impact with some of these previous cycles? I mean, based on your experience so far, like you said, you're running a lot of science experiments, how should we think about quantifying that? Is it you can get to an MVP in weeks instead of months or years or maybe not years, but months? You don't need a two pizza team, you can do one pizza team. How could we think about the quantified impact of AI and LLMs on software development? On software development, it's still early innings. I think the co-pilot model is fantastic. Most of the firms we're talking to about the benefit and the speed of an individual, so one developer productivity enhancements approaching 30 to 40% and you saw a great demo of it earlier on the main stage, we showed a chance where you could click right on a generative button in your IDE and it would produce the next bit of code that you want, still have a human in the loop, you still got to verify it. The great thing on code is the compiler's the ultimate check. It'll tell you if it works or not, it doesn't necessarily tell you if there are bugs in it or if it has the intended approach, but there is a gut check. That's a little different than on the creative side. I mean, so much of the chat TPs of the world, it's so focused in on creative, it's a little different on the code side. So I think you're going to see much more real world applicability there and we've got some great science experiments going on in our own R&D organization for that right now. Some teams 100% they're only allowed to code with a co-pilot experience. Okay, the data science, they report to you? No, so we have the center of data and analytics inside of SAS, so how we run our enterprise is a part of our enterprise solution, but the data science is Udo and the team out there in the field every day, that's all part of our R&D organization. So I have to ask you, we saw the security model shift to shift left, developers get in the code in the pipeline, right at point of it coding, you can implement policies and guardrails, so in fact the guardrails again, that was a good thing. Security thought in the beginning of the process, data is now coming in the same thing. So security we hear, software supply chain, security supply chain, data supply chains come up in the cube here today. What do you think about that concept of data supply chain? Where did that data come from? Where's it going? How is it being used? That's kind of an IT problem and a security problem and a compliance, potentially legal problem. It is. I mean, a lot of moving parts there. You're exactly right. So we think a lot about data hygiene. So again, the data sources, any of the work that makes it to a place where we feel like, hey, this is now usable for the enterprise, all the governing body over the data, the data dictionary, all the transparency around where it goes. That's our responsibility to feed the curiosity of the company. How they use that data, model against it, and then obviously output a model from it, that's really where the power of eye comes into play. So that capability, there was a talk today about the cards, those cards showing you exactly where that data originated, how it transformed, what it was combined with to ultimately get you to an output. That explainability is incredibly important and as you tie it back to generative. Because at that point, now you've got a question, okay, well how did you come up with that answer? Not just the speculative output of predicting the next word, but now coming back around to what data actually fed it. And that's beautiful segue back to the security shift left. Now the data is shifting somewhere because the developers are coding in line with data. So now that all has to be explainable, generative in a positive way, be native to the application. So you got developers out there in the slide. Developers on this side with the workbench and the app factor. Bookended, yeah. Okay, that validates what we've been saying and we coined the term the rise of the data developer. We're starting to see a developer persona emerging. You guys, for the first ones to have it on slide, vast data was the first storage company to call out in their marketing, the data developer persona. So anyway, a whole nother person is emerging in the data world. How do you see that? Do you agree? I totally agree. We'll give you a full credit and copyright response. But we absolutely agree. We're up against everything. And we have to cultivate that curiosity. Otherwise, how else do we gain, again, more traction adoption and usage of the platform? If we make it hard for the users and we won't ever get the outputs that the industries that we serve demand. So we absolutely agree with you on it. The data business, the world's changed. It's interesting times. What's the biggest challenge you're facing right now and advice you'd give to folks watching? Because you're in a role that's super important because you're setting the agenda, you're managing existing operations. But you also want to create an environment for your organization to accelerate business transfers and create creativity. Yeah. I mean, there are a few things to keep me up at night. Number one, because the CISOs with me, it's security, right? I got to get that right every day. I think number two, obviously the IT role of cost containment, especially Phenops, when you think about large language models running in a public cloud setting, how do you control that expense? So I've got to get that right. That's kind of earning the right to be there the next day, right for me. And then I think the last one is all around data culture, a data first mindset, and then driving data literacy throughout the company. At SAS, I have a privilege that that's just part of our DNA. I know many of my industry peers, I know many of the customers that we serve struggle with that. The data scientists are still kind of in a corner and there's not a appreciation for how data-driven insights can really empower your company. And ultimately make you resilient if you apply it the right way. So that is absolutely something that I continue to push and encourage. And I think my CIO peers out there, hopefully would nod their head in agreement, and say, you know, that is a responsibility that we shoulder now as kind of the modern CIO, if you will. You know, it's interesting. We've seen a lot of stuff, Dave and I, over 13 years doing theCUBE, you know, agile, design thinking, everything. And it's all good, right? Now we're seeing an evidence on systems thinking. Platforms are more conversations around platforms. What's your reaction to that idea of systems thinking as a mindset? Because you can be data first, I agree, check. But data is now an operating system. Things are working together. Yeah, I think we're all moving closer to business outcome thinking, right? So if you, again, thinking about the end in mind, solutions and platforms being in the middle, something I'm a big fan of. We think a lot about AI-driven operations and self-healing in our applications, how they can take care of themselves. You know, in the old days, it used to be set up monitoring management, a couple of tripwires, and then the guys all of a sudden jumped on the bridge. You know, today we're thinking about those solutions and those applications. We want everybody who's writing those to be closer to them, obviously closer to the operating model. And of course we want those solutions to drive the business outcomes that our companies expect back to those digital dividends that our boards. And Brian Harris was on earlier, he talked about the AI lifecycle from question to decision. That's right. As a lifecycle, repeatable. Yeah, absolutely. I mean, that's something that we continue to encourage. But, and it's not just with the data scientists that we use or those data engineers as you're referring to as well. It's all the way across our entire enterprise. Jay, thanks for coming on theCUBE. Really appreciate your commentary and thanks for sharing the data here on theCUBE. Really appreciate it. Thank you guys. We're bringing the data, bringing the commentary and color commentary here on theCUBE. I'm John Furrier with Dave Vellante. Stay with us for more coverage here on the floor of the Stas Explorer after this short break.