 Hello, and welcome back to Google Next 23. We're here on, what is it, Thursday? And it's the last day of Google Next. We're kicking this off, we're getting it going, we're all still have some great energy. The floor is still buzzing. This last day, I think the energy has been fantastic. I'm Rob Streche. I'm here joined with Dustin Kirkland. Once again this morning, we're getting things kicked off in a strong way. I've got to say, I'm really excited about this one. We're joined once again by PWC, and we have Keaton Alley-Gonker. Very close, very close, okay. I think that's a great rendition of my last video. I'm trying, I'm trying, but he's the chief data and AI officer for PWC, and also a partner over there. So welcome, and thank you for coming on with us today. Thank you, I feel the buzz too. I've been here since Sunday night, and it's Thursday, and it feels like a month's gone by in the four days, and it's been amazing. I mean, especially when you talk about data and AI, I felt this conference was all about data and AI, and given Gen AI and the buzz around that, there's just so much that's going on in the last four days. What have you been seeing here that's really caught your eye this week? Yeah, I think what's unique at this conference is typically you have either the services providers or you have the cloud providers, or you have the whole stack. So one thing that's been so unique is you have companies that represent industries that are, you know, like you have Wendy's back there with Google that's doing amazing stuff about AI assisted drive-throughs. You have a lot of the hardcore hardware companies over here that are talking about chips and how that lights up Gen AI. So it's been very interesting, it's truly for the first time I'm seeing the whole stack represented. So that's one, the second big theme is, as I've talked about 50 chief data, chief analytics, chief digital, chief information officers, and the interesting thing, they're kind of trying to figure out is a signal from the noise on Gen AI versus the core AI stack. And that dichotomy is what they're trying to get their head around. So it's been very interesting the last four days. Yeah, that's what I wanted to drill into, Gaten, is what you are hearing from customers without, you know, naming names unless you want to. Give us a sense of what are you hearing from customers here at this conference over these last couple of days. I think there's three or four really interesting themes that have emerged. The first is, you know, given the economic environment where clients are asking the question, do I move and how fast do I move my workloads to the cloud? And the big pivot I've started to see is in the last three to four years, clients are saying I'm going to go application first. Now most of my clients, you know, you talk to a CEO, the CEO is like, hey, the cloud is great, but when am I going to see the value? Am I going to see a doubling of my market cap? How do I get product to market faster? How do I decouple my supply chains? How do I manage risk? You go to a CFO, the CFO is, I've spent $200 million, where's my ROI? Well, I was going to say, where's the business value? Exactly. And I think, and we've talked to a few people from PWC and I think we like the fact that it's moving, you know, starting with the business value. Exactly. And driving into that. Exactly. Is that what you're seeing? I mean, again, with the CEOs and the CFOs, they hear the buzz, they're hearing it in the boardroom. They must be getting pressure to figure out what that is for them. They are, and I think the big shift when it's happened very rapidly in the last three to six months is an application cloud to value is shifted to value to what we need to move. The second thing that's very interesting is you're exactly right. In the first six months of this year, because of the Wall Street Journal and all the business publications, I mean, if you did a GNI search on GNI in those publications, you would get like, a gazillion hits. So I think the first six months is a lot about boards asking the question of the C-suite, how real is this? And what is it going to do to my industry? And the pivot that companies have made is, I'd say they've kind of gone down a couple of paths. There's maybe 10 or 20% of the industries that are truly at risk. The business model is going to get disintermediated and reinvented on GNI. They're hustling. Then I'd say there's 30 to 40 to 50% of the industries that have maybe two or three areas where they can get rapid productivity or rapid time to market. And then you have a long tail of industries that are not going to see the impact for a while. So I think some of the dust has settled, but one team that's come across the board is they've all realized they've got to invest in the data in AI core. Because GNI is only as good as the data in AI core and the risk and the regulatory framework that goes around that. Let me follow up on that. So C-suite, I think that's a great spot to drill into. Let me ask you a four part question. CEO, CTO, CFO, and CISO. How are each of those four, and I can repeat them if you need to, but that's C-suite. I mean, each of them have to be looking at GNI through a slightly different lens. Maybe the CEO's trying to bring it all together, but there's dramatic impacts to the CFO's area, the CTO's area, and then ultimately the CTO's. It's a great question, and you're right. I mean, what companies are starting to do is trying to bring all of it together. So in the finance organization, the big push that they're making is productivity. So when you go and you outsource finance, so the CFO's office is very focused on how do I get myself to becoming a digital finance office, and not just an old school record to report or accounts payable, GNI is going to play a big role there because these are repetitive, automated processes, and you're going to see a lot of compression on cost over there. So the CFO is taking an angle on what is GNI going to do to the finance function, but the flip side then is the CFO's also, I've seen a lot of CFO's get pretty aggressive in a good way, and they're talking to their COO's, the CMO's, the chief supply chain offices, and saying what are you going to do to this to monetize time to market or to monetize revenue growth? And especially in the marketing function, I feel GNI is going to have a huge lift. Like how do you get offers out faster? Like can you have prepackaged GNI content that you say, okay, this is the offer or the personalized message I want to send, how do I use GNI to take a three month cycle and get it down to two days? It's rapid. Yeah, and I think it's, having come from that side of the fence, especially in the marketing automation side with customization and being able to get the offers out and having spent time at Amazon myself and other places, it's really interesting. I think one of the ones that, I think what was interesting, what I haven't seen a lot of is the data layer stuff and data layer discussion here this week. And I don't see a lot of chief data officers here, per se. Do you think that's in the process of changing around here? Because a lot of people I talk to, they think the chief data officer may go away because AI is almost becoming so prevalent across the CTO and the CIO when they're coming together. Well, how do you feel about that? You know, it's a great question. I feel the importance of the data office is going to be 10x more than it ever has been. And to your point, the data office now, there's no AI without data. And the data office now has to actually do a couple of things. First is, most of our clients have got a massive multi-hundred million, multi-billion dollar application modernization journey. The executives on the business side are, I need the data. So effectively the chief data office is going to have to reconcile the two. So what they're going to have to do is to say, how do I decouple the data from the application modernization? To your point, how do I bring it into, in this case, a Google stack? How do I govern the data? The CISO then has to put privacy, provenance, trust, security, access management, roles-based access, and then you go to the AI layer. So I think the data layer is going to be more important. I mean, AI is as much about data as it is about what we're seeing here as the hardware side of the world. I agree, totally. Yeah, I've seen what you're describing firsthand in the financial services industry, where there's a bunch of data that maybe can't be commingled, certainly by humans, but maybe there's some opportunities with the right security and governance in place to use that to derive insights. The CFO, I want to come back to that because I think you talked about part of it, which is the driving efficiency. There's another part of it about just making a more efficient CFO's office, getting the case and queue reports out more efficiently and more accurately. It is, it is. No, it's so true because what executives will now have is not just the structured data that sits in all of your transaction system. What you need is unstructured data, which Jenny, I was very good at, if you train it to pick up emotion detection. So if let's say I'm at the CFO of an airline and there's bad weather, I can have hundreds of cameras at my airports that pick up sentiment analysis against man-on-man. And you can actually get real-time reporting that says you have a risk here for customer experience. So there's a lot more, and there's dark data. That's data you think you have or you have, but it's not in the system. So there's a lot going on and it's a very exciting time, to be honest. Yeah, I was just gonna say, you just hit on one of the things that I think is, also was, I went through one of the, I did the little tour and stuff like that. And they talked about the reporting and the analytics a little bit, but one of the things that hasn't necessarily been a full, fully pushed on, and I think that they're looking for partners like PWC to help with is really around, how do you do that at scale? How do you do analytics and reporting at scale? What are you seeing with your customers and how are you really helping them from a scale? That is actually a very key insight because one is, do I have the data? Second is, GenAI and these large-language models are going to change how we can drive value. The third is, how do you scale fast? And this is back to your point around CEOs, CFOs, CDOs, and now the CIOs. It's all about time to market. So here's what we're seeing the leaders do. The first thing the leaders are doing is they're investing a lot in creating a common data model. Like over here we had Google and Google's got a fantastic unified data model. So we're working with Telco, for example. How do you stitch network data with finance data at a GL level? How do you stitch that with customer data, device data, and with store data, or channel? And once you have that, that's where reporting comes alive. So the best companies are creating the unified data model, then they create a data mesh on top of that, which basically stitches all this together through a knowledge graph. On top of that then you have reporting as a product. Because then what you can do is you can just put in a GUI that says, hey, what were my sales today in the Southwest? And you don't need like 35 analysts going and getting the report. I mean, the AI can actually tell you that in a bespoke manner in seconds. And that is the future of where things are going. It's a very exciting time. I think one thing, you kind of hit on it and I could go on for an hour on this. And I know with his chops and my background, the rise of the data product manager has been one of these things we're seeing. And we don't have to go deep into this because again, I think we've been talking a lot on theCUBE about the rise of the data developer, not just the app developer, but developing the data, not just data engineering. And I think when you start to look at that, it seems like you have all these people starting to take a more, I guess you could say process-oriented approach, which I'm sure you guys push for your customers to work backwards from the customer. It could be an internal customer, external customer to do that. Now, you bring up a good point because if you think about value, honestly there's more tech today than there's adoption of tech. So even pre-GNAI, I mean, there's barely 10 to 20% adoption of tech and the best companies that are the most sophisticated data in AI-run companies. So what you're pointing out is really important. In addition to the technology layer, there also needs to be the people process and strategy. And if those don't tie in a very cohesive operating model, it's not going to come together. And the point of a product manager is so important because most of our clients organize vertically and data is a horizontal asset, it's a corporate asset. So how do you manage that? So having a data product manager that works between marketing, technology, data, your alliance partners is so important. That's a very evolving role. I love it. I'm biased as a former product manager. I love it. I knew you would be. So you like what I said, right? I do, I do. Is that something that PWC can help with directly? Can you provide data product managers or can you help train native data product managers within your clients organizations? We are doing that. We actually have an academy and we have a digital platform. Very cool. Because I think to your collective points, data engineering has rolled in exist 10 years back. And there are maybe, like you said, model tuners that have to figure out, okay, do I have a model gain or loss and is this the best model? Which LLM do you pick? You need prompt engineering. There's going to be eight to 10-year roles. Responsible AI is still a very young field. So there's going to be eight to 10-year roles that don't exist today, that are going to be scale roles that have to exist in three to five years. And that with the product management mindset and an operating model that ties strategy to technology. I think if these don't come together, you're not going to see the value. Yeah, in fact, we've been talking about that a lot on here, how my son's at Arizona State getting a CS degree right now. He's never used, or he's very rarely ever used VIRG. Because he's grown up in workspaces, with school and everything like that. I have no idea what he uses at school now, but I know his dev environments are all in the cloud. Unlike me having started out in high school on punch cards on a mainframe, which we were joking about over dinner last night. But it was, I think one of the things that, I think we should kind of, the elephant in the room that has been talked about by every Googler who's been here, is been trust and security. And so how do you see organizations really building that trust within there? I think we have a ways to go. Because I'd say a large number of the clients we serve, they're still in the early days of truly bringing the trust into the data. Because when you think about trust, it is multiple layers to that. First is, do you have a unifying data framework? And this is where CSOs are going to be even more important. Is do you trust the data? Who has access to the data? Is the data hashed and masked? What about PII? What about PHI? What about GDPR? So there needs to be a governing framework around data. And I think there's a role for government to work with industry and firms like us to come up with a uniform data framework that does not stymie innovation, but at the same time accelerates a common standard because right now there's multiple standards and there's the same thing of the AI layer. Because the scary thing about AI is you ask AI the question, it's going to unmask bias in the data. So there's a lot to be done here and I think the CSOs and the data privacy office is going to play an enhanced role in the coming years. It is a very, very kind of big gap that our clients really need to move on fast. No, I think that's so true. In kind of the last minute we have left, how can data and analytics really help, again with those people's processes and because you kind of started talking about with the CFO, how can I drive my supply chain more efficiently and things like that. How do you see that playing out in the organizations you're working with? Yeah, I think the hardest part is if you really wanted to scale fast, clients are going to ask for 3x or 5x more budget, it doesn't exist. So I feel the key things companies need to do is really think about where they want to place their bets. Like what is the disruption going to do and based on that, what are the big rocks in your strategy you want to light up and then to your point, how do you think about the right tech stack? How do you think about the governance around that and how do you get scale fast by thinking about a product mindset versus a service mindset? I think if you bring all these elements together, you could get 3x to 5x the velocity and value out of the same spend then kind of meandering your way through it. So it's multifaceted, but that's what I would offer up to some of my clients. Yeah, I think that that's, well, of course we're biased having been product guys. So, but I think that is so true and I've worked with a number of large companies as well and I think the ones that do it best are the ones that have that product mindset and really treat data as a product. Exactly. I can argue what the stack really terminology looks like above that. That I would love to pick up with you another time. You've been fantastic. I really appreciate you coming on. Keaton and really PWC's been doing a lot in this area and we really are excited to keep watching what you're doing and thank you for coming on. No, thank you for having us again. We're very excited and this was a fun conversation. They're fun times, it's going to be a great decade ahead, but there's lots to learn and lots to do. Absolutely. No shortage. No shortage. All right, well, we're the cube here at Google Next 23. We got a packed half day. Trying to keep our voices going and strong. We're joined by Lisa Martin, John Furrier, myself, co-host Dustin Kirkland with me. We're analyzing things, breaking it down, driving the signal out of the noise and trying to really help you understand what's going on and what you should be looking at as we get out of Google Next into next week and you start to go, hmm, what did I learn out of that? Stay tuned, we got more coming back.