>> Narrator: Live from Orlando, Florida. It's theCUBE! Covering .conf18. Brought to you by Splunk. >> Welcome back to Orlando, everybody. Splunk .conf18. This is theCUBE, the leader in live tech coverage. I'm Dave Vellante with my co-host, Stu Miniman. Carnival Cruise Lines is back. We heard from them yesterday, we heard them on the main stage of .conf. CEO is up there with Doug Merritt. Sheldon White is here. He's an enterprise architect at Carnival Cruise Line And Alex Taberras, who's the director of threat intelligence at Carnival. Gents, welcome to theCUBE. >> Thank you. >> Doing a lot of talk on security today. They've lined us up, which is great. We love the conversation. So much to learn. Alex, I'll start with you. When you think about security and threat intelligence, what are the big changes that you've seen over the last, whatever, pick a time. Half a decade? Decade? Couple of years even. >> Alex: So, it's just the amount of threats that are coming in now and how fast they're coming in, right? We can't seem to be keeping up with everything that's happening in the environment, everything that's happening outside, trying to get into our environment and cause all that damage, right? So, that's why Splunk is awesome, right? I get to see everything come in, real time. I'm able to quickly pinpoint any action I need to take, send it to my team and have them immediate right away. >> So, Sheldon, yesterday we had ship and shore from Carnival and he was talking about really different problems. You know, the folks on the ship, they got 250 thousand people on the ocean at any one point in time collecting data, trying to make a better experience, keep them connected. Folks on the shore, obviously, websites and things like that. Where do you fit into that mix of ship and shore? >> Sheldon: Right, so there's an entire value stream that we map out as enterprise architects. And so, what we do there is analyze all the customer touch points. And then we aggregate all of that information into a pipeline that we then address our audiences with those critical KPIs. Operational and infrastructure, the entire stack. >> Dave: You guys obviously have very strong relationship with Splunk. We heard from your CEO, Arnold Donald, right? >> Alex: Correct. >> Interesting name, I haven't messed that up yet so. (laughing) And so, where did that relationship start? Did it start in SecOps? Did it start in IT operations management? >> Alex: So, it really started in Devops, right? And they started... They purchased Splunk, I think back in like 2007, 2008. And they started looking at it, right? And I think I was talking to one of our other architects and it was one gig is what we started at, right? Now, we're upwards of 600 gigs. Just for security. So, it started there and it just kind of morphed into this huge relationship where we're partnering and touching all aspects of our business with Splunk. You know, and the Cloud and everything else. >> So, we heard, I don't know if you guys saw the key notes today, but we saw some announcements building on yesterday's Splunk next announcement. We heard some business workflow and some industrial IOT. I would think both of those are relevant for you guys. Not industrial IOT, but your IOT. Do you see Splunk permeating further into the organization? I guess, the answer's yes. You kind of already said that. But I'm interested in what role you guys play in facilitating that ? Are you kind of champions, evangelist, experts, consultants? How does that work? How do you see that (mumbles)? >> Sheldon: So, we see ourselves as internal consultants. We have our internal customers that depend on our guidance and our end-to-end view of the business processes. So, and now as enter our Cloud journey, into the second year of our Cloud journey, just we're able to accelerate our time to value for our internal customers to gain even greater insights into what's happening ship and shore. >> Dave: I wonder how, if you can talk about, how enterprise architecture has changed over the last decade even. You know, it used to be you were trying to harden the two tier or three tier architecture and harden top, don't touch it, it works. And then, of course, we all know, it created a lot of different stove pipes and a lot of data was locked into those stove pipes. That's changed, obviously. Cloud, now the Edge. Maybe because you guys were always sort of a distributed data company, you approached it differently. But I wondered if you could gives us (mumbles)? >> Sheldon: No, that's an interesting question. Because the evolution is not so much enterprise architect as it is eco system architect, right? So, now you have these massively distributed systems. So, you're really managing an eco system of internal and third party. And then all the relevant touch points, right? Like Alex mentioned, all that perimeters constantly shifting now. So, yeah, our focus is always aligning with the on-time business process and our internal customers. >> Yeah, wonder if we could dig into the Cloud a little. Alex, can we start with you? How does Cloud fit into your world of security? >> Alex: So, for me, the Cloud, as far as Splunk goes, it allows me to expand and contract as needed, right? So before, we used to have our on premise hardware, very finite RAM memory, I mean, disk space everything. So now, with the Cloud, I'm able to expand my environment as I move across all my North American brands, European brands, to be able to gather all that data, look at it and take action on it, right? >> Stu: And Sheldon, you're using AWS. We see they're, every software provider lives in AWS. It's often in the marketplace. We been seeing a lot this week that there's a deeper partnership. There's actually a lot of integration. Maybe give us your viewpoint on what you've seen on how Splunk and AWS work together to meet your requirements. >> Yeah. So, that's an interesting evolution as well of that partnership, right? So, you're starting to see things like the S3 API integration. So that you're removing storage from the critical path. And now that opens up different scale of possibilities, right? And internal opportunities. But yes, as you can see, leveraging the machine learning toolkit. I saw that one coming. It's going to be interesting to see how that keeps evolving, right? And also, like I was speaking to Alex, about the natural language capability. So, that also is well brought into the dimension of how our senior leadership with interact with these operational platforms. >> Yeah, I got to thank you. You're going to have your customer's natural language has to get into some of their rooms. It's definitely future. >> Sheldon: Oh, it's going to be apart of that value chain. Yeah, for sure. >> Dave: How does the S3 API integration affect you guys? Obviously, you got to put Syntax in an object store, which is going to scale. What does that mean for you guys? >> Sheldon: So, using the Splunk developer Cloud, we could develop all sorts of solutions to manage it intelligently how our storage, right? In near real time. So, we can completely automate and that end-to-end just integration with Splunk, how it ingest, how long that data stays relevant and how we offload it into things like Glacier. >> Dave: In the enablement, there is the S3 API. So, you're taking advantage of all the AWS automation tooling. >> Sheldon: Correct. >> Is that right? >> Sheldon: Correct. >> Alright. >> Sheldon: That's another example of that side integration. Not only with the S3 API. Lex, for the natural language. Obviously, TensorFlow and the machine learning toolkit. So, I think you're going to see that type of... those type of capabilities expanding as Splunk evolves. Next year, I'm sure they're going to have a ton of more, you know, announcements around how this evolution continues, right? >> Dave: So, you know, I was interested in the TensorFlow and Spark integration. And Stu and I were talking in an earlier segment. It's great, developers love that. We saw a lot of demos today that was like, looks so simple. Anybody could do it. Even I might be able to do it. But as practitioners of Splunk, is it really going to be that easy? Are business users actually going to be able to pick this stuff up and what are they going to have to do in order to take advantage of Splunk? Some training involved? >> Sheldon: Right, right. >> What's the learning curve going to be like? >> Sheldon: That's a great question, because there's a dual focus to this, right? First, is offloading from the developer. All that heavy lifting of creating this user interface and the dashboards, per say. Now, its all API driven. So, as you saw, maybe in the keynote this morning, that within the demo, was an API driven dashboard came together in several minutes. But one is offloading that and the second part is just enabling the business user with other capabilities, like natural language process. And they don't necessarily need to be on that screen. They can get acception reporting through emails and voice commands. So, training is also part of it, obviously. So, it's a multifaceted approach to leveraging these new capabilities. >> Dave: Are you guys responsible for the physical infrastructure of your ships? I mean, is that part of your purview? Okay. So, really there's is an industrial IOT component big time for you guys. >> Absolutely. >> Alex: And there's a huge push now for Maritime security, right? We saw what happened with Maersk and NotPetya virus, right? So, how it took them out of operation for about three weeks. So, this IOT is very, I think, awesome, right? I was speaking to some of the Splunk guys yesterday about it. How we could leverage that on our ships to gather that data, right, from our SCADA systems. And from our bridge and engine control systems to be able to view any kind of threat. Any kind of vulnerability that we might be seeing in the environment. How we can control that and how we can predict anything from happening, right? So, that's going to be very key to us. >> Dave: So, Splunk is going to take that data right off the machines. Which Stu and I were talking, that to us is a huge advantage. So many IT companies are coming and saying, "Hey! We're going to put a box at the edge". That's nice, but what about the data? So, Splunk's starting with the data, but it's the standards of that data. They're really driven by engineers and operations technology folks. Is Splunk sort of standard agnostic? Can they be able to ingest that data? What has to be done for you guys to take advantage of that? >> So, we'll have to ingest that data. And we'll have to, you know, look at it and see what we're seeing, right? This is all brand new to us as well. >> Dave: Right. >> Right. This whole Maritime thing has risen up in the past year, year and a half. So, we're going to have to look at the data and then kind of figure out what we want to see. Normalize it, you know, we'll probably get some PS services or something to assist us. Some experts. And then we just go from there, right? We build our dashboards and our reports. >> Dave: And predictive maintenance is a huge use case for you guys. >> Alex: Absolutely. >> I mean, to me, it's as important as the airlines. >> Alex: Absolutely, yes. >> So, I would think, anytime you... Well, first of all, real time during a journey. But anytime that journey is completed, you must bring in the inspectors and, I'm sure, very time consuming and precise. >> So, I know that some of our senior leadership, especially in the Maritime space, has now looking towards Splunk to do some of that predictive maintenance. To make sure that we have that right nuts and bolts, right? Per say, on the ship. To be able to fix any issue that might arise at sea while we're on there. >> Dave: Now, it's expect that the drive is going to be for human augmentation and of drive efficiency. >> Alex: Correct. >> You're not just going to trust the machines right out of the box. No way, right? >> Alex: No. But it's empowering those engineers, right? As we see with some of the dashboards that they're coming up with at the keynote. Empowering some of the those engineers that are in the engine room. That are in bridge. To be able to see those issues come up, right? And be able to track. >> Dave: Plus, I would imagine this is the kind of thing like an airline pilot. You're double checking, you're triple checking. So, you might catch misses earlier on in the cycle. >> Alex: Yeah. I could see it having huge impact. >> Stu: Yeah. Sheldon, I was just thinking through the other next announcement. I wonder if Splunk business flows sounds like something that might fit into your data pipeline? Get insights, understand satisfaction. Seems like it might be a fit. Is that of interest to you? >> Sheldon: Yeah, it sure is. Because we definitely want to, since we've evolved with kind of fragmented systems. We still have main frames, we still have whole call center environment that we need to ensure that it's parts of the end-to-end guest experience. So, for sure, we're getting into the whole early adopter program on the process flow. >> Yeah. Can you give us little insight? What kind of back and forth do you have with Splunk? What sort of things are you asking that would help make your jobs easier going forward? >> So, going forward, I know they're addressing a lot so the ingestion and data standardization. And now, with the decoupling of the storage, which is awesome, makes our lives a lot easier. But the evolution of the natural language and the integration with AWS natively is huge for us, as well as our Cloud program matures. And we start enabling Serverless architectures, for example. So, yeah. No, it's a very important part. >> Stu: Yeah. I mean, Serverless is actually something we're pretty interested. What are some of the early places that you're finding value there? >> Well, many people don't know this, but Carnival's also one of the largest travel agencies in the United States. So, we have the whole... Well, it's the whole global air travel platform that we're currently migrating to a Serverless architecture, integrates with Sabre. So, we're looking at things like open trace for that. And I know that our friends at Splunk are enabling capabilities for that type of management. >> Dave: And what's the business impact of Serverless there? You're just better utilization of resources? Faster time to value? Maybe you could describe. >> Yeah. Near real time processing. Scaling up and scaling down seasonally. Our key aspects of that. Removing the constraints of CPU and storage and-- >> Dave: Alex, has it changed the security paradigm at all? Serverless? How does it change it? >> Alex: So, it does. It let's me not have to worry so much about on premise stuff, right? As I did before. So, that helps a lot, right? And being able to scale up and down quickly as much data as we're ingesting is very key for us. >> Dave: You guys are heavy into Cloud, it's obvious. I wonder if you could share with us how you decide, kind of, what goes? If you're not all in on Cloud, right? It's not 100 percent Cloud? >> Sheldon: No, we could never be all in. >> No. >> Dave: And we've put forth that notion for years. We call it "true private cloud". That what you want to do is bring the Cloud experience to your data, wherever that data lives. There's certain data and workloads that you're not just going to put into the Cloud. >> Sheldon: That's correct. >> So, you would confirm that. That's the case. Like, you just said it. >> Correct. >> Dave: You're never going to put some of these workloads on Cloud. >> Well, we have floating data centers. So, we'll always be in a hybrid model. But there is a decision framework around how we create those application, migration pipelines. And the complexity and interdependencies between these platforms, some are easier to move than others. So, yeah. No, we're quite aware of-- >> Dave: And so, my follow up question is are you trying to bring that Cloud experience to those... to the floating data centers, wherever possible? And how is the industry doing? If you had a grade them in terms of their success. I mean, you certainly hear this from the big tech suppliers. "Oh, yes! We've got private Cloud" and "It's just like the public Cloud". And we know it's not and it doesn't have to be. >> Sheldon: Right. >> But if it can substantially mimic that public Cloud experience, it's a win for you guys. So, how is the industry doing in your view? >> So, I think it's a crawl, walk, run type of thing. Obviously, you have these floating cities and satellite bandwidth is a precious resource that we have to use wisely, right? So, we definitely are Edge computing strategy is evolving rapidly. What do we act upon at the Edge? What do we send to the Cloud? When do we send it? There also some business drivers behind this. For example, one of our early Cloud forays was in replicating a guest activity aboard the ship. So, we know if somebody buys a margarita off the coast of Australia, we know it five seconds later. And then, we could act upon that data. Casino or whatever data it may be in near real time. >> So, a lot of data stays at the floating data center, obviously. >> Correct. >> Much of it comes back to the Cloud. When it comes back to the Cloud is a decision, 'cause of the expense of the bandwidth. What do you do? You part the ship at the data center and put a big fire hose in there? (laughing) >> Alex: I wish it was that easy. >> You got a bunch of disc drives that you just take and load up? That's got to be a challenge. >> So, there business requirements, right? So, we have to figure out what application is more important, right? So, usually like our ship property management system, right. Where we have all our guests data, as far as their names, birth dates, all that stuff. That takes priority over a lot of other things, right. So, we have to use, like Sheldon said, that bandwidth wisely. 'Cause we don't really own a lot of the ports that we go into. So, we can't, just like you say, plug in a cable and move on, right? We still rely heavily on our satellites. So, bandwidth is our number on constraint and we have to, you know, we share it with our revenue generating guests as well. So, obviously, they take priority and a lot of factors go into that. >> Dave: And data's not shrinking. So, I'll give you guys the last word, if you could just sort of summarize, in your view, some of the big challenges that you're going to try to apply Splunk towards solving in the next near to mid term. >> Alex: Well, I'm more security focused. So, for me, its just making sure that I can get that data as fast as possible. I know that I saw yesterday at the keynote, the mobile app. That for me is going to be like one of the things I'm going to go like, research right away, right? 'Cause for me, its' getting that alert right away when something's going on, so that I can mitigate quickly, move fast and stop those threats from hitting our environment. >> Dave: Sheldon? >> Yes, I think the challenges are, like you mentioned earlier, about the stove pipes and how organizations evolve. Now, with this massive influx of data, that just making sense of it from a people, technology and processes standpoint. So that we could manage the chaos, so to speak, right? And make sure that we have an orderly end-to-end view of all the activity on the ships. >> Dave: Well, thank you guys. Stu and I are like kids in a candy shop, 'cause we getting to talk to so many customers this week. So, we really appreciate your time and your insights and the inspiration for your peers. So, thank you. >> Oh, thank you very much. >> Alex: Thank you for having us. >> Dave: You're welcome. Alright, keep it right there everybody. Stu and I will be back right after this short break. You're watching theCUBE Live from .conf18. Be right back. (techno music)

>> Live from Orlando, Florida, it's theCUBE, covering .conf18. Brought to you by Splunk. >> Welcome back to Splunk .conf18, #splunkconf18. You're here watching theCUBE, the leader in live-tech coverage. My name is Dave Vellante, and I'm with my cohost, Stu Miniman, and we're going to take a cruise with the data. Curt Persaud is here. He's the director of IT for Guest Technology at Carnival Cruise Lines. So, he's the ship. And Ariel Molina is here. He's the Senior Director of web development and enterprise architecture at Carnival Cruise Line. He's the shore. Gents, welcome to theCUBE. Good to see you. >> Happy to be here. Very, very. >> Thanks for having us guys. >> Dave, I sea what you did there. (laughs) >> Yeah, Stu, it's pretty good, huh. Well, this is kind of, you know, Splunk is known for a little tongue in cheek. >> Alright, let's keep this interview on course. >> (laughs) Alright, you got it. So Arnold Donald, your CEO, was on stage today with Doug Merritt, a very inspirational individual. You guys have an amazing company. You see those ads and just go "wow." Just makes you want to go. But Ariel, let's start with you, your role, what you guys are doing here. Just kick it off for us. >> So, no, it's fantastic, great to be here. Great energy in the conference today. The keynote was fantastic. It was great to see our CEO up there and really represent our company, really talk about, sort of, where we're heading and how Splunk helps us along that journey when it comes to data. Things are changing, they're moving faster every day, right? We're pressured into delivering more value, delivering innovation at a faster pace, and Splunk is a key enabler of that, for us. >> And Curt, at any one point in time, you guys said you have like 250,000 guests on the seas around the world. Wow! And everybody wants to be connected these days. So that's kind of your purview, right? >> Yeah, absolutely. Five, 10 years ago, what sold cruises was the ability to be disconnected. Right now, people want to be connected more than ever. So what we try to do, beyond just the connectivity, and giving them better bandwidth, and stuff like that, was to try to develop products onboard that helps them be connected, be social, but not miss out on the product that we're actually selling, which is the ship, the people, the crew, and the actual entertainment and the staff onboard. So we're trying to make people social, but not anti-social with some of the technologies that we're bringing onboard, as well. >> Doug Merritt said today, "we're all data emitters." And I think the number was you guys will service 13 million guests in any given year? So a huge, huge number of data emitters. And of course, Ariel, you obviously are analyzing a lot of data, as well. So, how has the use of data changed over the years at Carnival? Maybe you could kind of take us through that. >> Well, ultimately I think it's about personalizing the experience. So, how do we use the data to better understand what folks are looking for in that guest journey? We call the guest journey everything from planning a voyage, purchasing a voyage, purchasing all the auxiliary items that are up for sale, and then ultimately making it into the ship. So, what we're doing these days, is looking at mining this data, and looking for opportunities. On the dot-com side of things, obviously it's about resiliency and personalization. How do we deliver innovation through multiple releases, and then do so in a resilient way? And a lot of those innovations, typically, are around personalization. And we see that move the needle. We're incentivized to have more folks book online. That's ultimately good for the bottom line. So, data's a big part of that. Personalization, resiliency. >> Yeah, it's one of those interesting things we look at. Most people probably think of cruise ships as you're vacation or transportation, everything like that. You're a technology company now. You're tied in, you've got multiple mobile apps, before and during. Maybe bring us a little bit inside what that's like. >> Over the past three years, we've seen a great transformation in terms of the technologies that we're bringing on board. You name it, whether it's very high end tools, like Splunk and other APM tools that we use, to cutting-edge technology like AI, chatbots, facial recognition. We're using the full breadth of all these innovations, in terms of technology, to try to enhance guest experience. And to Ariel's point, the focus is really on trying to be very personal, trying to personalize this information, trying to personalize the guest experience, and using all those data points that we're capturing to really target what a custom experience looks for you. It's really interesting, because one of the things that we try to do in that personalization is try to manage those micro-moments. We're trying to get you what you want, we're trying to get you the feedback that you need in that micro-moment, so that you can do your transaction and move on to enjoying your cruise. >> There's something that you mentioned. You want a balance. You want people to take advantage of what's there. You used to think of a vacation like this, you'd disconnect yourself. Help understand that balance. >> You'd be surprised. We were just recently on a cruise, my family and I, and we don't cruise as often as you would imagine. >> Because you work for the company. >> Even though, when you do, it feels good to be a customer, right? There's so much activity going on on a ship on a given day. It's very hard to understand where to be at a certain point in time, and some people find that overwhelming. What things like the app does is really allow you to curate your day. To say hey, you like music? Let's focus on events that are music-oriented and that's going to be in Location XYZ on the ship. And they're going to be sequenced. So, that's personalizing the experience. But it's also ensuring that folks are really taking advantage of the full product. >> From our perspective, the technology should be in the background. It's more complementary. The real product is really the ship, the crew members, the activities, the entertainment on board. That's the product we really want people to really connect to. The stuff that we do is auxiliary in terms of, let me help you maximize those experiences on board. And that's what we're really trying to do. If we can get that done and accomplished, than we have done our jobs. >> So the app is the digital conduit to the physical experience >> Exactly. >> If you have a good app, it makes all the difference in the world. If you're at Disney, and you're trying to figure out what's next, what do the lines look like? You get a lot of people on a ship, and you want to prioritize. You all call that curating your experience. It's all about the app, as they say. What's the state of the app? The 1.0 probably needed a little work. Where are you know in the evolution? >> We're in a 2.0 release version of it. The original version, we started with what we called the meat and potatoes. The very basic stuff, that hey, where can I get food? What is the entertainment lineup for the day? We started off with some innovation in terms of being able to generate, we did a chat, kind of like, communication, so people could chat with their families onboard without having to purchase a plan or have any bandwidth needs. And then, as we evolved that, then we started to go into things that are more transactional. So, you're able to purchase your photos digitally through the app. We leveraged facial recognition software, so that if a photographer on a ship takes a picture of you, it recognizes that as you and puts your photo in your photo stream and your photo album. So, very, very convenient. We do things like sell shore excursions in terms of transactional stuff. You can sit at the pool and say "oh, tomorrow's a port day, "I'm going to be in the Bahamas. "Let me see what shore excursion I want to do. And you can do it directly from the app without even moving. So now, as we evolve that now, as Ariel said, now we're trying to leverage all that data now, to go beyond the transactions, and make things even more personalized. So, I know that you favor the casino, maybe you're a spa person, you want a facial. We'll target you and say hey, on your previous cruise you did this. Let's target you because we might have something special waiting for you onboard. >> And then carry that across the journey, right. So now they leave our ships. And how do we get them to come back to our ships? How do you create that conversation that's ongoing, notifications about what's going on on our ships. People follow their favorite cruise director. People follow a lot of the unique experiences there. How do you bring that to the online, to the dot-com experience? So that when they're thinking about that next cruise, they can remember what that last cruise was about, and they can know what's happening on each one of our ships in real-time. It's a journey. And technology definitely is a huge enabler for us and the experience. >> So what's the data architecture look like on there? We always talk on theCUBE about the innovation sandwich of the future. It used to be Moore's Law, doubling every two years. Okay, great. Now, it's data, plus machine intelligence, and you scale with the cloud. What's your data architecture look like? >> Well, I think it's early days. I think it's, I mean, they're all over the place, right? I think there's silos within the enterprise that are really maximizing data. I think that that trend continues to happen. But I think there's got to be, and the enterprise architecture world is sort of about wrangling that, and figuring out how data from different dispersed touch points affect that. So, it's early days. I do think that you're starting to see that machine learning algorithms do play a part. I'm seeing it personally, more in the operations side of the world. So all these systems, at the end of the day, they need to be resilient and they need to have high service levels. So, what I'm seeing now is tools, and at Splunk, you saw that today, being able to be really predictive about where the anomalies are. Traditionally, you were having to log errors and then interpret errors, and then that would be the way you action some of these things. The predictive nature of some of these tools are such that you're being proactive. So when you talk about data there's so many different places you can go. If you think about our technology stack, and that guest experience point of view, it's all about really maintaining that SLA's, resolving issues as quickly as possible. And there's a ton of data in that space, right? I mean, it's everywhere, there's a ton of signals. >> Well you guys know, we tend not to throw stuff away in technology. You sort of have to figure out how to integrate. >> A signal via the customer is probably one of those, as well. So at the end of the day, what more information are we collecting about our guest to ultimately personalize that experience? It's centered around that. >> And that's challenging, I mean, look at the airlines. And your app, which you love the airline apps. I mean, you're not, like, tethered to them. But the phone experience, and even the laptop experience, are a little bit different. Because of the data, it's very, very challenging. Have you figured that out? Or are you sort of figuring that out? >> That's API's, right? It's that experienced API layer. Being able to activate that data which is sitting in distinct silos and then do so across those experience apps, the experience channels, which is dot-com, the app, the chatbot, there's so many interfaces out there. But, yeah, it's a solid, mature API strategy that's going to get us there. >> And I think one of the things that our challenge is, as technology partners, is the ability to build those platforms so that the next wave of conversions, as you mentioned, there's some disjointed experience across the desktop view versus the mobile view, is to try to bring those conversions together. And in order to that, like Ariel said, maybe making some API extraction layers figuring out how to mine the data better, figuring out how to leverage insights from different tools or machines and sensors, we have a ton of sensors on these ships as well. And bringing all those things together to be able to put us in a position that when we do finally get a seamless conversion, we're ready for it from a technology and a platform perspective. >> It's obvious why data is important for your business. You actually did a press release with Splunk. Maybe explain a little about how Splunk Cloud fits into this discussion that we've been having? >> Well, Cloud really removes the barriers of experimentation. How do you right-size a problem you don't understand very well? I think Cloud really helps with that. We're looking forward to being able to be flexible. Flexibility in architecture, flexibility in infrastructure. So that's absolutely the use-case I think security's got a number of use-cases. You see it every day in the news. So yeah, more opportunities, I would say, it scales that flexibility that's taken us the cloud route. >> When you think Splunk, you think security. You got guys in the Knock. That's not where you guys are. You're kind of closer to the business. And so you're seeing Splunk, as I said before, permeate into other parts of the organization. You kind of expected somebody else to do that. I don't know, the Hadoop guys. And it's interesting, Splunk never used to talk about big data. Now that the big data era is, sort of, behind us, Splunk talks a lot about big data. It's kind of an interesting flip. >> I would say it's democratizing the data. That's the stuff I liked, that I heard today. How do you get these tools away from the IT operators that are writing these complex queries to get insights? And how do you elevate that up to the analysts, and the product managers? And how do they get access to those interfaces? You know, drag-and-drop, whatever you want to call it. But I think that where I see this happening more so than, machine learning, that's great and predictive. But just empowering others to really leverage that data. I would say Splunk is leading there and it's good to see some of that stuff today. >> Absolutely. It's putting the power where it really needs to be, where it's the end users, the guys making decisions, it's the product owners, the product managers, that are making those slight tweaks to that interface, or to that design, or to that experience, that makes a difference. And that's what we're trying to do, and leverage with tools like Splunk, as well. >> Even the simple visualization, right, the stuff that's out of the box is really important for the business user, right? >> The out of the box part's another thing that I saw today, which is more, sort of, curating for particular use-case, and saying hey, we're going to build that end-to-end and really turn it on and activate it a little sooner. So that infrastructure product we saw today, I think that's a big step forward. Where you're a platform, but at some point you're going to have to start being a little more vertical in the way that you bring to market, the way that they did with security. >> And Doug talked about, you know, Doug Merritt, that is, talked about data is messy, and the messiest landscape is the data. And then he talked about being able to organize that data in the moment. So, I think about, okay, just put it in the, we like to call data ocean, right, and just capture it. But then having the tools to be able to actually look at it in whatever schema you want, when you want it, is a challenge that people have. My question is, did he describe it accurately? I think yes. But then, can you actually do that with this messy data? >> I think it's a great concept. I'm interested to see how that plays out going forward. But I think in our world, we have several use-cases where that makes sense. We have a very captive audience for seven to 10 days. So we really have a very limited amount of time to make a really good impression. So, it's not only about attracting first-time cruisers; it's trying to get a repeat cruiser. So that limited time frame that we have to leave a really lasting impression is very limited. So things like recovery, in terms of getting metrics or data real-time, and being able to act on it immediately. Say you had a bad experience at the sushi bar. If we're able to grab that information, whatever data points that allow us to understand what happened, and then do a quick recovery, we may have a guest for a repeat cruise. Those are the things that we're trying to do. And, if what Doug is saying is something that they've kind of solved, or are able to try to solve in a good way, that is very powerful for us as well, and we definitely see leverage in that. >> Last question, Ariel, you're saying off-camera it's kind of early days. What's the future hold? I mean, that's going to blow our minds. Blow our minds! >> Oh, it's the predictive thing, right? It's bringing you your favorite drink before you're ready to have it, or something. I don't know. The cruise line business, the travel and hospitality space is a very fun space to work in. We get to really see our guests enjoy the product. And us, as technologists, we get to see how technology moves the needle. Continued innovation, right? If you're in the development side of the world, challenging yourself to deploy more often, to deliver more value more often. And if you're on the data side, how to get aggregated, compile all this this data, for ultimately what we're looking for, which is to enhance the guest experience. >> I mean, that real-time notion that you were talking about Curt, you can see that coming together and completely transforming the guest experience. So guys, thanks so much for coming on theCUBE. It was great to have you. Congratulations on all your success and good luck. Alright keep it right there everybody, we'll be back at Splunk .conf18. You're watching theCUBE. Dave Vellante with Stu Miniman. we'll be right back! (upbeat music)

>> Narrator: Live, from Washington D.C., it's theCUBE. Covering .conf2017, brought to you by Splunk. >> Well, welcome back to .conf2017. Here we are at Splunk's annual get together, with Dave Vellante, I'm John Walls. We are live in the Walter Washington Convention Center, in beautiful Washington D.C. I say that, proud to be a native. Actually raised here, lived here, fly the flag here. >> Wow. >> This is my place, Dave. >> Listen, I love this city. >> I do too. >> I love coming down here. Lots to do, my son's down here, so. >> But if we weren't here, where should we be, maybe on the deck of a Carnival cruise line ship right now? >> That would be good. >> I would like that. >> I would love to have theCUBE on the deck of a Carnival >> Maybe, maybe Ruel Waite can swing that. What do you think? Ruel Waite joins us. He is the manager of delivery and support for Carnival. And you got room for two on the next ship out of Miami? >> Listen, man, for you guys anything. >> I love that. Alright, you're hired. >> I can make it happen. >> Outstanding. Alright Ruel, thanks for being here with us. >> No problem. >> On theCUBE, glad to have you, and here at the show as well. Alright, so let's talk about first off, Splunk. What are you doing? Let's back up, in terms of what you do. Your core responsibilities and then we'll get into Splunk story after that. >> Yeah, so I manage the support operation for our ecommerce platform, as well as for the guest facing ship board application. So the ecommerce platforms is where you go and purchase your cabin on the web. You would also be able to purchase your show excursions, your spa treatments, as well. Or we have an e-retail site where if you have a friend who's sailing you can buy a bottle of champagne and have it in their room for when they get there. So all those purchasing perks now that we support on the ecommerce platform. And then the guest facing application, Shipboard, we're talking 'about the mobile application where guests chat and interact with each other or plan their day. We're talking about the Pixels application where guests are purchase their photos that they take throughout their cruise. And their some facial recognition stuff there as well. And the iTV that's in your room. So we have a separate, many different sort of applications that fit under that portfolio. >> Let's talk about the data. >> Yes. >> A lot of data that you just created. >> Right? >> Yup. >> What's the data pipeline look like, where does Splunk fit? >> We Splunk as much as we can and we're continuing to build that as we go. Our application logs are Splunk, everything we produce from the application. Also our performance metrics from our servers and our data and our network, and all those systems, we Splunk that because that's critical for us to triage issues that occurring. Because our operation is about monitoring what's happening, it's about resolving issues as quickly as possible, and it's about communicating to our business. So those three things are data essential to all of that. So we need to get as much as we can and we need to be able to get insights into it. >> Can you talk about where you started, you had mentioned off camera about four years ago, and how you've been able to inject automation into your processes and just take us through your journey. >> Yeah, so we started a few years ago with Splunk, and it was primarily a triage tool for us. So an incident would occur, we'd try to get it, and look at some logs, figure out what's going on. And as we've evolved it's become more of a proactive alerting tool for us, it's become a communication tool, a collaborative tool, for us. You know, we leverage things like the ITSI, right. That allows us to understand the base line behavior of our system. Once we base line that then we can understand the spikes, we can understand when things are changing, and that allows us to react and quickly identify things, defects in our system, things that are occurring, and resolve them. So once we kind of got our legs around okay, we get how to use Splunk to find stuff, now let's figure out how to get Splunk to tell us stuff. >> Okay. >> Right? And now once Splunk is telling us stuff, let's figure out how we tell the business that stuff. So that's kind of how we the journey we've had with Splunk. >> And Splunk's in that thread the whole way? >> The whole way. >> So from, >> The whole. >> So, ultimately then, right now what are you putting into practice that you didn't have available >> Yeah, sure. >> two, three years ago? >> Yeah sure, so one of the challenges we had was, with a typical ecommerce site you have several layers of the application, right. You have your web server, you have caching infrastructure, you have a database server, yet we have a mainframe reservation system as well. So there are several things involved with supporting all those different platforms. Now when we have an incident, it's sometimes challenging to, you know you get somebody on the phone, you're like hey what are you seeing over there on the mainframe side? Well I see this error occurring. Oh and the database side they're telling you okay, we're seeing some sort of timeout here, but we're not sure if it's related to the same thing you're talking about. And we didn't have a way to tie it together. But by using Splunk Transactions what we decided to do was we decided to log the session ID, the web servers session ID across all our layers, right, and push that through, and that allows us to tie those transactions together across those layers. And now when we have an incident we're able to, when we're talking to the mainframe we're saying hey guy, hey go look at this. And he say here's what I'm seeing. >> You can isolate it? >> We can isolate it, we can pull it together, and it's really helpful. >> So will you get to the point, or you were trying to get to the point, where you can automate the remediation? Or is that something you don't want to do 'cause you want humans involved? >> You know, automation is good. And whatever we can automate we try to do that. At this point we're not automating the resolution through Splunk at this time, but what we are doing is we are providing the on call, or the engineer that are responding with as much information as we can in order to have them quickly flip that switch. So if we have an alert that we know, hey this issue requires a recycle of an application pool, or some kind of other action like that, we can put that in our Splunk alert. And we say hey we're seeing this issue occur. That email and that text message that goes out actually tells the engineer that these are the suggested actions that you can take in order to quickly resolve this issue. >> Ruel, what are you hearing from the business side? What are the business drivers and how is that effecting what you're doing in IT generally, and specifically with data and Splunk? >> Okay so from business side we're looking at most bookings is the one of the major metrics that we look at. And our guest experience. So and on the web that means the site needs to be available, it needs to perform, and it needs to work. So what we really are trying to do with Splunk is understand those issues that are impacting our guests on the booking side. What that means is we need to know how well we're converting. And if we're looking at homepage performance, and we can now tell hey if our homepage loads in five seconds verses three seconds, there are how many fewer people make it to our payment page, which is huge for us. So that's something that we really try to hone in on. And it really helps us to collaborate with the business and understand, really, what is the revenue impact of these IT metrics that we're spitting out. >> But there could be other factors involved in that too, >> Yes. >> other variables, right? >> There are. >> You can't just you know this is, but you have enough of a track record the are a couple reasons to say okay, five seconds means this, we get a 30% conversion rate. We get three seconds, man, we got 'em hello, and, now we have a 50%, whatever. >> Yeah, but that is where, what I'm excited about at the conference is the machine learning capabilities that we've been hearing about. 'Cause that will allow us to then model how those different factors that go into when someone goes from the homepage to payment, you're totally right. There's several things that go into that. And what we want to be able to model, hey, on a normal day here's our guest behavior, whether we have a sale, how do our guests behavior differently, or on a Monday night at eight PM what is the behavioral trend. So it's all important to us. And getting the data behind it and being able to model that is going to be really key for us. >> Connect the dots for me on >> Yes. >> how you use machine learning, and how will that affect the business? You'll make different offers at different times, or? >> So what I mean is if I understand how guests behave I will know if I'm having an issue on the site. If there's something happening that's impacting their ability to book. 'Cause sometimes you do a release, you do your quality control, and then you go home, everything looks good. And sometimes hours later, sometimes days later unfortunately, something pops up that you introduced during that release. And understanding what that baseline is, right. So what Splunk has allowed us to do is say okay, here's what normal behavior is. And we're trying to grow this more, but what we've been using ITSI to say here's what that behavior really is. Based on what we kind of know are the metrics around booking. Here's what that behavior is. And we do a release and we see a spike, a change, and now we're able to say wait a minute, we never saw this error before. This error never existed in our system at any point. That was definitely something that was introduced right here in this release, we need to go ahead and resolve this as well. And sometimes you get some false positives there, if your development team is doing change the way they log a little bit you might get a spike. But that's cool because you get to go in immediately and figure out what those changes are, and you get a comfort level that you kind of understand how your system works. >> Let me ask you another question. You got some experience with Splunk. >> Yes. >> Obviously, you were just working with them. What, in your mind, is on their to do list? What do you want to see out of them? Doug, if I'm Doug. Tell me, where should I go, what should I do. >> What do I want Splunk to do. >> Any gripes, give me the good, the bad, and the ugly. >> For me, it's performance, performance, performance. I want to see my queries run as quickly as possible. I want to see things fast. I want to hit the button and it happens right away. Now obviously that's not going to, that's not realistic. But I like what some of the things that Splunk are doing. You look at the new metrics index that they've been talking about the last two days. So they've now isolated your time serious data and they're able to optimize the searches on time serious data seperate from your application logs. So, you know, your CPUs, your memory consumption, that data is not the same as your logging an error, or logging that a booking was created, or something like that. Those are kind of two different things. So they have kind of decoupled that and they're saying anything that's time serious I'm going to put it over here. And I'm going to optimize that query, and then you can handle your other logs separately. But the additional benefit of that is then you can take your time serious and you can look at a CPU spike and then you can take your event data and overlay it on top. And then you can see, hey wait a minute, this event is what caused that spike. So that's where the cool is. >> I think they call that mstats. Is that right, mstats? >> Yes, it's mstats, yes. >> How 'about the stuff that you saw this week in the keynotes, particularly today was the product stuff. A lot of security obviously. Anything that you've seen here at the show that excites you, that you really said alright, I got to have that, I got to learn more? >> Yeah, so the ITSI event analytics really seems like something's going to be cool for us. As I've said before, we utilize ITSI internally. So we put together a glass table that's shows us here are all the different components and the hierarchy of things. And when this goes red it effects these other layers. And it's really cool. But what they've added in is the ability to click a button and drill in to those components and then you have a view of hey, here are the events associated with that. That's really cool because now you're triaging in one place, now you get to the problem really quick. And you can emote directly into your Splunk queries. It really allows what we're looking for is just to resolve issues as quickly as possible. >> And you're describing, if I understand this correctly, you can visualize the dependencies, and you can take remedial action or identify, inform the business what to expect. >> Exactly. >> Be much more proactive, that's what people are talking about. >> Yeah, yeah. And we found that one of the surprising things we found with Splunk is that our business are users of Splunk as well, right. So it's always an IT tool, it's something that only the geeks are going to look at. And then all of a sudden you present a dashboard to a business user and they go ah. That's pretty, right. And then all of a sudden they want it more than you do. So that's what makes it great right, 'cause you can present the data however you want and you can put it in a way that different audiences can consume. And so it becomes a platform that goes across the organization, which is really, really cool. >> John: But your bottom line's all speed right? >> Yes, yeah. >> Take care of my problems faster, get my customer faster, deliver faster, come on Splunk. >> Come on, let's go. >> We want to go. >> Brings the weekend faster. >> Right, right. >> Get more sleep, get more sleep. >> Ruel, thanks for being with us. >> Oh. >> We appreciate that. >> And, we'll talk about the cruise. Leonard Nelson, our producer over here already said book him for a massage, the presidential suite. He wants one night, and then the champagne buffet please. >> It's done. >> Fast internet, though. >> Yeah. >> Fast internet, yeah. It's done. >> Alright. We're simple people, we don't need all that, but we'll talk later. >> Alright man, appreciate it, thank you. >> Thank you for being with us. Ruel Waite joining us from Carnival. Back with more from Splunk, .conf2017. 2015, where did that come from? 2017, it's been a long day. (upbeat music)

(upbeat music) >> Hello everyone. Welcome to theCube's presentation of the "AWS Startup Showcase." The topic this episode is AI and machine learning, top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering exciting startups from the AWS ecosystem. And this time we're talking about AI and machine learning. I'm your host, John Furrier. I'm excited I'm joined today by Robert Nishihara, who's the co-founder and CEO of a hot startup called Anyscale. He's here to talk about Ray, the open source project, Anyscale's infrastructure for foundation as well. Robert, thank you for joining us today. >> Yeah, thanks so much as well. >> I've been following your company since the founding pre pandemic and you guys really had a great vision scaled up and in a perfect position for this big wave that we all see with ChatGPT and OpenAI that's gone mainstream. Finally, AI has broken out through the ropes and now gone mainstream, so I think you guys are really well positioned. I'm looking forward to to talking with you today. But before we get into it, introduce the core mission for Anyscale. Why do you guys exist? What is the North Star for Anyscale? >> Yeah, like you mentioned, there's a tremendous amount of excitement about AI right now. You know, I think a lot of us believe that AI can transform just every different industry. So one of the things that was clear to us when we started this company was that the amount of compute needed to do AI was just exploding. Like to actually succeed with AI, companies like OpenAI or Google or you know, these companies getting a lot of value from AI, were not just running these machine learning models on their laptops or on a single machine. They were scaling these applications across hundreds or thousands or more machines and GPUs and other resources in the Cloud. And so to actually succeed with AI, and this has been one of the biggest trends in computing, maybe the biggest trend in computing in, you know, in recent history, the amount of compute has been exploding. And so to actually succeed with that AI, to actually build these scalable applications and scale the AI applications, there's a tremendous software engineering lift to build the infrastructure to actually run these scalable applications. And that's very hard to do. So one of the reasons many AI projects and initiatives fail is that, or don't make it to production, is the need for this scale, the infrastructure lift, to actually make it happen. So our goal here with Anyscale and Ray, is to make that easy, is to make scalable computing easy. So that as a developer or as a business, if you want to do AI, if you want to get value out of AI, all you need to know is how to program on your laptop. Like, all you need to know is how to program in Python. And if you can do that, then you're good to go. Then you can do what companies like OpenAI or Google do and get value out of machine learning. >> That programming example of how easy it is with Python reminds me of the early days of Cloud, when infrastructure as code was talked about was, it was just code the infrastructure programmable. That's super important. That's what AI people wanted, first program AI. That's the new trend. And I want to understand, if you don't mind explaining, the relationship that Anyscale has to these foundational models and particular the large language models, also called LLMs, was seen with like OpenAI and ChatGPT. Before you get into the relationship that you have with them, can you explain why the hype around foundational models? Why are people going crazy over foundational models? What is it and why is it so important? >> Yeah, so foundational models and foundation models are incredibly important because they enable businesses and developers to get value out of machine learning, to use machine learning off the shelf with these large models that have been trained on tons of data and that are useful out of the box. And then, of course, you know, as a business or as a developer, you can take those foundational models and repurpose them or fine tune them or adapt them to your specific use case and what you want to achieve. But it's much easier to do that than to train them from scratch. And I think there are three, for people to actually use foundation models, there are three main types of workloads or problems that need to be solved. One is training these foundation models in the first place, like actually creating them. The second is fine tuning them and adapting them to your use case. And the third is serving them and actually deploying them. Okay, so Ray and Anyscale are used for all of these three different workloads. Companies like OpenAI or Cohere that train large language models. Or open source versions like GPTJ are done on top of Ray. There are many startups and other businesses that fine tune, that, you know, don't want to train the large underlying foundation models, but that do want to fine tune them, do want to adapt them to their purposes, and build products around them and serve them, those are also using Ray and Anyscale for that fine tuning and that serving. And so the reason that Ray and Anyscale are important here is that, you know, building and using foundation models requires a huge scale. It requires a lot of data. It requires a lot of compute, GPUs, TPUs, other resources. And to actually take advantage of that and actually build these scalable applications, there's a lot of infrastructure that needs to happen under the hood. And so you can either use Ray and Anyscale to take care of that and manage the infrastructure and solve those infrastructure problems. Or you can build the infrastructure and manage the infrastructure yourself, which you can do, but it's going to slow your team down. It's going to, you know, many of the businesses we work with simply don't want to be in the business of managing infrastructure and building infrastructure. They want to focus on product development and move faster. >> I know you got a keynote presentation we're going to go to in a second, but I think you hit on something I think is the real tipping point, doing it yourself, hard to do. These are things where opportunities are and the Cloud did that with data centers. Turned a data center and made it an API. The heavy lifting went away and went to the Cloud so people could be more creative and build their product. In this case, build their creativity. Is that kind of what's the big deal? Is that kind of a big deal happening that you guys are taking the learnings and making that available so people don't have to do that? >> That's exactly right. So today, if you want to succeed with AI, if you want to use AI in your business, infrastructure work is on the critical path for doing that. To do AI, you have to build infrastructure. You have to figure out how to scale your applications. That's going to change. We're going to get to the point, and you know, with Ray and Anyscale, we're going to remove the infrastructure from the critical path so that as a developer or as a business, all you need to focus on is your application logic, what you want the the program to do, what you want your application to do, how you want the AI to actually interface with the rest of your product. Now the way that will happen is that Ray and Anyscale will still, the infrastructure work will still happen. It'll just be under the hood and taken care of by Ray in Anyscale. And so I think something like this is really necessary for AI to reach its potential, for AI to have the impact and the reach that we think it will, you have to make it easier to do. >> And just for clarification to point out, if you don't mind explaining the relationship of Ray and Anyscale real quick just before we get into the presentation. >> So Ray is an open source project. We created it. We were at Berkeley doing machine learning. We started Ray so that, in order to provide an easy, a simple open source tool for building and running scalable applications. And Anyscale is the managed version of Ray, basically we will run Ray for you in the Cloud, provide a lot of tools around the developer experience and managing the infrastructure and providing more performance and superior infrastructure. >> Awesome. I know you got a presentation on Ray and Anyscale and you guys are positioning as the infrastructure for foundational models. So I'll let you take it away and then when you're done presenting, we'll come back, I'll probably grill you with a few questions and then we'll close it out so take it away. >> Robert: Sounds great. So I'll say a little bit about how companies are using Ray and Anyscale for foundation models. The first thing I want to mention is just why we're doing this in the first place. And the underlying observation, the underlying trend here, and this is a plot from OpenAI, is that the amount of compute needed to do machine learning has been exploding. It's been growing at something like 35 times every 18 months. This is absolutely enormous. And other people have written papers measuring this trend and you get different numbers. But the point is, no matter how you slice and dice it, it' a astronomical rate. Now if you compare that to something we're all familiar with, like Moore's Law, which says that, you know, the processor performance doubles every roughly 18 months, you can see that there's just a tremendous gap between the needs, the compute needs of machine learning applications, and what you can do with a single chip, right. So even if Moore's Law were continuing strong and you know, doing what it used to be doing, even if that were the case, there would still be a tremendous gap between what you can do with the chip and what you need in order to do machine learning. And so given this graph, what we've seen, and what has been clear to us since we started this company, is that doing AI requires scaling. There's no way around it. It's not a nice to have, it's really a requirement. And so that led us to start Ray, which is the open source project that we started to make it easy to build these scalable Python applications and scalable machine learning applications. And since we started the project, it's been adopted by a tremendous number of companies. Companies like OpenAI, which use Ray to train their large models like ChatGPT, companies like Uber, which run all of their deep learning and classical machine learning on top of Ray, companies like Shopify or Spotify or Instacart or Lyft or Netflix, ByteDance, which use Ray for their machine learning infrastructure. Companies like Ant Group, which makes Alipay, you know, they use Ray across the board for fraud detection, for online learning, for detecting money laundering, you know, for graph processing, stream processing. Companies like Amazon, you know, run Ray at a tremendous scale and just petabytes of data every single day. And so the project has seen just enormous adoption since, over the past few years. And one of the most exciting use cases is really providing the infrastructure for building training, fine tuning, and serving foundation models. So I'll say a little bit about, you know, here are some examples of companies using Ray for foundation models. Cohere trains large language models. OpenAI also trains large language models. You can think about the workloads required there are things like supervised pre-training, also reinforcement learning from human feedback. So this is not only the regular supervised learning, but actually more complex reinforcement learning workloads that take human input about what response to a particular question, you know is better than a certain other response. And incorporating that into the learning. There's open source versions as well, like GPTJ also built on top of Ray as well as projects like Alpa coming out of UC Berkeley. So these are some of the examples of exciting projects in organizations, training and creating these large language models and serving them using Ray. Okay, so what actually is Ray? Well, there are two layers to Ray. At the lowest level, there's the core Ray system. This is essentially low level primitives for building scalable Python applications. Things like taking a Python function or a Python class and executing them in the cluster setting. So Ray core is extremely flexible and you can build arbitrary scalable applications on top of Ray. So on top of Ray, on top of the core system, what really gives Ray a lot of its power is this ecosystem of scalable libraries. So on top of the core system you have libraries, scalable libraries for ingesting and pre-processing data, for training your models, for fine tuning those models, for hyper parameter tuning, for doing batch processing and batch inference, for doing model serving and deployment, right. And a lot of the Ray users, the reason they like Ray is that they want to run multiple workloads. They want to train and serve their models, right. They want to load their data and feed that into training. And Ray provides common infrastructure for all of these different workloads. So this is a little overview of what Ray, the different components of Ray. So why do people choose to go with Ray? I think there are three main reasons. The first is the unified nature. The fact that it is common infrastructure for scaling arbitrary workloads, from data ingest to pre-processing to training to inference and serving, right. This also includes the fact that it's future proof. AI is incredibly fast moving. And so many people, many companies that have built their own machine learning infrastructure and standardized on particular workflows for doing machine learning have found that their workflows are too rigid to enable new capabilities. If they want to do reinforcement learning, if they want to use graph neural networks, they don't have a way of doing that with their standard tooling. And so Ray, being future proof and being flexible and general gives them that ability. Another reason people choose Ray in Anyscale is the scalability. This is really our bread and butter. This is the reason, the whole point of Ray, you know, making it easy to go from your laptop to running on thousands of GPUs, making it easy to scale your development workloads and run them in production, making it easy to scale, you know, training to scale data ingest, pre-processing and so on. So scalability and performance, you know, are critical for doing machine learning and that is something that Ray provides out of the box. And lastly, Ray is an open ecosystem. You can run it anywhere. You can run it on any Cloud provider. Google, you know, Google Cloud, AWS, Asure. You can run it on your Kubernetes cluster. You can run it on your laptop. It's extremely portable. And not only that, it's framework agnostic. You can use Ray to scale arbitrary Python workloads. You can use it to scale and it integrates with libraries like TensorFlow or PyTorch or JAX or XG Boost or Hugging Face or PyTorch Lightning, right, or Scikit-learn or just your own arbitrary Python code. It's open source. And in addition to integrating with the rest of the machine learning ecosystem and these machine learning frameworks, you can use Ray along with all of the other tooling in the machine learning ecosystem. That's things like weights and biases or ML flow, right. Or you know, different data platforms like Databricks, you know, Delta Lake or Snowflake or tools for model monitoring for feature stores, all of these integrate with Ray. And that's, you know, Ray provides that kind of flexibility so that you can integrate it into the rest of your workflow. And then Anyscale is the scalable compute platform that's built on top, you know, that provides Ray. So Anyscale is a managed Ray service that runs in the Cloud. And what Anyscale does is it offers the best way to run Ray. And if you think about what you get with Anyscale, there are fundamentally two things. One is about moving faster, accelerating the time to market. And you get that by having the managed service so that as a developer you don't have to worry about managing infrastructure, you don't have to worry about configuring infrastructure. You also, it provides, you know, optimized developer workflows. Things like easily moving from development to production, things like having the observability tooling, the debug ability to actually easily diagnose what's going wrong in a distributed application. So things like the dashboards and the other other kinds of tooling for collaboration, for monitoring and so on. And then on top of that, so that's the first bucket, developer productivity, moving faster, faster experimentation and iteration. The second reason that people choose Anyscale is superior infrastructure. So this is things like, you know, cost deficiency, being able to easily take advantage of spot instances, being able to get higher GPU utilization, things like faster cluster startup times and auto scaling. Things like just overall better performance and faster scheduling. And so these are the kinds of things that Anyscale provides on top of Ray. It's the managed infrastructure. It's fast, it's like the developer productivity and velocity as well as performance. So this is what I wanted to share about Ray in Anyscale. >> John: Awesome. >> Provide that context. But John, I'm curious what you think. >> I love it. I love the, so first of all, it's a platform because that's the platform architecture right there. So just to clarify, this is an Anyscale platform, not- >> That's right. >> Tools. So you got tools in the platform. Okay, that's key. Love that managed service. Just curious, you mentioned Python multiple times, is that because of PyTorch and TensorFlow or Python's the most friendly with machine learning or it's because it's very common amongst all developers? >> That's a great question. Python is the language that people are using to do machine learning. So it's the natural starting point. Now, of course, Ray is actually designed in a language agnostic way and there are companies out there that use Ray to build scalable Java applications. But for the most part right now we're focused on Python and being the best way to build these scalable Python and machine learning applications. But, of course, down the road there always is that potential. >> So if you're slinging Python code out there and you're watching that, you're watching this video, get on Anyscale bus quickly. Also, I just, while you were giving the presentation, I couldn't help, since you mentioned OpenAI, which by the way, congratulations 'cause they've had great scale, I've noticed in their rapid growth 'cause they were the fastest company to the number of users than anyone in the history of the computer industry, so major successor, OpenAI and ChatGPT, huge fan. I'm not a skeptic at all. I think it's just the beginning, so congratulations. But I actually typed into ChatGPT, what are the top three benefits of Anyscale and came up with scalability, flexibility, and ease of use. Obviously, scalability is what you guys are called. >> That's pretty good. >> So that's what they came up with. So they nailed it. Did you have an inside prompt training, buy it there? Only kidding. (Robert laughs) >> Yeah, we hard coded that one. >> But that's the kind of thing that came up really, really quickly if I asked it to write a sales document, it probably will, but this is the future interface. This is why people are getting excited about the foundational models and the large language models because it's allowing the interface with the user, the consumer, to be more human, more natural. And this is clearly will be in every application in the future. >> Absolutely. This is how people are going to interface with software, how they're going to interface with products in the future. It's not just something, you know, not just a chat bot that you talk to. This is going to be how you get things done, right. How you use your web browser or how you use, you know, how you use Photoshop or how you use other products. Like you're not going to spend hours learning all the APIs and how to use them. You're going to talk to it and tell it what you want it to do. And of course, you know, if it doesn't understand it, it's going to ask clarifying questions. You're going to have a conversation and then it'll figure it out. >> This is going to be one of those things, we're going to look back at this time Robert and saying, "Yeah, from that company, that was the beginning of that wave." And just like AWS and Cloud Computing, the folks who got in early really were in position when say the pandemic came. So getting in early is a good thing and that's what everyone's talking about is getting in early and playing around, maybe replatforming or even picking one or few apps to refactor with some staff and managed services. So people are definitely jumping in. So I have to ask you the ROI cost question. You mentioned some of those, Moore's Law versus what's going on in the industry. When you look at that kind of scale, the first thing that jumps out at people is, "Okay, I love it. Let's go play around." But what's it going to cost me? Am I going to be tied to certain GPUs? What's the landscape look like from an operational standpoint, from the customer? Are they locked in and the benefit was flexibility, are you flexible to handle any Cloud? What is the customers, what are they looking at? Basically, that's my question. What's the customer looking at? >> Cost is super important here and many of the companies, I mean, companies are spending a huge amount on their Cloud computing, on AWS, and on doing AI, right. And I think a lot of the advantage of Anyscale, what we can provide here is not only better performance, but cost efficiency. Because if we can run something faster and more efficiently, it can also use less resources and you can lower your Cloud spending, right. We've seen companies go from, you know, 20% GPU utilization with their current setup and the current tools they're using to running on Anyscale and getting more like 95, you know, 100% GPU utilization. That's something like a five x improvement right there. So depending on the kind of application you're running, you know, it's a significant cost savings. We've seen companies that have, you know, processing petabytes of data every single day with Ray going from, you know, getting order of magnitude cost savings by switching from what they were previously doing to running their application on Ray. And when you have applications that are spending, you know, potentially $100 million a year and getting a 10 X cost savings is just absolutely enormous. So these are some of the kinds of- >> Data infrastructure is super important. Again, if the customer, if you're a prospect to this and thinking about going in here, just like the Cloud, you got infrastructure, you got the platform, you got SaaS, same kind of thing's going to go on in AI. So I want to get into that, you know, ROI discussion and some of the impact with your customers that are leveraging the platform. But first I hear you got a demo. >> Robert: Yeah, so let me show you, let me give you a quick run through here. So what I have open here is the Anyscale UI. I've started a little Anyscale Workspace. So Workspaces are the Anyscale concept for interactive developments, right. So here, imagine I'm just, you want to have a familiar experience like you're developing on your laptop. And here I have a terminal. It's not on my laptop. It's actually in the cloud running on Anyscale. And I'm just going to kick this off. This is going to train a large language model, so OPT. And it's doing this on 32 GPUs. We've got a cluster here with a bunch of CPU cores, bunch of memory. And as that's running, and by the way, if I wanted to run this on instead of 32 GPUs, 64, 128, this is just a one line change when I launch the Workspace. And what I can do is I can pull up VS code, right. Remember this is the interactive development experience. I can look at the actual code. Here it's using Ray train to train the torch model. We've got the training loop and we're saying that each worker gets access to one GPU and four CPU cores. And, of course, as I make the model larger, this is using deep speed, as I make the model larger, I could increase the number of GPUs that each worker gets access to, right. And how that is distributed across the cluster. And if I wanted to run on CPUs instead of GPUs or a different, you know, accelerator type, again, this is just a one line change. And here we're using Ray train to train the models, just taking my vanilla PyTorch model using Hugging Face and then scaling that across a bunch of GPUs. And, of course, if I want to look at the dashboard, I can go to the Ray dashboard. There are a bunch of different visualizations I can look at. I can look at the GPU utilization. I can look at, you know, the CPU utilization here where I think we're currently loading the model and running that actual application to start the training. And some of the things that are really convenient here about Anyscale, both I can get that interactive development experience with VS code. You know, I can look at the dashboards. I can monitor what's going on. It feels, I have a terminal, it feels like my laptop, but it's actually running on a large cluster. And I can, with however many GPUs or other resources that I want. And so it's really trying to combine the best of having the familiar experience of programming on your laptop, but with the benefits, you know, being able to take advantage of all the resources in the Cloud to scale. And it's like when, you know, you're talking about cost efficiency. One of the biggest reasons that people waste money, one of the silly reasons for wasting money is just forgetting to turn off your GPUs. And what you can do here is, of course, things will auto terminate if they're idle. But imagine you go to sleep, I have this big cluster. You can turn it off, shut off the cluster, come back tomorrow, restart the Workspace, and you know, your big cluster is back up and all of your code changes are still there. All of your local file edits. It's like you just closed your laptop and came back and opened it up again. And so this is the kind of experience we want to provide for our users. So that's what I wanted to share with you. >> Well, I think that whole, couple of things, lines of code change, single line of code change, that's game changing. And then the cost thing, I mean human error is a big deal. People pass out at their computer. They've been coding all night or they just forget about it. I mean, and then it's just like leaving the lights on or your water running in your house. It's just, at the scale that it is, the numbers will add up. That's a huge deal. So I think, you know, compute back in the old days, there's no compute. Okay, it's just compute sitting there idle. But you know, data cranking the models is doing, that's a big point. >> Another thing I want to add there about cost efficiency is that we make it really easy to use, if you're running on Anyscale, to use spot instances and these preemptable instances that can just be significantly cheaper than the on-demand instances. And so when we see our customers go from what they're doing before to using Anyscale and they go from not using these spot instances 'cause they don't have the infrastructure around it, the fault tolerance to handle the preemption and things like that, to being able to just check a box and use spot instances and save a bunch of money. >> You know, this was my whole, my feature article at Reinvent last year when I met with Adam Selipsky, this next gen Cloud is here. I mean, it's not auto scale, it's infrastructure scale. It's agility. It's flexibility. I think this is where the world needs to go. Almost what DevOps did for Cloud and what you were showing me that demo had this whole SRE vibe. And remember Google had site reliability engines to manage all those servers. This is kind of like an SRE vibe for data at scale. I mean, a similar kind of order of magnitude. I mean, I might be a little bit off base there, but how would you explain it? >> It's a nice analogy. I mean, what we are trying to do here is get to the point where developers don't think about infrastructure. Where developers only think about their application logic. And where businesses can do AI, can succeed with AI, and build these scalable applications, but they don't have to build, you know, an infrastructure team. They don't have to develop that expertise. They don't have to invest years in building their internal machine learning infrastructure. They can just focus on the Python code, on their application logic, and run the stuff out of the box. >> Awesome. Well, I appreciate the time. Before we wrap up here, give a plug for the company. I know you got a couple websites. Again, go, Ray's got its own website. You got Anyscale. You got an event coming up. Give a plug for the company looking to hire. Put a plug in for the company. >> Yeah, absolutely. Thank you. So first of all, you know, we think AI is really going to transform every industry and the opportunity is there, right. We can be the infrastructure that enables all of that to happen, that makes it easy for companies to succeed with AI, and get value out of AI. Now we have, if you're interested in learning more about Ray, Ray has been emerging as the standard way to build scalable applications. Our adoption has been exploding. I mentioned companies like OpenAI using Ray to train their models. But really across the board companies like Netflix and Cruise and Instacart and Lyft and Uber, you know, just among tech companies. It's across every industry. You know, gaming companies, agriculture, you know, farming, robotics, drug discovery, you know, FinTech, we see it across the board. And all of these companies can get value out of AI, can really use AI to improve their businesses. So if you're interested in learning more about Ray and Anyscale, we have our Ray Summit coming up in September. This is going to highlight a lot of the most impressive use cases and stories across the industry. And if your business, if you want to use LLMs, you want to train these LLMs, these large language models, you want to fine tune them with your data, you want to deploy them, serve them, and build applications and products around them, give us a call, talk to us. You know, we can really take the infrastructure piece, you know, off the critical path and make that easy for you. So that's what I would say. And, you know, like you mentioned, we're hiring across the board, you know, engineering, product, go-to-market, and it's an exciting time. >> Robert Nishihara, co-founder and CEO of Anyscale, congratulations on a great company you've built and continuing to iterate on and you got growth ahead of you, you got a tailwind. I mean, the AI wave is here. I think OpenAI and ChatGPT, a customer of yours, have really opened up the mainstream visibility into this new generation of applications, user interface, roll of data, large scale, how to make that programmable so we're going to need that infrastructure. So thanks for coming on this season three, episode one of the ongoing series of the hot startups. In this case, this episode is the top startups building foundational model infrastructure for AI and ML. I'm John Furrier, your host. Thanks for watching. (upbeat music)

(upbeat instrumental) >> Hello and welcome to this CUBE conversation. I'm John Furrier, host of theCUBE, here in Palo Alto, California. Got a great conversation with Robert Nishihara who's the co-founder and CEO of Anyscale. Robert, great to have you on this CUBE conversation. It's great to see you. We did your first Ray Summit a couple years ago and congratulations on your venture. Great to have you on. >> Thank you. Thanks for inviting me. >> So you're first time CEO out of Berkeley in Data. You got the Databricks is coming out of there. You got a bunch of activity coming from Berkeley. It's like a, it really is kind of like where a lot of innovations going on data. Anyscale has been one of those startups that has risen out of that scene. Right? You look at the success of what the Data lakes are now. Now you've got the generative AI. This has been a really interesting innovation market. This new wave is coming. Tell us what's going on with Anyscale right now, as you guys are gearing up and getting some growth. What's happening with the company? >> Yeah, well one of the most exciting things that's been happening in computing recently, is the rise of AI and the excitement about AI, and the potential for AI to really transform every industry. Now of course, one of the of the biggest challenges to actually making that happen is that doing AI, that AI is incredibly computationally intensive, right? To actually succeed with AI to actually get value out of AI. You're typically not just running it on your laptop, you're often running it and scaling it across thousands of machines, or hundreds of machines or GPUs, and to, so organizations and companies and businesses that do AI often end up building a large infrastructure team to manage the distributed systems, the computing to actually scale these applications. And that's a, that's a, a huge software engineering lift, right? And so, one of the goals for Anyscale is really to make that easy. To get to the point where, developers and teams and companies can succeed with AI. Can build these scalable AI applications, without really you know, without a huge investment in infrastructure with a lot of, without a lot of expertise in infrastructure, where really all they need to know is how to program on their laptop, how to program in Python. And if you have that, then that's really all you need to succeed with AI. So that's what we've been focused on. We're building Ray, which is an open source project that's been starting to get adopted by tons of companies, to actually train these models, to deploy these models, to do inference with these models, you know, to ingest and pre-process their data. And our goals, you know, here with the company are really to make Ray successful. To grow the Ray community, and then to build a great product around it and simplify the development and deployment, and productionization of machine learning for, for all these businesses. >> It's a great trend. Everyone wants developer productivity seeing that, clearly right now. And plus, developers are voting literally on what standards become. As you look at how the market is open source driven, a lot of that I love the model, love the Ray project love the, love the Anyscale value proposition. How big are you guys now, and how is that value proposition of Ray and Anyscale and foundational models coming together? Because it seems like you guys are in a perfect storm situation where you guys could get a real tailwind and draft off the the mega trend that everyone's getting excited. The new toy is ChatGPT. So you got to look at that and say, hey, I mean, come on, you guys did all the heavy lifting. >> Absolutely. >> You know how many people you are, and what's the what's the proposition for you guys these days? >> You know our company's about a hundred people, that a bit larger than that. Ray's been going really quickly. It's been, you know, companies using, like OpenAI uses Ray to train their models, like ChatGPT. Companies like Uber run all their deep learning you know, and classical machine learning on top of Ray. Companies like Shopify, Spotify, Netflix, Cruise, Lyft, Instacart, you know, Bike Dance. A lot of these companies are investing heavily in Ray for their machine learning infrastructure. And I think it's gotten to the point where, if you're one of these, you know type of businesses, and you're looking to revamp your machine learning infrastructure. If you're looking to enable new capabilities, you know make your teams more productive, increase, speed up the experimentation cycle, you know make it more performance, like build, you know, run applications that are more scalable, run them faster, run them in a more cost efficient way. All of these types of companies are at least evaluating Ray and Ray is an increasingly common choice there. I think if they're not using Ray, if many of these companies that end up not using Ray, they often end up building their own infrastructure. So Ray has been, the growth there has been incredibly exciting over the, you know we had our first in-person Ray Summit just back in August, and planning the next one for, for coming September. And so when you asked about the value proposition, I think there's there's really two main things, when people choose to go with Ray and Anyscale. One reason is about moving faster, right? It's about developer productivity, it's about speeding up the experimentation cycle, easily getting their models in production. You know, we hear many companies say that they, you know they, once they prototype a model, once they develop a model, it's another eight weeks, or 12 weeks to actually get that model in production. And that's a reason they talk to us. We hear companies say that, you know they've been training their models and, and doing inference on a single machine, and they've been sort of scaling vertically, like using bigger and bigger machines. But they, you know, you can only do that for so long, and at some point you need to go beyond a single machine and that's when they start talking to us. Right? So one of the main value propositions is around moving faster. I think probably the phrase I hear the most is, companies saying that they don't want their machine learning people to have to spend all their time configuring infrastructure. All this is about productivity. >> Yeah. >> The other. >> It's the big brains in the company. That are being used to do remedial tasks that should be automated right? I mean that's. >> Yeah, and I mean, it's hard stuff, right? It's also not these people's area of expertise, and or where they're adding the most value. So all of this is around developer productivity, moving faster, getting to market faster. The other big value prop and the reason people choose Ray and choose Anyscale, is around just providing superior infrastructure. This is really, can we scale more? You know, can we run it faster, right? Can we run it in a more cost effective way? We hear people saying that they're not getting good GPU utilization with the existing tools they're using, or they can't scale beyond a certain point, or you know they don't have a way to efficiently use spot instances to save costs, right? Or their clusters, you know can't auto scale up and down fast enough, right? These are all the kinds of things that Ray and Anyscale, where Ray and Anyscale add value and solve these kinds of problems. >> You know, you bring up great points. Auto scaling concept, early days, it was easy getting more compute. Now it's complicated. They're built into more integrated apps in the cloud. And you mentioned those companies that you're working with, that's impressive. Those are like the big hardcore, I call them hardcore. They have a good technical teams. And as the wave starts to move from these companies that were hyper scaling up all the time, the mainstream are just developers, right? So you need an interface in, so I see the dots connecting with you guys and I want to get your reaction. Is that how you see it? That you got the alphas out there kind of kicking butt, building their own stuff, alpha developers and infrastructure. But mainstream just wants programmability. They want that heavy lifting taken care of for them. Is that kind of how you guys see it? I mean, take us through that. Because to get crossover to be democratized, the automation's got to be there. And for developer productivity to be in, it's got to be coding and programmability. >> That's right. Ultimately for AI to really be successful, and really you know, transform every industry in the way we think it has the potential to. It has to be easier to use, right? And that is, and being easier to use, there's many dimensions to that. But an important one is that as a developer to do AI, you shouldn't have to be an expert in distributed systems. You shouldn't have to be an expert in infrastructure. If you do have to be, that's going to really limit the number of people who can do this, right? And I think there are so many, all of the companies we talk to, they don't want to be in the business of building and managing infrastructure. It's not that they can't do it. But it's going to slow them down, right? They want to allocate their time and their energy toward building their product, right? To building a better product, getting their product to market faster. And if we can take the infrastructure work off of the critical path for them, that's going to speed them up, it's going to simplify their lives. And I think that is critical for really enabling all of these companies to succeed with AI. >> Talk about the customers you guys are talking to right now, and how that translates over. Because I think you hit a good thread there. Data infrastructure is critical. Managed services are coming online, open sources continuing to grow. You have these people building their own, and then if they abandon it or don't scale it properly, there's kind of consequences. 'Cause it's a system you mentioned, it's a distributed system architecture. It's not as easy as standing up a monolithic app these days. So when you guys go to the marketplace and talk to customers, put the customers in buckets. So you got the ones that are kind of leaning in, that are pretty peaked, probably working with you now, open source. And then what's the customer profile look like as you go mainstream? Are they looking to manage service, looking for more architectural system, architecture approach? What's the, Anyscale progression? How do you engage with your customers? What are they telling you? >> Yeah, so many of these companies, yes, they're looking for managed infrastructure 'cause they want to move faster, right? Now the kind of these profiles of these different customers, they're three main workloads that companies run on Anyscale, run with Ray. It's training related workloads, and it is serving and deployment related workloads, like actually deploying your models, and it's batch processing, batch inference related workloads. Like imagine you want to do computer vision on tons and tons of, of images or videos, or you want to do natural language processing on millions of documents or audio, or speech or things like that, right? So the, I would say the, there's a pretty large variety of use cases, but the most common you know, we see tons of people working with computer vision data, you know, computer vision problems, natural language processing problems. And it's across many different industries. We work with companies doing drug discovery, companies doing you know, gaming or e-commerce, right? Companies doing robotics or agriculture. So there's a huge variety of the types of industries that can benefit from AI, and can really get a lot of value out of AI. And, but the, but the problems are the same problems that they all want to solve. It's like how do you make your team move faster, you know succeed with AI, be more productive, speed up the experimentation, and also how do you do this in a more performant way, in a faster, cheaper, in a more cost efficient, more scalable way. >> It's almost like the cloud game is coming back to AI and these foundational models, because I was just on a podcast, we recorded our weekly podcast, and I was just riffing with Dave Vellante, my co-host on this, were like, hey, in the early days of Amazon, if you want to build an app, you just, you have to build a data center, and then you go to now you go to the cloud, cloud's easier, pay a little money, penny's on the dollar, you get your app up and running. Cloud computing is born. With foundation models in generative AI. The old model was hard, heavy lifting, expensive, build out, before you get to do anything, as you mentioned time. So I got to think that you're pretty much in a good position with this foundational model trend in generative AI because I just looked at the foundation map, foundation models, map of the ecosystem. You're starting to see layers of, you got the tooling, you got platform, you got cloud. It's filling out really quickly. So why is Anyscale important to this new trend? How do you talk to people when they ask you, you know what does ChatGPT mean for Anyscale? And how does the financial foundational model growth, fit into your plan? >> Well, foundational models are hugely important for the industry broadly. Because you're going to have these really powerful models that are trained that you know, have been trained on tremendous amounts of data. tremendous amounts of computes, and that are useful out of the box, right? That people can start to use, and query, and get value out of, without necessarily training these huge models themselves. Now Ray fits in and Anyscale fit in, in a number of places. First of all, they're useful for creating these foundation models. Companies like OpenAI, you know, use Ray for this purpose. Companies like Cohere use Ray for these purposes. You know, IBM. If you look at, there's of course also open source versions like GPTJ, you know, created using Ray. So a lot of these large language models, large foundation models benefit from training on top of Ray. And, but of course for every company training and creating these huge foundation models, you're going to have many more that are fine tuning these models with their own data. That are deploying and serving these models for their own applications, that are building other application and business logic around these models. And that's where Ray also really shines, because Ray you know, is, can provide common infrastructure for all of these workloads. The training, the fine tuning, the serving, the data ingest and pre-processing, right? The hyper parameter tuning, the and and so on. And so where the reason Ray and Anyscale are important here, is that, again, foundation models are large, foundation models are compute intensive, doing you know, using both creating and using these foundation models requires tremendous amounts of compute. And there there's a big infrastructure lift to make that happen. So either you are using Ray and Anyscale to do this, or you are building the infrastructure and managing the infrastructure yourself. Which you can do, but it's, it's hard. >> Good luck with that. I always say good luck with that. I mean, I think if you really need to do, build that hardened foundation, you got to go all the way. And I think this, this idea of composability is interesting. How is Ray working with OpenAI for instance? Take, take us through that. Because I think you're going to see a lot of people talking about, okay I got trained models, but I'm going to have not one, I'm going to have many. There's big debate that OpenAI is going to be the mother of all LLMs, but now, but really people are also saying that to be many more, either purpose-built or specific. The fusion and these things come together there's like a blending of data, and that seems to be a value proposition. How does Ray help these guys get their models up? Can you take, take us through what Ray's doing for say OpenAI and others, and how do you see the models interacting with each other? >> Yeah, great question. So where, where OpenAI uses Ray right now, is for the training workloads. Training both to create ChatGPT and models like that. There's both a supervised learning component, where you're pre-training this model on doing supervised pre-training with example data. There's also a reinforcement learning component, where you are fine-tuning the model and continuing to train the model, but based on human feedback, based on input from humans saying that, you know this response to this question is better than this other response to this question, right? And so Ray provides the infrastructure for scaling the training across many, many GPUs, many many machines, and really running that in an efficient you know, performance fault tolerant way, right? And so, you know, open, this is not the first version of OpenAI's infrastructure, right? They've gone through iterations where they did start with building the infrastructure themselves. They were using tools like MPI. But at some point, you know, given the complexity, given the scale of what they're trying to do, you hit a wall with MPI and that's going to happen with a lot of other companies in this space. And at that point you don't have many other options other than to use Ray or to build your own infrastructure. >> That's awesome. And then your vision on this data interaction, because the old days monolithic models were very rigid. You couldn't really interface with them. But we're kind of seeing this future of data fusion, data interaction, data blending at large scale. What's your vision? How do you, what's your vision of where this goes? Because if this goes the way people think. You can have this data chemistry kind of thing going on where people are integrating all kinds of data with each other at large scale. So you need infrastructure, intelligence, reasoning, a lot of code. Is this something that you see? What's your vision in all this? Take us through. >> AI is going to be used everywhere right? It's, we see this as a technology that's going to be ubiquitous, and is going to transform every business. I mean, imagine you make a product, maybe you were making a tool like Photoshop or, or whatever the, you know, tool is. The way that people are going to use your tool, is not by investing, you know, hundreds of hours into learning all of the different, you know specific buttons they need to press and workflows they need to go through it. They're going to talk to it, right? They're going to say, ask it to do the thing they want it to do right? And it's going to do it. And if it, if it doesn't know what it's want, what it's, what's being asked of it. It's going to ask clarifying questions, right? And then you're going to clarify, and you're going to have a conversation. And this is going to make many many many kinds of tools and technology and products easier to use, and lower the barrier to entry. And so, and this, you know, many companies fit into this category of trying to build products that, and trying to make them easier to use, this is just one kind of way it can, one kind of way that AI will will be used. But I think it's, it's something that's pretty ubiquitous. >> Yeah. It'll be efficient, it'll be efficiency up and down the stack, and will change the productivity equation completely. You just highlighted one, I don't want to fill out forms, just stand up my environment for me. And then start coding away. Okay well this is great stuff. Final word for the folks out there watching, obviously new kind of skill set for hiring. You guys got engineers, give a plug for the company, for Anyscale. What are you looking for? What are you guys working on? Give a, take the last minute to put a plug in for the company. >> Yeah well if you're interested in AI and if you think AI is really going to be transformative, and really be useful for all these different industries. We are trying to provide the infrastructure to enable that to happen, right? So I think there's the potential here, to really solve an important problem, to get to the point where developers don't need to think about infrastructure, don't need to think about distributed systems. All they think about is their application logic, and what they want their application to do. And I think if we can achieve that, you know we can be the foundation or the platform that enables all of these other companies to succeed with AI. So that's where we're going. I think something like this has to happen if AI is going to achieve its potential, we're looking for, we're hiring across the board, you know, great engineers, on the go-to-market side, product managers, you know people who want to really, you know, make this happen. >> Awesome well congratulations. I know you got some good funding behind you. You're in a good spot. I think this is happening. I think generative AI and foundation models is going to be the next big inflection point, as big as the pc inter-networking, internet and smartphones. This is a whole nother application framework, a whole nother set of things. So this is the ground floor. Robert, you're, you and your team are right there. Well done. >> Thank you so much. >> All right. Thanks for coming on this CUBE conversation. I'm John Furrier with theCUBE. Breaking down a conversation around AI and scaling up in this new next major inflection point. This next wave is foundational models, generative AI. And thanks to ChatGPT, the whole world's now knowing about it. So it really is changing the game and Anyscale is right there, one of the hot startups, that is in good position to ride this next wave. Thanks for watching. (upbeat instrumental)

(upbeat music) (upbeat music) >> Hi everybody, welcome back to our coverage of the Cloud Native Security Con. I'm Dave Vellante, here in our Boston studio. We're connecting today with Palo Alto, with John Furrier and Lisa Martin. We're also live from the show floor in Seattle. But right now, I'm here with Andy Thurai who's from Constellation Research, friend of theCUBE, and we're going to discuss the intersection of AI and security, the potential of AI, the risks and the future. Andy, welcome, good to see you again. >> Good to be here again. >> Hey, so let's get into it, can you talk a little bit about, I know this is a passion of yours, the ethical considerations surrounding AI. I mean, it's front and center in the news, and you've got accountability, privacy, security, biases. Should we be worried about AI from a security perspective? >> Absolutely, man, you should be worried. See the problem is, people don't realize this, right? I mean, the ChatGPT being a new shiny object, it's all the craze that's about. But the problem is, most of the content that's produced either by ChatGPT or even by others, it's an access, no warranties, no accountability, no whatsoever. Particularly, if it is content, it's okay. But if it is something like a code that you use for example, one of their site projects that GitHub's co-pilot, which is actually, open AI + Microsoft + GitHub's combo, they allow you to produce code, AI writes code basically, right? But when you write code, problem with that is, it's not exactly stolen, but the models are created by using the GitHub code. Actually, they're getting sued for that, saying that, "You can't use our code". Actually there's a guy, Tim Davidson, I think he's named the professor, he actually demonstrated how AI produces exact copy of the code that he has written. So right now, it's a lot of security, accountability, privacy issues. Use it either to train or to learn. But in my view, it's not ready for enterprise grade yet. >> So, Brian Behlendorf today in his keynotes said he's really worried about ChatGPT being used to automate spearfishing. So I'm like, okay, so let's unpack that a little bit. Is the concern there that it just, the ChatGPT writes such compelling phishing content, it's going to increase the probability of somebody clicking on it, or are there other dimensions? >> It could, it's not necessarily just ChatGPT for that matter, right? AI can, actually, the hackers are using it to an extent already, can use to individualize content. For example, one of the things that you are able to easily identify when you're looking at the emails that are coming in, the phishing attack is, you look at some of the key elements in it, whether it's a human or even if it's an automated AI based system. They look at certain things and they say, "Okay, this is phishing". But if you were to read an email that looks exact copy of what I would've sent to you saying that, "Hey Dave, are you on for tomorrow? Or click on this link to do whatever. It could individualize the message. That's where the volume at scale to individual to masses, that can be done using AI, which is what scares me. >> Is there a flip side to AI? How is it being utilized to help cybersecurity? And maybe you could talk about some of the more successful examples of AI in security. Like, are there use cases or are there companies out there, Andy, that you find, I know you're close to a lot of firms that are leading in this area. You and I have talked about CrowdStrike, I know Palo Alto Network, so is there a positive side to this story? >> Yeah, I mean, absolutely right. Those are some of the good companies you mentioned, CrowdStrike, Palo Alto, Darktrace is another one that I closely follow, which is a good company as well, that they're using AI for security purposes. So, here's the thing, right, when people say, when they're using malware detection systems, most of the malware detection systems that are in today's security and malware systems, use some sort of a signature and pattern scanning in the malware. You know how many identified malwares are there today in the repository, in the library? More than a billion, a billion. So, if you are to check for every malware in your repository, that's not going to work. The pattern based recognition is not going to work. So, you got to figure out a different way of identification of pattern of usage, not just a signature in a malware, right? Or there are other areas you could use, things like the usage patterns. For example, if Andy is coming in to work at a certain time, you could combine a facial recognition saying, that should he be in here at that time, and should he be doing things, what he is supposed to be doing. There are a lot of things you could do using that, right? And the AIOps use cases, which is one of my favorite areas that I work, do a lot of work, right? That it has use cases for detecting things that are anomaly, that are not supposed to be done in a way that's supposed to be, reducing the noise so it can escalate only the things what you're supposed to. So, AIOps is a great use case to use in security areas which they're not using it to an extent yet. Incident management is another area. >> So, in your malware example, you're saying, okay, known malware, pretty much anybody can deal with that now. That's sort of yesterday's problem. >> The unknown is the problem. >> It's the unknown malware really trying to understand the patterns, and the patterns are going to change. It's not like you're saying a common signature 'cause they're going to use AI to change things up at scale. >> So, here's the problem, right? The malware writers are also using AI now, right? So, they're not going to write the old malware, send it to you. They are actually creating malware on the fly. It is possible entirely in today's world that they can create a malware, drop in your systems and it'll it look for the, let me get that name right. It's called, what are we using here? It's called the TTPs, Tactics, Techniques and procedures. It'll look for that to figure out, okay, am I doing the right pattern? And then malware can sense it saying that, okay, that's the one they're detecting. I'm going to change it on the fly. So, AI can code itself on the fly, rather malware can code itself on the fly, which is going to be hard to detect. >> Well, and when you talk about TTP, when you talk to folks like Kevin Mandia of Mandiant, recently purchased by Google or other of those, the ones that have the big observation space, they'll talk about the most malicious hacks that they see, involve lateral movement. So, that's obviously something that people are looking for, AI's looking for that. And of course, the hackers are going to try to mask that lateral movement, living off the land and other things. How do you see AI impacting the future of cyber? We talked about the risks and the good. One of the things that Brian Behlendorf also mentioned is that, he pointed out that in the early days of the internet, the protocols had an inherent element of trust involved. So, things like SMTP, they didn't have security built in. So, they built up a lot of technical debt. Do you see AI being able to help with that? What steps do you see being taken to ensure that AI based systems are secure? >> So, the major difference between the older systems and the newer systems is the older systems, sadly even today, a lot of them are rules-based. If it's a rules-based systems, you are dead in the water and not able, right? So, the AI-based systems can somewhat learn from the patterns as I was talking about, for example... >> When you say rules-based systems, you mean here's the policy, here's the rule, if it's not followed but then you're saying, AI will blow that away, >> AI will blow that away, you don't have to necessarily codify things saying that, okay, if this, then do this. You don't have to necessarily do that. AI can somewhat to an extent self-learn saying that, okay, if that doesn't happen, if this is not a pattern that I know which is supposed to happen, who should I escalate this to? Who does this system belong to? And the other thing, the AIOps use case we talked about, right, the anomalies. When an anomaly happens, then the system can closely look at, saying that, okay, this is not normal behavior or usage. Is that because system's being overused or is it because somebody's trying to access something, could look at the anomaly detection, anomaly prevention or even prediction to an extent. And that's where AI could be very useful. >> So, how about the developer angle? 'Cause CNCF, the event in Seattle is all around developers, how can AI be integrated? We did a lot of talk at the conference about shift-left, we talked about shift-left and protect right. Meaning, protect the run time. So, both are important, so what steps should be taken to ensure that the AI systems are being developed in a secure and ethically sound way? What's the role of developers in that regard? >> How long do you got? (Both laughing) I think it could go for base on that. So, here's the problem, right? Lot of these companies are trying to see, I mean, you might have seen that in the news that Buzzfeed is trying to hire all of the writers to create the thing that ChatGPT is creating, a lot of enterprises... >> How, they're going to fire their writers? >> Yeah, they replace the writers. >> It's like automated automated vehicles and automated Uber drivers. >> So, the problem is a lot of enterprises still haven't done that, at least the ones I'm speaking to, are thinking about saying, "Hey, you know what, can I replace my developers because they are so expensive? Can I replace them with AI generated code?" There are a few issues with that. One, AI generated code is based on some sort of a snippet of a code that has been already available. So, you get into copyright issues, that's issue number one, right? Issue number two, if AI creates code and if something were to go wrong, who's responsible for that? There's no accountability right now. Or you as a company that's creating a system that's responsible, or is it ChatGPT, Microsoft is responsible. >> Or is the developer? >> Or the developer. >> The individual developer might be. So, they're going to be cautious about that liability. >> Well, so one of the areas where I'm seeing a lot of enterprises using this is they are using it to teach developers to learn things. You know what, if you're to code, this is a good way to code. That area, it's okay because you are just teaching them. But if you are to put an actual production code, this is what I advise companies, look, if somebody's using even to create a code, whether with or without your permission, make sure that once the code is committed, you validate that the 100%, whether it's a code or a model, or even make sure that the data what you're feeding in it is completely out of bias or no bias, right? Because at the end of the day, it doesn't matter who, what, when did that, if you put out a service or a system out there, it is involving your company liability and system, and code in place. You're going to be screwed regardless of what, if something were to go wrong, you are the first person who's liable for it. >> Andy, when you think about the dangers of AI, and what keeps you up at night if you're a security professional AI and security professional. We talked about ChatGPT doing things, we don't even, the hackers are going to get creative. But what worries you the most when you think about this topic? >> A lot, a lot, right? Let's start off with an example, actually, I don't know if you had a chance to see that or not. The hackers used a bank of Hong Kong, used a defect mechanism to fool Bank of Hong Kong to transfer $35 million to a fake account, the money is gone, right? And the problem that is, what they did was, they interacted with a manager and they learned this executive who can control a big account and cloned his voice, and clone his patterns on how he calls and what he talks and the whole name he has, after learning that, they call the branch manager or bank manager and say, "Hey, you know what, hey, move this much money to whatever." So, that's one way of kind of phishing, kind of deep fake that can come. So, that's just one example. Imagine whether business is conducted by just using voice or phone calls itself. That's an area of concern if you were to do that. And imagine this became an uproar a few years back when deepfakes put out the video of Tom Cruise and others we talked about in the past, right? And Tom Cruise looked at the video, he said that he couldn't distinguish that he didn't do it. It is so close, that close, right? And they are doing things like they're using gems... >> Awesome Instagram account by the way, the guy's hilarious, right? >> So, they they're using a lot of this fake videos and fake stuff. As long as it's only for entertainment purposes, good. But imagine doing... >> That's right there but... >> But during the election season when people were to put out saying that, okay, this current president or ex-president, he said what? And the masses believe right now whatever they're seeing in TV, that's unfortunate thing. I mean, there's no fact checking involved, and you could change governments and elections using that, which is scary shit, right? >> When you think about 2016, that was when we really first saw, the weaponization of social, the heavy use of social and then 2020 was like, wow. >> To the next level. >> It was crazy. The polarization, 2024, would deepfakes... >> Could be the next level, yeah. >> I mean, it's just going to escalate. What about public policy? I want to pick your brain on this because I I've seen situations where the EU, for example, is going to restrict the ability to ship certain code if it's involved with critical infrastructure. So, let's say, example, you're running a nuclear facility and you've got the code that protects that facility, and it can be useful against some other malware that's outside of that country, but you're restricted from sending that for whatever reason, data sovereignty. Is public policy, is it aligned with the objectives in this new world? Or, I mean, normally they have to catch up. Is that going to be a problem in your view? >> It is because, when it comes to laws it's always miles behind when a new innovation happens. It's not just for AI, right? I mean, the same thing happened with IOT. Same thing happened with whatever else new emerging tech you have. The laws have to understand if there's an issue and they have to see a continued pattern of misuse of the technology, then they'll come up with that. Use in ways they are ahead of things. So, they put a lot of restrictions in place and about what AI can or cannot do, US is way behind on that, right? But California has done some things, for example, if you are talking to a chat bot, then you have to basically disclose that to the customer, saying that you're talking to a chat bot, not to a human. And that's just a very basic rule that they have in place. I mean, there are times that when a decision is made by the, problem is, AI is a black box now. The decision making is also a black box now, and we don't tell people. And the problem is if you tell people, you'll get sued immediately because every single time, we talked about that last time, there are cases involving AI making decisions, it gets thrown out the window all the time. If you can't substantiate that. So, the bottom line is that, yes, AI can assist and help you in making decisions but just use that as a assistant mechanism. A human has to be always in all the loop, right? >> Will AI help with, in your view, with supply chain, the software supply chain security or is it, it's always a balance, right? I mean, I feel like the attackers are more advanced in some ways, it's like they're on offense, let's say, right? So, when you're calling the plays, you know where you're going, the defense has to respond to it. So in that sense, the hackers have an advantage. So, what's the balance with software supply chain? Are the hackers have the advantage because they can use AI to accelerate their penetration of the software supply chain? Or will AI in your view be a good defensive mechanism? >> It could be but the problem is, the velocity and veracity of things can be done using AI, whether it's fishing, or malware, or other security and the vulnerability scanning the whole nine yards. It's scary because the hackers have a full advantage right now. And actually, I think ChatGPT recently put out two things. One is, it's able to direct the code if it is generated by ChatGPT. So basically, if you're trying to fake because a lot of schools were complaining about it, that's why they came up with the mechanism. So, if you're trying to create a fake, there's a mechanism for them to identify. But that's a step behind still, right? And the hackers are using things to their advantage. Actually ChatGPT made a rule, if you go there and read the terms and conditions, it's basically honor rule suggesting, you can't use this for certain purposes, to create a model where it creates a security threat, as that people are going to listen. So, if there's a way or mechanism to restrict hackers from using these technologies, that would be great. But I don't see that happening. So, know that these guys have an advantage, know that they're using AI, and you have to do things to be prepared. One thing I was mentioning about is, if somebody writes a code, if somebody commits a code right now, the problem is with the agile methodologies. If somebody writes a code, if they commit a code, you assume that's right and legit, you immediately push it out into production because need for speed is there, right? But if you continue to do that with the AI produced code, you're screwed. >> So, bottom line is, AI's going to speed us up in a security context or is it going to slow us down? >> Well, in the current version, the AI systems are flawed because even the ChatGPT, if you look at the the large language models, you look at the core piece of data that's available in the world as of today and then train them using that model, using the data, right? But people are forgetting that's based on today's data. The data changes on a second basis or on a minute basis. So, if I want to do something based on tomorrow or a day after, you have to retrain the models. So, the data already have a stale. So, that in itself is stale and the cost for retraining is going to be a problem too. So overall, AI is a good first step. Use that with a caution, is what I want to say. The system is flawed now, if you use it as is, you'll be screwed, it's dangerous. >> Andy, you got to go, thanks so much for coming in, appreciate it. >> Thanks for having me. >> You're very welcome, so we're going wall to wall with our coverage of the Cloud Native Security Con. I'm Dave Vellante in the Boston Studio, John Furrier, Lisa Martin and Palo Alto. We're going to be live on the show floor as well, bringing in keynote speakers and others on the ground. Keep it right there for more coverage on theCUBE. (upbeat music) (upbeat music) (upbeat music) (upbeat music)

>>Okay. Now we're going to dig into the data landscape and cloud of course. And talk a little bit more about that with drew Allen. He's a managing director at Accenture drew. Welcome. Great to see you. Thank you. So let's talk a little bit about, you know, you've been in this game for a number of years. Uh, you've got a particular expertise in, in, in data and finance and insurance. I mean, you think about it within the data and analytics world, even our language is changing. You know, we don't say talk about big data so much anymore. We, we talk more about digital, you know, or, or, or data-driven when you think about sort of where we've come from and where we're going, what are the puts and takes that you have with regard to what's going on in the business today? >>Well, thanks for having me. Um, you know, I think some of the trends we're seeing in terms of challenges and puts some takes are that a lot of companies are already on this digital transformation journey. Um, they focused on customer experience is kind of table stakes. Everyone wants to focus on that and kind of digitizing their channels. But a lot of them are seeing that, you know, a lot of them don't even own their, their channels necessarily. So like we're working with a big cruise line, right. And yes, they've invested in digitizing what they own, but a lot of the channels that they sell through, they don't even own, right. It's the travel agencies or third-party real sellers. So having the data to know where, you know, where those agencies are, that that's something that they've discovered. And so there's a lot of big focus on not just digitizing, but also really understanding your customers and going across products because a lot of the data has built, been built up in individual channels and in digital products. >>And so bringing that data together is something that customers that have really figured out in the last few years is a big differentiator. And what we're seeing too, is that a big trend that the data rich are getting richer. So companies that have really invested in data, um, are having, uh, an outside market share and outside earnings per share and outside revenue growth. And it's really being a big differentiator. And I think for companies just getting started in this, the thing to think about is one of the missteps is to not try to capture all the data at once. The average company has, you know, 10,000, 20,000 data elements individually, when you want to start out, you know, 500, 300 critical data elements, about 5% of the data of a company drives 90% of the business value. So focusing on, on those key critical data elements is really what you need to govern first and really invest in first. And so that's something we tell companies at the beginning of their data strategy is first focus on those critical data elements, really get a handle on governing that data, organizing that data and building data products around >>That data. You can't boil the ocean. Right. And so, and I, I feel like pre pandemic, there was a lot of complacency. Oh yeah, we'll get to that. You know, not on my watch, I'll be retired before that, you know, it becomes a minute. And then of course the pandemic was, I call it sometimes a forced March to digital. So in many respects, it wasn't planned. It just ha you know, you had to do it. And so now I feel like people are stepping back and saying, okay, let's now really rethink this and do it right. But is there, is there a sense of urgency, do you think? >>Absolutely. I think with COVID, you know, we were working with, um, a retailer where they had 12,000 stores across the U S and they had didn't have the insights where they could drill down and understand, you know, with the riots and with COVID was the store operational, you know, with the supply chain of they having multiple, uh, distributors, what did they have in stock? So there are millions of data points that you need to drill down, down at the cell level, at the store level to really understand how's my business performing. And we like to think about it for like a CEO and his leadership team of like, think of it as a digital cockpit, right? You think about a pilot, they have a cockpit with all these dials and, um, dashboards, essentially understanding the performance of their business. And they should be able to drill down and understand for each individual, you know, unit of their work, how are they performing? That's really what we want to see for businesses. Can they get down to that individual performance to really understand how their businesses and >>The ability to connect those dots and traverse those data points and not have to go in and come back out and go into a new system and come back out. And that's really been a lot of the frustration where does machine intelligence and AI fit in? Is that sort of a dot connector, if you will, and an enabler, I mean, we saw, you know, decades of the, the AI winter, and then, you know, there's been a lot of talk about it, but it feels like with the amount of data that we've collected over the last decade and the, the, the low costs of processing that data now, it feels like it's, it's real. Where do you see AI fitting in? Yeah, >>I mean, I think there's been a lot of innovation in the last 10 years with, um, the low cost of storage and computing and these algorithms in non-linear, um, you know, knowledge graphs, and, um, um, a whole bunch of opportunities in cloud where what I think the, the big opportunity is, you know, you can apply AI in areas where a human just couldn't have the scale to do that alone. So back to the example of a cruise lines, you know, you may have a ship being built that has 4,000 cabins on the single cruise line, and it's going to multiple deaths that destinations over its 30 year life cycle. Each one of those cabins is being priced individually for each individual destination. It's physically impossible for a human to calculate the dynamic pricing across all those destinations. You need a machine to actually do that pricing. And so really what a machine is leveraging is all that data to really calculate and assist the human, essentially with all these opportunities where you wouldn't have a human being able to scale up to that amount of data >>Alone. You know, it's interesting. One of the things we talked to Mick Halston about earlier was just the everybody's algorithms are out of whack. You know, you look at the airline pricing, you look at hotels it's as a consumer, you would be able to kind of game the system and predict a, they can't even predict these days. And I feel as though that the data and AI are actually going to bring us back into some kind of normalcy and predictability, uh, w what do you see in that regard? >>Yeah, I think it's, I mean, we're definitely not at a point where when I talk to, you know, the top AI engineers and data scientists, we're not at a point where we have what they call broad AI, right? Where you can get machines to solve general knowledge problems, where they can solve one problem, and then a distinctly different problem, right? That's still many years away, but narrow AI, there's still tons of use cases out there that can really drive tons of business performance challenges, tons of accuracy challenges. So, for example, in the insurance industry, commercial lines, where I work a lot of the time, the biggest leakage of loss experience and pricing for commercial insurers is, um, people will go in as an agent and they'll select an industry to say, you know what, I'm a restaurant business. Um, I'll select this industry code to quote out a policy, but there's, let's say, you know, 12 dozen permutations, you could be an outdoor restaurant. >>You could be a bar, you could be a caterer, and all of that leads to different loss experience. So what this does is they built a machine learning algorithm. We've helped them do this, that actually at the time that they're putting in their name and address, it's crawling across the web and predicting in real time, you know, is this address actually, you know, a business that's a restaurant with indoor dining, does it have a bar is an outdoor dining, and it's that that's able to accurately more price the policy and reduce the loss experience. So there's a lot of that you can do, even with narrow AI that can really drive top line of business results. >>Yeah. I like that term narrow AI because getting things done is important. Let's talk about cloud a little bit because people talk about cloud first public cloud first doesn't necessarily mean public cloud only, of course. So where do you see things like what's the right operating model, the right regime hybrid cloud. We talked earlier about hybrid data help us squint through the cloud landscape. Yeah. >>I mean, I think for most right, most fortune 500 companies, they can't just their fingers and say, let's move all of our data centers to the cloud. They've got to move, you know, gradually. And it's usually a journey that's taking more than two to three plus years, even more than that in some cases. So they're half they have to move their data, uh, incrementally to the cloud. And what that means is that, that they have to move to a hybrid perspective where some of their data is on premise and some of it is publicly on the cloud. And so that's the term hybrid cloud essentially. And so what they've had to think about is from an intelligence perspective, the privacy of that data, where is it being moved? Can they reduce the replication of that data? Because ultimately you like, uh, replicating the data from on-premise to, to the cloud that introduces, you know, errors and data quality issues. So thinking about how do you manage, uh, you know, uh, on-premise and public cloud as a transition is something that Accenture thinks, thinks, and helps our clients do quite a bit. And how do you move them in a manner that's well-organized and well thought about? >>Yeah. So I've been a big proponent of sort of line of business lines of business becoming much more involved in, in the data pipeline, if you will, the data process, if you think about our major operational systems, they all have sort of line of business context in them. Then the salespeople, they know the CRM data and, you know, logistics folks. There they're very much in tune with ERP. I almost feel like for the past decade, the lines of business have been somewhat removed from the, the data team, if you will. And that, that seems to be changing. What are you seeing in terms of the line of line of business being much more involved in sort of end to end ownership if you will, if I can use that term of, uh, of the data and sort of determining things like helping determine anyway, the data quality and things of that nature. Yeah. >>I mean, I think this is where thinking about your data operating model and thinking about ideas of a chief data officer and having data on the CEO agenda, that's really important to get the lines of business, to really think about data sharing and reuse, and really getting them to, you know, kind of unlock the data because they do think about their data as a fiefdom data has value, but you've got to really get organizations in their silos to open it up and bring that data together because that's where the value is. You know, data doesn't operate. When you think about a customer, they don't operate in their journey across the business in silo channels. They don't think about, you know, I use only the web and then I use the call center, right? They think about that as just one experience. And that data is a single journey. >>So we like to think about data as a product. You know, you should think about a data in the same way. You think about your products as, as products, you know, data as a product, you should have the idea of like every two weeks you have releases to it. You have an operational resiliency to it. So thinking about that, where you can have a very product mindset to delivering your data, I think is very important for the success. And that's where kind of, there's not just the things about critical data elements and having the right platform architecture, but there's a soft stuff as well, like a product mindset to data, having the right data, culture, and business adoption and having the right value set mindset for, for data, I think is really, >>I think data as a product is a very powerful concept. And I think it maybe is uncomfortable to some people sometimes. And I think in the early days of big data, if you will, people thought, okay, data is a product going to sell my data, and that's not necessarily what you mean. You mean thinking about products or data that can fuel products that you can then monetize maybe as a product or as a, as, as a service. And I like to think about a new metric in the industry, which is how long does it take me to get from idea of I'm a business person. I have an idea for a data product. How long does it take me to get from idea to monetization? And that's going to be something that ultimately as a business person, I'm going to use to determine the success of my data team and my, my data architecture is, is that kind of thinking starting to really hit the marketplace. >>I mean, I insurers now are working, partnering with, you know, auto manufacturers to monetize, um, driver usage data, you know, on telematics to see, you know, driver behavior on how, you know, how auto manufacturers are using that data. That's very important to insurers, you know, so how an auto manufacturer can monetize that data is very important and also an insurance, you know, cyber insurance, um, are there news new ways we can look at how companies are being attacked with viruses and malware, and is there a way we can somehow monetize that information? So companies that are able to agily, you know, think about how can we, you know, collect this data, bring it together, think about it as a product, and then potentially, you know, sell it as a service is something that, um, company, successful companies are doing >>Great examples of data products, and it might be revenue generating, or it might be in the case of, you know, cyber, maybe it reduces my expected loss. Exactly. And it drops right to my bottom line. What's the relationship between Accenture and cloud era? Do you, I presume you guys meet at the customer, but maybe you could give us some insight as to yeah. So, >>Um, I I'm in the executive sponsor for, um, the Accenture cloud era partnership on the Accenture side. Uh, we do quite a lot of business together and, um, you know, Cloudera has been a great partner for us. Um, and they've got a great product in terms of the Cloudera data platform where, you know, what we do is as a big systems integrator for them, we help, um, you know, configure and we have a number of engineers across the world that come in and help in terms of, um, engineer architects and install, uh, cloud errors, data platform, and think about what are some of those, you know, value cases where you can really think about organizing data and bringing it together for all these different types of use cases. And really just as the examples we thought about. So the telematics, you know, um, in order to realize something like that, you're bringing in petabytes and huge scales of data that, you know, you just couldn't bring on a normal, uh, platform. You need to think about cloud. You need to think about speed of, of data and real-time insights and cloud errors, the right data platform for that. So, um, >>That'd be Cloudera ushered in the modern big data era. We, we kind of all know that, and it was, which of course early on, it was very services intensive. You guys were right there helping people think through there weren't enough data scientists. We've sort of all, all been through that. And of course in your wheelhouse industries, you know, financial services and insurance, they were some of the early adopters, weren't they? Yeah, >>Absolutely. Um, so, you know, an insurance, you've got huge amounts of data with loss history and, um, a lot with IOT. So in insurance, there's a whole thing of like sensorized thing in, uh, you know, taking the physical world and digitizing it. So, um, there's a big thing in insurance where, um, it's not just about, um, pricing out the risk of a loss experience, but actual reducing the loss before it even happens. So it's called risk control or loss control, you know, can we actually put sensors on oil pipelines or on elevators and, you know, reduce, um, you know, accidents before they happen. So we're, you know, working with an insurer to actually, um, listen to elevators as they move up and down and are there signals in just listening to the audio of an elevator over time that says, you know what, this elevator is going to need maintenance, you know, before a critical accident could happen. So there's huge applications, not just in structured data, but in unstructured data like voice and audio and video where a partner like Cloudera has a huge role apply. >>Great example of it. So again, narrow sort of use case for machine intelligence, but, but real value. True. We'll leave it like that. Thanks so much for taking some time. Thank you.

>>Have you ever wondered how we sequence the human genome, how your smartphone is so well smart, how we will ever analyze all the patient data for the new vaccines or even how we plan to send humans to Mars? Well, at Cloudera, we believe that data can make what is impossible today possible tomorrow we are the enterprise data cloud company. In fact, we provide analytics and machine learning technology that does everything from making your smartphone smarter, to helping scientists ensure that new vaccines are both safe and effective, big data, no problem out era, the enterprise data cloud company. >>So I think for a long time in this country, we've known that there's a great disparity between minority populations and the majority of population in terms of disease burden. And depending on where you live, your zip code has more to do with your health than almost anything else. But there are a lot of smaller, um, safety net facilities, as well as small academic medical colleges within the United States. And those in those smaller environments don't have the access, you know, to the technologies that the larger ones have. And, you know, I call that, uh, digital disparity. So I'm, Harry's in academic scientist center and our mission is to train diverse health care providers and researchers, but also provide services to underserved populations. As part of the reason that I think is so important for me hearing medical college, to do data science. One of the things that, you know, both Cloudera and Claire sensor very passionate about is bringing those height in technologies to, um, to the smaller organizations. >>It's very expensive to go to the cloud for these small organizations. So now with the partnership with Cloudera and Claire sets a clear sense, clients now enjoy those same technologies and really honestly have a technological advantage over some of the larger organizations. The reason being is they can move fast. So we were able to do this on our own without having to, um, hire data scientists. Uh, we probably cut three to five years off of our studies. I grew up in a small town in Arkansas and is one of those towns where the railroad tracks divided the blacks and the whites. My father died without getting much healthcare at all. And as an 11 year old, I did not understand why my father could not get medical attention because he was very sick. >>Since we come at my Harry are looking to serve populations that reflect themselves or affect the population. He came from. A lot of the data you find or research you find health is usually based on white men. And obviously not everybody who needs a medical provider is going to be a white male. >>One of the things that we're concerned about in healthcare is that there's bias in treatment already. We want to make sure those same biases do not enter into the algorithms. >>The issue is how do we get ahead of them to try to prevent these disparities? >>One of the great things about our dataset is that it contains a very diverse group of patients. >>Instead of just saying, everyone will have these results. You can break it down by race, class, cholesterol, level, other kinds of factors that play a role. So you can make the treatments in the long run. More specifically, >>Researchers are now able to use these technologies and really take those hypotheses from, from bench to bedside. >>We're able to overall improve the health of not just the person in front of you, but the population that, yeah, >>Well, the future is now. I love a quote by William Gibson who said the future is already here. It's just not evenly distributed. If we think hard enough and we apply things properly, uh, we can again take these technologies to, you know, underserved environments, um, in healthcare. Nobody should be technologically disadvantage. >>When is a car not just a car when it's a connected data driven ecosystem, dozens of sensors and edge devices gathering up data from just about anything road, infrastructure, other vehicles, and even pedestrians to create safer vehicles, smarter logistics, and more actionable insights. All the data from the connected car supports an entire ecosystem from manufacturers, building safer vehicles and fleet managers, tracking assets to insurers monitoring, driving behaviors to make roads safer. Now you can control the data journey from edge to AI. With Cloudera in the connected car, data is captured, consolidated and enriched with Cloudera data flow cloud Dara's data engineering, operational database and data warehouse provide the foundation to develop service center applications, sales reports, and engineering dashboards. With data science workbench data scientists can continuously train AI models and use data flow to push the models back to the edge, to enhance the car's performance as the industry's first enterprise data cloud Cloudera supports on-premise public and multi-cloud deployments delivering multifunction analytics on data anywhere with common security governance and metadata management powered by Cloudera SDX, an open platform built on open source, working with open compute architectures and open data stores all the way from edge to AI powering the connected car. >>The future has arrived. >>The Dawn of a retail Renaissance is here and shopping will never be the same again. Today's connected. Consumers are always on and didn't control. It's the era of smart retail, smart shelves, digital signage, and smart mirrors offer an immersive customer experience while delivering product information, personalized offers and recommendations, video analytics, capture customer emotions and gestures to better understand and respond to in-store shopping experiences. Beacons sensors, and streaming video provide valuable data into in-store traffic patterns, hotspots and dwell times. This helps retailers build visual heat maps to better understand custom journeys, conversion rates, and promotional effectiveness in our robots automate routine tasks like capturing inventory levels, identifying out of stocks and alerting in store personnel to replenish shelves. When it comes to checking out automated e-commerce pickup stations and frictionless checkouts will soon be the norm making standing in line. A thing of the past data and analytics are truly reshaping. >>The everyday shopping experience outside the store, smart trucks connect the supply chain, providing new levels of inventory visibility, not just into the precise location, but also the condition of those goods. All in real time, convenience is key and customers today have the power to get their goods delivered at the curbside to their doorstep, or even to their refrigerators. Smart retail is indeed here. And Cloudera makes all of this possible using Cloudera data can be captured from a variety of sources, then stored, processed, and analyzed to drive insights and action. In real time, data scientists can continuously build and train new machine learning models and put these models back to the edge for delivering those moment of truth customer experiences. This is the enterprise data cloud powered by Cloudera enabling smart retail from the edge to AI. The future has arrived >>For is a global automotive supplier. We have three business groups, automotive seating in studios, and then emission control technologies or biggest automotive customers are Volkswagen for the NPSA. And we have, uh, more than 300 sites. And in 75 countries >>Today, we are generating tons of data, more and more data on the manufacturing intelligence. We are trying to reduce the, the defective parts or anticipate the detection of the, of the defective part. And this is where we can get savings. I would say our goal in manufacturing is zero defects. The cost of downtime in a plant could be around the a hundred thousand euros. So with predictive maintenance, we are identifying correlations and patterns and try to anticipate, and maybe to replace a component before the machine is broken. We are in the range of about 2000 machines and we can have up to 300 different variables from pressure from vibration and temperatures. And the real-time data collection is key, and this is something we cannot achieve in a classical data warehouse approach. So with the be data and with clouded approach, what we are able to use really to put all the data, all the sources together in the classical way of working with that at our house, we need to spend weeks or months to set up the model with the Cloudera data lake. We can start working on from days to weeks. We think that predictive or machine learning could also improve on the estimation or NTC patient forecasting of what we'll need to brilliance with all this knowledge around internet of things and data collection. We are applying into the predictive convene and the cockpit of the future. So we can work in the self driving car and provide a better experience for the driver in the car. >>The Cloudera data platform makes it easy to say yes to any analytic workload from the edge to AI, yes. To enterprise grade security and governance, yes. To the analytics your people want to use yes. To operating on any cloud. Your business requires yes to the future with a cloud native platform that flexes to meet your needs today and tomorrow say yes to CDP and say goodbye to shadow it, take a tour of CDP and see how it's an easier, faster and safer enterprise analytics and data management platform with a new approach to data. Finally, a data platform that lets you say yes, >>Welcome to transforming ideas into insights, presented with the cube and made possible by cloud era. My name is Dave Volante from the cube, and I'll be your host for today. And the next hundred minutes, you're going to hear how to turn your best ideas into action using data. And we're going to share the real world examples and 12 industry use cases that apply modern data techniques to improve customer experience, reduce fraud, drive manufacturing, efficiencies, better forecast, retail demand, transform analytics, improve public sector service, and so much more how we use data is rapidly evolving as is the language that we use to describe data. I mean, for example, we don't really use the term big data as often as we used to rather we use terms like digital transformation and digital business, but you think about it. What is a digital business? How is that different from just a business? >>Well, digital business is a data business and it differentiates itself by the way, it uses data to compete. So whether we call it data, big data or digital, our belief is we're entering the next decade of a world that puts data at the core of our organizations. And as such the way we use insights is also rapidly evolving. You know, of course we get value from enabling humans to act with confidence on let's call it near perfect information or capitalize on non-intuitive findings. But increasingly insights are leading to the development of data, products and services that can be monetized, or as you'll hear in our industry, examples, data is enabling machines to take cognitive actions on our behalf. Examples are everywhere in the forms of apps and products and services, all built on data. Think about a real-time fraud detection, know your customer and finance, personal health apps that monitor our heart rates. >>Self-service investing, filing insurance claims and our smart phones. And so many examples, IOT systems that communicate and act machine and machine real-time pricing actions. These are all examples of products and services that drive revenue cut costs or create other value. And they all rely on data. Now while many business leaders sometimes express frustration that their investments in data, people, and process and technologies haven't delivered the full results they desire. The truth is that the investments that they've made over the past several years should be thought of as a step on the data journey. Key learnings and expertise from these efforts are now part of the organizational DNA that can catapult us into this next era of data, transformation and leadership. One thing is certain the next 10 years of data and digital transformation, won't be like the last 10. So let's get into it. Please join us in the chat. >>You can ask questions. You can share your comments, hit us up on Twitter right now. It's my pleasure to welcome Mick Holliston in he's the president of Cloudera mic. Great to see you. Great to see you as well, Dave, Hey, so I call it the new abnormal, right? The world is kind of out of whack offices are reopening again. We're seeing travel coming back. There's all this pent up demand for cars and vacations line cooks at restaurants. Everything that we consumers have missed, but here's the one thing. It seems like the algorithms are off. Whether it's retail's fulfillment capabilities, airline scheduling their pricing algorithms, you know, commodity prices we don't know is inflation. Transitory. Is it a long-term threat trying to forecast GDP? It's just seems like we have to reset all of our assumptions and make a feel a quality data is going to be a key here. How do you see the current state of the industry and the role data plays to get us into a more predictable and stable future? Well, I >>Can sure tell you this, Dave, uh, out of whack is definitely right. I don't know if you know or not, but I happen to be coming to you live today from Atlanta and, uh, as a native of Atlanta, I can, I can tell you there's a lot to be known about the airport here. It's often said that, uh, whether you're going to heaven or hell, you got to change planes in Atlanta and, uh, after 40 minutes waiting on algorithm to be right for baggage claim when I was not, I finally managed to get some bag and to be able to show up dressed appropriately for you today. Um, here's one thing that I know for sure though, Dave, clean, consistent, and safe data will be essential to getting the world and businesses as we know it back on track again, um, without well-managed data, we're certain to get very inconsistent outcomes, quality data will the normalizing factor because one thing really hasn't changed about computing since the Dawn of time. Back when I was taking computer classes at Georgia tech here in Atlanta, and that's what we used to refer to as garbage in garbage out. In other words, you'll never get quality data-driven insights from a poor data set. This is especially important today for machine learning and AI, you can build the most amazing models and algorithms, but none of it will matter if the underlying data isn't rock solid as AI is increasingly used in every business app, you must build a solid data foundation mic. Let's >>Talk about hybrid. Every CXO that I talked to, they're trying to get hybrid, right? Whether it's hybrid work hybrid events, which is our business hybrid cloud, how are you thinking about the hybrid? Everything, what's your point of view with >>All those descriptions of hybrid? Everything there, one item you might not have quite hit on Dave and that's hybrid data. >>Oh yeah, you're right. Mick. I did miss that. What, what do you mean by hybrid data? Well, >>David in cloud era, we think hybrid data is all about the juxtaposition of two things, freedom and security. Now every business wants to be more agile. They want the freedom to work with their data, wherever it happens to work best for them, whether that's on premises in a private cloud and public cloud, or perhaps even in a new open data exchange. Now this matters to businesses because not all data applications are created equal. Some apps are best suited to be run in the cloud because of their transitory nature. Others may be more economical if they're running a private cloud, but either way security, regulatory compliance and increasingly data sovereignty are playing a bigger and more important role in every industry. If you don't believe me, just watch her read a recent news story. Data breaches are at an all time high. And the ethics of AI applications are being called into question every day and understanding the lineage of machine learning algorithms is now paramount for every business. So how in the heck do you get both the freedom and security that you're looking for? Well, the answer is actually pretty straightforward. The key is developing a hybrid data strategy. And what do you know Dave? That's the business cloud era? Is it on a serious note from cloud era's perspective? Adopting a hybrid data strategy is central to every business's digital transformation. It will enable rapid adoption of new technologies and optimize economic models while ensuring the security and privacy of every bit of data. What can >>Make, I'm glad you brought in that notion of hybrid data, because when you think about things, especially remote work, it really changes a lot of the assumptions. You talked about security, the data flows are going to change. You've got the economics, the physics, the local laws come into play. So what about the rest of hybrid? Yeah, >>It's a great question, Dave and certainly cloud era itself as a business and all of our customers are feeling this in a big way. We now have the overwhelming majority of our workforce working from home. And in other words, we've got a much larger surface area from a security perspective to keep in mind the rate and pace of data, just generating a report that might've happened very quickly and rapidly on the office. Uh, ether net may not be happening quite so fast in somebody's rural home in, uh, in, in the middle of Nebraska somewhere. Right? So it doesn't really matter whether you're talking about the speed of business or securing data, any way you look at it. Uh, hybrid I think is going to play a more important role in how work is conducted and what percentage of people are working in the office and are not, I know our plans, Dave, uh, involve us kind of slowly coming back to work, begin in this fall. And we're looking forward to being able to shake hands and see one another again for the first time in many cases for more than a year and a half, but, uh, yes, hybrid work, uh, and hybrid data are playing an increasingly important role for every kind of business. >>Thanks for that. I wonder if we could talk about industry transformation for a moment because it's a major theme of course, of this event. So, and the case. Here's how I think about it. It makes, I mean, some industries have transformed. You think about retail, for example, it's pretty clear, although although every physical retail brand I know has, you know, not only peaked up its online presence, but they also have an Amazon war room strategy because they're trying to take greater advantage of that physical presence, uh, and ended up reverse. We see Amazon building out physical assets so that there's more hybrid going on. But when you look at healthcare, for example, it's just starting, you know, with such highly regulated industry. It seems that there's some hurdles there. Financial services is always been data savvy, but you're seeing the emergence of FinTech and some other challenges there in terms of control, mint control of payment systems in manufacturing, you know, the pandemic highlighted America's reliance on China as a manufacturing partner and, and supply chain. Uh it's so my point is it seems that different industries they're in different stages of transformation, but two things look really clear. One, you've got to put data at the core of the business model that's compulsory. It seems like embedding AI into the applications, the data, the business process that's going to become increasingly important. So how do you see that? >>Wow, there's a lot packed into that question there, Dave, but, uh, yeah, we, we, uh, you know, at Cloudera I happened to be leading our own digital transformation as a technology company and what I would, what I would tell you there that's been arresting for us is the shift from being largely a subscription-based, uh, model to a consumption-based model requires a completely different level of instrumentation and our products and data collection that takes place in real, both for billing, for our, uh, for our customers. And to be able to check on the health and wellness, if you will, of their cloud era implementations. But it's clearly not just impacting the technology industry. You mentioned healthcare and we've been helping a number of different organizations in the life sciences realm, either speed, the rate and pace of getting vaccines, uh, to market, uh, or we've been assisting with testing process. >>That's taken place because you can imagine the quantity of data that's been generated as we've tried to study the efficacy of these vaccines on millions of people and try to ensure that they were going to deliver great outcomes and, and healthy and safe outcomes for everyone. And cloud era has been underneath a great deal of that type of work and the financial services industry you pointed out. Uh, we continue to be central to the large banks, meeting their compliance and regulatory requirements around the globe. And in many parts of the world, those are becoming more stringent than ever. And Cloudera solutions are really helping those kinds of organizations get through those difficult challenges. You, you also happened to mention, uh, you know, public sector and in public sector. We're also playing a key role in working with government entities around the world and applying AI to some of the most challenging missions that those organizations face. >>Um, and while I've made the kind of pivot between the industry conversation and the AI conversation, what I'll share with you about AI, I touched upon a little bit earlier. You can't build great AI, can't grow, build great ML apps, unless you've got a strong data foundation underneath is back to that garbage in garbage out comment that I made previously. And so in order to do that, you've got to have a great hybrid dated management platform at your disposal to ensure that your data is clean and organized and up to date. Uh, just as importantly from that, that's kind of the freedom side of things on the security side of things. You've got to ensure that you can see who just touched, not just the data itself, Dave, but actually the machine learning models and organizations around the globe are now being challenged. It's kind of on the topic of the ethics of AI to produce model lineage. >>In addition to data lineage. In other words, who's had access to the machine learning models when and where, and at what time and what decisions were made perhaps by the humans, perhaps by the machines that may have led to a particular outcome. So every kind of business that is deploying AI applications should be thinking long and hard about whether or not they can track the full lineage of those machine learning models just as they can track the lineage of data. So lots going on there across industries, lots going on as those various industries think about how AI can be applied to their businesses. Pretty >>Interesting concepts. You bring it into the discussion, the hybrid data, uh, sort of new, I think, new to a lot of people. And th this idea of model lineage is a great point because people want to talk about AI, ethics, transparency of AI. When you start putting those models into, into machines to do real time inferencing at the edge, it starts to get really complicated. I wonder if we could talk about you still on that theme of industry transformation? I felt like coming into the pandemic pre pandemic, there was just a lot of complacency. Yeah. Digital transformation and a lot of buzz words. And then we had this forced March to digital, um, and it's, but, but people are now being more planful, but there's still a lot of sort of POC limbo going on. How do you see that? Can you help accelerate that and get people out of that state? It definitely >>Is a lot of a POC limbo or a, I think some of us internally have referred to as POC purgatory, just getting stuck in that phase, not being able to get from point a to point B in digital transformation and, um, you know, for every industry transformation, uh, change in general is difficult and it takes time and money and thoughtfulness, but like with all things, what we found is small wins work best and done quickly. So trying to get to quick, easy successes where you can identify a clear goal and a clear objective and then accomplish it in rapid fashion is sort of the way to build your way towards those larger transformative efforts set. Another way, Dave, it's not wise to try to boil the ocean with your digital transformation efforts as it relates to the underlying technology here. And to bring it home a little bit more practically, I guess I would say at cloud era, we tend to recommend that companies begin to adopt cloud infrastructure, for example, containerization. >>And they begin to deploy that on-prem and then they start to look at how they may move those containerized workloads into the public cloud. That'll give them an opportunity to work with the data and the underlying applications themselves, uh, right close to home in place. They can kind of experiment a little bit more safely and economically, and then determine which workloads are best suited for the public cloud and which ones should remain on prem. That's a way in which a hybrid data strategy can help get a digital transformation accomplish, but kind of starting small and then drawing fast from there on customer's journey to the we'll make we've >>Covered a lot of ground. Uh, last question. Uh, w what, what do you want people to leave this event, the session with, and thinking about sort of the next era of data that we're entering? >>Well, it's a great question, but, uh, you know, I think it could be summed up in, uh, in two words. I want them to think about a hybrid data, uh, strategy. So, uh, you know, really hybrid data is a concept that we're bringing forward on this show really for the, for the first time, arguably, and we really do think that it enables customers to experience what we refer to Dave as the power of, and that is freedom, uh, and security, and in a world where we're all still trying to decide whether each day when we walk out each building, we walk into, uh, whether we're free to come in and out with a mask without a mask, that sort of thing, we all want freedom, but we also also want to be safe and feel safe, uh, for ourselves and for others. And the same is true of organizations. It strategies. They want the freedom to choose, to run workloads and applications and the best and most economical place possible. But they also want to do that with certainty, that they're going to be able to deploy those applications in a safe and secure way that meets the regulatory requirements of their particular industry. So hybrid data we think is key to accomplishing both freedom and security for your data and for your business as a whole, >>Nick, thanks so much great conversation and really appreciate the insights that you're bringing to this event into the industry. Really thank you for your time. >>You bet Dave pleasure being with you. Okay. >>We want to pick up on a couple of themes that Mick discussed, you know, supercharging your business with AI, for example, and this notion of getting hybrid, right? So right now we're going to turn the program over to Rob Bearden, the CEO of Cloudera and Manny veer, DAS. Who's the head of enterprise computing at Nvidia. And before I hand it off to Robin, I just want to say for those of you who follow me at the cube, we've extensively covered the transformation of the semiconductor industry. We are entering an entirely new era of computing in the enterprise, and it's being driven by the emergence of data, intensive applications and workloads no longer will conventional methods of processing data suffice to handle this work. Rather, we need new thinking around architectures and ecosystems. And one of the keys to success in this new era is collaboration between software companies like Cloudera and semiconductor designers like Nvidia. So let's learn more about this collaboration and what it means to your data business. Rob, thanks, >>Mick and Dave, that was a great conversation on how speed and agility is everything in a hyper competitive hybrid world. You touched on AI as essential to a data first strategy and accelerating the path to value and hybrid environments. And I want to drill down on this aspect today. Every business is facing accelerating everything from face-to-face meetings to buying groceries has gone digital. As a result, businesses are generating more data than ever. There are more digital transactions to track and monitor. Now, every engagement with coworkers, customers and partners is virtual from website metrics to customer service records, and even onsite sensors. Enterprises are accumulating tremendous amounts of data and unlocking insights from it is key to our enterprises success. And with data flooding every enterprise, what should the businesses do? A cloud era? We believe this onslaught of data offers an opportunity to make better business decisions faster. >>And we want to make that easier for everyone, whether it's fraud, detection, demand, forecasting, preventative maintenance, or customer churn, whether the goal is to save money or produce income every day that companies don't gain deep insight from their data is money they've lost. And the reason we're talking about speed and why speed is everything in a hybrid world and in a hyper competitive climate, is that the faster we get insights from all of our data, the faster we grow and the more competitive we are. So those faster insights are also combined with the scalability and cost benefit they cloud provides and with security and edge to AI data intimacy. That's why the partnership between cloud air and Nvidia together means so much. And it starts with the shared vision making data-driven, decision-making a reality for every business and our customers will now be able to leverage virtually unlimited quantities of varieties, of data, to power, an order of magnitude faster decision-making and together we turbo charge the enterprise data cloud to enable our customers to work faster and better, and to make integration of AI approaches a reality for companies of all sizes in the cloud. >>We're joined today by NVIDIA's Mandy veer dos, and to talk more about how our technologies will deliver the speed companies need for innovation in our hyper competitive environment. Okay, man, you're veer. Thank you for joining us over the unit. >>Thank you, Rob, for having me. It's a pleasure to be here on behalf of Nvidia. We are so excited about this partnership with Cloudera. Uh, you know, when, when, uh, when Nvidia started many years ago, we started as a chip company focused on graphics, but as you know, over the last decade, we've really become a full stack accelerated computing company where we've been using the power of GPU hardware and software to accelerate a variety of workloads, uh, AI being a prime example. And when we think about Cloudera, uh, and your company, a great company, there's three things we see Rob. Uh, the first one is that for the companies that will already transforming themselves by the use of data, Cloudera has been a trusted partner for them. The second thing seen is that when it comes to using your data, you want to use it in a variety of ways with a powerful platform, which of course you have built over time. >>And finally, as we've heard already, you believe in the power of hybrid, that data exists in different places and the compute needs to follow the data. Now, if you think about in various mission, going forward to democratize accelerated computing for all companies, our mission actually aligns very well with exactly those three things. Firstly, you know, we've really worked with a variety of companies today who have been the early adopters, uh, using the power acceleration by changing the technology in their stacks. But more and more, we see the opportunity of meeting customers, where they are with tools that they're familiar with with partners that they trust. And of course, Cloudera being a great example of that. Uh, the second, uh, part of NVIDIA's mission is we focused a lot in the beginning on deep learning where the power of GPU is really shown through, but as we've gone forward, we found that GPU's can accelerate a variety of different workloads from machine learning to inference. >>And so again, the power of your platform, uh, is very appealing. And finally, we know that AI is all about data, more and more data. We believe very strongly in the idea that customers put their data, where they need to put it. And the compute, the AI compute the machine learning compute needs to meet the customer where their data is. And so that matches really well with your philosophy, right? And Rob, that's why we were so excited to do this partnership with you. It's come to fruition. We have a great combined stack now for the customer and we already see people using it. I think the IRS is a fantastic example where literally they took the workflow. They had, they took the servers, they had, they added GPS into those servers. They did not change anything. And they got an eight times performance improvement for their fraud detection workflows, right? And that's the kind of success we're looking forward to with all customers. So the team has actually put together a great video to show us what the IRS is doing with this technology. Let's take a look. >>My name's Joanne salty. I'm the branch chief of the technical branch and RAs. It's actually the research division research and statistical division of the IRS. Basically the mission that RAs has is we do statistical and research on all things related to taxes, compliance issues, uh, fraud issues, you know, anything that you can think of. Basically we do research on that. We're running into issues now that we have a lot of ideas to actually do data mining on our big troves of data, but we don't necessarily have the infrastructure or horsepower to do it. So it's our biggest challenge is definitely the, the infrastructure to support all the ideas that the subject matter experts are coming up with in terms of all the algorithms they would like to create. And the diving deeper within the algorithm space, the actual training of those Agra algorithms, the of parameters each of those algorithms have. >>So that's, that's really been our challenge. Now the expectation was that with Nvidia in cloud, there is help. And with the cluster, we actually build out the test this on the actual fraud, a fraud detection algorithm on our expectation was we were definitely going to see some speed up in prom, computational processing times. And just to give you context, the size of the data set that we were, uh, the SMI was actually working, um, the algorithm against Liz around four terabytes. If I recall correctly, we'd had a 22 to 48 times speed up after we started tweaking the original algorithm. My expectations, quite honestly, in that sphere, in terms of the timeframe to get results, was it that you guys actually exceeded them? It was really, really quick. Uh, the definite now term short term what's next is going to be the subject matter expert is actually going to take our algorithm run with that. >>So that's definitely the now term thing we want to do going down, go looking forward, maybe out a couple of months, we're also looking at curing some, a 100 cards to actually test those out. As you guys can guess our datasets are just getting bigger and bigger and bigger, and it demands, um, to actually do something when we get more value added out of those data sets is just putting more and more demands on our infrastructure. So, you know, with the pilot, now we have an idea with the infrastructure, the infrastructure we need going forward. And then also just our in terms of thinking of the algorithms and how we can approach these problems to actually code out solutions to them. Now we're kind of like the shackles are off and we can just run them, you know, come onto our art's desire, wherever imagination takes our skis to actually develop solutions, know how the platforms to run them on just kind of the close out. >>I rarely would be very missed. I've worked with a lot of, you know, companies through the year and most of them been spectacular. And, uh, you guys are definitely in that category. The, the whole partnership, as I said, a little bit early, it was really, really well, very responsive. I would be remiss if I didn't. Thank you guys. So thank you for the opportunity to, and fantastic. And I'd have to also, I want to thank my guys. My, uh, my staff, David worked on this Richie worked on this Lex and Tony just, they did a fantastic job and I want to publicly thank him for all the work they did with you guys and Chev, obviously also. Who's fantastic. So thank you everyone. >>Okay. That's a real great example of speed and action. Now let's get into some follow up questions guys, if I may, Rob, can you talk about the specific nature of the relationship between Cloudera and Nvidia? Is it primarily go to market or you do an engineering work? What's the story there? >>It's really both. It's both go to market and engineering and engineering focus is to optimize and take advantage of invidious platform to drive better price performance, lower cost, faster speeds, and better support for today's emerging data intensive applications. So it's really both >>Great. Thank you. Many of Eric, maybe you could talk a little bit more about why can't we just existing general purpose platforms that are, that are running all this ERP and CRM and HCM and you know, all the, all the Microsoft apps that are out there. What, what do Nvidia and cloud era bring to the table that goes beyond the conventional systems that we've known for many years? >>Yeah. I think Dave, as we've talked about the asset that the customer has is really the data, right? And the same data can be utilized in many different ways. Some machine learning, some AI, some traditional data analytics. So the first step here was really to take a general platform for data processing, Cloudera data platform, and integrate with that. Now Nvidia has a software stack called rapids, which has all of the primitives that make different kinds of data processing go fast on GPU's. And so the integration here has really been taking rapids and integrating it into a Cloudera data platform. So that regardless of the technique, the customer's using to get insight from that data, the acceleration will apply in all cases. And that's why it was important to start with a platform like Cloudera rather than a specific application. >>So I think this is really important because if you think about, you know, the software defined data center brought in, you know, some great efficiencies, but at the same time, a lot of the compute power is now going toward doing things like networking and storage and security offloads. So the good news, the reason this is important is because when you think about these data intensive workloads, we can now put more processing power to work for those, you know, AI intensive, uh, things. And so that's what I want to talk about a little bit, maybe a question for both of you, maybe Rob, you could start, you think about the AI that's done today in the enterprise. A lot of it is modeling in the cloud, but when we look at a lot of the exciting use cases, bringing real-time systems together, transaction systems and analytics systems and real time, AI inference, at least even at the edge, huge potential for business value and a consumer, you're seeing a lot of applications with AI biometrics and voice recognition and autonomous vehicles and the like, and so you're putting AI into these data intensive apps within the enterprise. >>The potential there is enormous. So what can we learn from sort of where we've come from, maybe these consumer examples and Rob, how are you thinking about enterprise AI in the coming years? >>Yeah, you're right. The opportunity is huge here, but you know, 90% of the cost of AI applications is the inference. And it's been a blocker in terms of adoption because it's just been too expensive and difficult from a performance standpoint and new platforms like these being developed by cloud air and Nvidia will dramatically lower the cost, uh, of enabling this type of workload to be done. Um, and what we're going to see the most improvements will be in the speed and accuracy for existing enterprise AI apps like fraud detection, recommendation, engine chain management, drug province, and increasingly the consumer led technologies will be bleeding into the enterprise in the form of autonomous factory operations. An example of that would be robots that AR VR and manufacturing. So driving quality, better quality in the power grid management, automated retail IOT, you know, the intelligent call centers, all of these will be powered by AI, but really the list of potential use cases now are going to be virtually endless. >>I mean, this is like your wheelhouse. Maybe you could add something to that. >>Yeah. I mean, I agree with Rob. I mean he listed some really good use cases. You know, the way we see this at Nvidia, this journey is in three phases or three steps, right? The first phase was for the early adopters. You know, the builders who assembled, uh, use cases, particular use cases like a chat bot, uh, uh, from the ground up with the hardware and the software almost like going to your local hardware store and buying piece parts and constructing a table yourself right now. I think we are in the first phase of the democratization, uh, for example, the work we did with Cloudera, which is, uh, for a broader base of customers, still building for a particular use case, but starting from a much higher baseline. So think about, for example, going to Ikea now and buying a table in a box, right. >>And you still come home and assemble it, but all the parts are there. The instructions are there, there's a recipe you just follow and it's easy to do, right? So that's sort of the phase we're in now. And then going forward, the opportunity we really look forward to for the democratization, you talked about applications like CRM, et cetera. I think the next wave of democratization is when customers just adopt and deploy the next version of an application they already have. And what's happening is that under the covers, the application is infused by AI and it's become more intelligent because of AI and the customer just thinks they went to the store and bought, bought a table and it showed up and somebody placed it in the right spot. Right. And they didn't really have to learn, uh, how to do AI. So these are the phases. And I think they're very excited to be going there. Yeah. You know, >>Rob, the great thing about for, for your customers is they don't have to build out the AI. They can, they can buy it. And, and just in thinking about this, it seems like there are a lot of really great and even sometimes narrow use cases. So I want to ask you, you know, staying with AI for a minute, one of the frustrations and Mick and I talked about this, the guy go problem that we've all studied in college, uh, you know, garbage in, garbage out. Uh, but, but the frustrations that users have had is really getting fast access to quality data that they can use to drive business results. So do you see, and how do you see AI maybe changing the game in that regard, Rob over the next several years? >>So yeah, the combination of massive amounts of data that have been gathered across the enterprise in the past 10 years with an open API APIs are dramatically lowering the processing costs that perform at much greater speed and efficiency, you know, and that's allowing us as an industry to democratize the data access while at the same time, delivering the federated governance and security models and hybrid technologies are playing a key role in making this a reality and enabling data access to be hybridized, meaning access and treated in a substantially similar way, your respect to the physical location of where that data actually resides. >>That's great. That is really the value layer that you guys are building out on top of that, all this great infrastructure that the hyperscalers have have given us, I mean, a hundred billion dollars a year that you can build value on top of, for your customers. Last question, and maybe Rob, you could, you can go first and then manufacture. You could bring us home. Where do you guys want to see the relationship go between cloud era and Nvidia? In other words, how should we, as outside observers be, be thinking about and measuring your project specifically and in the industry's progress generally? >>Yeah, I think we're very aligned on this and for cloud era, it's all about helping companies move forward, leverage every bit of their data and all the places that it may, uh, be hosted and partnering with our customers, working closely with our technology ecosystem of partners means innovation in every industry and that's inspiring for us. And that's what keeps us moving forward. >>Yeah. And I agree with Robin and for us at Nvidia, you know, we, this partnership started, uh, with data analytics, um, as you know, a spark is a very powerful technology for data analytics, uh, people who use spark rely on Cloudera for that. And the first thing we did together was to really accelerate spark in a seamless manner, but we're accelerating machine learning. We accelerating artificial intelligence together. And I think for Nvidia it's about democratization. We've seen what machine learning and AI have done for the early adopters and help them make their businesses, their products, their customer experience better. And we'd like every company to have the same opportunity. >>Okay. Now we're going to dig into the data landscape and cloud of course. And talk a little bit more about that with drew Allen. He's a managing director at Accenture drew. Welcome. Great to see you. Thank you. So let's talk a little bit about, you know, you've been in this game for a number of years. Uh, you've got particular expertise in, in data and finance and insurance. I mean, you know, you think about it within the data and analytics world, even our language is changing. You know, we don't say talk about big data so much anymore. We talk more about digital, you know, or, or, or data driven when you think about sort of where we've come from and where we're going. What are the puts and takes that you have with regard to what's going on in the business today? >>Well, thanks for having me. Um, you know, I think some of the trends we're seeing in terms of challenges and puts some takes are that a lot of companies are already on this digital journey. Um, they focused on customer experience is kind of table stakes. Everyone wants to focus on that and kind of digitizing their channels. But a lot of them are seeing that, you know, a lot of them don't even own their, their channels necessarily. So like we're working with a big cruise line, right. And yes, they've invested in digitizing what they own, but a lot of the channels that they sell through, they don't even own, right. It's the travel agencies or third party, real sellers. So having the data to know where, you know, where those agencies are, that that's something that they've discovered. And so there's a lot of big focus on not just digitizing, but also really understanding your customers and going across products because a lot of the data has built, been built up in individual channels and in digital products. >>And so bringing that data together is something that customers that have really figured out in the last few years is a big differentiator. And what we're seeing too, is that a big trend that the data rich are getting richer. So companies that have really invested in data, um, are having, uh, an outside market share and outside earnings per share and outside revenue growth. And it's really being a big differentiator. And I think for companies just getting started in this, the thing to think about is one of the missteps is to not try to capture all the data at once. The average company has, you know, 10,000, 20,000 data elements individually, when you want to start out, you know, 500, 300 critical data elements, about 5% of the data of a company drives 90% of the business value. So focusing on those key critical data elements is really what you need to govern first and really invest in first. And so that's something we, we tell companies at the beginning of their data strategy is first focus on those critical data elements, really get a handle on governing that data, organizing that data and building data products around >>That day. You can't boil the ocean. Right. And so, and I, I feel like pre pandemic, there was a lot of complacency. Oh yeah, we'll get to that. You know, not on my watch, I'll be retired before that, you know, is it becomes a minute. And then of course the pandemic was, I call it sometimes a forced March to digital. So in many respects, it wasn't planned. It just ha you know, you had to do it. And so now I feel like people are stepping back and saying, okay, let's now really rethink this and do it right. But is there, is there a sense of urgency, do you think? Absolutely. >>I think with COVID, you know, we were working with, um, a retailer where they had 12,000 stores across the U S and they had didn't have the insights where they could drill down and understand, you know, with the riots and with COVID was the store operational, you know, with the supply chain of the, having multiple distributors, what did they have in stock? So there are millions of data points that you need to drill down at the cell level, at the store level to really understand how's my business performing. And we like to think about it for like a CEO and his leadership team of it, like, think of it as a digital cockpit, right? You think about a pilot, they have a cockpit with all these dials and, um, dashboards, essentially understanding the performance of their business. And they should be able to drill down and understand for each individual, you know, unit of their work, how are they performing? That's really what we want to see for businesses. Can they get down to that individual performance to really understand how their business >>Is performing good, the ability to connect those dots and traverse those data points and not have to go in and come back out and go into a new system and come back out. And that's really been a lot of the frustration. W where does machine intelligence and AI fit in? Is that sort of a dot connector, if you will, and an enabler, I mean, we saw, you know, decades of the, the AI winter, and then, you know, there's been a lot of talk about it, but it feels like with the amount of data that we've collected over the last decade and the, the, the low costs of processing that data now, it feels like it's, it's real. Where do you see AI fitting? Yeah, >>I mean, I think there's been a lot of innovation in the last 10 years with, um, the low cost of storage and computing and these algorithms in non-linear, um, you know, knowledge graphs, and, um, um, a whole bunch of opportunities in cloud where what I think the, the big opportunity is, you know, you can apply AI in areas where a human just couldn't have the scale to do that alone. So back to the example of a cruise lines, you know, you may have a ship being built that has 4,000 cabins on the single cruise line, and it's going to multiple deaths that destinations over its 30 year life cycle. Each one of those cabins is being priced individually for each individual destination. It's physically impossible for a human to calculate the dynamic pricing across all those destinations. You need a machine to actually do that pricing. And so really what a machine is leveraging is all that data to really calculate and assist the human, essentially with all these opportunities where you wouldn't have a human being able to scale up to that amount of data >>Alone. You know, it's interesting. One of the things we talked to Nicolson about earlier was just the everybody's algorithms are out of whack. You know, you look at the airline pricing, you look at hotels it's as a consumer, you would be able to kind of game the system and predict that they can't even predict these days. And I feel as though that the data and AI are actually going to bring us back into some kind of normalcy and predictability, uh, what do you see in that regard? Yeah, I think it's, >>I mean, we're definitely not at a point where, when I talked to, you know, the top AI engineers and data scientists, we're not at a point where we have what they call broad AI, right? You can get machines to solve general knowledge problems, where they can solve one problem and then a distinctly different problem, right? That's still many years away, but narrow why AI, there's still tons of use cases out there that can really drive tons of business performance challenges, tons of accuracy challenges. So for example, in the insurance industry, commercial lines, where I work a lot of the time, the biggest leakage of loss experience in pricing for commercial insurers is, um, people will go in as an agent and they'll select an industry to say, you know what, I'm a restaurant business. Um, I'll select this industry code to quote out a policy, but there's, let's say, you know, 12 dozen permutations, you could be an outdoor restaurant. >>You could be a bar, you could be a caterer and all of that leads to different loss experience. So what this does is they built a machine learning algorithm. We've helped them do this, that actually at the time that they're putting in their name and address, it's crawling across the web and predicting in real time, you know, is this a address actually, you know, a business that's a restaurant with indoor dining, does it have a bar? Is it outdoor dining? And it's that that's able to accurately more price the policy and reduce the loss experience. So there's a lot of that you can do even with narrow AI that can really drive top line of business results. >>Yeah. I liked that term, narrow AI, because getting things done is important. Let's talk about cloud a little bit because people talk about cloud first public cloud first doesn't necessarily mean public cloud only, of course. So where do you see things like what's the right operating model, the right regime hybrid cloud. We talked earlier about hybrid data help us squint through the cloud landscape. Yeah. I mean, I think for most right, most >>Fortune 500 companies, they can't just snap their fingers and say, let's move all of our data centers to the cloud. They've got to move, you know, gradually. And it's usually a journey that's taking more than two to three plus years, even more than that in some cases. So they're have, they have to move their data, uh, incrementally to the cloud. And what that means is that, that they have to move to a hybrid perspective where some of their data is on premise and some of it is publicly on the cloud. And so that's the term hybrid cloud essentially. And so what they've had to think about is from an intelligence perspective, the privacy of that data, where is it being moved? Can they reduce the replication of that data? Because ultimately you like, uh, replicating the data from on-premise to the cloud that introduces, you know, errors and data quality issues. So thinking about how do you manage, uh, you know, uh on-premise and, um, public as a transition is something that Accenture thinks, thinks, and helps our clients do quite a bit. And how do you move them in a manner that's well-organized and well thought of? >>Yeah. So I've been a big proponent of sort of line of business lines of business becoming much more involved in, in the data pipeline, if you will, the data process, if you think about our major operational systems, they all have sort of line of business context in them. And then the salespeople, they know the CRM data and, you know, logistics folks there they're very much in tune with ERP, almost feel like for the past decade, the lines of business have been somewhat removed from the, the data team, if you will. And that, that seems to be changing. What are you seeing in terms of the line of line of business being much more involved in sort of end to end ownership, if you will, if I can use that term of, uh, of the data and sort of determining things like helping determine anyway, the data quality and things of that nature. Yeah. I >>Mean, I think this is where thinking about your data operating model and thinking about ideas of a chief data officer and having data on the CEO agenda, that's really important to get the lines of business, to really think about data sharing and reuse, and really getting them to, you know, kind of unlock the data because they do think about their data as a fiefdom data has value, but you've got to really get organizations in their silos to open it up and bring that data together because that's where the value is. You know, data doesn't operate. When you think about a customer, they don't operate in their journey across the business in silo channels. They don't think about, you know, I use only the web and then I use the call center, right? They think about that as just one experience and that data is a single journey. >>So we like to think about data as a product. You know, you should think about a data in the same way. You think about your products as, as products, you know, data as a product, you should have the idea of like every two weeks you have releases to it. You have an operational resiliency to it. So thinking about that, where you can have a very product mindset to delivering your data, I think is very important for the success. And that's where kind of, there's not just the things about critical data elements and having the right platform architecture, but there's a soft stuff as well, like a, a product mindset to data, having the right data, culture, and business adoption and having the right value set mindset for, for data, I think is really >>Important. I think data as a product is a very powerful concept and I think it maybe is uncomfortable to some people sometimes. And I think in the early days of big data, if you will, people thought, okay, data is a product going to sell my data and that's not necessarily what you mean, thinking about products or data that can fuel products that you can then monetize maybe as a product or as a, as, as a service. And I like to think about a new metric in the industry, which is how long does it take me to get from idea I'm a business person. I have an idea for a data product. How long does it take me to get from idea to monetization? And that's going to be something that ultimately as a business person, I'm going to use to determine the success of my data team and my data architecture. Is that kind of thinking starting to really hit the marketplace? Absolutely. >>I mean, I insurers now are working, partnering with, you know, auto manufacturers to monetize, um, driver usage data, you know, on telematics to see, you know, driver behavior on how, you know, how auto manufacturers are using that data. That's very important to insurers, you know, so how an auto manufacturer can monetize that data is very important and also an insurance, you know, cyber insurance, um, are there news new ways we can look at how companies are being attacked with viruses and malware. And is there a way we can somehow monetize that information? So companies that are able to agily, you know, think about how can we collect this data, bring it together, think about it as a product, and then potentially, you know, sell it as a service is something that, um, company, successful companies, you're doing great examples >>Of data products, and it might be revenue generating, or it might be in the case of, you know, cyber, maybe it reduces my expected loss and exactly. Then it drops right to my bottom line. What's the relationship between Accenture and cloud era? Do you, I presume you guys meet at the customer, but maybe you could give us some insight. >>Yeah. So, um, I, I'm in the executive sponsor for, um, the Accenture Cloudera partnership on the Accenture side. Uh, we do quite a lot of business together and, um, you know, Cloudera has been a great partner for us. Um, and they've got a great product in terms of the Cloudera data platform where, you know, what we do is as a big systems integrator for them, we help, um, you know, configure and we have a number of engineers across the world that come in and help in terms of, um, engineer architects and install, uh, cloud errors, data platform, and think about what are some of those, you know, value cases where you can really think about organizing data and bringing it together for all these different types of use cases. And really just as the examples we thought about. So the telematics, you know, um, in order to realize something like that, you're bringing in petabytes and huge scales of data that, you know, you just couldn't bring on a normal, uh, platform. You need to think about cloud. You need to think about speed of, of data and real-time insights and cloud era is the right data platform for that. So, um, >>Having a cloud Cloudera ushered in the modern big data era, we kind of all know that, and it was, which of course early on, it was very services intensive. You guys were right there helping people think through there weren't enough data scientists. We've sort of all, all been through that. And of course in your wheelhouse industries, you know, financial services and insurance, they were some of the early adopters, weren't they? Yeah, absolutely. >>Um, so, you know, an insurance, you've got huge amounts of data with loss history and, um, a lot with IOT. So in insurance, there's a whole thing of like sensorized thing in, uh, you know, taking the physical world and digitizing it. So, um, there's a big thing in insurance where, um, it's not just about, um, pricing out the risk of a loss experience, but actual reducing the loss before it even happens. So it's called risk control or loss control, you know, can we actually put sensors on oil pipelines or on elevators and, you know, reduce, um, you know, accidents before they happen. So we're, you know, working with an insurer to actually, um, listen to elevators as they move up and down and are there signals in just listening to the audio of an elevator over time that says, you know what, this elevator is going to need maintenance, you know, before a critical accident could happen. So there's huge applications, not just in structured data, but in unstructured data like voice and audio and video where a partner like Cloudera has a huge role to play. >>Great example of it. So again, narrow sort of use case for machine intelligence, but, but real value. True. We'll leave it like that. Thanks so much for taking some time. Yes. Thank you so much. Okay. We continue now with the theme of turning ideas into insights. So ultimately you can take action. We heard earlier that public cloud first doesn't mean public cloud only, and a winning strategy comprises data, irrespective of physical location on prem, across multiple clouds at the edge where real time inference is going to drive a lot of incremental value. Data is going to help the world come back to normal. We heard, or at least semi normal as we begin to better understand and forecast demand and supply and balances and economic forces. AI is becoming embedded into every aspect of our business, our people, our processes, and applications. And now we're going to get into some of the foundational principles that support the data and insights centric processes, which are fundamental to digital transformation initiatives. And it's my pleasure to welcome two great guests, Michelle Goetz. Who's a Kuba woman, VP and principal analyst at Forrester, and doing some groundbreaking work in this area. And Cindy, Mikey, who is the vice president of industry solutions and value management at Cloudera. Welcome to both of >>You. Welcome. Thank you. Thanks Dave. >>All right, Michelle, let's get into it. Maybe you could talk about your foundational core principles. You start with data. What are the important aspects of this first principle that are achievable today? >>It's really about democratization. If you can't make your data accessible, um, it's not usable. Nobody's able to understand what's happening in the business and they don't understand, um, what insights can be gained or what are the signals that are occurring that are going to help them with decisions, create stronger value or create deeper relationships, their customers, um, due to their experiences. So it really begins with how do you make data available and bring it to where the consumer of the data is rather than trying to hunt and Peck around within your ecosystem to find what it is that's important. Great. >>Thank you for that. So, Cindy, I wonder in hearing what Michelle just said, what are your thoughts on this? And when you work with customers at Cloudera, does, are there any that stand out that perhaps embody the fundamentals that Michelle just shared? >>Yeah, there's, there's quite a few. And especially as we look across, um, all the industries that we're actually working with customers in, you know, a few that stand out in top of mind for me is one is IQ via and what they're doing with real-world evidence and bringing together data across the entire, um, healthcare and life sciences ecosystems, bringing it together in different shapes and formats, making the ed accessible by both internally, as well as for their, um, the entire extended ecosystem. And then for SIA, who's working to solve some predictive maintenance issues within, there are a European car manufacturer and how do they make sure that they have, you know, efficient and effective processes when it comes to, uh, fixing equipment and so forth. And then also, um, there's, uh, an Indonesian based, um, uh, telecommunications company tech, the smell, um, who's bringing together, um, over the last five years, all their data about their customers and how do they enhance our customer experience? How do they make information accessible, especially in these pandemic and post pandemic times, um, uh, you know, just getting better insights into what customers need and when do they need it? >>Cindy platform is another core principle. How should we be thinking about data platforms in this day and age? I mean, where does, where do things like hybrid fit in? Um, what's cloud era's point >>Of view platforms are truly an enabler, um, and data needs to be accessible in many different fashions. Um, and also what's right for the business. When, you know, I want it in a cost and efficient and effective manner. So, you know, data needs to be, um, data resides everywhere. Data is developed and it's brought together. So you need to be able to balance both real time, you know, our batch historical information. It all depends upon what your analytical workloads are. Um, and what types of analytical methods you're going to use to drive those business insights. So putting and placing data, um, landing it, making it accessible, analyzing it needs to be done in any accessible platform, whether it be, you know, a public cloud doing it on-prem or a hybrid of the two is typically what we're seeing, being the most successful. >>Great. Thank you, Michelle. Let's move on a little bit and talk about practices and practices and processes as the next core principles. Maybe you could provide some insight as to how you think about balancing practices and processes while at the same time managing agility. >>Yeah, it's a really great question because it's pretty complex. When you have to start to connect your data to your business, the first thing to really gravitate towards is what are you trying to do? And what Cindy was describing with those customer examples is that they're all based off of business goals off of very specific use cases that helps kind of set the agenda about what is the data and what are the data domains that are important to really understanding and recognizing what's happening within that business activity and the way that you can affect that either in, you know, near time or real time, or later on, as you're doing your strategic planning, what that's balancing against is also being able to not only see how that business is evolving, but also be able to go back and say, well, can I also measure the outcomes from those processes and using data and using insight? >>Can I also get intelligence about the data to know that it's actually satisfying my objectives to influence my customers in my market? Or is there some sort of data drift or detraction in my, um, analytic capabilities that are allowing me to be effective in those environments, but everything else revolves around that and really thinking succinctly about a strategy that isn't just data aware, what data do I have and how do I use it, but coming in more from that business perspective to then start to be, data-driven recognizing that every activity you do from a business perspective leads to thinking about information that supports that and supports your decisions, and ultimately getting to the point of being insight driven, where you're able to both, uh, describe what you want your business to be with your data, using analytics, to then execute on that fluidly and in real time. And then ultimately bringing that back with linking to business outcomes and doing that in a continuous cycle where you can test and you can learn, you can improve, you can optimize, and you can innovate because you can see your business as it's happening. And you have the right signals and intelligence that allow you to make great decisions. >>I like how you said near time or real time, because it is a spectrum. And you know, one of the spectrum, autonomous vehicles, you've got to make a decision in real time, but, but, but near real-time, or real-time, it's, it's in the eyes of the holder, if you will, it's it might be before you lose the customer before the market changes. So it's really defined on a case by case basis. Um, I wonder Michelle, if you could talk about in working with a number of organizations, I see folks, they sometimes get twisted up and understanding the dependencies that technology generally, and the technologies around data specifically can have on critical business processes. Can you maybe give some guidance as to where customers should start, where, you know, where can we find some of the quick wins and high return, it >>Comes first down to how does your business operate? So you're going to take a look at the business processes and value stream itself. And if you can understand how people and customers, partners, and automation are driving that step by step approach to your business activities, to realize those business outcomes, it's way easier to start thinking about what is the information necessary to see that particular step in the process, and then take the next step of saying what information is necessary to make a decision at that current point in the process, or are you collecting information asking for information that is going to help satisfy a downstream process step or a downstream decision. So constantly making sure that you are mapping out your business processes and activities, aligning your data process to that helps you now rationalize. Do you need that real time near real time, or do you want to start grading greater consistency by bringing all of those signals together, um, in a centralized area to eventually oversee the entire operations and outcomes as they happen? It's the process and the decision points and acting on those decision points for the best outcome that really determines are you going to move in more of a real-time, uh, streaming capacity, or are you going to push back into more of a batch oriented approach? Because it depends on the amount of information and the aggregate of which provides the best insight from that. >>Got it. Let's, let's bring Cindy back into the conversation in your city. We often talk about people process and technology and the roles they play in creating a data strategy. That's that's logical and sound. Can you speak to the broader ecosystem and the importance of creating both internal and external partners within an organization? Yeah. >>And that's, uh, you know, kind of building upon what Michelle was talking about. If you think about datas and I hate to use the phrase almost, but you know, the fuel behind the process, um, and how do you actually become insight-driven? And, you know, you look at the capabilities that you're needing to enable from that business process, that insight process, um, you're extended ecosystem on, on how do I make that happen? You know, partners, um, and, and picking the right partner is important because a partner is one that actually helps under or helps you implement what your decisions are. Um, so, um, looking for a partner that has the capability that believes in being insight-driven and making sure that when you're leveraging data, um, you know, for within process on that, if you need to do it in a time fashion, that they can actually meet those needs of the business, um, and enabling on those, those process activities. So the ecosystem looking at how you, um, look at, you know, your vendors are, and fundamentally they need to be that trusted partner. Um, do they bring those same principles of value of being insight driven? So they have to have those core values themselves in order to help you as a, um, an end of business person enable those capabilities. So, so yeah, I'm >>Cool with fuel, but it's like super fuel when you talk about data, cause it's not scarce, right? You're never going to run out. So Michelle, let's talk about leadership. W w who leads, what does so-called leadership look like in an organization that's insight driven? >>So I think the really interesting thing that is starting to evolve as late is that organizations enterprises are really recognizing that not just that data is an asset and data has value, but exactly what we're talking about here, data really does drive what your business outcomes are going to be data driving into the insight or the raw data itself has the ability to set in motion. What's going to happen in your business processes and your customer experiences. And so, as you kind of think about that, you're now starting to see your CEO, your CMO, um, your CRO coming back and saying, I need better data. I need information. That's representative of what's happening in my business. I need to be better adaptive to what's going on with my customers. And ultimately that means I need to be smarter and have clearer forecasting into what's about ready to come, not just, you know, one month, two months, three months or a year from now, but in a week or tomorrow. >>And so that's, how is having a trickle down effect to then looking at two other types of roles that are elevating from technical capacity to more business capacity, you have your chief data officer that is shaping the exp the experiences, uh, with data and with insight and reconciling, what type of information is necessary with it within the context of answering these questions and creating a future fit organization that is adaptive and resilient to things that are happening. And you also have a chief digital officer who is participating because they're providing the experience and shaping the information and the way that you're going to interact and execute on those business activities, and either running that autonomously or as part of an assistance for your employees and for your customers. So really to go from not just data aware to data driven, but ultimately to be insight driven, you're seeing way more, um, participation, uh, and leadership at that C-suite level. And just underneath, because that's where the subject matter expertise is coming in to know how to create a data strategy that is tightly connected to your business strategy. >>Right. Thank you. Let's wrap. And I've got a question for both of you, maybe Cindy, you could start and then Michelle bring us home. You know, a lot of customers, they want to understand what's achievable. So it's helpful to paint a picture of a, of a maturity model. Uh, you know, I'd love to go there, but I'm not going to get there anytime soon, but I want to take some baby steps. So when you're performing an analysis on, on insight driven organization, city, what do you see as the major characteristics that define the differences between sort of the, the early, you know, beginners, the sort of fat middle, if you will, and then the more advanced, uh, constituents. >>Yeah, I'm going to build upon, you know, what Michelle was talking about as data as an asset. And I think, you know, also being data where, and, you know, trying to actually become, you know, insight driven, um, companies can also have data and they can have data as a liability. And so when you're data aware, sometimes data can still be a liability to your organization. If you're not making business decisions on the most recent and relevant data, um, you know, you're not going to be insight driven. So you've got to move beyond that, that data awareness, where you're looking at data just from an operational reporting, but data's fundamentally driving the decisions that you make. Um, as a business, you're using data in real time. You're, um, you're, you know, leveraging data to actually help you make and drive those decisions. So when we use the term you're, data-driven, you can't just use the term, you know, tongue in cheek. It actually means that I'm using the recent, the relevant and the accuracy of data to actually make the decisions for me, because we're all advancing upon. We're talking about, you know, artificial intelligence and so forth. Being able to do that, if you're just data where I would not be embracing on leveraging artificial intelligence, because that means I probably haven't embedded data into my processes. It's data could very well still be a liability in your organization. So how do you actually make it an asset? Yeah, I think data >>Where it's like cable ready. So, so Michelle, maybe you could, you could, you could, uh, add to what Cindy just said and maybe add as well, any advice that you have around creating and defining a data strategy. >>So every data strategy has a component of being data aware. This is like building the data museum. How do you capture everything that's available to you? How do you maintain that memory of your business? You know, bringing in data from your applications, your partners, third parties, wherever that information is available, you want to ensure that you're capturing and you're managing and you're maintaining it. And this is really where you're starting to think about the fact that it is an asset. It has value, but you may not necessarily know what that value is. Yet. If you move into a category of data driven, what starts to shift and change there is you're starting to classify label, organize the information in context of how you're making decisions and how you do business. It could start from being more, um, proficient from an analytic purpose. You also might start to introduce some early stages of data science in there. >>So you can do some predictions and some data mining to start to weed out some of those signals. And you might have some simple types of algorithms that you're deploying to do a next next best action for example. And that's what data-driven is really about. You're starting to get value out of it. The data itself is starting to make sense in context of your business, but what you haven't done quite yet, which is what insight driven businesses are, is really starting to take away. Um, the gap between when you see it, know it and then get the most value and really exploit what that insight is at the time when it's right. So in the moment we talk about this in terms of perishable insights, data and insights are ephemeral. And we want to ensure that the way that we're managing that and delivering on that data and insights is in time with our decisions and the highest value outcome we're going to have, that that insight can provide us. >>So are we just introducing it as data-driven organizations where we could see, you know, spreadsheets and PowerPoint presentations and lots of mapping to help make sort of longer strategic decisions, or are those insights coming up and being activated in an automated fashion within our business processes that are either assisting those human decisions at the point when they're needed, or an automated decisions for the types of digital experiences and capabilities that we're driving in our organization. So it's going from, I'm a data hoarder. If I'm data aware to I'm interested in what's happening as a data-driven organization and understanding my data. And then lastly being insight driven is really where light between business, data and insight. There is none it's all coming together for the best outcomes, >>Right? So people are acting on perfect or near perfect information or machines or, or, uh, doing so with a high degree of confidence, great advice and insights. And thank you both for sharing your thoughts with our audience today. It's great to have you. Thank you. Thank you. Okay. Now we're going to go into our industry. Deep dives. There are six industry breakouts, financial services, insurance, manufacturing, retail communications, and public sector. Now each breakout is going to cover two distinct use cases for a total of essentially 12 really detailed segments that each of these is going to be available on demand, but you can scan the calendar on the homepage and navigate to your breakout session for choice of choice or for more information, click on the agenda page and take a look to see which session is the best fit for you. And then dive in, join the chat and feel free to ask questions or contribute your knowledge, opinions, and data. Thanks so much for being part of the community and enjoy the rest of the day.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. >>Welcome to the cubes coverage of AWS reinvent 2020. The digital version I'm Lisa Martin and I have a couple of Cuba alumni joining me from Wien. We've got Danny Allen. It's C T O and S VP of product strategy And Dave Russell, VP of Enterprise Strategy, is here as well. Danny and a Welcome back to the Cube. >>Hi, Lisa. Great to be here. >>Hey, Lisa. Great to be here. Love talking with this audience >>It and thankfully, because of technologies like this in the zoom, were still able to engage with that audience, even though we would all be gearing up to be Go spending five days in Vegas with what 47,000 of our closest friends across, you know, and walking a lot. But I wanted Thio. Danny, start with you and you guys had them on virtually this summer. That's an event known for its energy. Talk to me about some of the things that you guys announced there. And how are your customers doing with this rapid change toe? work from home and this massive amount of uncertainty. >>Well, certainly no one would have predicted this the beginning of the year. There has been such transformation. There was a statement made earlier this year that we've gone through two years of transformation in just two months, and I would say that is definitely true. If you look both internally and bean our workforce, we have 4400 employees all of a sudden, 3000 of them that had been going into the office or working from home. And that is true of our customer base as well. There's a lot of remote, uh, remote employ, mental remote working, and so that has. You would think it would have impact on the digital systems. But what it's done is it's accelerated the transformation that organizations were going through, and that's been good in a number of different aspects. One certainly cloud adoption of clouds picked up things like Microsoft teams and collaboration software is certainly picked up, so it's certainly been a challenging year on many fronts. But on the on the other hand, it's also been very beneficial for us as well. >>Yeah, I've talked to so many folks in the last few months. There's silver linings everywhere. There's opportunity everywhere. But give our audience standing an overview of who them is, what you do and how you help customers secure their data. >>Sure, so VM has been in the backup businesses. What I'll say We started right around when virtualization was taking off a little before AWS and you see two left computing services on DWI would do back up a virtual environments. You know, over the last decade, we have grown into a $1 billion company doing backup solutions that enable cloud data management. What do you mean by that? Is we do backup of all kinds of different infrastructures, from virtual to cloud based Assad's based to physical systems, You name it. And then when we ingest that data, what we do is we begin to manage it. So an example of this is we have 400,000 customers, they're going back up on premises. And one of the things that we've seen this year is this massive push of that backup data into S three into the public cloud and s. So this is something that we help our customers with as they go through this transformation. >>And so you've got a team for a ws Cloud native solution. Talk to me a little bit about that. And how does that allow business is to get that centralized view of virtual physical SAS applications? >>Yeah, I think it all starts with architecture er and fundamentally beams, architectures. ER is based upon having a portable data format that self describing. So what >>does >>that mean? That means it reduces the friction from moving data that might have been born on premises to later being Stan Shih ated in, say, the AWS cloud. Or you can also imagine now new workloads being born in the cloud, especially towards the middle and end of this year. A lot of us we couldn't get into our data center. We had to do everything remotely. So we had to try to keep those lights on operationally. But we also had to begin to lift and shift and accelerate your point about silver linings. You know, if there is a silver lining, the very prepared really benefited. And I think those that were maybe a little more laggards they caught up pretty quickly. >>Well, that's good to hear stick big sticking with you. I'd love to get your perspectives on I t challenges in the last nine months in particular, what things have changed, what remains the same. And where is back up as a priority for the the I T folks and really the business folks, too? >>Yeah, I almost want to start with that last piece. Where? Where's backup? So back up? Obviously well understood as a concept, it's well funded. I mean, almost everybody in their right mind has a backup product, especially for critical data. But yet that all sounds very much the same. What's very, very different, though? Where are those workloads? Where do they need to be going forward? What are the service level agreements? Meaning that access times required for those workloads? And while we're arguably transitioning from certain types of applications to new applications, the vast majority of us are dead in the middle of that. So we've got to be able to embrace the new while also anchoring back to the past. >>Yeah, I'm not so easily sudden, done professionally or personally, Danny, I'd love to get your perspective on how your customer conversations have changed. You know, we're executives like you, both of you are so used to getting on planes and flying around and being able Thio, engage with your customers, especially events like Vermont, and reinvent What's the change been like? And from a business perspective, are you having more conversations at that business? Little as the end of the day. If you can't recover the data, that's the whole point, right? >>Yeah, it is. I would say the conversations really have four sentiments to them. The first is always starts with the pandemic and the impact of the pandemic on the business. The second from there is it talks about resource. We talked about resource management. That's resource management, both from a cost perspective. Customers trying to shift the costs from Capex models typically on premises into Op X cloud consumption models and also resource management as well. There's the shift from customers who are used to doing business one way, and they're trying to shift the resources to make it effective in a new and better way. I'd say the third conversation actually pivots from there to things like security and governance. One of the interesting things this year we've seen a lot of is ransomware and malware and attacks, especially because the attack surface has increased with people working from home. There is more opportunity for organizations to be challenged, and then, lastly, always pivots where it ends up his digital transformation. How do I get from where I used to be to where I want to be? >>Yeah, the ransomware increase has been quite substantial. I've seen a number of big. Of course you never want to be. The brand garment was head Carnival Cruise Line. I think canon cameras as well and you're talking about you know you're right, Danny. The attacks are toe surfaces, expanding. Um, you know, with unprotected cloud databases. I think that was the Facebook Tic Tac Instagram pack. And so it's and also is getting more personal, which we have more people from home, more distractions. And that's a big challenge that organizations need to be prepared for, because, really, it's not a matter of are we going to get a hit? But it's It's when, and we need to make sure that we have that resiliency. They've talked to us about how them enables customers toe have that resiliency. >>Yeah, you know, it's a multilayered approach like you know, any good defensive mechanism. It's not one thing it's trying to do all of the right things in advance, meaning passwords and perimeter security and, ideally, virtual private networks. But to your point, some of those things can fail, especially as we're all working remotely, and there's more dependence on now. Suddenly, perhaps not so. I t sophisticated people, too. Now do the right things on a daily basis and your point about how personal is getting. If we're all getting emails about, click on this for helpful information on the pandemic, you know there's the likelihood of this goes up. So in addition to try and do good things ahead of time, we've got some early warning detection capabilities. We can alert that something looks suspicious or a novelist, and bare bears out better investigation to confirm that. But ultimately, the couple of things that we do, they're very interesting and unique to beam are we can lock down copy of the backup data so that even internal employees, even somewhat at Amazon, can't go. If it's marked immutable and destroy it, remove it, alter it in any way before it's due to be modified or deleted, erased in any way. But one of the ones I'm most excited about is we can actually recover from an old backup and now introduce updated virus signatures to ensure we don't reintroduced Day zero threats into production environment. >>Is it across all workloads, physical virtual things like, you know, Microsoft or 65 slack talked about those collaboration tools that immune ability, >>so immune ability. We're expanding out into multiple platforms today. We've got it on on premises object storage through a variety of different partners. Actually, a couple dozen different partners now, and we have something very unique with AWS s three object lock that we you can really lock down that data and ensure that can't be compromised. >>That's excellent, Danny, over to you in terms of cloud adoption, you both talked about this acceleration of digital business transformation that we've all seen. I think everyone has whiplash from that and that this adoption of cloud has increased. We've seen a lot of that is being a facilitator like, are you working with clients who are sort of, you know, maybe Dave at that point you talked about in the beginning, like kind of on that on that. Bring in the beginning and we've got to transform. We've got to go to the cloud. How do you kind of help? Maybe facilitate their adoption of public health services like AWS with the technologies that the off first? >>Yeah, I'd say it's really two things everyone wants to say, Hey, we're disrupting the market. We're changing everything about the world around us. You should come with us. Being actually is a very different approach to this one is we provide stability through the disruption around you. So as your business is changing and evolving and you're going through digital transformation, we can give you the stability through that and not only the stability through that change, but we can help in that change. And what I mean by that is if you have a customer who's been on premises and running the workloads on premises for a long while, and maybe they've been sending their backups and deaths three and flagging that impute ability. But maybe now they want to actually migrate the workloads into E. C to weaken. Do that. It's a It's a three step three clicks and workflow to hit a button and say send it up into Easy to. And then once it's in AWS, we can protect the workload when it's there. So we don't just give the stability in this changing environment around us. But we actually help customers go through that transformation and help them move the workloads to the most appropriate business location for them. >>And how does that Danny contending with you from a cost optimization perspective? Of course, we always talk about cost as a factor. Um, I'm going to the cloud. How does that a facilitator of, like, being able to move some of those workloads like attitude that you talked about? Is that a facilitator of cost optimization? Lower tco? I would imagine at some point Yes, >>Yes, it is. So I have this saying the cloud is not a charity right there later in margin, and often people don't understand necessarily what it's going to cost them. So one of the fundamental things that we've had in being back up for a W s since the very beginning since version one is we give cost forecasting and it's not just a rudimentary cost forecasting. We look at the storage we looked compute. We looked at the networking. We look at what all of the different factors that go into a policy, and we will tell them in advance what it's going to cost. That way you don't end up in a position where you're paying a lot more than you expected to pay. And so giving that transparency, giving the the visibility into what the costs of the cloud migration and adoption are going to be is a critical motivator for customers actually to use our software. >>Awesome. And Dave, I'm curious if we look at some of the things trends wise that have gone on, what are you seeing? I t folks in terms of work from home, the remote workers, but I am imagine they're getting their hands on this. But do you expect that a good amount of certain types of folks from industries won't go back into the office because I ts realizing, like more cost optimization? Zor Hey, we don't need to be on site because we can leverage cloud capabilities. >>Yeah, I think it works, actually, in both directions least, I think we'll see employees continue to work remotely, so the notion of skyscrapers being filled with tens of thousands of people, you know, knowledge workers, as they were once called back in the day. That may not come to pass at least any time soon. But conversely to your point everybody getting back into the data center, you know, from a business perspective, the vast majorities of CEO so they don't wanna be in the real estate business. They don't wanna be in the brick and mortar and the power cooling the facilities business. So >>that was >>a trend that was already directionally happening. And just as an accelerant, I think 2000 and 20 and probably 2021 at least the first half just continues that trend. >>Yeah, Silicon Valley is a bit lonely. The freeways there certainly emptier, which is one thing. But it is. It's one of those things that you think you could be now granted folks that worked from home regardless of the functions they were in before. It's not the same. I think we all know that it's not the same working from home during a pandemic when there's just so much more going on. But at the same time, I think businesses are realizing where they can actually get more cost optimization. Since you point not wanting to manage real estate, big data centers, things like that, that may be a ah, positive spin on what this situation has demonstrated. Daddy Last question to you. I always loved it to hear about successful customers. Talk to me about one of your favorite reference customers that really just articulates beams value, especially in this time of helping customers with so many pivots. >>Well, the whole concept of digital transformation is clearly coming to the forefront with the pandemic. And so one of my favorite customers, for example, ducks unlimited up in Canada. They have i ot sensors where they're collecting data about about climate information. They put it into a repository and they keep it for 60 years. Why 60 years? Because who knows? Over the next 60 years, when these sensors in the data they're collecting may be able to solve problems like climate change. But if you >>look at it >>a broader sense, take that same concept of collection of data. I think we're in a fantastic period right now where things like Callum medicine. Um, in the past, >>it was >>kind of in a slow roll remote education and training was on kind of a slow roll. Climate change. Slow roll. Um, but now the pandemics accelerating. Ah, lot of that. Another customer, Royal Dutch Shell, for example. Traditionally in the oil and petrochemical industry, their now taking the data that they have, they're going through this transformation faster than ever before and saying, How do I move to sustainable energy? And so a lot of people look at 2020 and say, I want how does this year? Or, you know, this is not the transformation I want. I actually take the reverse of that. The customers that we have right now are taking the data sets that they have, and they're actually optimizing for a more sustainable future, a better future for us and for our Children. And I think that's a fantastic thing, and being obviously helps in that transformation. >>That's excellent. And I agree with you, Danny, you know, the necessity is the mother of invention. And sometimes when all of these challenges air exposed, it's hard right away to see what are the what are the positives right? What are the opportunities? But from a business perspective is you guys were talking about the beginning of our segment, you know, in the beginning was keeping the lights on. Well, now we've got to get from keeping the lights on, too. Surviving to pivoting well to thriving. So that hopefully 2021 this is good as everybody hopes it's going to be. Right, Dave? >>Yeah, absolutely. It's all data driven and you're right. We have to move from keep the lights up on going the operational aspect to growing the business in new ways and ideally transforming the business in new ways. And you can see we hit on digital transformation a number of times. Why? Because its data driven, Why do we intercept that with being well? Because if it's important to you, it's probably backed up and held for long term safekeeping. So we want to be able to better leverage the data like Danny mentioned with Ducks Unlimited. >>And of course, as we know, data volumes are only growing. So next time you're on day, you have to play us out with one of your guitars. Deal >>definitely, definitely will. >>Excellent for Dave Russell and Danny Allen. I'm Lisa Martin. Guys, thank you so much for joining. You're watching the Cube

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital Experience Brought to You by Dell Technologies. Welcome to the Cubes Coverage of Dell Technologies. World 2020. The Digital Experience. I'm Lisa Martin, and I'm pleased to welcome back one of our Cube alumni. John Shirley is with us. The vice president of unstructured storage product management. John. Welcome back to the Cube. >>Thank you for having me. It's great to be back. >>So so much has changed since we last saw you were very socially distant. But talk to me from from a storage and unstructured of data perspective, lot of changes in the year of 2020. >>Yeah, a lot of changes everywhere, but especially in our spaces. While we're seeing just a phenomenal amount of growth with storage. Still, that's continuing. But what we've really seen is things changing pretty pretty rapidly, actually, two new cloud based applications and it almost seems like everything that's happened during the pandemic has kind of been an accelerant to getting to that next level of technology. And so we're really excited to be working with our customers, really guide them in the journey to get into, you know, new cloud based applications, cloud native applications and really just helping them take advantage of all of this on structure data that's being generated. >>Yeah, we've heard about acceleration in so many facets this year and that it's, you know, we're accelerated by, you know, 24 to 36 months. Talk to me about, For example. I was talking Thio, Adele Technologies customer Earth down the other day. And, of course, the massive amount of video that they're generating 24 by seven by 3. 65 from all over the world. The edge, cloud core, So much growth there. How are you seeing customers be able to pivot quickly and adapt to how different things are? >>Yeah, you know, the interesting part two isn't just a collection of data anymore. It's how customers want to treat that data. And what we're seeing over and over again is that we get the video streams coming in. But there's also all of these sensors in the world and so marrying up the video streams with sensor information and keeping that in a repository so that you can do things like, uh, real Time analytics, but also be able to take that same data set and also get the historical view is becoming critically important. And that's the thing that's really changed, is how the data is being used yesterday that keeps coming in. But customers are really, really taking a different view in terms of how they want to go use that data. So we have a lot of tools that we've created over the last year or two that are helping our customers harness and really use that data, something that they just weren't able to do a couple years ago. >>Now we always talk about data as currency or data as gold or data equals trust and the most important factor for any businesses extracting value from that data. I think now, really time is even more important if you think of contact tracing, for example, or the accelerated work going on to develop a vaccine, so much access has to be now because data from yesterday isn't good enough. It's not gonna help solve some of these big use cases. What is she gonna key use cases that you're seeing accelerate in the last few months? >>You just hit it right on the head. So the way we look at it, it kind of two points within the timeline of data. That's the most valuable. And, of course, what you just said. Get the right away in the here. Now that's that's one of the times that is the most valuable toe have that data. But then if we kind of take a look at that data as it ages because it get less important, well, some of it might. But actually the data has a big scale data like data repository and be able to extract value out of that kind of holistically as a big set of data is extremely important as well. And so we we have tools, everything from our streaming data platform that talks about how we can extract value from that data, right as it's coming off the sensor of the videos video streams, we've got our power scale product, which provides very, very high performance storage so that customers 10 stream a bunch of data and get some of that AI and ml off of that data. And then we've got our PCs object storage based product what customers want exabytes of data, and they just want a really long term, robust storage repositories. So we've kind of got all the tools together that really helping our customers extract that value. >>Talk to me about doing a migration. That's always a big challenge, especially as many businesses live in a hybrid or multi cloud world where they've got or using public cloud services on from edge maybe, for example, but in terms of being able to get to the data and run algorithms on it to do a I. How can a customer give me, like a snapshot of a of an example infrastructure that, you see is common with customers that allows them to harness data wherever it is and be able to run a I on wherever it is without having to move it around and pale those charges and, of course, lose precious time? >>Yeah, that's a great question. What we're seeing a lot, too, is customers wanting to take advantage of things like the cloud, the power that compete in the cloud, and, uh, they don't necessarily want to move the data in and out of the cloud. But at the same time, you know, we want to make sure that the customers have the flexibility to choose which cloud that they want to go to. So we have multiple cloud offerings that were given to our customers, specifically the ability to take the data. We host the service for the customer so that it's all in all operated within the Dell EMC, uh, infrastructure team. And then we can map that data data up to the clouds. Whether they want to go to any of the Big three cloud providers, we could map that out. There's no egress fees, and they could go ahead and take advantage of the data very quickly, easily. >>So really, from a flexibility perspective, being able to meet them where they are, >>that's absolutely right. So whether the customers are in the edge or in their in their core or in the cloud will be there to help their needs. >>So this is the first Dell Technologies world that is digital, a lot of opportunity for folks. Thio learn and still be able to have as much engagement as possible. Talk to us about some of the things that you're excited about. The customers are gonna learn in terms of how you're helping them get more value out of the data faster in a time of such massive change. >>Yeah, so we're doing so much within the within the team. So earlier this year we introduced a new product called Power Scale which is taking our industry leading one FS software for scale out file. And we have put that in and really taken advantage of what we have within the Dell family and taking the best server hard work power edge. We've taken on one of one FS software married and together we're really extracting the best value of the data with those platforms. So again, the industry leading scale of file solution marrying that up with the industry leading server solution. And now we've got even though even more robust solution. On top of that, we have, uh, announced our objects scale solution. And so objects Scale is a knob decked store solution that's specifically targeted for customers running kubernetes. We've partnered up with our friends over at VM Ware and we've developed an object store specifically for developers on top of kubernetes environment, so that when customers want to go and start generating new applications with object store on new cloud native app they can really quickly spin up new object, store new buckets and start writing data. It's very simple and easy to use, and then when they want to grow at scale, we've got our PCs object store, too, into that petabytes scale. So it's it's very exciting. >>Can you give us an example of a customer that's that's already doing that That, you see, is really achieving some significant benefits? >>Yeah, yeah, So, uh, probably the one that's the most fun toe watches were working with a company that's doing amusement park rides and really taking a look at all the sensor information so that they can get predictive analytics in terms of the maintenance of the rides, making sure that if there is maintenance that needs to get done, they could get that fixed as quickly as possible so that customers going through those rights a. If, of course, they're going to be safety. Safety is always number one. But being able to make shape, make sure those rides are maintained so that the lines move quickly and they can keep customers going through. And you get us many people enjoying those rises. You can, and that's all coming from our streaming data platform, which is again taking that information. All of that sensors feet, and they need that that real time value that we talked about before to get that real time value. But they also get the historical view so they could see how the maintenance is kind of evolved over time. So that's that's one that's been, ah, lot of fun to work with here over the last couple. >>And hopefully we get to go back to amusement parks and calendar year 2021. Wouldn't that be nice? You mentioned safety and and that Yeah, that kind of makes me think about security. We've seen so much about increases like companies like Zoom, for example, with increased scrutiny on their data security, a more compliance requirements, Um, data protection being even mawr. Important as there was this massive pivot toe work from home seven months ago, and a lot of folks are still there are not going to be there. Tell me a little bit about some of the things that you're doing it to facilitate that this data, this massive increase in unstructured data, is managed securely so that if there's any sort of breach or incident, your customers air in good shape. >>We We have a lot of focus on security within the organization, and that's really across the board. That's really across all of Dell Technologies products. Eso We do a lot of things around encrypted drives to make sure that if the driver ever pulled out of the system, there's no way to go access that data. There's just no way to go do that without the original keys. You can't get those original kids when they're not in the system, so we make sure that we do a lot of hard enough the system at that level. We work very closely with the broader partner and ecosystem community to make sure that we provide things like ransom or protection, uh, isolated. So in case if something does happen a you identified as quickly as you can but be you make sure that you have a good data set, like a good golden copy of that data that you can always go back. Thio, >>you mentioned ransom where it's it's really been on the rise in 2020. I read a stat a couple days ago that every 11 seconds are Ransomware attack occurs when we think about how many new industries are exposed. I saw I read recently that the the New Zealand Stock Exchange was hit a couple of times. Carnival Cruise Line, the Department of veterans of There's a social media with Facebook Tick Toke Instagram on 235 million user profile straight from a unsecured cloud database. So not only is that threat landscape expanding, but we've got more people accessing. Um, you know, corporate networks with maybe personal devices for those phishing emails are probably even getting more sophisticated. >>Yeah, we spend. Like I said, we spend a lot of time. We have a whole security team within the storage group that does nothing but thanks about security and how we can harden the products to make sure they stay secure and robust. And we keep the bad, the bad people away. >>Now that's excellent. Alright, So any predictions what we might see in the next 6 to 9 months, who from Dell Technologies with respect to helping customers who are hopefully have pivoted from this survival mode to now being able to thrive, leverage data extract values from it to identify new revenue streams renew products are new innovation. What do you see on the horizon? >>Yeah, I see just the continued acceleration of the technology. I see Dell Technologies spending a lot of our time focused on solutions so that when we can go into a customer environment, we talk about solutions. We talk about how we can get time to value. So how quickly can we get up the customer up and running with a known good configuration? You know, supportable. It's enterprise grade on. We can have our customers spend time writing code and developing new applications and not worrying about how to go build that infrastructure. So you're gonna see a lot of things. A lot of partnerships across our entire infrastructure team, which internally we call I S G. And we're really working together is one SG team to make sure all of our networking, our storage and our compute and all of the software that goes around that we act as 111 overall family for our customers provide that solution. And we also partner very close with VM ware to provide that software layer. So that again when we go to our customers, uh, and they want to start a new project. We have all of the tools within our portfolio. Uh, we've been around for a very long time. We have very strong focus on both the horizontal, the various workloads that customers were running and also very specific vertical through the industry and teams that just are dedicated on that. So But I think you're going to see a lot more. Is the solution based approaches where we could go into customers? We can provide that solution, and it's up and running in the very, very short amount. All right, >>last question. You said you mentioned you guys have been doing this a long time. I know you've been with Dell for 10 years. What are the three things that you would say if you're in a customer situation and they're looking at Dell and maybe they're looking at HP, for example, or some other competitors? One of the three things that you think really differentiate what Dell Technologies can deliver with respect to extracting value from massive amounts of unstructured data. >>Absolutely. I mean, this is where I get really excited when I'm so proud to be at del, uh, because if I look at all of the advantages that we have that we could bring to our customers. We have just the knowledge. So I think first and foremost when it comes to on structure data, we have been the most prevalent player in the market. And again, if you take a look at different verticals, think about like media and entertainment. We've won an Emmy just because we've been around and we have the technology that's really met the needs. We, um but that's one. We have all of the deep knowledge, and that's really going to give a lot of benefit to our customers to we've got the breath of the portfolio. So not only do we have very specific knowledge in one area where actually cover all of the unstructured portfolio for our customers needs, whether that's file or object or streaming data might even be the data management data management. When we have data I Q. To help our customers understand that data. Our portfolio is really broad, so deep knowledge we have a broad portfolio and then we have the overall Dell Technologies family that that we go forward with. So again, it's not just about the unstructured data. It's everything that goes around that it's the servers. It's that computes all the infrastructure. But it's the software that's also our partners and that whole ecosystem that we built up across the technologies. That's what really makes us strong and really the best person to partner with >>excellent knowledge, bread and a large ecosystem. John, thank you so much for joining us on the Cube today, talking to us about all the exciting things that you're working on. What's to come? We appreciate your time. >>Thank you very much >>for John Shirley. I'm Lisa Martin. You're watching the Cubes Coverage of Dell Technologies World 2020.

>> Announcer: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. (soft music) >> Hi, Lisa Martin, with theCUBE here, talking with Caitlin Gordon, the VP of Product Marketing, at Dell technologies. Caitlin how are you? It's great to see you. >> I'm doing very well Lisa, thank you so much for having me. >> Nice to see you back on theCUBE. So lots of stuff going on in the news the last few months or so. A lot of stuff with respect to Cyber Recovery, Cyber Security, but talk to us about what's happening in the Purpose-Built Appliance Backup Appliance market. This market is growing. What's happening there, and talk to me about Dell's leadership role. >> Yeah, we've kind of come full circle. I've been in the data protection space for a while and I would say that, you know now we're looking at this as a $4 billion industry and security and protection has bubbled backup to the top of the list from an IT perspective. And one of the simplest, fastest ways to improve data protection is leveraging Backup Appliances. And there's really two segments within that. There's what I'll refer to as the target appliances and the integrated appliances. And we actually have had leadership in this space, since really the beginning. You know 50 cents of every dollar in this market is spent on Dell equipment. Where we see massive growth is really in that integrated appliance market. And those integrated appliances really simplify the deployment of not only the protection storage, but the protection software. So you can modernize your data protection, get much faster recovery, faster backups, as well as really get a smaller footprint, better efficiency, all in one single solution. And that's really where we've seen a lot of growth in the appliance market recently. >> Yeah. So as that, an integrated appliance market is growing twice as fast as targeted, give us a picture. You mentioned a few things, but kind of dig deeper into why customers are opting more and more for the integrated approach. >> Yeah that comes back to kind of a lot of the trends we see in IT overall. It's simplicity. It's ease of, how can you get to a better solution, a better outcome faster. And when it comes to integrated data protection appliances, it really it takes the guesswork out of it. You know, you have software and hardware, that's optimized to work together. You're really quick and easy to deploy, really simple to manage, 'cause it's all fully integrated and you get to a solution where you can get things like 65 one data reduction, get a very small footprint, get really fast improvements to not only backups, but probably even more importantly to recovery, get instant access to that data. And you really are able to with one purchase, transform all of your data protection. Now there's still a lot of great uses for target appliances as well of better flexibility. But, we've seen this overall you've seen this Lisa, every trend in probably IT and life, right? Simplicity. How can you get a faster, better answer? And integrated appliances really lean into that. It's as similar to what we see in the hyperconverged space, kind of in the primary storage and compute side of things. >> Yeah, I think we all want faster, simpler, better in every walk of life. One of the things this year that, in all of that lack of simplification, the complexity that we're living in that we've seen, is the rise of ransomware. It's not only on the rise, it's getting more personal. We've seen, you know, big companies, Garmin was attacked, one of the Cruise Lines was attacked, The New Zealand Stock Exchange, Facebook and Tik Tok were hacked. So we're starting to see so much more vulnerability and the ability of these hackers to expose more vulnerabilities. Have you seen that impacting your customers saying, "Hey, we need help here because now we have so many employees and devices, scattered." >> Yeah, unfortunately we have. You know, we've been talking about Ransomware Protection, Cyber Resiliency, Cyber Recovery with our customers for quite a number of years. And, now it's not a niche conversation just with financial institutions, it's a conversation with all of our customers. 'Cause either they've felt it or they've seen their competitors feel it and they need to protect themselves. So it has really become a conversation but it's not only our specialty sellers, but all of our sellers are having with our customers. And, it's really about not only being able to protect against them, which is an important part, but also recover from them. And that's really what our PowerProtect Cyber Recovery Solution is all about. And the exciting thing for us is that we actually have recently become the first Cyber Recovery Solution endorsed by Sheltered Harbor. Which really gives you an idea of the level of investment that we've made to provide that secure, automated air gap solution to give our customers that peace of mind. Because unfortunately this is becoming table stakes for any data protection out there today. >> Well, and as more and more, we see every company either becoming a data company or needing to become a data company to not just survive these times, but become successful as time goes on. To a point, it's one thing about protecting the data, but the actual need is to recover it should anything happen. Tell us a little bit more about Sheltered Harbor and what you guys were the first there to receive? Tell me a little bit more about that. >> Yeah, absolutely. Okay a little bit more on overall our solution and Sheltered Harbor is actually a consortium of organizations, primarily financial institutions that have really come together to define the standards, of what we need or Cyber Resiliency for Cyber Recovery. And for us with PowerProtect Cyber Recovery, we've worked closely with that organization, to meet those standards. And with that work and with that actual deploying in with one of our customers, we were able to become the first Cyber Recovery Solution endorsed by Sheltered Harbor to meet their standards there. And what's an important about our solution is that it's both that automated air-gapped solution for the data isolation, which is a part of it. But it's also, we have the CyberSense analytics and forensic tools that give you the ability to discover, to diagnose and to remediate against these attacks. So it gives you both sides of protecting that data air-gapping it, but also being able to intelligently discover and remediate against those attacks, if they do indeed happen. >> As VP of Product Marketing, I'm sure you're with customers often these days virtually. When you're having customer conversations, as you were singing out data protection and being able to recover and remediate, should anything like a ransomware attack happen, that's business critical. That's, you know, lifeline kind of stuff we're talking about. Have you seen the conversations within customer organizations shifts or is this now a board level or a C-level conversation in terms of data protection? >> Yeah, it's interesting. It's become a more frequent conversation. The people involved, are different. It's not just the backup administrators that are involved, it's really about the overall compliance strategy, the CSOs that are involved here. And it's becoming a corporate mandate as it really unfortunately needs to be at this point. So it's coming up more frequently, but also the types of people involved in that conversation have really changed the types of things we're having to talk about and build solutions for. So it's really changed that dynamic for us. And it's been great to really be on the front lines of that with our customers. You know, it started with those financial institutions and now it's really commonplace, to talk about this with everyone. >> So let's talk customers. Give us an example or two of some customers that are leveraging this new technology that are really achieving like the big deduplication ratio that you talked about, but also enabling their business to move forward. >> Yeah. One of my favorite ones for a couple of reasons I'll confess is, World Candy. Actually there are a World Corporation, but to me, they're a candy company. They actually make some chocolate out of Pennsylvania one of my favorites, chocolate covered pretzels. And they're a great example, right? 'Cause they're certainly not an IT specialty organization. They're trying to contract manufactured candy and they want to get things done as efficiently as possible. So they were looking a solution to overall modernize, their overall IT and that came with the combination of an Integrated Data Protection Appliance, as well as VXRail. And by implementing that, they were able to reduce their backup times from running overnight all night, to just two hours. They were able to get dedupe ratios of a 12O to one, 99.2% reduction, which is just incredible. And they were able to reduce their physical server footprint by 60%. So you can just imagine with an organization like this, that needs to run things as efficiently, as simply, as quickly as possible, how transformational that is. And, probably one of the other things that we find out of customers like this is, it's really about finding them a partner that can solve all of their problems in one place. And for data protection that's certainly one of the biggest things for PowerProtect is we now have a one-stop shop appliances software for all your data protection needs, large and small. And my favorite thing is actually our quote from this customer which is, he calls it a perfect partnership and that they have a single hand to high five. And we love to get those high fives from our customer and we really like to be that partner for them and to help them solve these challenges really no matter where their data is or what their challenges are. >> I like that a single can for a virtual high five. Speaking of partners, what's the channel play here? >> Yeah, absolutely. I mean, for us, Dell Technology is overall channel partners are absolutely critical and in the data protection space, probably even more so. So channel partners are a huge part of our go-to-market. And one of the reason that channel partners really like to work with us, with Dell technologies on the data protection side, is because of the breadth of that portfolio. And now with our most recent enhancements on the appliance side, you now have a full PowerProtect portfolio. Target appliances, integrated appliances, physical, virtual, as well as modern data protection software with PowerProtect data manager. And for our partners, and for us, it's so important that they can have one vendor to offer all of these solutions because we know that our customer's challenges are complex, they're diverse, their data sets are diverse and they need to be able to partner with someone, leverage us as a vendor, leverage our partners, leveraging us as a vendor to really give our customers that answer. And that could be very different needs. They have traditional applications, they have new modern applications in Kubernetes and the growth of, and the importance of those types of applications. Our partners don't want and our customers don't want to have to deal with multiple vendors. Multiple vendors actually can increase risk, increase costs. They want to keep that simple, efficient. And that's why partnering with us, with Dell Technologies, why our channel partners really find us to be such a critical vendor to work with on the data protection side. >> So you've shared some impressive stats about what the technology is able to deliver. You gave us the great World Candy company example in terms of the things I heard a big workforce productivity there, they've got big deduplication there. They're able to sounds like reduce their on-prem footprint. From an economic value perspective, help us understand what the economic value of the DP series and even maybe feedback from the analyst community. >> Yeah, we've actually got a recent study which I'd encourage you guys to go read and I will just kind of give you the Cliffs Notes version of it. Which shows you the advantages of leveraging Dell Technologies portfolio for data protection. You can have your cost to protect as low as 1 cent per gigabyte per month, which is impressive. And that's that efficiency that you can get with PowerProtect. It's a reduction in the administration costs for data reduction of 22%, a reduction of 84% in your Cloud resources and services. We all know that people have moved to Public Cloud and probably one of the biggest concerns is the cost of that. By implementing the right data protection solutions, leveraging our in-cloud backup and protection, you can actually significantly reduce that because of the way that we've implemented it. And overall, you can't argue with anything that reduces costs by 98%. So you can reduce your storage resource costs by 98% by leveraging the PowerProtect portfolio. And again, it's a recent ESG study, which you can find on our website and read more about that study and the economic elements that lead into that. But you can just see the dramatic impact that can have, not only are you protecting your most valuable asset of data, but you're doing so in a way that saves the company money, and time and resources. And we all know that's never been more critical than ever. >> Those are very impressive, but compelling stats. Last question, talking about the three waves that we know Dell technologies is writing, we've got VMware, Cloud, Cyber Recovery, give us a flavor of the launch and the news and the new capabilities for this one-stop shop with perspective of what's happening in Cyber Recovery today. >> Yeah, so we've got enhancements on all fronts. So we, let me go in order there. So we've got on the Cloud front our PowerProtect data manager, which we've talked about a lot this year. We continued to really enhance that. Some recent enhancements, the ability to deploy that in Azure and AWS Cloud, to be able to do in-Cloud data protection. On the VMware side as we talked about just recently at VMworld, we've got new integrations with Storage Based Policy Management to really simplify and automate protection for the Vadmins as well as protection administrators. The ability to support, real mission critical applications and VMs, that are something we're working on to be able to more intelligently protect those VMs that have become more challenging to protect in traditional methods as well as integration with protect VCF. And then lastly, I think we've covered a bit today is certainly on that Cyber Recovery, Cyber Resiliency solution. First one to be endorsed by Sheltered Harbor in providing that air gap solution, as well as that ability to discover to remediate from those attacks. And you can kind of get a sense of, where we're really focused on. Those are our big three areas in both our appliance as well as our software portfolio really focused on simplifying that for our customers. >> Well Caitlin, we thank you for joining us as per what theCUBE has seen for many years with Dell Technologies. Lots of innovation, continued innovation. We thank you so much for joining us on theCUBE today. >> Thanks so much for having me. It was great to be here, Lisa. >> Excellent. With Caitlin Gordon, I'm Lisa Martin. You're watching theCUBE. (soft music)

>> Live from Las Vegas, it's theCUBE covering Dell Technology's World 2019. Brought to you by Dell Technologies and it's ecosystem partners. >> Hi, welcome to theCUBE Lisa Martin with Stu Miniman and we are live at Dell Technologies World 2019 in Las Vegas with about 15,000 or so other people. There's about 4,000 of the Dell Technologies community of partners here as well. Day one as I mentioned, we're very pleased to welcome back one of our cube alumni, Karen Quintos, EVP and Chief Customer Officer from Dell Technologies, Karen, welcome back to theCUBE. >> Thank you, thank you. Always great to be with you all. >> So one of the things you walk down on stage this morning with Michael Dell and and the whole gang and you started to share a story that I'd love for you to share with our audience about this darling little girl, Phoebe from Manchester, England that has to do with this Dell Technologies partnership with Deloitte Detroit and 3D prosthetics. Can you share this story and what it meant about this partnership. >> Well we wanted to tell this story about Phoebe because we really wanted the audience to understand the innovation and all of what's done it with social good is really about the individual, You know, technology plays a key role but the face behind the technology and the innovation are people and you know, as you mention Phoebe is from Manchester, U.K. Her father wrote this blog about Phoebe's experience. Phoebe's aunt, Claire works for Deloitte. She had access to a lot of what they could do in terms of 3D printing and basically came to Dell and we were able to take it and scale it and accelerate it and speed it up with a engineer by the name of Seamus who saw what the precision workstation could do. So it was this small idea to help an amazing little girl like this that has now turned into this movement around how do we more rapidly, quickly scale 3D prosthetics so these children and adults can have a chance at a normal life so. >> What kind of prosthetics did you guys build for her? >> It's an arm, so the very first arm that we built for her when she was about five years old had the frozen Disney theme painted on it. I asked her father Keith what is the one that she's wearing now because she's now this like really super cool seven-year-old that goes to school and all of her classmates and friends around her see her as this rock star and the one that she has today is printed with unicorns and rainbows. So if you know anything about seven-year-old girls, it's all about unicorns and rainbows and she's done an amazing thing and she's inspired so many other people around the world, individuals, customers, partners like Deloitte and others that we're working with to really take this to a whole new level. >> Karen, I think back to Dell you know, if you think back a couple of decades ago you know, drove a lot of the some of the waves of technology change you know, think back to the PC, but in the early days it was you know supply chain and simple ordering in all these environments and when I've watched Dell move into the enterprise, a lot of that is, I need to be listening to my customer, I need to be much closer to them because it's not just ordering your SKU and having it faster and at a reasonable price but there's a lot more customization. Can you talk about how you're kind of putting that center, that customer in the center of the discussion and that feedback loops that you have with them, how that's changed in Dell. >> Yeah sure, so all of the basic fundamentals around you got to order, deliver, make the supply chain work to deliver for our customers still matters but it's gone beyond that to your point and probably the best way to talk about it is these six customer award winners that we recognized last night. I've gotten to know all six of those over the last year and while they are doing amazing things from a digital transformation using technology in the travel business, the automotive business, banking, financial services, insurance, kind of across the board, the thing that they say consistently is look, we didn't always have the answer in terms of what we needed but you came in, you listened, you rolled up your sleeves to try to figure out how you could design a solution that would meet the needs that we have and they said, that's why you're one of the most strategic partners that we have. Now you can do all those other things, right? You can supply chain ride and build and produce and all that but it's the design of a solution that helps us do the things that will allow us to be differentiated and you look at that list of six customers and brands that they represent, right, Carnival Cruise Lines, USAA, Bradesco, McLaren I mean, the list kind of goes on, they are the differentiators out there and we're really honored to be able to be working with them. >> So we're only a day one and it's only just after lunchtime but one of the things I think somatically that I heard this morning in the keynote with Michael and Pat and Jeff and Satya and yourself is, it's all about people. A couple interviews I did earlier today, same sort of thing, it's like we had the city of Las Vegas on. This is all driven by the people in for the people so that sense of community is really strong. I also noticed this year's theme of real transformation, parlays off last year's theme of make it real, it being digital transformation, IT, security, workforce transformation, what are some of the things that were like at Dell Technologies. Cloud this morning for example, VMware Cloud on Dell EMC that you guys specifically heard say from last year's attendees that are manifesting in some of the announcements today and some of the great things the 15 or so thousand people here are going to get to see and feel and touch at this year's event? >> Well, Lisa you nailed it. What you heard on stage today is what customers have been telling us over the last year. We unveiled about a month ago with a very small group of CIOs in Amia, our cloud strategy, our portfolio, the things that we're going to be able to do and one customer in particular immediately chimed in and said, we need you in the cloud and we need you in there now because you offer choice, you offer open, you offer simplicity, you offer integration and they're like, there's just too many choices and a lot of them are expensive. So what you heard on stage is absolutely a manifestation of what they told us. The other pieces, look, I think I think the industry and CIOs are very quickly realizing their workforce matters, making them happy and productive matters having them enabled that they can work flexibly wherever they want to really, really matters and you know, our Unified Workspace ONE solution is all about how we help them simplify, automate, streamline that experience with their workforce so their employees stick around. I mean, there's a war on talent and everybody's dealing with it and that experience is really, really important in particular to the gensies and the millennials. >> Karen, I love that point. Actually, I was really impressed this morning. In the press and analyst session this morning, there was a discussion of diversity and inclusion and the thing that I heard is, it's a business imperative, it's not, okay it's nice to do it or we should do it but no, this is actually critical to the business. Can you talk about what that means and what you hear from your customers and partners. >> Yes, yes, well, we're seeing it in spades and all of these technology jobs that are open, right. So look, all the research has shown that if you build a diverse team, you'll get to a more innovative solution and people generally get that but what they really get today is here in the U.S. alone, there's 1.1 million open technology jobs by the year 2024, half of them, half of them are going to be filled by the existing workforce. So there is this war in talent that is going to get bigger and bigger and bigger and I think that's what really has given a wake-up call to corporations around why this matters. I think the other piece that we're starting to see, not just around diversity but in our other social impact priorities around the environment as well as how we use our technology for good, look, customers want to do business with a corporation that has a soul and they stand for something and they're doing something, not just a bunch of talking heads but where it's really turning into action and they're being transparent about the journeys and where they're at with it. So it matters now to the current generation, the next generation, it matters to business leaders, matters to the financial services community, which you start to see you know, some of the momentum around you know, the black stones and state street. So it's really exciting that we're part of it and we're leading the way in a lot of number of areas. >> And it's something to that we talked about a lot on theCUBE, diversity and inclusion from many different levels, one of them being the business imperative that you talked about, the workforce needing to compete for this talent, but also how much different products and technologies and apps and APS and things can be with just thought diversity in and of itself and I think it's refreshing to what Stu was saying, hey, we're hearing this is a business imperative but you're also seeing proof in the pudding. This isn't just, we've got an imperative and we're going to do things nominally, you're seeing the efforts manifest. One of the, Draper Labs who was one of the customer award winners. That video that was shown this morning struck probably everyone's heart with the campfire in Paradise California. >> Tragic. >> I grew up close to there and that was something that only maybe, I get goosebumps, six months ago, so massively devastating and we think you know, that was 2018 but seeing how Dell Technologies is enabling this laboratory to investigate the potential toxins coming from all of this chart debris and how they're working to understand the social impact to all of us as they rebuild, I just thought it was a really nice manifestation of a social impact but also the technology breadth and differentiation that Dell has enabling. >> That was also why this story today was so great about Phoebe, right because it's where you can connect the human spirit with technology and scale and have an even bigger impact and there's so much that technology can help with today. You know, that that story about Phoebe. From the time that her aunt from Deloitte identified, you know, what we could do, all the way to the time that Phoebe got her first arm was less than seven months, seven months and you think about you know, some of the other prototypes that were out there, times would take years to be able to do it. So I love that you know, connection of human need with the human spirit and connecting and inspiring and motivating so many children and adults around the world. >> And what what are some of the next, speaking of Phoebe and the Deloitte digital 3D prosthetics partnership, what are some of the other areas we're going to see this technology that this little five-year-old from Manchester spurned. >> Well, I'll give you another example. So we, there was an individual in India, actually an employee of ours that designed an application to help figure out how to deploy healthcare monitoring in some of the remote villages in India where they don't have access to basic things that we take for granted. Monitoring your blood pressure, right, checking your cholesterol level and he created this application that a year later now, we have given kind of the full range of the Dell portfolio technology suite. So it is you know our application plus Pivotal plus VMware plus Dell EMC combined with the partnering that we've done with Tata Trust and the State of India, we've now deployed this healthcare solution called Life Care Solution to nearly 37 million rural residents, citizens in India. >> Wow 37 million. >> 37 million, so a small idea, you take from a really passionate individual, a person, a human being and figure out how you can really leverage that across the full gamut of what Dell can do, I think the results are incredible. >> Awesome, you guys also have a Women in Technology Executive Summit that you're hosting later this week. Let's talk about that in conjunction of what we talked a minute ago about, it's a business imperative as Stu pointed out, there are tangible, measurable results, tell us about this. >> Well, I'm kind of done honestly with a lot of the negativity around, oh, we're not making any progress, oh, we need to be moving fast and if you look at the amount of effort, energy and focus that is going into this space by so many companies and the public sector, it's remarkable and I've met a number of these CIOs over the last year or two, so we basically said let's invite 20 of them, let's share our passion, have made progress, care about solving this across their organization. A lot of us are working on the same things so if we simply got in a room and figured out, are their power in numbers and if we worked collectively together, could we accelerate progress. So that's what it's all about. So we have about 15 or 20 CEOs, both men and women and we'll be spending you know, six or seven hours together and we want to walk away with one or two recommendations on some things that we could collaborate on and have a faster, bigger impact. >> And I heard that, you mentioned collaboration, that's one of the vibes I also got from the keynote this morning when you saw Michael up there with Pat and Jeff and Satya, the collaboration within Dell Technologies, I think even talking with Stu and some of the things that have come out and that I've read, it seems to be more symbiosis with VMware but even some the, like I said, we're only in, I wouldn't even say halfway through day one and that is the spirit around here. We talk about people influence, but this spirit of collaboration is very authentic here. You are the first chief customer officer for Dell, if you look back at your tenure in this role, could you have envisioned where you are now? >> No, because it was like the first ever chief customer officer at Dell and you know, it really gave me a unique opportunity to build something from scratch and you know, there's been a number of other competitors as well as other companies that have announced in the last year or so the need to have a chief customer officer, the need to figure out how, which is a big remit of mine across Dell Technologies, how do we how do we eliminate the silos and connect the seams because that's where the value is going to be unlocked for our customers. That's what you saw on stage today. You saw the value of that with Jeff, with Pat, with Satya, some you know, one of our most important partners out there. Our customers don't want point solutions, they want them to be integrated, they want it to be streamlined, they don't be automated, they want us to speed time to value, they want us to streamline a lot of the back-office kind of mundane things that they're like, I don't want my people spending their time anymore and doing that and that's where we see Dell Technologies being so much more differentiated from other choices in the market. >> Yep, I agree with you. Well Karen, thank you so much for joining Stu and me on theCUBE this afternoon, sharing some of the stories, look forward to hearing next year what comes out of this year's as Women in Tech Exec Summit. Thank you so much for your time. >> Thank you very much, thank you. >> with Stu Miniman, I'm Lisa Martin, you're watching theCUBE, live day one of Dell Technology World from Las Vegas, thanks for watching. (light electronic music)

(techno music) >> Live from Mountain View California, it's the Cube covering DEVNET CREATE 2019, brought to you by CISCO. >> Hey, welcome back to the Cube. Lisa Martin with John Furrier covering, day two covering I should say, CISCO DEVNET CREATE 2019, at the Computer History Museum in Mountain View California. We're please to welcome Prashanth Shenoy, the VP of Product Marketing, Enterprise Networks and DEVNET at CISCO. Prashanth it's great to have you join John and me this afternoon. >> Great to be here. >> So, this event is growing year after year. John and I have been talking about this very strong sense of collaboration and community with the attendees that are here in person. One of the big things yesterday that Susie was talking about was this, What's coming in Wi-Fi? Talk to us about this next-gen Wi-Fi and how it's going to be so impactful to everyone. >> Yeah it's, it's a phenomenal technology inflection point this year, I feel. We can't believe it, but you know, when was the first Wi-Fi that got started? >> 2001. >> Pretty close, 1999. So this is the 20th Anniversary of Wi-Fi. It's come to be life, right? so it's now in its fourteenth. >> I'm off by two years. >> Right, so yeah, I know. (laughter) But, 802.11A was the first Wi-Fi technology, and the speeds were ... promised speeds were 54-megabits, okay? Ah, but the real speeds were, like, 6-mega or something, right? And now, this is the sixth generation of Wi-Fi, so we've come a long way and we take it for granted in our daily life. >> Absolutely, we do. >> I don't think I can think a day without having Wi-Fi. >> Everyone talks about Wi-Fi. The kids, What's the Wi-Fi password? (laughter) I change it all the time, kids, this ... parents, pro tip. Change the password. >> Yes. You got to listen. They'll call you, your kids will call you back. It's an important tip. >> Full-on security, yeah. >> But distance is been an issue, distance, and >> Yeah. >> Radio Frequency has certain >> Yeah propagation technique so, >> Yeah. >> Are you close to the router? That room doesn't have, this doesn't have it. So there's always been distance. And throughput. >> latency, throughput, capacity. >> Most people say who's streaming Netflix, Wi-Fi is down, so again people know this they experience it everyday. >> Exactly. >> What's the big hubbub about Wi-Fi 6? What's different? I got a little preview from Todd so I'll let you explain it but >> Yeah. >> What is the notable bullet points of why it's different? >> Yeah. >> And, Why it's a game changer? >> So it's, as with every technology, three things that it always brings up, better experiences, better capacity, increase capacity, and better battery savings, which I think is very important for users but more importantly useful for IOT applications, which is ... I'm very very excited on what its going to unleash when it comes to IOT. It's been in the fringe side of IOT, like oil and gas mining utilities is what we think when we think of IOT. And now we're going to think IOT in corporate space like this, right? Each one these devices are IOT devices now, like your HVAC systems, your lighting system, air conditioning systems, physical surveillance cameras. Everything with the Wi-Fi is IOT. And because of this increased capacity, an increase density, high density environment where this capacity becomes really critical, imagine 20 devices simultaneously using Wi-Fi to communicate high Bandwidth intensive application. That's when Wi-Fi 6 becomes really critical and powerful and that opens up a huge - >> So more coverage area. >> Yeah. >> With the Antenna. It's MIMO Antenna. >> Yeah. >> And Bandwidth, right? >> Capacity and Bandwidth, like compare to .11A, and even .11AX, right it's up to 4X better capacity, 4X better battery savings and the promised throughput of like six gigabits, right, so, But the key part here is simultaneously talking to multiple devices at the same time. And that is very very crucial because of technologies ... I don't want to geek out here, like OFDMA and all this etc. >> Well let's all ... architectural because one thing Susie brought up was, architectural shifts are going to be the big game, One of the game changes you brought up and you know Wi-Fi ... and I have seen it grow from the beginning, I remember when they first came out was a revelation and you know the battery power was an issue but it always was viewed as a peripheral to the network. >> Yeah. >> You bolt on Wi-Fi and just basically extend your land - >> Yeah. >> To use network parlance and now you're seeing people working on making it much more Core 1 Network. >> Absolutely. And Meraki kind of shows the benefit of having wireless and wired - >> Yeah. work together as one. >> Yeah, absolutely >> This seems to be the thesis behind Wi-Fi six. One core thing. >> Yeah. >> Not a bolt-on extension. >> No, absolutely. I think there's a saying which is the reality, behind every wireless there are tons of wires, right. So, 'cause everything that's connected to the wire infrastructure, and with the Wi-Fi 6 now having increased capacity and increased density, it's causing a cascading effect into the rest of the network infrastructure so it becomes highly, highly crucial when you architect your network infrastructure not just to think about wireless but what happens to the access switch, to the core, to the distribution, to the aggregation. And that has a compounding effect, like multi gig speeds in the access to 10 gig to 40 gig in the core going all the way to 100 gig, right, so, the whole performance and reliability to have that immersive experience that Wi-Fi six needs to bring in, needs to be there. >> so for developers and entrepreneurs out there who always look for the white space, CISCO is a big Multi-Billion dollar company. You guys got big market share, whenever there's big moves like this it causes a new change in the order, the pecking order - >> Yeah >> of companies, it changes the landscape. This is going to be a game changer because it's going to create the new opportunities to create new things. >> Yeah, absolutely. >> What are some of the things that you see out there you could share for people watching who are you know hacking around creating things who say, I want to create something big. What's the enablement? What are some of the things that you see happening that are going to be emerging out of this? >> Yeah, a lot of Fringe technologies that are fringe right now are going to be mainstream, like imagine 2006, When iPhone came in, right so and we were just having the discussion, like, that came in at the heels of major shift in connectivity, that's when 3G came in, right, at that point and multi-megabit capacity, and you saw new applications come in. Now Uber, Lyft, all these kind of applications were possible because of the connectivity. And now, Wi-Fi 6 along with 5G will unleash the next wave of applications. So, first thing is immersive applications, things that are VR, AR, it's used for gaming right now, and kids use this, you're going to see that come in hospitals, where surgeons can do remote surgeries, they can have high-density imagery of your brain, for example, as you're operating, being sent to a remote expert and on the fly, make decisions, right? Like, that is going to be pretty normal and standard, in fact, quite a few of our customers are testing this out, right? VR learning, for students, like, if I were to go ... Like, imagine if you are at the Lincoln Memorial in Washington, August 1963, right, listening to MLK "I Have A Dream" speech, and you're in the crowd, immersed in the VR, like, which student wouldn't have more recollection and really connect with that, right? >> I'm sorry, wait - >> You're going to see more and more of these, so it's a better way of learning, and really getting that learning sticking in your brain, you're going to see more of that happening. And the same goes with retail experience, you're shopping, it's going to completely change the way, because of all these immersive experiences. And then, because of the higher density, you're going to see entertainment venues like stadiums where everybody now wants to share their experience to the outside world, and livestream it, right? And I was talking to Carnival Cruise Line, who's one of our customers, and they call themselves City On The Sea, which means, a cruise ship is nothing but it has entertainment, casinos, hotels - >> Lots of food. (laughs) >> Lots of food, swimming pools Concerts happening, and when people took vacation they just wanted to disconnect from everything in the world, right? Now, it's completely reversed. They want to connect full-on, and share their experience in the land, right? And they want to stream it live, 4K. And, these cruise ships are transforming themselves to provide this always-on, fully-on immersive digital experience, and they're creating things like a mobile app to order pizza no matter where you are on the ship. Within five minutes they're going to find the exact location of where you are on the ship and deliver pizza to you, right? These kind of experiences will happen! >> And you know, the perfect storm in all this too, is that the Cloud earnings are coming out, we saw Microsoft's earnings yesterday, Amazon Web Series' earning >> Yeah. do proud of Amazon today, the Cloud stocks are up, the Clouds are growing at a massive scale, they're a power source for these application developers. >> Yeah. >> As well as the on-premise business. So you have, you now have the perfect developer environment - >> A hundred percent. >> To create these new wacky ideas that will be standard. I mean, what was once ... what we take as standard as you mentioned, was a wacky idea in 2006. >> Yeah. >> Location services, checking into a hotel with my phone and having - >> Yeah. >> Cars being delivered to me, what? Who does that? >> And this, this becomes a reality, and Cloud really increased the pace of innovation, right? Now it's kind of cheaper, you don't need to get your own server, you can kind of swipe your credit card, get a bunch of VM, start building applications, and now you have the required bandwidth capacity and density in your infrastructure, and you have the right devices right now to bring that experiences to you, right? So, now it's this trifecta of things, awesome devices, the network ready to deliver those experiences, and Cloud being able to scale out to build those experiences. >> Prashanth, I know you've got a big announcement coming up on the 29th, it's a virtual event, I think Cisco.com, they can probably find out with the URL where the event is, without revealing all the secret sauce, I know you guys had Wi-Fi 6 inside Cisco, >> Yeah. >> testing it out, I heard people in the hallway here, >> Yeah. >> Talking about it, um, and they're pretty animated in their commentary. Can you share the vibe and what's it like when the engineers look at the data, when they say, we just deployed the Wi-Fi 6, what was the reactions, um - >> Yeah. >> Were they blown away, was it mediocre, was it - >> Yeah. >> What were some of the things that they were saying, what was the feedback? >> We were piloting that, and the best way to look at it is, if you go to the wireless dev center on DevNet, you're going to see that we compared a 4K video running with Wi-Fi 6 and without Wi-Fi 6. I think the results speak for themselves. Like, the kind of experience that you're going to see, it's going to be beautiful, and when employees look at those things, and I talked about a few experiences, last week we had a thing called Cisco Beat which is internal employees that we rally around and talk about technology, but more importantly, what it means to us as human beings in a personal way, and what it means to our customers, and they were blown away with some of the applications that are going to be mainstream in all of the industries that I talked about, right? Like Healthcare, hospitality, education, entertainment venues, et cetera. >> What's the low-hanging fruit use cases? What's the things that are going to be right obvious, right out of the gate for companies to implement, in terms of deploying Wi-Fi 6 and seeing immediate benefits? >> Immediate benefits is high-density environment, period. Like student lecture halls, convention centers, areas like this, where everybody wants, like, understand what's going on, but be digitally and visually connected, right? It's not only about email checking anymore, That happens automatically. But if you're here and you want to watch Susie's keynote livestream right now, with high density, and 20 other people want to watch with you, on their devices, it's possible, without a hitch. So that seamless, always-on experience becomes a reality that people can easily test out in small environments, right? Not in their entire environment, where there are high-density of people, accessing multi-media applications or high-bandwidth applications, so I feel that's a low-hanging fruit. And then it's going to go more and more towards IOT applications where sensors are getting connected, like some of our customers are brewers, have hundreds and thousands of sensors in their farms, in brewing machines, and they want all of their data to come and look at that simultaneously for quality control, right? Beer, no matter where it's made, should taste consistent, right? So you can see that coming to life, because now all of these can be connected, and because of better density and better capacity and better battery savings for these IOT devices that Wi-Fi 6 provides, you make these applications possible. So you're going to see very vertical-specific applications coming more and more with Wi-Fi 6. >> Vertical-specific, because you mentioned a number of different customer examples, you know, ranging from retailer, to - >> Yeah. >> Carnival Cruise Line, it's now this connected city - >> Yeah. >> Are there any verticals you see where, when you're talking with customers they're not quite there yet? >> Yeah, that's an interesting thing, it's ... for a change, you always have these early adopters but there is a lot of laggers who are just watching, waiting on the sidelines saying, mm, that's not for me. With Wi-Fi 6, there's been a lot of industry excitement, I would say, like manufacturing full-on, right, just coming on board. Retail, higher education, are always in the early-adopter phase, because for them, and there has been studies shown to say this directly impacts their brand - >> Yes. >> like customer experience defines brand. >> Oh, absolutely. >> And Wi-Fi, equals customer experience these days, right? So, you're going to see all of these industries really, I think I haven't seen much in maybe financial services, if you will, I think that's the only thing that I can remember, transportation, big on, like, machine to machine communication, autonomous driving is possible now because of 5G and Wi-Fi 6, right? So, and you are seeing more and more of this industry - >> This is right in your wheelhouse, and you guys have been pushing the edge for a long time, SD Wind, campus networking This is not new to Cisco. >> Yeah. >> But now with Wi-Fi 6, it literally lights that up. >> Yeah. Yup. >> Pun intended. >> I mean, you can now enable those environments to be completely robust, fully addressable, data-driven - >> Yeah. I think data that you mentioned becomes very, very crucial in this, because, especially now when you have so many more users, so many more devices, so many more applications getting on the network, people are really trying to figure out, what do I do with this? How do I get visibility into ... am I delivering the right experience? Am I providing the right security, et cetera, right? So, data becomes extremely crucial, and you'll see emergence of ML and AI technology because it's going to be humanly impossible to look at all of the data and make sense. So you've got to do machines, do their job, figure out patterns, air on dwell time, foot traffic, predictive ways of saying things may break, the experience may change, and predicting that even before they happen, and giving the right insight to the IT in the line of business, so Wi-Fi 6 is going to open up a whole new slew of ML and AI-driven operations and management capability too, so that's pretty exciting. >> When are they going to pull up a GPU on the Wi-Fi 6 devices? >> (laughs) Oh, it's happening. >> It's ready? >> It is going to happen, because you can run Edge computing applications right on Wi-Fi 6 devices, so you're going to see all of that, so, application hosting capabilities with GPU powered applications are going to be there. >> Just a network connection, right? >> Yeah. So you are going to see that, and frankly even I don't know what some of the Edge computing applications with Wi-Fi 6 will be, but we are seeing more and more of these coming ... DevNet buying tech, yeah. >> Well we did some research, we keep on a part of our SiliconANGLES team, where we prove that it's easier and more cost-effective, rather than moving data around, you move compute to the Edge - >> Edge. >> And then you use the backhaul, 'cause it costs money to send data around the network. It's costly. >> Yeah, absolutely. Yeah, and the autonomous cars was one great example, right? Like, it's a life-and-death situation when you are letting the car drive itself, right? So, you can't send all the data to the Cloud and say, analyze it for me. There are instantaneous decisions to be made, in milli-micro- nanoseconds, that need to be done on the Edge. So I think autonomous cars are a great example of Edge computing that needs to happen right on the Edge. The learning can then start happening in the Cloud, right? As in when these things get more and more smarter, you send all this data, you correlate all the intelligence there, you send it back to the machines. So you're going to see these kind of Edge computing applications. >> So you're excited by Wi-Fi 6? >> Nah. >> (laughter) >> Wi-Fi 6, so that's an even number, is that to be odd numbers, or lucky, I mean, the naming convention? >> No! >> Is there a - >> We want to be better than 5G. (laughter) So 5G is fifth generation of cellular, >> Okay. >> Wi-Fi 6 is sixth generation of Wi-Fi, right? I mean it's - >> So you're going to trump the 5G with the 6, >> Yeah. >> Kind of get ahead of it. >> Because it is truly the sixth generation of Wi-Fi. >> Okay, that's what it is. >> If we were to go back in time we would call 802.11ac, Wi-Fi 5. Right? It's kind of not that easy to say, but yeah, so Wi-Fi 5 happened like three or four years back, and now it's Wi-Fi sixth gen, so. >> We'll have to do a deep dive in the studio sometime, >> Oh, absolutely. >> on getting into all the spectrum issues, you know, the channels - >> Yeah. >> And the antennas and chains and all that good stuff. >> Yeah. There's a lot to geek out on that. (laughs) >> Yeah, it's going to be fun. >> So you talked about, kind of before we wrap up here, you talked about, you know, everything really kind of being related to, or how this can help companies with brand, and brand is everything to any type of company - >> Yeah. >> We talk at every event we go to about how it's all about customer experience. >> Yeah. >> So my last question for you is, how is Wi-Fi 6 and some of these new technologies that clearly you're excited about, how do you think that's going to change the experience for your internal customers, and from being able to get things out faster, to your external Cisco customers? >> Yeah, when you say internal, our own employees - >> Yes. >> Our R and D? >> Yes, exactly. >> Absolutely. So I think, and one of the examples was shown right here, right, so, and I'm connecting the two answers that you had, like, there's a lot of technology details behind what we do, right, we spend tons of money doing R and D, but we wanted to expose that to our own customers, to our channel partners, and to our developers, right? So, this is something that Wi-Fi 6 brings a lot to our customers. So, all the goodness, the intelligence that we have hidden in our network, now gets exposed, through these APIs, to our developers, and to our own customers. So the internal customers of ours, which are engineers, Cisco IT, are tremendously excited to see what that unveils to us, right? And DevNet provides that platform where you can expose this through APIs, whether it's for security, whether it's for application experience, whether it's for better operations, and have new co-creation of applications that we haven't envisioned, new ways of ecosystem partners coming up and building new applications that we haven't envisioned. So, for our own R and D teams, it's pretty exciting. Because - >> Big catalyst. >> Yeah, just, exactly. You're just providing the platform, it's the catalyst for innovations, and that's what the internet was when we created that, right? We didn't know the internet of 20 years back is going to be the internet of today, and we didn't envision that, but here we are. >> Well the ETI's going to open up your market, because you're going to create an enablement to pass that forward, the opportunities to other developers to come up with the ideas. >> Yeah, absolutely. And that's the whole idea, is to provide them a platform to come up with innovations and ideas, and help share these ideas to other folks, right, because when the minds meld, it gets better and better. >> Build some good apps, make ... get it distributed on Wi-Fi 6, make some money, build a business, create a great app - >> Runs on your feet. It's step by step. >> It's a big inflection point. >> That's a pretty good motto. >> It's an inflection point. >> It is. It is truly, I believe, an inflection point. Mainly because, frankly, Wi-Fi 6 and 5G coming together, truly, because me and you as a user really don't care whether I'm on Wi-Fi or cellular, and we shouldn't, right, all I expect is no matter what I do, where I go, and I use my device, I should get the same consistent seamless experience. >> It works. >> Well I don't have the unlimited plan, so I'd love to have it - >> You would with that. on the Wi-Fi. (laughter) >> So you've got this virtual event next week on the 29th - >> Yeah. >> Is that going to tee up anything, any exciting things we're going to hear at Cisco Live a few weeks later? >> Oh yeah. Big time. Big time. (laughs) >> Any teasers you can give us? >> Without getting fired? Yeah, it's going to be tough. (laughter) No, yeah, I think things that we talked today are what we're going to explain more, and we're going to give more flavor on what Cisco's actually is actually doing from our products perspective, solutions, partnership perspective, to bring it to life, right? So, that's really exciting, so I highly encourage the folks that are watching this to register for this on Cisco.com Go Wired For Wireless event, so it's fun, because we've got a lot of industry experts, customers because that's where rubber meets the road - >> Absolutely. >> And that's where the top good applications, how far along they are, what are they testing, what are they trying out, and then we can geek out on all the technology, right? But it always starts with why, and why does it matter. So ... and that's why I'm excited, yeah. >> It sounds exciting. My cheeks are hurting from smiling. Prashanth, thank you so much ... right? ... for sharing your enthusiasm, your energy and expertise, it's been fun. We look forward to, uh, the virtual event next week, and hearing more about what's going on at Cisco Live. >> Thanks Lisa, thanks John. >> Well, our pleasure. For John Furrier, I'm Lisa Martin, you're watching The Cube live from day two of our coverage, of Cisco DevNet Create 2019. Thanks for watching. (techno music)

(energetic music) >> Live from Orlando, Florida, it's theCUBE covering .conf 18, brought to you by Splunk. >> We're back in Orlando, Splunk .conf 2018, I'm Dave Vellante, Stu Miniman, and this is theCUBE, the leader in live tech coverage. Doug Merritt is here, the CEO of Splunk, long time CUBE guest, great to see you again. >> Thank you, Dave, great to be here. >> So, loved the keynote yesterday and today. You guys have a lot of fun, I was laughing my you-know-what off at the auditions. They basically said, Doug wasn't a shoo in for the keynote, so they had these outtake auditions. They were really hilarious, you guys are a lot of fun. You got the great T-shirts, how do you feel? >> It's been a, my favorite time of year is .conf, both because there's usually so much that we're funneling to our customers at this time, but being here is just infectious, it's, and one of the things that always amazes me is it's almost impossible to tell who are the customers and who are the employees. That just, I think Devonia this morning said it's a family affair, and it's not just a family affair, it's that there's a shared passion, a shared, almost culture and value set, and there's, it just is a very inspiring and naturally flowing type of event and I know I'm biased because I'm the CEO of Splunk, but I don't, I just don't know of events that feel like our, like .conf does. There's a lot of great shows out there, but this has got a very unique feel to it. >> Well, we do a lot of shows, as you know, and I've always said, .conf, I think ServiceNow, does a great job obviously, re-invent the tableau shows. That energy is there, and the other thing is, we do, when we go to these shows, a lot of times, you'll look at the keynotes and say, are there any products being announced? You guys, that wasn't a problem here. You guys announced this -- >> Not this year. >> Bevy of products, I mean, it's clear the R and D is translating into stuff that people can consume, and obviously that you can sell, so that's huge. >> I'm really excited about the product roadmap right now, and it's, that was, when I got the job, almost three years ago, one of the key areas I leaned forward and the board was excited about it was, what, where or how are we going to take this product beyond the amazing index and search technology that we have? And this show, it takes a while to progress the roadmap to the point that you can get the type of volume that we have here, but this show was the first time that I felt that we had laid enough of the tracks, so you could see a much, much broader landscape of capabilities, and now it's a challenge of packaging and making sure our customers are successful with it, with the product that we just have, the products we've announced. >> Cloud caught a lot of companies and a lot of end user companies, flatfooted. You guys have embraced the cloud, not only with the AWS partnership, which we're going to talk about, but also the business model. You're successfully transitioning from a company with perpetual license model, to a ratable model, which is never easy. Wall Street is killing companies who try to do that. Why have you been successful doing that? You know, give us an update. >> Yeah, so five years ago, less than 20% of our contracts were, had any type of subscription orientation to it, whether it's a multi-year term or a cloud. We'd just launched our cloud four years ago. And we moved from there to we had told the street there would be 65% term in subscription by the end of this year and updated guidance at the end of the second quarter, which is just a month and change ago, that we've already hit the 75% mark that we were set in for next year, so it's been a pretty rapid progression and I think there're two elements that have helped us with that. One: cloud continues to catch fire and so the people's orientation on "Do I do something in the cloud?" four years ago they were much more nervous, so less nervous today. But data is growing at such a huge rate and people are still wrapping their heads around, "How do I take advantage of this data, how do I even begin to collect this data and then how do I take advantage of it?" And the elasticity that comes in the cloud and that comes with term contracts, we can flex out and flex back in, I think it's just a much more natural contracting motion than you bought this big, perpetual thing and pay maintenance on it, especially when someone is growing as fast as data is growing. >> Well and it requires you to communicate differently to the financial analysts. >> It does. >> Obviously, billings, you know, was an important metric. You've come up with some new metrics to help people understand the real health of the business. And one of the other metrics that strikes me, and you see this with some of the successful companies, I actually think Aneel Bhusri was sort of the modern version of this, is the number of seven figure deals. You're startin' to hit that, and it's not, the way he's phrased it was pretty good. It's not something you're trying to engineer, it's the outcome -- >> Yes. >> of having great, loyal customers, it's not something you try to micromanage. >> Right, and that's, just recently we dropped six figure deals, which, when I joined, you got this wonderful dynamic forecasting system that sits on top of sales for us, and so as head of sales, where I started, you're really paying attention to deals. I'd go down to a hundred thousand dollar deals that would track throughout the quarter. And now it's hard to get it down to the six figures 'cause we've got a big enough envelope of seven figure deals. So the business has changed pretty dramatically from where it was, but it is an outgrowth of our number one customer priority, which is, or number one corporate priority, which is customer success. 'Cause that investment by companies, when you get to a million dollars plus, in most cases that's a million annually, you better believe in and trust that vendor, 'cause that's no longer an easy, small departmental sale. You're usually at the CIO, CFO type level. So it's something that we're very honored by, that people trust us enough to get that footprint of Splunk to be that size and to feel like they're getting a value from Splunk to justify that purchase. >> Alright we'll get off the income statement, Stu, and you can read about all that stuff, and we're going to get into, we've got a lot of ground to cover with you, Doug. Jump in here, Stu. >> Yeah, so Doug, I've really enjoyed talking to some of your customers that, you know, most of them started on premises with you and now many of them, they're using Splunk cloud, it's really kind of a hybrid model, and it's been really interesting to watch the maturation of your partnership with Amazon, and being the leader in the cloud space. Give us a little bit of color as to what you're hearing from the customers, you said three, four years ago, you know, they were obviously a little bit more cautious around it, and bring us inside a little bit that partnership. >> Sure, so the first piece that, as part of Splunk, that I think is a little bit different than other vendors is because we are both a lower level infrastructural technology, right, data is, the way I frame what we do is there's these raw materials, which are all these different renditions of data around, and companies increasingly have to figure out how to gather together these different raw materials, put them together different ways, for the output that is driving their business. And we are the manufacturing parts provider that makes it easy for them to go and pick up any of these different compounds and then actually do what they want to do, which is make things happen with data. And that middle layer is really important and we have never taken a super strong stance either, we started on prem, but as we moved to cloud, we never took a strong stance saying everything should be in the cloud or everything should be on prem because data has gravity, there is physics to data. And it doesn't always make sense to move data around and it doesn't always make sense to keep data stagnant, so having that flexibility, being able to deploy your collection capability, whether it's ours or third party, your storage capability, and then your process and your search, what are you going to do with the data, anywhere that makes sense for a customer, I think, is important. And that's part of that hybrid story, is as people increasingly trust and interview us and other cloud vendors to build core apps and then house a lot of their data, we absolutely need to be there. And I think that momentum of the cloud is certainly as secure and, in many cases, more secure than my on prem footprint, and the velocity of invention that some like ABDS is driving allows me to be much more agile and effectively drive application development and leading edge capability, I think just has people continuing to trust the cloud service providers a little bit more. >> Yeah well, we're here in the pavilion, and seeing your ecosystem grow, we've been at re:Invent for about five years, that ecosystem is just so >> It's been amazing. >> massive and full, give us a little bit about the relationship with Amazon and how you look at that, how Amazon looks at a company like yours. >> Yeah, it's been, so one, whenever you're playing with a highly inventive and hugely successful company like Amazon, my orientation and what I convey back to the company is our job is to be more inventive, more agile, and continue to find value with our maniacal focus every day being the data landscape. Data is a service and outcomes is a service, so our job is run faster than Amazon. And I think that this show and our announcements help illustrate that our invention cycle is in high tilt gear and for what we do, we are leaning in in a really aggressive way to add that value. With that backdrop, Andy and I formed this partnership four years ago. He felt there's enough value in Splunk and we were a good enough partner and the way we consume their services that he would commission and quota their sales reps whenever a Splunk sale was done in the ADBS landscape, which I think has been really helpful for us, but we obviously are a huge customer of ADBS's and they become an increasingly large customer of ours and finally gave us approval with their three year renewal a quarter ago to publicly reference them as a sizeable customer for us. >> Oh, okay, congratulations on that. And something I've really, it's really crystallized for me: so many administrators out there, you look at their jobs, you know, what are they? It's like okay, I'm the security expert, I'm the network certified person. You're really, your users here, you know, they are the beacons of knowledge, they are the center of data, is really what they are. You know, Splunk's a tool, they're super excited about the product, but it's data at the center of what Splunk does and therefore, you're helping them in just such a critical aspect of what is happening in the industry today. >> Yeah, the key aspects of the keynote, of my keynote, were we are moving to a world where data is the product that people care about so the whole object is how do you make things happen with data and the people that can get that done increasingly are becoming the most valuable players on the field, so what infrastructure, what tooling, what capability exists that allows people from all departments, you know, we're very heavy within IT and security, but increasingly HR departments, finance departments, marketing departments, sales departments, manufacturing departments will not be successful without a really competent group of folks that understand how to make things happen with data and our job is to lower that bar so you don't have to go to Carnegie Mellon for four years and get a Masters in Computer Science and Data Science to be able to be that most valuable person on the field. >> I want to take a moment, I want to explain why I'm so bullish on Splunk. We had a conversation with Susan St. Ledger yesterday. Digital transformation is all about data. >> Yup. >> And you guys are all about data, there's the cliche which is "data is the new oil" and we've observed, well not really. I could put oil in my car, I can put oil in my house, I can't put it in both places, but data? I can use that same data in a lot of different use cases and that's exactly what you guys are doing now as you expand into line of business -- >> Yup. >> With Splunk Next. >> Yup. >> So you've announced that, you showed some cool demos today. I'd like you to talk about how you're going from your core peeps, the IT ops guys and the sec ops guys, and how, what your plan is to go to lines of business. More than just putting the data out there, you've come up with some new products that make it simpler, like business work flows, but what else are you doing from a go to market standpoint and a partnership standpoint, how do you see that playing out? >> Yeah, I think that the innovation on product, there are three key pillars that we're focusing on. Access data, any type of data, anywhere it lives. Make sure that we're driving actionable outcomes with that data, and acquisitions like Phantom and VictorOps have been a key pillar of that, but there's other things we're doing. And then, expand the capability of finding those outcomes to a much broader audience by lowering the bar. So the three key themes across the portfolio. But all of those are in service of the developers at a customer site, the developers in the ecosystem, to make it easier for them to actually craft a set of solutions that help a retailer, help a discrete manufacturer, help a hospital actually make things happen with data. 'Cause you could certainly start with a platform and build something specific for yourself but it's much easier if you start with a solution. And a lot of the emphasis we've been putting over the past two to three years is how do we up that platform game. And the many, many, 20 different product announcements that we rolled at this .conf and one of them that I'm also very excited about is our developer cloud where we've really enhanced the API layer that interacts with the different services that the entire Splunk portfolio represents. Not just the search and index pieces that people are familiar with but everything from orchestration to role based access to different types of visualization so a very broad API layer that's a well-mannered, restful set of APIs that allows third parties to much more crisply develop, excuse me, applications to compliment the 1800 apps that are already part of our Splunk base and right behind me is a developer pavilion where we've got the first hand full of early adopter OEM partners that are building their first sets of apps on top of that API framework. >> Dozens of them, it's actually worth walking around to see. Now, so that developer cloud is a lever, those developers are a lever for you to get into lines of business and build those relationships through the software, really, and through the apps. Same thing for IOT. >> Yup. >> Industrial IOT. Now, we've observed, and a lot of the IT companies that we see are trying to take a top down approach into IOT and we don't think it's going to work. It's, we talk about process engineers, it's operations technology people, they speak a different language. It's not going to be a top down, here, IT. >> A very different audience. >> It's going to be a bottoms up set of standards coming from the OT world. The brilliance of what you guys have, it's the data, you know, it's data coming off machines, data, you don't care. And so, you're in a good position to do a bottoms up in IOT and we heard some of that today. Now, there are some challenges. A lot of that data is still analog, okay, you can't really control that. A lot of the devices aren't instrumented, they're not connected, you can't control that. But once they become instrumented and connected and that analog data gets digitized, you're in a really good position, but then you got to build out the ecosystem as well. >> Yup. >> So talk about how you're addressing some of those challenges in industrial IOT. >> Yup, man, it's a great subject 'cause I think that the trying to rely on standards is the wrong approach. The velocity across this digital landscape is so high and my view over the past 30 years, I think it's only accelerated now, is there's going to be more and more varieties of data with different formats than there's ever been, and we've seen it in the past five years. Just look at the variety of services on top of AWS, which didn't even exist ten years ago, but and they now have hundreds of services and there is no organizing principle across those services as far as data definition. So it's a very chaotic data landscape and I don't think there's any way to manage it other than to embrace the chaos and work a little bit more bottoms up, you know, grab this data, don't worry about cleansing it, don't worry about structuring it, just make sure you have access to it and then make sure that you've got tools like Splunk that allow you to play with the data and try and find the patterns and the value inside of that data, which is where I think we're very uniquely suited as a technology set. Helping the ecosystem come to that realization is a key aspect of what we're doing. We're trying to attack it the same way we attacked the IT security piece which is pick a handful of verticals and really focus on the players, both the marquis anchor tenants, the BMWs, the Siemens', the Deutsche Bahn railroads of the world, as customers. And through that, get access to the key influencers and consultants and advisors to those industries and start to get that virtuous circle of "I actually have more data than I think I have." Even though there's some analog machines, there's so many different ways to attach to the signal that those machines are emitting and it may not be bi-directionally addressable, but at least you can see what's happening within those machines without a full manufacturing floor rip and replace. And everyone is excited about doing that. The advisors to the industry are excited, the industry themselves are excited. We had BMW on stage who walked through how they're using Splunk to help on everything from product design all the way through to predictive maintenance and feedback on the quality of the cars that they're rolling out. We've all heard stories that there's more lines of code in the Ford F150 and these other vehicles than there is within Facebook right now, so we all are dealing with rolling and sitting in building's and house's data centers. How do you make sure that you're able to pay attention what's happened within that data center? So I think that that is as big or bigger of an opportunity than what we've done with IT and security, it just has its own pace of understanding and adoption. >> Carnival Cruise Line, another one, Stu. We had those guys on today and they basically look, they have a lot of industrial equipment on those ships, so they're excited. >> Yeah, absolutely. Alright, so Doug, we started the beginning talking about the last couple years, how we measure Splunk has changed. Going to more subscription models, talk about how many customers you have. I look at developers, I look at IOT, whole different set of metrics. So if you look at Splunk Next, how do we measure you, going forward? What is success for your team and your customers going forward? >> Yeah, and the whole orientation around Splunk Next, as I'm sure Susan covered, it's not a product, it's a messaging framework. People are so used to Splunk being all about the collection of data within the index and searching in said index, and we're increasingly moving, we're complementing the index, the index is a incredibly unique piece of IP for us. But there's a lot of other modalities that can complement what that index does and Splunk Next represents all of our investments in next generation technologies that are helping in with everything from stream processing to distributed compute capability, next generation visualizations, et cetera. The metric that I care about over time is customer adoption and customer success. How many use cases are being deployed at different customers? How many companies, both customers and partners, are incorporating Splunk in what they do every day? You're getting OEM Splunk, making Splunk a backbone of their overall health and success. And ultimately that needs to translate into revenue, so revenue and bookings will always be a metric that we care about, but I think the leading indicators within theses different markets of rate of adoption of technology and, more importantly, the outcomes that they're driving as they adopt this technology, are going to be increasingly important. >> Yeah, I just have to tell you, when you talk about your customers not only excited, but it's a deeper partnership when you talk to insurance company out of Toronto that, like, they're talking to the people that they insure about, should they be using Splunk and how do they do that. It just, a much deeper, and you know, deeper than a partnership model for your customers. >> It's one of the things I love about this conference, is it's, we were talking about earlier, it's hard to tell the customers from the employees, like, there's a, there's a, this whole belief and purpose that everybody shares, which I adore about being here. But when you look at a sea of data, we've thought traditionally looked at the data we manufacture, typically data that's historic and at rest from our ERP systems. This next wave is certainly all the data that's happening within our organizations but increasingly it's all the data that's available in the world at large. And whether it's insurance or automotive or oil and gas, the services that I'm going to have to deliver to customers require me to farm data outside of my walls, data inside my walls, combine those two, to come up with unique value added services for my customers. So it's great to hear that, that our customers are on that journey 'cause that's where we all need to go to be successful. >> And there's a definitely alignment there. Doug, I know you're super busy, we got to go. Thanks so much for coming on theCUBE. Give you the last word, .conf 18 takeaways. >> (laughs) Unbelievable excitement and enthusiasm. A huge array of products that, I think, broaden the aperture of what Splunk does so dramatically that people are really trying to digest, "What should, how should I be thinking about Splunk moving forward?" And I'm, we started a whole series of transformations three years ago, and I'm really excited that they're all starting to land and I can't wait for the slow realization of the impact that our customers are counting on us to provide and that we'll increasingly be known for across the data landscape. >> Well and the landscape is messy and, as you said, the messiest part of that landscape is the data landscape. You guys are helping organize that, curate it. And hopefully we're helping curate some of the, from some of the noise and distracting to the signal to you on theCUBE. Doug, thanks so much for coming on theCUBE, great to see you again. >> Thank you Dave, thank you Stu, you guys do a great job. >> Thanks, we appreciate that. >> Thanks for being here with us. >> Alright, keep it right there, buddy. We'll be back with our next guest from .conf 18 from Orlando, we'll be right back. (digital music)

(upbeat music) >> Announcer: Live from Orlando Florida, it's theCUBE, covering .conf18. Brought to you by Splunk. >> Welcome back to .conf18 everybody. I'm Dave Vellante with Stu Miniman, and you're watching theCUBE, the leader in live tech coverage. We love to go out to the events, extract the signal from the noise. A lot of focus today, Stu, on security and Haiyan is here. Haiyan Song is the Senior Vice President and General Manager of Security at Splunk. Great to see you again. >> Thank you for having me. >> You're very welcome. Fifth time I think for you on theCUBE So you're super alum. And really always appreciate your deep knowledge. As I said, today was security day. A lot of customers talking about security. It's obviously a strong hold of Splunk. But, give us the update. What's new this year with you? We talked a year ago in D.C. What's happening with you guys? >> Well this is the year that we really went out and shared our vision of what SOC looks like in 2020. And we call it the Vision of SOC 2020. And on a very high level, we envision that in a couple of years with the technology like analytics, and operations, automation, orchestration, we envision that 90% of the Tier 1 work that a SOC analyst would be doing will be automated. And with that automation we are envisioning that most of the time, more than 50% of the time, the SOC analyst can actually focus on detection logic and really responding to things, that requires the human skills and insights. And we're also envisioning that by that time, there will be a place, one place, where things for response gets orchestrated versus people have to go to twenty different places trying to figure out what's going on. So, that sort of, from a business perspective but to deliver that, there's really, sort of ten, we share the ten big we call it core capabilities, that capability road map to SOC 2020. And for us, we feel really fortunate that with the acquisition of Phantom, we are really able to bring that full stack together, to deliver that capability. So we have data platform. You heard all the exciting news on what we are doing, with data fabric search, stream processing, and amplifying the performance analytics. You heard all those things that we're putting into IT, and security, ES, UBA, and then last but not least is the ability to orchestrate, to automate, to collaborate. So I think we're really uniquely positioned, because we can bring all three together. That's the full stack to deliver on that vision. >> So let's talk a little bit more about that vision. So, I mean my rudimentary understanding is you really had a reactive mode in the past. It's kind of herding cats, trying to figure out, okay I'm going to to try to respond to an incident. Then you started to use data and analytics to try to prioritize, to focus on those things that aren't going to be a false positive or of high value. What you're putting forth is a vision where a lot of that heavy lifting goes away. Machine intelligence is either augmenting, or making decisions about which items to go after. Talk more about that world. What does it look like? What's the role of the security professional in that new world? >> Yeah, there's two parts we do in the Security Operations Center. Detecting things and responding things and taking care of sort of the incidents. So a lot of the things you really touched on is how we have applied machine learning and analytics and really leveraging the business context. The feature we talked about, the distribute, the data fabric search is a really powerful tool. Now we can reach out and get lot more information to help you make better decisions to reduce the reshow of noise to signal, or signal to noise, and whichever way you want to see it up and down. So, that world we expect more machine learning, more data modeling, more threat modelings so we can really sort of incorporate business, sort of context, so risks become a one key thing to help people prioritize. That's our product ES, and UBA, and you heard about the whole predictive capabilities in IT. I think all of those will be sort of that world. And the second part of what we do is if something does happen now we really got the signal. What do you do about it? We envision that world lot of initial men did prep work. Like, oh I want to find out if this ID belongs to which organization? Is this really a signature in the virus total, sort of database and what happened, so that whole prep hopefully, will be done for you before you even get started into an incident. And furthermore, if we have responded to those type of incidents before, we actually would like to give you a recommendation, this is what happened before, this is what worked, and why don't you think about this playbook and automate this part? So, I think the world in 2020, is going to be a lot of augmentation. >> One of the things we've heard from a number of your customers, is security in DevOps and how they are using the DevOps mentality to make security more pervasive and integrated in everything they do. Could you explain how Splunk fits into that discussion? >> Yeah, so DevSecOps, I think that's, sort of, the term you might be eluding to and I think the cloud adoption, the acceleration, and the new IT is really, sort of, bringing that into focus for us. Splunk plays to that in several ways. We have a security business, we have a IT business, and you may have heard we just acquired another company called VictorOps after Phantom. So they're really helping the DevOps world and try to coordinate and enable collaboration. So we definitely expect that capability will show up in the security side to help the DevOps, DevSecOps' world and we are also, as a company, taking data security really seriously. So we are putting a lot of, you know, you saw the data stream processing and one of the capabilities to obfuscate credit card and for GDPR and a lot of other things, there's that mending. You got to give people the control of things so there is a lot of that. We're taking into consideration and putting that into the product and the other thing is, really, we ourselves operate probably one of the biggest, sort of, cloud capabilities on AWS and we have infused a lot of best practices around, how do you automate? How do you protect? How do you be compliant? And how do you insure customer have control? And there's a lot of work we're doing there and practicing DevSecOps ourselves. >> Haiyan, in thinking about the Splunk portfolio and in the context of the vision that you guys laid out, how does Splunks existing portfolio fit in to that vision and where are the gaps? What has to evolve, whether it's your capabilities, or the industry's MI, ML, or machine learning capabilities? Where are the gaps? >> So I think in many ways the ten core capabilities were laid out. I going to try to go through them in my head. So. >> Okay. >> Ingest. Detect. Predict. and then automate. Orchestrate. Recommend. Investigate. Case Management. Collaborate. And reporting. So those are the ten. When we were sharing with our audience, we actually look at our ES, UBA, and Phantom. We are able to give them all those capabilities to get started on their path for SOC 2020. But we also realize and recognize that all those capabilities, I'll give you an example, Case Management, now there is more and more requirements coming to the security side to say I want you to bring all the different things together, and I want you to take in the automated playbooks and how this plays into those, so there's always room for us to continue to enhance those capabilities. But, we also see the opportunity for us to bring all those things in a more seemless way into, sort of, one full stack, the full stack that gives you, you know, I don't know if you heard the term, powering the OODA Loop? Right, the observe, orient, decide, and act. And that was really, sort of, military strategy for the fighter pilots to say the whole premise is whoever can power that loop, and execute the fastest, wins. >> It's like readying fire but more data focused. >> More data focused, I like that. So for us, it's really how do we bring the portfolio together, so they can really power that loop in a very intuitive way. And in a very open way. I want to make sure that I iterate that reiterate our commitment to be open. There's data layer, there is analytics layer, there's operational layer. We want to be that company can bring the full stack make them work really well. But, in the meantime work well with other data, with other analytics, detection engines, and other ways to operate. So being open is very important. >> And you'll automate as many of those or all of those ten that you mentioned. Do you automate the run book? >> Automated run book is what Phantom is all about and the run book gets more and more sophisticated and I think we give people the ways to say if on day one, you don't want to automate everything, especially shutting down his email, then you have the choice. But, it's as you learn, as you become more confidence, and you have that under your control. How much you want to automate, and hopefully, as more automated actions are taken, we get to analyze those and start making recommendations so you become more comfortable with that. >> So I understand New York Presbyterian was in your session. And, you were talking about going beyond security. I often like to say that security and privacy are two different sides of the same coin. But, when they talked about going, well share with us, what you learned from them. >> Yeah you have really the best phrase to say they are both sides and as a security professional in the digitized world I don't think you have a boundary to say my job starts with SOC and ends with SOC. It goes way beyond. It goes into data privacy. It goes into even fraud analytics, because a lot of things are happening online. It also goes into compliance. And, it's interesting that we thought years ago, compliance was driving investment. I think now with GDPR, with some of the data privacy challenges we've seen, that's impacting the masses, the criticalness of compliance is actually coming back. So the story that I was super impressed that our customer, New York Presbyterian shared with us is they had a challenge of really managing all this sort of patient records, and try to understand the staff's activities. Because, the auditors have a certain set of things. You know you shouldn't be snooping around the patient's record, if its your neighbor, or your buddy. So they used Splunk and they powered, sort of, us with a lot of the data from various applications. They have probably 20 data sources, that's very healthcare centric. We partnered up, we had our product expert, and fraud experts on that. And, we built a privacy platform, a early version of that, and they showed it to their privacy officers, and they basically said we've not seen anything like this to give us the flexibility and ease of use to be able to bring everything together. And, they did even more than that. If you have time I'll share with you on the opiate diversion capabilities they started building with. >> Dave: Oh, yeah talk about that, yeah please >> So we were thinking, we're just going to help them with compliance that makes their organization more compliant and better, but they didn't stop there. They said well, based on the power we're able to, really, leverage from the Splunk platform, we see the data we have for our pharmacies, there's a lot of prescription, sort of, information and with the world that's battling the opiate epidemic, we think we can actually analyze the data and give us early patterns and earnings, warnings of what might be happening. So, that's the next project we're partnering up. And for us we have technology, and customer have domain knowledge, have data. I think that's a great partnership. And they are willing, they are wanting us to go evangelize 'cause they want the whole industry to benefit, they want the nation to benefit. >> Well we saw this week on 60 Minutes, did you see that story? The one pharmaceutical company got in big trouble and a doctor went to jail. The pharmaceutical company was shipping 500 million Oxycontin pills into Florida. This is a state with a population of 20 million. Something was wrong. Obviously those were hitting the streets. And, this individual this doctor went to jail for life. So, data analysis could identify that. >> Data was there. I think it's the inside to look for the ways, to look for those things and having that inside drive decisions is really the partnership we have with our customers >> We're seeing that, g'head Stu. >> Yeah I was just, you spoke on a panel of the Grace Hopper event. >> Haiyan: Last week. >> We've been hearing great messages of diversity at this show. You had the Carnival Cruise CEO up on stage giving some great discussion points yesterday. Maybe you could share a little bit of your experience at the show and the panel that you were on. >> The Grace Hopper is such an amazing event and we see so many college grads and people, sort of, starting their career and that is like the go to place. And I see all the big companies, big, or small actually, putting so much effort to try to really evangelize to that audience. 'Cause California just passed, the Governor just signed into law, they require a woman on the board, as part of the requirements because diversity is being proven to bring better decision making into the board and I, myself, can tell you that my security leadership team over the years become more and more diverse. I don't think diversity is just gender diversity. I think diversity needs to go beyond gender. It's background where people who are from the private sector, from the government, where people from different Geo's of the world. That sort of richness of perspective always give us the best, sort of, angles to think about and validating, and debating on our, sort of, strategies. And going back to Grace Hopper, the panel that I was on was really sharing with the people who are there, what are some of the things that you should be prepared for if you want a cyber security career. And the part is not try to, oh here's a high bar. We really try to encourage everyone, whether you're technical, or you just having great analytical skills. I think one of my fellow panelist, she made a comment I thought was super funny. She was a CEO of a company and she said, sometimes women just have to have enough confidence and to go take the risk, grab the opportunity. She use the word, sometimes you have to fake it until you prove it and until you make it. And she's really just encouraging the attendees, just step up take the opportunity. I am in total agreement with that. >> Lean in baby. >> Lean in. That's another way to do it. >> Haiyan thanks so much for coming back in theCUBE. Really great to see you again. >> Thank you for having me. >> You're very welcome. All right, keep it right there everybody. Stu and I will be right back with our next guest. Right after this short break. We're live from Orlando, Splunk .conf18 You're watching theCUBE. (upbeat music)