(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)

(upbeat music) >> Hey, everyone, welcome back to Las Vegas, "theCube" is on our fourth day of covering AWS re:Invent, live from the Venetian Expo Center. This week has been amazing. We've created a ton of content, as you know, 'cause you've been watching. But, there's been north of 55,000 people here, hundreds of thousands online. We've had amazing conversations across the AWS ecosystem. Lisa Martin, Paul Gillan. Paul, what's your, kind of, take on day four of the conference? It's still highly packed. >> Oh, there's lots of people here. (laughs) >> Yep. Unusual for the final day of a conference. I think Werner Vogels, if I'm pronouncing it right kicked things off today when he talked about asymmetry and how the world is, you know, asymmetric. We build symmetric software, because it's convenient to do so, but asymmetric software actually scales and evolves much better. And I think that that was a conversation starter for a lot of what people are talking about here today, which is how the cloud changes the way we think about building software. >> Absolutely does. >> Our next guest, Holger Mueller, that's one of his key areas of focus. And Holger, welcome, thanks for joining us on the "theCube". >> Thanks for having me. >> What did you take away from the keynote this morning? >> Well, how do you feel on the final day of the marathon, right? We're like 23, 24 miles. Hit the ball yesterday, right? >> We are going strong Holger. And, of course, >> Yeah. >> you guys, we can either talk about business transformation with cloud or the World Cup. >> Or we can do both. >> The World Cup, hands down. World Cup. (Lisa laughs) Germany's out, I'm unbiased now. They just got eliminated. >> Spain is out now. >> What will the U.S. do against Netherlands tomorrow? >> They're going to win. What's your forecast? U.S. will win? >> They're going to win 2 to 1. >> What do you say, 2:1? >> I'm optimistic, but realistic. >> 3? >> I think Netherlands. >> Netherlands will win? >> 2 to nothing. >> Okay, I'll vote for the U.S.. >> Okay, okay >> 3:1 for the U.S.. >> Be optimistic. >> Root for the U.S.. >> Okay, I like that. >> Hope for the best wherever you work. >> Tomorrow you'll see how much soccer experts we are. >> If your prediction was right. (laughs) >> (laughs) Ja, ja. Or yours was right, right, so. Cool, no, but the event, I think the event is great to have 50,000 people. Biggest event of the year again, right? Not yet the 70,000 we had in 2019. But it's great to have the energy. I've never seen the show floor going all the way down like this, right? >> I haven't either. >> I've never seen that. I think it's a record. Often vendors get the space here and they have the keynote area, and the entertainment area, >> Yeah. >> and the food area, and then there's an exposition, right? This is packed. >> It's packed. >> Maybe it'll pay off. >> You don't see the big empty booths that you often see. >> Oh no. >> Exactly, exactly. You know, the white spaces and so on. >> No. >> Right. >> Which is a good thing. >> There's lots of energy, which is great. And today's, of course, the developer day, like you said before, right now Vogels' a rockstar in the developer community, right. Revered visionary on what has been built, right? And he's becoming a little professorial is my feeling, right. He had these moments before too, when it was justifying how AWS moved off the Oracle database about the importance of data warehouses and structures and why DynamoDB is better and so on. But, he had a large part of this too, and this coming right across the keynotes, right? Adam Selipsky talking about Antarctica, right? Scott against almonds and what went wrong. He didn't tell us, by the way, which often the tech winners forget. Scott banked on technology. He had motorized sleds, which failed after three miles. So, that's not the story to tell the technology. Let everything down. Everybody went back to ponies and horses and dogs. >> Maybe goes back to these asynchronous behavior. >> Yeah. >> The way of nature. >> And, yesterday, Swami talking about the bridges, right? The root bridges, right? >> Right. >> So, how could Werner pick up with his video at the beginning. >> Yeah. >> And then talk about space and other things? So I think it's important to educate about event-based architecture, right? And we see this massive transformation. Modern software has to be event based, right? Because, that's how things work and we didn't think like this before. I see this massive transformation in my other research area in other platforms about the HR space, where payrolls are being rebuilt completely. And payroll used to be one of the three peaks of ERP, right? You would size your ERP machine before the cloud to financial close, to run the payroll, and to do an MRP manufacturing run if you're manufacturing. God forbid you run those three at the same time. Your machine wouldn't be able to do that, right? So it was like start the engine, start the boosters, we are running payroll. And now the modern payroll designs like you see from ADP or from Ceridian, they're taking every payroll relevant event. You check in time wise, right? You go overtime, you take a day of vacation and right away they trigger and run the payroll, so it's up to date for you, up to date for you, which, in this economy, is super important, because we have more gig workers, we have more contractors, we have employees who are leaving suddenly, right? The great resignation, which is happening. So, from that perspective, it's the modern way of building software. So it's great to see Werner showing that. The dirty little secrets though is that is more efficient software for the cloud platform vendor too. Takes less resources, gets less committed things, so it's a much more scalable architecture. You can move the events, you can work asynchronously much better. And the biggest showcase, right? What's the biggest transactional showcase for an eventually consistent asynchronous transactional application? I know it's a mouthful, but we at Amazon, AWS, Amazon, right? You buy something on Amazon they tell you it's going to come tomorrow. >> Yep. >> They don't know it's going to come tomorrow by that time, because it's not transactionally consistent, right? We're just making every ERP vendor, who lives in transactional work, having nightmares of course, (Lisa laughs) but for them it's like, yes we have the delivery to promise, a promise to do that, right? But they come back to you and say, "Sorry, we couldn't make it, delivery didn't work and so on. It's going to be a new date. We are out of the product.", right? So these kind of event base asynchronous things are more and more what's going to scale around the world. It's going to be efficient for everybody, it's going to be better customer experience, better employee experience, ultimately better user experience, it's going to be better for the enterprise to build, but we have to learn to build it. So big announcement was to build our environment to build better eventful applications from today. >> Talk about... This is the first re:Invent... Well, actually, I'm sorry, it's the second re:Invent under Adam Selipsky. >> Right. Adam Selipsky, yep. >> But his first year. >> Right >> We're hearing a lot of momentum. What's your takeaway with what he delivered with the direction Amazon is going, their vision? >> Ja, I think compared to the Jassy times, right, we didn't see the hockey stick slide, right? With a number of innovations and releases. That was done in 2019 too, right? So I think it's a more pedestrian pace, which, ultimately, is good for everybody, because it means that when software vendors go slower, they do less width, but more depth. >> Yeah. >> And depth is what customers need. So Amazon's building more on the depth side, which is good news. I also think, and that's not official, right, but Adam Selipsky came from Tableau, right? >> Yeah. So he is a BI analytics guy. So it's no surprise we have three data lake offerings, right? Security data lake, we have a healthcare data lake and we have a supply chain data lake, right? Where all, again, the epigonos mentioned them I was like, "Oh, my god, Amazon's coming to supply chain.", but it's actually data lakes, which is an interesting part. But, I think it's not a surprise that someone who comes heavily out of the analytics BI world, it's off ringside, if I was pitching internally to him maybe I'd do something which he's is familiar with and I think that's what we see in the major announcement of his keynote on Tuesday. >> I mean, speaking of analytics, one of the big announcements early on was Amazon is trying to bridge the gap between Aurora. >> Yep. >> And Redshift. >> Right. >> And setting up for continuous pipelines, continuous integration. >> Right. >> Seems to be a trend that is common to all database players. I mean, Oracle is doing the same thing. SAP is doing the same thing. MariaDB. Do you see the distinction between transactional and analytical databases going away? >> It's coming together, right? Certainly coming together, from that perspective, but there's a fundamental different starting point, right? And with the big idea part, right? The universal database, which does everything for you in one system, whereas the suite of specialized databases, right? Oracle is in the classic Oracle database in the universal database camp. On the other side you have Amazon, which built a database. This is one of the first few Amazon re:Invents. It's my 10th where there was no new database announced. Right? >> No. >> So it was always add another one specially- >> I think they have enough. >> It's a great approach. They have enough, right? So it's a great approach to build something quick, which Amazon is all about. It's not so great when customers want to leverage things. And, ultimately, which I think with Selipsky, AWS is waking up to the enterprise saying, "I have all this different database and what is in them matters to me." >> Yeah. >> "So how can I get this better?" So no surprise between the two most popular database, Aurora and RDS. They're bring together the data with some out of the box parts. I think it's kind of, like, silly when Swami's saying, "Hey, no ETL.". (chuckles) Right? >> Yeah. >> There shouldn't be an ETL from the same vendor, right? There should be data pipes from that perspective anyway. So it looks like, on the overall value proposition database side, AWS is moving closer to the universal database on the Oracle side, right? Because, if you lift, of course, the universal database, under the hood, you see, well, there's different database there, different part there, you do something there, you have to configure stuff, which is also the case but it's one part of it, right, so. >> With that shift, talk about the value that's going to be in it for customers regardless of industry. >> Well, the value for customers is great, because when software vendors, or platform vendors, go in depth, you get more functionality, you get more maturity you get easier ways of setting up the whole things. You get ways of maintaining things. And you, ultimately, get lower TCO to build them, which is super important for enterprise. Because, here, this is the developer cloud, right? Developers love AWS. Developers are scarce, expensive. Might not be want to work for you, right? So developer velocity getting more done with same amount of developers, getting less done, less developers getting more done, is super crucial, super important. So this is all good news for enterprise banking on AWS and then providing them more efficiency, more automation, out of the box. >> Some of your customer conversations this week, talk to us about some of the feedback. What's the common denominator amongst customers right now? >> Customers are excited. First of all, like, first event, again in person, large, right? >> Yeah. >> People can travel, people meet each other, meet in person. They have a good handle around the complexity, which used to be a huge challenge in the past, because people say, "Do I do this?" I know so many CXOs saying, "Yeah, I want to build, say, something in IoT with AWS. The first reference built it like this, the next reference built it completely different. The third one built it completely different again. So now I'm doubting if my team has the skills to build things successfully, because will they be smart enough, like your teams, because there's no repetitiveness and that repetitiveness is going to be very important for AWS to come up with some higher packaging and version numbers.", right? But customers like that message. They like that things are working better together. They're not missing the big announcement, right? One of the traditional things of AWS would be, and they made it even proud, as a system, Jassy was saying, "If we look at the IT spend and we see something which is, like, high margin for us and not served well and we announced something there, right?" So Quick Start, Workspaces, where all liaisons where AWS went after traditional IT spend and had an offering. We haven't had this in 2019, we don't have them in 2020. Last year and didn't have it now. So something is changing on the AWS side. It's a little bit too early to figure out what, but they're not chewing off as many big things as they used in the past. >> Right. >> Yep. >> Did you get the sense that... Keith Townsend, from "The CTO Advisor", was on earlier. >> Yep. >> And he said he's been to many re:Invents, as you have, and he said that he got the sense that this is Amazon's chance to do a victory lap, as he called it. That this is a way for Amazon to reinforce the leadership cloud. >> Ja. >> And really, kind of, establish that nobody can come close to them, nobody can compete with them. >> You don't think that- >> I don't think that's at all... I mean, love Keith, he's a great guy, but I don't think that's the mindset at all, right? So, I mean, Jassy was always saying, "It's still the morning of the day in the cloud.", right? They're far away from being done. They're obsessed over being right. They do more work with the analysts. We think we got something right. And I like the passion, from that perspective. So I think Amazon's far from being complacent and the area, which is the biggest bit, right, the biggest. The only thing where Amazon truly has floundered, always floundered, is the AI space, right? So, 2018, Werner Vogels was doing more technical stuff that "Oh, this is all about linear regression.", right? And Amazon didn't start to put algorithms on silicon, right? And they have a three four trail and they didn't announce anything new here, behind Google who's been doing this for much, much longer than TPU platform, so. >> But they have now. >> They're keen aware. >> Yep. >> They now have three, or they own two of their own hardware platforms for AI. >> Right. >> They support the Intel platform. They seem to be catching up in that area. >> It's very hard to catch up on hardware, right? Because, there's release cycles, right? And just the volume that, just talking about the largest models that we have right now, to do with the language models, and Google is just doing a side note of saying, "Oh, we supported 50 less or 30 less, not little spoken languages, which I've never even heard of, because they're under banked and under supported and here's the language model, right? And I think it's all about little bit the organizational DNA of a company. I'm a strong believer in that. And, you have to remember AWS comes from the retail side, right? >> Yeah. >> Their roll out of data centers follows their retail strategy. Open secret, right? But, the same thing as the scale of the AI is very very different than if you take a look over at Google where it makes sense of the internet, right? The scale right away >> Right. >> is a solution, which is a good solution for some of the DNA of AWS. Also, Microsoft Azure is good. There has no chance to even get off the ship of that at Google, right? And these leaders with Google and it's not getting smaller, right? We didn't hear anything. I mean so much focused on data. Why do they focus so much on data? Because, data is the first step for AI. If AWS was doing a victory lap, data would've been done. They would own data, right? They would have a competitor to BigQuery Omni from the Google side to get data from the different clouds. There's crickets on that topic, right? So I think they know that they're catching up on the AI side, but it's really, really hard. It's not like in software where you can't acquire someone they could acquire in video. >> Not at Core Donovan. >> Might play a game, but that's not a good idea, right? So you can't, there's no shortcuts on the hardware side. As much as I'm a software guy and love software and don't like hardware, it's always a pain, right? There's no shortcuts there and there's nothing, which I think, has a new Artanium instance, of course, certainly, but they're not catching up. The distance is the same, yep. >> One of the things is funny, one of our guests, I think it was Tuesday, it was, it was right after Adam's keynote. >> Sure. >> Said that Adam Selipsky stood up on stage and talked about data for 52 minutes. >> Yeah. Right. >> It was timed, 52 minutes. >> Right. >> Huge emphasis on that. One of the things that Adam said to John Furrier when they were able to sit down >> Yeah >> a week or so ago at an event preview, was that CIOs and CEOs are not coming to Adam to talk about technology. They want to talk about transformation. They want to talk about business transformation. >> Sure, yes, yes. >> Talk to me in our last couple of minutes about what CEOs and CIOs are coming to you saying, "Holger, help us figure this out. We have to transform the business." >> Right. So we advise, I'm going quote our friends at Gartner, once the type A company. So we'll use technology aggressively, right? So take everything in the audience with a grain of salt, followers are the laggards, and so on. So for them, it's really the cusp of doing AI, right? Getting that data together. It has to be in the cloud. We live in the air of infinite computing. The cloud makes computing infinite, both from a storage, from a compute perspective, from an AI perspective, and then define new business models and create new best practices on top of that. Because, in the past, everything was fine out on premise, right? We talked about the (indistinct) size. Now in the cloud, it's just the business model to say, "Do I want to have a little more AI? Do I want a to run a little more? Will it give me the insight in the business?". So, that's the transformation that is happening, really. So, bringing your data together, this live conversation data, but not for bringing the data together. There's often the big win for the business for the first time to see the data. AWS is banking on that. The supply chain product, as an example. So many disparate systems, bring them them together. Big win for the business. But, the win for the business, ultimately, is when you change the paradigm from the user showing up to do something, to software doing stuff for us, right? >> Right. >> We have too much in this operator paradigm. If the user doesn't show up, doesn't find the click, doesn't find where to go, nothing happens. It can't be done in the 21st century, right? Software has to look over your shoulder. >> Good point. >> Understand one for you, autonomous self-driving systems. That's what CXOs, who're future looking, will be talked to come to AWS and all the other cloud vendors. >> Got it, last question for you. We're making a sizzle reel on Instagram. >> Yeah. >> If you had, like, a phrase, like, or a 30 second pitch that would describe re:Invent 2022 in the direction the company's going. What would that elevator pitch say? >> 30 second pitch? >> Yeah. >> All right, just timing. AWS is doing well. It's providing more depth, less breadth. Making things work together. It's catching up in some areas, has some interesting offerings, like the healthcare offering, the security data lake offering, which might change some things in the industry. It's staying the course and it's going strong. >> Ah, beautifully said, Holger. Thank you so much for joining Paul and me. >> Might have been too short. I don't know. (laughs) >> About 10 seconds left over. >> It was perfect, absolutely perfect. >> Thanks for having me. >> Perfect sizzle reel. >> Appreciate it. >> We appreciate your insights, what you're seeing this week, and the direction the company is going. We can't wait to see what happens in the next year. And, yeah. >> Thanks for having me. >> And of course, we've been on so many times. We know we're going to have you back. (laughs) >> Looking forward to it, thank you. >> All right, for Holger Mueller and Paul Gillan, I'm Lisa Martin. You're watching "theCube", the leader in live enterprise and emerging tech coverage. (upbeat music)

The Cube Presents UI Path Forward five. Brought to you by UI Path. >>I Ready, Dave Ante with David Nicholson. We're back at UI Path forward. Five. We're getting ready for the big guns to come in, the two co CEOs, but we have a really special analyst panel now. We're excited to have Daniel Newman here. He's the Principal analyst at Future and Research. And Andy Dai, who's the Vice president and Principal Analyst at Constellation Research. Guys, good to see you. Thanks for making some time to come on the queue. >>Glad to be here. Always >>Good. So, >>Andy, you're deep into ai. You and I have been talking about having you come to our maor office. I'm, I'm really excited that we're able to meet here. What have you seen at the show so far? What are your big takeaways? You know, day one and a half? >>Yeah, well, so first of all, I'm d AI because my last name has AI and I >>Already talk about, >>So, but, but all jokes aside, there are a lot of good things I heard from the conference, right? I mean, one is the last two years because of the pandemic, the growth has been phenomenal for, for a lot of those robotic automation intelligent automation companies, right? So because the low hanging through position making processes have been already taken care of where they going to find the next growth spot, right? That was the question I was looking answers to. And they have some inverse, one good acquisition. They had intelligent document processing, but more importantly they're trying to move from detrimental rules based RPA automation into AI based, more probabilistic subjective decision making areas. That's a huge market, tons of money involved in it, but it's going to be a harder problem to solve. Love to see the execut. >>Well, it's also a big pivot for the, for the company. It started out as sort of a a point product and now is moving to, to platform. But to end of the macro is not in UI pass favor. It's not really in any, you know, tech company's favor, but especially, you know, a company that's going into a transition transitioning to go to market cetera. What are you seeing, what's your take on the macro? I mean, I know you follow the financial markets very closely. There's a lot of negative sentiment right now. Are you as negative as the sentiment? >>Well, the, the broad sentiment comes with some pretty good historical data, right? We've had probably one of the worst market years in multiple decades. And of course we're coming into a situation where all the, the factors are really not in our favor. You've got in interest rates climbing, you've got wildly high inflation, you've had a, you know, helicopters dumping money on the economy for a period of time. And we're, we're gonna get into this great reset is what I keep talking about. But, you know, I had the opportunity to talk to Bill McDermott recently on one of my shows and Bill's CEO of ServiceNow, in case anybody there doesn't know, but >>Former, >>Yeah, really well spoken guy. But you know, him and I kind of went back and forth and we came up with this kind of concept that we were gonna have to tech our way out of what's about to come. You can almost be certain recession is gonna come. But for companies like UiPath, I actually think there's a tremendous opportunity because the bottom line is companies are gonna be looking at their bottom line. A year ago it was all about growth a deal, like the Adobe Figma deal would've been, been lauded, people would've been excited. Now everybody's looking at going, how are they paying that price? Everybody's discounting the future growth. They're looking at the situation, say, what's gonna happen next? Well, bottom line is now they're looking at that. How profitable are we? Are you making money? Are you growing that bottom line? Are you creating earnings? We're >>Gonna come in >>Era, we're gonna come into an era where companies are gonna say, you know what? People are expensive. The inflationary cost of hiring is expensive. You know, what's less expensive? Investing in the cloud, investing in ai, investing in workflow and automation and things that actually enable businesses to expand, keep costs somewhat contained fixed costs, and scale their businesses and get themselves in a good position for when the economy turns to return to >>Grow. So since prior to the pandemic cloud containers, m l and RPA slash automation have been the big four that from a spending data standpoint have been above the line above all kind of the rest in terms of spending momentum up until last quarter, AI and RPA slash automation declined. So my question is, are those two areas discretionary or more discretionary than other technology investments you heard? >>Well, I, I think we're in a, a period where companies are, I won't say they've stopped spending, but you listened to Mark Benioff, you talked about the elongated sales cycle, right? I think companies right now are being very reflective and they're doing a lot of introspection. They're looking at their business and saying, We hired a lot of people. We hired really fast. Do we need to cut? Do we need to freeze? We've made investments in technology, are we getting a return on 'em? We all know that the analytics, whether it's you know, digital adoption platforms or just analytics in the business, say, What is all this money we've been spending doing for us and how productive are we? But I will tell you universally, the companies are looking at workflow automations that enable things. Whether that's onboarding customers, whether that's delivering experiences, whether that's, you know, full, you know, price to quote technologies, automate, automate, automate. By doing that, they're gonna bring down the cost, they're gonna control themselves as best as possible in a tough macro. And then when they come out of it, these processes are gonna be beneficiary in a, in a growth environment even more so, >>Andy UiPath rocketed to a leadership position, largely due to the, the product and the simplicity of the product relative to the competition. And then as you well know, they expanded into, you know, platform. So how do you see the competitive environment? A UI path is again focusing on that platform play Automation Anywhere couldn't get to public market. They had turnover at the go to market level. Chris Riley joined a lot of, lot of hope left Microsoft joined into the fray, obviously is having an impact that you're certainly seeing spending momentum around Microsoft. Then SAP service Now Salesforce, every software company the planet thinks they should get every dollar spent on software. You know, they, they see UI pass momentum and they say, Hey, we can, we can take some of that off the table. How do you see the competitive environment right now? >>So first of all, in in my mind, UI path is slightly better because of a couple of reasons. One, as you said, it's ease of use. >>They're able to customize it variable to what they want. So that's a real easy development advantage. And then the, when you develop the bots and equal, it takes on an average anywhere between two to maybe six weeks, generally speaking, in some industries regulated government might take more so that it's faster, quicker, easier than others in a sense. So people love using that. The second advantage of what they have in my mind is that not only they are available as a managed SA solution on, on cloud, on Azure Cloud, but also they have this version that you can install, maintain, manage any way you want, whether it's a public cloud or, or your own data center and so on so forth. That's not available with almost, not all of them have it, Few have it, but not all of the competitors have it. So they have an advantage there as well. Where it could become useful would be one of the areas that they haven't even expanded is the government. >>Government is the what, >>Sorry? The government. Yeah, related solutions, right? Defense, government, all of those areas when you go, which haven't even started for various reasons. For example, they're worried about laying off people, worried about cost, worried about automating things. There's a lot of hurdles to overcome. But once you overcome that, if you want to go there, nobody's going to use, or most of them will be very of using something on the cloud. So they have a solution for version variation of that. So they are set up to come to that next level. I mean, I don't know if you guys were at the keynote, the CEO talked about how their plans to go from 1 billion to 5 billion in ar. So they're set up to capture the market. But again, as you said, every big software company saw their momentum, they want to get into it, they want to compete with them. So >>Well, to get to 5 billion, they've gotta accelerate growth. I mean, if you do 20% cer over the next, you know, through the end of the decade, they don't quite get there. So they're gonna have to, you know, they lowered their forecast out of the high 20 or mid twenties to 18%. They're gonna have to accelerate that. And we've seen that before. We see it in cloud where cloud, you know, accelerates growth even though you got the lower large numbers. Go ahead Dave. >>Yeah, so Daniel, then how do we, how do we think of this market? How do we measure the TAM for total addressable market for automation? I mean, you know, what's that? What's that metric that shows how unautomated are we, how inefficient are we? Is there a, is there a 5% efficiency that can be gained? Is there a 40% efficiency that can be gained? Because if you're talking about, you know, how much much of the market can UI path capture, first of all, how big is the market? And then is UI path poised to take advantage of that compared to the actual purveyors of the software that people are interacting with? I'm interacting with an E R p, an ER P system that has built into it the ability to automate processes. Then why do I need 'EM UI path? So first, how do you evaluate TAM? Second, how do you evaluate whether UI Path is gonna have a chance in this market where RPAs built into the applications that we actually use? Yeah, >>I think that TAM is evolving, and I don't have it in front of me right now, but what I'll tell you about the TAM is there's sort of the legacy RPA tam and then there's what I would sort of evolve to call the IPA and workflow automation tam that is being addressed by many of these software companies that you asked in the competitive equation. In the, in the, in the question, what we're seeing is a world where companies are gonna say, if we can automate it, we will automate it. That's, it's actually non-negotiable. Now, the process in the ability to a arrive at automation at scale has long been a battle front within the nor every organization. We've been able to automate things for a long time. Why has it more been done? It's the same thing with analytics. There's been numerous studies in analytics that have basically shown companies that have been able to embrace, adopt, and implement analytics, have significantly better performances, better performances on revenue growth, better performances and operational cost management, better performances with customer experience. >>Guess what? Not everybody, every company can get to this. Now there's a couple of things behind this and I'm gonna, I'm gonna try to close my answer out cause I'm getting a little long winded here. But the first thing is automation is a cultural challenge in most organizations. We've done endless research on companies digitally transforming and automating their business. And what we've found is largely the technology are somewhat comparable. Meaning, you know, I, I've heard what he is saying about some of the advantages of partnership with Microsoft, very compelling. But you know what, all these companies that have automation offerings, whether it's you know, through a Salesforce, Microsoft, whether it's a specialized rpa like an Automation Anywhere or a UI path, their solutions can be deployed and successful. The company's ability to take the investment, implement it successfully and get buy in across the organization tends to always be the hurdle. An old CIO stat, 50% of IT projects fail. That stat is still almost accurate today. It's not 50% of technology is bad, but those failures are because the culture doesn't get behind it. And automation's a tricky one because there's a lot of people that feel on the outside rather than the inside of an automation transformation. >>So, Andy, so how do you think about the, to Dave's question, the SAPs the service nows trying to, you know, at least take some red crumbs off the table. They, they're gonna, they're gonna create these automation stove pipes, but in Automation Anywhere or, or UI path is a horizontal play, are they not? And so how do you think about that progression? Well, so >>First of all, all of this other companies, when they, they, whether it's a build, acquire, what have you, these guys already have what, five, seven years on them. So it's gonna be difficult for them to catch up with the Center of Excellence knowledge on the use cases, what they got to catch up with them. That's gonna be a lot of catch up. Just to give you an idea, Microsoft Power Automate has been there for a while, right? They're supposedly doing well as well, but they still choose to partner with the UiPath as well to get them to the next level. So there's going to be competition coming from all areas, but it's, it's about, you know, highlights. >>So, so who is the competition? Is it Microsoft chipping away an individual productivity? Is it a service now? Who's got a platform play? Is it themselves just being able to execute >>All plus also, but I think the, the most, I wouldn't say competition, but it's more people are not aware of what areas need to be automated, right? For example, one of the things I was talking about with a couple of customers is, so they have a automation hub where you can put the, the process and and task that need to be automated and then you prioritize and start working on it. And, and almost all of them that I speak to, they keep saying that most of the process and task identification that they need to do for automation, it's manual right now. So, which means it's limited, you have to go and execute it. When people find out and tell you that's what need to be fixed, you try to go and fix that. But imagine if there is a way, I mean the have solutions they're showcasing now if it becomes popular, if you're able to identify tasks that are very inefficient or or process that's very inefficient, automatically score them up saying that, you know what, this is what is going to be ROI and you execute on it. That's going to be huge. So >>I think ts right, there's no shortage of, of a market. I would, I would agree with you Rob Sland this morning talked about the progression. He sort of compared it to e R P of the early days. I sort of have a love hate with E R P cuz of the complexity of the implementation and the, and the cost. However, first of all, a couple points and I love to get your thoughts for you. If you went back, I know 25 years, you, you wouldn't have been able to pick SAP out of a lineup and say that's gonna be the leader in E R P and they ended up, you know, doing really, really well. But the more interesting angle is if you could have figured out the customers that were implementing e r p in, in a really high quality fashion, those are the companies that really did well. You buy their stocks, they really took off cuz they were killing their other industry competitors. So, fast forward to automation. Will automation live up to its hype and your opinion, will it be as transformative and will the, the practitioners of automation see the same type of uplift in their markets, in their market caps, in their competitiveness as did sort of the early adopters and the excellent adopters of brp? What are your thoughts? Well, >>I think it's an interesting comparison. Maybe answer it slightly different way. I think the future is that automation is a non-negotiable in every enterprise organization. I think if you're a large organization, we have absolutely filled our, our organizations with waste too much overhead, too much expense, too much technical debt and automation is an answer. This is the way we want to interact, right? We want a chat bot that actually gives us good answers that can answer on a Tuesday at 11:00 PM at night when we want to know if the right dog food, you know, and I'm saying that, you know, that's what we want. That's the outcome we want. And businesses have to be driven by the outcome. Here's what I'm not sure about, Dave, is we have an era where over the last three to five years, a lot of products have become companies and a lot of 'EM products became companies ended up in public markets. >>And so the RPA space is one of those areas that got this explosive amount of growth. And you look at it and there's two ways. Is this horizontally a business rpa or is this going to be something that's gonna be a target of those Microsofts and those SAPs and say, Look, we need hyper automation to be deeply integrated at the E R P crm, hcm SCM level. We're gonna build by this or we're gonna build this. And you're already hearing it in the partnerships, but this is how I think the story ends. I I think either the companies like UiPath get much bigger, they get much more rounded in their, in their offerings. Or you're gonna have a large company like a Microsoft come in and say, you know what? Buy it rather >>Than build can they can, they can, can this company, maybe not so much here, but can a company like Automation Anywhere stay acquisition? Well, >>I use the, I use the Service now as an, as a parallel because they're a company that I thought would always end up inside of a bigger company and now you're like, I think they're too big. I think they've they've dropped >>That, that chart. Yeah, they're acquisition proof. I would agree. But these guys aren't yet Nora's automation. They work for >>A while and it's not necessarily a bad thing. Sometimes getting bit bought is good, but what I mean is it's gonna be core and these big companies know it cuz they're all talking >>About, but as independent analysts, we want to see independent companies. >>I wanna see the right thing. >>It just makes it fun. >>The right thing >>Customers. Yeah, but you know, okay, Oracle buy more customers, more >>Customers. >>I'm kidding. Yeah, I guess it's the right thing. It just makes it more fun when you have really good independent competitors that >>We >>Absolutely so, and, and spend way more on r and d than these big companies who spend a lot more on stock buyback. But I know you gotta go. Thanks so much for spending some time, making time for Cube Andy. Great to see you. Good to see as well. All right, we are wrapping up day one, Dave Blan and Dave Nicholson live. You can hear the action behind us, forward in five on the Cube, right back.

(upbeat music) >> Okay, welcome back everyone. CUBE's coverage of re:MARS, here in Las Vegas, in person. I'm John Furrier, host of theCUBE. This is the analyst panel wrap up analysis of the keynote, the show, past one and a half days. We got two great guests here. We got Andy Thurai, Vice President, Principal Consultant, Constellation Research. Larry Carvalho, Principal Consultant at RobustCloud LLC. Congratulations going out on your own. >> Thank you. >> Andy, great to see you. >> Great to see you as well. >> Guys, thanks for coming out. So this is the session where we break down and analyze, you guys are analysts, industry analysts, you go to all the shows, we see each other. You guys are analyzing the landscape. What does this show mean to you guys? 'Cause this is not obvious to the normal tech follower. The insiders see the confluence of robotics, space, automation and machine learning. Obviously, it's IoTs, industrials, it's a bunch of things. But there's some dots to connect. Let's start with you, Larry. What do you see here happening at this show? >> So you got to see how Amazon started, right? When AWS started. When AWS started, it primarily took the compute storage, networking of Amazon.com and put it as a cloud service, as a service, and started selling the heck out of it. This is a stage later now that Amazon.com has done a lot of physical activity, and using AIML and the robotics, et cetera, it's now the second phase of innovation, which is beyond digital transformation of back office processes, to the transformation of physical processes where people are now actually delivering remotely and it's an amazing area. >> So back office's IT data center kind of vibe. >> Yeah. >> You're saying front end, industrial life. >> Yes. >> Life as we know it. >> Right, right. I mean, I just stopped at a booth here and they have something that helps anybody who's stuck in the house who cannot move around. But with Alexa, order some water to bring them wherever they are in the house where they're stuck in their bed. But look at the innovation that's going on there right at the edge. So I think those are... >> John: And you got the Lunar, got the sex appeal of the space, Lunar Outpost interview, >> Yes. >> those guys. They got Rover on Mars. They're going to have be colonizing the moon. >> Yes. >> I made a joke, I'm like, "Well, I left a part back on earth, I'll be right back." (Larry and Andy laugh) >> You can't drive back to the office. So a lot of challenges. Andy, what's your take of the show? Take us your analysis. What's the vibe, what's your analysis so far? >> It's a great show. So, as Larry was saying, one of the thing was that when Amazon started, right? So they were more about cloud computing. So, which means is they try to commoditize more of data center components or compute components. So that was working really well for what I call it as a compute economy, right? >> John: Mm hmm. >> And I call the newer economy as more of a AIML-based data economy. So when you move from a compute economy into a data economy, there are things that come into the forefront that never existed before, never popular before. Things like your AIML model creation, model training, model movement, model influencing, all of the above, right? And then of course the robotics has come long way since then. And then some of what they do at the store, or the charging, the whole nine yards. So, the whole concept of all of these components, when you put them on re:Invent, such a big show, it was getting lost. So that's why they don't have it for a couple of years. They had it one year. And now all of a sudden they woke up and say, "You know what? We got to do this!" >> John: Yeah. >> To bring out this critical components that we have, that's ripe, mature for the world to next component. So that's why- I think they're pretty good stuff. And some of the robotics things I saw in there, like one of them I posted on my Twitter, it's about the robot dog, sniffing out the robot rover, which I thought was pretty hilarious. (All laugh) >> Yeah, this is the thing. You're seeing like the pandemic put everything on hold on the last re:Mars, and then the whole world was upside down. But a lot of stuff pulled forward. You saw the call center stuff booming. You saw the Zoomification of our workplace. And I think a lot of people got to the realization that this hybrid, steady-state's here. And so, okay. That settles that. But the digital transformation of actually physical work? >> Andy: Yeah. >> Location, the walk in and out store right over here we've seen that's the ghost store in Seattle. We've all been there. In fact, I was kind of challenged, try to steal something. I'm like, okay- (Larry laughs) I'm pulling all my best New Jersey moves on everyone. You know? >> Andy: You'll get charged for it. >> I couldn't get away with it. Two double packs, drop it, it's smart as hell. Can't beat the system. But, you bring that to where the AI machine learning, and the robotics meet, robots. I mean, we had robots here on theCUBE. So, I think this robotics piece is a huge IoT, 'cause we've been covering industrial IoT for how many years, guys? And you could know what's going on there. Huge cyber threats. >> Mm hmm. >> Huge challenges, old antiquated OT technology. So I see a confluence in the collision between that OT getting decimated, to your point. And so, do you guys see that? I mean, am I just kind of seeing mirage? >> I don't see it'll get decimated, it'll get replaced with a newer- >> John: Dave would call me out on that. (Larry laughs) >> Decimated- >> Microsoft's going to get killed. >> I think it's going to have to be reworked. And just right now, you want do anything in a shop floor, you have to have a physical wire connected to it. Now you think about 5G coming in, and without a wire, you get minute details, you get low latency, high bandwidth. And the possibilities are endless at the edge. And I think with AWS, they got Outposts, they got Snowcone. >> John: There's a threat to them at the edge. Outpost is not doing well. You talk to anyone out there, it's like, you can't find success stories. >> Larry: Yeah. >> I'm going to get hammered by Amazon people, "Oh, what're you're saying that?" You know, EKS for example, with serverless is kicking ass too. So, I mean I'm not saying Outpost was wrong answer, it was a right at the time, what, four years ago that came out? >> Yeah. >> Okay, so, but that doesn't mean it's just theirs. You got Dell Technologies want some edge action. >> Yeah. >> So does HPE. >> Yes. >> So you got a competitive edge situation. >> I agree with that and I think that's definitely not Amazon's strong point, but like everything, they try to make it easy to use. >> John: Yeah. >> You know, you look at the AIML and they got Canvas. So Canvas says, hey, anybody can do AIML. If they can do that for the physical robotic processes, or even like with Outpost and Snowcone, that'll be good. I don't think they're there yet, and they don't have the presence in the market, >> John: Yeah. >> like HPE and, >> John: Well, let me ask you guys this question, because I think this brings up the next point. Will the best technology win or will the best solution win? Because if cloud's a platform and all software's open source, which you can make those assumptions, you then say, hey, they got this killer robotics thing going on with Artemis and Moonshot, they're trying to colonize the moon, but oh, they discovered a killer way to solve a big problem. Does something fall out of this kind of re:Mars environment, that cracks the code and radically changes and disrupts the IoT game? That's my open question. I don't know the answer. I'd love to get your take on what might be possible, what wild card's out there around, disrupting the edge. >> So one thing I see the way, so when IoT came into the world of play, it's when you're digitizing the physical world, it's IoT that does digitalization part of that actually, right? >> But then it has its own set of problems. >> John: Yeah. >> You're talking about you installing sensor everywhere, right? And not only installing your own sensor, but also you're installing competitor sensors. So in a given square feet how many sensors can you accommodate? So there are physical limitations on liabilities of bandwidth and networking all of that. >> John: And integration. >> As well. >> John: Your point. >> Right? So when that became an issue, this is where I was talking to the robotic guys here, a couple of companies, and one of the use cases they were talking about, which I thought was pretty cool, is, rather than going the sensor route, you go the robot route. So if you have either a factor that you want to map out, you put as many sensors on your robot, whatever that is, and then you make it go around, map the whole thing, and then you also do a surveillance in the whole nine yards. So, you can either have a fixed sensors or you can have moving sensors. So you can have three or four robots. So initially, when I was asking them about the price of it, when they were saying about a hundred thousand dollars, I was like, "Who would buy that?" (John and Larry laugh) >> When they then explained that, this is the use case, oh, that makes sense, because if you had to install, entire factory floor sensors, you're talking about millions of dollars. >> John: Yeah. >> But if you do the moveable sensors in this way, it's a lot cheaper. >> John: Yeah, yeah. >> So it's based on your use case, what are your use cases? What are you trying to achieve? >> The general purpose is over. >> Yeah. >> Which you're getting at, and that the enablement, this is again, this is the cloud scale open question- >> Yep. >> it's, okay, the differentiations isn't going to be open source software. That's open. >> It's going to be in the, how you configure it. >> Yes. >> What workflows you might have, the data streams. >> I think, John, you're bringing up a very good point about general purpose versus special purpose. Yesterday Zoox was on the stage and when they talked about their vehicle, it's made just for self-driving. You walk around in Vegas, over here, you see a bunch of old fashioned cars, whether they're Ford or GM- >> and they put all these devices around it, but you're still driving the same car. >> John: Yeah, exactly. >> You can retrofit those, but I don't think that kind of IoT is going to work. But if you redo the whole thing, we are going to see a significant change in how IoT delivers value all the way from the industrial to home, to healthcare, mining, agriculture, it's going to have to redo. I'll go back to the OT question. There are some OT guys, I know Rockwell and Siemens, some of them are innovating faster. The ones who innovate faster to keep up with the IT side, as well as the MLAI model are going to be the winners on that one. >> John: Yeah, I agree. Andy, your thoughts on manufacturing, you brought up the sensor thing. Robotics ultimately is, end of the day, an opportunity there. Obviously machine learning, we know what that does. As we move into these more autonomous builds, what does that look like? And is Amazon positioned well there? Obviously they have big manufacturers. Some are saying that they might want to get out of that business too, that Jassy's evaluating that some are saying. So, where does this all lead for that robotics manufacturing lifestyle, walk in, grab my food? 'Cause it's all robotics and AI at the end of the day, I got sensors, I got cameras, I got non-humans moving heavy lifting stuff, fixing the moon will be done by robots, not humans. So it's all coming. What's your analysis? >> Well, so, the point about robotics is on how far it has come, it is unbelievable, right? Couple of examples. One was that I was just talking to somebody, was explaining to them, to see that robot dog over there at the Boston Dynamics one- >> John: Yeah. >> climbing up and down the stairs. >> Larry: Yeah. >> That's more like the dinosaur movie opening the doors scene. (John and Larry laugh) It's like that for me, because the coordinated things, it is able to go walk up and down, that's unbelievable. But okay, it does that, and then there was also another video which is going on viral on the internet. This guy kicks the dog, robot dog, and then it falls down and it gets back up, and the sentiment that people were feeling for the dog, (Larry laughs) >> you can't, it's a robot, but people, it just comes at that level- >> John: Empathy, for a non-human. >> Yeah. >> But you see him, hey you, get off my lawn, you know? It's like, where are we? >> It has come to that level that people are able to kind of not look at that as a robot, but as more like a functioning, almost like a pet-level, human-level being. >> John: Yeah. >> And you saw that the human-like walking robot there as well. But to an extent, in my view, they are all still in an experimentation, innovation phase. It doesn't made it in the industrial terms yet. >> John: Yeah, not yet, it's coming. >> But, the problem- >> John: It's coming fast. That's what I'm trying to figure out is where you guys see Amazon and the industry relative to what from the fantasy coming reality- >> Right. >> of space in Mars, which is, it's intoxicating, let's face it. People love this. The nerds are all here. The geeks are all here. It's a celebration. James Hamilton's here- >> Yep. >> trying to get him on theCUBE. And he's here as a civilian. Jeff Barr, same thing. I'm here, not for Amazon, I bought a ticket. No, you didn't buy a ticket. (Larry laughs) >> I'm going to check on that. But, he's geeking out. >> Yeah. >> They're there because they want to be here. >> Yeah. >> Not because they have to work here. >> Well, I mean, the thing is, the innovation velocity has increased, because, in the past, remember, the smaller companies couldn't innovate because they don't have the platform. Now Compute is a platform available at the scale you want, AI is available at the scale. Every one of them is available at the scale you want. So if you have an idea, it's easy to innovate. The innovation velocity is high. But where I see most of the companies failing, whether startup or big company, is that you don't find the appropriate use case to solve, and then don't sell it to the right people to buy that. So if you don't find the right use case or don't sell the right value proposition to the actual buyer, >> John: Mm hmm. >> then why are you here? What are you doing? (John laughs) I mean, you're not just an invention, >> John: Eh, yeah. >> like a telephone kind of thing. >> Now, let's get into next talk track. I want to get your thoughts on the experience here at re:Mars. Obviously AWS and the Amazon people kind of combined effort between their teams. The event team does a great job. I thought the event, personally, was first class. The coffee didn't come in late today, I was complaining about that, (Larry laughs) >> people complaining out there, at CUBE reviews. But world class, high bar on the quality of the event. But you guys were involved in the analyst program. You've been through the walkthrough, some of the briefings. I couldn't do that 'cause I'm doing theCUBE interviews. What would you guys learn? What were some of the key walkaways, impressions? Amazon's putting all new teams together, seems on the analyst relations. >> Larry: Yeah. >> They got their mojo booming. They got three shows now, re:Mars, re:inforce, re:invent. >> Andy: Yeah. >> Which will be at theCUBE at all three. Now we got that coverage going, what's it like? What was the experience like? Did you feel it was good? Where do they need to improve? How would you grade the Amazon team? >> I think they did a great job over here in just bringing all the physical elements of the show. Even on the stage, where they had robots in there. It made it real and it's not just fake stuff. And every, or most of the booths out there are actually having- >> John: High quality demos. >> high quality demos. (John laughs) >> John: Not vaporware. >> Yeah, exactly. Not vaporware. >> John: I won't say the name of the company. (all laugh) >> And even the sessions were very good. They went through details. One thing that stood out, which is good, and I cover Low Code/No Code, and Low Code/No Code goes across everything. You know, you got DevOps No Low-Code Low-Code. You got AI Low Code/No Code. You got application development Low Code/No Code. What they have done with AI with Low Code/No Code is very powerful with Canvas. And I think that has really grown the adoption of AI. Because you don't have to go and train people what to do. And then, people are just saying, Hey, let me kick the tires, let me use it. Let me try it. >> John: It's going to be very interesting to see how Amazon, on that point, handles this, AWS handles this data tsunami. It's cause of Snowflake. Snowflake especially running the table >> Larry: Yeah. >> on the old Hadoop world. I think Dave had a great analysis with other colleagues last week at Snowflake Summit. But still, just scratching the surface. >> Larry: Yeah. >> The question is, how shared that ecosystem, how will that morph? 'Cause right now you've got Data Bricks, you've got Snowflake and a handful of others. Teradata's got some new chops going on there and a bunch of other folks. Some are going to win and lose in this downturn, but still, the scale that's needed is massive. >> So you got data growing so much, you were talking earlier about the growth of data and they were talking about the growth. That is a big pie and the pie can be shared by a lot of folks. I don't think- >> John: And snowflake pays AWS, remember that? >> Right, I get it. (John laughs) >> I get it. But they got very unique capabilities, just like Netflix has very unique capabilities. >> John: Yeah. >> They also pay AWS. >> John: Yeah. >> Right? But they're competing on prime. So I really think the cooperation is going to be there. >> John: Yeah. >> The pie is so big >> John: Yeah. >> that there's not going to be losers, but everybody could be winners. >> John: I'd be interested to follow up with you guys after next time we have an event together, we'll get you back on and figure out how do you measure this transitions? You went to IDC, so they had all kinds of ways to measure shipments. >> Larry: Yep. >> Even Gartner had fumbled for years, the Magic Quadrant on IaaS and PaaS when they had the market share. (Larry laughs) And then they finally bundled PaaS and IaaS together after years of my suggesting, thank you very much Gartner. (Larry laughs) But that just performs as the landscape changes so does the scoreboard. >> Yep. >> Right so, how do you measure who's winning and who's losing? How can we be critical of Amazon so they can get better? I mean, Andy Jassy always said to me, and Adam Salassi same way, we want to hear how bad we're doing so we can get better. >> Yeah. >> So they're open-minded to feedback. I mean, not (beep) posting on them, but they're open to critical feedback. What do you guys, what feedback would you give Amazon? Are they winning? I see them number one clearly over Azure, by miles. And even though Azure's kicking ass and taking names, getting back in the game, Microsoft's still behind, by a long ways, in some areas. >> Andy: Yes. In some ways. >> So, the scoreboard's changing. What's your thoughts on that? >> So, look, I mean, at the end of the day, when it comes to compute, right, Amazon is a clear winner. I mean, there are others who are catching up to it, but still, they are the established leader. And it comes with its own advantages because when you're trying to do innovation, when you're trying to do anything else, whether it's a data collection, we were talking about the data sensors, the amount of data they are collecting, whether it's the store, that self-serving store or other innovation projects, what they have going on. The storage compute and process of that requires a ton of compute. And they have that advantage with them. And, as I mentioned in my last article, one of my articles, when it comes to AIML and data programs, there is a rich and there is a poor. And the rich always gets richer because they, they have one leg up already. >> John: Yeah. >> I mean the amount of model training they have done, the billion or trillion dollar trillion parametrization, fine tuning of the model training and everything. They could do it faster. >> John: Yeah. >> Which means they have a leg up to begin with. So unless you are given an opportunity as a smaller, mid-size company to compete at them at the same level, you're going to start at the negative level to begin with. You have a lot of catch up to do. So, the other thing about Amazon is that they, when it comes to a lot of areas, they admit that they have to improve in certain areas and they're open and willing and listen to the people. >> Where are you, let's get critical. Let's do some critical analysis. Where does Amazon Websters need to get better? In your opinion, what criticism would you, in a constructive way, share? >> I think on the open source side, they need to be more proactive in, they are already, but they got to get even better than what they are. They got to engage with the community. They got to be able to talk on the open source side, hey, what are we doing? Maybe on the hardware side, can they do some open-sourcing of that? They got graviton. They got a lot of stuff. Will they be able to share the wealth with other folks, other than just being on an Amazon site, on the edge with their partners. >> John: Got it. >> If they can now take that, like you said, compute with what they have with a very end-to-end solution, the full stack. And if they can extend it, that's going to be really beneficial for them. >> Awesome. Andy, final word here. >> So one area where I think they could improve, which would be a game changer would be, right now, if you look at all of their solutions, if you look at the way they suggest implementation, the innovations, everything that comes out, comes out across very techy-oriented. The persona is very techy-oriented. Very rarely their solutions are built to the business audience or to the decision makers. So if I'm, say, an analyst, if I want to build, a business analyst rather, if I want to build a model, and then I want to deploy that or do some sort of application, mobile application, or what have you, it's a little bit hard. It's more techy-oriented. >> John: Yeah, yeah. >> So, if they could appeal or build a higher level abstraction of how to build and deploy applications for business users, or even build something industry specific, that's where a lot of the legacy companies succeeded. >> John: Yeah. >> Go after manufacturing specific or education. >> Well, we coined the term 'Supercloud' last re:Invent, and that's what we see. And Jerry Chen at Greylock calls it Castles in the Cloud, you can create these moats >> Yep. >> on top of the CapEx >> Yep. >> of Amazon. >> Exactly. >> And ride their back. >> Yep. >> And the difference in what you're paying and what you're charging, if you're good, like a Snowflake or a Mongo. I mean, Mongo's, they're just as big as Snow, if not bigger on Amazon than Snowflake is. 'Cause they use a lot of compute. No one turns off their database. (John laughs) >> Snowflake a little bit different, a little nuanced point, but, this is the new thing. You see Goldman Sachs, you got Capital One. They're building their own kind of, I call them sub clouds, but Dave Vellante says it's a Supercloud. And that essentially is the model. And then once you have a Supercloud, you say, great, I'm going to make sure it works on Azure and Google. >> Andy: Yep. >> And Alibaba if I have to. So, we're kind of seeing a playbook. >> Andy: Mm hmm. >> But you can't get it wrong 'cause it scales. >> Larry: Yeah, yeah. >> You can't scale the wrong answer. >> Andy: Yeah. >> So that seems to be what I'm watching is, who gets it right? Product market fit. Then if they roll it out to the cloud, then it becomes a Supercloud, and that's pure product market fit. So I think that's something that I've seen some people trying to figure out. And then, are you a supplier to the Superclouds? Like a Dell? Or you become an enabler? >> Andy: Yeah. >> You know, what's Dell Technologies do? >> Larry: Yeah. >> I mean, how do the box movers compete? >> Larry: I, the whole thing is now hybrid and you're going to have to see just, you said. (Larry laughs) >> John: Hybrid's a steady-state. I don't need to. >> Andy: I mean, >> By the way we're (indistinct), we can't get the chips, cause Broadcom and Apple bought 'em all. (Larry laughs) I mean there's a huge chip problem going on. >> Yes. I agree. >> Right now. >> I agree. >> I mean all these problems when you attract to a much higher level, a lot of those problems go away because you don't care about what they're using underlying as long as you deliver my solution. >> Larry: Yes. >> Yeah, it could be significantly, a little bit faster than what it used to be. But at the end of the day, are you solving my specific use case? >> John: Yeah. >> Then I'm willing to wait a little bit longer. >> John: Yeah. Time's on our side and now they're getting the right answers. Larry, Andy, thanks for coming on. This great analyst session turned into more of a podcast vibe, but you know what? (Larry laughs) To chill here at re:Mars, thanks for coming on, and we unpacked a lot. Thanks for sharing. >> Both: Thank you. >> Appreciate it. We'll get you back on. We'll get you in the rotation. We'll take it virtual. Do a panel. Do a panel, do some panels around this. >> Larry: Absolutely. >> Andy: Oh this not virtual, this physical. >> No we're live right now! (all laugh) We get back to Palo Alto. You guys are influencers. Thanks for coming on. You guys are moving the market, congratulations. Take a minute, quick minute each to plug any work you're doing for the people watching. Larry, what are you working on? Andy? You go after Larry, what you're working on. >> Yeah. So since I started my company, RobustCloud, since I left IDC about a year ago, I'm focused on edge computing, cloud-native technologies, and Low Code/No Code. And basically I help companies put their business value together. >> All right, Andy, what are you working on? >> I do a lot of work on the AIML areas. Particularly, last few of my reports are in the AI Ops incident management and ML Ops areas of how to generally improve your operations. >> John: Got it, yeah. >> In other words, how do you use the AIML to improve your IT operations? How do you use IT Ops to improve your AIML efficiency? So those are the- >> John: The real hardcore business transformation. >> Yep. >> All right. Guys, thanks so much for coming on the analyst session. We do keynote review, breaking down re:Mars after day two. We got a full day tomorrow. I'm John Furrier with theCUBE. See you next time. (pleasant music)

(upbeat music) >> Welcome to this Cube conversation sponsored by Citrix. This is the third and final installment in the Citrix launchpad series. We're going to be talking about the launchpad series for work. Lisa Martin here with Dion Hinchcliffe, VP and principal analyst at Constellation research. Dion, welcome to the program. >> No, thanks Lisa. Great to be here. >> So we have seen a tremendous amount of change in the last 18, 19 months. You know, we saw this massive scatter to work from home a year and a half ago. Now we're in this sort of distributed environment. That's been persisting for a long time. Talk to me about, we're going to be talking about some of the things that Citrix is seeing and some of the things that they're doing to help individuals and teams, but give me your lens from Constellation's perspective. What are some of the major challenges with this distributed environment that you've seen? >> Sure. Well, so we've gone from this, you know, the world of work, the way that it was now, we're all very decentralized, you know, work from anywhere. Remote work is really dominating, you know, white collar types of activities in the workplace and workplaces that in our homes for most of us even today. But that started to change. Some people are going back. Although I just recently spoke to a panel of CIOs that says they have no plans anytime soon, but they're very aware that they need to have workable plans for when we start sending people back to the office and there's this big divide. How are we going to make sure that we have one common culture? We have a collaborative organization when, you know, a good percentage of our workers are in the office, but also maybe as much as half the organization is at home. And so, how to make processes seamless, how to make people collaborate and make sure there's equity and inclusion so that the people at home aren't left out and then people in the office, maybe you don't have an unfair advantage. So those are all the conversations. And of course, because this is a technology revolution, remote work was enabled by technology. We're literally looking at it again for this hybrid work, this, you know, this divided organization that we're going to have. >> You mentioned culture that's incredibly important, but also challenging to do with this distribution. I was looking at some research that Citrix provided, asking individuals from a productivity perspective, and two thirds said, hey, for our organizations that have given us more tools for collaboration and communication, yes, we are absolutely more productive. But the kicker is, the same amount of people, about two thirds that answered the survey said, we've now got about ten tools. So complexity is more challenging. It's harder to work individually. It's harder to work in teams. And so Citrix is really coming to the table here with the launchpad series for work, saying let's help these individuals and these teams, because as we, we think, and I'm sure you have insight Dion on this as well, this hybrid model that we're starting to see emerge is going to be persistent for a while. >> Yeah. For the foreseeable future. Cause we don't know what the future holds. So we'll have to hold the hybrid model as the primary model. And we may eventually go back to the way that we were. But for the next several years, there's going to be that. And so we're trying to wrap our arms around that. And I think that we're seeing with things like the Citrix announcements, a wave of responses saying, all right, let's really design properly for these changes. You know, we kind of just adapted quickly when everyone went to remote last year and now we're actually adding features to streamline, to reduce the friction, to simplify remote work, which does use, you have to use more applications. You have to switch between different things. You have to, you know, your employee experience in the digital world is just more cluttered and complicated, but it doesn't have to be. And so I, you know, we can look to some of these announcements for last year, I think address some of that. >> Let's break some of that down because to your point, it doesn't have to be complex complicated. It shouldn't be. Initially this scatter was, let's do everything we can to ensure that our teams and our people can be productive, can communicate, can collaborate. And now, since this is going to be persistent for quite some time, to your point, let's design for this distributed environment, this hybrid workforce of the future. Talk to me about the, one of the things that Citrix is doing with Citrix workspace, the app personalization, I can imagine as an individual contributor, but also as a team leader, the ability to customize this to the way that I work best is critical. >> And it really is, especially when you know, you have workers, you know, 18 or 19 months worth of new hires that you've never met. They don't really feel like, you know, this is maybe their organization. But if you allow them to shape it a little bit, make it contextual for them. So they don't just come into this cookie cutter digital experience that actually is kind of more meaningful for them. It makes it easier for them to get their job done and things are the way that they want them and where they want them. I think that makes a lot of sense. And so the app personalization announcements is important for remote workers in particular, but all workers to say, hey, can I start tailoring, you know, parts of my employee experience? So they make more sense for me. And I feel like I belong a little bit more. I think it's significant. >> It is. Let's talk about it from a security perspective though. We've seen massive changes in the security landscape in the last year and a half. We've seen some Citrix data that I was looking at, said between 2019 and 2020, ransomware up 435%, malware up 358%. And of course the weakest link being humans. Talk to me from a Citrix workspace perspective about some of the things that they've done to ensure that those security policies can be applied. >> Well, and the part that I really liked about the launchpad announcements around work in terms of security was this much more intelligent analysis. You know, one of the most frustrating things is you're trying to get work done remotely and maybe you're you're in crunch mode and all of a sudden the security system clamps down because they think you're doing something that, you know, you might be sharing information you shouldn't be and now you can't, get your deadline met. I really liked how the analytics inside the new security features really try to make sure they're applying intelligent analysis of behavior. And only when it's clear that a bad actor is in there doing something, then they can restrict access, protect information. And so I have no doubt they'll continue to evolve the product so that it's even even more effective in terms of how it can include or exclude bad actors from doing things inside your system. And so this is the kind of intelligence security increasingly based on AI type technologies that I think that will keep our workers productive, but clamp down on the much higher rate of that activity we see out there. Because we do have so many more endpoints there's a thousand or more times more endpoints in today's organizations because of remote work. >> Right. And one of the things that we've seen with ransomware, I mentioned those numbers that Citrix was sharing. It's gotten so much more personalized, so it's harder and harder to catch these things. One of the things that I found interesting, Dion, that from a secure collaboration perspective, that Citrix is saying is that, you know, we need to go, security needs to go beyond the devices and the endpoints and the apps that an employee is using, which of which we said, there are at least 10 apps that are being used today and it needs to actually be applied at a content level, the content creation level. Talk to me about your thoughts about that. >> I think that's exactly right. So if you know the profile of that worker and the types of things they normally do, and you see unusual behavior that is uncharacteristic to that worker, because you know their patterns, the types of content, the locations of that content that they might normally have access to. And if they're just accessing things, you know, periodically, that's usually not a problem. When they suddenly access a large volume of information and appear to be downloading it, those are the types of issues and especially of content they don't normally use for their work. Then you can intervene and take more intelligent actions as opposed to just trying to limit all content for example. So that knowledge workers can actually get access to all that great information in your IT systems. You can now give them access to it, but when clearly something, something bad is happening, the system automatically does it and steps in. >> I was looking at some of the data with respect to updates to Citrix analytics that it can now auto change permissions on shared files to read only, I think you alluded to this earlier, when it detects that excess sharing is going on. >> And, inappropriate access sharing. So sometimes it's okay for a worker to access, you know, documents. But the big fear is that a bad actor gets access. They get a USB key and they download a bunch of files and they get a whole bunch of IP or important knowledge. Well, when you have a system that's continually monitoring and you know, the unblinking gaze of Citrix security capabilities are looking at the patterns, not just the content alone or just the device alone, but at the, at the usage patterns and saying, I can make this read only because that's clearly the, you know, we don't want them to be able to download this because this activity is completely out of bounds or very unusual. >> Right. One of the things also that Citrix is doing is integrating with Microsoft teams. I was listening to a fun quiz show the other day that said, what were the top two apps downloaded in 2020? And I guessed one of them correctly, Tiktok though. I still don't know how to use it. And the second one was Zoom, and I'm sure Microsoft teams is way up there. I was looking at some stats that said, I think as of the spring of 2020, there were 145 million daily users of Microsoft teams. So that, from a collaboration perspective, something that a lot of folks are dependent on during the pandemic. And now within Teams, I can access Microsoft workspace? Citrix workspace. >> Yes. Well, and it's more significant than it sounds because there's a real hunger to find a center of gravity for the employee experience. What do I put that? Where should they be spending most of their time? Where should I be training them to focus most of their attention? And obviously workers collaborate a lot and Teams as part of Office 365, is a juggernaut? You know, the rise of it during the pandemic has been incredible. And just to show this, I have a digital workplace advisory board. Its companies who are heading, are the farthest along in designing digital employee experiences, and 31% of them said, this January, they're planning on centralizing the employee experience in Teams. Now, if you're a Citrix customer, you have workspace you go, how do I, I don't want to be left out. This announcement allows you to say, you can have the goodness of teams and its capabilities and the power of Citrix workspace, and you have them in one place and really creating a true center of gravity and simplifying and streamlining the employee experience. You don't have this fragmented pieces. Everything's right there in one place, in one pane of glass. And so I like this announcement. It brings Citrix up to parody with a lot of their competitors and actually eclipses several of them as well. So I really like to see this. >> So then from within teams, I can access Citrix workspace. I can share documents with team members and collaborate as well as that kind of the idea. >> Yes. That is the idea, and of course, they'll continue to evolve that, but now you can do your work in Citrix workspace and when documents are involved and you want to bring your team in, they're already right there inside that experience. >> That ability to streamline things, so critical, given the fact that we're still in this distributed environment, I'm sure families are still dealing with some, some amount of remote learning, or there's still distractions from the, do I live at work, do I work from home environment? One of the grips I really felt for when this happened, Dion, was the contact center. I thought these poor people, more people now with shorter and shorter fuses trying to get updates on whatever it was that they were, if they had something ordered and of course all the shipping delays. And the contact center of course went (blowing sound) scattered as well. And we've got people working from home, trying to do their jobs. Talk to me about some of those things that Citrix is doing to enable with Google, those contact center workers to have a good experience so that ultimately the employee experience is good, so is the customer experience? >> The contact center worker has the toughest of all of the different employee profiles I've seen, they have the most they have to learn, the most number of applications. They're typically not highly skilled workers. So they might only just have a, you know, high school education. Yet, they're being asked to cram all of these technologies, each one with a different employee experience, and they don't stay very long as a result of that. You might train them for two months before they're effective and they only stay for six months on average. And so, both businesses really want to be able to streamline onboarding and provisioning a and getting them set up and effective. And they want it too, if you want happy contact center workers making your customers happy and staying around. And so this announcements really allows you to deploy pre-configured Citrix workspaces on, on Chrome OS so that, you know, if you need to field a whole bunch of workers or you have a big dose say you're a relief company and you have a lot of disaster care workers. You can certainly this issue that these devices very easily, they're ready to go with their employee experience and all the right things in place so they can be effective with the least amount of effort. So I guess, it's a big step forward for a worker that is often neglected and underserved. >> Right. Definitely often neglected. And you, you brought up a good point there. And one of the things that, that peaked in my mind, as you talked about, you know, the onboarding experience, the retention, well, these contact center folks are the front lines to the customer. So from a brand reputation perspective, that's on the line, for companies in every industry where people with short fuses are dealing with contact center folks. So the ability to onboard them to give them a much more seamless experience is critical for the brand reputation, customer retention for every industry, I would imagine. >> Absolutely. Especially when you're setting up a contact center or you have a new product launching and you want, you know, you've got to bring, onboard all these new workers, you can do it, and they are going to have the least challenges. They're going to be ready to go right out of the box, be able to receive their package, with their device and their Citrix employee experience, ready to go. You know, just turn the machine on and they're off to the races. And that's the vision and that's the right one. So I was glad to see that as well. >> Yeah. Fantastic. One of the things also that Citrix did, the Citrix workspace app builder, so that Citrix workspace can now be a system of record for certain things like collaboration, surveys, maybe even COVID-19 information, that system of record. Talk to me about why that's so critical for the distributed worker. >> So we've had this, this longstanding challenge in that we've had our systems of record, you know, these are CRM systems, ERP, things like that, which we use to run our business. And then we've had our collaboration tools and they're separate, even though we're collaborating on sales deals and we're collaborating on our supply chain. And so like, the team's announcement was in the same game. We can say, let's close that gap between our systems of record and our collaboration tools. Well, this announcement says, all right, well, we still have these isolated systems of record. How can we streamline them to build and start connecting together a little bit so that we have processes that might cross all of those things, right? It's still going to order comes in from the CRM system. Then you can complete it in the, in the ERP system, you know, ordering that product for them. So they actually get it. You know, and that's probably overkill, that scenario for this particular example. But for example, collecting data from workers saying, let's build some forms and collect some data and then feed it to this process, or this system record. You can do it much more easily than before, before you would have to hire a development team or a contractor to develop another system that would integrate, you know, CRM or ERP or whatever. Now you can do it very quickly inside that builder. First simple, basic applications, and get a lot of the low hanging fruit off your plate and more automated inside of your Citrix workspace. >> And automation has been one of the keys that we've seen to streamlining worker productivity in the last 18 months. Another thing that I was looking at is, you know, the fact that we have so many different apps and we're constantly switching apps, context is constantly changing. Is this sort of system of record going to allow or reduce the amount of context switching that employees have to do? >> Yep. Almost all of these announcements have some flavor to that saying, can we start bringing more systems together in one place? So you're not switching between applications. You don't have different and disconnected sets of data that if you need to, and if they are disconnected, you can connect them, right. That's what the app builder announcement again is about saying, all right, if you're already, always using these three applications to do something, and you're switching between them, maybe you can just build something that connect them into one experience and, you know, maybe a low level of IT person, or even a business user can do that. That's the big trend right now. >> That's so important for that continued productivity, as things will continue to be a little bit unstable, I guess, for awhile. One more thing that I saw that Citrix is announcing is integrations with, Wrike I've been a Wrike user myself. I like to have program project management tools that I can utilize to keep track of projects, but they've done a number of integrations, one of them with Wrike Signature, which I thought was really cool. So for, to secure e-signature within Wrike, based on a program or a project that you're working on. Talk to me about some of the boosts to Wrike that they've done and how you think that's going to be influential in the employee experience. >> Well, first let's just say that the Wrike acquisition was a really important one for Citrix to go above just the basic digital workplace and simple systems of record. This is a really a mass collaboration tool for managing work itself. And so they're, this is taking Citrix up the stack in the more sophisticated work scenarios. And, and when you, we are in more sophisticated work scenarios, you want to be able to pull in different data sets. So, you know, they have the Citrix ShareFile support. You want to be able to bring in really important things like, you know, signing contracts or signing sales deals or mortgage applications, or all sorts of exciting things that actually run in your business. And so, Wrike Signatures, support's really important so that when you have key processes that involve people putting signatures on documents, you can just build collaborative work management flows that, that take all that into account without having to leave the experience. Everything's in one place as much as possible. And this is the big push and we need to have all these different systems. We don't have too many apps. What we have is too many touchpoints, so lets start combining some of these. And so the Wrike integrations, really help you do that. >> Well, and ultimately it seems like what Citrix is doing with the work launchpad series. All the announcements here is really helping workers to work how and where they want to work. Which is very similar to what we say when we're talking about the end user customer experience. When tech companies like Citrix say, we have to meet our customers where they are, it sounds like that's the same thing that's happening here. >> It is. And I would just add on top of that and to make it all safe. So you can bring all these systems together, work from anywhere, and you can feel confident that you're going to do so securely and safely. And it's that whole package I think that's really critical here. >> You're right, I'm glad you brought up that security. All right, Dion take out your crystal ball for me. As we wrap things up, you're saying, you know, going into the future, we're going to be moving from this distributed workforce to this hybrid. What are some of the things that you see as really critical happening in the next six to nine months? >> Well, there's a real push to say, we need to bring in all the workers that we've hired over the last year. Maybe not bringing them in, in person, but can we use these collaborative tools and technologies to bring them, hold them closer so they get to know us. And so, you know, things like, having Microsoft teams integrated right into your Citrix workspace makes it easier for you to collaborate with remote workers and inside any process wherever you are. So whether you're in the office or not, it should bring workers closer, especially those remote ones that are at risk of being left out as they move to hybrid work. And then it's really important. And so the things like the app builder are going to also allow building those connections. And I think that workers and businesses are really going to try and build those bridges, because the number one thing I'm hearing from business leaders and IT leaders is, is it, you know, we're worried about splitting into two different organizations, the ones that are remote and the ones that are in the office and any way that we can bring all of them together in an easy way, in a natural way, situate the digital employee experience so that we really back or back to one company, one common culture, everybody has equal access and equity to the employee experience. That's going to be really important. And I think that Citrix launchpad announcements around work really are a step, a major step in the right direction for that. There's still more things that have to be done and all, all vendors are working on that. But it's nice to see. I really liked what Citrix is doing here to move the ball forward towards where we're all going. >> It is nice to see, and those connections are critically important. I happen to be at an in-person event last week, and several folks had just had been hired during the pandemic and just got to meet some of their teams. So in terms of, of getting that cultural alignment, once again, this is a great step towards that. Dion thank you for joining me on the program, talking about the Citrix launchpad series for work, all the great new things that they're announcing and sharing with us as some of the things that you see coming down the pike. We appreciate your time. >> Thanks Lisa, for having me. >> For Dion Hinchcliffe. I'm Lisa Martin. You're watching this Cube conversation. (upbeat music)

>>on >>the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Okay. Welcome back, everyone. That's the cubes. Live coverage here in Palo Alto, California. I'm John for your host with David Lantana in Boston. Massachusetts. Uh, we got a great panel here. Analysts just gonna break it down. Keynote analysis. Day one, we got Ah, longtime Web services expert analyst Diane Hinchcliffe, principal researcher at N V. P. It constantly research, but he goes way back. Dan, I remember, uh, 2000 and one time frame you and I'm >>reading Last time you and I hang out with Michael Arrington's house back in the TechCrunch days >>back when, you know you were on this was Web services. I mean, that's always, uh, serves on the architectures. They called it back then. This was the beginning. This really was the catalyst of cloud. If you think about virtualization and Web services in that era, that really spawned where we are today so great to >>have you on as an Amazon got their start saying that everyone could get whatever they want to on a P. I now right, >>all right? Well, we've been riding this wave. Certainly it's cotton now more clear for the mainstream America. And I quoted you in my story, uh, on Andy Jassy when I had my one on one with them because I saw your talk with star Bit of the weekend and in the way you kicked it off was the Pandemic four was forced upon everybody, which is true, and that caught my attention was very notable because you talked to a lot of C E. O s. Does jazz sees pitch resonate with them? In your opinion, what's your take on on that on that posture? Because we heard, hey, you know, get busy building or you're dying, right? So get busy building. That's what >>I thought that was a good message. But I mean on and certainly I saw tweets and said, Hey, he's just he's just directly talking to the CEO. But if you ask me, he's still talking to the CTO, right? The technology officer who's got a feels all this technology and bend it into the shape that it will serve the business. You talk to a CEO who wants is trying to get on the cloud their biggest challenges. I know I need armies of people who know all these brand new services. You saw the development velocity of all the things that they announced and things they re emphasized there was There was a lot of things that were bringing back again because they have so many things that they're offering to the public. But the developer skills or not, they're the partner skills are not there. So you talked to CEO, says All right, I buy in and and I have had to transform overnight because of the pandemic, my customers have moved, my workers have moved on, and I have to like, you know, redirect all my I t Overnight and Cloud is the best way to do that. Where's my where's all the skills for the training programs, the department programs that allow me to get access to large amounts of talent? Those are the types of things that the CEO is concerned about is from an operational perspective. We didn't hear anything about, like a sales force type trailhead where we're going to democratize cloud skills to the very far end of your organization. >>Yeah, they're just kind of scratching the service. They didn't mention that, you know, far Gates away to get into server list. I mean, this is ultimately the challenge Dave and Deena like, don't get your thoughts on this because I was talking Teoh a big time CTO and a big time see so and that perspectives were interesting. And here's the Here's the Here's what I want you to react Thio the sea level Say everything is gonna be a service. Otherwise we're gonna be extinct. Okay, that's true. I buy that narrative, Okay, Make it as a service. That's why not use it. And then they go to the C t. And they say, implement, They go Well, it's not that easy. So automation becomes a big thing. And then so there's this debate. Automate, automate, automate. And then everything becomes a service. Is it the cart before the horse? So is automation. It's the cart before the horse, for everything is a service. What do you guys think about that? >>We'll see. I mean, CEO is to Diane's point, are highly risk averse and they like services. And those services generally are highly customized. And I think the tell in the bevy of announcements the buffet have announces that we heard today was in the marketplace what you guys thought of this or if you caught this. But there was a discussion about curated professional services that were tied to software, and there were classic PDM services. But they were very, you know, tight eso sort of off the shelf professional services, and that's kind of how Amazon plays it. And they were designed to be either self serve. It's a Diane's point. Skill sets aren't necessarily there or third parties, not directly from Amazon. So that's a gap that Amazon's got too close. I mean, you talk about all the time without post installations, you know, going on Prem. You know who's gonna support and service those things. You know, that's a that's a white space right now. I think >>e think we're still reading the tea leaves on the announcements. But there was one announcement that was, I thought really important. And that was this VM Ware cloud for a W s. It says, Let's take your VM ware skills, which you've honed and and cultivated and built a talent base inside your organization to run VMS and let's make that work for a W s. So I thought the VM Ware cloud for a W s announcement was key. It was a sleeper. It didn't spend a lot of time on it. But the CEO ears are gonna perk up and say, Wait, I can use native born skills. I already have to go out to the cloud So I didn't think that they did have 11 announcement I thought was compelling in that >>in the spending data shows of VM Ware Cloud on AWS is really gaining momentum by the way, As you see in that open shift So you see in that hybrid zone really picking up. And we heard that from AWS today. John, you and I talked about it at the open I produces in >>Yeah, I want to double down on that point you made because I want to get your thoughts on this a Z analyst because you know, the VM ware is also tell. Sign to what I'm seeing as operating and developing Dev ops as they be called back in the day. But you gotta operate, i t. And if Jassy wants to go after this next tier of spend on premise and edge. He's gotta win the global i t posture game. He's gotta win hybrid. He's got to get there faster to your point. You gotta operate. It's not just develop on it. So you have a development environment. You have operational environment. I think the VM Ware thing that's interesting, cause it's a nice clean hand in glove. VM Ware's got operators who operate I t. And they're using Amazon to develop, but they work together. There's no real conflict like everyone predicted. So is that the tell sign is the operational side. The challenge? The Dev, How does Amazon get that global I t formula down? Is it the VM Ware partnership? >>I think part of it is there, finally learning to say that the leverage that the vast pool of operational data they have on their literally watching millions of organizations run all the different services they should know a lot and I say made that point today, he said, Well, people ask us all the time. You must have all these insights about when things were going right or wrong. Can you just tell us? And so I think the announcement around the Dev ops guru was very significant, also saying you don't necessarily have to again teach all your staff every in and out about how to monitor every aspect of all these new services that are much more powerful for your business. But you don't yet know how to manage, especially at scale. So the Dev Ops guru is gonna basically give a dashboard that says, based on everything that we've known in the past, we could give you insights, operational insights you can act on right away. And so I think that is again a tool that could be put in place on the operational side. Right. So b m where for cloud gives you migration ability, uh, of existing skills and workloads. And then the Dev Ops crew, if it turns out to be everything they say it is, could be a really panacea for unlocking the maturity curve that these operators have to climb >>on. AWS is in the business now of solving a lot of the problems that it sort of helped create. So you look at, for instance, you look at the sage maker Data Wrangler trying to simplify data workloads. The data pipeline in the cloud is very very complex and so they could get paid for helping simplify that. So that's a wonderful, virtuous circle. We've seen it before. >>Yeah. I mean, you have a lot of real time contact lens you've got, um, quick site. I mean, they have to kind of match the features. And And I want to get your guys thoughts on on hybrid because I think, you know, I'm still stuck on this, Okay? They won the as path and their innovations Great. The custom chips I buy that machine learning all awesome. So from the classic cloud I as infrastructure and platform as a service business looking good. Now, if you're thinking global, I t I just don't just not connecting the dots there. See Outpost? What's riel today for Amazon? Can you guys share E? I mean, if you were watching this keynote your head explode because you've got so many announcements. What's actually going on if you're looking at this is the CEO. >>So the challenge you have is the CEO. Is that your you have 10, 20 or 30 or more years of legacy hardware, including mainframes, right. Like so big insurance companies don't use mainframe because their claims systems have been developed in their very risk averse about changing them. Do you have to make all of this work together? Like, you know, we see IBM and Redhead are actually, you know, chasing that mainframe. Which angle, which is gonna die out where Amazon, I think is smart is saying, Look, we understand that container is gonna be the model container orchestration is gonna be how I t goes forward. The CEO is now buy into that. Last year, I was still saying, Are we gonna be able to understand? Understand? Kubernetes is the regular average i t person, which are not, you know, Google or Facebook level engineers Are there gonna be able to do do containers? And so we see the open sourcing of of the AWS is, uh, kubernetes, uh, server on. We see plenty of container options. That's how organizations could build cloud native internally. And when they're ready to go outside because we're gonna move, they're gonna move many times slower than a cloud native company to go outside. Everything is ready there. Um, I like what I'm seeing without posts. I like what I'm seeing with the hybrid options. The VM ware for cloud. They're building a pathway that says you can do real cloud. And I think the big announcement that was that. That s a really, uh, spend time on which is that PCs for everywhere. Um, a saying you're gonna be able to put Amazon services are compute services anywhere. You need it, e think that's a smart message. And that allows people to say I could eventually get toe one model to get my arms around this over time >>day. What does that mean for the numbers? I know you do a lot of research on spend customer data. Um, CEO is clearly no. This is gonna be the world's never go back to the same way it was. They certainly will accelerate cloud toe. What level depends upon where they are in their truth, as Jassy says. But >>what does >>the numbers look at? Because you're looking at the data you got Microsoft, You got Amazon. What's the customer spend look like where they're gonna be spending? >>Well, so a couple things one is that when you strip out the the SAS portion of both Google and Azure, you know, as we know, I asked him pass A W S is the leader, but there's no question that Microsoft is catching up. Says that we were talking about earlier. Uh, it's the law of large numbers Just to give you a sense Amazon this year we'll add. Q four is not done yet, but they'll add 10 billion over last year. And Jesse sort of alluded to that. They do that in 12 months. You know, uh, azure will add close to nine billion this year of incremental revenue. Google much, much smaller. And so So that's, you know, just seeing, uh, as you really catch up there for sure, you know, closing that gap. But still Amazon's got the lead. The other thing I would say is die on you and I were talking about this Is that you know Google is starting. Thio do a little bit better. People love their analytics. They love the built in machine learning things like like big query. And you know, even though they're much, much smaller there, another hedge people don't necessarily want to goto Microsoft unless they're Microsoft Shop. Google gives them that alternative, and that's been a bit of a tailwind for Google. Although I would say again, looking at the numbers. If I look back at where Azure and AWS were at this point where Google is with a few billion dollars in cloud the growth rates, I'd like to see Google growing a little faster. Maybe there's a covert factor there. >>Diane. I want to get your thoughts on this transition. Microsoft Oracle competition Um, Jesse knows he's got a deal with the elite Salesforce's out there. Oracle, Microsoft. Microsoft used to be the innovator. They had the they had the phrase embracing extend back in the day. Now Amazon's embracing and extending, but they gotta go through Oracle and Microsoft if they wanna win the enterprise on premise business and everybody else. Um, eso welcome to the party like Amazon. You What's your take on them versus Microsoft? Calling them out on sequel server licensing practices almost thrown him under the bus big time. >>Well, I think that's you know, we saw the evidence today that they're actually taking aim at Microsoft now. So Babel Fish, which allows you to run Microsoft sequel server workloads directly on Aurora. Uh, that that is what I call the escape pod that gives organizations an easy way That isn't Will parliament to redesign and re architect their applications to say, Just come over to AWS, right? We'll give you a better deal. But I think you've got to see Amazon have, um, or comprehensive sales plan to go into the C. E. O s. Go after the big deals and say, You know, we want to say the whole cloud suite, we have a stack that's unbeatable. You see our velocities, you know, best in class. Arguably against Microsoft is the big challenger, but we'll beat you on on a total cost of ownership. You know, your final bill. At the end of the day, we could we commit to being less than our competitors. Things like that will get the attention. But, you know, uh, Amazon is not known for cutting customized deals. Actually, even frankly, I'm hearing from very CEO is a very large, like Fortune 20 companies. They have very little wiggle room with Microsoft's anybody who's willing to go to the big enterprise and create custom deals. So if you build a sales team that could do that, you have a real shot and saying getting into the CEO's office and saying, You know, we want to move all the I t over and I'm seeing Microsoft getting winds like that. I'm not yet seeing Amazon and they're just gonna have to build a specialized sales team that go up against those guys and migration tools like we saw with Babel fish that says, If you want to come, we can get you over here pretty quick. >>I want to chime in on Oracle to John. I do. I think this is a blind spot somewhat for AWS, Oracle and mainframes. Jesse talks that talks like, Oh yeah, these people, they wanna get off there. And there's no question there are a number of folks that are unhappy, certainly with Oracle's licensing practices. But I talked to a lot of Oracle customers that are running the shops on Oracle database, and it's really good technology. It is world class for mission critical transaction workloads. Transaction workloads tend to be much, much smaller data set sizes, and so and Oracle's got, you know, decades built up, and so their their customers air locked in and and they're actually reasonably happy with the service levels they're getting out of Oracle. So yes, licensing is one thing, but there's more to the story and again, CEO or risk averse. To Diane's point, you're not just gonna chuck away your claim system. It's just a lot of custom code. And it's just the business case isn't there to move? >>Well, I mean, I would argue that Well, first of all, I see where you're coming from. But I would also argue that one of the things that Jesse laid out today that I thought was kind of a nuanced point was during the vertical section. I think it was under the manufacturing. He really laid out the case that I saw for startups and or innovation formula, that horizontal integration around the data. But then being vertically focused with the modern app with same machine learning. So what he was saying, and I don't think he did a good job doing it was you could disrupt horizontally in any industry. That's a that's a disruption formula, but you still could have that scale. That's cloud horizontal scalability, cloud. But the data gives you the ability to do both. I think bringing data together across multiple silos is critical, but having that machine learning in the vertical is the way you could different so horizontally. Scalable vertical specialization for the modern app, I think is a killer formula. And I think >>I think that's a I think it's a really strong point, John, and you're seeing that you're seeing in industries like, for instance, Amazon getting into grocery. And that's a data play. But I do like Thio following your point. The Contact Center solutions. I like the solutions play there and some of the stuff they're doing at the edge with i o T. The equipment optimization, the predictive maintenance, those air specialized solutions. I really like the solutions Focus, which several years ago, Amazon really didn't talk solution. So that's a positive sign, >>Diane, what do you think? The context And I think that was just such low hanging fruit for Amazon. Why not do it? You got the cloud scale. You got the Alexa knowledge, you know, got machine learning >>zone, that natural language processing maturity to allow them to actually monitor that. You know that that contact lens real time allows them a lot of supervisors to intervene them conversations before they go completely south, right? So allowing people to get inside decision windows they couldn't before. I think that's a really important capability. And that's a challenge with analytics in general. Is that generates form or insights than people know how to deal with? And it solutions like contact lens Real time? This is Let's make these insights actionable before it's broken. Let's give you the data to go and fix it before it even finishes breaking. And this is the whole predictive model is very powerful. >>Alright, guys, we got four minutes left. I wanted Segway and finish up with what was said in the keynote. That was a tell sign that gives us some direction of where the dots will connect in the future. There's a lot of stuff that was talked about that was, you know, follow on. That was meat on the bone from previous announcements. Where did Jassy layout? What? I would call the directional shift. Did you see anything particular that you said? Okay, that is solid. I mean, the zones was one I could see. What clearly is an edge piece. Where did you guys see? Um, some really good directional signaling from Jassy in terms of where they really go. Deal with start >>e I felt like Jassy basically said, Hey, we invented cloud. Even use these words we invented cloud and we're gonna define what hybrid looks like We're gonna bring our cloud model to the edge. And the data center just happens to be another edge point. And hey, I thought he laid down the gauntlet. E think it's a very powerful message. >>What do you think Jesse has been saying? That he laid out here, That's >>you laid out a very clear path to the edge that the Amazons marching to the edge. That's the next big frontier in the cloud. It isn't well defined. And that just like they defined cloud in the early days that they don't get out there and be the definitive leader in that space. Then they're gonna be the follower. I think so. We saw announcement after announcement around that you know, from the zones Thio the different options for outpost um, the five g announcement wavelength. All of those things says we're gonna go out to the very tippy edge is what I heard right out to your mobile devices. Right after the most obscure field applications imaginable. We're gonna have an appliance So we're gonna have a service that lets you put Amazon everywhere. And so I think the overarching message was This is a W s everywhere it z gonna go after 100% of I t. Eventually on DSO you can move to that. You know, this one stop shop? Um and you know, we saw him or more discussions about multi cloud, but it was interesting how they stand away from that. And this is what I think One area that they're going to continue to avoid. So it was interesting, >>John, I think I think the edges one by developers. And that's good news for Amazon. And good news for Microsoft. >>We'll see the facilities is gonna be good for me. I think guys, the big take away You guys nailed two of them there, but I think the other one was I think he's trying to speak to this new generation in a very professorial way. Talk about Clay Christensen was a professor at his business school at Harvard. We all know the book. Um, but there was this There was this a posture of speaking to the younger generation like hey, the old guy, the old that was running the mainframe. Wherever the old guys there, you could take over and run this. So it's kind of like more of a leadership preach of preaching like, Hey, it's okay to be cool and innovative, right now is the time to get in cloud. And the people who are blocking you are either holding on to what they built or too afraid to shift. Eso I think a Z we've seen through waves of innovation. You always have those people you know who are gonna stop that innovation. So I was very interesting. You mentioned that would service to the next generation. Um, compute. So he had that kind of posture. Interesting point. Yeah, just very, very preachy. >>E think he's talking to a group of people who also went through the through 2020 and they might be very risk averse and not bold anymore. And so, you know, I think that may have helped address that as well. >>All right, gentlemen, great stuff. Final word in the nutshell. Kena, What do you think about it in general? Will take away. >>Yeah, I I think we saw the continued product development intensity that Amazon is going to use to try and thrash the competition? Uh, the big vision. Um, you know, the real focus on developers first? Um and I think I t and C e O's second, I think before you could say they didn't really think about them too much at all. But now it's a close second. You know, I really liked what I saw, and I think it's It's the right move. I'd like to Seymour on on hybrid cloud migration than that, even when we saw them. >>All right, leave it there. Don. Thanks for coming on from this guest analyst segment. Appreciate you jumping in Cuba. Live. Thank you. >>Thanks. Alright. >>With acute virtual. I'm your host John per day Volonte here covering A W s live covering the keynote in real time State more for more coverage after the break

>> Announcer: Live, from Copenhagen, Denmark, it's theCUBE! Covering Nutanix.NEXT 2019. Brought to you by Nutanix. >> Welcome back everyone to theCUBE's live coverage of Nutanix.NEXT. We are at the Bella Center in Copenhagen, Denmark. I'm your host, Rebecca Knight, alongside of Stu Miniman, of course. We are joined by a good friend of theCUBE, Ray Wang, principal analyst and CEO of Constellation Research. Thank you so much for returning to theCUBE. >> Hey, how you doing? Good morning! >> Good morning, good morning! >> Good morning! (laughing) >> Good morning! >> I don't know. I get all my accents wrong out here. >> (laughing) So, you got a shout out on the main stage this morning, from Monica Kumar, congratulations on that. She talked about you and your research on the infinite role of computing. You also do a lot with the future of work. I know that that is really right in your wheelhouse right now. What are you hearing, what are you seeing, what kinds of conversations are you having that are interesting you? >> Yeah, so, this infinite computing option, it's one of the that we're talking about, the fact that you can scale out forever, right? And the problem that's holding us back has been technical debt, right? So all that legacy that everyone's got to figure out. It's like, my connections, my server, my disk-rack recovery, my disaster recovery, my backup, everything. It's a pain in the butt. And I'm still trying to get onto the cloud. So on that end, we're like, okay, all this stuff is holding us back, how do we get there? Now, the future of work is a little bit different. We're seeing a very very different set of work. People have talked about where we are the gig economy, but that's just one aspect of it. Everything is being decomposed into microservices. Large processes are becoming smaller and smaller microservices, they're being reusable, well our work and tasks are following the same way. We're getting smaller and smaller tasks, some are more repetitive, some are going to be automated, and it's really about where we actually find the difference between augmentation of humanity, and full automation, and that's where the next battle's going to be. >> Yeah, Ray, some of the discussions we've been having this week, is how do we really simplify the environment? The balance I hear from customers, on the one hand, they're always like, I don't have enough money, I don't have enough personnel, on the other hand, oh my gosh, that full automation sounds like you're going to put me out of a job. We know we're not putting everybody out of work in the next couple of years. There are challenges; we worry about the hollowing out of the center of the economy, but here, what Nutanix is trying to do, of course, is, I don't want to have to thrive in that complexity anymore, I want to be able to drive innovation, keep up with that, take advantage of that unlimited resources out there, so, where do you see, you've been here at the show, what are you hearing from the customers here? Anything different in Europe versus back in North America that you'd share about that journey onto the changing roles? >> Oh it's a great point. It's about simplifying everything where you can, it's about areas of automation where they make sense. Here in Europe it's slightly different because a lot of the focus in Europe has been about cost and efficiency, followed by of course regulatory. Those have been the two drivers. And they've been battling that in order to be, even they will look at some level of innovation. Where in the US, people are head on doing innovation, regulatory and operational efficiency at the same time. So that creates a very very different environment. But what we have noticed are some patterns, especially when we look at automation and AI; there are four areas out of seven where we see a lot more automation that's happening. The first one is massively repetitive tasks, those are things, yeah, got to get that out of the way, we don't do this very very well. The second one is really thinking about massive nodes of interaction. When you're connected to multiple places, multiple organizations, multiple instances, that's something where we start to get overwhelmed, and then of course, there's lots of volume. If you've got lots of volume or requests that are coming through, you can't possibly handle that, and that's a place where we see a lot of machine scale. And the last piece is really when you have to scale, humans don't scale very well. However, it's actually not a hollowing out of the middle; it's actually a hollowing out of the ends in a very, very real end, because really really simple tasks go away, super complex tasks go away, and the middle actually remains, and the middle is things that are complex that cannot be recreated by math, they're also areas that require a lot of creativity, humans make the rules, we break the rules, and then the last part is really fine motor skills and presence, the machines still aren't as good. So we still have some hope. So the middle stays, it's the hollowing out of the ends, the high end jobs and the low end jobs are the ones where we're going to see a lot of risk. >> So what does that mean? So we have, leaving the middle there, and as you said, the high end jobs and the low end jobs go away, but what does that mean in terms of the skills? In terms of what employers are looking for, in terms of what they need in their prospective applicants and hirees. >> That's a great point. Soft skills are important; it's the qualitative skills that become even more important, it's also being able to manage and orchestrate the hard skills; because you don't necessarily have to know how to do the calculation, you have to just know which algorithm to apply. >> Okay, and then also, these soft skills of managing people, I'm assuming too? Because computers are not so good at that either. >> Yes. Soft skills are managing people, but also manage the human and machine equation that's going to happen. Because we have to train the machines, the machines aren't going to know that level of intuition, and there's a large amount of training that's going to happen over time. >> All right. So, Ray, one of the things Nutanix is doing is, as they've been transforming to not only subscription, software's always been at their core, but they're starting to do not just infrastructure software, but application software. I know you live in that world quite a lot, so when you hear Nutanix talking about building databases, delivering these services, it's something that I look at, Amazon does some of that, but for the most part they're infrastructure and build on top of us. How do you think, how is Nutanix doing, what are some of the challenges for them, going up against some of the bellwethers out there in tech, and all the open source projects that are out there. >> So the challenge is always going to be, there is a one dominant player in every market. And what they're providing is an alternative to allow the orchestration of not having that, not only that dominant player, but a choice. So in every single market, they're focused on giving users choice, and giving the ability to aggregate, and bring everything into one single plane. That is tough to do, right? And the fact that they see that as their big hairy audacious goal, that's impressive. If you said they were going to do this three years ago, I wouldn't have believed them. >> Well yeah, I think back to, remember almost 10 years ago, VMware tried to get into applications, they bought Zimbra, they bought a few others. Cisco did like 26 adjacencies, they were going to take over video and do all these things, and we've seen lots of failures over the years. They refocused on their core, was a big thing that I heard, that the users seem to be excited about. Are there areas that you're find especially interesting as to where Nutanix is poking? >> So, I would say that Nutanix three years ago was a little bit sleepy. They got comfortable, they did the stuff that they did really well, and it feels like, maybe about 12 months ago, Dheeraj had a different vision. Like something snapped, something hit, he said this isn't working, we're going to change things, and we've seen a whole bunch of new talent come into play. We've also seen a huge expansion of what they're trying to do, and a cleanup of all those side projects that were all going on before. So I think they've actually honed in on, okay, if we can simplify this piece, this is a money-winning business for some time, and they're talking about 80% margins last quarter, I mean that's huge, and that's just trying to save customers money, and make their lives simpler. >> Do you think that they have the messaging right? Because, I mean, they're going to this Thoreauvian/Emersonian idea of simplify, simplify, simplify, and it does resonate, of course! What customer doesn't want a simpler computing experience? But do you think that they are reaching the right people, and they have obviously very passionate customers, but are they getting into new businesses. >> I think they're getting to the businesses that their customers are asking them to, those adjacencies are huge, I think and when you think about cleaning up technical debt, all that legacy debt that you actually have to fix, I mean, this is where you begin. It's so hard to make that cloud journey to begin with, it's even harder to carry all that legacy with you. And we're going to see a lot more of this going forward. >> All right. So, Ray, talk a little bit about, I loved an event you did last year, the people's centered digital future. Help explain to our audience what this is about, and where you're taking it again this year. >> So that event was a one-time event. We were celebrating the 70th anniversary of the United Nations founding, we were celebrating almost 50 years of the internet, and 50% of the world being connected to the internet. And part of the reason that was an important event was, we really felt that there was a need to get back to the roots of where the internet had begun, and more importantly, talk about where we are today in the world of privacy. One of the biggest challenges we have in the a digital world is that your personal data, your genomics, all this information about you is being brokered for free. And what we have to do is take that back. And by taking that back, what I mean is, we've got to make all these rights, property right. If we can make that a property right, we can leverage the existing rules and legislation that's there, and we can actually start paying people for that data through consent, and giving people that ability, on consent to data, could create lots of things, from universal basic income, to a brand new set of data economy that equalizes the playing field, while keeping the large tech giants. >> There's some of those big journeys that we went on, you talk about the internet, this year's 50th anniversary of the first walking on the moon, and you look at how entire countries rallied together, so much technology was-- >> Yeah, look at India. >> Spun off of what they've done there, it's like we need some rallying cries in today's day and age to solve some of these big day and age. Is that AI? Where are some of the big areas that you see tech needing to drive forward in the next decade? >> I think the big area's going to be around decentralization, giving individuals more empowerment. We've got large, big tech companies, that are, I'd say, imbalanced. We start companies right away, building monopolies on day one, and we don't open up those markets. And the question is, how do we create a level playing field for the individual to be to compete, to bring a new idea, and to innovate, if that's continuously stifled by big technology companies without an opportunity, we're in trouble. And so that starts by making data a property right, to the personal data. It starts by also creating marketplaces for that data, and those marketplaces have to have regulations, similar to capital market flows. The way treat exchanges, we treat marketplaces, we need to do the same thing with the way we do with data, and then the third piece, there has to be some level of a tax, that goes to all these data economies, so that they can fund the infrastructure and the watch dogs that are there. Now this is coming from a free market, I'm a free market capitalist, okay? I can't stand regulation, but I also realize that it's so important that we have a fair market. >> But do you, we know so much about how Americans are so much more cavalier about their privacy than even Europeans, what will it take to galvanize Americans to care about those little crumbs that they're leaving on the internet, that is the data that you say should be a property right, that we should be paid for? >> I think it's going to start with companies actually take, and do the right thing, where they actually give them that opportunity to monetize that information. >> Will they do that? >> I think the new set of startups are starting to do that, because they're looking at the risk that's being posed, at Facebook and Google and Amazon, on the anti-trust, DOJ, FCC, they're all coming in at the same time, the FTC, they're all wondering, do we break these companies up or not? The short answer is, I don't think they're going to, because we're competing with China, and when you're looking at that scale of data, where Amazon's transactions are only 1/10 of Ali Baba's? That's huge. So the consolidation has to happen, but we need to create a layer that actually democratizes and creates a fair trading play. >> And those startups, you think, can compete with established players? >> I think once we set the roles, and the ground rules, I think people are going to be able to do that, but once you free that data, what are we competing on now? You have to pay for my consent, you have to earn my business, you can't trade it for free, or just say, "Hey look, you are the product." That changes everything. >> Rebecca: Yeah, that's a good point. >> Ray, I know you spend a lot of time talking to, and giving advice to some of the leaders in technology, you're welcome to get into some specifics about Nutanix, or some of the cloud players, but what are some of the key themes, what are people getting right, and what are they still doing wrong? >> Okay, so theme number one, this is going to be a multicloud hybrid world for a long time. Anybody that's bucking the multicloud trend, they've missed the point, right? Because we want portability in data, there's only two or three players in every single market, if I can't move my data, my workloads, and my IO in and out, then you've actually created vendor lock-in from hell. And I think customers are going to protest against that. The second one, and you guys are probably following this trend a lot, is really about AI ethics and design principles for AI. So what is ethical AI? We've got five things that are important: The first one is make sure it's transparent. See the algorithms, see what they write. Second one, make sure it's explainable. Hey, bias is not a bad thing, so if I'm discriminating against redheads, with, left-handed, and that happened to like, I don't know, Oracle, fine. But, if that was unintended, and you're discriminating against that, then we have to get rid of that, right? And so we have to figure out how to reduce that kind of bias, if it's unwanted bias. If you discover that you're discriminating, and not being inclusive, you've got to make sure that you address that. So then the next part is, it's got to be reversible. And once you have that reversibility, we also make sure that we can train these systems over time. And then the last piece is, Musk could be right! Musk could be right, the machines might take over, but if you insert a human at the beginning of the process, and at the end of the process, you won't get taken over. >> I want to hear about what the future of work looks like for Ray Wang. You are on the road constantly, you are (laughs) you are moving your data from one place, you are everywhere, all the time. So what do you have on next, what's exciting you about your professional life? >> I think the challenge's that we are living in a world where there's too much information, too much content. And you guys say this all the time, right? Separating the signal from the noise. And people are willing to pay for that signal. But that is a very very tough job, right? It's about the analysis, the insights, and when you have that, people don't want to read through your reports. They don't want to watch through the videos. They just want to call you up and say, "Hey, what's going on?" And get the short version of it. And that's what's making it very interesting, because you would expect this would be in a chat bot, it'd be in a robo advisor, doesn't work that way. People still want the human connection, especially given all that data out there, they want the analysis and insights that you guys provide, that's very very important, but even more important right now, it's really about getting back to those relationships. I think people are very careful about the relationships they're keeping, they're also curating those relationships, and coming back to spending more time. And so we're seeing a lot more of in-person meetings, in-person events, very very small, curated conversations, and I think that's coming back. I mean that's why we do our conference every year, as well, we try to keep 200 to 300 people intimately together. >> Those human connections, not going away. (laughs) >> Nope, not going away, in an automated, AI, digital world! This is our post-digital future. >> That's excellent. Well Ray, thanks you so much for coming on theCUBE, it's always so much fun to talk to you. >> Hey, thanks a lot. >> High energy guy (laughs). >> Low energy. >> I'm Rebecca Knight for Stu Miniman, we will have more from the Bella Center at Nutanix.NEXT coming up in just a little bit. (upbeat music)

>>Live from Seattle, Washington. It's the cube covering Smartsheet engage 2019 brought to you by Smartsheet. >>Welcome back everyone to Seattle, Washington. We are here at Smartsheet engaged 2019 I'm your host, Rebecca Knight along with my cohost Jeff Frick. You're watching the cube. We are here with a cube alum, a cube veteran, Dion Hinchcliffe, VP and principal analyst at constellation research at at Washington DC. Thank you so much for returning to the cube. Absolutely. Thanks for having me. So we're here to talk with you about the future of work, which is a huge topic but a fascinating one. I want you to start by giving sort of a broad brush of what you see are the biggest changes right now happening in the workplace is driven by the new, the rise of digital technologies. >>Sure. I mean while it digital is infusing everything in the workplace these days, right? And so we've had the past waves of productivity tools and then mobile devices came through and then eventually augmented reality and virtual reality are going to literally change how we perceive the workplace. And then we have just everyday trends like remote working. And now people can work from anywhere, right? It's fantastic. And that's, that's really revolutionized a lot of things. There are things in 2% of the workforce per year is becoming a remote work force. Companies like ADP have a quarter of their workforce working from home, right? Accenture, same thing. They're getting rid of office space and they, they work out of their house unless there's a client site. And because you can create a, create the experience that you want. And one of the really big trends is this is this trend towards being able to shape the employee experience the way that you want to, using the tools that you prefer. >>And some people call this shadow it, other people call it innovation, right? And so that's one of the, one of the big changes. And then we have things like the gig economy, which is allowing people to build the lifestyles they want doing any kind of work they want when they want to, when they feel like it on their own terms. And that's, that's really quite exciting too. So all these, this confluence of forces all enabled them driven by technology. But it's also leading to a lot of what we call cognitive overload workers that are not lifelong learners are feeling overwhelmed by this. And that's another big challenge. >>Well, you also get this tools proliferation, which they're just not, they're just not word and, and Excel anymore. But you've got a tab open with Salesforce, you've got a tab open with Slack, you've got Gmail open, you've got docs open and you've got Smartsheet open. You might have a JIRA open. I mean, so how is that gonna sort itself out as we just kind of keep adding new tabs of apps that we have to keep up >>and we need all this technology to do better work. I mean the, these apps provide value except that it's increased in the onboarding time for workers. It's making it hard for us to train people. In some companies it's hard to retain people because they feel like they have to go to work and there's this onslaught of technologies they have to have tabs open and get their jobs done. And they do. And so we're seeing things like, you know, we're at the Smartsheet conference where, you know, how can we centralize work a little bit better, streamline it by integrating the tools and creating more focus in on what we're doing. And that's a very big trend. So my latest digital workplace trends report, we say this, we're seeing these hubs form, you know like Slack is another work hub that's become very popular inside of organizations. >>They have over 1100 application integrations that allow people to spend their time in one place and kind of work through all these other systems from one hub. So we're dealing with this complexity, you know, starting to be able to do this now, but it's early days still a big challenge. So what's a, what are you seeing now? So what's the, what is the answer then? I mean we have you just described all of these trends that are taking place that are making, making the work modern workplace so much more complex, dealing with workers who have, they're dealing with cognitive overload leaders who want more with less. What are some of the answers? What are some of the most exciting tools that you're seeing right now? We talked already about Smartsheet and Slack. We see the new digital experience platforms are emerging and low code and no code is also becoming popular. >>I'd be able to take the pieces of the applications you want and create more streamline experiences. So the CIO of Accenture, Andrew Wilson, solve his problem right away there. They're knowledge workers are just being choked by all of these tools, but yet we need the value they provide. So he began to divide up the employee experience, the 100 top moments and then he built experiences that enabled, you know, project management and onboarding and all of these key activities to be friction-free built out of their existing applications, but streamlined to just what they needed to do. And he used this as his top priority as a digital leader is to say, we've got to take as much complexity away so we can get at the values with streamlining and simplification. And we now have tools that allow that shaping to happen very quickly. It's almost reminds me of kind of the competition for Deb's right now. >>It's the competition for employees. And then we've talked a lot about the consumerization of it in mobile devices for the customer experience, but there hasn't been as much talk about leveraging that same kind of expected behavior, right? Or expected inner engagement interaction with the apps on the actual employee engagement side, which is probably as fierce of a battle as it is to get customers. Cause I think there's a lot more than 2% customers out available. But yeah, we only get 2% unemployment in the Bay area. Now it's creating effectively negative unemployment, right? Anything under 3%. So this is the challenges. Employee experience is usually low on the priority list for CEOs. They usually have analytics and cloud and cybersecurity and all these things that they have to get done that are higher priority. Yet customer experience is, is one of those priorities. But how does an employee give a good customer experience when they have a poor experience to deliver it with? Right. We're seeing you can do with talented people, is expecting to do a great job. And then give them a bunch of hard to use tools, right. Which is what's happening. So we are now finally seeing that prioritization go up a little bit because employee experience is part of delivering great customer experience and it's how you, how you create that experience to begin with. So small >>and leaders are seeing that as a priority of retaining their top people because they understand that their workers need to feel satisfied with their work life. >>Yeah. And now we have data on a lot of these things we didn't have before and I'm sure you've seen the numbers that are, most employees are disengaged at work. The majority, right between 50 and 60% depending on whose data you're looking at. That's an enormous untapped investment that workers are not performing the way that they could if they had better employee experiences. And what's disengaging is, as I mentioned, you know, giving a talented person allows you tools or allows you experience, right and expect them to do great is right. It doesn't happen. >>How much do you think AAL or excuse me, AI and machine learning will be able to offload enough of the mundane to flip the bit on how engaged they are in their job. >>Yeah, it's, it's interesting cause there's, you know, there's two sides of the coin there. Some people like a, a job that they can just kind of phone in and it's kind of rote and they can come in, they don't have to think too hard and then they can go home to their family and some people are hired on that basis. Right. Um, because that's the challenge. AI and machine learning will absolutely automate most rote work. If you look at like Adobe sensei, I was at the Adobe conference and, and they were talking about how all of these creative types, you'll have all these mundane tasks automated for them. And I could see everybody looking at each other going, I get paid to do. >>Right, right. >>So you know, it, you'll see things like robotic process automation is working. I mean, I hear anecdotes all the time from CIO is how they had, they cut like 25% out of their call center because they handed it over to the box. Right. You know, as bill processing, that's one of the, and sorting and matching bills, the invoices, it's a manual job even in today's world until very recently. So we are seeing that happen about the most rote level and it just, but it's just going to climb up from there. >>What do you see down the road though? I mean in terms of those, in terms of those employees who are raising their saying hands saying weed, I kind of want that job. I are you, are you seeing what's going to happen to those people? Are they going to have to learn new skills? Are they, are they going to be invested in by their companies? >>Well you hope so. You know, it's interesting. We see that all the big vendors now have these big education programs. Salesforce has Trailhead. SAP just announced open SAP where they giveaway massively open online courses. And you know, Microsoft has done this with Microsoft developers network way back in the day, trying to educate people. I mean you can get re-skilled for nothing for free now if you want to do it. But this is the challenges. Even though every technological revolution in the past, and it looks like this one too has totally changed the employment picture. Uh, uh, by and large it creates more jobs than we lose. And that looks like it's going to happen here. But the people who lose the jobs aren't the ones that tend to gain the jobs, the new jobs, right? Yeah. The, it's hard to take somebody who's, who's sorting bills and say, I need you to develop a new AI algorithms because that's where the next strategic jobs are going to be directing the AI to do all these things. Right. And so I think the short term is going to be dislocation and it's happening so fast that unless society, government, and enterprises really intervene that to upskill these folks, we are going to have a challenge. >>Well, we're in this really weird time too, in between, I mean, the classic one is long haul trucking, right? Which is perfect for autonomous vehicles, you know, to carry a lot of that freight and everyone pretty much agrees that's going to happen. At the same time, there's, there's a huge shortage of available truck drivers today. Uh, like there never has been. So as these weird, and again, it's probably not the best thing for a young kid to get into, right? Because it's not, doesn't have a lot great long. >>Right? Right. >>Well, and you know, you look at Uber and their stated direction is, is they want to get rid of all these drivers, right? They want it, they want self-driving taxis. And you know, we're getting close to where that might actually happen, right? Uh, and so the unskilled labor is going to be hit by far the worst. You have to become skilled labor in, in the digital economy. Uh, and so a big part of the future of work is going to be finding ways to, to get the skills into people's hands. You know, like Facebook and other large organizations don't even require a college degree. What they want people, the people that can deliver, they can take these things and create the, you know, the, the great products of the future. And so, you know, those everyone has to become a knowledge worker. >>And, and as Laird Hamilton said on the main stage today, it's the, it's the, the formula of learning to really understand when you're starting from a point of, wow, I don't know much about that. I bet. I guess I'd better learn about it. And then learning a lot about it along the way. We all have to be able to adapt and adopt those new, >>no, absolutely. Now the, uh, uh, and so w we see up-skilling and cross skilling becoming more transdisciplinary. So business people are becoming it folks now and it folks really business people, you know, we've had this business, it divide for a long time and cracks me up. I still go to big companies in the it departments using its own building. Right. But those days are going away. And now seeing that, you know, now as it people over on the business side that live there now. Right. You know, so we're seeing this kind of, this blending where digital is infusing everything and so you have to become digitally competent. Uh, and this is where we have to make that simpler. This is going back to the, you know, the, the, the digital workplace, the average user has had the number of applications they have to learn double or triple in the last just the last five years. Right. So it's a big challenge. >>So what should kids be majoring in today? What's your, >>Oh, a game design. Know the gaming industry is bigger than the movie by a large, large margin. Right. And, and that, that's where all the experience of these immersive experiences in virtual reality and augmented reality really come from. And then you can go into business. Right. You know, >>even sociology majors can design games. >>Yeah. It's just, you know, it's just get, like you said, it's, it's the poor tweeners right. That get bumped on the old and aren't necessarily in a position to take care of the new, yeah. I'll have to take care of. And unfortunately, uh, not a lot of great record of retraining today, but maybe that's going to have to be a much more significant investment because there just aren't the people to fill those positions, period. Right? Yeah. Well, and there's these big market places now you can build the career of your dreams. You'd go to Upwork or Gigster. I mean, these are big job markets where you can go and find work and do it from anywhere using a tablet you bought for $50 off Amazon. Right, right. You know, it just that most of you aren't even aware of that. They can do that. Right, right, right. >>So it's this fast changing world. Put a few bucks away for insurance and you've put a few bucks away in your 401k and you, yeah. You know, not just living off the cash plus a little bit to cover your costs, which unfortunately a lot of their, like the Uber drivers and the Lyft drivers are anyway, you know, they're not really banking that thing for building a, a career. Well, I've crawled to those platforms and it's interesting, entrepreneurial activities, very common in places like Asia, right? Where if, you know, they come here, they build businesses right away. Right. And they're used to that. So w and we lost some of that, but I think we were gave a economy is giving a lot of that back to us. We have to relearn it again, you know? Right. >>Well Deon, thank you so much for coming on the cube. It was a pleasure having you. Absolutely. Thanks. So Jeff. Thanks Rebecca. I'm Rebecca Knight for Jeff Frick. Stay tuned to more of the cubes live coverage of NJ engaged 2019.

>>live from Seattle, Washington. It's the key nude covering Smartsheet engaged 2019. Brought to you by smartsheet >>Welcome back, everyone to Seattle. Washington. We're here at smartsheet engaged 2019. I'm your host, Rebecca Night, along with my co host, Jeff. Rick, You're watching the Cube? We're here with a Cuba Lama Cube veteran Dion Hinchcliffe, VP and principal analyst at Constellation Research at a Washington D. C. Thank you so much for returning to the Cube. >>Absolutely. Thanks for having me. >>So we're here to talk with you about the future of work, which is a huge topic, but a fascinating one. I want you to start by giving sort of a broad brush of what you see are the biggest changes right now happening in the work force driven by the new the rise of digital technologies. >>Sure. I mean, well, it digital is infusing everything in the workplace these days, right? So, you know, we've had the past waves of productivity tools and mobile devices came through and then eventually augmented reality and virtual reality. You're gonna literally change how we perceive the workplace on then We have just, you know, everyday translate remote working and now people can work from anywhere, right. It's fantastic, and that's that's really revolutionized a lot of things. Things in 2% of the workforce per year is becoming a remote work force. Companies like 80 p have 1/4 of their work force working from home, right X century, something to get rid of office space. And they work out of their house. Unless there's a client site on because you can credit, create the experience that you want and one of the really big trends is this is this trend towards be able to shape the employees experience the way that you want to using the tools that you prefer. And so people call this shadow I t. Other people call it innovation, right? And so that's one of the big changes. Then we have things like the gig economy, which is allowing people to build the lifestyles they want. We're doing any kind of work they want when they want to, when they feel like it on their own terms on that's that's really quite exciting to use all these. This confluence of forces are enabled him driven by technology, but it's also leading to a lot of what we call cognitive overload workers. They're not lifelong learners are feeling overwhelmed by this, and that's another big challenge. >>But you also get this tools >>proliferation, which they're just not. They're just not word and excel anymore. But you've got a tab open with Salesforce. You've got a A tab open with slack. You got Gmail open. You've got Doc's open. He got smart cheat open. You might have Jiro open. I mean, so how is that gonna sort itself out as we just kind of keep adding new tabs of AB? So we have to keep in our >>way. And we need all this technology to do better work. Thes APS provide value, except that it's increasing the on boarding time for workers. It's making it hard for us, the train people. In some company. It's hard to retain people because they feel like they have to go to work. And there's this onslaught of technology. They have to have 30 tabs open to get their jobs done, and they do. And so we're seeing things that you know we're at the smartsheet conference where how can we centralize work a little bit better? Streamline it by integrating the tools and credit more focused on what we're doing. And that's a very big trend. S Oh, my latest digital workplace trends report. We say that we're seeing these hubs for me, like Slack is another workup that's become very popular inside of organizations. They have over 1100 application integrations that allow people to spend their time in one place and kind of work through all these other systems from one hub. So we're dealing with this complexity starting to be able to do this now. But it's early days still a big challenge. >>So so So what are you seeing now? So what? So what is the answer then? I mean, we have You've just described all of these trends that are taking place that they're making making the modern workplace so much more complex, dealing with workers who they're dealing with, cognitive overload leaders who want more with less What? What are some of the answers? What are some of the most exciting tools that you're seeing right now? >>Boys, we talked already about smartsheet and slack. We see the new digital experience platforms are emerging on low code and know code is also becoming popular to be able to take the pieces of the applications you want and create more streamlined experiences. So the CEO of Accenture, Andrew Wilson, you solve this problem right away Their their knowledge. Workers were being choked by all of these tools, but yet we need the value they provide. So he began to divide up the employees experience of the 100 top moments, and then he built experiences that enabled project management and on boarding and all these key activities to be friction free, built out of their existing applications. Streamlines, too, just what they needed to dio. And he views this as his top priority as a digital leaders and say, We've got to take much complexity away so we can get at the values with streamlining the simplification on. We now have tools that allow that shaping that happen very quickly. >>It's almost reminds me it's kind of the competition for Deb's right now competition for employees, and we've talked a lot about the consumer ization Oh, I t and mobile devices for the customer experience. But there hasn't been as much talk about leveraging that same kind of expected behaviour writer expected in her engagement interaction with the APS on the actual employee engagement side, which is probably as fierce of a battle as it is to get customers because I think there's a lot more than 2% customers out available, but we only got 2% unemployment in the Bay Area. Now. It's crazy, >>effectively, negative unemployment, right, right? Is that anything under 3%? Yes, so you know this is the challenges employees experience is usually low on the priority list for CEOs usually have analytics and cloud in cyber security and all these things that they have to get done that are higher priority. Yet customer experiences is one of those priorities. But how does an employee give a good customer experience when they have a poor experience to do it, deliver it with right? The worst thing you could do with talented people is expected to do a great job and then give him a bunch of hard to use tools, right? Which is what's happening. So we are now finally seeing that privatization go up a little bit because employees experiences part of delivering great customer experience. That is how you how do you create that experience to begin with so small progress >>and leaders air seeing that as a priority of retaining their top people because they understand that they're workers need to feel satisfied with their work life. >>Yeah, and now we have data on a lot of these things we didn't have before, you know? And I'm sure you've seen the numbers. Most employees air disengaged at work, the majority right between 50 and 60% depending on whose data you're looking at. That's an enormous untapped investment that that that workers are not performing the way that they could if they had better employees experiences and what's disengaged. As I mentioned, giving a talented person lousy tools are allows the experience and expecting the two greatest. It doesn't happen. How >>much do you >>think? A. L Excuse me. Aye, aye. And machine learning will be able to offload enough of the mundane to flip the bit on how engaged they are in their job. >>Yeah, it's interesting because there's, you know, there's two sides to a coin there. Some people like a job that they could just kind of phone in, and it's kind of wrote, and they can come in. They don't have to think too hard and then go home to their families. So people are hired on that basis, right? Because that's the challenge a I and machine learning will absolutely automate. Most wrote work if you look at like a dill bee sense A. I was at the adobe conference and they were talking about how all of these creative types you have all these mundane tasks automated for them, and I could see everybody looking at each other going. I >>could pay to >>do >>that creative rate. >>So you see the things like robotic process automation is working. I mean, I hear anecdotes all the time from CEOs how they how they cut 25% out of the call center because they handed it over to the box, right? You know, Bill processing. That's one of the, you know and sorting matching bills, the invoices, a manual job, even in today's world until very recently. So we are seeing that happen about the most wrote level and just, but it's just gonna climb up from there. >>What do you see down the road, though? I mean in terms of those in terms of those employees were raising their saying can saying I kind of want that job. Are you? Are you seeing what's gonna happen to those people? Are they going to have to learn new skills? Are they are they going to be invested in by their companies? >>We hope so. You know, it's interesting. We see that all the big vendors have these big education programs. Sales force has trailhead s a P just announced open ASAP where they give away massively open online courses on. And Microsoft has done this with Microsoft Developers Network way back in the day, trying to educate people. You can get Reese killed for nothing for free now if you want to do it. But this is the challenges, even though every technological revolution in the past it looks like this one, too, has you are really changed the employment picture. By and large, it creates more jobs than we lose on. That looks like it's gonna happen here. But the people who lose their jobs, not the ones that tend to gain the job, gets a new job. They often it's hard to take somebody who's who's sorting bills and say, I need you to develop a new way I algorithm because that's where you have executed jobs. They're gonna be directing the eye to do all these things right on. So I think the short term is gonna be dislocation. And it's happening so fast that unless society, government and enterprises really intervene that toe up skill, these folks, we are gonna have a challenge. >>We're in this really weird time to in between. I mean, the classic one is long haul trucking, right, which is perfect for autonomous vehicles. T carry a lot of that freight, and everyone pretty much agrees that's gonna happen. At the same time there's there's a huge shortage of available truck drivers today, like there never has been. So is he's weird, and it's probably not the best thing for a young kid to get into right, because doesn't have a lot of great long term, >>right? >>Well, you look at uber on their stated direction is they want to get rid of all these drivers they want. They want self driving taxis on, you know, we're getting close to where that might actually happen right on. So the unskilled labor is gonna be hit by far the worst you have to become skilled labor in the digital economy on a big part of the future of work is going to be finding ways to get the skills into people's hands on Facebook and other larger. They don't even require a college degree what they want people to people that can deliver that could take these things and create the, you know, the great products of the future. On DSO, you know, those everyone has to become a knowledge worker >>and in as layered, Hamilton said. On the main stage today, it's the formula of learning to really understand when you're starting from a point of Wow, I don't know much about that. I guess I better learn about it and then learning a lot about it along the way, we all have to be able to adapt and adopt those >>absolutely no the and so that way see up Skilling and cross killing becoming more trans disciplinary. So business people are becoming I t folks now and I t folks really business people. We had this business I t divide for a long time. It cracks me up. I still go to big companies in the I T department using its own building, right? But those days were going away. And I'll see that, you know now is that people over on the business side that live there now, right? So we're seeing this kind of blending where digital is infusing everything, and so you have to become digitally confident on this is where we have to make that simpler. This is going back to the digital workplace. The average user, as had the number of applications they have thio to learn double or triple in just the last five years. Right? So it's a big challenge. >>So what should kids be majoring in today? What's your >>Oh, uh, game design gaming industry is bigger than the movie industry by a large large margin, right? And that that's where all the experience of these immersive experiences and virtual reality and augmented reality >>a come >>from and then you can go into business, right? You know, >>even sociology majors, design games. >>Yeah, it's just, you know, just get like it's the poor tweeners, right that get bumped on the old and aren't necessarily in a position to take care of the new. And I want to take care of it. Unfortunately, not a lot of great record of retraining to date. But maybe that's gonna have to be a much more significant investment because there just aren't the people to fill those positions, period. >>Well, and there's a big market places now. You can build the career of your dreams. You goto up work or gig stir. I mean, these are big job markets where you go and find work and do it from anywhere. Using a tablet you bought for $50 off Amazon, right? You know, just that most you weren't even aware that they could do that. Right? So >>the world put a few bucks away for insurance and you put a few bucks away in your for one k and you, you know, just living off the cash, plus a little bit to cover your cost, which, unfortunately rather like the uber drivers in the lift drivers are Anyway, you know, they're not really thinking that thing for building a career. >>Well, I've crawled to those platforms and it's interesting. Entrepreneurial activity is very common in places like Asia, right? Where? Where you know, they come here, they build businesses right away, right, And they're used to that and we lost some of that. But I think we gave economy is giving a lot of that back to us. We have to relearn it again, you know. >>Great. Well, Dionne, thank you so much for coming on the Cube. It was a pleasure having you. >>Absolutely Thanks, Jeff. Thanks for >>I'm Rebecca Knight for Jeff. Rick. Stay tuned For more of the cubes. Live coverage of NJ engaged 2019.

>> from Santa Clara in the heart of Silicon Valley. It's the Q covering the Churchills 2019 brought to you by Silicon Angle Media. >> Hey, welcome back, everybody. Jefe Rick here with the Cube. We're in Santa Clara, California At the Churchills. It's the ninth annual kind of awards banquet at the Church O Club. It's on, and the theme this year is all about leadership. And we're excited to have not one of the winners, but one of the newest board members of the church, Oh, club. And someone is going to be interviewing some of the winners at a very many time. Cuba LEM Ray Wong, You know, from Constellation Research of founder, chief analyst >> and also >> a new board member for the Churchill Club Brigade, is >> also being back here. I love this event. There's one my favorite ones. You get to see all the cool interviews, >> right? So you're interviewing Grandstand from Pallet on for the life changer award. >> Yeah, so this is really incredible. I mean, this company has pretty much converge right. We're talking, It's media, It's sports, It's fitness. It's like social at the same time. And it's completely changed. So many people they've got more writers than soul cycle. Can you believe that? >> Yeah. I like to ride my bike outside, so I'm just not part of this whole thing. But I guess I guess on those bikes you can write anywhere >> you can write anywhere, anywhere with anyone. But it's not that. It's the classes, right? You basically hop on. You see the classes. People are actually pumping you up there. Okay, Go, go, go. You can see all the other riders are in the space. It's kind >> of >> addictive. Let's let's shift gears. Talk about leadership more generally, because things were a little rough right here in the Valley right now. And people are taking some hits and black eyes. You talk to a lot of leaders. She go to a tonic, shows you got more shows. A. We go to talk to a lot of CEOs when you kind of take a step back about what makes a good leader, what doesn't make a good leader? What are some of the things that jump into your head? >> You know, we really think about a dynamic leadership model. It's something conceit on my Twitter handle. It's basically the fact that you got a balance. All these different traits. Leaders have to perform in different ways in different situation. Something like Oh, wow, that's a general. They've done a great job commanding leadership. Other times we had individuals, a wonderful, empathetic leader, right? There's a balance between those types of traits that have to happen, and they curve like seven different dimensions and each of these dimensions. It's like sometimes you're gonna have to be more empathetic. Sometimes you got to be more realistic. Sometimes you're going to be harder. And I think right now we have this challenge because there's a certain style that's being imposed on all the leaders that might not be correct >> theater thing. The hypothesis for you to think about is, you know, when a lot of these people start the Silicon Valley companies the classic. It's not like they went to P and G and work their way up through the ranks. You know, they started a company, it was cool. And suddenly boom. You know, they get hundreds of millions of dollars, the I po and now you've got platforms that are impacting geopolitical things all over the world. They didn't necessarily sign up for that. That's not necessarily what they wanted to do, and they might not be qualified. So, you know, Is it? Is it fair to expect the leader of a tech company that just built some cool app that suddenly grew into, ah, ubiquitous platform over the world that many, many types of people are using for good and bad to suddenly be responsible? That's really interesting situation for these people. >> Well, that's what we talked about the need for responsive and responsible leadership. Those are two different types of traits. Look, the founding individual might not be the right person to do that, but they can surround themselves with team members that can do that. That could make sure that they're being responsive or responsible, depending on what's required for each of those traits. You know, great examples like that Black Mirror episode where you see the guru of, like, some slasher meet a guy. Some guys like Colin is like, you know, he wants to make sure that you know someone's paying attention to him. Well, the thing is like a lot of times, at least folks are surrounded by people that don't have that empathetic You might not have had what a founder is looking at, or it could be the flip side. The founder might not be empathetic. They're just gung ho, right, ready to build out the next set of features and capabilities that they wanted to d'oh! And they need that empathy that's around there. So I think we're going to start to see that mix and blend. But it's hard, right? I mean, going through a start up as a CEO and founder is very, very different than coming in through the corporate ranks. There's a >> very good running a company, you know. It's funny again. You go to a lot of shows. We get a lot of shows, a lot of key, knows a lot of CEO keynotes, and it's just interesting. Some people just seem to have that It factor one that jumps off the top is Dobie. You know, some people just seemed >> like the have it >> where they can get people to follow, and it's it's really weird. We just said John W. Thompson, on talking about Sathya changing the culture at Microsoft, with hundreds and hundreds of thousands of employees distributed all over the world. What a creative and amazing job to be able to turn that ship. >> Oh, it is. I mean, I can turn on the charm and just, like, get your view Lee excited about something just like that, right? And it's also about making sure you bring in the input and make people feel that they're inclusive. But you gotta make decisions at some point, too. Sometimes you have to make the tough choices. You cut out products, you cut out certain types of policies, or sometimes you gotta be much more responsive to customers. Right? Might look like you're eating crow. But you know what? At the inn today, cos they're really built around customers or state Kohler's stay close air bigger today than just shareholders. >> Right. Last question. Churchill Club. How'd you get involved? What makes you excited to jump on board? >> You know, this is like an institution for the valley, right? This is you know, if you think about like the top interviews, right? If you think about the top conversations, the interesting moments in the Valley, they've all happened here. And it's really about making sure that you know, the people that I know the people that you know there's an opportunity to re create that for the next set of generations. I remember coming here when it's like I go back, I think give Hey, just I don't hear anybody in 96 right? And just thinking like, Hey, what were the cool activities? What were the interesting conversations and the church? The club was definitely one of those, and it's time to give back. >> Very good. All right, well, congrats on that on that new assignment. And good luck with the interview tonight. Hey, thanks a lot. All right. He's Ray. I'm Jeff. You wanted the Cube with that? Churchill's in Santa Clara, California. Thanks for watching. We'll see you next time.

>> Live, from San Francisco. It's theCUBE. Covering IBM Think 2019. Brought to you by IBM. >> Welcome back to theCUBE's coverage of IBM Think 2019. Here in Moscone, we're talking so much multi clouds. It's been raining all day, really windy. To help us wrap up our third day, what we call theCUBE Insights, I have our co-CEO, Dave Vellante. I'm Stu Miniman and happy to welcome back to the program. It's been at least 15 times on the program, I think our counter is breaking as to how many you've been on, Ray Wang, who is the founder, chairman and analyst with Constellation Research, also the host of dDsrupTV who was gracious enough to have me on the podcast earlier this year, Ray. >> Little reciprocity there, Stu. >> Hey, we got to get you back on, this is awesome! Day three is wrap-up and this is going to be fun. >> Ray, as we say, theCUBE is everywhere, except it's really a subset of what you and the Constellation Research team do, we see you all over the place so thanks for taking time to join us. Alright, so tell us what's going on in your world, Ray. >> So what we're seeing here is actually really interesting, we've got a set of data-driven business models that are being lit up, and you see IBM everywhere in that network. And it's not about Cloud, it's not about AI, it's not about security, it's not about Blockchain. It's really about companies are actually building these digital networks, these business models, and they're lighting them up. IBM-Maersk, we saw things with insurance companies, you see it with food trust, you see it with healthcare. It's happening, and it's the top customers that are doing this. And so it's like we see a flicker of hope here at IBM that they're turning around, they're not just selling services, they're not just selling software, they're actually delivering these business models to executives and companies, and the early adopters are getting it. >> Ray that was one of the questions we had, is what's the theme of the show and-- >> There is no theme! >> You're giving us the theme here of what it should be because we talk digital, we talk cognitive, we talk all these other big thought-y words because we need to think while we're here, right? >> We need to think, we need to think! No, but the thing is this is a theme-less show, people can't figure it out but the main thing is, look, I've got a problem, this digital disruption is happening, my business models are changing. Help me be part of that shift, or I may go away! And people realize that and that's what they're starting to get, and you see that in all the reference customers the people that were on stage. The science slams were also really great. I don't know if you had a chance to catch those but the science slams were kind of a flicker into research, IBM research which is the heart of IBM, is coming up. They're going from concept to commercialization so much faster than they used to be, used to be research would do a project people are like, that's kind of cool, maybe I'll adopt it. They're now saying hey, let's get this into the market, let's get into academia, let's get early adopters on board. >> So Ray, what do you make of the Red Hat deal? What does it say about IBM's strategy? Do you like the deal? What does it say about the industry at large? >> It's a great question. The Red Hat deal to me was overpaid, however, at 20x multiples, that's what PE firms are paying. So every vendor is now competing with PE firms for assets. Red Hat, at about 9x, 10x? Makes a lot of sense, at 20x? It's kind of like, okay, is this the Hail Mary or is this the future strategy or is this basically what the new company is? I would have rather taken that money and put it into venture funds to continue what they're doing with these network models. That would have been a better strategy to me but Red Hat's a great company, you get a great team, you get great COs you get great tooling. >> So you would've rather seen tuck-ins to actually build that network effect that you've been alluding to. Of course that would have taken longer you know, wouldn't have solidified Ginni's legacy. So, it's a big move, a big move on the chessboard. >> Well the legacy's interesting, last year the stock was down some 20-some percent, it's up 20% since January so we're going to see what happens, but it's a doubt component. >> Well I've always said she inherited a bag of rocks from Palmisano at the peak of 2012 and then it got hit hard and she had to architect the transformation. It took, I don't know, five years plus, so, you know, she was dealt a tough hand, in my opinion. >> She had a bad hand, but we've had seven years to play this. I think that's what the market's saying. >> So it's on her, is what you're saying. >> It's now on her. She's got to turn this around, finish the legacy, but you've got a great CEO in waiting with the Red Hat guy. >> Jim Whitehurst you're saying? >> Yeah, he's good >> So she's what, Ginni is 60, 61? Is that about right? >> She's past the retirement age. Normally IBM CEOs would have gone through. >> 61 to 63 I think, is that range maybe, hey, women live longer so maybe they live longer as the CEO of IBM, I don't know. >> She did get a bad hand, but I think when you execute the strategy that money, here's the tough part. Investors are saying, hey, we'd rather take your money, back away from you through stock buybacks, dividends and mergers and acquisitions, and we don't trust you to do the innovation. That's happening to every company, including all of IBM's customers. The problem is if you do that, they're hedging against those companies too. The same investors are taking 50, 100 million, giving it to three kids in a start-up anywhere in the world and saying, hey, go disrupt these guys, so they're betting against their own investments and hedging. So that's the challenge she's up against. >> We talked about in our open for the show here. It's developers, though, that's the business model. We saw IBM struggle for years to get any real traction there, there's little pockets there, they've got great legacy in open source, but Red Hat's got developers. Ray, you go and see a lot of shows, who's doing well with developers out there? >> Microsoft redid their developer network by going younger with GitHub, whole bunch of other acquisitions, this is a great developer buy in that percent. But the other piece that we noticed here was it's the partner developers that are coming in in force. It's not your average developer. I'm going to build a coding and do a mobile app, it's people that work for large system integrators, large networks, small midsize VARs, those are where the developers are coming from and now they have a reason, right? Now they have a reason to build and I think that's been a good turnaround. >> How about Salesforce with the developer angle, what's your radar say there? >> It's not about the developer angle on the Salesforce side, what's interesting about the Salesforce side is Trailhead. This is, like, learning management meets gamification meets a whole LinkedIn training program in the back end. This is the way to actually take out LinkedIn without going after LinkedIn, by giving everyone a badge. There's a couple of million people actually on this thing. Think about this, all getting badges, all training each other, all doing customer support and experience, that's amazing! They crowd-source customer experience and learning right there. And they're building a community and they're building a movement. That's the thing, Salesforce is about a movement. >> Couple of others, SAP and Oracle, give us your update there. >> I think SAP's in the middle of trying to figure out what they have to do to make those investments. We see a lot of partnerships with Microsoft and IBM as they're doing the Cloud upgrades, that's an area. The acquisition of Qualtrics is another great example, 20x. 20x is the number people are now paying for for acquisitions and for assets on that end. And Oracle's going to be interesting to watch, post-Kurian to see how they come at it. They have a lot of the assets, they've got to put them together to get there, and then we've got all these interesting things like ServiceNow and Adobe on the other end. Like, ServiceNow is like, great platform! Awesome, people are building and extending the Cloud in ServiceNow, but no leadership! Right? I mean, you've got a consumer CEO trying to figure out enterprise, a consumer CMO trying to figure out enterprise, and they don't know if am I a platform or am I an app? You've got to figure that out now! People want to work with you! >> Well it is a company in transition at the top, for sure. >> But they can do nothing and still make a ton of money on the way out. >> And they've kicked butt since Donahoe came on, I mean just from a performance standpoint, amazing. >> Oh yeah, performance? You can do nothing and I think it's still going to coast but the thing is at some point it's going to come bite you, you got to figure that out. >> How do you think that Kurian will fit at Google, what's your take there? >> You know, early reactions on Kurian at Google is good, right? The developers are embracing him, he understands what the problems are. Let's be honest, I've said this many times to you guys in private and also in public, you know. It was a mess, it was a cluster before. I mean, you had three years, and you lost traction in the market, right? And it's because you didn't get enterprise, you couldn't figure out partners and, I mean, you paid sales people on consumption! Who does that? You're a sales rep, you're like, I'm not going to do this on consumption! Makes no sense! >> Ray, Kurian had been quoted that no acquisition is off the table, you know, they didn't buy GitHub, they didn't buy Red Hat, do you see them making a 10, 20 million dollar acquisition to get them into the enterprise space? >> Billion. >> Yeah, sorry, 20 billion. >> I think there's a lot that they go after. I know there's rumors about ServiceNow, there's a couple of other things. I think the first acquisition, if I were to make it would be Looker. I mean I love that thing that's on there and buy Snowflake too while you're at it. But we'll see what they do. I think the strategy is they've got to win back the trust of enterprises. People need to know, I'm buying your relationship, I have a relationship, I can count on you to be successful as opposed to, hey, you know, you can get this feature for less and if you do this on a sustained unit or, I want to know I can trust you and build that relationship and I think that's what they're going to focus on. >> Well, come on, isn't Google's business still ads? I mean, that's still where all their revenue is. >> It is, but the other category is $10 billion. That other category of devices and Cloud and all that? That's still a big category and that's where all the growth is. I mean look at this, it's a full frontal assault between Amazon and Google, Amazon Alexa versus Google Home, right? Amazon in ads, $10 billion in ads, going after Google's ad business. Amazon doing an AWS versus Google Cloud. Google's under assault right now! >> Give us the update on Constellation, your conference is really taking off, you've got great buzz in the industry, and congratulations on getting that off the ground. >> And the Tech for Good stuff, loved it. >> Thank you. We had great event, December 10th, talking about the future of the Internet. What it means in terms of, you know, digital rights, human rights in a digital age, was really that conference. Our big flagship conference is November 4th through 7th, it's at Half Moon Bay. We get about 250 CXOs together, about 100 vendors and tech folks that are visionaries and bring them together, that's doing well, and we do our healthcare summits. We brought on a new analyst, David Chou. David Chou, and if you've seen him before, he's like one of the top analysts for CIOs and chief data officers in the healthcare space, he's at HIMSS right now. >> He's awesome, we know him from Twitter. He's been on, he's great. >> Yeah, so we do healthcare summits twice a year and that's been picking up, some of the top thinkers in healthcare. We bring them in to Las Vegas, we do a brainstorming session, we work with them. They think about ideas and then we meet again, so. >> Alright, Ray, we want to give you the final word. We're halfway through IBM Think, what have you been thinking about this and any final musings on the industry? >> So I was very upset last year at how it was run. And I think this has run much better than last year. I think they did a good job. February in San Francisco? Never again, don't do that. I know it's May next year, is when this event's going to be. But I think the main thing is IBM's got to do more events than once a year. If you get enterprise marketing you realize it's at the beginning of the year, it's still sales kick-off and partners. March? March is like closing the quarter, so you do an event in April or May, and you do it in April or May but you have multiple events that are more targeted. This theme-less approach is not working. Right, partners are a little confused but they're here because it's once a year. But more importantly, build that pipeline over the quarters, don't just stop at a certain set of events, and I think they'll get very successful if they do that. >> Alright well, Ray, next time you come on the program, can you please bring a little bit of energy? We'll try to get you on early in the show when you're not so worn down. >> I know. >> Thanks as always. >> Appreciate you coming back on, man. >> Hey thanks, man, it's theCUBE! I love being on this thing.. >> Always a pleasure. >> Alright and, yeah, we always love helping you extract the signal from the noise. We're Dave Vellante, John Furrier, Lisa Martin. I'm Stu Miniman. Thanks for watching day three of theCUBE at IBM Think. Join us tomorrow, thanks for watching. (light music)

>> Hey, welcome back everybody! Jeff Frick here with theCUBE. We're at Zuora Subscribe at downtown San Francisco, and every time we go out to conferences, there's a pretty high probability we're going to run into this Cube alumni. Sure enough, here he is, Ray Wang. He's the founder and principal of Constellation Research. Ray, always great to see you. >> Hey Jeff, this is awesome, thanks for having me. >> And close to your hometown, what a thrill! >> This is, it's a local conference! What else can I ask for? >> So what do you think? Subscription economy, these guys have been at it for a while, 1200 people here, I'm a big Spotify fan, Amazon Prime, go back to Costco if you want to go back that far. But it seems to really be taking off. >> It is. About three years ago, digital transformation became a hot topic. And because it became a hot topic, it's really about how do I get products to be more like services. How do I get services to get into insights, and how do I make insights more like experiences and outcomes? And that natural transition as companies make a shift in business models is what's driving and fueling the subscription economy. >> It's interesting. Do you think they had to put the two and two together, that once the products become services now you can tap into that service, you can pull all kinds of data after that thing, you can have analytics, as opposed to shipping that product out the door it goes and maybe you see it every 15,000 miles for a checkup? >> You know what it is? It's basically, about three years ago, people started to realize this. Tien's been talking about this for ages, right? He's been talking about everything's a subscription economy, everything is going to be SAS-ified. And in tech world, everybody got that. But it was when companies like GE, which we saw together, a Caterpillar or a Ford, started to realize, "Hey we can do remote monitoring and sensing "with IOT on our cars, "and I can now figure out what's going on "and monitor them or give an upgrade, "or give a company an upgrade on their appliance, "or give an upgrade on their vehicle, "or do safety and compliance." Then people started realizing, "Oh, wow. "We're not just selling products. "We're in the services business." >> Right. It's funny, if you read the Elon Musk book, how the model years of Teslas, there's no such thing as a model year. It's what firmware version are you on, and then they upgrade. >> Oh, no, that's what we do all the time. You click on a little T, and it's like, boom, firmware. Oh, I get a new upgrade. Only the other day, you touch your head seat, there's like a lumbar support thing, the software popped up for headrest! I never knew I could change the headrest! It literally showed up two months ago. It's unbelievable. >> So, the cool thing, I think, that doesn't get enough play is the difference in the relationship when now you have a subscription-based relationship. That's a monthly recurring or annual recurring, you got to keep delivering value. You got to keep surprising you every morning, when you come out and get in your car, as opposed to that one time purchase. "Adios, we'll see you in however many years "until you get your next vehicle." >> Oh, that's a great example. And the Tesla, we got the Easter eggs over Christmas, right? So the Christmas holiday thing with the Model X that actually did Trans-Siberian Express to the Bellagio fountains with the doors that popped up. You're like, "Hey, what is this thing?" It's just an upgrade that shows up. You're like, "Okay." But you do. You do have to delight customers, you're always capturing their attention, and the fact is, hey, I might buy a toaster. And in that toaster, I might get an upgrade two to three years out. Or maybe, I just buy toasters, and I subscribe to them. And every three years, I get a new toaster. And I can choose between a model L or I can go upsell, get a different color, or I can change out a different set of features, but we're starting to see that. Or maybe, I get a hotel room or a vacation. And that hotel room is at level X, and if I get a couple more members of my family, I get to level Z, and I get to another level, where I lose all the kids, I go back to level A. But the point being is I'm buying a subscription to having an awesome vacation. And that is the type of things that we're talking about here. It's that freedom that Tien was talking about. >> Because he talked about the freedom from obsolescence, freedom from maintenance. There's a whole bunch of benefits that aren't necessarily surfaced when you consume stuff as a service versus consuming it as a product. >> It does. And sometimes it may cost more, but you're trading the convenience, you're trading the velocity of innovation, right? For some people, they just want to own the same thing, they're not going to make the move, but for other people, it's about getting the newest thing, getting delighted, having a new feature. And in some cases, it's about safety, right? This is regulatory compliant or I'm actually doing rev rec correctly, as they were talking about, ASC606. >> Alright, so you're getting out on the road a lot, it's June 6, and I won't tell anyone on air how many miles you already have, because Tamara is probably watching, and she'll be jealous, but biggest surprise is you see here or recently as this digital transformation just continues to gain speed. I'm doing a little research now, and maybe you can help me out. Looking back at digital photography, because it's like, "No, no, no, no, no." for the film, and then it's like, boom. I think these really steep inflection points, or up if you're on the right side, are coming. >> Let's stick to digital photography, that was a great one. There was the point, remember, where we actually had all those disposable cameras at parties that'd get developed, one hour developing. Then we get to back to the point where you just showed up at Costco, dropped something off, you'd get the disk and the photo. Then we had O-Photo, and now we have nothing. Everything just went away because of the phones. These things changed everything, right? I mean, they changed the way we look at photography to the point where, do we even have an album? I was breaking out albums basically three weeks ago, showing my kids, like "Hey, this is what a photo album looks like." And they were completely mystified. "Oh, you print these, how do they get printed?" I mean, they're asking the basic questions. That transformation is what we're having right now. "You own a car?" "You actually buy a PC?" I'm buying compute power. Kilowatts per hour for artificial intelligence in the next year. It's not going to be, I bought the server, I loaded it up, I got it tuned, I got it ready. So yeah, we are in the middle of that shift. But it's the fact that companies are willing to change their business models, and they're willing to break free in the post ERP era. A lot of this is just, my old ERP does not do billing, it doesn't understand the smallest unit of something I sell, and I've got to fix that. And more importantly, my customers, they want to buy it today. The want to buy it in pieces. They want to buy it even smaller pieces. They might buy it every other week, they might buy it-- we have no idea. Yeah, I've got to make sure I can do that. >> It's just interesting too that this is happening now. We're talking about autonomous cars. We see the Waymo cars all the time. The guy from Caterpillar, he's got to a whole autonomous fleet of mining vehicles that are operating today. >> 500,000! He's got 500,000 little trucks. Well, they're not little trucks, they can't fit in this building. >> They're big trucks. Apparently, they tried. >> But they're trying to get these trucks in. We used to think about, like "Hey, these are agricultural vehicles that can be remotely controlled by GPS, they also work for tanks." These are things that are actually doing runs. Now, it's a great reason. Think Australia. Out in Perth, it's about $150,000 to hire a driver. Just to go back and forth. So they figured, "This is just getting ridiculous. "We don't have enough people out here. "We can't convince enough people "to come drive these trucks. "Let's go automate that." That's a lot of the story of where a lot of this came from. >> Or he had a bad night, or broke up with his girlfriend, or distracted about this or that. The whole autonomous vehicle versus regular people driver-- all you've got to do is ride around on your bicycle in your neighborhood, and watch how many people stop at stop signs. Should we answer that question real fast? >> Oh, I do that in California. That's kind of bad, actually. >> Alright Ray. Well, thanks for taking a few minutes. I'm glad you get a weekend at home. Where you off to next, I should ask? >> Oh, it's going to be a crazy next few weeks. I'm going to be in London and Paris and Boston all next week. >> Oh, you're going to eat well. >> I'll try. >> Alright, he's Ray Wang. I'm Jeff Frick. You're watching the Cube from Zuora Subscribe. Thanks for watching.

>> Good Live from Las Vegas. It's the cute covering knowledge sixteen Brought to you by service. Now carry your host David, Dante and Jeffrey. >> Oh, >> welcome back to knowledge. Sixteen everybody, this is the cubicle wall to wall coverage. We got the events. We extract the signal from the noise. This is Day one for us will be going Three days of knowledge extraction from knowledge. Sixteen. Ray Wong is here. He's the founder and principal analyst and chairman of Consolation Research. Up and coming Smoking Hot research company. Ray, Always a pleasure to see you. Thanks for coming >> on. Excited to be here, man. It's been a world one week of events, so >> I'LL say So you were You were down. It's Sapphire, right? You were over it on Tampa Amplify And >> I'm Austin s Wait after this race, a >> normal week for you >> It's a normal week for all of us. >> So impressed You were telling us off camera that you were at one of the earlier knowledge events down in San Diego. So you've got a lot of experience with this company, >> you know, it was in a tent. It was outside they had detected. I think it's like a park. I'm not even sure what it was. I just But remember, there's one one hundred fifty people next. There's like five hundred people. Three years later, it's pretty wild. >> So they've come out of the blue and really, you know, escalated a lot of momentum. The latest billion dollar software company with a plan to get to four billion. So stepping back a second just looking at the software landscape, one has to be impressed with the progress that service now is made. What's your take on the industry and service now in particular? >> Well, I think what people don't understand this service now is a platform, right? There's a business model platform or the way that we used to look at paga or the way we used to look at a lot of those companies that were actually sit in the middle. That orchestration what's changes? Because everything's in the cloud. What we now have the ability to abstract orchestrating doing away that we've never seen before, right so you can take specific business problems. Take the heart of what's actually happened on the idle piece. Use it to not just manage the process, but also do the analytics and the monitoring. So when we get the things like Coyote coyotes really about having a set of smart services and being well. To put that in the construct is a lot of the opportunity that we see going forward >> so high. So I said three years ago in the Cube after I saw the platform capabilities and said, Wow, this is a collision course with sales force Investor's Business Daily wrote a article today. Collision course of sales for so glad they caught up with Theo. But But, I mean, it's you could kind of see it coming together. And now you Frank lays out this vision this morning. Have you got the AARP estate, the C R M estate and tea or a service management Rather kind of bridging those two. How do you see it? >> No, we definitely see this as a platform play. Now here's what's interesting is the lots of the developments, and you see this all the time has been happening in the APP to have side of the House package. APS have kind of been at a standstill for innovation compared to what's going on on the customs side. And so every so often we see that flip on platforms. This is the beginning of that flip, more than one person said. I it is going to be the end of the affair, right, because we're going to put all the intelligence into the interaction. You don't have to go to the specific app. No. And the fact is, what becomes important is the ability, the orchestration, the intelligence, the recommendation and what you want to build to get to the part where I'm making the right set of recommendations to augment the next set of processes. That's what gets really powerful and these platforms that are emerging on, What's the next set of clouds? That's going to be where we're going to see a lot of this advancement. >> So the FBI essentially becomes the product. Is that kind of? >> It's the orchestration of the AP eyes, the way the context was delivered against those AP eyes and more importantly, how we actually pulled together those journeys, like a couple things that we talk about time mass personalization of scale, lots of context, right, so rolls relationship, identity weather, location, time, all important, Then choose your own adventure journeys the ability to actually abstract different processes from different places and bring them together, and the more importantly, we call intention driven design, which is. I'm gonna give you three or four choices. Learn over time. Take that machine learning. Then apply that the next set of recommendations and then start building against that. And that power sits on the network. That power sits in these new platforms. >> So you're here speaking to the service now customers about customer experience, right? It's something we hear a lot about. Your an expert in that in that space. What did you say? What was the reaction? What was the feedback? >> Well, I think the important thing is we're seeing new business models and you hear me say this before it's we're in a post sale on demand, attention, economy. And what that means is everything after the sale is what's happening right now. That's the service. That's the experience. Peace. The on demand pieces were accessing smaller, smaller slices of a product. Maybe not even a product, a service, maybe not even a service, and incite maybe not even insight and experience. And then, more importantly, it's an attention to come. If you're not capturing my time and attention, which is mind share, or if you're not saving me time and money, I don't care. And That's what we're in. We're in. These business moms are built around. This is interesting. Came out of the Oracle Marketing cloud shows Well, same thing. Just smaller and smaller slices of attention based on the way you interact with all the other applications you have. You don't have time to give somebody the big story. You've got to get him when you can. They could be standing in line. They look at their phones, are in the middle of their kids, switching innings at the baseball game. And you got to get in that little tiny video that in between time is so important because you don't close there. You lose him, right? And it's not for something really big. It's move them along the needle down. The journal. Correct. >> What do you make of this, Dave? Dave Wright was just not talking about the new state of work. IBM has been talking about a new way to work in. He is kind of running the collaboration, you know, group. Now you you talk about millennials and how they work. What are you seeing in state of work? >> Well, a lot of the research we're looking on the future of work is by one of my colleagues, Alain Le Pastilles, and what he's been really looking as this shift in terms of conversations as a service. He's been looking at the shift in terms of intelligent collaboration. Right, and all this stuff is actually leading the point where we're actually using technology to augment ability. Teo do decisions had a lot more automation than we had him before. But then cognitive assistance pop up right and they help make a smarter. And they learned from our different our actions and all that's starting to come into the workplace, which is exciting and a little bit creepy and scary at the same time. >> So what's the What's the What's new with Constellation? You guys are growing. Bring it on. New analyst Cranking out Want to research? Your event keeps growing? Give us the update on Constellation. >> You know, I think the big thing is this digital transformation story we've been talking about for the last five to six years is huge. The next set is really not about transformation. It's about finding growth in times where there is no growth. That's where we're going to talk about the next five years at our conference. Really? Talking about what are those factors, right? We gotta jump start growth. Global GDP is growing two to three percent at best. Every company has a target of like five to ten. Someone's gonna lose, and it's gonna be very interesting. >> So you think that growth is going to come through productivity improvements or investments in technology? Actually, Dr sort of new productivity levels were taking away from >> someone else. I think we're taking me for someone else. That's what I'm really scared about, that they're smart growth that's sustainable and helps people with the jobs and the job transitions. And there's what we've been doing, which a lot of destructive Cross, which is actually limiting all of the jobs and actually making it harder to grow in the long run. >> Well, so yeah, we've talked about this on the Cuba lot machines replacing humans, which they've always replaced humans. But it seems to be now happening at the cognitive level. That's scary. I know you guys to the valley, wags. You know the seasonal nervous right now, You guys, you more sanguine? Then the VCs air >> well with these three big areas where we see a lot of investment. Deep learning happens to be one of them, right? We see a lot of medicine going off. Some of the smartest people I know are all focused and on deep learning. Very interesting thing. If you look at that university, California, Irvine there's a whole department around. This artificial intelligence that just lifted itself up became a private corporation. So there's very unfeeling things there. There's nanotech, which is also some erasers, things on the material science piece that's also playing a big role. And then, of course, there's stem cells in the biotech piece. Those three things are converging, and you know it's more than just building out the Star Trek roadmap that Apple's been doing. It's a lot bigger than that. There's some big societal shift that are happening. >> What, what's next for you? You say you're heading Teo. That's sweet, but we're So we work. We find Ray Juan. I'm >> off this sweet world, Max. There's a monetary it next week. There's a whole bunch of other events picking up in June as well as you. You're going to be at them, but I think we do our retreat every year at the end of the year. May June, we're going to be at Stanford, the faculty club. All the constellation folks get together on. Then we go back out into the field and it's a crazy summer as well. I don't know when this stops making, so yeah, you could always find him on Twitter That that's but I looked for you guys when I'm where you're at is where the events are. >> Well, hopefully our past will continue to cross. We love having you in the Cube was a great guests. Really appreciate your time. Thanks for coming on. >> You know, Thanks for having have a >> great conference. All right. They've travelled, right, everybody. We'LL be back after this short break. This's the Cube or live from knowledge. Sixteen, right? >> Service now is the

okay we're back here live ending up day one of IBM's information on demand exclusive coverage for SiliconANGLE and Wikibon and constellation research breaking down the day one analysis I'm John furrier and join my co-host E on the cube Dave vellante of course as usual and for this closing wrap up segment of day one we have analyst and founder of constellation research ray Wang former analyst big data guru software heading up the partner pavilion kicking off all the flying around the world your own event this month past month things going great how are you how are you doing we're going to great man there's a lot of energy in q3 q4 we've been watching people look at trying to spend down their budgets and I think people are just like worried that there's going to be nothing in 2014 right so they're just bending down we're seeing these big orders like tonight I've got to fly out to New York to close out a deal and help someone else that's basically it was a big day to deal that's going down this is how crazy it's going on and so it's been like this pretty much like for the last four or five weeks so flows budget flush I just wash this budget lunchtime what are you seeing for the deals out there give us some of the examples of some of the sizes and magnitude is it you know you know how are you up and run to get get some cash into secure what size scopes are you seeing up yeah i mean what we're seeing I mean it's anything from a quarter million into like five million dollar deals some of our platform we sing at all levels the one that's really hot we were talking about this that the tableau conference was the date of is right dative is is still really really hot but on the back end we're saying data quality pop-up we're seeing the integration piece play a role we also saw a little bit of content management but not the traditional content management that's coming in more about the text mining text analytics to kind of drive that I mean I'm not sure what are you guys seeing alone yeah so what we're seeing a lot of energy I've seen the budget flush we're not involved in the deals like you are Dave is but for me what I'm seeing is IT the cloud is being accepted I'll you know those has not talked about publicly is kind of a public secret is amazon is just destroying the value proposition of many folks out there with cloud they're just winning the developers hand over fist and you know i'm not sure pivotal with cloud family even catch up even OpenStack has really got some consume energy around we're following that so it opens stack yet amazon on the public cloud winning everything no money's pouring into the enterprise saying hey we got to build the infrastructure under the hood so you can't have the application edge if you don't have the engine so the 100 x price advantage and that's really a scary thing but I think softlayer gives IBM a shot here yeah we were talking about self leyva so you are seeing more I'm seeing it aight aight figure deals and big data right and it's starting to get up there so softly I'd love to get your take on soft layers we've been having a debate all day Oh softlayer jaws mckenna what do you what's your take you're saying it's a hosting I've been a look at first of all yeah I love putting a huge gap 9 million dollars per lock event data center hosting now if that's a footprint they can shave that and kind of give their customers some comfort I think that's the way i see it i mean just I haven't gone inside the numbers to see where it's going to be where this energy is but like we're software virtualization is going on where everyone's going on with virtualization the data center I'll give them a cloud play I just don't see ya didn't have one before I mean happy cloud I mean whistling private club Wow is their software involved I think it provides them with an option to actually deliver cloud services with a compression ratio on storage and a speed that they need to do to deliver mobile mobile data analytics right there's things that are there that are required so it gives them an option to be playing the cloud well I just saw I mean in the news coverage and the small inspection that we did I did was I just didn't reek of software innovation it's simply a data center large hosting big on you agree they didn't really have a northern wobblin driving him before this was brilliant on your Sun setting their previous all these chairs deal kind of musical chairs me for the music stops get something it was that kind of the deal no I think they are feel more like customers asking for something and they wanted IBM to have it yeah IBM works it's an irr play for IBM they're gonna make money on this team not a tuck under deal 900 million no I know but they'll make money on it that's IBM almost always does with it I'll leave it up to you guys to rip on I was your conference oh thanks hey constellation connecting enterprise was awesome we were at the half moon bay Ritz we had 220 folks that were there senior level individuals one of the shocking things for me was the fact that when we pulled the audience on day one two things happen that I would never imagine first thing as ninety percent of the folks downloaded our mobile app which was like awesome right so the network was with them the knowledge is with them when they leave the event and all the relationships the second thing that really shocked me we knew we had really good ratios but it was seventy-five percent of the audience that was line of business execs and twenty-five percent IT it was like we were we didn't have to preach to the choir it was amazing and the IT folks that were they were very very innovative on that end so it was awesome in that way so a lot like the mix the mix here is much more line of business execs the last week at hadoop world loose you know the t-shirt crowd right a lot of practitioners you know scoop I've flume hey we got the earth animals ever right oh but no this event is actually interesting IBM iod for me is like I didn't realize this when I didn't I looked at numbers when we're doing a partner event yesterday and there are thirteen thousand attendees here that actually makes that the biggest big data and analytics conference bigger than strata bigger than a whole bunch of other ones and so I mean this is pretty much the Nexus of what about open world big data over there but this is a big opera you see world any world cloud big data yeah hey the between no but so IBM's done a fantastic job of really transitioning this conference from sort of an eclectic swix db2 informix right I'm management routine fest right yeah and now it's like what are the business things I mean what are we trying to save around the world are they telling the story effectively it's a hard story to tell you got big data analytics cloud mobile in the middle and you got social business but then you got all this use case they have success stories if customers that creating business outcomes they telling the story effectively is it not enough speeds and fees is it too what's your take the stories are there we've seen like 122 case studies from the business partner side we just haven't seen them percolate out and I think they've got to do a better job evangelizing stories but what's interesting is like there's that remember we talked about this data to decision level there's that data level that was IBM right here's the database here's the structure here's the content management here's the unstructured stuff this is where it sets then there was that information management level which that they started to do which is really about cleaning the data connecting that data connecting to upstream and downstream systems getting into CRM and payroll and then they got to this level about insights which was all the Cognos stuff right so they've been building up the stat from data decisions so they got data information information to insight and then we're getting to this decision-making level which they haven't made a lot of the assets or acquisitions there but that's the predictive analytics that's the cognitive computing you can see how they're wrapping around there I mean there's a lot of vendors to buy there's a lot of opportunity out there's a lot to connect and they've been working on it for a while but I guess I got to ask you how they doing what's your report card from last year this year better better storytelling better messaging I think the stories are getting better but we're seeing them in more deals now right before we'd see a lot more SI p traditional SI p oracle you know kind of competes and a little bit of IBM Cognos now we're seeing them in a lot of end-to-end deals and what we're talking about it's not like I T deals these are line of business folks that say look I really need to change my shopping experience what do you guys have we see other things like you know the fraud examples that any was talking about those are hilarious I mean those are real I see em in every place right I mean even with Obamacare right there's gonna be massive amounts of fraud there any places that people going to want to go in and figure out how to connect or correct those kind of things yeah so so seeing the use cases emerge yeah and in particular me last week in a dupe world it was financial services you're talking risk you talk a marketing you're talking fraud protection to forecasting yep the big three and then underneath that is predicted predictive analytics so you know that's all sort of interesting what's your take on on Amazon these days you know they are crushing it on so many different unbelievable right on more billion this year maybe it's when you build a whole company which is basically on the premise of hey let's get people to offset our cost structure from November 15th to january first I mean it's pretty amazing what you can do it's like everyone's covering for it and even more funny it's like they're doing in the physical world with distribution centers I know if we talked about this before but what's really interesting is they've got last mile delivery UPS FedEx DHL can't cat can't handle their capacity so now the ability from digital to physical goods they've got that and beezus goes out and buys the post so he can make the post for example a national paper overnight again he can do home delivery things that they couldn't do before they can take digital ads bring that back in and so basically what they're doing on the cloud side they're also doing on the physical distribution side amazing isn't it they're almost the pushing towards sunday delivery right US Postal Service go into five day deliveries sort of the different directions amazon I'm Amazon's going to be the postal service by the time they're done we're all going to subsidize it so so I gotta get you take on the the Oracle early statement Larry Ellison said were the iphone for the data center that's his metaphor a couple of couple or global enrolls ago now you got open stack and though we kind of laugh at that but but amazon is like the iPhone you know it's disruptive its new its emerging like Apple was reading out of the ashes with Steve Jobs Oracle I think trying to shoehorn in an iphone positioning but if OpenStack if everyone's open and you got amazon here there is a plausible strategy scenario that says hey these guys can continue to to put the naysayers at the side of the road as they march forward to the enterprise and be the iphone they've turned the data center into an API so so we got the date as their lock in right so this sim lock in Apple has lock in so is that lock in what's your take of that scenario you think it's video in the open ecosystem world they're all false open because a walk-in also applies but but you've been even to this for a long time right and probably one of the things that you're seeing is that it's not about open versus closed it's about ubiquity right Microsoft was a closed evil empire back ten years ago now it's like oh the standard right it's like ok they're harmless Google was like open and now they're the evil empire right it just depends on the perception and the really is ubiquity Amazon's got ubiquity on it so i did is pushing their winning the developers the winning the developers they got the ecosystem they got ubiquity they've got a cost structure I mean I don't know what else could go wrong I think they could get s la's maybe and once that had I don't know what is Amazon's blind spot I mean s la's I think well a lumpy performance no one wants lumpy right they want the big Dayton who's got ever who's got better public as public cloud SL is denied well I think about what he just said us everybody no but here's think that's a public road statement not an amazon said let's crunch big data computation December fifteenth you tell me what this is all I want to know well I think I think an easy move is I mean this day you've got to do that on premise I just I just don't I just don't think that people are forecasting amazon the enterprise properly and you just set out the Washington Post that is a left-field move we can now look back and say okay I said makes sense amazon can continue to commoditize and disrupt and be innovative then shift and having some sort of on prem playing oh then it's over right then and then gets the stir days surrounded the castle but they really don't have a great arm tremblay have no on print but they could they could get one good I think they want to see well think they want to but I think with them what they figured out was let's go build some cool public service get everyone else to subsidize our main offerings right it's basically ultimate shared service everyone's subsidizing Amazon's destruction of their business right so if you're Macy is why the heck are you on amazon right you know if you're competing with them why the heck are you on Amazon you're basically digging your own grave I'm paying them to do it it's amazing I mean that's that's the brilliance of this goes invade they brag about it yeah digging your own brave like it's a you know put the compute power is great okay great but you're subsidizing Amazon's for the you know compute power so r a great shot great to have you here congratulations on your event constellation research awesome successful venues ahead last month top folks in you're doing a great job with your company and the end the day out today in the last word tell the folks what's happening with IBM what do you expect to hear from them tomorrow I know you're going to be another thing you had to fly to but what does IBM what's a trajectory coming out of the show for IBM what's your analysis I think the executives have figured out that the important audience here is really the line of business leaders and to figure out how to do couple things one democratize decision-making the second thing figure out how they can actually make it easy to consume IBM at different entry points and I think the third thing is really how can we focus on improving data visualization graphics I think you'll see something about that ray Wang on the cube cube alumni tech athlete entrepreneur new for his new firm not new anymore it's a couple years on his belt doing a great job but three years old congratulations we'll be back day two tomorrow stay with us here exclusive coverage of IBM information I'm John prairie with Dave vellante this is the cube will see you tomorrow the queue

(upbeat music) >> Hello, this is Dave Valente with theCUBE, and one of the most gratifying aspects of my role as a host of "theCUBE TV" is I get to cover a wide range of topics. And quite often, we're able to bring to our program a level of expertise that allows us to more deeply explore and unpack some of the topics that we cover throughout the year. And one of our favorite topics, of course, is data. Now, in 2021, after being in isolation for the better part of two years, a group of industry analysts met up at AWS re:Invent and started a collaboration to look at the trends in data and predict what some likely outcomes will be for the coming year. And it resulted in a very popular session that we had last year focused on the future of data management. And I'm very excited and pleased to tell you that the 2023 edition of that predictions episode is back, and with me are five outstanding market analyst, Sanjeev Mohan of SanjMo, Tony Baer of dbInsight, Carl Olofson from IDC, Dave Menninger from Ventana Research, and Doug Henschen, VP and Principal Analyst at Constellation Research. Now, what is it that we're calling you, guys? A data pack like the rat pack? No, no, no, no, that's not it. It's the data crowd, the data crowd, and the crowd includes some of the best minds in the data analyst community. They'll discuss how data management is evolving and what listeners should prepare for in 2023. Guys, welcome back. Great to see you. >> Good to be here. >> Thank you. >> Thanks, Dave. (Tony and Dave faintly speaks) >> All right, before we get into 2023 predictions, we thought it'd be good to do a look back at how we did in 2022 and give a transparent assessment of those predictions. So, let's get right into it. We're going to bring these up here, the predictions from 2022, they're color-coded red, yellow, and green to signify the degree of accuracy. And I'm pleased to report there's no red. Well, maybe some of you will want to debate that grading system. But as always, we want to be open, so you can decide for yourselves. So, we're going to ask each analyst to review their 2022 prediction and explain their rating and what evidence they have that led them to their conclusion. So, Sanjeev, please kick it off. Your prediction was data governance becomes key. I know that's going to knock you guys over, but elaborate, because you had more detail when you double click on that. >> Yeah, absolutely. Thank you so much, Dave, for having us on the show today. And we self-graded ourselves. I could have very easily made my prediction from last year green, but I mentioned why I left it as yellow. I totally fully believe that data governance was in a renaissance in 2022. And why do I say that? You have to look no further than AWS launching its own data catalog called DataZone. Before that, mid-year, we saw Unity Catalog from Databricks went GA. So, overall, I saw there was tremendous movement. When you see these big players launching a new data catalog, you know that they want to be in this space. And this space is highly critical to everything that I feel we will talk about in today's call. Also, if you look at established players, I spoke at Collibra's conference, data.world, work closely with Alation, Informatica, a bunch of other companies, they all added tremendous new capabilities. So, it did become key. The reason I left it as yellow is because I had made a prediction that Collibra would go IPO, and it did not. And I don't think anyone is going IPO right now. The market is really, really down, the funding in VC IPO market. But other than that, data governance had a banner year in 2022. >> Yeah. Well, thank you for that. And of course, you saw data clean rooms being announced at AWS re:Invent, so more evidence. And I like how the fact that you included in your predictions some things that were binary, so you dinged yourself there. So, good job. Okay, Tony Baer, you're up next. Data mesh hits reality check. As you see here, you've given yourself a bright green thumbs up. (Tony laughing) Okay. Let's hear why you feel that was the case. What do you mean by reality check? >> Okay. Thanks, Dave, for having us back again. This is something I just wrote and just tried to get away from, and this just a topic just won't go away. I did speak with a number of folks, early adopters and non-adopters during the year. And I did find that basically that it pretty much validated what I was expecting, which was that there was a lot more, this has now become a front burner issue. And if I had any doubt in my mind, the evidence I would point to is what was originally intended to be a throwaway post on LinkedIn, which I just quickly scribbled down the night before leaving for re:Invent. I was packing at the time, and for some reason, I was doing Google search on data mesh. And I happened to have tripped across this ridiculous article, I will not say where, because it doesn't deserve any publicity, about the eight (Dave laughing) best data mesh software companies of 2022. (Tony laughing) One of my predictions was that you'd see data mesh washing. And I just quickly just hopped on that maybe three sentences and wrote it at about a couple minutes saying this is hogwash, essentially. (laughs) And that just reun... And then, I left for re:Invent. And the next night, when I got into my Vegas hotel room, I clicked on my computer. I saw a 15,000 hits on that post, which was the most hits of any single post I put all year. And the responses were wildly pro and con. So, it pretty much validates my expectation in that data mesh really did hit a lot more scrutiny over this past year. >> Yeah, thank you for that. I remember that article. I remember rolling my eyes when I saw it, and then I recently, (Tony laughing) I talked to Walmart and they actually invoked Martin Fowler and they said that they're working through their data mesh. So, it takes a really lot of thought, and it really, as we've talked about, is really as much an organizational construct. You're not buying data mesh >> Bingo. >> to your point. Okay. Thank you, Tony. Carl Olofson, here we go. You've graded yourself a yellow in the prediction of graph databases. Take off. Please elaborate. >> Yeah, sure. So, I realized in looking at the prediction that it seemed to imply that graph databases could be a major factor in the data world in 2022, which obviously didn't become the case. It was an error on my part in that I should have said it in the right context. It's really a three to five-year time period that graph databases will really become significant, because they still need accepted methodologies that can be applied in a business context as well as proper tools in order for people to be able to use them seriously. But I stand by the idea that it is taking off, because for one thing, Neo4j, which is the leading independent graph database provider, had a very good year. And also, we're seeing interesting developments in terms of things like AWS with Neptune and with Oracle providing graph support in Oracle database this past year. Those things are, as I said, growing gradually. There are other companies like TigerGraph and so forth, that deserve watching as well. But as far as becoming mainstream, it's going to be a few years before we get all the elements together to make that happen. Like any new technology, you have to create an environment in which ordinary people without a whole ton of technical training can actually apply the technology to solve business problems. >> Yeah, thank you for that. These specialized databases, graph databases, time series databases, you see them embedded into mainstream data platforms, but there's a place for these specialized databases, I would suspect we're going to see new types of databases emerge with all this cloud sprawl that we have and maybe to the edge. >> Well, part of it is that it's not as specialized as you might think it. You can apply graphs to great many workloads and use cases. It's just that people have yet to fully explore and discover what those are. >> Yeah. >> And so, it's going to be a process. (laughs) >> All right, Dave Menninger, streaming data permeates the landscape. You gave yourself a yellow. Why? >> Well, I couldn't think of a appropriate combination of yellow and green. Maybe I should have used chartreuse, (Dave laughing) but I was probably a little hard on myself making it yellow. This is another type of specialized data processing like Carl was talking about graph databases is a stream processing, and nearly every data platform offers streaming capabilities now. Often, it's based on Kafka. If you look at Confluent, their revenues have grown at more than 50%, continue to grow at more than 50% a year. They're expected to do more than half a billion dollars in revenue this year. But the thing that hasn't happened yet, and to be honest, they didn't necessarily expect it to happen in one year, is that streaming hasn't become the default way in which we deal with data. It's still a sidecar to data at rest. And I do expect that we'll continue to see streaming become more and more mainstream. I do expect perhaps in the five-year timeframe that we will first deal with data as streaming and then at rest, but the worlds are starting to merge. And we even see some vendors bringing products to market, such as K2View, Hazelcast, and RisingWave Labs. So, in addition to all those core data platform vendors adding these capabilities, there are new vendors approaching this market as well. >> I like the tough grading system, and it's not trivial. And when you talk to practitioners doing this stuff, there's still some complications in the data pipeline. And so, but I think, you're right, it probably was a yellow plus. Doug Henschen, data lakehouses will emerge as dominant. When you talk to people about lakehouses, practitioners, they all use that term. They certainly use the term data lake, but now, they're using lakehouse more and more. What's your thoughts on here? Why the green? What's your evidence there? >> Well, I think, I was accurate. I spoke about it specifically as something that vendors would be pursuing. And we saw yet more lakehouse advocacy in 2022. Google introduced its BigLake service alongside BigQuery. Salesforce introduced Genie, which is really a lakehouse architecture. And it was a safe prediction to say vendors are going to be pursuing this in that AWS, Cloudera, Databricks, Microsoft, Oracle, SAP, Salesforce now, IBM, all advocate this idea of a single platform for all of your data. Now, the trend was also supported in 2023, in that we saw a big embrace of Apache Iceberg in 2022. That's a structured table format. It's used with these lakehouse platforms. It's open, so it ensures portability and it also ensures performance. And that's a structured table that helps with the warehouse side performance. But among those announcements, Snowflake, Google, Cloud Era, SAP, Salesforce, IBM, all embraced Iceberg. But keep in mind, again, I'm talking about this as something that vendors are pursuing as their approach. So, they're advocating end users. It's very cutting edge. I'd say the top, leading edge, 5% of of companies have really embraced the lakehouse. I think, we're now seeing the fast followers, the next 20 to 25% of firms embracing this idea and embracing a lakehouse architecture. I recall Christian Kleinerman at the big Snowflake event last summer, making the announcement about Iceberg, and he asked for a show of hands for any of you in the audience at the keynote, have you heard of Iceberg? And just a smattering of hands went up. So, the vendors are ahead of the curve. They're pushing this trend, and we're now seeing a little bit more mainstream uptake. >> Good. Doug, I was there. It was you, me, and I think, two other hands were up. That was just humorous. (Doug laughing) All right, well, so I liked the fact that we had some yellow and some green. When you think about these things, there's the prediction itself. Did it come true or not? There are the sub predictions that you guys make, and of course, the degree of difficulty. So, thank you for that open assessment. All right, let's get into the 2023 predictions. Let's bring up the predictions. Sanjeev, you're going first. You've got a prediction around unified metadata. What's the prediction, please? >> So, my prediction is that metadata space is currently a mess. It needs to get unified. There are too many use cases of metadata, which are being addressed by disparate systems. For example, data quality has become really big in the last couple of years, data observability, the whole catalog space is actually, people don't like to use the word data catalog anymore, because data catalog sounds like it's a catalog, a museum, if you may, of metadata that you go and admire. So, what I'm saying is that in 2023, we will see that metadata will become the driving force behind things like data ops, things like orchestration of tasks using metadata, not rules. Not saying that if this fails, then do this, if this succeeds, go do that. But it's like getting to the metadata level, and then making a decision as to what to orchestrate, what to automate, how to do data quality check, data observability. So, this space is starting to gel, and I see there'll be more maturation in the metadata space. Even security privacy, some of these topics, which are handled separately. And I'm just talking about data security and data privacy. I'm not talking about infrastructure security. These also need to merge into a unified metadata management piece with some knowledge graph, semantic layer on top, so you can do analytics on it. So, it's no longer something that sits on the side, it's limited in its scope. It is actually the very engine, the very glue that is going to connect data producers and consumers. >> Great. Thank you for that. Doug. Doug Henschen, any thoughts on what Sanjeev just said? Do you agree? Do you disagree? >> Well, I agree with many aspects of what he says. I think, there's a huge opportunity for consolidation and streamlining of these as aspects of governance. Last year, Sanjeev, you said something like, we'll see more people using catalogs than BI. And I have to disagree. I don't think this is a category that's headed for mainstream adoption. It's a behind the scenes activity for the wonky few, or better yet, companies want machine learning and automation to take care of these messy details. We've seen these waves of management technologies, some of the latest data observability, customer data platform, but they failed to sweep away all the earlier investments in data quality and master data management. So, yes, I hope the latest tech offers, glimmers that there's going to be a better, cleaner way of addressing these things. But to my mind, the business leaders, including the CIO, only want to spend as much time and effort and money and resources on these sorts of things to avoid getting breached, ending up in headlines, getting fired or going to jail. So, vendors bring on the ML and AI smarts and the automation of these sorts of activities. >> So, if I may say something, the reason why we have this dichotomy between data catalog and the BI vendors is because data catalogs are very soon, not going to be standalone products, in my opinion. They're going to get embedded. So, when you use a BI tool, you'll actually use the catalog to find out what is it that you want to do, whether you are looking for data or you're looking for an existing dashboard. So, the catalog becomes embedded into the BI tool. >> Hey, Dave Menninger, sometimes you have some data in your back pocket. Do you have any stats (chuckles) on this topic? >> No, I'm glad you asked, because I'm going to... Now, data catalogs are something that's interesting. Sanjeev made a statement that data catalogs are falling out of favor. I don't care what you call them. They're valuable to organizations. Our research shows that organizations that have adequate data catalog technologies are three times more likely to express satisfaction with their analytics for just the reasons that Sanjeev was talking about. You can find what you want, you know you're getting the right information, you know whether or not it's trusted. So, those are good things. So, we expect to see the capabilities, whether it's embedded or separate. We expect to see those capabilities continue to permeate the market. >> And a lot of those catalogs are driven now by machine learning and things. So, they're learning from those patterns of usage by people when people use the data. (airy laughs) >> All right. Okay. Thank you, guys. All right. Let's move on to the next one. Tony Bear, let's bring up the predictions. You got something in here about the modern data stack. We need to rethink it. Is the modern data stack getting long at the tooth? Is it not so modern anymore? >> I think, in a way, it's got almost too modern. It's gotten too, I don't know if it's being long in the tooth, but it is getting long. The modern data stack, it's traditionally been defined as basically you have the data platform, which would be the operational database and the data warehouse. And in between, you have all the tools that are necessary to essentially get that data from the operational realm or the streaming realm for that matter into basically the data warehouse, or as we might be seeing more and more, the data lakehouse. And I think, what's important here is that, or I think, we have seen a lot of progress, and this would be in the cloud, is with the SaaS services. And especially you see that in the modern data stack, which is like all these players, not just the MongoDBs or the Oracles or the Amazons have their database platforms. You see they have the Informatica's, and all the other players there in Fivetrans have their own SaaS services. And within those SaaS services, you get a certain degree of simplicity, which is it takes all the housekeeping off the shoulders of the customers. That's a good thing. The problem is that what we're getting to unfortunately is what I would call lots of islands of simplicity, which means that it leads it (Dave laughing) to the customer to have to integrate or put all that stuff together. It's a complex tool chain. And so, what we really need to think about here, we have too many pieces. And going back to the discussion of catalogs, it's like we have so many catalogs out there, which one do we use? 'Cause chances are of most organizations do not rely on a single catalog at this point. What I'm calling on all the data providers or all the SaaS service providers, is to literally get it together and essentially make this modern data stack less of a stack, make it more of a blending of an end-to-end solution. And that can come in a number of different ways. Part of it is that we're data platform providers have been adding services that are adjacent. And there's some very good examples of this. We've seen progress over the past year or so. For instance, MongoDB integrating search. It's a very common, I guess, sort of tool that basically, that the applications that are developed on MongoDB use, so MongoDB then built it into the database rather than requiring an extra elastic search or open search stack. Amazon just... AWS just did the zero-ETL, which is a first step towards simplifying the process from going from Aurora to Redshift. You've seen same thing with Google, BigQuery integrating basically streaming pipelines. And you're seeing also a lot of movement in database machine learning. So, there's some good moves in this direction. I expect to see more than this year. Part of it's from basically the SaaS platform is adding some functionality. But I also see more importantly, because you're never going to get... This is like asking your data team and your developers, herding cats to standardizing the same tool. In most organizations, that is not going to happen. So, take a look at the most popular combinations of tools and start to come up with some pre-built integrations and pre-built orchestrations, and offer some promotional pricing, maybe not quite two for, but in other words, get two products for the price of two services or for the price of one and a half. I see a lot of potential for this. And it's to me, if the class was to simplify things, this is the next logical step and I expect to see more of this here. >> Yeah, and you see in Oracle, MySQL heat wave, yet another example of eliminating that ETL. Carl Olofson, today, if you think about the data stack and the application stack, they're largely separate. Do you have any thoughts on how that's going to play out? Does that play into this prediction? What do you think? >> Well, I think, that the... I really like Tony's phrase, islands of simplification. It really says (Tony chuckles) what's going on here, which is that all these different vendors you ask about, about how these stacks work. All these different vendors have their own stack vision. And you can... One application group is going to use one, and another application group is going to use another. And some people will say, let's go to, like you go to a Informatica conference and they say, we should be the center of your universe, but you can't connect everything in your universe to Informatica, so you need to use other things. So, the challenge is how do we make those things work together? As Tony has said, and I totally agree, we're never going to get to the point where people standardize on one organizing system. So, the alternative is to have metadata that can be shared amongst those systems and protocols that allow those systems to coordinate their operations. This is standard stuff. It's not easy. But the motive for the vendors is that they can become more active critical players in the enterprise. And of course, the motive for the customer is that things will run better and more completely. So, I've been looking at this in terms of two kinds of metadata. One is the meaning metadata, which says what data can be put together. The other is the operational metadata, which says basically where did it come from? Who created it? What's its current state? What's the security level? Et cetera, et cetera, et cetera. The good news is the operational stuff can actually be done automatically, whereas the meaning stuff requires some human intervention. And as we've already heard from, was it Doug, I think, people are disinclined to put a lot of definition into meaning metadata. So, that may be the harder one, but coordination is key. This problem has been with us forever, but with the addition of new data sources, with streaming data with data in different formats, the whole thing has, it's been like what a customer of mine used to say, "I understand your product can make my system run faster, but right now I just feel I'm putting my problems on roller skates. (chuckles) I don't need that to accelerate what's already not working." >> Excellent. Okay, Carl, let's stay with you. I remember in the early days of the big data movement, Hadoop movement, NoSQL was the big thing. And I remember Amr Awadallah said to us in theCUBE that SQL is the killer app for big data. So, your prediction here, if we bring that up is SQL is back. Please elaborate. >> Yeah. So, of course, some people would say, well, it never left. Actually, that's probably closer to true, but in the perception of the marketplace, there's been all this noise about alternative ways of storing, retrieving data, whether it's in key value stores or document databases and so forth. We're getting a lot of messaging that for a while had persuaded people that, oh, we're not going to do analytics in SQL anymore. We're going to use Spark for everything, except that only a handful of people know how to use Spark. Oh, well, that's a problem. Well, how about, and for ordinary conventional business analytics, Spark is like an over-engineered solution to the problem. SQL works just great. What's happened in the past couple years, and what's going to continue to happen is that SQL is insinuating itself into everything we're seeing. We're seeing all the major data lake providers offering SQL support, whether it's Databricks or... And of course, Snowflake is loving this, because that is what they do, and their success is certainly points to the success of SQL, even MongoDB. And we were all, I think, at the MongoDB conference where on one day, we hear SQL is dead. They're not teaching SQL in schools anymore, and this kind of thing. And then, a couple days later at the same conference, they announced we're adding a new analytic capability-based on SQL. But didn't you just say SQL is dead? So, the reality is that SQL is better understood than most other methods of certainly of retrieving and finding data in a data collection, no matter whether it happens to be relational or non-relational. And even in systems that are very non-relational, such as graph and document databases, their query languages are being built or extended to resemble SQL, because SQL is something people understand. >> Now, you remember when we were in high school and you had had to take the... Your debating in the class and you were forced to take one side and defend it. So, I was was at a Vertica conference one time up on stage with Curt Monash, and I had to take the NoSQL, the world is changing paradigm shift. And so just to be controversial, I said to him, Curt Monash, I said, who really needs acid compliance anyway? Tony Baer. And so, (chuckles) of course, his head exploded, but what are your thoughts (guests laughing) on all this? >> Well, my first thought is congratulations, Dave, for surviving being up on stage with Curt Monash. >> Amen. (group laughing) >> I definitely would concur with Carl. We actually are definitely seeing a SQL renaissance and if there's any proof of the pudding here, I see lakehouse is being icing on the cake. As Doug had predicted last year, now, (clears throat) for the record, I think, Doug was about a year ahead of time in his predictions that this year is really the year that I see (clears throat) the lakehouse ecosystems really firming up. You saw the first shots last year. But anyway, on this, data lakes will not go away. I've actually, I'm on the home stretch of doing a market, a landscape on the lakehouse. And lakehouse will not replace data lakes in terms of that. There is the need for those, data scientists who do know Python, who knows Spark, to go in there and basically do their thing without all the restrictions or the constraints of a pre-built, pre-designed table structure. I get that. Same thing for developing models. But on the other hand, there is huge need. Basically, (clears throat) maybe MongoDB was saying that we're not teaching SQL anymore. Well, maybe we have an oversupply of SQL developers. Well, I'm being facetious there, but there is a huge skills based in SQL. Analytics have been built on SQL. They came with lakehouse and why this really helps to fuel a SQL revival is that the core need in the data lake, what brought on the lakehouse was not so much SQL, it was a need for acid. And what was the best way to do it? It was through a relational table structure. So, the whole idea of acid in the lakehouse was not to turn it into a transaction database, but to make the data trusted, secure, and more granularly governed, where you could govern down to column and row level, which you really could not do in a data lake or a file system. So, while lakehouse can be queried in a manner, you can go in there with Python or whatever, it's built on a relational table structure. And so, for that end, for those types of data lakes, it becomes the end state. You cannot bypass that table structure as I learned the hard way during my research. So, the bottom line I'd say here is that lakehouse is proof that we're starting to see the revenge of the SQL nerds. (Dave chuckles) >> Excellent. Okay, let's bring up back up the predictions. Dave Menninger, this one's really thought-provoking and interesting. We're hearing things like data as code, new data applications, machines actually generating plans with no human involvement. And your prediction is the definition of data is expanding. What do you mean by that? >> So, I think, for too long, we've thought about data as the, I would say facts that we collect the readings off of devices and things like that, but data on its own is really insufficient. Organizations need to manipulate that data and examine derivatives of the data to really understand what's happening in their organization, why has it happened, and to project what might happen in the future. And my comment is that these data derivatives need to be supported and managed just like the data needs to be managed. We can't treat this as entirely separate. Think about all the governance discussions we've had. Think about the metadata discussions we've had. If you separate these things, now you've got more moving parts. We're talking about simplicity and simplifying the stack. So, if these things are treated separately, it creates much more complexity. I also think it creates a little bit of a myopic view on the part of the IT organizations that are acquiring these technologies. They need to think more broadly. So, for instance, metrics. Metric stores are becoming much more common part of the tooling that's part of a data platform. Similarly, feature stores are gaining traction. So, those are designed to promote the reuse and consistency across the AI and ML initiatives. The elements that are used in developing an AI or ML model. And let me go back to metrics and just clarify what I mean by that. So, any type of formula involving the data points. I'm distinguishing metrics from features that are used in AI and ML models. And the data platforms themselves are increasingly managing the models as an element of data. So, just like figuring out how to calculate a metric. Well, if you're going to have the features associated with an AI and ML model, you probably need to be managing the model that's associated with those features. The other element where I see expansion is around external data. Organizations for decades have been focused on the data that they generate within their own organization. We see more and more of these platforms acquiring and publishing data to external third-party sources, whether they're within some sort of a partner ecosystem or whether it's a commercial distribution of that information. And our research shows that when organizations use external data, they derive even more benefits from the various analyses that they're conducting. And the last great frontier in my opinion on this expanding world of data is the world of driver-based planning. Very few of the major data platform providers provide these capabilities today. These are the types of things you would do in a spreadsheet. And we all know the issues associated with spreadsheets. They're hard to govern, they're error-prone. And so, if we can take that type of analysis, collecting the occupancy of a rental property, the projected rise in rental rates, the fluctuations perhaps in occupancy, the interest rates associated with financing that property, we can project forward. And that's a very common thing to do. What the income might look like from that property income, the expenses, we can plan and purchase things appropriately. So, I think, we need this broader purview and I'm beginning to see some of those things happen. And the evidence today I would say, is more focused around the metric stores and the feature stores starting to see vendors offer those capabilities. And we're starting to see the ML ops elements of managing the AI and ML models find their way closer to the data platforms as well. >> Very interesting. When I hear metrics, I think of KPIs, I think of data apps, orchestrate people and places and things to optimize around a set of KPIs. It sounds like a metadata challenge more... Somebody once predicted they'll have more metadata than data. Carl, what are your thoughts on this prediction? >> Yeah, I think that what Dave is describing as data derivatives is in a way, another word for what I was calling operational metadata, which not about the data itself, but how it's used, where it came from, what the rules are governing it, and that kind of thing. If you have a rich enough set of those things, then not only can you do a model of how well your vacation property rental may do in terms of income, but also how well your application that's measuring that is doing for you. In other words, how many times have I used it, how much data have I used and what is the relationship between the data that I've used and the benefits that I've derived from using it? Well, we don't have ways of doing that. What's interesting to me is that folks in the content world are way ahead of us here, because they have always tracked their content using these kinds of attributes. Where did it come from? When was it created, when was it modified? Who modified it? And so on and so forth. We need to do more of that with the structure data that we have, so that we can track what it's used. And also, it tells us how well we're doing with it. Is it really benefiting us? Are we being efficient? Are there improvements in processes that we need to consider? Because maybe data gets created and then it isn't used or it gets used, but it gets altered in some way that actually misleads people. (laughs) So, we need the mechanisms to be able to do that. So, I would say that that's... And I'd say that it's true that we need that stuff. I think, that starting to expand is probably the right way to put it. It's going to be expanding for some time. I think, we're still a distance from having all that stuff really working together. >> Maybe we should say it's gestating. (Dave and Carl laughing) >> Sorry, if I may- >> Sanjeev, yeah, I was going to say this... Sanjeev, please comment. This sounds to me like it supports Zhamak Dehghani's principles, but please. >> Absolutely. So, whether we call it data mesh or not, I'm not getting into that conversation, (Dave chuckles) but data (audio breaking) (Tony laughing) everything that I'm hearing what Dave is saying, Carl, this is the year when data products will start to take off. I'm not saying they'll become mainstream. They may take a couple of years to become so, but this is data products, all this thing about vacation rentals and how is it doing, that data is coming from different sources. I'm packaging it into our data product. And to Carl's point, there's a whole operational metadata associated with it. The idea is for organizations to see things like developer productivity, how many releases am I doing of this? What data products are most popular? I'm actually in right now in the process of formulating this concept that just like we had data catalogs, we are very soon going to be requiring data products catalog. So, I can discover these data products. I'm not just creating data products left, right, and center. I need to know, do they already exist? What is the usage? If no one is using a data product, maybe I want to retire and save cost. But this is a data product. Now, there's a associated thing that is also getting debated quite a bit called data contracts. And a data contract to me is literally just formalization of all these aspects of a product. How do you use it? What is the SLA on it, what is the quality that I am prescribing? So, data product, in my opinion, shifts the conversation to the consumers or to the business people. Up to this point when, Dave, you're talking about data and all of data discovery curation is a very data producer-centric. So, I think, we'll see a shift more into the consumer space. >> Yeah. Dave, can I just jump in there just very quickly there, which is that what Sanjeev has been saying there, this is really central to what Zhamak has been talking about. It's basically about making, one, data products are about the lifecycle management of data. Metadata is just elemental to that. And essentially, one of the things that she calls for is making data products discoverable. That's exactly what Sanjeev was talking about. >> By the way, did everyone just no notice how Sanjeev just snuck in another prediction there? So, we've got- >> Yeah. (group laughing) >> But you- >> Can we also say that he snuck in, I think, the term that we'll remember today, which is metadata museums. >> Yeah, but- >> Yeah. >> And also comment to, Tony, to your last year's prediction, you're really talking about it's not something that you're going to buy from a vendor. >> No. >> It's very specific >> Mm-hmm. >> to an organization, their own data product. So, touche on that one. Okay, last prediction. Let's bring them up. Doug Henschen, BI analytics is headed to embedding. What does that mean? >> Well, we all know that conventional BI dashboarding reporting is really commoditized from a vendor perspective. It never enjoyed truly mainstream adoption. Always that 25% of employees are really using these things. I'm seeing rising interest in embedding concise analytics at the point of decision or better still, using analytics as triggers for automation and workflows, and not even necessitating human interaction with visualizations, for example, if we have confidence in the analytics. So, leading companies are pushing for next generation applications, part of this low-code, no-code movement we've seen. And they want to build that decision support right into the app. So, the analytic is right there. Leading enterprise apps vendors, Salesforce, SAP, Microsoft, Oracle, they're all building smart apps with the analytics predictions, even recommendations built into these applications. And I think, the progressive BI analytics vendors are supporting this idea of driving insight to action, not necessarily necessitating humans interacting with it if there's confidence. So, we want prediction, we want embedding, we want automation. This low-code, no-code development movement is very important to bringing the analytics to where people are doing their work. We got to move beyond the, what I call swivel chair integration, between where people do their work and going off to separate reports and dashboards, and having to interpret and analyze before you can go back and do take action. >> And Dave Menninger, today, if you want, analytics or you want to absorb what's happening in the business, you typically got to go ask an expert, and then wait. So, what are your thoughts on Doug's prediction? >> I'm in total agreement with Doug. I'm going to say that collectively... So, how did we get here? I'm going to say collectively as an industry, we made a mistake. We made BI and analytics separate from the operational systems. Now, okay, it wasn't really a mistake. We were limited by the technology available at the time. Decades ago, we had to separate these two systems, so that the analytics didn't impact the operations. You don't want the operations preventing you from being able to do a transaction. But we've gone beyond that now. We can bring these two systems and worlds together and organizations recognize that need to change. As Doug said, the majority of the workforce and the majority of organizations doesn't have access to analytics. That's wrong. (chuckles) We've got to change that. And one of the ways that's going to change is with embedded analytics. 2/3 of organizations recognize that embedded analytics are important and it even ranks higher in importance than AI and ML in those organizations. So, it's interesting. This is a really important topic to the organizations that are consuming these technologies. The good news is it works. Organizations that have embraced embedded analytics are more comfortable with self-service than those that have not, as opposed to turning somebody loose, in the wild with the data. They're given a guided path to the data. And the research shows that 65% of organizations that have adopted embedded analytics are comfortable with self-service compared with just 40% of organizations that are turning people loose in an ad hoc way with the data. So, totally behind Doug's predictions. >> Can I just break in with something here, a comment on what Dave said about what Doug said, which (laughs) is that I totally agree with what you said about embedded analytics. And at IDC, we made a prediction in our future intelligence, future of intelligence service three years ago that this was going to happen. And the thing that we're waiting for is for developers to build... You have to write the applications to work that way. It just doesn't happen automagically. Developers have to write applications that reference analytic data and apply it while they're running. And that could involve simple things like complex queries against the live data, which is through something that I've been calling analytic transaction processing. Or it could be through something more sophisticated that involves AI operations as Doug has been suggesting, where the result is enacted pretty much automatically unless the scores are too low and you need to have a human being look at it. So, I think that that is definitely something we've been watching for. I'm not sure how soon it will come, because it seems to take a long time for people to change their thinking. But I think, as Dave was saying, once they do and they apply these principles in their application development, the rewards are great. >> Yeah, this is very much, I would say, very consistent with what we were talking about, I was talking about before, about basically rethinking the modern data stack and going into more of an end-to-end solution solution. I think, that what we're talking about clearly here is operational analytics. There'll still be a need for your data scientists to go offline just in their data lakes to do all that very exploratory and that deep modeling. But clearly, it just makes sense to bring operational analytics into where people work into their workspace and further flatten that modern data stack. >> But with all this metadata and all this intelligence, we're talking about injecting AI into applications, it does seem like we're entering a new era of not only data, but new era of apps. Today, most applications are about filling forms out or codifying processes and require a human input. And it seems like there's enough data now and enough intelligence in the system that the system can actually pull data from, whether it's the transaction system, e-commerce, the supply chain, ERP, and actually do something with that data without human involvement, present it to humans. Do you guys see this as a new frontier? >> I think, that's certainly- >> Very much so, but it's going to take a while, as Carl said. You have to design it, you have to get the prediction into the system, you have to get the analytics at the point of decision has to be relevant to that decision point. >> And I also recall basically a lot of the ERP vendors back like 10 years ago, we're promising that. And the fact that we're still looking at the promises shows just how difficult, how much of a challenge it is to get to what Doug's saying. >> One element that could be applied in this case is (indistinct) architecture. If applications are developed that are event-driven rather than following the script or sequence that some programmer or designer had preconceived, then you'll have much more flexible applications. You can inject decisions at various points using this technology much more easily. It's a completely different way of writing applications. And it actually involves a lot more data, which is why we should all like it. (laughs) But in the end (Tony laughing) it's more stable, it's easier to manage, easier to maintain, and it's actually more efficient, which is the result of an MIT study from about 10 years ago, and still, we are not seeing this come to fruition in most business applications. >> And do you think it's going to require a new type of data platform database? Today, data's all far-flung. We see that's all over the clouds and at the edge. Today, you cache- >> We need a super cloud. >> You cache that data, you're throwing into memory. I mentioned, MySQL heat wave. There are other examples where it's a brute force approach, but maybe we need new ways of laying data out on disk and new database architectures, and just when we thought we had it all figured out. >> Well, without referring to disk, which to my mind, is almost like talking about cave painting. I think, that (Dave laughing) all the things that have been mentioned by all of us today are elements of what I'm talking about. In other words, the whole improvement of the data mesh, the improvement of metadata across the board and improvement of the ability to track data and judge its freshness the way we judge the freshness of a melon or something like that, to determine whether we can still use it. Is it still good? That kind of thing. Bringing together data from multiple sources dynamically and real-time requires all the things we've been talking about. All the predictions that we've talked about today add up to elements that can make this happen. >> Well, guys, it's always tremendous to get these wonderful minds together and get your insights, and I love how it shapes the outcome here of the predictions, and let's see how we did. We're going to leave it there. I want to thank Sanjeev, Tony, Carl, David, and Doug. Really appreciate the collaboration and thought that you guys put into these sessions. Really, thank you. >> Thank you. >> Thanks, Dave. >> Thank you for having us. >> Thanks. >> Thank you. >> All right, this is Dave Valente for theCUBE, signing off for now. Follow these guys on social media. Look for coverage on siliconangle.com, theCUBE.net. Thank you for watching. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is "Breaking Analysis" with Dave Vellante. >> A decade of big data investments combined with cloud scale, the rise of much more cost effective processing power. And the introduction of advanced tooling has catapulted machine intelligence to the forefront of technology investments. No matter what job you have, your operation will be AI powered within five years and machines may actually even be doing your job. Artificial intelligence is being infused into applications, infrastructure, equipment, and virtually every aspect of our lives. AI is proving to be extremely helpful at things like controlling vehicles, speeding up medical diagnoses, processing language, advancing science, and generally raising the stakes on what it means to apply technology for business advantage. But business value realization has been a challenge for most organizations due to lack of skills, complexity of programming models, immature technology integration, sizable upfront investments, ethical concerns, and lack of business alignment. Mastering AI technology will not be a requirement for success in our view. However, figuring out how and where to apply AI to your business will be crucial. That means understanding the business case, picking the right technology partner, experimenting in bite-sized chunks, and quickly identifying winners to double down on from an investment standpoint. Hello and welcome to this week's Wiki-bond CUBE Insights powered by ETR. In this breaking analysis, we update you on the state of AI and what it means for the competition. And to do so, we invite into our studios Andy Thurai of Constellation Research. Andy covers AI deeply. He knows the players, he knows the pitfalls of AI investment, and he's a collaborator. Andy, great to have you on the program. Thanks for coming into our CUBE studios. >> Thanks for having me on. >> You're very welcome. Okay, let's set the table with a premise and a series of assertions we want to test with Andy. I'm going to lay 'em out. And then Andy, I'd love for you to comment. So, first of all, according to McKinsey, AI adoption has more than doubled since 2017, but only 10% of organizations report seeing significant ROI. That's a BCG and MIT study. And part of that challenge of AI is it requires data, is requires good data, data proficiency, which is not trivial, as you know. Firms that can master both data and AI, we believe are going to have a competitive advantage this decade. Hyperscalers, as we show you dominate AI and ML. We'll show you some data on that. And having said that, there's plenty of room for specialists. They need to partner with the cloud vendors for go to market productivity. And finally, organizations increasingly have to put data and AI at the center of their enterprises. And to do that, most are going to rely on vendor R&D to leverage AI and ML. In other words, Andy, they're going to buy it and apply it as opposed to build it. What are your thoughts on that setup and that premise? >> Yeah, I see that a lot happening in the field, right? So first of all, the only 10% of realizing a return on investment. That's so true because we talked about this earlier, the most companies are still in the innovation cycle. So they're trying to innovate and see what they can do to apply. A lot of these times when you look at the solutions, what they come up with or the models they create, the experimentation they do, most times they don't even have a good business case to solve, right? So they just experiment and then they figure it out, "Oh my God, this model is working. Can we do something to solve it?" So it's like you found a hammer and then you're trying to find the needle kind of thing, right? That never works. >> 'Cause it's cool or whatever it is. >> It is, right? So that's why, I always advise, when they come to me and ask me things like, "Hey, what's the right way to do it? What is the secret sauce?" And, we talked about this. The first thing I tell them is, "Find out what is the business case that's having the most amount of problems, that that can be solved using some of the AI use cases," right? Not all of them can be solved. Even after you experiment, do the whole nine yards, spend millions of dollars on that, right? And later on you make it efficient only by saving maybe $50,000 for the company or a $100,000 for the company, is it really even worth the experiment, right? So you got to start with the saying that, you know, where's the base for this happening? Where's the need? What's a business use case? It doesn't have to be about cost efficient and saving money in the existing processes. It could be a new thing. You want to bring in a new revenue stream, but figure out what is a business use case, how much money potentially I can make off of that. The same way that start-ups go after. Right? >> Yeah. Pretty straightforward. All right, let's take a look at where ML and AI fit relative to the other hot sectors of the ETR dataset. This XY graph shows net score spending velocity in the vertical axis and presence in the survey, they call it sector perversion for the October survey, the January survey's in the field. Then that squiggly line on ML/AI represents the progression. Since the January 21 survey, you can see the downward trajectory. And we position ML and AI relative to the other big four hot sectors or big three, including, ML/AI is four. Containers, cloud and RPA. These have consistently performed above that magic 40% red dotted line for most of the past two years. Anything above 40%, we think is highly elevated. And we've just included analytics and big data for context and relevant adjacentness, if you will. Now note that green arrow moving toward, you know, the 40% mark on ML/AI. I got a glimpse of the January survey, which is in the field. It's got more than a thousand responses already, and it's trending up for the current survey. So Andy, what do you make of this downward trajectory over the past seven quarters and the presumed uptick in the coming months? >> So one of the things you have to keep in mind is when the pandemic happened, it's about survival mode, right? So when somebody's in a survival mode, what happens, the luxury and the innovations get cut. That's what happens. And this is exactly what happened in the situation. So as you can see in the last seven quarters, which is almost dating back close to pandemic, everybody was trying to keep their operations alive, especially digital operations. How do I keep the lights on? That's the most important thing for them. So while the numbers spent on AI, ML is less overall, I still think the AI ML to spend to sort of like a employee experience or the IT ops, AI ops, ML ops, as we talked about, some of those areas actually went up. There are companies, we talked about it, Atlassian had a lot of platform issues till the amount of money people are spending on that is exorbitant and simply because they are offering the solution that was not available other way. So there are companies out there, you can take AoPS or incident management for that matter, right? A lot of companies have a digital insurance, they don't know how to properly manage it. How do you find an intern solve it immediately? That's all using AI ML and some of those areas actually growing unbelievable, the companies in that area. >> So this is a really good point. If you can you bring up that chart again, what Andy's saying is a lot of the companies in the ETR taxonomy that are doing things with AI might not necessarily show up in a granular fashion. And I think the other point I would make is, these are still highly elevated numbers. If you put on like storage and servers, they would read way, way down the list. And, look in the pandemic, we had to deal with work from home, we had to re-architect the network, we had to worry about security. So those are really good points that you made there. Let's, unpack this a little bit and look at the ML AI sector and the ETR data and specifically at the players and get Andy to comment on this. This chart here shows the same x y dimensions, and it just notes some of the players that are specifically have services and products that people spend money on, that CIOs and IT buyers can comment on. So the table insert shows how the companies are plotted, it's net score, and then the ends in the survey. And Andy, the hyperscalers are dominant, as you can see. You see Databricks there showing strong as a specialist, and then you got to pack a six or seven in there. And then Oracle and IBM, kind of the big whales of yester year are in the mix. And to your point, companies like Salesforce that you mentioned to me offline aren't in that mix, but they do a lot in AI. But what are your takeaways from that data? >> If you could put the slide back on please. I want to make quick comments on a couple of those. So the first one is, it's surprising other hyperscalers, right? As you and I talked about this earlier, AWS is more about logo blocks. We discussed that, right? >> Like what? Like a SageMaker as an example. >> We'll give you all the components what do you need. Whether it's MLOps component or whether it's, CodeWhisperer that we talked about, or a oral platform or data or data, whatever you want. They'll give you the blocks and then you'll build things on top of it, right? But Google took a different way. Matter of fact, if we did those numbers a few years ago, Google would've been number one because they did a lot of work with their acquisition of DeepMind and other things. They're way ahead of the pack when it comes to AI for longest time. Now, I think Microsoft's move of partnering and taking a huge competitor out would open the eyes is unbelievable. You saw that everybody is talking about chat GPI, right? And the open AI tool and ChatGPT rather. Remember as Warren Buffet is saying that, when my laundry lady comes and talk to me about stock market, it's heated up. So that's how it's heated up. Everybody's using ChatGPT. What that means is at the end of the day is they're creating, it's still in beta, keep in mind. It's not fully... >> Can you play with it a little bit? >> I have a little bit. >> I have, but it's good and it's not good. You know what I mean? >> Look, so at the end of the day, you take the massive text of all the available text in the world today, mass them all together. And then you ask a question, it's going to basically search through that and figure it out and answer that back. Yes, it's good. But again, as we discussed, if there's no business use case of what problem you're going to solve. This is building hype. But then eventually they'll figure out, for example, all your chats, online chats, could be aided by your AI chat bots, which is already there, which is not there at that level. This could build help that, right? Or the other thing we talked about is one of the areas where I'm more concerned about is that it is able to produce equal enough original text at the level that humans can produce, for example, ChatGPT or the equal enough, the large language transformer can help you write stories as of Shakespeare wrote it. Pretty close to it. It'll learn from that. So when it comes down to it, talk about creating messages, articles, blogs, especially during political seasons, not necessarily just in US, but anywhere for that matter. If people are able to produce at the emission speed and throw it at the consumers and confuse them, the elections can be won, the governments can be toppled. >> Because to your point about chatbots is chatbots have obviously, reduced the number of bodies that you need to support chat. But they haven't solved the problem of serving consumers. Most of the chat bots are conditioned response, which of the following best describes your problem? >> The current chatbot. >> Yeah. Hey, did we solve your problem? No. Is the answer. So that has some real potential. But if you could bring up that slide again, Ken, I mean you've got the hyperscalers that are dominant. You talked about Google and Microsoft is ubiquitous, they seem to be dominant in every ETR category. But then you have these other specialists. How do those guys compete? And maybe you could even, cite some of the guys that you know, how do they compete with the hyperscalers? What's the key there for like a C3 ai or some of the others that are on there? >> So I've spoken with at least two of the CEOs of the smaller companies that you have on the list. One of the things they're worried about is that if they continue to operate independently without being part of hyperscaler, either the hyperscalers will develop something to compete against them full scale, or they'll become irrelevant. Because at the end of the day, look, cloud is dominant. Not many companies are going to do like AI modeling and training and deployment the whole nine yards by independent by themselves. They're going to depend on one of the clouds, right? So if they're already going to be in the cloud, by taking them out to come to you, it's going to be extremely difficult issue to solve. So all these companies are going and saying, "You know what? We need to be in hyperscalers." For example, you could have looked at DataRobot recently, they made announcements, Google and AWS, and they are all over the place. So you need to go where the customers are. Right? >> All right, before we go on, I want to share some other data from ETR and why people adopt AI and get your feedback. So the data historically shows that feature breadth and technical capabilities were the main decision points for AI adoption, historically. What says to me that it's too much focus on technology. In your view, is that changing? Does it have to change? Will it change? >> Yes. Simple answer is yes. So here's the thing. The data you're speaking from is from previous years. >> Yes >> I can guarantee you, if you look at the latest data that's coming in now, those two will be a secondary and tertiary points. The number one would be about ROI. And how do I achieve? I've spent ton of money on all of my experiments. This is the same thing theme I'm seeing across when talking to everybody who's spending money on AI. I've spent so much money on it. When can I get it live in production? How much, how can I quickly get it? Because you know, the board is breathing down their neck. You already spend this much money. Show me something that's valuable. So the ROI is going to become, take it from me, I'm predicting this for 2023, that's going to become number one. >> Yeah, and if people focus on it, they'll figure it out. Okay. Let's take a look at some of the top players that won, some of the names we just looked at and double click on that and break down their spending profile. So the chart here shows the net score, how net score is calculated. So pay attention to the second set of bars that Databricks, who was pretty prominent on the previous chart. And we've annotated the colors. The lime green is, we're bringing the platform in new. The forest green is, we're going to spend 6% or more relative to last year. And the gray is flat spending. The pinkish is our spending's going to be down on AI and ML, 6% or worse. And the red is churn. So you don't want big red. You subtract the reds from the greens and you get net score, which is shown by those blue dots that you see there. So AWS has the highest net score and very little churn. I mean, single low single digit churn. But notably, you see Databricks and DataRobot are next in line within Microsoft and Google also, they've got very low churn. Andy, what are your thoughts on this data? >> So a couple of things that stands out to me. Most of them are in line with my conversation with customers. Couple of them stood out to me on how bad IBM Watson is doing. >> Yeah, bring that back up if you would. Let's take a look at that. IBM Watson is the far right and the red, that bright red is churning and again, you want low red here. Why do you think that is? >> Well, so look, IBM has been in the forefront of innovating things for many, many years now, right? And over the course of years we talked about this, they moved from a product innovation centric company into more of a services company. And over the years they were making, as at one point, you know that they were making about majority of that money from services. Now things have changed Arvind has taken over, he came from research. So he's doing a great job of trying to reinvent themselves as a company. But it's going to have a long way to catch up. IBM Watson, if you think about it, that played what, jeopardy and chess years ago, like 15 years ago? >> It was jaw dropping when you first saw it. And then they weren't able to commercialize that. >> Yeah. >> And you're making a good point. When Gerstner took over IBM at the time, John Akers wanted to split the company up. He wanted to have a database company, he wanted to have a storage company. Because that's where the industry trend was, Gerstner said no, he came from AMEX, right? He came from American Express. He said, "No, we're going to have a single throat to choke for the customer." They bought PWC for relatively short money. I think it was $15 billion, completely transformed and I would argue saved IBM. But the trade off was, it sort of took them out of product leadership. And so from Gerstner to Palmisano to Remedi, it was really a services led company. And I think Arvind is really bringing it back to a product company with strong consulting. I mean, that's one of the pillars. And so I think that's, they've got a strong story in data and AI. They just got to sort of bring it together and better. Bring that chart up one more time. I want to, the other point is Oracle, Oracle sort of has the dominant lock-in for mission critical database and they're sort of applying AI there. But to your point, they're really not an AI company in the sense that they're taking unstructured data and doing sort of new things. It's really about how to make Oracle better, right? >> Well, you got to remember, Oracle is about database for the structure data. So in yesterday's world, they were dominant database. But you know, if you are to start storing like videos and texts and audio and other things, and then start doing search of vector search and all that, Oracle is not necessarily the database company of choice. And they're strongest thing being apps and building AI into the apps? They are kind of surviving in that area. But again, I wouldn't name them as an AI company, right? But the other thing that that surprised me in that list, what you showed me is yes, AWS is number one. >> Bring that back up if you would, Ken. >> AWS is number one as you, it should be. But what what actually caught me by surprise is how DataRobot is holding, you know? I mean, look at that. The either net new addition and or expansion, DataRobot seem to be doing equally well, even better than Microsoft and Google. That surprises me. >> DataRobot's, and again, this is a function of spending momentum. So remember from the previous chart that Microsoft and Google, much, much larger than DataRobot. DataRobot more niche. But with spending velocity and has always had strong spending velocity, despite some of the recent challenges, organizational challenges. And then you see these other specialists, H2O.ai, Anaconda, dataiku, little bit of red showing there C3.ai. But these again, to stress are the sort of specialists other than obviously the hyperscalers. These are the specialists in AI. All right, so we hit the bigger names in the sector. Now let's take a look at the emerging technology companies. And one of the gems of the ETR dataset is the emerging technology survey. It's called ETS. They used to just do it like twice a year. It's now run four times a year. I just discovered it kind of mid-2022. And it's exclusively focused on private companies that are potential disruptors, they might be M&A candidates and if they've raised enough money, they could be acquirers of companies as well. So Databricks would be an example. They've made a number of investments in companies. SNEAK would be another good example. Companies that are private, but they're buyers, they hope to go IPO at some point in time. So this chart here, shows the emerging companies in the ML AI sector of the ETR dataset. So the dimensions of this are similar, they're net sentiment on the Y axis and mind share on the X axis. Basically, the ETS study measures awareness on the x axis and intent to do something with, evaluate or implement or not, on that vertical axis. So it's like net score on the vertical where negatives are subtracted from the positives. And again, mind share is vendor awareness. That's the horizontal axis. Now that inserted table shows net sentiment and the ends in the survey, which informs the position of the dots. And you'll notice we're plotting TensorFlow as well. We know that's not a company, but it's there for reference as open source tooling is an option for customers. And ETR sometimes like to show that as a reference point. Now we've also drawn a line for Databricks to show how relatively dominant they've become in the past 10 ETS surveys and sort of mind share going back to late 2018. And you can see a dozen or so other emerging tech vendors. So Andy, I want you to share your thoughts on these players, who were the ones to watch, name some names. We'll bring that data back up as you as you comment. >> So Databricks, as you said, remember we talked about how Oracle is not necessarily the database of the choice, you know? So Databricks is kind of trying to solve some of the issue for AI/ML workloads, right? And the problem is also there is no one company that could solve all of the problems. For example, if you look at the names in here, some of them are database names, some of them are platform names, some of them are like MLOps companies like, DataRobot (indistinct) and others. And some of them are like future based companies like, you know, the Techton and stuff. >> So it's a mix of those sub sectors? >> It's a mix of those companies. >> We'll talk to ETR about that. They'd be interested in your input on how to make this more granular and these sub-sectors. You got Hugging Face in here, >> Which is NLP, yeah. >> Okay. So your take, are these companies going to get acquired? Are they going to go IPO? Are they going to merge? >> Well, most of them going to get acquired. My prediction would be most of them will get acquired because look, at the end of the day, hyperscalers need these capabilities, right? So they're going to either create their own, AWS is very good at doing that. They have done a lot of those things. But the other ones, like for particularly Azure, they're going to look at it and saying that, "You know what, it's going to take time for me to build this. Why don't I just go and buy you?" Right? Or or even the smaller players like Oracle or IBM Cloud, this will exist. They might even take a look at them, right? So at the end of the day, a lot of these companies are going to get acquired or merged with others. >> Yeah. All right, let's wrap with some final thoughts. I'm going to make some comments Andy, and then ask you to dig in here. Look, despite the challenge of leveraging AI, you know, Ken, if you could bring up the next chart. We're not repeating, we're not predicting the AI winter of the 1990s. Machine intelligence. It's a superpower that's going to permeate every aspect of the technology industry. AI and data strategies have to be connected. Leveraging first party data is going to increase AI competitiveness and shorten time to value. Andy, I'd love your thoughts on that. I know you've got some thoughts on governance and AI ethics. You know, we talked about ChatGBT, Deepfakes, help us unpack all these trends. >> So there's so much information packed up there, right? The AI and data strategy, that's very, very, very important. If you don't have a proper data, people don't realize that AI is, your AI is the morals that you built on, it's predominantly based on the data what you have. It's not, AI cannot predict something that's going to happen without knowing what it is. It need to be trained, it need to understand what is it you're talking about. So 99% of the time you got to have a good data for you to train. So this where I mentioned to you, the problem is a lot of these companies can't afford to collect the real world data because it takes too long, it's too expensive. So a lot of these companies are trying to do the synthetic data way. It has its own set of issues because you can't use all... >> What's that synthetic data? Explain that. >> Synthetic data is basically not a real world data, but it's a created or simulated data equal and based on real data. It looks, feels, smells, taste like a real data, but it's not exactly real data, right? This is particularly useful in the financial and healthcare industry for world. So you don't have to, at the end of the day, if you have real data about your and my medical history data, if you redact it, you can still reverse this. It's fairly easy, right? >> Yeah, yeah. >> So by creating a synthetic data, there is no correlation between the real data and the synthetic data. >> So that's part of AI ethics and privacy and, okay. >> So the synthetic data, the issue with that is that when you're trying to commingle that with that, you can't create models based on just on synthetic data because synthetic data, as I said is artificial data. So basically you're creating artificial models, so you got to blend in properly that that blend is the problem. And you know how much of real data, how much of synthetic data you could use. You got to use judgment between efficiency cost and the time duration stuff. So that's one-- >> And risk >> And the risk involved with that. And the secondary issues which we talked about is that when you're creating, okay, you take a business use case, okay, you think about investing things, you build the whole thing out and you're trying to put it out into the market. Most companies that I talk to don't have a proper governance in place. They don't have ethics standards in place. They don't worry about the biases in data, they just go on trying to solve a business case >> It's wild west. >> 'Cause that's what they start. It's a wild west! And then at the end of the day when they are close to some legal litigation action or something or something else happens and that's when the Oh Shit! moments happens, right? And then they come in and say, "You know what, how do I fix this?" The governance, security and all of those things, ethics bias, data bias, de-biasing, none of them can be an afterthought. It got to start with the, from the get-go. So you got to start at the beginning saying that, "You know what, I'm going to do all of those AI programs, but before we get into this, we got to set some framework for doing all these things properly." Right? And then the-- >> Yeah. So let's go back to the key points. I want to bring up the cloud again. Because you got to get cloud right. Getting that right matters in AI to the points that you were making earlier. You can't just be out on an island and hyperscalers, they're going to obviously continue to do well. They get more and more data's going into the cloud and they have the native tools. To your point, in the case of AWS, Microsoft's obviously ubiquitous. Google's got great capabilities here. They've got integrated ecosystems partners that are going to continue to strengthen through the decade. What are your thoughts here? >> So a couple of things. One is the last mile ML or last mile AI that nobody's talking about. So that need to be attended to. There are lot of players in the market that coming up, when I talk about last mile, I'm talking about after you're done with the experimentation of the model, how fast and quickly and efficiently can you get it to production? So that's production being-- >> Compressing that time is going to put dollars in your pocket. >> Exactly. Right. >> So once, >> If you got it right. >> If you get it right, of course. So there are, there are a couple of issues with that. Once you figure out that model is working, that's perfect. People don't realize, the moment you decide that moment when the decision is made, it's like a new car. After you purchase the value decreases on a minute basis. Same thing with the models. Once the model is created, you need to be in production right away because it starts losing it value on a seconds minute basis. So issue number one, how fast can I get it over there? So your deployment, you are inferencing efficiently at the edge locations, your optimization, your security, all of this is at issue. But you know what is more important than that in the last mile? You keep the model up, you continue to work on, again, going back to the car analogy, at one point you got to figure out your car is costing more than to operate. So you got to get a new car, right? And that's the same thing with the models as well. If your model has reached a stage, it is actually a potential risk for your operation. To give you an idea, if Uber has a model, the first time when you get a car from going from point A to B cost you $60. If the model decayed the next time I might give you a $40 rate, I would take it definitely. But it's lost for the company. The business risk associated with operating on a bad model, you should realize it immediately, pull the model out, retrain it, redeploy it. That's is key. >> And that's got to be huge in security model recency and security to the extent that you can get real time is big. I mean you, you see Palo Alto, CrowdStrike, a lot of other security companies are injecting AI. Again, they won't show up in the ETR ML/AI taxonomy per se as a pure play. But ServiceNow is another company that you have have mentioned to me, offline. AI is just getting embedded everywhere. >> Yep. >> And then I'm glad you brought up, kind of real-time inferencing 'cause a lot of the modeling, if we can go back to the last point that we're going to make, a lot of the AI today is modeling done in the cloud. The last point we wanted to make here, I'd love to get your thoughts on this, is real-time AI inferencing for instance at the edge is going to become increasingly important for us. It's going to usher in new economics, new types of silicon, particularly arm-based. We've covered that a lot on "Breaking Analysis", new tooling, new companies and that could disrupt the sort of cloud model if new economics emerge. 'Cause cloud obviously very centralized, they're trying to decentralize it. But over the course of this decade we could see some real disruption there. Andy, give us your final thoughts on that. >> Yes and no. I mean at the end of the day, cloud is kind of centralized now, but a lot of this companies including, AWS is kind of trying to decentralize that by putting their own sub-centers and edge locations. >> Local zones, outposts. >> Yeah, exactly. Particularly the outpost concept. And if it can even become like a micro center and stuff, it won't go to the localized level of, I go to a single IOT level. But again, the cloud extends itself to that level. So if there is an opportunity need for it, the hyperscalers will figure out a way to fit that model. So I wouldn't too much worry about that, about deployment and where to have it and what to do with that. But you know, figure out the right business use case, get the right data, get the ethics and governance place and make sure they get it to production and make sure you pull the model out when it's not operating well. >> Excellent advice. Andy, I got to thank you for coming into the studio today, helping us with this "Breaking Analysis" segment. Outstanding collaboration and insights and input in today's episode. Hope we can do more. >> Thank you. Thanks for having me. I appreciate it. >> You're very welcome. All right. I want to thank Alex Marson who's on production and manages the podcast. Ken Schiffman as well. Kristen Martin and Cheryl Knight helped get the word out on social media and our newsletters. And Rob Hoof is our editor-in-chief over at Silicon Angle. He does some great editing for us. Thank you all. Remember all these episodes are available as podcast. Wherever you listen, all you got to do is search "Breaking Analysis" podcast. I publish each week on wikibon.com and silicon angle.com or you can email me at david.vellante@siliconangle.com to get in touch, or DM me at dvellante or comment on our LinkedIn posts. Please check out ETR.AI for the best survey data and the enterprise tech business, Constellation Research. Andy publishes there some awesome information on AI and data. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching everybody and we'll see you next time on "Breaking Analysis". (gentle closing tune plays)