>> Narrator: Live from San Juan, Puerto Rico It's theCUBE, covering Blockchain Unbound Brought to you by Blockchain Industries (upbeat music) >> Hey, welcome back everybody, we're live in Puerto Rico for the cryptocurrency, global blockchain, decentralized internet, Cube coverage in Puerto Rico part of Blockchain Unbound. I'm John Furrier, host of theCUBE here, also co-founder of SiliconANGLE Media Inc. And, we're here with a first Cube ever, father/son Cube segment where we're going to kind of break down a summary of the show but mainly get the take from a 22 year old. Here with me is my son Alex Furrier who's been doing the schedule and greeting all the guests. Alec has been also demoing our platform that we haven't formally announced but also Not that we have to but it's out there. theCUBE platform, all the back-end data Because it really is getting everyone here excited So, Alec, welcome to theCUBE. >> Thanks, great to be on, finally, after all these years (John chuckles) to be on, it's an honor. >> Well, thanks for all the hard work you did on the schedule but you're a young gun, you're 22 years old. This is an exciting crypto world for your generation. What's your reaction to the commentary you've heard, the stories you've heard, what's the young perspective on cryptocurrency, blockchain, what's the view? >> Totally, it's a totally crazy culture, right? So, there's a very big influx of young talent and talented minds at that, right? And, this is really changing the revolution landscape. It's accelerating the tech. These ideas are being freely shared whereas before there was bottlenecks in the collaboration aspect of the technological field, right? >> You're a gamer, I know that so you're the young eco-system You don't care about data lakes and data centers and cloud computing. What is your generation look at this as an opportunity? What's exciting about it? What's the perspective? >> Well, there's multiple perspectives. The main two I say, there's multiple perspectives. Main two, is one, there's a shit ton of way to make money. And you know, is there a scam? Is there a risk for my business? You know, blockchain is involved. And there's a little bit of that mumbo jumbo going along. But then, there's also the other side that are really into it and really applying the tech and know that this is the best way to collaborate with peers >> What's the coolest thing you've seen? >> The coolest thing I've seen is probably Hashgraph which is actually not on the blockchain and competitors of the blockchain. And that's actually increasing speeds and pretty much making the tech, the back-end infrastructure better. >> So, you dropped out of UCSB, you're going to maybe go back to school but you're also working as a product manager for our crypto project for SiliconANGLE Media, theCUBE, Cube Network, you were giving demos. What is, what are we doing? How would you explain what we're doing? And, what was some of the reactions to the demo that you were giving? >> All great reactions so far. People are very excited what we're building which is a reputation centrality metric. And, what this does, is allows us to track, what users are talking about, and where they're talking about it. And actually, rank their reputation leaderboard rankings by topic, by frequency, by impact down reverb in the entire network. And that allows us to appropriate connections between two people who have different social, culture and professional topics that they talk about. And allow them to create more value for the entire platform, for the community and more importantly, themselves. >> What is, what does that mean, what problem are we solving? >> So, we're solving the Facebook ad word problem of the old generation which is you as a user do not own your data. Right? >> Yep. >> So now, what we have is this user base struggling to find the monetary value in their social media platforms. But now, we are actually offering a way for them to reverse the paradigm and get paid for interacting with others, creating with others and contributing to the community through all of their social media outlets. >> What was the biggest thing that people reacted to at the demos, the variety of tools we showed them. What was the number one, couple of things that they reacted to, what jumped out at you? >> So, I would say what jumped out is, how blown away these people are. They really are, you know, elevated in their mindset when they think about these concepts. Because it expands their mind and when they realize that I can go and expand someone else's mind and their mind will essentially contribute to the entire community. And everyone's going to grow from one initial idea. >> What are you working on, the project? Please share with the folks, what've you been working on, what specific things that you do and you're managing. What's unique about the technology? Share some color commentary on the project. >> Yeah so right now we have a couple of projects going, and, for now, I'll just talk about the platform side of things which is the more futuristic vision. Specifically, we're creating trending communities so we could actually auto generate stories based on Twitter API data, right? And also, our own platform has even more complex metrics which we'll be rewarding people for, so people will get rewards for using our platform more than the Twitter. But we could still have native content versus in-network content being weighed differently. And so, what we're doing is routing metrics of weighted value with a contextual layer on top through natural language processing and machine learning. >> So, are some people saying "Oh, you're like Steam?" How do you respond to that? >> We're not like Steam. Steam is extremely powerhousey and it's momentum and it doesn't actually do topic weighing Right, so, and we also value attention of the crowd so what we're working on is, what do people influence with their reputation? Whereas Steam, it's like, where do people contribute? How much do they contribute? And so, what we want to do is, we say hey, you know if I get uploads on Reddit that should be weighed in the network somewhere else, right? Instead of having a overall karma, we should have one integrated karmic aspect of a topicality so that if my karma, I'm using karma as an analogy cause Reddit has the up votes karma, down votes karma. >> So what about blockchain, why are we So, how would you explain to someone Okay, you're theCUBE what is the blockchain? What is crypto mean for us? >> So, blockchain, we're using it to add a layer of trust and security to our network. So we want transparency within our network and that means we have to have a ledger for every single engagement, interaction like we tweet on the network, right? >> And the crypto, the token, does what? >> Crypto token will pretty much be able to be cashed out thru Ethereum, right, ERC20 but it would also have a weighted role in our two sided marketplace, bounty ask buy. And, that'll be the main medium of where people identify and exchange their reputation. >> How would you describe out platform to a user out there if they say, what do you like, or what are you disrupting, what aren't you like, what are you guys doing, what you disrupting? And why would I want to use your platform? >> Yeah, so I think we're disrupting, you know, multiple companies, right? And, the one I really associate with is a professional Steamit meets Brave Browser, BAT token versus Steam, right? So, BAT is attention only and attention is valuable. I'm here with you, you have a 20 minute interview with me. That's your attention, that's valuable but it's much more valuable than someone else who isn't interviewing, let's just say, someone who is less fortunate. But, that's also a real time aspect. So there's a time variable, there's a network variable and there's a topicality variable, you know the social graph, you got the interest graph, and then the value graph on top. >> So Alec, so if you had to describe what we do in one sentence, what would it be? Putting you on the spot. >> In one sentence, I would say we would call it, a decentralized media platform with rewards for the user base, based on reputation. >> Alright, my son Alec Furrier is also involved in our crypto project, part of theCUBE network coming soon, house of theCUBE is here, the crypto conference, and what better way to align with the crypto community then demoing our token enabled platform. Congratulations to you, Narendra, Kent, Jeff and the team doing a great job with theCUBE network. Cube alumni are all going to get coins, right? Not yet decided but great work Alec, thanks for sharing. It's theCUBE here, Puerto Rico. I'm John Furrier, my son Alec. Thanks for watching. (upbeat music)

(dramatic music) >> Narrator: Live from Washington D.C., it's cube conversations with John Furrier. >> Hello everyone, welcome to this special cube conversation, I'm John Furrier, the host of The Cube, co-founder of SiliconANGLE media Inc. We are here in the Washington D.C. Beltway area. We're actually at Amazon web services' public sector headquarters in Arlington, Virginia. My next guest is John Wood, he's the CEO and chairman of the board at Telos, a big provider of some of the big contracts, certainly with Amazon CIA, among others, welcome. >> Thank you very much. >> Thanks for joining me. >> I'm glad to be here. >> So, you guys have been pretty instrumental and we were talking to Teresa Carlson earlier, with an exclusive interview with her, and we talked about the shot heard around the Cloud. That was the CIA, Amazon win, four years ago. >> Yes. >> Kind of infiltrated the government area. It's almost a gestation period and now you got DOD action, a ton of other opportunities, but it really is an architectural mindset changeover from the old way. >> Yes You're involved in this, with Telos. What's your take, how are you guys involved, what's going on? >> Yeah, so it was groundbreaking, when the CIA made the determination that they were going to move to the Cloud, for sure. It kind of made everybody stand up and take notice, if the most security conscience organization in the world was considering it, why aren't I? And here we are, four years later, so where is the CIA now? Well now, the CIA is able to provision a server in a couple minutes, whereas the past, it used to take them almost a year. Now, with the use of automation tools like we have with Telos and the Xacta suite, the CIA is able to get their authority to operate in less than a week, when it used to take 18 months. So, I basically think what's happening is, the Cloud is providing an access point to IT modernization and the agency is showing that there is a blueprint that the rest of the government can also follow if they want to. >> One of the things we're involved in a lot of Blockchain covers, as well as kind of kicking the tires on Blockchain. You're in the middle of a Cloud gain with identity. Identity is the secret to having good scalable systems, because when you have good identity, good things happen. In Blockchain, some people say a theory about those. In IT, it's what identity you're going to use. How does the authority to operate challenge, you mentioned, become so important, because you're talking about massive amounts of time, I mean time savings. >> Wood: Yeah, so-- >> Just tease out the nuances of why it's so important to have that identity solution. >> So, in the past, there was no common language within which our cyber security professionals could engage with each other. Now, with the signing of the President's executive order on cyber security, the White House really is mandating the adoption if the NIST framework. What's relevant there is that on the one hand it provides you with a common language, but on the other hand, it's 11 hundred controls. So, as a result, automation is going to be key, to making sure that people can work with each other and making sure that, actually, the adoption actually takes off. >> They're safe, they know the trusted party. Is trust a big part of this and how does that--? >> I think what's happening, because the intelligence community has been working so closely together, and when I say the intelligence community, it's not just the traditional CIA, NSA, NRO, et cetera, it's also the military component of the intelligence community. So, you've got almost 38 assessors that are assessing C2S and SC2S. You know, the secret, if you will, Cloud, and the top secret Cloud, and those assessors all have been working in the same community under this framework and I think that has given them the confidence that the data is protected and as a result, they're heading much closer to reciprocity than ever before. >> There's been observations certainly on the Cube, we've said this many times with the past few years in tracking IT over the years, IT transformation, digital transformation, whatever you want to call it, buzz word. The reality is you had some progressives that would move faster and kick the tires, certainly financial services, in some areas you see that. Really, no problem. Then you had the folks who have just been consolidated down, didn't have a lot of budget and were lagging, waiting to adopt. Now there's no excuses, with cyber security, top of mind, with hacking, malware, ransomware, cyber warfare from nation states, sponsored states, an open source it's out of control. >> It is. >> So the security equations is forcing IT to move. The action has to be taken. What are you guys seeing in this area, because this is a big story and it's really putting a fire under everyone to move. >> And it's long over due. I co-wrote and article with our chief security officer in 2011, talking about why the Cloud was the way to go for federal, state, local, and education customers and at the end of the day, I think what's happening from a top cover perspective, the legislative community understands that. Obviously the Executive branch understands that, and now with editions like C2S the rest of the environment, the rest of the government can see what's possible. So, I believe the leadership within the government is ready for this change. They're seeing the benefit as it relates to C2S and SC2S and ultimately, the key is, the guys who run the contracts themselves, you got to make sure that those guys want that, to embrace that change too. >> Furrier: Yeah, so you have the-- >> And right now, 80, if you look across the government, 80% IT span is going back into maintenance. If you look at all my commercial customers, it's somewhere between 20 and 25%. What does that mean? It basically means that the government has a lot of legacy systems, which means that there's a lot of threats, and, which means there's a real cyber security problem. I believe fundamentally that by moving work loads to the Cloud, you'll be eliminating a lot of those cyber security problems. >> Yeah, it just means security is going to be the driver. The other thing I wanted to bring up, especially here in D.C., in public sector, is transparency. Now everyone can see everything. We're in a data-driven world, you can't hide either. The light is on, it's right there on the table. No more hiding. How has transparency been impacted in the procurement process, in the sales motions, the overall engagements with gov and public sector customers? >> I think, truth be told, there have been a lot of ideas that were sort of short-term and not really thoughtful, but the good news, as I said, is that the policy makers are really thinking and considering, trying to figure out how to make changes. Take for example, LPTA, low price technically acceptable. When I went to the congress and talked to both the House and the Senate side, and talked about how if I have one customer whose gotten hacked and the other customer has the same hack, but one happens to be a government customer and one's a commercial customer, the resources that we have are really trained, highly skilled, highly sought-after resources. Well, my commercial customers are willing to pay three to four hundred percent more than my government customers are. So when you have scarce resources, where are you going to apply them? You're going to apply them where the people are who are going to pay you. So my point to the Congress was simply to say, hey man, you get what you pay for. So ultimately, the good news is that, both on the House and the Senate side, that they elimanted LTPA, as it relates to cyber security, goods and services. So I believe, again, that there's a lot of, not just transparency happening, but there's a lot of people realizing that there are things that we can do. Procurement is kind of the last frontier for me. I have seen recently, I saw one of our government customers, where we were subcontracted, they went with something called an OTA, which stands for an other transaction agreement. Big problem in the government these days is everybody protest everything and there's really no downside to the protesting. With an OTA it's not protest-able. So I am seeing our government customers beginning to think about other means of actually doing things like procurement, and so that you can actually acquire. >> Are they going to have instant replay? (laughter) It sounds like the NFL, that call's not reversible. I mean, this is kind of, we're getting into all these rules and regulations where you've got protest, it seems that policy injection is not healthy at some level, because that point about what cost more on the commercial side, because of demand there, they understand the consequences and resource availability. To the government you just eliminated a policy that wasn't really helping. >> Right. >> So policy is a real consideration in here. >> I think so. Again though, it's a different environment than it was five or six years ago and I do think that there are some real positive things that are happening. I agree with you that there's a ground-swell of support behind the Cloud and certainly, players like us see the benefit associated with that shared security model. >> One of the things we've been observing and tracking on Sillaconangle and the Cube is this notion of public-private collaberation. Sharing data is a huge deal. Certainly, in Cyber people realize that data is valuable. Certainly, at Scale, you see patterns you might not see, customers on workloads, here and there, need to be identified. You're not sharing the data you don't know. So data sharing is a big deal, but also, collaborations between the private and public sector. Can you comment on what's going on there, because we're seeing some movement where, you're seeing some security agencies saying, "We'll share some stuff." >> Yeah. >> Furrier: You share some stuff with me, so you're seeing a little bit of the community developing heavily around data-sharing, what's you're take on that? >> So, I think we have a ways to go to make it work right, because if it was working right, you wouldn't see the very published, publicized hacks that have gone on. One of the things that the Congress can do is to provide incentives for the private sector to share more information, more quickly. When the Yahoo hacks occurred, it wasn't discovered until two or three years later. As a result, like I said, there's really no incentive and there's a perceived amount of liability. One of the things I'm asking some of our Congress people to consider is if you do share information, maybe, there's a limitation on liability and that provides, if you will, a mechanism and that provides an incentive for the private organization to work with the public organizations. >> So not to bury it, like Yahoo tried to bury that thing. >> Exactly. There's no sense in burying it. There should be no reason to bury. >> Okay, take a minute to talk about Telos, what you guys are doing, the chief executive. What's going on with the company, talk about the successes, where you guys are winning, your challenges and opportunities. >> Sure, we're in the business of, we do cyber security, we do identity and we do secure mobility. In the area of cyber security, I'm very proud about the fact that we're the database of record for intelligence community, many department of defense agencies use us, homeland security, a whole, department of safe-- There's a whole bunch of organizations that tend to work with us. I think that the issue for me has always been around investing in things that make our customers more efficient. So whether it's cyber security, it's one thing to provide the authority to operate, but I like to provide that authority to operate on a continuous basis. When we talk about identity, it's one thing to say that I am who I say I am, but it's another thing to let you know that I'm actually somebody that's trust worthy. So, we have a special relationship with the FBI that allows us to do real-time data look-ups on their people. We're the integrator of record for the common access card, the military ID card, we have been for a long time. From that, we built a business relationship with the TSA and now we have about 70 airports around America that use our service to do identity as a service for all their employees. >> Can you get me to cut the line at Pre? (laughter) >> You know, if you want to cut the line at TSA pre-- >> Quality of service opportunity and people will pay more for that. >> Absolutely. And plus, I think TSA pre-check wants to have a lot more people in that ecosystem too. No different than when the Easy Pass came into play years and years ago. I remember just zooming through the Easy Pass and wondering why people would want to stand in line, why would you, right? And then if you think about it, we're also involved with secure mobility, so we have a capability called Telos ghost that allows you to basically hide on a network. You're familiar with the notion of signal hopping? In World War two that's how we avoided detection by the enemy, so this is what we invented here with something around IP hopping. So as a result of that, whether you're a server-facing thing or a client-facing thing or a mobile device, you can't be seen on the network and if you can't be seen on the network, you can't be hacked. >> Well, that's awesome stuff. Your relationship with Amazon Web Services, talk about that, some of the things you're involved in. >> Yeah. >> The deals, the momentum. What's the relationship look like between you guys? >> So we have an enormous relationship with Amazon, most important part that we have, it started with the agency and I was in a meeting with Teresa Carlson, one of the senior people in the agency, and we wondered whether or not we could do for, we Telos, can do for the Cloud that which we've been doing for the enterprise for the better part of 15 to 17 years now, which is basically providing that authority to operate in an automated way. So we invested together and we were able to prove that we could absolutely do that. Now, what we're doing is we're basically copying and pasting that model to our customers across the government. >> And you guys put a stake in the ground, 2011. You were early. I mean 2008 was the beginning of the DevOps movement, you were in the heart of it in 2011. >> Wood: Yep. What's the biggest thing you've learned or observed or experienced over those years, since 2011? >> The biggest thing? >> Or just the most important. >> Wood: That is an enormous question. >> It could be the most important, the most relevant, most surprising-- >> Well the most important thing was I got married in 2012. (laughter) I have a four year old and two year old and a 14-year old, those are the most important. >> Was it really you who got married, was it your identity? >> Wood: It was really me and it was my identity. I will say, I think that the government is embracing efficiency. The government is embracing change. I think it started around 2014 or 15, and now it's really moving out. I think there's a lot of top cover, both from a policy side and an executive side and I'm seeing a lot of leadership from within the government itself of people who want to make the change happen. >> And there's also the competitive fairness question we're hearing, just here in town, yesterday, rumblings of one-source Cloud, multi-Cloud. Amazon is technically a one-source Cloud, but they've got an ecosystem. Should they have multi-Cloud in their requirements? All these things almost feel like that protest model is going on, like there's a little fud going everywhere from the other vendors. Do we expect to see more of that in your mind or less of it? (laughter) >> I think at the end of the day-- >> The chips are taken off the table. >> The people who don't want change are the ones, who are, if you will, very invested in the legacy. If those people are paid, time, material or cost blessed, they're not paid to be efficient. So there's going to be push back. On the other hand we've seen by the gigantic growth of the adoption of the Cloud and by the Cloud infrastructure and the Cloud ecosystem itself, there are enomorous opportunities for organizations out there. So I think people should embrace the change, I really do. I think, fundamentally, it's going to be a really big positive to this industry and into this region. >> I always say to Dave Vellante and my co-hosts, it's like no brainer, you look at the main frame, that was the generation when I was growing in the industry. I was the young gun, like main frame co-ball, who the hell wants that? Mini computer, eh, I want the client server. It's pretty obvious when you're in it. So I got to ask you with that in mind, Cloud is pretty obvious. Folks will understand DevOps and automation and those efficiences. You mentioned authority to operate as an example. Some of these numbers are pretty significant. So let's go down the problems that are important, what are the consequences, how do you quantify it, right? So the problem that people are trying to solve is how do I get resources, computing, software, whatever. Pretty important, because now you've got security, you've got all kinds of stuff. What are some of the consequences and you mentioned some benchmarks that you've quantified. You mentioned provisioning a server in a year. Is that really true? >> Wood: That's true! >> So give me some data on some of consequences, kind of the old way and new way. >> Well the old way if you're using the traditional procurement, it's like I said, one of the big issues is whether it was the culture or it's procurement roles or just the process to get an approval, it would take a year to get a server provisioned. Now, it's literally, you push a button and one to two minutes later you have a server, a new server. So you get ultimate scale, you get ultimate throughput, you pay as you go, you pay what you use. What's not to like? So that's all good. From the standpoint of security, because it's the NIST framework we can automate about 90% of that. That's 11 hundred controls, right? So we automate about 90% of those 11 hundred controls. Now, you get a whole bunch of auto inheritance, a whole bunch of things that can be automated are, and as a result, when NIST goes from one version of NIST to another version, all that happens automatically, and more importantly, as a cyber security professional, and I've only been at it since 1994. (laughter) I've been in it for relatively a long time as a CEO. As a cyber security professional, what I see is, as long as I can show a continuous monitoring of your current status, that's very relevant to the operational security professional. That's really good. So for us, we know that our customers are going to be a combination of Cloud, hybrid, and on-prem. These large organizations are going to take years and years and years to move to the Cloud, but they got to start, because now is the time. >> So automation and having that nice stack where it automatically updates and auto-provisioning, auto scaling, but the operational provisioning piece is really where the rubber meets the road, right? Is that what you're getting at? >> Well it's that. It's also you're consolidating your data centers. You don't need lots of them anymore. You can just focus on one, that's another big area. Another big area is, you can lift and shift your legacy IT infrastructure into the Cloud and then put the big investment into the new application as it's siting in there in the Cloud. >> Awesome, John, thanks for joining us here in the cube conversation. Here at Amazon Web Services Headquarters, breaking down the trends in GovCloud public sector as Cloud computing really levels the playing field, opens up new doors, new solutions, faster time to operate, in vi of other things, here in Washington, D.C., in Arlington, Virginia, I'm John Furrier. Thanks for watching. (dramatic music)

>> [Narrator] From the SiliconANGLE media office in Boston, Massachusets, it's theCUBE. Now, here's your host, Dave Vellante. >> Hi everybody, Dave Vellante here coming at you from our East Coast studios in Marlborough, MA just outside of Boston. What I wanted to do is give you a little recap of 2017 and what's happening and give you an update on SiliconANGLE Media. So as many of you know SiliconANGLE Media INC comprises three brands. TheCUBE, which as most of you know is we call it sometimes the ESPN of tech, it's our live and on demand video broadcasting element. And of course we have the research arm which is Wikibon and Wikibon.com And then, SiliconANGLE is our news site. And so I want to just, as I said, recap what went down in 2017 some of the things you may not know about. >> Last February, February first, actually we opened the new studio in Palo Alto, California. It's at 989 Commercial ST, you should check it out. It's sort of near the mountain view line but it's in Palo Alto, it's a great location, we have a large studio there. And throughout the year, in 2017 we held events, we had launches, but most importantly John Furrier, my business partner, is really running editorial content programs out of that studio. >> So every Thursday Furrier has high level key guests come in CEOs, VCs, in customers, and they just riff on what's going on in the industry and what's happening It's been an absolutely awesome resource for us and I really encourage you guys to go check it out. We did 135 show days last year. TheCUBE is run by our general manager, Jeff Frick and 135 show days meaning we broadcast live at 135 days at events last year, which is just incredible. >> It was our first year we ever did anything in China We did the Alibaba conference, the cloud show there that was very exciting. We did a number of shows in Europe and of course all the big shows in the United States as well >> We launched three websites last year. TheCUBE.net is the latest one. You know, a lot of times we talk about data driven media. If you go to theCube.net and check it out, you'll see something called theCUBE Alumni database. And theCUBE Alumni database contains virtually everybody who's ever been on theCUBE. So you can search CIOs, CEOs, developers, bloggers, analysts all the folks that have been on theCUBE you can see and they've got a profile page on each one of those so, we're collecting all that data SiliconANGLE.com we launched the new website >> SiliconANGLE is run by Rob Hof, who is our Editor-in-Chief Rob was the Silicon Valley beuro chief for business week for the better part of a decade, so we're really proud to have Rob on. He's been on for the last couple of years and just doing a great job with that site. >> And then Wikibon.com is run by Peter Burris he's our Chief Research Officer He's been with us now for the better part of 2 years and he's got that team cranking on all kinds of research in cloud and AI and data orientation, the edge, and infrastructure for emerging applications like AI. >> One of the areas we're most excited about that we launched in 2017 was a new capability called Clipper. So we have this tool called Video Clipper as you know, John Furrier and I, when we met we had this vision for data driven media and innovation and we launched this tool we call video clipper that was developed by Kent Libbey and his team one of our newer executives that we brought in last year on the product side. >> What Video Clipper does is we transcribe every video now that we do, we'll transcribe this video, and then we synchronize the transcript with the video and we're able to then search video, highlight a text, a paragraph let's say, push a button and boom we've got a clip and that clip is ready to be shared throughout various social media platforms like Twitter, and LinkedIn, and Facebook and the like So very, very excited about that tool you're going to be hearing more about that We don't sell it as a separate tool, we integrate it as part of our offerings and got some new offerings that we're bringing to customers in 2018. >> One of the other really exciting things in 2017 we brought in a new chief revenue officer his name is Greg Theriault, I'm going to introduce you to him today Greg Theriault is with me here in studio, Greg, it's great see you, thanks for spending some time with us. >> [Greg] Thank you, Dave, thanks for the opportunity I've never been more excited. Let me tell you a little bit about myself I live in Concord, MA right around the studio here and I came from the IT industry. I've been there for a long time. I used to be at a small systems integrator, kind of the size of SiliconANGLE Media, building client servers, computing, got certified in Novell, and then I jumped into sales. I worked most recently at Forester Research and was there for almost 18 years, two decades, building the sales capabilities, always wrapped around the customers, but I am thrilled to be here today >> [Dave] So, Novell, when our network goes down can you help us fix that? >> It was about 20 years ago but, you know the history with Novell >> Yeah, another Utah company that somehow didn't make it, but for a while they were a little monopoly. So you've been in the business now for a couple of decades maybe, you know, think about what has happened over the last 20 years, what kind of changes have you seen? Share with us your perspectives. >> I've never seen so much disruption from client server, to social computing, to AI, now it's digital disruption in everything and you hear about this all the time in the news that companies are becoming software companies look around the corner, GE is now GE digital, they're trying to reinvent themselves, very, very exciting times. AI machine learning, autonomous computing, and then right around the corner there's block chain I mean that's the big buzz these days Also there's the autonomous vehicles, and let em give you a quick story About two years ago my son was born and I was fortunate enough to have a breakfast with the CEO of Tesla, and I asked him "Hey, he was born, what's going to happen in 16 years?" and JB said to me quite candidly, he said "if your son is driving a car that's not autonomous it won't be safe and he won't need a license" So, things are happening at an epic speed I don't know I these prediction will be true but it is Telsa >> [Dave] Won't need a license, you know it's funny, I mean, I don't know how you feel about it but when I turned 16 it was one of the most exciting days of a young person's life. You wonder what the social implications of that is if you don't need a license, I don't know maybe they can start driving at 14 or 13, you know whatever but you know what I'm saying? >> [Greg] Yeah That was a really exciting time we couldn't wait to get our permits and "Dad can I drive you to the dump?" Right? It's like... >> Self driving cars and self driving refrigerators, I mean, it's moving fast it's at an epic speed right now >> Well everything, and again, you take that business it's all about the data, as I said in my intro we always talk about data driven media we got so much data, you talk about digital transformation, philosophy is digital meets data >> Right >> and you talked about GE you're seeing all these companies now getting disrupted because digital allows people to move so fast, it allows companies like Apple to get into financial servies and you're seeing Amazon become a content company and it's really all around the data, isn't it? >> [Greg] Absolutely >> So, I wonder if you could share with our audience, SiliconANGLE Media, small company you came from a much larger firm, a big brand, Forester, your former company. What attracted you to SiliconANGLE Media? >> I think it was the fact that I jumped on airplane and went out to Palo Alto and met with your general managers. I think the innovation and the speed, the speed around it's in your DNA and then you took social computing, combined it with really computing power. And then I saw the Video Clipper tool. It's the fastest application I've ever seen to clip video and that innovation, the speed really attracted me to the company, to build really powerful content >> [Dave] Yeah it's been quite a ride since I met John Furrier in 2010. You know, John at the time, said "Dave, whatever we do we have to innovate. "We have to continue to invest in R&D" And those R&D experiments they don't always pay off but when one hits, like the Clipepr tool, it can be a home run so we're very excited about that. Share with us your philosophy, what can we expect from Greg Theriault? >> [Greg] Sure, I appreciate that. Well I'm happy to be here I actually blogged on LinkedIn over the weekend about my transition here, and I think it starts off with my family, my son and my wife they helped me, they grounded me, but my philosophy on business is to really be customer focused to hire the right people, train and coach, and build a different mindset which I call the growth mindset the sales rep of the future is being disrupted right now just like very other function. And that is absolutely pivotal. I think the buyers change, Dave. Faster in two years than the past 100 years the buyer is in control, you have to build systems, processes and technologies wrapped around how do you help the customer be successful at drygrowth and that's the biggest shift going on right now I mean sales right now, again, is being disrupted so social selling and things like that, I want to bring that kind of discipline and processes to SiliconANGLE Media >> [Dave] Well, what about social selling? A lot of people will, when social media really started to come into play, a lot of people say "well, we sell to IT people, and IT people, they don't have time to go on Twitter, they don't do Facebook" What's your perspective, has that changed you know and what about that? >> It's changed faster than I could ever believe buyers buy differently but they also need to see the different presence in social that's Twitter, that's LinkedIn, and that's also you have to be on the phone, you have to be in front of customers but it absolutely is pivotal that the new, let's call it a digital rep, needs to understand the tools to listen. Listen to the customer first and foremost, and it's a new channel but it's a channel here for a long time. Again, it's disrupting sales at an epic pace >> [Dave] So what are your priorities, looking out, say, near term, mid-term, long term? >> [Greg] To wrap my hand around the customer base you have to innovate with them, with the team we build And also to build the collaborative culture I'm really into culture and the ability to kind of game-afy the culture, grow the business, accelerate the business, and also develop the team that we build. I mean, the aspirations to where do they want to be in a couple years will help build the business and that's a global business as well >> Well, of course, a lot of the action in the tech business is out in Silicon Valley, and you and I are based here in the East coast, What can we expect in terms of your presence in Silicon Valley? >> I'll be on a plane a lot, and I don't mind that at all I mean, it's a flat country right now So I'll be on a plane, but also the heat is in Boston, New York, Chicago, but the Valley is where it's at so I'm going to be jumping on plane in two weeks to meet with the team, I can't wait >> [Dave] Well, we're excited Greg, to have an executive of your callabor join our team. >> [Greg] Thank you, appreciate that >> Congratulations, and look forward to many, many years of productive growth and adding value for our clients with you >> [Greg] Likewise, thank you >> Alright, you're welcome. Thanks for watching everybody, this is Dave Vellante with Greg Theriault, we'll see you next time.

>> Narrator: Live from Las Vegas, it's the CUBE, covering AWS re:Invent 2017 presented by AWS, Intel, and our ecosystem of partners. >> Live here in Las Vegas is the CUBE's exclusive coverage of AWS re:Invent 2017, our fifth year of covering the massive growth of Amazon Web Services. I'm John Furrier, cofounder of SiliconANGLE Media, Inc. with my cohost, Keith Townsend, CTO advisor. We've got two amazing guests here from Druva, a hot startup, a hot company. Guss Gustafson is the executive chairman of the board, and Matt Morgan is the chief marketing officer at Druva. Been on the CUBE but many times. We've had you in the studio. You guys are doing extremely well. You've always got some news for us, always executing. You are like the Amazon of your sector. You've always got stuff going on, what's up? Tell us, share the news. >> Super excited about announcing a new technology, a new product line. It's called Druva Apollo, and Druva Apollo basically completes the cloud data protection story, so as you guys know, we've wrapped endpoints with data protection and data management, and we call it data management as a service. We've wrapped servers with the data management as a service. We took the data, we've protected within the cloud, but with Druva Apollo, what we've announced is cloud-to-cloud data protection, meaning that data that's born inside an EC2 existence, for example, can be wrapped with the same data protection and management as we do for endpoints and the servers. It really is an extension of the platform. Now you can start looking at the data holistically, any data, no matter if it's on the endpoint, the server, or now, within the cloud can be protected within the same controlled data set, getting the full global D2 technology, plus the governance and intelligence capability. I'm really excited about this announcement. >> Now, I want to ask a question on that, because one of the things we talked about in the past on is the cloud has changed the game around perimeter, no more perimeter. >> Right. >> There is no wall from the cloud, a lot of holes to get in there if you are a hacker, but you have a product leadership, but and ease-of-use. One of the things that the cloud is the ability to acquire the resources, right? The server list is out there, how do you guys compete in this now potentially data protection-less world, or is that a term? I mean, you've got to be seamless, but you've got to have good tech. How do you guys do both of those? >> I think you actually just underscore the paradigm shift that's happening. We used to think of data being localized to a server, to a machine, and you had to protect that machine. You wanted to, quote, backup hard drive if you will. Well, data is now in a serverless environment. It's in the air, it's in the cloud, it's tied to applications that may or may not be running within a specific instance. You don't have the control factors, right? >> John: Or some other database. >> That's exactly right. >> Mobile databases now. >> That's exactly the point, right? What we are doing is, because we are stateless, because we exist on these multiple planes, you can have a much more universal conversation around data protection and management, but there is another big "ah-hah" about moving to the cloud for data protection and management, and it's all about ease-of-use and simplicity. Now with a single login, with a single set of credentials, I can access and search across massive data sets that could contain all my endpoints, or it could contain multiple servers, or now it can contain cloud-to-cloud data protected instances. This is a very big deal. Think about the past. If I was a classic hardware acquisition play where I purchased specific silos of data storage or secondary storage, I needed to manage each and every one, and there could be hundreds. I would need to manage hundreds of logins. I had to keep them all up to date. All of that is gone with Druva. >> Let's talk about user experience. This is a developer-focused conference. >> Matt: Totally. >> I'm amazed at the number of shorts and hoodies I've seen (laughing) At a proper enterprise conference. >> John: It's a developer conference turned enterprise. >> Yes, developer conference-- >> Not an enterprise conference. (talking over each other) unlike everybody else. >> The infrastructure company having a hackerthon, for example, but developers don't care about servers. They care about data and interacting with that data. >> Matt: That's right. >> What is the developer experience for recovering and protecting data within Druva? Do they have to go through some backup grandfather-son, son-father set up to back up data? What is the experience? >> There are some vendors that actually still require that. (laughing) Some of them have acted like it's a breakthrough to put it in an appliance, but at the end of the day, it's the same conversation. It's just a localized piece of hardware. Druva's conversation is very different. Data protection is all about where that date is going to be managed and stored, and how you connect it up to the service. By being stateless, we've created an entire architecture that allows all of that data to be collected centrally. Once it's there, the developer has the capability to access it, but the real value comes on the governance side, and on the legal side, so if I'm in a situation where I need to manage critical corporate IP, and know it's protected, and and now I have an audit trail on that data, who has touched it, what they did with it, where it was copied. I have that information. I can search for that information. So it moves a little bit beyond a classic developer point of view and extends that data control to the other players. >> Gus, I want to get your take on this because you are the chairman of the board, but also you have a lot of industry experience. We are seeing a shift in the business now where scale of the cloud is causing a lot of disruption. You guys are taking advantage of it at Druva, but also you are seeing some deviants in Silicon Valley, in our backyard. You've got startups that were born before 2012 with the "go big or go home" attitude, Andreessen Horowitz in Sequoia writing fat checks, $100 million. They can't scale up to compete against this other scale. They got out-scaled, so they end up getting acquired, you know, accu-hired. Barracuda's going private. A gem in the valley, great company, no cloud strategy, so scale is dessimating and creating value at the same time. How should businesses look at this business model paradigm shift where it's not go big or go home, it's find a spot in the ecosystem (laughing) and milk it, or get a position. You can't compete. It's hard to compete against scale. >> Scale, you are right, the whole scale paradigm has now gone from-- it's beyond comprehension, to be honest with you. I think the other thing that we've learned, and this is how Druva looks at this, you can't compress experience. You can't compress learning and application of learning, and so for eight years plus we've been at this game thinking about scale, and in some cases, to be quite blunt, we experience it with our customers because there is no predetermined path in a lot of these things, but for companies of scale today, I think you would have to have a cloud-first mentality. That's what Druva brought to the party. I think we are seeing a lot of people that have looked at this and said, "How do I actually get all the way over here?" Our message is really simple. Let's just get started. Whatever applications or use cases it is to get started, whether it is endpoint, whether it's a server, cloud apps, but we've thought about and built the vision around the entire end-to-end strategy, so we will bump into things at scale. We will figure out how to handle those. We recently brought on board a customer with 75,000 employees, another one with another 50,000, and we've done this before, but those are new layers of scale. >> John : You guys are taking a pragmatic approach. >> We are. >> You guys aren't trying to overbuild, get over your skis, or whatever people call it, but don't create a situation where there is diseconomies. Leverage what you've got, and know your place in the world. >> If you don't mind, I'll just make a comment on the funding round we just received. We just received $80 million in net new funds. It was a preemptive interest in investing in the company. Quite honestly, we could have potentially taken more, but the focuses are on executing what we can actually do today. >> More discipline too. The less capital you take-- >> There is that. >> The more discipline. >> There is that, but you know when you think about the growth and the opportunity, in large part for us, it's all about staying pragmatic-focused and executing well on what is ahead of us. >> The product market fit is always one they talk about with the funding, but also it's the sales channels. If you try to compete with sales, say Amazon for instance, others have tried, it's hard. I know a few companies getting bought up by private equity left and right because they just, size wise, can never get there. >> Gus: Right. >> You guys are inside the tornado, as Jeffrey Moore would say, which is kind of the strategy for you guys. You get in the cloud, you've got product discipline. How is it going on the sales side? What are some of the metrics you are seeing? Any success metrics you can share customer success? >> Yeah, absolutely, and you know for us, AWS is a strategic partner and a great partner in terms of the alliance that we have around bring in net new customers to the cloud, working with them, et cetera, so in the last six months we've added 300 net new enterprise customers. That brings our total to well over 4,000 enterprise customers, and we've done that by, again, staying very focused around that first bite, a very simple approach, and then once people start, they see how simple it is, so you had asked about the developer experience, Keith, it is so simple. In some cases what we say in their actual experience is they don't believe us. When Matt was talking about the "ah-hah" moment, once they experience it, they continue to build, and build, and build. >> So the developers, again, we've talked about this because we are at a developer conference, they just want to solve problems. One of the things that we've always kind of harped on developers about, and Matt, you talked about this a little bit with governance, with data governance, GDPR is coming to be fully enforced May of next year. >> Matt: That's right. >> Very serious consequences for companies that don't kind of handle that. Have you guys seen an uptick in conversations around GDPR with customers and how Druva helps to mitigate some of the challenges around GDPR? >> Keith, one of the most amazing things that's come out of GDPR is the rise of this new executive persona, the chief data protection officer. >> John: Oh, another one! >> For a vendor that's in the data protection business, it is wonderful to have a C-level executive responsible for the value that we deliver. >> Some of the penalties is 4% of revenue. >> How many chiefs will there be these days? >> Well that is true, there are a lot of chiefs. >> There's a lot of chiefs in the kitchen. >> There are a lot of chiefs. >> More than Indians. Oh you guys are the-- >> Yeah, I'm going to defend the chief data protection officer. >> Keith: Yeah, we will keep that one. >> I'll keep that one. (laughing) You keep in mind that the risks that people are dealing with, and GDPR is an extension of extending individual rights to the data sets that are collected on them. The idea of the right to be forgotten. >> You guys have challenges not even within the customers, the external customers, but an organization with 75,000 users, they have rights in themselves, so there is this differentiation between protect internal corporate data and that policy, and keeping that data. If I'm a developer searching for data, I'm just searching for data, so how do I control, what's the controls available for making sure that that doesn't go afoul of GDPR? >> Absolutely, so we have phenomenal security capabilities that are built into our product both from an identification point of view giving rights and privileges, as well as protecting that data from any third-party access. All of this information is going to be compliant with these regulations beyond GDPR. There is enormous regulations around data that require us to keep our security levels as high as we go. In fact, we would argue that AWS itself is now typically more secure, more secure than your classic data set. >> Yeah, they've done the work. >> So we are building on top of the AWS security framework which gives you even better security, and because, this is important, it's off-site, logically by conception of the cloud, we also add immutability, so when you think about ransomware, ransomware is not going to crawl up to the cloud in the classic way that you would have the type of infections that have happened. Druva is going to give you the capability to ensure that that data is whole, and you can recover from those types of malware attacks. It's a little bit of a pivot from GDPR, but I think all of this stuff around data risks are related. >> Talking about the government, public sector market, you guys just got FedRAMP approved. >> Yes. >> It's a big certification. Congratulations. >> Thank you. >> What does that mean for your business? More customers coming in on the public-sector side? >> Public-sector is off the charts for us. The FedRAMP ATO certification, we are the only data protection vendor that has that, and it gives us the capability to qualify for data protection possibilities within the public-sector. I don't know if, Gus, you want to comment anymore on that. >> John: Visa cross is gonna love that. >> It's a massive market opportunity. It also puts you at a higher level in terms of, obviously, the security capabilities that they went through and tested to give us the ATO, which is the authority to operate in the FedRAMP sector. It opens up a tremendous amount of opportunity. When you look at, kind of, the Fortune one as far as US government, this is a massive opportunity for us. >> Well, save the date in Washington DC. This morning they announced the AWS Public Sector Summit on June 20th and 21st. The CUBE will be there. I've got the verbal. Well, we already have the deal with Theresa Carlson. The CUBE will be there probably with two sets too. That's turned into a re:Invent. It grew from a hotel room two years ago, to a ball room, to now the convention center, and they're expecting again, this year in DC, Amazon Public Sector Summit, everything, nonprofits, gov cloud, huge. >> Yeah, it's amazing. AWS has become the 21st-century operating system, and at first it was for individuals or small businesses, but now it is the enterprise. Look around, right? We are all re-platforming, if you will, to be able to provide this architecture as the best possibility. >> So you're betting on Amazon? >> Absolutely. >> Other clouds? >> So we are a multi-cloud provider. We have a solution that also runs on Microsoft Dejour. There's lots of customers that choose Dejour. They are Microsoft customers. They're customers that enjoy the different data centers that Microsoft offers, but the vast majority of our customers really embrace the AWS solution. >> You'll protect whatever the customer needs. Whatever environment they have, you'll support the major platforms? >> We're gonna support either one, and you've got to realize the idea of different data centers that are localized to different countries give you different soverignty options with Microsoft you may not get with AWS, at least not today. >> Yeah, and same with Google too. Google has not a big presence outside the US. >> That's right. >> So they're limited. >> So data protection is starting to become a catch-all term. The, what, $80 million in funding the last round? >> Gus: Yes. >> It's not just about data protection, but now multi-cloud data mobility. Being able to take my data, my hybrid IT data and move it to where I need to move it to. Can you talk about Druva's capability when it comes to data mobility? >> One of the most popular use cases of the acquisition of the Druva technology is all around MNA. The opportunity to bring in data from a variety of different endpoints and bring their customers new companies that are being acquired into the fold. You have all kinds of governance capabilities you could do on that data, and you could prevent the typical data leakage. The employee turnover, where people basically walk out the door. They take their hard drive with them, or take the computer. It's not being tracked, and you don't know what data was there, and you can't track it. With Druva, you have that data. They can take the hard drive. You know exactly what they took. You have information, and you have saved that IP for the company, and you gained that. If I'm acquiring a company, that information obviously is important to me. >> Thanks Gus, thanks for coming on the CUBE, thanks for the update. Congratulations on all the business success and public sector is right around the corner as well, another growing market. Back up and recovery data protection is hot in the cloud, it's hard to do. These guys have got a great solution in Druva. It's the CUBE bringing you more live coverage. We're taking a short break. We'll be right back with our next guest. Stay with us.

(upbeat music) >> Hello everyone, welcome to a special CUBE Conversation here at the SiliconANGLE Media, CUBE and Wikibon studio in Palo Alto. I'm John Furrier, co-founder of SiliconANGLE Media, Inc. I'm here with Peter Burris, head of research, for a special Amazon Web Services re:Invent preview. We just had a great session with Peter's weekly Action Item roundtable meeting with analysts surrounding the trend. So that'll be up on YouTube, check that out. Really in-depth conversation around what to expect at Amazon Web Service's re:Invent coming up in about a week and a half, and great content in there. But I want to go here, Peter, have a conversation with you back and forth, 'cause we've been having a debate, ping-ponging back and forth around what we think might happen. We certainly have some visibility in some of the news that might be happening at re:Invent. But you guys have been doing a great job with the research. I want to get your thoughts and I want to just have a conversation around Amazon Web Services. Continuing to kick ass, they've had a run on their own for many, many years now. But they got competition. The visibility in Wall Street is clear. They know the profitability. The numbers are all taking shape. Microsoft stock's up from 26 to wherever it is now. It's clear the cloud is the game. That's what's going on, and you have, again, the top three: Amazon, Azure, Google. And then, you can argue four through seven, including Alibaba and others, big game going on. This is causing a lot of opportunities, but disruption to business models, technology architectures, and ultimately how customers are going to deploy their IT and/or their digital business. Your thoughts? >> I think one of the most interesting things about this, John, is that in the first 10 years of the cloud, it was implied that it was a cost play. Don't do IT anymore, it's blah, blah, blah, blah, blah, do the cloud, do AWS. And I think that because the competition is so real now, and a lot of businesses are starting to realize what actually could be done if you're able to use your data in new and different ways, and dramatically accelerate and transform your businesses, that all this has become a value play. And the minute that it becomes a value play, in other words, new types of work, new types of capabilities, then for Amazon, for AWS, it becomes an ecosystem play. So I think one of the things that's most interesting about this re:Invent, is it's, from my opinion, it's going to be the first one where it's truly a strong ecosystem story. It's about how Amazon is providing services that the rest of the world's going to be able to consume and create new types of value through the Amazon ecosystem. >> Great point, I want to bring up a topic that we've been talking on theCUBE in some of my other CUBE Conversations, as it relates to the ecosystem is, in all these major ways, and we've seen many, you've covered many ways as an analyst over the years, there's always been a gestation period between a disruptive enabler, you could talk about TCP/IP, you could talk about HTTP, there's always a period of gestation. Sometimes it's accelerated now more than ever, but you start to see the impact of that disruptive enabler. Certainly cloud, and what Amazon has done, has been a disruptive enabler. Value's been created, more value's being created, more and more everyday we're seeing it. You're starting to see new things pop up from this gestation period, problems that become opportunities. And competitors that are now partners, partners that are now competitors. So a full changeover is happening in the landscape because of it. So the question for you is, what are you seeing, given your experience in seeing other ways before, what is starting to be clear in terms of visibility that are becoming known points of obvious straight and narrow trends that are happening with this cloud enabling? >> Well, let's talk about perhaps one of the biggest differences between traditional IT and cloud-oriented IT. And to kind of tell that story, I'll do something that a lot of people don't think about when they think about innovation. But if you really think about innovation, you got to break it down into two distinct acts. There's the act of inventing, which is an engineering act. It's, how do I take knowledge of physics, or knowledge of sociology, or knowledge of something, and invent something new that reflects my understanding of the problem and creating a solution? And then there's an innovation act, which is always a social act. It's getting people to change the way they do things. Businesses to change the way they do things. That's a social act. And one of the interesting things about the transition, this transition, this cloud-based transition, is we're moving into a world where the social acts are much more synonymous with the actual engineering act. And by that I mean, when something is positioned as a service, that the customer gets and just acts on it because they're now renting a service, that is truly an innovation process. You are adopting it as a service and embedding it more quickly. What we're seeing now in many respects, going back to your core point, is everything being done as a service, it means that the binding of the inventing and the innovating is much more strong, and much more immediate. And AWS re:Invent's been a forum where we see this. It's not just inventing or putting forward a new product that may get out to market in six months or nine months. It is, here is a service, people are consuming it, we're embedding it in our other AWS stuff. We're putting this AI into how folks are going to manage AWS, and the invention innovation process collapses very quickly. >> That's a good point. I would just give you some validation on that by seeing other trend points that talk about that social piece. You hear about social engineering in cyber security, that that's now a big part of how hackers are getting in, through social engineering. Open-source software is a social engineering act, 'cause it's got a community dynamic. Blockchains, huge social engineering around how these companies are forming. So I would 100% agree, that's a great, great point. The other thing I'd ask you to elaborate on is something that is a trend that's obvious, 'cause everyone talks about the old way, new way. Legacy is being disrupted. New players like Amazon are disrupting the people like Oracle. And Oracle thinks they're winning, Amazon thinks they're winning. The scoreboards aren't the same, but here's the question. Technology used to be built to solve technology problems. You build a box, you ship it, and it works. Software, craft it, ship it. It does work or it doesn't work. Now software and technology we can use to solve non-technology problems. This brings it to a whole nother level when you take your social comment, an invention. This is now a new dynamic that tend to be, I don't want to say minimized in the old days, but the old ways was, load some boxes, rack it up, and you got a PC on your desk. We could work effectively on a network. Now it's completely going non-technology problems, healthcare, verticals. >> Here's the way we look at it, John. >> John: What's your thoughts on that? >> Our simple bromide is that we are in the midst of the transition in computing. And by that I mean, for the first 50 years we talked about known process, unknown technology. By that I mean, for example, have you ever seen a GAAP accounting convention wandering out in the wild? No, it doesn't exist, it's manmade, it's artifice. There's nothing wrong with it. We all agree what an accounting thing is, but it's all highly stylized and extremely well-defined. It's a known process. And the first 50 years were about taking those known processes in accounting, and in HR, and a lot of other domains, and then saying, okay, what's the right technology to automate as much of this as possible? And we did a phenomenal job of it. It started with mainframes, then client/server. And was it this server, or that server? Unix or something else? TCP/IP or some other network? But that was the first 50 years of computing. Now we've got a lot of those things out. In fact, cloud kind of summarizes and puts forward a common set of experiences, still a lot of technology questions are going to be important. I don't want to suggest that that's not important. But increasingly it's, okay, what are the processes that we're going to try to automate? So we're now in a world where the technology's much more known, but the processes are incredibly unknown. So we went from a known-- >> So what is that impact to the cloud players, like Amazon? Because what I'm trying to figure out is, what will be the posture on the keynotes? Is it going to be a speeds and feeds show? Or is it going to be much more holistic, business impact, or societal impact? >> The obvious one is that Amazon increasingly has to be able to render these common building blocks for infrastructure up through to developers, and a new way of thinking about how do you solve problems. And so a lot more of what we're likely to see this year is Amazon continuing to move up the stack and say, here's how you're going to look at a problem, here's how you're going to solve the problem, here's the tooling, and here's the ecosystem that we're going to bring along with us. So it's much more problem-solving at the value level, going back to what we talked about earlier, problem solving that creates new types of business value, as opposed to problem solving to reduce the costs of existing infrastructure. >> Now we have a VIP chat on crowdchat.net/awsreinvent. If you want to participate, we're going to open it. We're going to keep it open for a long time, weigh in on that. We just had a great research meeting that you do weekly called Action Item, which is a format that's designed to flush out the latest and greatest research that's tied to current events or trends. And then unpack the action item for buyers and customers, large businesses in the industry. What's the summary for the meeting we just had here? A lot of stuff being talked about, Unigrid, we're talking about under the hood with data, a lot of good stuff. What's the bottom line? How do you up-level it for the CIO or CXO that's watching or listening, doesn't have time to get in the weeds? >> Well, I think the three fundamental conclusions that we reached this year is that we expect AWS to spend a lot of time talking about AI, both as a generalized way of moving up the stack, as we talked about. Here's the services the developers are going to work with. Here's the tool kits that they're going to utilize, et cetera, to solve more general problems. But also AI being embedded more deeply within AWS and how it runs as a service, and how it integrates and works with other clouds. So AI machine learning for IT operations management through AWS. So AI's going to be a major feature. The second one we think that we're going to hear a lot about is, Amazon's been putting forward this notion that they were going to facilitate migration of Legacy applications into AWS. That's been a slog, but we expect to see a more focused effort by going after specific big software houses, that have large installed bases of on-premise stuff, and see if they can't, with the software house, bring more of that infrastructure, or more of those installations, into AWS. Now, I don't want to call VMware an application house, but not unlike what they did with VMware about a year and a half ago. The last one is that we don't think that Amazon is going to put forward a general purpose IoT Edge solution this year. We think that they're going to reveal further what their approach to those problems are, which is, bigger networks, more PoPs. >> More scale. >> More scale, a lot of additional services for building applications that operate within that framework, but not that kind of, here's what the hybrid cloud by Amazon is going to look like. >> Let's talk about competition in China. Obviously, they kind of go hand in hand. Obviously, Andy Jassy and the Amazon Web Services team are seeing for the first time, massive competition. Obviously Microsoft stocks, I might have mentioned earlier. So you're starting to see the competition wheels cranking. Oracle's certainly been all over Amazon, we know that. Microsoft's just upping their game, trying to catch up, and their numbers are looking good. You got SAP playing the multicloud game. You got Google differentiating on things like TenserFlow and other AI and developer tools. This is interesting. This is the first time Amazon's really had some competition, I won't say nipping at its heels, but putting pressure. It's not the one game in town. People are talking multicloud, kind of talking about lock-in. And then you got the China situation. You got Alibaba, technically the number four cloud by some standards. Some will argue that position. The point is, it's massive. >> Yeah, I think it's by any reasonable standard. They are a big cloud player. >> So let's go through that. China, let's start with China. Amazon just announced, and the news was broken by the Wall Street Journal, who actually got it wrong and didn't correct their story for almost 24 hours. Really kind of screwed up the market, everyone thought that they were selling AWS to China. It was a unique deal. Rob Hof and the team reported and corrected, >> Peter: At SiliconANGLE. >> At siliconangle.com, we got it right, and that is is that it was a $300 million data center deal, not intellectual property, but this is the China playbook. >> They sold their physical assets. They didn't sell their IP. They didn't sell the services or the ability to provide the services. >> Based upon my reporting, and this is again still, the facts on the ground are loose, 'cause China, it's hard to get the data. But from what I can gather, they were already doing business in China. Apple went through this, even though they're hardware, they still have software. Everyone has that standoff, but ultimately getting into China requires a government-owned partner, or a Chinese company. Government-owned is quasi, you could argue that. And then they expand from there. Apple now has, I think, six stores or more in Shanghai and all over China. So this is a growth opportunity for Amazon if they play it right. Thoughts on that? I mean, obviously we cover a lot of the Chinese companies here. >> Well, I don't want to present myself as an expert on this, John. I've been watching, the Silicon Valley ANGLE reporting has been my primary information source. But I think that it's interesting. We talk about hard assets and soft assets. Hard assets are buildings, machines, and in the IT world, it's the hardware, it's the building, et cetera. And when China talks about ownership, they talk about ownership of those assets. And it sounds to me anyway, like AWS has done a very interesting thing, where they said, okay, fine, you want 51% of the hard assets? Have 51% of the hard, have 100% of the hard assets. But we are going to decide what those assets look like, and we are going to continue to own and operate the software that runs on those assets. So it sounds like, through that, they're going to provide a service into China, whatever the underlying hardware assets are running on. Interesting play. >> Well, we get the story right, and the story is, they're going into China, and they had to cut a deal. (laughs) That's the story. >> But for the hard assets. >> For the hard assets, they didn't get intellectual property. I think it's a good deal for Amazon. We'll see, we're going to watch that closely. I'm going to ask Andy Jassy that specific question. Now on the competition. The FUD is off the charts, fear, uncertainty and doubt. You see that in competitive markets, the competition throwing FUD. Sometimes it's really blatantly obvious FUD, sometimes it's just how they report numbers. I've been, not critical, but pointing out that Azure includes Office 365. Well when you start getting down that road, do you bundle in the sales floor as a cloud player? So all these things start to-- >> Peter: Yeah. >> Of course, so what is true cloud? Are people parsing the categories too narrowly, in your opinion? What's the opinion from the research team on, on what is cloud? >> Well, what is cloud? We like to talk about the cloud experience where the data demand's free or business. So the cloud experience is basically, it's self-provisioning, it's a service, it is continuous, and it allows you a range of different options about what assets you do or do not want to own, according to the physical realities, the legal realities, and intellectual property realities of the data that runs your business. So that's kind of what we mean by cloud. So let's talk about a couple of these. First-- >> Hold on, before you get to those, Andy Jassy said a couple years ago, he believes all enterprises will move to the cloud. (laughs) I mean, he was kind of, of course, he's buying 100% Amazon, and Amazon's defined as cloud. But he's kind of referring to that the enterprise on-premise current business model, and the associated technology, will move to cloud. Now, I'm not sure he would agree that the true private cloud is the same as Amazon. But if he cuts a deal with VMware like he did, is that AWS? So will his prediction come true? Ultimately, everyone's saying that will never be full cloud. >> I think this is one of those things where we got to be a little bit careful about trying to read too much into what he said. But here's what we think. Our advice to customers is don't think about moving your enterprise to the cloud, think about moving the cloud to your enterprise. And I think that's the whole basis for the hybrid cloud conversation that we're having. And the reason why we say the cloud experience where your data demands, is that there are physical realities that every enterprise is going to have to deal with, latency, bandwidth. There are legal realities that every enterprise is going to have to deal with. GDPR, what it means to handle privacy and handle data. And then there's finally intellectual property realities that every enterprise is going to have to deal with. Amazon not wanting to sell its IP to a Chinese partner, to comply with Chinese laws. Every business faces these issues. And they're not going to go away. And that's what's going to shape every businesses configuration of how they're using the cloud. >> And by the way, when I did ask him that question, it might have been three years ago. I can't actually remember, I'm losing my mind here. But at that time, cloud was not yet endorsed as the viable way. So he might have been referring to, again, I'm going to ask him this when I see him in my one on one. He might have been referring to old enterprise ways. So I mean-- >> Let's be honest. Amazon has done such an incredible job of making this a real thing. And our opinion is that they're going to continue to grow as fast as the cloud industry, however we define it. What we tend to define, we think that SaaS is going to be a big player, and it's going to be the biggest part of the player. We think Infrastructure as a Service is going to continue to be critically important. We think that the competition for developers is going to heat up in a big way. AI, machine learning, deep learning, all of those things are going to be part of that competition. In our view of things, we're going to see SaaS be much bigger in a few years. We're going to see this notion of true private cloud, which is a cloud experience on-premise with your assets, because you need to control your data in different ways, is going to be bigger than IaaS, but it's all going to be cloud. >> I mean, in all poise, my opinion and what I'm looking for this year, Peter, just to kind of wrap up the segment is, I think, and if you look at Amazon's new ad campaign, the builders, that's a topic that we talked about last year. >> Peter: Developers. >> Developers. We are living in a world where DevOps is now going mainstream. And there are still cultural issues around, what does that actually mean for a business? The personnel, how they operate, and some of the things you guys point out in your true private cloud report, illuminates those things. And that is, whoever can automate and create great tooling for the DevOps culture going forward, whatever that's called, new developers, new normal? Whatever it is, that to me is going to be the competitive landscape. >> Let me parse that slightly, or put it slightly differently. I think everybody put forward this concept of DevOps as, hey, business, redefine yourself around DevOps. And it hasn't gone as well as a lot of people thought it would. I think what's really going to happen, I don't think you're disagreeing with me, John, is that we need to bring more developers into cloud building that cloud experience, building more of the application value, building more of the enterprise value, in cloud. Now that's happening, and they are going to start snapping this DevOps concept into place. But I think it really is going to boil down to, how are developers going to fully embrace the cloud? What's it going to look like? It's going to be multicloud. Let's go back to the competition. Microsoft, you're right, but they're a big SaaS player. Companies are building enormous relations, big contracts, with Microsoft. They're going to be there. Google, last year they couldn't get out of their own way. Diane Greene comes in, we see a much more focused effort. There's some real engineering that's going on for Google Cloud Services, or Platform, that wasn't there before. Google is emerging as a big player. We're having a lot of conversations with users, where they're taking Google very seriously. IBM is still out there, still got some things going on. You've already mentioned Alibaba, Tencent, a whole bunch of other players in the globe. This is going to be a market that's going to be very, very contentious, but Amazon's going to get there first share. >> And I think we pointed out years ago, that DevOps will merge to cloud developers. You nailed it, I think you just said it. Okay, Peter Burris, here for the Amazon Web Service preview. Of course theCUBE will be there with two sets. We're going to have over 75 interviews over the course of 3 days. In the hall, look for theCUBE, if you've watched this video and you want to come by. If you got a ticket, it's sold out. But come by if you have a ticket. We'll be there, in Las Vegas, for Amazon Web Services re:Invent. I'm John Furrier, thanks for watching this CUBE Conversation from Palo Alto. (upbeat techno music)

(bright orchestral music) >> Hello everyone, and welcome to theCUBE Conversations here in Palo Alto Studios. For theCUBE, I'm John Furrier, the co-founder of SiliconANGLE Media inc. My next guest is Bruce Arthur, who's the Vice President of engineering at Banter.ai. Good friend, we've known each other for years, VP of engineering, developer, formerly at Apple. >> Yes. >> Worked on all the big products; the iPad-- had the the tin foil on your windows back in the day during Steve Jobs' awesome run there. Welcome to theCUBE. >> Thank you, it's good to be here. >> Yeah, great, you've got a ton of experience and I want to get your perspective as a developer, VP of engineering, entrepreneur, you're doing a startup around AI. Let's have a little banter. >> Sure. >> Banter.ai is a little bit a chat bot, but the rage is DevOps. Software really models change, infrastructure as code, cloud computing. Really a renaissance of software development going on right now. >> It is, it's changing a lot. >> What's your view on this? >> Well, so, years and years ago you would work really hard on your software. You would package it up in a box and you'd send it over the wall and you hope it works. And that seems very quaint now because now you write your software, you deploy it the first day, and you change it six times that day, and you're A/B-testing it, you're driving it forward, it's so much more interactive. It does require a different skillset. It also doesn't, how do I say this carefully? It used to be very easy to be craft, to have high craft and make a very polished product, but you didn't know if it was going to work. Today you know if it's going to work, but you often don't get to making sure it's high quality, high craft, high value. >> John: So, the iteration >> Exactly, the iteration runs so fast, which is highly valuable, but you sort of just a little bit of you miss the is this really something I am proud of and I can really work with it because you know, now the product definition can change so quickly, which is awesome but it is a big change. >> And that artisan crafting thing is interesting, but now some are saying that the UX side is interesting because, if you get the back end working, and you're iterating, you can still bring that artisan flavor back. We heard that cloud computing vendors like Amazon, and I was just in China for Alibaba, they're trying to bring this whole design artisan culture back. Your thoughts on the whole artisan craft in software, because now you have two stages, you have deploy, iterate, and then ultimately polish. >> Right, so, I think it's interesting, it used to be, engineering is so expensive and time-consuming. You have to design it upfront and you make one version of it and you're done. That has changed now that engineering has gotten easier. You have better tools, we have better things, you can make six versions and that used to be, so back in the day at Apple, you would make six versions, five of which Steve would hate and throw out, and eventually they would get better and better and better and then you would have something you're proud of. Now those are just exposed. Now everybody sees those, it's a very different process. So you, I think, the idea that you. Engineering used to be this scarce resource. It's becoming easier now to have many versions and have more engineers working on stuff, so now it is much more can I have three design teams, can they compete, can they make all good ideas, and then who's going to be the editor? Who evaluates them and decides I like this from this one, I like that, and now let's put this together to make the right product. >> So, at Apple, you mentioned Steve would reject, well, that's well-documented. >> Sure. >> It's publicly out there that he would like, really look at the design-side. Was it Waterfall-based, was it Agile, Scrum, did you guys, was it like, do you lay it all out in front of him and he points at it? What were some of the work flows like with Steve Jobs? >> So, when he was really excited about something he would want to meet with them every week. He'd want to see progress every week. He'd give lots of feedback every week, there'd be new ideas. It was very Steve-focused. I think the more constructive side of it was the design teams were always thinking about What can we build, how do we put it in front of him, and I remember there was a great quote from a designer that said. It's not that Steve designs great things, it's that you show him three things, and if you throw him three bad things, he'll pick the least bad. If you show him three great things, he'll pick the most great, But it's not, it was more about the, you've got to iterate in the process, you've got to try ideas, you take ideas from different people and some of them, like, they sound like a great idea. When we talk, it sounds really good. You build it, and you're like, that's just not, that's just not right. So, you want, how do I say this? You don't want to lock yourself in up front. You want to imagine them, you want to build them, you want to try 'em. >> And that's, I mean, I've gotten to know the family over the years, too, through some of the Palo Alto interactions, and that's the kind of misperception of Steve Jobs, was that he was the guy. He enabled people, he had that ethos that-- >> He was the editor, it's an old school journalism metaphor, which is, he had ideas, he wanted, but he also, he ran the team. He wanted to have people bring their ideas and come in. And then he decided, this is good, this is not. That's better, you can do better, let's try this. Or, sometimes, this whole thing stinks. It's just not going anywhere. So, like, it was much more of that. Now it's applied to software, and he was a marketing genius, about sort of knowing what people were going to go for, but there was a little bit of a myth for it, that there's one man designing everything. That is a very saleable marketing story. >> The mythical man. (laughs) >> Well, it's powerful, but no, there's a lot of people, and getting the best work of all those people. >> I mean, he's said on some of the great videos I've watched on YouTube over the years, Hire the best people, only work with the best, and they'll bring good stuff to the table. Now, I want to bring that kind of metaphor, one step further for this great learning lesson, again it's all well-documented on YouTube. Plenty of Steve videos there, but now when you go to DevOps, you mention the whole quality thing and you got to ship fast, iterate, you know there's a lot of moving fast break stuff as Zuckerberg would say, of Facebook, although he's edited his tune to say move fast and be reliable. (laughing) Welcome to the enterprise, welcome to software and operations. This is now a scale game at the enterprise side 'cause, you know, you start seeing open source software grow so much now, where a lot of the intellectual property might be only 10% of software. >> Right. >> You might be using other pieces. You're packaging it so that when you get it to the market, how do bring that culture? How do you get that innovation of, Okay, I'm iterating fast, how do I maintain the quality. What are some of your thoughts on that? Because you've got machine learning out there, you've got these cool things happening. >> Yup. So, you want, how do I say this? You just, you really need to leave time to schedule it. It needs to be in your list. There's a lot of figuring out what are we going to build and you have to try things, iterate things, see if they resonate with consumers. See if they resonate with people who want to pay. See if they resonate with investors. You have to figure than out fast, but then you have to know that, okay, this is a good prototype. Now I have to make it work better because the first version wouldn't scale well, now it has to scale, now it has to work right for people, now you have to have a review of: here's the bugs, here's the things that are not working. Why does this chatbot stop responding sometimes? What is causing that? Now, the great story is, with good DevOps, you actually have a system that's very good at finding and tracking those problems. In the old world, so the old world with the shrink-wrap software, you'd throw it over the fence. If it misbehaves, you will never know. Today you know. You've got alerts, you've got pagers going off, you've got logs, >> It's instrumented big-time. >> Yeah, exactly, you can find that stuff. So, since you can actually make, you can make very high-quality software because you have so much more data about what's going on with it, it's nice. And actually, chatbot software has this fascinating little side effect, with, because it's all chats and it's all text, there are no irreproducible bugs. You can go back and look at exactly what happened. I have a recording, I know exactly what happened, I know exactly what came in, I know what came out, and then I know that this failure happened. So, it's very reproducible, sort of, it's nice you can, it doesn't always work this way, but it's very easy to track down problems. >> It's event-based, it's really easy to manage. >> Exactly, and it's just text. You can just read it. It's not like I have to debug hacks, it's just these things were said and this thing died. >> No core dumps. (laughs) >> No, there's nothing that requires sophisticated analysis, well the code is one thing, but like, the sequence of events is very human-readable, very understandable. >> Alright, so let's talk about the younger generation. So, we've been around the block, you and I. We've talked, certainly many times around town, about the shifts, and we love these new waves. A lot of great waves coming in, we've seen many waves. What's going on, in your mind, with the younger generation? Because this is a, some exciting things happening. Decentralized internet. >> Bruce: Yup. >> There's blockchain, getting all the attention. Outside of the hype, Alpha VCs, Alpha engineers, Alpha entrepreneurs are really honing in on blockchain because they see the potential. >> Sure. >> Early people are seeing it. Then you've got cloud, obviously unlimited compute potentially, the new, you know, kind of agile market. All these young guys, they never shipped, actually never loaded Linux on a server. (laughing) So, like, what are you seeing for the younger guys? And what do you see as someone who's experienced, looking down at the next, you know, 20 year run we see. >> So, I think what I see that's most exciting is that we now have people solving very non-technical problems with technology. I think it used to be, you could build a computer, you could write code, but then, like, your space was limited to the computer in front of you. Like, I can do input and outputs. I can put things on the screen, I can make a video game, but it's in this box. Now everyone's thinking of much bigger, Solving bigger problems. >> John: Yeah, healthcare, we're seeing verticals. >> Yeah, healthcare's a massive one. You can, operation things, shipping products. I mean, who would've thought Amazon was going to be delivering things, basically. I mean, they're using technology to solve the physical delivery of objects. That is, the space of what people are tackling is massive. It' no longer just about silicon and programming, it's sort of, any problem out there, there's someone trying to apply technology, which is awesome and I think that's because these people these youngsters, they're digital natives. >> Yeah. >> They've come to expect that, of course video conferencing works, of course all these other items work. That I just need to figure out how to solve problems with them, and I'm hopeful we're going to see more human-sized problems solved. I think, you know, we have, technology has maybe exacerbated a few things and dislocated, cost a lot of people jobs. Disconnected some people from other sort of stabilizing forces, >> Fake news. (laughs) >> Fake news, you know, we need-- >> John: It's consequences, side effects. >> I hope we get people solving those problems because fake news should now be hard to solve. They'll figure it out, I think, but, like, the idea is, we need to, technology does have a bit of a responsibility to solve, fix some of the crap that it broke. Actually, there's things that need, old structures, journalism is an old profession. >> Yeah. >> And it used to actually have all these wonderful benefits, but when the classified business went down the tubes, it took all that stuff down. >> Yeah. >> And there needs to be a venue for that. There needs to be new outlets for people to sort of do research, look things up, and hold people to account. >> Yeah, and hopefully some of our tools we'll be >> I hope so. >> pulling out at Silicon Angle you'll be seeing some new stuff. Let's talk about, like just in general, some of the fashionable coolness around engineering. Machine learning, AI obviously tops the list. Something that's not as sexy, or as innovative things. >> Sure. >> Because you have machines and industrial manufacturing plant equipment to people's devices. Obviously you worked at Apple, so you understand that piece, with the watch and everything. >> Yup, >> So you've got, that's an internet, we're things, people are things too. So, machines and people are at the edge of the network. So, you've got this new kind of concept. What gets you excited? Talk about how you feel about those trends. >> So, there's a ton going on there. I think what's amazing is the idea that all these sensors and switches and all the remote pieces can start to have smarts on them. I think the downside of that is some of the early IoT stuff, you know, has a whole open SSL stack in it. And, you know, that can be out of date, and when you have security problems with that now your light switch has access to your tax returns and that's not really what you want. So, I think there's definitely, there's a world coming, I think, at a technical level, we need to make operating systems and tools and networking protocols that aren't general purpose because general purpose tools are hackable. >> John: Yeah. >> I need to have a sensor and a switch that know how to talk to each other, and that's it. They can't rewrite code, they can't rewrite their firmware, they can't, like, I want to be able to know that, you have a nice office here, if somebody came in and tried to hack your switches, would you ever know? And the answer's like, you'd have no idea, but when you have things that are on your network and that serve you, if they're a general, if they're a little general purpose computing device, they're a mess. Like, you know, a switch is simple. A microphone, a microphone is simple. There's an output from it, it needs, I think we, >> So differentiated software for device. >> Well, let's get back to old school. You studied operating systems back in the day. >> Yeah. >> A process can do whatever the hell it wants. It can read from memory, it can write to disk, it can talk to all these buses. It's a very, it can do, it's very general purpose. I don't want that in my switch. I want my switch to be sort of, much more of these old little micro-controller. >> Bounded. >> Yeah, it's in a little box. I mean, so the phone and the Mac have something called Sandbox, which sort of says, you get a smaller view of the world. You get a little piece of the disk, you can't see everything else, and those are parts of it, but I think you need even more. You need, sort of, this really, I don't want a general purpose thing, I want a very specific thing that says I'm allowed to do this and I'm allowed to talk to that server; I don't have access to the internet. I've got access to that server. >> You mentioned operating systems. I mean, obviously I grew up in the computer science genre of the '80s and you did as well. That was a revolution around Unix. >> Yes. >> And then Berkeley, BSD, and all that stuff that happened around the systems world, operating systems, was really the pioneers in computing at that time. It's interesting with cloud, it's almost a throwback now to systems thinking. >> Bruce: It's true, yeah. >> You know, people looking at, and you're discussing it. >> Bruce: Yeah, Yeah. >> It's a systems problem. >> Yeah, it is. >> It's just not in a box. >> Right, and I think we witnessed the, let's get everyone a general purpose computer and see what they can do. And that was amazing, but now you're like I don't want everything to be a general I want very specific, I want very little thing, dedicated things that do this really well. I don't want my thermostat actually tracking when I'm in the house. You know, I want it to know, eh, maybe there's someone in the house, but I don't want it to know it's me. I don't want it reporting to Google what's going on. I want it to track my temperature and manage that. >> Our Wikibon team calls the term Unigrid, I call it hypergrid because essentially it's grid computer; there's no differentiation between on-premise and cloud. >> Right. >> It's one pool of resource of compute and things processes. >> It is, although I think, and that's interesting, you want that, but again you want it, how do I say this? I get a little nervous when all of my data goes to some cloud that I can't control. Like, I would love if, I'll put it this way. If I have a camera in my house, and imagine I put security cameras up, I want that to sort of see what's going on, I don't want it to publish the video to anywhere that's out of my control. If it publishes a summary that says, oh, like, someone came to your door, I'm like, okay, that's a good, reasonable thing to know and I would want to get that. So, Palo Alto recently added, there's traffic cameras that are looking at traffic, and they record video, but everyone's very nervous about that fact. They don't want to be recorded on video. So, the camera, this is actually really good, the camera only reports number of cars, number of bikes, number of pedestrians, just raw numbers. So you're pushing the processing down to the end and you only get these very anonymous statistics out of it and that's the right model. I've got a device, it can do a lot of sophisticated processing, but it gives nice summary data that is very public, I don't think anyone's really >> There's a privacy issue there that they've factored into the design? >> Yes, exactly. It's privacy and it's also the appropriateness of the data, you don't want, yeah, people don't want a camera watching them when they go by, but they're happy and they're like, oh, yeah, that street has a big increase in traffic, And there's a lot of, there were accidents here and there's people running red lights. That's valuable knowledge, not the fact that it's you in your Tesla and you almost hit me. No. (laughs) >> Yeah, or he's speeding, slow down. >> Exactly, yeah, or actually if you recorded speeders the fact that there's a lot of speeding is very interesting. Who's doing it, okay, people get upset if that's recorded. >> Yeah, I'm glad that Palo Alto is solving their traffic problem, Palo Alto problems, as we say. In general, security's been a huge issue. We were talking before we came on, about just the security nightmare. >> Bruce: Yes. >> A lot of companies are out there scratching their heads. There's so much of digital transformation happening, that's the buzzword in the industry. What does that mean from your standpoint? Because engineers are now moving to the front lines. Developers, engineering, because now there's a visibility to not just the software, it's an end goal. They call it outcome. Do you talk to customers a lot around, through your entrepreneurial venture, around trying to back requirements into product and yet deliver value? Do you get any insight from the field of kind of problems, you know, businesses are generally tryna solve with tech? >> So, that's interesting, I think when we try to start tech companies, we usually have ideas and then we go test that premise on customers. Perhaps I'm not as adaptable as I should be. We're not actually going to customers and asking them what they want. We're asking them if this is the kind of thing that would solve their problems. And usually they're happy to talk to us. The tough one, then, is then are they going to become paying customers, there's talking and there's paying, and they're different lines. >> I mean, certainly is validation. >> Exactly, that's when you really know that they care. It is, it's a tough question. I think there's always, there's a category of entrepreneur that's always very knowledgable about a small number of customers and they solve their problems, and those people are successful and they're often, They often are more services-based, but they're solving problems because they know people. They know a lot of people, they know what their paying point are. >> Alright, so here's the real question I want to know is, have you been back to Apple in the new building? >> Have I been to, I have not been in the spaceship. (laughing) I have not been in the spaceship yet. I actually understand that in order to have the event there, they actually had to stop work on the rest of the building because the construction process makes everything so dirty; and they did not want everyone to see dirty windows, so they actually halted the construction, they scrubbed down the trees, they had the event, and now it's, but now it's back. >> Now it's back to, >> So, I'll get there at some point. >> Bruce Arthur it the Vice President of Banter.ai, entrepreneur, formerly of Apple, good friend, Final question for you, just what are you excited about these days and as you look out at the tooling and the computer science and the societal impact that is seen with cloud and all these technologies, and open source, what do you, what are you excited about? >> I'm most excited, I think we actually have now enough computing resources and enough tools at hand that we can actually go back and tackle some harder computer science problems. I think there's things that used to be so big that you're like, well, that's just not, That's too much data, we could never solve that. That's too much, that would take, you know, that would take a hundred computers a hundred years to figure out. Those are problems now that are becoming very tractable, and I think it's been the rise of, yeah, it starts with Google, but some other companies that sort of really made these very large problems are now tractable, and they're now solvable. >> And open source, your opinion on open source these days? >> Open source is great. >> Who doesn't love more code? (laughs) >> Well, I should back this up, Open source is the fastest way to share and to make progress. There are times where you need what's called proprietary, but in other words valuable, when you need valuable engineers to work on something and, you know, not knowing the providence or where something comes from is a little sticky, I think there's going to be space for both. I think open source is big, but there's going to be-- >> If you have a core competency, you really want to code it. >> Exactly, you want to write that up and you-- >> You can still participate in the communities. >> Right, and I think open source is also, it's awesome when it's following. If there's something else in front, it follows very fast, it does a very good job. It's very thorough, sometimes it doesn't know where to go and it sort of meanders, and that's when other people have advantages. >> Collective intelligence. >> Exactly. >> Bruce, thanks for coming on. I really appreciate it, good to see you. This is a Cube Conversation here in the Palo Alto studio, I'm John Furrier, thanks for watching. (light electronic music)

(techy music playing) >> Hello, everyone, welcome to theCUBE Studios here in Palo Alto, California. I'm John Furrier, the cofounder of SiliconANGLE Media Inc., also cohost of theCUBE. We're here for a CUBE Conversation on Thought Leader Thursday and I'm here with Chris Cummings, who's a senior manager, advisor, big-time industry legend, but he's also the Chasm Group, right now, doer, Crossing the Chasm, famous books and it's all about the future. Formerly an exec at Netapp, been in the storage and infrastructure cloud tech business, also friends of Stanford. Season tickets together to go to the tailgates, but big Cal game coming up of course, but more importantly a big-time influence in the industry and we're going to do some drill down on what's going on with cloud computing, all the buzzword bingo going on in the industry. Also, AWS, Amazon Web Services re:Invent is coming up, do a little preview there, but really kind of share our views on what's happening in the industry, because there's a lot of noise out there. We're going to try to get the signal from the noise, thanks for watching. Chris, thanks for coming in. >> Thank you so much for having me, glad to be here. >> Great to see you, so you know, you have seen a lot of waves of innovation and right now you're working with a lot of companies trying to figure out the future. >> That's right. >> And you're seeing a lot of significant industry shifts. We talk about it on theCUBE all the time. Blockchain from decentralization all the way up to massive consolidation with hyper-convergence in the enterprise. >> Mm-hmm. >> So a lot of action, and because of the day the people out in the marketplace, whether it's a developer or a CXO, CIO, CDO, whatever enterprise leader's doing the transformations. >> Chris Collins: We got all of them. >> They're trying to essentially not go out of business. A lot of great things are happening, but at the same time a lot of pressure on the business is happening. So, let's discuss that, I mean, you are doing this for work at the Chasm Group. Talk about your role, you were formerly at Netapp, so I know you know the storage business. >> Right. >> So we're going to have a great conversation about storage and impact infrastructure, but at the Chasm Group how are you guys framing the conversation? >> Yeah, Chasm Group is really all about helping these companies process their thinking, think about if they're going to get to be a platform out in the industry. You can't just go and become a platform in the industry, you got to go knock down problem, problem, problem, solution, solution, solution. So we help them prioritize that and think about best practices for achieving that. >> You know, Dave Alante, my co-CEO, copartner, co-founder at SiliconANGLE Media and I always talk about this all the time, and the expression we use is if you don't know what check mate looks like you shouldn't be playing chess, and a lot of the IT folks and CIOs are in that mode now where the game has changed so much that sometimes they don't even know what they're playing. You know, they've been leaning on this Magic Quadrant from Gartner and all these other analyst firms and it's been kind of a slow game, a batch kind of game, now it's real time. Whatever metaphor you want to use, the game has changed so the chessboard has changed. >> Chris: Mm-hmm. >> So I got to get your take on this because you've been involved in strategy, been on product, you worked at growth companies, big companies, start-ups, and now looking at the bigger picture, what is the game? I mean, right now if you could lay out the chessboard, what are people looking at, what is the game? >> So, we deal a lot with customer conversations and that's where it all kind of begins, and I think what we found is this era of pushing product and just throwing stuff out there. It worked for a while but those days are over. These folks are so overwhelmed. The titles you mentioned, CIO, CDO, all the dev ops people, they're so overwhelmed with what's going on out there. What they want is people to come in and tell them about what's happening out there, what are their peers doing and what problems are they trying to solve in order and drive it that way. >> And there's a lot of disruption on the product side. >> Yes. >> So tech's changing, obviously the business models are changing, that's a different issue. Let's consider the tech things, you have-- >> Mm-hmm. >> A tech perspective, let's get into the tech conversation. You got cloud, you got private cloud, hybrid cloud, multi-cloud, micro-machine learning, hyper-machine learning, hyper-cloud, all these buzzwords are out there. It's buzzwords bingo. >> Chris: Right. >> But also the reality is you got Amazon Web Services absolutely crushing it, no doubt about it. I mean, I've been looking at Oracle, I've been looking at Google, I've been looking at SAP, looking at IBM, looking at Alibaba, looking at Microsoft, the game is really kind of a cloak and dagger situation going on here. >> That's right. >> A lot of things shifting on the provider side, but no doubt scale is the big issue. >> Chris: That's right. >> So how does a customer squint through all this? >> The conversations that I've had, especially with the larger enterprises, is they know that they've got to be able to adopt and utilize the public cloud capabilities, but they also want to retain that degree of control, so they want to maintain, whether it's their apps, their dev ops, some pieces of their infrastructure on prem, and as you talked about that transition it used to be okay, well we thought about cloud was equal to private cloud, then it became public cloud. Hybrid cloud, people are hanging on to hybrid cloud, sometimes for the right reasons and sometimes for the wrong reasons. Right reasons are because it's critical for their business. You look at somebody, for instance, in media and entertainment. They can't just push everything out there. They've got to retain control and really have their hands around that content because they've got to be able to distribute it, right? But then you look at some others that are hanging on for the wrong reasons, and the wrong reasons are they want to have their control and they want to have their salary and they want to have their staff, so boy, hybrid sounds like a mix that works. >> So I'm going to be having a one-on-one with Andy Jassy next week, exclusive. I do that every year as part of theCUBE. He's a great guy, good friend, become a good friend, because we've been a fan of him when no one loved Amazon. We saw the early, obviously at SiliconANGLE, now he's the king of the industry, but he's a great manager, great executive, and has done a great job on his ethos of Bezos and Amazon. Ship stuff faster, lower prices, the flywheel that Amazon uses. Everything's kind of on that-- And they own Twitch, which we stream, too, and we love. But if you could ask Andy any questions what questions would you ask him if you get to have that one-on-one? >> Yeah, well, it stems from conversations I've had with customers, which was probably once a week I would be talking to a CIO or somebody on that person's staff, and they'd slide the piece of paper across and say this is my bill. I had no idea that this was what AWS was going to drive me from a billing perspective, and I think we've seen... You know, we've had all kinds of commentary out there about ingress fees, egress fees, all of that sort of stuff. I think the question for Andy, when you look at the amount of revenue and operating margin that they are generating in that business, how are they going to start diversifying that pricing strategy so that they can keep those customers on without having them rethink their strategy in the future. >> So are you saying that when they slide that piece of paper over that the fees are higher than expected or not... Or low and happy, they're happy with the prices. >> Oh, they're-- I think they're-- I think it's the first time they've ever thought that it could be as expensive as on-premise infrastructure because they just didn't understand when they went into this how much it was going to cost to access that data over time, and when you're talking about data that is high volume and high frequency data, they are accessing it quite a bit, as opposed to just stale, cold, dead stuff that they want to put off somewhere else and not have to maintain. >> Yeah, and one of the things we're seeing that we pointed at the Wikibon team is a lot of these pricings are... The clients don't know that they're being billed for something that they may not be using, so AI or machine learning could come in potentially. So this is kind of what you're getting at. >> Exactly. >> The operational things that Amazon's doing to keep prices low for the customer, not get bill shock. >> Chris: That's right. >> Okay, so that's cool. What else would you ask him about culture or is there anything you would ask him about his plans... What else would you ask him? >> I think another big thing would be just more plans on what's going to be done around data analytics and big data. We can call it whatever we want, but they've been so good at the semi-structured or unstructured content, you know, when we think about AWS and where AWS was going with S3, but now there's a whole new phenomenon going on around this and companies are as every bit as scared about that transition as they were about the prior cloud transition, so what really are their plans there when they think about that, and for instance, things like how does GPU processing come into play versus CPU processing. There's going to be a really interesting discussion I think you're going to have with him on that front. >> Awesome, let's talk about IT. IT and information technology departments formerly known as DP, data processing, information-- All that stuff's changed, but there were still guys that were buying hardware, buying Netapp tries that you used to work for, buying EMC, doing data domain, doing a lot of stuff. These guys are essentially looking at potentially a role where-- I mean, for instance, we use Amazon. We're a big customer, happy customer. >> Chris: Mm-hmm. >> We don't have those guys. >> Chris: Right. >> So if I'm an IT guy I might be thinking shit, I could be out of a job, Amazon's doing my job, so I'm not saying that's the case but that's certainly a fear. >> Chris: Absolutely. >> But the business models have to shift from old IT to new IT. >> Chris: Mm-hmm. >> What does that game look like? What is this new IT game? Is it more, not a department view, is it more of a holistic view, and what's the sentiment around the buyers and your customers that you talk to around how do they message to the IT guys, like, look, there's higher valued jobs you could go to. >> Right. >> You mention analytics... >> That's right. >> What's the conversation? Certainly some guys won't make the transition and might not make it, but what's the narrative? >> Well, I think that's where it just starts with what segment are you talking about, so if you look at it and say just break it down between the large enterprise, the uber enterprise that we've seen for so long, mid-size and smaller, the mid-size and smaller are gone, okay. Outside of just specific industries where they really need that control, media and entertainment might be an example. That mid-size business is gone for those vendors, right? So those vendors are now having to grab on and say I'm part of that cloud phenomenon, my hyper-cloud of the future. I'm part of that phenomenon, and that becomes really the game that they have to play, but when you look at those IT shops I think they really need to figure out where are they adding value and where are they just enabling value that's being driven by cloud providers, and really that's all they are is a facilitator, and they've got to shift their energy towards where am I adding value, and that becomes more that-- >> That's differentiation, that's where differentiation is, so non-differentiated labor is the term that Wikibon analysts use. >> Oh, okay. >> That's going down, the differentiated labor is either revenue generating or something operationally more efficient, right? >> That's right, and it's all going to be revenue generating now. I mean, I used to be out there talking about things like archiving, and archiving's a great idea. It's something where I'm going to save money, okay, but I got this many projects on my list if I'm a CIO of where I can save money. I'm being under pressure about how am I going to go generate money, and that's where I think people are really shifting their eyeballs and their attention, is more towards that. >> And you got IOT coming down the pike. I mean, we're hearing is from what I hear from CIOs when we have a few in-depth conversations is look, I got to get my development team ramped up and being more cloud native, more microservice and I got to get more app development going that drives revenue for my business, more efficiency. >> Chris: Right. >> I have a digital transformation across the company in terms of hiring culture and talent. >> Chris: Mm-hmm. >> And then I got pressure to do IOT. >> Chris: Right. >> And I got security, so of those five things, IOT tends to fall out, security takes preference because of the security challenges, and then that's already putting their plate full right there. >> That's right, that's real time and those people are-- >> Those are core issues. >> Putting too much pressure on that right now and then you're thinking about IT and in the meantime, by the way, most of these places don't have the dev ops shop that's operating on a flywheel, right? So you're not... What's it, Goldman Sachs has 5,000 developers, right? That's bigger than most tech companies, so as a consequence you start thinking about well, not everybody looks like that. What the heck are they going to do in the future. They're going to have to be thinking about new ways of accessing that type of capability. >> This is where the cloud really shines in my mind. I think in the cloud, too, it's starting to fragment the conversations. People will try to pigeonhole Amazon. I see Microsoft-- I've been very critical of Microsoft in their cloud because-- First of all, I love the move that they're making. I think it's a smart move business-wise, but they bundle in 365 Office, that's not really cloud, it's just SAS, so then you start getting into the splitting of the hairs of well, SAS is not included in cloud. But come on, SAS is cloud. >> Chris: Mm-hmm. >> Well, maybe Amazon should include their ecosystem that would be a trillion dollar revenue number, so all companies don't look the same. >> That's right. >> And so from an enterprise that's a challenge. >> Chris: Mm-hmm. >> Do I got to hire developers for Asger, do I got to hire developers for Amazon, do I got to hire developers for Google. >> Chris: Mm-hmm. >> There's no stack consistency across private enterprises to cloud. >> Chris: So I have-- >> Because I'm a storage guy, I've got Netapp drives and now I've got an Amazon thing. I like Amazon, but now I got to go Asger, what the hell do I do? >> I got EMCs here and I got Nimbles there and HP and I've still got tape from IBM from five decades ago, so, John, I got a great term for you that's going to be a key one, I think, in the ability. It's called histocompatibility, and this is really about... >> Oh, here we go. Let's get nerdy with the tape glasses on. >> It's really about the ability to be able to inter-operate with all this system and some of these systems are live systems, they're current systems. Some of it's garbage that should've been thrown out a long time ago and actually recycled. So I think histocompatibility is going to be a really, really big deal. >> Well, keep the glasses on. Let's get down in the weeds here. >> Okay. >> I like the-- With the pocket protector, if you had the pocket protector we'd be in good shape. >> Yep. >> So, vendors got to compete with these buzzwords, become buzzword bingo, but there are trends that you're seeing. You've done some analysis of how the positionings and you're also a positioning guru as well. There's ways to do it and that's a challenge is for suppliers, vendors who want to serve customers. They got to rise above the noise. >> Chris: That's right. >> That's a huge problem. What are you seeing in terms of buzzword bingo-- >> Oh, my goodness. >> Because like I said, I used to work for HP in the old days and they used to have an expression, you know, don't call it what it is because that's boring and make it exciting, so the analogy they used was sushi is basically cold, dead fish. (laughing) So, sushi is a name for cold, dead fish. >> Chris: Yeah. >> So you don't call your product cold, dead fish, you call it sushi. >> Chris: Right. >> That was the analogy, so in our world-- >> Chris: That was HP-UX. >> That was HP-UX, you know, HP was very engineering. >> Yes. >> That's not-- Sushi doesn't mean anything. It's cold, dead fish, that's what it is. >> Right. >> That's what it does. >> That's right. >> So a lot of vendors can error in that they're accurate and their engineers, they call it what it is, but there's more sex appeal with some better naming. >> Totally. >> What are you seeing in terms of the fashion, if you will, in terms of the naming conventions. Which ones are standing out, what's the analysis. >> Well, I think the analysis is this, you start with your adjectives with STEM words, John, and what I mean by that is things like histocompatibility. It could start with things like agility, flexibility, manageability, simplicity, all those sorts of things, and they've got to line those terms up and go out there, but I think the thing that right now-- >> But those are boring, I saw a press release saying we're more agile, we're the most effective software platform with agility and dev ops, like what the hell does that mean? >> Yeah, I think you also have to combine it with a heavy degree of hyperbole, right? So hyperbole, an off-the-cuff statement that is so extreme that you'd never really want to be tested on it, so an easy way to do that is to add hyper in front of all that. So it's hyper-manageability, right, and so I think we're going to see a whole new class of words. There are 361 great adjectives with STEMs, but-- >> Go through the list. >> Honestly. >> Go through the list that you have. >> I mean, there's so many, John, it's... >> So hyper is an easy one, right? >> Hyper is easy, I think that's a very simple one. I think now we also see that micro is so big, right, because we're talking about microservices and that's really the big buzzword in the industry right now. So everything's going to be about micro-segmenting your apps and then allowing those apps to be manifest and consumed by an uber app, and ultimately that uber app is an ultra app, so I think ultra is going to be another term that we see heading into the spectrum as well. >> And so histocompatibility is a word you mentioned, just here in my notes. >> Yep. >> You mentioned, so histo means historical. >> Exactly. >> So it means legacy. >> Chris: That's right. >> So basically backwards compatible would be the boring kind of word. >> Chris: That's right. >> And histocompatibility means we got you covered from legacy to cloud, right. >> Uh-huh. >> Or whatever. >> You bet. >> Micro-segmentility really talks to the granularity of data-driven things, right? >> That's right, another one would be macro API ability, it's kind of a mouthful, but everyone needs an API. I think we've seen that and because they're consuming so many different pieces and trying to assemble those they've got to have something that sits above. So macro API ability, I think, is another big one, and then lastly is this notion of mobility, right. We talk about-- As you said earlier, we talked about clouds and going from-- It's not just good enough to talk about hybrid cloud now, it's about multi-cloud. Well, multi-cloud means we're thinking about how we can place these apps and the data in all kinds of different spaces, but I've got to be able to have those be mobile, so hyper-mobility becomes a key for these applications as well. >> So hyper-scale we've seen, we've seen hyper-convergence. Hyper is the most popular-- >> Chris: Absolutely. >> Adjective with STEM, right? >> Chris: It's big. >> STEM words, okay, micro makes sense because, you know, micro-targeting, micro-segmentation, microservices, it speaks to the level of detail. >> Chris: Right. >> I love that one. >> Chris: Right. >> Which ones aren't working in your mind? We see anything that's so dead on arrival... >> Sure, I think there's a few that aren't working anymore. You got your agility, you got your flexibility, you got your manageability, and you got your simplicity. Okay, I could take all four of those and toss those over there in the trash because every vendor will say that they have those capabilities for you, so how does that help you distinguish yourself from anyone else. >> So that's old hat. >> It's just gone. >> Yeah, never fight fashion, as Jeremy Burton at EMC, now at Dell Technologies, said on theCUBE. I love that, so these are popular words. This is a way to stand out and be relevant. >> That's right. >> This is the challenge for vendors. Be cool and relevant but not be offensive. >> Yeah. >> All right, so what's your take on the current landscape for things like how do companies market themselves. Let's say they get the hyper in all the naming and the STEM words down. They have something compelling. >> Chris: Right. >> Something that's differentiated, something unique, how do companies stand out above the crowd, because the current way is advertising's not working. We're seeing fake news, you're seeing the analyst firms kind of becoming more old, slower, not relevant. I mean, does the Magic Quadrant really solve that problem or are they just putting that out there? If I'm a marketer, I'm a B2B marketer. >> Yeah. >> Obviously besides working with theCUBE and our team, so obviously great benefits. Plug there, but seriously, what do you advise? >> Yeah, I think the biggest thing is, you know, you think about marketing as not only reaching your target market, but also enabling your sales force and your channel partners, and frankly, the best thing that I've found in doing that, John, is starting every single piece that we would come up with with a number. How much value are we generating, whether it's zero clicks to get this thing installed. It's 90% efficiency, and then prove it. Don't just throw it out there and say isn't that good enough, but numbers matter because they're meaningful and they stimulate the conversation, and that's ultimately what all of this is. It's a conversation about is this going to be relevant for you, so that's the thing that I start with. >> So you're say being in the conversation matters. >> Absolutely. >> Yeah. >> Absolutely. >> What's the thought leadership view, what's your vision on how a company should be looking at thought leadership. Obviously you're seeing more of a real-time-- I call it the old world was batch marketing. >> Chris: Mm-hmm. >> E-mail marketing, do the normal things, get the white papers, do those things. You know, go to events, have a booth, and then the new way is real-time. >> Chris: Mm-hmm. >> Things are happening very fast-- >> That's right. >> In the market, people are connected now. It's a global, basically, message group. >> That's right. >> Twitter, LinkedIn, Facebook and all this stuff. >> It's really an unfulfilled need that you guys are really looking to fill, which is to provide that sort of real-time piece of it, but I think vendors trip over themselves and they think about I need a 50 page vision. They don't need a 50 page vision. What they need is here are a couple of dimensions on which this industry is going to change, and then commit to them. I think the biggest problem that many vendors have is they won't commit, they hedge, as opposed to they go all in behind those and one thing we talk about at Chasm Institute is if you're going to fail, fail fast, and that really means that you commit full time behind what you're pushing. >> Yeah, and of course what the Chasm, what it's based upon, you got to get to mainstream, get to early pioneers, cross the chasm. The other paradigm that I always loved from Jeffrey Moore was inside the tornado. Get inside the tornado because if you don't get in you're going to be spun out, so you've got to kind of get in the game, if you will. >> Chris: That's right. >> Don't overthink it, and this is where the iteration mindset comes in, "agile" start-up or "agile" venture. Okay, cool, so let's take a step back and reset to end the segment here. >> Mm-hmm. >> Re:Invent's coming up, obviously that's the big show of the year. VMworld, someone was commenting on Facebook VMworld 2008 was the big moment where they're comparing Amazon now to VMworld in 2008. >> Chris: Right. >> But you know, Pat Gelsinger essentially cut a great deal with Andy Jassy on Vmware. >> Chris: Right. >> And everything's clean, everything's growing, they're kicking ass. >> Chris: Mm-hmm. >> They got a private cloud and they got the hybrid cloud with Amazon. >> Yeah, it's that VMcloud on Amazon, that really seems to be the thing that's really driving their move into the future, and I think we're going to see from both of those folks, you are going to see so much on containers. Containerization, ultra-containers, hyper-containers, whatever it may be. If you're not speaking container language, then you are yesterday's news, right? >> And Kubernetes' certainly the orchestration piece right underneath it to kind of manage it. Okay, final point, what's in store for the legacy, because you're seeing a few major trends that we're pointing out and we're watching very closely, which really I put into two buckets. I know Wikibon's a more disciplined approach, I'm more simple about that. The decentralization trend we're seeing with Blockchain, which is kind of crazy and bubbly but very infrastructure relevant, this decentralized, disrupting, non-decentralized incumbence, so that's one trend and the other one is what cloud's doing to legacy IT vendors, Oracle, you know, these traditional manufacturers like that HP and Dell and all these guys, and Netapp which is transforming. So you've got disruption on both sides, cloud and like a decentralized model, apps, what's the position, view, from your standpoint, for these legacy guys? >> It's going to be quite an interesting one. I think they have to ride the wave, and I'll steal this from Peter Levine, from Andreessen, right? He talks about the end of cloud computing, and really what that is is just basically saying everything is going to be moving to the edge and there's going to be so much more compute at the edge with IOT and you can think about autonomous vehicles as the ultimate example of that, where you're talking about more powerful computers, certainly, than this that are sitting in cars all over the place, so that's going to be a big change, and those vendors that have been selling into the core data center for so long are going to have to figure out their way of being relevant in that universe and move towards that. And like we were talking about before, commit to that. >> Yeah. >> Right, don't just hedge, but commit to it and move. >> What's interesting is that I was talking with some executives at Alibaba when I was in China for part of the Alibaba Cloud Conference and Amazon had multiple conversations with Andy Jassy and his team over the years. It's interesting, a lot of people don't understand the nuances of kind of what's going on in cloud, and what I'm seeing is it's essentially, to your point, it's a compute game. >> Chris: Yeah. >> Right, so if you look at Intel for instance, Alibaba told me on my interview, they don't view Intel as a chip company anymore, they're a compute company, right, and CJ Bruno, one of the executives there, reaffirmed that. So Intel's looking at the big picture saying the cloud's a computer. Intel Inside is a series of compute, and you mentioned that the edge, Jassy is building a set of services with his team around core compute, which has storage, so this is essentially hyper-converged cloud. >> That's right. >> This is a pretty big thing. What's the one thing that people might not understand about this. If you could kind of illuminate this trend. I mean, the old Intel now turned into the new Intel, which is a monster franchise continuing to grow. >> Mm-hmm. >> Amazon, people see the numbers, they go oh, my god, they're a leader, but they have so much more headroom. >> Chris: Right, right. >> And they've got everyone else playing catch up. >> Yeah. >> What's the real phenomenon going on here? >> I think you're going to see more of this aggregation phenomenon where one vendor can't solve this entire problem. I mean, look at most recently, in the last two weeks, Intel and AMD getting together. Who would've thought that would happen? But they're just basically admitting we got a real big piece of the equation, Intel, and then AMD can fulfill this niche because they're getting killed by NVIDIA, but you're going to see just more of these industry conglomerations getting together to try and solve the problem. >> Just to end the segment, this is a great point. NVIDIA had a niche segment, graphics, now competing head to head with Intel. >> Chris: That's right. >> So essentially what's happening is the landscape is completely changing. Once competitors no longer-- New entrants, new competitors coming in. >> Chris: Mm-hmm. >> So this is a massive shift. >> Chris: It is. >> Okay, Chris Cummings here inside theCUBE. I'm John Furrier of CUBE Conversation. There's a massive shift happening, the game has changed and it's incumbent upon start-ups, venture capital, you know, Blockchain, ICOs or whatever's going on. Look at the new chessboard, look at the game and figure it out. Of course, we'll be broadcasting live at AWS re:Invent in a couple weeks. Stay tuned, more coverage, thanks for watching. (techy music playing)

(upbeat electronic music) >> Hello, everyone. Welcome to the Cube Studios in Palo Alto, California. I'm John Furrier, the co-host of the Cube and co-founder of SiliconANGLE Media Inc. I'm here with George Gilbert for a Wikibon conversation on the state of the big data. George Gilbert is the analyst at Wikibon covering big data. George, great to see you. Looking good. (laughing) >> Good to see you, John. >> So George, you're obviously covering big data. Everyone knows you. You always ask the tough questions, you're always drilling down, going under the hood, and really inspecting all the trends, and also looking at the technology. What are you working on these days as the big data analyst? What's the hot thing that you're covering? >> OK, so, what's really interesting is we've got this emerging class of applications. The name that we've used so far is modern operational analytic applications. Operational in the sense that they help drive business operations, but analytical in the sense that the analytics either inform or drive transactions, or anticipate and inform interactions with people. That's the core of this class of apps. And then there are some sort of big challenges that customers are having in trying to build, and deploy, and operate these things. That's what I want to go through. >> George, you know, this is a great piece. I can't wait to (mumbling) some of these questions and ask you some pointed questions. But I would agree with you that to me, the number one thing I see customers either fumbling with or accelerating value with is how to operationalize some of the data in a way that they've never done it before. So you start to see disciplines come together. You're starting to see people with a notion of digital business being something that's not a department, it's not a marketing department. Data is everywhere, it's horizontally scalable, and the smart executives are really looking at new operational tactics to do that. With that, let me kick off the first question to you. People are trying to balance the cloud, On Premise, and The Edge, OK. And that's classic, you're seeing that now. I've got a data center, I have to go to the cloud, a hybrid cloud. And now the edge of the network. We were just taking about Block Chain today, there's this huge problem. They've got the balance that, but they've got to balance it versus leveraging specialized services. How do you respond to that? What is your reaction? What is your presentation? >> OK, so let's turn it into something really concrete that everyone can relate to, and then I'll generalize it. The concrete version is for a number of years, everyone associated Hadoop with big data. And Hadoop, you tried to stand up on a cluster on your own premises, for the most part. It was on had EMR, but sort of the big company activity outside, even including the big tech companies was stand up a Hadoop cluster as a pilot and start building a data lake. Then see what you could do with sort of huge amounts of data that you couldn't normally sort of collect and analyze. The operational challenges of standing up that sort of cluster was rather overwhelming, and I'll explain that later, so sort of park that thought. Because of that complexity, more and more customers, all but the most sophisticated, are saying we need a cloud strategy for that. But once you start taking Hadoop into the cloud, the components of this big data analytic system, you have tons more alternatives. So whereas in Cloudera's version of Hadoop you had Impala as your MPP sequel database. On Amazon, you've got Amazon Redshift, you've got Snowflake, you've got dozens up MPP sequel databases. And so the whole playing field shifts. And not only that, Amazon has instrumented their, in that particular case, their application, to be more of a more managed service, so there's a whole lot less for admins to do. And you take that on sort of, if you look at the slides, you take every step in that pipeline. And when you put it on a different cloud, it's got different competitors. And even if you take the same step in a pipeline, let's say Spark on HDFS to do your ETL, and your analysis, and your shaping of data, and even some of the machine learning, you put that on Azure and on Amazon, it's actually on different storage foundation. So even if you're using the same component, it's different. There's a lot of complexity and a lot of trade off that you got to make. >> Is that a problem for customers? >> Yes, because all of a sudden, they have to evaluate what those trade offs are. They have to evaluate the trade off between specialization. Do I use the best to breed thing on one platform. And if I do, it's not compatible with what I might be running on prem. >> That'll slow a lot of things down. I can tell you right now, people want to have the same code base on all environments, and then just have the same seamless operational role. OK, that's a great point, George. Thanks for sharing that. The second point here is harmonizing and simplifying management across hybrid clouds. Again, back to your point. You set that up beautifully. Great example, open source innovation hits a roadblock. And the roadblock is incompatible components in multiple clouds. That's a problem. It's a management nightmare. How do harmonization about hybrid cloud work? >> You couldn't have asked it better. Let me put it up in terms of an X Y chart where on the x-axis, you have the components of an analytic pipeline. Ingest, process, analyze, predict, serve. But then on the y-axis, this is for an admin, not a developer. These are just some of the tasks they have to worry about. Data governance, performance monitoring, scheduling and orchestration, availability and recovery, that whole list. Now, if you have a different product for each step in that pipeline, and each product has a different way of handling all those admin tasks, you're basically taking all the unique activities on the y-axis, multiplying it by all the unique products on the x-axis, and you have overwhelming complexity, even if these are managed services on the cloud. Here now you've got several trade offs. Do I use the specialized products that you would call best to breed? Do I try and do end to end integration so I get simplification across the pipeline? Or do I use products that I had on-prem, like you were saying, so that I have seamless compatibility? Or do I use the cloud vendors? That's a tough trade off. There's another similar one for developers. Again, on the y-axis, for all the things that a developer would have to deal with, not all of them, just a sample. The data model and the data itself, how to address it, the programing model, the persistence. So on that y-axis, you multiply all those different things you have to master for each product. And then on the x-axis, all the different products and the pipeline. And you have that same trade off, again. >> Complexity is off the charts. >> Right. And you can trade end to end integration to simplify the complexity, but we don't really have products that are fully fleshed out and mature that stretch from one end of the pipeline to the other, so that's a challenge. Alright. Let's talk about another way of looking at management. This was looking at the administrators and the developers. Now, we're getting better and better software for monitoring performance and operations, and trying to diagnose root cause when something goes wrong and then remediate it. There's two real approaches. One is you go really deep, but on a narrow part of your application and infrastructure landscape. And that narrow part might be, you know, your analytic pipeline, your big data. The broad approach is to get end to end visibility across Edge with your IOT devices, across on-prem, perhaps even across multiple clouds. That's the breadth approach, end to end visibility. Now, there's a trade off here too as in all technology choices. When you go deep, you have bounded visibility, but that bounded visibility allows you to understand exactly what is in that set of services, how they fit together, how they work. Because the vendor, knowing that they're only giving you management of your big data pipeline, they can train their models, their machine learning models, so that whenever something goes wrong, they know exactly what caused it and they can filter out all the false positives, the scattered errors that can confuse administrators. Whereas if you want breadth, you want to see end to end your entire landscape so that you can do capacity planning and see if there was an error way upstream, something might be triggered way downstream or a bunch of things downstream. So the best way to understand this is how much knowledge do you have of all the pieces work together, and how much knowledge you have of all the pieces, the software pieces fit together. >> This is actually an interesting point. So if I kind of connect the dots for you here is the bounded root cause analysis that we see a lot of machine learning, that's where the automation is. >> George: Yeah. >> The unbounded, the breadth, that's where the data volume is. But they can work together, that's what you're saying. >> Yes. And actually, I hadn't even got to that, so thanks for taking it out. >> John: Did I jump ahead on that one? (laughing) >> No, no, you teed it out. (laughing) Because ultimately-- >> Well a lot of people want to know where it's going to be automated away. All the undifferentiated labored and scale can be automated. >> Well, when you talk about them working together. So for the deep depth first, there's a small company called Unravel Data that sort of modeled eight million jobs or workloads of big data workloads from high tech companies, so they know how all that fits together and they can tell you when something goes wrong exactly what goes wrong and how to remediate it. So take something like Rocana or Splunk, they look end to end. The interesting thing that you brought up is at some point, that end to end product is going to be like a data warehouse and the depth products are going to sit on top of it. So you'll have all the contextual data of your end to end landscape, but you'll have the deep knowledge of how things work and what goes wrong sitting on it. >> So just before we jump to the machine learning question which I want to ask you, what you're saying is the industry is evolving to almost looking like a data warehouse model, but in a completely different way. >> Yeah. Think of it as, another cue. (laughing) >> John: That's what I do, George. I help you out with the cues. (laughing) No, but I mean the data warehouse, everyone knows what that was. A huge industry, created a lot of value, but then the world got rocked by unstructured data. And then their bounded, if you will, view has got democratized. So creative destruction happened which is another word for new entrants came in and incumbents got rattled. But now it's kind of going back to what looks like a data warheouse, but it's completely distributed around. >> Yes. And I was going to do one of my movie references, but-- >> No, don't do it. Save us the judge. >> If you look at this starting in the upper right, that's the data lake where you're collecting all the data and it's for search, it's exploratory. As you get more structure, you get to the descriptive place where you can build dashboards to monitor what's going on. And you get really deep, that's when you have the machine learning. >> Well, the machine learning is hitting the low hanging fruit, and that's where I want to get to next to move it along. Sourcing machine learning capability, let's discuss that. >> OK, alright. Just to set contacts before we get there, notice that when you do end to end visibility, you're really seeing across a broad landscape. And when I'm showing my public cloud big data, that would be depth first just for that component. But you would do breadth first, you could do like a Rocana or a Splunk that then sees across everything. The point I wanted to make was when you said we're reverting back to data warehouses and revisiting that dream again, the management applications started out as saying we know how to look inside machine data and tell you what's going on with your landscape. It turns out that machine data and business operations data, your application data, are really becoming one and the same. So what used to be a transaction, there was one transaction. And that, when you summarized them, that went into the data warehouse. Then we had with systems of engagement, you had about 100 interaction events that you tracked or sort of stored for everything business transaction. And then when we went out to the big data world, it's so resource intensive that we actually had 1,000 to 10,000 infrastructure events for every business transaction. So that's why the data volumes have grown so much and why we had to go back first to data lake, and then curate it to the warehouse. >> Classic innovation story, great. Machine learning. Sourcing machine learning capabilities 'cause that's where the rubber starts hitting the road. You're starting to see clear skies when it comes to where machine learning is starting fit in. Sourcing machine learning capabilities. >> You know, even though we sort of didn't really rehearse this, you're helping cue me on perfectly. Let me make the assertion that with machine learning, we have the same shortage of really trained data scientists that we had when we were trying to stand up Hadoop clusters and do big data analytics. We did not have enough administrators because these were open source components built from essentially different projects, and putting them all together required a huge amount of skills. Data science requires, really, knowledge of algorithms that even really sophisticated programmers will tell you, "Jeez, now I need a PhD "to really understand how this stuff works." So the shortage, that means we're not going to get a lot of hand-built machine learning applications for a while. >> John: In a lot of libraries out there right now, you see TensorFlow from Google. Big traction with that application. >> George: But for PhDs, for PhDs. My contention is-- >> John: Well developers too, you could argue developers, but I'm just putting it out there. >> George: I will get to that, actually. A slide just on that. Let me do this one first because my contention is the first big application, widespread application of machine learning, is going to be the depth first management because it comes with a model built in of how all the big data workloads, services, and infrastructure fit together and work together. And if you look at how the machine learning model operates, when it knows something goes wrong, let's say an analytic job takes 17 hours and then just falls over and crashes, the model can actually look at the data layout and say we have way too much on one node, and it can change the settings and change the layout or the data because it knows how all the stuff works. The point about this is the vendor. In this particular example, Unravel Data, they built into their model an understanding of how to keep a big data workload running as opposed to telling the customer, "You have to program it." So that fits into the question you were just asking which is where do you get this talent. When you were talking about like TensorFlow, and Cafe, and Torch, and MXnet, those are all like assembly language. Yes, those are the most powerful places you could go to program machine learning. But the number of people is inversely proportional to the power of those. >> John: Yeah, those are like really unique specialty people. High, you know, the top guys. >> George: Lab coats, rocket scientists. >> John: Well yeah, just high end tier one coders, tier one brains coding away, AI gurus. This is not your working developer. >> George: But if you go up two levels. So go up one level is Amazon machine learning, Spark machine learning. Go up another level, and I'm using Amazon as an example here. Amazon has a vision service called Recognition. They have a speech generation service, Natural Language. Those are developer ready. And when I say developer ready, I mean developer just uses an API, you know, passes in the data that comes out. He doesn't have to know how the model works. >> John: It's kind of like what DevOps was for cloud at the end of the day. This slide is completely accurate in my opinion. And we're at the early days and you're starting to see the platforms develop. It's the classic abstraction layer. Whoever can extract away the complexity as AI and machine learning grows is going to be the winning platform, no doubt about it. Amazon is showing some good moves there. >> George: And you know how they abstracted away. In traditional programming, it was just building higher and higher APIs, more accessible. In machine learning, you can't do that. You have to actually train the models which means you need data. So if you look at the big cloud vendors right now. So Google, Microsoft, Amazon, and IBM. Most of them, the first three, they have a lot of data from their B to C businesses. So you know, people talking to Echo, people talking to Google Assistant or Siri. That's where they get enough of their speech. >> John: So data equals power? >> George: Yes. >> By having data, you have the ingredients. And the more data that you have, the more data that you know about, the more data that has information around it, the more effective it can be to train machine learning algorithms. >> Yes. >> And the benefit comes back to the people who have the data. >> Yes. And so even though your capabilities get narrower, 'cause you could do anything on TensorFlow. >> John: Well, that's why Facebook is getting killed right now just to kind of change tangents. They have all this data and people are very unhappy, they just released that the Russians were targeting anti-semitic advertising, they enabled that. So it's hard to be a data platform and still provide user utility. This is what's going on. Whoever has the data has the power. It was a Frankenstein moment for Facebook. So there's that out there for everyone. How do companies do the right thing? >> And there's also the issue of customer intellectual property protection. As consumers, we're like you can take our voice, you can take all our speech to Siri or to Echo or whatever and get better at recognizing speech because we've given up control of that 'cause we want those services for free. >> Whoever can shift the data value to the users. >> George: To the developers. >> Or to the developers, or communities, better said, will win. >> OK. >> In my opinion, that's my opinion. >> For the most part, Amazon, Microsoft, and Google have similar data assets. For the most part, so far. IBM has something different which is they work closely with their industry customers and they build progressively. They're working with Mercedes, they're working with BMW. They'll work on the connected car, you know, the autonomous car, and they build out those models slowly. >> So George, this slide is really really interesting and I think this should be a roadmap for all customers to look at to try to peg where they are in the machine learning journey. But then the question comes in. They do the blocking and tackling, they have the foundational low level stuff done, they're building the models, they're understanding the mission, they have the right organizational mindset and personnel. Now, they want to orchestrate it and implement it into action. That's the final question. How do you orchestrate the distributed machine learning feedback and the data coherency? How do you get this thing scaling? How do these machines and the training happen so you have the breadth, and then you could bring the machine learning up the curve into the dashboard? >> OK. We've saved the best for last. It's not easy. When I show the chevrons, that's the analytic data pipeline. And imagine in the serve and predict at the very end, let's take an IOT app, a very sophisticated one. which would be an autonomous car. And it doesn't actually have to be an autonomous one, you could just be collected a lot of information off the car to do a better job insuring it, the insurance company. But the key then is you're collecting data on a fleet of cars, right? You're collecting data off each one, but you're also collecting then the fleet. And that, in the cloud, is where you keep improving your model of how the car works. You run simulations to figure out not just how to design better ones in the future, but how to tune and optimize the ones that are on the road now. That's number three. And then in four, you push that feedback back out to the cars on the road. And you have to manage, and this is tricky, you have to make sure that the models that you trained in step three are coherent, or the same, when you take out the fleet data and then you put the model for a particular instance of a car back out on the highway. >> George, this is a great example, and I think this slide really represents the modern analytical operational role in digital business. You can't look further than Tesla, this is essentially Tesla, and now all cars as a great example 'cause it's complex, it's an internet (mumbling) device, it's on the edge of the network, it's mobility, it's using 5G. It encapsulates everything that you are presenting, so I think this is example, is a great one, of the modern operational analytic applications that supports digital business. Thanks for joining this Wikibon conversaion. >> Thank you, John. >> George Gilbert, the analyst at Wikibon covering big data and the modern operational analytical system supporting digital business. It's data driven. The people with the data can train the machines that have the power. That's the mandate, that's the action item. I'm John Furrier with George Gilbert. Thanks for watching. (upbeat electronic music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering InterConnect 2017. Brought to you by IBM. >> Okay, welcome back, everyone. We're live here at the Mandalay Bay in Las Vegas for the wrap-up of IBM InterConnect 2017. I'm John Furrier. My co-host this week, my partner in crime, co-CEO, co-founder of SiliconANGLE Media Inc. with myself, Dave Vellante. Dave, it's been a great week. I just feel like I have been Watsonized and Blockchained and cloud all week. As we wrap up InterConnect, I want to get your thoughts on IBM, the cloud business, the big data marketplace, some of the things that we're seeing at the 100 of events we go to. We've got our events coming up, we're going to be in Munich next month, we got DockerCon, but a lot of developer events coming up, but in general, we get to see the landscape, in some cases, that others don't see. Let's talk about that, so before we get into the landscape, let's about IBM, IBM's prospects. This show, just quick stat, almost double the online traffic we're seeing on IBMGO than World of Watson, which was the biggest show we've ever done with theCUBE that we've seen. So, an interest, it's a data point. Unpack the data, you can see that there's a lot of global interest in what IBM is doing right now with the cloud and with Watson, and certainly with Blockchain you add another disruptive enabler potentially to what will either be a brilliant IBM strategy or a complete crash and burn. I think this is an IBM go big or go home moment with Ginni Rometty. I love her messaging, I love her three pillars, enterprise strong, data first, cognitive to the core. That is solid messaging, all three pillars. To me, it's clear. IBM is at a reinvention moment, it's all coming together, but it's a go big or go home moment for them. >> Well, you know, John, I mean, Ginni when she took over, sorry, she was running strategy before she became CEO, I mean, IBM had a choice, they could go double down on infrastructure and go knock it out with Dell and EMC and HP, or they could go up the value chain. And my ongoing joke is Dell bought EMC, IBM buys some other company, and that to me underscores the differentiation in thinking. Oracle, I think, is a little different, but Oracle and IBM are somewhat similar, I think you'd agree, in that they've got a big SaaS portfolio, they're trying to vertically integrate, they're trying to drive high value margin businesses. The difference is IBM's much more services oriented than, say, an Oracle, and that's still, as I say, a big challenge for IBM. But I'm more, I'm a bull on IBM. >> Why is that? >> I think the strategy is, number one, they're relevant. We talked for years about how we weren't that excited about Microsoft because they weren't relevant. Satya Nadella came in, all of a sudden, they're relevant again. I think IBM is highly relevant in the minds of CEOs, CIOs, CCOs, CDOs, all the C-suite, IBM is super relevant there, just as are Accenture and Ernie Young and all the big SIs. But IBM's got tons of products beneath it, number one. Number two, despite the fact that, you called it out several years ago, they bought software for 2.4 billion, it was a bare metal hosting company, alright, but IBM's turning that into >> Bluemix. >> a cloud business with Bluemix, right. And they're building, bringing in acquisitions like Cleversafe, like Aspera, like Ustream, and others, where they're bringing services that are differentiated. You can only get Watson on IBM's cloud, you can only get IBM's Blockchain on IBM's cloud, so they're bringing in value-added services, and there's only one place you can get them, and I think that's a viable strategy that's going to throw off a lot of cash, and it's going to lead to success. >> And by the way, they're also continuing to invest in open source. So, again, that's-- >> That's the other piece. I wanted to talk to you, and this is your wheelhouse. IBM's open source mojo is not just lip service, alright. They have deep-rooted DNA in open source and their strategy around it, and they've proven that they can monetize open source. What's their model, I mean, explain the model because I think it's instructive. >> I mean, open source, there's a lot of different models. Red Hat-- >> For IBM, I mean. >> IBM's model of open source is very clear. If you look at what they've done with just Blockchain as a great example, they have mobilized their company, and they did it with Bluemix as well with the cloud, once they said, "We want to get in the cloud game," once, "We want to do Blockchain," they go open source at the core, then they get their entire brain trust workin' on it. It's not just a hand wave, some division, they're kind of reorganizing on the fly, they're kind of agile organization, which some may read as chaotic, but to me, I think that's just good management practice in this day and age. They get an open source project, and they drive that home, and they have people contributing and giving that to the community, and then adding value on top and differentiating. It's just classic 101, create some value, and create some differentiation with your products, and by the way, if you don't want to use our products, build your own, or hey, use the open source code. That's pretty much an over-simplified version of open source. >> But Blockchain's a great example of this, right? So, they see the leverage in open source project, they put all these resources in, and they say, okay, now let's build our product on top of that, let's get the open source community leverage and this is, let me ask you this, does IBM, so several years ago when IBM announced Bluemix, you were pretty critical. >> John: I was very critical. >> IBM has to win the developer audience or it's cooked in this game. >> That's what I said. >> How is it done, how would you grade them? >> I think they're doing very well. I think IBM is, again, to use your word, they're not putting lip service in it. So, I was joking with Meg Swanson last night, I saw Adam Gunther when they interviewed on theCUBE, and I was critical. I didn't say that their cloud was bad, I was just saying it's just not as, just got a lot of work to do, Amazon's kickin' ass, which we now know that happened, right. But they've done well. They've done well, they've ran hard, they've gone the table stakes on the enterprise. I still think they got some more work to do, we can analyze, I'm putting out my cloud ratings matrix, I'm going to put IBM on that list, I have Google and Amazon done. I'm going to add Microsoft Azure and IBM onto the mix in the comparison matrix. But IBM has done good with the developers. They've just invested 10 million in this announcement, and they're ramping up. I wouldn't say they're throwing just money at it, they got people, so I would give them, I'd give them a B-plus, A-minus score because they're hustlin', they're doing it. Are they totally blowing it out of the water? No, I don't think they're pushing hard enough there. I think they could give it some more gas, I think they could do more with it, personally thinking. But you know, Dr. Angel Diaz was on earlier today. They're going at their own pace. >> But you agree they're in the game. >> Oh, totally. >> Making good progress. >> They're totally, IBM is totally in the cloud game, and they don't get a lot of credit for it. Either does Oracle, by the way. Somehow, people seem to talk about Azure and Google. Google is so far behind, in my opinion, they're not even close. I think it's Amazon, Azure, IBM and Oracle and Google all kind of in that-- >> Juxtapose Oracle's developer cred, even though it owns Java, with IBM's. How would you compare the two? >> Very similar, I think. Different approaches, but again, to your point, IBM's relevant, Oracle's relevant. We had this question about VMware when they did the deal with AWS. They have customers and they have cash, so they're not going anywhere. It's not like IBM's a sinking ship. It's not like Oracle's a sinking ship. Now, that being said, there's a huge shift in the business, and I would say in that scenario, Google is in a very good position, so I've been very critical on Google only because they're trying to be acting like they're an enterprise flag. They're not, I mean, Google's got great tech, TensorFlow, machine learning. Google has great cloud tech, but in that game, they're up in the number one, two spot. But in the enterprise side, they're not close. They're workin' on that. So, that's my critique of Google. Microsoft has got the DNA for the enterprise, so Microsoft and Oracle to me are more similar than comparing IBM and Oracle. I'd say IBM is a lot more like Google and Amazon, kind of in-between, but Oracle and Microsoft look the same to me. Big install base, highly differentiated, stacks aren't perfect, but it looks good on paper, and they're gettin' business. And Oracle's earnings, by the way, were very explosive due to the cloud growth. >> Another question I like to ask sometimes is, okay, what would you have done differently if you had a choice? Like when Gerstner was running IBM, he chose to consolidate the company, essentially, not consolidate, but focus on services, one throat to choke, single-faced IBM. Great customer service and build the services business, buy-in, PWC, et cetera, that was the key. What could you have done differently that could've said, well-- >> John: For IBM? >> Yeah, at the time, you could have said, "We're spin out different product groups. "We're going to be the best at microprocessors, "or disk drives, or database, or software." >> I think IBM moved too slow. >> That's a historical example. Given what IBM's doing today, what would you have done differently if you were Ginni Rometty five or six years ago? >> I would've done what they're doing now three years ago. We were, when we started working with them with CUBE, IOD events, and Pulse. >> Dave: Information on Demand. >> You had a lot of silence. I think, if I had to go back and get a mulligan, if I was Ginni Rometty, I would've moved faster. >> Dave: Done that faster. >> Hindsight's 20-20 on that, but it wasn't that clear. But again, it's the big aircraft carrier, it can only move so fast. I think what they're doing now is good strategy, and they're price strong, data force, cognitive to the core is a good strategy. Now, cognitive is words for AI, and that's their word, cognitive 'cause of Watson, but essentially, machine learning and AI is going to be a big pillar there, and then, the data first is more of an architectural component that's very good. But in general, Dave, the cloud is, this is what's going on in my find. It's so obvious to me. The big data marketplace that was we defined by Cloudera and Hadoop and Hortonworks just never panned out. It morphed into a bigger picture, and so, Hadoop is part of, now, a bigger ecosystem. Cloud was growing very fast. Those two worlds are coming together and growing very rapidly independent with big data, with machine learning, AI, and IOT. They're coming together. The intersection of the big data and the cloud. >> Cloud-mapping data. That was Yuri Burton from 2005. >> But it's coming together really fast, and the IOT is the real business driver. I know there's not a lot of stuff shipping yet in the sim stuff out there, but merging IOT into IT into business process and into developer mindset, whether it's an Indiegogo up to full-on developers is the accelerant that's going to fuel the AI value. To me, that's the intersection point of big data and cloud, and that is the home run, that's the holy grail, and that's going to be disrupting some preexisting decisions by big vendors who made bets, and I'm talkin' about bets made in the past five years, not like bets made 20 years ago or 10 years ago. I think the IOT is going to really shape the game. The other thing I worry about now, in my opinion, is a lot of AI-washing. People say, "Oh, AI." You see people on the stage, "Oh, we did this with AI." There's no AI, it's augmented intelligence, which is basically predictive analytics. You know, true AI is not yet here, it's a little bit hyped up, not that I mind that. I think that the machine learning is the real meat on the bone right now, I think that's the core enabler. Machine learning is by far the most important trend in the computer science world today as it relates to integrating that capability into cloud native, microservices, and overall application. >> I agree, I mean, AI is still a heavy lift, but to me, the key, I go back to something you were saying, is developers. That's the lever that's going to give you the ability to move large mountains. If you don't have that developer community, and you don't have open source chops, you're going to struggle a little bit. You're going to be either in a swim lane like Oracle with its database and its red stack, and maybe you can break out of that, but I'm not sure it wants to. Or you're going to be stuck in infrastructure lane. >> Yeah, but the developers are driving all the action right now. My point about machine learning, if you look at the shows just recently, and certainly we have the history of the past year, machine learning is the sexiest trend in every show. Last show was Google Next, machine learning with TensorFlow, both open source. Machine learning's not new, it's just now accelerating the developer. The developers want to move faster, and I think things like machine learning, things like cognitive that IBM puts out there, are great catalysts. That's going to be a big thing we're going to watch, obviously, we have a big developer community at SiliconANGLE, so something to watch. >> What's next? We've got a chief data scientist summit next week in Silicon Valley, we're going to be at the-- >> Let's look at my Friday show this week. Every Friday I do the Silicon Valley Friday show with me and guests, we got that goin' on, so always check that out on soundcloud.com/johnfurrier, or check out my Facebook feed, facebook.com/johnfurrier. But in terms of CUBE events, we've got DataWorks in Munich on April 2nd, DockerCon in Austin, Oracle Marketing Sum Experience, Red Hat, Dell EMC World, Service Now, Open Stack, Big Data in London. >> It's going to be a busy spring. >> Lot of stuff going on. Great stuff. >> Deb, we'll see you in July. >> In bumper sticker, Dave, this show, encapsulate your thoughts. >> Well, I think it's all about cloud, data, and cognitive coming together in a way that allows business value and differentiation through the end customer. That's what this show is about to me. It's not about infrastructure, cloud and infrastructure, that's kind of table stakes. It's all about differentiation up the stack, creating, enabling new business models. >> My encapsulation is the enterprise strong, data first, cognitive to the core message that Ginni said, that translates into IBM's shoring up their base products and putting an innovation strategy around Blockchain and soon to be cognitive computing at a whole 'nother level, and I think they're going to have a real innovation strategy and continue to use what they did with Watson, the winning formula. Put something out there that's a guiding principle and draft the company behind it. I think that, to me, is my big walk away, and I think Blockchain will potentially level, has game-changing capabilities, and if that plays out like Watson's playing out, then IBM could be in great shape on both shoring up the base in cloud and their business and having an innovation strategy that extends them out. That to me is the reason why I'm bullish on them. So, great show, Dave Vellante. Thanks to the guys, thanks for everyone watching. That's it for us here in theCUBE. I'm John Furrier, Dave Vellante wrapping up IBM InterConnect 2017. Thanks for watching, stay with us, and follow us at theCUBE on Twitter and siliconangle.tv on the web. Thanks for watching. (electronic keyboard music)

>> Announcer: Live from Silicon Valley, it's theCUBE, covering Mobile World Congress 2017. Brought to you by Intel. >> Welcome back, everyone. We're here live in Palo Alto for SiliconANGLE Media's theCUBE's new studio, 4500 square feet in Palo Alto. Just moved in less than a month ago, and we're bringing you all the in-studio coverage of what's going on in Barcelona, Spain at Mobile World Congress. This is day two of two days of coverage. Here in the studio we're bringing people in that's in Silicon Valley into the studio, experts, entrepreneurs, venture capitalist investors, angel investors, and of course, analysts here from our own team, and we have Peter Burris with me here. And we're covering all the action. Of course, we have reporters and analysts and friends on the ground doing call-ins in Barcelona, bringing you all the action, and really, bringing the big story that's not being told, which is AI, IOT, and cloud-ready, cloud-native action is happening. This is the disruptor, the calm before the storm as we were saying earlier yesterday. Peter Burris, great to see you. We were talking yesterday morning on the kickoff, let's take that to the next level. Cloud-native, IOT, really the big story that's not being told at Mobile World Congress this year, mainly because it's just in everyone's face right now, and people are making sense of it. Your thoughts on this as you are looking at the research, looking at the marketplace, this is reality. The IOT is real. >> Oh, it's very much real, John. Let's start with why cloud and mobile are so important together. In many respects, the thing that made the cloud real is mobility because the minute that you don't know where your device is going to connect, where the termination point's going to be, then you don't want to have to control and own that network. And so in many respects, the whole concept of mobility catalyzed the need for the cloud because you didn't want to have to utilize a, you didn't want to have to build your own network to support people as they moved around. So the cloud as a front end, or as a set of capabilities that supports mobility is really crucial to this whole concept, and it's somewhat surprising that it's not more closely tied together at Mobile World Congress. But the most important thing that we could talk about obviously is that IOT is going to have a major impact on all kinds of different factors. It's going to have a major impact on the devices that are manufactured, it's going to have a major impact on what the scale efficiencies that you have in manufacturing, the nature of the sensors, the nature of microprocessors, how much memory gets put on stuff, how much flash memory is going to be manufactured over the next decade. All these things are going to have a significant impact on the concept of mobility and what it means and the networks it provided over the course of the next 10 years. >> Peter, I want to bring up something that you brought up yesterday, and I think this is important, that's why I wanted to do a real drill down on what seems to be a major paradigm shift and inflection point. We've been talking about autonomous vehicles, media entertainment, smart cities, smart homes. Those are all the sexy demos at Mobile World Congress. But the real change, as pointed out by Val Bercovici who just came in as CTO is that the sea change underneath it, and you pointed out yesterday the convergence between enterprise and consumers coming together is that this internet of things and people, IOTP, or IOTNP, 'cause things can be sensors and devices, are changing it, and what's obvious to us and now coming out of Mobile World Congress as it's just starting to be seen by the mainstream press and media and community is that the TelCos aren't used to dealing with rapidly provisioning things. They're used to a subscriber who buys a phone, dials up a service, gets provisioned and connected, and they have a number, and then they try to connect to the base station and get on the internet. That's simple, and those connections we all know fail, but now imagine that multiplied by millions and millions of devices that are going to be turned on and connected. This is a scale problem, this is a network problem, this is a physics problem. >> Well, it's a physics problem-- >> Explain your theory on this. >> Yeah, it's a physics problem at a very, very base level. Just talking about the TelCos for a second. You're absolutely right, John. We're talking about, when we talk about the scale problem in the TelCos, it's not that they don't know what to do with their networks, it's not that they don't know how to connect devices to the networks. They just don't know how to provide it at a service level. It's going to be demanded by the scale of the devices moving into and out of networks as we think about IOT and P, the TelCos have historically thought about, they've thought about the assets that they have in place, the rates that they charge for those assets, the returns they generate, the tariff rules they work with with governments around the globe. They tend to focus on, good or bad, 10, 20-year time horizons. >> And their P is phone, not people. >> That's right, their P is absolutely. Their P is phone, and I can, and you were probably around. I can remember when you could not buy a phone that didn't have, on a particular company's network, you still can't buy a phone on a network today. You buying a mobile phone and it goes, it's associated with-- >> You're buying a carrier. >> That's right, that's exactly right. And that's how TelCos want to work. Now, they're hoping that eventually they're going to find themselves in the position to be able to spin up devices very quickly, but the reality is that's not how provisioning works in the real world. It's one of the reasons why TelCos continue to get their lunches eaten by companies that are building out their own networks and doing a much better job of rapid provisioning. >> You and I were talking last night off-camera about this notion of IOT and P, and of course, we all believe in and we're passionate about it, but you made a comment that was interesting. It was that we're going to look back at this time in history as a moment where before and after kind of, before Christ, after Christ, however you want to look at it. I mean, there's always that AD, BC kind of thing going on where before, I always call it before Steve Jobs and iPhone. Now it's going to a whole other level with the societal changes from little things, like we had a guest on talking about waste disposal efficiency. Traffic light management, healthcare, every single digital service. NTT Docomo's investor was on yesterday. She was talking about investing in services and bringing AI as a service, not network services, lifestyle services. What do you mean by that, that this is going to be something that we're going to look back 50 years from now and say this was the moment? Can you expand your? >> Yeah, absolutely, John, and it's really actually pretty simple. If you take a look at how executives are starting to think, what's happening is for the first time, we're really starting to look at data as an asset. That's a big question, but let me try to break it down and be a little bit more concise about what I mean by that. When we think about IOT and P, we're thinking about the idea that we can distribute enormous, billions of devices that are going to be sources of data. They're going to be going into the analog world, put into the analog world, and they're going to take analog signals and turn them into, and transduce them into digital signals. Once those signals become digital, then they hit big data, they hit AI, they hit machine learning. That's what's catalyzing a lot of the social concerns about, well, what does it mean for machines to be more autonomous, to take more responsibility? What's going to happen with business accountability when business are increasingly relying on machines that quote, "think." When we think about these big societal changes, we're talking about the ability that IOT's providing, IOT and P is providing, that for the first time how we're going to capture enormous net-new data, how we're going to process that enormous net-new data, and then ultimately, what we call systems of enaction, how we're going to enact specific events back in the real world as a consequence of what machines say is the right thing to do. That is a demarcation point. It moves from a machine being regarded as a tool, and almost exclusively as a tool, something that performs work better but having that work be very well described and very well articulated and the concept clear to something that might actually introduce new work or do work differently. Take responsibility for how it performs work. That's a major sea change. And so when we say that it's going to be, we'll look back and say, "It was before this time "and after this time," it's because we are now in the position to economically be able to gather these streams of data, process them in ways that are unprecedented, and then have the results of that processing enact in unpredictable ways, and that's a major change. >> I don't know if we can talk about some of your research that's coming out, I dunno, can we touch on some of the points? This has yet to be released research from the Wikibon team headed up by Peter with SiliconANGLE Media. I want to just point out, 'cause I find this interesting, you say that there's a architectural decision point within IOTP, a new phrase, hashtag IOTP if you're interested in working with us, just hit us up at Twitter. But there's really four points you point, physics, the law, legal, of course, everything's legal. Physics, legal, economical, economics, and then, authority. >> Right. >> What do you mean by those four? Can you just take us through conceptually these are dimensions, they interplay, are they dependencies, are they interdependent, are they all intertwined? What's the rationale behind these architectural forces? >> When people think about information systems historically, they've been relatively well circumscribed. So, I have an employee that I'm going to provide a service to from a network that I control that has latency requirements and aren't that big a problem because at the end of the day a human being doesn't operate at nanosecond kind of levels, and I got a machine that's mine, and I own running an application that I've licensed. That is a very, very tightly bound unit. When we start introducing IOTP and some of these other things, now we're talking about emergent behaviors that might be far away that we don't control, we're working with partners, et cetera, and the basic architectural challenge of thinking about what do we have to do to get a handle on the requirements of the processing, 'cause at the end of the day these things are still computers, and they still have operational characteristics that have to be accommodated. We think that there's going to be four factors that are going to influence how what we call the edge zone expands or compresses based on the work that needs to be conducted. One is physics. You're not going to go faster than the speed of light, and in fact, generally speaking, if you look at the distance that you have to travel, you're going to be outside the automation zone. You're going to be outside the automation zone if light has to travel, at best, you're going to be about a 10th of the speed of light, so if your automation zone, if you want your automation zone to be about 100 miles, then it means that from there and back with the speed of light you're not going to be able to automate anything that takes longer than that, just for example. Physics is one. >> Physics and wireless is a great example of physics. >> Wireless is, yeah. >> And moving packets around. >> None of this stuff works without physics, right. The second one is legal, that the reality is is that while the laws of physics are relatively immutable as far as we know, there are also government regulations that are what they are, and that could include privacy, it can include requirements for disclosing things, and so, those also, borders are going to have an impact on this notion of automation zones, or edge zones as we call them. Economics is another one. It costs money to move data from point A to point B, and the question is how much data's going to move. A lot of people think that everything's going to go up to the cloud, it's going to be processed up there, and then some instruction's going to come down for automation. That's probably not the way it's going to work. Our findings are suggesting-- >> Not only is it the cost of data, I would argue that also the product design criteria will be impacted economically on that decision point. >> Absolutely. But that's based on how much does it cost to move the data around. The operational characteristics of a product or service are fundamentally, a digital product or service, are fundamentally tied to the cost of moving data. We think that 95-plus percent of the data's actually going to stay in the edge. And the last one is authority, and we kind of touched upon this a second ago in that we're now suggesting that machines are going to take actions without human intervention. Not just actions, but they're actually going to change the scope and nature of the actions that are going to be taken. What does that mean? What does it mean for a machine to act on behalf of a brand? Or on behalf of a person? People use a simple explanation, "Does the autonomous car take out the old lady "or the cub scouts if you got a problem? "Or does it do something else?" It's those kinds of things that we don't know the answer to. A lot of the questions of authority and how we distribute authority and how we codify authority and how we track authority is going to have a major impact on what limits to behavior we put on these things. >> There's also the security angle alone is another one, too, just like basic stuff. These are interesting. And you see these architectural forces. Are you calling them forces, factors, variables? >> Just factors simply because the concept of factor, or you can call it constraints, is the idea that your decision has to factor these things, so we're just calling 'em factors right now. >> Alright, so let's step back now, and look at some of the commentary from this week in Mobile World Congress and our interviews here in theCUBE as well as the remotes. Certainly the hallway conversation is the business model of the TelCos. Saar Gillia who was on yesterday brought up a point of, hey, where's the use cases? Show me the use case, and then I'll say yes. And it's this too complicated, he was not seeing the use cases, and he was saying, "I'd prefer more battery life than "more one gigabit wireless right now" given that's his current situation. The balancing of where to get started seems to be the number one theme. What do I do next, what's the first step? Will the bridge collapse that I'm trying to cross to this future? Or I can't see the other side? Is the world flat or round? These are kind of more personal feelings that people have around taking that leap of faith into this new world? How do you advise and package that together and assimilate that? I mean, do you, how should people look at that? >> I think it's a great question, and I wasn't part of the conversation yesterday, but let's look at that for example. Today, if you're using your phone, you effectively have a relatively simple number of sensors in your phone, relatively simple number of transducers, right. You have a chip that turns your analog voice into a digital signal, so there's that in there. You have some neat stuff that presents the screens, so there's that in there. You have a microphone, et cetera, that kind of stuff, but when we start thinking about 5G and what networking could become, as we talked about yesterday, it's not so much the absolute bandwidth speeds, and it certainly is not going to have any impact on latency for the most part. It really is the number of devices that you can support at one time. It allows for greater density of sources. Now, without looking at 5G, we can talk about a phone being able to support not just a few generators, or a few sources of data on that phone, but maybe dozens, so maybe things that, you know, the whole concept of wearables. Again, do I want to get involved in the use case? No, you and I are sitting here being analysts, and that's not our business. But are there going to be use cases for more wearable technology? Well, if you're sick, if you have a chronic disease, just for example, yeah, that's a use case. I could see people actually living much higher quality lives because they can support more sensors as a result of 5G, with greater security. Again, we go to the autonomous car. There's going to be a lot of sensors in an autonomous car. Most of them are going to operate locally, but having said that, it might be nice if we could actually have a very, very fast low-cost network with inside the car itself to handle a lot of that work. I think we've, human beings, developers, have always found new use cases when given more compute, more memory, and more networking. I don't think that's going to change. I think we're going to see more of that. >> Peter, what's your thoughts, if you had to summarize and encapsulate it into a narrative, Mobile World Congress 2017, now looking back at day two kind of coming to a close, seeing what's out there, how do you look at that? How would you tell someone here is the story of Mobile World Congress? Tell that story. >> To me, John, having looked at the stuff come over the transom and you know, a lot of new devices being talked about and generating a little bit of excitement, a lot of new this and a little bit of excitement, I think that the question for me is are we moving into a period where integration's going to matter again? And I think in many respects that's going to be kind of the subtext of what's coming out of Mobile World Congress. Is it good enough to have the best of breed device and this and that, with a software stack that's doing this and that? Or is there going to be more value to the enterprise and ultimately to the consumer by taking more of an end-to-end perspective? Apple from a consumer and an experience standpoint has done that and has, what is it? They're worth $150 billion more than any other company on the planet right now or something crazy like that? Don't quote me on that, but I think that's what somebody told me. >> Trillions of dollars in cash overseas, for sure. >> Yeah, so it's that notion of are we moving back into a world where integration is going to matter because we're going through a period of significant discontinuity. >> Integration is a great point, 'cause I see that, I do see that as a thing, and bring the Apple example. Apple, the way they develop might be different than say, what we see in an open source, for instance. If you look at what Intel's doing, and I look at Intel as a bellwether, and this is from my perspective, because they have such a huge long game in play, they have been the leader in my opinion in the tech industry playing the long game, and they have to because they make chips. And they're looking at the 5G as an ecosystem play, and they're admitting and saying it's not one vendor. They don't say take village, but they're basically saying it takes a village to rise all the tide or float all the boats, if you will. If you look at what Intel's doing, they're essentially saying that it's an integration game through their own moves, which is ecosystem, playing well together. Now, you could fight for best of breed on point solutions, whether it's a Snapdragon Qualcom, or Intel processor on the device. At the end of the day, it's, as we were saying, network function virtualization to make those dynamic networks work seem to be the key. To play in that, if as a society globally, to your four factors, it has to be an integration game. No one company can do those factors. >> You're absolutely right. Here's how I would say it to put a slightly different twist on it. The tech industry has moved from a product orientation to a service orientation, or is moving from a product orientation to a service orientation, from an orientation where we focus on what's the intrinsic value of what we're buying to what's the utility of what we're using. From a "Hey, let's a put a spend a lot of money upfront "and maybe we'll get to some point of time in the future "where it's valuable" to a, "Let's only pay for what we got." It's difficult to imagine the tech industry moving successfully into that service orientation without taking more of an integration approach to it. Certainly that's what Amazon's trying to do or AWS is trying to do, that's what Google is trying to do, that's what all the companies that are trying to move infrastructure into the cloud are trying to do, so I think that this is a general issue. If we're moving to a service orientation, we have to start taking the integration view on things. >> Awesome, great, Peter. You're watching theCUBE. This is SiliconANGLE Media, Inc., and SiliconANGLE Media, Inc. comprises of siliconangle.com, led by Rob Hof, that's our publishing journalism, wikibon.com led by Peter Burris and research, and theCUBE, our internet TV led by Jeff Frick, and of course CrowdChat is the data brand and the data science, and we love bringing you this great content. Pete, I'll give you quick plug because I know that you've been doing a ton of work building out the research team at Wikibon and expanding the work behind the firewall, it's a paid subscription. Some premium that we see on siliconangle.com for the most part. A great body of work on the research. I want to congratulate you, but give you an opportunity to share with the folks who are watching what's going on with research and some of the things that you're working on and why they should potentially reach out to Wikibon. >> Yeah, so we're focused on a couple of relatively simple things. We're not a huge team, so we tend to focus less on products, again, the idea of let's take a look at the intrinsic value of products, and we focus more on the impacts. What does it mean to get utility out of things? How do you get utility out of whatever you buy? The other thing we focus on is disruption, and we talked a lot about what are the disrupting factors. IOT, big data, and what we call the systems of enaction, all supported by significant changing infrastructure and new digital business models. So, it's kind of a combination of those five things that we are focusing our time and attention on. Ultimately, we want to be in a position to help our clients make decisions that improve the value of their business by better utilizing data through these digital models, digital business models that require these technology changes to go. >> Great, and it also helped show Mobile World Congress is about cloud-ready. You had a great report on Amazon we posted on siliconangle.com. What was the summary, bottom line that big body of work you did about Amazon that the headline was, "How big can Amazon be?" What was the key findings from your big assembled report on Amazon Web Service? >> The big finding is Amazon's going to get big, but the cloud's also going to get big, and we think that Amazon, the simple finding is, we think Amazon's going to hold share. That may not sound like much, but for the most part, most of the value's going to go into SaaS, most of the value's going to go into the use cases associated with stuff. That's where a lot of the money's going to go. Amazon holding share, given that they're one of the, in many respects, they created this whole thing, is actually a pretty stunning statement. And it all started, John, because when we went and we looked at our semi-annual update to what's going on in the cloud marketplace, the question that kept coming to us was, okay, so we think it's going to go this fast. Well, what's Amazon going to do with that? What's it going to mean to Amazon? How is Amazon's growth going to affect these things? And so, we started with that answer. We built our models and talked to a lot of users, built our scenarios, so we think that Amazon's going to continue to grow very fast, we think it's going to be a $40 billion company, $40 billion-plus company >> John: In revenue. >> In revenue, AWS. >> John: Not Amazon. >> Not Amazon, Amazon's a totally different beast. We'll see what Amazon does. But AWS will be about a $40-plus billion company in four or five years, and still have about eight-plus percent market share in the entire-- >> And Microsoft has changed their game, they're coming right after Amazon. >> Microsoft, Oracle, IBM, Google, and when you start talk internationally, Ali Baba, there's going to be a dozen companies that create enormous businesses. >> And there are companies that don't have a cloud that are late to the game and might not have a seat when the music stops in the old musical chair analogy, so certainly we know who they are. >> You know, what's going to happen to the TelCos? Good question. >> The world, we live in very exciting times as the saying goes. Peter Burris, great to have you, great commentary. Love what you're doing, I think the research around IOT and the edge is a fundamental architectural shift. You've got the four forces laid out. Congratulations, looking forward to doing more where there's totally going to be a game-changer. This will impact everything that we live, and it'll make the autonomous vehicles and the drones and the AI and smart cities a reality. Thanks for the commentary. More Mobile World Congress coverage here in Palo Alto, breaking it all down. We've got a couple late night call-ins, so stay with us. Hopefully, folks will be sauced up a bit, and maybe share some of the news and breaking stories from the hallway. More from theCUBE after this short break. Thanks for watching. (upbeat electronic music)

>> Hello, Wikibon community, and welcome to our 2017 predictions for the technology industry. We're very excited to be able to do this, today. This is one of the first times that Wikibon has undertaken something like this. I've been here since about April, 2016, and it's certainly the first time that I've been part of a gathering like this, with so many members of the Wikibon community. Today I'm joined with, or joined by, Dave Vellante, who's our co-CEO. So I'm the Chief Research Officer, here, and you can see me there on the left, that you can see this is from our being on TheCube at big data, New York City, this past September, and there's Dave on the right-hand side. Dave, you want to say hi? >> Dave: Hi everybody; welcome. >> So, there's a few things that we're going to do, here. The first thing I want to note is that we've got a couple of relatively simple webinar housekeeping issues. The first thing to note is everyone is muted. There is a Q&A option. You can hit the tab and a window will pop up and you can ask questions there. So if you hear anything that requires an answer, something we haven't covered or you'd like to hear again, by all means, hit that window, ask the question, and we'll do our best to get back to you. If you're a Wikibon customer, we'll follow up with you shortly after the call to make sure you get your question answered. If, however, you want to chat with your other members of the community, or with either Dave or myself, you want to comment, then there's also a chat option. On some of the toolbars, it's listed under the More button. So if you go to the More button, and you want to chat, you can probably find that there. Finally, we're also recording the webinar, and we will turn this into a Wikibon deliverable for the overall community. So, very excited to be doing this. Now, Dave, one of the things that we note on this slide is that we have TheCube in the lower left-hand corner. Why don't you take us through a little bit about who we are and what we're doing? >> Okay, great; thanks, Peter. So I think many of you or most of you know that SiliconANGLE Media Inc is sort of the umbrella company, and underneath SiliconAngle, we have three brands: the Wikibon research brand, which was started in the 2007 time frame. It's a community of IT practitioners. TheCube is, some people call it the ESPN of tech. We'll do 100 events this year, and we extensively use TheCUBE as a data-gathering mechanism and a way to communicate to our community. We've got some big shows coming up, pretty much every week, but of course we've got Amazon Reinvent coming up, and we'll be in London with HPE Discover. And so, we cover the world and cover technology, particularly in the enterprise, and then there's the SiliconANGLE publishing team, headed up by Rob Hoaf. It was founded by my co-CEO John Ferrier, and Rob Hoaf, former Business Week, is now leading that team. So those are the three main brands. We've got a new website coming out this month, on SiliconANGLE, so really excited about that and just thank the community for all your feedback and participation, so Peter, back to you. >> Thank you, Dave, so what you're going to hear today is what the analyst team here at Wikibon has pulled together for what we regard as some of the most interesting things that we think are going to happen over the next two years. Wikibon has been known for looking at disruptive technologies, and so while the focus, from a practical standpoint, in 2017, we do go further out. What is the overarching theme? Well, the overarching theme of our research and our conversations with the community is very simple. It's: put more data to work. The industry has developed incredible tools to gather data, to do analysis on data, to have applications use data and store data. I could go on with that list. But the data tends to be quite segmented and quite siloed to a particular application, a particular group, or a particular other activity. And the goal of digital business, in very simple terms, is to find ways to turn that data into an asset, so that it can be applied to other forms of work. That data could include customer data, operational data, financial data, virtually any data that we can imagine. And the number of sources that we're going to have over the next few years are going to be astronomical. Now, what we want to do is we want to find ways so that data can be freed up, almost like energy, in a physical sense, to dramatically improve the quality of the work that a firm produces. Whether it's from an engagement standpoint, or a customer experience standpoint, or actual operations, and increasingly automation. So that's the underlying theme. And as we go through all of these predictions, that theme will come out, and we'll reinforce that message during the course of the session. So, how are we going to do this? The first thing we're going to do is we're going to have six predictions that focus in 2017. Those six predictions are going to answer crucial questions that we're getting from the community. The first one is: what's driving system architecture? Are there new use cases, new applications, new considerations that are going to influence not only how technology companies create the systems and the storage and the networking and the database, and the middleware and the applications, but also how users are going to evolve the way they think about investing? The second one is: do micro-processor options matter? Through 20 years now, we've pretty much focused on one, limited class of micro-processor, the X386, er, the X86 architecture. But will these new workloads drive opportunities or options for new micro-processors? Do we have to worry about that? Thirdly, all this data has to be stored somewhere. Are we going to continue to store it, limited only on HDDs, or are other technologies going to come into vogue? Fourthly, in the 2017 time frame, we see the cloud, a lot's happening, professional developers have flocked to it, enterprises are starting to move to it in a big way, what does it mean to code in the cloud? What kinds of challenges are we going to face? Are they technological? Are they organizational, institutional? Are they sourcing? Related to that, obviously, is Amazon's had enormous momentum over the past few years. Do we expect that to continue? Is everybody else going to be continuing to play catch-up? And the last question for 2017 that we think is going to be very important is this notion of big data complexity. Big data has promised big things, and quite frankly has, except in some limited cases, been a little bit underwhelming. As some would argue, this last election showed. Now, we're going to move, after those six predictions, to 2022, where we'll have three predictions that we're going to focus on. One is: what is the new IT mandate? Is there a new IT mandate? Is it going to be the same old, same old, or is IT going to be asked to do new things? Secondly, when we think about Internet of Things, and we think about Augmented Reality or virtual reality, or some of these other new ways of engaging people, is that going to draw out new classes of applications? And then finally, after years of investing heavily in mobile applications, in mobile websites, and any number of other things, and thinking that there was this tight linkage where mobile equaled digital engagement, we're starting to see that maybe that's breaking, and we have to ask the question: is that all there is to digital engagement, or is there something else on the horizon that we're going to have to do? The last prediction, in 2027, we're going to take a stab here and say: will we all work for AI? So, these are the questions that we hear frequently from our clients, from our community. These are the predictions we're going to attend to and address. If you have others, let us know. If there's other things that you want us to focus on, let us know, but here's where we're starting. Alright. So let's start with 2017. What's driving system architecture? Our prediction for 2017 regarding this is very simple. The IoT edge use cases begin shaping decisions in system and application architecture. Now, the right-hand side, if you look at that chart, you can see a very, very important result of the piece of research that David Foyer recently did. And it shows IoT edge options, three-year costs. From left to right, moving all the data into the cloud over a normal data communications, telecommunications circuit, in the middle, moving that data into a central location, namely using cellular network technologies, which have different performance and security attributes, and then finally, keeping 95 percent of the data at the edge, processing it locally. We can see that the costs are overwhelming, favoring being smarter by how we design these applications and keeping more of that data local. And in fact, we think that so long as data and communications costs remain what they are, that there's going to be an irrevokeable pressure to alter key application architectures and ways of thinking to keep more of that crossing at the edge. The first point to note, here, is it means that data doesn't tend to move to the center as much as many are predicting, but rather, the cloud moves to the edge. The reason for that is that data movement isn't free. That means we're going to have even more distributed, highly autonomous apps, so none of those have to be managed in ways that sustain the firm's behavior in a branded, consistent way. And very importantly, because these apps are going to be distributed and autonomous, close to the data, it ultimately means that there's going to be a lot of operational technology players that impact the key decisions, here, that we're going to see made as we think about the new technologies that are going to be built by vendors and in the application architectures that are going to be deployed by users. >> So, Peter, let me just add to that. I think the key takeaway there is, as you mentioned, and I just don't want it to get lost, is 95 percent of the data, we're predicting, will stay at the edge. That's a much larger figure than I've seen from other firms or other commentary, and that's substantial, that's significant, it says it's not going to move. It's probably going to sit on flash, and the analytics will be done at the edge, as opposed to this sort of first bar, being cloud only. That 95 percent figure has been debated. It's somewhat controversial, but that's where we are today. Just wanted to point that out. >> Yeah, that's a great point, Dave. And the one thing to note, here, that's very important, is that this is partly driven by the cost of telecommunications or data communications, but there also are physical realities that have to be addressed. So, physics, the round trip times because of the speed of light, the need for greater autonomy and automation on the edge, OT and the decisions and the characteristics there, all of these will contribute strongly to this notion of the edge is increasingly going to drive application architectures and new technologies. So what's going to power those technologies? What's going to be behind those technologies? Let's start by looking at the CPUs. Do micro-processor options matter? Well, our prediction is that evolution in workloads, the edge, big data, which we would just, for now, put AI and machine learning, and cognitive underneath many of those big data things, almost as application forms, creates an opening for new micro-processor technologies, which are going to start grabbing market share from x86 servers in the next few years. Two to three percent next year, in 2017. And we can see a scenario where that number grows to double digits in the next three or four years, easily. Now, these micro-processors are going to come from multiple sources, but the factors driving this are, first off, the unbelievable explosion in devices served. That it's just going to require more processing power all over the place, and the processing power has to become much more cost-effective and much more tuned specifically to serving those types of devices. Data volumes and data complexity is another reason. Consumer economics is clearly driving a lot of these factors, has been for years, and it's going to continue to do so. But we will see new, ARM-based processors and other, and GPUs for big data apps, which have the advantage of being also supported in many of the consumer applications out there, driving this new trend. Now, the other two factors. Moore's Law is not out of room. We don't want to suggest that, but it's not the factor that it used to be. We can't presume that we're going to get double the performance out of a single class of technology every year or so, and that's going to remove any and all other types of micro-processor sets. So there's just not as much headroom. There's going to be an opportunity now to drive at these new workloads with more specialized technology. And the final one is: the legacy software issue's never going to go away; it's a big issue, it's going to remain a big issue. But, these new workloads are going to create so much new value in digital business settings, we believe, that it will moderate the degree to which legacy software keeps a hold on the server marketplace. So, we expect a lot of ARM-based servers that are lower cost, tuned and specialized, supporting different types of apps. A lot of significant opportunity for GPUs for big data apps, which do a great job running those kinds of graph-based data models. And a lot of room, still, for RISC in pre-packaged HCI solutions. Which we call: single managed entities. Others call: appliances. So we see a lot of room for new micro-processors in the marketplace over the next few years. >> I guess I'll add to that, and I'll be brief, just in the interest of time, the industry has marched to the cadence of Moore's Law for, as we know, many, many decades, and that's been the fundamental source of innovation. We see the innovation curve shifting and changing to become combinatorial, a combination of technologies. Peter mentioned GPU, certainly visualization's in there. AI, machine learning, deep learning, graph databases, combining to be the fundamental driver of innovation, going forward, so the answer here is: yes, they matter. Workloads are obviously the key. >> Great, Dave. So let's go to the next one. We talked about CPUs, well now, let's talk about HDDs. And more broadly, storage. So the prediction is that anything in a data center that physically moves gets less useful and loses share of wallet. Now, clearly that includes tape, but now it's starting to include HDDs. In our overall enterprise systems, storage systems revenue forecast, which is going to be published very, very shortly, we've identified that we think that the revenue attributable to HDD-based enterprise storage systems is going to drop over the next few years, while flash-based enterprise storage system revenue rises dramatically. Now, we're talking about storage system revenue here, Dave. We're not just talking about the HDDs, themselves. The HDD market starts, continues to grow, perhaps not as fast, partly because, even as the performance side of the HDD market starts to fade a bit, replaced by flash, that bulk, volume part of the HDD marketplace starts to substitute for tape. So, why is this happening? One of the main reasons it's happening is because the storage revenue, the storage systems revenue is very strongly influenced by software. And those software revenues are being bundled into the flash-based systems. Now, there's a couple reasons for this. First off, as we've predicted for quite some time, we do see a flash-only data center option on the horizon. It's coming well into focus. Number two is that, the good news is flash-based products are starting to come down and also are in sight of HDD-based products at the performance level. But very importantly, and here's one of the key notions of the value of data, and finding new ways to increase the use of data: flash, our research shows, offers superior business value, too, precisely because you can make so many copies of it and have a single set of data serve so many different applications and so many users, at scales that just aren't possible with traditional, HDD-based enterprise storage systems. Now, this applies to labor, too, Dave, doesn't it? >> Yeah, so a couple of points here. Yes, labor being one of those, sort of, areas that Peter's talking about are, ah, in jeopardy. We see about $200 billion over the next 10 years shifting from what we often refer to as non-differentiated IT labor, in provisioning and networking configuration and laying cable, et cetera, shifting from where it is today in services and/or on-prem IT labor, to vendor R&D or the cloud. So that's a very important point. I think I just wanted to add some color to what you were talking about before when you talked about HDD revenue continuing to grow, I think you were talking about, specifically, in the enterprise, in this storage systems view. And the other thing I want to add is, Peter, referenced sort of the business value of flash, as you, many of you know, David Floyer and Wikibon predicted, very early on, the impact that flash would have on spinning disk, and not only because of cost related to compression and de-duplication, but also this notion that Peter's talking about, of data sharing. The ability of development organizations to use the same data and minimize the number of copies. Now, the thing to watch, here, and kind of the wildcard is the hyperscale model. Hyperscalers, as we know, are consuming many, many, you know, exabytes and petabytes of data. They do things differently than is done in the enterprise, so that's something that we're watching very closely in terms of that model, that model being the hyperscale model, how it mimics or how it doesn't mimic what traditionally has occurred in the enterprise and how that will affect adoption of both flash and spinning disk. But as Peter said, we'll be releasing this data very shortly, and you'll be able to dig into it with is. >> And very importantly, Dave, in response to one of the comments in the chat, we're not talking about duplication of data everywhere, we're talking about the ability to provide logical and effective copies to single-data sources, so that, just because you can just drive a lot more throughput. So, that's the HDD. Now, let's turn to some of this notion of coding the cloud. What are we going to do with code in the cloud? Well our prediction is that the new cloud development stack, which is centered on containers and APIs, matures rapidly, but institutional habits in development constrain change. Now, why do we say that? I want to draw your attention to the graphic on the right-hand side. Now, this is what we think the mature, or the maturing cloud development stack looks like. As you can see, it's a lot of notions of containers, a lot of notions of other types of technologies. We'll see APIs interspersed throughout here as a primary way of getting to some of these container-based applications, services, microservices, et cetera, but this same, exact chart could be mapped back to SOA from 10 years ago, and even from some of the distributed computing environments that were put forward 20 years ago. The challenge here is that a sizable percentage, and we're estimating about 80 percent of in-house development, is still set up to work the old way. And so long as development organizations are structured to build monolithic apps or take care of monolithic apps, they will tend to create monolithic apps, with whatever technology's available to them. So, while we see these stacks becoming more vogue and more in use, we may not see, in 2017, shops being able to take full advantage of them. Precisely because the institutional work forms are going to change more slowly. Now, big data will partly contravene these habits. Why? Because big data is going to require quite different development approaches, because of the complexity associated of analytic pipelines, building analytic pipelines, managing data, figuring out how to move things from here to there, et cetera; there's some very, very complex data movement that takes place within big data applications. And some of these new application services, like Cognitive, et cetera, will require some new ways of thinking about how to do development. So, there will be a contravening force here, which we'll get to, shortly, but the last one is: ultimately, we think time-to-value metrics are going to be key. As KPI's move from project cost and taking care of the money, et cetera, and move more towards speed, as Agile starts to assert itself, as organizations start to, not only, build part of the development organization around Agile, but also Agile starts bleeding into other management locations, like even finance, then we'll start to see these new technologies really start asserting themselves and having a big impact. >> So, I would add to that, this notion of the iron triangle being these embedded processes, which as we all know, people, processes, and technology, people and process are the hardest to change, I'm interested, Peter, in your thoughts on, you hear a lot about Waterfall versus Agile; how will organizations, sort of, how will that affect organizations, in terms of their ability to adopt some of these, you know, new methodologies like Agile and Scrum? >> Well, the thing we're saying is the technology's going to happen fast, the Agile processes are being well-adopted, and are being used, certainly, in development, but I have had lots of conversations with CIOs, for example, over the last year and a half, two years ago, where they observed that they're having a very difficult time with reconciling the impedance mismatch between Agile development and non-Agile budgeting. And so, a lot of that still has to be worked out, and it's going to be tied back to how we think about the value of data, to be sure, but ultimately, again, it comes back to this notion of people, Dave, if the organization is not set up to fully take advantage of these new classes of technologies, if they're set up to deliver and maintain more monolithic applications, then that's what's going to tend to get built, and that's what's going to get, and that's what the organization is going to tend to have, and that's going to undermine some of the new value propositions that these technologies put forward. Well, what about the cloud? What kind of momentum does Amazon have? And our prediction for 2017 is that Amazon's going to have yet another banner year, but customers are going to start demanding a simplicity reset. Now, TheCUBE is going to be at Amazon Reinvent with John Ferrier and Steve Minnamon are going to be up there, I believe, Dave, and we're very excited. There's a lot of buzz happening about Reinvent. So follow us up there, through TheCUBE at Reinvent. But what I've done on the right-hand side is sent you a piece of Wikibon research. What we did is we wrote up, and we did an analysis of all of the AWS cases put forward, on their website, about how people are using AWS, and there's well over 650, or at least there were when we looked at it, and we looked at about two-thirds of them, and here's what we came up with. Somewhere in the vicinity of 80 percent, or so, of those cases are tied back to firms that we might regard as professional software delivery organizations. Whether they're stash or business services or games, provided games, or social networks. There's a smaller piece of the pie that's dedicated to traditional enterprise-type class of customers. But that's a growing and important piece, and we're not diminishing it at all, but the broad array of this pie chart, folks are relatively able to hire the people and apply the skills and devote the time necessary to learn some of the more complex, 75-plus Amazon services that are now available. The other part of the marketplace, the part that's moving into Amazon, the promise of Amazon is that it's simple, it's straightforward, and it is. Certainly more so than other options, but we anticipate that there will have to be a new type of, and Amazon's going to have to work even harder to simplify it, as it tries to attract more of that enterprise crowd. It's true that the flexibility of Amazon is certainly spawning complexity. We expect to see new tools, in fact, there are new tools on the market from companies like Appfield, for example, for handling and managing AWS billing and services, and that is, our CIOs are telling us, they're actually very helpful and very powerful in helping to manage those relationships, but the big issue here is that other folks, like VM Ware, have done research to suggest that the average shop has two to three big cloud relationships. That makes a lot of sense to us. As we start adding hybrid cloud into this and the complexities of inter-cloud communication and inter-cloud orchestration starts to become very real, that's going to even add more complexity, overall. >> So I'd add to that, just in terms of Amazon momentum, obviously those of you who follow what I read, you know, have been covering this for quite some time, but to me, the marginal economics of Amazon's model continue to be increasingly attractive. You can see it in the operating profits. Amazon's gap, operating profits, are in the mid-20s. 25, 26 percent. Just to give you a sense, EMC, who's an incredibly profitable company, its gap operating profits are in the teens. Amazon's non-gap operating profits are into 30 percent, so it's an incredibly profitable company. The more it grows, the more profitable it gets. Having said that, I think we agree with what Peter's saying in terms of complexity; think about API creep in Amazon. And different proprietary APIs for each of the data services, whether it's Kinesis or EC2 or S3 or Dynamo DB or EMR, et cetera, so the data complexity and the complexity of the data pipeline is growing, and I think that opens the door for the on-prem folks to at least mimic the public cloud experience to a great degree; as great a degree as possible. And you're seeing people, certainly, companies do that in their marketing, and starting to do that in the solutions that they're delivering. So by no means are we saying Amazon takes over the world, despite, you know, the momentum. There's a window open for those that can mimic, to the large extent, the public cloud capabilities. >> Yeah, very important point there. And as we said earlier, we do expect to see the cloud move closer to the edge, and that includes on-prem, in a managed way, as opposed to presuming that everything ends up in the cloud. Physics has something to say about that, as do some of the costs of data movement. Alright, so we've got one more 2017 prediction, and you can probably guess what it is. We've spent a lot of years and have a pretty significant place in spin big data, and we've been pretty aggressive about publishing what we think is going to happen in big data, or what is happening in big data, over the last year or so. One of the reasons why we think Amazon's momentum is going to increase is precisely because we think it's going to become a bigger target for big data. Why? Because big data complexity is a serious concern in many organizations today. Now, it's a serious concern because the spoke nature of the tools that are out there, many of which are individually extremely good, means that shops are spending an enormous amount of time just managing the underlying technology, and not as much time as they need to learning about how to solve big data problems, doing a great job of piloting applications, demonstrating to the business the financial returns are there. So as a result of this bespoked big data tool aggregates, we get multi-source, and we need to cobble it together from a lot of different technology sources, a lot of uncoordinated software and hardware updates that dramatically drive up the cost of on-prem administration. A lot of conflicting commitments, both from the business as well as from the suppliers, and very, very complex contracts. And as a result of that, we think that that's been one of the primary reasons why there's been so many pilot failures and why big data has not taken off the way that it probably should have. We think, however, that in 2017, we're going to see, and here's our prediction, we're going to see failure rates for big data pilots drop by 50 percent, as big vendors, IBM, Microsoft, AWS, and Google, amongst the chief ones, and we'll see if Oracle gets into that list, bring pre-packaged, problem-based analytic pipelines to market. And that's what we mean by this concept, here, of big data, single-managed entities. The idea that we can pull together, a company can pull together, or that it can pull together all the various elements necessary to provide the underlying infrastructure so that a shop can focus more time making sure that they understand the use-case, they understand how to go get the data necessary to serve that use-case, and understand how to pilot and deploy the application, because the underlying hardware and system software is pre-packaged and used. Now, we think that these, the SMEs, that are going to be most successful will be ones that are not predicated only on more proprietary software, but utilize a lot of open-source software. The ones that we see that are most successful today are in fact combining the pre-packaging of technology with the availability, or access, to the enormous value that the open-source market continues to build as it constructs new tools and delivers them out to big data applications. Ultimately, you've seen this before, or you've heard this before, from us: time-to-value becomes the focus. Similar to development, and we think that's one of the convergences that we have, here. We think that big data apps, or app patterns, will start to solidify. George Gilbert's done some leading-edge research on what some of those application patterns are going to be, and how those application patterns are going to drive analytic pipeline decisions, and very important, the process of building out the business capabilities necessary to build out the repeatable big data services to the business. Now, very importantly, some of these app patterns are going to be, are going to look like machine learning, cognitive AI, in many respects, all of these are part of this use-case to app trend that we see. So, we think that big data's kind of an umbrella for all of those different technology classes. It's going to be a lot of marketing done that tries to differentiate machine learning, cognitive AI. Technically, there are some differences, but from our perspective, they're all part of the effort of trying to ensure that we can pull together the technology in a more simple way so that it can be applied to complex business problems more easily. One more point I'll note, Dave, is that, and you adjust that world a lot, so I'd love to get your comments on this, but one of the more successful single-managed entities out there is, in fact, Watson from IBM, and it's actually a set of services and not just a device that you buy. >> Yeah, so a couple comments, there. One is that you can see the complexity in the market data, and we've been covering big data markets for a long time now, and there were two things that stood out when we started covering this. One is that software, as a percentage of the total revenue, is much lower than you would expect, in most markets. And that's because of the open-source contribution and the, you know, the multi-year collapse that we've seen in infrastructure software pricing. Largely due to open-source and cloud. The other piece of that is professional services, which have dominated spending within big data, because of the complexity. I think you're right, when you look at what happened at World of Watson and, you know, what IBM's trying to do, and others, in your prediction, there, are putting together a full, end-to-end data pipeline to do, you know, ingest and data wrangling and collaboration between data scientists, data engineers, and application developers and data quality people, and then bringing in the analytics piece. And essentially, you know, what many companies have done, and IBM included, they've cobbled together sets of tools and they've sort of layered on a way to interact with those tools, so the integration has still been slow in coming, but that's where the market is headed, so that we actually can build commercial, off the shelf applications. There's been a lack of those applications. I remember, probably four years ago, Mike Olsen at a (unintelligible) predicted: this will be the year of the big data app. And it still has not happened, so, and until it does, that complexity is going to reign. >> Yeah, and so it, again, as we said earlier, we anticipate that the big data, the need for developers to become more a part of the big data ecosystem, and the need for developers to get more value out of some of the other new cloud stacks are going to come together and will reinforce each other over the course of the next 24 to 36 months. So those were our 2017 predictions. Now let's take a look at our 2022 predictions, and we've got three. The first one is we do think a new IT mandate's on the horizon. Consistent with all these trends we've talked about, the idea of new ways of thinking about infrastructure and application architecture, based on the realities of the edge, new ways of thinking about how application developers need to participate in the value equation activities of big data, new ways of organizing to try to take greater advantage of the new processes, new technologies for development. We think, very strongly, that IT organizations will organize work to generate greater value from data assets by engineering proximity of applications and data. What do we mean by that? Well, proximity can mean physical proximity, but it also is something that we mean in terms of governance, tool similarity, infrastructure commonality, we think that over the next four to five years, we'll see a lot of effort to try to increase the proximity of not only data assets from a data standpoint, or the raw data, but also understanding from an infrastructure, governance skillset, et cetera, standpoint. So that we can actually do a better job of, again, generating more work out of our data by finding new and interesting ways of weaving together systems of records, big analytics, IOT, and a lot of other new application forms we see on the horizon, including one that I'll talk about in a second. Data value becomes a hot topic. We're going to have to do a better job, as a community, of talking about how data is valuable. How it creates (unintelligible) in the business, how it has to be applied, or has to be thought of as a source of value, in building out those systems. We talked earlier about the notion of people, process, and technology, well, we have to add to that: data. Data needs to be an asset that gets consumed as we think about how business changes. So data value's going to become a hot topic, and it's something we're focused on, as to what it means. We think, as Dave mentioned earlier, it's going to catalyze a true private cloud solutions for legacy applications. Now, I know Dave, you're going to want to talk about, in a second, what this might need. For example, things like the Amazon, VM Ware recent announcement. But it also means that strategic sourcing becomes reality. The idea of just going after the cheapest solution, or cost-optimized solution, which, don't get me wrong, don't get us wrong, is not going to go away, but it means that increasingly we're going to focus on new sourcing arrangements that facilitate creating greater proximity for those crucial aspects that make our shop run. >> Okay, so a couple of thoughts there, Peter. You know, there's a lot of talk, a couple years ago, and it's slowly beginning to happen, of bringing transaction and analytic systems together. What that oftentimes means is somebody takes their mainframe for the transactions and sticks it in finneban pipe into an exodata. I don't think that's what everybody envisioned when you started to sort of discuss that mean. So that's sort of happening slowly. But it's something that we're watching. This notion of data value, and shifting from, really a process economy to a data, or an insight, economy is something that's also occurring. You're seeing the emergence of the chief data officer. And our research shows that there are five things a chief data officer must do to really get started. The first is to understand data value, and how data contributes to the monetization of their company. So not monetizing the data, per se, and I think that's a mistake that a lot of people made, early on, is trying to figure out how to sell their data, but it's really to understand how data contributes to value for your organization. The second piece is how to access that data, who gets access to that data, and what data sources you have. And the third is the quality and trust of that data. And those are sequential things that our research shows a chief data officer has to do. And then the other, sort of parallel items, are relationship with the line of business and re-skilling. And those are complicated issues for most organizations to undertake, and something that's going to take, you know, many, many years to play out. The vast majorities of customers that we talk to say their data-driven, but aren't necessarily data-driven. >> Right, so, the one other thing I wanted to mention, Dave, is that we did some research, for example, on the VM Ware, Amazon relationship, and the reason why we were positive on it is quite simple. That it provides a path for VM Ware's customers, with their legacy applications running under VM Ware, to move those applications and the data associated with those applications, if they choose to, closer to some of the new, big data applications that are showing up in Amazon. So there's an example of this notion of making it more proximate, making applications and data more proximate, based on physics, based on governance, based on overall tooling and skilling, and we anticipate that that's going to become a new design center for a lot of shops over the course of the next few years. Now, coming to this notion of a new design center, the next thing we want to note is that, IoT, the Internet of Things, plus augmented reality, is going to have an impact on the marketplace. We got very excited about IoT, simply by thinking about the things, but our perspective is, increasingly, we have to recognize that people are going to always be a major feature, and perhaps the greatest value-creating feature, of systems. And augmented reality is going to emerge as a crucial actuator for the Internet of Things, and people. And that's kind of what we mean, is that augmented reality becomes an actuator for people. As will Chat Box and other types of technologies. Now, an actuator in an IoT sense is the devices or set of capabilities that take the results of models and actually turn that into a real-world behavior. So, if we think about this virtuous cycle that we have on the right-hand side, the internet, these are the three capabilities that we think people or firms are going to have to build out. They're going to have to build out an Internet of Things and People that are capable of capturing data, and turning analogue data into digital data, so that it can be moved into these big data applications. Again, with machine learning and AI and cognitive, sort of being part of that or underneath that umbrella, so that, then, we can build more models, more insights, more software that then translates into what we're calling systems of enaction. Or systems of "enaction", not "inaction". Systems of enaction. Businesses still serve customers, and these systems of enaction are going to generate real-world outcomes from these models and insights, and these real-world outcomes will certainly be associated with things, but they will also be associated with human being and people. And as a consequence of this, this we think is so powerful and is going to be so important over the course of the next five years that we anticipate that we will see a new set of disciplines focused on social discovery. Historically, in this industry, we've been very focused on turning insights or discovery about physics into hardware. Well, over the next few years, and Dave mentioned moving from the process to some new economy, we're going to see an enormous investment in understanding the social dynamics of how people work together and turn that into software. Not just how accountants do things, but how customers and enterprises come together to make markets happen, and through that social discovery, create these systems of enaction so that businesses can successfully, can successfully attend to and deliver the promises and the, ah, and the predictions that they're making through their other parts of their big data applications. >> So, Peter, you've pointed out many times that the big change, relative to processes, and historically, in the IT business, we've known what the processes are. The technology was sort of unknown, and mysterious. That's flipped. It's now, really the process is the unknown piece. That's the mysterious part. The technology is pretty well-understood. I think, as it relates to what you're talking about here with IoT and AR, what people tell us, the practitioners that are struggling with this, first of all, there's so much analogue data that people are trying to digitize, the other piece is there's a limited budget that folks have, and they're trying to figure out, alright, do I spend it on getting more data, and will that improve my data, increase my observation space? Or do I spend it on better models, and improving my models and iterating? And that's a trade-off that people have to make, and of course the answer is "both", but how those funds are allocated is something that organizations are really trying to better understand. There's a lot of trial and error going on. Because obviously, more data, in theory anyway, means you can make better decisions. But it's that iteration of that model, that trial and error and constant improvement, and both of those take significant resources. And budgets are still tight. >> Very true, Dave, and in fact, George Gilbert's research with the community is starting to demonstrate that more of the value's going to come from the models, as opposed to the raw data. We need the raw data to get to the models, but more of the value's going to come from the models. So that's where we think more people are going to focus their time and attention. Because the value will be in the insights and the models. But to go back to your point: where do you put your money? Well, you got to run these pilots, you got to keep up with your competitors, you got to serve customers better, so you're going to have to build all these models, sometimes in a bespoked way. But George is publishing an enormous amount of research right now that's very valuable to a lot of our community members that really shows how that pipeline, how those analytic pipelines or the capabilities associated with those analytic pipelines are starting to become better understood. So that we can actually start getting experience and generating efficiencies or generating a scale out of those analytic pipelines. And that's going to be a major feature underlying this basic trend. Now, this notion of people is really crucial, because as we think about the move to the Internet of Things and People, we have to ask ourselves: has digital engagement really, fully considered what it means to engage people throughout their customer journey? And the answer is: no, it hasn't. We believe that by 2022, IT will be given greater responsibility for management of demand chains. Working to unify customer journey designs and operations across all engagement functions. And by engagement functions, we mean marketing, sales, we mean product, we mean service, we mean fulfillment. That doesn't mean that they all report to IT. Don't mean that, at all. But it means that IT is going to have to, again, find ways to apply data from all these different sources so that it can, in fact, simplify and unify and bring together consistent design and operations so that all these functions can be successful and support reorganization if necessary, because the underlying systems provide that degree of unity and focus on customer success. Now, this is in strong opposition to the prediction made a few years ago, that marketing was going to emerge as the be-all and end-all, that's going to spend more than IT. That was silly, it hasn't happened, and you'd have to redefine marketing very aggressively to see that actually happening. But, when we think about this notion of putting more data to work, the first thing we note, and this is what all the digital natives have shown us, the data can transform a product into a service. That is the basis for a lot of the new business models we're talking about, a lot of these digital native business models and successes that they've had, and we think it's going to be a primary feature of the IT mandate to help business understand how data, more data can be put to work, transforming products into services. It also means, at a tactical level, that mobile applications have been way too focused on solving the seller's problems. We want to caution folks, don't presume that because your mobile application has gotten lost in some online store somewhere that that means that digital engagement's a failure. No, it means that you have to focus digital engagement on providing value throughout the customer journey, and not just from the problem to the solution, where the transaction for money takes place. Too much mobile applications, or too many mobile applications have been focused, in a limited way, on the marketers' problem within the business, of trying to generate, trying to generate awareness and demand. And it has to be, mobile has to be applied in a coherent and comprehensive way, across the entire journey. And ultimately, I hate to say this, but we think collaboration's going to make a comeback. But collaboration to serve customers. So the business can collaborate better inside, but in support of serving the customers. Major, major feature of what we think is going to happen over the course of the next couple years. >> I think the key point there is we all, there's many mobile apps that we love, and utilize, but there are a lot that are not so great. And the point that we've made to the community, quite often, is that it used to be that the brands had all the power, they had all the information, there was an asymmetry of information, the customer, the consumer didn't really know much about pricing. The web, obviously, has leveled that playing field and what many brands are trying to do is recreate that asymmetry and maybe got over their skis a little bit, before providing value to the customers. And I think your point is that, to the extent that you can provide value to that customer, that information advantage will come back to you. But you can't start with that information advantage. >> Great point, Dave. But it also means that we need to, that IT needs to look at the entire journey and see transactions and the discover, evaluate, buy, apply, use and fix throughout this entire journey and find ways of designing systems that provide value to customers at all times and in all places. So the demand chain notion, which historically has been focused on trying to optimize the value that the buyer gets in the buy process, at a cost-effective way, that notion of demand chain has to be applied to the entire engagement lifecycle. Alright, so that's 2022. Let's take a crack at our big prediction for 2027. And it's at, ah, it's on a lot of people's minds. Will we all work for AI? There've been a lot of studies done, over the course of the past year, year and a half, that have been kind of suggested that 47 percent of jobs are going to go away, for example. And that's not, that's not the only high end. Actually, folks have suggested much more, over the next 10, 15 years. Now, if you take a look at the right-hand side, you see a robot thinker. Now, you may not know this, but when The Thinker was actually first, when Rodan first constructed The Thinker, what he was envisioning was actually someone looking down into the seven levels of Hell as described by Dante. And I think that a lot of people would agree that the notion of no work is a Hell for a lot of people. We don't think that that's going to happen in the same way that most folks do. We believe that AI technology advances will far outpace the social advances. Some tasks will be totally replaced, but most jobs will only be partially replaced. We have to draw a clear distinction between the idea that a job performs only this or that task, as opposed to a job or an individual, an employee, as part of a complex community that ensures that a business is capable of serving customers. It doesn't mean we're not going to see more automation, but automation is going to focus mostly on replacing tasks. And to the degree that that task sets a particular job is replaced, then those jobs will be replaced. But ultimately, there's going to be a lot of social friction that gates how fast this happens. One of the key reasons for the social friction is something in behavioral economics that's known as loss avoidance. People are more afraid of losing something than they are of gaining something. And, whether it's a union or whether it's regulations or any number of other factors, that's going to gate the rate at which this notion that AI crushes employment occurs. AI will tend to compliment, not substitute for labor. And that's been a feature of technology for years. It doesn't, again, mean that some tasks and some task sets, sort of those in line with jobs, aren't replaced; there will be people put out of work as a consequence of this. But for the most part, we will see AI tend to compliment, not fully substitute for most jobs. Now this creates, also, a new design consideration. Historically, as technologists, we've looked at what can be done with technology, and we've asked: can we do it? And if the answer is "yes", we tend to go off and do it. And now, we need to start asking ourselves: should we do it? And this is not just a moral imperative. This has other implications, as well. So, for example, the remarkably bad impact that a lot of automated call centers have had on customer service from a customer experience standpoint. This has to become one of the features of how we think about bringing together, in these systems of enaction, all the different functions that are responsible for serving a customer. Asking ourselves: well, we can do it, from a technical standpoint, but should we do it from a customer experience, from a community relations, and even from a, ah, from a cultural imperative standpoint, as we move forward? >> Okay, I'll be brief, because we're wrapping up here, but first of all, machines have always replaced humans. When, largely with physical tasks, now we're seeing that occur with cognitive tasks. People are concerned, as Peter said. The middle class is obviously under fire. The median income in the United States has dropped from $55,000 in 1999 to just above $50,000 today. So, something's going on, and clearly you can look around and see whether it's an an airport with kiosks or billboards, electronic machines and cognitive functions are replacing human functions. Having said that, we're sanguine, because the, the story I'll tell is that the greatest chess player in the world is not a machine. When Deep Blue beat Gary Kasparov, what Gary Kasparov did is he started a competition to collaborate with other, you know, human chess players with machines, to beat the machine, and they succeeded at that, so this, again, I come back to this combination of technologies. Combinatorial technologies are really what's going to drive the innovation curve over the next, we think, 20 to 50 years. So, it's something that is far out there, in terms of our predictions, but it's also something that is relevant to the society, and obviously the technology industry. So thank you, everybody. >> So, we have one more slide, and it's Conclusions Slide, so let me hit these really quick, and before I do so, let me note that George, our big data analyst is George Gilbert. George Gilbert: G-I-L-B-E-R-T. Alright, so, very quickly, tech architecture question, we think edge IoT is going to have a major effect in how we think about architecture of the future. Micro-processor options? Yup, new micro-processor options are going to have an impact in the marketplace. Whither HDDs? For the performance side of storage, flash is coming on strong. Code in the cloud? Yes, the technologies are great, but development has to change its habits. Amazon momentum? Absolutely going to continue. Big data complexity? It's bad and we have to find ways to make it simpler so that we can focus more on the outcomes and the results, as opposed to the infrastructure and the tooling. 2022, new IT mandate? Drive the value of that data. Get more value out of your data. The Internet of Things and People is going to become the proper way of thinking about how these new systems of enaction work, and we anticipate that demand chain management is going to be crucial to extending the idea of digital engagement. Will we all work for AI? Dave just mentioned, as we said, there's going to be dislocation, there's going to be tasks that are replaced, but not by 2027. Alright, so thank you very much for your time, today. Here is how you can contact Dave and myself. We will be publishing this, the slides and this broadcast. Wikibon's going to deliver three coordinated predictions talks over the course of the next two days, so look for that. Go up to SiliconANGLE, we're up there a fair amount. Follow us on Twitter, and we want to thank you very much for staying with us during the course of this session. Have a great day.