(electronic music) (background party chatter) >> All right. Hello, everyone. Welcome to The Cube. Coming up, Bryan Talebi will be here with Ahura A.I? >> Ahura A.I. >> Ahura A.I. Bryan Talebi here with Ahura A.I. We are at The Cube post party networking event, special on the ground, extended coverage. Bryan, we were at The Futurist, not The Futurist Conference, The Future of Blockchain which was the Monaco Crypto Summit over at the Grimaldi Center. Now we're at the VIP gala, the prince is here, a lot of action's happening. You had a chance to look all the presentations we have all the heavy hitters here, kind of a movement going on, right? >> Absolutely. Well, first of all, I think it's absolutely amazing that Prince Albert II put this all together. He obviously understands the future and understands technology. It's absolutely brilliance. And Julio as well, I mean is incredible. So I take off my hat to all the people that put this event together and the speakers were brilliant. I mean, did you see all the speakers the technologies that they've built have the potential to radically transform billions of people's lives. >> It's interesting, you know, I've been covering crypto for a very long time and watched it emerge and then start exploding. And there's always been, and I saw this with the web too early on, legit versus not legit. And all early markets have the hype cycles go down and up, and you always kind of have that but now you're starting to see legitimate tie-in between physical digital assets where, and the confluence of the business value, societal value, government value, all across the spectrum. Every vertical, every use case is got a decentralized vibe going on right now because it's a forcing function. And, and here in Monaco, the price and the king they're leaning into it cause I think they see the future because they could answer their legacy. >> Yeah. Absolutely. And look, you're absolutely right about this because this downturn that we're facing, especially this new crypto winter, I think is the best thing that could possibly have happened to the crypto space because what it's doing is pushing out the let's call them the less than honest brokers within the crypto community, the people that were just in it for a buck, the pump and dumpers and so forth it's really pushing those folks out. And the companies that remain are the true technologists that aren't looking at crypto as just a speculative asset, but rather an underlying technology that can transform the way that we engage with the world in a decentralized way. >> Bryan, you know, we didn't mention in the intro but you also do investment. >> I do. >> You also have a lot of things going on. You got a great history, great pedigree of seeing the waves of innovation the best. That's something, an investment question, like are you in it for the money or are you in it for the make it happen mission? That becomes kind of like the probing question. Someone comes to the table, "Hey, I need some cash. We do funding." What's your exit strategy? "I want to make an exit in two years." Okay. You're out. (Bryan laughs) (John) But it's almost that easy now, right? >> Sure. >> (John) To figure out who's in it for the money. >> Sure. >> (John) Who's in it for the mission. Yeah, the mission's successful. You make a lot of money. >> That's exactly right. Look, one of my mentors once taught me is, money like power is only amassed in great amount if indirectly sought because money by itself is not intrinsically a motivator. And so, what we do at our AB+ Ventures, my venture capital fund, is we only invest, not only in companies that are impact driven and have the capacity to impact a billion people, but we invest in founders that are climbing their third or fourth mountain. So these are people who've already made their money. They either had a couple big exits at over a hundred million dollars or they became rock stars or they became astronauts. They did things where they achieved the highest levels of achievement. And now are building technologies because they believe that they're going to impact the world in a meaningful way. >> They kind of know it's important, right? They made some money, they've been successful. They have scar tissue and experience to apply almost I want to say for the legacy of it, but more for value. >> Yeah. >> For everybody. >> Absolutely. >> All right. So I got to ask about what your current venture, I know you got some good action going on. It's growing pretty good. As they say in golf, it's middle of the fairway. It's growing, got momentum. It's a turbine market. You probably has some offers on the table. I mean, I could imagine all the AI you got going on. Blockchain, very attracted. It's a hard problem, but it's the first inning. Not even. >> Yeah. >> What going on with the company? >> We're very early. Look, we've been building our technologies, the deep tech platform we've been building for four and a half years. There's a whole bunch of offers on the table to buy us. But look, the reality is right now is a fantastic hiring opportunity. There's a lot of amazing talent out there that now wants to come to us, which is great. Number one, number two, if you look back to the 2000 Dot-com bubble, what you saw is all of the companies that didn't really solve real problems went away and it left a more oxygen in the room for the companies that were really solving problems that needed to be solved. And those are now all trillion dollar companies. So, >> Well, Brian, you and I both got a little gray hair. So let's talk about the Dot-com bubble. The other thing, I'll add to that, by the way great commentary, is that everything that was like bullshit actually happened. People bought pet food online, >> Right. >> Groceries delivered to their house. So to your point, the things actually happen. See the visions and the aspirations were correct, timing and capital markets spree. >> Sure. >> Is there similarities going on in crypto? Is it the crypto winter, weeding out those pretenders? Is that what you're saying? >> Well, there's definitely a lot of similarities there but if you look at the example that you use, right, pets.com versus Amazon, people are still buying pet food online. I buy all my pet supplies for my two puppies online. However, if you look at the reason that Amazon works is because of their supply chain and the innovations that they created on being able to deliver anything to you within a day or two days in an extremely cost effective manner. It wasn't just because they had a website and they did some hand wavy stuff to say isn't this a good idea. You actually have to have the underlying operational capability and innovation from a technology standpoint to make it happen. And so, when we talk about crypto over the past number of years, and I've been in the crypto space for a long time, as you have there's been a lot of hand wavy stuff. There's been a lot of people like, "wouldn't this be a good idea?" but then you have the true operators that are able to find the underlying competitive advantages that actually make it work. And that's what I'm interested in. >> I'd love to get your thoughts on that. First of all, great point if you look at like, I was just commentating earlier I was asked the question what I think, and I said, well, I do a lot of lot of reporting and analysis on cloud computing. I watch what Amazon Web Service has done from many, many years ago. And all the followers now. Scale data, higher level services, they're all happening and it's creating a lot of value. Okay? That's going to come to crypto. And so, okay, the dots aren't connected there yet, but you've got this, but one of the things that has proven to be a success criteria, ecosystems. When you have enabling technology like DigitalBits, for instance, is kind the main powering of this ecosystem here, the value that's being created on top of it has to be a step function or multiple of the cost or operational cost to deploy the platform. Okay, so that's kind of in concert with everyone else. You product decentralized, what's your thoughts on that? Because now you have a lot of potential ecosystems that could connect together cause there's no one centralized ecosystem. >> (Bryan) Absolutely. >> But what is, what, how do you get that? How do you square that circle? So to speak. What's your take on that? How does ecosystems play into defi, decentralization, de-apps blockchain? >> So what you really talking about is interoperable, right? So again, if we use an analogy, if we look back to the late nineties, when Web 1.0 was really flourishing and then in the 2000s where everybody created their own websites, people went to the world wide web, but every company had their own website. They had their own social media platform. They had their entire Salesforce platform or what have you. So everyone had their entire separate organization. And so, I suspect that the future of crypto is going to be very similar, where there's going to be a bunch of different metaverses, a bunch of different ecosystems, but someone's going to come along, and I think there's a number of people on the back end that are actually working on this, Some of them are really brilliant, that are going to create an interoperable mechanism for people that jump from metaverse to metaverse from chain to chain in a completely easy experience from a user experience standpoint where you don't have to have a PhD in crypto, so to speak, that doesn't exist, but you don't have to have that level. >> Well, if you're working on crypto for the past five years you've got a PhD. >> Basically. >> The thesis is, you're still alive producing. (Brian laughs) Well, that's a good point. So I'm looking for like, this defacto enabler, right? Because TCP/IP was an example in the old days, you know, the levels of the stack that never, TCP/IP is part of the OSI model. It's just interconnect. That layer, nothing got above it, was open. It was just hard and top that TCP/IP the rest was all standard. Ethernet, token ring add that data layer and then cards. That worked, the industry could galvanize around that. I'm waiting for the crypto moment now, where, what is going to be that cloud (indistinct), Kubernetes and service matches and whatnot. What, is there anything on the horizon that you see that has that kind of coalescent ecosystem, let's get, if we all get behind this, we all win. Rather than chasing crumbs. >> Sure. >> You know, the bigger pie, rising tide, all that stuff. >> Well, so I think there's a really interesting analogy from a couple of hundred years ago on this. So most people don't realize that when the United States first had their railroad system which was the innovative infrastructure play at the time each state or each region had their own systems they had different size railroad. So what would happen if you were trying to ship a bunch of grain from one part of the country to the other you would take it by a train. You get to a train station, you'd have to take everything off, put it on a different train, on a different set of train tracks. You would go a couple states over. You'd have to do that again, go a couple states over. You have to do that again. Eventually what happened is the federal government came in and said, hey, we need to create a system of policies around one set of rules for all trains and all logistics across the country. And so, I do think there's a role for governments to come together, along with the operators and the companies to work collaboratively together to say, hey, what are the regulations? What are the rules of the road? How do we make sure we get all the scam artists out of the system? How do we create a system that actually works for everybody? Now, there's always dangers there, right? You have regulatory capture. Sometimes the government, oftentimes they're slow, they don't understand the technology. So they come down with a heavy hand. And so if it's done properly, and it's not just the United States alone, by the way, it's all the countries in the world. Now at this point, it's a global effort. >> There's money involved, too. >> Exactly. But if we are able to bring together people that are much smarter than me from the public and private sectors as well as the nonprofit sectors, together to come up with one set of rules I think that will enable crypto to massively expand across the entire globe. >> What are you passionate about right now? I know you got the investment fund for, you know, helping society and the planet, you get your project with your startup company, AI is in a hot area. What's going on? What's your top goals for the year? >> So there's two things. Number one, my company, Ahura A.I. is my baby. It's where I spend 70, 80 hours a week. We invent a technology that enables people to learn three to five times faster than traditional education. >> (John) Is that so? >> Because I believe that education is the first step. It's the first variable, that impacts all of the sustainable development goals, impacts the world in a very real way. >> And you're not wearing your UA pin. >> I'm not wearing my pin, I always point to it. >> I wanted to grab it, I saw it earlier. >> But then the second thing I'm super focused on is existential risk. Look, so I throw a lot of events where I bring together four categories of people, CEOs of impact driven companies, investors, whether they're VCs or billionaires or family offices, global experts, and celebrities that want to use their influence for good in the world. And one of the speakers that I had at one of my events is a guy at Stanford who runs their lab on existential risk and what he told the group, and what he told me, is according to Stanford and all the researchers, there's a one in six chance that we're all going to go extinct by 2050. One in six, that's a dice roll. And so to me, the most important thing I can do is bring people together that have capacity, have resources, have capabilities, to address these drivers of existential risk because selfishly, I don't want to live in a dystopian Hellscape. >> Exactly, yeah. Bryan, thanks for coming on. We're going to get back into dinner. Great to see you. >> Thank you very much. >> The Cube after dark, extended hours. Look at us, we're going the whole day. VIP gala, Prince Albert, the team, DigitalBits, The Cube, all here at the Yacht Club in Monaco. I'm John Furrier. Thanks for watching.

>> Narrator: From theCUBE Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a CUBE conversation. >> Hello everyone, welcome to this special CUBE virtual coverage of the AWS summit virtual online. This is an event that Amazon normally has in-person in San Francisco, but now it's virtual around the world, Seoul, Korea, in Tokyo, all over the world and Asia-Pacific and in North America. I'm John Furrier here joined with Stu Miniman. So Stu, we're kicking off AWS virtual with theCUBE virtual. I'm in Palo Alto with the quarantine crew. You're in Massachusetts, in Boston and the quarantine crew there. Stu, great to have you on to talk about AWS virtual summit. >> Yeah, John, it's great to see you. It's been, you know, interesting times doing all these remote interviews. As many of us say, I sure don't miss the planes and the hotels, but I do miss the communities. I do miss the hallway conversation, but great to see you John. Love the Midnight Madness shirt from re:Invent last year. >> Well, we want to thank Amazon for stepping up with some sponsorship for allow us to do the virtual CUBE alongside their virtual event, because now it's a global community. It's all virtual, there are no boundaries theCube has no boundary. Stu, we've got a great program. We have Corey Quinn coming up and expect to hear from him last week in AWS. He's known for, he's a rising star in the community, certainly CUBE guest and also guest host and analyst for theCUBE. We expect to hear all the latest from his big Zoom post controversy, to really what's going on in AWS, around what services are high. I know you're going to do a great interview with him, but let's start with Amazon. We're seeing a ton of activity. Obviously most recently, last week was the JEDI thing, which was an agency protest, kind of confidential. Microsoft blew that up big time with a post by their worldwide comms person Frank Shaw, countered by Drew Herdener, who's the comms global lead for AWS. And so a war of words is ensuing. This is again, pointing to the cloud native war that's going on with a JEDI conference. I mean, the JEDI contract for $10 billion, which is worth to Microsoft. This shows that the heat is on, Stu. This is a absolute bloodbath between AWS and Microsoft. We're seeing it play out now virtually with Amazon, A.I. large scale cloud. This is huge, this is another level. A DEFCON one basically, your thoughts? >> Yeah, John, you've covered this really well. It's been really interesting plot, number one, you talked about the security requirement, when AWS launched the GovCloud had the CIA as a client, early on many years ago. It was the green light for many companies that go from "Wait, is the cloud secure enough?" to "Well, if it's good enough for "the federal government in the U.S., "it's probably good enough for the enterprise." When Microsoft won JEDI, they didn't have all the certification, to meet what was in the contract. They had a ticking clock to make sure that they could meet those security engagements, as well as one of the pieces on the task board that moved was Oracle made a partnership announcement with Azure. We know the federal government uses Oracle quite a bit, so they can now run that in Azure and not have the penalties from Oracle. So that many have said, "Hey AWS, "why don't you kind of let that one piece of business go? "You've got federal business." But those ripple effects we understand from one contract kind of move things around. >> Well, my take on this is just the tempest in the teapot. Either Microsoft's got something that we don't know or they're running scared. My prediction, Stu, is that the clock is going to tick out. D.O.D. is going to award the contract again to Microsoft because I don't think the D.O.D. wants to change based upon the data that I'm getting from my reporting. And then ultimately Amazon will keep this going in court because Microsoft has been deficient on winning the deal. And that is by the judge and in government contracts, as you know, when you're deficient, you're ineligible. So essentially on the tech specs, Microsoft failed to meet the criteria of the contract and they're deficient. They still can't host top secret content even if they wanted to. This is going to be a game changer. If this comes out to be true, it will be a huge tech scandal. If it's true, then AWS is going to have egg on their face. Okay, so moving past JEDI, this speaks to the large scale problems that are having with COVID. You seeing Amazon, they're all working at home, but they still get to run the servers. They can do it, they've got cloud native, you got DevOps, but for their customers Stu, but people who are trying to do hybrid, what are you hearing in terms of the kinds of situations that people are doing? Are they still going to work with masks on? Are there still data centers that need to be managed? What are you hearing Stu, in the tech worlds do around COVID-19 and as the cloud becomes more apparent, it's obvious that if you're not cloud native, you're going to be on the wrong side of history here, it's pretty obvious. >> Well, absolutely John, there is a bit of a tailwind behind cloud or with COVID-19, everything from, you mentioned work from home. Everybody needs to be on their VPN. They need to access their services, where they are. If you've got a global workforce, if you thought that your infrastructure was going to be able to handle that, you might not be in for a good story. AWS is meeting that need. There's been some of the cloud providers that have had performance issues, that have had to prioritize which customers can get access to things. AWS is standing strong, they're meeting their customers and they're answering the call of cloud. We know that AWS puts a huge investment into their environment. If you compare an availability zone or from AWS, it is very, very sturdy. It's not just, you know, a small cluster and they say, "Hey, we can run all over the place." To be specific Azure, has been having some of those performance issues and there's been some concerns. Corey actually wrote a really good article talking about that it actually puts a bad view on public cloud in general, but we know not all public clouds are the same. So, Google has been doing quite well, managing the demand spike, so has AWS. Microsoft has needed to respond a little bit. >> Since you just mentioned, Microsoft's outages, Microsoft actually got caught on their 8K filing, which I just had me going through and I noticed that they said they had all this uptime for the cloud. It turns out it wasn't the cloud, it was the team's product. They had to actually put a strike a line through it legally. So a lot of people getting called out, but it doesn't matter, it's a crisis. I think that's not going to be a core issue. This is going to be what technology has been needed the most. And I got to ask you Stu, when was the last time you and I talked about virtual desktops? Because hey, if you're working at home and you're not at your desk, you might need some stuff on your desk. This is a real issue. I mean it's kind of a corner case in tech, but virtual desktops, if you're not at the office, you need to have that at home. This is a huge issue and it's been a surge of demand. >> Yeah, there were jokes in the community that, you know, finally at the year of V.D.I., but desktop as the service John, is an area that took a little while to get going. So, Dave Vellante and I were just having a conversation about this. You and Dave interviewed me when Amazon released workspaces and it was like, you know, Citrix is doing so well and V.D.I. isn't the hotness anymore, but desktop as a service, has grown, if you talk about desktop as a service compared to VDI, VDI is still a bit of a heavy lift. Even if you've got hyper converged infrastructure, roll this out, it's a couple of months to put these whole solutions together. Now if you have some of that infrastructure, can you scale it, can you build them up much faster? Yes you can. But if you're starting to enable your workforce a little bit faster, desktop as a service is going to be faster. AWS has a strong solution with workspaces. It really is that enablement and it's also putting pressure on the SaaS providers. One, they need scale and two, they need to be responsive that some of their customers need to scale up really fast and some of them need to dial things down. Always worry about, some of these contracts that the SaaS providers put you in. So, customers need to make sure they're being loud and clear with their providers. If you need help, if you need to adjust something, push back on them because they should be responsive, because we know that there is a broad impact on this, but it will not be a permanent impact. So, these are the times that companies need to work closely with customers, because otherwise you will, either make a customer for life or you will have somebody that will not be saying good about you for a long time. >> Well Stu, so let's just quickly run through some of the highlights so far on the virtual conference, virtual event. Obviously Amazon pre-announced last month, the Windows migration service, which has been a big part of their business. They've been doing it for 11 years. So we're going to have an interview with an AWS person to talk about that. Also AppFlow is announced as well as part of the virtual kind of private connects. So, you know, you're seeing that right here, large scale data lakes breaking down those silos, moving data from the cloud, from the console into the top applicants, like Salesforce is the big one. So that was kind of pre announced. The big story here is the Kendra availability and the augmented A.I. availability, among other things. This is this big story. This kind of shows the Amazon track record. They pre-announced that re:Invent and try to run as fast as they can to get it shipping. The focus of AI, the focus of large scale capacity, whether it's building on top of EC2, serverless, Lambda, A.I., all this is kind of coming together. Data, high capacity operational throughput and added value. That seems to be the highlights, your reaction? >> Yeah, so John, AppFlow is an interesting one, we were just talking about task providers. An area that we've been spending a lot of time talking with the East coast system is my data is all over the place. Yes, there's my data centers, public cloud, but there's all of these task providers. So, if I have data in ServiceNow I have it in Workday, I have it in Salesforce, how do I have connectors there? How do I secure that? How do I protect that? So Amazon, working with a broad ecosystem and helping to pull that together is definitely an interesting one to watch. Kendra definitely been some good buzz in the ecosystem for a while there. The question is on natural language processing and A.I., where are the customers with these deployments? Because some of them, if they're a little bit more longterm strategic might be the kind of projects that get put on pause rather than the ones that are critical for me to run the business today. >> And I just did a podcast with the VMware ecosystem last week talking about which projects will be funded, which ones won't. It brings up this new virtual work environment, where some people are going to get paid and some people aren't. If you're not core to the enterprise, you're probably not going to get paid. If you're not getting a phone call to come into work, you're probably going to get fired. So there will be projects that will be cut and projects that will be funded. Certainly virtual events, which I want to talk to you about in a minute, to applications that are driving revenue and or engagement around the new workforce. So the virtualization of business is happening. Now, we joke because we know server virtualization actually enabled the cloud, right? So I think there's going to be a huge Cambrian explosion of applications. So I want to get your thoughts, the folks you've been talking through the past few months, what are you hearing in terms of those kinds of projects that people are going to be leaning into and funding, versus ones they might put on hold? Have you heard anything? >> Yeah, well, John, it's interesting, when you go back at its core, what is AWS? And they want to enable build. So the last couple of years we've been talking about all of the new applications that will get built. That's not getting put on hold, John. What I do, not just to run the business but grow the business. I need to still have applications at the core of what we do. Data and application really are what driving companies today. So that piece is so critically important and therefore AWS is a very strategic partner there. >> Yeah, I've been seeing the same things too. I think the common trend that I would just add to that would be I'm seeing companies looking at the COVID crisis as an opportunity. And frankly in some cases an excuse to lay people off and that's kind of, you're seeing some of that. But at the end of the day that people are resetting, re-inventing and then putting new growth strategies together, that still doesn't change. business still needs to get done, so great point. All right, Stu, virtual events. We're here with the AWS summit. Normally we're on the show floor with theCUBE, we are here with the virtual CUBE doing our virtual thing. It's been interesting, Stu. A lot of our events have converted to virtual, some have been canceled but most of them have been been running on the virtual. We've been plugged in. But theCUBE is evolving, and I want to get your thoughts on how you see theCube evolving. I've been getting a lot of questions. This came up again on the VMware community podcast. How has theCUBE morphed? And I know that we've been working hard with a lot of our customers, how have we evolved? Because we're in the middle of this digital wave. This is a virtualization wave. theCUBE is in there. We've been successful, there's been different use cases. Some have been embedded into the software. Amazon's got their own run a show. But events are more than just running the show content. There's a lot more community behind this Stu, your thoughts on how theCUBE has evolved and what are you seeing? >> I'm glad John, you just mentioned community. So you and I have talked many times on air and did this too about theCUBE is as much a network and a community as it is a media company. So, first of all it's been so heartening over the last couple of months that we've been putting out content. We're still getting some great feedback from the community. One of the things I personally miss is, when we step off the stage and you walk the hallway and you bump into people that know and they ask you questions or they share some of the things that they're going through. That data that we always look for is something we still need. So I'm making sure to reach out to friends diving back into the social panels to make sure that we understand the pulse of what's going on. But, John, our community has always been online so a big piece of theCUBE is relatively unchanged other than we're doing all of the interviews remote. We have to deal with everyone's home systems and home network. Every once in a while you hear a dog barking in the background or a child running, but it actually humanized. So there's that opportunity for the communities to rally together. Some of my favorite interviews have been, the open source communities that are gathering together to work on common issues. A lot of them specifically for the global pandemic. And so there are some really good stories out there. I worry when you talk about companies that are saying, Hey, this is the-- (sound cuts out) There have been so many job losses, in this pandemic that it just is heartbreaking. So, we love when the tech community is helping to spur new opportunities, great new industries. I had a great interview that I did with our friends from A Cloud Guru and they've seen about a 20 to 30% increase on people taking the online training. And one of the main things that they're taking training on is the 101 courses on AWS, on Google and on Azure as well as an interesting point John, they said multicloud is something that has come up. So, 2020, we've been wondering is AWS going to admit that multicloud is a thing? Or are they going to stick with their hybrid message and ask that their partners not talk about multicloud? >> It's been interesting on the virtual queue, because we and Amazon's been a visionary in this and letting theCUBE be virtual with them. It's become a connective tissue, Stu, between the community and if you think about how much money the companies are saving by not running the physical events and with the layoffs as you mentioned, I think there could be an opportunity for theCUBE to be that connective tissue to bring people together. And I think that's the mission that we hope will unfold. But ultimately digital investments will probably go up from this. I'm seeing a lot of great conversion around, okay, so the content, what does it mean to me? Is that my my friend group, how are my friends involved? How do I learn, how do I discover? How do I connect? And I think the interesting thing about theCube is we've seen that upfront and I think there's a positive sign ahead, Stu, around virtualization of the media and the community and I think is going to be an economic opportunity and I hope that we could help people find either jobs or ways to reengage and reconnect. So again, re:Invent's coming, you've got VMworld, all these big shows too, they drop so much cash! Can you imagine if they put all that cash into the community? I think that's a viable scenario. >> Yeah, no, absolutely, John. There is big money in events. Yes, there are less costs. There are also almost none of them are charging for people to attend and very few of them are charging their sponsors. So, big shift in how we have to look at these. It needs to be a real focus on content. I mean, from our standpoint, John, from day one, and we've been doing this a decade now, in the early days when it was a wing and a prayer on the technology, it was always about the content and the best people help extract that signal from the noise. So, some things have changed, the mission overall stays the same. >> And you know what, Amazon is being humble. They're saying we're figuring it out. Of course, we're psyched that we're there with the virtual CUBE. Stu, thanks for spending the time kicking off this virtual coverage, wrap up. Not as good as face-to-face, love to be there on site, but I think it's going to be easier to get guests too Stu in the virtual world, but we're going to go to a hybrid as soon as it comes back to normal. It sounds like cloud Stu, public hybrid virtual. There it is. Stu, thanks so much. >> Thanks John. >> Okay, that's theCUBE coverage for AWS Summit Virtual Online. It's theCUBE virtual coverage. I'm John Furrier, Stu Miniman. Thanks for watching. Stay tuned for the next segment. (upbeat music)

>> Live from San Francisco, it's theCUBE. Covering Google Cloud Next 19. Brought to you by Google Cloud and its eco system partners. >> Hello everyone welcome back here to theCUBE live coverage here in San Francisco, California. We're in the Moscone Center on the ground floor here. Day three of three days of coverage for Google Cloud Next 2019. I'm John Furrier, my co-host, Dave Vellante, Stew Miniman out there getting stories out there He's also been hosting. Dave, great to see you! Evren, Director of Product Management at Google Cloud, doing all the data streaming the data. We're streaming data right now. >> Absolutely, this is it. This is it. >> So let's stream some data. So streaming data has certainly been around for awhile. Dave and I when we first started theCUBE ten years ago, it was part of Silk and Angle Media hadoop was just a small little project. That really kind of was the catalyst moment for around big data that's now evolved to it's own position. Now you have streaming data, you have cloud scale, the Cloud has really changed the game on big data. Changed the nature and dynamics of it and one of the things is streaming data, streaming analytics as a core value proposition for enterprises, and this is fairly new. >> Very true. >> What's your take on it and how does it relate to what's going on with Google Cloud? >> I am glad we're talking about that. This is an exciting time for us. Streaming like you said is growing. Batch is not going away, but streaming is actually overtaking a lot of the applications that we're seeing. Today we're seeing more streaming applications taking place than batch. One of the things that we're seeing is everybody is gathering data from all over the place from your websites, from your mobile phones, from your IoT devices, just like we're doing right now. There's data coming in and people want to make decisions real time whether it's in the banking industry, in your healthcare, retail, it doesn't matter which word cycle you're working with and we're seeing how those messages how those events are coming in and where the decisions are being made real time, milliseconds we're talking about. >> Why is it happening, what's the real catalyst here? Just tsunami of data, nature of the value, all of the above, what's the? >> We believe one of the things is like you mentioned Cloud really changed the game. Where people actually can reach globally data and messages at scale. We're talking about billions of messages coming in and processing capacity is available now we can actually process it and make a decision within milliseconds and get to the results. To me, that was the biggest catalyst. And we're seeing many of us have grown up using batch data, making decisions now everybody is talking about M.L. and A.I. You need that data coming in real time and we can actual process it and make the decision. To me, that's the catalyst. >> First of all we love streaming data, this topic. One we believe streaming where shooting video but data, real time, has been one of the keys you see self driving cars monging of data, mixing and matching of data to get better signal and better machine learning and I got to ask you, because batch is certainly the role for batch is kind of old school it's some old techniques it's been around for awhile, >> It's not going to go away though. >> It's not going to go away it's established it's place but the knee jerk reaction of existing old school people who haven't migrated to the new modern version they go to the batch kind of mind set. I want to get you're reaction. Data lakes, there's nothing flowing in a lake. Okay, so there is a role for a data lake streaming gives me the impression of like an ocean or a river or something moving fast. Talk about the differences because it's not just the data lake okay that's a batch kind of reaction. >> It is a complementary. Actually it's not going away because all of that data that we had in the back is something we're relying on to really augment and see what's changing. So if you're in a retail house you're buying something, you're going to make a decision and your support is actually behind it. OK here's Evren, he's actually shopping around this and he wants this for his son. That's what the models built around it is looking at what is my behavior and in the moment making a decision for me. So that's not going away. The other thing is batch users are able to take advantage of the technology today. If you look at our data flow, same set of codes, same set of capability can be used by the same folks that are used to batch. You don't have to change anything so that actually we help folks to be up skilled using the same set of tools and become much more experienced and experts in the streaming too. That's not going away we help both of the worlds. >> So, complementary. >> Very complementary. >> So data lakes are good for kind of setting the table if you have to store it somewhere but that's not the end game though. >> No. >> Okay. >> I wonder if we could talk about the evolution from batch to real time streaming. And my favorite example, because I think people can relate to it, is fraud detection. Ten years ago, it was up to the user to go through his or her bill, right? And then you started to get inundated with false positives, and now lately, last couple of years it's getting better and better. Fewer false positives, usually when you usually no news is good news. News is usually bad news now, so take that example and use that to describe how things have evolved. >> I am a student of AI I did my Master's and PhD in that and I went through that change in my career because we had to collect the data, batch it and analyze it, and actually make a decision about it and we had a lot of false positives and in some cases some negative misses too which you don't want that either. And what happened is our modeling capabilities became much better. With this rich data, and you actually tap into that data lake, you can go in there the data is there, and this is spread data we can pull in data from different sources and actually remove the outliers and make our decision real time right there. We didn't have the processing capability we didn't have a place like PostUp where globic can scan and bring in data at hundreds of gigabytes of data. That's messaging you want to deal with at scale no matter where it is and process that, that wasn't available for us. Now it's available it's like a candy shelf for technologists, all the technology is in our hands and we wanted all these things. >> You were talking about I think the simplicity of, I'm able to use my batch processes and apply them. One of the complaints I hear from developers sometimes is that the data pipeline is getting so complicated. You were talking about you're grabbing stuff from websites, from financial databases, and so depending on what data store you're using and what streaming tools you're using or other A.I. tools, the pipeline gets very complicated the A.P.Is start to get complicated but I'm hearing a story of simplicity. Can you elaborate on that and add some color? >> Yeah I'm glad you're asking that question you may have heard, yesterday we announced a whole bunch of new things and ease of use is the top of the line for us. Really are trying to make it easy. If you look at this eco pipeline we're building with data flow, it helps you end to end. Data engineered no matter which angle their coming in should be able to use their known skill sets and be able to build their pipelines end to end so that you can achieve your goals around streaming. We aren't really having to go through a lot of the clusters of the pipelines we are going to continue to push that ease of use over and over, we're not going to let it go because make it easier, everyone will adapt it faster. >> You mentioned you got a PhD in A.I., Master's in A.I., A.I. has been around for awhile. A lot of people have been saying that but machine learning certainly has changed the game. Machine learning plus cloud has been a real accelerant in the academic and now commercial aspects of A.I. So I want to get your thoughts on the notion of scale which you talk about, plus the diversity of data. So if you can bring in data at scale get more signaling points more access to data signaling the diversity of data becomes very key. But cleanliness, data cleaning, used to be an old practice of you get a bunch of data, stack it up, put it in a pile corpus, and you kind of go clean it. With streaming, if it's always flowing there's kind of a behavioral characteristic of data cleanliness, data monitoring, talk about that diversity of data clean data and how that feeds machine learning and makes better A.I. >> Good one, so that's where we actually are able to, if you look at PostUp, you're building joint your table set of datas with streaming set of datas you can actually put it into data filter it and make those analyses. And within both, we provide enough of a window for you to be able to go back, hey are there things that I should be looking at, up to seven days we can provide a snapshot because you will always find something you can go back, you know what I'm going to remove this outlier. All worrying about all the processing we do before we bring in the data so there's a lot of cleanliness that takes place but we have the built in tools we have the built in capabilities for everyone to get going. It's ready to scale for you from the moment you open it up. That's the beauty of it, that's the beauty of when you start from PostUp to data flow to streaming engine it's ready for you to run. >> Talk about what's changed though when people hear diversity of data they get scared, oh my god I work, heavy lifting. Now it's a benefit. What's easier now to deal with all of these diverse data sets, what's the easy revolution? >> So do you remember the big V's of big data right? Volume, velocity, variety. People were scared about the variety. Now I can actually bring in my data from different places. Again, let's go back to the shopping example. Where I shop, what I shop for, that actually defines my behavior around it. Those data sit somewhere else. We bring those in to make a decision about okay everyone wants to go buy a scooter or whatever else, that's the diversity of the data. We're now able to deal to with this at scale. That was not available we could actually bring in and render this, now everything is going to do this much more sequential. We're now able to bring all of them together process it at the same time and make the decision. >> What's the key products that will make all of those happen, take us through the portfolio if I want that would you just said which is a great value. It sounds like not a heavy lift all I have to do is point the data sources into this engine, what are the products that make up that capability? >> So if I look at the overall portfolio on Google Cloud from our data analysts point of view, so you actually can bring in your data through PostUp, lots of messaging capability globally and you can actually do it regionally because we have a lot of regional requirements coming from various countries and data flow is where we actually transfer the data. That's where you do the processing. And you use all of these advance analytics capabilities through your streaming engine that we released and you have your B query, you have your OMLs, you have all kinds of things that you can bring in you're big tables and what have you. That's all easily integrated end to end for any analyst to be able to use. >> What is beam? >> Beam ah that's great I'm so glad you asked that question I almost forgot! Beam is one of our open sources we donated the same set, just like we did with Koppernes few years ago, we donated to the open source it's growing. This year actually it won The Technology Awards. So the source is open the community really took it upon, they use that toolkit to build their pipelines you can use any kind of a code that you want Java, Gold, whatever you want to do it and they contribute. We use it internally and externally. It's one of those things that's going to grow. We have a lot of community events coming up this year. We might, and I've seen the increase, I'm really really proud of that community. >> Evren, I love the A.I. can't get my mind off your background and academic because I studied A.I. as well in the 80s and 90s all that good stuff. Young kids are flocking to computer science now because A.I. is very sexy, it's very intoxicating and it's so easy to deal with now. You guys had a hack-a-thon here with NCAA using data really kind of real time and kind of cool things are happening. So it's a moment now for A.I. this is the moment. What's your advice, you've been through the wars you've done your chore duty all those years now it's actually happening. What's your advice for young people who want to come in, get their hands dirty, build things, use A.I., what's your advice, how they should tackle that? >> I am living it, both of my sons one is finishing junior high, the other one is a senior in high school, their both in it. So when I hear my young kids come and say, "hey bubba we just built this using transfer flow." Like it is making me really proud. At the middle school level they were doing it. So the good news is we have all of this publicly available data for them. I encourage every one of them. If you look at what we provide from Google Cloud, you come in there, we have the data for them, we have the tools for them, it's all ready for them to play so schools get free access to it too. >> It's a major culture but how do they get someone who's interested but never coded before, how do they jump right in and get ingratiated and immersed into the code, what do they do? >> We have some community reaches that we're actually doing as Google. We go out to them and we're actually establishing centers to really build community events for them to really learn some new skills. And we're making this easy for them. And I'm happy to hear more and do it, but I'm an advocate I go to middle schools, I go to high schools, I go to colleges. Colleges are a different story. We provide school classes and we provide our technologies at the universities because enterprises need that talent, need that skill, when they graduate, their going to hire them just like I'm going to hire them into my organization. >> So my number one complaint my kids have about school, they're talking about kids that, oh school's going to be a waste it's so linear I can learn everything on YouTube and Google.com. All the stuff I learned in school I'm never going to use in the real world. So the question is, what skill should kids learn that could be applied to machine learning, thinking, the kind of constructs, data structures, or methodologies, what are some of the skills and classes that can tease out and be natural lead into computer science and machine learning A.I.? >> You know, actually their going to build up the skills. The languages will evolve and so forth. As long as they have that inner curiosity asking new questions, how can I find the answer a little faster, that will push them towards different sets of tools, different sets of areas. If you go to Berkeley in here, you will see a whole bunch of high school kids working side by side with graduate students asking those questions, developing those skill sets, but it's all coming down to their curiosity. >> And I think that applies for business too. I mean there's a big gap between the A.I. haves and have-nots I always say. And the good news here that my take away is, you're going to buy A.I, you're going to buy it from people like Google and you're going to build it and apply it, you're going to spend time applying it, and that's how these incumbents can close the gap and that's the good news here. >> Very true if you look at it, look at all the A.P.Is that we have. From text recognition to image recognition to whatever it is, those are all built models and I've seen some customers build some fantastic applications starting from there and they use their own data, bring it in, they update their model for their own businesses cases. >> It's composition it's composing. It's not coding it's composing. >> Exactly, it's composing. We are taking it to the next level. That abstraction is going to actually help others come into the field because they know their field of expertise, they can ask direct questions. You and I may not know it but, they will ask direct questions. And they will go with the tools available for them for the curiosity that they reach. >> Okay what's the coolest thing you're working on right now? >> Coolest thing, I just y'know streaming is my baby. We are working on, I want to solve all the streaming challenges, whatever the industry is. I really want to welcome everyone, bring you to us. I think, if I look at it, one of the things we discussed today was Antos was fantastic right? I mean we're really going to change the game for all enterprises to be able to provide those capabilities at the infrastructure. But imagine what we can do with all the data analytics capabilities we have on top of it. I think this is the next five years is going to be fantastic for us. >> What's the coolest use case thing you see emerging out of streaming? >> Ah you know, yesterday I actually had one of my clients with me onstage, AB Tasty. They had a fantastic capability that they built. They tried everything. And we were not their first choice, I'll be very open. They said the same thing to everybody, you guys were not our first choice. They went around, they looked at all the tool kits, everything. They came they used PostUp, they used data flow, they used engine, streaming engine. And they AB testing for marketing. And they do that at scale, billions of messages every minute, and they do it within seconds, milliseconds, 32 milliseconds at most. Because they have to make the decision. That was awesome, go check. I don't know if you're familiar with that. One of our customers, they provide these real time delivery. In India, imagine where things are. In global leaders, you can actually ask for a food to be delivered and they have to optimize, depending on what the traffic is and go with their scooters, and provide you this delivery. They aren't doing it as well. Okato, they believe, provide food in UK 70% of the population use our technologies for real time delivery. Those are some great examples. >> Evren, great insight, great to have you on. Just a final word here, next couple years, how do you see the trajectory of machine learning A.I. Analytics feeding into the value of making life easier society better, and businesses more productive? >> We are seeing really good pull from enterprises from every archival that you can think of. Regulated, retail, what have you. And we're going to solve some really hard problems whether it's in health care industry, financial industry, retail industry, we're going to make lives of people much easier. And their going to benefit from it at scale. And I believe we're just scratching the tip of it and you're seeing this energy in here. Year over year this has gotten better and better. I can't wait to see what's going to happen next year. >> Evren Eryurek great energy, expert at A.Is, streaming analytics, again this is early days of a brand new shift that's happening. You get on the right side of history it's A.I. machine learning, streaming analysts. Thanks for coming, I appreciate it. >> Thank you so much, take care guys. >> More live coverage here in theCUBE in San Francisco at Google next Cloud 2019. We'll be back after this short break.

>> Announcer: Live from Las Vegas, it's the CUBE. Covering IMB Think 2018. Brought to you by IBM. >> Hello everyone, welcome to the Cube here at IBM Think 2018. It's our flagship program where we extract the signal noise live entertainment and technology coverage here. Of course we're going to get all the data as well. Inderpal Bhandari, Global Chief Data Officer for IBM is here in the CUBE, CUBE alumni. The chief of the data for the entire company your job is pretty secure right now. Jean Merriman was talking about how data's the center of the value proposition, blockchain and A.I. Dave and I call it the innovation sandwich. You've got job security right now. >> (laughs) I guess you could put it that way. >> (laughs) So, obviously the data, all kidding aside, we've talked before in the CUBE, the importance of data and, you know, we're data driven, we're data geeks. This is a wonderful time to be in this world because the disruptive enabling that's going on with data is really been, I think, underplayed. It's been more of a tech conversation but the business benefits that this enables, I mean, just blockchain alone, what that could do for efficiencies in rewiring the value chains in a decentralized environment. And then what A.I. promises with the use of data to automate value creation, this is pretty spectacular. >> No, I would completely agree with you. I think it's a very exciting time to be in our industry. And, John, I think the challenge though, is what does it mean for the enterprise? If you put yourselves in the shoes of our customers, they're trying to understand, what does this really mean for the enterprise? What's an A.I. enterprise? What are the use cases for blockchain that play in the enterprise? And that's one of the major foci that I have within my organization, you know. And my role within IBM and the Global Chief Data Officer is to create an A.I. enterprise within IBM itself and then use that as a showcase for our customers so they're able to understand, clearly, what the use cases are that make a lot of sense. Because, frankly, IBM looks a lot like some of our customers. You know we are a large enterprise, we've been around for a while and the fits the profile for the large customers that we serve. >> Well, IBM is the perfect melting pot and Petri dish, if you will, to look at the future, 'cause you have legacy, you know, hundreds of years of being in business, so you've been around but you're also pushing the latest technologies. How has IBM been using the tech? Can you give an example, because this is the digital transformation challenge that most existing leaders have. You know, you don't need to be only five years old just to be, kind of, an old relic compared to what's on the table right now, the speed of innovation. So there has to be a constant energy on understanding how to create sustainable tech and business models and have that regenerate self-healing. I mean, this is a new normal that is just hitting us. How do you guys do it? Can you give some examples? >> Yes, no, absolutely. So we've taken the view that we want to transform our key processes within the company. And examples of these processes, they're not typical to us, they're typical of any large enterprise, you know, these could be procurement, supply chain, marketing, research, data. So we've got these end-to-end processes, which we are now transforming through the use of A.I. and blockchain, these kinds of technologies so that we are able to then re-use those as showcases. So in terms of examples of how we are making use of these today, they.. I'll give you some examples that are more, you know, just at a whole process level, for instance, supply chain. Trying to understand what are the risks to our supply chain based on emerging weather conditions, based on emerging political events. Trying to unravel all that and then essentially use that intelligent system to guide us to make the best decisions with regard to supply chain. That's kind of what I would call a process level example. I'll give you one example within data that seems to some extent quite trivial but actually there are literally thousands and thousands of such decision that are made everyday in a large enterprise. So one of the things that we do in my organization is try to understand if a client that we're dealing with is a government owned entity. And since we operate globally and there are rules that regulate how one deals with government owned entities, very important for us to get it right so that we do business ethically. And it's, you know, you might think, 'well that's a simple decision' it's actually quite complicated and a lot of different parties have a stake in the ground on this. You know, the legal department, the sales area. But now, the way the process is transforming is all that input is fed into an intelligence system that has an understanding of what we've done in the past. It can look at the external data, the news feeds that are available about that organization as well as what are the different points of view and then come to an understanding and then finally be able to explain back to us its rationale as to why it considers something a government owned entity or not. So every subject matter expert in the company should be able to make use of this technology. That's what an A.I. enterprise is and there are literally thousands and thousands such people within an enterprise. >> I mean, you're putting real complex data at their fingertips almost as easy as putting numbers on a spreadsheet. >> Inderpal: Yes. >> That's the kind of work that you guys are thinking. >> Yes, the way I would put it to you, it's more in the sense of engaging the subject matter expert in a dialog. So it's like you've got this intelligent system, Watson, that's working with this subject matter expert, taking them through the whole scenario. They come in with a use case in mind, I used the example of government owned entity or a risk insight for supply chain, they're coming in with a use case in mind, the system is guiding them through. Here's the internal data that's relevant. >> Yeah. >> Here's the external data that's relevant. Here's how you can link them. Here are the insights that you can draw from. So it's kind of a two-way street but it just ends being a much more accurate decision made much more quickly. >> Jean's talk on speech and the theme here at Think 2018 is, putting smart to work. I'll edit that for you in our conversation, putting smart data to work, 'cause that's what you're getting at here. How do you make data intelligent? I know, you know, I mean if you look at it, we can kind of go in the high levels in the clouds and look down and say, 'yeah, you know, that's a great mission.' You know it's hard as heck! >> It's it's very hard. >> So you've got an intelligent data, is it the right data, is it conceptually relevant, is it in the right place at the right time, does the application have the ability to ingest and use the data? >> How reliable it is? All that stuff comes into play and that's where, I think, you know, we've thought of IBM as having a very large portfolio of products that span from, you know, data management, data quality, those kinds of things, all the way to A.I. and Watson and so forth. Think of it more now as bringing together that portfolio into a cohesive data and cognitive framework or data and cognitive backbone for the enterprise. And that's really essentially what we're putting together. >> Inderpal I want to get your thoughts on something. I'm going to kind of go on a tangent since it just popped in my head. I wrote blog posted in 2007, way back in the day, 10 years ago, that said data's the new developer kit. And it's kind of a riff on that data's going to be the software. So we're seeing that now. I interviewed Rob Thomas earlier where he was talking about data containers. We're starting to get to that level with these Kubernetes and these cloud technologies, you now have new models emerging around data where people want to act on data, whether as a subject matter expert or developer. They are essentially develop users. So data's got to be programmable, it's got to be accessible. How do we get to a world where it's being developed on in a seamless way? Just like software's developed on. 'Cause most of the software, 90% of most software is open source, only 10%, put in a Linux foundation, is actually raw intellectual property. So you can almost think of data the same way. >> Inderpal: Yes, no no question. >> How does using data in a development context? What's your vision on that? >> So, you know, we have a blueprint to make an enterprise into am A.I. enterprise or a cognitive enterprise and it has four elements to it. One of the elements is actually data for precisely the reasons that you just annunciated. You know, developers, if they have to go off and search for data and try to find it then it's not a productive use of their time. So to some extent you have to bring the data eco system to them and that needs to be part of an A.I. enterprise. That that data is readily available for developers so that they're able to harness that. And so, now you get into all the hard questions, right? How to do you find it? What is the lineage of the data? So you need to have a super catalog enterprise-wide that enables all that and.. >> Hey, we're making up a new category as we speak it's called data ops. Data as code. We have DevOps as infrastructure's code. You know, I've been kind of, I was talking about this a year ago, didn't get any traction with the idea but what was circling in my head was if infrastructure as code, which was DevOps, which is now serverless when we look at the cloud computing as a set of programmable resources, you can almost make the stretch that data as code is a similar nirvana. >> Inderpal: Yes. >> Okay, it's available, I'm not searching for it but I don't need to reconstruct it, I don't need to essentially ingest it, yeah I'm ingesting it as a function, but, in a free-flowing world, what's your thoughts on that? What's your reaction to that? >> Well the way, you know, that's why setting up the central backbone for data and cognition is extremely important. And I think the right way to think about it is as a continuum. So you've got data and then you've got, essentially, API's on top of the data, that may, may be representing certain functions that you're running on the data. You think about that as a continuum because those functions end up with data as a result. Right? So you've got derived data. So, what the backbone needs to be able to do is give developers very quick access to all the raw data, the source data, as well as the derived data in terms that they can understand and it's easy for them to fathom what that is so that they're able to make judgments in conjunction with an intelligent system that guides them. >> Yeah, that's the key thing and that why Jean brought up Moore's law and Metcalfe's law in her speech because she's intimating at two things, faster smaller cheaper, performance improvements. Metcalfe's law is a network effect. Okay, so you know where I'm going with this, right?. So now we're in a network effect gamification world. We see blockchain, we see crypto currency, we see decentralized application developers coming on on board very quickly. So you have a world with token economics is becoming front and center and where I see innovation, certainly ICOs, initial point offerings are scaring me right now, but it is highlighting the innovation and arbitrage of an inefficient capital market, so, I just use that as a use case. But blockchain and crypto currency is an opportunity to create new business models from the enabling blockchain capability. How do you view that? Because we're still talking about data now. If you're freeing up more people to have more time to actually do their job, they're going to create new things maybe new business models and enter interstate token economics combined with blockchain, this is where we really see a lot of great innovation. Your thoughts in this area of token economics. >> Sure, yeah absolutely. So, I think there are two ways to think about it, one is in the transaction of business itself. What you're doing is you're bringing in a stakeholders for a particular business transaction and you're giving them a way to, a distributed way, a distributed way to arrive at the decision, right? As to whether or not to move forward. So, distributed consensus. You're making that very easy and simple of them so that they can rapidly reach a decision and make their decision, whether they're going to put in money, take out money et cetera. That's one aspect of it, and we literally have.. >> And by the way, consensus is now a new data source? >> Yes. >> And active real time.. >> Yes. >> Data set? >> Absolutely, it is creating, it is creating a data set, in and of its own right. So, but that's kind of one aspect of it, which is in the transaction of business, making it much more efficient, much faster and so forth. But I think it's also instructive to look at blockchain and apply it in terms of a second reuse to the process of managing data itself. So to the extent you're able to establish identities, to the extent you're able to establish permissions and roles. It's going to make governance of data much easier and much faster and much more efficient. These are typically very hard problems for enterprises to solve but I would say that as you go forward, maybe in this year or next year, you're going to see examples. >> And the opportunity too, is to actually break down some structural barriers. >> Yes. >> With this new technology. >> Absolutely. >> It's the bulldozer of innovation. It's not easy but there is a path. You guys have what, close to a hundred customers in blockchain? >> Yes. >> And it's a data story. Supply chain, blockchain, value chain, chain activities, interesting. >> It's going to just lead to a lot a lot more efficiency and accuracy as we move forward. >> Awesome! Inderpal Bhandari Global Chief Data Officer here on the CUBE, sharing his insights on data. We didn't even get to the good part around social data and graphs and all that great stuff that we love talking about. But more CUBE coverage is going to continue here. Day two coverage of IBM Think. I'm John Furrier, thanks for watching. (electronic music)

>> Narrator: Live from Las Vegas, it's theCUBE, covering Oracle Modern Customer Experience 2017, brought to you by Oracle. (bright, lively music) >> Hello and welcome back to a CUBE coverage of Oracle's Modern Customer Conference here at the Mandalay Bay in Las Vegas. I'm John Furrier with SiliconANGLE, theCUBE, with my co-host this week, Peter Burris, head of research at Wikibon.com, part of SiliconANGLE Media, and our next guest is Austin Miller, Product Marketing Director for Oracle Marketing Cloud. Welcome to theCUBE conversation. >> Thank you very much for having me. >> This coveted post-launch spot. >> Yeah, we have a lunch coma kicking in, but no, seriously, you have a really tough job because you're seeing the growth of the Platform Play, right, really robust horizontal platform, but how you got here through some really smart acquisitions but handled well, and integrated, we covered that last year. You guys are seeing some nice tailwinds with some momentum certainly around the expectations of what the customers want. >> Yeah, I think that one of the best things when we start thinking about, to your point, product integration, it's also the way that we are talking to our customers about how they can use the products together. It's not really enough just to have maybe one talk to another, but unless we prove out the use cases, you don't get the utilization, and I think this year what we've really seen is getting those use cases to actually start getting some traction in the field. >> So this integrated marketing idea seems to be the reality that everyone wants. >> Where are we on that progress bar, because this seems to be pretty much unanimous with customers, the question is how to get there, the journey, and the heroes that are going to drive and the theme of the conference. But the reality is this digital transformation is being forced for business change. >> Austin: Absolutely. >> And marketing is part of that digital fabric. >> I think that one of the most interesting things about this is if you look at kind of the history of when did the stacks start becoming actually part of the story, it was at a point where we didn't really necessarily even have the capabilities to do it. As a result many marketers who thought they were maybe buying into a stack approach got a little bit burned. I think now we are actually at that place where that value is not only something that they can see inherently and say "oh, I'd like all these applications to talk together," but it's actually feasible, it's something that they're going to be able to use, and they can be optimistic about, frankly. >> Where are they getting burned, you mentioned that, from buying into a full stack of software for a point solution, is that kind of what you meant? >> No, I think that in the marketing realm, when you're talking to marketers, it is very easy to think about all the horrible things that they have to deal with on a daily basis, all these problems. And the reality is that oftentimes you've had to have this conversation with them that says, you know, there are not going to be easy answers to hard problems. There are usually hard answers to hard problems. We can help alleviate some of that friction, especially when we start talking about data silos or things about interoperability, so being able to not just have integration, but pre-built function within these particular platforms, but realistically, it just wasn't something that we necessarily in the market in general were able to deliver on until somewhat recently. >> So, I am very happy that I heard you use the word "use cases," especially at a launch, because that's been one of the biggest challenges of both marketing technology when we think about big data, there's been such a focus on the technology, getting the technology right, and then the use cases and how it changed the way the business or the function did things, kind of either did or didn't happen. Talk about how a focus in use case is actually getting people to emphasize the outcomes, and how Oracle is helping people then turn that into technology decisions. >> This may sound almost counterintuitive, but in reality the way that use cases we see helping us the most is that it really helps spur about the organizational changes that we need in order to actually have some of this happen, 'cause it's very easy to say, "we have all this technology marketer and you should be using it all," but if you don't actually prove it out and how that's going to impact let's say the way that they're creating their marketing messages, on even a kind of not exciting basis, like how are you creating your emails, how are you creating your mobile messaging, how are you doing your website, and then start talking about those in actual use cases, it's very hard for people to organize their organizations around this kind of transformation. They need something tangible to hold onto. >> And the old way with putting things in buckets, >> Austin: Exactly. >> Right, so so hey we got one covered, move on to the next one ... >> Peter: Or by channels even. We got an email solution, or we got a web solution and as the customer moves amongst these different mechanisms, or engages differently with these mechanisms, the data then becomes, we've talked a lot about this, becomes the integration point, and that as you said affects a significant change on how folks think about organizing, but what do you think are going to be some of the big use cases if people are going to be ... you're providing advice and counsel to folks on the 2017. >> Yeah, so I think that talking about marketing-specific use cases is really important, especially when we start thinking about how am I using my first-party data that I may have within a particular channel. And I'm using that to contextually change the way I'm communicating to somebody on another channel. But if we kind of take that theme, and we think about let's not just expand it to marketing but let's really talk about customer experience, because as a customer, I go in-store, I go on email, I go on your mobile app, I don't view those as different things. That's just my experience with your brand. And even as we start getting to maybe some of the service things, am I calling a call center? The way that we're really thinking about marketing is not only bringing all this information across our traditional marketing channels, but how are we helping marketers drive organizational change beyond the traditional bounds of even their own marketing department into service, into sales, into on-store, because in reality that's where kind of the next step is. It's not just about, to your point, promotional emails. It's about how are we bringing this experience across the full spectrum. >> So it's really how is first-person data going to drive the role of marketer differently, the tasks of marketing as a consequence, and therefore how we institutionalize that work. >> Absolutely, and I think that you can see this in the investments that we've made in the ODC, Oracle Data Cloud. It's first step, let's start thinking about how we can start moving around on first-party data, that'll be a nice starting point, but then afterwards, how are we taking third-party data let's say from offline purchases, starting to incorporate that and that store's third-party data, 'cause then we really start getting to that simultaneously good experience or at least consistent experience across digital, across in-store, we start piecing together, but we really need to start at that baseline. >> A lot of people have been talking about the convergence of adtech and martech for years, and we had a CUBE alumni on our CUBE many years ago, when the Big Data movement started to happen, and he was a visionary, revolutionary kind of guy, Jeff Hammerbacher, the founder of Cloudera, who's now doing some pioneering work in New York City around science. He's since left Cloudera. But he said on theCUBE what really bothered him was some of the brightest minds in the industry were working on using data and put an ad in the right place. And he was being kind of critical of, use it for cooler things, but we look at what's happening on martech side, when you have customer experience, that same kind of principle of predictive thinking around how to use an asset can be applied to the customer journey, so now you bring up the question of A.I. If you broaden the scope of adtech and martech to say all things consumer, in any context, at any given time, you got to have an A.I. or machine learning approach to put the right thing at the right place at the right time that benefits the user >> Austin: It's not scalable. That's the reality of it. To you point, if you're going to start thinking about this across all these different channels, including advertising as well, the idea of being able to do these on a one-off basis, from a manual perspective, it's completely untenable, you're completely correct, but to that point, where you're talking about the best minds in the industry maybe dedicated to figuring out, "if I put a little target here, am I going to get somebody to click on that ad one time, or how am I placing it," that is very much the way that we were at the very beginning parts of marketing technology, where it was bash and blast messaging, how can we just kind of get the clicks and the engagement, and how do we send out >> John: spray and pray >> Exactly. And now I think that we are getting to a much more nuanced understanding of the way that we advertise because it's much more reliant on context, it's not just how can I get my stuff in front of somebody's eyeballs, it's how am I placing it when they're actually showing some sort of intention for maybe the products I already have. >> Adaptive intelligence is interesting to me because what that speaks to is, one, being adapted to a real time, not batch, spray and pray and the old methodology of database-driven things, no offense to the main database cache at Oracle, but it's a system of record, but now new systems of data are available, and that seems to be the key message here, that the customer experience is changing, multiple channels, that's omnichannel, there needs to be ... everyone's looking for the silver bullet. They think it's A.I., augmented intelligence or artificial intelligence. How do you see that product roadmap looking, because you're going to need to automate, you're going to need to use software differently to handle literally real time. >> Completely. I think that this is a really important distinction about the way that we view A.I. and how it factors into marketing technology and the way that I think a lot of people in the industry do. I think that once again this theme of there aren't easy answers to hard problems, it is very pleasant to think that I'm just going to have one product that's going to solve everything, from when I should send my next email, to if there's clean water in this particular area in a third-world country, and that's just something that maybe sounds nice, but it's not necessarily something that's actually tangible. The way that we view A.I. is it's something that's going to be embedded and actually built into each of these different functions so that we can do the mission-critical things on the actual practical level, and kind of make it real for marketers, make it something that's isn't just "oh, buy this and it will solve all your problems." >> So I'm going to ask you the question, the old adage, "Use the right tool for the right job, and if you're a hammer everything looks like a nail." A lot of people use email marketing that way, they're using it for notifications when in reality that's not the expectation of the consumer, some are building in a notification engine separate from email. All that stuff's kind of under the covers, in the weeds, but the bigger question to you is, I want to get your insight on this because you're talking to customers all the time, is as customers as you said need to change organizationally, they're essentially operationalizing this modern era of CX, customer experience, so it's a platform-based concept which pretty much everyone agrees on, but we're in the early innings of operationalizing this >> Austin: Oh yeah. >> So how do you see that evolving and what do you want customers to do to be set up properly if they're coming in for the first inning of their journey, or even if they're midstream with legacy stuff? >> I think that that's a really good perspective, because you don't want to necessarily force people to go through excruciating organizational change in preparation if we're in maybe the first inning, but it is really just about setting up the organization to adjust as realistically we get into the middle innings and into the later innings. And really the kind of beginning foundation of this is understanding that these arbitrary almost like tribal distinctions between who owns what channel, who's the email marketer, or who's the mobile person, they need to be broken down, and start thinking about things instead of these promotional blasts to your point, or even maybe reactionary notifications. How is this contributing to the number of times your brand is touching me in a day, or the way that I'm actually communicating, so I think that it's an interesting kind of perspective of how we were organizationally set up for that, but the short answer is that A.I. is going to fundamentally change the way that marketers are operating. It's not going to fundamentally change maybe everything that they're doing or it's not going to be replacing it. It's going to be a complementary role that they need to be ready to adjust to. >> So you are, you're in product, product management. >> Austin: Product marketing >> Product marketing. So you are at that interface between product and marketing, both moving more towards agile. How are you starting to use data differently and how would you advise folks like you in other businesses not selling software that might not have the same digital component today but might have a comparable digital component in the future, what would you tell them to do differently? >> So, I think that the first step is to actually have an honest assessment of what we have and what we don't have. I think that there's a lot of people who like to kind of close their eyes or maybe plug their ears and just sort of continue down the path of least resistance. >> Peter: Give me ... >> Oh, an honest assessment of what kind of data we do have today, what kind of data we might actually need, and then most importantly, is that actually feasible data to get. Because you can't >> you can wish it but you can't get it >> You can wave a magic wand and say these are the numbers that I need on this particular maybe interest level of these particular ... >> John: The fatal flaw is hoping that you're going to get data that you never get, or is ungettable. >> Or, this is really something that I think a lot, would resonate more with marketers is that we have now set up all these different points of interaction that are firehoses of data spraying it at me, I may be able to retroactively look at it and maybe garner some kind of insight, but there's just no real way for me to take that and make it actionable right away. It is a complete mess of data in a lot of these organizations. >> And that's where A.I. comes in. >> Austin: Absolutely. It's able to automate that, reaction ... >> Peter: Triage at a bare minimum. >> Correct >> So the first starts with data. What would be the second thing? >> So it's data, presume that you're going to need help on the triage and organizing that data. Is there a third thing? >> I would say that you're going down the right path with the steps there, but once again, we're all talking about these concepts that do require a great deal of specialization and a lot of actual understanding of the way we're dealing with data. So honest assessment is definitely that first part, but then do I have the actual people that I need in order to actually take action on this? Because it is a specialized kind of role that really hasn't traditionally been within marketing organizations. >> I know you guys have a big account-based, focus-account-based marketing, you know, doing all kinds of things, but I'm a person, I'm not a company, so that's a database saying "hey, what company do you work for?" And all the people who work for that company and their target list. I'm a person. I'm walking around, I've got a wearable, I might be doing a retail transaction, so the persona base seems to be the rage and seems to be the center and we heard from Mark Hurd's keynote, that's obviously his perspective and others as well so it's not like a secret, but how do you take it to the next level? An account base could help there too, but you need to organize around the person, and that seems to open up the identity question of okay, how do I know it's John? >> I think that goes beyond just personal taste, but into what does this person actually do at this company, because I can go in and give a headspinning presentation to maybe a C-level executive and say, "look at all this crazy stuff you can do," and meanwhile the guy who might be making the buying decision at the end of the table's looking at that and being like, "there's no way we can do that, we don't have the personnel to do that, there's no chance," and you have already dissension from the innards of the actual people who are making the buying decisions. The vision can't be so big that it resonates with no one. And you need to understand on a persona level what is actually resonated with them. 'Cause feasibility is a very important thing to our end user, and we need to actually incorporate that into our messaging, so it's not just so pie-in-the-sky visioning. >> I did a piece of research, sorry John, I did a piece of research a number of years ago that looked at the impact of selling mainly to the CIO. And if you sell successfully to the CIO, you can probably guarantee nine months additional time before the sale closes. >> Austin: Yeah. Because the CIO says "this is a great idea," and then everybody in the organization who's now responsible for doing it says "hold on, don't put this in my KPIs while I take a look at it and what it really means and blah blah blah. Don't make me responsible for this stuff." You just added nine months. >> Absolutely. I even have a very minute example for something that we rolled out. This was a great learning opportunity. Because we rolled out a feature called multi-variant testing. It's not important what exactly it is for the purposes of this, but basically it's the idea of you can take one email and eight versions of it, test it, and then send out the best one. Sounds great, right? I'm an executive, I'm like boy, I'm going to get every last ounce of revenue from my emails, I'm only going to send out the best content. If you don't pitch that right, the end user, all they hear is wait, the thing that I do one of, I have to create eight of now? Am I going to get to see my kids ever again? That's just the way you have to adjust ... >> And seven of 'em are going to be thrown away. I'm going to be called a failure. >> Exactly. So it's just not something that you can take for granted because marketers have a variety of different roles and a variety of firm responsibilities. >> And compound that with everything's going digital. >> Exactly. >> So (mumbles) Austin, great to have you on theCUBE. Spend the last minute though, I'd like you just to share for the last minute, what's the most important thing happening here at #ModernCX besides the simplicity of the messaging of modern era of customer expectations, experiences, all that's really awesome, but what should people know about that aren't here, watching. >> I'd just say that the one thing that at least resonates most with me, and this is once again coming from a product and sort of edging on marketing, is that the things that we've been talking about with not only A.I. but even just simple things like having systems that are communicating to each other, they're actually real and we're seeing that as real. You can actually see them working together in products and serving up experiences to customers that we're even doing now as part of the sales process and saying "hey, this is how you would actually do this," as opposed to just "here's our Chinese menu of different options. Pick what you want and then we can just kind of serve it up." Because I think that there's something that's very heartening to maybe marketers who have a little bit of, I don't know, doubt about whether or not this is real. It is real, it's here today, and we're able to execute on it. >> And that's the integration of a multi-product and technology solution. >> I would almost say that it's slightly different from that though, in terms of, it's not just integration of these pieces, it's integration that's pre-built, so we actually have it pre-built together and then we also have these tremendous, new, innovative features and functionality that are coming with those integrations. It's not just portability, it's actual use cases. >> Would you say that it's as real as the data? >> It's as real as the data. I think that that's ... >> If you have the data, then you can do what you need to do. >> That's a very, a very good point. >> Austin Miller, Product Marketing Director at Oracle Marketing Cloud. Thanks for sharing the data here on theCUBE where we're agile, agile marketing is the focus. I'm John Furrier, Peter Burris. More coverage from day one at Mandalay Bay for Oracle Modern Customer Experience show. We'll be right back with more after this short break. (bright, lively music)