>> From around the globe it's theCUBE with coverage of the GLOBAL.NEXT DIGITAL EXPERIENCE brought to you by Nutanix. >> Hi and welcome back, we're wrapping up our coverage of the Nutanix .Next Global Digital Experience, I'm Stu Miniman and I'm happy to welcome to the program, help us as I said wrap things up. We're going to be talking about running better, running faster and running anywhere. A theme that we've heard in the keynotes and throughout the two day event of the show. We have three VPs to help go through all the pieces coming up on the screen with first of all we have Greg Smith who's the vice president of product technical marketing right next to him is Madhukar Kumar, who is the vice president of product and solutions marketing and on the far end, the senior vice president Thomas Cornely, he is the senior vice president, as I said for product portfolio management. Gentlemen, thank you so much for joining us. >> Good to be here Stu. >> Alright, so done next to show we really enjoy, of course this the global event so not just the US and the European and Asia but what really gets to see across the globe and a lot going on. I've had the pleasure of watching Nutanix since the early days, been to most of the events and the portfolio is quite a bit bigger than just the original HCI solution. Thomas since you've got to portfolio management is under your purview, before we get into summarizing all of the new pieces and the expansion of the cloud and software and everything just give us if you could that overview of the portfolio as it's coming into the show. >> Yeah absolutely Stu. I mean as you said we've been doing this now for 10 plus years and we've grown the portfolio we developed products over the years and so what we rolled out at this conference is a new way and to talk about what we do at Nutanix and what we deliver in terms of set of offerings and we talk about the 4 D's. We start with our digital hyper converged infrastructure cartridges, dual core HCI stack that you can run on any server and that stack these two boards are data center services which combines our storage solutions, our business computing and data recovery solution and security solutions on DevOps services, which is our database automation services, our application delivery automation services and now our new common and that's one of the service offerings and then our desktop services catridges which is our core VDI offering and offering our discipline and service offerings. So put all these together this is what we talk about in the 4 D's, which is on Nutanix cloud platform that you can run on premises and now on any job. >> Well thank you Thomas for laying the ground work for us, Greg we're going to come to you first that run better theme as Thomas said and as we know HCI is at the core a lot of discussions this year of course, the ripple effect of the global pandemic has more people working remotely that's been a tailwind for many of the core offerings, but help us understand, how's that building out some of the new things that we should look at in the HCI. >> Yeah ,thanks too for Nutanix and our customers a lot of it begins with HCI, right. And what we've seen in the past year is really aggressive adoption for HCI, particularly in core data center and private cloud operations and customers are moving to HCI in our not only for greater simplicity, but to get faster provisioning and scaling. And I think from a workload perspective, we see two things, that ACI is being called upon to deliver even more demanding apps those with a really very low latency such as large scale database deployments. And we also see that HCI is expected to improve the economics of IT and the data center and specifically by increasing workload density. So we have a long history, a storied history of continually improving HCI performance. In fact every significant software release we've optimized the core data path and we've done it again. We've done it again with our latest HCI software release that we announced just this week as our next. The first enhancement we made was in 518, was to reduce the CPU overhead and latency for accessing storage devices such as SSD and NBME and we've done this by managing storage space on physical devices in the HCI software. So rather than rely on slower internal file systems and this new technology is called block store and our customers can take advantage of block store simply by upgrading to the new software released and we're seeing immediate performance gains of 20 to 25% for IOPS and latency. And then we built on top of that, we've added software support for Intel Optane by leveraging user space library, specifically SPDK or storage performance development kit. And SPDK allows Nutanix to access devices from user space and avoid expensive interrupts and systems calls. So with this support along with block store we're seeing application performance gains about this 56% or more. So we're just building our own a legacy of pushing performance and software and that's the real benefit of moving to HCI. >> And just to add to that too when it comes to run better I think one of the things that we think of running better is automation and operation then when it comes to automation and operation there are a couple of ways I would say significant announcements that we also did to. One is around Comm as a service. Comm is one of those products that our customers absolutely love and now with Comm as a service you have a SaaS plane, so you can just without installing anything or configuring anything you could just take advantage of that. And the other thing we also announced is something called Nutanix central and Nutanix central gives you the way to manage all your applications on Nutanix across all of your different clusters and infrastructure from a single place as well. So two big parts of a run better as well. >> Well, that's great and I've really, is that foundational layer, Madhukar if we talk about expanding out, running faster the other piece we've talked about for a few years is step one is you modernize the platform and then step two is really you have to modernize your application. So maybe help us understand that changing workload cloud native is that discussion that we've been having a few years now, what are you hearing from your customers and what new pieces do you have to expand and enable that piece of the overall stack? >> Yeah, so I think what you mentioned which is around cloud data the big piece over there is around Cybernetics's and they already had a carbon, so with carbon a lot of the things of complexities around managing cybernetics is all taken care of, but there are higher level aspects on it like you have to have observability, you have to have log, you have to have managed the ingress ,outgress which has a lot of complexity involved with, so if you're really just looking for building of applications what we found is that a lot of our customers are looking for a way to be able to manage that on their own. So what we announced which is carbon platform service enables you to do exactly that. So if you're really concerned about creating cloud native applications without really worrying too much about how do I configure the cybernetics clusters? How do I manage Histio? How do I manage all of that carbon platform service that actually encapsulates all of that to a sass plate So you can go in and create your cloud native application as quickly and as fast as possible, but just in a typical Nutanix style we wanted to give that freedom of choice to our customers as well. So if you do end up utilizing this what you can also choose is the end point where you want these application to run and you could choose any of the public clouds or the hyper scaler or you could use a Nutanix or an IOT as an endpoint as well. So that was one of the big announcements we've made. >> Great, Greg and Madhukar before we go on, it's one of the things that I think is a thread throughout but maybe doesn't get highlighted as much but security of course is been front and center for a while, but here in 2020 is even more emphasized things like ransomware, of course even more so today than it has been for a couple of years. So maybe could it just address where we are with security and any new pieces along there that we should understand? >> Yeah, I can start with that if I could. So we've long had security in our platform specifically micro-segmentation, fire walling individual workloads to provide least privilege access and what we've announced this week at .Next is we've extended that capability, specifically we've leveraged some of the capabilities in Xi beam and this is our SAS based service to really build a single dashboard for security operators. So with security central, again a cloud based SAS app, Nutanix customers can get a single pane from which they can monitor the entire security posture of their infrastructure and applications, it gives you asset reporting, asset inventory reporting, you can get automated compliance checks or HIPAA or PCI and others. So we've made security really easy in keeping with the Nutanix theme and it's a security central is a great tool for that security operations team so it's a big step for Nutanix and security. >> Yeah. >> To actually add on this one, one bit piece of security central is to make it easier, right. To see your various network bills and leverage the flow micro segmentation services and configure them on your different virtual machines, right? So it's really a key enabler here to kind of get a sense of what's going on in your environment and best configure and best protect and secure infrastructure. >> Thomas is exactly right. In fact, one of the things I wanted to chime into and maybe Greg you could speak a little bit more about it. One of my favorite announcements that we heard or at least I heard was the virtualized networking and coming from a cloud native world, I think that's a big deal. The ability to go create your virtual private cloud or VPCs and subnets and then be able to do it across multiple clouds. That's, something I think has been long time coming, so I was personally very, very pleased to hear that as well. Greg, do you want to add a little bit more? >> Yeah, that's a good point I'm glad you brought that up, when we talk about micro-segmentation that's one form of isolation, but what we've announced is virtual networking. So we really adopted some cloud principles, specifically virtual private clouds constructs that we can now bring into private cloud data centers. So this gives our customers the ability to define and deploy virtual networks or overlays this sort of sit on top of broadcast domains and VLANs and it provides isolation for different environments. So a number of great use cases, we see HCI specifically being relied upon for fast provisioning in a new environment. But today the network has always been sort of an impediment to that we're sort of stuck with physical network plants, switches and routers. So what virtual networking allows us to do is through APIs, is to create an isolated network a virtual private cloud on a self service basis. This is great for organizations that increasing operating as service providers and they need that tenant level segmentation. It's also good for developers who need isolated workspace and they want to spin it up quickly. So we see a lot of great use cases for virtual networks and it just sort of adds to our full stack so we've software defined compute, we've software defined storage, now we're completing that with software defining networking. >> And if I have it right in my notes the virtual networking that's in preview today correct? >> Yes, we announce it this week and we are announcing upcoming availability, so we have number of customers who are already working with us to help define it and ready to put it into their environments. The virtual private network is upcoming from Nutanix. >> Yeah, so I absolutely I've got, Mudhakar, I've got a special place in my heart for the networking piece that's where a lot of my background is, but there was a different announcement that got a little bit more of my attention and Thomas we're going to turn to you to talk a little bit more about clusters. I got to speak with Monica and Tarkin, ahead of the conference when you had the announcement with AWS, for releasing Nutanix clusters and this is something we've been watching for a bit, when you talk about the multicloud messaging and how you're taking the Nutanix software and extending it even further that run anywhere that you have talk about in the conference. So Thomas if you could just walk us through the announcements as I said something we've been excited, I've been watching this closely for the last couple of years with Nutanix and great to see some of the pieces really starting to accelerate. >> Well absolutely and as you said this is something that's been core to the strategy in terms of getting and enabling customers to go and do more with hybrid cloud and public cloud and if you go back a few weeks when we announced clusters on AWS this was a few weeks back now, we talked of HCI is a prerequisite to getting the most of your hybrid cloud infrastructure, which is the new HCI in our mind and what we covered at .Next was this great announcement with Microsoft Azure, right, and just leveraging their technologies bringing some of their control plan onto our cloud platform but also now adding clusters on Azure and announcing that we'll be doing this in a few months. Enabling the customers to go and take the same internet cloud platform the same consistent set of operations and technology services from data center services, DevOps services and desktop services and deploying those anywhere on premises, on AWS or on Microsoft Azure and again for us cloud is not a destination. This is not a now we just accomplished something. This is a new way of operating, right? And so it's touching, giving customers options in terms of where they want to go to count so we keep on adding new counts as we go but also it's a new form of consuming infrastructure, right? From an economist perspective you probably know, you don't extend it you're pressing into the moving to is fiction based offering on all of our solutions and our entire portfolio and as we go and enable these clusters offering, we're not making consumptions more granular to non customers do not consume our software on an hourly basis or a monthly basis. So again this is kind of that next step of cloud is not just technology, it's not a destination it's a new way of operating and consuming technology. >> Why think about the flexibility that this brings to existing new techs customers it gives them enormous choices in terms of new infrastructure and whether they set up new clusters. So think about in text a customer today. They may have data centers throughout the US or in Europe and in Asia Pacific, but now they have a choice rather than employ their Nutanix environment, in an existing data center or Colo, they can put it into AWS and they can manage it exactly the same. So it just provides near infinite choice for our customers of how they deploy HCI and our full software stack. In addition to the consumption that Thomas talked about, consumption choices. >> Yeah, just to add to that again I should have said this is also one of my favorite announcements as well, yesterday. We Greg, myself, Thomas, we were talking to some industry analysts and they were talking about, Hey, you know how there is a need for pods where you have compute, you have network and you have storage altogether, and now people want to run it across multiple different destination but they have to have the freedom of choice. Today using one different kind of hardware tomorrow you want to use something else. They should be portability for that, so with clusters, I think what we have been able to do is to take that concept and apply it across public cloud. So the same whether you want to call it a pod or whatever but compute, storage, networking. Now you have the freedom of choice of choosing a public cloud as an end point where you want to run it. So absolutely one of those I would say game-changing announcements that we have made more recently. >> Yeah-- >> To close that loop actually and talk about portability as enabling quality of occupations. But also one thing that's really unique in terms of how we're delivering this to customers is probability of licenses. The fact that you have a subscription term license for on premises you can very easily now repay the license if you decide to move a workload and move a cluster from one premises to your count of choice, that distance is also affordable. But so again, full flexibility for these customers, freedom of choice from a technology perspective but also a business perspective. >> Well, one of the things I think that really brings home how real this solution is, it's not just about location, Thomas as you said, it's not about a destination, but it's about what you can do with those workloads. So one of the use cases I saw during the conference was talking about a very long partner of a Nutanix Citrix and how that plays out in this clusters type of environment so maybe if you could just illustrate that as one of those proof points is how customers can leverage the variety of choice. >> Yeah, we're very excited about this one, right? Because given what we're currently going through as a humanity right now, across the world with COVID situation, and the fact that we all have now to start looking at working from home, enabling scaling of existing infrastructure and doing it without having to go and rethink your design enabling this clusters in our Citrix solution is just paramount. Because what it will ask you to do is if you say you started and you had an existing VDI solution on premises using Citrix, extending that now and you putting new capacity in every location where you can go and spin this up in any AWS region or Azure region, no one has to go and the same images, the same processes, the same operations of your original desktop infrastructure would apply regardless of where you're moving now your workforce to work remotely. And this is again it's about making this very easy and keeping that consistency operations, from managing the desktops to managing that core infrastructure that is now enabled by using different clusters on Azure or AWS. >> Well, Thomas back in a previous answer, I thought you were teeing something up when you said we will be entering a new era. So when you talk about workloads that are going to the cloud, you talk about modernization probably the hottest area that we have conversations with practitioners on is what's happening in the database world. Of course, there's migrations, there's lots of new databases on there, and Nutanix era is helping in that piece. So maybe if we could as kind of a final workload talk about how that's expanding and what updates you have for the database. >> Absolutely and so I mean Eras is one of our key offerings when it comes to a database automation and really enabling teams to start delivering database as a service to their own and users. We just announced Era 2.0 which is now taking Era to a whole other level, allowing you to go and manage your devices on cross clusters. And this is very topical in this current use case, because we're talking of now I can use era to go in as your database that might be running on premises for production and using Era to spin up clones for test drive for any team anywhere potentially in cloud then using clusters on the all kind of environments. So those use cases of being which more leverage the power of the core is same structure of Nutanix for storage management for efficiency but also performance and scaling doing that on premises and in unique cloud region that you may want to leverage, using Era for all the automation and ensuring that you keep on with your best practices in terms of deploying and hacking your databases is really critical. So Era 2.0 great use cases here to go and just streamline how you onboard databases on top of HCI whether you're doing HCI on premises or HCI in public town, and getting automation of those operations at any scale. >> Yeah, hey Tom has mentioned a performance and Era has been a great extension to the portfolio sitting on top of our HCI. As you know Stu database has long been a popular workload to run it all HCI, particularly Nutanix and it extends from scalability performance. A lot of I talked about earlier in terms of providing that really low latency to support the I-Ops, to support the transactions per second, that are needed these very demanding databases. Our customers have had great success running SAP, HANA, Oracle SQL server. So I think it's a combination of Era and what we're doing as Thomas described as well as just getting a rock solid foundational HCI platform to run it on and so that's what we're very excited about to go forward in the database world. >> Wonderful, well look, we covered a lot of ground here. I know we probably didn't hit everything there but it's been amazing to watch Nutanix really going from simplicity at its core and software driving it to now that really spiders out and touches a lot of pieces. So I'll give you each just kind of final word as you having conversations with your customers, how do they think of Nutanix today and expect that we have a little bit of diversity and the answers but it's one of those questions I think the last couple of years you've asked when people register for .Next. So it's, I'm curious to hear what you think on that. Maybe Greg if we start with you and kind of go down the line. >> Yeah, for me what sums it up is Nutanix makes IT simple, It makes IT invisible and it allows professionals to move away from the care and feeding structure and really spend more time with the applications and services that power their business. >> And I agree with Greg I think the two things that always come up, one is the freedom of choice, the ability for our customers to be able to do so many different things, have so many more choices and we continue to do that every time we add something new or we announce something new and then just to add onto what Greg said is to try and make the complexities invisible, so if there are multiple layers, abstract them out so that our customers are really focused on doing things that really matter versus trying to manage all the other underlying layers, which adds more complexity. >> Yeah You could just kind of send me to it up right. In the end, internet is becoming much more than HCI, as hyper converged infrastructure this is not taking it to another level with the hybrid cloud infrastructure and when you look at what's been built over the last few years from the portfolio points that we now have, I think it was just growing recognition that internet actually delivers this cloud platform that you can all average to go and get to a consistency of services, operations and business operations in any location, on premises through our network constant providers through our Nutanix cloud offerings and hyper scaler with Nutanix clusters. So I think things are really changing, the company is getting to a whole other level and I couldn't be more excited about what's coming out now the next few years as we keep on building and scaling our cloud platform. >> And I'll just add my perspective as a long time watcher of Nutanix. For so long IT was the organization where you typically got an answer of no, or they were very slow to be able to react on it. It was actually a quote from Alan Cohen at the first .Next down in Miami he said, "we take need to take those nos "and those slows and get them to say go." So the ultimate, what we need is of course reacting to the business, taking those people, eliminating some of the things that were burdensome or took up too much time and you're freeing them up to be able to really create value for the business. Want to thank Greg, Madhukar, Thomas, thank you so much for helping us wrap up, theCUBE is always thrilled to be able to participate in .Next great community customers really engaged and great to talk with all three of you. >> Thank you. >> Alright so that's a rack for theCUBES coverage of the Nutanix Global.Next digital experience. Go to thecube.com. thecube.net is the website where you can go see all of the previous interviews we've done with the executives, the partners, the customers. I'm Stu Miniman and as always thank you for watching theCUBE.

>> Narrator: Live from Washington, D.C., it's theCUBE covering Boomi World 19. Brought to you by Boomi. >> Welcome back to theCUBE, the leader in live tech coverage. I'm Lisa Martin, John Furrier is my co-host, and we are at Boomi World 2019 in Washington, D.C. Very pleased to be joined by the founder of Boomi and the co-founder and CEO of Guru, Rick Nucci. Hey, Rick. >> Hello. >> Lisa: Welcome to theCUBE. >> Thanks for having me, this is very cool setup. >> Lisa: Yeah, isn't it?! >> Rick: Yeah. >> So this is a founder of Boomi. It's pretty cool to have a celebrity on our stage. >> Rick: I'm not a celebrity. (laughs) >> (laughs) Talk to us about all that back in the day back in Philadelphia when you had this idea for what now has become a company that has 9,000+ customers in 80+ countries. >> Yeah, I'm beyond proud of this team and just how well they have done and made this business into what it is today. Yeah, way back in 2007, we were really looking at the integration market, and back then, cloud was really an unknown future. It was creeping up the Hype Cycle of the Gartner. Hype Cycle's my favorite thing they do. A lot of people were dismissing it as a fad, and we were early adopters of cloud internally at Boomi. We were early users of Salesforce and NetSuite and just thought and made a bet and a lot of this stuff is luck as any founder will tell you, any honest founder will tell you. And recognize that, hey, if the world were to move to cloud, how would you actually think about the integration problem? Because it would be very different than how you would think about it in the on-prem days when you have everything in your own data center behind your own four walls. In this world, everything's different. Security's a huge deal, the way data moves and has to mediate between firewalls is a big deal. And none of these products are built like this and so, really wanted as a team, and I remember these early conversations and had the willingness to take a big bet and swing for the fences and what I mean by that is really build a product from the ground up in this new paradigm, new cloud, and take a bet and say, hey, if cloud does take off, this will be awesome for Boomi. If not, well, we'll be in the line of all the other startups that have come and gone. And I think we ended up in a good spot. >> Yeah, that's a great point, Rick, about the founders being honest. And a lot of it is hard work, but having a vision and making multiple bets and big bets. I remember, when EC2 came out, it was a startup dream, too, by the way. You could just purchase a data center. But it wasn't fully complete, it was actually growing very fast. More services were coming on, they were web services, so that was API-based concepts back then. When was the crossover point for you guys going, "okay, we got this, the bets are coming in. "We're going to double down, we're going to double down on this." What were some of those moments where you started to get visibility that was a good bet? And what did you do? >> Yeah, what it really was was the rise of SaaS, very specifically, and the rise of business applications that were being re-architected in the cloud. And everybody knew about Salesforce, but there weren't a lot of other things back then. And there was NetSuite and a handful of others, but then, you started to see additional business units start to build cloud, and you had, in the HR space, with success factors in Taleo and marketing automation space with Eloqua and Marketo. CRM space, we all know that story, e-commerce space procurement, and you start to see these best-of-breed products rise up which is amazing, but as that was happening, it was proliferating the integration problem. And so what became really clear to us, I think, as we were going through this and finding product market fit for Boomi, again, back in 2007, 2008, that was the pattern that emerged, like hey, every time someone buys one of these products, they are going to have to integrate 'cause you're talking about employee data, customer data. You have to integrate this with your other systems and that was going to create an opportunity for us and that was where we were like, okay, I think we're onto something. >> You know, to date, we've been doing theCUBE for 10 years. We made a big bet that people, authentic conversation would be a good bet, turns out it worked. We love it, things going great, but now, we're living in a world now that's getting more complex and I want to get your thoughts that Dave Vellante, myself, Stu who have been talking about how clouds changed and we were goofing on the Web 2.0 metaphor by saying, Cloud 1.0, Cloud 2.0. But I want to get your thoughts on how you might see this because, if you say Cloud 1.0 was Amazon, compute storage, AtScale, cloud NATO, all started there. Pretty straightforward if you're going to be born in the cloud, then you could work with some things there, but to bring multicloud and for enterprises to adopt with this integration challenge, Cloud 2.0 unveils some new things like, for instance, network management now is observability. Configuration management is now automation (chuckles). So you start to see things emerge differently in this Cloud 2.0 operating model. How do you see Cloud 2.0? Do you believe that, one, there's a Cloud 2.0 the way I said it, and if so, what is your version of what Cloud 2.0 would look like? >> Yeah, I think, yes, definitely think things are changing and the way that I think about it is that we're continuing to unbundle, and what I mean by unbundle is we're continuing to proliferate... Buyers are willing to buy and, therefore, we're continuing to proliferate relatively narrower and narrower and deeper and deeper capabilities and functionalities. And one big driver of that is AI, specifically, machine learning, and not the hypey stuff, but the real stuff. It's funny, man, when you compare, right now, AI, and what I was just talking about, it's the same thing all over again. It's Hype Cycle crawling up the thing, okay. But now, I think the recipe for good AI products that really do solve problems is that they're very intentionally narrow and they're very deep because they're gathering good training data and they're built to solve a very specific problem. So I think-- >> Like domain expertise, domain-specific-- >> Exactly, industry expertise, domain expertise, use case. If you're gathering training data about a knowledge worker, the data you'll gather is very different if you're a salesperson or an HR professional or an engineer. And I think the AI companies that are getting it right, are really dialed in and focused on that, so as a result, you see this proliferation of things that might be layered on top of big platforms like CRM's and technologies like Slack, which is creating a place for all this to come together, but you're seeing this unbundling where you're getting more and more kind of almost microservices, not quite, but very fine-tuned, specific things coming together. >> So machine learning, I totally agree with you, it's definitely hype, but the hardcore machine learning has a math side to it and a cognition side, cognitive learning thing. But, also, data is a common thread here. I mentioned domain-specific. >> Rick: All about the data. >> So, if data's super important, you want domain expertise which I agree with, but also there's now a horizontal scalability with observation data. The more data you have, the better at machine learning. It may or may not, depending on what the context is, so you have contextual data, this is a (chuckles) hard thing. What's your view on this because this is where people maybe get caught around the axis of machine learning hype and not nearly narrowing on what their data thinking is. >> Rick: 100%. >> What's your--? >> 100%, I think people will tend to fall in the trap of focusing on the algorithms that they're building and not recognizing that, without the data, the algorithms are useless. Right? >> Lisa: Right. >> And that it's really about how, as a ML problem that you're trying to tackle. Are you gathering data that's good, high-quality, scalable, accurate, protected, and safe? Because now, for different reasons, but again, just like when we were moving to cloud, security and privacy are utmost important because, for any AI to do its job well, it has to gather a lot of data out of the enterprise and store it and train off of that. >> It's interesting a lot of the cloud play. I mean sales was just a unicorn right out of the gate and they were a pioneer, that's what it is. They were cloud before cloud was cloud as we know it today. But you see a lot of things like the marketing automation cloud platform. It's a marketing cloud, I got a sales cloud. Almost seem too monolithic and you see people trying to unbundle that. I think you're right. Or break it apart 'cause the data is stuck in this full-stack model because, if you agree with your sets, horizontal scalability and vertical integration is the architecture. Technically, that's half-stack. (chuckles) >> Yes, yes. >> John: So half-stack developers are evaluable now. >> Totally, and yes, I like that term. The other problem that I think you're getting at is tendency isolation of that data. A lot of things were built with that in mind, meaning that the best AI you're going to build is only going to be what you can derive from one customer's set of data. Whereas, now, people are designing things intentionally such that the more customers that are using the thing, the better and smarter it gets. And so, to your point about monolithic, I think the opportunity that the next wave of startups have is that they can design in that world and that just means that their technology will get better faster 'cause it'll be able to learn from more data and-- >> This hasn't been changing a lot in cloud. I want to get your thoughts because you guys at Boomi here are on a single-tenant instance model because the collective intelligence of the data benefits everybody as more people come in. That's a beautiful fly, we'll feel a lot like Amazon model to me. But the old days, multi-tenancy was the holy grail. Maybe that came from the telcos or whatever, hosting world. What's your view on single-tenant instance on a SaaS business versus, say, multiten... There's trade-offs and pros and cons. What's your opinion, where do you lean on this one? >> Yeah, I mean we, both Boomi and Guru, so two eras worth or whatever. You have to have some level of tenancy isolation for some level of what you do. And, at Boomi, what we did is we separated the sensitive, private data. Boomi has customers processing payroll through its product, so very, very sensitive stuff absolutely has to be protected and isolated per tenant, and Boomi and Guru is signing up for that, and the clauses that we sign to are security agreements. But what you can decouple from that is more of the metadata or the attributes about that data and that customer, so Boomi, you're referring to, launched way back when Boomi Suggest which basically learned. As all the people were building data maps, connecting different things together, Boomi could learn from all that and go, oh, you're trying to do this. Well, these however many other customers, let me suggest how these maps are drawn, and Guru, we're following a very similar pattern, so Guru, we store knowledge which also tends to be IP for a company and so, yes, we absolutely adhere to the fact that only a handful of our employees can ever see that stuff, and that's 'cause they're in devops, and they needed to keep things running, but all the tenants are protected from one another. No one could ever leak to another one. But there are things about organization and structure and tagging and learnings you can get that are not that sensitive stuff that does make the product better from an AI perspective the more people that use it. And so, I don't know that I'm giving you one or another, but I think it does come down to how you intentionally design your data to it. >> John: Decoupling is the critical piece. >> Absolutely. >> This is the cloud architecture. Decouple, use API's to connect highly cohesive elements, and the platform can be cohesive if shared. >> Absolutely, and you can still get all the benefits of scalability and elastic growth and, yeah, 100%. >> Along that uncoupling line, tell us a little bit briefly about what Guru is and then I want to talk about some of the use cases. I know I'm a big Slack user; you probably are too, John. Talk to us about what you're doing there, but just give our folks a sense of what Guru is and all that good stuff. >> Sure, I mean Guru's, in some ways, like Boomi, rethinking a very old problem, in this case, it's knowledge management. That's a concept we've talked about for a long time and I think, these days, it has really become something that does impact a company's ability to scale and grow reliably, so very specifically, what we do is we bring the knowledge that employees need to do their job to them when they need it. So imagine if you're a customer support agent and you're supporting Spotify, you're an employee of Spotify. And I write in and I want to know about the new Hulu partnership. As an agent, you use Guru to look up and give me that answer and you don't have to go to a portal, you don't have to go to some other place to do that. Guru's sitting there right next to your ticket or your chat as you're having it in real time, saying, hey, there's asking about Hulu. This is the important things you want to know and talk about. And then the other half of that is, we make sure that that doesn't go still. The classic problem with knowledge products is the information, when you're talking about something like product knowledge, changes all the time. And the world we live in is moving faster and faster and faster, so we used to ship product once a year, once every two years. Now we ship product every month, sometimes couple times a month. >> Can you get a Guru bot for our journalism and our Cube hosts? We can be real time. >> Hey! >> I would be happy to do that. >> That'd be great! >> (laughs) Guru journalist. >> Actually, you're able to set it right in there where your ears are-- >> Lisa: I'll take it. >> Just prompting you, exactly. So, and then you asked about Slack, that's a really great partner for us. They were an early investor in the company. They're a customer, but together, if you think about where a lot of knowledge exchange happens in Slack, it's, hey, I need to know something. I think I can go slack John 'cause I think he'll know the answer. He knows about this. And you're like the 87th person who's asked me that same thing over again. Well, with Guru being integrated into Slack, you can just say, "Guru, give them the answer." And you don't have to repeat yourself. And that expert fatigue problem is a real thing. >> John: That's a huge issue. >> Absolutely. >> And, as your company grows and more and more people are, oh, poor John's getting buried for being the expert, one of the reasons he got you there. Now he's getting burned out and buried from it. And so we seek to solve that problem and then, post-Guru, a company will scale faster, they'll onboard their employees faster, they'll launch products better, 'cause everyone will know what to talk about-- >> It's like a frequently asked questions operating system. >> Rick: Exactly. >> At a moment's notice. >> Technology, right? And making it living 'cause all those FAQ's change all the time. >> And that's the important part too is keeping it relevant, 24 by 7. >> Rick: Absolutely. >> Which is difficult. >> Contextual data analysis is really hard. What's the secret sauce? >> The secret sauce is that we live where you work. The secret sauce is that we focus very specifically on specific workflows like that customer support agent and so, by knowing what you're doing and what ticket you're working on and what chat you're having with a customer, Guru can be anticipatory over time and start to say, "hey, you probably "want to talk to him about this," and bring that answer to you. It's because we live where you work. And that was frankly accidental in a lot of ways. We were trying to solve the problem of knowledge living where you work, and then what we realized is, wow, there's a lot of interesting stuff that we can learn and give back to the customer about what problems they're solving and when they're using Guru and why, and that only makes the product better. So that's really, I think, the thing that, if you ask our typical customers, really gets them excited. They'll say, hey, because of Guru, I feel more confident when I'm on the phone, that I'm always going to give the right answer. >> That's awesome. >> I love hearing customers talk about or even have business leaders talk about some of the accidental discoveries or capabilities, but just how, over time, more and more and more value gets unlocked if you can actually, really extract value from that data. Last question, Rick, I need to know what's in a name? The name Boomi, the name Guru? >> Yes, well, I'll start with the less exciting answer which I always get asked about, which is Boomi, which is a Hindi word that means "earth" or "from the earth". And, sometimes, if you're ordering at the Indian restaurant, you'll see B-H-O-M-I and that might be the vegetables on the menu. That name came from an early employee of the company. I wish I could say that it had a connection to business (laughs). It really doesn't, it just was like, it looks cool, and people tend to remember the name. And honestly, there have been so many moments in the early, early days where we were like, should we change the name, it doesn't really. And we're like you know what? People tend to, it sticks with them, it's kind of exciting, and we kept it. Guru, on the flip side, one of our early employees came up with that name too, and I think she was listening to me talk about what we were doing and she's like, oh, that thing is like a guru to you. And so the brand promise is that you feel like a guru in your area of expertise within a company and that our product plays a relatively small role in you having that, feeling confident about that expertise. >> I love that, awesome. Rick, thank you so much for joining John and me on theCUBE today, we appreciate it. >> Thank you. >> John: Thanks. >> For John Furrier, I'm Lisa Martin. You're watching theCUBE from Boomi World 2019. Thanks for watching. (upbeat electronic music)

>> Live from New York, it's theCUBE covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. (techy music) >> Welcome back to the Big Apple, everybody. This is theCUBE, the leader in live tech coverage. My name is Dave Vellante, I'm here with my cohost Peter Burris, and this is our week-long coverage of CUBENYC. It used to be, really, a big data theme. It sort of evolved into data, AI, machine learning. Ronan Schwartz is here, he's the senior vice president and general manager of cloud, big data, and data integration at data integration company Informatica. Great to see you again, Ronan, thanks so much for coming on. >> Thanks for inviting me, it's a good, warm day in New York. >> Yeah, the storm is coming and... Well, speaking of storms, the data center is booming. Data is this, you know, crescendo of storms (chuckles) have occurred, and you guys are at the center of that. It's been a tailwind for your business. Give us the update, how's business these days? >> So, we finished Q2 in a great, great success, the best Q2 that we ever had, and the third quarter looks just as promising, so I think the short answer is that we are seeing the strong demand for data, for technologies that supports data. We're seeing more users, new use cases, and definitely a huge growth in need to support... To support data, big data, data in the cloud, and so on, so I think very, very good Q2 and it looks like Q3's going to be just as good, if not better. >> That's great, so there's been a decades-long conversation, of course, about data, the value of data, but more often than not over the history of recent history, when I say recent I mean let's say 20 years on, data's been a problem for people. It's been expensive, how do you manage it, when do you delete it? It's sort of this nasty thing that people have to deal with. Fast forward to 2010, the whole Hadoop movement, all of a sudden data's the new oil, data's... You know, which Peter, of course, disagrees with for many reasons. >> No, it's... >> We don't have to get into it. >> It's subtlety. >> It's a subtlety, but you're right about it, and well, maybe if we have time we can talk about that, but the bromide of... But really focused attention on data and the importance of data and the value of data, and that was really a big contribution that Hadoop made. There were a lot of misconceptions. "Oh, we don't need the data warehouse anymore. "Oh, we don't need old," you know, "legacy databases." Of course none of those are true. Those are fundamental components of people's big data strategy, but talk about the importance of data and where Informatica fits. >> In a way, if I look into the same history that you described, and Informatica have definitely been a player through this history. We divide it into three eras. The first one is when data was like this thing that sits below the application, that used the application to feed the data in and if you want to see the data you go through the application, you see the data. We sometimes call that as Data 1.0. Data 2.0 was the time that companies, including Informatica, kind of froze and been able to give you a single view of the data across multiple systems, across your organization, and so on, because we're Informatica we have the ETL with data quality, even with master data management, kind of came into play and allowed an organization to actually build analytics as a system, to build single view as a system, et cetera. I think what is happening, and Hadoop was definitely a trigger, but I would say the cloud is just as big of a trigger as the big data technologies, and definitely everything that's happening right now with Spark and the processing power, et cetera, is contributing to that. This is the time of the Data 3.0 when data is actually in the center. It's not a single application like it was in the Data 2.0. It's not this thing below the application in Data 1.0. Data is in the center and everything else is just basically have to be connected to the data, and I think it's an amazing time. A big part of digitalization is the fact that the data is actually there. It's the most important asset the organization has. >> Yeah, so I want to follow up on something. So, last night we had a session Peter hosted on the future of AI, and he made the point, I said earlier data's the new oil. I said you disagreed, there's a nuance there. You made the point last night that oil, I can put oil in my car, I can put oil in my house, I can't do both. Data is the new currency, people said, "Well, I can spend a dollar or I can spend "a dollar on sports tickets, I can't do both." Data's different in that... >> It doesn't follow the economics of scarcity, and I think that's one of the main drivers here. As you talk about 1.0, 2.0, and 3.0, 1.0 it's locked in the application, 2.0 it's locked in a model, 3.0 now we're opening it up so that the same data can be shared, it can be evolved, it can be copied, it can be easily transformed, but their big issue is we have to sustain overall coherence of it. Security has to remain in place, we have to avoid corruption. Talk to us about some of the new demands given, especially that we've got this, more data but more users of that data. As we think about evidence-based management, where are we going to ensure that all of those new claims from all of those new users against those data sources can be satisfied? >> So, first, I truly like... This is a big nuance, it's not a small one. (laughs) The fact that you have better idea actually means that you do a lot of things better. It doesn't mean that you do one thing better and you cannot do the other. >> Right. I agree 100%, I actually contribute that for two things. One is more users, and the other thing is more ways to use the data, so the fact that you have better data, more data, big data, et cetera, actually means that your analytics is going to be better, right, but it actually means that if you are looking into hyperautomation and AI and machine learning and so on, suddenly this is possible to do because you have this data foundation that is big enough to actually support machine learning processes, and I think we're just in the beginning of that. I think we're going to see data being used for more and more use cases. We're in the integration business and in the data management business, and we're seeing, within what our customers are asking us to support, this huge growth in the number of patterns of how they want the data to be available, how they want to bring data into different places, into different users, so all of that is truly supporting what you just mentioned. I think if you look into the Data 2.0 timeframe, it was the time that a single team that is very, very strong with the right tools can actually handle the organization needs. In what you described, suddenly self-service. Can every group consume the data? Can I get the data in both batch and realtime? Can I get the data in a massive amount as well as in small chunks? These are all becoming very, very central. >> And very use case, but also user and context, you know, we think about time, dependent, and one of the biggest challenges that we have is to liberate the data in the context of the multiple different organization uses, and one of the biggest challenges that customers have, or that any enterprise has, and again, evidence-based management, nice trend, a lot of it's going to happen, but the familiarity with data is still something that's not, let's say broadly diffused, and a lot of the tools for ensuring that people can be made familiar, can discover, can reuse, can apply data, are modestly endowed today, so talk about some of these new tools that are going to make it easier to discover, capture, catalog, sustain these data assets? >> Yeah, and I think you're absolutely right, and if this is such a critical asset, and data is, and we're actually looking into more user consuming the data in more ways, it actually automatically create a bottleneck in how do I find the data, how do I identify the data that I need, and how am I making this available in the right place at the right time? In general, it looks like a problem that is almost unsolvable, like I got more data, more users, more patterns, nobody have their budget tripled or quadrupled just to be able to consume it. How do you address that, and I think Informatica very early have identified this growing need, and we have invested in a product that we call the enterprise data catalog, and it's actually... The concept of a catalog or a metadata repository, a place that you can actually identify all the data that exists, is not necessarily a new concept-- >> No, it's been around for years. >> Yes, but doing it in an enterprise-unified way is unique, and I think if you look into what we're trying to basically empower any user to do I basically, you know, we all using Google. You type something and you find it. If you're trying to find data in the organization in a similar way, it's a much harder task, and basically the catalog and Informatica unified, enterprise-unified catalog is doing that, leveraging a lot of machine learning and AI behind the scenes to basically make this search possible, make basically the identification of the data possible, the curation of the data possible, and basically empowering every user to find the data that he wants, see recommendation for other data that can work with it, and then basically consume the data in the way that he wants. I totally think that this will change the way IT is functioning. It is actually an amazing bridge between IT and the business. If there is one place that you can search all your data, suddenly the whole interface between IT and the business is changing, and Informatica's actually leading this change. >> So, the catalog gives you line-of-sight on all, (clears throat) all those data sources, what's the challenge in terms of creating a catalog and making it performant and useful? >> I think there are a few levels of the challenge. I chose the word enterprise-unified intelligent catalog deliberately, and I think each one of them is kind of representing a different challenge. The first challenge is the unified. There is technical metadata, this is the mapping and the processes that move data from one place to the other, then there is business metadata. These are the definition the business is using, and then there is the operational metadata as well, as well as the physical location and so on. Unifying all of them so that you can actually connect and see them in one place is a unique challenge that at this stage we have already completely addressed. The second one is enterprise, and when talking about enterprise metadata it means that you want all of your applications, you want application in the cloud, you want your cloud environment, your big data environment. You want, actually, your APIs, you want your integration environment. You want to be able to collect all of this metadata across the enterprise, so unified all the types, enterprise is the second one. The third challenge is actually the most exciting one, is how can you leverage intelligence so it's not limited by the human factor, by the amount of people that you have to actually put the data together, right? >> Mm-hm. >> And today we're using a very, very sophisticated, interesting logarithm to run on the metadata and be able to tell you that even though you don't know how the data got from here to here, it actually did get from here to here. >> Mm-hm. >> It's a dotted line, maybe somebody copied it, maybe something else happened, but the data is so similar that we can actually tell you it came from one place. >> So, actually, let me see, because I think there's... I don't think you missed a step, but let me reveal a step that's in there. One of the key issues in the enterprise side of things is to reveal how data's being used. The value of data is tied to its context, and having catalogs that can do, as you said, the unified, but also the metadata becomes part of how it's used makes that opportunity, that ability to then create audit trails and create lineage possible. >> You're absolutely right, and I think it actually is one of the most important things, is to see where the data came from and what steps did it go to. >> Right. >> There's also one other very interesting value of lineage that I think sometimes people tend to ignore is who else is using it? >> Right. >> Who else is consuming it, because that is actually, like, a very good indicator of how good the data is or how common the data is. The ability to actually leverage and create this lineage is a mandatory thing. The ability to create lineage that is inferred, and not actually specifically defined, is also very, very interesting, but we're now doing, like, things that are, I think, really exciting. For example, let's say that a user is looking into a data field in one source and he is actually identifying that this is a certain, specific ID that his organization is using. Now we're able to actually automatically understand that this field actually exists in 700 places, and actually, leverage the intelligence that he just gave us and actually ask him, "Do you want it to be automatically updated everywhere? "Do you want to do it in a step-by-step, guided way?" And this is how you actually scale to handle the massive amount of data, and this is how organizations are going to learn more and more and get the data to be better and better the more they work with the data. >> Now, Ronan, you have hard news this week, right? Why don't you update us on what you've announced? >> So, I think in the context for our discussion, Informatica announced here, actually today, this morning in Strata, a few very exciting news that are actually helping the customer go into this data journey. The first one is basically supporting data across, big data across multi-clouds. The ability to basically leverage all of these great tools, including the catalog, including the big data management, including data quality, data governance, and so on, on AWS, on Azure, on GCP, basically without any effort needed. We're even going further and we're empowering our user to use it in a serverless mode where we're actually allowing them full control over the resources that are being consumed. This is really, really critical because this is actually allowing them to do more with the data in a lower cost. I think the last part of the news that is really exciting is we added a lot, a lot of functionality around our Spark processing and the capabilities of the things that you can do so that the developers, the AI and machine learning can use their stuff, but at the same time we actually empower business users to do more than they ever did before. So, kind of being able to expand the amount of users that can access the data, wanting a more sophisticated way, and wanting a very simple but still very powerful way, I think this is kind of the summary of the news. >> And just a quick followup on that. If I understand it, it's your full complement of functionality across these clouds, is that right? You're not neutering... (chuckles) >> That is absolutely correct, yes, and we are seeing, definitely within our customers, a growing choice to decide to focus their big data efforts in the cloud, it makes a lot of sense. The ability to scale up and down in the cloud is significantly superior, but also the ability to give more users access in the cloud is typically easier, so I think Informatica have chosen as the market we're focusing on enterprise cloud data management. We talked a lot about data management. This is a lot about the cloud, the cloud part of it, and it's basically a very, very focused effort in optimizing things across clouds. >> Cloud is critical, obviously. That's how a lot of people want to do business. They want to do business in a cloud-like fashion, whether it's on-prem or off-prem. A lot of people want things to be off-prem. Cloud's important because it's where innovation is happening, and scale. Ronan, thanks so much for coming on theCUBE today. >> Yeah, thank you very much and I did learn something, oil is not one of the terms that I'm going to use for data in the future. >> Makes you think about that, right? >> I'm going to use something different, yes. >> It's good, and I also... My other takeaway is, in that context, being able to use data in multiple places. Usage is a proportional relationship between usage and value, so thanks for that. >> Excellent. >> Happy to be here. >> And thank you, everybody, for watching. We will be right back right after this short break. You're watching theCUBE at #CUBENYC, we'll be right back. (techy music)

(intense orchestral music) >> Hello and welcome to this special VMworld preview, I'm John Furrier, co-host of theCUBE, here in the Silicon Valley, Palo Alto offices for theCUBE. I'm here with Peter Burris, head of research at SiliconANGLE media and Wikibon team. We're hear kickin' off, what we're going to talk about at VMworld, what we expect to see at the event in Las Vegas; and what are some of the highlights from the news, what's going to be discussed. Peter, great to see you. >> Great to be here John. >> I know you've been workin' hard, we're going to talk about this new true private cloud report that you put out, very comprehensive, a lot to go through, so, we're going to digest that, we're going to unpack that. But first, we're going to have theCUBE there for you know three days. >> Two sets right? >> Two sets. So, second year in a row we have two sets at VMworld. 72 thought leaders and interviews in the middle of the hang space, if you're going to to to VMworld, go to the hang space and look for us, come say hello there's some little cough areas to hang out. Come visit us, say hello, check in if you're an influencer, we're going to come preview some new technology we're going to show there, so, don't forget to ask about that, take a look at the video or the variety of tools we have with theCUBE Digital Tooling and Video Services. But, most notably, there's going to be a lot of headline news, Andy Jassy's going to be giving a keynote, we've got that confirmed on Twitter; and a lot of discussion around the future of the data center, future of IT, certainly of how cloud and on-premises are going to intersect. This is has been a groundbreaking report from Wikibon for the third year of the true private cloud report. So let's unpack that, because I think this is a notable backdrop to VMworld is that as everyone's been saying hybrid cloud, now multi cloud, essentially the same thing. The cloud is a great resource, on-premises (laughs) is not going away. It used to be aspirational to have this notion of having cloud operations. Your report is now definitively saying it's no longer aspirational, it's actually happening. So take a minute to explain the report in it's third year some of the key findings. >> Well the, we might want to, we want to step back a little bit and say what's goin' on with VMware? Because VMware's progress and both what it's enabling, and what constraints it still faces, are going to have a lot to do with what happens in the report. But speaking about the report specifically, True private cloud was a concept that David Floyer, Stu Miniman, kind of devised a number of years ago, and the simple observation is that ultimately a lot of hardware vendors, a lot of system vendors, were just taking the word cloud and slapping it on their hardware and saying oh here's our replacement strategy, does it have anything to do with cloud? Well, kind of, yeah, but not really. And their observation was increasingly, customers are going to want that cloud experience and the basic notion of true private cloud, and what all of our research shows, is that inevitably what's going to happen is the customer's not going to move their data to the public cloud en mass; there's going to be certainly some important elements that are going to there, it's no question about that, but then increasingly they're going to try to bring cloud, the cloud operating model, the cloud experience, down to where the data resides; and that's going to be at the edge, and that's going to be at what others call the core, on-premises. And near premises, so, you know side-by-side with public cloud players in in a number of different hosting companies. So the very concept is the requirements or the attributes of the data are going to dictate where the workloads operate, and increasingly those, that's going to demand an on-premises capability that still satisfies the basic notions of cloud. >> Great, that's a great backdrop. Now let's talk about VMware, and let's, I have something that I want to talk about the direct cloud report, we'll get into that. VMware had two or three years ago, Pat Gelsinger was under the gun, you know with the pressure of the Dell merger looming, what the future is going to be in there. Since then the performance of VMware has been spectacular financially, he's really proud of that. Some new products pivoting, I want to get what you're hearing first, but what I'm hearing is and I want to give you something, give you a chance to respond, I want to get your reaction. VMware has seen some acceleration over the years around vSphere, around kind of good, stable, that haven't lost anything with vSphere, so, one of their core products, virtualization storage; but their large accounts are stable in the Fortune 500, losing some business maybe in the lower accounts, but as the AWS, Azures, and Google Cloud, cloud native players are growing, the emerging products are front and center for VMware. vSAN, NSX, obviously the driver which we'll want to double click on, and the vCHS, the VMware vCloud Hybrid Service. These are, specifically the vSAN getting momentum, and these emerging products, how important is that for VMware? Obviously their stability is IT footprint. But why is the cloud driving some of these new emerging behaviors? >> Look, every company wish they had the install base that VMware has, and that install base is predicated on VDI, or Video Desktop Integration, Virtual Desktop Integration. It's vSAN, which is the use of VMware as a basis for virtualizing storage, and obviously all the stuff that's associated with virtualizing hardware. You know, John, it's interesting, if you think about what made the cloud possible, certainly AWS took on the heavy duty the heavy lifting associated with actually creating a business, and it's obviously you know very successful, but it all started with the idea of virtualization, and the notion that you could in fact bring virtualization in on top of hardware sources and generate a lot of not only cost avoidance, but also increasing flexibilities; you can get better utilization but also increase your flexibility, and that's one of the things that made the cloud possible. And so if we think about the VMware install base, that's where it all starts. It's the ability to get greater utilization and greater flexibility on-premise, and now it's moving into the cloud. So we got three basic questions for for VMware that we're looking at. One, there's been a lot of chatter about the relationship between Dell EMC and VMware, and what does that mean? You know Dell EMC is carrying a pretty significant debt load these days, and, there is visibility in where it's going to go, but VMware, as a brand is worth an enormous amount of money. So how does Dell EMC better you know increasingly attach itself to VMware is an interesting question, and what does that mean for the ecosystem? >> Having perverse incentives possibly versus-- >> Possibly, possibly, but we want to get that, there has to be a constant promise from VMware that they're going to take care of the ecosystem first with Dell EMC as a big participant in that. So that's the first thing, especially these days with all the financial chatter. Second thing is, this AWS agreement is really really important, and a lot of people are questioning is it a one way street? Do you just, you know, sure we have virtualization in cloud, we got virtualization here, does it make it easy to bring stuff up to VMware? What happens once it, or up to AWS, what happens once workloads get up there? Is AWS going to try to you know facilitate a migration? That's still a very very challenging technical problem, but we'll see a lot more, Andy Jassy has the keynote as you said, about how that partnership is working and where it's actually going. Because there will be a requirement also to be able to take workloads out of AWS, and out of public clouds, and bring 'em down on-premise. >> Hence the two-way street that you're looking for. >> Got to be a two-way street. A simple example, we're going to see increasing, in the AI world, we're going to see more modeling occurring in cloud, more training occurring in cloud, and more inferencing learning out on the edge and the core. Well, we want to see, you know VMware certainly wants to see more of those workloads being virtualized. And that leads to the third question what's the VMware story with IOT, with the edge? That is very very unclear at this point in time, and there's a lot of work that's going to have to be required to put into. And so I think that those are the three things that we're really focusing on, and how does VMware answer those questions can have a lot to do with future architectures, future business models, and future partnerships. >> And it's important, I think the edge one is clearly obvious that the don't have much announced, but that have to put a stake in the ground at some point. >> Absolutely. And you know, the reality is, the edge has real-time, often is associated with real-time, high performance, every throughput, very lightweight execution. >> Uses the cloud, uses the data center. >> Uses the cloud, uses the cloud, uses you know servos computing is an example, containers, those things all don't require a virtualized machine. >> I want to get your reactions on something, I sent an email out to a bunch of buyers, of friends in the network of theCUBE alumni and our networks and I asked them a question, I said: what do you think about VMware's prospects going forward as a buyer of technology, as you're transforming your organization from the obvious on-premise operating model to hybrid? Which they're all doing pretty much, and are agreeing to it. So the aspirational aspect was confirmed, to your point. So they responded, (laughs) and they said look it, VMware remains largely flat across server, infrastructure, storage, and virtualization buying. >> In terms of growth? >> No, what they're buying and growth, growth, no they're not really paying much attention to that, they're saying it's pretty flat, we're not going anywhere it's not going down, it's not going up per se, in the core segments. They said the main thing is going to be the emerging technology so vSAN, NSX, and vCHS. Then I asked 'em I said: What do you like about VMware, what do you think they're strong in? They said: well, we like the fact that they got, that they have technology, okay, and if they can keep the technology lead we're interested, so that's a question also, I'll get that in a second, the relationships that they've had with VMware, the supplier relationships, rinse reset a feature of products, and then compatibility with their existing IT footprint. I then asked 'em what're you worried about? (laughs) And they said: well, if there's a discussion about replacing VMware, it's around price cost and technology lag. Your reaction to those two points? >> First point is, again, there's no question that VMware has a great install base of customers that are thinking about what it's going to mean, and I think the most important observation is that, and we'll learn more about how many enterprises really are starting to move their virtual machines up to AWS, for example, more than VMware next week. But I also think that it provides cover for you know a CIO or VP of infrastructure to say yeah I'm going to continue to invest here, and I'm going to, you know, have the option of moving to something else. And there will be a lot more options for what you do with a VMware virtual machine in the future. Regarding the question of whether it's flat or not, I think one of the reasons why that perception is there, is because the hardware business overall has been flat, and VMware is a derivative of play in the hardware business, so, at least until recently. In many respects now it's dragging some of it forward because VMware allows you to put off additional hardware purchases. So we'll see where that cycle ends up, we might be at the nadir of that cycle, but I certainly think that we're seeing-- >> It's mature for sure, I mean. >> It's mature. But it used to be that you'd buy new hardware and then you'd put VMware on top of it to virtualize it, so you could get more productivity out of it. But as hardware's slowed down, why would you buy more VMware? But I think what's happening now is people are thinking first in terms of buying VMware, and what workloads you need to put on there, how they want to set those workloads up, and then looking for hardware to do that, and increasingly looking through the cloud. The third thing I'd say is that look, the VMware cloud foundation, and NSX, are two incredibly important technologies. For example-- >> Well hold on before you go there, 'cause I want to drill down on this because, one of the things that I mentioned in there which is a key word is existing IT footprint; this is a reality, some call it legacy. Having an IT footprint with VMware is not going to get you in trouble because of the path of the cloud, 'cause you've got cloud native, things like Kubernetes down the road, but that footprint's the base foundation. So as NSX comes in, (laughs) and the cloud foundation, interesting new lever. How does those enabling components fit for the enterprise who's sittin' there sayin' I got an existing IT footprint, I got all these clouds on the horizon, why NSX, why is the vCloud foundation important? >> Yeah, so let's start with VCF, VCF provides, or is a, takes you maybe 75, 80% of the way there to that cloud experience on-premises; a VMware based cloud experience on-premises. So, it's a really nice bundling of technology, that provides a relatively simple way of deploying, configuring, maintaining, and ultimately retiring workloads. So, it's a nice package for a lot of enterprises that have that VMware experience. That's a different story from NSX, so, on the cloud foundation standpoint, if you need to demonstrate to your board and to your CXO, and to your line of business people, that you are not just have an option to go to the cloud, but you're actually bringing that experience more to the business, a lot of customers are kickin' the tires on VCF, and it's a good thing to do. NSX is a little bit different. NSX, if we think about the long term, there has always been a need to flatten networks in the enterprise. Having that network, and that network, and that network, and trying to inter-network them together through bridging and gateways, is extremely problematic, even at the network level. It requires-- >> In terms of sprawl and complexity, or both? >> In terms of complexity, in terms of the amount of processing, I mean the cost of doing address translation and takin' packets and re-formatting them for different workloads in the network; very, very difficult to do. Now, you add programmability atop of that, 'cause at the end of the day, cloud is effectively a network program model. Very, you know, hey, you got a big problem on your hands. Somebody at some point in time is going to make, is going to build a $50 billion company around the idea of inter-networking clouds. I don't know who it is. >> Cisco wants to do it. >> Cisco would like to do it, but Cisco, quite frankly, probablyyyy, you know they could have started this process five or six years ago, and they didn't get out there. VMware took some steps to do that. NSX is a pretty good candidate right now, if we're thinking about how we build inter-networked multi cloud environments. >> So, you used the example before you came on camera, that you have this segment that in the old world of network stacks SNA, DECnet, variety whether stacks had proprietary things and bridges happened, to your point, to your explanation. And then TCP/IP came up and flattened it, TCP/IP. >> Yeah, just flattened it all out, made 'em all go away. >> So clouds aren't networks, but they're cloud environments, same concept, but flattening 'em out. >> Well, they are networks, at the end of the day they really are networks. >> They're a network of machines. >> Yeah, they're a network of services, they're a network of machines. >> So, explain the flattening piece, is it, are we still in the early stages of that, are you seeing visibility? >> Very much so. >> What are some data points around this? >> So the, and you said earlier, that the multi cloud, hybrid cloud are really the same, well today they are. We might envision a day when they're not, here's why. Hybrid cloud is I got this cloud, I got that cloud, it's more of a where is the data located, how am I going to run those environments together. Multi cloud is I got multiple clouds that I have to inter-network, and I have to bring together. I want to run a job in one of the Oracle application clouds, that also touches some of the machine learning that you get out of Google Cloud, and increase and include some of the retail capabilities you get out of AWS. That is a very very realistic scenario, it's going to happen, people are doing that kind of stuff right now. >> And that's the preferred outcome people are looking for? >> That's the preferred outcome that people are lookin' for. Well, each of those different environments are going to have an economic incentive to say yeah, that's great do that, but bring more of the workload into my cloud, 'cause I'm going to create interfaces that are a little bit better at working together than you know you can get from the inter-networking side. Well, they'll still have to stay open, but you know some of those environments are going to be better at that than others; but at the end of the day you want no penalty whatsoever, other than latency and where the data's located from amongst these different services. And so eventually what we're going to want to do is we're going to see the inter-networking itself flatten, where're the jobs, how the jobs are set up: flattened. Make it easier to move data, and jobs or workloads out of one cloud and be able to put it in another, because of any number of different reasons. And so, that's-- >> Yeah, competitive advantage, different economics, different product features >> Regulatory regimes change, you know what happens if if in Germany they decide to do something else from other than GDPR, what's it going to mean? >> So is NSX going to be that connector, you kind of think? >> NSX-- >> Has the opportunity. >> Has the potential to be that kind of connector. So an enterprise that's looking at how they can increase their set of options, their flexibility, their ability to bring networking closer to workload. NSX is as good of, that I know about, that we know about, as good an option out there as any. >> I want to ask you before we move onto the true private cloud versus private cloud and that whole report you did to private cloud in the third year. We're seeing a trend around the operating side, the personas are developing Google Cloud Next conference, the notion of an SRE, you know sight reliability engineer. Public cloud has always been known as developer friendly, very developer oriented, cloud native, all the developers love containers, Kubernetes, Istio, and a lot of cool services are coming out. But now with VMware, they kind of own the IT footprint from an operating model, operating the networks. The bridging of those two worlds are kind of coming together, right now we don't see a lot of cross over yet between pure cloud native developers in VMware ecosystem. Your thought on that connection to those personas, how it relates to how the ecosystem's rolling out, your thoughts? >> Yeah, you know John, I think that's going to be the big challenge for the next couple of years, literally, in the next couple of years. That ultimately, developers love the public cloud because they can avoid operations of people. Increasingly the public cloud players are going to have to provide platforms. And you know everybody talks about I, you know infrastructure as a service versus pass as a service, or platform as a service. But when, in Amazon, Google, Azure, Oracle, IBM Software, all of these guys are going to have to add capabilities that are that much more intriguing and interesting to developers. Bringing the enterprise developer into this ecosystem is the next big round of competition, 'cause those people aren't going to go away, they're too important to the future of business. And, to the degree that VMware can provide, and I think this is the best that they can do, a neutral platform for those guys as opposed to starting to introduce you know machine learning services on VMware or or, you know, anything beyond some of the platform stuff that Dell EMC has Pivotal, and what not, on VMware. Yeah, we can expect to see greater integration for that, but I think ultimately what VMware needs to be is a phenomenal target for stuff that's written over here, that needs to run over there, and have it run on VMware, I think that's ultimately what's going to happen. >> Alright Peter, great stuff, now let's talk about the true private cloud report, 'cause I think VMworld is always a beacon, always a bellwether for what's going on in IT, with respect to on-premises private cloud, or true private cloud, or hybrid cloud, IBM as well, and some others, they're always a leader in engineering. Before we get into the report, first describe the difference between what true private cloud is and what people have called private cloud. Because the term private cloud's been kicked around, going back I think 2012 I first heard-- >> Oh, private cloud, I first heard the term private cloud in probably 2005, 2006. >> But you guys have nailed this definition called true private cloud. What does it mean, what's the difference? >> So, the idea is, the cloud experience wherever the data requires it, and increasingly data is going to require it at the edge, in the core, in the data center, you know, local to the business; because of latency issues, because of cost of bandwidth issues, because of regulatory issues, because of IP control issues, any number of other issues, there's going to be an increasing distribution of data; workloads are going to follow that distribution of data, and the systems have to be there to run it. But we want to have a common vision of how those workloads are operated, and a common model for how we pay to run those workloads. So when you think about true private cloud, it's basically, we want the cloud experience, which includes, you know simplicity, the one throat to choke, the regular and non-invasive upgrades and enhancements to software; we want to add to it, kind of the management interfaces that we're associating with the cloud, but also the pay as you go, and the flexibility to scale up and the greater plasticity to be able to add services. We want all of that, but in a footprint on premise. >> And that's for true private cloud? >> And that's what we mean by true private cloud. Now if you go back a few years, companies would you know, you'd get a hardware company that'd say oh look, cloud is Linux plus some manned control interfaces, no problem, we can put that directly into our operating system or have a management interface on our platform, now we can go on cloud. >> And put it in your data center. >> And put it in your data center. But you still paid for everything up front, you have to deal with software patches and upgrades, because it's software that's installed. >> So it's an operating model, how you're consuming technology, how you're buying it. >> Operating model, how you consume the technology, and the flexibility, and the future of the modern application approach, which is services oriented, and networks and data. >> And so one of the findings obviously, you're pretty strong on this sayin' this is no long aspirational, it's realistic. What does the report show, what're the numbers, how did you break down the report? >> Sure. >> What are the categories, and what are some of the data? >> So the aspirational notion was that we kept talking about true private cloud, but, the hardware vendors were slow to actually deliver on it, especially on that service oriented approach as opposed to a product oriented approach. By that I mean product approach is, you buy it all upfront, and it's caviat after I'm a consumer, service oriented approach is you know we have enough belief in what we're selling that you're only paying for the services you consume, which is what AWS and Azure and others do. So we're seeing that actually happen. That's number one. You take a loot at what HPE's with a technology called GreenLake. IBM is advancing it's cause with software. Dell EMC is doing some interesting things with both VMware but also some related types of technologies. All of that is happening right now, so the server companies, or traditional server companies, are introducing true and honest to goodness capabilities that mimic the cloud, so that's happening. The second thing that's happening is you know the AWSs the Google Clouds, and the big hyper scalers, are also starting to introduce technology that allows at least elements of their platform to run on-premise. The big holdover was AWS, but now, through snowballs, through their their kind of ranked box, data box, you can now put a fair amount of processing on there, and a fair amount of AWS stuff, and you can actually run workloads down on this box. So it extends the AWS platform out to locations in a very novel way. So we're seeing on the one hand the server companies truly will introduce technology and services that actually do a better job of mimicking the cloud. We're seeing the cloud players come up with technologies that allow them to extend their footprint, their cloud presence, down to where data needs to reside, and that's where everybody's goin' right now, everybody's goin for that spot in the marketplace. >> So, you have categories here, on-premise-- >> We have on-premise, which is kind of the traditional true private cloud, and the leaders from a hardware packaging standpoint are Dell EMC, HPE are two of the big leaders. Then we have-- >> Cisco's right behind them. >> Cisco's right behind 'em. We've got what we call the near-premise, or the host of true private cloud, and this is where you have AWS right next to your private cloud box so that they can communicate really fast, or it's hosted. IBM is very big here, but there is a number of other players-- >> IBM's got a sizable lead, it's 12% by your numbers, and Rackspace coming second and four-- >> Rackspace is good. And then you've got some very interesting and very important smaller players, like Expedient for example. And then-- >> So there's two main categories, there's hosted, >> Correct. >> And then on-premise. >> On-premise. >> And then there's another category >> So near premise, and on-premise. >> Near premise and on-premise or hosted. >> And there's the ecosystem side, there's a software that's actually utilized to do this, this is where VMware excels in. >> Explain what the ecosystem, so you called true private cloud ecosystem pull through shares, what is that? >> So, we have, so, VMware as we've been talking about, is one of those technologies that allows one to devise a true private cloud platform. Increasingly that's what they're doing, with some of the technologies that we're talking about. And so ultimately they are putting the software out to customers and customers are defaulting to that software, as their approach to building that true private cloud, and then pulling hardware through as a second decision. So the first decision is I'm going to build my cloud, my private cloud, my true private cloud with VMware, and I'll find hardware that doesn't get in the way. >> So it's leaders who are pulling hardware sales. >> It's the software leaders that are putting the software for building true private clouds out there, and then through partnerships dragging hardware in. >> And so there, they're there and everyone wants to talk to them. So that's VMware (laughs) 24% >> That's VMware, Nutanix is moving along. >> HPE, Microsoft, IBM. >> HPE's in there. >> Interesting, that's awesome. And any other findings that you've found, in terms of growth? Number sizes I think this year you had 21 billion roughly 2017. >> Yeah, it's just over 20, it's 20.3 billion, it's going to go to, you know over 260 billion in 10 years, it's going to be bigger than the infrastructure as a service marketplace, it is the true private cloud segment, the on-premise segment for the first time exceeded the size of the near premise segment as the software matures, as you figure out how to make these business models go. But this is going to be, you know Diane Greene said something very very interesting at Google Next. And she said look, nobody really understands how this business is going to work in 10 years, and she's right. Some companies clearly have a better understanding than others. >> So do you think your numbers are short or over? >> I think-- >> But that implies you know. (laughs) >> Well no, I don't know if it's short or over, but let me give you an example. That our numbers presume a relatively constant approach in thinking about how we price and how we generate exchange for this stuff. But how fast the cloud operating model, that pay as you go moves into the true private space, is going to have an enormous implication on what those revenues look like. The degree to which companies demand a three year commitment like Salesforce is starting to do with SaaS. It's going to have an enormous implication on how those revenues actually get realized. >> Well, we've debated this, you and I have debated this before with Dave as well, Dave this it's a trillion, Dave Vellante, so, you know I think you're sure, I think you took a conservative approach, and you know just my personal observation. >> Well we think the overall cloud market's going to be, if we add SaaS in there, it's going to be 260 to 300, probably a total of 700 billion, something like that, and so it's pretty sizable. So we're just talking about that on-premise true private cloud. >> Yeah, the true private cloud you know, $250 billion by 2027. Okay, so I got to ask you a question, since, I like that Diane Greene quote by the way, just kidding you about the forecast numbers, but, I think she's right. So I got to ask you, what is your observation around what this report says vis-a-vis the buyer market out there who are squinting through the fud, and, all these rankings around who's got the most market share. We hear, you know there was a post on Forbes from my friend Bob Evans that said: oh, Microsoft's number one in cloud! So, how you define cloud is a function of how you define cloud. Someone defines it by bundling an office and apps and, eventually, the level of granularity is going to have to be at least segmented a bit. How do you view how customers should keep a score card for market share, leadership, and besides customers, and number of services, I mean is there an approach that anything coming out of this data you would see and saying maybe the market might want to be sized this way, maybe we should be thinking about not so much market share numbers on some graph on some analyst firm. Is there any thoughts on that? Because it's a big thing, and true private cloud's just one sector. >> Yeah, yeah. >> You've got SaaS, and you've got PaaS, and you've got-- >> So I think John, there've been at least, you know we could probably say there're more, but just making it up off the top of my head, there have been at least three eras that users focused on. Era number one is the hardware as the asset, how do we get the most out of our hardware. That dominated probably until the late '80s or so. And then it became the application as the asset, and then we bought into the application, and we bought hardware and all the other stuff underneath that application, and that was pretty much the 2000's, up until maybe 2010. And now we're thinking of data as the asset, and what does that mean? What it means is that ultimately, I think that the way that, we think that the way that architecture is going to be thought of, is not on application architecture, but around data architecture; I don't mean data architecture like a DBA, I mean what is your brand promise, what, what activities do you have to deliver that brand promise, what data and services do you need to perform those activities. Get that data in as close as you possibly can to those activities, wherever they have to be performed, so that you can perform them predictably, reliably, at the lowest cost, and in the greatest, shortest period of time. So I would start with the idea, you know what I'm going to focus on where my data's going to be located to run my business, that's where I would focus. The second thing, as I think when we think about market shares, and we think about a lot of these other questions, it's okay which, this is a transformative period of time, which of these companies is going to be most likely to deliver a product now, but also create better options for how I do stuff in the future; and we like to talk to our clients about the idea of buy the stuff that provides the best portfolio of options on future data value. And so, data today, and helping think about architecture, work with companies that are demonstrating that they're going to be able to create the options that you need in the future, 'cause this is going to change a lot over the next five, six, eight years. And so, you want to work with companies that are demonstrating that they're able to create new technology, through IP, through things like opensource, >> Okay so the question is-- >> Are sharing it appropriately too. >> So, who's number one? Again, I don't think this is going to be one score, I think it's going to be level of services, how many services you're using. There was one angle I wanted to do, but I can't, I'm still having a hard time. But I guess I'll ask ya, to put ya on the spot. If I'm a customer, Peter, who's the number one in cloud, gimme the top three players. >> AWS, Azure, Google. >> Okay, (claps once) there ya go. (laughs) The top three clouds. Well we're going to keep an eye on it-- >> Let's go to four though, so AWS, Azure, Google, and then again, from that true private cloud-- >> IBM. >> Because that's a, no, no, it's got to be Vmware; because that's, that's where the pull through is right now, right. But when you think about it, the big question is is AWS and Google Cloud going to come down to the edge, and down to the true private cloud as fast as some of these other cloud players are going to go up to the bigger cloud? If I were to pick the one that's most likely to win, it's located somewhere near ribbon. So Microsoft or... In Seattle area AWS. Again, again, it's so early, I think if people, going to have to figure out what to do, that's going to determine the winners and losers. Certainly a true private cloud report, great report. Check out the true private cloud report from Wikibon.com, go to wikibon.com and check it out, preview for VMworld. I'm John Furrier with Peter Burris, a lot of exciting news, two large sets, 72 interviews, three days, come visit theCUBE team, we got to full team down there, we're going to have a lot of our team down there lookin' to talk to you. Join our community, join our network, we're going to have a lot of fun, and also learn a lot at VMworld, talk to some really smart people. Thanks for watching. (intense orchestral music)

>> So as folks make their way over from Javits I'm going to give you the least interesting part of the evening and that's my segment in which I welcome you here, introduce myself, lay out what what we're going to do for the next couple of hours. So first off, thank you very much for coming. As all of you know Wikibon is a part of SiliconANGLE which also includes theCUBE, so if you look around, this is what we have been doing for the past couple of days here in the TheCUBE. We've been inviting some significant thought leaders from over on the show and in incredibly expensive limousines driven them up the street to come on to TheCUBE and spend time with us and talk about some of the things that are happening in the industry today that are especially important. We tore it down, and we're having this party tonight. So we want to thank you very much for coming and look forward to having more conversations with all of you. Now what are we going to talk about? Well Wikibon is the research arm of SiliconANGLE. So we take data that comes out of TheCUBE and other places and we incorporated it into our research. And work very closely with large end users and large technology companies regarding how to make better decisions in this incredibly complex, incredibly important transformative world of digital business. What we're going to talk about tonight, and I've got a couple of my analysts assembled, and we're also going to have a panel, is this notion of software is eating the Edge. Now most of you have probably heard Marc Andreessen, the venture capitalist and developer, original developer of Netscape many years ago, talk about how software's eating the world. Well, if software is truly going to eat the world, it's going to eat at, it's going to take the big chunks, big bites at the Edge. That's where the actual action's going to be. And what we want to talk about specifically is the entangling of the internet or the industrial internet of things and IoT with analytics. So that's what we're going to talk about over the course of the next couple of hours. To do that we're going to, I've already blown the schedule, that's on me. But to do that I'm going to spend a couple minutes talking about what we regard as the essential digital business capabilities which includes analytics and Big Data, and includes IIoT and we'll explain at least in our position why those two things come together the way that they do. But I'm going to ask the august and revered Neil Raden, Wikibon analyst to come on up and talk about harvesting value at the Edge. 'Cause there are some, not now Neil, when we're done, when I'm done. So I'm going to ask Neil to come on up and we'll talk, he's going to talk about harvesting value at the Edge. And then Jim Kobielus will follow up with him, another Wikibon analyst, he'll talk specifically about how we're going to take that combination of analytics and Edge and turn it into the new types of systems and software that are going to sustain this significant transformation that's going on. And then after that, I'm going to ask Neil and Jim to come, going to invite some other folks up and we're going to run a panel to talk about some of these issues and do a real question and answer. So the goal here is before we break for drinks is to create a community feeling within the room. That includes smart people here, smart people in the audience having a conversation ultimately about some of these significant changes so please participate and we look forward to talking about the rest of it. All right, let's get going! What is digital business? One of the nice things about being an analyst is that you can reach back on people who were significantly smarter than you and build your points of view on the shoulders of those giants including Peter Drucker. Many years ago Peter Drucker made the observation that the purpose of business is to create and keep a customer. Not better shareholder value, not anything else. It is about creating and keeping your customer. Now you can argue with that, at the end of the day, if you don't have customers, you don't have a business. Now the observation that we've made, what we've added to that is that we've made the observation that the difference between business and digital business essentially is one thing. That's data. A digital business uses data to differentially create and keep customers. That's the only difference. If you think about the difference between taxi cab companies here in New York City, every cab that I've been in in the last three days has bothered me about Uber. The reason, the difference between Uber and a taxi cab company is data. That's the primary difference. Uber uses data as an asset. And we think this is the fundamental feature of digital business that everybody has to pay attention to. How is a business going to use data as an asset? Is the business using data as an asset? Is a business driving its engagement with customers, the role of its product et cetera using data? And if they are, they are becoming a more digital business. Now when you think about that, what we're really talking about is how are they going to put data to work? How are they going to take their customer data and their operational data and their financial data and any other kind of data and ultimately turn that into superior engagement or improved customer experience or more agile operations or increased automation? Those are the kinds of outcomes that we're talking about. But it is about putting data to work. That's fundamentally what we're trying to do within a digital business. Now that leads to an observation about the crucial strategic business capabilities that every business that aspires to be more digital or to be digital has to put in place. And I want to be clear. When I say strategic capabilities I mean something specific. When you talk about, for example technology architecture or information architecture there is this notion of what capabilities does your business need? Your business needs capabilities to pursue and achieve its mission. And in the digital business these are the capabilities that are now additive to this core question, ultimately of whether or not the company is a digital business. What are the three capabilities? One, you have to capture data. Not just do a good job of it, but better than your competition. You have to capture data better than your competition. In a way that is ultimately less intrusive on your markets and on your customers. That's in many respects, one of the first priorities of the internet of things and people. The idea of using sensors and related technologies to capture more data. Once you capture that data you have to turn it into value. You have to do something with it that creates business value so you can do a better job of engaging your markets and serving your customers. And that essentially is what we regard as the basis of Big Data. Including operations, including financial performance and everything else, but ultimately it's taking the data that's being captured and turning it into value within the business. The last point here is that once you have generated a model, or an insight or some other resource that you can act upon, you then have to act upon it in the real world. We call that systems of agency, the ability to enact based on data. Now I want to spend just a second talking about systems of agency 'cause we think it's an interesting concept and it's something Jim Kobielus is going to talk about a little bit later. When we say systems of agency, what we're saying is increasingly machines are acting on behalf of a brand. Or systems, combinations of machines and people are acting on behalf of the brand. And this whole notion of agency is the idea that ultimately these systems are now acting as the business's agent. They are at the front line of engaging customers. It's an extremely rich proposition that has subtle but crucial implications. For example I was talking to a senior decision maker at a business today and they made a quick observation, they talked about they, on their way here to New York City they had followed a woman who was going through security, opened up her suitcase and took out a bird. And then went through security with the bird. And the reason why I bring this up now is as TSA was trying to figure out how exactly to deal with this, the bird started talking and repeating things that the woman had said and many of those things, in fact, might have put her in jail. Now in this case the bird is not an agent of that woman. You can't put the woman in jail because of what the bird said. But increasingly we have to ask ourselves as we ask machines to do more on our behalf, digital instrumentation and elements to do more on our behalf, it's going to have blow back and an impact on our brand if we don't do it well. I want to draw that forward a little bit because I suggest there's going to be a new lifecycle for data. And the way that we think about it is we have the internet or the Edge which is comprised of things and crucially people, using sensors, whether they be smaller processors in control towers or whether they be phones that are tracking where we go, and this crucial element here is something that we call information transducers. Now a transducer in a traditional sense is something that takes energy from one form to another so that it can perform new types of work. By information transducer I essentially mean it takes information from one form to another so it can perform another type of work. This is a crucial feature of data. One of the beauties of data is that it can be used in multiple places at multiple times and not engender significant net new costs. It's one of the few assets that you can say about that. So the concept of an information transducer's really important because it's the basis for a lot of transformations of data as data flies through organizations. So we end up with the transducers storing data in the form of analytics, machine learning, business operations, other types of things, and then it goes back and it's transduced, back into to the real world as we program the real world and turning into these systems of agency. So that's the new lifecycle. And increasingly, that's how we have to think about data flows. Capturing it, turning it into value and having it act on our behalf in front of markets. That could have enormous implications for how ultimately money is spent over the next few years. So Wikibon does a significant amount of market research in addition to advising our large user customers. And that includes doing studies on cloud, public cloud, but also studies on what's happening within the analytics world. And if you take a look at it, what we basically see happening over the course of the next few years is significant investments in software and also services to get the word out. But we also expect there's going to be a lot of hardware. A significant amount of hardware that's ultimately sold within this space. And that's because of something that we call true private cloud. This concept of ultimately a business increasingly being designed and architected around the idea of data assets means that the reality, the physical realities of how data operates, how much it costs to store it or move it, the issues of latency, the issues of intellectual property protection as well as things like the regulatory regimes that are being put in place to govern how data gets used in between locations. All of those factors are going to drive increased utilization of what we call true private cloud. On premise technologies that provide the cloud experience but act where the data naturally needs to be processed. I'll come a little bit more to that in a second. So we think that it's going to be a relatively balanced market, a lot of stuff is going to end up in the cloud, but as Neil and Jim will talk about, there's going to be an enormous amount of analytics that pulls an enormous amount of data out to the Edge 'cause that's where the action's going to be. Now one of the things I want to also reveal to you is we've done a fair amount of data, we've done a fair amount of research around this question of where or how will data guide decisions about infrastructure? And in particular the Edge is driving these conversations. So here is a piece of research that one of our cohorts at Wikibon did, David Floyer. Taking a look at IoT Edge cost comparisons over a three year period. And it showed on the left hand side, an example where the sensor towers and other types of devices were streaming data back into a central location in a wind farm, stylized wind farm example. Very very expensive. Significant amounts of money end up being consumed, significant resources end up being consumed by the cost of moving the data from one place to another. Now this is even assuming that latency does not become a problem. The second example that we looked at is if we kept more of that data at the Edge and processed at the Edge. And literally it is a 85 plus percent cost reduction to keep more of the data at the Edge. Now that has enormous implications, how we think about big data, how we think about next generation architectures, et cetera. But it's these costs that are going to be so crucial to shaping the decisions that we make over the next two years about where we put hardware, where we put resources, what type of automation is possible, and what types of technology management has to be put in place. Ultimately we think it's going to lead to a structure, an architecture in the infrastructure as well as applications that is informed more by moving cloud to the data than moving the data to the cloud. That's kind of our fundamental proposition is that the norm in the industry has been to think about moving all data up to the cloud because who wants to do IT? It's so much cheaper, look what Amazon can do. Or what AWS can do. All true statements. Very very important in many respects. But most businesses today are starting to rethink that simple proposition and asking themselves do we have to move our business to the cloud, or can we move the cloud to the business? And increasingly what we see happening as we talk to our large customers about this, is that the cloud is being extended out to the Edge, we're moving the cloud and cloud services out to the business. Because of economic reasons, intellectual property control reasons, regulatory reasons, security reasons, any number of other reasons. It's just a more natural way to deal with it. And of course, the most important reason is latency. So with that as a quick backdrop, if I may quickly summarize, we believe fundamentally that the difference today is that businesses are trying to understand how to use data as an asset. And that requires an investment in new sets of technology capabilities that are not cheap, not simple and require significant thought, a lot of planning, lot of change within an IT and business organizations. How we capture data, how we turn it into value, and how we translate that into real world action through software. That's going to lead to a rethinking, ultimately, based on cost and other factors about how we deploy infrastructure. How we use the cloud so that the data guides the activity and not the choice of cloud supplier determines or limits what we can do with our data. And that's going to lead to this notion of true private cloud and elevate the role the Edge plays in analytics and all other architectures. So I hope that was perfectly clear. And now what I want to do is I want to bring up Neil Raden. Yes, now's the time Neil! So let me invite Neil up to spend some time talking about harvesting value at the Edge. Can you see his, all right. Got it. >> Oh boy. Hi everybody. Yeah, this is a really, this is a really big and complicated topic so I decided to just concentrate on something fairly simple, but I know that Peter mentioned customers. And he also had a picture of Peter Drucker. I had the pleasure in 1998 of interviewing Peter and photographing him. Peter Drucker, not this Peter. Because I'd started a magazine called Hired Brains. It was for consultants. And Peter said, Peter said a number of really interesting things to me, but one of them was his definition of a customer was someone who wrote you a check that didn't bounce. He was kind of a wag. He was! So anyway, he had to leave to do a video conference with Jack Welch and so I said to him, how do you charge Jack Welch to spend an hour on a video conference? And he said, you know I have this theory that you should always charge your client enough that it hurts a little bit or they don't take you seriously. Well, I had the chance to talk to Jack's wife, Suzie Welch recently and I told her that story and she said, "Oh he's full of it, Jack never paid "a dime for those conferences!" (laughs) So anyway, all right, so let's talk about this. To me, things about, engineered things like the hardware and network and all these other standards and so forth, we haven't fully developed those yet, but they're coming. As far as I'm concerned, they're not the most interesting thing. The most interesting thing to me in Edge Analytics is what you're going to get out of it, what the result is going to be. Making sense of this data that's coming. And while we're on data, something I've been thinking a lot lately because everybody I've talked to for the last three days just keeps talking to me about data. I have this feeling that data isn't actually quite real. That any data that we deal with is the result of some process that's captured it from something else that's actually real. In other words it's proxy. So it's not exactly perfect. And that's why we've always had these problems about customer A, customer A, customer A, what's their definition? What's the definition of this, that and the other thing? And with sensor data, I really have the feeling, when companies get, not you know, not companies, organizations get instrumented and start dealing with this kind of data what they're going to find is that this is the first time, and I've been involved in analytics, I don't want to date myself, 'cause I know I look young, but the first, I've been dealing with analytics since 1975. And everything we've ever done in analytics has involved pulling data from some other system that was not designed for analytics. But if you think about sensor data, this is data that we're actually going to catch the first time. It's going to be ours! We're not going to get it from some other source. It's going to be the real deal, to the extent that it's the real deal. Now you may say, ya know Neil, a sensor that's sending us information about oil pressure or temperature or something like that, how can you quarrel with that? Well, I can quarrel with it because I don't know if the sensor's doing it right. So we still don't know, even with that data, if it's right, but that's what we have to work with. Now, what does that really mean? Is that we have to be really careful with this data. It's ours, we have to take care of it. We don't get to reload it from source some other day. If we munge it up it's gone forever. So that has, that has very serious implications, but let me, let me roll you back a little bit. The way I look at analytics is it's come in three different eras. And we're entering into the third now. The first era was business intelligence. It was basically built and governed by IT, it was system of record kind of reporting. And as far as I can recall, it probably started around 1988 or at least that's the year that Howard Dresner claims to have invented the term. I'm not sure it's true. And things happened before 1988 that was sort of like BI, but 88 was when they really started coming out, that's when we saw BusinessObjects and Cognos and MicroStrategy and those kinds of things. The second generation just popped out on everybody else. We're all looking around at BI and we were saying why isn't this working? Why are only five people in the organization using this? Why are we not getting value out of this massive license we bought? And along comes companies like Tableau doing data discovery, visualization, data prep and Line of Business people are using this now. But it's still the same kind of data sources. It's moved out a little bit, but it still hasn't really hit the Big Data thing. Now we're in third generation, so we not only had Big Data, which has come and hit us like a tsunami, but we're looking at smart discovery, we're looking at machine learning. We're looking at AI induced analytics workflows. And then all the natural language cousins. You know, natural language processing, natural language, what's? Oh Q, natural language query. Natural language generation. Anybody here know what natural language generation is? Yeah, so what you see now is you do some sort of analysis and that tool comes up and says this chart is about the following and it used the following data, and it's blah blah blah blah blah. I think it's kind of wordy and it's going to refined some, but it's an interesting, it's an interesting thing to do. Now, the problem I see with Edge Analytics and IoT in general is that most of the canonical examples we talk about are pretty thin. I know we talk about autonomous cars, I hope to God we never have them, 'cause I'm a car guy. Fleet Management, I think Qualcomm started Fleet Management in 1988, that is not a new application. Industrial controls. I seem to remember, I seem to remember Honeywell doing industrial controls at least in the 70s and before that I wasn't, I don't want to talk about what I was doing, but I definitely wasn't in this industry. So my feeling is we all need to sit down and think about this and get creative. Because the real value in Edge Analytics or IoT, whatever you want to call it, the real value is going to be figuring out something that's new or different. Creating a brand new business. Changing the way an operation happens in a company, right? And I think there's a lot of smart people out there and I think there's a million apps that we haven't even talked about so, if you as a vendor come to me and tell me how great your product is, please don't talk to me about autonomous cars or Fleet Managing, 'cause I've heard about that, okay? Now, hardware and architecture are really not the most interesting thing. We fell into that trap with data warehousing. We've fallen into that trap with Big Data. We talk about speeds and feeds. Somebody said to me the other day, what's the narrative of this company? This is a technology provider. And I said as far as I can tell, they don't have a narrative they have some products and they compete in a space. And when they go to clients and the clients say, what's the value of your product? They don't have an answer for that. So we don't want to fall into this trap, okay? Because IoT is going to inform you in ways you've never even dreamed about. Unfortunately some of them are going to be really stinky, you know, they're going to be really bad. You're going to lose more of your privacy, it's going to get harder to get, I dunno, mortgage for example, I dunno, maybe it'll be easier, but in any case, it's not going to all be good. So let's really think about what you want to do with this technology to do something that's really valuable. Cost takeout is not the place to justify an IoT project. Because number one, it's very expensive, and number two, it's a waste of the technology because you should be looking at, you know the old numerator denominator thing? You should be looking at the numerators and forget about the denominators because that's not what you do with IoT. And the other thing is you don't want to get over confident. Actually this is good advice about anything, right? But in this case, I love this quote by Derek Sivers He's a pretty funny guy. He said, "If more information was the answer, "then we'd all be billionaires with perfect abs." I'm not sure what's on his wishlist, but you know, I would, those aren't necessarily the two things I would think of, okay. Now, what I said about the data, I want to explain some more. Big Data Analytics, if you look at this graphic, it depicts it perfectly. It's a bunch of different stuff falling into the funnel. All right? It comes from other places, it's not original material. And when it comes in, it's always used as second hand data. Now what does that mean? That means that you have to figure out the semantics of this information and you have to find a way to put it together in a way that's useful to you, okay. That's Big Data. That's where we are. How is that different from IoT data? It's like I said, IoT is original. You can put it together any way you want because no one else has ever done that before. It's yours to construct, okay. You don't even have to transform it into a schema because you're creating the new application. But the most important thing is you have to take care of it 'cause if you lose it, it's gone. It's the original data. It's the same way, in operational systems for a long long time we've always been concerned about backup and security and everything else. You better believe this is a problem. I know a lot of people think about streaming data, that we're going to look at it for a minute, and we're going to throw most of it away. Personally I don't think that's going to happen. I think it's all going to be saved, at least for a while. Now, the governance and security, oh, by the way, I don't know where you're going to find a presentation where somebody uses a newspaper clipping about Vladimir Lenin, but here it is, enjoy yourselves. I believe that when people think about governance and security today they're still thinking along the same grids that we thought about it all along. But this is very very different and again, I'm sorry I keep thrashing this around, but this is treasured data that has to be carefully taken care of. Now when I say governance, my experience has been over the years that governance is something that IT does to make everybody's lives miserable. But that's not what I mean by governance today. It means a comprehensive program to really secure the value of the data as an asset. And you need to think about this differently. Now the other thing is you may not get to think about it differently, because some of the stuff may end up being subject to regulation. And if the regulators start regulating some of this, then that'll take some of the degrees of freedom away from you in how you put this together, but you know, that's the way it works. Now, machine learning, I think I told somebody the other day that claims about machine learning in software products are as common as twisters in trail parks. And a lot of it is not really what I'd call machine learning. But there's a lot of it around. And I think all of the open source machine learning and artificial intelligence that's popped up, it's great because all those math PhDs who work at Home Depot now have something to do when they go home at night and they construct this stuff. But if you're going to have machine learning at the Edge, here's the question, what kind of machine learning would you have at the Edge? As opposed to developing your models back at say, the cloud, when you transmit the data there. The devices at the Edge are not very powerful. And they don't have a lot of memory. So you're only going to be able to do things that have been modeled or constructed somewhere else. But that's okay. Because machine learning algorithm development is actually slow and painful. So you really want the people who know how to do this working with gobs of data creating models and testing them offline. And when you have something that works, you can put it there. Now there's one thing I want to talk about before I finish, and I think I'm almost finished. I wrote a book about 10 years ago about automated decision making and the conclusion that I came up with was that little decisions add up, and that's good. But it also means you don't have to get them all right. But you don't want computers or software making decisions unattended if it involves human life, or frankly any life. Or the environment. So when you think about the applications that you can build using this architecture and this technology, think about the fact that you're not going to be doing air traffic control, you're not going to be monitoring crossing guards at the elementary school. You're going to be doing things that may seem fairly mundane. Managing machinery on the factory floor, I mean that may sound great, but really isn't that interesting. Managing well heads, drilling for oil, well I mean, it's great to the extent that it doesn't cause wells to explode, but they don't usually explode. What it's usually used for is to drive the cost out of preventative maintenance. Not very interesting. So use your heads. Come up with really cool stuff. And any of you who are involved in Edge Analytics, the next time I talk to you I don't want to hear about the same five applications that everybody talks about. Let's hear about some new ones. So, in conclusion, I don't really have anything in conclusion except that Peter mentioned something about limousines bringing people up here. On Monday I was slogging up and down Park Avenue and Madison Avenue with my client and we were visiting all the hedge funds there because we were doing a project with them. And in the miserable weather I looked at him and I said, for godsake Paul, where's the black car? And he said, that was the 90s. (laughs) Thank you. So, Jim, up to you. (audience applauding) This is terrible, go that way, this was terrible coming that way. >> Woo, don't want to trip! And let's move to, there we go. Hi everybody, how ya doing? Thanks Neil, thanks Peter, those were great discussions. So I'm the third leg in this relay race here, talking about of course how software is eating the world. And focusing on the value of Edge Analytics in a lot of real world scenarios. Programming the real world for, to make the world a better place. So I will talk, I'll break it out analytically in terms of the research that Wikibon is doing in the area of the IoT, but specifically how AI intelligence is being embedded really to all material reality potentially at the Edge. But mobile applications and industrial IoT and the smart appliances and self driving vehicles. I will break it out in terms of a reference architecture for understanding what functions are being pushed to the Edge to hardware, to our phones and so forth to drive various scenarios in terms of real world results. So I'll move a pace here. So basically AI software or AI microservices are being infused into Edge hardware as we speak. What we see is more vendors of smart phones and other, real world appliances and things like smart driving, self driving vehicles. What they're doing is they're instrumenting their products with computer vision and natural language processing, environmental awareness based on sensing and actuation and those capabilities and inferences that these devices just do to both provide human support for human users of these devices as well as to enable varying degrees of autonomous operation. So what I'll be talking about is how AI is a foundation for data driven systems of agency of the sort that Peter is talking about. Infusing data driven intelligence into everything or potentially so. As more of this capability, all these algorithms for things like, ya know for doing real time predictions and classifications, anomaly detection and so forth, as this functionality gets diffused widely and becomes more commoditized, you'll see it burned into an ever-wider variety of hardware architecture, neuro synaptic chips, GPUs and so forth. So what I've got here in front of you is a sort of a high level reference architecture that we're building up in our research at Wikibon. So AI, artificial intelligence is a big term, a big paradigm, I'm not going to unpack it completely. Of course we don't have oodles of time so I'm going to take you fairly quickly through the high points. It's a driver for systems of agency. Programming the real world. Transducing digital inputs, the data, to analog real world results. Through the embedding of this capability in the IoT, but pushing more and more of it out to the Edge with points of decision and action in real time. And there are four capabilities that we're seeing in terms of AI enabled, enabling capabilities that are absolutely critical to software being pushed to the Edge are sensing, actuation, inference and Learning. Sensing and actuation like Peter was describing, it's about capturing data from the environment within which a device or users is operating or moving. And then actuation is the fancy term for doing stuff, ya know like industrial IoT, it's obviously machine controlled, but clearly, you know self driving vehicles is steering a vehicle and avoiding crashing and so forth. Inference is the meat and potatoes as it were of AI. Analytics does inferences. It infers from the data, the logic of the application. Predictive logic, correlations, classification, abstractions, differentiation, anomaly detection, recognizing faces and voices. We see that now with Apple and the latest version of the iPhone is embedding face recognition as a core, as the core multifactor authentication technique. Clearly that's a harbinger of what's going to be universal fairly soon which is that depends on AI. That depends on convolutional neural networks, that is some heavy hitting processing power that's necessary and it's processing the data that's coming from your face. So that's critically important. So what we're looking at then is the AI software is taking root in hardware to power continuous agency. Getting stuff done. Powered decision support by human beings who have to take varying degrees of action in various environments. We don't necessarily want to let the car steer itself in all scenarios, we want some degree of override, for lots of good reasons. They want to protect life and limb including their own. And just more data driven automation across the internet of things in the broadest sense. So unpacking this reference framework, what's happening is that AI driven intelligence is powering real time decisioning at the Edge. Real time local sensing from the data that it's capturing there, it's ingesting the data. Some, not all of that data, may be persistent at the Edge. Some, perhaps most of it, will be pushed into the cloud for other processing. When you have these highly complex algorithms that are doing AI deep learning, multilayer, to do a variety of anti-fraud and higher level like narrative, auto-narrative roll-ups from various scenes that are unfolding. A lot of this processing is going to begin to happen in the cloud, but a fair amount of the more narrowly scoped inferences that drive real time decision support at the point of action will be done on the device itself. Contextual actuation, so it's the sensor data that's captured by the device along with other data that may be coming down in real time streams through the cloud will provide the broader contextual envelope of data needed to drive actuation, to drive various models and rules and so forth that are making stuff happen at the point of action, at the Edge. Continuous inference. What it all comes down to is that inference is what's going on inside the chips at the Edge device. And what we're seeing is a growing range of hardware architectures, GPUs, CPUs, FPGAs, ASIC, Neuro synaptic chips of all sorts playing in various combinations that are automating more and more very complex inference scenarios at the Edge. And not just individual devices, swarms of devices, like drones and so forth are essentially an Edge unto themselves. You'll see these tiered hierarchies of Edge swarms that are playing and doing inferences of ever more complex dynamic nature. And much of this will be, this capability, the fundamental capabilities that is powering them all will be burned into the hardware that powers them. And then adaptive learning. Now I use the term learning rather than training here, training is at the core of it. Training means everything in terms of the predictive fitness or the fitness of your AI services for whatever task, predictions, classifications, face recognition that you, you've built them for. But I use the term learning in a broader sense. It's what's make your inferences get better and better, more accurate over time is that you're training them with fresh data in a supervised learning environment. But you can have reinforcement learning if you're doing like say robotics and you don't have ground truth against which to train the data set. You know there's maximize a reward function versus minimize a loss function, you know, the standard approach, the latter for supervised learning. There's also, of course, the issue, or not the issue, the approach of unsupervised learning with cluster analysis critically important in a lot of real world scenarios. So Edge AI Algorithms, clearly, deep learning which is multilayered machine learning models that can do abstractions at higher and higher levels. Face recognition is a high level abstraction. Faces in a social environment is an even higher level of abstraction in terms of groups. Faces over time and bodies and gestures, doing various things in various environments is an even higher level abstraction in terms of narratives that can be rolled up, are being rolled up by deep learning capabilities of great sophistication. Convolutional neural networks for processing images, recurrent neural networks for processing time series. Generative adversarial networks for doing essentially what's called generative applications of all sort, composing music, and a lot of it's being used for auto programming. These are all deep learning. There's a variety of other algorithm approaches I'm not going to bore you with here. Deep learning is essentially the enabler of the five senses of the IoT. Your phone's going to have, has a camera, it has a microphone, it has the ability to of course, has geolocation and navigation capabilities. It's environmentally aware, it's got an accelerometer and so forth embedded therein. The reason that your phone and all of the devices are getting scary sentient is that they have the sensory modalities and the AI, the deep learning that enables them to make environmentally correct decisions in the wider range of scenarios. So machine learning is the foundation of all of this, but there are other, I mean of deep learning, artificial neural networks is the foundation of that. But there are other approaches for machine learning I want to make you aware of because support vector machines and these other established approaches for machine learning are not going away but really what's driving the show now is deep learning, because it's scary effective. And so that's where most of the investment in AI is going into these days for deep learning. AI Edge platforms, tools and frameworks are just coming along like gangbusters. Much development of AI, of deep learning happens in the context of your data lake. This is where you're storing your training data. This is the data that you use to build and test to validate in your models. So we're seeing a deepening stack of Hadoop and there's Kafka, and Spark and so forth that are driving the training (coughs) excuse me, of AI models that are power all these Edge Analytic applications so that that lake will continue to broaden in terms, and deepen in terms of a scope and the range of data sets and the range of modeling, AI modeling supports. Data science is critically important in this scenario because the data scientist, the data science teams, the tools and techniques and flows of data science are the fundamental development paradigm or discipline or capability that's being leveraged to build and to train and to deploy and iterate all this AI that's being pushed to the Edge. So clearly data science is at the center, data scientists of an increasingly specialized nature are necessary to the realization to this value at the Edge. AI frameworks are coming along like you know, a mile a minute. TensorFlow has achieved a, is an open source, most of these are open source, has achieved sort of almost like a defacto standard, status, I'm using the word defacto in air quotes. There's Theano and Keras and xNet and CNTK and a variety of other ones. We're seeing range of AI frameworks come to market, most open source. Most are supported by most of the major tool vendors as well. So at Wikibon we're definitely tracking that, we plan to go deeper in our coverage of that space. And then next best action, powers recommendation engines. I mean next best action decision automation of the sort of thing Neil's covered in a variety of contexts in his career is fundamentally important to Edge Analytics to systems of agency 'cause it's driving the process automation, decision automation, sort of the targeted recommendations that are made at the Edge to individual users as well as to process that automation. That's absolutely necessary for self driving vehicles to do their jobs and industrial IoT. So what we're seeing is more and more recommendation engine or recommender capabilities powered by ML and DL are going to the Edge, are already at the Edge for a variety of applications. Edge AI capabilities, like I said, there's sensing. And sensing at the Edge is becoming ever more rich, mixed reality Edge modalities of all sort are for augmented reality and so forth. We're just seeing a growth in certain, the range of sensory modalities that are enabled or filtered and analyzed through AI that are being pushed to the Edge, into the chip sets. Actuation, that's where robotics comes in. Robotics is coming into all aspects of our lives. And you know, it's brainless without AI, without deep learning and these capabilities. Inference, autonomous edge decisioning. Like I said, it's, a growing range of inferences that are being done at the Edge. And that's where it has to happen 'cause that's the point of decision. Learning, training, much training, most training will continue to be done in the cloud because it's very data intensive. It's a grind to train and optimize an AI algorithm to do its job. It's not something that you necessarily want to do or can do at the Edge at Edge devices so, the models that are built and trained in the cloud are pushed down through a dev ops process down to the Edge and that's the way it will work pretty much in most AI environments, Edge analytics environments. You centralize the modeling, you decentralize the execution of the inference models. The training engines will be in the cloud. Edge AI applications. I'll just run you through sort of a core list of the ones that are coming into, already come into the mainstream at the Edge. Multifactor authentication, clearly the Apple announcement of face recognition is just a harbinger of the fact that that's coming to every device. Computer vision speech recognition, NLP, digital assistance and chat bots powered by natural language processing and understanding, it's all AI powered. And it's becoming very mainstream. Emotion detection, face recognition, you know I could go on and on but these are like the core things that everybody has access to or will by 2020 and they're core devices, mass market devices. Developers, designers and hardware engineers are coming together to pool their expertise to build and train not just the AI, but also the entire package of hardware in UX and the orchestration of real world business scenarios or life scenarios that all this intelligence, the submitted intelligence enables and most, much of what they build in terms of AI will be containerized as micro services through Docker and orchestrated through Kubernetes as full cloud services in an increasingly distributed fabric. That's coming along very rapidly. We can see a fair amount of that already on display at Strata in terms of what the vendors are doing or announcing or who they're working with. The hardware itself, the Edge, you know at the Edge, some data will be persistent, needs to be persistent to drive inference. That's, and you know to drive a variety of different application scenarios that need some degree of historical data related to what that device in question happens to be sensing or has sensed in the immediate past or you know, whatever. The hardware itself is geared towards both sensing and increasingly persistence and Edge driven actuation of real world results. The whole notion of drones and robotics being embedded into everything that we do. That's where that comes in. That has to be powered by low cost, low power commodity chip sets of various sorts. What we see right now in terms of chip sets is it's a GPUs, Nvidia has gone real far and GPUs have come along very fast in terms of power inference engines, you know like the Tesla cars and so forth. But GPUs are in many ways the core hardware sub straight for in inference engines in DL so far. But to become a mass market phenomenon, it's got to get cheaper and lower powered and more commoditized, and so we see a fair number of CPUs being used as the hardware for Edge Analytic applications. Some vendors are fairly big on FPGAs, I believe Microsoft has gone fairly far with FPGAs inside DL strategy. ASIC, I mean, there's neuro synaptic chips like IBM's got one. There's at least a few dozen vendors of neuro synaptic chips on the market so at Wikibon we're going to track that market as it develops. And what we're seeing is a fair number of scenarios where it's a mixed environment where you use one chip set architecture at the inference side of the Edge, and other chip set architectures that are driving the DL as processed in the cloud, playing together within a common architecture. And we see some, a fair number of DL environments where the actual training is done in the cloud on Spark using CPUs and parallelized in memory, but pushing Tensorflow models that might be trained through Spark down to the Edge where the inferences are done in FPGAs and GPUs. Those kinds of mixed hardware scenarios are very, very, likely to be standard going forward in lots of areas. So analytics at the Edge power continuous results is what it's all about. The whole point is really not moving the data, it's putting the inference at the Edge and working from the data that's already captured and persistent there for the duration of whatever action or decision or result needs to be powered from the Edge. Like Neil said cost takeout alone is not worth doing. Cost takeout alone is not the rationale for putting AI at the Edge. It's getting new stuff done, new kinds of things done in an automated consistent, intelligent, contextualized way to make our lives better and more productive. Security and governance are becoming more important. Governance of the models, governance of the data, governance in a dev ops context in terms of version controls over all those DL models that are built, that are trained, that are containerized and deployed. Continuous iteration and improvement of those to help them learn to do, make our lives better and easier. With that said, I'm going to hand it over now. It's five minutes after the hour. We're going to get going with the Influencer Panel so what we'd like to do is I call Peter, and Peter's going to call our influencers. >> All right, am I live yet? Can you hear me? All right so, we've got, let me jump back in control here. We've got, again, the objective here is to have community take on some things. And so what we want to do is I want to invite five other people up, Neil why don't you come on up as well. Start with Neil. You can sit here. On the far right hand side, Judith, Judith Hurwitz. >> Neil: I'm glad I'm on the left side. >> From the Hurwitz Group. >> From the Hurwitz Group. Jennifer Shin who's affiliated with UC Berkeley. Jennifer are you here? >> She's here, Jennifer where are you? >> She was here a second ago. >> Neil: I saw her walk out she may have, >> Peter: All right, she'll be back in a second. >> Here's Jennifer! >> Here's Jennifer! >> Neil: With 8 Path Solutions, right? >> Yep. >> Yeah 8 Path Solutions. >> Just get my mic. >> Take your time Jen. >> Peter: All right, Stephanie McReynolds. Far left. And finally Joe Caserta, Joe come on up. >> Stephie's with Elysian >> And to the left. So what I want to do is I want to start by having everybody just go around introduce yourself quickly. Judith, why don't we start there. >> I'm Judith Hurwitz, I'm president of Hurwitz and Associates. We're an analyst research and fault leadership firm. I'm the co-author of eight books. Most recent is Cognitive Computing and Big Data Analytics. I've been in the market for a couple years now. >> Jennifer. >> Hi, my name's Jennifer Shin. I'm the founder and Chief Data Scientist 8 Path Solutions LLC. We do data science analytics and technology. We're actually about to do a big launch next month, with Box actually. >> We're apparent, are we having a, sorry Jennifer, are we having a problem with Jennifer's microphone? >> Man: Just turn it back on? >> Oh you have to turn it back on. >> It was on, oh sorry, can you hear me now? >> Yes! We can hear you now. >> Okay, I don't know how that turned back off, but okay. >> So you got to redo all that Jen. >> Okay, so my name's Jennifer Shin, I'm founder of 8 Path Solutions LLC, it's a data science analytics and technology company. I founded it about six years ago. So we've been developing some really cool technology that we're going to be launching with Box next month. It's really exciting. And I have, I've been developing a lot of patents and some technology as well as teaching at UC Berkeley as a lecturer in data science. >> You know Jim, you know Neil, Joe, you ready to go? >> Joe: Just broke my microphone. >> Joe's microphone is broken. >> Joe: Now it should be all right. >> Jim: Speak into Neil's. >> Joe: Hello, hello? >> I just feel not worthy in the presence of Joe Caserta. (several laughing) >> That's right, master of mics. If you can hear me, Joe Caserta, so yeah, I've been doing data technology solutions since 1986, almost as old as Neil here, but been doing specifically like BI, data warehousing, business intelligence type of work since 1996. And been doing, wholly dedicated to Big Data solutions and modern data engineering since 2009. Where should I be looking? >> Yeah I don't know where is the camera? >> Yeah, and that's basically it. So my company was formed in 2001, it's called Caserta Concepts. We recently rebranded to only Caserta 'cause what we do is way more than just concepts. So we conceptualize the stuff, we envision what the future brings and we actually build it. And we help clients large and small who are just, want to be leaders in innovation using data specifically to advance their business. >> Peter: And finally Stephanie McReynolds. >> I'm Stephanie McReynolds, I had product marketing as well as corporate marketing for a company called Elysian. And we are a data catalog so we help bring together not only a technical understanding of your data, but we curate that data with human knowledge and use automated intelligence internally within the system to make recommendations about what data to use for decision making. And some of our customers like City of San Diego, a large automotive manufacturer working on self driving cars and General Electric use Elysian to help power their solutions for IoT at the Edge. >> All right so let's jump right into it. And again if you have a question, raise your hand, and we'll do our best to get it to the floor. But what I want to do is I want to get seven questions in front of this group and have you guys discuss, slog, disagree, agree. Let's start here. What is the relationship between Big Data AI and IoT? Now Wikibon's put forward its observation that data's being generated at the Edge, that action is being taken at the Edge and then increasingly the software and other infrastructure architectures need to accommodate the realities of how data is going to work in these very complex systems. That's our perspective. Anybody, Judith, you want to start? >> Yeah, so I think that if you look at AI machine learning, all these different areas, you have to be able to have the data learned. Now when it comes to IoT, I think one of the issues we have to be careful about is not all data will be at the Edge. Not all data needs to be analyzed at the Edge. For example if the light is green and that's good and it's supposed to be green, do you really have to constantly analyze the fact that the light is green? You actually only really want to be able to analyze and take action when there's an anomaly. Well if it goes purple, that's actually a sign that something might explode, so that's where you want to make sure that you have the analytics at the edge. Not for everything, but for the things where there is an anomaly and a change. >> Joe, how about from your perspective? >> For me I think the evolution of data is really becoming, eventually oxygen is just, I mean data's going to be the oxygen we breathe. It used to be very very reactive and there used to be like a latency. You do something, there's a behavior, there's an event, there's a transaction, and then you go record it and then you collect it, and then you can analyze it. And it was very very waterfallish, right? And then eventually we figured out to put it back into the system. Or at least human beings interpret it to try to make the system better and that is really completely turned on it's head, we don't do that anymore. Right now it's very very, it's synchronous, where as we're actually making these transactions, the machines, we don't really need, I mean human beings are involved a bit, but less and less and less. And it's just a reality, it may not be politically correct to say but it's a reality that my phone in my pocket is following my behavior, and it knows without telling a human being what I'm doing. And it can actually help me do things like get to where I want to go faster depending on my preference if I want to save money or save time or visit things along the way. And I think that's all integration of big data, streaming data, artificial intelligence and I think the next thing that we're going to start seeing is the culmination of all of that. I actually, hopefully it'll be published soon, I just wrote an article for Forbes with the term of ARBI and ARBI is the integration of Augmented Reality and Business Intelligence. Where I think essentially we're going to see, you know, hold your phone up to Jim's face and it's going to recognize-- >> Peter: It's going to break. >> And it's going to say exactly you know, what are the key metrics that we want to know about Jim. If he works on my sales force, what's his attainment of goal, what is-- >> Jim: Can it read my mind? >> Potentially based on behavior patterns. >> Now I'm scared. >> I don't think Jim's buying it. >> It will, without a doubt be able to predict what you've done in the past, you may, with some certain level of confidence you may do again in the future, right? And is that mind reading? It's pretty close, right? >> Well, sometimes, I mean, mind reading is in the eye of the individual who wants to know. And if the machine appears to approximate what's going on in the person's head, sometimes you can't tell. So I guess, I guess we could call that the Turing machine test of the paranormal. >> Well, face recognition, micro gesture recognition, I mean facial gestures, people can do it. Maybe not better than a coin toss, but if it can be seen visually and captured and analyzed, conceivably some degree of mind reading can be built in. I can see when somebody's angry looking at me so, that's a possibility. That's kind of a scary possibility in a surveillance society, potentially. >> Neil: Right, absolutely. >> Peter: Stephanie, what do you think? >> Well, I hear a world of it's the bots versus the humans being painted here and I think that, you know at Elysian we have a very strong perspective on this and that is that the greatest impact, or the greatest results is going to be when humans figure out how to collaborate with the machines. And so yes, you want to get to the location more quickly, but the machine as in the bot isn't able to tell you exactly what to do and you're just going to blindly follow it. You need to train that machine, you need to have a partnership with that machine. So, a lot of the power, and I think this goes back to Judith's story is then what is the human decision making that can be augmented with data from the machine, but then the humans are actually training the training side and driving machines in the right direction. I think that's when we get true power out of some of these solutions so it's not just all about the technology. It's not all about the data or the AI, or the IoT, it's about how that empowers human systems to become smarter and more effective and more efficient. And I think we're playing that out in our technology in a certain way and I think organizations that are thinking along those lines with IoT are seeing more benefits immediately from those projects. >> So I think we have a general agreement of what kind of some of the things you talked about, IoT, crucial capturing information, and then having action being taken, AI being crucial to defining and refining the nature of the actions that are being taken Big Data ultimately powering how a lot of that changes. Let's go to the next one. >> So actually I have something to add to that. So I think it makes sense, right, with IoT, why we have Big Data associated with it. If you think about what data is collected by IoT. We're talking about a serial information, right? It's over time, it's going to grow exponentially just by definition, right, so every minute you collect a piece of information that means over time, it's going to keep growing, growing, growing as it accumulates. So that's one of the reasons why the IoT is so strongly associated with Big Data. And also why you need AI to be able to differentiate between one minute versus next minute, right? Trying to find a better way rather than looking at all that information and manually picking out patterns. To have some automated process for being able to filter through that much data that's being collected. >> I want to point out though based on what you just said Jennifer, I want to bring Neil in at this point, that this question of IoT now generating unprecedented levels of data does introduce this idea of the primary source. Historically what we've done within technology, or within IT certainly is we've taken stylized data. There is no such thing as a real world accounting thing. It is a human contrivance. And we stylize data and therefore it's relatively easy to be very precise on it. But when we start, as you noted, when we start measuring things with a tolerance down to thousandths of a millimeter, whatever that is, metric system, now we're still sometimes dealing with errors that we have to attend to. So, the reality is we're not just dealing with stylized data, we're dealing with real data, and it's more, more frequent, but it also has special cases that we have to attend to as in terms of how we use it. What do you think Neil? >> Well, I mean, I agree with that, I think I already said that, right. >> Yes you did, okay let's move on to the next one. >> Well it's a doppelganger, the digital twin doppelganger that's automatically created by your very fact that you're living and interacting and so forth and so on. It's going to accumulate regardless. Now that doppelganger may not be your agent, or might not be the foundation for your agent unless there's some other piece of logic like an interest graph that you build, a human being saying this is my broad set of interests, and so all of my agents out there in the IoT, you all need to be aware that when you make a decision on my behalf as my agent, this is what Jim would do. You know I mean there needs to be that kind of logic somewhere in this fabric to enable true agency. >> All right, so I'm going to start with you. Oh go ahead. >> I have a real short answer to this though. I think that Big Data provides the data and compute platform to make AI possible. For those of us who dipped our toes in the water in the 80s, we got clobbered because we didn't have the, we didn't have the facilities, we didn't have the resources to really do AI, we just kind of played around with it. And I think that the other thing about it is if you combine Big Data and AI and IoT, what you're going to see is people, a lot of the applications we develop now are very inward looking, we look at our organization, we look at our customers. We try to figure out how to sell more shoes to fashionable ladies, right? But with this technology, I think people can really expand what they're thinking about and what they model and come up with applications that are much more external. >> Actually what I would add to that is also it actually introduces being able to use engineering, right? Having engineers interested in the data. Because it's actually technical data that's collected not just say preferences or information about people, but actual measurements that are being collected with IoT. So it's really interesting in the engineering space because it opens up a whole new world for the engineers to actually look at data and to actually combine both that hardware side as well as the data that's being collected from it. >> Well, Neil, you and I have talked about something, 'cause it's not just engineers. We have in the healthcare industry for example, which you know a fair amount about, there's this notion of empirical based management. And the idea that increasingly we have to be driven by data as a way of improving the way that managers do things, the way the managers collect or collaborate and ultimately collectively how they take action. So it's not just engineers, it's supposed to also inform business, what's actually happening in the healthcare world when we start thinking about some of this empirical based management, is it working? What are some of the barriers? >> It's not a function of technology. What happens in medicine and healthcare research is, I guess you can say it borders on fraud. (people chuckling) No, I'm not kidding. I know the New England Journal of Medicine a couple of years ago released a study and said that at least half their articles that they published turned out to be written, ghost written by pharmaceutical companies. (man chuckling) Right, so I think the problem is that when you do a clinical study, the one that really killed me about 10 years ago was the women's health initiative. They spent $700 million gathering this data over 20 years. And when they released it they looked at all the wrong things deliberately, right? So I think that's a systemic-- >> I think you're bringing up a really important point that we haven't brought up yet, and that is is can you use Big Data and machine learning to begin to take the biases out? So if you let the, if you divorce your preconceived notions and your biases from the data and let the data lead you to the logic, you start to, I think get better over time, but it's going to take a while to get there because we do tend to gravitate towards our biases. >> I will share an anecdote. So I had some arm pain, and I had numbness in my thumb and pointer finger and I went to, excruciating pain, went to the hospital. So the doctor examined me, and he said you probably have a pinched nerve, he said, but I'm not exactly sure which nerve it would be, I'll be right back. And I kid you not, he went to a computer and he Googled it. (Neil laughs) And he came back because this little bit of information was something that could easily be looked up, right? Every nerve in your spine is connected to your different fingers so the pointer and the thumb just happens to be your C6, so he came back and said, it's your C6. (Neil mumbles) >> You know an interesting, I mean that's a good example. One of the issues with healthcare data is that the data set is not always shared across the entire research community, so by making Big Data accessible to everyone, you actually start a more rational conversation or debate on well what are the true insights-- >> If that conversation includes what Judith talked about, the actual model that you use to set priorities and make decisions about what's actually important. So it's not just about improving, this is the test. It's not just about improving your understanding of the wrong thing, it's also testing whether it's the right or wrong thing as well. >> That's right, to be able to test that you need to have humans in dialog with one another bringing different biases to the table to work through okay is there truth in this data? >> It's context and it's correlation and you can have a great correlation that's garbage. You know if you don't have the right context. >> Peter: So I want to, hold on Jim, I want to, >> It's exploratory. >> Hold on Jim, I want to take it to the next question 'cause I want to build off of what you talked about Stephanie and that is that this says something about what is the Edge. And our perspective is that the Edge is not just devices. That when we talk about the Edge, we're talking about human beings and the role that human beings are going to play both as sensors or carrying things with them, but also as actuators, actually taking action which is not a simple thing. So what do you guys think? What does the Edge mean to you? Joe, why don't you start? >> Well, I think it could be a combination of the two. And specifically when we talk about healthcare. So I believe in 2017 when we eat we don't know why we're eating, like I think we should absolutely by now be able to know exactly what is my protein level, what is my calcium level, what is my potassium level? And then find the foods to meet that. What have I depleted versus what I should have, and eat very very purposely and not by taste-- >> And it's amazing that red wine is always the answer. >> It is. (people laughing) And tequila, that helps too. >> Jim: You're a precision foodie is what you are. (several chuckle) >> There's no reason why we should not be able to know that right now, right? And when it comes to healthcare is, the biggest problem or challenge with healthcare is no matter how great of a technology you have, you can't, you can't, you can't manage what you can't measure. And you're really not allowed to use a lot of this data so you can't measure it, right? You can't do things very very scientifically right, in the healthcare world and I think regulation in the healthcare world is really burdening advancement in science. >> Peter: Any thoughts Jennifer? >> Yes, I teach statistics for data scientists, right, so you know we talk about a lot of these concepts. I think what makes these questions so difficult is you have to find a balance, right, a middle ground. For instance, in the case of are you being too biased through data, well you could say like we want to look at data only objectively, but then there are certain relationships that your data models might show that aren't actually a causal relationship. For instance, if there's an alien that came from space and saw earth, saw the people, everyone's carrying umbrellas right, and then it started to rain. That alien might think well, it's because they're carrying umbrellas that it's raining. Now we know from real world that that's actually not the way these things work. So if you look only at the data, that's the potential risk. That you'll start making associations or saying something's causal when it's actually not, right? So that's one of the, one of the I think big challenges. I think when it comes to looking also at things like healthcare data, right? Do you collect data about anything and everything? Does it mean that A, we need to collect all that data for the question we're looking at? Or that it's actually the best, more optimal way to be able to get to the answer? Meaning sometimes you can take some shortcuts in terms of what data you collect and still get the right answer and not have maybe that level of specificity that's going to cost you millions extra to be able to get. >> So Jennifer as a data scientist, I want to build upon what you just said. And that is, are we going to start to see methods and models emerge for how we actually solve some of these problems? So for example, we know how to build a system for stylized process like accounting or some elements of accounting. We have methods and models that lead to technology and actions and whatnot all the way down to that that system can be generated. We don't have the same notion to the same degree when we start talking about AI and some of these Big Datas. We have algorithms, we have technology. But are we going to start seeing, as a data scientist, repeatability and learning and how to think the problems through that's going to lead us to a more likely best or at least good result? >> So I think that's a bit of a tough question, right? Because part of it is, it's going to depend on how many of these researchers actually get exposed to real world scenarios, right? Research looks into all these papers, and you come up with all these models, but if it's never tested in a real world scenario, well, I mean we really can't validate that it works, right? So I think it is dependent on how much of this integration there's going to be between the research community and industry and how much investment there is. Funding is going to matter in this case. If there's no funding in the research side, then you'll see a lot of industry folk who feel very confident about their models that, but again on the other side of course, if researchers don't validate those models then you really can't say for sure that it's actually more accurate, or it's more efficient. >> It's the issue of real world testing and experimentation, A B testing, that's standard practice in many operationalized ML and AI implementations in the business world, but real world experimentation in the Edge analytics, what you're actually transducing are touching people's actual lives. Problem there is, like in healthcare and so forth, when you're experimenting with people's lives, somebody's going to die. I mean, in other words, that's a critical, in terms of causal analysis, you've got to tread lightly on doing operationalizing that kind of testing in the IoT when people's lives and health are at stake. >> We still give 'em placebos. So we still test 'em. All right so let's go to the next question. What are the hottest innovations in AI? Stephanie I want to start with you as a company, someone at a company that's got kind of an interesting little thing happening. We start thinking about how do we better catalog data and represent it to a large number of people. What are some of the hottest innovations in AI as you see it? >> I think it's a little counter intuitive about what the hottest innovations are in AI, because we're at a spot in the industry where the most successful companies that are working with AI are actually incorporating them into solutions. So the best AI solutions are actually the products that you don't know there's AI operating underneath. But they're having a significant impact on business decision making or bringing a different type of application to the market and you know, I think there's a lot of investment that's going into AI tooling and tool sets for data scientists or researchers, but the more innovative companies are thinking through how do we really take AI and make it have an impact on business decision making and that means kind of hiding the AI to the business user. Because if you think a bot is making a decision instead of you, you're not going to partner with that bot very easily or very readily. I worked at, way at the start of my career, I worked in CRM when recommendation engines were all the rage online and also in call centers. And the hardest thing was to get a call center agent to actually read the script that the algorithm was presenting to them, that algorithm was 99% correct most of the time, but there was this human resistance to letting a computer tell you what to tell that customer on the other side even if it was more successful in the end. And so I think that the innovation in AI that's really going to push us forward is when humans feel like they can partner with these bots and they don't think of it as a bot, but they think about as assisting their work and getting to a better result-- >> Hence the augmentation point you made earlier. >> Absolutely, absolutely. >> Joe how 'about you? What do you look at? What are you excited about? >> I think the coolest thing at the moment right now is chat bots. Like to be able, like to have voice be able to speak with you in natural language, to do that, I think that's pretty innovative, right? And I do think that eventually, for the average user, not for techies like me, but for the average user, I think keyboards are going to be a thing of the past. I think we're going to communicate with computers through voice and I think this is the very very beginning of that and it's an incredible innovation. >> Neil? >> Well, I think we all have myopia here. We're all thinking about commercial applications. Big, big things are happening with AI in the intelligence community, in military, the defense industry, in all sorts of things. Meteorology. And that's where, well, hopefully not on an every day basis with military, you really see the effect of this. But I was involved in a project a couple of years ago where we were developing AI software to detect artillery pieces in terrain from satellite imagery. I don't have to tell you what country that was. I think you can probably figure that one out right? But there are legions of people in many many companies that are involved in that industry. So if you're talking about the dollars spent on AI, I think the stuff that we do in our industries is probably fairly small. >> Well it reminds me of an application I actually thought was interesting about AI related to that, AI being applied to removing mines from war zones. >> Why not? >> Which is not a bad thing for a whole lot of people. Judith what do you look at? >> So I'm looking at things like being able to have pre-trained data sets in specific solution areas. I think that that's something that's coming. Also the ability to, to really be able to have a machine assist you in selecting the right algorithms based on what your data looks like and the problems you're trying to solve. Some of the things that data scientists still spend a lot of their time on, but can be augmented with some, basically we have to move to levels of abstraction before this becomes truly ubiquitous across many different areas. >> Peter: Jennifer? >> So I'm going to say computer vision. >> Computer vision? >> Computer vision. So computer vision ranges from image recognition to be able to say what content is in the image. Is it a dog, is it a cat, is it a blueberry muffin? Like a sort of popular post out there where it's like a blueberry muffin versus like I think a chihuahua and then it compares the two. And can the AI really actually detect difference, right? So I think that's really where a lot of people who are in this space of being in both the AI space as well as data science are looking to for the new innovations. I think, for instance, cloud vision I think that's what Google still calls it. The vision API we've they've released on beta allows you to actually use an API to send your image and then have it be recognized right, by their API. There's another startup in New York called Clarify that also does a similar thing as well as you know Amazon has their recognition platform as well. So I think in a, from images being able to detect what's in the content as well as from videos, being able to say things like how many people are entering a frame? How many people enter the store? Not having to actually go look at it and count it, but having a computer actually tally that information for you, right? >> There's actually an extra piece to that. So if I have a picture of a stop sign, and I'm an automated car, and is it a picture on the back of a bus of a stop sign, or is it a real stop sign? So that's going to be one of the complications. >> Doesn't matter to a New York City cab driver. How 'about you Jim? >> Probably not. (laughs) >> Hottest thing in AI is General Adversarial Networks, GANT, what's hot about that, well, I'll be very quick, most AI, most deep learning, machine learning is analytical, it's distilling or inferring insights from the data. Generative takes that same algorithmic basis but to build stuff. In other words, to create realistic looking photographs, to compose music, to build CAD CAM models essentially that can be constructed on 3D printers. So GANT, it's a huge research focus all around the world are used for, often increasingly used for natural language generation. In other words it's institutionalizing or having a foundation for nailing the Turing test every single time, building something with machines that looks like it was constructed by a human and doing it over and over again to fool humans. I mean you can imagine the fraud potential. But you can also imagine just the sheer, like it's going to shape the world, GANT. >> All right so I'm going to say one thing, and then we're going to ask if anybody in the audience has an idea. So the thing that I find interesting is traditional programs, or when you tell a machine to do something you don't need incentives. When you tell a human being something, you have to provide incentives. Like how do you get someone to actually read the text. And this whole question of elements within AI that incorporate incentives as a way of trying to guide human behavior is absolutely fascinating to me. Whether it's gamification, or even some things we're thinking about with block chain and bitcoins and related types of stuff. To my mind that's going to have an enormous impact, some good, some bad. Anybody in the audience? I don't want to lose everybody here. What do you think sir? And I'll try to do my best to repeat it. Oh we have a mic. >> So my question's about, Okay, so the question's pretty much about what Stephanie's talking about which is human and loop training right? I come from a computer vision background. That's the problem, we need millions of images trained, we need humans to do that. And that's like you know, the workforce is essentially people that aren't necessarily part of the AI community, they're people that are just able to use that data and analyze the data and label that data. That's something that I think is a big problem everyone in the computer vision industry at least faces. I was wondering-- >> So again, but the problem is that is the difficulty of methodologically bringing together people who understand it and people who, people who have domain expertise people who have algorithm expertise and working together? >> I think the expertise issue comes in healthcare, right? In healthcare you need experts to be labeling your images. With contextual information where essentially augmented reality applications coming in, you have the AR kit and everything coming out, but there is a lack of context based intelligence. And all of that comes through training images, and all of that requires people to do it. And that's kind of like the foundational basis of AI coming forward is not necessarily an algorithm, right? It's how well are datas labeled? Who's doing the labeling and how do we ensure that it happens? >> Great question. So for the panel. So if you think about it, a consultant talks about being on the bench. How much time are they going to have to spend on trying to develop additional business? How much time should we set aside for executives to help train some of the assistants? >> I think that the key is not, to think of the problem a different way is that you would have people manually label data and that's one way to solve the problem. But you can also look at what is the natural workflow of that executive, or that individual? And is there a way to gather that context automatically using AI, right? And if you can do that, it's similar to what we do in our product, we observe how someone is analyzing the data and from those observations we can actually create the metadata that then trains the system in a particular direction. But you have to think about solving the problem differently of finding the workflow that then you can feed into to make this labeling easy without the human really realizing that they're labeling the data. >> Peter: Anybody else? >> I'll just add to what Stephanie said, so in the IoT applications, all those sensory modalities, the computer vision, the speech recognition, all that, that's all potential training data. So it cross checks against all the other models that are processing all the other data coming from that device. So that the natural language process of understanding can be reality checked against the images that the person happens to be commenting upon, or the scene in which they're embedded, so yeah, the data's embedded-- >> I don't think we're, we're not at the stage yet where this is easy. It's going to take time before we do start doing the pre-training of some of these details so that it goes faster, but right now, there're not that many shortcuts. >> Go ahead Joe. >> Sorry so a couple things. So one is like, I was just caught up on your incentivizing programs to be more efficient like humans. You know in Ethereum that has this notion, which is bot chain, has this theory, this concept of gas. Where like as the process becomes more efficient it costs less to actually run, right? It costs less ether, right? So it actually is kind of, the machine is actually incentivized and you don't really know what it's going to cost until the machine processes it, right? So there is like some notion of that there. But as far as like vision, like training the machine for computer vision, I think it's through adoption and crowdsourcing, so as people start using it more they're going to be adding more pictures. Very very organically. And then the machines will be trained and right now is a very small handful doing it, and it's very proactive by the Googles and the Facebooks and all of that. But as we start using it, as they start looking at my images and Jim's and Jen's images, it's going to keep getting smarter and smarter through adoption and through very organic process. >> So Neil, let me ask you a question. Who owns the value that's generated as a consequence of all these people ultimately contributing their insight and intelligence into these systems? >> Well, to a certain extent the people who are contributing the insight own nothing because the systems collect their actions and the things they do and then that data doesn't belong to them, it belongs to whoever collected it or whoever's going to do something with it. But the other thing, getting back to the medical stuff. It's not enough to say that the systems, people will do the right thing, because a lot of them are not motivated to do the right thing. The whole grant thing, the whole oh my god I'm not going to go against the senior professor. A lot of these, I knew a guy who was a doctor at University of Pittsburgh and they were doing a clinical study on the tubes that they put in little kids' ears who have ear infections, right? And-- >> Google it! Who helps out? >> Anyway, I forget the exact thing, but he came out and said that the principle investigator lied when he made the presentation, that it should be this, I forget which way it went. He was fired from his position at Pittsburgh and he has never worked as a doctor again. 'Cause he went against the senior line of authority. He was-- >> Another question back here? >> Man: Yes, Mark Turner has a question. >> Not a question, just want to piggyback what you're saying about the transfixation of maybe in healthcare of black and white images and color images in the case of sonograms and ultrasound and mammograms, you see that happening using AI? You see that being, I mean it's already happening, do you see it moving forward in that kind of way? I mean, talk more about that, about you know, AI and black and white images being used and they can be transfixed, they can be made to color images so you can see things better, doctors can perform better operations. >> So I'm sorry, but could you summarize down? What's the question? Summarize it just, >> I had a lot of students, they're interested in the cross pollenization between AI and say the medical community as far as things like ultrasound and sonograms and mammograms and how you can literally take a black and white image and it can, using algorithms and stuff be made to color images that can help doctors better do the work that they've already been doing, just do it better. You touched on it like 30 seconds. >> So how AI can be used to actually add information in a way that's not necessarily invasive but is ultimately improves how someone might respond to it or use it, yes? Related? I've also got something say about medical images in a second, any of you guys want to, go ahead Jennifer. >> Yeah, so for one thing, you know and it kind of goes back to what we were talking about before. When we look at for instance scans, like at some point I was looking at CT scans, right, for lung cancer nodules. In order for me, who I don't have a medical background, to identify where the nodule is, of course, a doctor actually had to go in and specify which slice of the scan had the nodule and where exactly it is, so it's on both the slice level as well as, within that 2D image, where it's located and the size of it. So the beauty of things like AI is that ultimately right now a radiologist has to look at every slice and actually identify this manually, right? The goal of course would be that one day we wouldn't have to have someone look at every slice to like 300 usually slices and be able to identify it much more automated. And I think the reality is we're not going to get something where it's going to be 100%. And with anything we do in the real world it's always like a 95% chance of it being accurate. So I think it's finding that in between of where, what's the threshold that we want to use to be able to say that this is, definitively say a lung cancer nodule or not. I think the other thing to think about is in terms of how their using other information, what they might use is a for instance, to say like you know, based on other characteristics of the person's health, they might use that as sort of a grading right? So you know, how dark or how light something is, identify maybe in that region, the prevalence of that specific variable. So that's usually how they integrate that information into something that's already existing in the computer vision sense. I think that's, the difficulty with this of course, is being able to identify which variables were introduced into data that does exist. >> So I'll make two quick observations on this then I'll go to the next question. One is radiologists have historically been some of the highest paid physicians within the medical community partly because they don't have to be particularly clinical. They don't have to spend a lot of time with patients. They tend to spend time with doctors which means they can do a lot of work in a little bit of time, and charge a fair amount of money. As we start to introduce some of these technologies that allow us to from a machine standpoint actually make diagnoses based on those images, I find it fascinating that you now see television ads promoting the role that the radiologist plays in clinical medicine. It's kind of an interesting response. >> It's also disruptive as I'm seeing more and more studies showing that deep learning models processing images, ultrasounds and so forth are getting as accurate as many of the best radiologists. >> That's the point! >> Detecting cancer >> Now radiologists are saying oh look, we do this great thing in terms of interacting with the patients, never have because they're being dis-intermediated. The second thing that I'll note is one of my favorite examples of that if I got it right, is looking at the images, the deep space images that come out of Hubble. Where they're taking data from thousands, maybe even millions of images and combining it together in interesting ways you can actually see depth. You can actually move through to a very very small scale a system that's 150, well maybe that, can't be that much, maybe six billion light years away. Fascinating stuff. All right so let me go to the last question here, and then I'm going to close it down, then we can have something to drink. What are the hottest, oh I'm sorry, question? >> Yes, hi, my name's George, I'm with Blue Talon. You asked earlier there the question what's the hottest thing in the Edge and AI, I would say that it's security. It seems to me that before you can empower agency you need to be able to authorize what they can act on, how they can act on, who they can act on. So it seems if you're going to move from very distributed data at the Edge and analytics at the Edge, there has to be security similarly done at the Edge. And I saw (speaking faintly) slides that called out security as a key prerequisite and maybe Judith can comment, but I'm curious how security's going to evolve to meet this analytics at the Edge. >> Well, let me do that and I'll ask Jen to comment. The notion of agency is crucially important, slightly different from security, just so we're clear. And the basic idea here is historically folks have thought about moving data or they thought about moving application function, now we are thinking about moving authority. So as you said. That's not necessarily, that's not really a security question, but this has been a problem that's been in, of concern in a number of different domains. How do we move authority with the resources? And that's really what informs the whole agency process. But with that said, Jim. >> Yeah actually I'll, yeah, thank you for bringing up security so identity is the foundation of security. Strong identity, multifactor, face recognition, biometrics and so forth. Clearly AI, machine learning, deep learning are powering a new era of biometrics and you know it's behavioral metrics and so forth that's organic to people's use of devices and so forth. You know getting to the point that Peter was raising is important, agency! Systems of agency. Your agent, you have to, you as a human being should be vouching in a secure, tamper proof way, your identity should be vouching for the identity of some agent, physical or virtual that does stuff on your behalf. How can that, how should that be managed within this increasingly distributed IoT fabric? Well a lot of that's been worked. It all ran through webs of trust, public key infrastructure, formats and you know SAML for single sign and so forth. It's all about assertion, strong assertions and vouching. I mean there's the whole workflows of things. Back in the ancient days when I was actually a PKI analyst three analyst firms ago, I got deep into all the guts of all those federation agreements, something like that has to be IoT scalable to enable systems agency to be truly fluid. So we can vouch for our agents wherever they happen to be. We're going to keep on having as human beings agents all over creation, we're not even going to be aware of everywhere that our agents are, but our identity-- >> It's not just-- >> Our identity has to follow. >> But it's not just identity, it's also authorization and context. >> Permissioning, of course. >> So I may be the right person to do something yesterday, but I'm not authorized to do it in another context in another application. >> Role based permissioning, yeah. Or persona based. >> That's right. >> I agree. >> And obviously it's going to be interesting to see the role that block chain or its follow on to the technology is going to play here. Okay so let me throw one more questions out. What are the hottest applications of AI at the Edge? We've talked about a number of them, does anybody want to add something that hasn't been talked about? Or do you want to get a beer? (people laughing) Stephanie, you raised your hand first. >> I was going to go, I bring something mundane to the table actually because I think one of the most exciting innovations with IoT and AI are actually simple things like City of San Diego is rolling out 3200 automated street lights that will actually help you find a parking space, reduce the amount of emissions into the atmosphere, so has some environmental change, positive environmental change impact. I mean, it's street lights, it's not like a, it's not medical industry, it doesn't look like a life changing innovation, and yet if we automate streetlights and we manage our energy better, and maybe they can flicker on and off if there's a parking space there for you, that's a significant impact on everyone's life. >> And dramatically suppress the impact of backseat driving! >> (laughs) Exactly. >> Joe what were you saying? >> I was just going to say you know there's already the technology out there where you can put a camera on a drone with machine learning within an artificial intelligence within it, and it can look at buildings and determine whether there's rusty pipes and cracks in cement and leaky roofs and all of those things. And that's all based on artificial intelligence. And I think if you can do that, to be able to look at an x-ray and determine if there's a tumor there is not out of the realm of possibility, right? >> Neil? >> I agree with both of them, that's what I meant about external kind of applications. Instead of figuring out what to sell our customers. Which is most what we hear. I just, I think all of those things are imminently doable. And boy street lights that help you find a parking place, that's brilliant, right? >> Simple! >> It improves your life more than, I dunno. Something I use on the internet recently, but I think it's great! That's, I'd like to see a thousand things like that. >> Peter: Jim? >> Yeah, building on what Stephanie and Neil were saying, it's ambient intelligence built into everything to enable fine grain microclimate awareness of all of us as human beings moving through the world. And enable reading of every microclimate in buildings. In other words, you know you have sensors on your body that are always detecting the heat, the humidity, the level of pollution or whatever in every environment that you're in or that you might be likely to move into fairly soon and either A can help give you guidance in real time about where to avoid, or give that environment guidance about how to adjust itself to your, like the lighting or whatever it might be to your specific requirements. And you know when you have a room like this, full of other human beings, there has to be some negotiated settlement. Some will find it too hot, some will find it too cold or whatever but I think that is fundamental in terms of reshaping the sheer quality of experience of most of our lived habitats on the planet potentially. That's really the Edge analytics application that depends on everybody having, being fully equipped with a personal area network of sensors that's communicating into the cloud. >> Jennifer? >> So I think, what's really interesting about it is being able to utilize the technology we do have, it's a lot cheaper now to have a lot of these ways of measuring that we didn't have before. And whether or not engineers can then leverage what we have as ways to measure things and then of course then you need people like data scientists to build the right model. So you can collect all this data, if you don't build the right model that identifies these patterns then all that data's just collected and it's just made a repository. So without having the models that supports patterns that are actually in the data, you're not going to find a better way of being able to find insights in the data itself. So I think what will be really interesting is to see how existing technology is leveraged, to collect data and then how that's actually modeled as well as to be able to see how technology's going to now develop from where it is now, to being able to either collect things more sensitively or in the case of say for instance if you're dealing with like how people move, whether we can build things that we can then use to measure how we move, right? Like how we move every day and then being able to model that in a way that is actually going to give us better insights in things like healthcare and just maybe even just our behaviors. >> Peter: Judith? >> So, I think we also have to look at it from a peer to peer perspective. So I may be able to get some data from one thing at the Edge, but then all those Edge devices, sensors or whatever, they all have to interact with each other because we don't live, we may, in our business lives, act in silos, but in the real world when you look at things like sensors and devices it's how they react with each other on a peer to peer basis. >> All right, before I invite John up, I want to say, I'll say what my thing is, and it's not the hottest. It's the one I hate the most. I hate AI generated music. (people laughing) Hate it. All right, I want to thank all the panelists, every single person, some great commentary, great observations. I want to thank you very much. I want to thank everybody that joined. John in a second you'll kind of announce who's the big winner. But the one thing I want to do is, is I was listening, I learned a lot from everybody, but I want to call out the one comment that I think we all need to remember, and I'm going to give you the award Stephanie. And that is increasing we have to remember that the best AI is probably AI that we don't even know is working on our behalf. The same flip side of that is all of us have to be very cognizant of the idea that AI is acting on our behalf and we may not know it. So, John why don't you come on up. Who won the, whatever it's called, the raffle? >> You won. >> Thank you! >> How 'about a round of applause for the great panel. (audience applauding) Okay we have a put the business cards in the basket, we're going to have that brought up. We're going to have two raffle gifts, some nice Bose headsets and speaker, Bluetooth speaker. Got to wait for that. I just want to say thank you for coming and for the folks watching, this is our fifth year doing our own event called Big Data NYC which is really an extension of the landscape beyond the Big Data world that's Cloud and AI and IoT and other great things happen and great experts and influencers and analysts here. Thanks for sharing your opinion. Really appreciate you taking the time to come out and share your data and your knowledge, appreciate it. Thank you. Where's the? >> Sam's right in front of you. >> There's the thing, okay. Got to be present to win. We saw some people sneaking out the back door to go to a dinner. >> First prize first. >> Okay first prize is the Bose headset. >> Bluetooth and noise canceling. >> I won't look, Sam you got to hold it down, I can see the cards. >> All right. >> Stephanie you won! (Stephanie laughing) Okay, Sawny Cox, Sawny Allie Cox? (audience applauding) Yay look at that! He's here! The bar's open so help yourself, but we got one more. >> Congratulations. Picture right here. >> Hold that I saw you. Wake up a little bit. Okay, all right. Next one is, my kids love this. This is great, great for the beach, great for everything portable speaker, great gift. >> What is it? >> Portable speaker. >> It is a portable speaker, it's pretty awesome. >> Oh you grabbed mine. >> Oh that's one of our guys. >> (lauging) But who was it? >> Can't be related! Ava, Ava, Ava. Okay Gene Penesko (audience applauding) Hey! He came in! All right look at that, the timing's great. >> Another one? (people laughing) >> Hey thanks everybody, enjoy the night, thank Peter Burris, head of research for SiliconANGLE, Wikibon and he great guests and influencers and friends. And you guys for coming in the community. Thanks for watching and thanks for coming. Enjoy the party and some drinks and that's out, that's it for the influencer panel and analyst discussion. Thank you. (logo music)

>> [Announcer] Live from New York, it's the Cube. Covering the IBM Machine Learning Launch Event. Brought to you by IBM. Now here are your hosts Dave Vellante and Stu Miniman. >> Welcome back to New York City everybody, this is the CUBE. We're here live at the IBM Machine Learning Launch Event. Bringing analytics and transactions together on Z, extending an announcement that IBM made a couple years ago, sort of laid out that vision, and now bringing machine learning to the mainframe platform. We're here with Jim Kobielus. Jim is the Director of IBM's Community Engagement for Data Science and a long time CUBE alum and friend. Great to see you again James. >> Great to always be back here with you. Wonderful folks from the CUBE. You ask really great questions and >> Well thank you. >> I'm prepared to answer. >> So we saw you last week at Spark Summit so back to back, you know, continuous streaming, machine learning, give us the lay of the land from your perspective of machine learning. >> Yeah well machine learning very much is at the heart of what modern application developers build and that's really the core secret sauce in many of the most disruptive applications. So machine learning has become the core of, of course, what data scientists do day in and day out or what they're asked to do which is to build, essentially artificial neural networks that can process big data and find patterns that couldn't normally be found using other approaches. And then as Dinesh and Rob indicated a lot of it's for regression analysis and classification and the other core things that data scientists have been doing for a long time, but machine learning has come into its own because of the potential for great automation of this function of finding patterns and correlations within data sets. So today at the IBM Machine Learning Launch Event, and we've already announced it, IBM Machine Learning for ZOS takes that automation promised to the next step. And so we're real excited and there'll be more details today in the main event. >> One of the most funs I had, most fun I had last year, most fun interviews I had last year was with you, when we interviewed, I think it was 10 data scientists, rock star data scientists, and Dinesh had a quote, he said, "Machine learning is 20% fun, 80% elbow grease." And data scientists sort of echoed that last year. We spent 80% of our time wrangling data. >> [Jim] Yeah. >> It gets kind of tedious. You guys have made announcements to address that, is the needle moving? >> To some degree the needle's moving. Greater automation of data sourcing and preparation and cleansing is ongoing. Machine learning is being used for that function as well. But nonetheless there is still a lot of need in the data science, sort of, pipeline for a lot of manual effort. So if you look at the core of what machine learning is all about, it's supervised learning involves humans, meaning data scientists, to train their algorithms with data and so that involves finding the right data and then of course doing the feature engineering which is a very human and creative process. And then to be training the data and iterating through models to improve the fit of the machine learning algorithms to the data. In many ways there's still a lot of manual functions that need expertise of data scientists to do it right. There's a lot of ways to do machine learning wrong you know there's a lot of, as it were, tricks of the trade you have to learn just through trial and error. A lot of things like the new generation of things like generative adversarial models ride on machine learning or deep learning in this case, a multilayered, and they're not easy to get going and get working effectively the first time around. I mean with the first run of your training data set, so that's just an example of how, the fact is there's a lot of functions that can't be fully automated yet in the whole machine learning process, but a great many can in fact, especially data preparation and transformation. It's being automated to a great degree, so that data scientists can focus on the more creative work that involves subject matter expertise and really also application development and working with larger teams of coders and subject matter experts and others, to be able to take the machine learning algorithms that have been proved out, have been trained, and to dry them to all manner of applications to deliver some disruptive business value. >> James, can you expand for us a little bit this democratization of before it was not just data but now the machine learning, the analytics, you know, when we put these massive capabilities in the broader hands of the business analysts the business people themselves, what are you seeing your customers, what can they do now that they couldn't do before? Why is this such an exciting period of time for the leveraging of data analytics? >> I don't know that it's really an issue of now versus before. Machine learning has been around for a number of years. It's artificial neural networks at the very heart, and that got going actually in many ways in the late 50s and it steadily improved in terms of sophistication and so forth. But what's going on now is that machine learning tools have become commercialized and refined to a greater degree and now they're in a form in the cloud, like with IBM machine learning for the private cloud on ZOS, or Watson machine learning for the blue mixed public cloud. They're at a level of consumability that they've never been at before. With software as a service offering you just, you pay for it, it's available to you. If you're a data scientist you being doing work right away to build applications, derive quick value. So in other words, the time to value on a machine learning project continues to shorten and shorten, due to the consumability, the packaging of these capabilities and to cloud offerings and into other tools that are prebuilt to deliver success. That's what's fundamentally different now and it's just an ongoing process. You sort of see the recent parallels with the business intelligence market. 10 years ago BI was reporting and OLEP and so forth, was only for the, what we now call data scientists or the technical experts and all that area. But in the last 10 years we've seen the business intelligence community and the industry including IBM's tools, move toward more self service, interactive visualization, visual design, BI and predictive analytics, you know, through our cognos and SPSS portfolios. A similar dynamic is coming in to the progress of machine learning, the democratization, to use your term, the more self service model wherein everybody potentially will be able to be, to do machine learning, to build machine learning and deep learning models without a whole of university training. That day is coming and it's coming fairly rapidly. It's just a matter of the maturation of this technology in the marketplace. >> So I want to ask you, you're right, 1950s it was artificial neural networks or AI, sort of was invented I guess, the concept, and then in the late 70s and early 80s it was heavily hyped. It kind of died in the late 80s or in the 90s, you never heard about it even the early 2000s. Why now, why is it here now? Is it because IBM's putting so much muscle behind it? Is it because we have Siri? What is it that has enabled that? >> Well I wish that IBM putting muscle behind a technology can launch anything to success. And we've done a lot of things in that regard. But the thing is, if you look back at the historical progress of AI, I mean, it's older than me and you in terms of when it got going in the middle 50s as a passion or a focus of computer scientists. What we had for the last, most of the last half century is AI or expert systems that were built on having to do essentially programming is right, declared a rule defining how AI systems could process data whatever under various scenarios. That didn't prove scalable. It didn't prove agile enough to learn on the fly from the statistical patterns within the data that you're trying to process. For face recognition and voice recognition, pattern recognition, you need statistical analysis, you need something along the lines of an artificial neural network that doesn't have to be pre-programmed. That's what's new now about in the last this is the turn of this century, is that AI has become predominantly now focused not so much on declarative rules, expert systems of old, but statistical analysis, artificial neural networks that learn from the data. See the, in the long historical sweep of computing, we have three eras of computing. The first era before the second world war was all electromechanical computing devices like IBM's start of course, like everybody's, was in that era. The business logic was burned into the hardware as it were. The second era from the second world war really to the present day, is all about software, programming, it's COBAL, 4trans, C, Java, where the business logic has to be developed, coded by a cadre of programmers. Since the turn of this millennium and really since the turn of this decade, it's all moved towards the third era, which is the cognitive era, where you're learning the business rules automatically from the data itself, and that involves machine learning at its very heart. So most of what has been commercialized and most of what is being deployed in the real world working, successful AI, is all built on artificial neural networks and cognitive computing in the way that I laid out. Where, you still need human beings in the equation, it can't be completely automated. There's things like unsupervised learning that take the automation of machine learning to a greater extent, but you still have the bulk of machine learning is supervised learning where you have training data sets and you need experts, data scientists, to manage that whole process, that over time supervised learning is evolving towards who's going to label the training data sets, especially when you have so much data flooding in from the internet of things and social media and so forth. A lot of that is being outsourced to crowd sourcing environments in terms of the ongoing labeling of data for machine learning projects of all sorts. That trend will continue a pace. So less and less of the actual labeling of the data for machine learning will need to be manually coded by data scientists or data engineers. >> So the more data the better. See I would argue in the enablement pie. You're going to disagree with that which is good. Let's have a discussion [Jim Laughs]. In the enablement pie, I would say the profundity of Hadup was two things. One is I can leave data where it is and bring code to data. >> [Jim] Yeah. >> 5 megabytes of code to petabyte of data, but the second was the dramatic reduction in the cost to store more data, hence my statement of the more data the better, but you're saying, meh maybe not. Certainly for compliance and other things you might not want to have data lying around. >> Well it's an open issue. How much data do you actually need to find the patterns of interest to you, the correlations of interest to you? Sampling of your data set, 10% sample or whatever, in most cases that might be sufficient to find the correlations you're looking for. But if you're looking for some highly deepened rare nuances in terms of anomalies or outliers or whatever within your data set, you may only find those if you have a petabyte of data of the population of interest. So but if you're just looking for broad historical trends and to do predictions against broad trends, you may not need anywhere near that amount. I mean, if it's a large data set, you may only need five to 10% sample. >> So I love this conversation because people have been on the CUBE, Abi Metter for example said, "Dave, sampling is dead." Now a statistician said that's BS, no way. Of course it's not dead. >> Storage isn't free first of all so you can't necessarily save and process all the data. Compute power isn't free yet, memory isn't free yet, so forth so there's lots... >> You're working on that though. >> Yeah sure, it's asymptotically all moving towards zero. But the bottom line is if the underlying resources, including the expertise of your data scientists that's not for free, these are human beings who need to make a living. So you've got to do a lot of things. A, automate functions on the data science side so that your, these experts can radically improve their productivity. Which is why the announcement today of IBM machine learning is so important, it enables greater automation in the creation and the training and deployment of machine learning models. It is a, as Rob Thomas indicated, it's very much a multiplier of productivity of your data science teams, the capability we offer. So that's the core value. Because our customers live and die increasingly by machine learning models. And the data science teams themselves are highly inelastic in the sense that you can't find highly skilled people that easily at an affordable price if you're a business. And you got to make the most of the team that you have and help them to develop their machine learning muscle. >> Okay, I want to ask you to weigh in on one of Stu's favorite topics which is man versus machine. >> Humans versus mechanisms. Actually humans versus bots, let's, okay go ahead. >> Okay so, you know a lot of discussions, about, machines have always replaced humans for jobs, but for the first time it's really beginning to replace cognitive functions. >> [Jim] Yeah. >> What does that mean for jobs, for skill sets? The greatest, I love the comment, the greatest chess player in the world is not a machine. It's humans and machines, but what do you see in terms of the skill set shift when you talk to your data science colleagues in these communities that you're building? Is that the right way to think about it, that it's the creativity of humans and machines that will drive innovation going forward. >> I think it's symbiotic. If you take Watson, of course, that's a star case of a cognitive AI driven machine in the cloud. We use a Watson all the time of course in IBM. I use it all the time in my job for example. Just to give an example of one knowledge worker and how he happens to use AI and machine learning. Watson is an awesome search engine. Through multi-structure data types and in real time enabling you to ask a sequence of very detailed questions and Watson is a relevance ranking engine, all that stuff. What I've found is it's helped me as a knowledge worker to be far more efficient in doing my upfront research for anything that I might be working on. You see I write blogs and I speak and I put together slide decks that I present and so forth. So if you look at knowledge workers in general, AI as driving far more powerful search capabilities in the cloud helps us to eliminate a lot of the grunt work that normally was attended upon doing deep research into like a knowledge corpus that may be preexisting. And that way we can then ask more questions and more intelligent questions and really work through our quest for answers far more rapidly and entertain and rule out more options when we're trying to develop a strategy. Because we have all the data at our fingertips and we've got this expert resource increasingly in a conversational back and forth that's working on our behalf predictively to find what we need. So if you look at that, everybody who's a knowledge worker which is really the bulk now of the economy, can be far more productive cause you have this high performance virtual assistant in the cloud. I don't know that it's really going, AI or deep learning or machine learning, is really going to eliminate a lot of those jobs. It'll just make us far smarter and more efficient doing what we do. That's, I don't want to belittle, I don't want to minimize the potential for some structural dislocation in some fields. >> Well it's interesting because as an example, you're like the, you're already productive, now you become this hyper-productive individual, but you're also very creative and can pick and choose different toolings and so I think people like you it's huge opportunities. If you're a person who used to put up billboards maybe it's time for retraining. >> Yeah well maybe you know a lot of the people like the research assistants and so forth who would support someone like me and most knowledge worker organizations, maybe those people might be displaced cause we would have less need for them. In the same way that one of my very first jobs out of college before I got into my career, I was a file clerk in a court in Detroit, it's like you know, a totally manual job, and there was no automation or anything. You know that most of those functions, I haven't revisited that court in recent years, I'm sure are automated because you have this thing called computers, especially PCs and LANs and so forth that came along since then. So a fair amount of those kinds of feather bedding jobs have gone away and in any number of bureaucracies due to automation and machine learning is all about automation. So who knows where we'll all end up. >> Alright well we got to go but I wanted to ask you about... >> [Jim] I love unions by the way. >> And you got to meet a lot of lawyers I'm sure. >> Okay cool. >> So I got to ask you about your community of data scientists that you're building. You've been early on in that. It's been a persona that you've really tried to cultivate and collaborate with. So give us an update there. What's your, what's the latest, what's your effort like these days? >> Yeah, well, what we're doing is, I'm on a team now that's managing and bringing together all of our program for community engagement programs for really for across portfolio not just data scientists. That involves meet ups and hack-a-thons and developer days and user groups and so forth. These are really important professional forums for our customers, our developers, our partners, to get together and share their expertise and provide guidance to each other. And these are very very important for these people to become very good at, to help them, get better at what they do, help them stay up to speed on the latest technologies. Like deep learning, machine learning and so forth. So we take it very seriously at IBM that communities are really where customers can realize value and grow their human capital ongoing so we're making significant investments in growing those efforts and bringing them together in a unified way and making it easier for like developers and IT administrators to find the right forums, the right events, the right content, within IBM channels and so forth, to help them do their jobs effectively and machine learning is at the heart, not just of data science, but other professions within the IT and business analytics universe, relying more heavily now on machine learning and understanding the tools of the trade to be effective in their jobs. So we're bringing, we're educating our communities on machine learning, why it's so critically important to the future of IT. >> Well your content machine is great content so congratulations on not only kicking that off but continuing it. Thanks Jim for coming on the CUBE. It's good to see you. >> Thanks for having me. >> You're welcome. Alright keep it right there everybody, we'll be back with our next guest. The CUBE, we're live from the Waldorf-Astoria in New York City at the IBM Machine Learning Launch Event right back. (techno music)

in Cambridge Massachusetts it's the cube covering the MIT chief data officer and information quality symposium now here are your hosts Stu miniman and George Gilbert you're watching the cube SiliconANGLE media's flagship program we go out to lots of technology shows and symposiums like this one here help extract the signal from the noise I'm Stu miniman droid joined by George Gilbert from the Wikibon research team and really thrilled to have on the program the keynote speaker from this MIT event Tom Davenport whose pressure at babson author of some books including a new one that just came out and thank you so much for joining us my pleasure great to be here all right so uh you know so many things your morning keynote that I know George and I want to dig into I guess I'll start with you talk about the you know for eras of you called it data today used to be formation from the information sorry but you said you started with when it was three eras of analytics and now you've came to information so I'm just curious we you know we get caught up sometimes on semantics but is there a reason why you switch from you know analytics to information now well I'm not sure it's a permanent switch I just did it for this occasion but you know I I think that it's important for even people who aren't who don't have as their job doing something with analytics to realize that analytics or how we turn data into information so kind of on a whim I change it from four errors of analytics 24 hours of information to kind of broaden it out in a sense and make people realize that the whole world is changing it's not just about analytics ya know I it resonated with me because you know in the tech industry so much we get caught up on the latest tool George will be talking about how Hadoop is moving to spark and you know right if we step back and look from a longitudinal view you know data is something's been around for a long time but as as you said from Peter Drucker's quote when we endow that with relevance and purpose you know that that's when we get information so yeah and that's why I got interested in analytics a year ago or so it was because we weren't thinking enough about how we endowed data with relevance and purpose turning it into knowledge and knowledge management was one of those ways and I did that for a long time but the people who were doing stuff with analytics weren't really thinking about any of the human mechanisms for adding value to to data so that moved me in analytics direction okay so so Tommy you've been at this event before you know you you've taught in written and you know written books about this about this whole space so willing I'm old no no its you got a great perspective okay so bring us what's exciting you these days what are some of our big challenges and big opportunities that we're facing as kind of kind of humanity and in an industry yeah well I think for me the most exciting thing is they're all these areas where there's just too much data and too much analysis for humans to to do it anymore you know when I first started working with analytics the idea was some human analysts would have a hypothesis about how to do that about what's going on in the data and you'd gather some data and test that hypothesis and so on it could take weeks if not months and now you know we need me to make decisions in milliseconds on way too much data for a human to absorb even in areas like health care we have 400 different types of cancer hundreds of genes that might be related to cancer hundreds of drugs to administer you know we have these decisions have to be made by technology now and so very interesting to think about what's the remaining human role how do we make sure those decisions are good how do we review them and understand them all sorts of fascinating new issues I think along those lines come you know in at a primitive level in the Big Data realm the tools are kind of still emerging and we want to keep track of every time someone's touched it or transformed it but when you talk about something as serious as cancer and let's say we're modeling how we decide to or how we get to a diagnosis do we need a similar mechanism so that it's not either/or either the doctor or you know some sort of machine machine learning model or cognitive model some waited for the model to say here's how I arrived at that conclusion and then for the doctor to say you know to the patient here's my thinking along those lines yeah I mean I think one can like or just like Watson it was being used for a lot of these I mean Watson's being used for a lot of these oncology oriented projects and the good thing about Watson in that context is it does kind of presume a human asking a question in the first place and then a human deciding whether to take the answer the answers in most cases still have confidence intervals you know confidence levels associated with them so and in health care it's great that we have this electronic medical record where the physicians decision of their clinicians decision about how to treat that patient is recorded in a lot of other areas of business we don't really have that kind of system of record to say you know what what decision did we make and why do we make it and so on so in a way I think health care despite being very backward in a lot of areas is kind of better off than then a lot of areas of business the other thing I often say about healthcare is if they're treating you badly and you die at least there will be a meeting about it in a healthcare institution in business you know we screw up a decision we push it under the rug nobody ever nobody ever considered it what about 30 years ago I think it was with Porter's second book you know and the concept of the value chain and sort of remaking the the understanding of strategy and you're talking about the you know the AP AP I economy and and the data flows within that can you help tie your concept you know the data flows the data value chain and the api's that connect them with the porters value chain across companies well it's an interesting idea I think you know companies are just starting to realize that we are in this API economy you don't have to do it all yourself the smart ones have without kind of modeling it in any systematic way like the porter value chain have said you know we we need to have other people linking to our information through api's google is fairly smart i think in saying will even allow that for free for a while and if it looks like there's money to be made in what start charging for access to those api so you know building the access and then thinking about the the revenue from it is one of the new principles of this approach but i haven't seen its i think would be a great idea for paper to say how do we translate the sort of value chain ideas a michael porter which were i don't know 30 years ago into something for the api oriented world that we live in today which you think would you think that might be appropriate for the sort of platform economics model of thinking that's emerging that's an interesting question i mean the platform people are quite interested in inner organizational connections i don't hear them as talking as much about you know the new rules of the api economy it's more about how to two sided and multi-sided platforms work and so on Michael Porter was a sort of industrial economist a lot of those platform people are economists so from that sense it's the same kind of overall thinking but lots of opportunity there to exploit I think so tell me what want to bring it back to kind of the chief data officer when one of the main themes of the symposium here I really like you talked about kind of there needs to be a balance of offense and defense because so much at least in the last couple of years we've been covering this you know governance and seems to be kind of a central piece of it but it's such an exciting subject it's exciting subject but you know you you put that purely in defense on and you know we get excited the companies that are you know building new products you know either you know saving or making more money with with data Kenny can you talk a little bit about kind of as you see how this chief data officer needs to be how that fits into your kind of four arrows yeah yeah well I don't know if I mentioned it in my talk but I went back and confirmed my suspicions that the sama Phi odd was the world's first chief data officer at Yahoo and I looked at what Osama did at Yahoo and it was very much data product and offense or unity established yahoo research labs you know not everything worked out well at Yahoo in retrospect but I think they were going in the direction of what interesting data products can can we create and so I think we saw a lot of kind of what I call to point o companies in the in the big data area in Silicon Valley sing it's not just about internal decisions from data it's what can we provide to customers in terms of data not just access but things that really provide value that means data plus analytics so you know linkedin they attribute about half of their membership to the people you may know data product and everybody else as a people you may know now well we these companies haven't been that systematic about how you build them and how do you know which one to actually take the market and so on but I think now more and more companies even big industrial companies are realizing that this is a distinct possibility and we oughta we ought to look externally with our data for opportunities as much as supporting internal and I guess for you talk to you know companies like Yahoo some of the big web companies the whole you know Big Data meme has been about allowing you know tools and processes to get to a broader you know piece of the economy you know the counterbalance that a little bit you know large public clouds and services you know how much can you know a broad spectrum of companies out there you know get the skill set and really take advantage of these tools versus you know or is it going to be something that I'm going to still going to need to go to some outside chores for some of this well you know I think it's all being democratized fairly rapidly and I read yesterday the first time the quote nobody ever got fired for choosing amazon web services that's a lot cheaper than the previous company in that role which was IBM where you had to build up all these internal capabilities so I think the human side is being democratized they're over a hundred company over 100 universities in the US alone that have analytics oriented degree programs so i think there's plenty of opportunity for existing companies to do this it's just a matter of awareness on the part of the management team I think that's what's lacking in most cases they're not watching your shows i guess and i along the lines of the you know going back 30 years we had a preference actually a precedent where the pc software sort of just exploded onto the scene and it was i want control over my information not just spreadsheets you know creating my documents but then at the same time aighty did not have those guardrails to you know help help people from falling off you know their bikes and getting injured what are the what tools and technologies do we have for both audiences today so that we don't repeat that mistake ya know it's a very interesting question and I think you know spreadsheets were great you know the ultimate democratization tool but depending on which study you believe 22 eighty percent of them had errors in them and there was some pretty bad decisions that were made sometimes with them so we now have the tools so that we could tell people you know that spreadsheet is not going to calculate the right value or you should not be using a pie chart for that visual display I think vendors need to start building in those guardrails as you put it to say here's how you use this product effectively in addition to just accomplishing your basic task but you wouldn't see those guardrails extending all the way back because of data that's being provisioned for the users well I think ultimately if we got to the point of having better control over our data to saying you should not be using that data element it's not you know the right one for representing you know customer address or something along those lines we're not there yet and the vast majority of companies I've seen a few that have kind of experimented with data watermarks or something to say yes this is the one that you're allowed to to use has been certified as the right one for that purpose but we need to do a lot more in that regard yeah all right so Tommy you've got a new book that came out earlier this year only humans need apply winners and losers in the age of smart machines so ask you the same question we asked eric donaldson and Auntie McAfee when they wrote the second Machine Age you know are we all out of job soon well I think big day and I have become a little more optimistic as we look in some depth at at the data I mean one there are a lot of jobs evolving working with these technologies and you know it's just somebody was telling me the other day that is that I was doing a radio interview from my book and the guy was hung who said you know I've made a big transition into podcasting he said but the vast majority of people in radio have not been able to make that transition so if you're willing to kind of go with the flow learn about new technologies how they work I think there are plenty of opportunities the other thing to think about is that these transitions tend to be rather slow I mean we had about in the United States in 1980 about half a million bank tellers since then we've had ATMs online banking etc give so many bank tellers we have in 2016 about half a million it's rather shocking i think i don't know exactly what they're all doing but we're pretty slow in making these transitions so i think those of us sitting here today or even watching her probably okay we'll see some job loss on the margins but anybody who's willing to keep up with new technologies and add value to the smart machines that come into the workplace i think is likely to be okay okay do you have any advice for people that either are looking at becoming you know chief data officers well yeah as I as you said balanced offense and defense defense is a very tricky area to inhabit as a CDO because you if you succeed and you prevent you know breaches and privacy problems and security issues and so on nobody gives you necessarily any credit for it or even knows that it's helps of your work that you were successful and if you fail it's obviously very visible and bad for your career too so I think you need to supplement defense with offense activities are analytics adding valued information digitization data products etc and then I think it's very important that you make nice with all the other data oriented c-level executives you know you may not want to report to the CIO or if you have a cheap analytics officer or chief information security officer chief digitization officer chief digital officer you gotta present a united front to your organization and figure out what's the division of labor who's going to do what in too many of these organizations some of these people aren't even talking to each other and it's crazy really and very confusing to the to the rest of the organization about who's doing what yeah do you see the CDO role but you know five years from now being a standalone you know peace in the organization and you know any guidance on where that should sit is structurally compared to say the CIO yeah I don't you know I I've said that ideally you'd have a CIO or somebody who all of these things reported to who could kind of represent all these different interests of the rest of the organization that doesn't mean that a CDO shouldn't engage with the rest of the business I think CIO should be very engaged with the rest of the business but i think this uncontrolled proliferation has not been a good thing it does mean that information and data are really important to organization so we need multiple people to address it but they need to be coordinated somehow in a smart CEO would say you guys get your act together and figure out sort of who does what tell me a structure I think multiple different things can work you can have it inside of IT outside of IT but you can at least be collaborating okay last question I've got is you talked about these errors and you know that they're not you know not one dies in the next one comes and you talked about you know we know how slow you know people especially are to change so what happened to the company that are still sitting in the 10 or 20 era as we see more 30 and 40 companies come yeah well it's not a good place to be in general and I think what we've seen is this in many industries the sophisticated companies with regard to IT are the ones that get more and more market share the the late adopters end up ultimately going out of business I mean you think about in retail who's still around Walmart was the most aggressive company in terms of Technology Walmart is the world's largest company in moving packages around the world FedEx was initially very aggressive with IT UPS said we better get busy and they did it to not too much left of anybody else sending packages around the world so I think in every industry ultimately the ones that embrace these ideas tend to be the ones who who prosper all right well Tom Davenport really appreciate this morning's keynote and sharing with our audience everything that's happening in the space will be back with lots more coverage here from the MIT CDO IQ symposium you're watching the q hi this is christopher