(upbeat music) >> Live from Barcelona, Spain. It's theCUBE covering Cisco Live Europe, brought to you by Cisco and its ecosystem partners. >> Hello everyone welcome back to theCUBE's live coverage here in Barcelona, Spain for Cisco Live Europe 2019. I'm John Furrier with theCUBE, with Stu Miniman and Dave Alonte also here doing interviews. Our next guests, two guests from the DNA center platform, Cisco, the agent platform team, Prakash Rajamani, director of product management, Cisco and Ronnie Ray, vice president of product management, Cisco, the DNA center platform growing 70% of the use cases, software distractions, API automation. Congratulations. Great success. Thanks for joining us. >> Thanks John. >> Big Fan of the DNA center. You guys have made great progress. Take a step through us. The positioning, how things are rolling, what's some of the feedback? Where's the DNA center platform at right now for Cisco? >> Yup. >> So DNA center was launched about 80 months back and it's probably one of the products in Cisco that has completely started to transform how we do the selling motions. So this is one of the key drivers of Cisco moving into light sensing mode switch, more software like. Now as part of how we do management Typically and traditionally it has been very much a manual driven process there's some reporting but it is a lot of expert light capabilities that you need to have to do management of the infrastructure then it's kind of moving that access to where you can now do machine-lift management. Of course it doesn't solve all the use cases absolutely as you mentioned, more than 70% but there's a whole host of new capabilities that you have to put on top and that's where developers come in because this is a platform that's built for developers to be able to extend it's capabilities to really look at solving problems for our customers. >> I think you know, after listening to all the announcements in temp based networking, ACI anywhere, hyperflex anywhere, data at the center of the value, data centered as you guys say, it's clever but I think it highlights what you guys are doing because you're talking about programmability of the network as two worlds collide actually three worlds collide, Cloud, On Premises and Edge into one network, you have a network, the network is key it's getting bigger, to cross domains is a big theme here, these are hard problems that are being solved by Cisco more complex cause there's more moving parts but it still has to operate as one network. This is essentially highlights the success of the DNA platform, am I kind of getting it right or is that kind of in line with how you guys see it? >> Sure, I mean I think Cisco DNA centered I mean if you look at the evolution we started in the network domain. You're absolutely right we have kind of extended to the brand change, there's nine integrations that are happening with the data center integrations, happening with the cloud, so yeah absolutely looking at the fabric that we launched about 18 months back now extending and stretching to all of those domains and wherever users connect and wherever users go to and that's of Cisco data center but think about that as we kind of do that, yes there is a change that also required not just in the product but also in the IT process because earlier companies had silos of things and now those silos will be forced to work together and CI was one that our network folks that support us because really they want to see cross domain bring power to the organizations but we are the enabler of making that happen. >> No brainer. >> Prakash, I'd love for you to take us inside ya know, we love looking at the product management piece here because you've had a lot of constituencies. You've got the internal product teams that all I'm sure want to get in and mature and expand their used cases. You've got all your partners that are building the platform. You've got the customers asking for feedback You've got a - ya know, a lot of options to choose from which is a good thing but you've obviously got limited resources. So take us inside that, what you've learned over the last year and how you helped prioritize and move this product forward so fast over the last 18 months. >> So one of the main things we did when we started with Data Center is to start thinking and having the vision to get a data center platform. With that in mind, every feature, every capability that we built in the product was built API first before we built a UI around it. Right? That has helped us immensely in the last couple releases we've started delivering features as APIs even before it had a face to it, and I think that has helped us prioritize and make sure that we are able to meet the demands going demands of customer or partner we had a customer who was like "I need this feature now" and we were hands strapped, we had a big back log, we couldn't get things done but the fact that we were able to get the APIs we were able to work with the customer and say "Hey here you can wire these three APIs and you can get what you're looking for" and he was like "Wow, that's so simple and I'm on my own" he was happy, we are happy we are able to manage our back log better. So I think the main strategy for us that's working is going API first on a pragmatic basis. This is us moving completely software driven as Ronnie was highlighting earlier in that relevant process that is helping us get there and that's part of it >> Well, it's customers a lot I mean they get to roll their own if you will without having be customized, it's still standardized with the APIs >> That's right, right? I mean the benefit is as you start getting into the 30% used case where "Hey, what's coming out of the box is not meeting exactly what I do today" we provide very grander APIs to very business driven, simplified interned APIs. The grander APIs allows the customer who wants to say I want A, B and then D and E to move forward compared to intern based API who is using the pride in the simplicity in driving that formula. >> Yeah, Ronnie I'm wondering if we can up level for a second here cause feedback I've gotten over the last year. Ya know, a year ago we heard Cisco is moving heavily towards software. When I talked to a lot of the partners both technology partners and channel partners they said this had a ripple effect inside Cisco it's not so much okay here's the skews and here's the new boards and here's the products but I need to sell a solution and therefore that's platforms that I have to have and therefore everything needs to work together and I have to think API first and like it does significant changes to how Cisco is, the joke I used to have is Cisco is like 100 companies and some people were like "Well, maybe it's 100, maybe it's 200." But today it's now something like platform is a unifying place, is that what is your solution set part of that drive and is that something you're seeing more broadly inside Cisco? >> Certainly, I think you're absolutely right that is does have a unifying effect if I might put it that way. >> Yeah Right? Because there's so many different capabilities that existed in different tools that are coalescing on Cisco data central and which is becoming part of the platform which is now customizable by our entire development community but think how fast that happens in a now within the sales force, within Cisco as a company there is no more cross domain knowledge that'll be required because now it operates different parts it can tune different things, that also means that is supposed to change the business model because going into software and kind of bringing it together and is increasing Cisco is obviously ya know foyering into softer subscriptions, this is a key product that's kind of supporting that, so in many ways it's not just the technology, it's not just APIs but also as a business process that's changing Cisco just like it'll change customers. >> One of the things we're seeing is a lot of design thinking principles this year. Love the new positioning bridged to the future bridged to tomorrow, wherever it goes but it's clean. Connecting the worlds are connecting together through the network get that. What has been some of the challenges and opportunities you guys are seeing around simplicity? Love this API, exposing API allows for customization, I love the broader intent based templates are great but it's hard to make things simple. Can you just elaborate on how you guys are thinking about the product short, medium, long term in terms of continuing to work the back log, I'm sure the feature list is growing like crazy but you got a challenge to make it simpler. >> Absolutely >> How hard is it? What does it entail? Share some insight there. >> So lets take the question in two parts and Prakash can talk to the product simplicity because that is a certainly something that we've got to manage very very carefully but think about also when simple doesn't just mean usable product, it also means a product that can fit into the ecosystem and make the process simpler. So there's a lot of deeper understanding that we are developing through the learning as we work with customers and how do we embed how do we make customers life easier how do we make the process easier and then after goal is how do we make their operational expenses lower? Because we want them to go faster, we want them to go faster at a lower cost and so there's a certainly both learning and investment that's happening there and the product side Prakash. >> On the product side it's about how we used to build to how we are building right now the way we used to do was a new feature comes in it goes to the device layer first the device team builds it puts CLI around it ships it off, sends it to the management team and the management team says "Oh, I got to support this feature" They go, they wrap a UI around it to support the feature, ships. Now we have flipped it turn completely around we start with like what is a customer's work field? What do they need to do and how can we do it in the minimal steps? Once we identify that we push that down to saying "Here is what the user interface looks like here are the three steps that they need to do. That trickles down to saying what we need as an APA on the device layer to develop the feature so we've gone down from going a bottom up way to build a product to a top down, customer driven, used case driven way to build a product. That means we are addressing the customer head on from a simplicity perspective and that's basically what has made us successful in moving the ball forward on this one. >> What has been some of the customer feedback? Can you share some anecdotes around some of the early customers you started rolling this out and what are the ones receiving on the receiving end today saying? >> So when you see from a simplicity feedback perspective I have a large retail store rolling out like maybe 60 APs in a single store over night and they've gone from having that be done over three nights to one person spending 20 minutes putting all the APs up going to the tool and the tool recognizing everything that's come up and deployed. So it's a night and day transformation on how it used to be to how it is right now. So the simplicity >> Sounds like the old way was >> Sounds like you saved a night in a day >> Manually configure, go put a wireless ping to it >> Yep, the old way was yeah you go you plugged the AP, you come back you look at the tool, the AP is there >> Check the channel, stuff is there. >> Map it to the right controller, do all the mappings Now you don't have to do anything just plug the APs and upload preloaded to say these APs are going to the store. The tool takes care of the rest of the stuff that's how simple it is become >> It's almost like old way new way What why are we doing that? And it's good when they have consistent environments with policies there's definitely more expansion. I get that, what about other used cases? Wireless is one hot one, I could see that branch off it's deployments what are some of the popular used cases that you're seeing in the customer base I know you got a broad base but what are the ones what are the patterns that are emerging out of this? >> So let me start another then have Ronnie chime in on the used cases he's seen. Some of the ones that are probably very transformational is that on the policy based used case, we have companies turning around and creating small subdivisions within their organizations. We have a large government in Yasha who is deploying that, they have 20 divisions. Earlier to do that it's extremely complex. They have to go in, they have to understand what division, who is using on which device, which ports mapped to them, just planning that it says it's so huge. For the new policy different approach that we have going, they don't have to know about anything they just need to know Prakash works for division A, Ronnie works for division B assign me to respective divisions, as I come in my policy gets right over to the network. I deploy the network as is, as I speak that is basically the level of simplicity that has changed and that all ties back to doing your network from a policy perspective not a networking from a feature perspective. >> Got it, Ronnie any comments on used case on your end? >> Yeah absolutely so think about we've talked about assurance we launched segmentation that's doing very very well of course even with when all of the public acknowledgement that goes with it but an interesting used case that's come up which is in fact in the keynote this week at Cisco live is about IUT extensions. So Data seto owa is extending to the factory floor, the production equipment and transportation and these are tremendous neo opportunities that are both for companies to kind of look at IT and OT and how this comes together, again going back to the unification simplification theme that do many more things at the same time they try to make it in a rationally much more operable. >> Okay so lot of progress in 18 months give us the road map going forward. We're at the beginning of 2019 what you'll be looking for, can a high level show show us what we should expect to see down the road >> K so from a road map perspective it's in a think about that we've been very focused on getting the customer value. Now the lens is kind of shifting to how do we deal with large enterprise capabilities? So both the hardening of the system itself, how do we look at, for example multiple clusters opening up in diverse locations will give us geo diversity and support there from that perspective and high availability. So these are enterprise class features every large customer requires it and as they move from smaller deployments to full scale deployments that is something that the labs look to need >> Yeah, Prakash when I heard you talking about things I need to think a little bit differently. It's like okay I'm used to going into the deploy and it's going to take me three days wait how do I learn about the fact that I can do it now in a couple of hours? What kind of training or retraining or education is that part of what you're doing in your team or where does that happen? >> It's part of the education, part of the videos we double up and publish to customers so that they don't think about this as I'm going to approach my same 20 steps and think that I'm going do that through data center except that I'm going to do that through a user interface. The first thing that we tell them is like "You're going to do 20" You're going to do two. Right? So the immediate feedback is oh does it address everything I want to do? And so that's the 70% used case more would rather say yes it addresses only thing is we have simplified it, we have compressed it so you don't have to go and go through all these 20 steps but instead get it done in two, so the watts have helped some of the trainings that you have done has helped even talking to from a sales process the customer to know "Hey this is what I'm embracing" so when they come in they don't come in with I'm going to run my network the same way but no no I'm going to run it differently has helped us immensely to make the transition >> Well guys, congratulations on a great successful product, big fan I love that thing, I think it's going to be the future there's a lot more head room there that's cause we're looking at automations the devnet zone we're in is showing massive growth. The appetite for automation the appetite for configuration and scale and managing the complexity is a sweet spot I think that you guys had a nice formally hear looking forward to it. Final question for these guys Ronnie and Prakash are going to both answer it. Say something about DNA center platform that people should pay attention to that they might not hear in the mainstream chatter that's important that they should maybe want to kick the tires or understand it further, an area that they should know about that they might not hear about or they should know about what's the most important feature. Share some, share some insight. >> So again just looking at a little bit into the future of Cisco data center platform, right now we're kind of talking of APIs, there's capability that's coming in the future that will also deal with work flows and the work flows will be built on something which is machine built so there will be a lot of analytics in fact in a data center not only does automation but also extends data analytics so a lot of cool stuff that'll come there and again we'll talk about it more as we get to the next Cisco live. >> Prakash anything? >> I'm going to go a little more ground level people tend to talk about simplicity, talk about how we can do things way differently with data center and people tend to forget that we have not forgotten the network engineer who has been managing the network. We have APIs for you to do the same things you've done all along, create articles create re-lance, do some of the basic networking stuff so that it's not about this just as simple we also have the more detailed breakdown of the API so that you can still continue to know the nuts and the bolts and other things as well as much as the simple stuff so it's the >> It's an empowering all personas in the network from network engineer low level getting down and dirty to large scale automations, whatever the use case is you got the empowerment. >> Yep that's basically what I would like to >> That's awesome, well congratulations Again big fan, DNA center takeover here in the Devnet zone I'm John Furrier with Stu Miniman Cube coverage day two of three days stay with us for more after this short break. (electronic music plays)

>> Live from Orlando, Florida, it's theCUBE, covering Cisco Live 2018 brought to you by Cisco, NetApp, and theCUBE's ecosystem partners. >> Welcome back everyone. This is theCUBE's live coverage here in Orlando, Florida, for Cisco Live 2018. I'm John Furrier with theCUBE. Stu Miniman, my co-host, for the next two more days. We're in three days of coverage. Our next two guests here from Cisco Ronnie Ray, Vice President of Cisco, and Prakash Rajamani, Director of Project Management at Cisco. Guys, welcome to theCube. Thanks for coming on. >> Thank you, John. >> So all the buzz is about the DevNet developer aspect, the rise of the network engineer moving up to the stack while taking care of business in the software-defined data center, software-defined service provider. Everything is software-defined. You guys are involved in the DNA Center Platform. We talked about the DNA Center, the product. This is a real innovation environment for you guys, so take a minute to explain, what is the DNA Center Platform? And how does that compare from the DNA Center? How should customers think about this? What is it? what's the offering? >> Absolutely. So if we just walk back about a year. A year ago we launched DNA Center. DNA Center is the product, and that supported things, like SD-Access, which is absolutely a new innovation about Software-Defined campuses. Through the year, we've launched showrooms, through the year we've launched Enterprise Network Functions Virtualization, we have capabilities in automation, and these are all product capabilities that DNA Center has. What we're doing today and this week in Cisco live and in the DevNet area right now is that we have launched DNA Center platform, which is the ability to open up and expose all of the APIs and the STKs that now makes DNA Center a product that our customers, our partners and developers out there can now work on and create new value. It could be apps, it could be integrations, it could be new devices, third-party devices that Cisco's never supported before, but they can now make that supportable in DNA Center because we're giving them the tools to do that. >> So this is not so much a customer thing, it's more of a partner or app, is that kind of how this goes? So if I'm a partner, makes sense. is this kind of where it's different? I mean, where's the line here, or is it open for everybody? >> It is for everybody. If you are a networking expert and you've done CLI in the past, what we are doing is making API simpler, we are making them intent-based, which means that they can achieve a lot more and this is open to you as a networking expert, you as an application developer, you as a partner that is providing, creating your services for your end customer or client. All of you can now use DNA Center platform to create new value. >> This is great, it's for everyone. So this is where, if I get this right, we love this notion of DevOps on cloud, Susie and you guys have been talking about network programmability. Is this kind of where it is? We're talking about network programmability, is this where the APIs shine, and what's our vision? >> This is truly network programmability, in fact in the past what we've talked about is device programmability, but now what you're doing in DNA Center platform is really expressing intent and using APIs that apply across the whole network. Prakash can probably give you some examples of what these intent APIs look like. >> I think as Ronnie said, we like to call it Network DevOps, I think Susie calls it that too. And this is the way in which Network DevOps is conductible. There are two kinds of target market that we look at. One is the network engineer who understands everything network-centric, who knows all the nuances, and are very comfortable with those, but then being able to achieve those through a programmable API, that's one market. The way we want to go with the intent API is for the software engineers who want to be able to say, I want to prioritize YouTube traffic less than my network, and I want to prioritize my custom-built app as the most critical for my enterprise, as the most critical on my network. And I want to express that as an intent through an API, and then let the DNA Center platform take care of making that real on the network without having to worry about all the technologies and all the, >> How to provision it, what's going on under the hood, essentially to them it's a call. >> To them it's a call, and it's taken care of. >> That is actually seamless to the software developer, by the way, who doesn't want to get in the weeds of networking. The networking guys who are under the hood, what does it mean for them? They get to provide services to the developers, so it sounds like everyone's winning here. What's the benefit to the network engineers? They get scalability? I see the benefits to the software developer, that's awesome, but where's the network engineer, what are they getting out of it? >> They can achieve more things faster, they can get deeper, and this is absolutely making it simpler for them operationally to run their network. So they can basically free up time to do other tasks, like design and architecture that typically is, very hard to explain. >> Cooler tasks. (laughs) Not boring, mundane, cut and paste the scripts, CLI scripts, to another device. >> Absolutely and that's one part. The other part is about the cool new apps that they can create because there are use cases, even if you look at all the show floor, the companies that are here in Cisco Live and that they come every year, there are use cases out there that even collectively as an industry we cannot solve, that needs to be solved in the context of the company and the environment that you're in and so the network expert that's sitting in a customer environment can say, "Okay, I have this problem, let me solve it, "let me go build-" >> But they're gettable problems to solve now. Because now you're taking off more time, but also cloud and some of the software-defined things are now at the disposal to create that creativity. Is that what you're getting at, this is the new opportunity. Is that what Chuck was kind of referring to in his keynote around getting at these new use cases? >> Certainly, this opens up a new use case because this is a new way to program across the entire network in a much more simpler fashion than it's ever been done before. >> So when I hear a new way to program, I want to understand, what's the learning curve for this? If somebody understands the rocky APIs, is this a short learning curve, if they don't, is it a longer learning curve? >> So what we have done from a learning curve perspective, we have worked with a development team, we have learning labs where somebody who's not familiar with programming completely can start with the basics of, okay, how do I get started with DNA Center platform APIs and get started and go through a sequence of learning labs to get them completely familiarized with everything. Somebody like what you said, like a Meraki person, who's already using the Meraki API, for them, anybody who understands REST XML APIs can just turn around and there's a bunch of new APIs available that they can understand, program, try within the product, and then get sample codes and then build on top of that. So it's that easy as that. >> It was interesting, I was walking through the show floor, talking to some of the customers here, and for some of them, what's off the shelf is good, but I hear them griping about, not about Cisco, some of the partners, like "I can't customize what I need." One of the challenges we've always had in IT is, it's great if you can take the off the shelf, but everybody needs to tweak and adjust what they have. How's that addressed with this solution? >> From a customer's perspective, because we provide in our product we provide a specific set of capabilities, but when it comes to API, we make it much, much, much richer and granular so that people can create any workflow that they want. The workflows that we create in the API context is in three formats. We have what we call as tasks, which are individual operations that we perform, and then we group the tasks and offer them as workflows. And we group the workflows and offer them as an intent. So as a user, based on what level of granular they need, you can go to the lowest level task, or you can go all the way up to the intent based on your skillset and then use them and customize them as it fits your needs. >> So they can get up and running pretty quickly, sounds like, and if you know APIs then it's just JSON, it's all the same XML, all the great stuff, but I gotta ask where this goes from here because one of the things we were talking about before we came on camera is, we've been covering all the Linux Foundation, the Cloud Native Computing Foundation, CNCF, you've got Docker Containers, and containers now have been a great thing. Pretty much check, standard, everyone's using containers. And it's great, put a container around it, a lot of great things could happen. Kubernetes and then microservices around Service Meshes, Diane Greene mentioned in her keynote with Chuck Robbins, Istio was a big hot, one of the hottest projects in the Linux foundation, so that's kind of microservices, this sounds like it's got a lot of levels of granularity. I love that word because now when you get to that point, you can really make the software targeted and strong and bullet-proof. How is that on the road map, where does someone who's actually looking at microservices as a North Star, what does your offering mean for them? Is it right in line? What's the progression, what's the road map? >> So, from a microservice perspective, DNA Center as a product itself is completely microservice-based architecture. There's 110 microservices today that make up what is DNA Center. This gives us a flexibility to really update every single service, every single capability, and make it almost like giving customers ability to do this every two weeks or every four weeks, new changes, new announcements, in a very simple fashion. That's kind of how the part is being built. What we eventually want to do is extend the platform as an ability for partners and others to build microservices that can be built and deployed within DNA Center over time. That's further down the road, but given that solution and given the strategy where we are as a product architecture that lends us to extend that to them. >> It's natural extension, so basically you're cloudified. You've got all the APIs, so if a customer wants to sling APIs, customers want to integrate in, like you mentioned, ServiceNow, they can do that easily today, and then you've got some extensibility in the road map to be kind of Cloud Native when things start growing. Timing's everything, it's kind of evolving right now heavily at the Cloud Native. >> I mean that's the benefit of this architecture, that you can really pick and choose where you want to run over time. We are right now on a box, an appliance that helps us solve the solution, but there's nothing that stops us from going anywhere. >> So Ronnie, I want you to talk about the significance, this is an open platform. I've watched Cisco my entire career, and always Cisco's been heavily involved in standards, but takes arrows from people as to how they do this. This is open, what does that mean? And what's that mean to your customers? >> Absolutely, this is basically opening up Cisco to industry-wide innovation. So until now, if you look at everything that we've done on DNA Center and on some of the other Cisco platforms that Cisco developed, but we are now getting to a point where with DevNet, now with 500,000 developers registered, we have the critical mass to basically say the industry can come and develop on top of Cisco platforms. And so this is completely new kinds of innovation that we will see, use cases that we've never thought of, and this will happen. And of course we will continue to contribute to all whether it's IETF or whether it's OpenConfig, all of these in with the YANG models that we are doing across the industry, those will continue, the open source confirmations that we do, but this is really saying, okay, let's provide our best customers and our partners and of course the individual developer that's out there a way to today build new creations and maybe tomorrow there's a part to monetize that. >> It's interesting you bring that up, I love the open. We love open, we're open content. You guys are now open networking, for lack of a better description. Chuck Robbins talked about in his keynote, one of the things I was really impressed on, he highlighted something that we've been talking about, is that the geo-political, the geo-technical world, is a huge factor, you look at just cloud computing, you've got Regis, you've got GDPR, I mean all these things going on, you mentioned assurances off camera, this is like a huge deal, right. You've got a global tech landscape, you've got global tech compliance issues, so you got this now open source and it's whatever fourth generation where it's part of the entrepreneurial fabric. So Ronnie, I've got to ask you, you've been an entrepreneur before. With bringing entrepreneurship into networking, what's the guiding principles, what's your inspirational view on this because this is really, not only save time for engineers, it makes them part of an open collaborative culture, like open source which you're used to, bringing an entrepreneurial vibe to it. >> Absolutely. >> This is a big dynamic, what's your view on this? >> It's a huge dynamic and I can talk from personal experience, you know when I've done start-ups and I've raised money or put my own money into it, 70% of your calories go in building a platform. So you're just looking at how do I store data, how do I process data, how to I look at availability of systems, and 30% of it really goes into building a use case. What we are doing with DNA Center platform is basically saying forget about the 70%. We will give you normalized data, whether it's for Cisco equipment or whether it's for third-party equipment. So the STK will allow you to bring in Juniper or Huawei or Aruba or whoever that's out there and you can bring that into DNA Center, so now you have a view of the entire network, Cisco and Non-Cisco. You have normalized data for all of those and you can configure all of those, you can image update all of those. It's very very powerful. Just from an ISV standpoint, individual available standpoint now you are kind of unlocking, making this almost democratic. >> You've done the heavy-lifting. >> Yep, absolutely. >> That's what Cloud is all about, but talk about the creativity because you mentioned that entrepreneurial, a lot of the energy goes into trying to find the fatal flaw, is the product gonna be product-market fit, you do all that heavy-lifting and bootstrap it, right now it's simply, okay, I can sling some APIs together, get a prototype, then the creativity starts. Talk about the creativity impact. How do you see that impacting some of these new use cases, these hard problems. This is gonna come from, not some guy coming out of business school saying, "Hey, I'm gonna go hire "some engineers and solve that big, hard problem." It's gonna come organically, this is a huge deal. >> This is a huge deal, and because we're making it simpler it can come from any quarters, it doesn't have to be an established company, it can be an individual person that can't solve any use case, and then we ask Cisco, not only do we have, and of course the majority share in the market, but will also we have the platforms, like DevNet, and DevNet now has an equal system exchange, so if something that's cool can float up in the exchange can be voted on, can become something that becomes an absolutely easy part to monetization for somebody, that basically saying, "Okay, how do I marry business "and how do I take network and bring them together." >> This is awesome and it's also external to Cisco, but talk about the global impact. Just outside North America, massive growth, you're seeing things going on in Europe, but really in the Asia and China, huge growth markets going on. When you go to China, talk about mobility, they have mobility nailed down. India is absolutely on fire, growing like crazy. The talent, this is a melting pot of tech talent. How do you make all that work from a Cisco standpoint because what you want to do is bring the goods to everybody, that's open source. >> Absolutely, so think about any of the logical place that people go to with, given the way that the platform is already built, which is, it is Cloud Native. We've not in the cloud yet, but at some point the platform will go to cloud. And we are looking at harnessing the creative talent worldwide, whether it be in Asia or whether it be in Europe, or whether it be in the Americas, really doing that new value creation and taking that to the masses. And Cisco has the right to claim this market, we are absolutely in support of folks that want to do that. That's why DevNet has all of the learning labs and the sandboxes and everything else that's there in support, these are free to use. We want people to come and learn and co-create on the platform. >> And making it open and collaborative, the community aspect of it. >> Absolutely. >> Alright, final question while you guys are here, obviously you're at the Cisco perspective, but put your industry landscape hat on, people who couldn't make Cisco Live this year here in Orlando, they might be watching this video either live or on demand when it goes up to YouTube. What's the big story, I mean obviously what you guys are doing, across the whole show, what's the most important stories that are developing here this week that people should pay attention to deeply? >> So in terms of looking at the openness of the platform, Cisco is an open platform, API is really the new CLI because that's the way that you'll talk to the network. And think about what Chuck said at the opening keynote, this starts from the user, the things that you want to do to the applications, wherever they live, whether it be in a cloud, in a multi-cloud environment, Cisco is bringing all of that together. >> Prakash, what's your thoughts? >> Adding on to Ronnie's point, the openness and something that new that we are doing, not just from campus perspective, but campus, branch, data center, and making it open across everything, which is what Dave Goeckeler covered today in his keynote, I think that's something that Cisco is not just looking at one infrastructure, but across all of his portfolio and making it unique is really something that people should take away from this one. >> That's awesome. Great stuff, well guys, thanks for sharing. Thanks for co-sharing, co-developing content with us. I gotta say just from the hallway conversations, people are impressed that you guys are taking a very practical approach, not trying to boil over the ocean here with all these capabilities and announcements, focusing on the network value, where it fits in, and being Cloud Native from day one with microservices is a good start, so congratulations. >> Thank you. >> Thanks for sharing. Live coverage here in theCUBER. Day two of Cisco Live, I'm John Furrier with Stu Miniman. More live coverage, stay with us here at day two as we start winding down day two here at Cisco Live in Orlando, Florida, be right back.

>> Narrator: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE Conversation. >> Hey, welcome back everybody Jeff Frick here with theCUBE. We're coming to you today from our Palo Alto studios with theCUBE conversation, talking about data, and we're excited to have our next guest. He's been on a number of times, many times, CUBE alum, really at the forefront of helping companies and customers be more data centric in their activities. So we'd like to welcome onto the show Satyen Sangani. He is the co founder and CEO of Alation. Satyen, great to see you. >> Great to see you, Jeff. It's good to see you again in this new world, a new format. >> It is a new world, a new format, and what's crazy is, in March and April we were talking about this light switch moment, and now we've just turned the calendar to October and it seems like we're going to be doing this thing for a little bit longer. So, it is kind of the new normal, and even I think when it's over, I don't think everything's going to go back to the way it was, so here we are, but you guys have some exciting news to announce, so let's just jump to the news and then we'll get into a little bit more of the nitty gritty. So what do you got coming out today, right? >> Yeah its so. >> What we are announcing today is basically Alation 2020, which is probably one of the biggest releases that I've been with, that we've had since I've been with the company. We with it are releasing three things. So in some sense, there's a lot of simplicity to the release. The first thing that we're releasing is a new experience around what we call the business user experience, which will bring in a whole new set of users into the catalog. The second thing that we're announcing is basically around Alation analytics and the third is around what we would describe as a cloud-native architecture. In total, it brings a fully transformative experience, basically lowering the total cost of getting to a data management experience, lower and data intelligent experience, much lower than previously had been the case. >> And you guys have a really simple mission, right? You're just trying to help your customers be more data, what's the right word? Data centric, use data more often and to help people actually make that decision. And you had an interesting quote in another interview, you talked about trying to be the Yelp for information which is such a nice kind of humanizing way to think about it because data isn't necessarily that way and I think, you mentioned before we turned on the cameras, that for a lot of people, maybe it's just easier to ignore the data. If I can just get the decision through, on a gut and intuition and get onto my next decision. >> Yeah, you know it's funny. I mean, we live in a time where people talk a lot about fake news and alternative facts and our vision is to empower a curious and rational world and I always smile when I say that a little bit, because it's such a crazy vision, right? Like how you get people to be curious and how do you get people to think rationally? But you know, to us, it's about one making the data really accessible, just allowing people to find the data they need when and as they want it. And the second is for people to be able to think scientifically, teaching people to take the facts at their disposal and interpret them correctly. And we think that if those two skills existed, just the ability to find information and interpret it correctly, people can make a lot better decisions. And so the Yelp analogy is a perfect one, because if you think about it, Yelp did that for local businesses, just like Amazon did it for really complicated products on the web and what we're trying to do at Alation is, in some sense very simple, which is to just take information and make it super usable for people who want to use it. >> Great, but I'm sure there's the critics out there, right? Who say, yeah, we've heard this before the promise of BI has been around forever and I think a lot of peoples think it just didn't work whether the data was too hard to get access to, whether it was too hard to manipulate, whether it was too hard to pull insights out, whether there's just too much scrubbing and manipulating. So, what is some of the secret sauce to take? What is a very complex world? And again and you got some very large customers with some giant data sets and to, I don't want to say humanize it, but kind of humanize it and make it easier, more accessible for that business analyst not just generally, but more specifically when I need it to make a decision. >> Yeah I mean, it's so funny because, making something, data is like a lot of software death by 1000 cuts. I mean you look at something from the outside and it looks really, really, really simple, but then you kind of dwell into any problem and that can be CRM something like Salesforce, or it can be something like service now with ITSM, but these are all really, really complicated spaces and getting into the depths and the detail of it is really hard. And data is really no different, like data is just the sort of exhaust from all of those different systems that exist inside of your company. So the detail around the data in your company is exhaustingly minute. And so, how do you make something like that simple? I think really the biggest challenge there is progressively revealing complexity, right? Giving people the right amount of information at the right amount of time. So, one of the really clever things that we do in this business user experience is we allow people to search for and receive the information that's most relevant to them. And we determined that relevance based upon the other people in the enterprise that happen to be using that data. And we know what other people are using in that company, because we look at the logs to understand which data sources are used most often, and which reports are used most often. So right after that, when you get something, you just see the name of the report and it could be around the revenues of a certain product line. But the first thing that you see is who else uses it. And that's something that people can identify with, you may not necessarily know what the algorithm was or what the formula might be, how the business glossary term relates to some data model or data artifact, but you know the person and if you know the person, then you can trust the information. And so, a lot of what we do is spend time on design to think about what is it that a person expects to see and how do they verify what's true. And that's what helps us really understand what to serve up to somebody so that they can navigate this really complicated, relevant data. >> That's awesome, cause there's really a signal to noise problem, right? And I think I've heard you speak before. >> Yeah >> And of course this is not new information, right? There's just so much data, right? The increasing proliferation of data. And it's not that there's that much more data, we're just capturing a lot more of it. So your signal to noise problem just gets worse and worse and worse. And so what you're talking about is really kind of helping filter that down to get through a lot of that, a lot of that noise, so that you can find the piece of information within the giant haystack. That is what you're looking for at this particular time in this particular moment. >> Yeah and it's a really tough problem. I mean, one of the things that, it's true that we've been talking about this problem for such a long time. And in some instance, if we're lucky, we're going to be talking about it for a lot longer because it used to be that the problem was, back when I was growing up, you were doing research on a topic and you'd go to the card catalog and you'd go to the Dewey decimal system. And in your elementary school or high school library, you might be lucky if you were to find, one, two or three books that map to the topic that you were looking for. Now, you go to Google and you find 10,000 books. Now you go inside of an enterprise and you find 4,000 relational database tables and 200 reports about an artifact that you happened to be looking for. And so really the problem is what do I trust? And what's correct and getting to that level of accuracy around information, if there's so much information out there is really the big problem of our time and I think, for me it's a real privilege to be able to work on it because I think if we can teach people to use information better and better then they can make better decisions and that can help the world in so many different. >> Right, right, my other favorite example that everybody knows is photographs, right? Back when you only got 24 and a roll and cost you six bucks to develop it. Those were pretty special and now you go buy a fancy camera. You can shoot 11, 11 frames a second. You go out and shoot the kids at the soccer game. You come home with 5,000 photos. How do you find the good photo? It's a real, >> Yeah. >> It's a real problem. If you've ever faced something like that, it's kind of a splash of water in the face. Like where do I even begin? But the other piece that you talk about a lot, which is slightly different but related is context, and in favorite concept, it's like 55, right? That's a number, but if you don't have any context for that number, is it a temperature? Is it cold inside the building? Is it a speed? Is it too slow on i5? Or is it fast because I'm on a bicycle going down a Hill and without context data is just, it's just a number. It doesn't mean anything. So you guys really by adding this metadata around the data are adding a lot more contextual information to help figure out kind of what that signal is from the noise. >> Yap, you'll get facts from anywhere, right? Like, you're going to have a Hitchcock, you've got a 55 or 42, and you can figure out like what the meaning of the universe is and apparently the answer is 42 and what does that mean? It might mean a million different things and that, to me, that context is the difference between, suspecting and knowing. And there's the difference between having confidence and basically guessing. And I think to the extent that we can provide more of that over time, that's, what's going to make us, an ever more valuable partner to the customers that we satisfy today. >> Right, well, I do know why 42 is always the answer 'cause that's Ronnie Lot and that's always the answer. So, that one I know that's an easy one. (both chuckles) But it is really interesting and then you guys just came out. I heard Aaron Kalb on, one of your co-founders the other day and we talked about this new report that you guys have sponsored the Data Culture Report and really, putting some granularity on a Data Culture Index and I thought it was pretty interesting and I'm excited that you guys are going to be doing this, longitudinally because whether you do or do not necessarily agree with the method, it does give you a number, It does give you a score, It's a relatively simple formula. And at least you can compare yourself over time to see how you're tracking. I wonder if you could share, I mean, the thing that jumps out right off the top of that report is something we were talking about before we turned the cameras on that, people's perception of where they are on this path doesn't necessarily map out when you go bottoms up and add the score versus top down when I'm just making an assessment. >> Yeah, it's funny, it's kind of the equivalent of everybody thinks they're an above average driver or everybody thinks they're above average in terms of obviously intelligence. And obviously that mathematically is not possible or true, but I think in the world of data management, we all talk about data, we all talk about how important it is to use data. And if you're a data management professional, you want people in your company to use more data. But ironically, the discipline of data management doesn't actually use a lot of data itself. It tends to be a very slow methodical process driven gut oriented process to develop things like, what data models exist and how do I use my infrastructure and where do I put my data and which data quality is best? Like all of those things tend to be, somewhat heuristic driven or gut driven and they don't have to be and a big part of our release actually is around this product called Alation Analytics. And what we do with that product is really quite interesting. We start measuring elements of how your organization uses data by team, by data source, by use case. And then we give you transparency into what's going on with the data inside of your landscape and eco-system. So you can start to actually score yourself both internally, but also as we reveal in our customer success methodology against other customers, to understand what it is that you're doing well and what it is that you're doing badly. And so you don't need necessarily to have a ton of guts instinct anymore. You can look at the data of yourselves and others to figure out where you need to improve. And so that's a pretty exciting thing and I think this notion that says, look, you think you're good, but are you really good? I mean, that's fundamental to improvement in business process and improvement in data management, improvement in data culture fundamentally for every company that we work with. >> Right, right and if you don't know, there's a problem, and if you're not measuring it, then there's no way to improve on it, right? Cause you can't, you don't know, what you're measuring is. >> Right. >> But I'm curious of the three buckets that you guys measured. So you measured data search and discovery was bucket number one, data literacy, you know what you do once you find it and then data governance in terms of managing. It feels like that the search and discovery, which is, it sounds like what you're primarily focused on is the biggest gap because you can't get to those other two buckets unless you can find and understand what you're looking for. So is that JIve or is that really not problem, is it more than manipulation of the data once you get it? >> Yeah, I mean we focus really. We focus on all three and I think that, certainly it's the case that it's a virtuous cycle. So if you think about kind of search and discovery of data, if you have very little context, then it's really hard to guide people to the right bit of information. But if I know for example that a certain data is used by a certain team and then a new member of that team comes on board. Then I can go ahead and serve them with exactly that bit of data, because I know that the human relationships are quite tight in the context graph on the back end. And so that comes from basically building more context over time. Now that context can come from a stewardship process implemented by a data governance framework. It can come from, building better data literacy through having more analytics. But however, that context is built and revealed, there tends to be a virtuous cycle, which is you get more, people searching for data. Then once they've searched for the data, you know how to necessarily build up the right context. And that's generally done through data governance and data stewardship. And then once that happens, you're building literacy in the organization. So people then know what data to search for. So that tends to be a cycle. Now, often people don't recognize that cycle. And so they focus on one thing thinking that you can do one to the exclusion of the others, but of course that's not the case. You have to do all three. >> Great and I would presume you're using some good machine, Machine Learning and Artificial Intelligence in that process to continue to improve it over time as you get more data, the metadata around the data in terms of the usage and I think, again I saw in another interview there talking about, where should people invest? What is the good data? What's the crap data? what's the stuff we shouldn't use 'cause nobody ever uses it or what's the stuff, maybe we need to look and decide whether we want to keep it or not versus, the stuff that's guiding a lot of decisions with Bob, Mary and Joe, that seems to be a good investment. So, it's a great application of applied AI Machine Learning to a very specific process to again get you in this virtuous cycle. That sounds awesome. >> Yeah, I know it is and it's really helpful to, I mean, it's really helpful to think about this, I mean the problem, one of the biggest problems with data is that it's so abstract, but it's really helpful to think about it in just terms of use cases. Like if I'm using a customer dataset and I want to join that with a transaction dataset, just knowing which other transaction datasets people joined with that customer dataset can be super helpful. If I'm an analyst coming in to try to answer a question or ask a question, and so context can come in different ways, just in the same way that Amazon, their people who bought this product also bought this product. You can have all of the same analogies exist. People who use this product also use that product. And so being able to generate all that intelligence from the back end to serve up simple seeming experience on the front end is the fun part of the problem. >> Well I'm just curious, cause there's so many pieces of this thing going on. What's kind of the, aha moment when you're in with a new customer and you finish the install and you've done all the crawling and where all the datasets are, and you've got some baseline information about who's using what I mean, what is kind of the, Oh, my goodness. When they see this thing suddenly delivering results that they've never had at their fingertips before. >> Yeah, it's so funny 'cause you can show Alation as a demo and you can show it to people with data sets that are fake. And so we have this like medical provider data set that, we've got in there and we've got a whole bunch of other data sets that are in there and people look at it and interestingly enough, a lot of time, they're like, Oh yeah, I can kind of see it work and I can kind of like understand that. And then you turn it on against their own data. The data they have been using every single day and literally their faces change. They look at the data and they say, Oh my God, like, this is a dataset that Steven uses, I didn't even know that Steven thought that this data existed and, Oh my God, like people are using this data in this particular way. They shouldn't be using that data at all, Like I thought I deprecated that dataset two years ago. And so people have all of these interesting insights and it's interesting how much more real it gets when you turn it on against the company's systems themselves. And so that's been a really fun thing that I've just seen over and over again, over the course of multiple years where people just turn on the cup, they turn on the product and all of a sudden it just changes their view of how they've been doing it all along. And that's been really fun and exciting. >> That's great yeah, cause it means something to them, right? It's not numbers on a page, It's actually, it's people, it's customers, it's relationships, It's a lot of things. That's a great story and I'm curious too, in that process, is it more often that they just didn't know that there were these other buckets of reports and other buckets of data or was it more that they just didn't have access to it? Or if they did, they didn't really know how to manipulate it or to integrate it into their own workflow. >> Yeah, It's kind of funny and it's somewhat role dependent, but it's kind of all of the above. So, if you think about it, if you're a data management professional, often you kind of know what data sources might exist in the enterprise, but you don't necessarily know how people are using the data. And so you look at data and you're like, Oh my God, I can't believe this team is using this data for this particular purpose. They shouldn't be doing that. They should be using this other data set. I deprecated that data set like two years ago. And then sometimes if you're a data scientist, you're you find, Oh my gosh, there's this new database that I otherwise didn't realize existed. And so now I can use that data and I can process that for building some new machine learning algorithms. In one case we've had a customer where they had the same data set procured five different times. So it was a pure, it was a data set that cost multiple hundreds of thousands of dollars. They were spending $2 million overall on a data set where they could have been spending literally one fifth of that amount. And then you had a sort of another case finally, where you're basically just looking at it and saying, Hey, I remember that data set. I knew I had that dataset, but I just don't remember exactly where it was. Where did I put that report? And so it's exactly the same way that you would use Google. Sometimes you use it for knowledge discovery, but sometimes you also use it for just remembering the thing you forgot. >> Right but, but the thing, like I remember when people were trying to put Google search in that companies just to find records not necessarily to support data efforts and the knock was always, you didn't have enough traffic to drive the algorithm to really have effective search say across a large enterprise that has a lot of records, but not necessarily a lot of activity. So, that's a similar type of problem that you must have. So is it really extracting that extra context of other people's usage that helps you get around kind of that you just don't have a big numbers? >> Yeah, I mean that kind of is fundamentally the special sauce. I mean, I think a lot of data management has been this sort of manual brute force effort where I get a whole bunch of consultants or a whole bunch of people in the room and we do this big documentation session. And all of a sudden we hope that we've kind of, painted the golden gate bridge is at work. But, knowing that three to six months later, you're going to have to go back and repaint the golden gate bridge overall all over again, if not immediately, depending on the size and scale of your company. The one thing that Google did to sort of crawl the web was to really understand, Oh, if a certain webpage was linked to super often, then that web page is probably a really useful webpage. And when we crawled the logs, we basically do the exact same thing. And that's really informed getting a really, really specific day one view of your data without having to have a whole bunch of manual effort. And that's been really just dramatical. I mean, it's been, it's allowed people to really see their data very quickly and new different ways and I think a big part of this is just friction reduction, right? We'd all love to have an organized data world. We'd love to organize all the information in a company, but for anybody has an email inbox, organizing your own inbox, let alone organizing every database in your company just seems like a specificity in effort. And so being able to focus people on what's the most important thing has been the most important thing. And that's kind of why we've been so successful. >> I love it and I love just kind of the human factors kind of overlay, that you've done to add the metadata with the knowledge of who is accessing these things and how are they accessing it. And the other thing I think is so important Satyen is, we talk about innovation all the time. Everybody wants more innovation and they've got DevOps so they can get software out faster, et cetera, et cetera. But, I fundamentally believe in my heart of hearts that it's much more foundational than that, right? That if you just get more people, access to more information and then the ability to manipulate and clean knowledge out of that information and then actually take action and have the power and the authority to take action. And you have that across, everyone in the company or an increasing number of people in the company. Now suddenly you're leveraging all those brains, right? You're leveraging all that insight. You're leveraging all that kind of First Line experience to drive kind of a DevOps type of innovation with each individual person, as opposed to, kind of classic waterfall with the Chief Innovation Officer, Doing PowerPoints in his office, on his own time. And then coming down from the mountain and handing it out to everybody to go build. So it's a really a kind of paradox that by adding more human factors to the data, you're actually making it so much more usable and so much more accessible and ultimately more valuable. >> Yeah, it's funny we, there's this new term of art called data intelligence. And it's interesting because there's lots of people who are trying to define it and there's this idea and I think IDC, IDC has got a definition and you can go look it up, but if you think about the core word of intelligence, it basically DevOps down to the ability to acquire information or skills, right? And so if you then apply that to companies and data, data intelligence then stands to reason. It's sort of the ability for an organization to acquire, information or skills leveraging their data. And that's not just for the company, but it's for every individual inside of that company. And we talk a lot about how much change is going on in the world with COVID and with wildfires here in California. And then obviously with the elections and then with new regulations and with preferences, cause now that COVID happened everybody's at home. So what products and what services do you have to deliver to them? And all of this change is, basically what every company has to keep up with to survive, right? If capitalism is creative destruction, the world's getting destroyed, like, unfortunately more often than we'd like it to be,. >> Right. >> And so then you're say there going, Oh my God, how do I deal with all of this? And it used to be the case that you could just build a company off of being really good at one thing. Like you could just be the best like logistics delivery company, but that was great yesterday when you were delivering to restaurants. But since there are no restaurants in business, you would just have to change your entire business model and be really good at delivering to homes. And how do you go do that? Well, the only way to really go do that, is to be really, really intelligent throughout your entire company. And that's a function of data. That's a function of your ability to adapt to a world around you. And that's not just some CEO cause literally by the time it gets to the CEO, it's probably too late. Innovations got to be occurring on the ground floor. And people have got to repackage things really quickly. >> I love it, I love it. And I love the other human factor that we talked about earlier. It's just, people are curious, right? So if you can make it easy for them to fulfill their curiosity, they're going to naturally seek out the information and use it versus if you make it painful, like a no fun lesson, then people's eyes roll in and they don't pay attention. So I think that it's such an insightful way to address the problem and really the opportunity and the other piece I think that's so different when you're going down the card catalog analogy earlier, right? Is there was a day when all the information was in that library. And if you went to the UCLA psych library, every single reference that you could ever find is in that library, I know I've been there, It was awesome, but that's not the way anymore, right? You can't have all the information and it's pulling your own information along with public information and as much information as you can. where you start to build that competitive advantage. So I think it's a really great way to kind of frame this thing where information in and of itself is really not that valuable. It's about the context, the usability, the speed of these ability and that democratization is where you really start to get these force multipliers and using data as opposed to just talking about data. >> Yeah and I think that that's the big insight, right? Like if you're a CEO and you're kind of looking at your Chief Data Officer or Chief Data and Analytics Officer. The real question that you're trying to ask yourself is, how often do my people use data? How measurable is it? Like how much do people, what is the level at which people are making decisions leveraging data and that's something that, you can talk about in a board room and you can talk about in a management meeting, but that's not where the question gets answered. The question gets really answered in the actual behaviors of individuals. And the only way to answer that question, if you're a Chief Analytics Officer or somebody who's responsible for data usage within the company is by measuring it and managing it and training it and making sure it's a part of every process and every decision by building habit and building those habits are just super hard. And that's, I think the thing that we've chosen to be sort of the best in the world at, and it's really hard. I mean, we're still learning about how to do it, but, from our customers and then taking that knowledge and kind of learning about it over time. >> Right, well, that's fantastic. And if it wasn't hard, it wouldn't be valuable. So those are always the best problems to solve. So Satyen, really enjoyed the conversation. Congratulations to you and the team on the new release. I'm sure there's lots of sweat, blood and tears that went into that effort. So congrats on getting that out and really great to catch up. Look forward to our next catch up. >> You too Jeff, It's been great to talk. Thank you so much. >> All right, take care. All righty Satyen and I'm Jeff, you're watching theCUBE. We'll see you next time. Thanks for watching. (ethereal music)

(electronic music) >> Narrator: From the SiliconANGLE Media office in Boston Massachusetts, it's the CUBE. Now here are your hosts, Dave Vellante and Stu Miniman. >> Cloud is dead, it's all going to the edge. Or is it? Hi everybody, this is Dave Vellante and I'm here with Stu Miniman. Stu, where does this come from, this narrative that the cloud is over? >> Well Dave, you know, clouds had a good run, right? It's been over a decade. You know, Amazon's dominance in the marketplace but Peter Levine from Andreessen Horowitz did an article where he said, cloud is dead, the edge is killing the dead. The Edge is killing the cloud and really we're talking about IoT and IoT's huge opportunity. Wikibon, Dave we've been tracking for many years. We did you know the original forecast for the Industrial Internet and obviously there's going to be lots more devices at the edge so huge opportunity, huge growth, intelligence all over the place. But in our viewpoint Dave, it doesn't mean that cloud goes away. You know, we've been talking about distributed architectures now for a long time. The cloud is really at the core of this building services that surround the globe, live in just hundreds of places for all these companies so it's nuanced. And just as the cloud didn't overnight kill the data center and lots of discussion as to what lives in the data center, the edge does not kill the cloud and it's really, we're seeing some major transitions pull and push from some of these technologies. A lot of challenges and lots to dig into. >> So I've read Peter Levine's piece, I thought was very thought-provoking and quite well done. And of course, he's coming at that from the standpoint of a venture capitalist, all right. Do I want to start you know, do I want to pour money into the trend that is now the mainstream? Or do I want to get ahead of it? So I think that's what that was all about but here's my question Stu is, in your opinion will the activity that occurs at the edge, will it actually drive more demand from the cloud? So today we're seeing the infrastructure, the service business is growing at what? Thirty five percent? Forty percent? >> Sure, sure. Amazon's growing at the you know, 35 to 40 percent. Google, Microsoft are growing double that right now but overall you're right. >> Yeah, okay and so, and then of course the enterprise players are flat if they're lucky. So my question is will the edge actually be a tailwind for the cloud, in your opinion? >> Yeah, so first on your comment there from an investment standpoint, totally can understand why edge is greenfield opportunity. Lots of different places that I can place bets and probably can win as opposed to if I think that today I'm going to compete against the hyperscale cloud guys. You know, they're pouring 10 billion dollars a year into their infrastructure. They have huge massive employment so the bar to entry is a lot higher. I'm sorry, the second piece was? >> So will the edge drive more demand for the cloud? >> Yeah, absolutely. I think it does Dave because you know, let's take something like autonomous vehicles. Something that we talk about. I need intelligence of the edge. I can't wait for some instruction to go back to the cloud before my Tesla plows into an individual. I need to know that it's there but the models themselves, really I've got all the compute in the cloud. This is where I'm going to train all of my models but I need to be able to update and push those to the edge. If I think about a lot of the industrial applications. Flying a plane is, you know, things need to happen locally but all the anomalies and new things that we run into there's certain pieces that need to be updated to the cloud. So you know, it's kind of a multi-layer. If we look at how much data will there be at the edge, well there's probably going to be more data at the edge than there will be in the central cloud. But how much activity, how much compute do I need, how much things do I need to actually work on. The cloud is probably going to be that central computer still and it's not just a computer, as I said, a distributed architecture. That's where, you know. When we've looked at big data in the early days Dave, when we can put those data lines in the cloud. I've got thousands or millions of compute cycles that I can throw at this at such a lower price and use that there as opposed to at the edge especially. What kind of connectivity do I have? Am i isolated from those other pieces? If you go back to my premise of we're building distributed architectures, the edge is still very early. How do I make sure I secure that? Do I have the network? There's lots of things that I'm going to build in a tiny little component and have that be there. And there's lots of hardware innovation going on at that edge too. >> Okay, so let's talk about how this plays out a little bit and you're talking about a distributed model and it's really to me a distributed data model. The research analysts at Wikibon have envisioned this three-tier data model where you've got data at the edge, which you may or may not persist. You've got some kind of consolidation or aggregation layer where it's you know, it's kind of between the edge and the deep data center and then you've got the cloud. Now that cloud can be an on-prem cloud or it could be the public cloud. So that data model, how do you see that playing out with regard to the adoption of cloud, the morphing of cloud and the edge and the traditional data center? >> Yeah we've been talking about intelligent devices at the edge for a couple decades now. I mean, I remember I built a house in like 1999 and the smart home was already something that people were talking about then. Today, great, I've got you know. I've got my Nest if I have, I probably have smart assistants. There's a lot of things I love-- >> Alexa. >> Saw on Twitter today, somebody's talking like I'm waiting for my light bulbs to update their firmware from the latest push so, some of its coming but it's just this slow gradual adoption. So there's the consumer piece and then there's the business aspect. So, you know, we are still really really early in some of these exciting edge uses. Talk about the enterprise. They're all working on their strategy for how devices and how they're going to work through IoT but you know this is not something that's going to happen overnight. It's they're figuring out their partnerships, they're figuring out where they work, and that three-tiered model that you talked about. My cloud provider, absolutely hugely important for how I do that and I really see it Dave, not as an or but it's an and. So I need to understand where I collect my data, where it's at certain aspects are going to live, and the public cloud players are spending a lot of time working on on that intelligence, the intelligence layer. >> And Stu, I should mention, so far we're talking about really, the infrastructure as a service layer comprises database and middleware. We haven't really addressed the the SAS space and we're not going to go deep into that but just to say. I mean look, packaged software as we knew it is dead, right? SAS is where all the action is. It's the highest growth area, it's the highest value area, so we'll cover that in another segment. So we're really talking about that, the stack up to the middleware, the database, and obviously the infrastructures as a service. So when you think about the players here, let's start with AWS. You've been to I think, every AWS re:Invent maybe, with the exception of one. You've seen the evolution. I was just down in D.C. the other day and they have this chart on the wall, which is their releases, their functional releases by year. It's just, it's overwhelming what they've done. So they're obviously the leader. I saw a recent Gartner Magic Quadrant. It looked like, I tweeted it, it looked like Ronnie Turcotte looking back on Secretariat from the Belmont and whatever it was. 1978, I think it was. (laughs) 31 lengths. I mean, massive domination in the infrastructure as a service space. What do you see going on? >> Yeah so, Dave, absolutely. Today the cloud is, it's Amazon's market out there. Interestingly if you say, okay what's some of the biggest threats in the infrastructure as a service? Well, maybe China, Dave. You know, Alibaba was one that you look at there. But huge opportunity for what's happened at the edge. If you talk about intelligence, you talk about AI, talk about machine learning. Google is actually the company that most people will talk about it, can kind of have a leadership. Heck, I've even seen discussion that maybe we need antitrust to look at Google because they're going to lock things up. You know, they have Android, they have Google Home, they have all these various pieces. But we know Dave, they are far behind Amazon in the public cloud market and Amazon has done a lot, especially over the last two years. You're right, I've been to every Amazon re:Invent except for the first one and the last two years, really seen a maturation of that growth. Not just you know, devices and partnerships there but how do they bring their intelligence and push that out to the edge so things like their serverless technology, which is Lambda. They have Lambda Greengrass that can put to the edge. The serverless is pervading all of their solutions. They've got like the Aurora database-- >> And serverless is profound, not just that from the standpoint of application development but just an entire new business model is emerging on top of serverless and Lambda really started all that but but carry on. >> Yeah and when you look in and you say okay, what better use case than IoT for, well I need infrastructure but I only need it when I need it and I want to call it for when it's there. So that kind of model where I should be able to build by the microsecond and only use what I need. That's something that Amazon is at the forefront, clear leadership position there and they should be able to plug in and if they can extend that out to the edge, starting new partnerships. Like the VMware partnerships, interesting. Red Hat's another partnership they have with OpenShift to be able to get that out to more environments and Amazon has a tremendous ecosystem out there and absolutely is on their radar as to how their-- >> They're crushing it So we were at Google Next last year. Big push, verbally anyway, to the enterprise. They've been making some progress, they're hiring a lot of people out of formerly Cisco, EMC, folks that understand the enterprise but beyond sort of the AI and sort of data analytics, what kind of progress has Google made relative to the leader? >> So in general, enterprise infrastructure service, they haven't made as much progress as most of us watching would expect them to make. But Dave, you mentioned something, data. I mean, at the center of everything we're talking about is the data. So in some ways is Google you know, come on Google, they're smarter than the rest of us. They're skating to where the puck is Dave and infrastructure services, last decades argument if it's the data and the intelligence, Google's got just brilliant people. They're working at the some of these amazing environments. You look at things like Google's Spanner. This is distributed architecture. Say how do I plug in all of these devices and help the work in a distributed gradual work well. You know, heck, I'd be reading the whitepapers that Google's doing in understanding that they might be really well positioned in this 3D chess match that were playing. >> Your eyes might bleed. (laughs) I've read the Google Spanner, I was very excited about it. Understood, you know, a little bit of it. Okay, let's talk about Microsoft. They're really of the big cloud guys. They're really the one that has a partnership strategy to do both on-prem and public cloud. What are your thoughts on that now that sort of Azure stack is starting to roll out with some key partners? >> Yeah absolutely, it's the one that you know. Dave, if you use your analogy looking back, it's like well the next one, it's gaining a little bit, gaining a little bit but still far back. There is Microsoft. Where Microsoft has done best of course is their portfolio of business applications that they have. That they've really turned the green light on for enterprises to adopt SAS with Office 365. Azure stack, it's early days still but companies that use Microsoft, they trust Microsoft. Microsoft's done phenomenal working with developers over the last couple of years. Very prominent like the Kubernetes shows that I've been attending recently. They've absolutely got a play for serverless that we were talking about. I'm not as up to speed as to where Microsoft sits for kind of the IoT edge discussions. >> But you know they're playing there. >> Yeah, absolutely. I mean, Microsoft does identity better than anyone. Active Directory is still the standard in enterprises today. So you know, I worry that Microsoft could be caught in the middle. If Google's making the play for what's next, Microsoft is still chasing a little bit what Amazon's already winning. >> Okay and then we don't have enough time to really talk about China, you mentioned it before. Alibaba's you know, legit. Tencent, Baidu obviously with their captive market in China, they're going to do a lot of business and they're going to move a lot of compute and storage and networking but maybe address that in another segment. I want to talk about the traditional enterprise players. Dell EMC, IBM, HPE, Cisco, where do they stand? We talk a lot at Wikibond about true private cloud. The notion that you can't just stick all your data into the public cloud. Andy Jassy may disagree with that but there are practical realities and certainly when you talk to CIOs they they underscore that. But that notion of true private cloud hasn't allowed these companies to really grow. Now of course IBM and Oracle, I didn't mention Oracle, have a different strategy and Oracle's strategy is even more different. So let's sort of run through them. Let's take the arms dealers. Dell EMC, HPE, Cisco, maybe you put Lenovo in there. What's their cloud strategy? >> Well first of all Dave I think most of them, they went through a number of bumps along the road trying to figure out what their cloud strategy is. Most of them, especially let's take, if you take the compute or server side of the business, they are suppliers to all the service providers trying to get into the hyperscalers. Most of them have, they all have some partnership with Microsoft. There's a Assure stack and they're saying, okay hey, if I want an HPE server in my own data center and in Azure, Microsoft's going to be happy to provide that for you. But David, it's not really competing against infrastructure as a service and the bigger question is as that market has kind of flattened out and we kind of understand it, where is the opportunity for them in IoT. We saw, you know Dave. Last five years or so, can I have a consumer business and an enterprise business in the same? HPE tore those two apart. Michael Dell has kept them together. IBM spun off to Lenovo everything that was on the more consumer side of the business. Where will they play or will companies like Google, like Apple, the ones that you know, Dave. They are spending huge amounts of money in chips. Look at Google and what they're doing with TP use. Look at Apple, I believe it was, there was an Israeli company that they bought and they're making chips there. There's a different need at the edge and sure, company like Dell can create that but will they have the margin, will they have the software, will they have the ecosystem to be able to compete there? Cisco, I haven't seen on the compute side, them going down that path but I was at Cisco Live and a big talk there. I really like the opening keynote and we had a sit down on the CUBE with the executive, it said really if I look out to like 2030. If Cisco still successful and we're thinking about them, we don't think of them as a network company anymore. They are a software company and therefore, things like collaboration, things like how it's kind of a new version of networking that's not on ports and boxes. But really as I think about my data, think about my privacy and security, Cisco absolutely has a play there. They've done some very large acquisitions in that space and they've got some deep expertise there. >> But again, Dell, HPE, Cisco, predominantly arms dealers. Obviously don't have, HPE at one point had a public cloud, they've pulled back. HP's cloud play really is cloud technology partners that they acquire. That at least gives them a revenue stream into the cloud. Now maybe-- >> But it's a consultancy. >> It's a consultancy, maybe it's a one-way trip to the cloud but I will say this about CTP. What it does is it gives HPE a footprint in that business and to the extent that they're a trusted service provider for companies trying to move into the cloud. They can maybe be in the catbird seat for the on-prem business but again, largely an arms dealer. it's going to be a lower margin business certainly than IBM and Oracle, which have applications. They own their own public cloud with the Oracle public cloud and IBM cloud, formerly SoftLayer, which was a two billion dollar acquisition several years ago. So those companies from a participation standpoint, even a tiny market share is compared to Amazon, Google, and Microsoft. They're at least in that cloud game and they're somewhat insulated from that disruption because of their software business and their large install base. Okay, I want to sort of end with, sort of where we started. You know, the Peter Levine comment, cloud is dead, it's all going to the edge. I actually think the cloud era, it's kind of, it's here, we're kind of. It's kind of playing out as many of us had expected over the last five years. You know what blew me away? Is Alexa, who would have thought that Amazon would be a leader in this sort of natural language processing marketplace, right? You would have thought it would come from, certainly Google with all the the search capability. You would have thought Apple with Siri, you know compared to Alexa. So my point is Amazon is able to do that because it's got a data model. It's a data company, all these companies, including Apple, Google, Microsoft, Amazon, Facebook. The largest market cap companies in the world, they have data at the core. Data is foundational for those companies and that's why they are in such a good position to disrupt. So cloud, SAS, mobile, social, big data, to me still these are kind of the last 10 years. The next 10 years are going to be about AI, machine intelligence, deep learning, machine learning, cognitive. We're trying to even get the names right but it starts with the data. So let me put forth the premise and get your commentary. and tie it back in the cloud. So the innovation, in the next 10 years is going to come from data and to the extent that your data is not in silos, you're going to be in a much better position than if it is. Number two is your application of artificial intelligence, you know whatever term you want to use, machine intelligence, etc. Data plus AI, plus I'll bring it back to cloud, cloud economics. If you don't have those cloud economics then you're going to be at a disadvantage of innovation. So let's talk about what we mean by cloud economics. You're talking about the API economy, talking about global scale, always on. Very importantly something we've talked about for years, virtually zero marginal costs at volume, which you're never going to get on-prem because this creates a network effect. And the other thing it does from an innovation context, it attracts startups. Or startups saying, hey I want to build on-prem. No, they don't want to build in the cloud. So it's data plus artificial intelligence plus cloud economics that's going to drive innovation in the next ten years. What are your thoughts? >> Yeah Dave, absolutely. Something I've been saying for the last couple of years, we watched kind of the the customer flywheel that the public clouds have. Data is that next flywheel so companies that can capture that. You mentioned Amazon and Alexa, one of the reasons that Amazon can basically sell that as a loss is lots of those people, they're all Amazon Prime customers and they're ordering more things from Amazon and they're getting so much data that drive all of those other services. Where is Amazon going to threaten in the future? Everywhere. It is basically what they see. The thing we didn't discuss there Dave, you know I love your premise there, is it's technology plus people. What's going to happen with jobs? You and I did the sessions with Andy McAfee and Eril Brynjolfsson, it's racing with the machine. Where is, we know that people plus machines always beat so we spent the last five years talking about data scientist, the growth of developers and developers and the new king makers. So you know what are those new jobs, what are those new roles that are going to help build the solutions where people plus machine will win and what does that kind of next generation of workforce going to look like? >> Well I want to add to that Stu, I'm glad you brought that up. So a friend of mine David Michelle is just about to publish a new book called Seeing Digital. And in that book, I got an advance copy, in there he talks about companies that have data at their core and with human expertise around the data but if you think about the vast majority of companies, it's human expertise and the data is kind of bolted on. And the data lives in silos. Those companies are in a much more vulnerable position in terms of being disrupted, than the ones that have a data model that everybody has access to with human expertise around it. And so when you think about digital disruption, no industry is safe in my opinion, and every industry has kind of its unique attributes. You know, obviously publishing and books and music have disrupted very quickly. Insurance hasn't been disrupted, banking hasn't been disrupted, although blockchain it's probably going to affect that. So again, coming back to this tail-end premise is the next 10 years is going to be about that digital disruption. And it's real, it's not just a bunch of buzzwords, a cloud is obviously a key component, if not the key component of the underlying infrastructure with a lot of activity in terms of business models being built on top. All right Stu, thank you for your perspectives. Thanks for covering this. We will be looking for this video, the outputs, the clips from that. Thanks for watching everybody. This is Dave Vellante with Stu Miniman, we'll see you next time. (electronic music)