>> Host: Live from San Diego, California, it's the CUBE, covering Cisco Live US 2019. Brought to you by Cisco and its ecosystem partners. >> Hi, welcome back to the CUBE, Lisa Martin with Stu Miniman, covering day one of Cisco Live from sunny San Diego. We're pleased to welcome back a couple of our alumni. To my right Eric Herzog, CMO of IBM Storage. Eric it's always great to have you. >> Great. >> And you fashion choices on the CUBE. >> Always wear a Hawaiian shirt for the CUBE. >> I know, it's a thing. And we've also got Evaristus Mainsah, General Manager of IBM Cloud Private Ecosystem. Evaristus it's great to have you back on the program. >> Thank you very much, delighted to be here. >> So guys here we are, we're in the dove nut zone. Lots of collaboration, lots of conversations day one of Cisco Live. But this events been around for 30 years. Long time, I think Chuck Robins said this morning what also turned 30 this year is Tetris. Anybody a big fan of Tetris? So, so much progress, so much change. I know you've seen a lot of it. Eric lets start with you. The global economy, what are the impacts it's having on IT? >> Well I'd say the number one thing is everyone is recognized the most valuable asset is data. It's not gold, it's not silver, it's not plutonium and it definitely isn't oil, it's all about data. And whether it be a global Fortune 500, a midsize company or Herzogs Bar & Grill, data is your most valuable asset. So at IBM Storage, what we've done is making sure that our focus is on being data-driven. It's all about solutions, it's not about speeds and feeds. Of course, having done this for 35 years I could have whacked poetically on speeds and feeds. And even if you have some speeds and feeds that Stu may not even remember anymore. That said, it's really about data, it's not about storage speeds and feeds. How really storage is that critical foundation for applications, workloads and use cases. And that's what's most important. >> Yeah, so Eric, when they rolled out on stage this morning that 30 year old box with ribbon cable, yeah, that predated a little bit when I was looking at IT. But, I remember when I started in IT, when we talked about security, the main thing was lock the door of the cabinet that everything was in there, because it was kind of self-contained. Security's gone through a few changes in the last you know 20 25 years though. Maybe you could talk a little bit about that kind of security resiliency. Obviously, something that's impacted the network for a long time, something that IBM sees front and center. >> What I think the big deal is what most people think when they think of security, is I got to buy security software. So I got to call up IBM Security or RSA or the Intel Security Division and buy some security software. And while that's great the reality is as many people have written about, in fact Wikibon SiliconANGLE's written about it. Close to 98% of all enterprises, and I mean big enterprises now are going to get to be broken into. And you've seen this all over the news. So the key thing is once they're inside, storage can help you with a cyber resiliency play. And at IBM Storage whether that be data at rest encryption. Whether that be malware or ransomware protection. We put together a whole set of technology that when the bad guys in the house they can't steal the TV. Because we've locked it down. It's almost as if it was in a safe. Maybe it's almost like the cloak in science fiction where you can't even see the Romulan ship, because it's cloaked. Well guess what, that's what IBM storage can do for your data and it is your most valuable asset. So critical to cyber resiliency. >> So helping customers go from reactive to this expectation breach has happened very very frequently every few seconds to being proactive? >> Yeah, I mean. >> Eventually predictive? >> Well what we do is for example with our Spectrum Protect software. When there's a malware or ransomware attack, what happens is they always go after you're secondary data sets first. I know that sounds weird but they go after your backups, your snapshots and your replicas. 'Cause when they attack your primary data, if they've you can just recover from a backup they can't hold you for $10 million of ransom. So our Spectrum Protect software for example, when it sees anomalous activity in backup data sets, sends an email on a warning out to all the admins and says you have weird activity going on, you might want to check it out and that way you would know. Because secondarity is attacked first in a cyber resiliency strategy. >> You know, the other thing we're seeing a lot is just the scope of what's happening in IT. When you talk about things like scale and you talk about you know edge computing and so much change going on. There's got to be AI in there or machine learning to help us because humans alone can't keep up with what's going on here. Tell us a little bit about that Eric. >> So Big data and AI is like the hot topic right now. Cyber resiliency is important 'cause people obviously have been buying security software for a while. So it's more what we do is really an adjunct to that. In the case of Big data and AI, it's a brand new open field. Everyone is looking for solutions in both of those spaces. We have created a complete set of data infrastructure we've called the AI pipeline. It involves not only physical storage arrays but a whole bunch of software. In fact our Spectrum Discover software which allows you to create metadata catalogs about file and object data is being expanded. And we already publicly said this in the second half To include EMC and Netapp and AWS, not just IBM Storage. So it's a critical thing, you've got to make sure the other thing is when you're using AI. Let's say you're going to use AI to run a factory. If the storage goes down, those robots aren't working. So storage is that critical underlying foundation. A in a Big data network load to be able to have this pipeline to get the data. But if you don't have the resiliency, the performance and the availability of the underlying storage everything shuts down if the storage fails. 'Cause the AI software won't run. So that's how we see fitting in to their both the critical foundation also this AI data pipeline with all of our software. >> So before we get in to this Cisco partnership with Evaristus, it's one more question Eric for you. As Chief Marketing Officer, you talk about the customers all of the time. In that example that you just gave about the criticality of storage for AI where are you having conversations within customer organizations. Is it at the level of the storage girls and guys or has it gone up to lines of business to executives. >> Yeah so, from an AI perspective it runs a gamut. It could be sometimes the storage people. Sometimes the infrastructure people. A lot of times it's actually in the line of business or at the data scientist level. On the Big data side it's a little bit more mature so people know they need to do analytics versus AI. And so when you look at it from that perspective on that side it's often the storage guy but it's also the data scientist as well. So that's who we talk to to get things rolling. And it's not, we don't just talk to the storage admin for either of them, because they're both so new and they have such a big impact on the data scientists and the analytic engine committees inside of those giant enterprises. >> I can imagine eventually maybe question for you. Of that conversation elevating it up to the sweet sweet. Because if you can't access the data, if it can't be protected, what good is it? Right, it's really, to say it's the lifeblood is a silly thing, but we say it all the time. But it's critical, it's table stakes. >> Well one of the things that's interesting is I just got my Fortune 500 magazine at home, that had the Fortune 500 list in it. And there was an interesting article on AI and the enterprise. And they did a survey according to Fortune magazine, 50% of the CIO's that are in the Fortune 500 said they're using AI and Big data of some type. So it's sweeping the world. And it started of course in HPC in the academics. But now it's going into all enterprises of all types. >> Alright so we've talked a few years about the Versastack Partnership. But the last year or so we've really been talking about where Hybrid cloud and multi-cloud fit in to this. We talked a little bit at IBM Think. Evaristus we talked at another show about some of the IBM Cloud Private. Give us the update where we are with customers and how that fits, Eric lets start with you and Evaristus just go into the partnership. >> Sure from a storage version perspective, we've been talking about a Hybrid multi-cloud now for several years. And in fact I did a presentation two years ago at Cisco Live on Hybrid cloud using Versastack. Today I gave one on the data driven enterprise and why hybrid multi-cloud is important to use. So that was the 30 minutes presentation I did today. So I think the key thing is we make sure that we A our Hybrid it's not going to all public or all private. And we can move data seamlessly back and forth. And then also multi-cloud. When you look at enterprise shops, they're not just going to use IBM Cloud. I wish they would I'm an IBM shareholder but they're not. They use IBM, they're going to use ABS, they're going to use Amazon and in many cases they're going to use some smaller cloud provider. So we make sure that we can move data around across any multi-cloud of various different providers to accompany. But also Hybrid cloud as well. >> So the status talk to use about you know from a partnership Cisco IBM Cloud Private perspective, what's going on there Evaristus? >> Well Thank you very much. Well IBM and Cisco have been partners for a long long time. And what we are doing now is given the realities, the fact that those clients have found themselves in a multi-cloud environment, >> Hybrid multi-cloud environment. What we can do to help clients so they can develop they can test, they can manage the applications in a consistent manner, whether they are on prime or in the cloud. And there are a couple of initiatives that we are announcing. One of them is that IBM Cloud Private is going to run on Hyperflex, so Cisco's Hyperflex. As well as hyperflex, hyper-conversed infrastructure. What it means is a client who currently has hyperflex can have IBM Cloud Private on it. Which effectively means they have themselves a Private Cloud environment that also connects to other public cloud environments and allows you to really begin to work within a Hybrid cloud environment the way that most clients need to. The second initiative is that we will have ACI pods or V pods, virtual ACI, running in the IBM public cloud. Which basically means that again, Cisco customers, ACI Network customers who currently use the produce on Prime will be able to use exactly you know the same control pane to manage their deployments and to manage their security preferences on Prime as they do in the cloud. And this again surrounding the Public Cloud is running on bare metal on the IBM Cloud. >> Alright, Evaristus can you bring us inside a little bit the applications you know. Eric talked about you know data we know is so important. Really it's the applications that are driving that. It's where we're seeing the most change in customers, as to how they're moving or building new applications. And in Hyber cloud it's one of the biggest questions for customers is what do they do with that application portfolio? >> Yes so what we're seeing is clearly because you know. Clients have now lots of different Public clouds. They also have Private clouds to deal with them. They have lots of applications that are currently that need to move right. We believe 20% of those applications have moved, the remaining 80% are still on Prime. And so the trend that we are really seeing is applications moving to the cloud. And the two ways of doing it you could do this by simply lifting and shifting on VM, you get the contraction benefit of your stack right. So you can some cost impacts. But the really interesting way that you see lots of clients moving is modernizing the applications. Because the real valued driver with infinite cloud is not so much cost as innovation. And when you convert those applications into Microsoft this is the right and let me run them in containers it gives them plenty of flexibility. And wasting lots of clients that want to use IBM Cloud Private as a platform to enable that modernization journey. >> So as every industry is living in this Hybrid multi-cloud world for many reasons. But it sounds like to me is that the IBM Cisco relationship is deepening as a result to enable these organizations that are in these very amorphous environments. You know as we see the explosion of Edge and Mobile, that's what it sounds like to me. Is that this long standing partnership is getting deeper and maybe a stronger foundation. To help customers not just live in this Hybrid multi-cloud world but be successful so that their businesses gain competitive advantage. They can identify new products and services and revenue streams. >> Yeah, I think multi-cloud and Hybrid cloud actually requires partnerships. Because as Eric said later on of course you like everybody to be on the IBM Cloud and it's a great cloud. But we recognize that many clients who have a variety of different plights to deal with. They have a variety of different infrastructures. And that's why when you look at IBM Cloud Private which is you know our offering that really enables that Hybrid cloud. It is designed to managed that. So It is multi-model, so if you want to run it as a VM you can, you want to run your containers, you can run serverless, you can run them bare metal. But also, it supports a range of different infrastructure. So not only does it run on Z, it runs on power, it runs on Spectrum Storage. We announce running now on Hyperflex. It also runs on other peoples Public clouds. It runs on Azure, it runs on Amazon web services, it runs on Google Cloud platform, it runs on the IBM Cloud. And the intent here is to enable clients to basically manage and work with that infrastructure as if it was one. The way that Stu said in the data center where you locked everything up. Well it's not like that anymore. But the most that we can do is to enable clients to treat all of that infrastructure as one. And that's what sort of aim to do with our platforms. >> Alright, I guess last question I'd like to get both of your comments on. Is your advice for customers, you know, customers have that they have a lot of you know existing things that they have to deal with, that they're looking to modernize. What advice do you give them? Where do you start them you know I guess you know one of the things you're starting where they are. But you know what are some of the first steps and recommendations that you have for customers today? >> We have a process that works really well, which is called the IBM Garage. Which is effectively a way that we used to co-create with our clients to solve the immediate problems. So a client for example, who is looking at app modernization but isn't sure where to start, which app. What we do is we get their teams together with our teams line of business together with IT and our teams and we spend a couple of days in a design thinking workshop to identify a minimum viable product. Which is something that solves a problem not big enough that it will take forever, but big enough to matter. Then we get our teams to work side-by-side, we code it, we test it, we deploy it, we'll run it in the IBM Cloud. We manage it, at like in one week sprints. And then you spend another few days at the end of week four or five to do a see retrospective to see whether it solved the problem as you expected. And if it did, you pick the next piece of work to continue your journey. So before you know, five weeks in, you have your first application modernized. Or you have your first cloud negative ready. >> Now from a storage perspective it's a little bit easier. We supported storage on bare metal. We supported storage in all the virtual environments. KVM, OVM, obviously VM we're in Hyper V. And now, we've been supporting containers for over two years. So we say is leave no data behind. If certain data needs to stay on bare metal, that's fine we can support that. But we can also transparently migrate data back and forth between the various tiers of container-based virtualization-based or the old style bare metal. So from our perspective, we help them move data around where they need it. And if they're still running in a hybridized world in this case, containers, virtual and bare metal that's fine. If they just go containers that's fine. If they just go virtual it's fine. So for us, because of what we've been supporting now for several years, we can help them on that journey. And traverse from any one of those three layers, which is where data sits in today's data centers and cloud environments. >> So overall a lot of collaboration, a lot of customer choice. Gentlemen, Thank you for joining Stu and me the program this afternoon, great to have you back. >> Thank you >> Great, Thank you. Glad to be on the CUBE. >> Oooh our pleasure. For Stu Miniman, I am Lisa Martin. You're watching the CUBE, live from day one of our coverage on Cisco Live. Thanks for watching. (energetic music)

(upbeat music) >> Live From Miami Beach, Florida. It's theCUBE! Covering VeeamON 2019. Brought to you by Veeam! >> Welcome back to Miami everybody this is theCUBE, the leader in live tech coverage. My name is Dave Vellante I'm here with my co-host Peter Burris. Two days of wall to wall coverage of VeeamON 2019. They selected the Fontainebleau Hotel in hip, swanky Miami. Tad Brockway is here he's the corporate VP of Azure Storage, good to see you! >> Yeah great to see you thank you for having me. >> So you're at work for a pretty hip company, Microsoft Azure is where all the growth is, 70 plus percent growth, and you doing some cool stuff with storage. So let's get into it. Let's start with your role and kind of your swim lane if you will. >> So our team is responsible for our storage platform that includes our disc service for IAS virtual machines, our scale our storage we call Azure blob storage. We have support for files as well with a product called Azure Files, we support SMB based files, NFS based files, we have a partnership with NetApp, we're bring Azure NetApp files is what we call it, we're bringing NetApp on tap into our data centers delivering that as a first priority service we're pretty excited about that. And then a number of other services around those core capabilities. >> And that's really grown over the last several years, optionality is really the watch word there right, giving customers as many options, file, block, object, etc. How would you summarize the Azure Storage strategy? >> I like that point, optionality and really flexibility for customers to approach storage in whatever way makes sense. So there may be customers, there are customers who are developing brand new cloud base taps, maybe they'll go straight to object storage or blobs. There are many customers who have data sets and work loads on-prem that are NFS based and SMB based, they can bring those assets to our cloud as well. We're the only vendor in the industry that has a server side implementation of HDFS. So for analytics workloads we bring file system semantics for those large scale HDFS workloads. We bring them into our storage environment so that the customer can do all of the things that are possible with a file system hierarchy's for organizing their data, use ACl's to protect their data assets and that's a pretty revolutionary thing that we've done but to your point though, optionality is the key and being able to do all of those things for all of those different access types, and then being able to do that for multiple economic tiers as well from hot storage all the way down to our archive storage tier. >> And I short changed you on your title cause your also responsible for media and edge, so that includes Azure stack is that right? >> Right so we have Azure stack as well within our area and DataBox and DataBox edge, DataBox edge and Azure stack are our edge portfolio platforms. So the customers can bring cloud based applications right into their on-prem environments. >> Peter you were making a point this morning about the cloud and it's distributed nature, can you make that point I'd love to hear Tad's reaction and response. >> So Tad we've been arguing in our research here Wikibon SiliconANGLE for quite some time. The common parlance the common concept of cloud, move everything to the center was wrong. We've been saying this for probably four or five years, and we believe very strongly that the cloud really is a technology for further distributing data, further distributing computing so that you can locate data approximate to the activity that it's going to support. But do so in a way that's coherent, comprehensive, and quite frankly confident. That's what's been missing in the industry for a long time so if you look at it that way, tell us a little bit about how that approach, that thinking informs what you're doing with Azure and specifically one of the other challenges is how does then data services impact that? So maybe we'll come to that in a second I'm sure. >> Great insight by the way, I agree that the assumption had been that everything is going to move to these large data centers in the cloud and I think that is happening for sure, but what we're seeing now is that there's a greater understanding of the longer term requirements for compute and that there are a bunch of workloads that need to be in proximity to where the data is being generated and to where it's being acted upon, and there are tons of scenarios here. Manufacturing is an example where we have one of our customers who's using our DataBox edge product to monitor an assembly line as parts come out of the assembly line our DataBox edge device is used with a camera system attached to it, AI inferencing to detect defects in the assembly line, and then stop the assembly line with very low latency where a round trip to the cloud and back to do all the AI inferencing and then do the command and control to stop the assembly line that would just be too much round trip time so in many different verticals we're seeing this awareness that there are very good reasons to have compute and storage on-prem, and so that's why we're investing in Azure stack and DataBox edge in particular. Now you asked well how does data factor in to that, because it turns out in a world of IoT and basically an infinite number of devices over time, more and more data is going to be generated. That data needs to be archived somewhere so that's where public cloud comes in and all the elasticity and the scale economies of cloud. But in terms of processing that data you need to be able to have a nice strong connection between what's going on in the public cloud and what's going on on-prem, so the killer scenario here is AI. Being able to grab data as it's being generated on-prem, write it into a product like DataBox edge, DataBox edge is a storage gateway device so you can map your cameras in the use case I mentioned or for other scenarios you can route the data directly into a file share, an NFS, blob, or SMB file share, drop into DataBox edge, then DataBox edge will automatically copy it over to the cloud, but allow for local processing to local applications as if it were, in fact it is local, running in a hot SSD NVME tier, and the beautiful thing about DataBox edge it includes an FPGA device to do AI inference offloading. So this is a very modern device that intersects a whole bunch oft things all on one very simple, self contained unit. Then the data flows into the cloud where it can be archived permanently in the cloud, and then AI models can be updated using the elastic scale of cloud compute, then those models can be brought back on-prem for enhanced processing over time. So you can sort of see this virtuous cycle happening over time where the edge is getting smarter and smarter and smarter. >> So that's what you mean kind of when you talked about the intelligent cloud and the intelligent edge, I was going to ask you you just kind of explained it and you can automate this, use machine intelligence to actually determine where the data should land and minimize human involvement. You talked about driving marginal cost of storing your data to zero, which we've always talked about doing that from the standpoint of reducing or even eliminating labor cost through automation, but you've also got some cool projects to reduce the cost for storing a bit. >> Yeah. >> Maybe you could talk about some of those projects a little bit. >> Thats right so, and that was mentioned in the keynote this morning and so our vision is that we want for our customers to be able to keep their artifacts that they store on our cloud platform for thousands of years and if you think about sort of the history of humanity that's not outside the question at all, in fact wouldn't it be great to have everything that was ever generated by humankind for the thousands of years of modern or human history. We'll be able to do that with technology that we're developing so we're investing in technology to store data virtually indefinitely on glass, as well as even in DNA, and by investing in those advance types of storage that is going to allow us to drive that marginal cost down to zero over time. >> Epigenetic storage systems. I want to come back to this notion of services though, and where the data's located. From our research what we see is we see as you said, data being proximate or being housed, approximator created and acted upon, but that increasingly businesses want the options to be able to replicate that, replicates a strong word it's a loaded word, but to be able to do something similar in some other location if the action is taking place in that location too. That's what Kubernetes is kind of about, and server list computing and some of these other things are about. But it's more than just the data, it's the data, it's the data services, it's the meditate associated with that , how do you foresee at Microsoft and what role might they play in this notion of a greater federation of data services that make possible a policy driven, back up, restore, data protection architecture that's really driven by what the business needs and where the actions taking place. Is that something you were seeing in a direction that you see it going? >> Yeah absolutely and so I'll talk conceptually about our strategy in that regard and where we see that going for customers, and then maybe we can come back to the Veeam partnership as well cause I think this is all connected up. Our approach to storage, our view is that you should be able to drop all your data assets into a single storage system like we talked about that supports all the different protocols that are required, can automatically tier from very hot storage all the way down to overtime glass and DNA, and we do all of that within one storage system and then the movement across those different vertical and horizontal slices that can all be done programmatically or via policy. So customers can make a choice in the near term about how they drop their data into the cloud but then they have a lot of flexibility to do all kinds of things with it over time, and then with that we layer on the Microsoft whole set of analytics services. So all of our data and analytics products, they layer on top of this disaggregated storage system so there can be late binding of the type of processing that's used including AI to reason over that data relatively to where and how and when the data entered into the platform. So that's sort of modularity, it really future proofs the use of data over the long haul we're really excited about that, and then those data assets can then be replicated to use your term to other regions around the globe as well using our backbone. So the customers can use our network, our network is a customers network, and then the way that docs into the partnership with Veeam is that just as I mentioned in the keynote this morning, data protection is a use case that is just fundamental to enterprise IT. We can make together with customers and with Veeam, we can make data protection better today using the cloud and with the work that Veeam has done in integrating with 0365, the integration from there into Azure storage and then over time customers can start down this path of something that feels sort of mundane and it's just been a part of daily life at enterprise IT, and then that becomes an entry point into our broader longterm data strategy in the cloud. >> But following up on this if we agree that data is not going to be entirely centralized, but it's going to be more broadly distributed and that there is a need for a common set of capabilities around data protection which is a very narrowly defined term today and is probably going to evolve over the next few years. >> I agree with that. >> We think you're going to have a federated model for data protection that provides for local autonomous data protection activities that is consistent with the needs of those local data assets, but under a common policy based framework that a company like Veeam's going to be able to provide. What do you think? >> So first of all a core principle of ours is that while we're creating these platforms for large data sets to move into Azure the most important thing is that customers own their own data. So there's this balance that has to be reached in terms of cloud scale and the federated nature of cloud and these common platforms and ways of approaching data, while simultaneously making sure that customers and users are in charge of their own data assets. So those are the principles that we'll use to guide our innovation moving forward and then I agree I think we're going to see a lot of innovation when it comes to taking advantage of cloud scale, cloud flexibility and economics but also empowering customers to advantage of these things but do it on their terms. I think the futures pretty bright in that regard. >> And the operative term there is their terms. Obviously Microsoft has always had a large on-prem install base and the software estate, and so you've embraced hybrid to use that term, with your strategies. You never sort of run away from it, you never said everything's going to go into the cloud, and that's now evolving to the edge. And so my question is what are the big gaps, not necessarily organizationally or process wise, but from a technology standpoint that the industry, generally in Microsoft specifically, have to fill to make that sort of federated vision a reality. >> I mean we're just at the early stages of all this for sure in fact as we talked about this morning, the notion of hybrid which started out with use cases like backup is rapidly evolving toward a more sort of modern enduring view. I think in a lot of ways hybrid was used as this kind of temporary stop along a path to cloud, and back to our earlier discussion for by some I guess, maybe there's a debate you all are having there. But what we're seeing is the emergence of edge is being and enduring location for compute and for data, and that's where the concept of intelligent edge comes in. So the model that I talked about earlier today is about extending on-prem data assets into the cloud, where as intelligent edge is taking cloud concepts and bringing them back to the edge, in an enduring way. So it's pretty neat stuff. >> And a big part of that is much of the data if not most of the data, the vast majority even might stay at the edge permanently and of course you want to run your models up in the cloud. >> That's right, at least for realtime processing. >> Right you just don't have the time to do the round trip. Alright Tad I'll give you the last word on Azure, direction, your relationship with Veeam, the conference, take your pick. >> Yeah well I thank you, thanks great to be here. As I mentioned earlier today the partnership with Veeam and then this conference in particular is great because I really love the idea of solving a very real and urgent problem for customers today, and then helping them along that journey to the cloud so that's one of the things that makes my job a great one. >> Well we talk about digital transformation all the time on theCUBE it's real, it's not just a buzz word, it can happen without the cloud but it's not all in the central location, it's extending now to other locations. >> It reflects your data assets. >> And where your data wants to live. So Tad thanks very much for coming to theCUBE it was great to have you. >> Thanks guys! >> Alright keep it right there everybody we'll be back with our next guest. This is VeeamOn 2019 and you're watching theCUBE. (upbeat music)

(bright orchestra music) >> Hi, I'm Peter Burris. Welcome to a special digital community event brought to you by Wikibon and theCUBE. Sponsored by Dell EMC. Today we're gonna spend quite some time talking about some of the trends in the relationship between hardware and AI. Specifically, we're seeing a number of companies doing some masterful work incorporating new technologies to simplify the infrastructure required to take full advantage of AI options and possibilities. Now at the end of this conversation, series of conversations, we're gonna run a CrowdChat, which will be your opportunity to engage your peers and engage thought leaders from Dell EMC and from Wikibon SiliconANGLE and have a broader conversation about what does it mean to be better at doing AI, more successful, improving time to value, et cetera. So wait 'til the very end for that. Alright, let's get it kicked off. Tom Burns is my first guest. And he is the Senior Vice President and General Manager of Networking Solutions at Dell EMC. Tom, it's great to have you back again. Welcome back to theCUBE. >> Thank you very much. It's great to be here. >> So Tom, this is gonna be a very, very exciting conversation we're gonna have. And it's gonna be about AI. So when you go out and talk to customers specifically, what are you hearing then as they describe their needs, their wants, their aspirations as they pertain to AI? >> Yeah, Pete, we've always been looking at this as this whole digital transformation. Some studies say that about 70% of enterprises today are looking how to take advantage of the digital transformation that's occurring. In fact, you're probably familiar with the Dell 2030 Survey, where we went out and talked to about 400 different companies of very different sizes. And they're looking at all these connected devices and edge computing and all the various changes that are happening from a technology standpoint, and certainly AI is one of the hottest areas. There's a report I think that was co-sponsored by ServiceNow. Over 62% of the CIO's and the Fortune 500 are looking at AI as far as managing their business in the future. And it's really about user outcomes. It's about how do they improve their businesses, their operations, their processes, their decision-making using the capability of compute coming down from a class perspective and the number of connected devices exploding bringing more and more data to their companies that they can use, analyze, and put to use cases that really make a difference in their business. >> But they make a difference in their business, but they're also often these use cases are a lot more complex. They're not, we have this little bromide that we use that the first 50 years of computing were about known process, unknown technology. We're now entering into an era where we know a little bit more about the technology. It's gonna be cloud-like, but we don't know what the processes are, because we're engaging directly with customers or partners in much more complex domains. That suggests a lot of things. How are customers dealing with that new level of complexity and where are they looking to simplify? >> You actually nailed it on the head. What's happening in our customers' environment is they're hiring these data scientists to really look at this data. And instead of looking at analyzing the data that's being connected, that's being analyzed and connected, they're spending more time worried about the infrastructure and building the components and looking about allocations of capacity in order to make these data scientists productive. And really, what we're trying to do is help them get through that particular hurdle. So you have the data scientists that are frustrated, because they're waiting for the IT Department to help them set up and scale the capacity that they need and infrastructure that they need in order to do their job. And then you got the IT Departments that are very frustrated, because they don't know how to manage all this infrastructure. So the question around do I go to the cloud? Do I remain on-prem? All of this is things that our companies, our customers, are continuing to be challenged with. >> Now, the ideal would be that you can have a cloud experience but have the data reside where it most naturally resides, given physics, given the cost, given bandwidth limitations, given regulatory regimes, et cetera. So how are you at Dell EMC helping to provide that sense of an experience based on what the work load is and where the data resides, as opposed to some other set of infrastructure choices? >> Well, that's the exciting part is that we're getting ready to announce a new solution called the Ready Solutions for AI. And what we've been doing is working with our customers over the last several years looking at these challenges around infrastructure, the data analytics, the connected devices, but giving them an experience that's real-time. Not letting them worry about how am I gonna set this up or management and so forth. So we're introducing the Ready Solutions for AI, which really focuses on three things. One is simplify the AI process. The second thing is to ensure that we give them deep and real-time analytics. And lastly, provide them the level of expertise that they need in a partner in order to make those tools useful and that information useful to their business. >> Now we want to not only provide AI to the business, but we also wanna start utilizing some of these advanced technologies directly into the infrastructure elements themselves to make it more simple. Is that a big feature of what the ready system for AI is? >> Absolutely, as I said, one of the key value propositions is around making AI simple. We are experts at building infrastructure. We have IP around compute, storage, networking, infinity band. The things that are capable of putting this infrastructure together. So we have tested that based upon customers' input, using traditional data analytics, libraries, and tool sets that the data scientists are gonna use, already pre-tested and certified. And then we're bringing this to them in a way which allows them through a service provisioning portal to basically set up and get to work much faster. The previous tools that were available out there, some from our competition. There were 15, 20, 25 different steps just to log on, just to get enough automation or enough capability in order to get the information that they need. The infrastructure allocated for this big data analytics through this service portal we've actually gotten it down to around five clicks with a very user-friendly GUI, no CLI required. And basically, again, interacting with the tools that they're used to immediately right out of the gate like in stage three. And then getting them to work in stage four and stage five so that they're not worried about the infrastructure, not worried about capacity, or is it gonna work. They basically are one, two, three, four clicks away, and they're up and working on the analytics that everyone wants them to work on. And heaven knows, these guys are not cheap. >> So you're talking about the data scientists. So presumably when you're saying they're not worried about all those things, they're also not worried about when the IT Department can get around to doing it. So this gives them the opportunity to self-provision. Have I got that right? >> That's correct. They don't need the IT to come in and set up the network to do the CLI for the provisioning, to make sure that there is enough VM's or workloads that are properly scheduled in order to give them the capacity that they need. They basically are set with a preset platform. Again, let's think about what Dell EMC is really working towards and that's becoming the infrastructure provider. We believe that the silos, the service storage, and networking are becoming eliminated, that companies want a platform that they can enable those capabilities. So you're absolutely right. The part about the simplicity or simplifying the AI process is really giving the data scientists the tools they need to provision the infrastructure they need very quickly. >> And so that means that the AI or rather the IT group can actually start acting more like a DevOps organization as opposed to a specialist in one or another technology. >> Correct, but we've also given them the capability by giving the usual automation and configuration tools that they're used to coming from some of our software partners, such as Cloudera. So in other words, you still want the IT Department involved, making sure that the infrastructure is meeting the requirements of the users. They're giving them what they want, but we're simplifying the tools and processes around the IT standpoint as well. >> Now we've done a lot of research into what's happening in the big data now is likely to happen in the AI world. And a lot of the problems that companies had with big data was they conflated or they confused the objectives, the outcome of a big data project, with just getting the infrastructure to work. And they walked away often, because they failed to get the infrastructure to work. So it sounds though what you're doing is you're trying to take the infrastructure out of the equation while at the same time going back to the customer and saying, "Wherever you want this job "to run or this workload to run, you're gonna get the same "experience irregardless." >> Correct, but we're gonna get an improved experience as well. Because of the products that we've put together in this particular solution, combined with our compute, our scale-out mass solution from a storage perspective, our partnership with Mellon Oshman infinity band or ethernet switch capability. We're gonna give them deeper insights and faster insights. The performance and scalability of this particular platform is tremendous. We believe in certain benchmark studies based upon the Reznik 50 benchmark. We've performed anywhere between two and half to almost three times faster than the competition. In addition from a storage standpoint, all of these workloads, all of the various characteristics that happen, you need a ton of IOPS. >> Yeah. >> And there's no one in the industry that has the IOP performance that we have with our All-Flash Isilon product. The capabilities that we have there we believe are somewhere around nine times the competition. Again, the scale-out performance while simplifying the overall architecture. >> Tom Burns, Senior Vice President of Networking and Solutions at Dell EMC. Thanks for being on theCUBE. >> Thank you very much. >> So there's some great points there about this new class of technology that dramatically simplifies how hardware can be deployed to improve the overall productivity and performance of AI solutions. But let's take a look at a product demo. >> Every week, more customers are telling us they know AI is possible for them, but they don't know where to start. Much of the recent progress in AI has been fueled by open source software. So it's tempting to think that do-it-yourself is the right way to go. Get some how-to references from the web and start building out your own distributive deep-learning platform. But it takes a lot of time and effort to create an enterprise-class AI platform with automation for deployment, management, and monitoring. There is no easy solution for that. Until now. Instead of putting the burden of do-it-yourself on your already limited staff, consider Dell EMC Ready Solutions for AI. Ready Solutions are complete software and hardware stacks pre-tested and validated with the most popular open source AI frameworks and libraries. Our professional services with proven AI expertise will have the solution up and running in days and ready for data scientists to start working in weeks. Data scientists will find the Dell EMC data science provisioning portal a welcome change for managing their own hardware and software environments. The portal lets data scientists acquire hardware resources from the cluster and customize their software environment with packages and libraries tested for compatibility with all dependencies. Data scientists choose between JupyterHub notebooks for interactive work, as well as terminal sessions for large-scale neural networks. These neural networks run across a high-performance cluster of power-edge servers with scalable Intel processors and scale-out Isilon storage that delivers up to 18 times the throughput of its closest all-flash competitor. IT pros will experience that AI is simplified as Bright Cluster Manager monitors your cluster for configuration drift down to the server BIOS using exclusive integration with Dell EMC's open manage API's for power-edge. This solution provides comprehensive metrics along with automatic health checks that keep an eye on the cluster and will alert you when there's trouble. Ready Solutions for AI are the only platforms that keep both data center professionals and data scientists productive and getting along. IT operations are simplified and that produces a more consistent experience for everyone. Data scientists get a customizable, high-performance, deep-learning service experience that can eliminate monthly charges spent on public cloud while keeping your data under your control. (upbeat guitar music) >> It's always great to see the product videos, but Tom Burns mentioned something earlier. He talked about the expansive expertise that Dell EMC has and bringing together advanced hardware and advanced software into more simple solutions that can liberate business value for customers, especially around AI. And so to really test that out, we sent Jeff Frick, who's the general manager and host of theCUBE down to the bowels of Dell EMC's operations in Austin, Texas. Jeff went and visited the Dell EMC HPC and AI Innovation Lab and met with Garima Kochhar, who's a tactical staff Senior Principal Engineer. Let's hear what Jeff learned. >> We're excited to have with us our next guest. She's Garima Kochhar. She's on the tactical staff and the Senior Principal Engineer at Dell EMC. Welcome. >> Thank you. >> From your perspective what kinda changing in the landscape from high-performance computing, which has been around for a long time, into more of the AI and machine learning and deep learning and stuff we hear about much more in business context today? >> High-performance computing has applicability across a broad range industries. So not just national labs and supercomputers, but commercial space as well. And our lab, we've done a lot of that work in the last several years. And then the deep learning algorithms, those have also been around for decades. But what we are finding right now is that the algorithms and the hardware, the technologies available, have hit that perfect point, along with industries' interest with the amount of data we have to make it more, what we would call, mainstream. >> So you can build an optimum solution, but ultimately you wanna build industry solutions. And then even subset of that, you invite customers in to optimize for what their particular workflow or their particular business case which may not match the perfect benchmark spec at all, right? >> That's exactly right. And so that's the reason this lab is set up for customer access, because we do the standard benchmarking. But you want to see what is my experience with this, how does my code work? And it allows us to learn from our customers, of course. And it allows them to get comfortable with their technologies, to work directly with the engineers and the experts so that we can be their true partners and trusted advisors and help them advance their research, their science, their business goals. >> Right. So you guys built the whole rack out, right? Not just the fun shiny new toys. >> Yeah, you're right. So typically, when something fails, it fails spectacularly. Right, so I'm you've heard horror stories where there was equipment on the dock and it wouldn't fit in the elevator or things like that, right? So there are lots of other teams that handle, of course Dell's really good at this, the logistics piece of it, but even within the lab. When you walk around the lab, you'll see our racks are set up with power meters. So we do power measurements. Whatever best practices in tuning we come up with, we feed that into our factories. So if you buy a solution, say targeted for HPC, it will come with different BIOS tuning options than a regular, say Oracle, database workload. We have this integration into our software deployment methods. So when you have racks and racks of equipment or one rack of equipment or maybe even three servers, and you're doing an installation, all the pieces are baked-in already and everything is easy, seamless, easy to operate. So our idea is... The more that we can do in building integrated solutions that are simple to use and performant, the less time our customers and their technical computing and IT Departments have to spend worrying about the equipment and they can focus on their unique and specific use case. >> Right, you guys have a services arm as well. >> Well, we're an engineering lab, which is why it's really messy, right? Like if you look at the racks, if you look at the work we do, we're a working lab. We're an engineering lab. We're a product development lab. And of course, we have a support arm. We have a services arm. And sometimes we're working with new technologies. We conduct training in the lab for our services and support people, but we're an engineering organization. And so when customers come into the lab and work with us, they work with it from an engineering point of view not from a pre-sales point of view or a services point of view. >> Right, kinda what's the benefit of having the experience in this broader set of applications as you can apply it to some of the newer, more exciting things around AI, machine learning, deep learning? >> Right, so the fact that we are a shared lab, right? Like the bulk of this lab is High Performance Computing and AI, but there's lots of other technologies and solutions we work on over here. And there's other labs in the building that we have colleagues in as well. The first thing is that the technology building blocks for several of these solutions are similar, right? So when you're looking at storage arrays, when you're looking at Linux kernels, when you're looking at network cards, or solid state drives, or NVMe, several of the building block technolgies are similar. And so when we find interoperability issues, which you would think that there would never be any problems, you throw all these things together, they always work like-- >> (laughs) Of course (laughs). >> Right, so when you sometimes, rarely find an interoperability issue, that issue can affect multiple solutions. And so we share those best practices, because we engineers sit next to each other and we discuss things with each other. We're part of the larger organization. Similarly, when you find tuning options and nuances and parameters for performance or for energy efficiency, those also apply across different domains. So while you might think of Oracle as something that it's been done for years, with every iteration of technology there's new learning and that applies broadly across anybody using enterprise infrastructure. >> Right, what gets you excited? What are some of the things that you see, like, "I'm so excited that we can now apply "this horsepower to some of these problems out there?" >> Right, so that's a really good point, right? Because most of the time when you're trying to describe what you do, it's hard to make everybody understand. Well, not what you're doing, right? But sometimes with deep technology it's hard to explain what's the actual value of this. And so a lot of work we're doing in terms of excess scale, it's to grow like the... Human body of knowledge forward, to grow the science happening in each country moving that forward. And that's kind of, at the higher end when you talk about national labs and defense and everybody understands that needs to be done. But when you find that your social media is doing some face recognition, everybody experiences that and everybody sees that. And when you're trying to describe the, we're all talking about driverless cars or we're all talking about, "Oh, it took me so long, "because I had this insurance claim and then I had "to get an appointment with the appraisor "and they had to come in." I mean, those are actual real-world use cases where some of these technologies are going to apply. So even industries where you didn't think of them as being leading-edge on the technical forefront in terms of IT infrastructure and digital transformation, in every one of these places you're going to have an impact of what you do. >> Right. >> Whether it's drug discovery, right? Or whether it's next-generation gene sequencing or whether it's designing the next car, like pick your favorite car, or when you're flying in an aircraft the engineers who were designing the engine and the blades and the rotors for that craft were using technologies that you worked with. And so now it's everywhere, everywhere you go. We talked about 5G and IoT and edge computing. >> Right. >> I mean, we all work on this collectively. >> Right. >> So it's our world. >> Right. Okay, so last question before I let you go. Just being, having the resources to bear, in terms of being in your position, to do the work when you've got the massive resources now behind you. You have Dell, the merger of EMC, all the subset brands, Isilon, so many brands. How does that help you do your job better? What does that let you do here in this lab that probably a lot of other people can't do? >> Yeah, exactly. So when you're building complex solutions, there's no one company that makes every single piece of it, but the tighter that things work together the better that they work together. And that's directly through all the technologies that we have in the Dell technologies umbrella and with Dell EMC. And that's because of our super close relationships with our partners that allows us to build these solutions that are painless for our customers and our users. And so that's the advantage we bring. >> Alright. >> This lab and our company. >> Alright, Garima. Well, thank you for taking a few minutes. Your passion shines through. (laughs) >> Thank you. >> I really liked hearing about what Dell EMC's doing in their innovation labs down at Austin, Texas, but it all comes together for the customer. And so the last segment that we wanna bring you here is a great segment. Nick Curcuru, who's the Vice President of Big Data Analytics at Mastercard is here to talk about how some of these technologies are coming together to speed value and realize the potential of AI at Mastercard. Nick, welcome to theCUBE. >> Thank you for letting me be here. >> So Mastercard, tell us a little bit about what's going on at Mastercard. >> There's a lot that's going on with Mastercard, but I think the most exciting things that we're doing out of Mastercard right now is with artificial intelligence and how we're bringing the ability for artificial intelligence to really allow a seamless transition when someone's actually doing a transaction and also bringing a level of security to our customers and our banks and the people that use Mastercards. >> So AI to improve engagement, provide a better experience, but that's a pretty broad range of things. What specifically kinds of, when you think about how AI can be applied, what are you looking to? Especially early on. >> Well, let's actually take a look at our core business, which is being able to make sure that we can secure a payment, right? So at this particular point, people are used to, we're applying AI to biometrics. But not just a fingerprint or a facial recognition, but actually how you interact with your device. So you think of like the Internet of Things and you're sitting back saying, "I'm using, "I'm swiping my device, my mobile device, "or how I interact with a keyboard." Those are all key signatures. And we, with our company, new data that we've just acquired are taking that capability to create a profile and make that a part of your signature. So it's not just beyond a fingerprint. It's not just beyond a facial. It's actually how you're interacting so that we know it's you. >> So there's a lot of different potential sources of information that you can utilize, but AI is still a relatively young technology and practice. And one of the big issues for a lot of our clients is how do you get time to value? So take us through, if you would, a little bit about some of the challenges that Mastercard and anybody would face to try to get to that time to value. >> Well, what you're really seeing is looking for actually a good partner to be with when you're doing artificial intelligence, because again, at that particular point, you try to get to scale. For us, it's always about scale. How can we roll this across 220 countries? We're 165 million transactions per hour, right? So what we're looking for is a partner who also has that ability to scale. A partner who has the global presence, who's learning. So that's the first step. That's gonna help you with your time to value. The other part is actually sitting back and actually using those particular partners to bring their expertise that they're learning to combine with yours. It's no longer just silos. So when we talk about artificial intelligence, how can we be learning from each other? Those open source systems that are out there, how do we learn from that community? It's that community that allows you to get there. Again, those that are trying to do it on their own, trying to do it by themselves, they're not gonna get to the point where they need to be. In other words, in a six month time to value it's gonna take them years. We're trying to accelerate that, you say, "How can we get out of those algorithms operating for us "the way we need them to provide the experiences "that people want quickly." And that's with good partners. >> 165 million transactions per hour is only likely to go up over the course of the next few years. That creates an operational challenge. AI is associated with a probabilistic set of behaviors as opposed to categorical. Little bit more difficult to test, little bit more difficult to verify, how is the introduction of some of these AI technologies impacting the way you think about operations at Mastercard? >> Well, for the operations, it's actually when you take a look there's three components, right? There's right there on the edge. So when someone's interacting and actually doing the transaction, and then we'll look at it as we have a core. So that core sits there, right? Basically, that's where you're learning, right? And then there's actually, what we call, the deep learning component of it. So for us, it's how can we move what we need to have in the core and what we need to have on the edge? So the question for us always is we want that algorithm to be smart. So what three to four things do we need that algorithm to be looking for within that artificial intelligence needs to know that it then goes back into the core and retrieves something, whether that's your fingerprint, your biometrics, how you're interacting with that machine, to say, "Yes, that's you. "Yes, we want that transaction to go through." Or, "No, stop it before it even begins." It's that interaction and operational basis that we're always have a dynamic tension with, but it's how we get from the edge to the core. And it's understanding what we need it to do. So we're breaking apart what we have to have that intelligence to be able to create a decision for us. So that's how we're trying to manage it, as well as of course, the hardware that goes with it and the tools that we need in order to make that happen. >> When we get on the hardware just a little bit, so that historically different applications put pressure on different components within a stack. One of the observations that we've made is that the transition from spinning disk to flash allows companies like Mastercard to think about just persisting data to actually delivering data. >> Yeah. >> Much more rapidly. How does some of the, how does these AI technologies, what kinda new pressures do they put on storage? >> Well, they put a tremendous pressure, because that's actually again, the next tension or dynamics that you have to play with. So what do you wanna have on disk? What do you need flash to do? Again, if you look at some people, everyone's like, "Oh, flash will take over everything." It's like no, flash has, there's a reason for it to exist, and understanding what that reason is and understanding, "Hey, I need that to be able to do this "in sub-seconds, nanoseconds," I've heard the term before. That's what you're asking flash to do. When you want deep learning, that, I want it on disk. I want to be taking all those millions of billions of transactions that we're gonna see and learn from them. All the ways that people will be trying to attack me, right? The bad guys, how am I learning from everything that I'm having that can sit there on disk and let it continue to run, that's the deep learning. The flash is when I wanna create a seamless transaction with a customer, or a consumer, or from a business to business. I need to have that decision now. I need to know it is you who is trying to swipe or purchase something with my mobile device or through the, basically through the Internet. Or how am I actually even swiping or inserting, tipping my card in that particular machine at a merchant. That's we're looking at how we use flash. >> So you're looking at perhaps using older technologies or different classes technologies for some of the training elements, but really moving to flash for the interfacing piece where you gotta deliver the real-time effort right now. >> And that's the experience. And that's what you're looking for. And that's you're looking, you wanna be able to make sure you're making those distinctions. 'Cause again there's no longer one or the other. It's how they interact. And again, when you look at your partners, it's the question now is how are they interacting? Am I actually, has this been done at scale somewhere else? Can you help me understand how I need to deploy this so that I can reduce my time to value, which is very, very important to create that seamless, frictionless transaction we want our consumers to have. >> So Nick, you talked about how you wanna work with companies that demonstrate that they have expertise, because you can't do it on your own. Companies that are capable of providing the scale that you need to provide. So just as we talk about how AI is placing pressure on different parts of the technology stack, it's got also to be putting pressure on the traditional relationships you have with technology suppliers. What are you looking for in suppliers as you think about these new classes of applications? >> Well, the part is you're looking at, for us it's do you have that scale that we're looking at? Have you done this before, that global scale? Again, in many cases you can have five guys in a garage that can do great things, but where has it been tested? When we say tested, it's not just, "Hey, we did this "in a pilot." We're talking it's gotta be robust. So that's one thing that you're looking for. You're looking for also a partner we can bring, for us, additional information that we don't have ourselves, right? In many cases, when you look at that partner they're gonna bring something that they're almost like they are an adjunct part of your team. They are your bench strength. That's what we're looking for when we look at it. What expertise do you have that we may not? What are you seeing, especially on the technology front, that we're not privy to? What are those different chips that are coming out, the new ways we should be handling the storage, the new ways the applications are interacting with that? We want to know from you, because again, everyone's, there's a talent, competition for talent, and we're looking for a partner who has that talent and will bring it to us so that we don't have to search it. >> At scale. >> Yeah, especially at scale. >> Nick Curcuro, Mastercard. Thanks for being on theCUBE. >> Thank you for having me. >> So there you have a great example of what leading companies or what a leading company is doing to try to take full advantage of the possibilities of AI by utilizing infrastructure that gets the job done simpler, faster, and better. So let's imagine for a second how it might affect your life. Well, here's your opportunity. We're now gonna move into the CrowdChat part of the event, and this is your chance to ask peers questions, provide your insights, tell your war stories. Ultimately, to interact with thought leaders about what it means to get ready for AI. Once again, I'm Peter Burris, thank you for watching. Now let's jump into the CrowdChat.

>> Live from San Francisco, it's theCUBE. Covering Google Cloud Next, 2018. Brought to you by Google Cloud, and its ecosystem partners. >> Hi this is Peter Burris from Wikibon SiliconANGLE, stepping in for John Furrier and Dave Vellante. Continuing our CUBE coverage here at Google Next 2018 from Moscone South, an impressive array of talent, and that includes my next guest, Jon Thomas. Jon is the director of Product Management for Digital Services Management Cloud Services at BMC Software. Welcome to theCUBE, Jon. >> Thank you for having me. >> You know this is a really an interesting topic for me, because as an old infrastructure hack, someone who's been in IT operations in a couple different worlds, as well as being an Industry Analyst in infrastructure, it's important that we not lose sight of the fact that there's a lot of expertise out there regarding how we run complex systems, that the Cloud companies are demonstrating, but the business as an adopt Cloud services, nonetheless, has to sustain. So talk to us a little bit about what BMC is doing to try to bring some of that knowledge over 30 years of working in the data center, and apply it for businesses as they become better Cloud citizens. >> Yeah, thank you for asking. So, BMC is worked with some of the largest IT organizations. In fact, over 80% of the Fortune 500 use BMC software to help them manage IT. At this point when I go out and talk to those customers, they're all on a Cloud journey. And the really exciting thing is that the conversations stop becoming if we're going to use public cloud, but it's going to be a how to use public cloud, and really at this point, it's about how do we use it in a way that we can scale that out, scale that innovation out within the organization. And we're seeing that those organizations are actually, in a lot of times, they're reorganizing in order to really facilitate that innovation. And so BMC, like you said, is taking that expertise that we've had and helping them manage the data center asset, and to apply the same learnings that we had with a new spin to actually work with the public cloud as they start to adopt public cloud. >> So, give us an example of a few of the more modern approaches in the cloud that are being employed by BMC to ensure that you get the type of control, manageability, and automation, that BMC customers have gotten used to on-premise. >> Yeah, I mean, one great example is for a very long time BMC has had the BladeLogic Product Line, and we've helped customers to make sure that they can harden their servers and their network devices and their databases on premise. Now as they move the public cloud, there's a big question, what does it mean to even harden a public cloud configuration? And a lot of organizations are trying to understand what is their responsibility in that shared responsibility model. And so one thing that we've done is take that knowledge about hardening assets and apply it to public cloud resources, and also just in the way that we do it. You know if you think about traditionally, IT's gotten a bad rap as being Captain No, and now with the public cloud and the ability of application teams that go directly to the public cloud, IT has to just change the way that it's providing its services to their consumers, their internal consumers. So, now instead of putting a big block in the process, instead we're enabling IT to provide services. Because application teams, they don't want to be insecure. They're not out there nefariously trying to break things and leave data out there. >> They may sometimes not know when they're not being secure. >> Exactly, and so IT's new and changing role is about how do you provide services and consultation to your business to be a facilitator. And so with the products that we're offering now, we think we've taken that history, and that legacy, and our heritage, and hardening in that data center, and then applying that same model to the public cloud, but in a model that fits for how you leverage public cloud resources. >> I presume that a customer that decides to go with, say, Google or Google Cloud-- >> Yeah. >> Or decides to go with Amazon or AWS, is going to use your product and exploit the best or the capabilities of both clouds. as they are uniquely provided, is that accurate? >> Absolutely. Yeah when we talk to our customers, very few of them have the luxury of only using one public cloud vendor. Whether it's based off of decisions from application teams or even acquisitions, a lot of times they have to manage across multiple clouds on top of all of that on-premise infrastructure that they still have to manage. And so we do, we try to help to simplify that complexity for them by bringing it all together into one visibility, into what is the state of the risk of their cloud services. >> But to employ, or to be able to exploit the best that each of those platforms has, while at the same time from an overall manageability standpoint, being able to provide a common view to those different resources, have I got that right? >> Exactly, exactly. >> Now, how does that tie back in to the data center? One of the things that we've seen over the course of the last week is something that Wikibon has been calling it your private cloud. The idea that there are going to be circumstances when an enterprise's data requires that you move the cloud to the data, as opposed to moving the data to the cloud. >> Yeah. >> And there's no doubt there's going to be a lot of data that's going to, for any number of physical, legal, election property control reasons, will be on-premise, or within the confines of the business. So, how do you envision that the practices and tooling and automation regimes that are currently on-premise, and what we're doing now on the cloud are going to start together, come together over the next few years. So we can put data where it naturally should be. >> Yeah, I'm glad you asked that. It's just, some of the tools and some of the reasons that we're able to help our customers on their cloud journey is because we have that knowledge of their on-premise infrastructure. So being able to do things like discover what they have on-prem, and understand the dependencies, helps us to be really uniquely positioned to help them with cloud migration. And migration might not be just from on-premise to cloud, it could be from cloud back to on-premise, it could be between clouds or even between different regions based off of the need of the business at that time. >> So that's migration, what about overall classes of integration that might allow a DevOps person, for example, to be able to look at an application that spans multiple places, or multiple locations, but still be able to administrate as a coherent resource? >> Yeah, so in that same discovery capabilities that we have, we've extended those out to the public cloud as well, so we can discover on-premise, in the public cloud, so that whenever you need it, you can go to a single place and understand what's the state of your infrastructure, no matter where it exists. >> So what do you think of Google Next? Are you having good conversations with customers? Do you see Google Cloud coming on more? And how does BMC going to make it easier for everybody? >> Absolutely, we're really excited by the progress that Google Cloud is making and we're seeing a lot of adoption in particular certain segments of our businesses are really, really fond of Google Cloud. And what we're doing is trying to make sure that from the tools that we have that we're integrating into Google Cloud, so that it gives our customers that choice to pick what's the right cloud for them at the right time and for the right circumstances, and then still get that simplification by putting it all into the same tool where they can get in the single view. >> Now every company has a challenge as they migrate to the cloud, both from a standpoint of where the applications are being developed, where the applications are being run. But also, strategically, the cloud has a pretty significant impact. BMC seems to be one of those companies that's able to partly, I would presume in large measure, because of 30 years of really working with the customers is having a relatively facile time enacting that transformation. Give us a sense, especially in the Product Management Committee, thinking about how BMC's going to provide value in the cloud. What is BMC think the future of cloud and cloud management looks like? >> Well, we see it's evolving. Right now a lot of organizations are creating centralized Cloud Centers of Excellence just to figure out how, like I said, to scale out best practices within their organization. And right now, those teams really have a couple of areas of focus. Number one is the migration, so figuring out how to do their migration projects. Number two is how do we do security of those resources, so being able to understand what's their risk posture, and set up some governance around that, we say a cloud with guard rails. And the last thing is last year was really a time of customers coming to us because they had 10 ex-million dollar surprise builds. And so one of the things that we want to do to help facilitate the use of public cloud, because we believe that it can be as safe or safer, as efficient or more efficient, is to take away those concerns that would keep a company from feeling like they're able to migrate more workloads to the cloud, or build more applications to the cloud. >> So, Jon, I'm going to do kind of a lightning round here. >> Alright. >> I'm going to put something in front of you and I want you to respond as best as you can from a standpoint of how the value proposition's going to play out. Let's start with speed to value. How does the tooling that you're providing improve speed to value, especially to those companies that are looking for greater flexibility than strategies? >> Well, speed to value, one of the biggest things is in order to have real data up in the public cloud, organizations just need to understand what is their risk posture, make sure that those services that they're creating are hardened. And so with our true side cloud security product, we're able to give them that visibility so that they can get the check mark to move quickly to go to market with the solutions they're creating in the public cloud. >> The second thing, modern application development, containers, Kubernetes, those types of things. >> Yeah, absolutely. In the same platform that we support the public cloud, it's really all new modern innovations. So we also support Kubernetes, and Docker as well, so you bring that all into the same platform and the same visibility. >> Big data, advanced analytics, and AI. >> So as companies want to leverage AI, that's one of the examples where they're trying to figure out as they do it, what are their costs going to do? New services, we've heard stories where people turn on a brand new service and then find out that that service costs them a lot of money. And so with some of our expense management for a cloud tools, we're able to do baselines of their spending and start to forecast out, identify when you have something that is going to come and surprise you later on. >> Can't talk about cloud without talking security. >> Absolutely. Yeah, so through true side cloud security, we're helping organizations to not only identify where they might have a risky configurations that might leave them open to data breaches, but also built in automated remediation so that you can take action, and to bring yourself to a very safe place. >> One of the big challenges of the cloud on a global basis is privacy, trust, local. How does GDPR fit into this mix, for example? >> Well, one of the requirements that GDPR is really to have state of the art, that's what they say. And so you have to have state of the art controls in place. So with our solution, especially just like cloud security, that allows organizations to be able to not only have state of the art prosthesis in place and tools to access their risk, but to also prove it. And I think that's a big aspect. >> IoT. >> IoT is also something that's coming up a lot in our customer base, so being able to manage those same cloud resources in terms of the cost of the resources and the security as well. >> Serverless? >> Yeah, Serverless. In fact, internally when we developed our application, we used a lot of Serverless. So we love cloud native artifacts, we believe that they really can help application teams to develop applications quicker. And so one of the things that we provide is the ability to look at hardening of applications built on cloud native resources. >> Now you've already mentioned cost, but what's it cost to? How do you use the tooling to get the most out of your expenditures in the cloud? >> So, first off we give you the visibility in to what you're spending, and then run that through machine learning to search and do forecasting to help you identify when you're going to overrun your cost, but the second part of that is to actually look at optimization. So we're examining out your accounts to understand, do you have idle VM's that are out there? Do you have ones that were over revision? Different ways that we can help bring down your cost to make it sure that your maximizing your cost in the public cloud. >> Okay, so, the next two years at BMC, going to continue to drive its affinity with these new cloud-based workloads. What are you most excited about as you look out at working with customers over the next couple of years? >> Really looking at the adoption going bigger. And, right now, and they talked about it in The Keynote this morning, the number of workloads in the public cloud, it is still relatively small to what they have on-premise. And so we believe that as organizations start to do hardware refreshes, starts to do data center consolidation projects, they're going to start looking into public cloud more and more, and we're going to see more and more resources making their way to the public cloud, and we find that very exciting. >> A wide opportunity for thought leadership, isn't there Jon? >> Absolutely. >> Alright, Jon Thomas, who's been crucial to driving a lot of the product management efforts around some of BMC's cloud management software. Thanks very much for being on theCUBE, Jon. >> Thanks for having me. >> Okay, we'll be right back with more coverage from Google Next, thanks for watching.

>> Narrator: From Berlin, Germany, it's theCUBE (relaxing music) covering DataWorks Summit Europe 2018. (relaxing music) Brought to you by Hortonworks. (quieting music) >> Well, hello and welcome to theCUBE. We're here at DataWorks Summit 2018 in Berlin, Germany, and it's been a great show. Who we have now is we have IBM. Specifically we have Dave McDonnell of IBM, and we're going to be talkin' with him for the next 10 minutes or so about... Dave, you explain. You are in storage for IBM, and IBM of course is a partner of Hortonworks who are of course the host of this show. So Dave, have you been introduced, give us your capacity or roll at IBM. Discuss the partnership of Hortonworks, and really what's your perspective on the market for storage systems for Big Data right now and going forward? And what kind of work loads and what kind of requirements are customers coming to you with for storage systems now? >> Okay, sure, so I lead alliances for the storage business unit, and Hortonworks, we actually partner with Hortonworks not just in our storage business unit but also with our analytics counterparts, our power counterparts, and we're in discussions with many others, right? Our partner organization services and so forth. So the nature of our relationship is quite broad compared to many of our others. We're working with them in the analytics space, so these are a lot of these Big Data Data Lakes, BDDNA a lot of people will use as an acronym. These are the types of work loads that customers are using us both for. >> Mm-hmm. >> And it's not new anymore, you know, by now they're well past their first half dozen applications. We've got customers running hundreds of applications. These are production applications now, so it's all about, "How can I be more efficient? "How can I grow this? "How can I get the best performance and scalability "and ease of management to deploy these "in a way that's manageable?" 'cause if I have 400 production applications, that's not off in any corner anymore. So that's how I'd describe it in a nutshell. >> One of the trends that we're seeing at Wikibon, of course I'm the lead analyst for Big Data Analytics at Wikibon under SiliconANGLE Media, we're seeing a trend in the marketplace towards I wouldn't call them appliances, but what I would call them is workload optimized hardware software platforms so they can combine storage with compute and are optimized for AI and machine learning and so forth. Is that something that you're hearing from customers, that they require those built-out, AI optimized storage systems, or is that far in the future or? Give me a sense for whether IBM is doing anything in that area and whether that's on your horizon. >> If you were to define all of IBM in five words or less, you would say "artificial intelligence and cloud computing," so this is something' >> Yeah. that gets a lot of thought in Mindshare. So absolutely we hear about it a lot. It's a very broad market with a lot of diverse requirements. So we hear people asking for the Converged infrastructure, for Appliance solutions. There's of course Hyper Converged. We actually have, either directly or with partners, answers to all of those. Now we do think one of the things that customers want to do is they're going to scale and grow in these environments is to take a software-defined strategy so they're not limited, they're not limited by hardware blocks. You know, they don't want to have to buy processing power and spend all that money on it when really all they need is more data. >> Yeah. >> There's pros and cons to the different (mumbles). >> You have power AI systems, I know that, so that's where they're probably heading, yeah. >> Yes, yes, yes. So of course, we have packages that we've modeled in AI. They feed off of some of the Hortonworks data lakes that we're building. Of course we see a lot of people putting these on new pieces of infrastructure because they don't want to put this on their production applications, so they're extracting data from maybe a Hortonworks data lake number one, Hortonworks data lake number two, some of the EDWs, some external data, and putting that into the AI infrastructure. >> As customers move their cloud infrastructures towards more edge facing environments, or edge applications, how are storage requirements change or evolving in terms of in the move to edge computing. Can you give us a sense for any sort of trends you're seeing in that area? >> Well, if we're going to the world of AI and cognitive applications, all that data that I mighta thrown in the cloud five years ago I now, I'm educated enough 'cause I've been paying bills for a few years on just how expensive it is, and if I'm going to be bringing that data back, some of which I don't even know I'm going to be bringing back, it gets extremely expensive. So we see a pendulum shift coming back where now a lot of data is going to be on host, ah sorry, on premise, but it's not going to stay there. They need the flexibility to move it here, there, or everywhere. So if it's going to come back, how can we bring customers some of that flexibility that they liked about the cloud, the speed, the ease of deployment, even a consumption based model? These are very big changes on a traditional storage manufacturer like ourselves, right? So that's requiring a lot of development in software, it's requiring a lot of development in our business model, and one of the biggest thing you hear us talk about this year is IBM Cloud Private, which does exactly that, >> Right. and it gives them somethin' they can work with that's flexible, it's agile, and allows you to take containerized based applications and move them back and forth as you please. >> Yeah. So containerized applications. So if you can define it for our audience, what is a containerized application? You talk about Docker and orchestrate it through Kubernetes and so forth. So you mentioned Cloud Private. Can you bring us up to speed on what exactly Cloud Private is and in terms of the storage requirements or storage architecture within that portfolio? >> Oh yes, absolutely. So this is a set of infrastructure that's optimized for on-premise deployment that gives you multi-cloud access, not just IBM Cloud, Amazon Web Services, Microsoft Azure, et cetera, and then it also gives you multiple architectural choices basically wrapped by software to allow you to move those containers around and put them where you want them at the right time at the right place given the business requirement at that hour. >> Now is the data storager persisted in the container itself? I know that's fairly difficult to do in a Docker environment. How do ya handle persistence of data for containerized applications within your architecture? >> Okay, some of those are going to be application specific. It's the question of designing the right data management layer depending on the application. So we have software intelligence, some of it from open source, some of which we add on top of open source to bring some of the enterprise resilience and performance needed. And of course, you have to be very careful if the biggest trend in the world is unstructured data. Well, okay fine, it's a lot of sensor data. That's still fairly easy to move around. But once we get into things like medical images, lots of video, you know, HD video, 4K video, those are the things which you have to give a lot of thought to how to do that. And that's why we have lots of new partners that we work with the help us with edge cloud, which gives that on premise-like performance in really a cloud-like set up. >> Here's a question out of left field, and you may not have the answer, but I would like to hear your thoughts on this. How has Blockchain, and IBM's been making significant investments in blockchain technology database technology, how is blockchain changing the face of the storage industry in terms of customers' requirements for a storage systems to manage data in distributed blockchains? Is that something you're hearing coming from customers as a requirement? I'm just tryin' to get a sense for whether that's, you know, is it moving customers towards more flash, towards more distributed edge-oriented or edge deployed storage systems? >> Okay, so yes, yes, and yes. >> Okay. So all of a sudden, if you're doing things like a blockchain application, things become even more important than they are today. >> Yeah. >> Okay, so you can't lose a transaction. You can't have a storage going down. So there's a lot more care and thought into the resiliency of the infrastructure. If I'm, you know, buying a diamond from you, I can't accept the excuse that my $100,000 diamond, maybe that's a little optimistic, my $10,000 diamond or yours, you know, the transaction's corrupted because the data's not proper. >> Right. >> Or if I want my privacy, I need to be assured that there's good data governance around that transaction, and that that will be protected for a good 10, 20, and 30 years. So it's elevating the importance of all the infrastructure to a whole different level. >> Switching our focus slightly, so we're here at DataWorks Summit in Berlin. Where are the largest growth markets right now for cloud storage systems? Is it Apache, is it the North America, or where are the growth markets in terms of regions, in terms of vertical industries right now in the marketplace for enterprise grade storage systems for big data in the cloud? >> That's a great question, 'cause we certainly have these conversations globally. I'd say the place where we're seeing the most activity would be the Americas, we see it in China. We have a lot of interesting engagements and people reaching out to us. I would say by market, you can also point to financial services in more than those two regions. Financial services, healthcare, retail, these are probably the top verticals. I think it's probably safe to assume, and we can the federal governments also have a lot of stringent requirements and, you know, requirements, new applications around the space as well. >> Right. GDPR, how is that impacting your customers' storage requirements. The requirement for GDPR compliance, is that moving the needle in terms of their requirement for consolidated storage of the data that they need to maintain? I mean obviously there's a security, but there's just the sheer amount of, there's a leading to consolidation or centralization of storage, of customer data, that would seem to make it easier to control and monitor usage of the data. Is it making a difference at all? >> It's making a big difference. Not many people encrypt data today, so there's a whole new level of interest in encryption at many different levels, data at rest, data in motion. There's new levels of focus and attention on performance, on the ability for customers to get their arms around disparate islands of data, because now GDPR is not only a legal requirement that requires you to be able to have it, but you've also got timelines which you're expected to act on a request from a customer to have your data removed. And most of those will have a baseline of 30 days. So you can't fool around now. It's not just a nice to have. It's an actual core part of a business requirement that if you don't have a good strategy for, you could be spending tens of millions of dollars in liability if you're not ready for it. >> Well Dave, thank you very much. We're at the end of our time. This has been Dave McDonnell of IBM talking about system storage and of course a big Hortonworks partner. We are here on day two of the DataWorks Summit, and I'm James Kobielus of Wikibon SiliconANGLE Media, and have a good day. (upbeat music)

>> Narrator: From Berlin, Germany, it's theCUBE. Covering Dataworks Summit Europe 2018. Brought to you by Hortonworks. >> Hello, welcome to theCUBE. We're here at Dataworks Summit 2018 in Berlin, Germany. I'm James Kobielus. I'm the lead analyst for Big Data Analytics, within the Wikibon team of SiliconAngle Media. Our guest is John Kreisa. He's the VP for Marketing at Hortonworks, of course, the host company of Dataworks Summit. John, it's great to have you. >> Thank you Jim, it's great to be here. >> We go long back, so you know it's always great to reconnect with you guys at Hortonworks. You guys are on a roll, it's been seven years I think since you guys were founded. I remember the founding of Hortonworks. I remember when it splashed in the Wall Street Journal. It was like oh wow, this big data thing, this Hadoop thing is actually, it's a market, it's a segment and you guys have built it. You know, you and your competitors, your partners, your ecosystem continues to grow. You guys went IPO a few years ago. Your latest numbers are pretty good. You're continuing to grow in revenues, in customer acquisitions, your deal sizes are growing. So Hortonworks remains on a roll. So, I'd like you to talk right now, John, and give us a sense of where Hortonworks is at in terms of engaging with the marketplace, in terms of trends that you're seeing, in terms of how you're addressing them. But talk about first of all the Dataworks Summit. How many attendees do you have from how many countries? Just give us sort of the layout of this show. >> I don't have all of the final counts yet. >> This is year six of the show? >> This is year six in Europe, absolutely, thank you. So it's great, we've moved it around different locations. Great venue, great host city here in Berlin. Super excited about it, I know we have representatives from more than 51 countries. If you think about that, drawing from a really broad set of countries, well beyond, as you know, because you've interviewed some of the folks beyond just Europe. We've had them from South America, U.S., Africa, and Asia as well, so really a broad swath of the open-source and big data community, which is great. The final attendance is going to be 1,250 to 1,300 range. The final numbers, but a great sized conference. The energy level's been really great, the sessions have been, you know, oversubscribed, standing room only in many of the popular sessions. So the community's strong, I think that's the thing that we really see here and that we're really continuing to invest in. It's something that Hortonworks was founded around. You referenced the founding, and driving the community forward and investing is something that has been part of our mantra since we started and it remains that way today. >> Right. So first of all what is Hortonworks? Now how does Hortonworks position itself? Clearly Hadoop is your foundation, but you, just like Cloudera, MapR, you guys have all continued to evolve to address a broader range of use-cases with a deeper stack of technology with fairly extensive partner ecosystems. So what kind of a beast is Hortonworks? It's an elephant, but what kind of an elephant is it? >> We're an elephant or riding on the elephant I'd say, so we're a global data management company. That's what we're helping organizations do. Really the end-to-end lifecycle of their data, helping them manage it regardless of where it is, whether it's on-premise or in the cloud, really through hybrid data architectures. That's really how we've seen the market evolve is, we started off in terms of our strategy with the platform based on Hadoop, as you said, to store, process, and analyze data at scale. The kind of fundamental use-case for Hadoop. Then as the company emerged, as the market kind of continued to evolve, we moved to and saw the opportunity really, capturing data from the edge. As IOT and kind of edge-use cases emerged it made sense for us to add to the platform and create the Hortonworks DataFlow. >> James: Apache NiFi >> Apache NiFi, exactly, HDF underneath, with associated additional open-source projects in there. Kafka and some streaming and things like that. So that was now move data, capture data in motion, move it back and put it into the platform for those large data applications that organizations are building on the core platform. It's also the next evolution, seeing great attach rates with that, the really strong interest in the Apache NiFi, you know, the meetup here for NiFi was oversubscribed, so really really strong interest in that. And then, the markets continued to evolve with cloud and cloud architectures, customers wanting to deploy in the cloud. You know, you saw we had that poll yesterday in the general session about cloud with really interesting results, but we saw that there was really companies wanting to deploy in a hybrid way. Some of them wanted to move specific workloads to the cloud. >> Multi-cloud, public, private. >> Exactly right, and multi-data center. >> The majority of your customer deployments are on prem. >> They are. >> Rob Bearden, your CEO, I think he said in a recent article on SiliconAngle that two-thirds of your deployments are on prem. Is that percentage going down over time? Are more of your customers shifting toward a public cloud orientation? Does Hortonworks worry about that? You've got partnerships, clearly, with the likes of IBM, AWS, and Microsoft Dasher and so forth, so do you guys see that as an opportunity, as a worrisome trend? >> No, we see it very much as an opportunity. And that's because we do have customers who are wanting to put more workloads and run things in the cloud, however, there's still almost always a component that's going to be on premise. And that creates a challenge for organizations. How do they manage the security and governance and really the overall operations of those deployments as they're in the cloud and on premise. And, to your point, multi-cloud. And so you get some complexity in there around that deployment and particularly with the regulations, we talked about GDPR earlier today. >> Oh, by the way, the Data Steward Studio demo today was really, really good. It showed that, first of all, you cover the entire range of core requirements for compliance. So that was actually the primary announcement at this show; Scott Gnau announced that. You demoed it today, I think you guys are off on a good start, yeah. We've gotten really, and thank you for that, we've gotten really good feedback on our DataPlane Services strategy, right, it provides that single pane of glass. >> I should say to our viewers that Data Steward Studio is the second of the services under the DataPlane, the Hortonworks DataPlane Services Portfolio. >> That's right, that's exactly right. >> Go ahead, keep going. >> So, you know, we see that as an opportunity. We think we're very strongly positioned in the market, being the first to bring that kind of solution to the customers and our large customers that we've been talking about and who have been starting to use DataPlane have been very, very positive. I mean they see it as something that is going to help them really kind of maintain control over these deployments as they start to spread around, as they grow their uses of the thing. >> And it's built to operate across the multi-cloud, I know this as well in terms of executing the consent or withdrawal of consent that the data subject makes through what is essentially a consent portal. >> That's right, that's right. >> That was actually a very compelling demonstration in that regard. >> It was good, and they worked very hard on it. And I was speaking to an analyst yesterday, and they were saying that they're seeing an increasing number of the customers, enterprises, wanting to have a multi-cloud strategy. They don't want to get locked into any one public cloud vendor, so, what they want is somebody who can help them maintain that common security and governance across their different deployments, and they see DataPlane Services is the way that's going to help them do that. >> So John, how is Hortonworks, what's your road map, how do you see the company in your go to market evolving over the coming years in terms of geographies, in terms of your focuses? Focus, in terms of the use-cases and workloads that the Hortonworks portfolio addresses. How is that shifting? You mentioned the Edge. AI, machine learning, deep learning. You are a reseller of IBM Data Science Experience. >> DSX, that's right. >> So, let's just focus on that. Do you see more customers turning to Hortonworks and IBM for a complete end-to-end pipeline for the ingest, for the preparation, modeling, training and so forth? And deployment of operationalized AI? Is that something you see going forward as an evolution path for your capabilities? >> I'd say yes, long-term, or even in the short-term. So, they have to get their data house in order, if you will, before they get to some of those other things, so we're still, Hortonworks strategy has always been focused on the platform aspect, right? The data-at-rest platform, data-in-motion platform, and now a platform for managing common security and governance across those different deployments. Building on that is the data science, machine learning, and AI opportunity, but our strategy there, as opposed to trying to trying to do it ourselves, is to partner, so we've got the strong partnership with IBM, resell their DSX product. And also other partnerships around to deliver those other capabilities, like machine learning and AI, from our partner ecosystem, which you referenced. We have over 2,300 partners, so a very, very strong ecosystem. And so, we're going to stick to our strategy of the platforms enabling that, which will subsequently enable data science, machine learning, and AI on top. And then, if you want me to talk about our strategy in terms of growth, so we already operate globally. We've got offices in I think 19 different countries. So we're really covering the globe in terms of the demand for Hortonworks products and beginning implements. >> Where's the fastest growing market in terms of regions for Hortonworks? >> Yeah, I mean, international generally is our fastest growing region, faster than the U.S. But we're seeing very strong growth in APAC, actually, so India, Asian countries, Singapore, and then up and through to Japan. There's a lot of growth out in the Asian region. And, you know, they're sort of moving directly to digital transformation projects at really large scale. Big banks, telcos, from a workload standpoint I'd say the patterns are very similar to what we've seen. I've been at Hortonworks for six and a half years, as it turns out, and the patterns we saw initially in terms of adoption in the U.S. became the patterns we saw in terms of adoption in Europe and now those patterns of adoption are the same in Asia. So, once a company realizes they need to either drive out operational costs or build new data applications, the patterns tend to be the same whether it's retail, financial services, telco, manufacturing. You can sort of replicate those as they move forward. >> So going forward, how is Hortonworks evolving as a company in terms of, for example with GDPR, Data Steward, data governance as a strong focus going forward, are you shifting your model in terms of your target customer away from the data engineers, the Hadoop cluster managers who are still very much the center of it, towards more data governance, towards more business analyst level of focus. Do you see Hortonworks shifting in that direction in terms of your focus, go to market, your message and everything? >> I would say it's not a shifting as much as an expansion, so we definitely are continuing to invest in the core platform, in Hadoop, and you would have heard of some of the changes that are coming in the core Hadoop 3.0 and 3.1 platform here. Alan and others can talk about those details, and in Apache NiFi. But, to your point, as we bring and have brought Data Steward Studio and DataPlane Services online, that allows us to address a different user within the organization, so it's really an expansion. We're not de-investing in any other things. It's really here's another way in a natural evolution of the way that we're helping organizations solve data problems. >> That's great, well thank you. This has been John Kreisa, he's the VP for marketing at Hortonworks. I'm James Kobielus of Wikibon SiliconAngle Media here at Dataworks Summit 2018 in Berlin. And it's been great, John, and thank you very much for coming on theCUBE. >> Great, thanks for your time. (techno music)

(upbeat music) >> Welcome to a CUBEConversation. I'm Peter Burris with Wikibon SiliconAngle. I am having a great conversation today with Derek Manky, who's a global securities strategist at Fortinet. >> Yes sir. >> Lots to talk about, Derek. I don't want to be too topical here, but still, why don't you tell us exactly what a global security strategist does. >> Yeah. So, obviously I've got a global region. We're looking at the past, the present, and the future. When I say that, we're looking at past events, learning from security, we're looking at present events, reacting to them, trying to beat the bad guys to the punch, doing advanced research on darknet, but also looking at statistical trends and modeling, a lot like a weather forecast. So, we're doing modeling as to where threats in the future, based on our expertise, knowledge and, obviously, a global telemetry base of data. Billions and billions of data points we look at. >> Everybody knows that this is enormous, that security in the past informed the current, and we are all worried about the future, but let's talk about where we are right now. >> Derek: Sure, sure, yeah. >> What is the state of things in global cybersecurity? >> It's flashing red, unfortunately, we're in this state. And what I mean by this is, CSOs and the likes always have to look at flashing red on their dashboards. They're a lot like car alarms and we get so many events that are happening day in and day out and we need to start looking at them and prioritizing: How do we respond to these events? What's the severity level of these? What are these events? And the context around that and why it matters. We look at a lot of events that are happening today, obviously we get into the IoT world, that's here, mobile threats are here. We've gone from, just from one year ago, we had about 2% of the global tax that we see were mobile, that number is reaching close to 10% now, so mobile threat activity is accounting for nearly 10% of all global activity that we're seeing. IoT is the next rising star that we're seeing in that as well. That's really the state that we're seeing. >> So, there's no really new normal in global cybersecurity, it's constantly changing, so give us an assessment and some insights into how the threat target is changing. What is the surface area and the surface attack area that we're worried about as we go forward? >> Sure. Up and to the right. What I mean by that is when I say that, we're seeing, obviously, volume increasing, and we're seeing the level of sophistication increasing in the threats as well. A lot more automated clever techniques are being put into threats. The attack surface is shifting into the IoT world, as I mentioned. Some of the top attacks we're seeing are CCTV cameras, which by the way, are not closed-circuit anymore, IP security cameras, we're looking at DVRs, consumer-grade routers, printers, all of these different devices now that are not just, obviously Windows-based as well. Because of that, the amount of volume of threats is increasing that attack surface, there's much more interconnectivity into these devices, which is a very large issue. We're dealing with a zero-patch environment now, as well. The reality is there's just not enough patches readily available for these devices too. And again, that comes back to the security strategy piece, we have to strategize. >> We're used to thinking about PCs being attacked, or servers being attacked, what happens if your router gets hacked in this way? Give us a little insight into how that propagates into a problem. >> Yeah, so worm-like activity, we look at a lot of, what I'm calling, shadow nets. These are IoT botnets. What I mean by that is you get a piece of code like Mirai, Hajime, there's also other flavors of this that we're seeing out there that basically look to propagate like a worm, spread from router to router, or different device to different device, plant malicious code. And then, once they have that, obviously, the device is compromised and it can be used for anything. It can be used for altering DNS traffic, hijacking credentials, it can be used to launch a DDos attack, like we saw with Mirai last year, as well. It's also being used now for more sophisticated attacks, so we look at like the Hajime botnet. Unlike Mirai, which I would consider more of a non-intelligent botnet, it's just using brute force techniques, Hajime is using automated techniques to download new password lists and try different attacks using updated and dynamic intelligence as being built into this automated code now as well. >> That sounds like it's an enormous amount of fun (laughs). We're talking mainly about devices at this point in time, but when we think about digital business, Wikibon likes to say that digital business is different from business in how a digital business uses data. And the idea that data is increasingly becoming an asset and is a differentiator for your business, especially in how you do things from engagement standpoint. How is the idea of data as an asset and the need for these new threats, this new landscape, going to come together over the course of the next few years? >> Yeah, absolutely. That's a really good point, what you bring up. Data is highly sought after by these threats. The initial stage of attack is building infrastructure and that's been done. We talk about these IoT botnets as gaining a foothold into networks where data is either stored or in transit, especially on mobile. And when we look at how data is stored or in transit, often enough it's stored for too long, it's too persistent, it's not stored properly, it's not hashed or salted and these sorts of techniques, and it's often, it may be going to the wrong places, or giving permission to the wrong users. These threats now that have a foothold onto these devices, can easily scrape and use data, send to their command and control operators, botnet operators, and then that data, as you are very well aware, can be used multiple times. We're seeing this data used, obviously, sold through crime services, sold on data dumps, on darknet. It's being used for things like identity theft, money mules, and laundering. We worked on a case last year with the EFCC in Nigeria, and INTERPOL, that's the expert working panel I'm on, we took down a $60 million crime ring. The heart of that crime ring was money laundering and that all revolves around identity theft, as well, which is all data. >> Right. So, let's build on this a little bit because one of the things I think people frequently get wrong is they don't understand data as an asset and that a crucial feature of it is it can be copied, and can be applied in two places at once. Now, that has a lot of business implication, but let's talk about the security implication. If somebody steals my money, I immediately know that my money is gone. If somebody steals my data, I may not know that my data is gone because it can be copied, and it can be reapplied and reused and I may never know it. Now, we're looking at a recent breach here at a big supplier's credit services, 165 million accounts being hacked. That might have only taken five minutes to download the data associated with those 165 million accounts, but that was probably a persistent, a few months, or maybe years getting to that point. What does a business have to do differently, from a security standpoint, to actually be able to capture those smaller events that may not have immediate proximate damage, but lead to a big hack like this? >> Yeah, absolutely, that's a really good point. Obviously, the threat landscape is extremely volatile. There's a lot of different characteristics or features you have to look for in these attacks. You're completely right, most of these attacks we see can play resident for months on networks. In fact, they want to lay as silent and as stealthy as possible. As I said, it's much more tricky today because threats are becoming more sophisticated to try to obfuscate into data flows and to try to remain silent on networks. What can be done, from an organization standpoint, is absolutely turning it around, looking at detection first. Threat intelligence, applying threat intelligence to detection. You need advanced threat intelligence to be able to find advanced threats. We're talking about solutions like SIEM, and so forth. Once you can see that threat activity on the network, that's key. Obviously, launching into incident response, how we deal with this, shut down that threat to mitigate the window because, otherwise, if you have a wide open window, obviously, more data is going to be leaked, the more data is leaked, the more damage and collateral damage is going to be done. >> And that's, still we're talking about consumers, which are problematic. But, when we start talking about critical infrastructure, we're talking about the social fabric itself. >> Yes. >> What new visibility, because Fortinet and auto research are on this, what visibility does Fortinet have into what's going on with some of the new critical infrastructure security--? >> Yeah, so looking at our threat landscape report, unfortunately, this is the normal still. I wouldn't say it's the new normal, in this case, because we're seeing 90% of organizations that are still facing attacks on application vulnerabilities that are three years or older. When we look critical infrastructure, it is over nine times, if we look at all industries, and just compare critical infrastructure to that baseline, so we're nine times higher with the tax on these application vulnerabilities. And so, the problem, unfortunately, with critical infrastructure, we're still seeing a lot of attacks on these IoT devices that are connected, the CCTV cameras, other things like that, that can be used as launchpads because they're not traditionally inspected by security. They're in a tough position with critical infrastructure, also healthcare, and ICU, critical care networks, because they're resistant to patch sometimes because if the patch is done, it could break. They have critical services and processes behind there that it could break it, but at the same time, what we're experiencing is that they're under rapid fire and if they don't patch, it's going to be much more damage done because we're seeing tremendous volume on the tax to those vulnerable applications lying on the networks. >> We now have a situation where we're trying to secure our critical infrastructure, which affects everybody, individuals have to be more cognizant of the role that a breach in their home network or their IoT devices can play. Increasingly, we're thinking about: How do we start putting together the idea of brand trust and security? Talk a little bit about how security is going to enter into the lexicon of brand, brand preference, and starting with what brands are going to have to do to transmit their commitment to security. >> Yeah, so again, we're talking about digital assets, when it comes to that. I think when it comes to brand integrity, if we flashback 10 years, I think, people had a false sense of security. They wouldn't really think twice about where their data is going, how that data is stored, and so forth. But, now that we're seeing consumers having a direct impact, when there are these massive data breaches, I think consumers are finally starting to become much more security conscious. That mentality, switching from that false sense of security, is really going to start having them have that cyber hygiene and have that daily thought process of where's my data going and they should have this. Where is my data going? Who is storing that? What are their security practices? Being able to readily access that sort of information on security posture. I think it's going to be critical moving forward-- >> So, what is it? Because this is very complex stuff, there are a limited number of people in the world who understand this really deeply. You're one of them, obviously. What does a consumer, then, have to know about security to be able to make that type of assessment? Because that's going to lead to some new conventions that we can start to promulgate and diffuse for how to get smarter about things. Is there like one or two things that someone has to be really aware of right now, questions that they can ask to get to that point where you're saying that they could be, therefore, smarter about how to evaluate different brands? >> I think they really have to, just at a basic level, treat their identity, treat their information, like the keys to their car, or their keys to their house, and their family's. It has to be personal, and so they have to be able to understand that they have a part to play, but they also have to understand that if I walk into a house and I leave the keys on the table somewhere and walk out, that somebody else can still easily access that. As opposed to me putting the keys to my car in a locker when I'm somewhere else. That is what they have to understand is that their assets, where they store those assets, and how they transmit those assets, is ultimately going to come back and impact them. >> If Wikibon says that digital business is about a business using data differently, in a matter of respects about what we're talking about, is digital life is a recognition, an acknowledgement, that data is playing a different role in your life and being really, really clear about that as an asset in the way that you conduct yourself. >> Yeah. And I think moving forward, that's just going to become even more critical. As I said, we're going to have more and more, as I said, with the world of IoT coming now, there's going to be more and more impact on daily life, there are more transit points for those data to go to. >> But the reality is, even though you're right, people don't, we might have been saying, "What about digital security?" a number of years ago because it wasn't on the forefront of everybody's minds. There are things that people can do to be smarter about this, treat your digital identity as an asset and be careful about it, but the reality is, most of us aren't really going to be smart enough to really make good decisions in this regard, we're going to rely on automation. Also, as you said earlier, we know that the bad guys are doing more with automation. Even if automation is not the complete goal, how are we going to fight more automation, on the bad guys' side, as we try to have more people involved in these good digital security practices? >> Yeah, there's a couple of approaches to that. First of all, number one, there is a severe, this is not a surprise or news, but there's a severe shortage in cybersecurity professionals out there. As you said, not a lot of people understand this stuff deeply, especially when we get down to the consumer level. How can we arm them to defend against all of this automation that the black hats are doing? We need to fight automation with automation. We need defensive measures, we need scalable security solutions, interconnected security solutions, security solutions that integrate threat intelligence, as well, to be able to identify the different stages of these threats. And the key here is quickly reacting to that because these threats are moving so quickly from the black hats' side, automated defense layers need to be able to identify those aspects of the threats and then make decisions, this is the key part, make a decision. This is what I call actionable intelligence. A security solution that can make a decision on its own, it's what I refer to as an expert system, is what's required to be able to block those, so that the people who don't know anything about these threats and worse, respond to them too slowly, don't have to do those measures. This is the idea of having an integrated intelligent security fabric. >> And where are we going to get that? >> Our approach is the security fabric. This is the Fortinet security fabric where we can take integrated intelligence, scale it up and make automated decisions that humans, we don't have to get rid of the humans, but we can repurpose the humans for that nature. >> Derek, once again, great insight. I think we'll call it a wrap there. Once again, this has been a CUBEConversation. I'm Peter Burris, Wikibon, and Derek Manky, who's the global securities strategist at Fortinet. Derek you and I have had, a couple of times, have talked, and every time it's been really insightful. The work you guys do is absolutely essential in today's world, so thank you very much for doing that. >> Yeah, it's a pleasure, anytime. >> Until we have another opportunity to speak again, track CUBEConversations, let's get the signal out of the noise. (upbeat music)

>> Welcome to our Palo Alto studio, all of you coming in over the airwaves. It's a wonderful opportunity today to talk about something very important with Computer Associates or, CA Tech, as they're now known. And I want to highlight one point about the slide title, the title they chose for the day, we chose for the day, Cloud and Hybrid IT Analytics for Digital Business. One of the most interesting things that you're going to hear about today is that it's going to keep coming back to business challenges and business problems. At the end of the day that's what the focus needs to be on. While we certainly do want to do more with the technology we have and drive greater effectiveness and utilization out of the technology that we use in our digital business, increasingly the ability to tie technology decisions to business outcomes is possible and all IT professionals must make that effort, as well as all IT vendors, if the community is going to be successful. Now what I'm going to talk about specifically is how cloud monitoring plays inside this drive to increase the effectiveness of business through digital technologies. And to do that, I'm going to talk about a few things. The first thing I'm going to talk about is what is a digital business and how does it impact strategic technology capabilities? Now the reason why this is so important is because there's an enormous amount of conversation in the industry about digital businesses, multi-channel for digital businesses, customer experience for digital businesses, some other attribute. And while those are all examples or potential benefits of digital business, at its core digital business is something else. We want to articulate what that is because it informs all decisions that we're going to make about a lot of different things. The second thing I'm going to talk about is this notion of advanced analytics and how advanced analytics are crucial to not only achieving the outcomes of digital business but also to sustain the effort in the transformation process. And as you might expect, if we're going to use analytics to improve our effectiveness, then we have to be in a position to gather the data that we need from the variety of resources necessary to succeed with a digital business strategy. Those are the three things I'm going to talk about but let's start with this first one. What is digital business and how does it impact technology capabilities? Now to do that, I want to show you something that we're quite proud of here at Wikibon SiliconANGLE because we're a research firm and a company that's dedicated to helping communities make better decision. The power of digital community is clear. It's a very, very important resource, overall, inside any business. And what we do is we have a tool that we call CrowdChat. And the purpose of CrowdChat is to bring together members of the community and surface the best insights they have about their undertakings. Now I'm not using this to just pitch what CrowdChat is, I really want to talk through how this is a representation of the power of digital community. I want to point you to a few things in this slide. First off, note that it's, very importantly, this was from a CrowdChat that we did on 31 January 2017 but the thing to note here is a couple of things. Now let's see if I can click through them here. Well the first thing to note is that it reached 3.4 million people linked to the technology decision making. Think about that. Wikibon SiliconANGLE is not a huge company. We're a very focused company that strongly emphasizes the role that technology can play in helping to make decisions and improve business outcomes. But this CrowdChat reached 3.4 million decision makers as part of our ongoing effort. And it clearly is an indication, ultimately, that today customers, in fact, are at the center of what goes on within digital business decision making. So customers are at the centers of these crucial market information flows. Now this is going to be something we come back to over and over and over. It used to be that folks who sold stuff were the primary centers of what happened with the information flows of the industry. But through social media, tools like CrowdChat and others, today customers are in a much better position overall to establish their voices and share their insights about what works and what doesn't work. In many respects, that is the core focus of digital business. So that leads us to this question of what is digital business. Now I am a fan of Peter Drucker. It's hard to argue with Peter Drucker and it's one of the reasons I start with Peter Drucker is because people don't typically argue with me when I start there. And Peter Drucker famously said many years ago that the purpose of a business is to create and keep a customer. Now you can go on about what about shareholder value, what about employees, and those are all true things. There's no question that that's also important. But the fundamental keeps coming back that if you don't have customers and you don't provide a great experience for those customers, you're not going to have a business. So what's the difference between digital business and business? The biggest difference between digital business and business and in fact how we properly define the concept of digital business is that digital businesses apply data to create and keep customers. That's the basis of digital business. It's how do you use your data assets to differentiate your business and especially to provide a superior experience, a superior value proposition, and superior outcomes for your customers. That is the core of digital business. If you're using data to differentiate how you engage customers, how you provide that experience for customers, and how you improve their outcomes, then you are more digital business than you were yesterday. If you use more data, you are more digital business than your competition. So this is a way of properly thinking about the role of digital business. And to summarize it slightly differently, what we strongly believe is that what decision makers have to do over the course of the next number of years is find ways to put their data to work. That is the fundamental goal of an IT professional today. And increasing, increasingly the goal of many business professionals. Find ways to apply data so that you can increase the work the firm does for customers. That's kind of the simple thread we're trying to pull here. Data, put to work, superior customer experience. Now at the centerpiece of this simple prescriptive is an enormous amount of complexity. A lot of decisions that have to be made because most businesses are not organized around their data. Most businesses don't institutionalize the way they engage customers or perform their work based on what their data assets can provide. Most businesses are built around the hardware, at least if you're an IT person, they're built around the hardware assets or maybe even the application assets. But increasingly it's become incumbent on CIOs and IT leaders to recognize that the central value of the business, at least that they work with, is the data and how that data performs work for the business. So that leads to the second question. Given the enormity of data in the future of digital business, we have to ask the question, "Well what role "is advanced analytics playing to keep us on track "as we thing about, ultimately, driving forward "for a digital business?" Now we draw this picture out to customers to try to explain the things that they'll have to do to become an increasingly digital business. And it starts with this idea that a digital business transformation requires investment in new capabilities, new business capabilities that foster the role that digital assets can play within the business that simplify making decisions about where to put people and how to institutionalize work and ultimately help sustain the value of the data within the business over time. And a way to think about it is that any digital business has to establish the capabilities to better capture data create catalysts from data. Now what do we mean by that? We mean basically that data is a catalyst for action. Data can actually be the source of value if you're a media company, for example. But in most businesses data is a catalyst, the next best action, a better prediction of superior forecast, a faster and simpler, and less expensive report for compliance purposes. Data is a catalyst. So we capture it and we translate it into a catalyst that then can actually guide action. That's the simple set of capabilities that we have to deploy here. Capturing data, turning it into the catalysts that then have consequential impacts in front of customers, provides superior experience and better business. Now if we try to map those prescriptions for business capabilities onto industry buzzwords, here's what we end with. Capture Data, well that's the centerpiece of what the industrial internet of things is about, or the internet of things is about, if we're talking mainly about small devices in a consumer world. Capturing data is essential and IIoT is going to be crucial to that effort as well as mobile computing and other types of things. We like to talk about it sometimes is the internet of things and people. Big data and analytics should be properly thought of as helping businesses turn those streams of information into models and insights that can lead to action. So that's what the whole purpose of what big data analytics is all about. It's not to just capture more data and store more data, it's about using that data that comes from a lot of different locations and turning it into catalysts, sources of value within the business. And the final one is branded customer experience. At the end of the day, what we're talking about is how we're going to use digital technology to better engage our customers, better engage our partners, better engage our markets, and better engage our employees. And increasingly, as customers demonstrate a preference for greater utilization of digital technology in their lives, the whole notion of a branded experience is going to be tied back to how well we provide these essential digital capabilities to our customers in our markets. So analytics plays an incredibly important role here because we've always been pretty good at capturing data and we've always, we're getting better I guess I should say, at utilizing insights from that data that could be gleaned on an episodic basis and turning that into some insight for a customer. Usually really smart people in sales or marketing or manufacturing or product management play that role. But what we're talking about is operationalizing, turning data into value for customers on a continuous ongoing basis. And Analytics is crucial for that and analytics also is crucial to ensure that we could stay on track as we effect these transformations and transitions. Now I want to draw your attention, obviously, to an important piece as we go forward here. And that is this notion how do we capture that data so that it is appropriately prepped and set up so that we can create value from analytics. And that's going to be the basis of the third point that I'm going to talk about. Why is hybrid cloud monitoring emerging as a crucial transformation tool? Now monitoring has been around for a long time. We've been monitoring individual assets to ensure we get greater efficiency and utilization. CA's been a master of that for 30, 35 years. Increasingly though, we need to think about how systems come together in a lot of different ways to increase what we call the plasticity of the infrastructure. The ability of the infrastructure to not only scale but to reconfigure itself in response to the crucial new work that digital businesses have to perform. So how's that going to play out? It's become very popular within the industry to talk about how data is going to move to the cloud. And that's certainly going to happen. There's going to be a lot of data that ends up in the cloud. But as we think about the realities of moving data, data is not just an ephemeral thing. Data has real physical characteristics, real legal implications. And ultimately intellectual property is increasingly rendered in the form of data. And so we have to be very careful how we think about data being moved across the enterprise into any number of different locations. It's one of the most strategic decisions that a board of directors is going to make. How do we handle and take care of our data assets? Now I want to focus just on one element of that. Hopefully provide a simple proof point to make this argument. And that is, if we looked at how data is generated, for example, in an Edge setting. Say we looked at the cost of moving data from a wind farm. A relatively small straightforward wind farm with a number of different sensors. What does it cost to move that data to the cloud? And that's provided here. If we think about the real costs of data, the cost of moving data from an Edge situation, even in a relatively simple example, back to the cloud can be dramatic. Hundreds of thousands of dollars. Limitations based on latencies, concerns about traversing borders that have legal jurisdictions, and obviously also, as I said, the intellectual property realities. But the bottom line here is that it shows that it's going to be much cheaper to process the data in place, process the data close to where the action needs to be taken, than to move it all to the cloud. And we think that's going to become a regular feature of how we think about setting up infrastructure in business in the future. Increasingly, it's not going to be about moving data to the cloud only, we're going to have additional options about moving cloud and cloud services to the data. Increasingly this is going to be the tact that businesses are going to take. It's find ways to move that sense of control, that notion of quality of service, and that flexibility in how we provision infrastructure so that the cloud experience comes to where the event needs to take place. That going forward will be the centerpiece of a lot of technology decision making. It doesn't mean we're not going to move data to the cloud it just means that we're going to be smart about when we do it, how we do it, and understanding when it makes more sense to move the cloud or the cloud set of services closer to the event so that we can process it in place. Now this is a really crucial concern because it suggests there's going to be a greater distribution of data and not a greater centralization of data. And you can probably see where I'm going with this. Greater distribution of data ultimately means that there's going to be a lot more things that require that we have to have visibility into their performance, visibility into how they work. If it was all going to be in one place then we could let someone else actually handle a lot of those questions about what's going on, how is it working. But as our businesses become more digital and our data assets become more central to how we provide customer experience, it means that the resources that we use to generate value out of those assets have to be managed and monitored appropriately. Now we have done a lot of work around this and what our research pretty strongly shows is that over the next 10 years, we're going to see three things happen. First off, we're going to see a lot of investment in public cloud options both in the form of SaaS as well as infrastructure as a service. So that will continue. There's no question that we're going to see some of the big public cloud suppliers become more important. But our expectation also, is we will see significant net new investment in what we call true private cloud. The idea of moving those cloud services on premise so that we can support local events that need high quality data and that kind of capability. The second thing I want to point out here is that while we do expect to see significant net new efficiencies and how we run all these resources, if we look at the cost of labor over the course of operational labor over the course of the next decade, we do expect to see the cost go down about around 7%. So we will see greater productivity in the world of IT labor. But it's not going to crash like many people predict. And one of the reasons it's not going to crash is because of the incredible net new reports of digital assets. But the third thing to note here is that we are not going to see the type of massive dumping of traditional infrastructure that many people predict. There's too many assets, too much value already in place in a lot of systems, and instead what we're going to see is a blending of all of these different capabilities in a rational way so that the business can achieve the digital outcomes that it seeks. The challenge, though, over the course of the next decade, however, is going to be to find ways, while we're going to have all these different resources, be a feature of our technology plan, be a feature of how we run our business. Historically we've tended to think about these in silos and the monitoring challenge that we put in place was to better generate efficiencies out of an individual asset. Well as we go forward, increasingly we need to think about how not one resource works, but how all these resources work. It's time for business to think about the internet not as something that's external, but as the basis for their computing. The internet is a computer. How we slice it up for our business is a statement about how we're going to build a set of distributive capabilities but weave them together so that we have a set of resources that can, in fact, reflect the business needs and support business requirements. And monitoring becomes crucial to that because as we move forward the goal needs to be to be able to enfranchise, federate a lot of these distributive resources into a working coherent statement of how computing serves our business. And that's going to require an approach that is much more focused on how things come together and how things can be bought into a coherent whole as opposed just the efficiency of any single tool or any single device. That's where digital business has to go, how can we bring all of these resources together into a coherent whole that supports our business needs. And that is the goal of the next generation of monitoring is to make that possible. Okay, so as we think about what we've talked about we basically made a couple of points here. The first when we talked about what is digital business, the first point that I made is data is the digital business asset. That's what we're trying to do here is use data to improve the effectiveness of the outcomes that we seek for customers. Digital business elevates IT but forces real and material changes. The second point that I made is how are advanced analytics helping. Well analytics turns business, or turns data into business catalysts that ultimately guide and shape customer experience. Crucial point. And the last point that I want to make is when we think about cloud monitoring remember that if we move forward in the digital world, as you make choices, your brand fails when your infrastructure fails. So as a consequence for those of you who are in the midst of thinking about the future role that monitoring is going to play in your world, choose your suppliers carefully. It's not about having a tool for a device, it's about thinking about how all of this can be, how monitoring can bring a lot of different resources into a coherent picture to ensure that your business is able to process, compute, store, and effect dramatic improvements to customer experience across the entire infrastructure asset. And the last thought that I'll leave you with is that CA Tech has been one of the companies of the vanguard of thinking about how this is going to work over the next decade in the industry.

>> Why don't I spend just a couple minutes talking about what we mean by digital enactment, turning data in models and models into action. And then we'll jump directly into, I'll introduce the panelists after that, and we'll jump directly into the questions. So Wikibon SiliconAngle has been on a mission for quite sometime now to really understand what is the nature of digital transformation, or digital disruption. And historically, when we've talked about digital, people talk about a variety of different characteristics of it, so we'll talk about new types of channels and activity on the web, and a many number of other things. But to really make sense of this, we kind of felt that we had to go to a set of basic principles, and utilize those basic principles to build our observations up. And so what we started with is a simple observation that, if it's not digital, or if it's not data, it ain't digital. By that we mean fundamentally the idea of digital business is how are we going to use data as an asset to differentially drive our business forward? And if we borrowed from Drucker, Drucker used to like to talk about the idea that business exists to create sustained customers, and so we would say that digital business is about applying data assets to differentially create sustained customers. Now to do that successfully, we have to be able to, as businesses, be able to establish a set of strategic business capabilities that will allow us to differentially use data assets. And we think that there are a couple of core strategic business capabilities required. One is human beings and most businesses operate in the analog world, so it's how do we take that analog data and turn it into digital data that we can then process. So that's the first one, the notion of an IOT as a transducer of information so that we can generate these very rich data streams. Secondly we have to be able to do something with those data streams, and that's the basis of big data. So we utilize big data to create models, to create insights, and increasingly through a more declarative style, actually create new types of software systems that will be crucial to driving the business forward. That's the second capability. The third capability is one that we're still coming to understand, and that is we have to take the output of those models, the output of those insights, and then turn them back into some event that has a consequential moment in the real world, or what we call systems of an action. And so the three core business capabilities that have to be built are this capture data through IOT, big data to process it, systems of an action also through IOT, through actuators, to actually that have a consequential action in the real world. So that's the basis of what we're talking about. We're going to take Flavio's vision that he just laid out, and then we, in this panel, are going to talk about some of the business capabilities necessary to make that happen, and then after this, David Foyer will lead a panel on specifically some of the lower level technologies that are going to make it work. Make sense guys? >> Sounds good (mumbles). >> Okay, so let me introduce the panelists. Over, down there on the end, Ted Connell. Ted is from Intel, I don't know if we can get the slide up that has their names and their titles. Ted, why don't you very quickly introduce yourself. >> Yeah, thank you very much. I run Solution Architecture for the manufacturing and industrial vertical, where we put together end to end ecosystem solutions that solve our clients business problems. So we're not selling silicone or semiconductors, we're solving our clients problems, which as Flavio said, requires ecosystem solutions of software, system integrators, and other partners to come together to put together end solutions. >> Excellent, next to Ted is Steve Madden of Equinix. >> Yeah, Steve Madden. Equinix is the largest interconnection, global interconnection company and a lot of the ecosystems that you'll be hearing about, come together inside our locations. And one of the things I do in there is work with our big customers on industry vertical level solutions, IOT being one of them. >> Phu Hoang, from Data Torrent. >> Hi, my name's Phu Hoang, I'm co-founder and chief strategy of a company called Data Torrent, and at Data Torrent, our mission is really to build out solutions to allow enterprises to process big data in a streaming fashion. So that whole theme around ingestion, transformation, analytics, and taking action in sub second on massive data is what we're focusing on. >> And you're familiar with Flavio. Flavio, will you take a second to introduce yourself. >> Yes, thank you, I am leading a company that is trying to manifest the vision highlighted here, building a platform. Not so much the applications, we are hosting the applications (mumbles) the data management and so forth. And trying to apply the industrial vertical first. Big enough to keep us busy for quite a while. >> So in case you didn't know this, we have an interesting panel, we have use case, application, technol infrastructure, and platform. So what' we'll try to do is over the next, say, 10 minutes or so, we're going to spend a little bit of time, again, talking about some of these business capabilities. Let me start off by asking each of you a question, and I will take, if anybody is really burning to ask a question, raise your hand, I'll do my best to see you and I'll share the microphone for just long enough for you to ask it. Okay, so first question, digital business is data. That means we have to think about data differently. Ted, at Intel, what is Intel doing when they think about data as an asset? >> So, Intel has been working on what is now being called Fog, and big data analytics for over a generation. The modern xeon server we're selling, the wire in the electronics if you will, is 10 silicon atoms wide. So to control that process, we've had to do what is called Industry 4.0 20 years ago. So all of our production equipment has been connected for 20 years, we're running... One of our factories will produce a petabyte of data a day, and we're running big data analytics, including machine learning on the stuff currently. If you look at an Intel factory, we have 2,000 fit clients on the factory floor supported by 600 servers in our data center at the factory, just to control the process and run predictive yield analytics. >> Peter: So that's your itch? >> Our competitive advantage at Intel is the factory. We are a manufacturer, we're a world class manufacturer. Our front end factories have zero people in it, not that we don't like people, but we had to fully automate the factory because as I speak, tens of thousands of water molecules are leaving my mouth, and if one of those water molecules lands on a silicon, it ain't going to work. So we had to get people physically out of the factory, and so we were forced by Moore's Law, and the product we build, to build out what became Fog, when they came up with the term seven years ago, we just came to that conclusion because of cost, latency, and security, it made sense to, you know, look, you got data, you got compute, there's a network between. It doesn't matter where you do the compute, bring the compute to the data, the data to the compute. You're doing a compute function, it doesn't matter where you do it. So Fog is not complicated, it's just a distributed data center. >> So when you think about some of the technologies necessary to make this work, it's not just batch, we're going to be doing a lot of stuff in real time, continuously. So Phu, talk a little bit about the system software, the infrastructure software that has to be put in place to ensure that this works for them. >> I think that's great. A little bit about our background, the company was founded by a bunch of ex-Yahoos that had been out for 12, 15 years from the early days. So we sort of grew up in that period where we had to learn about big data, learn about making all the mistakes of big data, and really seeing that nowadays, it's not good enough to get insight, you have to get insight in a timely fashion enough to actually do something about it. And for a lot of enterprise, especially with human being carrying around mobile phones and moving around all over the place, and sensors sending thousands, if not millions of events per second, the need for the business to understand what's going on and react, have insight and react sub second, is crucial. And what that means is the stuff that used to be batch, offline, you know, can kind of go down, now has to be continuous, 24 by seven. You can't lose data, you got to be able to recover and come back to where you were as if nothing has happened with no human intervention. There's a lot of theme around no human intervention, because this stuff is so fast, you can't involve human beings in it, then you're not reacting fast enough. >> Can I real quickly add one thing first? >> Peter: Sure. >> We think of data at Intel in half life terms. >> Yeah, that's exactly right. >> The data has valuable right now. If you wait a second, literally a second, the data has a little bit of value. You wait two second, it's historical data you can run regressions, and tell you why you screwed up, but you ain't going to fix anything. >> Exactly. >> If you want to do anything with your data, you got to do it now. >> So that, ultimately, we need to develop experience, a creed experience about what we're doing. And the stuff we're doing in applications will eventually find itself into platforms. So Flavio, talk to us a little bit about the types of things that are going to end up in the platform to ensure that these use cases are made available to, certainly, businesses that perhaps aren't as sophisticated as Intel. >> Yes, so in many ways, we are learning from what is going on in the Cloud, and has to come through this continuum, all the way into the machines. This break between what's going inside the machine, and old 1980 microprocessor and the server, and the Cloud server with virtualization on the other side cannot leave. So it has to be a continuum of computing so you can move the same function, the same container, all the way through first. Second, you really have to take the real time very, very seriously, particularly at the edge, but even in the back so that when you have these end to end continuum, you can decide where you do what. And I think that one of the models that was in that picture with a concentric circle is really telling what we need to learn first. Bring the data back and learn, and that can take time. But then you can have models that are lightweight, that can be brought down to the front, and impact the reaction to the data there. And we heard from a car company, a big car company, how powerful this was when they learned that the angle of a screwdriver, and a few other parameters, can determine the success of screwing something into a body of a car, that could go well, or could go very, very bad and be very costly. So all the learning, massive data, can come down to a simple model that can save a lot of money and improve efficiency. But that has to be hosted along this continuum. >> So from a continuum, it means we still have to have machines somewhere to do something. >> Touching the ground, touching the physical world requires machines, actuators. >> Peter: Absolutely, so Steve, what is Equinix doing to simplify the thinking through of some of these infrastructure issues? >> Yeah, I mean, the biggest thing that people find when they start looking at millions of devices, millions of data capture points, transferring those data real time and streaming it, is one thing hasn't changed and that's physics. So where those things are, where they need to go, where the data needs to move to and how fast, starts with having to figure out your own topology of how you're moving that data. As much as it's easy to say we're just going to buy a platform and choose a device, and we'll clink them together, there's still a lot of other things that need to be solved, physics being the first one. The second one, primarily, is volumes. So how much bandwidth and (mumbles) you're going to require. How much of that data are you going to back haul to centralized data center before you send it up to a Cloud? How much of it are you going to leave at the edge? Where do you place that becomes a bigger deal. And the third one is pretty much every industry has to deal with regulations. Regulations control what you can and can't do in terms of IT delivery, where you can place stuff, where you cannot place stuff, data that can leave the country, data that can't. So all these things mean that you need to have a thought through process of where you're placing certain functions, and what you're defining as your itch between the digital and physical world. And Equinix is an interconnection company that's sitting there as a neutral party across all the networks, all the clouds, all the enterprises, all the providers to help people figure that out. >> So before I ask the audience a question, now that I'm down here so I can see you so be prepared, I'm going to ask some of you a question. When you think about the strategic business capabilities necessary to succeed, what is the first thing that the business has to do? So why don't I just take Ted, and just go right on down the line. >> Yeah, so I think this is really, really important. I work with many, many clients around the world who are doing five, 10, 15 POCs, pilots, and the internet things, and they haven't thought through a codified strategy. So they're doing five things that will never fit together, that you will never scale, and the learnings you're using, you really can't do that much with. So coming up with what is my architecture, what is my stack going to look like, how am I going to push data, what is my data... You know, because when you connect to these things, I can't tell you how much data you're going to get. You're going to be overwhelmed by the data, and that's why we all go to the edge, and I got to process this data real time. And oh, by the way, if I only have one source of data, like I'm connecting to production equipment, you're not going to learn anything. 98% of that data's useless, you got to contextualize the data with either an inspection step, or some kind of contextualization that tells you if this then that. You need the then that, without that, your data is basically worthless. So now you're pulling multiple sources of data together in real time to make an understanding. And so understanding what that architecture looks like, spend the time upfront. Look, most of us are engineers, you know five percent additional work upfront saves you 95% on the backend, that's true here. So think through the architecture, talk to some of us who have been working in this area for a long time. We'll share our architecture, we have reference architecture that we're working with companies. How do you go from industry 2.0 or industry 3.0, to industry 4.0? And there is a logical path to do it, but ultimately, where we're going to end up is a software defined universe. I mean, what's a cloud? It's a software defined data center. Now we're doing software defined networks, software defined storages, ultimately we're going to be doing software defined systems because it's cheaper. You get better capital utilization, better asset utilization, so we will go there, so what does that mean for you infrastructure, and what are you going to do from an architectural perspective, and then take all of your POCs and pilots, and force them to do that specifically around security. People are doing POCs with security that they don't even have any protocols, they're violating all their industry standards doing POCs, and that's going to get thrown out. It's wasted time, wasted effort, don't do it. >> Steve, a couple sentences? >> Yeah, essentially it's not going to be any prizes for me saying think interconnection first. A lot of our customers, if we look at what they've done with us, everyone from GE to real time facial recognition at the edge, it all comes down to how are you wired, topology wise, first. You can't use the internet for risk reasons, you can't necessarily pay for multiple (mumbles) bandwidth costs, et cetera. So low latency, 80% lower latency, seven times of bandwidth at half the cost is a scalable infrastructure to move (mumbles) around the planet. If you don't have that, the rest of the stuff (mumbles) breakdown. >> Peter: Phu? >> Well I would say that analytics is hard, analytics in real time is even harder. And I think with us talking to our customers, I feel for them, they're confused. There's like a million solutions out there, everybody's trying to claim to do the same thing. I think it's both sides, consumers have to get more educated, they have to be more intelligent about their POCs, but as an industry, we also have to get better at thinking about how do we help our customer succeed. It's not about let me give you some open source, and then let me spend the next 10 months charging you professional services to help you. We ought to think about software tools and enterprise tools to really help the customer be able to think about their total cost (mumbles) and time to value to handle this thing, because it's not easy. >> Peter: Flavio. >> Yeah, we're facing an interesting situation where the customers are ready, the needs are there, the marketing is going to be huge, but the plot, the solution, is not trivial. It is maturing and we are all trying to understand how to do it. And this is the confusion that you see in many of these half baked solution (mumbles). Everything is coming together, and you have to go up the stalk and down the stalk with full confidence, that's not easy. So we all have to really work together. Give ourselves time, be feeling that we are in a competitive world, preparing for addressing together a huge market. And trying to mature these solutions that then will be replicated more and more, but we have to be patient with each other, and with the technologies that are maturing and they're not fully there and understood. But the market is amazing. >> Peter: So we have a Twitter question. >> Man: It's being live streamed, the audience is really engaged online as well, digital. So we have a question from Twitter from Lauren Cooney saying, "Would like to know what industries would "be most impacted with digitization "over the next five years." >> Which one won't be? (men laughing) All of them, what we've seen, the business model is the data. I mean, our CEOs calling data the new gold. I mean, it's the new oil. So I don't know of anything, unless you're doing something that is just physical therapy, but that even data, you can do data on that. So yeah, everything, yeah, I don't know of anything that won't be. >> I think the real question is how is it going to move through industries. Obviously it's going to start with some of the digital native, it's all ready deep into that, deep into media, we're moving through the media right now. Intel's clearly a digital company, and you've been working, you've been on this path for quite some time. >> Let me give you a stat. Intel has a 105,000 people, and 144,000 servers. So we're about 1.5 server to people, that's what kind of computation we're (mumbles). >> Peter: We can help you work on that. >> If you do like the networking started by (mumbles) the internet, then content delivery, and media, hard media, et cetera, is gone. Financial services and trading exchanges pretty much show what digital market's going to be in the future. Cloud showed up, and now, I think he's right, it's effecting every industry. Manufacturing, industrial, health professional services are the top three right now. But people who shop to ask for help went from every industry on every country, for that matter. >> Our customers are, you know, the top players in almost every vertical. You start out as a small company thinking that you're going to attack one vertical, but as you start to talk about the capability, everybody (mumbles) wait, you're solving my problem. >> Peter: (mumbles) are followers, is what you mean. >> Yeah, because what business would say, hey, I don't want to know what's going on with my business, and I don't want to take any action. >> Add to that it's an ecosystem of ecosystems. No one, by themselves, is going to solve anything. They have to partner and connect with other people to solve the solution. >> So I'll close the panel by making these kind of summary comments, the business capabilities that we think are going to be most important are, first off, when we talk about the internet of things, we like to talk about the internet of things and people. That the people equation doesn't go away. So we're building on mobile, we're building on other things, but if there's a strategic capability that's going to be required, it's going to be how is this going to impact folks who actually create value in the business. The second one, I'll turn it around, is that IT organizations have gone through a number of different range wars, if you will, over the past 20 years. I lived through IT versus telecom, for example. The IT, OT conflict, or potential conflict, is non trivial. There's going to be some serious work that has to be done, so I would add to the conversation that we've heard thus far, the answers that we've heard thus far, is the degree to which people are going to be essential to making this work, and how we diffuse this knowledge into our employees, and into our IT and professional communities is going to be crucial, especially with developers because Flavio, if we are, right now, trying to figure stuff out, it really matures when we think about the developer world. Okay, so I want to close the first panel and get ready for the second panel. So thank you very much, and thank you very much to our panelists. (audience applauding) And if we could bring David Foyer and the second panel up, we'll get going on panel two. Oh, we're going to get together for a picture. (exciting rhythmic music)

>> Announcer: Live, from Las Vegas, Nevada, it's theCUBE, covering Accelerate 2017. Brought to you by Fortinet. Now, here are your hosts, Lisa Martin and Peter Burris. >> Welcome back to theCUBE, I'm Lisa Martin joined by Peter Burris. We have been in Vegas all day at Accelerate 2017. What an exciting, buzz-filled day that we've had, Peter. I feel like we've learned, I've learned a lot myself, but also just that the passion and the opportunity for helping companies become more secure, as security is evolving, is really palpable. >> Well, yeah, I totally agree with you, Lisa. In fact, if there's one kind of overarching theme of what we heard and what we experienced, it's this is one of the first conferences, security conferences, that I've been to, where we spent more time talking about business opportunity, business outcome, the role that security is going to play in facilitating business change. And we spent a lot less time talking about security speed, security feeds, geeking out about underlying security technologies. And I think that portends a pretty significant seismic shift in how people regard security. We'll still always have to be very focused and understand those underlying technologies in the speeds and feeds, but increasingly, the business conversation is creeping into, and in fact, starting to dominate how we regard security. It's past become reviewed in a digital world, it has to become viewed as a strategic business asset, and not just as the thing you do to make sure your devices don't get stolen or appropriated. >> Right, and that context was set from the beginning with the keynotes this morning. The CEO Ken Xie, a lot of folks that we talked to today, said he normally gets quite technical in keynotes, and today kept things really at a business level. >> And we heard that many people thought it was the best keynote they've seen him give in a long time. >> That's right, that's a great point. >> And one of his key messages was that at the end of the day, digital business is not about some new observations on channels or new observations on products. It really is about how you use data to differentiate, differentially create sustained customers. My words, not his, but it's very, very much in line. The difference between any business and digital business is how you use your data. And we heard that over and over and over today, and how security, technologies, and practices, and capabilities have to evolve to focus more on what businesses want to do with data. That is where, certainly Fortinet, sees the market going, and they're trying to steer their customers so that they can take advantages of those opportunities. >> Right, and that's a great point that you made. Their CFO, who we had on the program as well, Drew Del Matto, talked about in his keynote, that it's critical for a company to be able to have digital trust. We talk about trust in lots of different contexts, but what does trust mean to a business? >> As you said, he's the CFO. It's interesting, CFO is typically focused on things like is the ownership getting a return on the capital that they've invested in this company? It was very, to me anyway, refreshing to hear a CFO expressly state data is becoming an increasing feature of the capital stock of the company. And we have to take explicit steps to start to protect it and secure it, because in fact, it's through security that data is turned into an asset. If you don't secure your data, it's everywhere. It's easily copied, it flies around. Data and security-- at least data asset, the concept of data asset, and security, are inextricably bound because it is through security that you create the asset notion of data. The thing that generates value. Because if you don't, it's everywhere. It's easy to copy. I thought he did a wonderful job of starting to tie together the idea of data in business in a very straightforward, tactical, CFO approach. It was a good conversation about where business people are starting to think about how this is going to evolve. >> He also talked about the role of the CSO, and there was a panel during the general session of three CSOs from different industries. That's an interesting evolution as security has evolved from perimeter only to web, to cloud, to-- Now, where we need to be as Ken Xie talked about, we're at this third generation. It's about fabric. He talked about that, and the importance of that, and the capabilities. But it's also interesting to hear security's now a conversation in the boardroom. This is not something that is simply owned by a CIO or CSO, that that role has to facilitate a company becoming a digital army in order to create value from that data. A lot of folks said today, too, that mindset of "If I can't see it, I can't protect it." >> Yeah, we heard that this morning from the CFO, we also heard it from George, the CSO of Azure, Microsoft Azure. We heard the relationship, the evolving role of the CSO, or the Chief Security Officer multiple times today. Security's hard. This is not easy stuff. We can bring a lot of automation, and we can bring a lot of technology to bear on making it easier and simplifying it. And we heard a lot about how that's happening. But this is a hard, hard thing to do, for a lot of reasons. But it's one that must be done, especially in a digital world. And the role, or the impact on the CSO role, is profound. You're not going to have everybody in the organization-- They all have a stake in it, but they're all not going to perform security routines, necessarily. Yet, it's too big, as we heard from George, for one person. We have to start increasingly thinking about security as a strategic business capability that may be championed by the CSO, but is going to be undertaking in a lot of different places. One of the things that the Microsoft gentleman, George-- >> Lisa: George Moore. >> George Moore bought up, was the idea that increasingly, if you do security right, you can secure things at a relatively technical level and present them as services so that other parts of the business can start to consume them, and they become part of their security architecture. And it goes into their products, or it goes into their services, or it goes into how they engage customers. >> Facilitating scale. >> Or whatever else it might be, logistics. I think that that is a very powerful way of thinking about how security's going to work through a fabric, being able to present a hierarchy of security capabilities that go all the way out to your customers and actually allow you to engage your customers at a security conversation level. Which is, we also heard that talked about a little bit today. The role, the brand value of trust, but we still don't have an answer for how that's going to play out. >> If we look at some of the other things that were talked about in Ken's keynote, hyperconnectivity. From the proliferation of mobile and IoT, which IoT devices, there's 20 billion that are predicted to be connected by 2020, which is just a few years away. As that sounds, well it doesn't sound, it is increasing the threat surface, and we are also hearing from some of the folks that were on the program today, Derek Manky being one of them, who wrote a great blog just published recently on Fortinet talking about the major trends that are being seen and the challenges there. I think we're also seeing that companies like Fortinet and their suite of technology alliance partners like Microsoft, like Nazomi, going all the way out to the endpoints and back, that these companies are coming together to collaborate, to start mitigating the risks that are increasingly there with the threat surface being larger. I think there was a lot more positivity than I honestly anticipated. When you hear of all these attacks that it's daily, and that's such a common thing. The collaboration of the technology and the integration is exciting to hear where these companies are going to be able to limit damage. >> And to put one more number on it, the 20 billion devices, but it's what those devices are doing. Again, George Moore from Microsoft Azure talked about I think he said, it was 800 billion events that they're dealing with a day. And in 2017, Microsoft Azure is going to cross a threshold of dealing with one trillion events a day that they have to worry about from a security standpoint. If you think about that industry wide, Microsoft Azure's big, but there are others. We're talking today, probably somewhere, I just estimated, he said, "Yeah, that sounds about right," about five trillion, five trillion events a day that businesses have to worry about in aggregate from a security standpoint. And that number is just going to keep growing exponentially. In a year's time, he talked about three, four, five x. So we're talking about hundreds of trillions of events. >> Staggering numbers. >> Within the next decade or so. There is virtually no way that human beings are set up to deal with those kinds of numbers. It's going to require great technology-- >> Automation. >> That provides great automation. That nonetheless, works with humans so that the discretion that human beings bring, the smarts, and the collaboration that human beings bring to bear. The value that they create stays there. We're going to see more productivity coming out of these incredibly smart people that are doing security, because the tooling's going to improve and make it possible. And if it doesn't happen, then that's going to put a significant break on how fast a lot of this digital business evolution takes place. >> Another point that was quite prevalent among our conversations today, was that there isn't, with the exception maybe of healthcare, it's quite an agnostic problem that enterprises are facing in terms of security threats. When we talked to Derek, he mentioned healthcare being one because that information is so pervasive. It's very personal and private. But something that kind of surprised me, I almost thought we might see or hear about a hierarchy, maybe healthcare, financial services. But really, what everyone talked about today, was that the security threats are really pervasive across all industries. All the way, even to industrial control systems and HVAC systems. Which shows you the breadth of the challenge ahead. But to your point, and some of the points that some of our guests made, it's going to be a combination of the humans and the machines coming together to combat these challenges. >> Well I think what we're seeing is that there's a high degree of data specialization within a lot of industries based on different terms, different tactics, different risk profiles, et cetera. But that many of the algorithms necessary to understand exceptions or deal with anomalies, or one of those other things, are applicable across a lot of different industries. What we are likely to see over the next few years is we're still likely to see some of that specialization by industry, by data. Nonetheless, become featured in the output, but the algorithms are going to be commonly applied. They'll get better and better and better. There's still likely to be some degree of specialization if only because the data itself is somewhat specialized, but the other thing that we heard is that it's pretty clear that the bad guys want to get access. Well, let's put it this way, not all data is of equal value. And the bad guys want to get access to the data that is especially valuable to them. A lot of that data is in healthcare systems. To bring these common algorithms that specialize data to secure the especially challenging problems associated with healthcare is a real, real big issue for a lot of businesses today. Not just healthcare businesses, but people who are buying insurance for their employees, et cetera. >> Exactly, it becomes a pervasive problem. You were mentioning today that this was very much a business conversation versus speeds and fees. We did hear about a couple of technologies moving forward that are going to be key to driving security forward. Analytics, data science, in fact-- We also talked about kind of the difference between security fabric which Fortinet rolled out last year, and a platform and how businesses are kind of mobilized around that, and the differences there. Control versus spreading that out. One of the things that Forinet did about, I think it was in June of last year, was they acquired AccelOps. Bringing in monitoring, bringing in realtime analytics. A lot of our guests talked about the essentialness of that realtime capability to discover, detect, remediate, and clear things up. From a 2017 perspective, besides analytics and data science, what are some of the other things you see here as essential technologies to facilitate where the security evolution trajectory is going? >> I think in many respects, it comes back to some of the things we just talked about. That as digital business increasingly-- Let's step back. The way we define, at Wikibon SiliconANGLE, what digital business is, what differentiates your digital business from any other kind of business is data. It's how you use your data to create and sustain customers. That's a pretty big world. There's a lot of-- You know, most of us operate in the analog world. There's some very interesting ways of turning that analog information into digital information. There's voice, there's photographs, there's a lot of other-- We talked a little bit about industrial internetive things. There's an enormous set of investments being made today to turn the analog world that all of us operate in, and the processes that we normally think about, into digital representations that then can be turned into models for action, models for insight, new software systems that can then have an impact on how the business actually operates. And I think that, if we think the notion of analytics and data science, and by the way, security's one of those places where that set of disciplines have really, really matured through fraud detection and other types of things. But I think what we're going to look at, is as new types of data are created by different classes of business or different classes of industry, or different roles and responsibilities, that that data, too, will have to be made secure. What we're going to see, is as the world figures out new ways of using data to create new types of value, that the security industry is going to have to be moving in lockstep so that security doesn't once again become the function that says no to everything, but rather the function that says, "Yeah, we can do that." We can go from idea to execution really fast, because we know how to make that data secure. >> Well, Peter, it's been such a pleasure, an honor, co-hosting with you today. Thank you so much for sharing the desk with me. >> Absolutely, Lisa. >> Look forward to doing it some other time. And we want to thank you so much for joining us on theCUBE today as well. I want to also point you to some of the upcoming events. Go to SiliconANGLE.tv. Next week, we've got the VTUG Winter Warmer going on. You'll also be able to see that on the website. Women and Data Science with yours truly in early February. And then the Spark Summit in February, Feb 7-9 in Boston. Again, that's SiliconANGLE.tv. For my co-host Peter Burris, I'm Lisa Martin. Thanks so much for watching, and we'll see ya next time. (techno music)