(light music) >> Hi everybody. Welcome to this Cube Conversation. I'm really pleased to announce a collaboration with Rob Strechay. He's a guest cube analyst, and we'll be working together to extract the signal from the noise. Rob is a long-time product pro, working at a number of firms including AWS, HP, HPE, NetApp, Snowplow. I did a stint as an analyst at Enterprise Strategy Group. Rob, good to see you. Thanks for coming into our Marlboro Studios. >> Well, thank you for having me. It's always great to be here. >> I'm really excited about working with you. We've known each other for a long time. You've been in the Cube a bunch. You know, you're in between gigs, and I think we can have a lot of fun together. Covering events, covering trends. So. let's get into it. What's happening out there? We're sort of exited the isolation economy. Things were booming. Now, everybody's tapping the brakes. From your standpoint, what are you seeing out there? >> Yeah. I'm seeing that people are really looking how to get more out of their data. How they're bringing things together, how they're looking at the costs of Cloud, and understanding how are they building out their SaaS applications. And understanding that when they go in and actually start to use Cloud, it's not only just using the base services anymore. They're looking at, how do I use these platforms as a service? Some are easier than others, and they're trying to understand, how do I get more value out of that relationship with the Cloud? They're also consolidating the number of Clouds that they have, I would say to try to better optimize their spend, and getting better pricing for that matter. >> Are you seeing people unhook Clouds, or just reduce maybe certain Cloud activities and going maybe instead of 60/40 going 90/10? >> Correct. It's more like the 90/10 type of rule where they're starting to say, Hey I'm not going to get rid of Azure or AWS or Google. I'm going to move a portion of this over that I was using on this one service. Maybe I got a great two-year contract to start with on this platform as a service or a database as a service. I'm going to unhook from that and maybe go with an independent. Maybe with something like a Snowflake or a Databricks on top of another Cloud, so that I can consolidate down. But it also gives them more flexibility as well. >> In our last breaking analysis, Rob, we identified six factors that were reducing Cloud consumption. There were factors and customer tactics. And I want to get your take on this. So, some of the factors really, you got fewer mortgage originations. FinTech, obviously big Cloud user. Crypto, not as much activity there. Lower ad spending means less Cloud. And then one of 'em, which you kind of disagreed with was less, less analytics, you know, fewer... Less frequency of calculations. I'll come back to that. But then optimizing compute using Graviton or AMD instances moving to cheaper storage tiers. That of course makes sense. And then optimize pricing plans. Maybe going from On Demand, you know, to, you know, instead of pay by the drink, buy in volume. Okay. So, first of all, do those make sense to you with the exception? We'll come back and talk about the analytics piece. Is that what you're seeing from customers? >> Yeah, I think so. I think that was pretty much dead on with what I'm seeing from customers and the ones that I go out and talk to. A lot of times they're trying to really monetize their, you know, understand how their business utilizes these Clouds. And, where their spend is going in those Clouds. Can they use, you know, lower tiers of storage? Do they really need the best processors? Do they need to be using Intel or can they get away with AMD or Graviton 2 or 3? Or do they need to move in? And, I think when you look at all of these Clouds, they always have pricing curves that are arcs from the newest to the oldest stuff. And you can play games with that. And understanding how you can actually lower your costs by looking at maybe some of the older generation. Maybe your application was written 10 years ago. You don't necessarily have to be on the best, newest processor for that application per se. >> So last, I want to come back to this whole analytics piece. Last June, I think it was June, Dev Ittycheria, who's the-- I call him Dev. Spelled Dev, pronounced Dave. (chuckles softly) Same pronunciation, different spelling. Dev Ittycheria, CEO of Mongo, on the earnings call. He was getting, you know, hit. Things were starting to get a little less visible in terms of, you know, the outlook. And people were pushing him like... Because you're in the Cloud, is it easier to dial down? And he said, because we're the document database, we support transaction applications. We're less discretionary than say, analytics. Well on the Snowflake earnings call, that same month or the month after, they were all over Slootman and Scarpelli. Oh, the Mongo CEO said that they're less discretionary than analytics. And Snowflake was an interesting comment. They basically said, look, we're the Cloud. You can dial it up, you can dial it down, but the area under the curve over a period of time is going to be the same, because they get their customers to commit. What do you say? You disagreed with the notion that people are running their calculations less frequently. Is that because they're trying to do a better job of targeting customers in near real time? What are you seeing out there? >> Yeah, I think they're moving away from using people and more expensive marketing. Or, they're trying to figure out what's my Google ad spend, what's my Meta ad spend? And what they're trying to do is optimize that spend. So, what is the return on advertising, or the ROAS as they would say. And what they're looking to do is understand, okay, I have to collect these analytics that better understand where are these people coming from? How do they get to my site, to my store, to my whatever? And when they're using it, how do they they better move through that? What you're also seeing is that analytics is not only just for kind of the retail or financial services or things like that, but then they're also, you know, using that to make offers in those categories. When you move back to more, you know, take other companies that are building products and SaaS delivered products. They may actually go and use this analytics for making the product better. And one of the big reasons for that is maybe they're dialing back how many product managers they have. And they're looking to be more data driven about how they actually go and build the product out or enhance the product. So maybe they're, you know, an online video service and they want to understand why people are either using or not using the whiteboard inside the product. And they're collecting a lot of that product analytics in a big way so that they can go through that. And they're doing it in a constant manner. This first party type tracking within applications is growing rapidly by customers. >> So, let's talk about who wins in that. So, obviously the Cloud guys, AWS, Google and Azure. I want to come back and unpack that a little bit. Databricks and Snowflake, we reported on our last breaking analysis, it kind of on a collision course. You know, a couple years ago we were thinking, okay, AWS, Snowflake and Databricks, like perfect sandwich. And then of course they started to become more competitive. My sense is they still, you know, compliment each other in the field, right? But, you know, publicly, they've got bigger aspirations, they get big TAMs that they're going after. But it's interesting, the data shows that-- So, Snowflake was off the charts in terms of spending momentum and our EPR surveys. Our partner down in New York, they kind of came into line. They're both growing in terms of market presence. Databricks couldn't get to IPO. So, we don't have as much, you know, visibility on their financials. You know, Snowflake obviously highly transparent cause they're a public company. And then you got AWS, Google and Azure. And it seems like AWS appears to be more partner friendly. Microsoft, you know, depends on what market you're in. And Google wants to sell BigQuery. >> Yeah. >> So, what are you seeing in the public Cloud from a data platform perspective? >> Yeah. I think that was pretty astute in what you were talking about there, because I think of the three, Google is definitely I think a little bit behind in how they go to market with their partners. Azure's done a fantastic job of partnering with these companies to understand and even though they may have Synapse as their go-to and where they want people to go to do AI and ML. What they're looking at is, Hey, we're going to also be friendly with Snowflake. We're also going to be friendly with a Databricks. And I think that, Amazon has always been there because that's where the market has been for these developers. So, many, like Databricks' and the Snowflake's have gone there first because, you know, Databricks' case, they built out on top of S3 first. And going and using somebody's object layer other than AWS, was not as simple as you would think it would be. Moving between those. >> So, one of the financial meetups I said meetup, but the... It was either the CEO or the CFO. It was either Slootman or Scarpelli talking at, I don't know, Merrill Lynch or one of the other financial conferences said, I think it was probably their Q3 call. Snowflake said 80% of our business goes through Amazon. And he said to this audience, the next day we got a call from Microsoft. Hey, we got to do more. And, we know just from reading the financial statements that Snowflake is getting concessions from Amazon, they're buying in volume, they're renegotiating their contracts. Amazon gets it. You know, lower the price, people buy more. Long term, we're all going to make more money. Microsoft obviously wants to get into that game with Snowflake. They understand the momentum. They said Google, not so much. And I've had customers tell me that they wanted to use Google's AI with Snowflake, but they can't, they got to go to to BigQuery. So, honestly, I haven't like vetted that so. But, I think it's true. But nonetheless, it seems like Google's a little less friendly with the data platform providers. What do you think? >> Yeah, I would say so. I think this is a place that Google looks and wants to own. Is that now, are they doing the right things long term? I mean again, you know, you look at Google Analytics being you know, basically outlawed in five countries in the EU because of GDPR concerns, and compliance and governance of data. And I think people are looking at Google and BigQuery in general and saying, is it the best place for me to go? Is it going to be in the right places where I need it? Still, it's still one of the largest used databases out there just because it underpins a number of the Google services. So you almost get, like you were saying, forced into BigQuery sometimes, if you want to use the tech on top. >> You do strategy. >> Yeah. >> Right? You do strategy, you do messaging. Is it the right call by Google? I mean, it's not a-- I criticize Google sometimes. But, I'm not sure it's the wrong call to say, Hey, this is our ace in the hole. >> Yeah. >> We got to get people into BigQuery. Cause, first of all, BigQuery is a solid product. I mean it's Cloud native and it's, you know, by all, it gets high marks. So, why give the competition an advantage? Let's try to force people essentially into what is we think a great product and it is a great product. The flip side of that is, they're giving up some potential partner TAM and not treating the ecosystem as well as one of their major competitors. What do you do if you're in that position? >> Yeah, I think that that's a fantastic question. And the question I pose back to the companies I've worked with and worked for is, are you really looking to have vendor lock-in as your key differentiator to your service? And I think when you start to look at these companies that are moving away from BigQuery, moving to even, Databricks on top of GCS in Google, they're looking to say, okay, I can go there if I have to evacuate from GCP and go to another Cloud, I can stay on Databricks as a platform, for instance. So I think it's, people are looking at what platform as a service, database as a service they go and use. Because from a strategic perspective, they don't want that vendor locking. >> That's where Supercloud becomes interesting, right? Because, if I can run on Snowflake or Databricks, you know, across Clouds. Even Oracle, you know, they're getting into business with Microsoft. Let's talk about some of the Cloud players. So, the big three have reported. >> Right. >> We saw AWSs Cloud growth decelerated down to 20%, which is I think the lowest growth rate since they started to disclose public numbers. And they said they exited, sorry, they said January they grew at 15%. >> Yeah. >> Year on year. Now, they had some pretty tough compares. But nonetheless, 15%, wow. Azure, kind of mid thirties, and then Google, we had kind of low thirties. But, well behind in terms of size. And Google's losing probably almost $3 billion annually. But, that's not necessarily a bad thing by advocating and investing. What's happening with the Cloud? Is AWS just running into the law, large numbers? Do you think we can actually see a re-acceleration like we have in the past with AWS Cloud? Azure, we predicted is going to be 75% of AWS IAS revenues. You know, we try to estimate IAS. >> Yeah. >> Even though they don't share that with us. That's a huge milestone. You'd think-- There's some people who have, I think, Bob Evans predicted a while ago that Microsoft would surpass AWS in terms of size. You know, what do you think? >> Yeah, I think that Azure's going to keep to-- Keep growing at a pretty good clip. I think that for Azure, they still have really great account control, even though people like to hate Microsoft. The Microsoft sellers that are out there making those companies successful day after day have really done a good job of being in those accounts and helping people. I was recently over in the UK. And the UK market between AWS and Azure is pretty amazing, how much Azure there is. And it's growing within Europe in general. In the states, it's, you know, I think it's growing well. I think it's still growing, probably not as fast as it is outside the U.S. But, you go down to someplace like Australia, it's also Azure. You hear about Azure all the time. >> Why? Is that just because of the Microsoft's software state? It's just so convenient. >> I think it has to do with, you know, and you can go with the reasoning they don't break out, you know, Office 365 and all of that out of their numbers is because they have-- They're in all of these accounts because the office suite is so pervasive in there. So, they always have reasons to go back in and, oh by the way, you're on these old SQL licenses. Let us move you up here and we'll be able to-- We'll support you on the old version, you know, with security and all of these things. And be able to move you forward. So, they have a lot of, I guess you could say, levers to stay in those accounts and be interesting. At least as part of the Cloud estate. I think Amazon, you know, is hitting, you know, the large number. Laws of large numbers. But I think that they're also going through, and I think this was seen in the layoffs that they were making, that they're looking to understand and have profitability in more of those services that they have. You know, over 350 odd services that they have. And you know, as somebody who went there and helped to start yet a new one, while I was there. And finally, it went to beta back in September, you start to look at the fact that, that number of services, people, their own sellers don't even know all of their services. It's impossible to comprehend and sell that many things. So, I think what they're going through is really looking to rationalize a lot of what they're doing from a services perspective going forward. They're looking to focus on more profitable services and bringing those in. Because right now it's built like a layer cake where you have, you know, S3 EBS and EC2 on the bottom of the layer cake. And then maybe you have, you're using IAM, the authorization and authentication in there and you have all these different services. And then they call it EMR on top. And so, EMR has to pay for that entire layer cake just to go and compete against somebody like Mongo or something like that. So, you start to unwind the costs of that. Whereas Azure, went and they build basically ground up services for the most part. And Google kind of falls somewhere in between in how they build their-- They're a sort of layer cake type effect, but not as many layers I guess you could say. >> I feel like, you know, Amazon's trying to be a platform for the ecosystem. Yes, they have their own products and they're going to sell. And that's going to drive their profitability cause they don't have to split the pie. But, they're taking a piece of-- They're spinning the meter, as Ziyas Caravalo likes to say on every time Snowflake or Databricks or Mongo or Atlas is, you know, running on their system. They take a piece of the action. Now, Microsoft does that as well. But, you look at Microsoft and security, head-to-head competitors, for example, with a CrowdStrike or an Okta in identity. Whereas, it seems like at least for now, AWS is a more friendly place for the ecosystem. At the same time, you do a lot of business in Microsoft. >> Yeah. And I think that a lot of companies have always feared that Amazon would just throw, you know, bodies at it. And I think that people have come to the realization that a two pizza team, as Amazon would call it, is eight people. I think that's, you know, two slices per person. I'm a little bit fat, so I don't know if that's enough. But, you start to look at it and go, okay, if they're going to start out with eight engineers, if I'm a startup and they're part of my ecosystem, do I really fear them or should I really embrace them and try to partner closer with them? And I think the smart people and the smart companies are partnering with them because they're realizing, Amazon, unless they can see it to, you know, a hundred million, $500 million market, they're not going to throw eight to 16 people at a problem. I think when, you know, you could say, you could look at the elastic with OpenSearch and what they did there. And the licensing terms and the battle they went through. But they knew that Elastic had a huge market. Also, you had a number of ecosystem companies building on top of now OpenSearch, that are now domain on top of Amazon as well. So, I think Amazon's being pretty strategic in how they're doing it. I think some of the-- It'll be interesting. I think this year is a payout year for the cuts that they're making to some of the services internally to kind of, you know, how do we take the fat off some of those services that-- You know, you look at Alexa. I don't know how much revenue Alexa really generates for them. But it's a means to an end for a number of different other services and partners. >> What do you make of this ChatGPT? I mean, Microsoft obviously is playing that card. You want to, you want ChatGPT in the Cloud, come to Azure. Seems like AWS has to respond. And we know Google is, you know, sharpening its knives to come up with its response. >> Yeah, I mean Google just went and talked about Bard for the first time this week and they're in private preview or I guess they call it beta, but. Right at the moment to select, select AI users, which I have no idea what that means. But that's a very interesting way that they're marketing it out there. But, I think that Amazon will have to respond. I think they'll be more measured than say, what Google's doing with Bard and just throwing it out there to, hey, we're going into beta now. I think they'll look at it and see where do we go and how do we actually integrate this in? Because they do have a lot of components of AI and ML underneath the hood that other services use. And I think that, you know, they've learned from that. And I think that they've already done a good job. Especially for media and entertainment when you start to look at some of the ways that they use it for helping do graphics and helping to do drones. I think part of their buy of iRobot was the fact that iRobot was a big user of RoboMaker, which is using different models to train those robots to go around objects and things like that, so. >> Quick touch on Kubernetes, the whole DevOps World we just covered. The Cloud Native Foundation Security, CNCF. The security conference up in Seattle last week. First time they spun that out kind of like reinforced, you know, AWS spins out, reinforced from reinvent. Amsterdam's coming up soon, the CubeCon. What should we expect? What's hot in Cubeland? >> Yeah, I think, you know, Kubes, you're going to be looking at how OpenShift keeps growing and I think to that respect you get to see the momentum with people like Red Hat. You see others coming up and realizing how OpenShift has gone to market as being, like you were saying, partnering with those Clouds and really making it simple. I think the simplicity and the manageability of Kubernetes is going to be at the forefront. I think a lot of the investment is still going into, how do I bring observability and DevOps and AIOps and MLOps all together. And I think that's going to be a big place where people are going to be looking to see what comes out of CubeCon in Amsterdam. I think it's that manageability ease of use. >> Well Rob, I look forward to working with you on behalf of the whole Cube team. We're going to do more of these and go out to some shows extract the signal from the noise. Really appreciate you coming into our studio. >> Well, thank you for having me on. Really appreciate it. >> You're really welcome. All right, keep it right there, or thanks for watching. This is Dave Vellante for the Cube. And we'll see you next time. (light music)

[Music] the cyber security landscape has changed dramatically over the past 24 to 36 months rapid cloud migration has created a new layer of security defense sure but that doesn't mean csos can relax in many respects it further complicates or at least changes the cso's scope of responsibilities in particular the threat surface has expanded and that creates more seams and csos have to make sure their teams pick up where the hyperscaler clouds leave off application developers have become a critical execution point for cyber assurance shift left is the kind of new buzz phrase for devs but organizations still have to shield right meaning the operational teams must continue to partner with secops to make sure infrastructure is resilient so it's no wonder that an etr's latest survey of nearly 1500 cios and it buyers that business technology executives cite security as their number one priority well ahead of other critical technology initiatives including collaboration software cloud computing and analytics rounding out the top four but budgets are under pressure and csos have to prioritize it's not like they have an open checkbook they have to contend with other key initiatives like those just mentioned to secure the funding and what about zero trust can you go out and buy zero trust or is it a framework a mindset in a series of best practices applied to create a security consciousness throughout the organization can you implement zero trust in other words if a machine or human is not explicitly allowed access then access is denied can you implement that policy without constricting organizational agility the question is what's the most practical way to apply that premise and what role does infrastructure play as the enforcer how does automation play in the equation the fact is that today's approach to cyber resilient type resilience can't be an either or it has to be an and conversation meaning you have to ensure data protection while at the same time advancing the mission of the organization with as little friction as possible and don't even talk to me about the edge that's really going to keep you up at night hello and welcome to the special cube presentation a blueprint for trusted infrastructure made possible by dell technologies in this program we explore the critical role that trusted infrastructure plays in cyber security strategies how organizations should think about the infrastructure side of the cyber security equation and how dell specifically approaches securing infrastructure for your business we'll dig into what it means to transform and evolve toward a modern security infrastructure that's both trusted and agile first up are pete gear and steve kenniston they're both senior cyber security consultants at dell technologies and they're going to talk about the company's philosophy and approach to trusted infrastructure and then we're going to speak to paris our godaddy who's a senior consultant for storage at dell technologies to understand where and how storage plays in this trusted infrastructure world and then finally rob emsley who heads product marketing for data protection and cyber security he's going to take a deeper dive with rob into data protection and explain how it has become a critical component of a comprehensive cyber security strategy okay let's get started pete gear steve kenniston welcome to the cube thanks for coming into the marlboro studios today great to be here dave thanks dave good to see you great to see you guys pete start by talking about the security landscape you heard my little rap up front what are you seeing i thought you wrapped it up really well and you touched on all the key points right technology is ubiquitous today it's everywhere it's no longer confined to a monolithic data center it lives at the edge it lives in front of us it lives in our pockets and smartphones along with that is data and as you said organizations are managing sometimes 10 to 20 times the amount of data that they were just five years ago and along with that cyber crime has become a very profitable uh enterprise in fact it's been more than 10 years since uh the nsa chief actually called cybercrime the biggest transfer of wealth in history that was 10 years ago and we've seen nothing but accelerating cybercrime and really sophistication of how those attacks are are perpetrated and so the new security landscape is really more of an evolution we're finally seeing security catch up with all of the technology adoption all the build out the work from home and work from anywhere that we've seen over the last couple of years we're finally seeing organizations and really it goes beyond the i.t directors it's a board level discussion today security's become a board level discussion so yeah i think that's true as well it's like it used to be the security was okay the sec ops team you're responsible for security now you've got the developers are involved the business lines are involved it's part of onboarding for most companies you know steve this concept of zero trust it was kind of a buzzword before the pandemic and i feel like i've often said it's now become a a mandate but it's it's it's still fuzzy to a lot of people how do you guys think about zero trust what does it mean to you how does it fit yeah i thought again i thought your opening was fantastic in this whole lead into to what is zero trust it had been a buzzword for a long time and now ever since the federal government came out with their implementation or or desire to drive zero trust a lot more people are taking a lot more seriously because i don't think they've seen the government do this but ultimately let's see ultimately it's just like you said right if you don't have trust to those particular devices applications or data you can't get at it the question is and and you phrase it perfectly can you implement that as well as allow the business to be as agile as it needs to be in order to be competitive because we're seeing with your whole notion around devops and the ability to kind of build make deploy build make deploy right they still need that functionality but it also needs to be trusted it needs to be secure and things can't get away from you yeah so it's interesting we attended every uh reinforce since 2019 and the narrative there is hey everything in this in the cloud is great you know and this narrative around oh security is a big problem is you know doesn't help the industry the fact is that the big hyperscalers they're not strapped for talent but csos are they don't have the the capabilities to really apply all these best practices they're they're playing whack-a-mole so they look to companies like yours to take their your r d and bake it into security products and solutions so what are the critical aspects of the so-called dell trusted infrastructure that we should be thinking about yeah well dell trusted infrastructure for us is a way for us to describe uh the the work that we do through design development and even delivery of our it system so dell trusted infrastructure includes our storage it includes our servers our networking our data protection our hyper-converged everything that infrastructure always has been it's just that today customers consume that infrastructure at the edge as a service in a multi-cloud environment i mean i view the cloud as really a way for organizations to become more agile and to become more flexible and also to control costs i don't think organizations move to the cloud or move to a multi-cloud environment to enhance security so i don't see cloud computing as a panacea for security i see it as another attack surface and another uh aspect in front that organizations and and security organizations and departments have to manage it's part of their infrastructure today whether it's in their data center in a cloud or at the edge i mean i think it's a huge point because a lot of people think oh the data's in the cloud i'm good it's like steve we've talked about oh why do i have to back up my data it's in the cloud well you might have to recover it someday so i don't know if you have anything to add to that or any additional thoughts on it no i mean i think i think like what pete was saying when it comes to when it comes to all these new vectors for attack surfaces you know people did choose the cloud in order to be more agile more flexible and all that did was open up to the csos who need to pay attention to now okay where can i possibly be attacked i need to be thinking about is that secure and part of the part of that is dell now also understands and thinks about as we're building solutions is it is it a trusted development life cycle so we have our own trusted development life cycle how many times in the past did you used to hear about vendors saying you got to patch your software because of this we think about what changes to our software and what implementations and what enhancements we deliver can actually cause from a security perspective and make sure we don't give up or or have security become a whole just in order to implement a feature we got to think about those things yeah and as pete alluded to our secure supply chain so all the way through knowing what you're going to get when you actually receive it is going to be secure and not be tampered with becomes vitally important and pete and i were talking earlier when you have tens of thousands of devices that need to be delivered whether it be storage or laptops or pcs or or whatever it is you want to be tr you want to know that that that those devices are can be trusted okay guys maybe pete you could talk about the how dell thinks about it's its framework and its philosophy of cyber security and then specifically what dell's advantages are relative to the competition yeah definitely dave thank you so i we've talked a lot about dell as a technology provider but one thing dell also is is a partner in this larger ecosystem we realize that security whether it's a zero trust paradigm or any other kind of security environment is an ecosystem with a lot of different vendors so we look at three areas uh one is protecting data in systems we know that it starts with and ends with data that helps organizations combat threats across their entire infrastructure and what it means is dell's embedding security features consistently across our portfolios of storage servers networking the second is enhancing cyber resiliency over the last decade a lot of the funding and spending has been in protecting or trying to prevent cyber threats not necessarily in responding to and recovering from threats right we call that resiliency organizations need to build resiliency across their organization so not only can they withstand a threat but they can respond recover and continue with their operations and the third is overcoming security complexity security is hard it's more difficult because of the the things we've talked about about distributed data distributed technology and and attack surfaces everywhere and so we're enabling organizations to scale confidently to continue their business but know that all all the i.t decisions that they're making um have these intrinsic security features and are built and delivered in a consistent security so those are kind of the three pillars maybe we could end on what you guys see as the key differentiators uh that people should know about that that dell brings to the table maybe each of you could take take a shot at that yeah i i think first of all from from a holistic portfolio perspective right the secure supply chain and the secure development life cycle permeate through everything dell does when building things so we build things with security in mind all the way from as pete mentioned from from creation to delivery we want to make sure you have that that secure device or or asset that permeates everything from servers networking storage data protection through hyper converge through everything that to me is really a key asset because that means you can you understand when you receive something it's a trusted piece of your infrastructure i think the other core component to think about and pete mentioned as dell being a partner for um making sure you can deliver these things is that even though those are that's part of our framework these pillars are our framework of how we want to deliver security it's also important to understand that we are partners and that you don't need to rip and replace but as you start to put in new components you can be you can be assured that the components that you're replacing as you're evolving as you're growing as you're moving to the cloud as you're moving to more on-prem type services or whatever that your environment is secure i think those are two key things got it okay pete bring us home yeah i think one of one of the big advantages of dell uh is our scope and our scale right we're a large technology vendor that's been around for decades and we develop and sell almost every piece of technology we also know that organizations are might make different decisions and so we have a large services organization with a lot of experienced services people that can help customers along their security journey depending on uh whatever type of infrastructure or solutions that they're looking at the other thing we do is make it very easy to consume our technology whether that's traditional on-premise in a multi-cloud environment uh or as a service and so the best of breed technology can be consumed in any variety of fashion and know that you're getting that consistent secure infrastructure that dell provides well and dell's forgot the probably top supply chain not only in the tech business but probably any business and so you can actually take take your dog food and then and allow other your champagne sorry allow other people to you know share share best practices with your with your customers all right guys thanks so much for coming thank you appreciate it okay keep it right there after this short break we'll be back to drill into the storage domain you're watching a blueprint for trusted infrastructure on the cube the leader in enterprise and emerging tech coverage be right back you

the cyber security landscape has changed dramatically over the past 24 to 36 months rapid cloud migration has created a new layer of security defense sure but that doesn't mean csos can relax in many respects it further complicates or at least changes the ciso's scope of responsibilities in particular the threat surface has expanded and that creates more seams and cisos have to make sure their teams pick up where the hyperscaler clouds leave off application developers have become a critical execution point for cyber assurance shift left is the kind of new buzz phrase for devs but organizations still have to shield right meaning the operational teams must continue to partner with secops to make sure infrastructure is resilient so it's no wonder that in etr's latest survey of nearly 1500 cios and it buyers that business technology executives cite security as their number one priority well ahead of other critical technology initiatives including collaboration software cloud computing and analytics rounding out the top four but budgets are under pressure and csos have to prioritize it's not like they have an open checkbook they have to contend with other key initiatives like those just mentioned to secure the funding and what about zero trust can you go out and buy xero trust or is it a framework a mindset in a series of best practices applied to create a security consciousness throughout the organization can you implement zero trust in other words if a machine or human is not explicitly allowed access then access is denied can you implement that policy without constricting organizational agility the question is what's the most practical way to apply that premise and what role does infrastructure play as the enforcer how does automation play in the equation the fact is that today's approach to cyber resilient type resilience can't be an either or it has to be an and conversation meaning you have to ensure data protection while at the same time advancing the mission of the organization with as little friction as possible and don't even talk to me about the edge that's really going to keep you up at night hello and welcome to the special cube presentation a blueprint for trusted infrastructure made possible by dell technologies in this program we explore the critical role that trusted infrastructure plays in cyber security strategies how organizations should think about the infrastructure side of the cyber security equation and how dell specifically approaches securing infrastructure for your business we'll dig into what it means to transform and evolve toward a modern security infrastructure that's both trusted and agile first up are pete gear and steve kenniston they're both senior cyber security consultants at dell technologies and they're going to talk about the company's philosophy and approach to trusted infrastructure and then we're going to speak to paris arcadi who's a senior consultant for storage at dell technologies to understand where and how storage plays in this trusted infrastructure world and then finally rob emsley who heads product marketing for data protection and cyber security he's going to take a deeper dive with rob into data protection and explain how it has become a critical component of a comprehensive cyber security strategy okay let's get started pete gear steve kenniston welcome to the cube thanks for coming into the marlboro studios today great to be here dave thanks dave good to see you great to see you guys pete start by talking about the security landscape you heard my little rap up front what are you seeing i thought you wrapped it up really well and you touched on all the key points right technology is ubiquitous today it's everywhere it's no longer confined to a monolithic data center it lives at the edge it lives in front of us it lives in our pockets and smartphones along with that is data and as you said organizations are managing sometimes 10 to 20 times the amount of data that they were just five years ago and along with that cyber crime has become a very profitable enterprise in fact it's been more than 10 years since uh the nsa chief actually called cyber crime the biggest transfer of wealth in history that was 10 years ago and we've seen nothing but accelerating cyber crime and really sophistication of how those attacks are perpetrated and so the new security landscape is really more of an evolution we're finally seeing security catch up with all of the technology adoption all the build out the work from home and work from anywhere that we've seen over the last couple of years we're finally seeing organizations and really it goes beyond the i t directors it's a board level discussion today security's become a board level discussion yeah i think that's true as well it's like it used to be the security was okay the secops team you're responsible for security now you've got the developers are involved the business lines are involved it's part of onboarding for most companies you know steve this concept of zero trust it was kind of a buzzword before the pandemic and i feel like i've often said it's now become a mandate but it's it's it's still fuzzy to a lot of people how do you guys think about zero trust what does it mean to you how does it fit yeah i thought again i thought your opening was fantastic in in this whole lead into to what is zero trust it had been a buzzword for a long time and now ever since the federal government came out with their implementation or or desire to drive zero trust a lot more people are taking a lot more seriously because i don't think they've seen the government do this but ultimately let's see ultimately it's just like you said right if if you don't have trust to those particular devices uh applications or data you can't get at it the question is and and you phrase it perfectly can you implement that as well as allow the business to be as agile as it needs to be in order to be competitive because we're seeing with your whole notion around devops and the ability to kind of build make deploy build make deploy right they still need that functionality but it also needs to be trusted it needs to be secure and things can't get away from you yeah so it's interesting we attended every uh reinforce since 2019 and the narrative there is hey everything in this in the cloud is great you know and this narrative around oh security is a big problem is you know doesn't help the industry the fact is that the big hyperscalers they're not strapped for talent but csos are they don't have the the capabilities to really apply all these best practices they're they're playing whack-a-mole so they look to companies like yours to take their r your r d and bake it into security products and solutions so what are the critical aspects of the so-called dell trusted infrastructure that we should be thinking about yeah well dell trusted infrastructure for us is a way for us to describe uh the the work that we do through design development and even delivery of our it system so dell trusted infrastructure includes our storage it includes our servers our networking our data protection our hyper converged everything that infrastructure always has been it's just that today customers consume that infrastructure at the edge as a service in a multi-cloud environment i mean i view the cloud as really a way for organizations to become more agile and to become more flexible and also to control costs i don't think organizations move to the cloud or move to a multi-cloud environment to enhance security so i don't see cloud computing as a panacea for security i see it as another attack surface and another uh aspect in front that organizations and and security organizations and departments have to manage it's part of their infrastructure today whether it's in their data center in a cloud or at the edge i mean i think it's a huge point because a lot of people think oh data's in the cloud i'm good it's like steve we've talked about oh why do i have to back up my data it's in the cloud well you might have to recover it someday so i don't know if you have anything to add to that or any additional thoughts on it no i mean i think i think like what pete was saying when it comes to when it comes to all these new vectors for attack surfaces you know people did choose the cloud in order to be more agile more flexible and all that did was open up to the csos who need to pay attention to now okay where can i possibly be attacked i need to be thinking about is that secure and part of the part of that is dell now also understands and thinks about as we're building solutions is it is it a trusted development life cycle so we have our own trusted development life cycle how many times in the past did you used to hear about vendors saying you got to patch your software because of this we think about what changes to our software and what implementations and what enhancements we deliver can actually cause from a security perspective and make sure we don't give up or or have security become a whole just in order to implement a feature we got to think about those things yeah and as pete alluded to our secure supply chain so all the way through knowing what you're going to get when you actually receive it is going to be secure and not be tampered with becomes vitally important and pete and i were talking earlier when you have tens of thousands of devices that need to be delivered whether it be storage or laptops or pcs or or whatever it is you want to be you want to know that that that those devices are can be trusted okay guys maybe pete you could talk about the how dell thinks about it's its framework and its philosophy of cyber security and then specifically what dell's advantages are relative to the competition yeah definitely dave thank you so we've talked a lot about dell as a technology provider but one thing dell also is is a partner in this larger ecosystem we realize that security whether it's a zero trust paradigm or any other kind of security environment is an ecosystem uh with a lot of different vendors so we look at three areas one is protecting data in systems we know that it starts with and ends with data that helps organizations combat threats across their entire infrastructure and what it means is dell's embedding security features consistently across our portfolios of storage servers networking the second is enhancing cyber resiliency over the last decade a lot of the funding and spending has been in protecting or trying to prevent cyber threats not necessarily in responding to and recovering from threats right we call that resiliency organizations need to build resiliency across their organization so not only can they withstand a threat but they can respond recover and continue with their operations and the third is overcoming security complexity security is hard it's more difficult because of the things we've talked about about distributed data distributed technology and and attack surfaces everywhere and so we're enabling organizations to scale confidently to continue their business but know that all all the i.t decisions that they're making um have these intrinsic security features and are built and delivered in a consistent security so those are kind of the three pillars maybe we could end on what you guys see as the key differentiators that people should know about that that dell brings to the table maybe each of you could take take a shot at that yeah i think first of all from from a holistic portfolio perspective right the uh secure supply chain and the secure development life cycle permeate through everything dell does when building things so we build things with security in mind all the way from as pete mentioned from from creation to delivery we want to make sure you have that that secure device or or asset that permeates everything from servers networking storage data protection through hyper converge through everything that to me is really a key asset because that means you can you understand when you receive something it's a trusted piece of your infrastructure i think the other core component to think about and pete mentioned as dell being a partner for making sure you can deliver these things is that even though those are that's part of our framework these pillars are our framework of how we want to deliver security it's also important to understand that we are partners and that you don't need to rip and replace but as you start to put in new components you can be you can be assured that the components that you're replacing as you're evolving as you're growing as you're moving to the cloud as you're moving to a more on-prem type services or whatever that your environment is secure i think those are two key things got it okay pete bring us home yeah i think one of one of the big advantages of dell is our scope and our scale right we're a large technology vendor that's been around for decades and we develop and sell almost every piece of technology we also know that organizations are might make different decisions and so we have a large services organization with a lot of experienced services people that can help customers along their security journey depending on whatever type of infrastructure or solutions that they're looking at the other thing we do is make it very easy to consume our technology whether that's traditional on-premise in a multi-cloud environment uh or as a service and so the best of breed technology can be consumed in any variety of fashion and know that you're getting that consistent secure infrastructure that dell provides well and dell's forgot the probably top supply chain not only in the tech business but probably any business and so you can actually take take your dog food and then and allow other billionaire champagne sorry allow other people to you know share share best practices with your with your customers all right guys thanks so much for coming thank you appreciate it okay keep it right there after this short break we'll be back to drill into the storage domain you're watching a blueprint for trusted infrastructure on the cube the leader in enterprise and emerging tech coverage be right back concern over cyber attacks is now the norm for organizations of all sizes the impact of these attacks can be operationally crippling expensive and have long-term ramifications organizations have accepted the reality of not if but when from boardrooms to i.t departments and are now moving to increase their cyber security preparedness they know that security transformation is foundational to digital transformation and while no one can do it alone dell technologies can help you fortify with modern security modern security is built on three pillars protect your data and systems by modernizing your security approach with intrinsic features and hardware and processes from a provider with a holistic presence across the entire it ecosystem enhance your cyber resiliency by understanding your current level of resiliency for defending your data and preparing for business continuity and availability in the face of attacks overcome security complexity by simplifying and automating your security operations to enable scale insights and extend resources through service partnerships from advanced capabilities that intelligently scale a holistic presence throughout it and decades as a leading global technology provider we'll stop at nothing to help keep you secure okay we're back digging into trusted infrastructure with paris sarcadi he's a senior consultant for product marketing and storage at dell technologies parasaur welcome to the cube good to see you great to be with you dave yeah coming from hyderabad awesome so i really appreciate you uh coming on the program let's start with talking about your point of view on what cyber security resilience means to to dell generally but storage specifically yeah so for something like storage you know we are talking about the data layer name and if you look at cyber security it's all about securing your data applications and infrastructure it has been a very mature field at the network and application layers and there are a lot of great technologies right from you know enabling zero trust advanced authentications uh identity management systems and so on and and in fact you know with the advent of you know the the use of artificial intelligence and machine learning really these detection tools for cyber securities have really evolved in the network and the application spaces so for storage what it means is how can you bring them to the data layer right how can you bring you know the principles of zero trust to the data layer uh how can you leverage artificial intelligence and machine learning to look at you know access patterns and make intelligent decisions about maybe an indicator of a compromise and identify them ahead of time just like you know how it's happening and other ways of applications and when it comes to cyber resilience it's it's basically a strategy which assumes that a threat is imminent and it's a good assumption with the severity of the frequency of the attacks that are happening and the question is how do we fortify the infrastructure in the switch infrastructure to withstand those attacks and have a plan a response plan where we can recover the data and make sure the business continuity is not affected so that's uh really cyber security and cyber resiliency and storage layer and of course there are technologies like you know network isolation immutability and all these principles need to be applied at the storage level as well let me have a follow up on that if i may the intelligence that you talked about that ai and machine learning is that do you do you build that into the infrastructure or is that sort of a separate software module that that points at various you know infrastructure components how does that work both dave right at the data storage level um we have come with various data characteristics depending on the nature of data we developed a lot of signals to see what could be a good indicator of a compromise um and there are also additional applications like cloud iq is the best example which is like an infrastructure wide health monitoring system for dell infrastructure and now we have elevated that to include cyber security as well so these signals are being gathered at cloud iq level and other applications as well so that we can make those decisions about compromise and we can either cascade that intelligence and alert stream upstream for uh security teams um so that they can take actions in platforms like sign systems xtr systems and so on but when it comes to which layer the intelligence is it has to be at every layer where it makes sense where we have the information to make a decision and being closest to the data we have we are basically monitoring you know the various parallels data access who is accessing um are they crossing across any geo fencing uh is there any mass deletion that is happening or a mass encryption that is happening and we are able to uh detect uh those uh patterns and flag them as indicators of compromise and in allowing automated response manual control and so on for it teams yeah thank you for that explanation so at dell technologies world we were there in may it was one of the first you know live shows that that we did in the spring certainly one of the largest and i interviewed shannon champion and a huge takeaway from the storage side was the degree to which you guys emphasized security uh within the operating systems i mean really i mean powermax more than half i think of the features were security related but also the rest of the portfolio so can you talk about the the security aspects of the dell storage portfolio specifically yeah yeah so when it comes to data security and broadly data availability right in the context of cyber resiliency dell storage this you know these elements have been at the core of our um a core strength for the portfolio and the source of differentiation for the storage portfolio you know with almost decades of collective experience of building highly resilient architectures for mission critical data something like power max system which is the most secure storage platform for high-end enterprises and now with the increased focus on cyber security we are extending those core technologies of high availability and adding modern detection systems modern data isolation techniques to offer a comprehensive solution to the customer so that they don't have to piece together multiple things to ensure data security or data resiliency but a well-designed and well-architected solution by design is delivered to them to ensure cyber protection at the data layer got it um you know we were talking earlier to steve kenniston and pete gear about this notion of dell trusted infrastructure how does storage fit into that as a component of that sort of overall you know theme yeah and you know and let me say this if you could adjust because a lot of people might be skeptical that i can actually have security and at the same time not constrict my organizational agility that's old you know not an ore it's an end how do you actually do that if you could address both of those that would be great definitely so for dell trusted infrastructure cyber resiliency is a key component of that and just as i mentioned you know uh air gap isolation it really started with you know power protect cyber recovery you know that was the solution more than three years ago we launched and that was first in the industry which paved way to you know kind of data isolation being a core element of data management and uh for data infrastructure and since then we have implemented these technologies within different storage platforms as well so that customers have the flexibility depending on their data landscape they can approach they can do the right data isolation architecture right either natively from the storage platform or consolidate things into the backup platform and isolate from there and and the other key thing we focus in trusted infrastructure dell infra dell trusted infrastructure is you know the goal of simplifying security for the customers so one good example here is uh you know being able to respond to these cyber threats or indicators of compromise is one thing but an i.t security team may not be looking at the dashboard of the storage systems constantly right storage administration admins may be looking at it so how can we build this intelligence and provide this upstream platforms so that they have a single pane of glass to understand security landscape across applications across networks firewalls as well as storage infrastructure and in compute infrastructure so that's one of the key ways where how we are helping simplify the um kind of the ability to uh respond ability to detect and respond these threads uh in real time for security teams and you mentioned you know about zero trust and how it's a balance of you know not uh kind of restricting users or put heavy burden on you know multi-factor authentication and so on and this really starts with you know what we're doing is provide all the tools you know when it comes to advanced authentication uh supporting external identity management systems multi-factor authentication encryption all these things are intrinsically built into these platforms now the question is the customers are actually one of the key steps is to identify uh what are the most critical parts of their business or what are the applications uh that the most critical business operations depend on and similarly identify uh mission critical data where part of your response plan where it cannot be compromised where you need to have a way to recover once you do this identification then the level of security can be really determined uh by uh by the security teams by the infrastructure teams and you know another you know intelligence that gives a lot of flexibility uh for for even developers to do this is today we have apis um that so you can not only track these alerts at the data infrastructure level but you can use our apis to take concrete actions like blocking a certain user or increasing the level of authentication based on the threat level that has been perceived at the application layer or at the network layer so there is a lot of flexibility that is built into this by design so that depending on the criticality of the data criticality of the application number of users affected these decisions have to be made from time to time and it's as you mentioned it's it's a balance right and sometimes you know if if an organization had a recent attack you know the level of awareness is very high against cyber attacks so for a time you know these these settings may be a bit difficult to deal with but then it's a decision that has to be made by security teams as well got it so you're surfacing what may be hidden kpis that are being buried inside for instance the storage system through apis upstream into a dashboard so that somebody could you know dig into the storage tunnel extract that data and then somehow you know populate that dashboard you're saying you're automating that that that workflow that's a great example and you may have others but is that the correct understanding absolutely and it's a two-way integration let's say a detector an attack has been detected at a completely different layer right in the application layer or at a firewall we can respond to those as well so it's a two-way integration we can cascade things up as well as respond to threats that have been detected elsewhere um uh through the api that's great all right hey api for power skill is the best example for that uh excellent so thank you appreciate that give us the last word put a bow on this and and bring this segment home please absolutely so a dell storage portfolio um using advanced data isolation um with air gap having machine learning based algorithms to detect uh indicators of compromise and having rigor mechanisms with granular snapshots being able to recover data and restore applications to maintain business continuity is what we deliver to customers uh and these are areas where a lot of innovation is happening a lot of product focus as well as you know if you look at the professional services all the way from engineering to professional services the way we build these systems the way we we configure and architect these systems um cyber security and protection is a key focus uh for all these activities and dell.com securities is where you can learn a lot about these initiatives that's great thank you you know at the recent uh reinforce uh event in in boston we heard a lot uh from aws about you know detent and response and devops and machine learning and some really cool stuff we heard a little bit about ransomware but i'm glad you brought up air gaps because we heard virtually nothing in the keynotes about air gaps that's an example of where you know this the cso has to pick up from where the cloud leaves off but that was in front and so number one and number two we didn't hear a ton about how the cloud is making the life of the cso simpler and that's really my takeaway is is in part anyway your job and companies like dell so paris i really appreciate the insights thank you for coming on thecube thank you very much dave it's always great to be in these uh conversations all right keep it right there we'll be right back with rob emsley to talk about data protection strategies and what's in the dell portfolio you're watching thecube data is the currency of the global economy it has value to your organization and cyber criminals in the age of ransomware attacks companies need secure and resilient it infrastructure to safeguard their data from aggressive cyber attacks [Music] as part of the dell technologies infrastructure portfolio powerstor and powermax combine storage innovation with advanced security that adheres to stringent government regulations and corporate compliance requirements security starts with multi-factor authentication enabling only authorized admins to access your system using assigned roles tamper-proof audit logs track system usage and changes so it admins can identify suspicious activity and act with snapshot policies you can quickly automate the protection and recovery process for your data powermax secure snapshots cannot be deleted by any user prior to the retention time expiration dell technologies also make sure your data at rest stays safe with power store and powermax data encryption protects your flash drive media from unauthorized access if it's removed from the data center while adhering to stringent fips 140-2 security requirements cloud iq brings together predictive analytics anomaly detection and machine learning with proactive policy-based security assessments monitoring and alerting the result intelligent insights that help you maintain the security health status of your storage environment and if a security breach does occur power protect cyber recovery isolates critical data identifies suspicious activity and accelerates data recovery using the automated data copy feature unchangeable data is duplicated in a secure digital vault then an operational air gap isolates the vault from the production and backup environments [Music] architected with security in mind dell emc power store and powermax provides storage innovation so your data is always available and always secure wherever and whenever you need it [Music] welcome back to a blueprint for trusted infrastructure we're here with rob emsley who's the director of product marketing for data protection and cyber security rob good to see a new role yeah good to be back dave good to see you yeah it's been a while since we chatted last and you know one of the changes in in my world is that i've expanded my responsibilities beyond data protection marketing to also focus on uh cyber security marketing specifically for our infrastructure solutions group so certainly that's you know something that really has driven us to you know to come and have this conversation with you today so data protection obviously has become an increasingly important component of the cyber security space i i don't think necessarily of you know traditional backup and recovery as security it's to me it's an adjacency i know some companies have said oh yeah now we're a security company they're kind of chasing the valuation for sure bubble um dell's interesting because you you have you know data protection in the form of backup and recovery and data management but you also have security you know direct security capability so you're sort of bringing those two worlds together and it sounds like your responsibility is to to connect those those dots is that right absolutely yeah i mean i think that uh the reality is is that security is a a multi-layer discipline um i think the the days of thinking that it's one uh or another um technology that you can use or process that you can use to make your organization secure uh are long gone i mean certainly um you actually correct if you think about the backup and recovery space i mean people have been doing that for years you know certainly backup and recovery is all about the recovery it's all about getting yourself back up and running when bad things happen and one of the realities unfortunately today is that one of the worst things that can happen is cyber attacks you know ransomware malware are all things that are top of mind for all organizations today and that's why you see a lot of technology and a lot of innovation going into the backup and recovery space because if you have a copy a good copy of your data then that is really the the first place you go to recover from a cyber attack and that's why it's so important the reality is is that unfortunately the cyber criminals keep on getting smarter i don't know how it happens but one of the things that is happening is that the days of them just going after your production data are no longer the only challenge that you have they go after your your backup data as well so over the last half a decade dell technologies with its backup and recovery portfolio has introduced the concept of isolated cyber recovery vaults and that is really the you know we've had many conversations about that over the years um and that's really a big tenant of what we do in the data protection portfolio so this idea of of cyber security resilience that definition is evolving what does it mean to you yeah i think the the analyst team over at gartner they wrote a very insightful paper called you will be hacked embrace the breach and the whole basis of this analysis is so much money has been spent on prevention is that what's out of balance is the amount of budget that companies have spent on cyber resilience and cyber resilience is based upon the premise that you will be hacked you have to embrace that fact and be ready and prepared to bring yourself back into business you know and that's really where cyber resiliency is very very different than cyber security and prevention you know and i think that balance of get your security disciplines well-funded get your defenses as good as you can get them but make sure that if the inevitable happens and you find yourself compromised that you have a great recovery plan and certainly a great recovery plan is really the basis of any good solid data protection backup and recovery uh philosophy so if i had to do a swot analysis we don't have to do the wot but let's focus on the s um what would you say are dell's strengths in this you know cyber security space as it relates to data protection um one is we've been doing it a long time you know we talk a lot about dell's data protection being proven and modern you know certainly the experience that we've had over literally three decades of providing enterprise scale data protection solutions to our customers has really allowed us to have a lot of insight into what works and what doesn't as i mentioned to you one of the unique differentiators of our solution is the cyber recovery vaulting solution that we introduced a little over five years ago five six years parapatek cyber recovery is something which has become a unique capability for customers to adopt uh on top of their investment in dell technologies data protection you know the the unique elements of our solution already threefold and it's we call them the three eyes it's isolation it's immutability and it's intelligence and the the isolation part is really so important because you need to reduce the attack surface of your good known copies of data you know you need to put it in a location that the bad actors can't get to it and that really is the the the the essence of a cyber recovery vault interestingly enough you're starting to see the market throw out that word um you know from many other places but really it comes down to having a real discipline that you don't allow the security of your cyber recovery vault to be compromised insofar as allowing it to be controlled from outside of the vault you know allowing it to be controlled by your backup application our cyber recovery vaulting technology is independent of the backup infrastructure it uses it but it controls its own security and that is so so important it's like having a vault that the only way to open it is from the inside you know and think about that if you think about you know volts in banks or volts in your home normally you have a keypad on the outside think of our cyber recovery vault as having its security controlled from inside of the vault so nobody can get in nothing can get in unless it's already in and if it's already in then it's trusted exactly yeah exactly yeah so isolation is the key and then you mentioned immutability is the second piece yeah so immutability is is also something which has been around for a long time people talk about uh backup immunoability or immutable backup copies so immutability is just the the the additional um technology that allows the data that's inside of the vault to be unchangeable you know but again that immutability you know your mileage varies you know when you look across the uh the different offers that are out there in the market especially in the backup industry you make a very valid point earlier that the backup vendors in the market seems to be security washing their marketing messages i mean everybody is leaning into the ever-present danger of cyber security not a bad thing but the reality is is that you have to have the technology to back it up you know quite literally yeah no pun intended and then actually pun intended now what about the intelligence piece of it uh that's that's ai ml where does that fit for sure so the intelligence piece is delivered by um a solution called cybersense and cybersense for us is what really gives you the confidence that what you have in your cyber recovery vault is a good clean copy of data so it's looking at the backup copies that get driven into the cyber vault and it's looking for anomalies so it's not looking for signatures of malware you know that's what your antivirus software does that's what your endpoint protection software does that's on the prevention side of the equation but what we're looking for is we're looking to ensure that the data that you need when all hell breaks loose is good and that when you get a request to restore and recover your business you go right let's go and do it and you don't have any concern that what you have in the vault has been compromised so cyber sense is really a unique analytic solution in the market based upon the fact that it isn't looking at cursory indicators of of um of of of malware infection or or ransomware introduction it's doing full content analytics you know looking at you know has the data um in any way changed has it suddenly become encrypted has it suddenly become different to how it was in the previous scan so that anomaly detection is very very different it's looking for um you know like different characteristics that really are an indicator that something is going on and of course if it sees it you immediately get flagged but the good news is is that you always have in the vault the previous copy of good known data which now becomes your restore point so we're talking to rob emsley about how data protection fits into what dell calls dti dell trusted infrastructure and and i want to come back rob to this notion of and not or because i think a lot of people are skeptical like how can i have great security and not introduce friction into my organization is that an automation play how does dell tackle that problem i mean i think a lot of it is across our infrastructure is is security has to be built in i mean intrinsic security within our servers within our storage devices uh within our elements of our backup infrastructure i mean security multi-factor authentication you know elements that make the overall infrastructure secure you know we have capabilities that you know allow us to identify whether or not configurations have changed you know we'll probably be talking about that a little bit more to you later in the segment but the the essence is is um security is not a bolt-on it has to be part of the overall infrastructure and that's so true um certainly in the data protection space give us the the bottom line on on how you see dell's key differentiators maybe you could talk about dell of course always talks about its portfolio but but why should customers you know lead in to dell in in this whole cyber resilience space um you know staying on the data protection space as i mentioned the the the work we've been doing um to introduce this cyber resiliency solution for data protection is in our opinion as good as it gets you know the you know you've spoken to a number of our of our best customers whether it be bob bender from founders federal or more recently at delton allergies world you spoke to tony bryson from the town of gilbert and these are customers that we've had for many years that have implemented cyber recovery vaults and at the end of the day they can now sleep at night you know that's really the the peace of mind that they have is that the insurance that a data protection from dell cyber recovery vault a parapatex cyber recovery solution gives them you know really allows them to you know just have the assurance that they don't have to pay a ransom if they have a an insider threat issue and you know all the way down to data deletion is they know that what's in the cyber recovery vault is good and ready for them to recover from great well rob congratulations on the new scope of responsibility i like how you know your organization is expanding as the threat surface is expanding as we said data protection becoming an adjacency to security not security in and of itself a key component of a comprehensive security strategy rob emsley thank you for coming back in the cube good to see you again you too dave thanks all right in a moment i'll be back to wrap up a blueprint for trusted infrastructure you're watching the cube every day it seems there's a new headline about the devastating financial impacts or trust that's lost due to ransomware or other sophisticated cyber attacks but with our help dell technologies customers are taking action by becoming more cyber resilient and deterring attacks so they can greet students daily with a smile they're ensuring that a range of essential government services remain available 24 7 to citizens wherever they're needed from swiftly dispatching public safety personnel or sending an inspector to sign off on a homeowner's dream to protecting restoring and sustaining our precious natural resources for future generations with ever-changing cyber attacks targeting organizations in every industry our cyber resiliency solutions are right on the money providing the security and controls you need we help customers protect and isolate critical data from ransomware and other cyber threats delivering the highest data integrity to keep your doors open and ensuring that hospitals and healthcare providers have access to the data they need so patients get life-saving treatment without fail if a cyber incident does occur our intelligence analytics and responsive team are in a class by themselves helping you reliably recover your data and applications so you can quickly get your organization back up and running with dell technologies behind you you can stay ahead of cybercrime safeguarding your business and your customers vital information learn more about how dell technology's cyber resiliency solutions can provide true peace of mind for you the adversary is highly capable motivated and well equipped and is not standing still your job is to partner with technology vendors and increase the cost of the bad guys getting to your data so that their roi is reduced and they go elsewhere the growing issues around cyber security will continue to drive forward thinking in cyber resilience we heard today that it is actually possible to achieve infrastructure security while at the same time minimizing friction to enable organizations to move quickly in their digital transformations a xero trust framework must include vendor r d and innovation that builds security designs it into infrastructure products and services from the start not as a bolt-on but as a fundamental ingredient of the cloud hybrid cloud private cloud to edge operational model the bottom line is if you can't trust your infrastructure your security posture is weakened remember this program is available on demand in its entirety at thecube.net and the individual interviews are also available and you can go to dell security solutions landing page for for more information go to dell.com security solutions that's dell.com security solutions this is dave vellante thecube thanks for watching a blueprint for trusted infrastructure made possible by dell we'll see you next time

(upbeat music) >> Hi, welcome to this year's Annual Wikibon Predictions. This is our 2018 version. Last year, we had a very successful webinar describing what we thought was going to happen in 2017 and beyond and we've assembled a team to do the same thing again this year. I'm very excited to be joined by the folks listed here on the screen. My name is Peter Burris. But with me is David Floyer, Jim Kobielus is remote. George Gilbert's here in our Pal Alto studio with me. Neil Raden is remote. David Vellante is here in the studio with me. And Stuart Miniman is back in our Marlboro office. So thank you analysts for attending and we look forward to a great teleconference today. Now what we're going to do over the course of the next 45 minutes or so is we're going to hit about 13 of the 22 predictions that we have for the coming year. So if you have additional questions, I want to reinforce this, if you have additional questions or things that don't get answered, if you're a client, give us a call. Reach out to us. We'll leave you with the contact information at the end of the session. But to start things off we just want to make sure that everybody understands where we're coming from. And let you know who is Wikibon. So Wikibon is a company that starts with the idea of what's important as to research communities. Communities are where the action is. Community is where the change is happening. And community is where the trends are being established. And so we use digital technologies like theCUbE, CrowdChat and others to really ensure that we are surfacing the best ideas that are in a community and making them available to our clients so that they can succeed successfully, they can be more successful in their endeavors. When we do that, our focus has always been on a very simple premise. And that is that we're moving to an era of digital business. For many people, digital business can mean virtually anything. For us it means something very specific. To us, the difference between business and digital business is data. A digital business uses data to differentially create and keep a customer. So borrowing from what Peter Drucker said if the goal of business is to create customers and keep and sustain customers, the goal of digital business is to use data to do that. And that's going to inform an enormous number of conversations and an enormous number of decisions and strategies over the next few years. We specifically believe that all businesses are going to have establish what we regard as the five core digital business capabilities. First, they're going to have to put in place concrete approaches to turning more data into work. It's not enough to just accrete data, to capture data or to move data around. You have to be very purposeful and planful in how you establish the means by which you turn that data into work so that you can create and keep more customers. Secondly, it's absolutely essential that we build kind of the three core technology issues here, technology capabilities of effectively doing a better job of capturing data and IoT and people, or internet of things and people, mobile computing for example, is going to be a crucial feature of that. You have to then once you capture that data, turn it into value. And we think this is the essence of what big data and in many respects AI is going to be all about. And then once you have the possibility, kind of the potential energy of that data in place, then you have to turn it into kinetic energy and generate work in your business through what we call systems of agency. Now, all of this is made possible by this significant transformation that happens to be conterminous with this transition to digital business. And that is the emergence of the cloud. The technology industry has always been defined by the problems it was able to solve, catalyzed by the characteristics of the technology that made it possible to solve them. And cloud is crucial to almost all of the new types of problems that we're going to solve. So these are the five digital business capabilities that we're going to talk about, where we're going to have our predictions. Let's start first and foremost with this notion of turn more data into work. So our first prediction relates to how data governance is likely to change in a global basis. If we believe that we need to turn more data into work well, businesses haven't generally adopted many of the principles associated with those practices. They haven't optimized to do that better. They haven't elevated those concepts within the business as broadly and successfully as they have or as they should. We think that's going to change in part by the emergence of GDPR or the General Data Protection Regulation. It's going to go in full effect in May 2018. A lot has been written about it. A lot has been talked about. But our core issues ultimately are is that the dictates associated with GDPR are going to elevate the conversation on a global basis. And it mandates something that's now called the data protection officer. We're going to talk about that in a second David Vellante. But if is going to have real teeth. So we were talking with one chief privacy officer not too long ago who suggested that had the Equifax breach occurred under the rules of GDPR that the actual finds that would have been levied would have been in excess of 160 billion dollars which is a little bit more than the zero dollars that has been fined thus far. Now we've seen new bills introduced in Congress but ultimately our observation and our conversations with a lot of data chief privacy officers or data protection officers is that in the B2B world, GDPR is going to strongly influence not just our businesses behavior regarding data in Europe but on a global basis. Now that has an enormous implication David Vellante because it certainly suggest this notion of a data protection officer is something now we've got another potential chief here. How do we think that's going to organize itself over the course of the next few years? >> Well thank you Peter. There are a lot of chiefs (laughs) in the house and sometimes it gets confusing as the CIO, there's the CDO and that's either chief digital officer or chief data officer. There's the CSO, could be strategy, sometimes that could be security. There's the CPO, is that privacy or product. As he says, it gets confusing sometimes. On theCUbE we talked to all of these roles so we wanted to try to add some clarity to that. First thing we want to say is that the CIO, the chief information officer, that role is not going away. A lot of people predict that, we think that's nonsense. They will continue to have a critical role. Digital transformations are the priority in organizations. And so the chief digital officer is evolving from more than just a strategy role to much more of an operation role. Generally speaking, these chiefs tend to report in our observation to the chief operating officer, president COO. And we see the chief digital officer as increasing operational responsibility aligning with the COO and getting incremental responsibility that's more operational in nature. So the prediction really is that the chief digital officer is going to emerge as a charismatic leader amongst these chiefs. And by 2022, nearly 50% of organizations will position the chief digital officer in a more prominent role than the CIO, the CISO, the CDO and the CPO. Those will still be critical roles. The CIO will be an enabler. The chief information security officer has a huge role obviously to play especially in terms of making security a teams sport and not just falling on IT's shoulders or the security team's shoulders. The chief data officer who really emerged from a records and data management role in many cases, particularly within regulated industries will still be responsible for that data architecture and data access working very closely with the emerging chief privacy officer and maybe even the chief data protection officer. Those roles will be pretty closely aligned. So again, these roles remain critical but the chief digital officer we see as increasing in prominence. >> Great, thank you very much David. So when we think about these two activities, what we're really describing is over the course of the next few years, we strongly believe that data will be regarded more as an asset within business and we'll see resources devoted to it and we'll see certainly management devoted to it. Now, that leads to the next set of questions as data becomes an asset, the pressure to acquire data becomes that much more acute. We believe strongly that IoT has an enormous implication longer term as a basis for thinking about how data gets acquired. Now, operational technology has been in place for a long time. We're not limiting ourselves just operational technology when we talk about this. We're really talking about the full range of devices that are going to provide and extend information and digital services out to consumers, out to the Edge, out to a number of other places. So let's start here. Over the course of the next few years, the Edge analytics are going to be an increasingly important feature overall of how technology decisions get made, how technology or digital business gets conceived and even ultimately how business gets defined. Now David Floyer's done a significant amount of work in this domain and we've provided that key finding on the right hand side. And what it shows is that if you take a look at an Edge based application, a stylized Edge based application and you presume that all the data moves back to an centralized cloud, you're going to increase your costs dramatically over a three year period. Now that moderates the idea or moderates the need ultimately for providing an approach to bringing greater autonomy, greater intelligence down to the Edge itself and we think that ultimately IoT and Edge analytics become increasingly synonymous. The challenge though is that as we evolve, while this has a pressure to keep more of the data at the Edge, that ultimately a lot of the data exhaust can someday become regarded as valuable data. And so as a consequence of that, there's still a countervailing impression to try to still move all data not at the moment of automation but for modeling and integration purposes, back to some other location. The thing that's going to determine that is going to be rate at which the cost of moving the data around go down. And our expectation is over the next few years when we think about the implications of some of the big cloud suppliers, Amazon, Google, others, that are building out significant networks to facilitate their business services may in fact have a greater impact on the common carriers or as great an impact on the common carriers as they have on any server or other infrastructure company. So our prediction over the next few years is watch what Amazon, watch what Google do as they try to drive costs down inside their networks because that will have an impact how much data moves from the Edge back to the cloud. It won't have an impact necessarily on the need for automation at the Edge because latency doesn't change but it will have a cost impact. Now that leads to a second consideration and the second consideration is ultimately that when we talk about greater autonomy at the Edge we need to think about how that's going to play out. Jim Kobielus. >> Jim: Hey thanks a lot Peter. Yeah, so what we're seeing at Wikibon is that more and more of the AI applications, more of the AI application development involves AI and more and more of the AI involves deployment of those models, deep learning machine learning and so forth to the Edges of the internet of things and people. And much of that AI will be operating autonomously with little or no round-tripping back to the cloud. What that's causing, in fact, we're seeing really about a quarter of the AI development projects (static interference with web-conference) as Edge deployment. What that involves is that more and more of that AI will be, those applications will be bespoke. They'll be one of a kind, or unique or an unprecedented application and what that means is that, you know, there's a lot of different deployment scenarios within which organizations will need to use new forms of learning to be able to ready that data, those AI applications to do their jobs effectively albeit to predictions of real time, guiding of an autonomous vehicle and so forth. Reinforcement learning is the core of what many of these kinds of projects, especially those that involve robotics. So really software is hitting the world and you know the biggest parts are being taken out of the Edge, much of that is AI, much of that autonomous, where there is no need or less need for real time latency in need of adaptive components, AI infused components where as they can learn by doing. From environmental variables, they can adapt their own algorithms to take the right actions. So, they'll have far reaching impacts on application development in 2018. For the developer, the new developer really is a data scientist at heart. They're going to have to tap into a new range of sources of data especially Edge sourced data from the senors on those devices. They're going to need to do commitment training and testing especially reinforcement learning which doesn't involve trained data so much as it involves being able to build an algorithm that can learn to maximum what's called accumulative reward function and if you do the training there adaptly in real time at the Edge and so forth and so on. So really, much of this will be bespoke in the sense that every Edge device increasingly will have its own set of parameters and its own set of objective functions which will need to be optimized. So that's one of the leading edge forces, trends, in development that we see in the coming year. Back to you Peter. >> Excellent Jim, thank you very much. The next question here how are you going to create value from data? So once you've, we've gone through a couple trends and we have multiple others about what's going to happen at the Edge. But as we think about how we're going to create value from data, Neil Raden. >> Neil: You know, the problem is that data science emerged rapidly out of sort of a perfect storm of big data and cloud computing and so forth. And people who had been involved in quantitative methods you know rapidly glommed onto the title because it was, lets face it, it was very glamorous and paid very well. But there weren't really good best practices. So what we have in data science is a pretty wide field of things that are called data science. My opinion is that the true data scientists are people who are scientists and are involved in developing new or improving algorithms as opposed to prepping data and applying models. So the whole field really kind of generated very quickly, in really, just in a few years. To me I called it generation zero which is more like data prep and model management all done manually. And it wasn't really sustainable in most organizations because for obvious reasons. So generation one, then some vendors stepped up with tool kits or benchmarks or whatever for data scientists and made it a little better. And generation two is what we're going to see in 2018, is the need for data scientists to no longer prep data or at least not spend very much time with it. And not to do model management because the software will not only manage the progression of the models but even recommend them and generate them and select the data and so forth. So it's in for a very big change and I think what you're going to see is that the ranks of data scientists are going to sort of bifurcate to old style, let me sit down and write some spaghetti code in R or Java or something and those that use these advanced tool kits to really get the work done. >> That's great Neil and of course, when we start talking about getting the work done, we are becoming increasingly dependent upon tools, aren't we George? But the tool marketplace for data science, for big data, has been somewhat fragmented and fractured. And hasn't necessarily focused on solving the problems of the data scientists. But in many respects focusing the problems that the tools themselves have. What's going to happen in the coming year when we start thinking about Neil's prescription that as the tools improve what's going to happen to the tools. >> Okay so, the big thing that we see supporting what Neil's talking about, what Neil was talking about is partly a symptom of a product issue and a go to market issue where the produce issue was we had a lot of best of breed products that were all designed to fit together. That in the broader big data space, that's the same issue that we faced with more narrowly with ArpiM Hadoop where you know, where we were trying to fit together a bunch of open source packages that had an admin and developer burden. More broadly, what Neil is talking about is sort of a richer end to end tools that handle both everything from the ingest all to the way to the operationalization and feedback of the models. But part of what has to go on here is that with open source, these open source tools the price point and the functional footprints that many of the vendors are supporting right now can't feed an enterprise sales force. Everyone talks with their open source business models about land and expand and inside sales. But the problem is once you want to go to wide deployment in an enterprise, you still need someone negotiating commercial terms at a senior level. You still need the technical people fitting the tools into a broader architecture. And most of the vendors that we have who are open source vendors today, don't have either the product breadth or the deal size to support traditional enterprise software. An account team would typically a million and a half to two million quota every year so we see consolidation and the consolidation again driven by the need for simplicity for the admins and the developers and for business model reasons to support enterprise sales force. >> All right, so what we're going to see happen in the course of the coming year is a lot of specialization and recognition of what is data science, what are the practices, how is it going to work, supported by an increasing quality of tools and a lot of tool vendors are going to be left behind. Now the third kind of notion here for those core technology capabilities is we still have to enact based on data. The good new is that big data is starting to show some returns in part because of some of the things that AI and other technologies are capable of doing. But we have to move beyond just creating the potential for, we have to turn that into work and that's what we mean ultimately by this notion of systems of agency. The idea that data driven applications will increasingly be act on behalf of a brand, on behalf of a company and building those systems out is going to be crucial. It's going to have a whole new set of disciplines and expertise required. So when we think about what's going to be required, it always starts with this notion of AI. A lot of folks are presuming however, that AI is going to be relatively easy to build or relatively easy to put together. We have a different opinion George. What do we think is going to happen as these next few years unfold related to AI adoption in large enterprises? >> Okay so, let's go back to the lessons we learned from sort of the big data, the raw, you know, let's put a data link in place which was sort of the top of everyone's agenda for several years. The expectation was it was going to cure cancer, taste like chocolate and cost a dollar. And uh. (laughing) It didn't quite work out that way. Partly because we had a burden on the administrator again of so many tools that weren't all designed to fit together, even though they were distributed together. And then the data scientists, the guys who had to take all this data that wasn't carefully curated yet. And turn that into advanced analytics and machine learning models. We have many of the same problems now with tool sets that are becoming more integrated but at lower levels. This is partly what Neil Raden was just talking about. What we have to recognize is something that we see all along, I mean since the beginning of (laughs) corporate computing. We have different levels of extraction and you know at the very bottom, when you're dealing with things like Tensorflow or MXNet, that's not for mainstream enterprises. That's for you know, the big sophisticated tech companies who are building new algorithms on those frameworks. There's a level above that where you're using like a spark cluster in the machine learning built into that. That's slightly more accessible but when we talk about mainstream enterprises taking advantage of AI, the low hanging fruit is for them to use the pre-trained models that the public cloud vendors have created with all the consumer data on speech, image recognition, natural language processing. And then some of those capabilities can be further combined into applications like managing a contact center and we'll see more from like Amazon, like recommendation engines, fulfillment optimization, pricing optimization. >> So our expectation ultimately George is that we're going to see a lot of this, a lot of AI adoption happen through existing applications because the vendors that are capable of acquiring a talent, taking or experimenting, creating value, software vendors are going to be where a lot of the talent ends up. So Neil, we have an example of that. Give us an example of what we think is going to happen in 2018 when we start thinking about exploiting AI and applications. >> Neil: I think that it's fairly clear to be the application of what's called advanced analytics and data science and even machine learning. But really, it's rapidly becoming a commonplace in organizations not just at the bottom of the triangle here. But I like the example of SalesForce.com. What they've done with Einstein, is they've made machine learning and I guess you can say, AI applications available to their customer base and why is that a good thing? Because their customer base already has a giant database of clean data that they can use. So you're going to see a huge number of applications being built with Einstein against Salesforce.com data. But there's another thing to consider and that is a long time ago Salesforce.com built connectors to a zillion times of external data. So, if you're a SalesForce.com customer using Einstein, you're going to be able to use those advanced tools without knowing anything about how to train a machine learning model and start to build those things. And I think that they're going to lead the industry in that sense. That's going to push their revenue next year to, I don't know, 11 billion dollars or 12 billion dollars. >> Great, thanks Neil. All right so when we think about further evidence of this and further impacts, we ultimately have to consider some of the challenges associated with how we're going to create application value continually from these tools. And that leads to the idea that one of the cobblers children, it's going to gain or benefit from AI will in fact be the developer organization. Jim, what's our prediction for how auto-programming impacts development? >> Jim: Thank you very much Peter. Yeah, automation, wow. Auto-programming like I said is the epitome of enterprise application development for us going forward. People know it as co-generation but that really understates the control of auto-programming as it's evolving. Within 2018, what we're going to see is that machine learning driven co-generation approach of becoming the forefront of innovation. We're seeing a lot of activity in the industry in which applications use ML to drive the productivity of developers for all kinds of applications. We're also seeing a fair amount of what's called RPA, robotic process automation. And really, how they differ is that ML will deliver or will drive co-generation, from what I call the inside out meaning, creating reams of code that are geared to optimize a particular application scenario. This is RPA which really takes over the outside in approach which is essentially, it's the evolution of screen scraping that it's able to infer the underlined code needed for applications of various sorts from the external artifacts, the screens and from sort of the flow of interactions and clips and so forth for a given application. We're going to see that ML and RPA will compliment each other in the next generation of auto-programming capabilities. And so, you know, really application development tedium is really the enemy of, one of the enemies of productivity (static interference with web-conference). This is a lot of work, very detailed painstaking work. And what they need is to be better, more nuanced and more adaptive auto-programming tools to be able to build the code at the pace that's absolutely necessary for this new environment of cloud computing. So really AI-related technologies can be applied and are being applied to application development productivity challenges of all sorts. AI is fundamental to RPA as well. We're seeing a fair number of the vendors in that stage incorporate ML driven OCR and natural language processing and screen scraping and so forth into their core tools to be able to quickly build up the logic albeit to drive sort of the verbiage outside in automation of fairly complex orchestration scenario. In 2018, we'll see more of these technologies come together. But you know, they're not a silver bullet. 'Cause fundamentally and for organizations that are considering going deeply down into auto-programming they're going to have to factor AI into their overall plans. They need to get knowledgeable about AI. They're going to need to bring more AI specialists into their core development teams to be able to select from the growing range of tools that are out there, RPA and ML driven auto-programming. Overall, really what we're seeing is that the AI, the data scientists, who's been the fundamental developer of AI, they're coming into the core of development tools and skills in organizations. And they're going to be fundamental to this whole trend in 2018 and beyond. If AI gets proven out in auto-programming, these developers will then be able to evangelize the core utility of the this technology, AI. In a variety of other backend but critically important investments that organizations will be making in 2018 and beyond. Especially in IT operations and in management, AI is big in that area as well. Back to you there, Peter. >> Yeah, we'll come to that a little bit later in the presentation Jim, that's a crucial point but the other thing we want to note here regarding ultimately how folks will create value out of these technologies is to consider the simple question of okay, how much will developers need to know about infrastructure? And one of the big things we see happening is this notion of serverless. And here we've called it serverless, developer more. Jim, why don't you take us through why we think serverless is going to have a significant impact on the industry, at least certainly from a developer perspective and developer productivity perspective. >> Jim: Yeah, thanks. Serverless is really having an impact already and has for the last several years now. Now, everybody, many are familiar in the developer world, AWS Lambda which is really the ground breaking public cloud service that incorporates the serverless capabilities which essentially is an extraction layer that enables developers to build stateless code that executes in a cloud environment without having to worry about and to build microservices, we don't have to worry about underlined management of containers and virtual machines and so forth. So in many ways, you know, serverless is a simplification strategy for developers. They don't have to worry about the underlying plumbing. They can worry, they need to worry about the code, of course. What are called Lambda functions or functional methods and so forth. Now functional programming has been around for quite a while but now it's coming to the form in this new era of serverless environment. What we'll see in 2018 is that we're predicting is that more than 50% of lean microservices employees, in the public cloud will be deployed in serverless environments. There's AWS and Microsoft has the Azure function. IMB has their own. Google has their own. There's a variety of private, there's a variety of multiple service cloud code bases for private deployment of serverless environments that we're seeing evolving and beginning to deploy in 2018. They all involve functional programming which really, along, you know, when coupled with serverless clouds, enables greater scale and speed in terms of development. And it's very agile friendly in the sense that you can quickly Github a functionally programmed serverless microservice in a hurry without having to manage state and so forth. It's very DevOps friendly. In the very real sense it's a lot faster than having to build and manage and tune. You know, containers and DM's and so forth. So it can enable a more real time and rapid and iterative development pipeline going forward in cloud computing. And really fundamentally what serverless is doing is it's pushing more of these Lamba functions to the Edge, to the Edges. If you're at an AWS Green event last week or the week before, but you notice AWS is putting a big push on putting Lambda functions at the Edge and devices for the IoT as we're going to see in 2018. Pretty much the entire cloud arena. Everybody will push more of the serverless, functional programming to the Edge devices. It's just a simplification strategy. And that actually is a powerful tool for speeding up some of the development metabolism. >> All right, so Jim let me jump in here and say that we've now introduced the, some of these benefits and really highlighted the role that the cloud is going to play. So, let's turn our attention to this question of cloud optimization. And Stu, I'm going to ask you to start us off by talking about what we mean by true private cloud and ultimately our prediction for private cloud. Do we have, why don't you take us through what we think is going to happen in this world of true private cloud? >> Stuart: Sure Peter, thanks a lot. So when Wikibon, when we launched the true private cloud terminology which was about two weeks ago next week, two years ago next week, it was in some ways coming together of a lot of trends similar to things that you know, George, Neil and James have been talking about. So, it is nothing new to say that we needed to simplify the IT stack. We all know, you know the tried and true discussion of you know, way too much of the budget is spent kind of keeping lights on. What we'd like to say is kind of running the business. If you squint through this beautiful chart that we have on here, a big piece of this is operational staffing is where we need to be able to make a significant change. And what we've been really excited and what led us to this initial market segment and what we're continuing to see good growth on is the move from traditional, really siloed infrastructure to you want to have, you know, infrastructure where it is software based. You want IT to really be able to focus on the application services that they're running. And what our focus for the this for the 2018 is of course it's the central point, it's the data that matters here. The whole reason we've infrastructured this to be able to run applications and one of the things that is a key determiner as to where and what I use is the data and how can I not only store that data but actually gain value from that data. Something we've talked about time and again and that is a major determining factor as to am I building this in a public cloud or am I doing it in you know my core. Is it something that is going to live on the Edge. So that's what we were saying here with the true private cloud is not only are we going to simplify our environment and therefore it's really the operational model that we talked about. So we often say the line, cloud is not a destination. But it's an operational model. So a true private cloud giving me some of the you know, feel and management type of capability that I had had in the public cloud. It's, as I said, not just virtualization. It's much more than that. But how can I start getting services and one of the extensions is true private cloud does not live in isolation. When we have kind of a core public cloud and Edge deployments, I need to think about the operational models. Where data lives, what processing happens need to be as environments, and what data we'll need to move between them and of course there's fundamental laws of physics that we need to consider in that. So, the prediction of course is that we know how much gear and focus has been on the traditional data center. And true private cloud helps that transformation to modernization and the big focus is many of these applications we've been talking about and uses of data sets are starting to come into these true private cloud environments. So, you know, we've had discussions. There's Spark, there's modern databases. Many of these, there's going to be many reasons why they might live in the private cloud environment. And therefore that's something that we're going to see tremendous growth and a lot of focus. And we're seeing a new wave of companies that are focusing on this to deliver solutions that will do more than just a step function for infrastructure or get us outside of our silos. But really helps us deliver on those cloud native applications where we pull in things like what Jim was talking about with serverless and the like. >> All right, so Stu, what that suggests ultimately is that data is going to dictate that everything's not going to end up in the private or in the public cloud or centralized public clouds because of latency costs, data governance and IP protection reasons. And there will be some others. At bare minimum, that means that we're going to have it in most large enterprises as least a couple of clouds. Talk to us about what this impact of multi cloud is going to look like over the course of the next few years. >> Stuart: Yeah, critical point there Peter. Because, right, unfortunately, we don't have one solution. There's nobody that we run into that say, oh, you know, I just do a single you know, one environment. You know it would be great if we only had one application to worry about. But as you've done this lovely diagram here, we all use lots of SaaS and increasingly, you know, Oracle, Microsoft, SalesForce, you know, all pushing everybody to multiple SaaS environments that has major impacts on my security and where my data lives. Public clouds, no doubt is growing at leaps and bounds. And many customers are choosing applications to live in different places. So just as in data centers, I would kind of look at it from an application standpoint and build up what I need. Often, there's you know, Amazon doing phenomenal. But you know, maybe there's things that I'm doing with Azure. Maybe there's things that's I'm doing with Google or others as well as my service providers for locality, for you know, specialized services, that there's reasons why people are doing it. And what customers would love is an operational model that can actually span between those. So we are very early in trying to attack this multi cloud environment. There's everything from licensing to security to you know, just operationally how do I manage those. And a piece of them that we were touching on in this prediction year, is Kubernetes actually can be a key enabler for that cloud native environment. As Jim talked about the serverless, what we'd really like is our developer to be able to focus on building their application and not think as much about the underlined infrastructure whether that be you know, racket servers that I built myself or public cloud infrastructures. So we really want to think more it's at the data and application level. It's SaaS and pass is the model and Kubernetes holds the promise to solve a piece of this puzzle. Now Kubernetes is not by no means a silver bullet for everything that we need. But it absolutely, it is doing very well. Our team was at the Linux, the CNCF show at KubeCon last week and there is you know, broad adoption from over 40 of the leading providers including Amazon is now a piece. Even SalesForce signed up to the CNCF. So Kubernetes is allowing me to be able to manage multi cloud workflows and therefore the prediction we have here Peter is that 50% of developing teams will be building, sustaining multi cloud with Kubernetes as a foundational component of that. >> That's excellent Stu. But when we think about it, the hardware of technology especially because of the opportunities associated with true private cloud, the hardware technologies are also going to evolve. There will be enough money here to sustain that investment. David Floyer, we do see another architecture on the horizon where for certain classes of workloads, we will be able to collapse and replicate many of these things in an economical, practical way on premise. We call that UniGrid, NVME is, over fabric is a crucial feature of UniGrid. >> Absolutely. So, NVMe takes, sorry NVMe over fabric or NVMe-oF takes NVMe which is out there as storage and turns it into a system framework. It's a major change in system architecture. We call this UniGrid. And it's going to be a focus of our research in 2018. Vendors are already out there. This is the fastest movement from early standards into products themselves. You can see on the chart that IMB have come out with NVMe over fabrics with the 900 storage connected to the power. Nine systems. NetApp have the EF750. A lot of other companies are there. Meta-Lox is out there looking for networks, for high speed networks. Acceler has a major part of the storage software. So and it's going to be used in particular with things like AI. So what are the drivers and benefits of this architecture? The key is that data is the bottleneck for application. We've talked about data. The amount of data is key to making applications more effective and higher value. So NVMe and NVMe over fabrics allows data to be accessed in microseconds as opposed to milliseconds. And it allows gigabytes of data per second as opposed to megabytes of data per second. And it also allows thousands of processes to access all of the data in very very low latencies. And that gives us amazing parallelism. So what's is about is disaggregation of storage and network and processes. There are some huge benefits from that. Not least of which is you save about 50% of the processor you get back because you don't have to do storage and networking on it. And you save from stranded storage. You save from stranded processor and networking capabilities. So it's overall, it's going to be cheaper. But more importantly, it makes it a basis for delivering systems of intelligence. And systems of intelligence are bringing together systems of record, the traditional systems, not rewriting them but attaching them to real time analytics, real time AI and being able to blend those two systems together because you've got all of that additional data you can bring to bare on a particular problem. So systems themselves have reached pretty well the limit of human management. So, one of the great benefits of UniGrid is to have a single metadata lab from all of that data, all of those processes. >> Peter: All those infrastructure elements. >> All those infrastructure elements. >> Peter: And application. >> And applications themselves. So what that leads to is a huge potential to improve automation of the data center and the application of AI to operations, operational AI. >> So George, it sounds like it's going to be one of the key potential areas where we'll see AI be practically adopted within business. What do we think is going to happen here as we think about the role that AI is going to play in IT operations management? >> Well if we go back to the analogy with big data that we thought was going to you know, cure cancer, taste like chocolate, cost a dollar, and it turned out that the application, the most wide spread application of big data was to offload ETL from expensive data warehouses. And what we expect is the first widespread application of AI embedded in applications for horizontal use where Neil mentioned SalesForce and the ability to use Einstein as SalesForce data and connected data. Now because the applications we're building are so complex that as Stu mentioned you know, we have this operational model with a true private cloud. It's actually not just the legacy stuff that's sucking up all the admin overhead. It's the complexity of the new applications and the stringency of the SLA's, means that we would have to turn millions of people into admins, the old you know, when the telephone networks started, everyone's going to have to be an operator. The only way we can get past this is if we sort of apply machine learning to IT Ops and application performance management. The key here is that the models can learn how the infrastructure is laid out and how it operates. And it can also learn about how all the application services and middleware works, behaving independently and with each other and how they tie with the infrastructure. The reason that's important is because all of a sudden you can get very high fidelity root cause analysis. In the old management technology, if you had an underlined problem, you'd have a whole sort of storm of alerts, because there was no reliable way to really triangulate on the or triage the root cause. Now, what's critical is if you have high fidelity root cause analysis, you can have really precise recommendations for remediation or automated remediation which is something that people will get comfortable with over time, that's not going to happen right away. But this is critical. And this is also the first large scale application of not just machine learning but machine data and so this topology of collecting widely desperate machine data and then applying models and then reconfiguring the software, it's training wheels for IoT apps where you're going to have it far more distributed and actuating devices instead of software. >> That's great, George. So let me sum up and then we'll take some questions. So very quickly, the action items that we have out of this overall session and again, we have another 15 or so predictions that we didn't get to today. But one is, as we said, digital business is the use of data assets to compete. And so ultimately, this notion is starting to diffuse rapidly. We're seeing it on theCUbE. We're seeing it on the the CrowdChats. We're seeing it in the increase of our customers. Ultimately, we believe that the users need to start preparing for even more business scrutiny over their technology management. For example, something very simple and David Floyer, you and I have talked about this extensively in our weekly action item research meeting, the idea of backing up and restoring a system is no longer in a digital business world. It's not just backing up and restoring a system or an application, we're talking about restoring the entire business. That's going to require greater business scrutiny over technology management. It's going to lead to new organizational structures. New challenges of adopting systems, et cetera. But, ultimately, our observations is that data is going to indicate technology directions across the board whether we talk about how businesses evolve or the roles that technology takes in business or we talk about the key business capability, digital business capabilities, of capturing data, turning it into value and then turning into work. Or whether we talk about how we think about cloud architecture and which organizations of cloud resources we're going to utilize. It all comes back to the role that data's going to play in helping us drive decisions. The last action item we want to put here before we get to the questions is clients, if we don't get to your question right now, contact us. Send us an inquiry. Support@silicongangle.freshdesk.com. And we'll respond to you as fast as we can over the course of the next day, two days, to try to answer your question. All right, David Vellante, you've been collecting some questions here. Why don't we see if we can take a couple of them before we close out. >> Yeah, we got about five or six minutes in the chat room, Jim Kobielus has been awesome helping out and so there's a lot of detailed answer there. The first, there's some questions and comments. The first one was, are there too many chiefs? And I guess, yeah. There's some title inflation. I guess my comment there would be titles are cheap, results aren't. So if you're creating chief X officers just for the, to check a box, you're probably wasting money. So you've got to give them clear roles. But I think each of these chiefs has clear roles to the extent that they are you know empowered. Another comment came up which is we don't want you know, Hadoop spaghetti soup all over again. Well true that. Are we at risk of having Hadoop spaghetti soup as the centricity of big data moves from Hadoop to AI and ML and deep learning? >> Well, my answer is we are at risk of that but that there's customer pressure and vendor economic pressure to start consolidating. And we'll also see, what we didn't see in the ArpiM big data era, with cloud vendors, they're just going to start making it easier to use some of the key services together. That's just natural. >> And I'll speak for Neil on this one too, very quickly, that the idea ultimately is as the discipline starts to mature, we won't have people that probably aren't really capable of doing some of this data science stuff, running around and buying a tool to try to supplement their knowledge and their experience. So, that's going to be another factor that I think ultimately leads to clarity in how we utilize these tools as we move into an AI oriented world. >> Okay, Jim is on mute so if you wouldn't mind unmuting him. There was a question, is ML a more informative way of describing AI? Jim, when you and I were in our Boston studio, I sort of asked a similar question. AI is sort of the uber category. Machine learning is math. Deep learning is a more sophisticated math. You have a detailed answer in the chat. But maybe you can give a brief summary. >> Jim: Sure, sure. I don't want too pedantic here but deep learning is essentially, it's a lot more hierarchical deeper stacks of neural network of layers to be able to infer high level extractions from data, you know face recognitions, sentiment analysis and so forth. Machine learning is the broader phenomenon. That's simply along a different and part various approaches for distilling patterns, correlations and algorithms from the data itself. What we've seen in the last week, five, six tenure, let's say, is that all of the neural network approaches for AI have come to the forefront. And in fact, the core often market place and the state of the art. AI is an ancient paradigm that's older than probably you or me that began and for the longest time was rules based system, expert systems. Those haven't gone away. The new era of AI we see as a combination of both statical approaches as well as rules based approaches, and possibly even orchestration based approaches like graph models or building broader context or AI for a variety of applications especially distributed Edge application. >> Okay, thank you and then another question slash comment, AI like graphics in 1985, we move from a separate category to a core part of all apps. AI infused apps, again, Jim, you have a very detailed answer in the chat room but maybe you can give the summary version. >> Jim: Well quickly now, the most disruptive applications we see across the world, enterprise, consumer and so forth, the advantage involves AI. You know at the heart of its machine learning, that's neural networking. I wouldn't say that every single application is doing AI. But the ones that are really blazing the trail in terms of changing the fabric of our lives very much, most of them have AI at their heart. That will continue as the state of the art of AI continues to advance. So really, one of the things we've been saying in our research at Wikibon `is that the data scientists or those skills and tools are the nucleus of the next generation application developer, really in every sphere of our lives. >> Great, quick comment is we will be sending out these slides to all participants. We'll be posting these slides. So thank you Kip for that question. >> And very importantly Dave, over the course of the next few days, most of our predictions docs will be posted up on Wikibon and we'll do a summary of everything that we've talked about here. >> So now the questions are coming through fast and furious. But let me just try to rapid fire here 'cause we only got about a minute left. True private cloud definition. Just say this, we have a detailed definition that we can share but essentially it's substantially mimicking the public cloud experience on PRIM. The way we like to say it is, bringing the cloud operating model to your data versus trying to force fit your business into the cloud. So we've got detailed definitions there that frankly are evolving. about PaaS, there's a question about PaaS. I think we have a prediction in one of our, you know, appendices predictions but maybe a quick word on PaaS. >> Yeah, very quick word on PaaS is that there's been an enormous amount of effort put on the idea of the PaaS marketplace. Cloud Foundry, others suggested that there would be a PaaS market that would evolve because you want to be able to effectively have mobility and migration and portability for this large cloud application. We're not seeing that happen necessarily but what we are seeing is that developers are increasingly becoming a force in dictating and driving cloud decision making and developers will start biasing their choices to the platforms that demonstrate that they have the best developer experience. So whether we call it PaaS, whether we call it something else. Providing the best developer experience is going to be really important to the future of the cloud market place. >> Okay great and then George, George O, George Gilbert, you'll follow up with George O with that other question we need some clarification on. There's a question, really David, I think it's for you. Will persistent dims emerge first on public clouds? >> Almost certainly. But public clouds are where everything is going first. And when we talked about UniGrid, that's where it's going first. And then, the NVMe over fabrics, that architecture is going to be in public clouds. And it has the same sort of benefits there. And NV dims will again develop pretty rapidly as a part of the NVMe over fabrics. >> Okay, we're out of time. We'll look through the chat and follow up with any other questions. Peter, back to you. >> Great, thanks very much Dave. So once again, we want to thank you everybody here that has participated in the webinar today. I apologize for, I feel like Hans Solo and saying it wasn't my fault. But having said that, none the less, I apologize Neil Raden and everybody who had to deal with us finding and unmuting people but we hope you got a lot out of today's conversation. Look for those additional pieces of research on Wikibon, that pertain to the specific predictions on each of these different things that we're talking about. And by all means, Support@silicongangle.freshdesk.com, if you have an additional question but we will follow up with as many as we can from those significant list that's starting to queue up. So thank you very much. This closes out our webinar. We appreciate your time. We look forward to working with you more in 2018. (upbeat music)

>> Announcer: From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now here's your host, Dave Vellante. >> In the early days of big data and Hadoop, the focus was really on operational efficiency where ROI was largely centered on reduction of investment. Fast forward 10 years and you're seeing a plethora of activity around machine learning, and deep learning, and artificial intelligence, and deeper business integration as a function of machine intelligence. Welcome to this Cube conversation, The Skinny on Machine Intelligence. I'm Dave Vellante and I'm excited to have Jim Kobielus here up from the District area. Jim, great to see you. Thanks for coming into the office today. >> Thanks a lot, Dave, yes great to be here in beautiful Marlboro, Massachusetts. >> Yes, so you know Jim, when you think about all the buzz words in this big data business, I have to ask you, is this just sort of same wine, new bottle when we talk about all this AI and machine intelligence stuff? >> It's actually new wine. But of course there's various bottles and they have different vintages, and much of that wine is still quite tasty, and let me just break it out for you, the skinny on machine intelligence. AI as a buzzword and as a set of practices really goes back of course to the early post-World War II era, as we know Alan Turing and the Imitation Game and so forth. There are other developers, theorists, academics in the '40s and the '50s and '60s that pioneered in this field. So we don't want to give Alan Turing too much credit, but he was clearly a mathematician who laid down the theoretical framework for much of what we now call Artificial Intelligence. But when you look at Artificial Intelligence as a ever-evolving set of practices, where it began was in an area that focused on deterministic rules, rule-driven expert systems, and that was really the state of the art of AI for a long, long time. And so you had expert systems in a variety of areas that became useful or used in business, and science, and government and so forth. Cut ahead to the turn of the millennium, we are now in the 21st century, and what's different, the new wine, is big data, larger and larger data sets that can reveal great insights, patterns, correlations that might be highly useful if you have the right statistical modeling tools and approaches to be able to surface up these patterns in an automated or semi-automated fashion. So one of the core areas is what we now call machine learning, which really is using statistical models to infer correlations, anomalies, trends, and so forth in the data itself, and machine learning, the core approach for machine learning is something called Artificial Neural Networks, which is essentially modeling a statistical model along the lines of how, at a very high level, the nervous system is made up, with neurons connected by synapses, and so forth. It's an analog in statistical modeling called a perceptron. The whole theoretical framework of perceptrons actually got started in the 1950s with the first flush of AI, but didn't become a practical reality until after the turn of this millennium, really after the turn of this particular decade, 2010, when we started to see not only very large big data sets emerge and new approaches for managing it all, like Hadoop, come to the fore. But we've seen artificial neural nets get more sophisticated in terms of their capabilities, and a new approach for doing machine learning, artificial neural networks, with deeper layers of perceptrons, neurons, called deep learning has come to the fore. With deep learning, you have new algorithms like convolutional neural networks, recurrent neural networks, generative adversarial neural networks. These are different ways of surfacing up higher level abstractions in the data, for example for face recognition and object recognition, voice recognition and so forth. These all depend on this new state of the art for machine learning called deep learning. So what we have now in the year 2017 is we have quite a mania for all things AI, much of it is focused on deep learning, much of it is focused on tools that your average data scientist or your average developer increasingly can use and get very productive with and build these models and train and test them, and deploy them into working applications like going forward, things like autonomous vehicles would be impossible without this. >> Right, and we'll get some of that. But so you're saying that machine learning is essentially math that infers patterns from data. And math, it's new math, math that's been around for awhile or. >> Yeah, and inferring patterns from data has been done for a long time with software, and we have some established approaches that in many ways predate the current vogue for neural networks. We have support vector machines, and decision trees, and Bayesian logic. These are different ways of approaches statistical for inferring patterns, correlations in the data. They haven't gone away, they're a big part of the overall AI space, but it's a growing area that I've only skimmed the surface of. >> And they've been around for many many years, like SVM for example. Okay, now describe further, add some color to deep learning. You sort of painted a picture of this sort of deep layers of these machine learning algorithms and this network with some depth to it, but help us better understand the difference between machine learning and deep learning, and then ultimately AI. >> Yeah, well with machine learning generally, you know, inferring patterns from data that I said, artificial neural networks of which the deep learning networks are one subset. Artificial neural networks can be two or more layers of perceptrons or neurons, they have relationship to each other in terms of their activation according to various mathematical functions. So when you look at an artificial neural network, it basically does very complex math equations through a combination of what they call scalar functions, like multiplication and so forth, and then you have these non-linear functions, like cosine and so forth, tangent, all that kind of math playing together in these deep structures that are triggered by data, data input that's processed according to activation functions that set weights and reset the weights among all the various neural processing elements, that ultimately output something, the insight or the intelligence that you're looking for, like a yes or no, is this a face or not a face, that these incoming bits are presenting. Or it might present output in terms of categories. What category of face is this, a man, a woman, a child, or whatever. What I'm getting at is that so deep learning is more layers of these neural processing elements that are specialized to various functions to be able to abstract higher level phenomena from the data, it's not just, "Is this a face," but if it's a scene recognition deep learning network, it might recognize that this is a face that corresponds to a person named Dave who also happens to be the father in the particular family scene, and by the way this is a family scene that this deep learning network is able to ascertain. What I'm getting at is those are the higher level abstractions that deep learning algorithms of various sorts are built to identify in an automated way. >> Okay, and these in your view all fit under the umbrella of artificial intelligence, or is that sort of an uber field that we should be thinking of. >> Yeah, artificial intelligence as the broad envelope essentially refers to any number of approaches that help machines to think like humans, essentially. When you say, "Think like humans," what does that mean actually? To do predictions like humans, to look for anomalies or outliers like a human might, you know separate figure from ground for example in a scene, to identify the correlations or trends in a given scene. Like I said, to do categorization or classification based on what they're seeing in a given frame or what they're hearing in a given speech sample. So all these cognitive processes just skim the surface, or what AI is all about, automating to a great degree. When I say cognitive, but I'm also referring to affective like emotion detection, that's another set of processes that goes on in our heads or our hearts, that AI based on deep learning and so forth is able to do depending on different types of artificial neural networks are specialized particular functions, and they can only perform these functions if A, they've been built and optimized for those functions, and B, they have been trained with actual data from the phenomenon of interest. Training the algorithms with the actual data to determine how effective the algorithms are is the key linchpin of the process, 'cause without training the algorithms you don't know if the algorithm is effective for its intended purpose, so in Wikibon what we're doing is in the whole development process, DevOps cycle, for all things AI, training the models through a process called supervised learning is absolutely an essential component of ascertaining the quality of the network that you've built. >> So that's the calibration and the iteration to increase the accuracy, and like I say, the quality of the outcome. Okay, what are some of the practical applications that you're seeing for AI, and ML, and DL. >> Well, chat bots, you know voice recognition in general, Siri and Alexa, and so forth. Without machine learning, without deep learning to do speech recognition, those can't work, right? Pretty much in every field, now for example, IT service management tools of all sorts. When you have a large network that's logging data at the server level, at the application level and so forth, those data logs are too large and too complex and changing too fast for humans to be able to identify the patterns related to issues and faults and incidents. So AI, machine learning, deep learning is being used to fathom those anomalies and so forth in an automated fashion to be able to alert a human to take action, like an IT administrator, or to be able to trigger a response work flow, either human or automated. So AI within IT service management, hot hot topic, and we're seeing a lot of vendors incorporate that capability into their tools. Like I said, in the broad world we live in in terms of face recognition and Facebook, the fact is when I load a new picture of myself or my family or even with some friends or brothers in it, Facebook knows lickity-split whether it's my brother Tom or it's my wife or whoever, because of face recognition which obviously depends, well it's not obvious to everybody, depends on deep learning algorithms running inside Facebook's big data network, big data infrastructure. They're able to immediately know this. We see this all around us now, speech recognition, face recognition, and we just take it for granted that it's done, but it's done through the magic of AI. >> I want to get to the development angle scenario that you specialize in. Part of the reason why you came to Wikibon is to really focus on that whole application development angle. But before we get there, I want to follow the data for a bit 'cause you mentioned that was really the catalyst for the resurgence in AI, and last week at the Wikibon research meeting we talked about this three-tiered model. Edge, as edge piece, and then something in the middle which is this aggregation point for all this edge data, and then cloud which is where I guess all the deep modeling occurs, so sort of a three-tier model for the data flow. >> John: Yes. >> So I wonder if you could comment on that in the context of AI, it means more data, more I guess opportunities for machine learning and digital twins, and all this other cool stuff that's going on. But I'm really interested in how that is going to affect the application development and the programming model. John Farrier has a phrase that he says that, "Data is the new development kit." Well, if you got all this data that's distributed all over the place, that changes the application development model, at least you think it does. So I wonder if you could comment on that edge explosion, the data explosion as a result, and what it means for application development. >> Right, so more and more deep learning algorithms are being pushed to edge devices, by that I mean smartphones, and smart appliances like the ones that incorporate Alexa and so forth. And so what we're talking about is the algorithms themselves are being put into CPUs and FPGAs and ASICs and GPUs. All that stuff's getting embedded in everything that we're using, everything's that got autonomous, more and more devices have the ability if not to be autonomous in terms of making decisions, independent of us, or simply to serve as augmentation vehicles for our own whatever we happen to be doing thanks to the power of deep learning at the client. Okay, so when deep learning algorithms are embedded in say an internet of things edge device, what the deep learning algorithms are doing is A, they're ingesting the data through the sensors of that device, B, they're making inferences, deep learning algorithmic-driven inferences, based on that data. It might be speech recognition, face recognition, environmental sensing and being able to sense geospatially where you are and whether you're in a hospitable climate for whatever. And then the inferences might drive what we call actuation. Now in the autonomous vehicle scenario, the autonomous vehicle is equipped with all manner of sensors in terms of LiDAR and sonar and GPS and so forth, and it's taking readings all the time. It's doing inferences that either autonomously or in conjunction with inferences that are being made through deep learning and machine learning algorithms that are executing in those intermediary hubs like you described, or back in the cloud, or in a combination of all of that. But ultimately, the results of all those analytics, all those deep learning models, feed the what we call actuation of the car itself. Should it stop, should it put on the brakes 'cause it's about to hit a wall, should it turn right, should it turn left, should it slow down because it happened to have entered a new speed zone or whatever. All of the decisions, the actions that the edge device, like a car would be an edge device in this scenario, are being driven by evermore complex algorithms that are trained by data. Now, let's stay with the autonomous vehicle because that's an extreme case of a very powerful edge device. To train an autonomous vehicle you need of course lots and lots of data that's acquired from possibly a prototype that you, a Google or a Tesla, or whoever you might be, have deployed into the field or your customers are using, B, proving grounds like there's one out by my stomping ground out in Ann Arbor, a proving ground for the auto industry for self-driving vehicles and gaining enough real training data based on the operation of these vehicles in various simulated scenarios, and so forth. This data is used to build and iterate and refine the algorithms, the deep learning models that are doing the various operations of not only the vehicles in isolation but the vehicles operating as a fleet within an entire end to end transportation system. So what I'm getting at, is if you look at that three-tier model, then the edge device is the car, it's running under its own algorithms, the middle tier the hub might be a hub that's controlling a particular zone within a traffic system, like in my neck of the woods it might be a hub that's controlling congestion management among self-driving vehicles in eastern Fairfax County, Virginia. And then the cloud itself might be managing an entire fleet of vehicles, let's say you might have an entire fleet of vehicles under the control of say an Uber, or whatever is managing its own cars from a cloud-based center. So when you look at the tiering model that analytics, deep learning analytics is being performed, increasingly it will be for various, not just self-driving vehicles, through this tiered model, because the edge device needs to make decisions based on local data. The hub needs to make decisions based on a wider view of data across a wider range of edge entities. And then the cloud itself has responsibility or visibility for making deep learning driven determinations for some larger swath. And the cloud might be managing both the deep learning driven edge devices, as well as monitoring other related systems that self-driving network needs to coordinate with, like the government or whatever, or police. >> So envisioning that three-tier model then, how does the programming paradigm change and evolve as a result of that. >> Yeah, the programming paradigm is the modeling itself, the building and the training and the iterating the models generally will stay centralized, meaning to do all these functions, I mean to do modeling and training and iteration of these models, you need teams of data scientists and other developers who are both adept as to statistical modeling, who are adept at acquiring the training data, at labeling it, labeling is an important function there, and who are adept at basically developing and deploying one model after another in an iterative fashion through DevOps, through a standard release pipeline with version controls, and so forth built in, the governance built in. And that's really it needs to be a centralized function, and it's also very compute and data intensive, so you need storage resources, you need large clouds full of high performance computing, and so forth. Be able to handle these functions over and over. Now the edge devices themselves will feed in the data in just the data that is fed into the centralized platform where the training and the modeling is done. So what we're going to see is more and more centralized modeling and training with decentralized execution of the actual inferences that are driven by those models is the way it works in this distributive environment. >> It's the Holy Grail. All right, Jim, we're out of time but thanks very much for helping us unpack and giving us the skinny on machine learning. >> John: It's a fat stack. >> Great to have you in the office and to be continued. Thanks again. >> John: Sure. >> All right, thanks for watching everybody. This is Dave Vellante with Jim Kobelius, and you're watching theCUBE at the Marlboro offices. See ya next time. (upbeat music)