from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi and welcome to the cube Studios for another cube conversation where we go in-depth with thought leaders driving innovation across the tech industry I'm your host Peter Buress every enterprise has to concern themselves with how they're going to go about ensuring the appropriate access to those crucial applications that run the business this is especially a key question in domains where the applications our seminal feature of the operations how can we set up IT so users see what they should see can access what they can access and that we have control over all about how these systems work and have that conversation we're here with Tony Ferguson an IT infrastructure architect at man energy solutions Tony welcome to the cube yeah thank you so Tony before we get into this crucial question about the appropriate level of visibility and the need for security between people users and applications tell us a little bit about man energy solutions yeah so we're a german-based company I'm working out of Copenhagen but we're part of the Volkswagen Group we have 16 thousand users globally across a hundred locations our company we we make large diesel entrants you also make smaller versions in our own factory and yeah in our company we have a course a lot of my irt on the actual engine and of course we have corporate IT and my job is to secure all of this infrastructure so specifically some of these big diesel engines as I understanding are being placed in locations and use cases that have an absolute requirements for security for example driving a ship is a major feature of the way that your engines are being used within the world so if I got that right yeah yeah that's correct and yeah and then the scale of this you know the number of engines and the number of vessels we need to access and the data we collect it is critical infrastructure we also have power plants so it's really important that we secure this infrastructure so it's a it's a it's a very it's an infrastructure that has very interesting physical characteristics but also has very interesting security characteristics as you went into thinking about how you're going to improve the applicability of the overall infrastructure that you use to drive your business use cases what were some of the issues that you find yourself struggling with yes so yeah a lot of issues actually one of the first things is that we wanted to authenticate the actual engineer and we wanted to make sure that the right people got to the right assets and we wanted to make sure that a thing dication was strong so like the two-factor multi-factor authentication and we wanted to show that the all the data between their engineer and the vessel was encrypted and another big problem for us is scale we need to scale the solution and one of the one of the things as these get brought for us is namespace routing we had the ability to really scale the system without using IP addresses were actually networking so this solved really a lot of problems for us and trying to get those engineers to all of the assets and the IOT on the engine now one of the things that you noted in your as you move forward was this notion of a black cloud where you could formalize the clock the types of relationships you wanted between your engineer users and other users and the Eric the applications you were running on a global scale basis to actually ensure the reliability of the product you had out in the field tell us a little bit about this notion of black cloud yeah so it ties it into a little bit around zero trust but how I see black cloud and how I would describe it is you know everything is dark right so if there's an attacker and he scans port scans of my infrastructure he won't see anything so so basically we would use their tech surface that means that there's no answer back and by doing this we we remove all these vulnerabilities all these zero-day vulnerabilities were remove this and in the same time we stall out that engineer to commit to their assets now how does that work in an environment that is as physically constrained as you know integrating or networking internet working with seagoing vessels yeah so of course a lot of this connectivity is over satellite and of course it's across the internet so it's important that we encrypt into end and it's important that we allow the right engineers to the right customers and we're able to access all these resources and to do Federation and make sure there's strong authentication for our customers we can we really tell them that this all the similar structure is completely secured dark and it's extremely difficult to to come into this black cloud so you've got a challenge the challenge that we've set up here is that you've got a use case that is constrained by the characteristics of the physical infrastructure where the security needs are absolutely paramount and still has to scale and very importantly be evolvable to allow you to be able to provide future classes of services that will further differentiate and improve your business that suggests that these decisions you had to make about the characteristics of the solution was gonna have an enormous impact ultimately on what you could achieve tell us a little bit about the thought process as you went through as you chose a set of sub technology suppliers to help you build out this black cloud and this application set yeah so we looked at a lot of different solutions but a lot of these solutions were based around the old knit work style right around VPNs around having files and around having ACLs and a lot of this is really network centric and what we were looking for is something that was more application centric something that moved up the stack and started to look at policy around what the user would want access to so putting those users and applications together and create meaningful policy based on the DNS rather than on the IP layer and this was really important for us to be able to scale and really make meaningful policy so in many respects it allowed you to not to necessarily de-emphasize but refocus your network design engineering and management efforts from device level assets and perimeter level assets to some of the assets that are really driving new classes of value the applications the users and the data that these engines are streaming and the models that you're using to assure optimal performance of them have I got that right yeah that's exactly right it's extremely important that that we don't have electrical movement you know we look today there's all sorts of were mobile malware attacks ransomware and you know you can imagine if something got into into this cloud that you wouldn't want to let remove so it's not just about the products but it's also about making sure that all these assets are designed from the ground up that that dark as well all right that even on the interns that they can't speak to each other all these very limited connectivity there Tony this has been a fascinating conversation about how you've taken this notion of a black cloud and applied it to a really crucial business case within man energy but I got to believe that this sets you up for a range of other use cases that the investments you've made here are gonna offer new classes of payback in a lot of different use cases how are you going to roll this black cloud concept using Z scalar out to the rest of the organization and the rest of the work that's being performed yeah it's a good question um so when we first looked at this technology we thought it was perfect for consultants because we could have very specific access policies and just allow them to the SS we will be required but then we also saw that there were so many other user cases here for example we are moving our applications from our data center to AWS and to Azura and as we move those applications the users need to connect to this so where would you have this black cloud and have the connectivity to it but we're not opening this to the Internet so you know as far as you're concerned I don't even have any resources or a service in AWS because it's black it's dark so there's a huge amount of security that we can add to this and then there's also a lot of other user cases like company mergers we had to buy a company so we could use this technology to to move to another company together because you don't need to worry about the network anymore you just worried about getting applications to users so I there's a number of great applications for this technology and I really see that this technology will really grow and I'm really excited about it so moving away from a physical orientation of the network to a more logical application and user oriented services or any care orientated a vision of the network has opened up a lot of strategic possibilities what's been the cost impact yes so it what's quite interesting we when you move to the cloud and move to a company like Z scalar is there a software company so forget about all the hardware you can imagine we have a hundred locations globally so we don't have to install all the hardware we don't have to have VPN concentrators we just have to have some software on the client some software the connectors in the cloud and then Z scalar do the magic so for the business they really love this technology because it is very simple it's sitting in the background they don't have to log on to the VPN all the time so it's very seamless for the user and for us we save a lot of money on buying hardware and appliances excellent Tony Ferguson I want to thank you very much for being on the cube Tony Tony Ferguson's the IT infrastructure architect at man energy solutions I'm Peter Burris once again until we have another cube conversation you [Music]

(upbeat music) >> Live from Miami Beach, Florida. It's the CUBE. Covering VeeamON 2019. Brought to you by Veeam. >> Hi everybody, welcome back to Miami. This is the CUBE the leader in Live tech coverage. We go out to the events and we extract the signal from the noise. We're here at VeeamON day one, 2019 at the Fountain Blue hotel in Miami. Danny Allen is here. He's the vice president of product strategy at Veeam, welcome. >> Thank you, I always love being here with you. >> Good to see you. >> Fresh off the Keynote we we're joking that you're up against South Beach and you had packed out room none the less. People we're into it. It was like the KoolAid injection of, the firehose of feature announcements. Eight demo's in about an hour, hour and a half. So congratulations for getting that done. >> Thank you. >> Feels like a weight off your shoulders I'm sure, you've been working on that for a long long time as have your engineers. Okay, well let's get into it. Where do you want to start? Veeam? Product strategy? I mean it's an exciting time for you guys. You know, dial back several years ago there were a lot of nice incremental improvements. But there's a lot of like, really see changing things that are going on. Your take? >> So I would argue the last ten years is all about modernizing the data center. Even though, people have been talking about clouds significantly, over the last ten years. The adoption hasn't really been there. So now were at this inflection point where all these organizations are saying okay, now I really have to do something about cloud. The fight for dominance in the cloud management era is only really beginning now. And will unfold over the next few years. >> That's some interesting competitive dynamics going on. There's a lot of money (clears throat) pouring into the space. Why do you think Veeam is in such a good position to be as, what Ratmir called the dominant player in cloud data management? >> So, two things really. So we have this maniacal focus on make it simple, make it realiable, make it flexible. But one of the other things, that I think that really drives this, is-- so those are differentiators. Simplicity, reliability, flexibility. But we have an unfair advantage. In that we have 350,000 customers that are giving us guidance on how to make it simple, how to make it reliable, how to be flexible. >> You know one of the other things-- you've been at this like I said since seven A.M this morning >> (giggles) >> We're at the Analyst and the media briefing. You just did the Keynote. You talked about the five stages the Veeam availability platform. And it was refreshing, to actually hear a company who's traditionally a back-up software company. Say, start with backup. Right? >> Yes. >> Everybody is sort of running away from the term. And you're saying start there. Backup cloud mobility, visibility, orchestration, automation. So you sort of laid out this journey, but the core of it, is back-up. Because that's kind of what you guys are all about, right? >> That's how you get your data. Everyone wants to talk about artificial intelligence, in power points. And machine learning, more real of course. And I want to talk about where we're goin', but we're not there today. I mean, we have customers that struggle with back-up. And they struggle with back-up in their data center and in the cloud. So, I always highlight to customers, yes, we want to go there. And we'll help you get there. But start with back-up, because that's about aggregating you data into one place. >> So, you're talkin' about the customers being just sort of starting really, their to really dig into the cloud. I mean obviously, I don't know what the stat is it's lke 20% of work loads are in the cloud. 80% to go, depending on who's data that you're looking at. And, typically you would think the vendor community leads-- >> Mhm. >> The user adoption. Okay, that makes sense. But so what are the specific things now that we're I guess, let's see, 2006 it all started. >> Yes. >> 2010, we started really paying attention to it. So, now that we're a decade and change in, what are the learnings on how was that affecting your product strategy? >> So, one of things, the initial thought, 2010 to 2015 maybe, people thought well I'll just pick up my data center and move it over here and drop it in the cloud. What they quickly learned is, I always say the cloud is not a charity, right? They layer in margin, and so just picking up infrastructure and moving it somewhere else doesn't necessarily leverage the cloud for what it's good at. And so, I don't -- Sometimes what we actually see is reprioritization, like the data goes back on premises after it moves to the cloud. But we are beginning to see, cloud native applications that are designed for cloud. And that's where I think it's really interesting. Looking at Kubernetes looking at functions as a service. That is where I think the cloud, is really going to find it's legs over the next few years. >> Yeah, you talked about that in your Keynote That you're going to need backup for things like Lambda and functions in the service. You're going to need back up for containers. And that's a whole new world. It's not just back-up, as we were talkin' earlier, about data assurance. Spitting down containers, spitting them up. Making it harder for the bad guys to sort of figure out where the vulnerabilities are. So, that's clearly part of the I don't want to say road map 'cause Ratmir said well we don't really have these strict roadmaps. >> Yes. >> But part of the vision. >> Yes, absolute part of the vision and strategies. So what we do is, we keep our finger in the pulse of what is happening. Like I say, we have an unfair advantage. 350,000 customers. How many of them are actually moving to the cloud? What are they moving to the cloud? Are they building in the cloud? So, having that visibility into how this cloud adoption is taking place, is giving us an advantage that frankly other companies don't have. So, we invest in understanding that and then being ready when the scale actually tips. >> Mhm. >> So, one of the things that I find particularly interesting, is that back-up, and restore, we've said it a couple times today has historically been a bolt on. Something, that you do as an afterthought. Something you do once a system's been built. But it's this transformation, this move to digital business, puts data at the center of a companies strategy and value proposition. It means that now, this whole notion, this whole-- what back-up does, and why it's now important, is because it comes, it becomes for the first time, central to what a companies strategic business capabilities are. How is that shifting, as a product guy? How is is that shifting, how you balance and how you get information about features and functions, and no road maps, but what you do next? >> We always look at how we can enable that next generation of activities. So, you made and interesting comment there. You said people always bolt on back-up after the fact. And I look back, I come out of the security industry. People will bolt security on, after they've built the system. We only really became better as an industry, when we built security into the applications, rather than-- >> Something we're still learning to do. >> Yes. And we're only now so people are still bolting on back-up I would argue that we're now going through this phase of building data management into our platforms. Building data management in is more than just back-up it's an insuring that all of the data you have the visibility across it, that you can unlock it, that you can distribute it. Because if we're only looking at data in a reactive way we're missing the greater opportunity to make our businesses run faster. >> Yeah well, faster and better, we're diminishing the value of the options that we have on how we use our data. >> Yes. >> And that's not what you want to do in digital business setting. We talk about assurance. >> Mhm. >> Data assurance, so data protection is one thing. But that seems kind of like what's already happened. Whereas, data assurances, ensure that your data is in the right place, at the right time, with the right set of services and the right set of meta data, so that you can spin up that Kubernetes cluster if that's where you need it. >> Yes. >> How does that notion, that kind of forward reaching, turning data into asset, generating new strategic value streams out of your data, from a platform like Veeam, how does that comport with this notion of assurance? >> Good question, and-- a challenge that we have frankly as Veeam, is that our typical buyer has been the back-up data center assurance buyer. And as you begin to look at how do we expand beyond this to unlock data and build systems in all of sudden there's new constituent in play. It's not the IT administrator anymore. It's the compliance team, it's the security team. It's whatever team happens to be involved. So, we operate this in a few different ways. One is certainly at a marketing level. Just the messaging around Ransome, where the messaging around compliance, and GDPR, the messaging around how you can do more with data. But frankly one of the big things that we do is events like this. We have CIO's and administrator's of IT I was speaking with CIO last night, he started out as an administrator of Veeam, sorry VMware ten years ago, and now he's a CIO. And so, these events are what enable us to get the message out, about what Veeam is actually capable of. >> That's not an uncommon profile by the way. >> (giggles) Yes. >> One more question if I may, that you got a strong security background and now your in data protection. Those two, groups, are looking at many of the same problems. >> Mhm. >> And if you think about where the security guys are going, they're increasingly looking at what they can do with data from a services standpoint that goes beyond security. And you look at what data protection is doing, you're looking at how you can start to add more data security attributes to the platform that you have. Where does this-- where does security and data protection intersect? And come together? And when do you think? >> Well, frankly, I believe that Veeam is at the intersection of that now. >> Okay. >> And to take it one step further, I think it's not only data protection, data security but also data privacy. So the advent of GDPR and regulatory around users ownership of data. And I think it's going to get worse, before it gets better. I'll be honest on that because we have this patchwork of regulations with no central model and if I was a CIO I would be pulling my hair out. But I believe that Veeam is well positioned because we're at the intersection and we can see all of this data. That's why visibility is the center stage in that five stage journey. Because you move from being reactive to being proactive with the data. And proactive in terms in of security, and proactive in terms of data privacy. >> You know it's like a three sides coin, if that's even such a thing. >> (giggles) >> We've talked about security, has shifted from one of pure defensive to responsive. How do I respond? How quickly can I respond? You know, data protection has always been about recovery. >> Mhm. >> I think two of you're firehose, demo's today were about fast recovery. From backups and >> Yes. >> And then recovery to (mumbles). And I feel like I kind of agree. It could get worse, before it gets better. When you think about privacy, it sort of reminds me of the early days of the federal rules of civil procedure and you're trying to plug holes with email archiving. And it was just a band aid. >> Mhm. >> You mention GDPR, a couple of times. How have you seen, GDPR sort of affect the way in which people think about data protection and data management? Has it been a real up-tick? In awareness? Is it still, sort of like the email archiving, plugging the finger in the dike, what's your take? >> Well, I just wrote an article on this actually because May 25th is the one year anniversary of the implementation of GDPR. But its done a few different things. One is, its raised user awareness and organizational awareness of the issue of data privacy as opposed to security. That the users have some ownership over their data. So if nothing else, its just raised awareness for the users and the organizations. The second thing though, that its done, its actually put, there's legal teeth to this. There's been hefty fines associated with it. And I think those are only going to increase. The market is only really getting started. We're going to see how it plays out over the next five years. But I expect to see more GDPR compliance issues and more fines associated with it. But ultimately it's better for the end user because they get the ownership that frankly they deserve with their data. >> So this right to be forgotten, is sort of central to GDPR. So, how can the backup corpus, you know be a linch pin of the right to be forgotten. Or, potentially the smoking gun if it's not found. >> (giggles) >> Thoughts on that. >> So, we've introduced specific capabilities for this. So, around GDPR, we have simple things. When I say simple things, complex. But we've made it simple for customers to tag data. Does this belong in this country, versus this country, this country. Because actually when you recover forget about whether it's privacy data, are you even allowed to do that. But secondly, we introduced a step and update for this was back in January that when you recover data you can actually say, I want to run this script to eliminate that user's data from being recovered. Because if they exercise the right to be forgotten, after the database is in archive, and then you recover it, their data is back all of a sudden. So we introduce, a sandbox environment. Where you could run all of the same GDPR right to be forgotten scripts, so when it's recovered that's eliminated. And our focus has always been make those types of processes really simple for our end customers. >> So I got excited this morning when you were talking about the fast recovery from back-up. >> Mhm. >> And you talked about replication and where that fits. But being able to have the architecture and the meta data access, to be able to do a fast recovery from back-up is pretty profound. >> Yes. >> It seems like your architecture is designed in a way that it's not a do-over. As a product guy that's probably quite helpful. But I wonder if you could just describe sort of the-- the tenants of the architecture. Just in terms of what it means for Veeams future, future proofing, however, you know buzzword we want to throw at it. But there's an architectural component that was my takeaway from this mornings conversation, that's fundamental. >> Yeah, one of the fundamental components is our data is self-describing. If you have WinZip and I have 7-Zip I can send you a file, and you can open it. And we did that with different programs entirely. So when we do a backup, there's no dependency on a central server, or central management environment. And that's really important when you move things from the cloud, to on premises to another cloud, to different environments because otherwise they all need to talk to one another. In order to understand , what the data is that they receive. Another problem with that model, is that if that central management environment goes down you've lost everything. With a self-describing format, what it means is even if the Veeam Software blows up and goes away if I have that VBK file, I can recover all of the data in it. And that is very unique to us. Because if you do your data protection in the cloud, you just need to move the data on premises and I can open it on premises with a completely different software stack if you will. If you acquire a new company I can open their back-ups even assuming I have the keys, and permissions and security and all of that. Even though it was managed or backed up with completely different management stack. >> So, your saying if a competitor loses their catalog. >> Yes. >> They're screwed. >> Yes. >> And that's not the case with you guys. >> We actually had this happen just recently in a (mumbles). Customer had both a competitor and our software. And they were hit with Ransomware. The Ransomware hit the meta data catalog and they lost everything in the competitors software. Now, because they were following the 3-2-1 rule and they had one data offsite with Veeam, they were able to recover 100% of the Veeam back-ups, and 0% of the competitors back-ups. >> Well, that's story. >> (giggles) >> Yeah but-- >> Case study in the making. >> But that is actually, it's fundamental to the model. And it's not only fundamental for block storage back-ups but also the way we introduce object storage, and cloud object storage models as well. That it's completely self describing even if your Veeam software goes away and your ten years down the road. You can still get that data back. >> Okay. We got to give you the final word here. Day one, we're wrapping up day one with Danny Allen. Head of Product Strategy at Veeam. Your Keynote, the Analyst Feedback, Customer Feedback, summarize it all into a bumper sticker. You know, take as much time as you like. >> (laughter) Well, this is an exciting year for us. So we've, we have a really strong focus on cloud. You probably see that all over. Cloud data management we're talking about over and over again. >> It's on the wall. >> It's on the wall. It's on the pins that we wear. It's everywhere. So cloud data management, and another thing that you, I don't know whether people have noticed but we've put the focus back on our buyer. Who is the technical decision maker. So rather than talking about the environment ten years from now, and artificial intelligence, and machine learning. While we get excited about those things, we've brought it back to our core buyer. Because the budget today is for back-up. The budget is not for artificial intelligence. There is a budget for back-up. So we've done two things. Focus on cloud, and focus on that technical decision maker and it seems to be resonating. Customers all day long, great, great conversations. >> Well, it's pragmatic. You guys are pragmatic company, always have been. Danny Allen, thanks so much for coming on the CUBE. It was great to see you. >> Thank you very much . >> All right and thank you. That's a wrap for day one. Peter Buress and I will be back tomorrow again wall to wall coverage. This is the CUBE, and we're here at VeeamON 2019, in Miami. We'll see you tomorrow. (electronic music)

(upbeat electronic music) >> Welcome back, everyone, to this special Cube conversation. I'm John Furrier in the Cube's Palo Alto Studios. My next guest is Dr. Tom Bradicich, he's a friend of the Cube, works at Hewlett Packard Enterprise, heads up the IOT. He's general manager and vice president of servers, converged edge, IOT systems. But we're here to talk about, not so much HPE but really that work that Tom's done in a topic called First Mover, a book that he's writing. It hasn't come out yet, so we'll get an early preview of what it's like to create a category innovation and how to use process to your advantage, not make it your enemy. (chuckles) How to use creativity and how to motivate people and how to sell it through organizations, whether it's venture capitalists or managers. Tom, you've got great experience, thanks for spending time to come into the studio. >> Great to be here, thanks for having me and I'm happy to have this discussion. >> If you go back to the Cube videos, folks watching that know you, seen all the videos at HPE Discover or HP Discover, back in the day, you had a great career. You were an engineer, built the first notebook computer with IBM, you've done a lot of groundbreaking things and I like the topic of your book, First Mover, 'cause it speaks to your mindset. Entrepreneurial, innovative, breaking through walls, you probably got a lot of scar tissue. So, I want to talk about that. Because this is what the opportunity many entrepreneurs have as you know, in the Cube, we really believe that a renaissance in software development is coming. It's so obvious, open source is growing at a extraordinary pace, reuse of code. >> Right. >> You got IOT. >> You're involved in, you got crypto currency, block chain, all these new waves are coming all at once. >> Yes. >> I wish I was 22 again. >> Because this is a great opportunity to innovate. But this improving things, what are some of those things? Let's jump in, what do you see as the playbook? What have you learned and what can you share? >> Well, sure, I've been blessed, I've had a career where I've been able to do a lot of innovation but also, I like to separate the notion of innovation from differentiation. Now see, it's possible to be innovated and not different. Like it's possible for you and I to have the same new suit. It's new, it's innovative, but it's not different. And differentiation is really where one can have a first mover advantage because differentiation by definition is new, is innovation. But it's not always the other way around. So, I always tell my teams and I always focus on, how can we be two things, both different and better. It's possible also to be different and not as good. You can have the highest failure rate in the industry, you're different but that's not good. >> Right? >> Yeah. >> So, the concept here is how do you be different, not just new and innovative but how to be different and how do you be good. And I've actually faced three risks in mostly the big corporate culture that we've had to innovation. And the first risk is, of course, the obvious one, will customers buy it, that's called market risk. Is it something that's good enough to be purchased at a profit? The second risk is, can it be manufactured at quality and at a rate of consumption. The third risk is your own company, does the company have what it takes, actually, to take on the risk of a brand new product category, not just a new product. But a new category of products that, by definition, have never been done before. And when one can do that, when one can figure that out, and I've had some significant experience with this, you can catapult your careers, you can catapult your company and your customers to new levels because you enjoy the benefits of the first mover. That's the name of the book, The First Mover. >> Well, I'm looking forward to seeing it. But I want to ask, this is super important because a lot of people are really good at something and they run hard, they break through a wall but might have missed something. So, you kind of bring up this holistic picture. What are some of the things that folks should focus in on? Say I have a breakthrough idea, I have a prototype I've been running, it's in market, I think it's the best thing since sliced bread, I'm pushing it hard, people are just going to lap this up, this is going to be great, I know it's innovative but no one else knows it. >> Right, right, yeah. >> What do I do? >> What's the process, what do you recommend? >> Well, what I like to do is portion the benefits into two categories. There's supply side benefits that's to your company. Why is this good for your company to do this? And then there are demand side benefits. Meaning, why is it good for the customer? Most people tend to focus mostly on the demand side. Oh, it's solves this problem and the customers will love it and that's important and I would call that a necessary but not a sufficient condition. The other condition is why is this good for your company? And many times, when it's a brand new product category, those inside a company aren't quite in tune with why it's good for the customer. Because, again, it's a new thing, it's a new product category. Why is an automobile better than a horse and buggy, right? Why is a laptop computer better than a desktop computer? These are the ideas where it may be intuitive, it may be instructive to talk about that but when you can get a business model first and start with that, well, the reason is, we can enjoy this margin. The reason is, we can enjoy this particular first mover advantage, the halo effect, the reputation of being the leader. The reason is because we can penetrate a new market. The reason is we can now overcome a falling revenue in a shrinking tam. Now we can accelerate in another tam, perhaps, as well. So, by coming up with both the demand side and the supply side, you have a better case to go forward for support and funding inside a big corporation. >> There's always product market fit, I hear the buzzwords, I got to get the cashflow positive, break even. There's always a motivating force to get something done. How should someone organize the order of their operations to get something done, to the market, if it's an innovative, groundbreaking, differentiating? Because a lot of the big challenge is, some people call it landing span, I heard that buzzword too but you get a champion inside a company and that champion embraces it and most people think, oh man, I got a customer. But then that person has to sell it through and then it has to be operationalized, meaning, people got to get used to it. These are really challenges. >> They are, yes. >> What is your view of how an entrepreneur or a business executive or practitioner to get through that? >> Well, you have to get people on your side and it's really important. Somebody's got to believe in, either, you not even understanding what you're proposing but they'd say, well, you have a track record. For some reason, I believe what you're saying. And then, secondly, getting customers. So, I have personally never done anything major without a customer that I call an inspiration customer. That's a name I just made up. So, a customer, by definition, is an end user that will buy something from you, that's the definition of a customer. And an inspiration customer is one that will help you that is okay with seeing your dirty laundry, okay with mistakes you might make because they see the value in it and they also see the value in them being a first mover. And I like to tell my team, we want to be a first mover and a trendsetter, so our customers can also be trendsetters in their business as well. So therefore, by getting that customer support, and that's in the form of POCs or in trials or in just customer testimony, combine that now with a second dimension called the analyst community, which you're team resides in as well, also saying well, I think this is good as well, brings a lot credibility because there's a saying, a verse in the bible that a prophet is not without honor except in his own home town. Now, if you think about that, a lot of times, you're own company that you reside in has a lower point of view because it's very consumed with, indeed, what is next and doing the right thing, by the way. I have to make this quarter, right. We have to protect the brand. We have to keep the cashflow coming in. These are all important things, so how do you get someone to focus on that? Many times, it's not you anymore, it's outside. And I call that the second C. The first C is internal, the company. The second C is your customers and the community. That also could include, by the way, analysts, the media, other experts, consultants, those type of Cs around there. Now the third C is the competition. This is a little bit controversial. What happens when the idea is now exploited by the competition first; sometimes that is a motivator for a company to jump on it as well and make the market. But, again, if you follow the competition, you're not the first mover, you don't enjoy the benefits of first mover advantage. Higher margin, the halo effect of being the innovators and also, learning, that's an important one. When you're a first mover, you're out there learning so that you can respond to the second generation in a better way. >> I like the notion of differentiation and innovation as two different variables. >> Yes. >> Because it's super important. You can be different and not innovative. You can be innovative and not different. Again, it's all contextual but I want to get back to the pioneering of the first movers. So, statistically speaking, a lot of the best entrepreneurs are first movers and they're often "misunderstood", you hear that all the time. >> Yes. >> Or being a visionary is the difference being 10 years in the future versus an hour, can make the difference between success. (chuckles) We are crazy on one end and you're brilliant on the other because the time to value catches up with that profit, if you will. So the question is that, how does first movers continue to win 'cause I've seen situations where first movers come in, get a position and win and stay, keep the lead. Other times, first movers come in, set the market up, create all the attention and then have arrows on their back. >> And a second mover enjoys the benefit. >> Yeah, so the second mover comes in, bigger scale, so this competition, competitive strategy overlaid on this. Which even complicates it even further. >> Indeed, yes. >> So, your thoughts on that. >> Yes, indeed. Well, one way to look at this is the way to move forward is again, when you can get some momentum that's not you. That's the number one as a... >> John: Market growth, number of subscribers, doing the internet as a trend. >> Yes. >> Mobile users. >> Yes. >> And a third party consultant who's highly respected, a greaser, an analyst. I ran into an analyst recently in a coffee shop who agreed with some of this first mover work we're doing and converged edge systems, which is a new class of products as well. But it's really important that you can't be discouraged, let me point this out. What I tell my team, and I tell students, I lecture at universities and I've been edge professor, those younger in their career, is if you cast and vision and you have an idea and nobody gets it, don't be discouraged, that's a good sign. That's sounds a little funny. Why is it a good sign? Because if everybody gets it right away, it's likely not that novel, it's likely rather ordinary, it's likely been thought of before as well. So, by the very nature and definition that the average person might think it's discouraging. Oh, nobody understands me, nobody gets this idea, should be an encouragement, and a motivation. Now the risk here, is people not getting it is also a sign of a stupid idea. So, usually, when people don't get it, it's either, really not good. >> Or really good. >> Or really amazing that, eventually, they'll come around to it. I had a boss in one of my career opportunities told me to stop working on a product. I don't want to give too much detail, but he literally told me that. And I said, I didn't want to be insubordinate to a boss, we have them and I said, can I please just keep working on it, okay, don't let it interfere with the other stuff. Dah, dah, dah. Today that market is a nine billion dollar market as well. >> Of that product that you-- >> Of that very product that I was told by a very astute person, one of my colleagues, my bosses, that I don't see the future in this, let's not do this, you know, as well. But, being able to have a second thing. So, number one is don't be discouraged by people not getting it. By definition, that's supposed to happen. >> Yeah. >> When you have new-- >> Good point, you want to finish that? >> I just want to get-- >> Get one more thing. >> If I may add a second one. And as you're moving forward with this as well is seek out and find those who do agree with you and stick with them very, very closely. And I have, I can say a couple of names. There's one, we've created this new product class called Converge Edge Systems. Alan Andriole is senior vice president at HP. >> Cube alumni. >> And he's a Cube alumni. >> Super smart. And I'm pointing him out because he has publicly taken on this idea that this product category can really, really work and he's worked-- >> John: Cloud Nine? >> Oh, the converge edge system called Edgeline. >> Okay, got it. >> The Edgeline product brand. >> You know it as well. So therefore, when you find someone who had authority-- >> Eagles fly together, you want to get a good peer group. >> Absolutely. >> Here's a question for you. >> One of my experiences, and I want to just get your reaction and add on to it, your thoughts is, most entrepreneurs or pioneers are misunderstood, so I agree, don't be discouraged, but also, keep validating and be a data seeker, get the data. But a lot of the times, just getting something in the market or getting it going creates movement and inertia to get rolling and sometimes the original idea is actually the big idea turns into it as you get more data. An example is like Air B&B wasn't... What it is, it was basically air mattresses and selling cereal. >> Yes, yes. >> That was the original story, right. And then it turned into, but conceptually, it was the same thing, so you don't have to be 100% right on the semantics. >> It's well known that most startups don't end up being successful with the product they start with. That's well known fact but that's true also in large companies with a product idea as well. So, you have to have this interesting balance. It's very interesting as I've thought about this in study. You have to have deep philosophical and conviction of principles. And here's why: If you don't, you will be swayed by everybody's opinion and you'll never get anything done because oh, well, that's a good idea, maybe I should do this well, that's a good idea, maybe I should do this. Now, I'm not saying that's bad to listen to others but if you don't have a grounding of principles. Example, we established the seven principles of the IOT over two years ago, and we've held on to them and created the success we have based on those principles. Now that's not to say we didn't modify them a little bit but the point is, we were convicted with something and when somebody would come up with a counter to it, we had a way to defend our convictions, if you will, in internal debates and external debates as well. And then, secondly, you got to be also okay with being the sole inhabitant of that field of discourse. Being a visionary can be a very lonely job because of that, right. And, again, it's because you are and your team is, it's not always a lone person right, the team is actually creating something that literally nobody's ever seen before. Nobody understand before. >> What process do you wrap around this? Because Dave Alonzo and I always talk about this on the Cube and after the Cube is that the process has to be your friend, not your enemy. It has to work for you. >> I always say that, yeah. >> Also says that as well on Amazon. But also Charlie Munger, Warren Buffet's partner always says I'm not a big fan of master plans, meaning, because become a slave to the plan rather than the opportunity. >> Yep, yep. >> So these are process kind of things, right. So how does an innovator that's a first mover that wants to create a category, 'cause categories killers or category creators are huge opportunities financially. So they create a lot of value wealth and opportunity. What process is best? Is there a view, is it conditional on certain things? What's your thoughts on... >> Well, let me say, I'm going to give you a big company or a medium size company context, not a startup, I think they're distinctly different. I have limited experience with a startup but I've had significant experience with bigger, medium and large, now, companies as well. You can't try to change the system because now you have two variables. You got this new product that nobody's ever heard of and now you're trying to change the whole system. Now, again, this is just advice for bigger companies. So be careful how many things you want to change, how many things you want to stop. So you want to take this new thing and align it with existing processes and existing core competencies as much as you can, even though it's new, it has to have some alignment; I'll give you an example. When we built the converged edge systems, the Edgeline brand, we aligned it with compute. It's not only compute, but we aligned it with compute, why? Because HPE or HP, at the time, was and is and now, number one in compute when it comes to data center. Compute systems when it comes to high performance computing and mission critical, right. So therefore, that was easy to understand so you're okay, you're familiar with this, but now, let me tell you this new twist on it. And I would assume, and I don't know this for sure, but I would assume Steve Jobs and the Apple team that was thinking of this smartphone concept, the iPhone as well, they had to align it with some level of compute capabilities, right. And if you notice, as it emerged, it also included something that already exists called the iPod which was already aligned with their laptop computers and their desktops, right. Your music would be downloaded as an app to connectivity, but now you can take it with you and by the way, now I'll add a phone to it and so this incrementally built and by the way, you ain't seen nothing yet, I'm going to add a GPS system, I'm going to add a camera, your flashlight, your wallet, I'm going to add all that in. So, I think, by incrementally moving but not upsetting the system, like you said, in a large company really, really helps because you can't change everything too quickly. You got to be okay being alone-- >> Well, I want to interrupt you there for a second. Peter Buress and I talk all the time; I love his quote, Peter Buress, head Cube on research says, the iPhone was a computer that happened to make phone calls. Okay, and that's the smartphone, it's category creator and we know what happened, the rest is history. However, you mentioned talking to customers, having an inspiration customer, I love that concept. Because you need a muse as an innovator. You got to have someone you can trust that knows what you're trying to do that understands the mission. If Steve Jobs went into the marketplace and did market research, he would have probably had the customer feedback to build the best Blackberry. A better Blackberry or another device. Instead, he used is gut, was on his mission and then he understood the inspirational customer, whether it was real or not, he was going down a different road. It takes guts but also some discipline. >> I hear you and I agree with this 100%. When I had the great fortune of leading a team that created the first enterprise blade server or converge system, and today that is pushing about a 10 billion dollar market opportunity, and not one customer asked me for it. Now, that doesn't mean I didn't listen, okay. But I had to bring it to them. So here's the difference, we're not responding to trends, this is a key point, we're creating a trend. And what I tell my team is, you must create trends, not follow them. Many of competitors, are by the way making good money and doing good business, I'm not knocking that, but I'm saying they're not creating a trend, they're actually following one. They're in an exploding tam. >> Pretty lucrative trend. >> It can be. >> Very mature, big market. >> Dave Thomas with Wendy's followed a trend called hamburgers and he did pretty well. He didn't create the hamburger market but he followed one. Now, this is really rather interesting. So when you come in, and then you're saying I want to actually set a trend and create one, it really gives you this opportunity to redefine what is happening. So now, quick story, you may have heard this, maybe your viewers have heard this. A manager of a shoe company sends two guys to an island. He says, I want you to sell shoes on this island. They get to the island, the first guy calls back and says, boss, this is terrible, everybody is barefoot. There's no opportunity to sell shoes. This is terrible, I'm coming home. The second guy calls and says, boss, you're not going to believe this, there's not a shoe on this island and I have a tam that's 100% of the market to sell shoes. I believe, as you pointed out, Steve Jobs didn't go and say well, what apps do you own on your Blackberry. What he did is reversed it and this is what we're doing, we're reversing, we're saying, if you could watch a full length high definition movie in your hand, would you? Well, I can but I can't do it on this device. But if you could, right. So now, in the IOT, I hear this all the time from my competitors and even some colleagues out in the industry, well, we ask them what apps they run at the Edge. We ask them what they do at the Edge. That's good, that's necessary but not sufficient. You have to say, but if you had this product, wouldn't you, for example, run an entire database? Would you compile your machine learning models at the Edge, do it in the cloud now, wouldn't you do that, if you had it? Well, I never thought of that because I don't have that capability, just like, well, I never thought of being able to take pictures and watch full length high definition movies 'cause I never had it. But what if you did, would you do it? So you always got to be setting that trend, not responding to it only. >> That's awesome. >> Dr. Tom Bradicich, writing a book called First Mover really about being innovative. Give you the final word, thanks for coming in, appreciate you sharing the advice. What's going on with HPE and your IOT work? Take a minute to talk about what's happening at HPE. >> Well, thanks, pretty exciting, we've been able to move forward with some really great customer wins. I'm hoping to go public with them. We're in many ways, I know this is an abused term, but we're revolutionizing the industrial IOT in particular and manufacturing floors. We have the large auto-manufacturer that has chosen Edgeline as the standard to produce more and more vehicles per day. That's their goal, how many more vehicles can I get into my customer's hands per day. We have snack company making potato chips. Looking at what we're doing with sulfur, defining operations. We have even, we've talked about this before, space travel, engage with what the space edge is all about. In many ways, we're potato chips to space ships. >> Data centers on Mars. >> Data centers everywhere. >> And then, also, converging OT, just like the smartphone converged the camera and the GPS system, we're converging control systems, data acquisition systems. It's pretty exciting, I've been fortunate to have a company and our new CEO, Antonia Neery, has been very supportive, I was with him this morning and we talked about that new, first-of-a-kind product that we have at this auto-- >> So, is Antonio going to let us come in and do an exclusive interview since he's been a Cube alumni multiple times? >> Yes, I think he should. >> Tell him we said hello. >> I will, I will. >> Tom, great to see you. >> Thanks for having me. >> Tom Bradicich, great thought leader, really around category killers, category creators, being innovative and different, that's the key to success. Thanks for sharing. This is the Cube Conversation here in Palo Alto, I'm John Furrier, thanks for watching. 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>> (Narrator) Live from Madrid, Spain. It's The Cube covering HP Discover Madrid 2017, brought to you by Hewlett Packard Enterprise. >> We're back at HPE Discover Madrid 2017. This is The Cube, the leader in live tech coverage. My name is Dave Vellante and I'm with my co-host for the week, Peter Burris. Cat Graves is here, she's a research scientist at Hewlett Packard Enterprises. And she's joined by Natalia Vassilieva. Cube alum, senior research manager at HPE. Both with the labs in Palo Alto. Thanks so much for coming on The Cube. >> Thank you for having us. >> You're welcome. So for decades this industry has marched to the cadence of Moore's Law, bowed down to Moore's Law, been subservient to Moore's Law. But that's changing, isn't it? >> Absolutely. >> What's going on? >> I can tell Moore's Law is changing. So we can't increase the number, of course, on the same chip and have the same space. We can't increase the density of the computer today. And from the software perspective, we need to analyze more and more data. We are now marching calls into the area of artificial intelligence when we need to train larger and larger models, we need more and more compute for that. And the only possible way today to speed up the training of those modules, to actually enable the AI, is to scale out. Because we can't put more cores on the chip. So we try to use more chips together But then communication bottlenecks come in. So we can't efficiently use all of those chips. So for us on the software side, on the part of people who works how to speed up the training, how to speed up the implementation of the algorithms, and the work of those algorithms, that's a problem. And that's where Cat can help us because she's working on a new hardware which will overcome those troubles. >> Yeah, so in our lab what we do is try and think of new ways of doing computation but also doing the computations that really matter. You know, what are the bottlenecks for the applications that Natalia is working on that are really preventing the performance from accelerating? Again exponentially like Moore's Law, right? We'd like to return to Moore's Law where we're in that sort of exponential growth in terms of what compute is really capable of. And so what we're doing in labs is leveraging novel devices so, you've heard of memristor in the past probably. But instead of using memristor for computer memory, non volatile memory for persistent memory driven computer systems, we're using these devices instead for doing computation itself in the analog domain. So one of our first target applications, and target core computations that we're going after is matrix multiplication. And that is a fundamental mathematical building block for a lot of different machine learning, deep learning, signal processing, you kind of name it, it's pretty broad in terms of where it's used today. >> So Dr. Tom Bradicich was talking about the dot product, and it sounds like it's related. Matrix multiplications, suddenly I start breaking out in hives but is that kind of related? >> That's exactly what it is. So, if you remember your linear algebra in college, a dot product is exactly a matrix multiplication. It's the dot in between the vector and the matrix. The two itself, so exactly right. Our hardware prototype is called the dot product engine. It's just cranking out those matrix multiplications. >> And can you explain how that addresses the problem that we're trying to solve with respect to Moore's Law? >> Yeah, let me. You mentioned the problem with Moore's Law. From me as a software person, the end of Moore's Law is a bad thing because I can't increase their compute power anymore on the single chip. But for Cat it's a good thing because it forced her to think what's unconventional. >> (Cat) It's an opportunity. >> It's an opportunity! >> It forced her to think, what are unconventional devices which she can come up with? And we also have to mention they understand that general purpose computing is not always a solution. Sometimes if you want to speed up the thing, you need to come up with a device which is designed specifically for the type of computation which you care about. And for machine learning technification, again as I've mentioned, these matrix-matrix multiplications matrix-vector multiplications, these are the core of it. Today if you want to do those AI type applications, you spend roughly 90% of the time doing exactly that computation. So if we can come up with a more power efficient and a more effective way of doing that, that will really help us, and that's what dot product engine is solving. >> Yes, an example some of our colleagues did in architectural work. Sort of taking the dot product engine as the core, and then saying, okay if I designed a computer architecture specifically for doing convolutional neural networks. So image classification, these kinds of applications. If I built this architecture, how would it perform? And how would it compare to GPUs? And we're seeing 10 to 100 X speed up over GPUs. And even 15 X speed up over if you had a custom-built, state of the art specialized digital Asic. Even comparing to the best that we can do today, we are seeing this potential for a huge amount of speed up and also energy savings as well. >> So follow up on that, if I may. So you're saying these alternative processors like GPUs, FGPAs, custom Asics, can I infer from that that that is a stop-gap architecturally, in your mind? Because you're seeing these alternative processors pop up all over the place. >> (Cat) Yes. >> Is that a fair assertion? >> I think that recent trends are obviously favoring a return to specialized hardware. >> (Dave) Yeah, for sure. Just look at INVIDIA, it's exploding. >> I think it really depends on the application and you have to look at what the requirements are. Especially in terms of where there's a lot of power limitations, right, GPUs have become a little bit tricky. So there's a lot of interest in the automotive industry, space, robotics, for more low power but still very high performance, highly efficient computation. >> Many years ago when I was actually thinking about doing computer science and realized pretty quickly that I didn't have the brain power to get there. But I remember thinking in terms of there's three ways of improving performance. You can do it architecturally, what do you do with an instruction? You can do it organizationally, how do you fit the various elements together? You can do it with technology, which is what's the clock speed, what's the underlying substrate? Moore's Law is focused on the technology. Risk, for example, focused on architecture. FPGAs, arm processors, GPUs focus on architecture. What we're talking about to get back to that doubling the performance every 18 months from a computing standpoint not just a chip standpoint, now we're talking about revealing and liberating, I presume, some of the organization elements. Ways of thinking about how to put these things together. So even if we can't get improvements that we've gotten out of technology, we can start getting more performance out of new architectures. But organizing how everything works together. And make it so that the software doesn't have to know, or the developer, doesn't have to know everything about the organization. Am I kind of getting there with this? >> Yes, I think you are right. And if we are talking about some of the architectural challenges of today's processors, not only we can't increase the power of a single device today, but even if we increase the power of a single device, then the challenge would be how do you bring the data fast enough to that device? So we will have problems with feeding that device. And again, what dot product engine does, it does computations in memory, inside. So you limit the number of data transfers between different chips and you don't face the problem of feeding their computation thing. >> So similar same technology, different architecture, and using a new organization to take advantage of that architecture. The dot product engine being kind of that combination. >> I would say that even technology is different. >> Yeah, my view of it we're actually thinking about it holistically. We have in labs software working with architects. >> I mean it's not just a clock speed issue. >> It's not just a clock speed issue. It's thinking about what computations actually matter, which ones you're actually doing, and how to perform them in different ways. And so one of the great things as well with the dot product engine and these kind of new computation accelerators, is with something like the memory driven computing architecture. We have now an ecosystem that is really favoring accelerators and encouraging the development of these specialized hardware pieces that can kind of slot in in the same architecture that can scale also in size. >> And you invoke that resource in an automated way, presumably. >> Yeah, exactly. >> What's the secret sauce behind that? Is that software that does that or an algorithm that chooses the algorithm? >> A gen z. >> A gen z's underlying protocol is to make the device talk to the data. But at the end of the system software, it's algorithms also which will make a decision at every particular point which compute device I should use to do a particular task. With memory driven computing, if all my data sits in the shared pool of memory and I have different heterogeneous compute devices, being able to see that data and to talk to that data, then it's up to the system management software to allocate the execution of a particular task to the device which does that the best. In a more power efficient way, in the fastest way, and everybody wins. >> So as a software person, you now with memory driven computing have been thinking about developing software in a completely different way. Is that correct? >> (Natalia) Yeah. You're not thinking about going through I/O stack anymore and waiting for a mechanical device and doing other things? >> It's not only the I/O stack. >> As I mentioned today, the only possibility for us to decrease the time of processing for the algorithms is to scale out. That means that I need to take into account the locality of the data. It's not only when you distribute the computation across multiple nodes, even if we have some number based which is we have different sockets in a single system. With local memory and the memory which is remote to that socket but which is local to another socket. Today as a software programmer, as a developer, I need to take into account where my data sits. Because I know in order to accept the data on a local memory it'll take me 100 seconds to accept my data. In the remote socket, it will take me longer. So when I developed the algorithm in order to prevent my computational course to stall and to wait for the data, I need to schedule that very carefully. With memory driven computing, giving an assumption that, again, all memory not only in the single pool, but it's also evenly accessible from every compute device. I don't need to care about that anymore. And you can't even imagine such a relief it is! (laughs) It makes our life so much easier. >> Yeah, because you're spending a lot of time previously trying to optimize your code >> Yes for that factor of the locality of the data. How much of your time was spent doing that menial task? >> Years! In the beginning of Moore's Law and the beginning of the traditional architectures, if you turn to the HPC applications, every HPC application device today needs to take care of data locality. >> And you hear about when a new GPU comes out or even just a slightly new generation. They have to take months to even redesign their algorithm to tune it to that specific hardware, right? And that's the same company, maybe even the same product sort of path lined. But just because that architecture has slightly changed changes exactly what Natalia is talking about. >> I'm interested in switching subjects here. I'd love to spend a minute on women in tech. How you guys got into this role. You're both obviously strong in math, computer backgrounds. But give us a little flavor of your background, Cat, and then, Natalia, you as well. >> Me or you? >> You start. >> Hm, I don't know. I was always interested in a lot of different things. I kind of wanted to study and do everything. And I got to the point in college where physics was something that still fascinated me. I felt like I didn't know nearly enough. I felt like there was still so much to learn and it was constantly challenging me. So I decided to pursue my Ph.D in that, and it's never boring, and you're always learning something new. Yeah, I don't know. >> Okay, and that led to a career in technology development. >> Yeah, and I actually did my Ph.D in kind of something that was pretty different. But towards the end of it, decided I really enjoyed research and was just always inspired by it. But I wanted to do that research on projects that I felt like might have more of an impact. And particularly an impact in my lifetime. My Ph.D work was kind of something that I knew would never actually be implemented in, maybe a couple hundred years or something we might get to that point. So there's not too many places, at least in my field in hardware, where you can be doing what feels like very cutting edge research, but be doing it in a place where you can see your ideas and your work be implemented. That's something that led me to labs. >> And Natalia, what's your passion? How did you arrive here? >> As a kid I always liked different math puzzles. I was into math and pretty soon it became obvious that I like solving those math problems much more than writing about anything. I think in middle school there was the first class on programming, I went right into that. And then the teacher told me that I should probably go to a specialized school and that led me to physics and mathematics lyceum and then mathematical department at the university so it was pretty straightforward for me since then. >> You're both obviously very comfortable in this role, extremely knowledgeable. You seem like great leaders. Why do you feel that more women don't pursue a career in technology. Do you have these discussions amongst yourselves? Is this something that you even think about? >> I think it starts very early. For me, both my parents are scientists, and so always had books around the house. Always was encouraged to think and pursue that path, and be curious. I think its something that happens at a very young age. And various academic institutions have done studies and shown when they do certain things, its surmountable. Carnegie Mellon has a very nice program for this, where they went for the percentage of women in their CS program went from 10% to 40% in five years. And there were a couple of strategies that they implemented. I'm not gonna get all of them, but one was peer to peer mentoring, when the freshmen came in, pairing them with a senior, feeling like you're not the only one doing what you're doing, or interested in what you're doing. It's like anything human, you want to feel like you belong and can relate to your group. So I think, yeah. (laughs) >> Let's have a last word. >> On that topic? >> Yeah sure, or any topic. But yes, I'm very interested in this topic because less than 20% of the tech business is women. Its 50W% of the population. >> I think for me its not the percentage which matters Just don't stay in the way of those who's interested in that. And give equal opportunities to everybody. And yes, the environment from the very childhood should be the proper one. >> Do you feel like the industry gives women equal opportunity? >> For me, my feeling would be yes. You also need to understand >> Because of your experience Because of my experience, but I also originally came from Russia, was born in St. Petersburg, and I do believe that ex-Soviet Union countries has much better history in that. Because the Soviet Union, we don't have man and woman. We have comrades. And after the Second World War, there was women who took all hard jobs. And we used to get moms at work. All moms of all my peers have been working. My mom was an engineer, my dad is an engineer. From that, there is no perception that the woman should stay at home, or the woman is taking care of kids. There is less of that. >> Interesting. So for me, yes. Now I think that industry going that direction. And that's right. >> Instructive, great. Well, listen, thanks very much for coming on the Cube. >> Sure. >> Sharing the stories, and good luck in lab, wherever you may end up. >> Thank you. >> Good to see you. >> Thank you very much. >> Alright, keep it right there everybody. We'll be back with our next guest, Dave Vallante for Peter Buress. We're live from Madrid, 2017, HPE Discover. This is the Cube.

>> Narrator: Live from San Jose, in the heart of Silicon Valley, it's the Cube, covering DataWorks Summit 2017. Brought to you by Hortonworks. >> Hey, welcome back to the Cube, I'm Lisa Martin with my co-host, Peter Buress. We are live on day one of the DataWorks Summit in Silicon Valley. We've had a great day so far, talking about innovation across different, different companies, different use cases, it's been really exciting. And now, please welcome our next guest, David Lyle from Informatica. You are driving business transformation services. >> Yes. >> Lisa: Welcome to the Cube. >> Well thank you, it's good to be here. >> It's great to have you here. So, tell us a little about Informatica World, Peter you were there with the Cube. Just recently some of the big announcements that came out of there, Informatica getting more aggressive with cloud movement, extending your master data management strategy, and you also introduce a set of AI capabilities around meta-data. >> David: Exactly. >> So, looking at those three things, and your customer landscape, what's going on with Informatica customers, where are you seeing these great new capabilities be, come to fruition? >> Absolutely, well one of the areas that is really wonderful that we're using in every other aspect of our life is using the computer to do the logical things it should, and could, be doing to help us out. So, in this announcement at Informatica World, we talked about the central aspect of meta-data finally being the true center of Informatica's universe. So bringing in meta-data-- >> And customer's universes. >> Well, and customer's universes, so the, not seeing it as something that sits over here that's not central, but truly the thing that, is where you should be focusing your attention on. And so Informatica has some card carrying PhD artificial intelligence machine learning engineers, scientists, that we have hired, that have been working for several years, that have built this new capability called CLAIRE. That's the marketing term for it, but really what it is, it's helping to apply artificial intelligence against that meta-data, to use the computer to do things for the developer, for the analyst, for the architect, for the business people, whatever, that are dealing with these complex data transformation initiatives that they're doing. Where in the past what's been happening is whatever product you're using, the product is basically keeping track of all the things that the scientist or analyst does, but isn't really looking at that meta-data to help suggest the things, that they, that maybe has already been done before. Or domains of data. Why, how come you have to tell the system that this is an address? Can't the system identify that when data looks like this, it's an address already? We think about Shazam and all these other apps that we have on our phones that can do these fantastic things with music. How come we can't do those same things with data? Well, that's really what CLAIRE can actually do now is discover these things and help. >> Well, I want to push now a little bit. >> David: Sure, sure. >> So, historically meta-data was the thing that you created in the modeling activity. >> David: Right. >> And it wasn't something that you wanted to change, or was expected to change frequently. >> In fact, in the world of transaction processing, you didn't want to change. >> Oh, yeah. And especially you get into finance apps, and things like that, you want to keep that slow. >> Exactly. >> Yeah. >> And meta-data became one of those things that often had to be secured in a different way, and was one of those reasons why IT was always so slow. >> Yeah. >> Because of all these concerns about what's the impact on meta-data. >> Yeah. >> We move into this big data world, and we're bringing forward many of the same perspectives on how we should treat meta-data, and what you guys are doing is saying "that's fine, keep the meta-data of that data, but do a better job of revealing it, and how it connects-- >> David: Exactly. >> and how it could be connected." And we talked about this with Bill Schmarzo just recently-- >> Good friend of mine. >> Yeah, the data that's in that system can also be applied to that system. >> Yeah. >> It doesn't have to be a silo. And what CLAIRE is trying to do is remove some of the artificial barriers-- >> Exactly. >> Of how we get access to data that are founded by organization, or application, or system. >> David: Right. >> And make it easier to find that data, use that data, and trust the data. >> Exactly. >> Peter: I got that right? >> You've totally got that right. So, if we think about all these systems in a organization as this giant complex air ball, that in the past we may have had pockets of meta-data here and there that weren't really exposed, or controlled in the right way in the first place. But now bringing it together. >> But also valuable in the context of the particular database or system-- >> Yep. >> that was running. It wasn't the meta-data that was guarded as valuable-- >> Right. that just provided documentation for what was in the data. >> Exactly, exactly. So, but now with this ability to see it, really for the first time, and understand how it connects and impacts with other systems, that are exchanging data with this, or viewing data with this. We can understand then if I need, occasionally, to make a change to the general ledger, or something, I can now understand what impact on different KPIs, and the calculations stream of tableaux, business objects, cognos, micro strategy, quick, whatever. That, what else do I need to change? What else do I need to test? That's something computers are good at. Something that humans have had to do manually up to this point. And that, that's what computers are for. >> Right. >> So questions for you on the business side. Since we look at-- >> Yeah. >> Businesses are demanding real time access to data to make real time decisions, manage costs, be competitive, and that's driving cloud, it's driving IOTs, it's driving big data and analytics. You talked about CLAIRE, and the implications of it across different people within an organization. >> Right. Meta-data, how does a C-Sweet, or a senior manager care-- >> David: Good point. >> About meta-data? >> They don't, and that's why we don't talk about the word architecture. Typically we see sweet folks we don't use the word meta-data. We see sweet folks, instead we talk about things like solving the problem of time, to get the application, or information that you need, reducing that time by being able to see and change and retest the things that need to be. So we just change the discussion to either dollars, or time, or of course those are really equivalent. >> But really facilitated by this-- >> Exactly. >> Artificial intelligence. >> It's facilitated by this artificial intelligence. It can also then lead to the, when we get into data lakes, ensuring that those data lakes are, understood better, trusted better, that people are being able to see what other people are actually using. And in other words we kind of bring, somewhat, the Amazon.com website model to the data lake, so that people know, okay, if I'm looking of a product, or data set, that looks like this for my, our, processing data science utility, or what I want to do. Then these are the data sets that are out there, that may be useful. This is how many people have used them, or who those other people are, and are those people kind of trusted, valid, people that have done similar stuff to what I want to do before? Anyway, all that information we're used to when we buy products from Amazon, we bring that now to the data lake that you're putting together, so that you can actually prevent it, kind of, from being a swamp and actually get value at it. Once again, it's the meta-data that's the key to that, of getting the value out of that data. >> Have you seen historically that, you're working with customers that, have or are already using hadoop. >> David: That's right. >> They've got data lakes. >> Oh yeah. >> Have you seen that historically they haven't really thought about meta-data as driving this much value before, is this sort of a, not a new problem, but are you seeing that it's not been part of their-- >> It's a new. >> strategic approach. >> That's right, it's a new solution. I think you talk to anybody, and they knew this problem was coming. That with a data lake, and the speed that we're talking about, if you don't back that up with the corresponding information that you need to really digest, you can create a new mess, a new hairball, faster than you ever created the original hairball you're trying to fix in the first place. >> Lisa: Nobody likes a hairball. >> Nobody likes a hairball, exactly. >> Well it also seems as though, for example at the executive level, do I have a question? Can I get this question answered? How do I get this question answered? How can I trust the answer that I get? In many respects that's what you guys are trying to solve. >> David: Exactly, exactly. >> So, it's not, hey what you need to do is invest a whole bunch in the actual data, or copying data, or moving a bunch of data around. You're just starting with the prob, with the observation, with the proposition. Yes, you can answer this question, here's how you're going to do it, and you can trust it because of this trail-- >> David: Exactly. >> Of activities based on the meta-data. >> Exactly, exactly. So, it's about helping to, hate to use the phrase again, but "detangle" that hairball, so that, or at least manage it a bit, so that we can begin to move faster and solve these problems with a hell of a lot more confidence. So we have-- >> Can we switch gears? >> Absolutely. >> Certainly. >> Let's switch gears and talk about transformations. >> Yeah. >> I know that's something that is near and dear to your heart, and something you're spending a lot of time with clients in. >> Yeah. >> How, how do you approach, when a customer comes to you, how are they approaching the transformation, and what are they, what's the conversation that you're having with them? >> Well, it's interesting that the phrase has, and I'm even thinking of changing our group's title to digital transformation services, not just because it's hot, but because, frankly, the fluid or the thing, the glue, that really makes that happen is data in these different environments. But the way that we approach it is by, well understanding what the business capabilities are that are affected by the transformation that is being discussed. Looking at and prioritizing those capabilities based upon the strategic relevance of that capability, along with the opportunity to improve, and multiplying those together, we can then take those and rank those capabilities, and look at it in conjunction with, what we call a business view of the company. And from that we can understand what the effects are on the different parts of the organization, and create the corresponding plans, or roadmaps that are necessary to do this digital transformation. We actually bought a little stealth acquisition of a company two years ago, that's kind of the underpinnings of what my team does, that is extremely helpful in being able to drive these kinds of complex transformations. In fact, big companies, a lot, several in this room in a way, are going through the transformation of moving from a traditional software license sale transaction with the customer to a subscription, monthly transaction. That changes marketing. That changes sales. That changes customer support. That changes R&D. Everything Changes. >> Everything, yeah. >> How do you coordinate that? What is the data that you need in order to calculate a new KPI for how I judge how well I'm doing in my company? Annual recurring revenue, or something. It's a, these are all, they get into data governance. You get into all these different aspects, and that's what our team's tool and approach is actually able to credibly go in, and lay out this road map for folks that is shocking, kind of, in how it's making complex problems manageable. Not necessarily simple. Actually it was Bill Schmarzo, on the, he told me this 15 years ago. Our problem is not to make simple problems mundane, our problem, or what we're trying to do, is make complex problems manageable. I love that. >> Sounds like something-- >> I love that. >> Bill would say. >> That's an important point though about not saying "we're going to make it simple-- >> No. >> we're going to make it manageable." >> David: Exactly. >> Because that's much more realistic. >> David: Right. >> Don't you think? >> David: Exactly, exactly. The fact-- >> I dunno, if we can make them simple, that's good too. >> That would be nice. >> Oh, we'd love that >> Yeah. >> Oh yeah. >> When it happens, it's beautiful. >> That's art. >> Right, right. >> Well, your passion and your excitement for what you guys have just announced is palpable. So, obviously just coming off that announcement, what's next? We look out the rest of the calendar year, what's next for Informatica and transforming digital businesses? >> I think it is, you could say the first 20 years, almost, of Informatica's existence was building that meta-data center of gravity, and allowing people to put stuff in, I guess you could say. So going forward, the future is getting value out. It's continually finding new ways to use, in the same way, for instance, Apple is trying to improve Siri, right? And each release they come out with more capabilities. Obviously Google and Amazon seems to be working a little better, but nevertheless, it's all about continuous improvement. Now, I think, the things that Informatica is doing, is moving that, power of using that meta-data also towards helping our customers more directly with the business aspect of data in a digital transformation. >> Excellent. Well, David, thank you so much for joining us on the Cube. We wish you continued success, I'm sure the Cube be back with Informatica in the next round. >> Excellent. >> Thanks for sharing your passion and your excitement for what you guys are doing. Like I said, it was very palpable, and it's always exciting to have that on the show. So, thank you for watching. I'm Lisa Martin, for my co-host Peter Burress, we thank you for watching the Cube again. And we are live on day one of the Dataworks summit from San Jose. Stick around, we'll be right back.

>> Narrator: Live from San Jose, California It's the CUBE, covering Big Data Silicon Valley 2017. >> Okay welcome back everyone, live in Silicon Valley, this is the CUBE coverage of Big Data Week, Big Data Silicon Valley, our event, in conjunction with Strata Hadoop. This is the CUBE for two days of wall-to-wall coverage. I'm John Furrier with Analyst from Wikibon, George Gilbert our Big Data as well as Peter Buress, covering all of the angles. And our next guest is Wei Wang, Senior Director of Product Market at Hortonworks, a CUBE alumni, and Oliver Chiu, Senior Product Marketing Manager for Big Data and Microsoft Cloud at Azure. Guys, welcome to the CUBE, good to see you again. >> Yes. >> John: On the CUBE, appreciate you coming on. >> Thank you very much. >> So Microsoft and Hortonworks, you guys are no strangers. We have covered you guys many times on the CUBE, on HD insights. You have some stuff happening, here, and I was just talking about you guys this morning on another segment, like, saying hey, you know the distros need a Cloud strategy. So you have something happening tomorrow. Blog post going out. >> Wei: Yep. >> What's the news with Microsoft? >> So essentially I think that we are truly adopting the CloudFirst. And you know that we have been really acquiring a lot of customers in the Cloud. We have that announced in our earnings that more than a quarter of our customers actually already have a Cloud strategy. I want to give out a few statistics that Gardner told us actually last year. The increase for their end users went up 57% just to talk about Hadoop and Microsoft Azure. So what we're here, is to talk about the next generation. We're putting our latest and greatest innovation in which comes in in the package of the release of HDP2.6, that's our last release. I think our last conversation was on 2.5. So 2.6's great latest and newest innovations to put on CloudFirst, hence our partner, here, Microsoft. We're going to put it on Microsoft HD Insight. >> That's super exciting. And, you know, Oliver, one of the things that we've been really fascinated with and covering for multiple years now is the transformation of Microsoft. Even prior to Satya, who's a CUBE alumni by the way, been on the CUBE, when we were at XL event at Stanford. So, CEO of Microsoft, CUBE alumni, good to have that. But, it's interesting, right? I mean, the Open Compute Project. They donated a boatload of IP into the open-source. Heavily now open-source, Brendan Burns works for Microsoft. He's seeing a huge transformation of Microsoft. You've been working with Hortonworks for a while. Now, it's kind of coming together, and one of the things that's interesting is the trend that's teasing out on the CUBE all the time now is integration. He's seeing this flash point where okay, I've got some Hadoop, I've got a bunch of other stuff in the enterprise equation that's kind of coming together. And you know, things like IOT, and AIs all around the corner as well. How are you guys getting this all packaged together? 'Cause this kind of highlights some of the things that are now integrated in with the tools you have. Give us an update. >> Yeah, absolutely. So for sure, just to kind of respond to the trend, Microsoft kind of made that transformation of being CloudFirst, you know, many years ago. And, it's been great to partner with someone like Hortonworks actually for the last four years of bringing HD Insight as a first party Microsoft Cloud service. And because of that, as we're building other Cloud services around in Azure, we have over 60 services. Think about that. That's 60 PAZ and IAZ services in Microsoft, part of the Azure ecosystem. All of this is starting to get completely integrated with all of our other services. So HD Insight, as an example, is integrated with all of our relational investments, our BI investments, our machine learning investments, our data science investments. And so, it's really just becoming part of the fabric of the Azure Cloud. And so that's a testament to the great partnership that we're having with Hortonworks. >> So the inquiry comment from Gardner, and we're seeing similar things on the Wikibon site on our research team, is that now the legitimacy of say, of seeing how Hadoop fits into the bigger picture, not just Hadoop being the pure-play Big Data platform which many people were doing. But now they're seeing a bigger picture where I can have Hadoop, and I can have some other stuff all integrating. Is that all kind of where this is going from you guys' perspective? >> So yeah, it's again, some statistics we have done tech-validate service that our customers are telling us that 43% of the responders are actually using that integrated approach, the hybrid. They're using the Cloud. They're using our stuff on-premise to actually provide integrated end-to-end processing workload. They are now, I think, people are less think about, I would think, a couple years ago, people probably think a little bit about what kind of data they want to put in the Cloud. What kind of workload they want to actually execute in the Cloud, versus their own premise. I think, what we see is that line starting to blur a little bit. And given the partnership we have with Microsoft, the kind of, the enterprise-ready functionalities, and we talk about that for a long time last time I was here. Talk about security, talk about governance, talk about just layer of, integrated layer to manage the entire thing. Either on-premise, or in the Cloud. I think those are some of the functionalities or some of the innovations that make people a lot more at ease with the idea of putting the entire mission-critical applications in the Cloud, and I want to mention that, especially with our blog going out tomorrow that we will actually announce the Spark 2.1. In which, in Microsoft Azure HD Insight, we're actually going to guarantee 99.9% of SLA. Right, so it's, for that, it's for enterprise customers. In which many of us have together that is truly an insurance outfield, that people are not just only feel at ease about their data, that where they're going to locate, either in the Cloud or within their data center, but also the kind of speed and response and reliability. >> Oliver, I want to queue off something you said which was interesting, that you have 60 services, and that they're increasingly integrated with each other. The idea that Hadoop itself is made up of many projects or services and I think in some amount of time, we won't look at it as a discrete project or product, but something that's integrated with together makes a pipeline, a mix-and-match. I'm curious if you can share with us a vision of how you see Hadoop fitting in with a richer set of Microsoft services, where it might be SQL server, it might be streaming analytics, what that looks like and so the issue of sort of a mix-and-match toolkit fades into a more seamless set of services. >> Yeah, absolutely. And you're right, Hadoop and Wei will definitely reiterate this, is that Hadoop is a platform right, and certainly there is multiple different workloads and projects on that platform that do a lot of different things. You have Spark that can do machine learning and streaming, and SQL-like queries, and you have Hadoop itself that can do badge, interactive, streaming as well. So, you see kind of a lot of workloads being built on open-source Hadoop. And as you bring it to the Cloud, it's really for customers that what we found, and kind of this new Microsoft that is often talked about, is it's all about choice and flexibility for our customers. And so, some customers want to be 100% open-source Apache Hadoop, and if they want that, HD Insight is the right offering, and what we can do is we can surround it with other capabilities that are outside of maybe core Hadoop-type capabilities. Like if you want to media services, all the way down to, you know, other technologies nothing related to, specifically to data and analytics. And so they can combine that with the Hadoop offering, and blend it into a combined offering. And there are some customers that will blend open-source Hadoop with some of our Azure data services as well, because it offers something unique or different. But it's really a choice for our customers. Whatever they're open to, whatever their kind of their strategy for their organization. >> Is there, just to kind of then compare it with other philosophies, do you see that notion that Hadoop now becomes a set of services that might or might not be mixed and matched with native services. Is that different from how Amazon or Google, you know, you perceive them to be integrating Hadoop into their sort of pipelines and services? >> Yeah, it's different because I see Amazon and Google, like, for instance, Google kind of is starting to change their philosophy a little bit with introduction of dataproc. But before, you can see them as an organization that was really focused on bringing some of the internal learnings of Google into the marketplace with their own, you can say proprietary-type services with some of the offerings that they have. But now, they're kind of realizing the value that Hadoop, that Apache Hadoop ecosystem brings. And so, with that comes the introduction of their own manage service. And for AWS, their roots is IAZ, so to speak, is kind of the roots of their Cloud, and they're starting to bring kind of other systems, very similar to, I would say Microsoft Strategy. For us, we are all about making things enterprise-ready. So that's what the unique differentiator and kind of what you alluded to. And so for Microsoft, all of our data services are backed by 99.9% service-level agreement including our relationship with Hortonworks. So that's kind of one, >> Just say that again, one more time. >> 99.9% up-time, and if, >> SLA. >> Oliver: SLA and so that's a guarantee to our customers. So if anything we're, >> John: One more time. >> It's a guarantee to our customers. >> No, this is important. SLA, I mean Google Next didn't talk much about last week their Cloud event. They talked about speed thieves, >> Exactly >> Not a lot of SLAs. This is mandate for the enterprise. They care more about SLA so, not that they don't care about price, but they'd much rather have solid, bulletproof SLAs than the best price. 'Cause the total cost of ownership. >> Right. And that's really the heritage of where Microsoft comes from, is we have been serving our on-premises customers for so long, we understand what they want and need and require for a mission-critical enterprise-ready deployment. And so, our relationship with Hortonworks absolutely 99.9% service-level agreement that we will guarantee to our customers and across all of the Hadoop workloads, whether it would be Hive, whether it would be Spark, whether it'd be Kafka, any of the workloads that we have on HD Insight, is enterprise-ready by virtue, mission-critical, built-in, all that stuff that you would expect. >> Yeah, you guys certainly have a great track record with enterprise. No debate about that, 100%. Um, back to you guys, I want to take a step back and look at some things we've been observing kicking off this week at the Strata Hadoop. This is our eighth year covering, Hadoop world now has evolved into a whole huge thing with Big Data SV and Big Data NYC that we run as well. The bets that were made. And so, I've been intrigued by HD Insights from day one. >> Yep. >> Especially the relationship with Microsoft. Got our attention right away, because of where we saw the dots connecting, which is kind of where we are now. That's a good bet. We're looking at what bets were made and who's making which bets when, and how they're panning out, so I want to just connect the dots. Bets that you guys have made, and the bets that you guys have made that are now paying off, and certainly we've done before camera revolution analytics. Obviously, now, looking real good middle of the fairway as they say. Bets you guys have made that hey, that was a good call. >> Right, and we think that first and foremost, we are sworn to work to support machine learning, we don't call it AI, but we are probably the one that first to always put the Spark, right, in Hadoop. I know that Spark has gained a lot of traction, but I remember that in the early days, we are the ones that as a distro that, going out there not only just verbally talk about support of Spark, but truly put it in our distribution as one of the component. We actually now in the last version, we are actually allows also flexibility. You know Spark, how often they change. Every six weeks they have a new version. And that's kind of in the sense of running into paradox of what actually enterprise-ready is. Within six weeks, they can't even roll out an entire process, right? If they have a workload, they probably can't even get everyone to adopt that yet, within six weeks. So what we did, actually, in the last version, in which we will continue to do, is to essentially support multiple versions of Spark. Right, we essentially to talk about that. And the other bet we have made is about Hive. We truly made that as kind of an initiative behind project Stinger initiative, and also have ties now with LAP. We made the effort to join in with all the other open-source developers to go behind this project that make sure that SQL is becoming truly available for our customers, right. Not only just affordable, but also have the most comprehensive coverage for SQL, and C20-11. But also now having that almost sub-second interactive query. So I think that's the kind of bet we made. >> Yeah, I guess the compatibility of SQL, then you got the performance advantage going on, and this database is where it's in memory or it's SSD, That seems to be the action. >> Wei: Yeah. >> Oliver, you guys made some good bets. So, let's go down the list. >> So let's go down memory lane. I always kind of want to go back to our partnership with Hortonworks. We partnered with Hortonworks really early on, in the early days of Hortonworks' existence. And the reason we made that bet was because of Hortonworks' strategy of being completely open. Right, and so that was a key decision criteria for Microsoft. That we wanted to partner with someone whose entire philosophy was open-source, and committing everything back to the Apache ecosystem. And so that was a very strategic bet that we made. >> John: It was bold at the time, too. >> It was very bold, at the time, yeah. Because Hortonworks at that time was a much smaller company than they are today. But we kind of understood of where the ecosystem was going, and we wanted to partner with people who were committing code back into the ecosystem. So that, I would argue, is definitely one really big bet that was a very successful one and continues to play out even today. Other bets that we've made and like we've talked about prior is our acquisition of Revolution Analytics a couple years ago and that's, >> R just keeps on rolling, it keeps on rolling, rolling, rolling. Awesome. >> Absolutely. Yeah. >> Alright, final words. Why don't we get updated on the data science experiences you guys have. Is there any update there? What's going on, what seems to be, the data science tools are accelerating fast. And, in fact, some are saying that looks like the software tools years and years ago. A lot more work to do. So what's happening with the data science experience? >> Yeah absolutely and just tying back to that original comment around R, Revolution Analytics. That has become Microsoft, our server. And we're offering that, available on-premises and in the Cloud. So on-premises, it's completely integrated with SQL server. So all SQL server customers will now be able to do in-database analytics with R built-in-to-the-core database. And that we see as a major win for us, and a differentiator in the marketplace. But in the Cloud, in conjunction with our partnership with Hortonworks, we're making Microsoft R server, available as part of our integration with Azure HD Insights. So we're kind of just tying back all that integration that we talked about. And so that's built in, and so any customer can take R, and paralyze that across any number of Hadoop and Sparknotes in a managed service within minutes. Clusters will spin up, and they can just run all their data science models and train them across any number of Hadoop and Sparknotes. And so that is, >> John: That takes the heavy lifting away on the cluster management side, so they can focus on their jobs. >> Oliver: Absolutely. >> Awesome. Well guys, thanks for coming on. We really appreciate Wei Wang with Hortonworks, and we have Oliver Chiu from Microsoft. Great to get the update, and tomorrow 10:30, the CloudFirst news hits. CloudFirst, Hortonworks with Azure, great news, congratulations, good Cloud play for Hortonworks. To CUBE, I'm John Furrier with George Gilbert. More coverage live in Silicon Valley after this short break.

>> Announcer: Live from Las Vegas, Nevada it's theCube. Covering, Accelerate 2017. Brought to you by Fortinet. Now, here are your hosts, Lisa Martin, and Peter Buress. (tech music) >> Lisa: Hi, welcome back to theCube. We are Silicon Angle's Flagship Program, where we go out to the events and extract the signal to the noise, bringing it directly to you. Today, we are in beautiful Las Vegas with Fortinet. It's their Accelerate 2017 Event. I'm your host, Lisa Martin, joined by my cohost, Peter Buress. And we're very excited to be joined by a Technology Alliance Partner, Nozomi Networks, Edgard Capdevielle. You are the CEO? >> Yes, that's right. >> And, welcome to theCube. >> Thank you, happy to be here. >> So, a couple of great things that Nozomi announced, just a couple of months ago, one was, they just secured fantastic $7.5 million in Series A Funding. And the second thing they announced was you, as the new CEO, so congratulations on your new post. >> Thank you very much, thank you. >> So, Nozomi is focused on the Industrial Control Systems Industry. What was it about this particular opportunity, that attracted you to want to lead Nozomi? >> Yeah, great question. Two things mainly. One, is the team. The two founders are truly rock stars, they have a great background in Cyber Security, and how do we apply Artificial Intelligence to Industrial Cyber Security. And two was, I had been working with the founders for a little bit, and I saw, with my own eyes, how the customers adopted the technology, how easy it was to deploy in an industrial setting, which tends to have a lot of friction. Not a lot of equipment gets into those networks. And the ease of proof of concepts, I saw it with my own eyes. And the frictionless interactions, made me join. >> So Nozomi was started in 2013, you're already monitoring over 50,000 industrial installations. >> That's right. >> Some of the themes that we've talked about, at the event today, so far, with Fortinet's senior leaders, is the evolution of security, where they're positioning, really at this third generation of that. As we're seeing that, and we're seeing that in order for businesses to digitalize successfully, they have to have trust in that data. What is Nozomi seeing, in terms of your industrial customers? What are some of the biggest concerns that they have, regarding security? And how are you working with Fortinet, to help mitigate or limit damage from cyber attacks? >> A lot of our customers in our space, are going through what's called IT/OT Conversions. OT networks, have traditionally been serial, point to point, run over two step para copper and they've recently adopted ethernet. When you adopt ethernet, you have a gravitational force, which is to connect. So these OT networks used to be air gaps, segregated, and now they're being converged with IT technology, under sometimes, IT operation. And therefore, they start suffering the traditional IT attacks. Those traditional IT attacks, are particularly harmful when it comes to industrial, critical infrastructure. And they require special technology that understands those protocols, to be able to detect anomalies, and white list or black list, certain activities. >> Give some example, of an IOT network. So, what is, you say critical infrastructure, gives us some examples, what are we talking about? >> IOT's a very broad term. We focus very specifically on industrial IOT. >> Or, industrial IOT. >> Industrial IOT, could be a network that controls a refining, so the refining process in a refinery. It could be electrical distribution, any form of electrical generation, oil and gas, upstream or downstream. Manufacturing, everything that moves in manufacturing, is controlled by an industrial control networks. Pharma, in the same subsegment, if you will. Some transportation, we're based in San Francisco, so our barge system is controlled with industrial control systems. >> So, we're talking about, as you say critical infrastructure, we're talking about things that, where getting control of some element of that critical infrastructure, >> Correct. >> Especially in the process manufacturing businesses, can have enormously harmful effects? >> Correct. >> On not only business, but an entire community? >> The disruption that it can cause is tremendous. From lights out in a city, to harm to people, in a transportation case, oil and gas case. Environmental damage, leakage. The damage can be tremendous. And that's basically, one of the huge differences between IT and OT. In IT, if your network blinks, your email may be two seconds late, my print job may need to be resent. In OT, you may not be able to turn off that valve, or stop this process from happening, or receive an alarm in time. >> Right, so like, I live in Palo Alto. Not too from me is, some of the big refineries up in Richmond, California. And not too long ago, they had an OT outage, and it led to nearly a billion dollars worth of damage, to that plant, and to the local environment. >> Correct. >> So this is real serious stuff. >> So with a product like Nozomi, you can detect anomalies. Anomalies come in three flavors. One could be equipment damage, malfunction. The other one could be human error, which is very very common. And the other one could be cyber. Any one of those could be an anomaly, and if it tries to throw the process into a critical state, we would detect that, and that's where ... >> Talking about cyber, from a cyber attack perspective, what is it about industrial control systems that makes them such a target? >> Yeah. It is that they had been used to be isolated networks, just like I said. When IT and OT converges, are taking networks that used to be serial security was not really a concern, in industrial control networks, you don't really have identity, you don't have authentication. You're just starting to have encryption. Basically, if you drop a command in the network, that command will get executed. So, it's about the vulnerability of those. >> Vulnerability, maybe it's an easy target? And then from a proliferation perspective, we mentioned the evolution of security. But, the evolution of cyber attacks, the threat surface is increasing. What is the potential, give us some examples, some real world examples, of the proliferation that a cyber attack, >> That is a great question. >> And an industrial control system, can have on a retailer or a bank, energy company? >> The industry was put in the map in 2010, with Stuxnet. Stuxnet was the first attack, everybody talked about Stuxnet for a while. And it was very hard to create a market out of that, because it was done really by a nation's state, and it was done like once. Since then, 2010, 2013 and now 'til today, attacks have increased in frequency dramatically, and in use cases. Not only are nation states attacking each other, like in the case now of the Ukraine, but now you have traditional security use cases, your malicious insider, you're compromised insiders, doing industrial cyber attacks. In 2015, the Department of Homeland Security reported 295, industrial cyber attacks, in our nation's critical infrastructure. And those are not mandated, they don't have a reporting mandate, so those are voluntary reports. >> Wow. >> So that number, could be two or three times as big. If you think about it, from 2010, we've gone from once a year, to 2015 once per day. So, it's happening. It's happening all the time. And it's increasing not only in frequency, but in sophistication. >> So, it's 295 reported. But there's a bunch of unreported, >> Correct. >> That we know about, and then there's a bunch that we don't know about? >> Correct. >> So, you're talking about potentially thousands of efforts? And you're trying with Fortinet and others, to bring technology, as well as, a set of best practices and thought leadership, for how to mitigate those problems? >> That's right. With Fortinet, we have a very comprehensive solution. We basically combine Fortinet's sophistication or robustness from a cyber security platform, with Nozomi's industrial knowledge. Really, we provide anomaly detection, we detect, like I said, any sort of anomaly, when it comes to error, cyber, or malfunction. And we feed it to Fortinet. Fortinet can be our enforcement arm if you will, to stop, quarantine, block, cyber attacks. >> So, Nozomi's building models, based on your expertise of how industrial IOT works, >> That's right. >> And you're deploying those models with clients, but integrating the back into the Fortinet sandbox, and other types of places. So, when problems are identified, it immediately gets published, communicated to Fortinet, and then all Fortinet customers get visibility into some of those problems? >> We connect with Fortinet in two ways. One, is we have 40 SIM, so we alert everybody. We become part of the information, security information environment. But we also used Nozomi Fortigates, to block, to become active in the network. Our product is 100% passive. We have to be passive to be friendly deployed in industrial networks. But, for the level of attack or the level or risk is very high, you can actually configure Fortinet to receive a command from Fortinet, and from Nozomi, and actually block or quarantine a particular contaminated node, or something like that. Does that make sense? >> Oh, totally. Makes 100%, because as you said, so you let Fortinet do the active work, of actually saying yes or no, something can or cannot happen, based on the output of your models? >> That's right. Yep. >> So, when you think about IOT, or industrial IOT, there's an enormous amount of investment being made of turning all these analog feeds, into digital signals, that then can be modeled. Tell us a little bit about how your customers are altering their perspective on, what analog information needs to be captured, so that your models can get smarter and smarter, and better and better at predicting and anticipating and stopping problems. >> When it comes to industrial models, you need to pretty much capture all the data. So, we size the deployment of our product based on the number of nodes or PLC's that exist in an industrial network. We have designed our product to scale, so the more information or the more number of nodes, the better our models are going to be, and our products will scale to build those models. But, capturing all the data is required. Not only capturing, but parsing all the data, and extracting the insides and the correlations between all the data, is a requirement for us to have the accuracy in anomaly detection that we have. >> What is the customer looking at in terms of going along that, that seems like an arduous task, a journey. What does, and you don't have to give us a customer name, but what does that journey look like, working together with Nozomi, and Fortinet, to facilitate that transformation, from analog to digital, if all the information is critical? >> That transformation is happening already. A lot of these industrial networks are already working on top of ethernet, a standard DCPIP. The way the journey works for us, is we provide, as soon as we show up, an immediate amount of visibility. These networks don't have the same tool sets from a visibility and asset management perspective that IT networks have. So, the first value add is visibility. We capture an incredible amount of information. And the first and best way to deploy it initially, is with, let me look at my network, understand how many PLC's do I have, how the segmentation should be properly done. And then, during all this time, our model building is happening, we're learning about the physical process and about the network. After we've done with the learning our system, determines that now it's ready to enforce, or detect anomalies, and we become at that point, active in anomaly detection. At that point, the customer may connect us with Fortinet, and we may be able to enforce quarantine activities, or blocking activities, if the problem requires it. >> Is there any one particular, use case that sticks out in your mind, as a considerable attack, that Nozomi has helped to stop? >> We obviously can't name any one in particular, but when it comes to defending yourself against cyber criminals, we have defended companies against malicious insiders. Sometimes, an employee didn't like how something may have happened, with them or with somebody else, and that person leaves the company, but nobody removed their industrial credentials. And they decide to do something harmful, and it's very hard. Industrial malicious insider activity, is extremely hard to pinpoint, extremely hard to troubleshoot. Industrial issues in general, are very hard to troubleshoot. So, one of the things that Nozomi adds a lot of value is, is allowing troubleshooting from the keyboard, without eliminating trucks and excel sheets, you quickly can pinpoint a problem, and stop the bad things before they happen. >> One more quick question for you. With the announcements that Fortinet has made today, regarding, you mentioned some of the products, what are you looking forward to most in 2017, in terms of being able to take it to the next level with your customers? To help them, help themselves? >> Listen, the solution works amazingly well. We have to tell more people about it. I think the critical infrastructure has not had the attention in prior years, and I think this year's going to be a year where, ICS security is going to be, and Fortinet of course, is very aware of this, is going to be a lot more relevant for a lot more people. The number of attacks, and the you know, the attacks surface that will never be, it's all playing so that, this year's going to be a big year. >> Yeah, I think we were talking, before we started, that the U.S. Department of Homeland Security, has just identified the U.S. Election System, as a critical infrastructure. >> That's right. >> So maybe it's going to take more visible things, that have global implications, to really help move this forward. >> I think the one point I would make when it comes to government, government has been great, if you make an analogy, this is an analogy that I have on the top of my head, if you look at cars in the automotive industry, seat belts and airbags have saved a lot of lives. We don't have that in industrial cyber security. And we need the government to tell us, what are the seat belts? And what are the minimum set of requirements that are electrical, infrastructures should be able to sustain? And that way, it makes the job easier for a lot of us, because nobody can tell you today, how much security to invest, and what's the mix of security solutions that you should have. And therefore, in the places where you don't have a lot of investment, you don't have none. And you become very vulnerable. Today, if you want to ship a car, and you want your car to be driven on the road, it has to have airbags, and it has to have seat belts, and that makes it a minimum bar for proper operation, if you will. >> But the proper, the way it typically works, is government is going to turn to folks like yourself, to help advise and deliver visibility, into what should be the appropriate statements about regulation, and what needs to be in place. So, it's going to be interesting because you and companies like you, will in fact be able to generate much of the data, that will lead to hopefully, less ambiguous types of regulations. >> Yes, that's right. That's right. I agree 100%. >> Wow, it's an exciting prospect. Edgard Capdevielle, thank you so much. CEO of Nozomi Networks, it's been a pleasure to have you on the program today. >> Thank you. >> On behalf of my cohost Peter Buress, Peter, thank you. We thank you for watching theCube, but stick around, we've got some more up, so stay tuned. (tech music)