>> Live from Midtown Manhattan, it's theCUBE. Covering BigData New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. (calm electronic music) >> Basil Faruqui, who's the Solutions Marketing Manger at BMC, welcome to theCUBE. >> Thank you, good to be back on theCUBE. >> So first of all, heard you guys had a tough time in Houston, so hope everything's gettin' better, and best wishes to everyone down in-- >> We're definitely in recovery mode now. >> Yeah and so hopefully that can get straightened out quick. What's going on with BMC? Give us a quick update in context to BigData NYC. What's happening, what is BMC doing in the big data space now, the AI space now, the IOT space now, the cloud space? >> So like you said that, you know, the data link space, the IOT space, the AI space, there are four components of this entire picture that literally haven't changed since the beginning of computing. If you look at those four components of a data pipeline it's ingestion, storage, processing, and analytics. What keeps changing around it, is the infrastructure, the types of data, the volume of data, and the applications that surround it. And the rate of change has picked up immensely over the last few years with Hadoop coming in to the picture, public cloud providers pushing it. It's obviously creating a number of challenges, but one of the biggest challenges that we are seeing in the market, and we're helping costumers address, is a challenge of automating this and, obviously, the benefit of automation is in scalability as well and reliability. So when you look at this rather simple data pipeline, which is now becoming more and more complex, how do you automate all of this from a single point of control? How do you continue to absorb new technologies, and not re-architect our automation strategy every time, whether it's it Hadoop, whether it's bringing in machine learning from a cloud provider? And that is the issue we've been solving for customers-- >> Alright let me jump into it. So, first of all, you mention some things that never change, ingestion, storage, and what's the third one? >> Ingestion, storage, processing and eventually analytics. >> And analytics. >> Okay so that's cool, totally buy that. Now if your move and say, hey okay, if you believe that standard, but now in the modern era that we live in, which is complex, you want breath of data, but also you want the specialization when you get down to machine limits highly bounded, that's where the automation is right now. We see the trend essentially making that automation more broader as it goes into the customer environments. >> Correct >> How do you architect that? If I'm a CXO, or I'm a CDO, what's in it for me? How do I architect this? 'Cause that's really the number one thing, as I know what the building blocks are, but they've changed in their dynamics to the market place. >> So the way I look at it, is that what defines success and failure, and particularly in big data projects, is your ability to scale. If you start a pilot, and you spend three months on it, and you deliver some results, but if you cannot roll it out worldwide, nationwide, whatever it is, essentially the project has failed. The analogy I often given is Walmart has been testing the pick-up tower, I don't know if you've seen. So this is basically a giant ATM for you to go pick up an order that you placed online. They're testing this at about a hundred stores today. Now if that's a success, and Walmart wants to roll this out nation wide, how much time do you think their IT department's going to have? Is this a five year project, a ten year project? No, and the management's going to want this done six months, ten months. So essentially, this is where automation becomes extremely crucial because it is now allowing you to deliver speed to market and without automation, you are not going to be able to get to an operational stage in a repeatable and reliable manner. >> But you're describing a very complex automation scenario. How can you automate in a hurry without sacrificing the details of what needs to be? In other words, there would seem to call for repurposing or reusing prior automation scripts and rules, so forth. How can the Walmart's of the world do that fast, but also do it well? >> Yeah so we do it, we go about it in two ways. One is that out of the box we provide a lot of pre-built integrations to some of the most commonly used systems in an enterprise. All the way from the Mainframes, Oracles, SAPs, Hadoop, Tableaus of the world, they're all available out of the box for you to quickly reuse these objects and build an automated data pipeline. The other challenge we saw, and particularly when we entered the big data space four years ago was that the automation was something that was considered close to the project becoming operational. Okay, and that's where a lot of rework happened because developers had been writing their own scripts using point solutions, so we said alright, it's time to shift automation left, and allow companies to build automations and artifact very early in the developmental life cycle. About a month ago, we released what we call Control-M Workbench, its essentially a community edition of Control-M, targeted towards developers so that instead of writing their own scripts, they can use Control-M in a completely offline manner, without having to connect to an enterprise system. As they build, and test, and iterate, they're using Control-M to do that. So as the application progresses through the development life cycle, and all of that work can then translate easily into an enterprise edition of Control-M. >> Just want to quickly define what shift left means for the folks that might not know software methodologies, they don't think >> Yeah, so. of left political, left or right. >> So, we're not shifting Control-M-- >> Alt-left, alt-right, I mean, this is software development, so quickly take a minute and explain what shift left means, and the importance of it. >> Correct, so if you think of software development as a straight line continuum, you've got, you will start with building some code, you will do some testing, then unit testing, then user acceptance testing. As it moves along this chain, there was a point right before production where all of the automation used to happen. Developers would come in and deliver the application to Ops and Ops would say, well hang on a second, all this Crontab, and these other point solutions we've been using for automation, that's not what we use in production, and we need you to now go right in-- >> So test early and often. >> Test early and often. So the challenge was the developers, the tools they used were not the tools that were being used on the production end of the site. And there was good reason for it, because developers don't need something really heavy and with all the bells and whistles early in the development lifecycle. Now Control-M Workbench is a very light version, which is targeted at developers and focuses on the needs that they have when they're building and developing it. So as the application progresses-- >> How much are you seeing waterfall-- >> But how much can they, go ahead. >> How much are you seeing waterfall, and then people shifting left becoming more prominent now? What percentage of your customers have moved to Agile, and shifting left percentage wise? >> So we survey our customers on a regular basis, and the last survey showed that eighty percent of the customers have either implemented a more continuous integration delivery type of framework, or are in the process of doing it, And that's the other-- >> And getting close to a 100 as possible, pretty much. >> Yeah, exactly. The tipping point is reached. >> And what is driving. >> What is driving all is the need from the business. The days of the five year implementation timelines are gone. This is something that you need to deliver every week, two weeks, and iteration. >> Iteration, yeah, yeah. And we have also innovated in that space, and the approach we call jobs as code, where you can build entire complex data pipelines in code format, so that you can enable the automation in a continuous integration and delivery framework. >> I have one quick question, Jim, and I'll let you take the floor and get a word in soon, but I have one final question on this BMC methodology thing. You guys have a history, obviously BMC goes way back. Remember Max Watson CEO, and Bob Beach, back in '97 we used to chat with him, dominated that landscape. But we're kind of going back to a systems mindset. The question for you is, how do you view the issue of this holy grail, the promised land of AI and machine learning, where end-to-end visibility is really the goal, right? At the same time, you want bounded experiences at root level so automation can kick in to enable more activity. So there's a trade-off between going for the end-to-end visibility out of the gate, but also having bounded visibility and data to automate. How do you guys look at that market? Because customers want the end-to-end promise, but they don't want to try to get there too fast. There's a diseconomies of scale potentially. How do you talk about that? >> Correct. >> And that's exactly the approach we've taken with Control-M Workbench, the Community Edition, because earlier on you don't need capabilities like SLA management and forecasting and automated promotion between environments. Developers want to be able to quickly build and test and show value, okay, and they don't need something that is with all the bells and whistles. We're allowing you to handle that piece, in that manner, through Control-M Workbench. As things progress and the application progresses, the needs change as well. Well now I'm closer to delivering this to the business, I need to be able to manage this within an SLA, I need to be able to manage this end-to-end and connect this to other systems of record, and streaming data, and clickstream data, all of that. So that, we believe that it doesn't have to be a trade off, that you don't have to compromise speed and quality for end-to-end visibility and enterprise grade automation. >> You mentioned trade offs, so the Control-M Workbench, the developer can use it offline, so what amount of testing can they possibly do on a complex data pipeline automation when the tool's offline? I mean it seems like the more development they do offline, the greater the risk that it simply won't work when they go into production. Give us a sense for how they mitigate, the mitigation risk in using Control-M Workbench. >> Sure, so we spend a lot of time observing how developers work, right? And very early in the development stage, all they're doing is working off of their Mac or their laptop, and they're not really connected to any. And that is where they end up writing a lot of scripts, because whatever code business logic they've written, the way they're going to make it run is by writing scripts. And that, essentially, becomes the problem, because then you have scripts managing more scripts, and as the application progresses, you have this complex web of scripts and Crontabs and maybe some opensource solutions, trying to simply make all of this run. And by doing this on an offline manner, that doesn't mean that they're losing all of the other Control-M capabilities. Simply, as the application progresses, whatever automation that the builtin Control-M can seamlessly now flow into the next stage. So when you are ready to take an application into production, there's essentially no rework required from an automation perspective. All of that, that was built, can now be translated into the enterprise-grade Control M, and that's where operations can then go in and add the other artifacts, such as SLA management and forecasting and other things that are important from an operational perspective. >> I'd like to get both your perspectives, 'cause, so you're like an analyst here, so Jim, I want you guys to comment. My question to both of you would be, lookin' at this time in history, obviously in the BMC side we mention some of the history, you guys are transforming on a new journey in extending that capability of this world. Jim, you're covering state-of-the-art AI machine learning. What's your take of this space now? Strata Data, which is now Hadoop World, which is Cloud Air went public, Hortonworks is now public, kind of the big, the Hadoop guys kind of grew up, but the world has changed around them, it's not just about Hadoop anymore. So I'd like to get your thoughts on this kind of perspective, that we're seeing a much broader picture in big data in NYC, versus the Strata Hadoop show, which seems to be losing steam, but I mean in terms of the focus. The bigger focus is much broader, horizontally scalable. And your thoughts on the ecosystem right now? >> Let the Basil answer fist, unless Basil wants me to go first. >> I think that the reason the focus is changing, is because of where the projects are in their lifecycle. Now what we're seeing is most companies are grappling with, how do I take this to the next level? How do I scale? How do I go from just proving out one or two use cases to making the entire organization data driven, and really inject data driven decision making in all facets of decision making? So that is, I believe what's driving the change that we're seeing, that now you've gone from Strata Hadoop to being Strata Data, and focus on that element. And, like I said earlier, the difference between success and failure is your ability to scale and operationalize. Take machine learning for an example. >> Good, that's where there's no, it's not a hype market, it's show me the meat on the bone, show me scale, I got operational concerns of security and what not. >> And machine learning, that's one of the hottest topics. A recent survey I read, which pulled a number of data scientists, it revealed that they spent about less than 3% of their time in training the data models, and about 80% of their time in data manipulation, data transformation and enrichment. That is obviously not the best use of a data scientist's time, and that is exactly one of the problems we're solving for our customers around the world. >> That needs to be automated to the hilt. To help them >> Correct. to be more productive, to deliver faster results. >> Ecosystem perspective, Jim, what's your thoughts? >> Yeah, everything that Basil said, and I'll just point out that many of the core uses cases for AI are automation of the data pipeline. It's driving machine learning driven predictions, classifications, abstractions and so forth, into the data pipeline, into the application pipeline to drive results in a way that is contextually and environmentally aware of what's goin' on. The history, historical data, what's goin' on in terms of current streaming data, to drive optimal outcomes, using predictive models and so forth, in line to applications. So really, fundamentally then, what's goin' on is that automation is an artifact that needs to be driven into your application architecture as a repurposable resource for a variety of-- >> Do customers even know what to automate? I mean, that's the question, what do I-- >> You're automating human judgment. You're automating effort, like the judgments that a working data engineer makes to prepare data for modeling and whatever. More and more that can be automated, 'cause those are pattern structured activities that have been mastered by smart people over many years. >> I mean we just had a customer on with a Glass'Gim CSK, with that scale, and his attitude is, we see the results from the users, then we double down and pay for it and automate it. So the automation question, it's an option question, it's a rhetorical question, but it just begs the question, which is who's writing the algorithms as machines get smarter and start throwing off their own real-time data? What are you looking at? How do you determine? You're going to need machine learning for machine learning? Are you going to need AI for AI? Who writes the algorithms >> It's actually, that's. for the algorithm? >> Automated machine learning is a hot, hot not only research focus, but we're seeing it more and more solution providers, like Microsoft and Google and others, are goin' deep down, doubling down in investments in exactly that area. That's a productivity play for data scientists. >> I think the data markets going to change radically in my opinion. I see you're startin' to some things with blockchain and some other things that are interesting. Data sovereignty, data governance are huge issues. Basil, just give your final thoughts for this segment as we wrap this up. Final thoughts on data and BMC, what should people know about BMC right now? Because people might have a historical view of BMC. What's the latest, what should they know? What's the new Instagram picture of BMC? What should they know about you guys? >> So I think what I would say people should know about BMC is that all the work that we've done over the last 25 years, in virtually every platform that came before Hadoop, we have now innovated to take this into things like big data and cloud platforms. So when you are choosing Control-M as a platform for automation, you are choosing a very, very mature solution, an example of which is Navistar. Their CIO's actually speaking at the Keno tomorrow. They've had Control-M for 15, 20 years, and they've automated virtually every business function through Control-M. And when they started their predictive maintenance project, where they're ingesting data from about 300,000 vehicles today to figure out when this vehicle might break, and to predict maintenance on it. When they started their journey, they said that they always knew that they were going to use Control-M for it, because that was the enterprise standard, and they knew that they could simply now extend that capability into this area. And when they started about three, four years ago, they were ingesting data from about 100,000 vehicles. That has now scaled to over 325,000 vehicles, and they have no had to re-architect their strategy as they grow and scale. So I would say that is one of the key messages that we are taking to market, is that we are bringing innovation that spans over 25 years, and evolving it-- >> Modernizing it, basically. >> Modernizing it, and bringing it to newer platforms. >> Well congratulations, I wouldn't call that a pivot, I'd call it an extensibility issue, kind of modernizing kind of the core things. >> Absolutely. >> Thanks for coming and sharing the BMC perspective inside theCUBE here, on BigData NYC, this is the theCUBE, I'm John Furrier. Jim Kobielus here in New York city. More live coverage, for three days we'll be here, today, tomorrow and Thursday, and BigData NYC, more coverage after this short break. (calm electronic music) (vibrant electronic music)

>> From Tavern on the Green, in Central Park, New York, it's theCUBE, covering Veritas Vision Solution Day, brought to you by Veritas. >> Welcome back to the beautiful Tavern on the Green, in the heart of Central Park. You're watching theCUBE, the leader in live tech coverage. My name's Dave Vellante. We're covering Vertias Solution Days, #VtasVision. Veritas used to have the big, single tent, big tent customer event, and decided this year, it's going to go belly to belly. Go out to 20 cities, intimate customer events where they can really sit down with customers across from the table; certainly, this beautiful venue is the perfect place to do that. Harish Venkat is here as the VP of Marketing and Global Sales Enablement at Veritas. Thanks for coming on, Harish. >> Yeah, thanks for having me. >> So, we're going to change it up a little bit. Let's hit the Escape key a few times and talk about >> Yeah. >> some of the big mega trends that you're seeing. You spend a lot of time with customers. You had some intimate conversations today. What do you see as the big trends driving the marketplace? >> So at my level, what I observe with the highest thing is simplicity, instant gratification, is two things that customers love. Forget about customers, even we as individuals, we love simplicity and instant gratification. Examples around that, you know, think about back in the days where you had to take a picture, process the film, and then realize, "oh my god, the film's not even worth watching." Now you have digital photography, you take millions of pictures, and instantly you view the picture, and keep whatever you want, delete whatever you don't want. A small example of how simplicity and instant gratification is changing the world. In fact, if you listen to Warren Buffett, he'll say, "Invest in companies that is making your life a lot easier," so, if I spread that across the entire industry, I can go on with examples like Netflix disrupting Blockbuster because it made it easy for customers to watch movies at their time, and making it easy for consumption. You look at showrooming concept, where you go to Best Buy's of the world and many others, and look at a product, but you don't buy it right there. You go to your phone and say, "okay, do I do a price compare?" And then order it on the phone, where someone delivers it to your house So the list goes on and on, and the underpinning result as a result of this is disruption, all right? You look at Fortune 500 companies, just in the last decade. Over 52% of those companies have been disrupted and the underpinning phenomenon is all about instant gratification and simplicity. >> And Amazon is another great example of, I remember when my wife said to me, "Dave, you got to invest in this company." It was like... 1997. >> Yeah. >> Invest in this company, Amazon? >> Yeah. >> At the time, it was mostly books, but they started to get into other retail, so right-- >> We missed that boat, didn't we? >> I actually did, but I sold, ah! (laughs) >> I never lost money making a profit, so okay. So, at the same time, customer... Customers just can't get there... >> Yeah. >> Overnight, so what are some of the challenges that they have in getting to that level of simplicity? >> Yeah, so you look at IT spend, and when you look at the breakdown of IT spend, you'll see that about 87%, and in many cases, even greater than 90%, they spend just to keep the lights on and these are well-established companies that I'm talking about. In fact, I was doing a Keynote in, in Minneapolis one time and a CIO came and said, "Harish, I totally disagree." "In my company, it's 96%." >> (Dave laughs) >> Just to keep the lights on! So you're talking about less than 10% of your IT spend gone towards innovation, and then you look at emerging companies who are spending almost 100% all around innovation, leveraging the clouds of the world, leveraging the latest and greatest technology, and then doing these disruptions, and making things simple for consumption, and as a result, the disruption happens, so I think we have an opportunity to re-balance the equation in the enterprise space, and making it more available for innovation than just keeping the lights on. >> So part of that... the equation of shifting that needle, moving that needle, if you will, just eliminating non-value-producing activities that are expensive. We know, still, IT is still very labor-intensive, so we got to take that equation down and shift it. Are you seeing companies have success in shifting, re-training people toward digital initiatives and removing some of the heavy lifting, and what's driving that? >> Yeah, so I think it's a journey, right? So, I mean, the entire notion of journeying to the cloud is one of the big initiative to take out heavily manual-intensive, data center-intensive, which is costing a lot of money. If I can just shift all of those workloads to the cloud, that'll help me re-balance the equation. I view the concept of data intensity, which is really two variables to it. Back to your point, if I can take the non-core activity, rely on my partner ecosystem to say what is best in class solutions that I can use as my foundation layer, and then innovate on top of it, then yes, you have the perfect winning formula to really have a lot of market share and wallet share. If you're trying to do the entire stack by yourself, good luck. You'll be one of those guys who will be disrupted. There is no doubt. >> So well, okay, that says partnerships are very important. >> Without a doubt. >> You're not too alone. >> Channel is very important. >> Yes. >> So, so what do you see, in terms of the ebb and flow in the industry, of partnerships, how those are forming? Hear a lot about "co-opetition," which is kind of an interesting term, that is now, we're living. >> Yeah. >> What's your, what's your observation about partnerships, and how companies are able to leverage them? What's best practice there? >> Yeah, so just as Veritas, we're a data protection leader company. We have incredible market share and wallet share, amongst the Fortune 500 and Fortune 100 companies, but even within our incredible standing, we have to rely on other partners. We don't do everything on our own. We have incredible relationship with our cloud service providers, with the hyper-converged system to the world, like Nutanix. We just announced Pure today, so when we combine those partnerships, we can offer incredible solutions for our customers, who can then take care of the first variable that I talked about, and then innovate on top of it. So I think partner ecosystem is extremely important. For customers, it's very important that they pick the right players, so they don't have to worry about the data, and they can continually focus on innovation. >> We were talking to NBC Universal today, and one of themes in my take-aways was he's trying to get to the... he's a, basically a data protector, backup administrator, essentially, but he's trying to get to the point where he can get the business lines to self-serve. >> Yeah. >> And that seems to me to be part of the simplicity. Now... an individual like that, got to re-skill. Move toward a digital transformation. Move that needle so it's not 90% keeping the lights on. It's maybe you get to 50/50. >> Yeah. What are you seeing in terms of training and re-education of both existing people and maybe even how young people are being educated, your thoughts? >> Yeah, I think the young people coming out of college, they're already tuned to this, so to me, those are the disruptors of the world. You got to keep an eye on those millennials of the world because you don't have to train them more, because they're coming out of college, you know. They don't have the legacy background. They don't have the data centers of the world. They are already in the cloud. They're born in the cloud, sort of individuals, so I think the challenge is more about existing individuals who have the pedigree of all the journey that, you and I, we have seen, and how do you re-tune yourself to the modern world? And I think that presents an opportunity to say, "Okay look, if you don't adapt real quick," "you don't have a chance to survive" "in this limited amount of time you have in the IT space," but having said that, we're also seeing that you have some time window, and that time window will continue to shrink, so when we talk about this transformation journey, you can see year after year, the progress that, that's been made in the transformation, this leap and bound, and that's all related to Moore's Law. You think about computer and storage, it's becoming a lot cheaper, and so the innovation rate is continuing to go up. So you have very limited window: adapt or die. >> So, Harish, we were talking about, we've talked about digital transformation. We talk about simplifying; we're talking about agility. We're talking about shifting budget priorities, all very important initiatives. How is Veritas helping customers achieve these goals, so that they can move the needle from 90% keep the lights on to maybe 50/50, and put more into innovation. >> So four major themes: one is data protection. If you don't have your core enterprise asset, which is your data protected, then you can't really innovate anything on top of it. You'll constantly be worrying about what happens if I have a ransomware attack, what if I have a data outage, so Veritas takes care of it, back to the notion that you pick the best players to take care of the fundamental layer, which is around the data. The second thing that I... I would say Veritas can help is the journey to the cloud. Cloud, again, is another instrument for you to take out cost out of your data center. You're agile, you're nimble, so you can focus on innovation. Do you see the trend? So again, Veritas helps you with that journey to the cloud. It allows to move data and application to the cloud. When you're in the cloud, we protect your data in the cloud. The third thing I would say is doing more with less. I talked about the IT equation already. Software-defined storage allows you to do that. And the last thing I would say is compliance. We can't get away from compliance, the fact that Veritas has solutions to have visibility around the data. You can classify the data. You can always be compliant working with Veritas. You take care of these four layers, you don't have to worry about your data asset. You can worry about innovation at that point. >> So it, to me, it's sort of a modern version of the rebirth of Veritas. When Veritas first started, I always used to think of it as a data management company, not just a backup company. >> Right. >> And that's really what we're talking about here today, evolving toward a data-centric approach, that full life cycle of data management, simplifying that, bringing the cloud experience to your data wherever it is. Could be "on-prem." >> Yeah. >> Could be in the cloud, sort of this API-based architecture, microservices, containers... >> Yep. >> All the kind of interesting buzzwords today, but they enable agility in a cloud-like experience, that Netflix-like experience that you were talking about. >> Absolutely, right, so we're super excited. The one thing I would also say is what our latest net backup, 812, the other thing that I talked about, which is simplicity and ease of use: we are addressing both of that in addition to the robust brand that we have around protecting data. So you now you have simplicity, ease of use, instant gratification, all the basic ingredients, and Veritas is here to protect them. >> Harish, it's been a great day. Thanks for helping me close out the segment here. This venue is really terrific. It's been a while since I've been at Tavern on the Green. Some of you guys, I don't think you've ever seen it before. Seth's down here; he's, he's a city boy but we country bumpkins up in Massachusetts, we love coming down here, in the heart of Yankee country. So thanks very much-- >> Of course. >> For helping me close out here, great segment. All right, thanks for watching, everybody. We're out here, from New York City, Tavern on the Green. You've been watching theCUBE; I'm Dave Vellante. We'll see you next time. (light electronic music)

>> From Tavern on the Green in Central Park, New York, it's theCUBE, covering Veritas Vision Solution Day. Brought to you by Veritas. (robotic music) >> We're back in the heart of Central Park. We're here at Tavern on the Green. Beautiful location for the Veritas Vision Day. You're watching theCUBE, my name is Dave Vellante. We go out to the events, we extract the signal from the noise, we got the CEO of Veritas here, Greg Hughes, newly minted, nine months in. Greg, thanks for coming on theCUBE. >> It's great to be here Dave, thank you. >> So let's talk about your nine. What was your agenda your first nine months? You know they talk about the 100 day plan. What was your nine month plan? >> Yeah, well look, I've been here for nine months, but I'm a boomerang. So I was here from 2003 to 2010. I ran all of global services, during that time and became the chief strategy officer after that. Was here during the merger by Semantic. And then ran the Enterprise Product Group. So I had all the products and all the engineering teams for all the Enterprise products. And really my starting point is the customer. I really like to hear directly from the customer. So I've spent probably 50% of my time out and about, meeting with customers. And at this point, I've met with a 100 different accounts all around the world. And what I'm hearing, makes me even more excited to be here. Digital transformation is real. These customers are investing a lot in digitizing their companies. And that's driving an explosion of data. That data all needs to be available and recoverable and that's where we step in. We're the best at that. >> Okay, so that was sort of alluring to you. You're right, everybody's trying to get digital transformation right. It changes the whole data protection equation. It kind of reminds me, in a much bigger scale, of virtualization. You remember, everybody had to rethink their backup strategies because you now have less physical resources. This is a whole different set of pressures, isn't it? It's like you can't go down, you have to always have access to data. Data is-- >> 24 by seven. >> Increasingly valuable. >> Yup. >> So talk a little bit more about the importance of data, the role of data, and where Veritas fits in. >> Well, our customers are using new, they're driving new applications throughout the enterprise. So machine learning, AI, big data, internet of things. And that's all driving the use of new data management technologies. Cassandra, Hadoop, Open Sequel, MongoDB. You've heard all of these, right? And then that's driving the use of new platforms. Hyper-converged, virtual machines, the cloud. So all this data is popping up in all these different areas. And without Veritas, it can exist, it'll just be in silos. And that becomes very hard to manage and protect it. All that data needs to be protected. We're there to protect everything. And that's really how we think about it. >> The big message we heard today was you got a lot of different clouds, you don't want to have a different data protection strategy for each cloud. So you've got to simplify that for people. Sounds easy, but from an R&D perspective, you've got a large install base, you've been around for a long, long time. So you've got to put investments to actually see that through. Talk about your R&D and investment strategy. >> Well, our investment strategy's very simple. We are the market share leader in data protection and software-defined storage. And that scale, gives us a tremendous advantage. We can use that scale to invest more aggressively than anybody else, in those areas. So we can cover all the workloads, we can cover wherever our customers are putting their data, and we can help them standardize on one provider of data protection, and that's us. So they don't have to have the complexity of point products in their infrastructure. >> So I wonder if we could talk, just a little veer here, and talk about the private equity play. You guys are the private equity exit. And you're seeing a lot of high profile PE companies. It used to be where companies would go to die, and now it's becoming a way for the PE guys to actually get step-ups, and make a lot of money by investing in companies, and building communities, investing in R&D. Some of the stuff we've covered. We've followed Syncsort, BMC, Infor, a really interesting company, what's kind of an exit from PE, right? Dell, the biggest one of all. Riverbed, and of course Veritas. So, there's like a new private equity playbook. It's something you know well from your Silver Lake days. Describe what that dynamic is like, and how it's changed. >> Oh look, private equity's been involved in software for 10 or 15 years. It's been a very important area of investment in private equity. I've worked for private equity firms, worked for software companies, so I know it very well. And the basic idea is, continue the investment. Continue in the investment in the core products and the core customers, to make sure that there is continued enhancement and innovation, of the core products. With that, there'll be continuity in customer relationships, and those customer relationships are very valuable. That's really the secret, if you will, of the private equity playbook. >> Well and public markets are very fickle. I mean, they want growth now. They don't care about profits. I see you've got a very nice cash flow, you and some of the brethren that I mentioned. So that could be very attractive, particularly when, you know, public markets they ebb and flow. The key is value for customers, and that's going to drive value for shareholders. >> That's absolutely right. >> So talk about the TAM. Part of a CEOs job, is to continually find new ways, you're a strategy guy, so TAM expansion is part of the role. How do you look at the market? Where are the growth opportunities? >> We see our TAM, or our total addressable market, at being around $17 billion, cutting across all of our areas. Probably growing into high single digits, 8%. That's kind of a big picture view of it. When I like to think about it, I like to think about it from the themes I'm hearing from customers. What are our customers doing? They're trying to leverage the cloud. Most of our customers, which are large enterprises. We work with the blue-chip enterprises on the planet. They're going to move to a hybrid approach. They're going to on-premise infrastructure and multiple cloud providers. So that's really what they're doing. The second thing our customers are worried about is ransomware, and ransomware attacks. Spearfishing works, the bad guys are going to get in. They're going to put some bad malware in your environment. The key is to be resilient and to be able to restore at scale. That's another area of significant investment. The third, they're trying to automate. They're trying to make investments in automation, to take out manual labor, to reduce error rate. In this whole world, tape should go away. So one of the things our customers are doing, is trying to get rid of tape backup in their environment. Tape is a long-term retention strategy. And then finally, if you get rid of tape, and you have all your secondary data on disc or in the cloud, what becomes really cool, is you can analyze all that data. Out of bound, from the primary storage. That's one of the bigger changes I've seen since I've returned back to Veritas. >> So $17 billion, obviously, that transcends backup. Frankly, we go back to the early days of Veritas, I always thought of it as a data management company and sort of returned to those roots. >> Backup, software defined storage, compliance, all those areas are key to what we do. >> You mentioned automation. When you think about cloud and digital transformation, automation is fundamental, we had NBCUniversal on earlier, and the customer was talking about scripts and how scripts are fragile and they need to be maintained and it doesn't scale. So he wants to drive automation into his processes as much as possible, using a platform, a sort of API based, modern, microservices, containers. Kind of using all those terms. What does that mean for you guys in terms of your R&D roadmap, in terms of the investments that you're making in those types of software innovations? >> Well actually one of the things we're talking about today is our latest release of NetBackup 812, which had a significant investment in APIs and that allow our customers to use the product and automate processes, tie it together with their infrastructure, like ServiceNow, or whatever they have. And we're going to continue full throttle on APIs. Just having lunch with some customers just today, they want us to go even further in our APIs. So that's really core to what we're doing. >> So you guys are a little bit like the New England Patriots. You're the leader, and everybody wants to take you down. So you always start-- >> Nobody's confused me for Tom Brady. Although my wife looks... I'll stack her up against Giselle anytime, but I'm no Tom Brady. >> So okay, how do you maintain your leadership and your relevance for customers? A lot of VC money coming into the marketplace. Like I said, everybody wants to take the leader down. How do you maintain your leadership? >> We've been around for 25 years. We're very honored to have 95% of the Fortune 100, are our customers. If you go to any large country in the world it's very much like that. We work with the bluest of blue-chips, the biggest companies, the most complex, the most demanding (chuckling), the most highly regulated. Those are our customers. We steer the ship based on their input, and that's why we're relevant. We're listening to them. Our customer's extremely relevant. We're going to help them protect, classify, archive their data, wherever it is. >> So the first nine months was all about hearing from customers. So what's the next 12 to 18 months about for you? >> We're continuing to invest, delighted to talk about partnerships, and where those are going, as well. I think that's going to be a major emphasis of us to continue to drive our partnerships. We can't do this alone. Our customers use products from a variety of other players. Today we had Henry Axelrod, from Amazon Web Services, here talking about how we're working closely with Amazon. We announced a really cool partnership with Pure Storage. Our customers that use Pure Storage's all-flash arrays, they know their data's backed up and protected with Veritas and with NetBackup. It's continually make sure that across this ecosystem of partners, we are the one player that can help our large customers. >> Great, thank you for mentioning that ecosystem is a key part of it. The channel, that's how you continue to grow. You get a lot of leverage out of that. Well Greg, thanks very much for coming on theCUBE. Congratulations on your-- >> Dave, thank you. >> On the new role. We are super excited for you guys, and we'll be watching. >> I enjoyed it, thank you. >> All right. Keep it right there everybody we'll be back with our next guest. This is Dave Vellante, we're here in Central Park. Be right back, Veritas Vision, be right back. (robotic music)

>> From Tavern on the Green in Central Park, New York, it's theCUBE, covering Veritas Vision Solution Day. Brought to you by Veritas. >> Hi everybody, welcome back to Tavern on the Green. We're in the heart of Central Park in New York City, the Big Apple. My name is Dave Vellante and you're watching theCUBE, the leader in live tech coverage. We're here at the Veritas Solution Day #VtasVision. Veritas used to have a big main tent day where they brought in all the customers. Now they're going out, belly-to-belly, 20 cities. Dave Raffo is here, he's the editorial director for TechTarget Storage. Somebody who follows this space very closely. David good to see you, welcome to theCUBE. >> Yeah, it's great to be on theCUBE. I always hear and watch you guys but never been on before. >> Well you're now an alum, I got to get him a sticker. So, we were talking about VMworld just now, and that show, last two years, one of the hottest topics anyway, was cloud, multi-cloud, Kubernetes of course was a hot topic. But, data protection was right up there. Why, in your view, is data protection such a hot topic right now? >> Well there's a lot of changes going on. First of all, couple years ago it was backup, nobody calls it backup anymore right. The whole market is changing. Data protection, you have newer guys like Cohesity and Ruberik, would come out with a, you know, architecture. They're basically, from scratch, they built scale-out and that's changing the way people look at data protection. You have all of the data protection guys, the Dell EMC, CommVault, Veeam, they're all kind of changing a little. And Veritas, the old guys, have been doing it forever. And now they're changing the way that they're reacting to the competition. The cloud is becoming a major force in where data lives, and you have to protect that. So there's a lot of changes going on in the market. >> Yeah I was talking to a Gartner analyst recently, he said they're data suggested about 2/3 of the customers that they talk to, within the next, I think, 18 months, are going to change they're backup approach or reconsider how they do backup or data protection as it were, as you just said. What do you think is driving that? I mean, people cite digital transformation they cite cloud, they cite big data, all the buzz words. You know, where there's smoke, there's fire, I guess. But what are your thoughts? >> Yeah, it's a little bit of all of those things, because the IT infrastructure is changing, virtualization containers, everything, every architectural change changes the way you protect and manage your data, right. So, we're seeing a lot of those changes, and now people are reacting to it and everybody's figuring out still how to use the cloud and where the data is going to live. So then, you know, how do you protect that data? >> And of course, when you listen to vendors talk, data protection, backup, recovery, it's very sexy when you talk to the customers they're just, oftentimes, drinking from the fire hose, right. Just trying to solve the next problem that they have. But what are you hearing from the customers? TechTarget obviously has a big community. You guys do a lot of events. You talk personally to a lot of customers, particularly when there are new announcements. And what does the landscape look like to you? >> So they're all, you know like I said, everybody's looking at the cloud. They're looking at all these, how they're going to use these things. They're not sure yet, but they want data protection, data management that will kind of fit in no matter which direction they go. It's kind of, you know, we know we're looking at where we're going to be in five years and now we want to know how we're going to protect, how we're going to manage our data, how we're going to use it, move it from cloud to cloud. So, you know, it's kind of like, it's a lot of positioning going on now. A lot of planing for the future. And they're trying to figure out what's the best way they're going to be able to do all this stuff. >> Yeah, so, you know the hot thing, it used to be, like you said, backup. And then of course, people said backup is one thing, recovery is everything. You know, so it was the old bromide, my friend Fred Moore, I think coined that term, back in the old storage tech days. But when you think about cloud, and you think about the different cloud suppliers, they've all got different approaches, they're different walled gardens, essentially. And they've got different processes for at least replicating, backing up data. Where do you see customers, in terms of having that sort of single abstraction layer, the single data protection philosophy or strategy and set of products for multi-cloud? >> Well, where they are is they're not there, and they're, you know, far from it, but that's where they want to be. So, that's where a lot of the vendor positioning is going. A lot of the customers are looking to do that. But another thing that's changing it is, you know, people aren't using Oracle, SQL databases all the time anymore either. They're using the NoSQL MongoDB. So that change, you know, you need different products for that too. So, the whole, almost every type of product, hyper-converged is changing backup. So, you know, all these technologies are changing the way people actually are going to protect their data. >> So you look at the guys with the big install base, obviously Veritas is one, guys like IBM, certainly CommVault and there are others that have large install bases. And the new guys, the upstarts, they're licking their chops to go after them. What do you see as, let's take Veritas as an example, the vulnerabilities and the strengths of a company like that? >> So the vulnerabilities of an old company that's been around forever is that, the newer guys are coming with a clean sheet of paper and coming up and developing their products around technologies that didn't exist when NetBackup was created, right. So the strength is that, for Veritas, they have huge install base. They have all the products, technology they need. They have a lot of engineers so they can get to the board, drawing board, and figure it out and add stuff. And what they're trying to do is build around NetBackup saying all these companies are using NetBackup, so let's expand that, let's build archiving in, let's build, you know, copy data protect, copy data management into that. Let's build encryption, all of that, into NetBackup. You know, appliances, they're going farther, farther and farther into appliances. Seems like nobody wants to just buy backup software, and backup hardware as separate, which they were forever. So you know, we're seeing the integration there. >> Well that brings up another good point, is you know, for years, backup's been kind of one size fits all. So that meant you were either over protected, or under protected. It was maybe an after thought, a bolt-on, you put in applications, put it in a server, an application on top of it. You know, install Linux, maybe some Oracle databases. All of the a sudden, oh, we got to back this thing up. And increasingly, people are saying, hey, I don't want to just pay for insurance, I'd like to get more value. And so, you're hearing a lot of talk about governance, certainly security, ransomware is now a big topic, analytics. What are you seeing, in terms of, some of those additional value, those value adds beyond that, is it still just insurance, or are we seeing incremental value to customers? >> Yeah, well I think everybody wants incremental value. They have the data, now it's not just, like you said, insurance. It's like how is this going to, how am I going to use this data? How's it going to help my business? So, the analytics is a big thing that everybody's trying to get in. You know, primary and secondary storage everybody's adding analytics. AI, how we use AI, machine learning. You know, how we're going to back up data from the edge into and out of thing. What are we going to do with all this data? How are we going to collect it, centralize it, and then use it for our business purposes? So there's, you know, it's a wide open field. Remember it used to be, people would say backup, nobody ever changes their backup, nobody wants to change backup. Now surveys are saying within the next two years or so, more than 50% of people are looking to either add a backup product, or just change out their whole backup infrastructure. >> Well that was the interesting about when, you know, the ascendancy of Data Domain, as you recall, you were following the company back then. The beauty of that architecture was, you don't have to change your backup processes. And now, that's maybe a challenge for a company like that. Where people are, because of digital, because of cloud, they're actually looking to change their backup processes. Not unlike, although there are differences, but a similar wave, remember the early days of virtualization, you had, you're loosing physical resources, so you had to rethink backup. Are you seeing similar trends today, with cloud, and digital? >> Yeah, the cloud, containers, microservices, things like that, you know, how do you protect that data? You know people, some people are still struggling with virtualization, you know, like, there's so many more VMs being created so quickly, and that you know, a lot of the backup products still haven't caught up to that. So, I mean Veeam has made an awful great business around dealing with VM backup, right? >> Right. >> Where was everybody else before that? Nobody else could do it. >> We storage guys, we're like the cockroaches of the industry. We're just this, storage just doesn't seem to die. You know the joke is, there's a hundred people in storage and 99 seats. But you've been following it for a long time. Yeah, you see all the hot topics like cloud and multi-cloud and digital transformation. Are you surprised at the amount of venture capital over the last, you know, four or five years, that has flooded in to storage, that continues to flood in to storage? And you see some notable successes, sure some failures, but even those failures, you're seeing the CEOs come out and sell to new companies and you're seeing the rise of a lot of these startups and a lot of these unicorns. Does it surprise you, or is that kind of your expectation? >> Well, I mean, like you said, that's the way it's always been in storage. When you look at storage compared to networking and compute, how many startups are there in those other areas. Very few, but storage keeps getting funded. A couple of years ago, I used to joke, if you said I do Flash, people would just throw hundreds of millions of dollars at you, then it was cloud. There always seem to be like a hot topic, a hot spot, that you can get money from VCs. And there's always four or five, at least, storage vendors who are in that space. >> Yeah, the cloud, the storage cloud AI blockchain company is really the next unicorn right? >> Right, yeah, if you know the right buzz words you can get money. And there's never just one right, there's always a couple in that same area and then one or two make it. >> Yeah, or, and or, if you've done before, right, you're seeing that a lot. I mean, you see what the guys like, for instance at Datrium are doing. Brian Biles he did it a Data Domain, and now he's, they just did a giant raise. >> Qumulo. >> Yeah, you know, Qumulo, for sure. Obviously the Cohesity are sort of well known, in terms of how they've done giant raises. So there's massive amount of capital now pouring in, much of which will go into innovation. It's kind of, it's engineering and it's you know, go to market and marketing. So, you know, no doubt, that that innovation curve will continue. I guess you can't bet against data growth. >> Right, you know, yeah, right, everybody knows data is going to grow. They're saying it's the new oil, right. Data is the big thing. The interesting thing with the funding stuff now is the, not the new companies, but the companies that have been around a little bit, and it's now time for them to start showing revenue. And where in the past it was easier for them to get money, now it seems a little tougher for those guys. So, you know, we could see more companies go away without getting bought up or go public but-- >> Okay, great. Dave, thanks very much for coming on theCUBE. >> Alright. >> It was great to have you. >> Thanks for having me on. >> Alright keep it right there everybody. We'll be back with our next guest. You're watching theCUBE from Veritas Vision in Central Park. We'll be right back. (theCUBE theme music)

from Tavern on the Green in Central Park New York it's the to covering Veritas vision solution day brought to you by Veritas welcome back to New York City everybody we're in the heart of Central Park at the Tavern on the Green beautiful location here a lot of customers coming in to see and hear veritas solution days we talked to Scott general earlier about sort of why these solution days very intimate customer events around the world David Moyes is here he's the vice president of software to find storage and appliances at Veritas David thanks for coming on oh thanks for having me you're very welcome so wait appliances you guys were software company what what's going on we are we are a software company and we have been a software company for a very long time and we will continue to be a software company for a very long time but what we find is that you know customers oftentimes they want to deploy software but then they find that there's a lot of additional challenges that come with that there's the maintenance of the actual server infrastructure there's patching of the operating system there's vulnerabilities that show up those additional operational costs sometimes outweigh the benefits of just buying a purpose-built appliance and those purpose-built appliances can sometimes you know how workflows built-in to them that just make them so easy to use that you know one person could potentially operate petabytes of an appliance versus a whole army of people trying to maintain hundreds or thousands of individual servers so you put out a stat this morning which I wasn't aware of you guys have more than half the market I think it was an IDC stat maybe was Gartner more than half the market for integrated the purpose-built backup appliances is that right that's right so for backup appliances purpose-built back of appliances that actually hosts the backup software we have more than 50% market share people have that much trust in the appliances that we build and find that simplicity so compelling that they want to buy it and that form factor from us you know about a decade ago I wrote a piece in the early days of wiki bond talking about services oriented storage and and when we think about software-defined storage what you described today was actually sets of granular services that I can invoke when I need them or not if I don't need them very cloud like I mean I could replace the s in Software Defined with s and services and that's your kind of philosophy and approach isn't it it is in fact what we find is that people want data services on top of that data when you're reporting enterprise data and you protecting exabyte so that apprise data the way the Veritas does having it just sit there and do nothing is is kind of wasteful to a large extent unless you're just waiting for a disaster to occur or data corruption or something like that if we can support to think about what are the governance capabilities lineage audit all of the different things that we could potentially build as services that are then can be downloaded onto that purpose-built backup appliance those all become added value to the customer but you you did what you described was not just another you're not just dropping in another stovepipe appliance you talked about having visibility across the entire portfolio you really stress that a lot you said several times you can't get this from any other backup vendor or any other vendor really talk about that a little bit well what's interesting is that look from the moment that data is actually born from a primary application and then it's protected its protected into a backup solution and then it's probably put into some sort of a storage solution either maybe at a duplication storage solution then it's moved to an even cheaper tier and eventually off to the cloud or somewhere else each of those are disparate if we can actually build a connector framework that can actually extract that information and bring it all together so that we can start to make assertions about hey how did this data flow how did it originate what kind of information do I have do I even need it anymore after seven years should I purge it or should I delete it is it even a risk to my organization to keep it you can only do that when you can make those associations across the lifecycle of that data and so we tracked the data through its entire lifecycle and that's only through the integration of all that product portfolio and that's something that you're not going to see with the small point solutions that are being built by startups and it's even very rare to see in some of the larger companies that build these solutions you know dude I'm glad you mentioned that about getting rid of data because so often today in the in the news media and you here in vendor presentations people talk about keeping data forever yeah that's that's dangerous in a lot of case a lot of general councils out there don't want to keep data forever there's data that you want to delete if in fact you can because of the compliance risks that it brings to your company you don't want to keep working process or some rogue email that floats around the organization get rid of that keep what you have to and get rid of the rest right and then the problem is if you don't know what you have and also you don't know how many places that that data is propagated how can you possibly delete it all we've helped customers in some cases that I'm not going to mention who they are but we've helped them delete up to 50 percent of their data after it's aged out and I've talked to banks before and I've asked them like hey after seven years what do you do with your data they say well we just keep it because we don't know what it was originated for we don't even know what's in it and therefore it's too risky for us to go and delete but at the same time to your point it may be even too risky to keep in some cases especially a liability to keep that deal and what's the what's the technology enable are there is that your catalog you're sort of copy data management self so it's a combination of things the catalog is what helps us understand what we have and where it is and how it's actually moved through that lifecycle and then we have a component called the Veritas information classifier and that component allows us to crack open the data and actually determine what's inside whether it's personally identifiable information social security numbers there's a number of different patterns we can look for actually document types and we can actually tag that data and say hey this data has information that's pertains to a specific individuals so for example if I'm following gdpr rules I can now find out all the data where it's propagated specific to an individual who said now I want all my stuff deleted and that's a very powerful technique so such as the data it's the metadata associated with that taste well that's a that I think it's a unique capability in terms of being integrated into a solution and so that's that's cool I also want to talk about Acme financial services yeah is artificial company or a real company but but an anonymized company that you talked about moving to your system your appliance based system they were able to reduce TCO by 40% shave two thirds of their hardware infrastructure away come back to that is I have sort of a tongue-in-cheek there get rid of tape at least we're possible no new tape I think is what you that's right and then save 20 million dollars a year in reduced downside downtime costs so my tongue and cheek is who everybody remembers the the no hardware agenda of signs you know bhai live in Massachusetts that we used to see those right next to the EMC facilities and you're only a hardware agenda it seems with your appliance is to get rid of hardware is exactly right look we we are actually putting out technology that allows you to take ten heads or ten servers and consolidate it down to two or three to make the total cost of ownership for your product less because at the end of day it's in our benefit right we're a software vendor we want to maintain ourselves as a software vendor we want to take Hardware out of the equation to the extent that's possible but we don't want to do it at the expense of simplicity and so striking that balance is what's most important yeah and and you also have talked about a little you showed a little leg if you will on a road map that's right I'm one of the things that struck me and it's sort of there today but but even more in the future is the ability to scale compute and storage independently and more granular chunks explain what that is and why that's important to customers well you know if you think about it the way that these integrated backup appliances work even back up people just dismiss backup appliances they just grow and grow and grow to a certain capacity they scale up and scale up and at some point the performance just starts to tank and taper off so what if you could actually grow them almost in a node-based architecture think about its compute and storage that you grow together and as you add more compute you add more storage and so that means that I can do more micro services or I can provide better deduplication but my deduplication doesn't slow down when I go from two petabyte to four petabyte because my compute has actually grown in lockstep with my storage you made a big deal about eliminating tape where possible and you also and I want to push it this a little bit talked about the economics of solution relative to tape I was somewhat surprised because the conventional wisdom would say tape is you know pennies on the dollar come here to to disc based solutions how is it that you're able to make that claim well there's a couple of different things that come to play number one is that again through the cloud catalyst capability of net backup we can actually keep data deduplicated before we send it to our disk based solution which means that it stays in some cases 50 to one deduplicated and you're not necessarily gonna get that capability on tape you'd have to rehydrate don't have to rehydrate so if you talk about about pennies well multiply that by 50 because if your data duplicates that much that's the kind of thing you're talking about the second part is the operational cost of actually maintaining tape now if I'm keeping data for seven years thirty years the lifetime of a patient that tape infrastructure age is out and I'm doing tape migrations all the time those are not cheap and sometimes though tape infrastructures not even available anymore the other compatibility is not there that's right the other thing is well the other thing is is the network costs right if you're going to be pushing stuff over the network absolutely your network and your and you pushing bigger things over the network you have more network infrastructure just for the purposes of moving data from one here tier to another tier it's wasteful David talked about the Flex appliance what I took away from that is it sort of allows for services oriented deployment fast migration it allows you to sort of test out new services to see whether or not you like it what what is that what does the customer have to do to exploit that capability today the customer would buy our high-end backup appliance which is the 53:40 they would buy the Flex software which is a software package that allows them to basically augment that's appliance so that it can actually maintain that catalog and then quickly deploy them those services as I showed in the demo in three minutes or less we can deploy a service on from the service catalog in the future you should expect that we should we will begin to build that into all of our appliances that's just the way of doing things it becomes a service-oriented architecture and that catalog is just going to be a natural way of us operating ok I want to also ask you about another capability that you discussed which was your ability to look across the portfolio and identify predictive failures yeah everybody talks about machine intelligence being used you know in that use case IOT you hear about that along what are you guys doing what's unique well you know in some cases what we're doing is not completely unique I mean there are some companies that have done this pretty well they've done it for their own point solutions I think where it gets interesting is when you say look we're not just building a point solution we have a number of different products again for the entire lifecycle of data from the moment it's born and if I can integrate all of the telemetry that I get from those different products I can now start to get predictive about things that might have happened not just at one stage within one product but might happen down the road when I go to move it into my long-term retention as my long-term retention ready for it is it going to impact the performance of my long-term retention solution and so therefore should I think about scaling on my long-term retention solution independent of you know so or ahead of the actual growth of my purpose-built Beco appliance right so it's that portfolio view that makes it so powerful right it's okay two things actually the portfolio view and also the full lifecycle view as well that's right something you've been hitting on not just a point product all right David I know you Jam it a lot of a lot of customer conversations here in New York so I gotta let you go but thanks so much for stopping by the cube appreciate my pleasure I really appreciate your time thank you welcome all right keep it right there everybody you're watching the cube live from Veritas solution days in New York City right in the heart of Central Park we'll be right at right back after this short break [Music]

the foundation of having that data management platform is absolutely fundamental and necessary to do good machinery without good data without good data management you can't do good ml or AI sounds sort of simple but very true the dupes of all the nature and velocity of data has evolved in the last five screens taking over the world and being in charge of you and us being dominated by them as often we say in culture now it's about having this really beautiful interface between technology objects I can take the traditional tools using like Jupiter no spark tensorflow you know those packages with kubernetes on top of the databases of service and some object stores I have a much easier stack to work in able everyone to make data-driven decisions but make sure that they're interpreting that data in the right way right give them enough guidance don't let them just kind of attack the well [Music]

>> Live from New York, it's theCUBE, covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. >> Okay, welcome back, everyone. We're here live in New York City for CUBE NYC. This is our ninth year covering the big data ecosystem, now it's AI, machine-learning, used to be Hadoop, now it's growing, ninth year covering theCUBE here in New York City. I'm John Furrier, with Dave Vellante. Our next guest, Josh Rogers, CEO of Syncsort. I'm going back, long history in theCUBE. You guys have been on every year. Really appreciate chatting with you. Been fun to watch the evolution of Syncsort and also get the insight. Thanks for coming on, appreciate it. >> Thanks for having me. It's great to see you. >> So you guys have constantly been on this wave, and it's been fun to watch. You guys had a lot of IP in your company, and then just watching you guys kind of surf the big data wave, but also make some good decisions, made some good calls. You're always out front. You guys are on the right parts of the wave. I mean now it's cloud, you guys are doing some things. Give us a quick update. You guys got a brand refresh, so you got the new logo goin' on there. Give us a quick update on Syncsort. You got some news, you got the brand refresh. Give us a quick update. >> Sure. I'll start with the brand refresh. We refreshed the brand, and you see that in the web properties and in the messaging that we use in all of our communications. And, we did that because the value proposition of the portfolio had expanded so much, and we had gained so much more insight into some of the key use cases that we're helping customers solve that we really felt we had to do a better job of telling our story and, probably most importantly, engage with the more senior level within these organizations. What we've seen is that when you think about the largest enterprises in the world, we offer a series of solutions around two fundamental value propositions that tend to be top of mind for these executives. The first is how do I take the 20, 30, 40 years of investment in infrastructure and run that as efficiently as possible. You know, I can't make any compromises on the availability of that. I certainly have to improve my governance and secureability of that environment. But, fundamentally, I need to make sure I could run those mission-critical workloads, but I need to also save some money along the way, because what I really want to do is be a data-driven enterprise. What I really want to do is take advantage of the data that gets produced in these transactional applications that run on my AS400 or IBM I-infra environment, my mainframe environment, even in my traditional data warehouse, and make sure that I'm getting the most out of that data by analyzing it in a next-generation set of-- >> I mean one of the trends I want to get your thoughts on, Josh, cause you're kind of talking through the big, meagatrend which is infrastructure agnostic from an application standpoint. So the that's the trend with dev ops, and you guys have certainly had diverse solutions across your portfolio, but, at the end of the day, this is the abstraction layer customers want. They want to run workloads on environments that they know are in production, that work well with applications, so they almost want to view the infrastructure, or cloud, if you will, same thing, as just agnostic, but let the programmability take care of itself, under the hood, if you will. >> Right, and what we see is that people are absolutely kind of into extending and modernizing existing applications. This is in the large enterprise, and those applications and core components will still run on mainframe environments. And so, what we see in terms of use cases is how do we help customers understand how to monitor that, the performance of those applications. If I have a tier that's sitting on the cloud, but it's transacting with the mainframe behind the firewall, how do I get an end-to-end view of application performance? How do I take the data that ultimately gets logged in a DB2 database on the mainframe and make that available in a next-generation repository, like Hadoop, so that I can do advanced analytics? When you think about solving both the optimization and the integration challenge there, you need a lot of expertise in both sides, the old and the new, and I think that's what we uniquely offer. >> You guys done a good job with integration. I want to ask quick question on the integration piece. Is this becoming more and more table stakes, but also challenging at the same time? Integration and connecting systems together, if their stateless, is no problem, you use APIs, right, and do that, but as you start to get data that needs state information, you start to think to think about some of the challenges around different, disparate systems being distributed, but networked, in some cases, even decentralized, so distributed networking is being radically changed by the data decisions on the architecture, but also integration, call it API 2.0 or this new way to connect and integrate. >> Yeah, so what we've tried to focus on is kind of solving that piece between these older applications that run these legacy platforms and making them available to whatever the consumer is. Today, we see Kafka and in Amazon we see Kinesis as kind of key buses delivering data as a service, and so the role that we see ourselves playing and what we announced this week is an ability to track changed data, deliver it in realtime in these older systems, but deliver it to these new targets: Kafka, Kinesis, and whatever comes next. Because really that's the fundamental partner we're trying to be to our customers is we will help you solve the integration challenge between this infrastructure you've been building for 30 years and this next-generation technology that lets you get the next leg of value out of your data. >> So Jim, when you think about the evolution of this whole big data space, the early narrative in the trade press was, well, NoSQL is going to replace Oracle and DB2, and the data lake is going to replace the EDW, and unstructured data is all that matters, and so forth. And now, you look at what's really happened is the EDW is a fundamental component of making decisions and insights, and SQL is the killer app for Hadoop. And I take an example of say fraud detection, and when you think and this is where you guys sit in the middle from the standpoint of data quality, data integration, in order to do what we've done in the past 10 years take fraud detection down from well, I look at my statement a month or two later and then call the credit card company, it's now gone to a text that's instantaneous. Still some false positives, and I'm sure working on that even. So maybe you could describe that use case or any other, your favorite use case, and what your role is there in terms of taking those different data sources, integrating them, improving the data quality. >> So, I think when you think about a use case where I'm trying to improve the SLA or the responsiveness of how do manage against or detect fraud, rather than trying to detect it on a daily basis, I'm trying to detect it at transaction time. The reality is you want to leverage the existing infrastructure you have. So if you have a data warehouse that has detailed information about transaction history, maybe that's a good source. If you have an application that's running on the mainframe that's doing those transaction realtime, the ultimate answer is how do I knit together the existing infrastructure I have and embed the additional intelligence and capability I need from these new capabilities, like, for example, using Kafka, to deliver a complete solution. What we do is we help customers kind of tie that together, Specifically, we announced this integration I mentioned earlier where we can take a changed data element in a DB2 database and publish it into Kafka. That is a key requirement in delivering this real-time fraud detection if I in fact am running transactions on a mainframe, which most of the banks are. >> Without ripping and replacing >> Why would you want to rip out an application >> You don't. >> your core customer file when you can just extend it. >> And you mentioned the Cloudera 6 certification. You guys have been early on there. Maybe talk a little about that relationship, the engineering work that has to get done for you to be able to get into the press release day one. >> We just mentioned that my first time on theCUBE was in 2013, and that was on the back of our initial product release in the big data world. When we brought the initial DMX-h release to market, we knew that we needed to have deep partnerships with Cloudera and the key platform providers. I went and saw Mike Olson, I introduced myself, he was gracious enough to give me an hour, and explain what we thought we could do to help them develop more value proposition around their platform, and it's been a terrific relationship. Our architecture and our engineering and product management relationship is such that it allows us to very rapidly certify and work on their new releases, usually within a couple a days. Not only can customers take advantage of that, which is pretty unique in the industry, but we get some some visibility from Cloudera as evidenced by Tendu's quote in the press release that was released this week, which is terrific. >> Talk about your business a little bit. You guys are like a 50-year old startup. You've had this really interesting history. I remember you from when I first started in the industry following you guys. You've restructured the company, you've done some spin outs, you've done some M and A, but it seems to be working. Talk about growth and progress that you're making. >> We're the leader in the Big Iron to Big Data market. We define that as allowing customers to optimize their traditional legacy investments for cost and performance, and then we help them maximize the value of the data that get generated in those environments by integrating it with next-generation analytic environments. To do that, we need a broad set of capability. There's a lot of different ways to optimize existing infrastructure. One is capacity management, so we made an acquisition about a year ago in the capacity management space. We're allowing customers to figure out how do I make sure I've got not too much and not too little capacity. That's an example of optimization. Another area of capability is data quality. If I'm maximize the value of the data that gets produced in these older environments, it would be great that when it lands in these next-generation repositories it's as high quality as possible. We acquired Trillium about a year ago, or actually coming up >> How's that comin'? >> on two years ago and we think that's a great capability for our customers It's going terrific. We took their core data quality engine, and now it runs natively on a distributed Hadoop infrastructure. We have customers leveraging it to deliver unprecedented volume of matching, so not only breakthrough performance, but this whole notion of write once, run anywhere. I can run it on an SMP environment. I can run it on Hadoop. I can run it Hadoop in the cloud. We've seen terrific growth in that business based on our continued innovation, particularly pointing it at the big data space. >> One of the things that I'm impressed with you guys is you guys have transformed, so having a transformation message to your customers is you have a lot of credibility, but what's interesting is is that the world with containers and Kubernetes now and multi-cloud, you're seeing that you don't have to kill the legacy to bring in the new stuff. You can see you can connect systems, when you guys have done with legacy systems, look at connect the data. You don't have to kill that to bring in the new. >> Right >> You can do cloud-native, you can do some really cool things. >> Right. I think there's-- >> This rip and replace concept is kind of going away. You put containers around it too. That helps. >> Right. It's expensive and it's risky, so why do that. I think that's the realization. The reality is that when people build these mission-critical systems, they stay in place for not five years, but 25 years. The question is how do you allow the customers to leverage what they have and the investment they've made, but take advantage of the next wave, and that's what we're singularly focused on, and I think we're doing a great job of that, not just for customers, but also for these next-generation partners, which has been a lot of fun for us. >> And we also heard people doing analytics they want to have their own multi-tenent, isolated environments, which goes to don't screw this system up, if it's doing a great job on a mission-critical thing, don't bundle it, just connect it to the network, and you're good. >> And on the cloud side, we're continuing to look at our portfolio and say what capabilities will customers want to consume in a cloud-delivery model. We've been doing that in the data quality space for quite awhile. We just launched and announced over the last about three months ago capacity management as a service. You'll continue to see, both on the optimization side and on the integration side, us continuing to deliver new ways for customers to consume the capabilities they need. >> That's a key thing for you guys, integration. That's pretty much how you guys put the stake in the ground and engineer your activities around integration. >> Yeah, we start with the premise that your going to need to continue to run this older investments that you made, and you're going to need to integrate the new stuff with that. >> What's next? What's goin' on the rest of the year with you guys? >> We'll continue to invest heavily in the realtime and changed-data capture space. We think that's really interesting. We're seeing a tremendous amount of demand there. We've made a series of acquisitions in the security space. We believe that the ability to secure data in the core systems and its journey to the next-generation systems is absolutely critical, so we'll continue to invest there. And then, I'd say governance, that's an area that we think is incredibly important as people start to really take advantage of these data lakes they're building, they have to establish real governance capabilities around those. We believe we have an important role to play there. And there's other adjacencies, but those are probably the big areas we're investing in right now. >> Just continuing to move the ball down the field in the Syncsort cadence of acquisitions, organic development. Congratulations. Josh, thanks for comin' on. To John Rogers, CEO of Syncsort, here inside theCUBE. I'm John Furrier with Dave Vellante. Stay with us for more big data coverage, AI coverage, cloud coverage here. Part of CUBE NYC, we're in New York City live. We'll be right back after this short break. Stay with us. (techno music)

(upbeat music) >> Live from New York, it's theCUBE. Covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. >> Okay, welcome back everyone to theCUBE NYC. This is theCUBE's live coverage covering CubeNYC Strata Hadoop Strata Data Conference. All things data happen here in New York this week. I'm John Furrier with Peter Burris. Our next guest is Basil Faruqui lead solutions marketing manager digital business automation within BMC returns, he was here last year with us and also Big Data SV, which has been renamed CubeNYC, Cube SV because it's not just big data anymore. We're hearing words like multi cloud, Istio, all those Kubernetes. Data now is so important, it's now up and down the stack, impacting everyone, we talked about this last year with Control M, how you guys are automating in a hurry. The four pillars of pipelining data. The setup days are over; welcome to theCUBE. >> Well thank you and it's great to be back on theCUBE. And yeah, what you said is exactly right, so you know, big data has really, I think now been distilled down to data. Everybody understands data is big, and it's important, and it is really you know, it's quite a cliche, but to a larger degree, data is the new oil, as some people say. And I think what you said earlier is important in that we've been very fortunate to be able to not only follow the journey of our customers but be a part of it. So about six years ago, some of the early adopters of Hadoop came to us and said that look, we use your products for traditional data warehousing on the ERP side for orchestration workloads. We're about to take some of these projects on Hadoop into production and really feel that the Hadoop ecosystem is lacking enterprise-grade workflow orchestration tools. So we partnered with them and some of the earliest goals they wanted to achieve was build a data lake, provide richer and wider data sets to the end users to be able to do some dashboarding, customer 360, and things of that nature. Very quickly, in about five years time, we have seen a lot of these projects mature from how do I build a data lake to now applying cutting-edge ML and AI and cloud is a major enabler of that. You know, it's really, as we were talking about earlier, it's really taking away excuses for not being able to scale quickly from an infrastructure perspective. Now you're talking about is it Hadoop or is it S3 or is it Azure Blob Storage, is it Snowflake? And from a control-end perspective, we're very platform and technology agnostic, so some of our customers who had started with Hadoop as a platform, they are now looking at other technologies like Snowflake, so one of our customers describes it as kind of the spine or a power strip of orchestration where regardless of what technology you have, you can just plug and play in and not worry about how do I rewire the orchestration workflows because control end is taking care of it. >> Well you probably always will have to worry about that to some degree. But I think where you're going, and this is where I'm going to test with you, is that as analytics, as data is increasingly recognized as a strategic asset, as analytics increasingly recognizes the way that you create value out of those data assets, and as a business becomes increasingly dependent upon the output of analytics to make decisions and ultimately through AI to act differently in markets, you are embedding these capabilities or these technologies deeper into business. They have to become capabilities. They have to become dependable. They have to become reliable, predictable, cost, performance, all these other things. That suggests that ultimately, the historical approach of focusing on the technology and trying to apply it to a periodic or series of data science problems has to become a little bit more mature so it actually becomes a strategic capability. So the business can say we're operating on this, but the technologies to take that underlying data science technology to turn into business operations that's where a lot of the net work has to happen. Is that what you guys are focused on? >> Yeah, absolutely, and I think one of the big differences that we're seeing in general in the industry is that this time around, the pull of how do you enable technology to drive the business is really coming from the line of business, versus starting on the technology side of the house and then coming to the business and saying hey we've got some cool technologies that can probably help you, it's really line of business now saying no, I need better analytics so I can drive new business models for my company, right? So the need for speed is greater than ever because the pull is from the line of business side. And this is another area where we are unique is that, you know, Control M has been designed in a way where it's not just a set of solutions or tools for the technical guys. Now, the line of business is getting closer and closer, you know, it's blending into the technical side as well. They have a very, very keen interest in understanding are the dashboards going to be refreshed on time? Are we going to be able to get all the right promotional offers at the right time? I mean, we're here at NYC Strata, there's a lot of real-time promotion happening here. The line of business has direct interest in the delivery and the timing of all of this, so we have always had multiple interfaces to Control M where a business user who has an interest in understanding are the promotional offers going to happen at the right time and is that on schedule? They have a mobile app for them to do that. A developer who's building up complex, multi-application platform, they have an API and a programmatic interface to do that. Operations that has to monitor all of this has rich dashboards to be able to do that. That's one of the areas that has been key for our success over the last couple decades, and we're seeing that translate very well into the big data place. >> So I just want to go under the hood for a minute because I love that answer. And I'd like to pivot off what Peter said, tying it back to the business, okay, that's awesome. And I want to learn a little bit more about this because we talked about this last year and I kind of am seeing it now. Kubernetes and all this orchestration is about workloads. You guys nailed the workflow issue, complex workflows. Because if you look at it, if you're adding line of business into the equation, that's just complexity in and of itself. As more workflows exist within its own line of business, whether it's recommendations and offers and workflow issues, more lines of business in there is complex for even IT to deal with, so you guys have nailed that. How does that work? Do you plug it in and the lines of businesses have their own developers, so the people who work with the workflows engage how? >> So that's a good question, with sort of orchestration and automation now becoming very, very generic, it's kind of important to classify where we play. So there's a lot of tools that do release and build automation. There's a lot of tools that'll do infrastructure automation and orchestration. All of this infrastructure and release management process is done ultimately to run applications on top of it, and the workflows of the application need orchestration and that's the layer that we play in. And if you think about how does the end user, the business and consumer interact with all of this technology is through applications, k? So the orchestration of the workflow's inside the applications, whether you start all the way from an ERP or a CRM and then you land into a data lake and then do an ML model, and then out come the recommendations analytics, that's the layer we are automating today. Obviously, all of this-- >> By the way, the technical complexity for the user's in the app. >> Correct, so the line of business obviously has a lot more control, you're seeing roles like chief digital officers emerge, you're seeing CTOs that have mandates like okay you're going to be responsible for all applications that are facing customer facing where the CIO is going to take care of everything that's inward facing. It's not a settled structure or science involved. >> It's evolving fast. >> It's evolving fast. But what's clear is that line of business has a lot more interest and influence in driving these technology projects and it's important that technologies evolve in a way where line of business can not only understand but take advantage of that. >> So I think it's a great question, John, and I want to build on that and then ask you something. So the way we look at the world is we say the first fifty years of computing were known process, unknown technology. The next fifty years are going to be unknown process, known technology. It's all going to look like a cloud. But think about what that means. Known process, unknown technology, Control M and related types of technologies tended to focus on how you put in place predictable workflows in the technology layer. And now, unknown process, known technology, driven by the line of business, now we're talking about controlling process flows that are being created, bespoke, strategic, differentiating doing business. >> Well, dynamic, too, I mean, dynamic. >> Highly dynamic, and those workflows in many respects, those technologies, piecing applications and services together, become the process that differentiates the business. Again, you're still focused on the infrastructure a bit, but you've moved it up. Is that right? >> Yeah, that's exactly right. We see our goal as abstracting the complexity of the underlying application data and infrastructure. So, I mean, it's quite amazing-- >> So it could be easily reconfigured to a business's needs. >> Exactly, so whether you're on Hadoop and now you're thinking about moving to Snowflake or tomorrow something else that comes up, the orchestration or the workflow, you know, that's as a business as a product that's our goal is to continue to evolve quickly and in a manner that we continue to abstract the complexity so from-- >> So I've got to ask you, we've been having a lot of conversations around Hadoop versus Kubernetes on multi cloud, so as cloud has certainly come in and changed the game, there's no debate on that. How it changes is debatable, but we know that multiple clouds is going to be the modus operandus for customers. >> Correct. >> So I got a lot of data and now I've got pipelining complexities and workflows are going to get even more complex, potentially. How do you see the impact of the cloud, how are you guys looking at that, and what are some customer use cases that you see for you guys? >> So the, what I mentioned earlier, that being platform and technology agnostic is actually one of the unique differentiating factors for us, so whether you are an AWS or an Azure or a Google or On-Prem or still on a mainframe, a lot of, we're in New York, a lot of the banks, insurance companies here still do some of the most critical processing on the mainframe. The ability to abstract all of that whether it's cloud or legacy solutions is one of our key enablers for our customers, and I'll give you an example. So Malwarebytes is one of our customers and they've been using Control M for several years. Primarily the entire structure is built on AWS, but they are now utilizing Google cloud for some of their recommendation analysis on sentiment analysis because their goal is to pick the best of breed technology for the problem they're looking to solve. >> Service, the best breed service is in the cloud. >> The best breed service is in the cloud to solve the business problem. So from Control M's perspective, transcending from AWS to Google cloud is completely abstracted for them, so runs Google tomorrow it's Azure, they decide to build a private cloud, they will be able to extend the same workflow orchestration. >> But you can build these workflows across whatever set of services are available. >> Correct, and you bring up an important point. It's not only being able to build the workflows across platforms but being able to define dependencies and track the dependencies across all of this, because none of this is happening in silos. If you want to use Google's API to do the recommendations, well, you've got to feed it the data, and the data's pipeline, like we talked about last time, data ingestion, data storage, data processing, and analytics have very, very intricate dependencies, and these solutions should be able to manage not only the building of the workflow but the dependencies as well. >> But you're defining those elements as fundamental building blocks through a control model >> Correct. >> That allows you to treat the higher level services as reliable, consistent, capabilities. >> Correct, and the other thing I would like to add here is not only just build complex multiplatform, multiapplication workflows, but never lose focus of the business service of the business process there, so you can tie all of this to a business service and then, these things are complex, there are problems, let's say there's an ETL job that fails somewhere upstream, Control M will immediately be able to predict the impact and be able to tell you this means the recommendation engine will not be able to make the recommendations. Now, the staff that's going to work under mediation understands the business impact versus looking at a screen where there's 500 jobs and one of them has failed. What does that really mean? >> Set priorities and focal points and everything else. >> Right. >> So I just want to wrap up by asking you how your talk went at Strata Hadoop Data Conference. What were you talking about, what was the core message? Was it Control M, was it customer presentations? What was the focus? >> So the focus of yesterday's talk was actually, you know, one of the things is academic talk is great, but it's important to, you know, show how things work in real life. The session was focused on a real-use case from a customer. Navistar, they have IOT data-driven pipelines where they are predicting failures of parts inside trucks and buses that they manufacture, you know, reducing vehicle downtime. So we wanted to simulate a demo like that, so that's exactly what we did. It was very well received. In real-time, we spun up EMR environment in AWS, automatically provision control of infrastructure there, we applied spark and machine learning algorithms to the data and out came the recommendation at the end was that, you know, here are the vehicles that are-- >> Fix their brakes. (laughing) >> Exactly, so it was very, very well received. >> I mean, there's a real-world example, there's real money to be saved, maintenance, scheduling, potential liability, accidents. >> Liability is a huge issue for a lot of manufacturers. >> And Navistar has been at the leading edge of how to apply technologies in that business. >> They really have been a poster child for visual transformation. >> They sure have. >> Here's a company that's been around for 100 plus years and when we talk to them they tell us that we have every technology under the sun that has come since the mainframe, and for them to be transforming and leading in this way, we're very fortunate to be part of their journey. >> Well we'd love to talk more about some of these customer use cases. Other people love about theCUBE, we want to do more of them, share those examples, people love to see proof in real-world examples, not just talk so appreciate it sharing. >> Absolutely. >> Thanks for sharing, thanks for the insights. We're here Cube live in New York City, part of CubeNYC, we're getting all the data, sharing that with you. I'm John Furrier with Peter Burris. Stay with us for more day two coverage after this short break. (upbeat music)

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

Live from New York, it's theCUBE. Covering theCUBE, New York City 2018. Brought to you by SiliconANGLE Media and its ecosystem partners. >> Okay, welcome back everyone. This is theCUBE live in New York City for our CUBE NYC event, #cubenyc. This is our ninth year covering the big data ecosystem going back to the original Hadoop world, now it's evolved to essentially all things AI, future of AI. Peter Burris is my cohost. He gave a talk two nights ago on the future of AI presented in his research. So it's all about data, it's all about the cloud, it's all about live action here in theCUBE. Our next guest is David Richards, who's been in the industry for a long time, seen the evolution of Hadoop, been involved in it, has been a key enabler of the technology, certainly enabling cloud recovery replication for cloud, welcome back to theCUBE. It's good to see you. >> It's really good to be here. >> I got to say, you've been on theCUBE pretty much every year, I think every year, we've done nine years now. You made some predictions and calls that actually happened. Like five years ago you said the cloud's going to kill Hadoop. Yeah, I think you didn't say that off camera, but it might (laughing) maybe you said it on camera. >> I probably did, yeah. >> [John] But we were kind of pontificating but also speculating, okay, where does this go? You've been right on a lot of calls. You also were involved in the Hadoop distribution business >>back in the day. Oh god. >> You got out of that quickly. (laughing) You saw that early, good call. But you guys have essentially a core enabler that's been just consistently performing well in the market both on the Hadoop side, cloud, and as data becomes the conversation, which has always been your perspective, you guys have had a key in part of the infrastructure for a long time. What's going on? Is it still doing deals, what's? >> Yes, I mean, the history of WANdisco's play and big data in Hadoop has been, as you know because you've been with us for a long time, kind of an interesting one. So we back in sort of 2013, 2014, 2015 we built a Hadoop-specific product called Non-Stop NameNode and we had a Hadoop distribution. But we could see this transition, this change in the market happening. And the change wasn't driven necessarily by the advent of new technology. It was driven by overcomplexity associated with deploying, managing Hadoop clusters at scale because lots of people, and we were talking about this off-camera before, can deploy Hadoop in a fairly small way, but not many companies are equipped or built to deploy massive scale Hadoop distributions. >> Sustain it. >> They can't sustain it, and so the call that I made you know, actions speak louder than words. The company rebuilt the product, built a general purpose data replication platform called WANdisco Fusion that, yes, supported Hadoop but also supported object store and cloud technologies. And we're now seeing use cases in cloud certainly begin to overtake Hadoop for us for the first time. >> And you guys have a patent that's pretty critical in all this, right? >> Yeah. So there's some real IP. >> Yes, so people often make the mistake of calling us a data replication business, which we are, but data replication happens post-consensus or post-agreement, so the very heart of WANdisco of 35 patents are all based around a Paxos-based consensus algorithm, which wasn't a very cool thing to talk about now with the advent of blockchain and decentralized computing, consensus is at the core of pretty much that movement, so what WANdisco does is a consensus algorithm that enables things like hybrid cloud, multi cloud, poly cloud as Microsoft call it, as well as disaster recovery for Hadoop and other things. >> Yeah, as you have more disparate parts working together, say multi cloud, I mean, you're really perfectly positioned for multi cloud. I mean, hybrid cloud is hybrid cloud, but also multi cloud, they're two different things. Peter has been on the record describing the difference between hybrid cloud and multi cloud, but multi cloud is essentially connecting clouds. >> We're on a mission at the moment to define what those things actually are because I can tell you what it isn't. A multi cloud strategy doesn't mean you have disparate data and processes running in two different clouds that just means that you've got two different clouds. That's not a multi cloud strategy. >> [Peter] Two cloud silos. >> Yeah, correct. That's kind of creating problems that are really going to be bad further down the road. And hybrid cloud doesn't mean that you run some operations and processes and data on premise and a different siloed approach to cloud. What this means is that you have a data layer that's clustered and stretched, the same data that's stretched across different clouds, different on-premise systems, whether it's Hadoop on-premise and maybe I want to build a huge data lake in cloud and start running complex AI and analytics processes over there because I'm, less face it, banks et cetera ain't going to be able to manage and run AI themselves. It's already being done by Amazon, Google, Microsoft, Alibaba, and others in the cloud. So the ability to run this simultaneously in different locations is really important. That's what we do. >> [John] All right, let me just ask this directly since we're filming and we'll get a clip out of this. What is the definition of hybrid cloud? And what is the definition of multi cloud? Take, explain both of those. >> The ability to manage and run the same data set against different applications simultaneously. And achieve exactly the same result. >> [John] That's hybrid cloud or multi cloud? >> Both. >> So they're the same. >> The same. >> You consider hybrid cloud multi cloud the same? >> For us it's just a different end point. It's hybrid kind of mean that you're running something implies on-premise. A multi cloud or poly cloud implies that you're running between different cloud venues. >> So hybrid is location, multi is source. >> Correct. >> So but let's-- >> [David] That's a good definition. >> Yes, but let's unpack this a little bit because at the end of the day, what a business is going to want to do is they're going to want to be able to run apply their data to the best service. >> [David] Correct. >> And increasingly that's what we're advising our clients to think about. >> [David] Yeah. >> Don't think about being an AWS customer, per se, think about being a customer of AWS services that serve your business. Or IBM services that serve your business. But you want to ensure that your dependency on that service is not absolute, and that's why you want to be able to at least have the option of being able to run your data in all of these different places. >> And I think the market now realizes that there is not going to be a single, dominant vendor for cloud infrastructure. That's not going to happen. Yes, it happened, Oracle dominated in relational data. SAP dominated for ERP systems. For cloud, it's democratized. That's not going to happen. So everybody knows that Amazon probably have the best serverless compute lambda functions available. They've got millions of those things already written or in the process of being written. Everybody knows that Microsoft are going to extend the wonderful technology that they have on desktop and move that into cloud for analytics-based technologies and so on. The Google have been working on artificial intelligence for an elongated period of time, so vendors are going to arbitrage between different cloud vendors. They're going to choose the best of brood approach. >> [John] They're going to go to Google for AI and scale, they're going to go to Amazon for robustness of services, and they're going to go to Microsoft for the Suite. >> [Peter] They're going to go for the services. They're looking at the services, that's what they need to do. >> And the thing that we'll forget, that we don't at WANdisco, is that that requires guaranteed consistent data sets underneath the whole thing. >> So where does Fusion fit in here? How is that getting traction? Give us some update. Are you working with Microsoft? I know we've been talking about Amazon, what about Microsoft? >> So we've been working with Microsoft, we announced a strategic partnership with them in March where we became a tier zero vendor, which basically means that we're partnered with them in lockstep in the field. We executed extremely well since that point and we've done a number of fairly large, high-profile deals. A retailer, for example, that was based in Amazon didn't really like being based in Amazon so had to build a poly cloud implementation to move had to buy scale data from AWS into Azure, that went seamlessly. It was an overnight success. >> [John] And they're using your technology? >> They're using our technology. There's no other way to do that. I think the world has now, what Microsoft and others have realized, CDC technology changed data capture. Doesn't work at this kind of scale where you batch up a bunch of changes and then you ship them, block shipping or whatever, every 15 minutes or so. We're talking about petabyte scale ingest processes. We're talking about huge data lakes, that that technology simply doesn't work at this kind of scale. >> [John] We've got a couple minutes left, I want to just make sure we get your views on blockchain, you mentioned consensus, I want to get your thoughts on that because we're seeing blockchain is certainly experimental, it's got, it's certainly powering money, Bitcoin and the international markets, it's certainly becoming a money backbone for countries to move billions of dollars out. It's certainly in the tank right now about 600 million below its mark in January, but blockchain is fundamentally supply chain, you're seeing consensus, you're seeing some of these things that are in your realm, what's your view? >> So first of all, at WANdisco, we separate the notion of cryptocurrency and blockchain. We see blockchain as something that's been around for a long time. It's basically the world is moving to decentralization. We're seeing this with airlines, with supermarkets, and so on. People actually want to decentralize rather that centralize now. And the same thing is going to happen in the financial industry where we don't actually need a central transaction coordinator anymore, we don't need a clearinghouse, in other words. Now, how do you do that? At the very heart of blockchain is an incorrect assumption. So must people think that Satoshi's invention, whoever that may be, was based around the blockchain itself. Blockchain is pieced together technologies that doesn't actually scale, right? So it takes game-theoretic approach to consensus. And I won't get, we don't have enough time for me to delve into exactly what that means, but our consensus algorithm has already proven to scale, right? So what does that mean? Well, it means that if you want to go and buy a cup of coffee at the Starbucks next door, and you want to use a Bitcoin, you're going to be waiting maybe half an hour for that transaction to settle, right? Because the-- >> [John] The buyer's got to create a block, you know, all that step's in one. >> The game-theoretic approach basically-- >> Bitcoin's running 500,000 transactions a day. >> Yeah. That's eight. >> There's two transactions per second, right? Between two and eight transactions per second. We've already proven that we can achieve hundreds of thousands, potentially millions of agreements per second. Now the argument against using Paxos, which is what our technology's based on, is it's too complicated. Well, no shit, of course it's too complicated. We've solved that problem. That's what WANdisco does. So we've filed a patent >> So you've abstracted the complexity, that's your job. >> We've extracted the complexity. >> So you solve the complexity problem by being a complex solution, but you're making and abstracting it even easier. >> We have an algorithmic not a game-theoretic approach. >> Solving the scale problem Correct. >> Using Paxos in a way that allows real developers to be able to build consensus algorithm-based applications. >> Yes, and 90% of blockchain is consensus. We've solved the consensus problem. We'll be launching a product based around Hyperledger very soon, we're already in tests and we're already showing tens of thousands of transactions per second. Not two, not 2,000, two transactions. >> [Peter] The game theory side of it is still going to be important because when we start talking about machines and humans working together, programs don't require incentives. Human beings do, and so there will be very, very important applications for this stuff. But you're right, from the standpoint of the machine-to-machine when there is no need for incentive, you just want consensus, you want scale. >> Yeah and there are two approaches to this world of blockchains. There's public, which is where the Bitcoin guys are and the anarchists who firmly believe that there should be no oversight or control, then there's the real world which is permission blockchains, and permission blockchains is where the banks, where the regulators, where NASDAQ will be when we're trading shares in the future. That will be a permission blockchain that will be overseen by a regulator like the SEC, NASDAQ, or London Stock Exchange, et cetera. >> David, always great to chat with you. Thanks for coming on, again, always on the cutting edge, always having a great vision while knocking down some good technology and moving your IP on the right waves every time, congratulations. >> Thank you. >> Always on the next wave, David Richards here inside theCUBE. Every year, doesn't disappoint, theCUBE bringing you all the action here. Cube NYC, we'll be back with more coverage. Stay with us; a lot more action for the rest of the day. We'll be right back; stay with us for more after this short break. (upbeat music)

>> Live from New York, it's the Cube (funky music) covering the Cube New York City 2018 brought to you by SiliconANGLE Media and its ecosystem partners. (funky music) >> Everyone welcome back to theCUBE live in New York City for Cube NYC. This is our live broadcast - two days of coverage around the big data world, AI, the future of Cloud analytics. I'm John Furrier, my cohost Peter Burris. Our next guest is Paul Appleby, CEO Kinetica. Thanks for coming back to theCUBE - good to see you. >> Great to be back again and great to visit in New York City - it's incredible to be here on this really important week. >> Last time we chatted was in our big data Silicon Valley event, which is going to be renamed Cube SV, because it's not just data anymore; there's a lot of Cloud involved, a lot of new infrastructure. But analytics has certainly changed. What's your perspective now in New York as you're in here hearing all the stories around the show and you talk to customers - what's the update from your perspective? Because certainly we're hearing a lot of Cloud this year - Cloud, multi Cloud, analytics, and eyeing infrastructure, proof in the pudding, that kind of thing. >> I'm going to come back to the Cloud thing because I think that's really important. We have shifted to this sort of hybrid multi Cloud world, and that's our future - there is no doubt about it, and that's right across all spectre of computing, not just as it relates to data. But I think this evolution of data has continued this journey that we've all been on from whatever you want to call it - systems or record - to the world of big data where we're trying to gain insights out of this massive oceans of data. But we're in a world today where we're leveraging the power of analytics and intelligence, AI machine learning, to make fundamental decisions that drive some action. Now that action may be to a human to make a decision to interact more effectively with a customer, or it could be to a machine to automate some process. And we're seeing this fundamental shift towards a focus on that problem, and associated with that, we're leveraging the power of Cloud, AI, ML, and all the rest of it. >> And the human role in all this has been talked about. I've seen in the US in the political landscape, data for good, we see Facebook up there being basically litigated publicly in front of the Senate around the role of data and the elections. People are talking in the industry about the role of humans with machines is super important. This is now coming back as a front and center issue of hey, machines do great intelligence, but what about the human piece? What's your view on the human interaction component, whether it's the curation piece, the role of the citizen analyst, or whatever we're calling it these days, and what machines do to supplement that? >> Really good question - I've spent a lot of time thinking about this. I've had the incredible privilege of being able to attend the World Economic Forum for the last five years, and this particular topic of how Robotics Automation Artificial Intelligence machine learning is impacting economies, societies, and ultimately the nature of work has been a really big thread there for a number of years. I've formed a fundamental view: first of all, any technology can be used for good purposes and bad purposes, and it's - >> It always is. >> And it always is, and it's incumbent upon society and government to apply the appropriate levels of regulation, and for corporations to obviously behave the right way, but setting aside those topics - because we could spend hours talking about those alone - there is a fundamental issue, and this is this kind of conversation about what a lot of people like to describe as the fourth industrial revolution. I've spent a lot of time, because you hear people bandy that around - what do they really mean, and what are we really talking about? I've looked at every point in time where there's been an industrial revolution - there's been a fundamental shift of work that was done by humans that's now done by machines. There's been a societal uproar, and there're being new forms of work created, and society's evolved. What I look at today is yes, there's a responsibility and a regular treaside to this, but there's also a responsibility in business and society to prepare our workers and our kids for new forms of work, cause that's what I really think we should be thinking about - what are the new forms of work that are actually unlocked by these technologies, rather than what are the roles that are displaced by this steam powered engine. (laughs softly) >> Well, Paul, we totally agree with you. There's one other step in this process. It kind of anticipates each of these revolutions, and that is there is a process of new classes of asset formation. Mhm. So if you go back to when we put new power trains inside row houses to facilitate the industrial revolution in the early 1800s, and you could say the same thing about transportation, and what the trains did and whatnot. There's always this process of new asset formation that presaged some of these changes. Today it's data - data's an asset cause businesses ultimately institutionalize, or re institutionalize, their work around what they regard as valuable. Now, when we start talking about machines telling other machines what to do, or providing options or paring off options for humans so they have clear sets of things that they can take on, speed becomes a crucial issue, right? At the end of the day, all of this is going to come back to how fast can you process data? Talk to us a little bit about how that dynamic and what you guys are doing to make it possible is impacting business choices. >> Two really important things to unpack there, and one I think I'd love to touch on later, which is data as an asset class and how corporations should treat data. You talk about speed, and I want to talk about speed in the context of perishability, because the truth is if you're going to drive these incredible insights, whether it's related to a cyber threat, or a terrorist threat, or an opportunity to expand your relationship with a customer, or to make a critical decision in a motor vehicle in an autonomous operating mode, these things are about taking massive volumes of streaming data, running analytics in real time, and making decisions in real time. These are not about gleaning insights from historic pools or oceans of data; this is about making decisions that are fundamental to - >> Right now. >> The environment that you're in right now. You think about the autonomous car - great example of the industrial Internet, one we all love to talk about. The mechanical problems associated with autonomy have been solved, fundamentally sensors in cars, and the automated processes related to that. The decisioning engines - they need to be applied at scale in millions of vehicles in real time. That's an extreme data problem. The biggest problem solved there is data, and then over time, societal and regulatory change means that this is going to take some time before it comes to fruition. >> We were just saying - I think it was 100 Teslas generating 100 terabytes of data a day based on streams from its fleet of cars its customers have. >> We firmly believe that longer term, when you get to true autonomy, each car will probably generate around ten terabytes of data a day. That is an extremely complex problem to solve, because at the end of the day, this thinking that you're able to drive that data back to some centralized brain to be making those decisions for and on behalf of the cars is just fundamentally flawed. It has to happen in the car itself. >> Totally agree. >> This is putting super computers inside cars. >> Which is kind of happening - in fact, that 100 terabytes a day is in fact the data that does get back to Tesla. >> Yeah. >> As you said, there's probably 90% of the data is staying inside the car, which is unbelievable scale. >> So the question I wanted to ask you - you mentioned the industrial revolution, so every time there's a new revolution, there's an uproar, you mentioned. But there's also a step up of new capabilities, so if there's new work being developed, usually entrepreneur activity - weird entrepreneurs figured out that everyone says they're not weird anymore; it's great. But there's a step up of new capability that's built. Someone else says hey, the way we used to do databases and networks was great for moving one gig Ethernet on top of the rack; now you got 10 terabytes coming off a car or wireless spectrum. We got to rethink spectrum, or we got to rethink database. Let's use some of these GPUs - so a new step up of suppliers have to come in to support the new work. What's your vision on some of those things that are happening now - that you think people aren't yet seeing? What are some of those new step up functions? Is it on the database side, is it on the network, is it on the 5G - where's the action? >> Wow. Because who's going to support the Teslas? (Paul laughs) Who's going to support the new mobile revolution, the new iPhones the size of my two hands put together? What's your thoughts on that? >> The answer is all of the above. Let me talk about that and what I mean by that. Because you're looking at it from the technology perspective, I'd love to come back and talk about the human perspective as well, but from the technology perspective, of course leveraging power is going to be fundamental to this, because if you think about the types of use cases where you're going to have to be gigathreading queries against massive volumes of data, both static and streaming, you can't do that with historic technology, so that's going to be a critical part of it. The other part of it that we haven't mentioned a lot here but I think we should bring into it is if you think about these types of industrial Internet use cases, or IOT - even consumer Internet IOT related use cases - a lot of the decisioning has to occur out of the H. It cannot occur in a central facility, so it means actually putting the AI or ML engine inside the vehicle, or inside the cell phone tower, or inside the oil rig, and that is going to be a really big part of you know, shifting back to this very distributive model of machine lining in AI, which brings very complex questions in of how you drive governance - (John chuckles) >> And orchestration around employing Ai and ML models at massive scale, out to edge devices. >> Inferencing at the edge, certainly. It's going to be interesting to see what happens with training - we know that some of the original training will happen at the center, but some of that maintenance training? It's going to be interesting to see where that actually - it's probably going to be a split function, but you're going to need really high performing databases across the board, and I think that's one of the big answers, John, is that everybody says oh, it's all going to be in software. It's going to be a lot of hard word answers. >> Yep. >> Well the whole idea is just it's provocative to think about it and also intoxicating if you also want to go down that rabbit hole... If you think about that car, okay, if they're going to be doing century machine learning at the edge - okay, what data are you working off of? There's got to be some storage, and then what about real time data coming from other either horizontally scalable data sets. (laughs) So the question is, what do they have access to? Are they optimized for the decision making at that time? >> Mhm. >> Again, talk about the future of work - this is a big piece, but this is the human piece as well. >> Yeah. >> Are our kids going to be in a multi massive, multi player online game called Life? >> They are. >> They are now. They're on Fortnite, they're on Call of Duty, and all this gaming culture. >> But I think this is one of the interesting things, because there's a very strong correlation between information theory and thermodynamics. >> Mhm. >> They're the same exact - in physics, they are the identical algorithms and the identical equations. There's not a lot of difference, and you go back to the original revolution, you have a series of row houses, you put a power supply all the way down, you can run a bunch of looms. The big issue is entropy - how much heat are you generating? How do you get greater efficiency out of that single power supply? Same thing today: we're worried about the amount of cost, the amount of energy, the amount of administrative overhead associated with using data as an asset, and the faster the database, the more natural it is, the more easy it is to administer, the more easy it is to apply to a lot of different cases, the better. And it's going to be very, very interesting over the next few year to see how - Does database come in memory? Does database stay out over there? A lot of questions are going to be answered in the next couple years as we try to think about where these information transducers actually reside, and how they do their job. >> Yeah, and that's going to be driven yes, partially by the technology, but more importantly by the problems that we're solving. Here we are in New York City - you look at financial services. There are two massive factors in financial services going on what is the digital bank of the future look like, and how the banks interact with their customers, and how you get that true one-to-one engagement, which historically has been virtually impossible for companies that have millions or tens of millions of customers, so fundamental transformation of customer engagement driven by these advanced or excelerated analytics engines, and the pair of AI and ML, but then on the other side if you start looking at really incredibly important things for the banks like risk and spread, historically because of the volumes of data, it's been virtually impossible for them to present their employees with a true picture of those things. Now, with these accelerated technologies, you can take all the historic trading data, and all of the real time trading data, smash that together, and run real time analytics to make the right decisions in the moment of interaction with a customer, and that is incredibly powerful for both the customer, but also for the bank in mitigating risk, and they're the sorts of things we're doing with banks up and down the city here in New York, and of course, right around the world. >> So here's a question for you, so with that in mind - this is kind of more of a thought exercise - will banks even be around in 20 years? >> Wow. (laughs) >> I mean, you've got block chains saying we're going to have new crypto models here, if you take this Tesla with ten terabytes going out every second or whatever that number is. If that's the complex problem, banking should be really easy to solve. >> I think it's incumbent on boards in every industry, not just banking, to think about what existential threats exist, because there are incredibly powerful, successful companies that have gone out of existence because of fundamental shifts and buying behaviors or technologies - I think banks need to be concerned. >> Every industry needs to be concerned. >> Every industry needs to be concerned. >> At the end of the day, every board needs to better understand how they can reduce their assets specificities, right? How they can have their assets be more fungible and more applicable or appropriable to multiple different activities? Think about a future where data and digital assets are a dominant feature of business. Asset specificities go down; today their very definition of vertical industry is defined by the assets associated with bottling, the assets associated with flying, the assets associated with any number of other things. As aspect specialist needs to go down because of data, it changes even the definition of industry, let alone banking. >> Yeah, and auto industry's a great example. Will we own cars in the future? Will we confirm them as a service? >> Exactly. >> Car order manufacturers need to come to terms with that. The banks need to come to terms with the fact that the fundamental infrastructure for payments, whether it's domestic or global, will change. I mean, it is going to change. >> It's changing. It's changing. >> It has to change, and it's in the process of changing, and I'm not talking about crypto, you know, what form of digital currency exists in the future, we can argue about forever, but a fundamental underlying platform for real time exchange - that's just the future. Now, what does that mean for banks that rely heavily on payments as part of their core driver of profitability? Now that's a really important thing to come to terms with. >> Or going back to the point you made earlier. We may not have banks, but we have bankers. There's still going to be people who're providing advice in council, helping the folks understand what businesses to buy, what businesses to sell. So whatever industry they're in, we will still have the people that bring the extra taste to the data. >> Okay, we got to break it there, we've run out of time. Paul, love to chat further about future banking, all this other stuff, and also, as we live in a connected world, what does that mean? We're obviously connected to data; we certainly know there's gonnna be a ton of data. We're bringing that to you here, New York City, with Cube NYC. Stay with us for more coverage after the short break. (funky music)

>> Live from New York. It's theCUBE. Covering theCUBE New York City, 2018. Brought to you by SiliconANGLE Media, and it's ecosystem partners. >> I'm John Furrier with Peter Burris, our next two guests are Jim Franklin with Dell EMC Director of Product Management Anant Chintamaneni, who is the Vice President of Products at BlueData. Welcome to theCUBE, good to see you. >> Thanks, John. >> Thank you. >> Thanks for coming on. >> I've been following BlueData since the founding. Great company, and the founders are great. Great teams, so thanks for coming on and sharing what's going on, I appreciate it. >> It's a pleasure, thanks for the opportunity. >> So Jim, talk about the Dell relationship with BlueData. What are you guys doing? You have the Dell-ready solutions. How is that related now, because you've seen this industry with us over the years morph. It's really now about, the set-up days are over, it's about proof points. >> That's right. >> AI and machine learning are driving the signal, which is saying, 'We need results'. There's action on the developer's side, there's action on the deployment, people want ROI, that's the main focus. >> That's right. That's right, and we've seen this journey happen from the new batch processing days, and we're seeing that customer base mature and come along, so the reason why we partnered with BlueData is, you have to have those softwares, you have to have the contenders. They have to have the algorithms, and things like that, in order to make this real. So it's been a great partnership with BlueData, it's dated back actually a little farther back than some may realize, all the way to 2015, believe it or not, when we used to incorporate BlueData with Isilon. So it's been actually a pretty positive partnership. >> Now we've talked with you guys in the past, you guys were on the cutting edge, this was back when Docker containers were fashionable, but now containers have become so proliferated out there, it's not just Docker, containerization has been the wave. Now, Kubernetes on top of it is really bringing in the orchestration. This is really making the storage and the network so much more valuable with workloads, whether respective workloads, and AI is a part of that. How do you guys navigate those waters now? What's the BlueData update, how are you guys taking advantage of that big wave? >> I think, great observation, re-embrace Docker containers, even before actually Docker was even formed as a company by that time, and Kubernetes was just getting launched, so we saw the value of Docker containers very early on, in terms of being able to obviously provide the agility, elasticity, but also, from a packaging of applications perspective, as we all know it's a very dynamic environment, and today, I think we are very happy to know that, with Kubernetes being a household name now, especially a tech company, so the way we're navigating this is, we have a turnkey product, which has containerization, and then now we are taking our value proposition of big data and AI and lifecycle management and bringing it to Kubernetes with an open source project that we launched called Cube Director under our umbrella. So, we're all about bringing stateful applications like Hadoop, AI, ML to the community and to our customer base, which is some of the largest financial services in health care customers. >> So the container revolution has certainly groped developers, and developers have always had a history of chasing after the next cool technology, and for good reason, it's not like just chasing after... Developers tend not to just chase after the shiny thing, they chased after the most productive thing, and they start using it, and they start learning about it, and they make themselves valuable, and they build more valuable applications as a result. But there's this interesting meshing of creators, makers, in the software world, between the development community and the data science community. How are data scientists, who you must be spending a fair amount of time with, starting to adopt containers, what are they looking at? Are they even aware of this, as you try to help these communities come together? >> We absolutely talk to the data scientists and they're the drivers of determining what applications they want to consume for the different news cases. But, at the end of the day, the person who has to deliver these applications, you know data scientists care about time to value, getting the environment quickly all prepared so they can access the right data sets. So, in many ways, most of our customers, many of them are unaware that there's actually containers under the hood. >> So this is the data scientists. >> The data scientists, but the actual administrators and the system administrators were making these tools available, are using containers as a way to accelerate the way they package the software, which has a whole bunch of dependent libraries, and there's a lot of complexity our there. So they're simplifying all that and providing the environment as quickly as possible. >> And in so doing, making sure that whatever workloads are put together, can scaled, can be combined differently and recombined differently, based on requirements of the data scientists. So the data scientist sees the tool... >> Yeah. >> The tool is manifest as, in concert with some of these new container related technologies, and then the whole CICD process supports the data scientist >> The other thing to think about though, is that this also allows freedom of choice, and we were discussing off camera before, these developers want to pick out what they want to pick out what they want to work with, they don't want to have to be locked in. So with containers, you can also speed that deployment but give them freedom to choose the tools that make them best productive. That'll make them much happier, and probably much more efficient. >> So there's a separation under the data science tools, and the developer tools, but they end up all supporting the same basic objective. So how does the infrastructure play in this, because the challenge of big data for the last five years as John and I both know, is that a lot of people conflated. The outcome of data science, the outcome of big data, with the process of standing up clusters, and lining up Hadoop, and if they failed on the infrastructure, they said it was a failure overall. So how you making the infrastructure really simple, and line up with this time of value? >> Well, the reality is, we all need food and water. IT still needs server and storage in order to work. But at the end of the day, the abstraction has to be there just like VMware in the early days, clouds, containers with BlueData is just another way to create a layer of abstraction. But this one is in the context of what the data scientist is trying to get done, and that's the key to why we partnered with BlueData and why we delivered big data as a service. >> So at that point, what's the update from Dell EMC and Dell, in particular, Analytics? Obviously you guys work with a lot of customers, have challenges, how are you solving those problems? What are those problems? Because we know there's some AI rumors, big Dell event coming up, there's rumors of a lot of AI involved, I'm speculating there's going to be probably a new kind of hardware device and software. What's the state of the analytics today? >> I think a lot of the customers we talked about, they were born in that batch processing, that Hadoop space we just talked about. I think they largely got that right, they've largely got that figured out, but now we're seeing proliferation of AI tools, proliferation of sandbox environments, and you're psyched to see a little bit of silo behavior happening, so what we're trying to do is that IT shop is trying to dispatch those environments, dispatch with some speed, with some agility. They want to have it at the right economic model as well, so we're trying to strike a better balance, say 'Hey, I've invested in all this infrastructure already, I need to modernize it, and that I also need to offer it up in a way that data scientists can consume it'. Oh, by the way, we're starting to see them start to hire more and more of these data scientists. Well, you don't want your data scientists, this very expensive, intelligent resource, sitting there doing data mining, data cleansing, detail offloads, we want them actually doing modeling and analytics. So we find that a lot of times right now as you're doing an operational change, the operational mindset as you're starting to hire these very expensive people to do this very good work, at the corest of the data, but they need to get productive in the way that you hired them to be productive. >> So what is this ready solution, can you just explain what that is? Is it a program, is it a hardware, is it a solution? What is the ready solution? >> Generally speaking, what we do as a division is we look for value workloads, just generally speaking, not necessarily in batch processing, or AI, or applications, and we try and create an environment that solves that customer challenge, typically they're very complex, SAP, Oracle Database, it's AI, my goodness. Very difficult. >> Variety of tools, using hives, no sequel, all this stuff's going on. >> Cassandra, you've got Tensorflow, so we try fit together a set of knowledge experts, that's the key, the intellectual property of our engineers, and their deep knowledge expertise in a certain area. So for AI, we have a sight of them back at the shop, they're in the lab, and this is what they do, and they're serving up these models, they're putting data through its paces, they're doing the work of a data scientist. They are data scientists. >> And so this is where BlueData comes in. You guys are part of this abstraction layer in the ready solutions. Offering? Is that how it works? >> Yeah, we are the software that enables the self-service experience, the multitenancy, that the consumers of the ready solution would want in terms of being able to onboard multiple different groups of users, lines of business, so you could have a user that wants to run basic spark, cluster, spark jobs, or you could have another user group that's using Tensorflow, or accelerated by a special type of CPU or GPU, and so you can have them all on the same infrastructure. >> One of the things Peter and I were talking about, Dave Vellante, who was here, he's at another event right now getting some content but, one of the things we observed was, we saw this awhile ago so it's not new to us but certainly we're seeing the impact at this event. Hadoop World, there's now called Strata Data NYC, is that we hear words like Kubernetes, and Multi Cloud, and Istio for the first time. At this event. This is the impact of the Cloud. The Cloud has essentially leveled the Hadoop World, certainly there's some Hadoop activity going on there, people have clusters, there's standing up infrastructure for analytical infrastructures that do analytics, obviously AI drives that, but now you have the Cloud being a power base. Changing that analytics infrastructure. How has it impacted you guys? BlueData, how are you guys impacted by the Cloud? Tailwind for you guys? Helpful? Good? >> You described it well, it is a tailwind. This space is about the data, not where the data lives necessarily, but the robustness of the data. So whether that's in the Cloud, whether that's on Premise, whether that's on Premise in your own private Cloud, I think anywhere where there's data that can be gathered, modeled, and new insights being pulled out of, this is wonderful, so as we ditched data, whether it's born in the Cloud or born on Premise, this is actually an accelerant to the solutions that we built together. >> As BlueData, we're all in on the Cloud, we support all the three major Cloud providers that was the big announcement that we made this week, we're generally available for AWS, GCP, and Azure, and, in particular, we start with customers who weren't born in the Cloud, so we're talking about some of the large financial services >> We had Barclays UK here who we nominated, they won the Cloud Era Data Impact Award, and what they're actually going through right now, is they started on Prem, they have these really packaged certified technology stacks, whether they are Cloud Era Hadoop, whether they are Anaconda for data science, and what they're trying to do right now is, they're obviously getting value from that on Premise with BlueData, and now they want to leverage the Cloud. They want to be able to extend into the Cloud. So, we as a company have made our product a hybrid Cloud-ready platform, so it can span on Prem as well as multiple Clouds, and you have the ability to move the workloads from one to the other, depending on data gravity, SLA considerations. >> Compliancy. >> I think it's one more thing, I want to test this with you guys, John, and that is, analytics is, I don't want to call it inert, or passive, but analytics has always been about getting the right data to human beings so they can make decisions, and now we're seeing, because of AI, the distinction that we draw between analytics and AI is, AI is about taking action on the data, it's about having a consequential action, as a result of the data, so in many respects, NCL, Kubernetes, a lot of these are not only do some interesting things for the infrastructure associated with big data, but they also facilitate the incorporation of new causes of applications, that act on behalf of the brand. >> Here's the other thing I'll add to it, there's a time element here. It used to be we were passive, and it was in the past, and you're trying to project forward, that's no longer the case. You can do it right now. Exactly. >> In many respects, the history of the computing industry can be drawn in this way, you focused on the past, and then with spreadsheets in the 80s and personal computing, you focused on getting everybody to agree on the future, and now, it's about getting action to happen right now. >> At the moment it happens. >> And that's why there's so much action. We're passed the set-up phase, and I think this is why we're hearing, seeing machine learning being so popular because it's like, people want to take action there's a demand, that's a signal that it's time to show where the ROI is and get action done. Clearly we see that. >> We're capitalists, right? We're all trying to figure out how to make money in these spaces. >> Certainly there's a lot of movement, and Cloud has proven that spinning up an instance concept has been a great thing, and certainly analytics. It's okay to have these workloads, but how do you tie it together? So, I want to ask you, because you guys have been involved in containers, Cloud has certainly been a tailwind, we agree with you 100 percent on that. What is the relevance of Kubernetes and Istio? You're starting to see these new trends. Kubernetes, Istio, Cupflow. Higher level microservices with all kinds of stateful and stateless dynamics. I call it API 2.0, it's a whole other generation of abstractions that are going on, that are creating some goodness for people. What is the impact, in your opinion, of Kubernetes and this new revolution? >> I think the impact of Kubernetes is, I just gave a talk here yesterday, called Hadoop-la About Kubernetes. We were thinking very deeply about this. We're thinking deeply about this. So I think Kubernetes, if you look at the genesis, it's all about stateless applications, and I think as new applications are being written folks are thinking about writing them in a manner that are decomposed, stateless, microservices, things like Cupflow. When you write it like that, Kubernetes fits in very well, and you get all the benefits of auto-scaling, and so control a pattern, and ultimately Kubernetes is this finite state machine-type model where you describe what the state should be, and it will work and crank towards making it towards that state. I think it's a little bit harder for stateful applications, and I think that's where we believe that the Kubernetes community has to do a lot more work, and folks like BlueData are going to contribute to that work which is, how do you bring stateful applications like Hadoop where there's a lot of interdependent services, they're not necessarily microservices, they're actually almost close to monolithic applications. So I think new applications, new AI ML tooling that's going to come out, they're going to be very conscious of how they're running in a Cloud world today that folks weren't aware of seven or eight years ago, so it's really going to make a huge difference. And I think things like Istio are going to make a huge difference because you can start in the cloud and maybe now expand on to Prem. So there's going to be some interesting dynamics. >> Without hopping management frameworks, absolutely. >> And this is really critical, you just nailed it. Stateful is where ML will shine, if you can then cross the chasma to the on Premise where the workloads can have state sharing. >> Right. >> Scales beautifully. It's a whole other level. >> Right. You're going to the data into the action, or the activity, you're going to have to move the processing to the data, and you want to have nonetheless, a common, seamless management development framework so that you have the choices about where you do those things. >> Absolutely. >> Great stuff. We can do a whole Cube segment just on that. We love talking about these new dynamics going on. We'll see you in CF CupCon coming up in Seattle. Great to have you guys on. Thanks, and congratulations on the relationship between BlueData and Dell EMC and Ready Solutions. This is Cube, with the Ready Solutions here. New York City, talking about big data and the impact, the future of AI, all things stateful, stateless, Cloud and all. It's theCUBE bringing you all the action. Stay with us for more after this short break.

- [Announcer] Live, from New York, it's theCUBE. Covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media, and its ecosystem partners. - Hello everyone, welcome back to CUBE NYC. This is a CUBE special presentation of something that we've done now for the past couple of years. IBM has sponsored an influencer panel on some of the hottest topics in the industry, and of course, there's no hotter topic right now than AI. So, we've got nine of the top influencers in the AI space, and we're in Hell's Kitchen, and it's going to get hot in here. (laughing) And these guys, we're going to cover the gamut. So, first of all, folks, thanks so much for joining us today, really, as John said earlier, we love the collaboration with you all, and we'll definitely see you on social after the fact. I'm Dave Vellante, with my cohost for this session, Peter Burris, and again, thank you to IBM for sponsoring this and organizing this. IBM has a big event down here, in conjunction with Strata, called Change the Game, Winning with AI. We run theCUBE NYC, we've been here all week. So, here's the format. I'm going to kick it off, and then we'll see where it goes. So, I'm going to introduce each of the panelists, and then ask you guys to answer a question, I'm sorry, first, tell us a little bit about yourself, briefly, and then answer one of the following questions. Two big themes that have come up this week. One has been, because this is our ninth year covering what used to be Hadoop World, which kind of morphed into big data. Question is, AI, big data, same wine, new bottle? Or is it really substantive, and driving business value? So, that's one question to ponder. The other one is, you've heard the term, the phrase, data is the new oil. Is data really the new oil? Wonder what you think about that? Okay, so, Chris Penn, let's start with you. Chris is cofounder of Trust Insight, long time CUBE alum, and friend. Thanks for coming on. Tell us a little bit about yourself, and then pick one of those questions. - Sure, we're a data science consulting firm. We're an IBM business partner. When it comes to "data is the new oil," I love that expression because it's completely accurate. Crude oil is useless, you have to extract it out of the ground, refine it, and then bring it to distribution. Data is the same way, where you have to have developers and data architects get the data out. You need data scientists and tools, like Watson Studio, to refine it, and then you need to put it into production, and that's where marketing technologists, technologists, business analytics folks, and tools like Watson Machine Learning help bring the data and make it useful. - Okay, great, thank you. Tony Flath is a tech and media consultant, focus on cloud and cyber security, welcome. - Thank you. - Tell us a little bit about yourself and your thoughts on one of those questions. - Sure thing, well, thanks so much for having us on this show, really appreciate it. My background is in cloud, cyber security, and certainly in emerging tech with artificial intelligence. Certainly touched it from a cyber security play, how you can use machine learning, machine control, for better controlling security across the gamut. But I'll touch on your question about wine, is it a new bottle, new wine? Where does this come from, from artificial intelligence? And I really see it as a whole new wine that is coming along. When you look at emerging technology, and you look at all the deep learning that's happening, it's going just beyond being able to machine learn and know what's happening, it's making some meaning to that data. And things are being done with that data, from robotics, from automation, from all kinds of different things, where we're at a point in society where data, our technology is getting beyond us. Prior to this, it's always been command and control. You control data from a keyboard. Well, this is passing us. So, my passion and perspective on this is, the humanization of it, of IT. How do you ensure that people are in that process, right? - Excellent, and we're going to come back and talk about that. - Thanks so much. - Carla Gentry, @DataNerd? Great to see you live, as opposed to just in the ether on Twitter. Data scientist, and owner of Analytical Solution. Welcome, your thoughts? - Thank you for having us. Mine is, is data the new oil? And I'd like to rephrase that is, data equals human lives. So, with all the other artificial intelligence and everything that's going on, and all the algorithms and models that's being created, we have to think about things being biased, being fair, and understand that this data has impacts on people's lives. - Great. Steve Ardire, my paisan. - Paisan. - AI startup adviser, welcome, thanks for coming to theCUBE. - Thanks Dave. So, uh, my first career was geology, and I view AI as the new oil, but data is the new oil, but AI is the refinery. I've used that many times before. In fact, really, I've moved from just AI to augmented intelligence. So, augmented intelligence is really the way forward. This was a presentation I gave at IBM Think last spring, has almost 100,000 impressions right now, and the fundamental reason why is machines can attend to vastly more information than humans, but you still need humans in the loop, and we can talk about what they're bringing in terms of common sense reasoning, because big data does the who, what, when, and where, but not the why, and why is really the Holy Grail for causal analysis and reasoning. - Excellent, Bob Hayes, Business Over Broadway, welcome, great to see you again. - Thanks for having me. So, my background is in psychology, industrial psychology, and I'm interested in things like customer experience, data science, machine learning, so forth. And I'll answer the question around big data versus AI. And I think there's other terms we could talk about, big data, data science, machine learning, AI. And to me, it's kind of all the same. It's always been about analytics, and getting value from your data, big, small, what have you. And there's subtle differences among those terms. Machine learning is just about making a prediction, and knowing if things are classified correctly. Data science is more about understanding why things work, and understanding maybe the ethics behind it, what variables are predicting that outcome. But still, it's all the same thing, it's all about using data in a way that we can get value from that, as a society, in residences. - Excellent, thank you. Theo Lau, founder of Unconventional Ventures. What's your story? - Yeah, so, my background is driving technology innovation. So, together with my partner, what our work does is we work with organizations to try to help them leverage technology to drive systematic financial wellness. We connect founders, startup founders, with funders, we help them get money in the ecosystem. We also work with them to look at, how do we leverage emerging technology to do something good for the society. So, very much on point to what Bob was saying about. So when I look at AI, it is not new, right, it's been around for quite a while. But what's different is the amount of technological power that we have allow us to do so much more than what we were able to do before. And so, what my mantra is, great ideas can come from anywhere in the society, but it's our job to be able to leverage technology to shine a spotlight on people who can use this to do something different, to help seniors in our country to do better in their financial planning. - Okay, so, in your mind, it's not just a same wine, new bottle, it's more substantive than that. - [Theo] It's more substantive, it's a much better bottle. - Karen Lopez, senior project manager for Architect InfoAdvisors, welcome. - Thank you. So, I'm DataChick on twitter, and so that kind of tells my focus is that I'm here, I also call myself a data evangelist, and that means I'm there at organizations helping stand up for the data, because to me, that's the proxy for standing up for the people, and the places and the events that that data describes. That means I have a focus on security, data privacy and protection as well. And I'm going to kind of combine your two questions about whether data is the new wine bottle, I think is the combination. Oh, see, now I'm talking about alcohol. (laughing) But anyway, you know, all analogies are imperfect, so whether we say it's the new wine, or, you know, same wine, or whether it's oil, is that the analogy's good for both of them, but unlike oil, the amount of data's just growing like crazy, and the oil, we know at some point, I kind of doubt that we're going to hit peak data where we have not enough data, like we're going to do with oil. But that says to me that, how did we get here with big data, with machine learning and AI? And from my point of view, as someone who's been focused on data for 35 years, we have hit this perfect storm of open source technologies, cloud architectures and cloud services, data innovation, that if we didn't have those, we wouldn't be talking about large machine learning and deep learning-type things. So, because we have all these things coming together at the same time, we're now at explosions of data, which means we also have to protect them, and protect the people from doing harm with data, we need to do data for good things, and all of that. - Great, definite differences, we're not running out of data, data's like the terrible tribbles. (laughing) - Yes, but it's very cuddly, data is. - Yeah, cuddly data. Mark Lynd, founder of Relevant Track? - That's right. - I like the name. What's your story? - Well, thank you, and it actually plays into what my interest is. It's mainly around AI in enterprise operations and cyber security. You know, these teams that are in enterprise operations both, it can be sales, marketing, all the way through the organization, as well as cyber security, they're often under-sourced. And they need, what Steve pointed out, they need augmented intelligence, they need to take AI, the big data, all the information they have, and make use of that in a way where they're able to, even though they're under-sourced, make some use and some value for the organization, you know, make better use of the resources they have to grow and support the strategic goals of the organization. And oftentimes, when you get to budgeting, it doesn't really align, you know, you're short people, you're short time, but the data continues to grow, as Karen pointed out. So, when you take those together, using AI to augment, provided augmented intelligence, to help them get through that data, make real tangible decisions based on information versus just raw data, especially around cyber security, which is a big hit right now, is really a great place to be, and there's a lot of stuff going on, and a lot of exciting stuff in that area. - Great, thank you. Kevin L. Jackson, author and founder of GovCloud. GovCloud, that's big. - Yeah, GovCloud Network. Thank you very much for having me on the show. Up and working on cloud computing, initially in the federal government, with the intelligence community, as they adopted cloud computing for a lot of the nation's major missions. And what has happened is now I'm working a lot with commercial organizations and with the security of that data. And I'm going to sort of, on your questions, piggyback on Karen. There was a time when you would get a couple of bottles of wine, and they would come in, and you would savor that wine, and sip it, and it would take a few days to get through it, and you would enjoy it. The problem now is that you don't get a couple of bottles of wine into your house, you get two or three tankers of data. So, it's not that it's a new wine, you're just getting a lot of it. And the infrastructures that you need, before you could have a couple of computers, and a couple of people, now you need cloud, you need automated infrastructures, you need huge capabilities, and artificial intelligence and AI, it's what we can use as the tool on top of these huge infrastructures to drink that, you know. - Fire hose of wine. - Fire hose of wine. (laughs) - Everybody's having a good time. - Everybody's having a great time. (laughs) - Yeah, things are booming right now. Excellent, well, thank you all for those intros. Peter, I want to ask you a question. So, I heard there's some similarities and some definite differences with regard to data being the new oil. You have a perspective on this, and I wonder if you could inject it into the conversation. - Sure, so, the perspective that we take in a lot of conversations, a lot of folks here in theCUBE, what we've learned, and I'll kind of answer both questions a little bit. First off, on the question of data as the new oil, we definitely think that data is the new asset that business is going to be built on, in fact, our perspective is that there really is a difference between business and digital business, and that difference is data as an asset. And if you want to understand data transformation, you understand the degree to which businesses reinstitutionalizing work, reorganizing its people, reestablishing its mission around what you can do with data as an asset. The difference between data and oil is that oil still follows the economics of scarcity. Data is one of those things, you can copy it, you can share it, you can easily corrupt it, you can mess it up, you can do all kinds of awful things with it if you're not careful. And it's that core fundamental proposition that as an asset, when we think about cyber security, we think, in many respects, that is the approach to how we can go about privatizing data so that we can predict who's actually going to be able to appropriate returns on it. So, it's a good analogy, but as you said, it's not entirely perfect, but it's not perfect in a really fundamental way. It's not following the laws of scarcity, and that has an enormous effect. - In other words, I could put oil in my car, or I could put oil in my house, but I can't put the same oil in both. - Can't put it in both places. And now, the issue of the wine, I think it's, we think that it is, in fact, it is a new wine, and very simple abstraction, or generalization we come up with is the issue of agency. That analytics has historically not taken on agency, it hasn't acted on behalf of the brand. AI is going to act on behalf of the brand. Now, you're going to need both of them, you can't separate them. - A lot of implications there in terms of bias. - Absolutely. - In terms of privacy. You have a thought, here, Chris? - Well, the scarcity is our compute power, and our ability for us to process it. I mean, it's the same as oil, there's a ton of oil under the ground, right, we can't get to it as efficiently, or without severe environmental consequences to use it. Yeah, when you use it, it's transformed, but our scarcity is compute power, and our ability to use it intelligently. - Or even when you find it. I have data, I can apply it to six different applications, I have oil, I can apply it to one, and that's going to matter in how we think about work. - But one thing I'd like to add, sort of, you're talking about data as an asset. The issue we're having right now is we're trying to learn how to manage that asset. Artificial intelligence is a way of managing that asset, and that's important if you're going to use and leverage big data. - Yeah, but see, everybody's talking about the quantity, the quantity, it's not always the quantity. You know, we can have just oodles and oodles of data, but if it's not clean data, if it's not alphanumeric data, which is what's needed for machine learning. So, having lots of data is great, but you have to think about the signal versus the noise. So, sometimes you get so much data, you're looking at over-fitting, sometimes you get so much data, you're looking at biases within the data. So, it's not the amount of data, it's the, now that we have all of this data, making sure that we look at relevant data, to make sure we look at clean data. - One more thought, and we have a lot to cover, I want to get inside your big brain. - I was just thinking about it from a cyber security perspective, one of my customers, they were looking at the data that just comes from the perimeter, your firewalls, routers, all of that, and then not even looking internally, just the perimeter alone, and the amount of data being pulled off of those. And then trying to correlate that data so it makes some type of business sense, or they can determine if there's incidents that may happen, and take a predictive action, or threats that might be there because they haven't taken a certain action prior, it's overwhelming to them. So, having AI now, to be able to go through the logs to look at, and there's so many different types of data that come to those logs, but being able to pull that information, as well as looking at end points, and all that, and people's houses, which are an extension of the network oftentimes, it's an amazing amount of data, and they're only looking at a small portion today because they know, there's not enough resources, there's not enough trained people to do all that work. So, AI is doing a wonderful way of doing that. And some of the tools now are starting to mature and be sophisticated enough where they provide that augmented intelligence that Steve talked about earlier. - So, it's complicated. There's infrastructure, there's security, there's a lot of software, there's skills, and on and on. At IBM Think this year, Ginni Rometty talked about, there were a couple of themes, one was augmented intelligence, that was something that was clear. She also talked a lot about privacy, and you own your data, etc. One of the things that struck me was her discussion about incumbent disruptors. So, if you look at the top five companies, roughly, Facebook with fake news has dropped down a little bit, but top five companies in terms of market cap in the US. They're data companies, all right. Apple just hit a trillion, Amazon, Google, etc. How do those incumbents close the gap? Is that concept of incumbent disruptors actually something that is being put into practice? I mean, you guys work with a lot of practitioners. How are they going to close that gap with the data haves, meaning data at their core of their business, versus the data have-nots, it's not that they don't have a lot of data, but it's in silos, it's hard to get to? - Yeah, I got one more thing, so, you know, these companies, and whoever's going to be big next is, you have a digital persona, whether you want it or not. So, if you live in a farm out in the middle of Oklahoma, you still have a digital persona, people are collecting data on you, they're putting profiles of you, and the big companies know about you, and people that first interact with you, they're going to know that you have this digital persona. Personal AI, when AI from these companies could be used simply and easily, from a personal deal, to fill in those gaps, and to have a digital persona that supports your family, your growth, both personal and professional growth, and those type of things, there's a lot of applications for AI on a personal, enterprise, even small business, that have not been done yet, but the data is being collected now. So, you talk about the oil, the oil is being built right now, lots, and lots, and lots of it. It's the applications to use that, and turn that into something personally, professionally, educationally, powerful, that's what's missing. But it's coming. - Thank you, so, I'll add to that, and in answer to your question you raised. So, one example we always used in banking is, if you look at the big banks, right, and then you look at from a consumer perspective, and there's a lot of talk about Amazon being a bank. But the thing is, Amazon doesn't need to be a bank, they provide banking services, from a consumer perspective they don't really care if you're a bank or you're not a bank, but what's different between Amazon and some of the banks is that Amazon, like you say, has a lot of data, and they know how to make use of the data to offer something as relevant that consumers want. Whereas banks, they have a lot of data, but they're all silos, right. So, it's not just a matter of whether or not you have the data, it's also, can you actually access it and make something useful out of it so that you can create something that consumers want? Because otherwise, you're just a pipe. - Totally agree, like, when you look at it from a perspective of, there's a lot of terms out there, digital transformation is thrown out so much, right, and go to cloud, and you migrate to cloud, and you're going to take everything over, but really, when you look at it, and you both touched on it, it's the economics. You have to look at the data from an economics perspective, and how do you make some kind of way to take this data meaningful to your customers, that's going to work effectively for them, that they're going to drive? So, when you look at the big, big cloud providers, I think the push in things that's going to happen in the next few years is there's just going to be a bigger migration to public cloud. So then, between those, they have to differentiate themselves. Obvious is artificial intelligence, in a way that makes it easy to aggregate data from across platforms, to aggregate data from multi-cloud, effectively. To use that data in a meaningful way that's going to drive, not only better decisions for your business, and better outcomes, but drives our opportunities for customers, drives opportunities for employees and how they work. We're at a really interesting point in technology where we get to tell technology what to do. It's going beyond us, it's no longer what we're telling it to do, it's going to go beyond us. So, how we effectively manage that is going to be where we see that data flow, and those big five or big four, really take that to the next level. - Now, one of the things that Ginni Rometty said was, I forget the exact step, but it was like, 80% of the data, is not searchable. Kind of implying that it's sitting somewhere behind a firewall, presumably on somebody's premises. So, it was kind of interesting. You're talking about, certainly, a lot of momentum for public cloud, but at the same time, a lot of data is going to stay where it is. - Yeah, we're assuming that a lot of this data is just sitting there, available and ready, and we look at the desperate, or disparate kind of database situation, where you have 29 databases, and two of them have unique quantifiers that tie together, and the rest of them don't. So, there's nothing that you can do with that data. So, artificial intelligence is just that, it's artificial intelligence, so, they know, that's machine learning, that's natural language, that's classification, there's a lot of different parts of that that are moving, but we also have to have IT, good data infrastructure, master data management, compliance, there's so many moving parts to this, that it's not just about the data anymore. - I want to ask Steve to chime in here, go ahead. - Yeah, so, we also have to change the mentality that it's not just enterprise data. There's data on the web, the biggest thing is Internet of Things, the amount of sensor data will make the current data look like chump change. So, data is moving faster, okay. And this is where the sophistication of machine learning needs to kick in, going from just mostly supervised-learning today, to unsupervised learning. And in order to really get into, as I said, big data, and credible AI does the who, what, where, when, and how, but not the why. And this is really the Holy Grail to crack, and it's actually under a new moniker, it's called explainable AI, because it moves beyond just correlation into root cause analysis. Once we have that, then you have the means to be able to tap into augmented intelligence, where humans are working with the machines. - Karen, please. - Yeah, so, one of the things, like what Carla was saying, and what a lot of us had said, I like to think of the advent of ML technologies and AI are going to help me as a data architect to love my data better, right? So, that includes protecting it, but also, when you say that 80% of the data is unsearchable, it's not just an access problem, it's that no one knows what it was, what the sovereignty was, what the metadata was, what the quality was, or why there's huge anomalies in it. So, my favorite story about this is, in the 1980s, about, I forget the exact number, but like, 8 million children disappeared out of the US in April, at April 15th. And that was when the IRS enacted a rule that, in order to have a dependent, a deduction for a dependent on your tax returns, they had to have a valid social security number, and people who had accidentally miscounted their children and over-claimed them, (laughter) over the years them, stopped doing that. Well, some days it does feel like you have eight children running around. (laughter) - Agreed. - When, when that rule came about, literally, and they're not all children, because they're dependents, but literally millions of children disappeared off the face of the earth in April, but if you were doing analytics, or AI and ML, and you don't know that this anomaly happened, I can imagine in a hundred years, someone is saying some catastrophic event happened in April, 1983. (laughter) And what caused that, was it healthcare? Was it a meteor? Was it the clown attacking them? - That's where I was going. - Right. So, those are really important things that I want to use AI and ML to help me, not only document and capture that stuff, but to provide that information to the people, the data scientists and the analysts that are using the data. - Great story, thank you. Bob, you got a thought? You got the mic, go, jump in here. - Well, yeah, I do have a thought, actually. I was talking about, what Karen was talking about. I think it's really important that, not only that we understand AI, and machine learning, and data science, but that the regular folks and companies understand that, at the basic level. Because those are the people who will ask the questions, or who know what questions to ask of the data. And if they don't have the tools, and the knowledge of how to get access to that data, or even how to pose a question, then that data is going to be less valuable, I think, to companies. And the more that everybody knows about data, even people in congress. Remember when Zuckerberg talked about? (laughter) - That was scary. - How do you make money? It's like, we all know this. But, we need to educate the masses on just basic data analytics. - We could have an hour-long panel on that. - Yeah, absolutely. - Peter, you and I were talking about, we had a couple of questions, sort of, how far can we take artificial intelligence? How far should we? You know, so that brings in to the conversation of ethics, and bias, why don't you pick it up? - Yeah, so, one of the crucial things that we all are implying is that, at some point in time, AI is going to become a feature of the operations of our homes, our businesses. And as these technologies get more powerful, and they diffuse, and know about how to use them, diffuses more broadly, and you put more options into the hands of more people, the question slowly starts to turn from can we do it, to should we do it? And, one of the issues that I introduce is that I think the difference between big data and AI, specifically, is this notion of agency. The AI will act on behalf of, perhaps you, or it will act on behalf of your business. And that conversation is not being had, today. It's being had in arguments between Elon Musk and Mark Zuckerberg, which pretty quickly get pretty boring. (laughing) At the end of the day, the real question is, should this machine, whether in concert with others, or not, be acting on behalf of me, on behalf of my business, or, and when I say on behalf of me, I'm also talking about privacy. Because Facebook is acting on behalf of me, it's not just what's going on in my home. So, the question of, can it be done? A lot of things can be done, and an increasing number of things will be able to be done. We got to start having a conversation about should it be done? - So, humans exhibit tribal behavior, they exhibit bias. Their machine's going to pick that up, go ahead, please. - Yeah, one thing that sort of tag onto agency of artificial intelligence. Every industry, every business is now about identifying information and data sources, and their appropriate sinks, and learning how to draw value out of connecting the sources with the sinks. Artificial intelligence enables you to identify those sources and sinks, and when it gets agency, it will be able to make decisions on your behalf about what data is good, what data means, and who it should be. - What actions are good. - Well, what actions are good. - And what data was used to make those actions. - Absolutely. - And was that the right data, and is there bias of data? And all the way down, all the turtles down. - So, all this, the data pedigree will be driven by the agency of artificial intelligence, and this is a big issue. - It's really fundamental to understand and educate people on, there are four fundamental types of bias, so there's, in machine learning, there's intentional bias, "Hey, we're going to make "the algorithm generate a certain outcome "regardless of what the data says." There's the source of the data itself, historical data that's trained on the models built on flawed data, the model will behave in a flawed way. There's target source, which is, for example, we know that if you pull data from a certain social network, that network itself has an inherent bias. No matter how representative you try to make the data, it's still going to have flaws in it. Or, if you pull healthcare data about, for example, African-Americans from the US healthcare system, because of societal biases, that data will always be flawed. And then there's tool bias, there's limitations to what the tools can do, and so we will intentionally exclude some kinds of data, or not use it because we don't know how to, our tools are not able to, and if we don't teach people what those biases are, they won't know to look for them, and I know. - Yeah, it's like, one of the things that we were talking about before, I mean, artificial intelligence is not going to just create itself, it's lines of code, it's input, and it spits out output. So, if it learns from these learning sets, we don't want AI to become another buzzword. We don't want everybody to be an "AR guru" that has no idea what AI is. It takes months, and months, and months for these machines to learn. These learning sets are so very important, because that input is how this machine, think of it as your child, and that's basically the way artificial intelligence is learning, like your child. You're feeding it these learning sets, and then eventually it will make its own decisions. So, we know from some of us having children that you teach them the best that you can, but then later on, when they're doing their own thing, they're really, it's like a little myna bird, they've heard everything that you've said. (laughing) Not only the things that you said to them directly, but the things that you said indirectly. - Well, there are some very good AI researchers that might disagree with that metaphor, exactly. (laughing) But, having said that, what I think is very interesting about this conversation is that this notion of bias, one of the things that fascinates me about where AI goes, are we going to find a situation where tribalism more deeply infects business? Because we know that human beings do not seek out the best information, they seek out information that reinforces their beliefs. And that happens in business today. My line of business versus your line of business, engineering versus sales, that happens today, but it happens at a planning level, and when we start talking about AI, we have to put the appropriate dampers, understand the biases, so that we don't end up with deep tribalism inside of business. Because AI could have the deleterious effect that it actually starts ripping apart organizations. - Well, input is data, and then the output is, could be a lot of things. - Could be a lot of things. - And that's where I said data equals human lives. So that we look at the case in New York where the penal system was using this artificial intelligence to make choices on people that were released from prison, and they saw that that was a miserable failure, because that people that release actually re-offended, some committed murder and other things. So, I mean, it's, it's more than what anybody really thinks. It's not just, oh, well, we'll just train the machines, and a couple of weeks later they're good, we never have to touch them again. These things have to be continuously tweaked. So, just because you built an algorithm or a model doesn't mean you're done. You got to go back later, and continue to tweak these models. - Mark, you got the mic. - Yeah, no, I think one thing we've talked a lot about the data that's collected, but what about the data that's not collected? Incomplete profiles, incomplete datasets, that's a form of bias, and sometimes that's the worst. Because they'll fill that in, right, and then you can get some bias, but there's also a real issue for that around cyber security. Logs are not always complete, things are not always done, and when things are doing that, people make assumptions based on what they've collected, not what they didn't collect. So, when they're looking at this, and they're using the AI on it, that's only on the data collected, not on that that wasn't collected. So, if something is down for a little while, and no data's collected off that, the assumption is, well, it was down, or it was impacted, or there was a breach, or whatever, it could be any of those. So, you got to, there's still this human need, there's still the need for humans to look at the data and realize that there is the bias in there, there is, we're just looking at what data was collected, and you're going to have to make your own thoughts around that, and assumptions on how to actually use that data before you go make those decisions that can impact lots of people, at a human level, enterprise's profitability, things like that. And too often, people think of AI, when it comes out of there, that's the word. Well, it's not the word. - Can I ask a question about this? - Please. - Does that mean that we shouldn't act? - It does not. - Okay. - So, where's the fine line? - Yeah, I think. - Going back to this notion of can we do it, or should we do it? Should we act? - Yeah, I think you should do it, but you should use it for what it is. It's augmenting, it's helping you, assisting you to make a valued or good decision. And hopefully it's a better decision than you would've made without it. - I think it's great, I think also, your answer's right too, that you have to iterate faster, and faster, and faster, and discover sources of information, or sources of data that you're not currently using, and, that's why this thing starts getting really important. - I think you touch on a really good point about, should you or shouldn't you? You look at Google, and you look at the data that they've been using, and some of that out there, from a digital twin perspective, is not being approved, or not authorized, and even once they've made changes, it's still floating around out there. Where do you know where it is? So, there's this dilemma of, how do you have a digital twin that you want to have, and is going to work for you, and is going to do things for you to make your life easier, to do these things, mundane tasks, whatever? But how do you also control it to do things you don't want it to do? - Ad-based business models are inherently evil. (laughing) - Well, there's incentives to appropriate our data, and so, are things like blockchain potentially going to give users the ability to control their data? We'll see. - No, I, I'm sorry, but that's actually a really important point. The idea of consensus algorithms, whether it's blockchain or not, blockchain includes games, and something along those lines, whether it's Byzantine fault tolerance, or whether it's Paxos, consensus-based algorithms are going to be really, really important. Parts of this conversation, because the data's going to be more distributed, and you're going to have more elements participating in it. And so, something that allows, especially in the machine-to-machine world, which is a lot of what we're talking about right here, you may not have blockchain, because there's no need for a sense of incentive, which is what blockchain can help provide. - And there's no middleman. - And, well, all right, but there's really, the thing that makes blockchain so powerful is it liberates new classes of applications. But for a lot of the stuff that we're talking about, you can use a very powerful consensus algorithm without having a game side, and do some really amazing things at scale. - So, looking at blockchain, that's a great thing to bring up, right. I think what's inherently wrong with the way we do things today, and the whole overall design of technology, whether it be on-prem, or off-prem, is both the lock and key is behind the same wall. Whether that wall is in a cloud, or behind a firewall. So, really, when there is an audit, or when there is a forensics, it always comes down to a sysadmin, or something else, and the system administrator will have the finger pointed at them, because it all resides, you can edit it, you can augment it, or you can do things with it that you can't really determine. Now, take, as an example, blockchain, where you've got really the source of truth. Now you can take and have the lock in one place, and the key in another place. So that's certainly going to be interesting to see how that unfolds. - So, one of the things, it's good that, we've hit a lot of buzzwords, right now, right? (laughing) AI, and ML, block. - Bingo. - We got the blockchain bingo, yeah, yeah. So, one of the things is, you also brought up, I mean, ethics and everything, and one of the things that I've noticed over the last year or so is that, as I attend briefings or demos, everyone is now claiming that their product is AI or ML-enabled, or blockchain-enabled. And when you try to get answers to the questions, what you really find out is that some things are being pushed as, because they have if-then statements somewhere in their code, and therefore that's artificial intelligence or machine learning. - [Peter] At least it's not "go-to." (laughing) - Yeah, you're that experienced as well. (laughing) So, I mean, this is part of the thing you try to do as a practitioner, as an analyst, as an influencer, is trying to, you know, the hype of it all. And recently, I attended one where they said they use blockchain, and I couldn't figure it out, and it turns out they use GUIDs to identify things, and that's not blockchain, it's an identifier. (laughing) So, one of the ethics things that I think we, as an enterprise community, have to deal with, is the over-promising of AI, and ML, and deep learning, and recognition. It's not, I don't really consider it visual recognition services if they just look for red pixels. I mean, that's not quite the same thing. Yet, this is also making things much harder for your average CIO, or worse, CFO, to understand whether they're getting any value from these technologies. - Old bottle. - Old bottle, right. - And I wonder if the data companies, like that you talked about, or the top five, I'm more concerned about their nearly, or actual $1 trillion valuations having an impact on their ability of other companies to disrupt or enter into the field more so than their data technologies. Again, we're coming to another perfect storm of the companies that have data as their asset, even though it's still not on their financial statements, which is another indicator whether it's really an asset, is that, do we need to think about the terms of AI, about whose hands it's in, and who's, like, once one large trillion-dollar company decides that you are not a profitable company, how many other companies are going to buy that data and make that decision about you? - Well, and for the first time in business history, I think, this is true, we're seeing, because of digital, because it's data, you're seeing tech companies traverse industries, get into, whether it's content, or music, or publishing, or groceries, and that's powerful, and that's awful scary. - If you're a manger, one of the things your ownership is asking you to do is to reduce asset specificities, so that their capital could be applied to more productive uses. Data reduces asset specificities. It brings into question the whole notion of vertical industry. You're absolutely right. But you know, one quick question I got for you, playing off of this is, again, it goes back to this notion of can we do it, and should we do it? I find it interesting, if you look at those top five, all data companies, but all of them are very different business models, or they can classify the two different business models. Apple is transactional, Microsoft is transactional, Google is ad-based, Facebook is ad-based, before the fake news stuff. Amazon's kind of playing it both sides. - Yeah, they're kind of all on a collision course though, aren't they? - But, well, that's what's going to be interesting. I think, at some point in time, the "can we do it, should we do it" question is, brands are going to be identified by whether or not they have gone through that process of thinking about, should we do it, and say no. Apple is clearly, for example, incorporating that into their brand. - Well, Silicon Valley, broadly defined, if I include Seattle, and maybe Armlock, not so much IBM. But they've got a dual disruption agenda, they've always disrupted horizontal tech. Now they're disrupting vertical industries. - I was actually just going to pick up on what she was talking about, we were talking about buzzword, right. So, one we haven't heard yet is voice. Voice is another big buzzword right now, when you couple that with IoT and AI, here you go, bingo, do I got three points? (laughing) Voice recognition, voice technology, so all of the smart speakers, if you think about that in the world, there are 7,000 languages being spoken, but yet if you look at Google Home, you look at Siri, you look at any of the devices, I would challenge you, it would have a lot of problem understanding my accent, and even when my British accent creeps out, or it would have trouble understanding seniors, because the way they talk, it's very different than a typical 25-year-old person living in Silicon Valley, right. So, how do we solve that, especially going forward? We're seeing voice technology is going to be so more prominent in our homes, we're going to have it in the cars, we have it in the kitchen, it does everything, it listens to everything that we are talking about, not talking about, and records it. And to your point, is it going to start making decisions on our behalf, but then my question is, how much does it actually understand us? - So, I just want one short story. Siri can't translate a word that I ask it to translate into French, because my phone's set to Canadian English, and that's not supported. So I live in a bilingual French English country, and it can't translate. - But what this is really bringing up is if you look at society, and culture, what's legal, what's ethical, changes across the years. What was right 200 years ago is not right now, and what was right 50 years ago is not right now. - It changes across countries. - It changes across countries, it changes across regions. So, what does this mean when our AI has agency? How do we make ethical AI if we don't even know how to manage the change of what's right and what's wrong in human society? - One of the most important questions we have to worry about, right? - Absolutely. - But it also says one more thing, just before we go on. It also says that the issue of economies of scale, in the cloud. - Yes. - Are going to be strongly impacted, not just by how big you can build your data centers, but some of those regulatory issues that are going to influence strongly what constitutes good experience, good law, good acting on my behalf, agency. - And one thing that's underappreciated in the marketplace right now is the impact of data sovereignty, if you get back to data, countries are now recognizing the importance of managing that data, and they're implementing data sovereignty rules. Everyone talks about California issuing a new law that's aligned with GDPR, and you know what that meant. There are 30 other states in the United States alone that are modifying their laws to address this issue. - Steve. - So, um, so, we got a number of years, no matter what Ray Kurzweil says, until we get to artificial general intelligence. - The singularity's not so near? (laughing) - You know that he's changed the date over the last 10 years. - I did know it. - Quite a bit. And I don't even prognosticate where it's going to be. But really, where we're at right now, I keep coming back to, is that's why augmented intelligence is really going to be the new rage, humans working with machines. One of the hot topics, and the reason I chose to speak about it is, is the future of work. I don't care if you're a millennial, mid-career, or a baby boomer, people are paranoid. As machines get smarter, if your job is routine cognitive, yes, you have a higher propensity to be automated. So, this really shifts a number of things. A, you have to be a lifelong learner, you've got to learn new skillsets. And the dynamics are changing fast. Now, this is also a great equalizer for emerging startups, and even in SMBs. As the AI improves, they can become more nimble. So back to your point regarding colossal trillion dollar, wait a second, there's going to be quite a sea change going on right now, and regarding demographics, in 2020, millennials take over as the majority of the workforce, by 2025 it's 75%. - Great news. (laughing) - As a baby boomer, I try my damnedest to stay relevant. - Yeah, surround yourself with millennials is the takeaway there. - Or retire. (laughs) - Not yet. - One thing I think, this goes back to what Karen was saying, if you want a basic standard to put around the stuff, look at the old ISO 38500 framework. Business strategy, technology strategy. You have risk, compliance, change management, operations, and most importantly, the balance sheet in the financials. AI and what Tony was saying, digital transformation, if it's of meaning, it belongs on a balance sheet, and should factor into how you value your company. All the cyber security, and all of the compliance, and all of the regulation, is all stuff, this framework exists, so look it up, and every time you start some kind of new machine learning project, or data sense project, say, have we checked the box on each of these standards that's within this machine? And if you haven't, maybe slow down and do your homework. - To see a day when data is going to be valued on the balance sheet. - It is. - It's already valued as part of the current, but it's good will. - Certainly market value, as we were just talking about. - Well, we're talking about all of the companies that have opted in, right. There's tens of thousands of small businesses just in this region alone that are opt-out. They're small family businesses, or businesses that really aren't even technology-aware. But data's being collected about them, it's being on Yelp, they're being rated, they're being reviewed, the success to their business is out of their hands. And I think what's really going to be interesting is, you look at the big data, you look at AI, you look at things like that, blockchain may even be a potential for some of that, because of mutability, but it's when all of those businesses, when the technology becomes a cost, it's cost-prohibitive now, for a lot of them, or they just don't want to do it, and they're proudly opt-out. In fact, we talked about that last night at dinner. But when they opt-in, the company that can do that, and can reach out to them in a way that is economically feasible, and bring them back in, where they control their data, where they control their information, and they do it in such a way where it helps them build their business, and it may be a generational business that's been passed on. Those kind of things are going to make a big impact, not only on the cloud, but the data being stored in the cloud, the AI, the applications that you talked about earlier, we talked about that. And that's where this bias, and some of these other things are going to have a tremendous impact if they're not dealt with now, at least ethically. - Well, I feel like we just got started, we're out of time. Time for a couple more comments, and then officially we have to wrap up. - Yeah, I had one thing to say, I mean, really, Henry Ford, and the creation of the automobile, back in the early 1900s, changed everything, because now we're no longer stuck in the country, we can get away from our parents, we can date without grandma and grandpa setting on the porch with us. (laughing) We can take long trips, so now we're looked at, we've sprawled out, we're not all living in the country anymore, and it changed America. So, AI has that same capabilities, it will automate mundane routine tasks that nobody wanted to do anyway. So, a lot of that will change things, but it's not going to be any different than the way things changed in the early 1900s. - It's like you were saying, constant reinvention. - I think that's a great point, let me make one observation on that. Every period of significant industrial change was preceded by the formation, a period of formation of new assets that nobody knew what to do with. Whether it was, what do we do, you know, industrial manufacturing, it was row houses with long shafts tied to an engine that was coal-fired, and drove a bunch of looms. Same thing, railroads, large factories for Henry Ford, before he figured out how to do an information-based notion of mass production. This is the period of asset formation for the next generation of social structures. - Those ship-makers are going to be all over these cars, I mean, you're going to have augmented reality right there, on your windshield. - Karen, bring it home. Give us the drop-the-mic moment. (laughing) - No pressure. - Your AV guys are not happy with that. So, I think the, it all comes down to, it's a people problem, a challenge, let's say that. The whole AI ML thing, people, it's a legal compliance thing. Enterprises are going to struggle with trying to meet five billion different types of compliance rules around data and its uses, about enforcement, because ROI is going to make risk of incarceration as well as return on investment, and we'll have to manage both of those. I think businesses are struggling with a lot of this complexity, and you just opened a whole bunch of questions that we didn't really have solid, "Oh, you can fix it by doing this." So, it's important that we think of this new world of data focus, data-driven, everything like that, is that the entire IT and business community needs to realize that focusing on data means we have to change how we do things and how we think about it, but we also have some of the same old challenges there. - Well, I have a feeling we're going to be talking about this for quite some time. What a great way to wrap up CUBE NYC here, our third day of activities down here at 37 Pillars, or Mercantile 37. Thank you all so much for joining us today. - Thank you. - Really, wonderful insights, really appreciate it, now, all this content is going to be available on theCUBE.net. We are exposing our video cloud, and our video search engine, so you'll be able to search our entire corpus of data. I can't wait to start searching and clipping up this session. Again, thank you so much, and thank you for watching. We'll see you next time.

Live from New York It's theCUBE! Covering theCUBE New York City 2018 Brought to you by Silicon Angle Media and it's ecosystem partners >> Hey welcome back and we're live in theCUBE in New York city. It's our 2nd day of two days of coverage CUBE NYC. The hashtag CUBENYC Formerly Big data NYC renamed because it's about big data, it's about the server, it's about Cooper _________'s multi-cloud data. It's all about data, and that's the fundamental change in the industry. Our next guest is Yaron Haviv, who's the CTO of Iguazio, key alumni, always coming out with some good commentary smart analysis. Kind of a guest host as well as an industry participant supplier. Welcome back to theCUBE. Good to see you. >> Thank you John. >> Love having you on theCUBE because you always bring some good insight and we appreciate that. Thank you so much. First, before we get into some of the comments because I really want to delve into comments that David Richards said a few years ago, CEO of RenDisco. He said, "Cloud's going to kill Hadoop". And people were looking at him like, "Oh my God, who is this heretic? He's crazy. What is he talking about?" But you might not need Hadoop, if you can run server less Spark, Tensorflow.... You talk about this off camera. Is Hadoop going to be the open stack of the big data world? >> I don't think cloud necessary killed Hadoop, although it is working on that, you know because you go to Amazon and you know, you can consume a bunch of services and you don't really need to think about Hadoop. I think cloud native serve is starting to kill Hadoop, cause Hadoop is three layers, you know, it's a file system, it's DFS, and then you have server scheduling Yarn, then you have applications starting with map produce and then you evolve into things like Spark. Okay, so, file system I don't really need in the cloud. I use Asfree, I can use a database as a service, as you know, pretty efficient way of storing data. For scheduling, Kubernetes is a much more generic way of scheduling workloads and not confined to Spark and specific workloads. I can run with Dancerflow, I can run with data science tools, etc., just containerize. So essentially, why would I need Hadoop? If I can take the traditional tools people are now evolving in and using like Jupiter Notebooks, Spark, Dancerflow, you know, those packages with Kubernetes on top of a database as a service and some object store, I have a much easier stack to work with. And I could mobilize that whether it's in the cloud, you know on different vendors. >> Scale is important too. How do you scale it? >> Of course, you have independent scaling between data and computation, unlike Hadoop. So I can just go to Google, and use Vquery, or use, you know, DynamoDB on Amazon or Redchick, or whatever and automatically scale it down and then, you know >> That's a unique position, so essentially, Hadoop versus Kubernetes is a top-line story. And wouldn't that be ironic for Google, because Google essentially created Map Produce and Coudera ran with it and went public, but when we're talking about 2008 timeframe, 2009 timeframe, back when ventures with cloud were just emerging in the mainstream. So wouldn't it be ironic Kubernetes, which is being driven by Google, ends up taking over Hadoop? In terms of running things on Kubernetes and cloud eight on Visa Vis on premise with Hadoop. >> The poster is tend to give this comment about Google, but essentially Yahoo started Hadoop. Google started the technology  and couple of years after Hadoop started, with Google they essentially moved to a different architecture, with something called Percolator. So Google's not too associated with Hadoop. They're not really using this approach for a long time. >> Well they wrote the map-produced paper and the internal conversations we report on theCUBE about Google was, they just let that go. And Yahoo grabbed it. (cross-conversation) >> The companies that had the most experience were the first to leave. And I think it may respect what you're saying. As the marketplace realizes the outcomes of the dubious associate with, they will find other ways of achieving those outcomes. It might be more depth. >> There's also a fundamental shift in the consumption where Hadoop was about a ranking pages in a batch form. You know, just collecting logs and ranking pages, okay. The chances that people have today revolve around applying AI to business application. It needs to be a lot more concurring, transactional, real-time ish, you know? It's nothing to do with Hadoop, okay? So that's why you'll see more and more workers, mobilizing different black server functions, into service pre-canned services, etc. And Kubernetes playing a good role here is providing the trend. Transport for migrating workloads across cloud providers, because I can use GKE, the Google Kubenetes, or Amazon Kubernetes, or Azure Kubernetes, and I could write a similar application and deploy it on any cloud, or on Clam on my own private cluster. It makes the infrastructure agnostic really application focused. >> Question about Kubernetes we heard on theCUBE earlier, the VP of Project BlueData said that Kubernetes ecosystem and community needs to do a better job with Stapla, they nailed Stapflalis, Stafle application support is something that they need help on. Do you agree with that comment, and then if so, what alternatives do you have for customers who care about Stafe? >> They should use our product (laughing) >> (mumbling) Is Kubernetes struggling there? And if so, talk about your product >> So, I think that our challenge is rounded that there are many solutions in that. I think that they are attacking it from a different approach Many of them are essentially providing some block storage to different containers on really cloud 90. What you want to be able is to have multiple containers access the same data. That means either sharing through file systems, for objects or through databases because one container is generating, for example, ingestion or __________. Another container is manipulating that same data. A third container may look for something in the data, and generate a trigger or an action. So you need shared access to data from those containers. >> The rest of the data synchronizes all three of those things. >> Yes because the data is the form of state. The form of state cannot be associated with the same container, which is what most of where I am very active and sincere in those committees, and you have all the storage guys in the committees, and they think the block story just drag solution. Cause they still think like virtual machines, okay? But the general idea is that if you think about Kubernetes is like the new OS, where you have many processes, they're just scattered around. In OS, the way for us to share state between processes an OS, is whether through files, or through databases, in those form. And that's really what >> Threads and databases as a positive engagement. >> So essentially I gave maybe two years ago, a session at KubeCon in Europe about what we're doing on storing state. It's really high-performance access from those container processes to our database. Impersonate objects, files, streams or time series data, etc And then essentially, all those workloads just mount on top of and we can all share stape. We can even control the access for each >> Do you think you nailed the stape problem? >> Yes, by the way, we have a managed service. Anyone could go today to our cloud, to our website, that's in our cloud. It gets it's own Kubernetes cluster, a provision within less than 10 minutes, five to 10 minutes. With all of those services pre-integrated with Spark, Presto, ______________, real-time, these services functions. All that pre-configured on it's own time. I figured all of these- >> 100% compatible with Kubernetes, it's a good investment >> Well we're just expanding it to Kubernetes stripes, now it's working on them, Amazon Kubernetes, EKS I think, we're working on AKS and GK. We partner with Azure and Google. And we're also building an ad solution that is essentially exactly the same stock. Can run on an edge appliance in a factory. You can essentially mobilize data and functions back and forth. So you can go and develop your work loads, your application in the cloud, test it under simulation, push a single button and teleport the artifacts into the edge factory. >> So is it like a real-time Kubernetes? >> Yes, it's a real-time Kubernetes. >> If you _______like the things we're doing, it's all real-time. >> Talk about real-time in the database world because you mentioned time-series databases. You give objects store versus blog. Talk about time series. You're talking about data that is very relevant in the moment. And also understanding time series data. And then, it's important post-event, if you will, meaning How do you store it? Do you care? I mean, it's important to manage the time series. At the same time, it might not be as valuable as other data, or valuable at certain points and time, which changes it's relationship to how it's stored and how it's used. Talk about the dynamic of time series.. >> Figured it out in the last six or 12 months that since real-time is about time series. Everything you think about real-time censored data, even video is a time-series of frames, okay And what everyone wants to do is just huge amount of time series. They want to cross-correlate it, because for example, you think about stock tickers you know, the stock has an impact from news feeds or Twitter feeds, or of a company or a segment. So essentially, what they need to do is something called multi-volume analysis of multiple time series to be able to extract some meaning, and then decide if you want to sell or buy a stock, as in vacation example. And there is a huge gap in the solution in that market, because most of the time series databases were designed for operational databases, you know, things that monitor apps. Nothing that injects millions of data points per second, and cross-correlates and run real-time AI analytics. Ah, so we've essentially extended because we have a programmable database essentially under the hoop. We've extended it to support time series data with about 50 to 1 compression ratio, compared to some other solutions. You know we've break with the customer, we've done sizing, they told them us they need half a pitabyte. After a small sizing exercise, about 10 to 20 terabytes of storage for the same data they stored in Kassandra for 500 terabytes. No huge ingestion rates, and what's very important, we can do an in-flight with all those cross-correlations, so, that's something that's working very well for us. >> This could help on smart mobility. Kenex 5G comes on, certainly. Intelligent edge. >> So the customers we have, these cases that we applied right now is in financial services, two or three main applications. One is tick data and analytics, everyone wants to be smarter learning on how to buy and sell stocks or manage risk, the second one is infrastructure, monitoring, critical infrastructure, monitoring is SLA monitoring is be able to monitor network devices, latencies, applications, you now, transaction rate, or that, be able to predict potential failures or escalation We have similar applications; we have about three Telco customers using it for real-time time. Series analytics are metric data, cybersecurity attacks, congestion avoidance, SLA management, and also automotive. Fleet management, file linking, they are also essentially feeding huge data sets of time series analytics. They're running cross-correlation and AI logic, so now they can generate triggers. Now apply to Hadoop. What does Hadoop have anything to do with those kinds of applications? They cannot feed huge amounts of datasets, they cannot react in real-time, doesn't store time-series efficiently. >> Hapoop (laughing) >> You said that. >> Yeah. That's good. >> One, I know we don't have a lot of time left. We're running out of time, but I want to make sure we get this out here. How are you engaging with customers? You guys got great technical support. We can vouch for the tech chops that you guys have. We seen the solution. If it's compatible to Kubernetes, certainly this is an alternative to have really great analytical infrastructure. Cloud native, goodness of your building, You do PFC's, they go to your website, and how do you engage, how do you get deals? How do people work with you? >> So because now we have a cloud service, so also we engage through the cloud. Mainly, we're going after customers and leads, or from webinars and activities on the internet, and we sort of follow-up with those customers, we know >> Direct sales? >> Direct sales, but through lead generation mechanism. Marketplace activity, Amazon, Azure, >> Partnerships with Azure and Google now. And Azure joint selling activities. They can actually resale and get compensated. Our solution is an edge for Azure. Working on similar solution for Google. Very focused on retailers. That's the current market focus of since you think about stores that have a single supermarket will have more than a 1,000 cameras. Okay, just because they're monitoring shelves in real-time, think about Amazon go, kind of replication. Real-time inventory management. You cannot push a 1,000 camera feeds into the cloud. In order to analyze it then decide on inventory level. Proactive action, so, those are the kind of applications. >> So bigger deals, you've had some big deals. >> Yes, we're really not a raspberry pie-kind of solution. That's where the bigger customers >> Got it. Yaron, thank you so much. The CTO of Iguazio Check him out. It's actually been great commentary. The Hadoop versus Kubernetes narrative. Love to explore that further with you. Stay with us for more coverage after this short break. We're live in day 2 of CUBE NYC. Par Strata, Hadoop Strata, Hadoop World. CUBE Hadoop World, whatever you want to call it. It's all because of the data. We'll bring it to ya. Stay with us for more after this short break. (upbeat music)

(lively peaceful music) >> Live, from New York, it's The Cube. Covering "The Cube New York City 2018." Brought to you by SiliconANGLE Media and its ecosystem partners. >> Well, everyone, welcome back to The Cube special conversation here in New York City. We're live for Cube NYC. This is our ninth year covering the big data ecosystem, now evolved into AI, machine learning, cloud. All things data in conjunction with Strata Conference, which is going on right around the corner. This is the Cube studio. I'm John Furrier. Dave Vellante. Our next guest is Mick Hollison, who is the CMO, Chief Marketing Officer, of Cloudera. Welcome to The Cube, thanks for joining us. >> Thanks for having me. >> So Cloudera, obviously we love Cloudera. Cube started in Cloudera's office, (laughing) everyone in our community knows that. I keep, keep saying it all the time. But we're so proud to have the honor of working with Cloudera over the years. And, uh, the thing that's interesting though is that the new building in Palo Alto is right in front of the old building where the first Palo Alto office was. So, a lot of success. You have a billboard in the airport. Amr Awadallah is saying, hey, it's a milestone. You're in the airport. But your business is changing. You're reaching new audiences. You have, you're public. You guys are growing up fast. All the data is out there. Tom's doing a great job. But, the business side is changing. Data is everywhere, it's a big, hardcore enterprise conversation. Give us the update, what's new with Cloudera. >> Yeah. Thanks very much for having me again. It's, it's a delight. I've been with the company for about two years now, so I'm officially part of the problem now. (chuckling) It's been a, it's been a great journey thus far. And really the first order of business when I arrived at the company was, like, welcome aboard. We're going public. Time to dig into the S-1 and reimagine who Cloudera is going to be five, ten years out from now. And we spent a good deal of time, about three or four months, actually crafting what turned out to be just 38 total words and kind of a vision and mission statement. But the, the most central to those was what we were trying to build. And it was a modern platform for machine learning analytics in the cloud. And, each of those words, when you unpack them a little bit, are very, very important. And this week, at Strata, we're really happy on the modern platform side. We just released Cloudera Enterprise Six. It's the biggest release in the history of the company. There are now over 30 open-source projects embedded into this, something that Amr and Mike could have never imagined back in the day when it was just a couple of projects. So, a very very large and meaningful update to the platform. The next piece is machine learning, and Hilary Mason will be giving the kickoff tomorrow, and she's probably forgotten more about ML and AI than somebody like me will ever know. But she's going to give the audience an update on what we're doing in that space. But, the foundation of having that data management platform, is absolutely fundamental and necessary to do good machine learning. Without good data, without good data management, you can't do good ML or AI. Sounds sort of simple but very true. And then the last thing that we'll be announcing this week, is around the analytics space. So, on the analytic side, we announced Cloudera Data Warehouse and Altus Data Warehouse, which is a PaaS flavor of our new data warehouse offering. And last, but certainly not least, is just the "optimize for the cloud" bit. So, everything that we're doing is optimized not just around a single cloud but around multi-cloud, hybrid-cloud, and really trying to bridge that gap for enterprises and what they're doing today. So, it's a new Cloudera to say the very least, but it's all still based on that core foundation and platform that, you got to know it, with very early on. >> And you guys have operating history too, so it's not like it's a pivot for Cloudera. I know for a fact that you guys had very large-scale customers, both with three letter, letters in them, the government, as well as just commercial. So, that's cool. Question I want to ask you is, as the conversation changes from, how many clusters do I have, how am I storing the data, to what problems am I solving because of the enterprises. There's a lot of hard things that enterprises want. They want compliance, all these, you know things that have either legacy. You guys work on those technical products. But, at the end of the day, they want the outcomes, they want to solve some problems. And data is clearly an opportunity and a challenge for large enterprises. What problems are you guys going after, these large enterprises in this modern platform? What are the core problems that you guys knock down? >> Yeah, absolutely. It's a great question. And we sort of categorize the way we think about addressing business problems into three broad categories. We use the terms grow, connect, and protect. So, in the "grow" sense, we help companies build or find new revenue streams. And, this is an amazing part of our business. You see it in everything from doing analytics on clickstreams and helping people understand what's happening with their web visitors and the like, all the way through to people standing up entirely new businesses based simply on their data. One large insurance provider that is a customer of ours, as an example, has taken on the challenge and asked us to engage with them on building really, effectively, insurance as a service. So, think of it as data-driven insurance rates that are gauged based on your driving behaviors in real time. So no longer simply just using demographics as the way that you determine, you know, all 18-year old young men are poor drivers. As it turns out, with actual data you can find out there's some excellent 18 year olds. >> Telematic, not demographics! >> Yeah, yeah, yeah, exactly! >> That Tesla don't connect to the >> Exactly! And Parents will love this, love this as well, I think. So they can find out exactly how their kids are really behaving by the way. >> They're going to know I rolled through the stop signs in Palo Alto. (laughing) My rates just went up. >> Exactly, exactly. So, so helping people grow new businesses based on their data. The second piece is "Connect". This is not just simply connecting devices, but that's a big part of it, so the IOT world is a big engine for us there. One of our favorite customer stories is a company called Komatsu. It's a mining manufacturer. Think of it as the ones that make those, just massive mines that are, that are all over the world. They're particularly big in Australia. And, this is equipment that, when you leave it sit somewhere, because it doesn't work, it actually starts to sink into the earth. So, being able to do predictive maintenance on that level and type and expense of equipment is very valuable to a company like Komatsu. We're helping them do that. So that's the "Connect" piece. And last is "Protect". Since data is in fact the new oil, the most valuable resource on earth, you really need to be able to protect it. Whether that's from a cyber security threat or it's just meeting compliance and regulations that are put in place by governments. Certainly GDPR is got a lot of people thinking very differently about their data management strategies. So we're helping a number of companies in that space as well. So that's how we kind of categorize what we're doing. >> So Mick, I wonder if you could address how that's all affected the ecosystem. I mean, one of the misconceptions early on was that Hadoop, Big Data, is going to kill the enterprise data warehouse. NoSQL is going to knock out Oracle. And, Mike has always said, "No, we are incremental". And people are like, "Yeah, right". But that's really, what's happened here. >> Yes. >> EDW was a fundamental component of your big data strategies. As Amr used to say, you know, SQL is the killer app for, for big data. (chuckling) So all those data sources that have been integrated. So you kind of fast forward to today, you talked about IOT and The Edge. You guys have announced, you know, your own data warehouse and platform as a service. So you see this embracing in this hybrid world emerging. How has that affected the evolution of your ecosystem? >> Yeah, it's definitely evolved considerably. So, I think I'd give you a couple of specific areas. So, clearly we've been quite successful in large enterprises, so the big SI type of vendors want a, want a piece of that action these days. And they're, they're much more engaged than they were early days, when they weren't so sure all of this was real. >> I always say, they like to eat at the trough and then the trough is full, so they dive right in. (all laughing) They're definitely very engaged, and they built big data practices and distinctive analytics practices as well. Beyond that, sort of the developer community has also begun to shift. And it's shifted from simply people that could spell, you know, Hive or could spell Kafka and all of the various projects that are involved. And it is elevated, in particular into a data science community. So one of additional communities that we sort of brought on board with what we're doing, not just with the engine and SPARK, but also with tools for data scientists like Cloudera Data Science Workbench, has added that element to the community that really wasn't a part of it, historically. So that's been a nice add on. And then last, but certainly not least, are the cloud providers. And like everybody, they're, those are complicated relationships because on the one hand, they're incredibly valuable partners to it, certainly both Microsoft and Amazon are critical partners for Cloudera, at the same time, they've got competitive offerings. So, like most successful software companies there's a lot of coopetition to contend with that also wasn't there just a few years ago when we didn't have cloud offerings, and they didn't have, you know, data warehouse in the cloud offerings. But, those are things that have sort of impacted the ecosystem. >> So, I've got to ask you a marketing question, since you're the CMO. By the way, great message UL. I like the, the "grow, connect, protect." I think that's really easy to understand. >> Thank you. >> And the other one was modern. The phrase, say the phrase again. >> Yeah. It's the "Cloudera builds the modern platform for machine learning analytics optimized for the cloud." >> Very tight mission statement. Question on the name. Cloudera. >> Mmhmm. >> It's spelled, it's actually cloud with ERA in the letters, so "the cloud era." People use that term all the time. We're living in the cloud era. >> Yes. >> Cloud-native is the hottest market right now in the Linux foundation. The CNCF has over two hundred and forty members and growing. Cloud-native clearly has indicated that the new, modern developers here in the renaissance of software development, in general, enterprises want more developers. (laughs) Not that you want to be against developers, because, clearly, they're going to hire developers. >> Absolutely. >> And you're going to enable that. And then you've got the, obviously, cloud-native on-premise dynamic. Hybrid cloud and multi-cloud. So is there plans to think about that cloud era, is it a cloud positioning? You see cloud certainly important in what you guys do, because the cloud creates more compute, more capabilities to move data around. >> Sure. >> And (laughs) process it. And make it, make machine learning go faster, which gives more data, more AI capabilities, >> It's the flywheel you and I were discussing. >> It's the flywheel of, what's the innovation sandwich, Dave? You know? (laughs) >> A little bit of data, a little bit of machine itelligence, in the cloud. >> So, the innovation's in play. >> Yeah, Absolutely. >> Positioning around Cloud. How are you looking at that? >> Yeah. So, it's a fascinating story. You were with us in the earliest days, so you know that the original architecture of everything that we built was intended to be run in the public cloud. It turns out, in 2008, there were exactly zero customers that wanted all of their data in a public cloud environment. So the company actually pivoted and re-architected the original design of the offerings to work on-prim. And, no sooner did we do that, then it was time to re-architect it yet again. And we are right in the midst of doing that. So, we really have offerings that span the whole gamut. If you want to just pick up you whole current Cloudera environment in an infrastructure as a service model, we offer something called Altus Director that allows you to do that. Just pick up the entire environment, step it up onto AWUS, or Microsoft Azure, and off you go. If you want the convenience and the elasticity and the ease of use of a true platform as a service, just this past week we announced Altus Data Warehouse, which is a platform as a service kind of a model. For data warehousing, we have the data engineering module for Altus as well. Last, but not least, is everybody's not going to sign up for just one cloud vendor. So we're big believers in multi-cloud. And that's why we support the major cloud vendors that are out there. And, in addition to that, it's going to be a hybrid world for as far out as we can see it. People are going to have certain workloads that, either for economics or for security reasons, they're going to continue to want to run in-house. And they're going to have other workloads, certainly more transient workloads, and I think ML and data science will fall into this camp, that the public cloud's going to make a great deal of sense. And, allowing companies to bridge that gap while maintaining one security compliance and management model, something we call a Shared Data Experience, is really our core differentiator as a business. That's at the very core of what we do. >> Classic cloud workload experience that you're bringing, whether it's on-prim or whatever cloud. >> That's right. >> Cloud is an operating environment for you guys. You look at it just as >> The delivery mechanism. In effect. Awesome. All right, future for Cloudera. What can you share with us. I know you're a public company. Can't say any forward-looking statements. Got to do all those disclaimers. But for customers, what's the, what's the North Star for Cloudera? You mentioned going after a much more hardcore enterprise. >> Yes. >> That's clear. What's the North Star for you guys when you talk to customers? What's the big pitch? >> Yeah. I think there's a, there's a couple of really interesting things that we learned about our business over the course of the past six, nine months or so here. One, was that the greatest need for our offerings is in very, very large and complex enterprises. They have the most data, not surprisingly. And they have the most business gain to be had from leveraging that data. So we narrowed our focus. We have now identified approximately five thousand global customers, so think of it as kind of Fortune or Forbes 5000. That is our sole focus. So, we are entirely focused on that end of the market. Within that market, there are certain industries that we play particularly well in. We're incredibly well-positioned in financial services. Very well-positioned in healthcare and telecommunications. Any regulated industry, that really cares about how they govern and maintain their data, is really the great target audience for us. And so, that continues to be the focus for the business. And we're really excited about that narrowing of focus and what opportunities that's going to build for us. To not just land new customers, but more to expand our existing ones into a broader and broader set of use cases. >> And data is coming down faster. There's more data growth than ever seen before. It's never stopping.. It's only going to get worse. >> We love it. >> Bring it on. >> Any way you look at it, it's getting worse or better. Mick, thanks for spending the time. I know you're super busy with the event going on. Congratulations on the success, and the focus, and the positioning. Appreciate it. Thanks for coming on The Cube. >> Absolutely. Thank you gentlemen. It was a pleasure. >> We are Cube NYC. This is our ninth year doing all action. Everything that's going on in the data world now is horizontally scaling across all aspects of the company, the society, as we know. It's super important, and this is what we're talking about here in New York. This is The Cube, and John Furrier. Dave Vellante. Be back with more after this short break. Stay with us for more coverage from New York City. (upbeat music)