(upbeat music) >> Welcome back to "theCube's" coverage of Mobile World Congress 2021. We're here in person and remote. This is a physical and virtual. It's a hybrid event, and "theCube's" got wall-to-wall coverage. I'm John Furrier, your host of "theCube." We've got a great guest here, Adolfo Hernandez, Vice-President, Global Telco Business Unit for Amazon Web Services, AWS. Adolfo, thank you for coming on remotely for this virtual hybrid Mobile World Congress. >> Thanks for having me, John, exciting. >> You have an impressive background in telecom industry. Over the years the technology industry has been great innovation. We've seen, I mean, how many Gs have been we've gone through, but I remember the days when wifi wasn't even around. So (laughing) You got a complete change in the past couple decades. This year, more than ever with the pandemic coming through this, you're starting to see some clear visibility on the trends, and also, this is the first Mobile World Congress in person since 2019, so a lot has changed. What is your view on the marketplace, and what is your message you're telling the telecom industry from Amazon's and your perspective? What do you see? >> Yeah, you're absolutely right, John. This is a fascinating time to be on the cloud, to be at Mobile World Congress. I remember Mobile World Congress 2020 was the first event that actually got canceled. So that was the beginning of the pandemic. And now, here we are, a year and a bit later, working with the leading telecommunications operators with the leading telecommunication sides based on solution providers and what better place that would be in doing that with AWS in this very transformational time in this space. We are supporting telecom operators around the world, as they reinvent communications in many different ways. This is not just one more G, we are definitely transforming the industry. Like any industry, we see telecom operators having to get simplification on their operations and transforming the IT side of the house. So they've go the internal IT, that needs a big transformation, they also got the network IT, everything related with OSS and BSS, and they need to migrate that to the cloud. And we've got a lot of experience by doing that with telcos around the world, to really help them accelerate that journey to the cloud. And we can help them with data center consolidation, migrations and a number of things. So we've got examples like GiffGaff, which is one of the largest MVNOs, and one of the first ones in Europe to go all in on the AWS cloud and they move all the data and the heart of the business there. So once you're sort of dealing with the network, the IT transformation, then you've got to go and look at how do you reinvent and accelerate the delivery of 5G connectivity? Well, that's very current as we're doing now. And we really want to help them because when they accelerate to the cloud, they get more flexibility, they get more agility, they get more cost effectiveness. And if you think about how traditional telco networks were built, where you have to provision a lot of systems you have to provision a lot on the base stations, and then you needed to provision a lot of systems on the Ram side, and then you needed to put aggregation centers, traffic centers, and then you would have the headquarters, and then you would have all the network functions, going from the radio all the way into the center. All of the systems needed to be provision for peak capacity. They sort of famous Mother's Day moment. As you move to the cloud, you can provision on the different parts of the cloud, you can provision on the AWS Outpost, you can provision on locals phone, you can provision on regions, and you leverage right away the experience that we've got on all of our infrastructure, reducing costs, getting a lot of flexibility and being able to embark, just and consume what you need. And, an example of that, it's been a Telefonica Vivo in Brazil. We talked about that a couple of weeks ago, and they've accelerated their move by deploying a 5G standalone cloud native platform. And that gives them a lot of automation capabilities. It gives them faster CI/CD/CT. So really cool stuff that you couldn't do in the old ways of building networks- >> It's interesting you mentioned CI/CD pipeline and developers. To me that's what comes to my mind when I think of AWS, the enablement of developers, now the enterprise. Now you've got the telco cloud and Amazon is not known for being a 5G player, but you guys are enabling a lot of 5G. Could you address that question? How is Amazon web services enabling 5G? What's your answer to that? >> So first of all, I have to say that 5G is an absolutely great example that this is a lot about moving to the cloud. 5G is cloud native, it's cloud friendly. You can virtualize pretty much every function. You can separate every function from the hardware and the software move everything to the cloud. And that is really lending itself to move to a cloud delivery model. As we were talking about earlier, we are enabling people to go and take the AWS infrastructure like AWS Outpost and bringing all the AWS infrastructure, all the services, all the APIs and all the tools that you have on AWS, virtually to any single location. And that allows you to really deploy themes like thousands of cell sites across a run, you couldn't do that before. On the AWS local zones, you can take everything that compute storage databases and a lot of different services. And those are perfect for large metro areas where you need to do a lot of network traffic aggregation, and this makes them really good to deploy in parts of the network core. Again, that's another re-innovation. And then you can look at then the regions and the regions have everything that you need from a compute storage and services perspective. And that those are really well suited for BSS for OSS to keeping the network running and to do all of that. And you can do that today, leveraging existing infrastructure. You don't have to acquire that, you don't have to provision, that you don't have to provision for the peak capacity and then you don't have to install and manage, and I think that's a serious breakthrough for the industry. >> Okay, so let me just capture that, 'cause I heard a bunch of things that I really like, cloud native 5G. What does cloud native 5G mean for the telco industry specifically? >> Well, I think if I had to put it down to one thing, it's just about making it really easy to roll out. And it's about being able to deploy easily to automate easily, so you can free up investment and you can free up resources and you can free up overhead. You can really start taking advantage of all that flexibility and scalability and automation that you get with the cloud and you apply that to a network, and that is the very first time we're able to do that in wireless. And it's just going to give you a lot of advantages. Look at Dish. We made this announcement with Dish that they are moving with one of the industry first 5G cloud native networks out there. Look at the example I talked about earlier, Telefonica Vivo, we're doing that 5G standalone solution. So you're going to be seeing, this is just the beginning, but this is going to be not the end because there's a lot of interest in getting these benefits. >> I saw the Dave Brown announcement with Dish a while back just recently. So I want to ask you, does Graviton processors play a role on the Dish deal? Do you mind answering that? If you comment on that? >> Yeah, I think you might remember Dave Brown being very proud of everything that Graviton2 processors can do in terms of increase in the price performance, helping telco operators, not only with the price performance factor, but also with the energy equation. So it's just really exciting to have that differentiation and being able to deliver that innovation and that value to telco operators in a cloud native 5G network. >> I got to ask you about some of the open source and cloud scale things coming together. That's a big trend I'm seeing here at Mobile World Congress. Openness, multi-vendor, scaling up quickly, provisioning stuff fast and easy, leveraging existing technologies and of course, developer friendly. So with that, I got to ask you, what's all the big deal about with this Open RAN. Obviously radios are key and wireless. What does Open RAN mean? Can you take us through, what's the importance of this? >> Yeah, Open RAN is an industry wide or mostly industry-wide initiative to look into effectively trying to apply some of these open and sharing models to the RAN. You've got vendors and you've got telco operators participating. But what we do and you know as well John, 'cause you've been working with AWS for a while, you know, that we're very customer focused, and 90% of what we do is what we hear that they are trying to solve because it's the things that matter to them. So what we engage with them, what we engage with somebody like Dish, and they tell us that they are interested in Open RAN, we will go and partner with the right partners who can provide the right solution to deliver on that Open RAN. And you've seen we signed agreements with the likes of Nokia to do research and solutions on cloud RAN. You also saw a couple of weeks ago, we did another collaboration announcement with Mavenir, to deliver not only cloud run, but I said of 5G solutions like IMS, the 4G 5G converge packet, or messaging and others. So we are engaging with the complete ecosystem on our customer's behalf to deliver whatever thereafter, and Open RAN is one of these topics and that we're delivering to operators like Deutsche and others in the market. >> Do you think that this new shift with cloud is going to increase the surface area? 'Cause that to me is the big theme I'm seeing what this new shift, as we look at, even telco cloud and the Edge, it's the classic surface area. And this is well known in the security world, but the there's no perimeter anymore. The surface area for security is everywhere. So things have changed. But telco just seems like the edge is expanding, you got satellite, you got space, you got more 5G, more commercial, so much more surface area. What's the impact going to be to the industry and to applications? >> Well, I think what we're seeing is 5G comes out there because there is a need for more data, more bandwidth obviously increased security, new standards, but there is also about latency, latency reduction. And I think that's really going to change the paradigm as we inject these increased responsiveness, these low latency, closer to the edge, and we bring the applications and we bring the compute and we bring storage as we do with wavelength right through to the edge as we are doing with Verizon, Vodafone, KDDI, SK Telecom and operators around the world. This is going to enable a number of transformational use cases for society, whether they are in virtual reality, whether they are with autonomous driving, whether it's about automating and getting more intelligence into manufacturing processes, there is just so much potential to transform society. And it all comes back with these sort of new 5G and some of the themes that enables moving closer to the edge. So as I said, really interesting times. >> Adolfo Hernandez, Vice President of Global Telco Business Unit with Amazon Web Services. Thanks for the great insight here on "theCube" for our Mobile World Congress coverage. Really, really great insight. Thanks so much. >> Thanks, John, delighted to be here. >> If you don't mind, I'd like to just quickly shift gears to something while I got you here on the industry. Adolfo you're very well known in the industry for someone who knows how to turn things around. You've done that in the past. You've been part of growth companies, you've been part of companies that have refocused. Telco has been a big change over people looking at this new opportunity as a growth opportunity. And people are looking at divesting some non-critical divisions and looking at acquisitions. I mean the private equity's on fire right now, and you're starting to see a lot more formation because there's more visibility into territory to take, there's more opportunities to be had. So there's more potential revenue than there is you can do on the cost cutting side. So everyone I talked to who's been in the industry has got their eyes are really popping out of their head, they're saying there's more opportunities if we can reconfigure our resources to take advantage of cloud. You're an expert in this area. For the folks out there who are in the boardrooms, cranking away thinking through how to organize for the cloud scale, what would be your advice to those teams? >> Well, I mean, there's a lot of insight to be had from the experience that AWS we've gained through the years, of doing this IT. And you definitely have to get a top down vision. Obviously it's really got to start at the C-suite, is moving to the cloud for what it bring. Either faster pace of innovation, the cost reduction, the agility. And that's you've got to be thinking about going to the cloud top down. Then the next thing you've got to go and say, "Okay, what are the parts of my operation "that I can go after with cloud? "Where do I start? "Do I start with the IT applications? "Do I start with some new go-to market initiatives? "Do I start by infusing some machine learning capabilities "into existing operations? "Do I start by building a data links "that I can go and monetize, "or I can go on and use to generate "best at customer service, "or I can go and fundamentally transform my networks?" Now, every telco's going to start in in different place, but I would say is you've got to start looking at that agility, that faster innovation, that better use of resources that cloud brings to telco for the very first time in a time in, in decades. And then if you're going to do that, I would strongly recommend people to talk to the provider that's got the capabilities, the broader set of services, the deepest set of services, and the most relevant experience to do that, 'cause we've been doing that in IT, and we've been working on telcos now for five plus years. And we've got pretty much every relationship. And as you know, John, this is really important. In telco you depend on collaborations on ISBs on software vendors, and every vendor out there, every software company out there will develop certainly on AWS. So we would be delighted to engage with them and help them move forward. >> Yeah, and Andy Jassy the CEO of AWS last year at re:Invent really made that the hallmark of his keynote around get those teams together, the executives top-down be a builder, think like a builder. McKinsey just put out a report, trillion dollar opportunities that no one sees yet that's coming. So a lot of emphasis on revenue, new revenue opportunities that are coming. And certainly this has been something that telcos been looking for for a long time. So great opportunity and thank you for sharing your insight. Appreciate it. >> Thanks, John. >> Okay this is "theCube's" coverage of ABS Mobile World Congress, 2021, I'm John Furrier, your host. Thanks for watching.

(upbeat music) >> From ''theCUBE studios,'' in Palo Alto, in Boston, connecting with thought leaders all around the world. This is ''theCUBE Conversation.'' >> Hi, I'm John Walls. And as we're all aware, technology continues to evolve these days at an incredible pace and it's changing the way industries are doing their business all over the world and that's certainly true in telecommunications, CSPs all around the globe are developing plans on how to leverage the power of 5G technology and their network operations are certainly central to that mission. That is the genesis of ''IBM's Cloud for Telecommunications Service.'' That's a unified open hybrid architecture, that was recently launched and was developed to provide telecoms with the solutions they need to meet their very unique network demands and needs. I want us to talk more about that. I'm joined by Steve Canepa, who is the Global GM and Managing Director of the communication sector at IBM. Steve, good to see you today. >> Yeah, you too, John. >> And Jeffrey Hammond. So, he's the Principal Analyst and Vice President at Forrester. Jeffrey, thank you for your time as well today. Good to see you. >> Thanks a lot. It's great to be here. >> Yeah, Steve, let's just jump right in. First off, I mean, to me, the overarching question is, why telecom, I know that IBM has been very focused on providing these kinds of industries specific services, you've done very well in finance, now you're shifting over to telecom. What was the driver there? >> First, great to be with you today, John, and, you know, if we look at the marketplace, especially in 2020, I think the one thing that's, everyone can agree with, is that the rate and pace of change is just really accelerating and is a very, very dynamic marketplace. And so, if we look at the way both our personal lives are now guided by connectivity, and the use of multiple devices throughout the day, the same with our professional lives. So, connectivity really sits at the heart of how value and solutions are delivered and for businesses, this is becoming a critical issue. So, as we work with the telecommunication providers around the world, we're helping them transform their business to make it much more agile, to make it open and make them deliver new services much more quickly and to engage digitally with their clients to bring that kind of experience that we all expect now, so, that the rate pace of change, and the need for the telecommunications industry to bring new value, is really driving a tremendous opportunity for us to work with them. >> Jeffrey what's happening in the telecom space? That, I mean, these aren't just small trends, right? These are tectonic shifts that are going on in terms of their new capabilities and their needs. I'm sure this digital transformation has been driven in some part by COVID, but there are other forces going on here, I would assume too. What do you see from your analyst seat? >> Yeah, I look at it, you know, from a glass half full and a glass half empty approach. From a half empty approach, the shifts to remote work and remote learning, and from traditional retail channels, brick and mortar channels to digital ones, have really put a strain on the existing networking infrastructure, especially, at the Edge, but they've also demonstrated just how critical it is to get that right. You know, as an example, I'm actually talking to you today over my hotspot on my iPhone. So, I think a lot more about the performance of my local cell tower now than I ever did a year ago. and I want it to be as good as it can possibly be and give me as many capabilities as it can. From a glass half full perspective, the opportunities that a modernized network infrastructure gives us are, I think, more readily apparent than ever, you know, most of my wife's doctor's appointments have shifted to remote appointments and every time she calls up to connect, I kind of cringe in the other room and it's like, are they going to get video working? Are they going to get audio working? Are they actually going to have to shift to an old-style phone call to make this happen? Well, things like 5G really are poised to solve those kinds of challenges. They promise, 5G promises, exponential improvements in connectivity speed, capacity, and reductions in latency that are going to allow us to look at some really interesting workloads, IOT workloads, automation workloads, and a lot of Edge use cases. I think 5G sets the stage or Edge compute. Expanding Edge compute scenarios, make it possible to distribute data and services where businesses can best optimize their outcomes, whether it's IOT enabled assets, whether it's connected environments, whether it's personalization, whether it's rich content, AI, or even extended reality workloads. So, you might seem like, that's what a little over the horizon, but it's actually not that far away. And as companies gain the ability to manage and analyze and localize their data, and unlocks real-time insights in a way that they just haven't had before, it can drive expanded engagement and automation in close proximity to the end point devices and customers. And none of that happens without the telco providers and the infrastructure that they own being on board and providing the capabilities for developers like me to take advantage of the infrastructure that they've put in place. So, my perspective on it is, that transformation, that digital transformation, is not going to happen on its own. Someone's got to provision the infrastructure, someone's got to write the code, someone's got to get the services as close to my cell tower or to the Edge as possible and so, that's one of the reasons that when we ask decision makers in the telco space about their priorities from a business perspective, what they tell us is, one of their top three priorities is, we need to improve our ability to innovate and the other two are, we need to grow our revenue and we need to improve our product and services. What's going on from a software perspective in the telco space, is set to make all three of those possible, from my perspective. >> You know, Steve, Jeffrey just unpacked an awful lot there, did a really nice job of that. So, let's talk about first off, that telco relationship IBM's had, or has. You work with data, the 10 largest communication service providers in the world, and I'm sure you're on this journey with them, right? They've been telling you about their challenges and you recognize their needs. This is, you have had maybe some specific examples of that dialogue, that has progressed as your relationship has matured and you provide a different service to them. What are they telling you? What did they tell you say, '' This is where we have got to get better. We've got to get a little sharper, a little leaner.'' And then how did IBM respond to that? >> Yeah, I mean, critical to what Jeffrey just shared is under the covers. You know, 5G is going to take five times the cost that 4G took to deploy. So, if you're a telco, you have to get much more efficient. You have to drive a much more effective TCO into cost of deploying and managing and running that network architecture. When the network becomes a software defined platform, it opens up the opportunity to use open source, open technology, and to drive a tremendous ecosystem of innovation that you can then capture that value onto that open software network. And as the Edge emerged as compute and storage and connectivity, both to the Edge as Jeffrey described, then the opportunity to deliver B2B use cases to take advantage of the latency improvements with 5G, take advantage of the bandwidth capabilities that you have moving video and AI out to the Edge, so, you can create insights as a service. These are the underlying transformations that the telcos are making right now to capture this value. And in fact, we have an institute for business value on our website. You can see some of the surveys and analysis we've done but 84% of the telco clients say, you know, '' Improving the automation and the intelligence of this network platform becomes critical.'' So, from our standpoint, we see a tremendous opportunity to create an open architecture to allow the telcos to regain control of their architecture so that they can pick the solutions and services that work best for them to create value for their customers and then allows them to deploy them incredibly quickly. In fact, just this last week, we announced a milestone with Bharti, a project that we're doing in India, already has over 300 million subscribers. We've taken their ability to deploy their run environment, one of the core domains of the network, where you actually do the access over the cell towers. We've improved that from weeks down to a few days. In fact, our objective is to get to a few minutes. Applying that kind of automation dramatically improves the kind of service they can deliver. When we talk about relationships we have with Vodafone, AT$T, Verizon, about working with them on their mobile Edge compute platforms, it will allow them to extend their network. In fact, with our cloud announcement that you highlighted at the top, we announced a capability called the IBM Cloud Satellite and what IBM Cloud Satellite does is, it's built with Red Hat, so, it's open architecture, it takes advantage of the millions and millions of upstream developers, that are developing every single day to build a foundational shift architecture that allows us to deploy these services so quickly and we can move that capability right now to the Edge. What that means for a telco, is they can deploy those services wherever they want to deploy them, on their private infrastructure or on a public cloud, on a customer's premise, that gives them the flexibility. The automation allows them to do it smartly and very quickly and then in partnering with clients, they can create new end Edge services, things like, you know, manufacturing 4.0 you may have heard of or as you mentioned, advanced healthcare services. Every single industry is going to take advantage of these changes and we're really excited about the opportunity to work in combination with the telcos and speed the pace of innovation in the market. >> Jeffrey, I'd like to go back to the Bharti there. I was going to get into it a little bit later but Steve brought it up. This major Indian CSP, as you mentioned, 300 million subs, 400 million around the world. What does that say to you in terms of its commitment and its, the needs that are being addressed and how it's going to fundamentally change the way it is doing business as far as setting the pace in the telecom industry? >> Well, I think, one of the things that highlights it is, you know, this isn't just a U.S phenomenon or a European phenomenon. Indeed, in some cases we're seeing countries outside the U.S in advance, moving faster, Switzerland, as an example. We expect 90% of the population in Germany to be covered by 5G By 2025, we expect 90% of the population in South Korea to be covered by 2026, 160 million connections in in China as well. So, in some ways, what's happening in the telco world is mirroring what has happened in the public cloud world, which is the world's gone flat. And that's great from a developer perspective because that means that I don't have to learn specialized technologies or specialized services, in order to look at these network infrastructure platforms as part of the addressable surface that I have. That's one of the things that I think has always held the larger developer population back and has kept them from taking advantage of the telco networks. Is, they've always been bit of a black box to the vast majority of developers, you know, IP goes in, IP comes out but that's about all the control I have, unless I want to go and dig deep into those, you know, industry specific specifications. I was cleaning out my office last week because I'm in the process of moving and I came across my '' IMS Explained Handbook from 2006,'' and I remember going deep into that because, you know, we were told that that's going to make it so that IT infrastructure and telco infrastructure is going to converge and it did to a little bit, but not in a way that all the developers out there could really take advantage of telco infrastructure. And then I remember the next thing was like, well, '' Java Amiens on the front end with mobile clients, that's going to make everything different and we're going to be able to build apps everywhere.'' What ended up being was we would write once and test everywhere, across all the different devices that we had to support. And you know, what really drove you equity? Was the iPhone and apps that we could use HTML like technology or that we could use Java to build and it exploded. And we got millions of applications on the front end of the network. What I see potentially happening now, is the same thing on the backend infrastructure side, because the reality is for any developer that is trying to build modern applications, that's trying to take advantage of cloud native technologies, things start with containers and specifically, OCI compliant containers. That is the basis for how we think about building services and handing them off to operators to run them for us. And with what's going on here, by building on top of OpenShift, you take that, you know, essentially de facto standard of containers as the way that we communicate on the infrastructure side globally, from a software development perspective and you make that the entry point for developers into the modern telco outcome system. And so, basically, it means that if I want to push all the way out to the Edge and I want to get as close as I possibly can, as long as I can give you a container to execute that capability, I'm well on the way to making that a reality, that's a game changer in my opinion. >> Yeah, I was on. >> Just to pick it, just if I could, just to pick up on that because I think Jeffrey made a really important point. So, it's kind of like, in a way, an auntie to the ball here is this open architecture because it empowers the entire ecosystem and it allows the telcos to take advantage of enormous innovation that's happening in the marketplace. And that's why, you know, the 35 ecosystem partners that we announced when we announced the IBM Cloud for telco, that's why they're so important because it allows you to have choice. But the other piece, which he hinted at, I wanted to just underscore, is today, in it kind of the first wave of cloud, only about 20% of the applications move to cloud. They were mostly funny digital applications. In fact, we moved our funny digital applications as well into Watson, we have over 1.5 billion customers of telcos today around the world that can access Watson, through our various chatbot and call center or an agent assist solutions we've deployed. But the 80% of applications that haven't moved yet, haven't moved because it's tough to move them, because they're mission critical, they need, you know, regulatory controls, they have to have world-class security, they need to be able to provide data sovereignty as you're operating in different countries around the world and you have to make sure that you have the data in places that you need, these are the attributes, that kind of open up the opportunity for all these other workloads to move. And those are the exact kind of capabilities that we've built into the IBM Cloud for telco, so that we can enable telcos to move their applications into this environment safely, securely, and do it, as Jeffrey described, on an open architecture that gives them that agility and flexibility. And we're seeing it happen real time, you know, I'll just give you another quick example, Vodafone India, their CTO has said publicly and moving to this cloud architecture, he sees it as a universal cloud architecture, so, they're going to run not just their internal it workloads, not just their network services, their voice data and multimedia network services workloads, but also their B2B enterprise workloads, as Jeffrey was starting to describe. Those workloads that are going to move out to the Edge. And by being able to run on a common platform, he's said publicly that they're seeing an 80% improvement in their CapEx, a 50% improvement in their OPEX, and then 90% improvement in the cost to get productions and services deployed. So, the ability to embrace this open architecture and to have the underlying capabilities and attributes in a cloud platform that responds to the specific needs of telco and enterprise workloads, we think is a really powerful combination. >> Steve, the ecosystem, Jeffrey, you brought it up as well. So, I'd like, just to give you a moment to talk about that a little bit, not a small point, by any means you have nearly 40 partners lined up in this respect, from a hardware vendor, software vendors, SAS providers. I mean, it's a pretty impressive lineup and what kind of a statement is that in your, from your perspective, that you're making to the marketplace when you bring that kind of breadth and depth, that kind of bench, basically the game? >> From our view, it's exciting, and we're only getting started. I mean, we literally have not made the announcement, just a matter of a couple of months ago, and every day that passes, we have additional partners that see the power in joining this open architecture approach that we've put in place. The reason that it delivers such values for all the players, you know, one of the hallmarks of a platform approach is that for every player that joins the platform, it brings value to all the players on the cloud. So as we build this ecosystem and we take the leverage of the open source community, and we build on the power of OpenShift and containers, as Jeffrey was saying, we're creating momentum in the marketplace and back to my very first point I made, when the market's moving really quickly, you've got to be agile. And to be agile in today's market, you have to infuse automation at scale, you have to infuse security at scale and you have to infuse intelligence at scale. And that's exactly what we can help the telcos do, and do it in partnership with these enterprise clients. Instinctively >> One of the values of that is that, you know, we're seeing the larger trend in the cloud native space of folks that used to build packaged software services, is essentially taking advantage of these architectural capabilities and containerizing their applications as part of their future strategy. I mean, just two weeks ago, Salesforce basically said, we're reinvisioning Salesforce as a set of containerized workloads that we deliver, SAP is going in very much the same direction. So as you think about these business workloads, where you get data coming from the infrastructure and you want to go all the way back to the back office and you want to make sure that data gets updated in your supply chain management system, being able to do that with a consistent architecture makes these integration challenges just an order of magnitude easier. I actually want to drill in on that data point for a minute because I think that that's also key to understanding what's going on here, because, you know, during the early days of the public cloud and even WebDuo before that, one of the things that drove WebDuo was the idea that data is the new Intel inside and in some ways that was around centralized data because we had 40 or 50 years to get all the data into the data centers and into the, and then put it in the public cloud. But that's not what is happening today. So much of the new data is actually originating at the Edge and increasingly it needs to stay at the Edge if for no other reason than to make sure that the folks that are trying to use it well aren't running up huge ingestion costs, trying to move it all back to the public cloud providers, analyze it and then push it back out and do that within the realm of the laws of physics. So, you know, one of the big things that's driving the Edge is, in the move toward the Edge, and the interest in 5G is that allows us to do more with data where the data originates. So, as an example, a manufacturer that I've been working with that basically came across exactly that problem, as they stood up more and more connected devices, they were seeing their data ingestion volume spiking and kind of running ahead of their budgets for data ingestion but they were like, well, we can't just leave this data and discard it at the Edge, because what happens if it turns out to be valuable for the maintenance, preventative maintenance use cases that we want to run, or for the machine wear characteristics that we want to run. So, we need to find a way to get our models out close to the data so we don't have to bring it all back to the core. In retailing, personalization is something that a lot of folks are looking at right now and even clientelling and that's, again, another situation where you want to get the data close to where the customer actually lives from a geographic basis and into the hands of the person that's in the store but you don't want to necessarily have to go and install a lot of complex hardware in the retail outlet because then somebody has to manage, you know, those servers and manage all those capabilities. So, you know, in the case of the retailer that I was working with, what they wanted was to get that capability as close as possible to the store, but no closer. And the idea of essentially a virtual back office that they could stand up whenever they opened up a new retail outlet, or even had a franchisee open up an outlet, was an extremely powerful concept and that's the kind of thing that you can do when you're saying,'' Well it's just a set of containers and if I have a, you know, essentially a control plane that I deploy it to, then I can do that on top of that telco provider that they sign up to be a strategic services provider.'' There are lots of other interesting scenarios, tourism, if you think about, you know, the tourist economies that we have around the world and the data that, you know, mobile devices throw off that let us get anonymized information about who's coming, where they're going, what they're spending, how long they're staying, there's a huge set of data there that you can use to grow revenue. You know, other types of use cases, transportation? We see, you know, municipal governments kind of looking at how they can use anonymized data around commute patterns to impact their planning. That's all data that's coming from the the telco infrastructure. >> You know, when we're talking about these massive advantages, right, as this hybrid cloud approach about skill ones, build one's, easy management, efficient management, all of these things, Steve, I think we almost, we'd be derelict to duty if we didn't talk about security a little bit. Just ultimately at the end of the day, you've got to provide this as you pointed out, world-class secure environment. And so, in terms of the hybrid approach, what kind of considerations do you have to make that are special to that and that are being deployed and have been considered >> You know, that's a great point. One of the benefits to Comms from moving to an open architecture, is that you componentize the framework of that architecture, and you have suppliers supplying applications for the various different services that we just talked through. And the ability then to integrate security is essentially a foundational element to the entire Premack architecture. We've stayed very compliant with the Nanci framework architecture and the way that we've worked with the telcos and bringing forth a solution, because we specifically want them to have the choice but how is that choice being married with the kind of security you just talked about. And to Jeffrey's point, you know, when you move those applications out to the Edge and that data, you know, many of the analysts are saying now by 2025, as much as 75% of the data created in the world will happen at the Edge. So, this is a massive shift. And when that shift occurs, you have to have the security to make sure that you're going to take care of that data in the way that it should be and that meets all regulatory, you know, governance already rules and regulations. So, that becomes really critical. The other piece though, is just the amount of value that gets created. The reason that data is at the Edge is because now you can act on it at the Edge, you can extract insights and in fact, most of the analysts will say,'' In the next three years, we'll see $675 billion of new value created at the Edge with these kinds of applications.'' And going back on the manufacturing example, I mean, we're already working today with manufacturers and they already had, you know, hundreds of IOT sensors deployed in the factory and we have an Edge application manager that extends right out to the far Edge, if you will, right out onto that factory floor to help get intelligence from those devices. But now think about adding to that the AI capabilities, the video capabilities, watching that manufacturing line to make sure every product that comes off that line is absolutely perfect, Watching the employees to make sure they're staying in safety zones, you know, watching the actual equipment itself to make sure it is performing the way it's supposed to, maybe using an analytics and AI capabilities to predict, you know, issues that might arise before they even happen, so you can take preventative action. This kind of intelligence, you know, makes the business run smarter, faster, more effective. So, that's where we see tremendous service. So, it's not just the fact that data will be created and it will be higher fidelity data to include the analytics, AI, you don't include unstructured data like video data and image data, audio data, but the ability to then extract insights and value out of it. And this is why we believe the ecosystem we talked about earlier, our partnership with the telco's and the ability to bring ecosystem partners and they can add value is just a tremendous momentum that we're going to build. >> Well, the market opportunity is certainly great. As you pointed out, a lot of additional value yet to be created, significant value and obviously, a lot of money to be spent as well by telcos, by some estimates, a hundred billion plus, just by the year 2022 and getting this new software defined platforms up and running. So, congratulations to IBM for this launch and we wish you continued success, Steve, in that endeavor and thank you for your time and Jeffrey, thank you as well for your insights from Forester. >> Always a pleasure. (upbeat music)

>> (upbeat music) [Narrator] From around the globe, it's theCUBE with digital coverage of AnsibleFest 2020, brought to you by Red Hat. >> Hello welcome back to theCUBE virtual coverage of AnsibleFest 2020 Virtual. We're not face to face this year. I'm John Furrier, your host. We're bringing it together remotely. We're in the Palo Alto Studios with theCUBE and we're going remote for our guests this year. And I hope you can come together online enjoy the content. Of course, go check out the events site on Demand Live. And certainly I have a lot of great content. I've got a great guest Skyla Loomis Vice president, for the Z Application Platform at IBM. Also known as IBM Z talking Mainframe. Skyla, thanks for coming on theCUBE Appreciate it. >> Thank you for having me. So, you know, I've talked many conversations about the Mainframe of being relevant and valuable in context to cloud and cloud native because if it's got a workload you've got containers and all this good stuff, you can still run anything on anything these days. By integrating it in with all this great glue layer, lack of a better word or oversimplifying it, you know, things going on. So it's really kind of cool. Plus Walter Bentley in my previous interview was talking about the success of Ansible, and IBM working together on a really killer implementation. So I want to get into that, but before that let's get into IBM Z. How did you start working with IBM Z? What's your role there? >> Yeah, so I actually just got started with Z about four years ago. I spent most of my career actually on the distributed platform, largely with data and analytics, the analytics area databases and both On-premise and Public Cloud. But I always considered myself a friend to Z. So in many of the areas that I'd worked on, we'd, I had offerings where we'd enabled it to work with COS or Linux on Z. And then I had this opportunity come up where I was able to take on the role of leading some of our really core runtimes and databases on the Z platform, IMS and z/TPF. And then recently just expanded my scope to take on CICS and a number of our other offerings related to those kind of in this whole application platform space. And I was really excited because just of how important these runtimes and this platform is to the world,really. You know, our power is two thirds of our fortune 100 clients across banking and insurance. And it's you know, some of the most powerful transaction platforms in the world. You know doing hundreds of billions of transactions a day. And you know, just something that's really exciting to be a part of and everything that it does for us. >> It's funny how distributed systems and distributed computing really enable more longevity of everything. And now with cloud, you've got new capabilities. So it's super excited. We're seeing that a big theme at AnsibleFest this idea of connecting, making things easier you know, talk about distributed computing. The cloud is one big distribute computer. So everything's kind of playing together. You have a panel discussion at AnsibleFest Virtual. Could you talk about what your topic is and share, what was some of the content in there? Content being, content as in your presentation? Not content. (laughs) >> Absolutely. Yeah, so I had the opportunity to co-host a panel with a couple of our clients. So we had Phil Allison from Black Knight and Pat Lane from Allstate and they were really joining us and talking about their experience now starting to use Ansible to manage to z/OS. So we just actually launched some content collections and helping to enable and accelerate, client's use of using Ansible to manage to z/OS back in March of this year. And we've just seen tremendous client uptake in this. And these are a couple of clients who've been working with us and, you know, getting started on the journey of now using Ansible with Z they're both you know, have it in the enterprise already working with Ansible on other platforms. And, you know, we got to talk with them about how they're bringing it into Z. What use cases they're looking at, the type of culture change, that it drives for their teams as they embark on this journey and you know where they see it going for them in the future. >> You know, this is one of the hot items this year. I know that events virtual so has a lot of content flowing around and sessions, but collections is the top story. A lot of people talking collections, collections collections, you know, integration and partnering. It hits so many things but specifically, I like this use case because you're talking about real business value. And I want to ask you specifically when you were in that use case with Ansible and Z. People are excited, it seems like it's working well. Can you talk about what problems that it solves? I mean, what was some of the drivers behind it? What were some of the results? Could you give some insight into, you know, was it a pain point? Was it an enabler? Can you just share why that was getting people are getting excited about this? >> Yeah well, certainly automation on Z, is not new, you know there's decades worth of, of automation on the platform but it's all often proprietary, you know, or bundled up like individual teams or individual people on teams have specific assets, right. That they've built and it's not shared. And it's certainly not consistent with the rest of the enterprise. And, you know, more and more, you're kind of talking about hybrid cloud. You know, we're seeing that, you know an application is not isolated to a single platform anymore right. It really expands. And so being able to leverage this common open platform to be able to manage Z in the same way that you manage the entire rest of your enterprise, whether that's Linux or Windows or network or storage or anything right. You know you can now actually bring this all together into a common automation plane in control plane to be able to manage to all of this. It's also really great from a skills perspective. So, it enables us to really be able to leverage. You know Python on the platform and that's whole ecosystem of Ansible skills that are out there and be able to now use that to work with Z. >> So it's essentially a modern abstraction layer of agility and people to work on it. (laughs) >> Yeah >> You know it's not the joke, Hey, where's that COBOL programmer. I mean, this is a serious skill gap issues though. This is what we're talking about here. You don't have to replace the, kill the old to bring in the new, this is an example of integration where it's classic abstraction layer and evolution. Is that, am I getting that right? >> Absolutely. I mean I think that Ansible's power as an orchestrator is part of why, you know, it's been so successful here because it's not trying to rip and replace and tell you that you have to rewrite anything that you already have. You know, it is that glue sort of like you used that term earlier right? It's that glue that can span you know, whether you've got rec whether you've got ACL, whether you're using z/OSMF you know, or any other kind of custom automation on the platform, you know, it works with everything and it can start to provide that transparency into it as well, and move to that, like infrastructure as code type of culture. So you can bring it into source control. You can have visibility to it as part of the Ansible automation platform and tower and those capabilities. And so you, it really becomes a part of the whole enterprise and enables you to codify a lot of that knowledge. That, you know, exists again in pockets or in individuals and make it much more accessible to anybody new who's coming to the platform. >> That's a great point, great insight.& It's worth calling out. I'm going to make a note of that and make a highlight from that insight. That was awesome. I got to ask about this notion of client uptake. You know, when you have z/OS and Ansible kind of come in together, what are the clients area? When do they get excited? When do they know that they've got to do? And what are some of the client reactions? Are they're like, wake up one day and say, "Hey, yeah I actually put Ansible and z/OS together". You know peanut butter and chocolate is (mumbles) >> Honestly >> You know, it was just one of those things where it's not obvious, right? Or is it? >> Actually I have been surprised myself at how like resoundingly positive and immediate the reactions have been, you know we have something, one of our general managers runs a general managers advisory council and at some of our top clients on the platform and you know we meet with them regularly to talk about, you know, the future direction that we're going. And we first brought this idea of Ansible managing to Z there. And literally unanimously everybody was like yes, give it to us now. (laughs) It was pretty incredible, you know? And so it's you know, we've really just seen amazing uptake. We've had over 5,000 downloads of our core collection on galaxy. And again that's just since mid to late March when we first launched. So we're really seeing tremendous excitement with it. >> You know, I want to want to talk about some of the new announcements, but you brought that up. I wanted to kind of tie into it. It is addictive when you think modernization, people success is addictive. This is another theme coming out of AnsibleFest this year is that when the sharing, the new content you know, coders content is the theme. I got to ask you because you mentioned earlier about the business value and how the clients are kind of gravitating towards it. They want it.It is addictive, contagious. In the ivory towers in the big, you know, front office, the business. It's like, we've got to make everything as a service. Right. You know, you hear that right. You know, and say, okay, okay, boss You know, Skyla, just go do it. Okay. Okay. It's so easy. You can just do it tomorrow, but to make everything as a service, you got to have the automation, right. So, you know, to bridge that gap has everything is a service whether it's mainframe. I mean okay. Mainframe is no problem. If you want to talk about observability and microservices and DevOps, eventually everything's going to be a service. You got to have the automation. Could you share your, commentary on how you view that? Because again, it's a business objective everything is a service, then you got to make it technical then you got to make it work and so on. So what's your thoughts on that? >> Absolutely. I mean, agility is a huge theme that we've been focusing on. We've been delivering a lot of capabilities around a cloud native development experience for folks working on COBOL, right. Because absolutely you know, there's a lot of languages coming to the platform. Java is incredibly powerful and it actually runs better on Z than it runs on any other platform out there. And so, you know, we're seeing a lot of clients you know, starting to, modernize and continue to evolve their applications because the platform itself is incredibly modern, right? I mean we come out with new releases, we're leading the industry in a number of areas around resiliency, in our security and all of our, you know, the face of encryption and number of things that come out with, but, you know the applications themselves are what you know, has not always kept pace with the rate of change in the industry. And so, you know, we're really trying to help enable our clients to make that leap and continue to evolve their applications in an important way, and the automation and the tools that go around it become very important. So, you know, one of the things that we're enabling is the self service, provisioning experience, right. So clients can, you know, from Open + Shift, be able to you know, say, "Hey, give me an IMS and z/OS connect stack or a kicks into DB2 stack." And that is all under the covers is going to be powered by Ansible automation. So that really, you know, you can get your system programmers and your talent out of having to do these manual tasks, right. Enable the development community. So they can use things like VS Code and Jenkins and GET Lab, and you'll have this automated CICB pipeline. And again, Ansible under the covers can be there helping to provision those test environments. You know, move the data, you know, along with the application, changes through the pipeline and really just help to support that so that, our clients can do what they need to do. >> You guys got the collections in the hub there, so automation hub, I got to ask you where do you see the future of the automating within z/OS going forward? >> Yeah, so I think, you know one of the areas that we'd like to see go is head more towards this declarative state so that you can you know, have this declarative configuration defined for your Z environment and then have Ansible really with the data and potency right. Be able to, go out and ensure that the environment is always there, and meeting those requirements. You know that's partly a culture change as well which goes along with it, but that's a key area. And then also just, you know, along with that becoming more proactive overall part of, you know, AI ops right. That's happening. And I think Ansible on the automation that we support can become you know, an integral piece of supporting that more intelligent and proactive operational direction that, you know, we're all going. >> Awesome Skyla. Great to talk to you. And so insightful, appreciate it. One final question. I want to ask you a personal question because I've been doing a lot of interviews around skill gaps and cybersecurity, and there's a lot of jobs, more job openings and there are a lot of people. And people are with COVID working at home. People are looking to get new skilled up positions, new opportunities. Again cybersecurity and spaces and event we did and want to, and for us its huge, huge openings. But for people watching who are, you know, resetting getting through this COVID want to come out on the other side there's a lot of online learning tools out there. What skill sets do you think? Cause you brought up this point about modernization and bringing new people and people as a big part of this event and the role of the people in community. What areas do you think people could really double down on? If I wanted to learn a skill. Or an area of coding and business policy or integration services, solution architects, there's a lot of different personas, but what skills can I learn? What's your advice to people out there? >> Yeah sure. I mean on the Z platform overall and skills related to Z, COBOL, right. There's, you know, like two billion lines of COBOL out there in the world. And it's certainly not going away and there's a huge need for skills. And you know, if you've got experience from other platforms, I think bringing that in, right. And really being able to kind of then bridge the two things together right. For the folks that you're working for and the enterprise we're working with you know, we actually have a bunch of education out there. You got to master the mainframe program and even a competition that goes on that's happening now, for folks who are interested in getting started at any stage, whether you're a student or later in your career, but you know learning, you know, learn a lot of those platforms you're going to be able to then have a career for life. >> Yeah. And the scale on the data, this is so much going on. It's super exciting. Thanks for sharing that. Appreciate it. Want to get that plug in there. And of course, IBM, if you learn COBOL you'll have a job forever. I mean, the mainframe's not going away. >> Absolutely. >> Skyla, thank you so much for coming on theCUBE Vice President, for the Z Application Platform and IBM, thanks for coming. Appreciate it. >> Thanks for having me. >> I'm John Furrier your host of theCUBE here for AnsibleFest 2020 Virtual. Thanks for watching. (upbeat music)

(upbeat music) >> Live, from Las Vegas it's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host Rebecca Knight along with my co-host John Furrier. We are joined by Adam Mariano, he is the Vice-President Health Informatics at HighPoint Solutions. Thanks for coming on theCUBE! >> Thank you for having me. >> So tell our viewers a little bit about HighPoint Solutions, what the company does and what you do there. >> Sure, HighPoint is a consulting firm in the Healthcare and Life Sciences spaces. If it's data and it moves we probably can assist with it. We do a lot of data management, we implement the full Infomatica stack. We've been an Infomatica partner for about 13 years, we were their North American partner of the year last year. We're part of a much larger organization, IQVIA, which is a merger of IMS quintiles, large data asset holder, big clinical research organization. So we're very much steeped in the healthcare data space. >> And what do you do there as Vice President of Health and Formatics? >> I'm in an interesting role. Last year I was on the road 51 weeks. So I was at over a hundred facilities, I go out and help our customers or prospective customers or just people we've met in the space, get strategic about how they're going to leverage data as a corporate asset, figure out how they're going to use it for clinical insight, how they're going to use it for operational support in payer spaces. And really think about how they're going to execute on their next strategy for big data, cloud strategy, digital re-imaginment of the health care space and the like. >> So we know that healthcare is one of the industries that has always had so much data, similar to financial services. How are the organizations that you're working with, how are they beginning to wrap their brains around this explosion of data? >> Well it's been an interesting two years, the last augur two years there isn't a single conversation that hasn't started with governance. And so it's been an interesting space for us. We're a big MDM proponent, we're a big quality proponent, and you're seeing folks come back to basics again, which is I need data quality, I need data management from a metadata perspective, I need to really get engaged from a master data management perspective, and they're really looking for integrated metadata and governance process. Healthcare's been late to the game for about five or six years behind other industries. I think now that everybody's sort of gone through meaningful use and digital transformation on some level, we're now arcing towards consumerism. Which really requires a big deep-dive in the data. >> Adam, data governance has been discussed at length in the industry, certainly recently everyone knows GDPR's one year anniversary, et cetera, et cetera. But the role of data is really critical applications for SAS and new kinds of use cases, and the term Data Provisioning as a service has been kicked around. So I'd love to get your take on what that means, what is the definition, what does it mean? Data Provisioning as a service. >> The industry's changed. We've sort of gone through that boomerang, alright, we started deep in the sort of client server, standard warehouse space. Everything was already BMS. We then, everybody moved to appliances, then everybody came back and decided Hadoop, which is now 15 year old technology, was the way to go. Now everybody's drifting to Cloud, and you're trying to figure out how am I going to provision data to all these self-service users who are now in the sort of bring your own tools space. I'd like to use Tablo, I'd like to use Click. I like SAS. People want to write code to build their own data science. How can you provision to all those people, and do so through a standard fashion with the same metadata with the same process? and there isn't a way to do that without some automation at this point. It's really just something you can't scale, without having an integrated data flow. >> And what's the benefits of data provisioning as a service? What's the impact of that, what does it enable? >> So the biggest impact is time to market. So if you think about warehousing projects, historically a six month, year-long project, I can now bring data to people in three weeks. In two days, in a couple of hours. So thinking about how I do ingestion, if you think about the Informatica stack, something like EDC using enterprise data catalog to automatically ingest data, pushing that out into IDQ for quality. Proving that along to AXON for data governance and process and then looking at enterprise data lake for actual self-service provisioning. Allowing users to go in and look at their own data assets like a store, pick things off the shelf, combine them, and then publish them to their favorite tools. That premise is going to have to show up everywhere. It's going to have to show up on AWS, and on Amazon, and on Azure. It's going to have to show up on Google, it's going to have to show up regardless of what tool you're using. And if you're going to scale data science in a real meaningful way without having to stack a bunch of people doing data munging, this is the way it's going to have to go. >> Now you are a former nurse, and you now-- >> I'm still a nurse, technically. >> You're still a nurse! >> Once a nurse, always a nurse. Don't upset the nurses. >> I've got an ear thing going on, can you help me out here? (laughter) >> So you have this really unique vantage point, in the sense that you are helping these organizations do a better job with their data, and you also have a deep understanding of what it's like to be the medical personnel on the other side, who has to really implement these changes, and these changes will really change how they get their jobs done. How would you say, how does that change the way you think about what you do? And then also what would you say are the biggest differences for the nurses that are on the floor today, in the hospital serving patients? >> I think, in America we think about healthcare we often talked about Doctors, we only talk about nurses in nursing shortages. Nurses deliver all the care. Physicians see at this point, the way that medicine is running, physicians see patients an average two to four minutes. You really think about what that translates to if you're not doing a surgery on somebody, it's enough time to talk to them about their problem, look at their chart and leave. And so nursing care is the point of care, we have a lot of opportunity to create deflection and how care is delivered. I can change quality outcomes, I can change safety problems, I can change length of stay, by impacting how long people keep IVs in after they're no longer being used. And so understanding the way nursing care is delivered, and the lack of transparency that exists with EMR systems, and analytics, there's an opportunity for us to really create an open space for nursing quality. So we're talking a lot now to chief nursing officers, who are never a target of analytics discussion. They don't necessarily have the budget to do a lot of these things, but they're the people who have the biggest point of control and change in the way care is delivered in a hospital system. >> Care is also driven by notifications and data. >> Absolutely. >> So you can't go in a hospital without hearing all kinds of beeps and things. In AI and all the things we've been hearing there's now so many signals, the question is what they pay attention to? >> Exactly. >> This becomes a really interesting thing, because you can get notifications, if everything's instrumented, this is where kind of machine learning, and understanding workflows, outcomes play a big part. This is the theme of the show. It's not just the data and coding, it's what are you looking for? What's the problem statement or what's the outcome or scenario where you want the right notification, at the right time or a resource, is the operating room open? Maybe get someone in. These kinds of new dynamics are enabled by data, what's your take on all this? >> I think you've got some interesting things going on, there's a lot of signal to noise ratio in healthcare. Everybody is trying to build an algorithm for something. Whether that's who's going to overstay their visit, who's going to be readmitted, what's the risk for somebody developing sepsis? Who's likely to follow up on a pharmacy refill for their medication? We're getting into the space where you're going to have to start to accept correlation as opposed to causation, right? We don't have time to wait around for a six month study, or a three year study where you employ 15,000 patients. I've got three years of history, I've got a current census for the last year. I want to figure out, when do I have the biggest risk for falls in a hospital unit? Low staffing, early in their career physicians and nurses? High use of psychotropic meds? There are things that, if you've been in the space, you can pretty much figure out which should go into the algorithm. And then being pragmatic about what data hospitals can actually bring in to use as part of that process. >> So what you're getting at is really domain expertise is just as valuable as coding and wrangling data, and engineering data. >> In healthcare if you don't have SMEs you're not going to get anything practical done. And so we take a lot of these solutions, as one of the interesting touch points of our organization, I think it's where we shine, is bringing that subject matter expertise into a space where pure technology is not going to get it done. It's great if you know how to do MDM. But if you don't know how to do MDM in healthcare, you're going to miss all the critical use cases. So it really - being able to engage that user base, and the SMEs and bring people like nurses to the forefront of the conversation around analytics and how data will be used to your point, which signals to pay attention to. It's critical. >> Supply chains, another big one. >> Yeah. >> Impact there? >> Well it's the new domain in MDM. It's the one that was ignored for a long time. I think people had a hard time seeing the value. It's funny I spoke at 10 o'clock today, about supply chain, that was the session that I had with Nathan Rayne from BJC. We've been helping them embark on their supply chain journey. And from all the studies you look at it's one of the easiest places to find ROI with MBM. There's an unbelievable amount of ways- >> Low hanging fruit. >> $24.5 billion in waste a year in supply chain. It's just astronomical. And it's really easy things, it's about just in time supplies, am I overstocking, am I losing critical supplies for tissue samples, that cost sometimes a $100,000, because a room has been delayed. And therefore that tissue sits out, it ends up expiring, it has to be thrown away. I'll bring up Nathan's name again, but he speaks to a use case that we talked about, which is they needed a supply at a hospital within the system, 30 miles away another hospital had that supply. The supply costs $40,000. You can only buy them in packs of six. The hospital that needed the supply was unaware that one existed in the system, they ordered a new pack of six. So you have a $240,000 price that you could have resolved with a $100 Uber ride, right? And so the reality is that supply could have been shipped, could have been used, but because that wasn't automated and because there was no awareness you couldn't leverage that. Those use cases abound. You can get into the length of stay, you can get into quality of safety, there's a lot of great places to create wins with supply chain in the MDM space. >> One of the conversations we're having a lot in theCUBE, and we're having here at Informatica World, it centers around the skills gap. And you have a interesting perspective on this, because you are also a civil rights attorney who is helping underserved people with their H1B visas. Can you talk a little bit about the visa situation, and what you're seeing particularly as it relates to the skills gap? >> We're in an odd time. We'll leave it at that. I won't make a lot of commentary. >> Yes. >> I'm a civil rights and immigration attorney, and on the immigration side I do a lot of pro bono work with primarily communities of color, but communities at risk looking to help adjust their immigration status. And what you've had is a lot of fear. And so you have, well you might have an H1B holder here, you may have somebody who's on a provisional visa, or family members, and because those family members can no longer come over, people are going home. And you're getting people who are now returning. So we're seeing a negative immigration of places like Mexico, you're seeing a lot of people take their money, and their learnings and go back to India and start companies there and work remotely. So we're seeing a big up-tick in people who are looking for staffing again. I think the last quarter or so has been a pretty big ramp-up. And I think there's going to continue to be this hole, we're going to have to find new sources of talent if we can't bring people in to do the jobs. We're still also, I think it just speaks to our STEM education the fact that we're not teaching kids. I have a 28 year old daughter who loves technology, but I can tell you, her education when she was a kid, was lacking in this technology space. I think it's really an opportunity for us to think about how do we train young people to be in the new data economy. There's certainly an opportunity there today. >> And what about the, I mean you said you were talking about your daughter's education. What would you have directed her toward? What kinds of, when you look ahead to the jobs of the future, particularly having had various careers yourself, what would you say the kids today should be studying? >> That's two questions. So my daughter, I told her do what makes you happy. But I also made her learn Sequel. >> Be happy, but learn Sequel. >> But learn sequel. >> Okay! >> And for kids today I would say look, if you have an affinity and you think you enjoy the computer space, so you think about coding, you like HTML, you like social media. There are a plethora of jobs in that space and none of them require you to be an architect. You can be a BA, you can be a quality assurance person, you can be a PM. You can do analysis work. You can do data design, you can do interface design, there's a lot of space in there. I think we often reject kids who don't go to college, or don't have that opportunity. I think there's an opportunity for us to reach down into urban centers and really think about how we make alternate pathways for kids to get into the space. I think all the academies out there, you're seeing rise, Udemy, and a of of these other places that are offering academy based programs that are three, six months long and they're placing all of their students into jobs. So I don't think that the arc that we've always chased which is you've got to come from a brand named school to get into the space, I don't think it's that important. I think what's important is can I get you the clinical skill, so that you've understood how to move data around, how to process it, how to do testing, how to do design, and then I can bring you into the space and bring you in as an entry level employee. That premise I think is not part of the American dream but it should be. >> Absolutely, looking for talent in these unexpected places. >> College is not the only in point. We're back to having I think vocational schools for the new data economy, which don't exist yet. That's an opportunity for sure. >> And you said earlier, domain expertise, in healthcare as an example, points to what we've been hearing here at the conference, is that with data understanding outcomes and value of the data actually is just as important, as standing up, wrangling data, because if you don't have the data-- >> You make a great point. The other thing I tell young people in my practice, young people I interact with, people who are new to the space is, okay I hear you want to be a data scientist. Learn the business. So if you don't know healthcare get a healthcare education. Come be on this project as a BA. I know you don't want to be a BA, that's fine. Get over it. But come be here and learn the business, learn the dialogue, learn the economy of the business, learn who the players are, learn how data moves through the space, learn what is the actual business about. What does delivering care actually look like? If you're on the payer side, what does claims processing look like from an end to end perspective? Once you understand that I can put you in any role. >> And you know digital four's new non-linear ways to learn, we've got video, I see young kids on YouTube, you can learn anything now. >> Absolutely. >> And scale up your learning at a pace and if you get stuck you can just keep getting through it no-- >> And there are free courses everywhere at this point. Google has a lot of free courses, Amazon will let you train for free on their platform. It's really an opportunity-- >> I think you're right about vocational specialism is actually a positive trend. You know look at the college University scandals these days, is it really worth it? (laughter) >> I got my nursing license through a vocational school originally. But the nursing school, they didn't have any technology at that point. >> But you're a great use case. (laughter) Excellent Adam, thank you so much for coming on theCUBE it's been a pleasure talking to you. >> Thank you. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE. (techno music)

>> Announcer: Live from Orlando, Florida. It's TheCUBE covering PentahoWorld 2017. Brought to you by Hitachi Ventara. >> Welcome back to TheCUBE's live coverage of PentahoWorld brought to you by Hitachi Ventara, we are wrapping up day one. I'm your host Rebecca Knight along with my cohosts today James Kobielus and Dave Vellante. Guys, day one is done what have we learned? What's been the most exciting thing that you've seen at this conference? >> The most exciting thing is that clearly Hitachi Ventara which of course, Pentaho is a centerpiece is very much building on their strong background and legacy and open analytics, and pushing towards open analytics in the Internet of things, their portfolio, the whole edge to outcome theme, with Brian Householder doing a sensational Keynote this morning, laying out their strategic directions now Dave had a great conversation with him on TheCUBE earlier but I was very impressed with the fact that they've got a dynamic leader and a dynamic strategy, and just as important Hitachi, the parent company, has clearly put together three product units that make sense. You got strong data integration, you got a strong industrial IOT focus, and you got a really strong predictive and machine learning capability with Pentaho for the driving the entire pipeline towards the edge. Now that to me shows that they've got all the basic strategic components necessary to seize the future, further possibilities. Now, they brought a lot of really good customers on, including our latest one from IMS, Hillove, to discuss exactly what they're doing in that area. So I was impressed with the amount of solid substance of them seizing the opportunity. >> Well so I go back two years, when TheCUBE first did PentagoWorld 2015, and the story then was pretty strong. You had a company in big data, they seemingly were successful, they had a lot of good customer references, they achieved escape velocity, and had a nice exit under Quentin Galavine, who was the CEO at the time and the team. And they had a really really good story, I thought. But I was like okay, now what? We heard about conceptually we're going to bring the industrial internet and analytics together, and then it kind of got quiet for two years. And now, you're starting to see the strategy take shape in typical Hitachi form. They tend not to just rush in to big changes and transformations like this, they've been around for a long time, a very thoughtful company. I kind of look at Hitachi limited in a way, as an IBM like company of Japan, even though they do industrial equipment, and IBM's obviously in a somewhat different business, but they're very thoughtful. And so I like the story the problem I see is not enough people know about the story. Brian was very transparent this morning, how many people do business with Hitachi? Very few. And so I want to see the ecosystem grow. The ecosystem here is Hitachi, a couple of big data players, I don't see any reason why they can't explode this event and the ecosystem around Hitachi Ventara, to fulfill it's vision. I think that that's a key aspect of what they have to do. >> I want to see-- >> What will be the tipping point? Just to get as you said, I mean it's the brand awareness, and every customer we had on the show really said, when he when he said that my eyes lit up and I thought oh wow, we could actually be doing more stuff with Hitachi, there's more here. >> I want to see a strong developer focus, >> Yeah. >> Going forward, that focuses on AI and deep learning at the at the edge. I'm not hearing a lot of that here at PentahoWorld, of that rate now. So that to me is a strategic gap right now and what they're offering. When everybody across the IT and data and so forth is going real deep on things like frameworks like TensorFlow and so forth, for building evermore sophisticated, data driven algorithms with the full training pipeline and deployment and all that, I'm not hearing a lot of that from the Pentaho product group or from the Hitachi Ventara group here at this event. So next year at this event I would like to hear more of what they're doing in that area. For them to really succeed, they're going to have to have a solid strategy to migrate up there, openstack to include like I said, a bit of TensorFlow, MXNet, or some of the other deep learning tool kits that are becoming essentially defacto standards with developers. >> Yeah, so I mean I think the vision's right. Many of the pieces are in place, and the pieces that aren't there, I'm actually not that worried about, because Hitachi has the resources to go get them, either build them organically, which has proven it can do overtime, or bring in acquisition. Hitachi is a decent acquire of companies. Its content platform came in on an acquisition, I've seen them do some hardware acquisitions, some have worked, some haven't. But there's a lot of interesting software players out there and I think there's some values, frankly. The big data, tons of money poured in to this open source world, hard to make money in opensource, which means I think companies like Hitachi could pick off to do some M and A and find some value. Personally, I think if the numbers right at a half a billion dollars, I personally think that that was pretty good value for Hitachi. You see in all these multi billion dollar acquisitions going left and right. And so the other thing is the fact that Hitachi under the leadership under Brian Householder and others, was able to shift its model from 80% hardware, now it's 50/50 software and services I'd like to dig into that a little bit. They're a public company but you can't really peel the onion on the Hitachi Ventara side, so it kind of is what they say it is, I would imagine that's a lot of infrastructure software, kind of like EMC's a software company. >> James: Right. >> But nonetheless, they're moving towards a subscription model, they're committed to that, and I think that the other thing is that a lot of costumers. We come to a lot of shows and they struggle to get costumers on with substantive stories, so we heard virtually every costumer we talked to today is like Here's how I'm using Pentaho, here's how it's affecting. Not like super sexy stories yet, I mean that's what the IOT and the edge piece come in, but fundamental plumbing around big data, Pentaho seems like a pretty important piece of it. >> Their fundamental-- >> Their fundamental plumbing that's really saving them a lot of money too, and having a big ROI. >> They're fairly blue-chip as a solution provider of a full core data of a portfolio of Pentaho. I think of them in many ways as sort of like SAP, not a flashy vendor, but a very much a solid blue-chip in their core markets >> Right. >> I'm just naming another vendor that I don't see with a strong AI focus yet. >> Yeah. >> Pentaho, nothing to sneeze at when you have one customer after another like we've had here, rolling out some significant work they've been doing with Pantaho for quite a while, not to sneeze at their delivering value but they have to rise to the next level of value before long, to avoid be left in the dust. >> You got this data obviously they're going to be capturing more more data with the devices. >> James: Yeah. >> And The relationship with Hitachi proper, the elevator makers is still a little fuzzy to me, I'm trying to understand how that all shakes up, but my question for you Jim is: okay so let's assume for second they're going to have this infrastructure in place because they are industrial internet, and they got the analytics platform, maybe there's some holes that they can fill in, one being AI and some of the deep learning stuff, can't they get that somewhere? I mean there's so much action going on-- >> Yes. >> In the AI world, can't they bring that in and learn how to apply it overtime? >> Of course they can. First of all they can acquire and tap their own internal expertise. They've got like Mark Hall for example on the panel, they've obviously got a deep bench of data scientist like him who can take it to that next level, that's important. I think another thing that Hitachi Ventara needs to do to take it to the next level is they need a strong robotics portfolio. It's really talking about industrial internet of things, it's robotics with AI inside. I think they're definitely a company that could go there fairly quickly, a wide range of partners they can bring in or acquire to get fairly significant in terms of not just robotics in general, but robotics for a broad range of use cases where the AI is not so much the supervise learning and stuff that involves training, but things like reinforcement learning, and there's a fair amount of smarts and academe on Reinforcement learning for in body cognition, for robots, that's out there in terms of that's like the untapped space other than the broad AI portfolio, reinforcement learning. If somebody's going to innovate and differentiate themselves in terms of the enterprise, in terms of leveraging robotics in a variety of applications, it's going to to be somebody with a really strong grounding and reinforcement learning and productizing that and baking that in to an actual solution portfolio, I don't see yet the Google's and the IBM's and the Microsofts going there, and so if these guys want to stand out, that's one area they might explore. >> Yeah, and I think to pick up on that, I think this notion of robotics process automation, that market's going to explode. We were at a conference this week in Boston, the data rowdy of Boston, the chief data officer conference at the Park Plaza, 20 to 25% of the audiences, the CDO's in the audience had some kind of RPA, robotic process automation, initiative going on which I thought was astoundingly high. And so it would seem to me that Hitachi's going to be in a good position to capture all that data. The other thing that Brian stressed, which a lot of companies without a cloud will stress, is that it's your data, you own the data, we're not trying to resell that data, monetize that data, repackage that data. I pushed him a little bit on well what about that data training models, and where do those models go? And he says Look we are not in the business of taking models and you know as a big consultancy, and bringing it over to other competitors. Now Hitachi does have consultancy, but it's sort of in a focus, but as Brian said in his keynote, you have to listen to what people say and then watch them to see how they act. >> Rebecca: Do they walk the walk? >> How they respond. >> Right. >> And so that's you have to make your decision, but I do think that's going to be a very interesting field to watch because Hitachi's going to have so much data in their devices. Of course they're going to mine that data for things like predictive analytics, those devices are going to be in factories, they're going to be in ecosystems, and there's going to be a battle for who owns the data, and it's going to be really interesting to see how that shakes out. >> So I want to ask you both, as you've both have said, we've had a lot of great customer stories here on TheCUBE today. We had a woman who does autonomous vehicles, we had a gamer from Finland, we had a benefit scientist out of Massachusetts, Who were your favorite customer stories and what excited you most about their stories? >> James: Hmmm. >> Well I know you like the car woman. >> Well, yeah the car woman, >> The car woman. >> Ella Hillel. >> Ella Hillel, Yes. >> The PHD. That was really what I found many things fascinating, I was on a panel with Ella as well as she was on TheCUBE, what I found interesting I was expecting her to go to town on all things autonomous driving, self driving vehicles, and so forth, was she actually talked about the augmentation of the driver, passenger experience through analytics, dashboards in the sense that dashboards that help not only drivers but insurance companies and fleet managers, to do behavioral modification to help them modify the behavior, to get the most out of their vehicular experience, like reducing wear and tear on tires, and by taking better roads, or revising I thought that's kind of interesting; build more of the recommendation engine capability into the overall driving experience. That depends on an infrastructure of predictive analytics and big data, but also metered data coming from the vehicle and so forth. I found that really interesting because they're doing work clearly in that area, that's an area that you don't need levels one through five of self driving vehicles to get that. You can get that at any level of that whole model, just by bringing those analytics somehow into an organic way hopefully safely, into your current driving experience, maybe through a heads-up display that's integrated through your GPS or whatever might be, I found that interesting because that's something you could roll out universally, and it can actually make a huge difference in A: safety, B: people's sort of pleasure with the driving experience, Fahrvergnugen that's a Volkswagon, and then also see how people make the best use of their own vehicular assets in an era where people still mostly own their own car. >> Well for me if there's gambling involved-- >> Rebecca: You're there. >> It was the gaming, now not only because of the gambling, and we didn't find out how to beat the house Leonard, maybe next time, but it was confirmation of the three-tier data model from from edge-- >> James: Yes. >> To gateway to cloud, and that the cloud is two vectors; the on-premise and the off-premise cloud, and the fact that as a gaming company who designs their own slot machines it's an edge device, and they're basically instrumenting that edge device for real-time interactions. He said that most of the data will go back, I'm not sure. Maybe in that situation it might, maybe all the data will go back like weather data, it all comes back, But generally speaking I think there's going to be a lot of analog data at the edge that's going to be digitize that maybe you don't have to save and persist. But anyway, confirmation of that three-tiered data model I think is important because I think that is how Brian talked about it, we all know the pendulum is swinging, swung away from mainframe to decentralize back to the centralized data center and now it's swinging again to a much more distributed sort of data architecture. So it was good to hear confirmation of that, and I think it's again, it's really early innings in terms of how that all shakes out. >> Great, and we'll know more tomorrow at Pentaho day two, and I look forward to to being up here again with both of you tomorrow. >> Likewise. >> Great, this has been TheCUBE's live coverage of PentahoWorld brought to you by Hitachi Ventara, I'm Rebecca Knight for Jim Kobielus and Dave Vellante, we'll see you back here tomorrow.

>> Narrator: Live from Orlando, Florida, it's the Cube, covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to the Cube's live coverage of PentahoWorld brought to you by Hitachi Vantara I'm your host Rebecca Knight, along with my co-host James Kobielus. We're joined by Dr. Allaa Hilal She is the Director of Innovation at IMS Thanks so much for coming on the Cube Allaa >> Thanks, I'm excited to be here. >> So you described you mission this morning, as is, the mission to enable the connected car. Tell our viewers what is the connected car? >> That is a very interesting question. So, to us, to us at IMS, we define the connected vehicle in a little bit of a different way. So, most people define it as being connected to the internet. But, having it connected to the internet is not very useful to us drivers. But having it connected to you, the driver, is the key, is the essential point. And this is how we define the connected vehicle. So, if it's, by connecting to you, we need to connect it to the internet, then that's a by product. But the key is giving you an actionable insights as you're driving along, doing you daily commute. And as I mentioned this morning, you spend about four point five years, of you life, in a vehicle. That's a long time. It's a lot of time on your behalf. So, if you, if we are able to make this commute, or your time in a vehicle more productive, then you get to enjoy this ride a little bit more. >> So augmenting the driver, or passengers, experience with analytics, as opposed to what people usually think of, which is self-driving autonomous vehicles, am I-- >> So, it's one step of the way. You cannot have an autonomous vehicle without having connected vehicles. Because, if you think about it, if you're having autonomous vehicle that has a horrible user experience, then what are you really doing? Right? Nobody will want to ride it. So. >> So, talk about, what are some examples of these actionable insights that you could give someone as their driving along? >> So, imagine this: so, if you're driving in the middle of highway, and you, and we know your destination in advance, but we know that there's no parking space, and we can redirect you to another parking spot. That's an actionable insight that would be useful. If we now that you're driving, and because of the way you're driving, your premiums will go up because you impose a little bit more higher risk, we can give you coaching, and feed-back on how you can get to be a better driver and save some money. Think about it another way. You can be driving in harsh breaking, harsh acceleration, imposing wear and tear on your tires. That will cost you money because you would need to change them. If we give you this information early on, you're incentivized to change your behavior a little bit to prolong the lifetime of your vehicle, as well as save some gas. >> So, IMS is a long-time IOT customer, can you tell us how you've been able to stay relevant? >> Oh, that's a very interesting question. So, definitely some, it's been an interesting, ever-changing market. So, we focus on delivering a suite of services. Not just one service, with one provider. We actually provide a suite of services, and we can enable different one at different times. So we're not just a usage-based company, we're a connected car company. That means that we enable road-usage charging. So, you know road-usage charging, right? So, like, multiple states in North America, as well as in Europe, different countries, are focused now on having road chargings. Instead of you paying the gas-tax, at the gas pump every time you put gas in the car, to off-set the cost of the infrastructure, you pay the road-usage charge. >> Rebecca: A toll. >> A toll. Well, similar to a toll, but it's different because you're already paying it somehow. So, a toll is choice, you need to take this road, you pay the tolls for it >> James: Yes. >> But, for road-usage charging, it's trying to have a fair system to offset the cost of the infrastructure. The way it was done before, using the gas-tax, everybody had to use gas, everybody buys gas, and then they pay a little bit of money that goes to the infrastructure. Now you have hybrid vehicles, now we have fuel efficient vehicles, as well as you have electric vehicles, that are imposing wear and tear one the roads, but there's not money coming to the government to help offset this cost. So they are trying to have a more fair system where we all contribute to the roads that we're driving in. >> So what's the metering infrastructure to enable road-usage based, road charges? >> Okay, so, road-usage charging is actually quite interesting, so, you think it has a lot of different additional over-head that you need. But it actually is not. It's you can, we as a company, enable road-usage charging through an OBD dongle that you add on your vehicle. >> Yes, yes. >> And that's enough for us to get all the information needed. Whether it's just millage information, without GPS, again-- >> James: A diagnostic port. >> It's a diagnostic port, yes. >> Yes, yes. So it has multiple ways, right? So you can enable it, road-usage charging has multiple flavors of it. So one of them with GPS informations, so we only charge you on public roads, not private roads. So, if you have, like if you're driving on a campus, or like a big a campus at work, you're not pay, you're not charged for that. You only pay for public roads. If we don't have GPS, we do millage based approach. Where we collect this data and we provide it to the government, to do, to charge you for it. And the nice thing about it, they actually do a gas rebate, so gas-tax rebate, so you get to claim these millages, claim what you're paying for road-use charging and you rebate your gas-tax. Another flavor of it would be based on OBD two, sorry, other then OBD two, is mobile phone. So we can use the mobile phone to collect similar data and again, understand where you are, and accordingly charge you. Send the information to the government to charge you as such. >> As it relates to the internet of things that are, those are approaches, that would you, regard those are both IOT related approaches? Is there other any other, like, metering technologies that you are exploring? For gathering this data, in a way that's more or less invisible? >> So, I would definitely consider this as an IOT because, again, the IOT is having the sensors embedded in multiple services. >> Yes, yes. So, definitely to me, that's an IOT application. That being said, there are existing tooling approaches which are like cameras, and sensors, at entry points, and exit points. These are road-side infrastructures, you can also have, like, lane, high occupancy lanes, where, if you're in it they can take a picture, or sense how many people are in the vehicle. So, there are a lot of technologies that enables road-usage charging. That being said, I think using an OBD two, or a mobile phone is one of the most seamless things that you can use simply because you plug it in once, and you don't have to interact with it. >> So how is Pentaho, how are partnered with Pentaho to manage all this data, to drive these programs? >> Actually, that's an interesting question. >> Yeah exactly! >> We're at PentahoWorld, so This is the right question to ask here. (laughs) So, Pentaho has helped us to accelerate the ETL: the extract, transform, and load process. Especially since we're collecting data from diverse sources, from heterogeneous platforms, whether it's from an OBD two, from a mobile phone, or even from vehicles themselves. So collecting data from all of this different sources, Pentaho enabled us to ingest it fast, extract it, transform it, and load it. It also helped with with, data integration. So, the pentaho data integration platform helped us to work with multiple sources. Get stuff fast, get it ready. And, above all, it helped with the visualization because, we work with different clients, and each of them require a different report, or view of the data, in aggregated ways. Pentaho definitely helped us accelerate and adapt fast to the requirement of our clients. >> Are the clients, are they fleet managers? Are the clients insurance companies? Just give us a sense of the sort of dashboards you provide to them. And I'm using "dashboards" in a double entendre sense. To what extent can this technology be embedded in the dashboards of the future? Connected cars. To help drivers and passengers to modify their behavior while their using the road system. >> So I will answer that onto two parts. So the first, who are our clients? So we work with, definitely, insurance companies, some of the top ones in the world. Which would need data in a different form. We work with governments, we provide them for road-usage charging, for example, work with governments, so we provide them a different view of the data as their requirement. Work with fleet managers, fleet insurance company, which is commercial lines. We also provide information to the end-driver, to the end-user, because, how can you change, help them change their behavior? How can you give them actionable insights if your not interacting with them? So all of these are different end-points to our data and how we're exposing it. Regarding, what can we show in the dashboard, if you thin about it, today in some sense we're showing some information, we're showing, actually, a lot of information. So we have the mobile app, that acts as an interface, or a touch-point between us and the end-user. Because, at the end of the day, the end-user is the one who owns the data, it's not IMS, it's the end-user who owns the data. And he's allowing us to use it to give him insights to get insurance discounts or, know how much he's being charged for road-usage charging or, like, enabled services like road-side assistance, and others. So, the mobile app, is our interaction point and we have like, screens, that show the logs of your trip, and like, what good did you do, what bad did you do. We have analytics on this behavioral side. Where are you in terms of percentile of all different drivers. So that also gives you an encouragement and we always focus on positive feed-back to help you enhance and change your driving to the better. >> What are you doing in terms of data-masking, anonymization, to protect the privacy of this data that's being processed through, through your applications. >> So, definitely I-- >> James: We're very privacy sensitive obviously. >> No, yeah, and we are very, very aware of it. We're actually-- >> And how are you using Pentaho in that regard? >> We're very, very aware of it and we're very, very security conscious. If you thin about it, who are our clients? Our insurance company who are security focused, and then governments are security focused. And so, with, when you work with like, such big companies, and big institutions, that are very aware of security, you need also, to step up and show that. And this is why, we're (mumbles) certified in many, many areas. So, we're very, very aware of privacy. We never use any PII. And our PII officer, we have a security officer that is very, very, very strict. Let me tell you that. (laughs) And, when we use data, we use it an aggregated and anonymized format. So, you cannot, and we use differential privacy on it, so you cannot identify one person added, or removed out of it. So we use all of these different measures. And all the data that is being sent form the device, is double encrypted on a VPN, as well as sent on a binary format to our back-end, through a secure system. Devices are unhackable because they are designed such as that you cannot receive input. It's just made to send out input. So we work on privacy and security. We are actually privacy and security focused institute. And this is why we have been chosen by top tier insurers, as well as governments, to work with. >> So how far are we from fully autonomous vehicles? I mean, in your keynote, you talked about how actually people think we're further along in the journey then we actually are. But can you walk us through, the next, sort of next steps, and then give us an estimate? >> Tell me when to ditch my car right now >> Yeah, exactly! That's what I want to know. >> Okay, that's an interesting question, I'm sure it's a very controversial one, because, everybody would have a different opinion. I know somebody on my team, and if he's watching he would say "In the next three years and I will have "my next autonomous vehicle." and it all falls back to the definition of autonomy, right? So there, as I mentioned this morning, there are five levels of autonomy. So level zero is having no autonomy whatsoever. So it's like you 1970 or 1960 car, that you drive, you enjoy, but, it does nothing except enables you to drive. You have them, your level one autonomy, which will enable one feature only, so, it's either cruise-control, automatic breaking. One thing to assist you. So it's one thing. The you have level two, that enables two or more things at the same time, but you need to be fully alert and aware. Level three, while it can drive a little bit autonomously, but you need to be alert, fully engaged and ready to engage at any time. Ready to go at any time. Level four, it is autonomous under certain conditions. So, for example, autonomous on highway, or autonomous in specific cities, but not autonomous in others. Level five is autonomous everywhere, all the time. This is what we all are waiting for. Where we can sit-- >> I want tenterhooks. >> Exactly. Where you can-- >> Yes, I want to sleep while I'm driving (laughs) >> I want to bing on Netflix or catch-up on all the reading >> Right. Exactly. >> I have a lot of Game of Thrones on my, yes. >> Exactly. (laughs) Exactly. So, it depends on how you define autonomy, and this is where defines where we are on the progress. So, if you look at Tesla and Google car, we're actually somewhere between level two and level three. Multiple systems are engaged, but you need to be fully alert and ready to intervene at any time. We're still not at the phase where you can lay back and relax and sleep. >> What is your opinion, finally, how many years are we looking? >> Okay, depends on the levels, so if I say level three, yeah, well, we have it. Now, >> Yeah (laughs) If we are talking about-- >> You're hedging >> (laughs) level four, I would expect, okay, so level four and level five has its challenges. Level four, I would expect it to be between five to 10 years, somewhere in between. But level five is a little bit further. And the reason is multiple things: I would say 15 to 20, and I'll tell you why. Number one, you would have multiple cars coexisting on the road. And humans decisions are subjective, and are not always predictable. So, you would always need to default to human intervention when needed. Road infrastructure takes a long time to be developed, and for government investment. Third one, you need human acceptance, and trust into these systems, so I can trust my six-year-old daughter to sit there and I would not be afraid for her life. So, these things take time to develop, and this is hwy I'm saying 15 to 20 years. >> Okay, you heard it hear first folks. Alright? 15 to 20 years. >> Great >> I'm all for it. Allaa, thanks so much for coming on the Cube. It was a great conversation. >> I really enjoyed it so much. Thanks for having me. >> I'm Rebecca Knight for James Kobielus, we will have more form the Cube at PentahoWorld in just a little bit. (electronic music)

>> Announcer: Live, from Orlando, Florida, it's The Cube. Covering PentahoWorld 2017. Brought to you by, Hitachi Vantara. >> Welcome back to Orlando, everybody. This is PentahoWorld, #pworld17 and this is The Cube, The leader in live tech coverage. My name is Dave Vellante and I'm here with my co-host, Jim Kobielus Donna Prlich is here, she's the Chief Product Officer of Pentaho and a many-time Cube guest. Great to see you again. >> Thanks for coming on. >> No problem, happy to be here. >> So, I'm thrilled that you guys decided to re-initiate this event. You took a year off, but we were here in 2015 and learned a lot about Pentaho and especially about your customers and how they're applying this, sort of, end-to-end data pipeline platform that you guys have developed over a decade plus, but it was right after the acquisition by Hitachi. Let's start there, how has that gone? So they brought you in, kind of left you alone for awhile, but what's going on, bring us up to date. >> Yeah, so it's funny because it was 2015, it was PentahoWorld, second one, and we were like, wow, we're part of this new company, which is great, so for the first year we were really just driving against our core. Big-Data Integration, analytics business, and capturing a lot of that early big-data market. Then, probably in the last six months, with the initiation of Hitachi Ventara which really is less about Pentaho being merged into a company, and I think Brian covered it in a keynote, we're going to become a brand new entity, which Hitachi Vantara is now a new company, focused around software. So, obviously, they acquired us for all that big-data orchestration and analytics capability and so now, as part of that bigger organization, we're really at the center of that in terms of moving from edge to outcome, as Brian talked about, and how we focus on data, digital transformation and then achieving the outcome. So that's where we're at right now, which is exciting. So now we're part of this bigger portfolio of products that we have access to in some ways. >> Jim: And I should point out that Dave called you The CPO of Pentaho, but in fact you're the CPO of Hitachi Vantara, is that correct? >> No, so I am not. I am the CPO for the Pentaho product line, so it's a good point, though, because Pentaho brand, the product brand, stays the same. Because obviously we have 1,800 customers and a whole bunch of them are all around here. So I cover that product line for Hitachi Vantara. >> David: And there's a diverse set of products in the portfolios >> Yes. >> So I'm actually not sure if it makes sense to have a Chief Products officer for Hitachi Vantara, right? Maybe for different divisions it makes sense, right? But I've got to ask you, before the acquisition, how much were you guys thinking about IOT and Industrial IOT? It must have been on your mind, at about 2015 it certainly was a discussion point and GE was pushing all this stuff out there with the ads and things like that, but, how much was Pentaho thinking about it and how has that accelerated since the acquisition? >> At that time in my role, I had product marketing I think I had just taken Product Management and what we were seeing was all of these customers that were starting to leverage machine-generated data and were were thinking, well, this is IOT. And I remember going to a couple of our friendly analyst folks and they were like, yeah, that's IOT, so it was interesting, it was right before we were acquired. So, we'd always focus on these blueprints of we've got to find the repeatable patterns, whether it's Customer 360 in big data and we said, well they're is some kind of emerging pattern here of people leveraging sensor data to get a 360 of something. Whether it's a customer or a ship at sea. So, we started looking at that and going, we should start going after this opportunity and, in fact, some of the customers we've had for a long time, like IMS, who spoke today all around the connected cars. They were one of the early ones and then in the last year we've probably seen more than 100% growth in customers, purely from a Pentaho perspective, leveraging Machine-generated data with some other type of data for context to see the outcome. So, we were seeing it then, and then when we were acquired it was kind of like, oh this is cool now we're part of this bigger company that's going after IOT. So, absolutely, we were looking at it and starting to see those early use cases. >> Jim: A decade or more ago, Pentaho, at that time, became very much a pioneer in open-source analytics, you incorporated WECA, the open-source code base for machine-learning, data mining of sorts. Into the core of you're platform, today, here, at the conference you've announced Pentaho 8.0, which from what I can see is an interesting release because it brings stronger integration with the way the open-source analytic stack has evolved, there's some Spark Streaming integration, there's some Kafaka, some Hadoop and so forth. Can you give us a sense of what are the main points of 8.0, the differentiators for that release, and how it relates to where Pentaho has been and where you're going as a product group within Hiatachi Vantara. >> So, starting with where we've been and where we're going, as you said, Anthony DeShazor, Head of Customer Success, said today, 13 years, on Friday, that Pentaho started with a bunch of guys who were like, hey, we can figure out this BI thing and solve all the data problems and deliver the analytics in an open-source environment. So that's absolutely where we came form. Obviously over the years with big data emerging, we focused heavily on the big data integration and delivering the analytics. So, with 8.0, it's a perfect spot for us to be in because we look at IOT and the amount of data that's being generated and then need to address streaming data, data that's moving faster. This is a great way for us to pull in a lot of the capabilities needed to go after those types of opportunities and solve those types of challenges. The first one is really all about how can we connect better to streaming data. And as you mentioned, it's Spark Streaming, it's connecting to Kafka streams, it's connecting to the Knox gateway, all things that are about streaming data and then in the scale-up, scale-out kind of, how do we better maximize the processing resources, we announced in 7.1, I think we talked to you guys about it, the Adaptive Execution Layers, the idea that you could choose execution engine you want based on the processing you need. So you can choose the PDI engine, you can choose Spark. Hopefully over time we're going to see other engines emerge. So we made that easier, we added Horton Work Support to that and then this concept of, so that's to scale up, but then when you think about the scale-out, sometimes you want to be able to distribute the processing across your nodes and maybe you run out of capacity in a Pentaho server, you can add nodes now and then you can kind-of get rid of that capacity. So this concept of worker-nodes, and to your point earlier about the Hitachi Portfolio, we use some of the services in the foundry layer that Hitachi's been building as a platform. >> David: As a low balancer, right? >> As part of that, yes. So we could leverage what they had done which if you think about Hitachi, they're really good at storage, and a lot of things Pentaho doesn't have experience in, and infrastructure. So we said, well why are we trying to do this, why don't we see what these guys are doing and we leverage that as part of the Pentaho platform. So that's the first time we brought some of their technology into the mix with the Pentaho platform and I think we're going to see more of that and then, lastly, around the visual data prep, so how can we keep building on that experience to make data prep faster and easier. >> So can I ask you a really Columbo question on that sort-of load-balancing capabilities that you just described. >> That's a nice looking trench coat you're wearing. >> (laughter) gimme a little cigar. So, is that the equivalent of a resource negotiator? Do I think of that as sort of your own yarn? >> Donna: I knew you were going to ask me about that (laughter) >> Is that unfair to position it that way? >> It's a little bit different, conceptually, right, it's going to help you to better manage resources, but, if you think about Mesos and some of the capabilities that are out there that folks are using to do that, that's what we're leveraging, so it's really more about sometimes I just need more capacity for the Pentaho server, but I don't need it all the time. Not every customer is going to get to the scale that they need that so it's a really easy way to just keep bringing in as much capacity as you need and have it available. >> David: I see, so really efficient, sort of low-level kind of stuff. >> Yes. >> So, when you talk about distributed load execution, you're pushing more and more of the processing to the edge and, of course, Brian gave a great talk about edge to outcome. You and I were on a panel with Mark Hall and Ella Hilal about the, so called, "power of three" and you did a really good blog post on that the power of the IOT, and big data, and the third is either predictive analytics or machine learning, can you give us a quick sense for our viewers about what you mean by the power of three and how it relates to pushing more workloads to the edge and where Hitachi Vantara is going in terms of your roadmap in that direction for customers. >> Well, its interesting because one of the things we, maybe we have a recording of it, but kind of shrink down that conversation because it was a great conversation but we covered a lot of ground. Essentially that power of three is. We started with big data, so as we could capture more data we could store it, that gave us the ability to train and tune models much easier than we could before because it was always a challenge of, how do I have that much data to get my model more accurate. Then, over time everybody's become a data scientist with the emergence of R and it's kind of becoming a little bit easier for people to take advantage of those kinds of tools, so we saw more of that, and then you think about IOT, IOT is now generating even more data, so, as you said, you're not going to be able to process all of that, bring all that in and store it, it's not really efficient. So that's kind of creating this, we might need the machine learning there, at the edge. We definitely need it in that data store to keep it training and tuning those models, and so what it does is, though, is if you think about IMS, is they've captured all that data, they can use the predictive algorithms to do some of the associations between customer information and the censor data about driving habits, bring that together and so it's sort of this perfect storm of the amount of data that's coming in from IOT, the availability of the machine learning, and the data is really what's driving all of that, and I think that Mark Hall, on our panel, who's a really well-known data-mining expert was like, yeah, it all started because we had enough data to be able to do it. >> So I want to ask you, again, a product and maybe philosophy question. We've talked on the Cube a lot about the cornucopia of tooling that's out there and people who try to roll their own and. The big internet companies and the big banks, they get the resources to do it but they need companies like you. When we talk to your customers, they love the fact that there's an integrated data pipeline and you've made their lives simple. I think in 8.0 I saw spark, you're probably replacing MapReduce and making life simpler so you've curated a lot of these tools, but at the same time, you don't own you're own cloud, you're own database, et cetera. So, what's the philosophy of how you future-proof your platform when you know that there are new projects in Apache and new tooling coming out there. What's the secret sauce behind that? >> Well the first one is the open-source core because that just gave us the ability to have APIs, to extend, to build plugins, all of that in a community that does quite a bit of that, in fact, Kafka started with a customer that built a step, initially, we've now brought that into a product and created it as part of the platform but those are the things that in early market, a customer can do at first. We can see what emerges around that and then go. We will offer it to our customers as a step but we can also say, okay, now we're ready to productize this. So that's the first thing, and then I think the second one is really around when you see something like Spark emerge and we were all so focused on MapReduce and how are we going to make it easier and let's create tools to do that and we did that but then it was like MapReduce is going to go away, well there's still a lot of MapReduce out there, we know that. So we can see then, that MapReduce is going to be here and, I think the numbers are around 50/50, you probably know better than I do where Spark is versus MapReduce. I might be off but. >> Jim: If we had George Gilbert, he'd know. >> (laughs) Maybe ask George, yeah it's about 50/50. So you can't just abandon that, 'cause there's MapReduce out there, so it was, what are we going to do? Well, what we did in the Hadoop Distro days is we created a adaptive, big data layer that said, let's abstract a layer so that when we have to support a new distribution of Hadoop, we don't have to go back to the drawing board. So, it was the same thing with the execution engines. Okay, let's build this adaptive execution layer so that we're prepared to deal with other types of engines. I can build the transformation once, execute it anywhere, so that kind of philosophy of stepping back if you have that open platform, you can do those kinds of things, You can create those layers to remove all of that complexity because if you try to one-off and take on each one of those technologies, whether it's Spark or Flink or whatever's coming, as a product, and a product management organization, and a company, that's really difficult. So the community helps a ton on that, too. >> Donna, when you talk to customers about. You gave a great talk on the roadmap today to give a glimpse of where you guys are headed, your basic philosophy, your architecture, what are they pushing you for? Where are they trying to take you or where are you trying to take them? (laughs) >> (laughs) Hopefully, a little bit of both, right? I think it's being able to take advantage of the kinds of technologies, like you mentioned, that are emerging when they need them, but they also want us to make sure that all of that is really enterprise-ready, you're making it solid. Because we know from history and big data, a lot of those technologies are early, somebody has to get their knees skinned and all that with the first one. So they're really counting on us to really make it solid and quality and take care of all of those intricacies of delivering it in a non-open-source way where you're making it a real commercial product, so I think that's one thing. Then the second piece that we're seeing a lot more of as part of Hitachi we've moved up into the enterprise we also need to think a lot more about monitoring, administration, security, all of the things that go at the base of a pipeline. So, that scenario where they want us to focus. The great thing is, as part of Hitachi Vantara now, those aren't areas that we always had a lot of expertise in but Hitachi does 'cause those are kind of infrastructure-type technologies, so I think the push to do that is really strong and now we'll actually be able to do more of it because we've got that access to the portfolio. >> I don't know if this is a fair question for you, but I'm going to ask it anyway, because you just talked about some of the things Hitachi brings and that you can leverage and it's obvious that a lot of the things that Pentaho brings to Hitachi, the family but one of the things that's not talked about a lot is go-to-market, Hitachi data systems, traditionally don't have a lot of expertise at going to market with developers as the first step, where in your world you start. Has Pentaho been able to bring that cultural aspect to the new entity. >> For us, even though we have the open-source world, that's less of the developer and more of an architect or a CIO or somebody who's looking at that. >> David: Early adopter or. >> More and more it's the Chief Data Officer and that type of a persona. I think that, now that we are a entity, a brand new entity, that's a software-oriented company, we're absolutely going to play a way bigger role in that, because we brought software to market for 13 years. I think we've had early wins, we've had places where we're able to help. In an account, for instance, if you're in the data center, if that's where Hitachi is, if you start to get that partnership and we can start to draw the lines from, okay, who are the people that are now looking at, what's the big data strategy, what's the IOT strategy, where's the CDO. That's where we've had a much better opportunity to get to bigger sales in the enterprise in those global accounts, so I think we'll see more of that. Also there's the whole transformation of Hitachi as well, so I think there'll be a need to have much more of that software experience and also, Hitachi's hired two new executives, one on the sales side from SAP, and one who's now my boss, Brad Surak from GE Digital, so I think there's a lot of good, strong leadership around the software side and, obviously, all of the expertise that the folks at Pentaho have. >> That's interesting, that Chief Data Officer role is emerging as a target for you, we were at an event on Tuesday in Boston, there were about 200 Chief Data Officers there and I think about 25% had a Robotic Process Automation Initiative going on, they didn't ask about IOT just this little piece of IOT and then, Jim, Data Scientists and that whole world is now your world, okay great. Donna Prlich, thanks very much for coming to the Cube. Always a pleasure to see you. >> Donna: Yeah, thank you. >> Okay, Dave Velonte for Jim Kobielus. Keep it right there everybody, this is the Cube. We're live from PentahoWorld 2017 hashtag P-World 17. Brought to you by Hitachi Vantara, we'll be right back. (upbeat techno)

(upbeat music) >> Announcer: Live from Orlando, Florida, it's the Cube. Covering Pentaho World 2017 brought to you by Hitachi Vantara. >> Welcome back to the Cube's live coverage of Pentaho World brought to you of course by Hitachi Vantara. I am your host Rebecca Knight along with my co-host, Dave Vellante. We're joined by Anthony Deshazor. He is the Chief Solution's Architect and SVP of Customer Success at Pentaho. Thanks so much for coming on the Cube. >> Thank you for having me. Wonderful to be here. >> So before the cameras were rolling, we were talking a little bit about your career. You've been at this company for 12 years. >> Anthony: 12 years. >> And in different iterations of the company. >> Anthony: Right. >> Tell our viewers a little bit about how the company has evolved and also your role has evolved. >> One of the things that I really have watched Pentaho go through is the evolution to be more customer-centric. We began as a technology company. A bunch of geeks getting together. Had some neat tech, we could write some code and it was fun. We enjoyed it, but now as we start getting more customers we realized the technology had to serve the customer versus the customer serving the technology. That's wonderful transformation to go through to figure out how do you take that technology, bend it to the will of the customer and have that customer at the center of all your conversations. That was something that took us about six years to go through. Where we had all the geeks, kind of out of the room and put them in the back. I was one of the geeks so I got excused for some of those strategy conversations. But we got some good sales guys involved, some good marketing people who really brought that customer focus. Along the way we built better solutions 'cause we were listening more to our customers. It's interesting when you hear what people want to do you have a better chance of actually achieving it versus, let me build it and they will come. Other way, what do they need now let me build that. >> And really you said you were a geek, but you also really straddled the non-geek side too-- >> Anthony: Right. >> Because you can speak the other side. How do you do that, what is sort of the secret sauce to? >> I actually attribute that to some of my non-Pentaho, non-technical training. I'm actually a pastor of a church in Orlando, Florida. So I've done a lot of theological studies, a lot of homiletics that teach you how to stand on a stage and how to relate to people, even at a distance. And that actually comes through when you talk one on one with people. They feel like you're actually listening to them. And I actually attribute that all to that training. >> But the underline architecture still has to be malleable in order to accommodate-- >> Absolutely. >> That vision that you just put forth. It's kind of like that platforms versus products. >> Anthony: Yes. >> You built a platform not a product. And if you don't start with a vision of a platform you get a bunch of products. It don't necessarily tie together. Take us back to the early days. Was that part of the design thinking? >> Actually it was. Our five founders at Pentaho had that in their DNA. We had done three startups. I've been luckily enough or maybe stupid enough to do three of their startups. They had done three, I have done all three. But at the very core it was we needed to build something that was embeddable. That can work in process. Something that can be molded to the client's problem. We understood that whatever we built will never be enough. It would never be able to solve all of the problems. So if we put gates around it, it would reduce what we can do. So we wanted to build something that was extendable. Something that was a platform that if we didn't have the functionality you could easily build it. That's one of the reasons why went open source originally. Where all the code was open source. Anyone could extend it, anyone could bend it. Just because we understood there's no way for us, sitting in an ivory tower, to really figure out what's needed. >> And these decisions were made in the early to mid 2000's. >> Anthony: Yes. >> So they way predated Hadoop. >> Anthony: Yes. >> Then you had Hadoop saying okay, we're just going to bring compute to the data. And totally different data paradigm and platform approach. >> Anthony: Yes, yes. >> Was it that sort of philosophy that allowed you to adapt or did you have to do a heavy lift to adapt? >> Actually it wasn't a heavy lift. The legend has it, I wasn't in the conversation but our founding CEO had a conversation with one of our architects. I think they were having drinks or something at one of the local bars or pubs around Orlando, around the Orlando office. They begin to talk about Hadoop, pulled out a white napkin and just drew some things on the back of the napkin. A week later we had our first integration with Haddook. That's built upon that extendable, pluggable architecture that was there at the core. So that's really allowed up to adapt to new technologies to really catch the waves early and maybe sometimes anticipate the waves. >> So in this latest iteration of the company, Hitachi Vantara what can customers expect? >> The one way I can describe it is that it's maturity. You get the size of Hitachi Vantara behind you, you can do things that you could not do with a small company. As great as Pentaho was as a standalone company I believe we'll be that much bigger when you have the whole weight of Hitachi Anatara standing behind you. We had our strategic advisory board yesterday and one of the things I shared with those customers is that now you will see us attack things that we could not even fathom before. We have more developers so we can move features further, faster. We have more people in different regions so now we can do more services, help customers better in far regions like an Apac region for example. Where we struggled in the past as a standalone company. When you have a support center. A whole geography dedicated to Hitachi Vantara already there, it's now how do we instead of build the infrastructure just add that analytic DNA to the infrastructure that already exists. So that's what I think customers will experience very quickly. We can do more faster. We can do more in different locations. And we can even do more at a higher level of efficiency and quality if you would, because we have that backing of Hitachi Vantara. >> You were sharing this off camera. You do a lot of traveling, you talk to a lot of customers. >> Yes. >> You spend a lot of time in the aluminum tube. When you talk to customers and you compare it to now versus in the early days. The technology when you guys started was sort of mysterious and today the technology, there's plenty of it, it's abundant and it's pretty well understood. Sometimes it's hard to make work. But when you guys talk about digital transformation. >> Anthony: Sure. >> And disruption, be the disruptor, not the disruptee. A big thing that's changing is the processes within organizations. Those are largely unknown. It used to be very well known processes. Accounting or HR or whatever it was. Now the processes they're changing everyday. >> Yes. >> Do you have those conversations with customers and how are you as a company adapting and supporting that premise. >> One of the things I've noticed is that we have new roles introduced everyday. (laughter) All of a sudden, we had a data engineer. They used to be called DBA's, now they're data engineers. Now we have data scientists. Some companies I know they have data janitors and we have data prep. All these people now new roles in the organization all related to data. What we've been looking at is how do we make sure that every person, no matter their role understands how to use the data. My interest and my focus here at Pentaho is not just around architecture but also customer success. And we learned very quickly in the last two years as we've been on this customer success journey, you can install the best technology. It can be absolutely pristine from an architectural standpoint. You can get awards on architecture. But if you can't get the people to adapt, to adopt and use the software, use the solution you've basically just wasted your time. So what we've been focused on, how do we identify those new roles? How do we identify what skills do they need? How do we do training on the solution that was built so that no matter what their role is they understand how the solution can add value. How does the solution improve your job? Improve your life experience, maybe get things done faster. Maybe do more than you used to be able to do. But we've gotten out of the old tradition that there's a training department, accounting department. There used to be a time, I'm old enough to say this, where there was business analytics team but now every team has business analytics in it. It's part of someone's job to analyze the data. Even if that's not their primary function. So it's that, how do you make sure that no matter the role they have the skills and they access the data. >> How are you fostering collaboration between those roles? You always hear the stories of data scientists spend 80% of their trying to-- >> Anthony: Clean your data. >> Mess with the data, right. But you're right you've got the data engineer, the quality engineer, the application developer now-- >> Anthony: Yes. >> Data's now the new development kit. >> Anthony: It is. >> So how are you approaching the collaboration across those roles? >> So one of the things we've challenged our customers with is do you have a center of excellence? Doesn't have to be a dedicated center of excellence. It can be a concept or virtual team. But do you have a forum where people can collaborate? If you're doing analytics in a silo, if you're doing data integration in a silo and people are not talking to each other you're missing opportunities for efficiency, for innovation, even for understanding, wait if I do this that allows you to do this better. So how do you create that center of excellence? We have services now, professional services team are working with our customers to start that concept. Let's train one or two people. Make them the go to people for everyone else. >> Rebecca: Evangelists. >> Exactly, they become the evangelist. That helps us in two ways. One it helps us when it comes to getting people to use the technology in the right way. When you have a platform that means people have to use it correctly. You can build some amazing things with Pentaho, but you can also build some pretty, let's just say non-efficient things with the same platform. And then of course, me being the customer guy, they're going to blame the technology and I have to have that very delicate conversation, like not real good technology. It's the builder, it's what you built that's the problem. So we have some experts there that we can train and have them be the guardians, if you would. The custodians of the quality of the solutions. To make sure there's consistency and best practices. But the other side, we're also a renewable based company where we want to get the subscriptions, we want to get the renewals. So if I have evangelist there that can help the company use the solutions, adopt the solutions, that makes the renewal conversations that much easier. >> So I want to talk to you about measuring success. >> Anthony: Sure. >> Because one of the things that came out in the keynote today was Pentaho's underlying principles of social innovation and not just saving companies money or making them more money but also doing good in the world and bettering society. So how do you pitch that to customers? How do customers respond? How do you approach that idea? >> It's a hard one at times, because most companies are focused, I need to solve my problem. I don't care what we're doing about the rest of the world. I have this major pain point. This is what I need you to focus on. >> And fair enough. >> Absolutely, that's what they're paying the money for. That's where we start. We start there, can we get into start solving some problems together. And as the partnership develops, now what else can we do? So it's not just let me go sell this one solution. Let's partner for your good but for the good of the whole society. Are there things we can do that actually make not only your job easier, bring you money, but actually make things better. So some of the customers I love you heard IMS, you heard Dr. Alaina there Ella, excuse me today. I met with some of the other ones that are working with IMS, Dr. Ben. That story's actually close to my heart, 'cause who doesn't want to save money on their insurance but who also doesn't want better and safer cars? That's a social innovation story. Absolutely we're driving down the costs, we're helping companies manage their risks, understand their risks around insurance. But then we're also helping them get feedback on what makes cars better. What makes them safer? How can we avoid accidents? That is social innovation, that's what we're looking for. That's what Brian talked about with that double bottom line. How can we help you achieve your business goals but go beyond that to better society. >> We've heard a lot about transformations. Hitachi's own transformation, Pentaho, pre Hadoop, the Hadoop big data mime. You guys caught that wave. Now you're sort of entering, I don't know if it's your third wave or not. (laughter) Could be your fifth, tenth, I don't know. But there's another big wave coming. >> Anthony: Absolutely. >> Which is industrial IOT, Brian talked about IT and OT coming together. >> Anthony: Coming together. >> And it's early days but what are you seeing in the customer base. It was interesting, Brian very transparent, said how many Hitachi customers are out there? A few hands went up. >> Great, great. >> But not a ton. So as I say it is early days, but on paper the potential is enormous. >> Anthony: Great. >> It's a trillion dollar market, makes a lot of since, you see a lot of big industrial giants going after this and you've got some real assets you can bring to bear. >> Anthony: Right. >> What are the conversations like with customers and where do you see that all going? >> The way we approached customers and what I hear from customers, they don't really mention the word IOT. >> Dave: Okay. >> Most of them don't understand that they have an IOT problem. All they know is, I have this problem. So we're using IOT is to say, you have that outcome. You desire that outcome and to get that outcome you need to get data from all your devices. We have an IOT platform that can help you do that. So where the word even IOT comes up for us, is only in the solution not in the problem. Where I think some companies are missing the mark 'cause they're selling the technology. We have an IOT platform, please come buy our platform. Well, we've been a platform play forever with Pentaho and we understand that if you go there with a blank slate and say here, here's my platform come buy it, people don't understand it. They don't see the value. But if you can come and say, what's the problem you have? What's the outcome you're looking for? Let's focus on the outcome and back our way into the technology. And that's how we're approaching customers. That seems to be working so far. We have some IOT customers today that did not realize that they were doing IOT. >> The big product announcement today with Pentaho 8. What can we expect? >> Scale, that's the one word I would use for Pentaho 8. This is one of the best releases I think we've had. We have a new functionality called Work Nodes. We have customers who have been implementing something similar to this in the field for years. We've now productized it, it allows customers to scale out. We've heard from Brian and from others that to do this right you have to do it at scale. You have to provide this data, this analytics at scale. What our Worker Nodes allows customers to do is spin ups, spin down, distribute the workload on prim in the cloud. We don't really care, it's just we have a workload. You've given us a set of nodes we can work on we're just distribute the workload throughout that and when we're done we can spin them down. That elasticity, that flexibility as absolutely needed for today's data solutions. >> Great, Anthony thank you, you were a great guest. Thanks for coming on the Cube. >> Thank you for having me, thank you. >> I'm Rebecca Knight for Dave Vellante. We will have more from Pentaho World just after this. (upbeat music)

>> Announcer: Live from Las Vegas, it's the Cube, covering InterConnect 2017, brought to you by IBM. >> Okay, welcome back everyone. We are live in Mandalay Bay in Las Vegas for Day Three coverage of IBM InterConnect 2017. This is the Cube's exclusive coverage of IBM's Cloud Show and their Watson Data, IoT and more. I'm John Furrier, my co-host Dave Vellante. Our next two guests are Matt Kalmenson, VP of North American Sales and Cloud Service Providers at Veeam and Andy Vandeveld, VP of Global Strategic Alliances at Veeam. Guys, welcome to the Cube. >> Andy: Thank you. >> Matt: Thank you for having us. >> Andy, I want you to just set the table. We are familiar with Veeam. We're going to do your event. You've got a big event coming up in New Orleans. The Cube will be there. We've been watching you guys for many, many years. The Cube is on its eighth season. I think Season One, 2010 at VMworld. You've been very, very successful. But you're not a public company, but yet you guys act like a public company. You release your revenue and earnings. Set the table about what Veeam is, where you guys are at, and the current status. >> Sure. We're a company that's been around for 10 years, founded by our founders, Andrei Baronov and Ratmir Timashev. The company has grown significantly in the data availability space over the 10 years. We just announced our earnings, or our revenue, for the first time just last quarter. Last year we did $607 million in total revenue. And that's at a 28% growth. So we're a very high-growth company, even though we're at significant run rate of revenue. We've got 2,500 employees worldwide. We'll grow that substantially this year. We've got 240,000 customers worldwide. We're growing 4,000 new customers a month. So we're really a growth company. But we're a privately-held company. We like it that way. It allows us to do things that public companies might not be able to do because of their quarterly reporting requirements. We can make investments where we think investments need to be made for the future, as opposed to having to always watch the profitability. >> Yeah, the 30-day shot clock, as they say, or the quarterly shot clock, 90-day shot clock I mean. So you guys are very successful. Congratulations. And that's, by the way, a great story that you guys kind of act like a public company without being public. So it's like the best of both worlds. You guys are doing well. Congratulations. What's the secret sauce for you guys? Just for the sound bite. We'll get into some of the questions. We have some specific questions about IBM InterConnect. But why is Veeam winning? What's happening? Because you guys are really moving the needle. Quickly explain the secret formula of why Veeam is so successful. >> Well, I think it cuts across a couple of different dimensions. One is, we have a really great culture within the company. And so we have a culture of innovation. People feel like they're invested in the success of the company. And everybody is joining in that. And I think that really helps. We have great technology. We used to have an "It Just Works" tagline. Customers love that, particularly when we talk about their backup and data-protection solutions. They don't want to have to have people monitoring it on a regular basis. It just works. So I think customers love the technology. We have a great employee base, great executive team, and we have great partnerships, like the one here with IBM. And I think those are all key to the success. >> So I want to go back a little bit and sort of set the table on some of the big mega-trends that led to Veeam's ascendancy. When you go back to the early days of virtualization, you had this situation where you had underutilized servers. And then VMware essentially allowed us to consolidate those servers and dramatically increase the utilization, 'cause applications running on these servers, the servers were highly underutilized. The one application that needed all that sort of dedicated server power was backup. So when virtualization went from nothing to whatever, 60, 70% of the market, backup got choked. And it needed an answer. And one of those answers was Veeam. And it shot up and exploded as a company. You've done very, very well. There's more to it than that, distribution channels and so forth. Now we enter the Cloud era. And people now talk beyond backup, about availability. So what can we learn from the virtualization era? What's similar, what's different now? And why is the discussion shifting from one of backup to one of sort of always-on availability? >> So, it's a really good question. And if you think about the trends that we've seen, we've gone through this trend to a completely virtualized world. Yet when we still talk to CIOs, and Veeam's gone out and done studies where we've talked to CIOs, and when we talk to them, we hear that they still have the same challenges that they've had in the past. And that is, over 90% of them are still saying that their most critical needs are application uptime and their access to data. So when we go out and talk to hundreds and thousands of CIOs, they say, "We still have these needs: "access to our applications and access to data." Yet when we talk to them about how those needs are being met, over 80% of them say there's this gap. There's this gap, and while they still have those needs, those needs are not being met. And we call that the availability gap. And Andy and I were talking this morning over a cup of coffee, and he said, "You know what the availability gap is? He said, "Think about it like this." Think about when you're using your cell phone, and that cell phone is going down to 10%, 9%, 8% and 1%. And you get that feeling inside that "I'm about to lose service." And we all know that feeling when you lose connectivity on your cell phone. Now think about that as the CIO or someone who's relying upon that data. That's the availability gap that we see in the marketplace. And that's the gap that Veeam bridges. We bridge that availability gap. So we've addressed that from a virtualization perspective and, now, moving into the physical world too. But now as we move forward, we're seeing another dynamic change in the marketplace, of course. And that's Cloud. Now consumers want to think about different ways to consume technology. They want it on-prem. They want a managed solution. They want in a public cloud. They want it in a private cloud. And the way Veeam addresses that solution is essentially by saying, "However you want to consume your technology, "that's okay by us." If you want to consume your virtualized environment and have it backed up on premises, fantastic. If you want it backed up and managed by a managed service provider, that's okay too. If you want to have that data and information and back it up in a public cloud, great. Or in a private cloud. Or move it between those environments. We'll have the solutions to meet those needs. So we're going to meet this need of having uptime of applications, uptime of data, and availability of data, minimizing that availability gap that these CIOs are facing and allow them to manage and run data and applications and have it available to them no matter what scenario or platform they're running it in. So that's a vision that's more than just selling backup insurance. >> Matt: Absolutely. >> I mean, you just kind of answered it, but I'll ask it generally. How do you guys communicate your vision to CIOs? >> Well, I think we communicate it just like Matt said. When you talk about backup, that is sort of a yesterday story. It's really about making sure that those customers can get access to their data and that they can keep their applications, and, frankly, their businesses up and running. So when we go in and have a conversation with a CIO, we can delineate for them the specific business impacts of not having a robust availability platform. And that takes on different dimensions from a product perspective. So it's not just backup and recovery anymore. It's backup and recovery, but it's availability. It's, how do you orchestrate data across platforms? These are the source of new issues that Veeam has been addressing for the past few years. And I think it's what gives us an advantage in the data protection space. >> Now, it's a very competitive market. A lot of legacy vendors, of which IBM is one of them. But yet you're here at InterConnect as a major IBM partner. Help us understand what the relationship is with IBM, where it fits in the organization. Is it just Cloud? Is it across the entire organization? Fill us in. >> Yeah, so it is a strategic partnership for us. It's not just a single business-unit partnership. We're across the business units inside of IBM. And sure, there's IBM Spectrum Protect, which is a competitive product. But there are so many more opportunities for Veeam and IBM to win together that we're not going to worry about the few areas where there's some overlap. We just announced a few months ago that we're integrating, doing snapshot integration, for IBM Storwize and SAN Volume Controller, which we'll provide in our next version, version 10. It's coming out later this year. And that's a big thing. We don't do storage integration, snapshot integration, with all storage vendors. So when we can make a commitment like that, it's a meaningful commitment to the partnership. And so we have this great relationship on the storage side and other parts, but the genesis of the partnership actually started in the Cloud area with Matt's team and some guys on Matt's team that really drove hard to get a foothold in the relationship. So I'll let Matt talk about the Cloud relationship. >> Thanks Andy, and it's been a great relationship, because, while Andy focuses on the global alliances, I have a little bit more of a narrow focus around the Cloud, which really isn't so narrow. So we tend to team up together very well. And what really got our relationship kicked off was having the VMware Cloud Foundation, which runs on the IBM Cloud, where Veeam is the essential backup product that runs the management components of that platform. So, anything on the VMware Cloud Foundation, which sits on the IBM Cloud platform, is backed up and managed by Veeam. So that's now available. And that was really the genesis of the relationship from a Cloud perspective, so that was very, very exciting. >> And Bluemix, they're in the mix? >> Bluemix is in the mix. And that VMware Cloud Foundation actually leverages the Bluemix platform. And then there's several layers of the Bluemix Cloud platform. And now we're going to be in the Bluemix catalog, what is called the IMS catalog, which will be for everyone who's looking to provision a cloud service, can go ahead and pull down and choose to provision VMware and some infrastructure and other services and have it backed up by Veeam. >> So that deal between IBM and VMware was a real catalyst for your relationship? >> Matt: A real catalyst for us. >> Now, of course, VMware's done other deals. They just did one with Amazon recently. But my understanding is the IBM relationship-- >> Well, Pat's been clear. It's a multi-cloud world. So the thing that's clear from this show is, multicloud is what's happening. So that's-- >> Well, what this has given us the ability to do is say, no matter what your customer looks like, there's an opportunity for us to partner and work together. So if you think about the VCF, the VMware Cloud Foundation, might be some organizations that are enterprise in scope, that have a large, on-premises type of deployment. So we're looking for large automation platforms that are looking to automate moving to the Cloud or maybe move back from the Cloud to on-prem, but nevertheless have these very high-end availability needs and business continuity needs. Now, if you think about the IMS platform in Bluemix, which might be a traditional hit-the-keyboard and looking for some infrastructure that you might spin up in a born-in-the-cloud company, from day one, we'll have some services available there for you as well. So you can go from a small SMB company that might be born in the Cloud to a legacy Fortune 100 company that has some kind of cloud foundation needs. And between the partnerships of our organizations we have solutions to meet those needs. >> One of the interesting things to me about Veeam is when you started out, when you were in your eating glass mode, you were going to VMUGs and doing all that sort of hard work with the hardcore VMware practitioners. Now you're on your way to a billion dollars. And you're striking partnerships with companies like IBM. How have the conversations changed in terms of who you sell to, who you're interacting with. Obviously more CIOs are probably paying attention to the investments that they're making. How has that changed? >> Well, just from the Veeam perspective, these partnerships are extremely important. Companies like IBM have relationships with enterprises that go back decades. And, for us, that's an opportunity for us to leverage their trusted advisor status with those decision makers in the enterprise. Our business started, and we have a very robust small and medium-size business. We have a strong and growing enterprise business. And we're looking for the enterprise as our growth vehicle to get to a billion dollars. So partnering with enterprise-class partners like IBM is really a key force. >> I mean, you guys can bring your value proposition pretty much to any environment. To your cell phone analogy about the battery power, which we've all seen. But, you know, Dave's on Verizon. I'm on AT&T. So this is the same dynamic in the Cloud. This is where you guys are looking for the growth. Am I getting that right? >> Yeah, I think that's a pretty good analogy. And the way I kind of think of it is, we have the best solutions in the marketplace for availability needs, regardless of the size of the organization, the end-user needs, regardless of the go-to-market strategy and regardless of the platform. So by building, and as we continue to move up market and aggressively build partnerships like the ones with IBM, it allows to address the business needs no matter what those business needs are. And partnerships like the ones with IBM allow us to scale to great lengths. >> Matt and Andy, I want to ask you a question for the folks watching, 'cause here at IBM InterConnect, the IBM relationship that you guys outlined, what's the major to-do for Veeam this year? I mean, in terms of, as you accelerate. You've got 600 million in revenue. What's the core message that you're sending the marketplace in terms of where that growth's going to come from? And what's the tag, what's the bumper sticker for Veeam right now? >> I think it's around the Cloud. I think that's an area where we're putting a heavy investment. We're hiring great people. And for us, we see that data protection is going to have to span the Cloud environment. Now, it's going to be on-prem, it's going to be in the Cloud, it's going to be a hybrid. But from our perspective >> Matt: It's everywhere. >> Yeah, becoming much more robust in the Cloud is really going to be a focus area for us this year. >> Yeah, I would agree. I would tack onto that continuing to scale into the enterprises very aggressively. We've built out a large enterprise organization strictly focused on the enterprise. We've had the technology to address the enterprise needs, but now we've dedicated sales teams and organizational structures just to address the enterprise. And continuing to bring out our Cloud sales organizations and make sure that everyone within our organizations also has a benefit by not only understanding the Cloud business, but our sales teams are compensated to sell Cloud solutions. So it's not like we have a stovepipe organization that just goes sell Cloud, and then somebody else who goes out and sells an on-prem solution. We have teams that are focused on compensation that works together so that our teams can go out and send the message of, "consumption's your decision". We want to help you make the right business decision. We want to help make the right technological decisions. But how you consume, that's up to you. And we're here to help you coach, here to help guide, here to help show some maps on how you can do that. We know we have the right availability solution no matter what needs or what consumption model of what path you want to go down. >> And the enterprise has certainly changed. And you guys understand the enterprise readiness. And you've got product leadership. So that seems to me to be the magic. >> And also the relationship with an organization like IBM because that helps us bridge those gaps. >> Well, congratulations guys, for great success and a good relationship with IBM. Great story. Love the story of being private with this kind of transparency. It's rare, and so congratulations Andy, Matt. >> Thank you. >> Thanks for joining the Cube. More live coverage. Stay with us all day, Day Three of exclusive coverage of IBM InterConnect 2017. I'm John Furrier with Dave Vellante. Stay with us. More after this short break.

>> Announcer: Live from Santa Fe, California, it's the Cube covering Big Data Silicon Valley 2017. >> Welcome, back, everyone. We're here live in Silicon Valley in San Jose for Big Data SV in conjunct with stratAHEAD Hadoop part two. Three days of coverage here in Silicon Valley and Big Data. It's our eighth year covering Hadoop and the Hadoop ecosystem. Now expanding beyond just Hadoop into AI, machine learning, IoT, cloud computing with all this compute is really making it happen. I'm John Furrier with my co-host George Gilbert. Our next guest is Arik Pelkey who is the senior director of product marketing at Pentaho that we've covered many times and covered their event at Pentaho world. Thanks for joining us. >> Thank you for having me. >> So, in following you guys I'll see Pentaho was once an independent company bought by Hitachi, but still an independent group within Hitachi. >> That's right, very much so. >> Okay so you guys some news. Let's just jump into the news. You guys announced some of the machine learning. >> Exactly, yeah. So, Arik Pelkey, Pentaho. We are a data integration and analytics software company. You mentioned you've been doing this for eight years. We have been at Big Data for the past eight years as well. In fact, we're one of the first vendors to support Hadoop back in the day, so we've been along for the journey ever since then. What we're announcing today is really exciting. It's a set of machine learning orchestration capabilities, which allows data scientists, data engineers, and data analysts to really streamline their data science processes. Everything from ingesting new data sources through data preparation, feature engineering which is where a lot of data scientists spend their time through tuning their models which can still be programmed in R, in Weka, in Python, and any other kind of data science tool of choice. What we do is we help them deploy those models inside of Pentaho as a step inside of Pentaho, and then we help them update those models as time goes on. So, really what this is doing is it's streamlining. It's making them more productive so that they can focus their time on things like model building rather than data preparation and feature engineering. >> You know, it's interesting. The market is really active right now around machine learning and even just last week at Google Next, which is their cloud event, they had made the acquisition of Kaggle, which is kind of an open data science. You mentioned the three categories: data engineer, data science, data analyst. Almost on a progression, super geek to business facing, and there's different approaches. One of the comments from the CEO of Kaggle on the acquisition when we wrote up at Sylvan Angle was, and I found this fascinating, I want to get your commentary and reaction to is, he says the data science tools are as early as generations ago, meaning that all the advances and open source and tooling and software development is far along, but now data science is still at that early stage and is going to get better. So, what's your reaction to that, because this is really the demand we're seeing is a lot of heavy lifing going on in the data science world, yet there's a lot of runway of more stuff to do. What is that more stuff? >> Right. Yeah, we're seeing the same thing. Last week I was at the Gardener Data and Analytics conference, and that was kind of the take there from one of their lead machine learning analysts was this is still really early days for data science software. So, there's a lot of Apache projects out there. There's a lot of other open source activity going on, but there are very few vendors that bring to the table an integrated kind of full platform approach to the data science workflow, and that's what we're bringing to market today. Let me be clear, we're not trying to replace R, or Python, or MLlib, because those are the tools of the data scientists. They're not going anywhere. They spent eight years in their phD program working with these tools. We're not trying to change that. >> They're fluent with those tools. >> Very much so. They're also spending a lot of time doing feature engineering. Some research reports, say between 70 and 80% of their time. What we bring to the table is a visual drag and drop environment to do feature engineering a much faster, more efficient way than before. So, there's a lot of different kind of desperate siloed applications out there that all do interesting things on their own, but what we're doing is we're trying to bring all of those together. >> And the trends are reduce the time it takes to do stuff and take away some of those tasks that you can use machine learning for. What unique capabilities do you guys have? Talk about that for a minute, just what Pentaho is doing that's unique and added value to those guys. >> So, the big thing is I keep going back to the data preparation part. I mean, that's 80% of time that's still a really big challenge. There's other vendors out there that focus on just the data science kind of workflow, but where we're really unqiue is around being able to accommodate very complex data environments, and being able to onboard data. >> Give me an example of those environments. >> Geospatial data combined with data from your ERP or your CRM system and all kinds of different formats. So, there might be 15 different data formats that need to be blended together and standardized before any of that can really happen. That's the complexity in the data. So, Pentaho, very consistent with everything else that we do outside of machine learning, is all about helping our customers solve those very complex data challenges before doing any kind of machine learning. One example is one customer is called Caterpillar Machine Asset Intelligence. So, their doing predictive maintenance onboard container ships and on ferry's. So, they're taking data from hundreds and hundreds of sensors onboard these ships, combining that kind of operational sensor data together with geospatial data and then they're serving up predictive maintenance alerts if you will, or giving signals when it's time to replace an engine or complace a compressor or something like that. >> Versus waiting for it to break. >> Versus waiting for it to break, exactly. That's one of the real differentiators is that very complex data environment, and then I was starting to move toward the other differentiator which is our end to end platform which allows customers to deliver these analytics in an embedded fashion. So, kind of full circle, being able to send that signal, but not to an operational system which is sometimes a challenge because you might have to rewrite the code. Deploying models is a really big challenge within Pentaho because it is this fully integrated application. You can deploy the models within Pentaho and not have to jump out into a mainframe environment or something like that. So, I'd say differentiators are very complex data environments, and then this end to end approach where deploying models is much easier than ever before. >> Perhaps, let's talk about alternatives that customers might see. You have a tool suite, and others might have to put together a suite of tools. Maybe tell us some of the geeky version would be the impendent mismatch. You know, like the chasms you'd find between each tool where you have to glue them together, so what are some of those pitfalls? >> One of the challenges is, you have these data scientists working in silos often times. You have data analysts working in silos, you might have data engineers working in silos. One of the big pitfalls is not really collaborating enough to the point where they can do all of this together. So, that's a really big area that we see pitfalls. >> Is it binary not collaborating, or is it that the round trip takes so long that the quality or number of collaborations is so drastically reduced that the output is of lower quality? >> I think it's probably a little bit of both. I think they want to collaborate but one person might sit in Dearborn, Michigan and the other person might sit in Silicon Valley, so there's just a location challenge as well. The other challenge is, some of the data analysts might sit in IT and some of the data scientists might sit in an analytics department somewhere, so it kind of cuts across both location and functional area too. >> So let me ask from the point of view of, you know we've been doing these shows for a number of years and most people have their first data links up and running and their first maybe one or two use cases in production, very sophisticated customers have done more, but what seems to be clear is the highest value coming from those projects isn't to put a BI tool in front of them so much as to do advanced analytics on that data, apply those analytics to inform a decision, whether a person or a machine. >> That's exactly right. >> So, how do you help customers over that hump and what are some other examples that you can share? >> Yeah, so speaking of transformative. I mean, that's what machine learning is all about. It helps companies transform their businesses. We like to talk about that at Pentaho. One customer kind of industry example that I'll share is a company called IMS. IMS is in the business of providing data and analytics to insurance companies so that the insurance companies can price insurance policies based on usage. So, it's a usage model. So, IMS has a technology platform where they put sensors in a car, and then using your mobile phone, can track your driving behavior. Then, your insurance premium that month reflects the driving behavior that you had during that month. In terms of transformative, this is completely upending the insurance industry which has always had a very fixed approach to pricing risk. Now, they understand everything about your behavior. You know, are you turning too fast? Are you breaking too fast, and they're taking it further than that too. They're able to now do kind of a retroactive look at an accident. So, after an accident, they can go back and kind of decompose what happened in the accident and determine whether or not it was your fault or was in fact the ice on the street. So, transformative? I mean, this is just changing things in a really big way. >> I want to get your thoughts on this. I'm just looking at some of the research. You know, we always have the good data but there's also other data out there. In your news, 92% of organizations plan to deploy more predictive analytics, however 50% of organizations have difficulty integrating predictive analytics into their information architecture, which is where the research is shown. So my question to you is, there's a huge gap between the technology landscapes of front end BI tools and then complex data integration tools. That seems to be the sweet spot where the value's created. So, you have the demand and then front end BI's kind of sexy and cool. Wow, I could power my business, but the complexity is really hard in the backend. Who's accessing it? What's the data sources? What's the governance? All these things are complicated, so how do you guys reconcile the front end BI tools and the backend complexity integrations? >> Our story from the beginning has always been this one integrated platform, both for complex data integration challenges together with visualizations, and that's very similar to what this announcement is all about for the data science market. We're very much in line with that. >> So, it's the cart before the horse? Is it like the BI tools are really driven by the data? I mean, it makes sense that the data has to be key. Front end BI could be easy if you have one data set. >> It's funny you say that. I presented at the Gardner conference last week and my topic was, this just in: it's not about analytics. Kind of in jest, but it drove a really big crowd. So, it's about the data right? It's about solving the data problem before you solve the analytics problem whether it's a simple visualization or it's a complex fraud machine learning problem. It's about solving the data problem first. To that quote, I think one of the things that they were referencing was the challenging information architectures into which companies are trying to deploy models and so part of that is when you build a machine learning model, you use R and Python and all these other ones we're familiar with. In order to deploy that into a mainframe environment, someone has to then recode it in C++ or COBOL or something else. That can take a really long time. With our integrated approach, once you've done the feature engineering and the data preparation using our drag and drop environment, what's really interesting is that you're like 90% of the way there in terms of making that model production ready. So, you don't have to go back and change all that code, it's already there because you used it in Pentaho. >> So obviously for those two technologies groups I just mentioned, I think you had a good story there, but it creates problems. You've got product gaps, you've got organizational gaps, you have process gaps between the two. Are you guys going to solve that, or are you currently solving that today? There's a lot of little questions in there, but that seems to be the disconnect. You know, I can do this, I can do that, do I do them together? >> I mean, sticking to my story of one integrated approach to being able to do the entire data science workflow, from beginning to end and that's where we've really excelled. To the extent that more and more data engineers and data analysts and data scientists can get on this one platform even if their using R and WECCA and Python. >> You guys want to close those gaps down, that's what you guys are doing, right? >> We want to make the process more collaborative and more efficient. >> So Dave Alonte has a question on CrowdChat for you. Dave Alonte was in the snowstorm in Boston. Dave, good to see you, hope you're doing well shoveling out the driveway. Thanks for coming in digitally. His question is HDS has been known for mainframes and storage, but Hitachi is an industrial giant. How is Pentaho leveraging Hitatchi's IoT chops? >> Great question, thanks for asking. Hitatchi acquired Pentaho about two years ago, this is before my time. I've been with Pentaho about ten months ago. One of the reasons that they acquired Pentaho is because a platform that they've announced which is called Lumata which is their IoT platform, so what Pentaho is, is the analytics engine that drives that IoT platform Lumata. So, Lumata is about solving more of the hardware sensor, bringing data from the edge into being able to do the analytics. So, it's an incredibly great partnership between Lumata and Pentaho. >> Makes an eternal customer too. >> It's a 90 billion dollar conglomerate so yeah, the acquisition's been great and we're still very much an independent company going to market on our own, but we now have a much larger channel through Hitatchi's reps around the world. >> You've got IoT's use case right there in front of you. >> Exactly. >> But you are leveraging it big time, that's what you're saying? >> Oh yeah, absolutely. We're a very big part of their IoT strategy. It's the analytics. Both of the examples that I shared with you are in fact IoT, not by design but it's because there's a lot of demand. >> You guys seeing a lot of IoT right now? >> Oh yeah. We're seeing a lot of companies coming to us who have just hired a director or vice president of IoT to go out and figure out the IoT strategy. A lot of these are manufacturing companies or coming from industries that are inefficient. >> Digitizing the business model. >> So to the other point about Hitachi that I'll make, is that as it relates to data science, a 90 billion dollar manufacturing and otherwise giant, we have a very deep bench of phD data scientists that we can go to when there's very complex data science problems to solve at customer sight. So, if a customer's struggling with some of the basic how do I get up and running doing machine learning, we can bring our bench of data scientist at Hitatchi to bear in those engagements, and that's a really big differentiator for us. >> Just to be clear and one last point, you've talked about you handle the entire life cycle of modeling from acquiring the data and prepping it all the way through to building a model, deploying it, and updating it which is a continuous process. I think as we've talked about before, data scientists or just the DEV ops community has had trouble operationalizing the end of the model life cycle where you deploy it and update it. Tell us how Pentaho helps with that. >> Yeah, it's a really big problem and it's a very simple solution inside of Pentaho. It's basically a step inside of Pentaho. So, in the case of fraud let's say for example, a prediction might say fraud, not fraud, fraud, not fraud, whatever it is. We can then bring that kind of full lifecycle back into the data workflow at the beginning. It's a simple drag and drop step inside of Pentaho to say which were right and which were wrong and feed that back into the next prediction. We could also take it one step further where there has to be a manual part of this too where it goes to the customer service center, they investigate and they say yes fraud, no fraud, and then that then gets funneled back into the next prediction. So yeah, it's a big challenge and it's something that's relatively easy for us to do just as part of the data science workflow inside of Pentaho. >> Well Arick, thanks for coming on The Cube. We really appreciate it, good luck with the rest of the week here. >> Yeah, very exciting. Thank you for having me. >> You're watching The Cube here live in Silicon Valley covering Strata Hadoop, and of course our Big Data SV event, we also have a companion event called Big Data NYC. We program with O'Reilley Strata Hadoop, and of course have been covering Hadoop really since it's been founded. This is The Cube, I'm John Furrier. George Gilbert. We'll be back with more live coverage today for the next three days here inside The Cube after this short break.

>> Announcer: Live from New York, it's theCUBE, covering the IBM Machine Learning Launch Event, brought to you by IBM. Now, here are your hosts, Dave Vellante and Stu Miniman. >> Welcome back to New York City, everybody. We're here at the Waldorf Astoria covering the IBM Machine Learning Launch Event, bringing machine learning to the IBM Z. Bryan Smith is here, he's the vice president of R&D and the CTO of Rocket Software, powering the path to digital transformation. Bryan, welcome to theCUBE, thanks for coming on. >> Thanks for having me. >> So, Rocket Software, Waltham, Mass. based, close to where we are, but a lot of people don't know about Rocket, so pretty large company, give us the background. >> It's been around for, this'll be our 27th year. Private company, we've been a partner of IBM's for the last 23 years. Almost all of that is in the mainframe space, or we focused on the mainframe space, I'll say. We have 1,300 employees, we call ourselves Rocketeers. It's spread around the world. We're really an R&D focused company. More than half the company is engineering, and it's spread across the world on every continent and most major countries. >> You're esstenially OEM-ing your tools as it were. Is that right, no direct sales force? >> About half, there are different lenses to look at this, but about half of our go-to-market is through IBM with IBM-labeled, IBM-branded products. We've always been, for the side of products, we've always been the R&D behind the products. The partnership, though, has really grown. It's more than just an R&D partnership now, now we're doing co-marketing, we're even doing some joint selling to serve IBM mainframe customers. The partnership has really grown over these last 23 years from just being the guys who write the code to doing much more. >> Okay, so how do you fit in this announcement. Machine learning on Z, where does Rocket fit? >> Part of the announcement today is a very important piece of technology that we developed. We call it data virtualization. Data virtualization is really enabling customers to open their mainframe to allow the data to be used in ways that it was never designed to be used. You might have these data structures that were designed 10, 20, even 30 years ago that were designed for a very specific application, but today they want to use it in a very different way, and so, the traditional path is to take that data and copy it, to ETL it someplace else they can get some new use or to build some new application. What data virtualization allows you to do is to leave that data in place but access it using APIs that developers want to use today. They want to use JSON access, for example, or they want to use SQL access. But they want to be able to do things like join across IMS, DB2, and VSAM all with a single query using an SQL statement. We can do that relational databases and non-relational databases. It gets us out of this mode of having to copy data into some other data store through this ETL process, access the data in place, we call it moving the applications or the analytics to the data versus moving the data to the analytics or to the applications. >> Okay, so in this specific case, and I have said several times today, as Stu has heard me, two years ago IBM had a big theme around the z13 bringing analytics and transactions together, this sort of extends that. Great, I've got this transaction data that lives behind a firewall somewhere. Why the mainframe, why now? >> Well, I would pull back to where I said where we see more companies and organizations wanting to move applications and analytics closer to the data. The data in many of these large companies, that core business-critical data is on the mainframe, and so, being able to do more real time analytics without having to look at old data is really important. There's this term data gravity. I love the visual that presents in my mind that you have these different masses, these different planets if you will, and the biggest, massivest planet in that solar system really is the data, and so, it's pulling the smaller satellites if you will into this planet or this star by way of gravity because data is, data's a new currency, data is what the companies are running on. We're helping in this announcement with being able to unlock and open up all mainframe data sources, even some non-mainframe data sources, and using things like Spark that's running on the platform, that's running on z/OS to access that data directly without having to write any special programming or any special code to get to all their data. >> And the preferred place to run all that data is on the mainframe obviously if you're a mainframe customer. One of the questions I guess people have is, okay, I get that, it's the transaction data that I'm getting access to, but if I'm bringing transaction and analytic data together a lot of times that analytic data might be in social media, it might be somewhere else not on the mainframe. How do envision customers dealing with that? Do you have tooling them to do that? >> We do, so this data virtualization solution that I'm talking about is one that is mainframe resident, but it can also access other data sources. It can access DB2 on Linux Windows, it can access Informix, it can access Cloudant, it can access Hadoop through IBM's BigInsights. Other feeds like Twitter, like other social media, it can pull that in. The case where you'd want to do that is where you're trying to take that data and integrate it with a massive amount of mainframe data. It's going to be much more highly performant by pulling this other small amount of data into, next to that core business data. >> I get the performance and I get the security of the mainframe, I like those two things, but what about the economics? >> Couple of things. One, IBM when they ported Spark to z/OS, they did it the right way. They leveraged the architecture, it wasn't just a simple port of recompiling a bunch of open source code from Apache, it was rewriting it to be highly performant on the Z architecture, taking advantage of specialty engines. We've done the same with the data virtualization component that goes along with that Spark on z/OS offering that also leverages the architecture. We actually have different binaries that we load depending on which architecture of the machine that we're running on, whether it be a z9, an EC12, or the big granddaddy of a z13. >> Bryan, can you speak the developers? I think about, you're talking about all this mobile and Spark and everything like that. There's got to be certain developers that are like, "Oh my gosh, there's mainframe stuff. "I don't know anything about that." How do you help bridge that gap between where it lives in the tools that they're using? >> The best example is talking about embracing this API economy. And so, developers really don't care where the stuff is at, they just want it to be easy to get to. They don't have to code up some specific interface or language to get to different types of data, right? IBM's done a great job with the z/OS Connect in opening up the mainframe to the API economy with ReSTful interfaces, and so with z/OS Connect combined with Rocket data virtualization, you can come through that z/OS Connect same path using all those same ReSTful interfaces pushing those APIs out to tools like Swagger, which the developers want to use, and not only can you get to the applications through z/OS Connect, but we're a service provider to z/OS Connect allowing them to also get to every piece of data using those same ReSTful APIs. >> If I heard you correctly, the developer doesn't need to even worry about that it's on mainframe or speak mainframe or anything like that, right? >> The goal is that they never do. That they simply see in their tool-set, again like Swagger, that they have data as well as different services that they can invoke using these very straightforward, simple ReSTful APIs. >> Can you speak to the customers you've talked to? You know, there's certain people out in the industry, I've had this conversation for a few years at IBM shows is there's some part of the market that are like, oh, well, the mainframe is this dusty old box sitting in a corner with nothing new, and my experience has been the containers and cool streaming and everything like that, oh well, you know, mainframe did virtualization and Linux and all these things really early, decades ago and is keeping up with a lot of these trends with these new type of technologies. What do you find in the customers that, how much are they driving forward on new technologies, looking for that new technology and being able to leverage the assets that they have? >> You asked a lot of questions there. The types of customers certainly financial and insurance are the big two, but that doesn't mean that we're limited and not going after retail and helping governments and manufacturing customers as well. What I find is talking with them that there's the folks who get it and the folks who don't, and the folks who get it are the ones who are saying, "Well, I want to be able "to embrace these new technologies," and they're taking things like open source, they're looking at Spark, for example, they're looking at Anaconda. Last week, we just announced at the Anaconda Conference, we stepped on stage with Continuum, IBM, and we, Rocket, stood up there talking about this partnership that we formed to create this ecosystem because the development world changes very, very rapidly. For a while, all the rage was JDBC, or all the rage was component broker, and so today it's Spark and Anaconda are really in the forefront of developers' minds. We're constantly moving to keep up with developers because that's where the action's happening. Again, they don't care where the data is housed as long as you can open that up. We've been playing with this concept that came up from some research firm called two-speed IT where you have maybe your core business that has been running for years, and it's designed to really be slow-moving, very high quality, it keeps everything running today, but they want to embrace some of their new technologies, they want to be able to roll out a brand-new app, and they want to be able to update that multiple times a week. And so, this two-speed IT says, you're kind of breaking 'em off into two separate teams. You don't have to take your existing infrastructure team and say, "You must embrace every Agile "and every DevOps type of methodology." What we're seeing customers be successful with is this two-speed IT where you can fracture these two, and now you need to create some nice integration between those two teams, so things like data virtualization really help with that. It opens up and allows the development teams to very quickly access those assets on the mainframe in this case while allowing those developers to very quickly crank out an application where quality is not that important, where being very quick to respond and doing lots of AB testing with customers is really critical. >> Waterfall still has its place. As a company that predominately, or maybe even exclusively is involved in mainframe, I'm struck by, it must've been 2008, 2009, Paul Maritz comes in and he says VMWare our vision is to build the software mainframe. And of course the world said, "Ah, that's, mainframe's dead," we've been hearing that forever. In many respects, I accredit the VMWare, they built sort of a form of software mainframe, but now you hear a lot of talk, Stu, about going back to bare metal. You don't hear that talk on the mainframe. Everything's virtualized, right, so it's kind of interesting to see, and IBM uses the language of private cloud. The mainframe's, we're joking, the original private cloud. My question is you're strategy as a company has been always focused on the mainframe and going forward I presume it's going to continue to do that. What's your outlook for that platform? >> We're not exclusively by the mainframe, by the way. We're not, we have a good mix. >> Okay, it's overstating that, then. It's half and half or whatever. You don't talk about it, 'cause you're a private company. >> Maybe a little more than half is mainframe-focused. >> Dave: Significant. >> It is significant. >> You've got a large of proportion of the company on mainframe, z/OS. >> So we're bullish on the mainframe. We continue to invest more every year. We invest, we increase our investment every year, and so in a software company, your investment is primarily people. We increase that by double digits every year. We have license revenue increases in the double digits every year. I don't know many other mainframe-based software companies that have that. But I think that comes back to the partnership that we have with IBM because we are more than just a technology partner. We work on strategic projects with IBM. IBM will oftentimes stand up and say Rocket is a strategic partner that works with us on hard problem-solving customers issues every day. We're bullish, we're investing more all the time. We're not backing away, we're not decreasing our interest or our bets on the mainframe. If anything, we're increasing them at a faster rate than we have in the past 10 years. >> And this trend of bringing analytics and transactions together is a huge mega-trend, I mean, why not do it on the mainframe? If the economics are there, which you're arguing that in many use cases they are, because of the value component as well, then the future looks pretty reasonable, wouldn't you say? >> I'd say it's very, very bright. At the Anaconda Conference last week, I was coming up with an analogy for these folks. It's just a bunch of data scientists, right, and during most of the breaks and the receptions, they were just asking questions, "Well, what is a mainframe? "I didn't know that we still had 'em, "and what do they do?" So it was fun to educate them on that. But I was trying to show them an analogy with data warehousing where, say that in the mid-'90s it was perfectly acceptable to have a separate data warehouse separate from your transaction system. You would copy all this data over into the data warehouse. That was the model, right, and then slowly it became more important that the analytics or the BI against that data warehouse was looking at more real time data. So then it became more efficiencies and how do we replicate this faster, and how do we get closer to, not looking at week-old data but day-old data? And so, I explained that to them and said the days of being able to do analytics against old data that's copied are going away. ETL, we're also bullish to say that ETL is dead. ETL's future is very bleak. There's no place for it. It had its time, but now it's done because with data virtualization you can access that data in place. I was telling these folks as they're talking about, these data scientists, as they're talking about how they look at their models, their first step is always ETL. And so I told them this story, I said ETL is dead, and they just look at me kind of strange. >> Dave: Now the first step is load. >> Yes, there you go, right, load it in there. But having access from these platforms directly to that data, you don't have to worry about any type of a delay. >> What you described, though, is still common architecture where you've got, let's say, a Z mainframe, it's got an InfiniBand pipe to some exit data warehouse or something like that, and so, IBM's vision was, okay, we can collapse that, we can simplify that, consolidate it. SAP with HANA has a similar vision, we can do that. I'm sure Oracle's got their vision. What gives you confidence in IBM's approach and legs going forward? >> Probably due to the advances that we see in z/OS itself where handling mixed workloads, which it's just been doing for many of the 50 years that it's been around, being able to prioritize different workloads, not only just at the CPU dispatching, but also at the memory usage, also at the IO, all the way down through the channel to the actual device. You don't see other operating systems that have that level of granularity for managing mixed workloads. >> In the security component, that's what to me is unique about this so-called private cloud, and I say, I was using that software mainframe example from VMWare in the past, and it got a good portion of the way there, but it couldn't get that last mile, which is, any workload, any application with the performance and security that you would expect. It's just never quite got there. I don't know if the pendulum is swinging, I don't know if that's the accurate way to say it, but it's certainly stabilized, wouldn't you say? >> There's certainly new eyes being opened every day to saying, wait a minute, I could do something different here. Muscle memory doesn't have to guide me in doing business the way I have been doing it before, and that's this muscle memory I'm talking about of this ETL piece. >> Right, well, and a large number of workloads in mainframe are running Linux, right, you got Anaconda, Spark, all these modern tools. The question you asked about developers was right on. If it's independent or transparent to developers, then who cares, that's the key. That's the key lever this day and age is the developer community. You know it well. >> That's right. Give 'em what they want. They're the customers, they're the infrastructure that's being built. >> Bryan, we'll give you the last word, bumper sticker on the event, Rocket Software, your partnership, whatever you choose. >> We're excited to be here, it's an exciting day to talk about machine learning on z/OS. I say we're bullish on the mainframe, we are, we're especially bullish on z/OS, and that's what this even today is all about. That's where the data is, that's where we need the analytics running, that's where we need the machine learning running, that's where we need to get the developers to access the data live. >> Excellent, Bryan, thanks very much for coming to theCUBE. >> Bryan: Thank you. >> And keep right there, everybody. We'll be back with our next guest. This is theCUBE, we're live from New York City. Be right back. (electronic keyboard music)

>> Announcer: Live from Boston, Massachusetts this is the CUBE covering Spark Summit East 2017 brought to you by Databricks. Now, here are your hosts Dave Vellante and George Gilbert. >> Welcome back to Boston everybody this is the CUBE. We're live here at Spark Summit East in the Hynes Convention Center. This is the CUBE, check out SiliconANGLE.com for all the news of the day. Check out Wikibon.com for all the research. I'm really excited about this session here. Al Essa is here, he's the vice president of analytics and R&D at McGraw-Hill Education. And I'm so excited because we always talk about digital transformations and transformations. We have an example of 150 year old company that has been, I'm sure, through many transformations. We're going to talk about a recent one. Al Essa, welcome to the CUBE, thanks for coming on. >> Thank you, pleasure to be here. >> So you heard my little narrative up front. You, obviously, have not been with the company for 150 years (laughs), you can't talk about all the transformations, but there's certainly one that's recent in the last couple of years, anyway which is digital. We know McGraw Hill is a print publisher, describe your business. >> Yeah, so McGraw Hill Education has been traditionally a print publisher, but beginning with our new CEO, David Levin, he joined the company about two years ago and now we call ourselves a learning science company. So it's no longer print publishing, it's smart digital and by smart digital we mean we're trying to transform education by applying principles of learning science. Basically what that means is we try to understand, how do people learn? And how they can learn better. So there are a number of domains, cognitive science, brain sciences, data science and we begin to try to understand what are the known knowns in these areas and then apply it to education. >> I think Marc Benioff said it first, at least the first I heard he said there were going to be way more Saas companies that come out of non-tech companies than tech companies. We're talking off camera, you're a software company. Describe that in some detail. >> Yeah, so being a software company is new for us, but we've moved pretty quickly. Our core competency has been really expert knowledge about education. We work with educators, subject matter experts, so for over a hundred years, we've created vetted content, assessments, and so on. So we have a great deal of domain expertise in education and now we're taking, sort of the new area of frontiers of knowledge, and cognitive science, brain sciences. How can learners learn better and applying that to software and models and algorithms. >> Okay, and there's a data component to this as well, right? >> So yeah, the way I think about it is we're a smart digital company, but smart digital is fueled by smart data. Data underlies everything that we do. Why? Because in order to strengthen learners, provide them with the optimal pathway, as well as instructors. We believe instructors are at the center of this new transformation. We need to provide immediate, real-time data to students and instructors on, how am I doing? How can I do better? This is the predictive component and then you're telling me, maybe I'm not on the best path. So what's my, "How can I do better?" the optimal path. So all of that is based on data. >> Okay, so that's, I mean, the major reason. Do you do any print anymore? Yes, we still do print, because there's still a huge need for print. So print's not going to go away. >> Right. Okay, I just wanted to clarify that. But what you described is largely a business model change, not largely, it is a business model change. But also the value proposition is changing. You're providing a new service, related, but new incremental value, right? >> Yeah, yeah. So the value proposition has changed, and here again, data is critical. Inquiring minds want to know. Our customers want to know, "All right, we're going to use your technology "and your products and solutions, "show us "rigorously, empirically, that it works." That's the bottom line question. Is it effective? Are the tools, products, solutions, not just ours, but are our products and solutions have a context. Is the instruction effective? Is it effective for everyone? So all that is reliant on data. >> So how much of a course, how much of the content in a course would you prepare? Is it now the entire courseware and you instrument the students interaction with it? And then, essentially you're selling the outcomes, the improved outcomes. >> Yeah, I think that's one way to think about it. Here's another model change, so this is not so much digital versus non-digital, but we've been a closed environment. You buy a textbook from us, all the material, the assessments is McGraw Hill Education. But now a fundamental part of our thinking as a software company is that we have to be an open company. Doesn't mean open as in free, but it's an open ecosystem, so one of the things that we believe in very much is standards. So there's a standard body in education called IMS Global. My boss, Stephen Laster, is on the board of IMS Global. So think of that as, this encompasses everything from different tools working together, interoperability tools, or interoperability standards, data standards for data exchange. So, we will always produce great content, great assessments, we have amazing platform and analytics capability, however, we don't believe all of our customers are going to want to use everything from McGraw Hill. So interoperability standards, data standards is vital to what we're doing. >> Can you explain in some detail this learning science company. Explain how we learn. We were talking off camera about sort of the three-- >> Yeah, so this is just one example. It's well known that memory decays exponentially, meaning when you see some item of knowledge for the first time, unless something happens, it goes into short-term memory and then it evaporates. One of the challenges in education is how can I acquire knowledge and retain knowledge? Now most of the techniques that we all use are not optimal. We cram right before an exam. We highlight things and that creates the illusion that we'll be able to recall it. But it's an illusion. Now, cognitive science and research in cognitive science tells us that there are optimal strategies for acquiring knowledge and recalling it. So three examples of that are effort for recall. If you have to actively recall some item of knowledge, that helps with the stickiness. Another is space practice. Practicing out your recall over multiple sessions. Another one is interleaving. So what we do is, we just recently came out with a product last week called, StudyWise. What we've done is taken those principles, written some algorithms, applies those algorithms into a mobile product. That's going to allow learners to optimize their acquisition and recall of knowledge. >> And you're using Spark to-- >> Yeah, we're using Spark and we're using Databricks. So I think what's important there is not just Spark as a technology, but it's an ecosystem, it's a set of technologies. And it has to be woven together into a workflow. Everything from building the model and algorithm, and those are always first approximations. We do the best we can, in terms of how we think the algorithm should work and then deploy that. So our data science team and learning science team builds the models, designs the models, but our IT team wants to make sure that it's part of a workflow. They don't want to have to deal with a new set of technologies, so essentially pressing the button goes into production and then it doesn't stop there, because as Studywise has gone on the market last week, now we're collecting data real-time as learners are interacting with our products. The results of their interactions is coming in to our research environment and we're analyzing that data, as a way of updating our models and tuning the models. >> So would it be fair to say that it was interesting when you talked about these new ways of learning. If I were to create an analogy to Legacy Enterprise apps, they standardize business transactions and the workflows that went with them. It's like you're picking out the best practices in learning, codifying them into an application. And you've opened it up so other platforms can take some or all and then you're taking live feedback from the models, but not just tuning the existing model, but actually adding learning to the model over time as you get a better sense for how effort of recall works or interleaving works. >> Yeah, I think that's exactly right. I do want to emphasize something, an aspect of what you just said is we believe, and it's not just we believe, the research in learning science shows that we can get the best, most significant learning gains when we place the instructor, the master teacher, at the center of learning. So, doing that, not just in isolation, but what we want to do is create a community of practitioners, master teachers. So think of the healthcare analogy. We have expert physicians, so when we have a new technique or even an old technique, What's working? What's not working? Let's look at the data. What we're also doing is instrumenting our tools so that we can surface these insights to the master practitioners or master teachers. George is trying this technique, that's working or not working, what adjustments do we need to make? So it's not just something has to happen with the learner. Maybe we need to adjust our curriculum. I have to change my teaching practices, my assessments. >> And the incentive for the master practitioners to collaborate is because that's just their nature? >> I think it is. So let's kind of stand back, I think the current paradigm of instruction is lecture mode. I want to impart knowledge, so I'm going to give a lecture. And then assessment is timed tests. In the educational, the jargon for that is summit of assessments, so lecture and tests. That's the dominant paradigm in education. All the research evidence says that doesn't work. (laughs) It doesn't work, but we still do it. >> For how many hundreds of years? >> Yeah. Well, it was okay if we needed to train and educate a handful of people. But now, everyone needs to be educated and it's lifelong learning rate, so that paradigm doesn't work. And the research evidence is overwhelming that it doesn't work. We have to change our paradigm where the new paradigm, and this is again based on research, is differentiated instruction. Different learners are at different stages in their learning and depending on what you need to know, I'm at a different stage. So, we need assessments. Assessments are not punitive, they're not tests. They help us determine what kind of knowledge, what kind of information each learner needs to know. And the instructor helps with the differentiated instruction. >> It's an alignment. >> It's an alignment, yeah. Really to take it to the next stage, the master practitioners, if they are armed with the right data, they can begin to compare. All right, practices this way of teaching for these types of students works well, these are the adjustments that we need to make. >> So, bringing it down to earth with Spark, these models of how to teach, or perhaps how to differentiate the instruction, how to do differentiated assessments, these are the Spark models. >> Yeah, these are the Spark models. So let's kind of stand back and see what's different about traditional analytics or business intelligence and the new analytics enabled by Spark, and so on. First, traditional analytics, the questions that you need to be able to answer are defined beforehand. And then they're implemented in schemas in a data warehouse. In the new order of things, I have questions that I need to ask and they just arise right now. I'm not going to anticipate all the questions that I might want to be able to ask. So, we have to be enable the ability to ask new questions and be able to receive answers immediately. Second, the feedback loop, traditional analytics is a batch mode. Overnight, data warehouse gets updated. Imagine you're flying an airplane, you're the pilot, a new weather system emerges. You can't wait a week or six months to get a report. I have to have corrective course. I have to re-navigate and find a new course. So, the same way, a student encounters difficulty, tell me what I need to do, what course correction do I need to apply? The data has to come in real-time. The models have to run real-time. And if it's at scale, then we have to have parallel processing and then the updates, the round trip, data back to the instructor or the student has to be essentially real-time or near real-time. Spark is one of the technologies that's enabling that. >> The way you got here is kind of interesting. You used to be CIO, got that big Yale brain (laughs) working for you. You're not a developer, I presume, is that right? >> No. >> How did you end up in this role? >> I think it's really a passion for education and I think this is at McGraw Hill. So I'm a first generation college student, I went to public school in Los Angeles. I had a lot of great breaks, I had great teachers who inspired me. So I think first, it's education, but I think we have a major, major problem that we need to solve. So if we look at... So I spent five years with the Minnesota state colleges and university system, most of the colleges, community colleges are open access institutions. So let me just give you a quick statistic. 70% of students who enter community colleges are not prepared in math and english. So seven out of 10 students need remediation. Of the seven out of 10 students who need remediation, only 15% not 5-0, one-five succeed to the next level. This is a national tragedy. >> And that's at the community college level? >> That's at the community college level. We're talking about millions of students who are not making it past the first gate. And they go away thinking they've failed, they incurred debt, their life is now stuck. So this is playing itself out, not to tens of thousands of students, but hundreds of thousands of students annually. So, we've got to solve this problem. I think it's not technology, but reshaping the paradigm of how we think about education. >> It is a national disaster, because often times that's the only affordable route for folks and they are taking on debt, thinking okay, this is a gateway. Al, we have to leave it there. Awesome segment, thanks very much for coming to the CUBE, really appreciate it. >> Thank you very much. >> All right, you're welcome. Keep it right there, my buddy, George and I will be back with our next guest. This is the CUBE, we're live from Boston. Be right back. (techno music) >> Narrator: Since the dawn of the cloud