[Announcer] Live, from Las Vegas, it's theCube. Covering VMworld 2018, brought to you by VMware and its ecosystem partners. >> Welcome back, we're in Las Vegas and you're watching theCube. My cohost is John Trayor, I'm Stu Miniman, happy to welcome back to the program, Bob Madaio. >> Thanks Stu, I'm so glad to be here. >> Alright, so Bob, you're the Vice President of Infrastructure Solutions Marketing, and big difference, last year, you were with, was it, HDS? >> I was with Hitachi Data Systems. >> And now it's Hitachi Vantara and you had a little less facial hair when I talked to you last. >> You know it was around about November, I said, I might do theCube again next year, so I need to look different. >> Yeah, I had a, I had one Cube guy this year, he had got like almost a mountain man beard. And the week before he shaved it because he was coming back on theCube. And he was like, yeah I didn't but beards, no beards, diversity is what we like on theCube. >> Got to make the videos look different right? So next year I'll be beardless again, we'll do it again. It'll be fun. >> Alright, so I guess the follicle discussion's interesting but the Hitachi story is an interesting one you know Hitachi of course, huge company, lot of different technologies there, but bring us up to speed with what came together with Hitachi Vantara. >> Sure for a reminder of what we did, so we were Hitachi Data Systems, that's a part of the company I was with and again Hitachi Limited, out of Japan very large, you know, over 80 billion dollar organization of which Hitachi Data Systems used to be the IT arm that took those solutions to market around the world. What we did is, many might remember we had purchased Pentaho the open source analytics, you know, great a ETL blending capability. We brought them into the family with Hitachi Data Systems. We also had a sister company that was called Hitachi Insight Group. Hitachi Insight Group was really there to kick start our efforts around IoT. And if you've heard us talk about our Lumada Platform, if you look at what strings all this together, we've been having a lot, and I'm sure we'll talk about it here, of conversations about data. How do we help our customers with data? You know of course we've had a history in storage, but how do we bring it together, analyze it, bring new things, make that infrastructure more flexible? So that was what Vantara was. It was the bringing together of those three entities, and we continue to add. So, we're doing more and going to bring more companies into the fold. >> I love that Bob. I mean, we know the trend we've been watching for quite a number of years is, IT is going from that cost center over on the side that said, "No" to, to survive, you'd better be responding to the business, tie closely to the business or the business will go elsewhere. >> Yes indeed. >> For those solutions. And in the same way storage can't just be some growing expense that, you know, is I can't manage it I can't do this, to data is the life blood of business today. >> You know it's, I get to see some customers, not always as many as I'd like, but work closely with our field. And I was at this great customer of ours who's a regional bank and I was talking to, it was fundamentally a very storage-y audience. And I was, let's be honest, I was the corporate guy, I was a bit of the appetizer before the technical team, and I was having a data conversation. And we have this thing that, maybe we'll talk about, we call it the stairway of value and how customers should think of their value getting, you know, more important and how they can help, IT can help expose that value. And I was going through this model and I was worried, man, I might be losing this audience. But the lead person was writing, and I kind of stopped and said, is this relevant to you guys? And he said you know, I'm presenting on data to the organization next week, and I'm taking notes. And so what we're seeing is storage people, they also want to be able to have this conversation. How can what we do to make storage more accessible, move that data more quickly, valuable to the organization, to your point. >> Stairway to value, that's the power ballad for IT of the future, right? >> It does have a nearly built-in theme song, although we haven't gone there yet. For us actually, what it is is, we talk about the base layer sort of storing and protecting, and then it's enriching the data. So if you think of, often that's a meta data conversation. How do we bring more value, explain that data, make it easier to use? Then we get up into activate. That's blending maybe. You have the static systems. You want to build a repository for analytics. How do we help you activate that? And that really puts you on the path for monetization and that's the M. So we call it our SEAM model. >> So Bob, I love Hitachi Vantara and the idea of it. I got to say I understood Hitachi Data Systems better, because it's a storage, it was a you know, very, very smart, super smart storage company and you could compare it to other storage companies. But now, I'm just curious, in your, as you go and talk to customers, does that change who you talk with? And because, Hitachi Vantara as part of Hitachi too is about the industrialization of, of data and everything from oil fields and all the way down to, you know, a box in my office that might store my data. So, you know, but you've got the open source crowd and data scientists, you've got all the industrial and medical and health care stuff. As well as still these super smart storage scientists there. So how do you start that conversation and who do you end up talking with? >> You know it's very interesting. I mean, there are some companies that we approach at a Hitachi level and they're going to be major manufacturing projects or government projects, and we can bring the whole set. But oftentimes we are leveraging where a customer knows us and branching into new areas. So the storage base that we have is the most obvious to leverage. But what we're doing now is things like, we have some IT automation and analytics tools that help our customers know what's going on with the systems in their environment and how to take processes out. Well we can bring Pentaho in and tie in non-storage systems, non-IT systems like security, power and cooling, and really give a whole new dashboard. So that's a new entrance. And then the IT team can be an advocate to help you meet new business people. We also go in and speak to the business of course. We can do that through, we have IT governance go to markets, and data analytics go to market motion. And we're beginning to blend those better and better, but to be fair there are still some silos in how we talk to the customer depending on the audience. But what we see is if we can use data as a bridge, that's when the audiences sometimes meet each other, you know, for the first time it seems. >> Alright, so I love some of where this is going. Let's think down a level. You do the infrastructure solutions group, >> I do, yeah. >> So when you talk about, you know, CI and HCI and all those pieces. We're talking multi-cloud, a lot of this stuff, you know, what's the latest? What are the conversations you're having with customers about that? >> Yeah, very good. And really for us it's all been about agility. You know, data agility sure, but you can't make your data agile if you're infrastructure is very, you know, static. And so it doesn't take much to convince a customer we can build them trusted storage. That's like telling them the sky is blue, if they know Hitachi, they know this. What our conversations are now is about, what about the rest of your applications that surround this? On that trusted storage, how do we cloud enable it? What can we do there? On HCI and converge, obviously here at VMware, we partner deeply with VMware, so we are working with, for instance, how do we run some of our applications in the AWS VMware cloud, as well as be that HCI or that rack scale system on-site that manages it? So we're really having this agility conversation. How do we build the systems to be ready for this onslaught of data? Because it's great to have an IoT conversation, which remains of interest, but the reality is the systems folks need to be ready for that wave that's coming at them, and current process of just, I'm going to add controllers, I mean, that's not the way to think about it anymore. There's new types of systems that are making customer lives easier. >> Right, so from the VMware standpoint, I believe on-site is part of the partnership. >> Yeah, yeah, yeah. >> Anything in particular VMware specific. >> Yeah, across the portfolio. So of course on the storage we work with Vwalls and we do all the protection and integration with SRM and the like but, but really what's of course, hot, is hyper converge and we have our unified compute platform, HC line, that is based on Vsan. And that's doing very well. We actually had a session here with some great customers. So both Kanagra brands and MCL told their stories of picking Hitachi and they've seen some great results. So that business is doing very well. We've also introduced what we call unified compute platform RS for the rack-scale infrastructure. And we do a number of things with that. So we'll do bare metal systems for, you know, analytics workloads, but what's really got people excited is we sell a complete package with VMware cloud foundation. And that gets customers, not only ready to get up6 to hybrid cloud quickly on-site, but really have that hybrid ability. And so we're beginning to do things like certify core applications so they can test. We have a tech preview out on our Hitachi content platform, Object Store, it's actually in the app store for VMware's AWS cloud. Now, it's a tech preview from us because we know it works, but there's a scale thing. And you know Hitachi, right? It's going to work perfectly before we let our customers go crazy. So, we're really getting into those hybrid conversations and also enabling it as a service. I don't know if we talked about the as-a-service cloud that we have on offer too. >> But, no, please do, yeah. >> On top of all that technology, one of the hot offerings we have is called Hitachi Enterprise Cloud. And we have a VMware based offering, which has been doing very well and a much newer container-based platform. So on the VMware offering, really it's all of the VMware tools, but a customer never touches any of it. They don't touch our storage, the servers. It is an as-a-service model that we come in with services, help them bring in their applications, help build the service catalog for that customer and really, all they do is consume the service. So while the hardware might be on-site, it's really, they're largely indifferent to it. We do all the underlying capabilities, upgrades and such and they just provide out services to the business. So it's a really great option that people don't even know we offer. >> Yeah, well absolutely. You've had a number of conversations here at the show, it's, the customers have, the companies that have decades of appearance, you start with that base level of trust and therefore, you can help customers. You might not be the bleeding edge, but when you're there, customers know, oh wait, you're going to be around. I know that this thing's baked and ready when we get there. >> You know, the bleeding edge is fun, and there maybe some things we do, but I think it's fair enough that maybe that isn't always us. But I, but I think I have heard us called the adults in the room from time to time. And over at the booth we hear a lot of these. You know, we've been playing with this but it's going to get real. What do you guys do in this space? And while it is maybe some fun marketing being that bleeding edge, it is great to know that when it really matters, customers always trust us. And that's a huge vote of confidence. >> Alright, yep, Bob, really appreciate the update there. Absolutely, technologies like IoT, rapidly go from, this is super early but I need things we can trust. So absolutely, congrats on the progress. >> Thank you. >> We look forward to seeing, you know, how Hitachi and you know, the beard look next time we see you. >> Look forward to it. >> For John Troyer, I'm Stu Miniman. Back with more coverage from VMworld 2018 soon. Thanks for watching theCube.

>> Narrator: Live from Orlando, Florida it's theCUBE covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to sunny Orlando everybody. This is theCUBE, the leader in live tech coverage, and this is our second day covering PentahoWorld 2017. theCUBE was here in 2015 when Pentaho had just been recently acquired by Hitachi. We then, let's see, around September timeframe we saw Hitachi rebrand, Hitachi Data Systems rebrand as Hitachi Vantara, bringing together three components of its business, the Hitachi Data Systems business, the Hitachi Insights business, and of course, the Pentaho Analytics platform. We heard yesterday from Brian Householder, the president and COO of Hitachi Vantara, what the strategy was. I thought he was a very crisp, clear presenter. The strategy made a lot of sense, it resonated. Obviously a lot of execution to be done. And then subsequently at the last two days we've heard largely from Pentaho practitioners who are applying this end to end analytics platform to really transform their businesses, to really become data driven supporting those digital transformations. So pretty positive story overall. A lot of work to be done. We got to see how this whole edge to outcome plays out. Sounds good. There's got to be some execution there. We got to see the ecosystem grow for sure. These guys got a great story. This conference should explode. >> It's really a validation for Pentaho. They've been on the market for more than a decade now as the spearhead for the open source analytics revolution in business analytics, and in predictive modeling, and in data integration, all of it open source. And they've come very far and they're really a blue chip solution program. I think this show has been a great validation of Pentaho's portfolio presence in the market. Now Hitachi Vantara has a gem of a core asset. Clearly, the storage market, the data center converged infrastructure, the core Hitachi Data Systems product lines, are starting to experience the low growth that such a mature space experiences. And clearly they're placing a strong bet on Hitachi Vantara that the IoT, that the edge analytics market, will just boom wide open. Hitachi Insight Group, which was only created last year by their corporate parent, was chartered to explore opportunities in IoT. They've got the Lumata platform. They had, Hitachi Next, their conference last month, focused on IoT. I think that's really the capstone, the Lumata portfolio, in this overall story. Now, I think what we're hearing this week is that great, they've got the components, the building blocks, of potential growth, but I don't think they're going to be able to achieve takeoff growth until such time, Hitachi Vantara, they have a stronger, more credible reach out to the developer community, specifically the developers who are building the AI and machine learning for deployment to the edge. That will require to have credibility in that space. Clearly it's going to have to be the new set of frameworks, such as TensorFlow, and MXNet, and Fee-an-o, and so forth. They're going to need some sort of a modeling framework or abstraction from it that sits on top of the Pentaho platform or really across all of their offerings, including Lumata, and enables a developer to using, the mainstream application developer to use code, whether it be Python or R or Java, whatever, to build the deep learning and AI models at the highest level of abstraction, the business level of abstraction, then to automatically compile those models, which are computational graphs, down to formats that are optimized and efficient to run on devices of all sorts, chip sets of all sorts, that are increasingly resource constrained. They're not there yet. I'm not hearing that overall developer story at this show. I think they've got a lot of smart people, including Brian, pushing them in that direction. Hopefully next year's PentahoWorld or however they may rebrand this show, I think they'll probably have more of that put together, but we'll keep on waiting to see. >> And that's something that I pushed on a little bit this week. In particular, that requires a whole new go to market where the starting point is developers and then you're nurturing those developers. And certainly Pentaho has experience with community editions, but that was more to get enterprise buyers to kind of try before they buy. As you know well, Jim, the developer community is, they're very fickle, they're persnickety, they're demanding, and they're super smart, and they can be your best advocates or they'll just ignore you. That's just kind of the way it is with developers. And if you can appeal to them you can get a foothold in markets. We've seen it. Look at what Microsoft has done, look at what Amazon has done, certainly Docker, you know, on and on and on. >> Community marketing that's full bore (mumbles) user groups, developer days, hackathons, the whole nine yards, I'm not seeing a huge emphasis on community marketing in that really evangelistic sense. They need to go there seriously. They need to win the hearts and minds of the next generation developer, the next generation developer who actually won't care about whether it's TensorFlow backends or the other ones. What they will care is the high level framework, and really a collaborative framework, that's a solution provider gives them for their teams to collaborate on building and training and deploying all this stuff. I'm not hearing from this solution provider, devops really, here this year. Hopefully in the coming years there will be. Other vendors are a bit further along than they are. We see a bit further along IBM is. We see a bit further along like Cloudera and others are in putting together really a developer friendly ecosystem of components within a broader data lake framework. >> Yeah, and that's not been the historical Pentaho DNA. However, as you know, to reach out, have a community effort to reach out to developers requires resources and commitment, and it's not a one shot deal. But, it also requires a platform, and what we're seeing today is the formation of that. The reformation of Hitachi into Hitachi Vantara with a lot of resources that has a vision of a platform, of which Pentaho is a critical component, but it's going to take a lot of effort, a lot of cultivating. I presume they're having those conversations internally. They're not ready to have them externally, which is I presume why they're not having them. But that's something that we're going to certainly watch for in the coming years. What else? You gave a talk this afternoon. >> Yeah, AI is Eating the Edge, and it was well received. In fact, when I prepared my thoughts and my research about a month ago for this event I was thinking, "Am I way too far ahead?" This is Pentaho. I've been of course familiar with them since their inception. I thought, "Are there other users? "Are there developers? "Is their community going deep into AI "and all the IoT stuff?" And the last day or so here at this event it's like, "Whoa, everybody here is into that. "They know this stuff." So, not only was I relieved that I wouldn't have to explain the ABCs of all that, they were ahead of me in terms of the questions I got. The questions are, once again, what framework should we adopt for AI, the whole TensorFlow, all those framework wars, which I think are sort of overblown and they will be fairly soon, it'll be irrelevant, but those kinds of questions. Those are actually developer level questions that people are just here and they're coming to me with. >> Well, you know, I tell you, I'm no expert in frameworks, but my advice would be whatever framework you adopt you're probably not going to be using that same framework down the road. So you have to be flexible as an organization. A lot of technical leaders tell me this is look, technology is going to come and it's going to go. We got to have great people. We've got to be able to respond to the market requirements. We have to have processes that allow us to be proactive and responsive, and that your choice of framework should ensure that it doesn't constrict you in those areas. >> And you know, the framework that actually appeals to this crowd, including the people in my room, it's a wiki bot framework, it's also what Brian Hopkins of Forrester presented, the three tier architecture. There's the edge devices. There are the gateways or hubs. There's the cloud. We call them primary, secondary, tertiaries. Whatever you call them, you put different data, you put different analytics on each of those tiers. And then really in many ways in a modular fashion then you begin to orchestrate with Kubernetes and so forth these AI infused apps and these distributed architectures, like self driving vehicles or whatever. And the buzz I've been getting here, including in my session, everybody is saying, "Yeah, that's exactly the way to go." In other words, thinking in those terms prevents you as a developer from thinking that AI has to be some monolithic frigging stack on one single node. No, it actually has to be massively parallel and distributed, because these are potentially very compute intensive applications. I think there's a growing realization in the developer community that when you're talking about developing AI you're really talking about developing two core workloads. There's the inferencing, which is where the magic happens in terms of predictions and classifications, but even more resource consumptive is the training that has to happen in the cloud, and that's data, that's exabytes, petabytes intensive potentially. That's compute intensive. Very different workload. That definitely needs to happen in the cloud primarily. There's a little bit of federated training that goes out to the edge, but that's really the exception right now. So there's a growing realization in the developer community that boy, we better get a really good platform for training. And actually they could leverage, we've seen it in our research of wiki bot is that, many AI developers, many deep learning developers, actually leverage their Spark clusters for training of TensorFlow and so forth, because of in memory massive parallelism, so forth and so on. I think there will be a growing realization in the developer community that the investments they've been making in Hadoop and Spark will just be leveraged for this growing stack, for training if nothing else. >> Well, in 8.0 that was sort of the big buzz here. And you and I talked at the open with Rebecca, our other co-host, about 8.0 A lot of incremental improvements. But you know what, in talking to customers that's kind of what they want. They want Pentaho to do a good job of incorporating, curating, open source content, open source platforms and products, bringing them into their system, and making sure that their customers can take advantage of them. That's what they consistently kept asking for. They weren't freaked out about lack of AI and lack of deep learning and ML and Weka is fine. Now maybe it's a blind spot, I don't know. >> No, no, actually I've had 24 hours since they announced to chew on it. In fact, I have a SiliconANGLE article going up fairly soon with essentially my trip report and my basic takeaway. And actually what I like about 8.0 is that it focuses on streaming, bringing open source analytic streaming more completely into the Pentaho data integration platform, in other words, their stronger interoperability with Spark streaming, with Kafka, and so forth, but also they have the ability within 8.0 to better match realtime streaming workloads to execution engines in a distributed fabric. In other words, what I think that represents not only in terms of Hitachi Vantara's portfolio, but in terms of where the industry is going with all things to do with big data applications whether or not they involve AI is streaming is coming into the mainstream, pun intended, and data at rest platforms are starting to become marginalized in a lot of applications. In other words, Hadoop is data at rest par excellence, so are a fair number of other no SQL platforms. Those are not going away. Those are the core of your data lakes. But most development is being developed now, most AI and machine learning is being developed for streaming environments that increasingly are edge oriented. So Pentaho, Hitachi Vantara, for 8.0 have put in the right incremental features for the market that lies ahead. So in many ways I think that was actually a well thought out release for this particular event. >> Great. Okay, some of the highlights here. We had a lot of different industries, gaming, we had experts on autonomous vehicles, we had the NASDAQ guys on, that was a very interesting segment, the German police interview you did, the chief data officer of community colleges in Indiana. So, a lot of diversity, which underscores the platformness of Pentaho. It's not some industry specific system. It is a horizontal capabilities platform. Final thoughts on the show, some interesting things that you saw, things you learned? >> Yeah, on the show itself, they did a really good job. Hitachi Vantara, of course it's a new brand, but it's an old company, and it's even an old established set of product teams that have come together in a hurry essentially, though it's really been two years since the acquisition. They did a really good job of presenting a unified go to market message. That's a good start They've done a good job of the fact that they had these two shows in a rapid sequence, Hitachi Next, which was IoT and Lumata, but it was Hitachi Vantara, and now this one where it's all data analytics. The fact that here in the peak of fall event season they had these two shows really highlighting their innovations and their romance for those two core of their portfolio, and have done a good job of positioning themselves in each case, that shows that the teams are orchestrating well in terms of at least go to market presenting their value prop. I think in terms of the actual, we've had a lot of great customer and partner interviews on this show. And I think, you mentioned gaming first, I wasn't actually on the gaming related CUBE interview, but gaming is a hot, of course it's a hot, hot market for AI increasingly. A lot of AI that gets developed now for lots of applications involves simulations of whatever scenario you're building, including like autonomous vehicles. So gaming is in many ways a set of practices that are well established and mature that are becoming fundamental to development of all AI, because you're developing synthetic data based on simulation environments. The fact that Hitachi Vantara has strong presence as a data provider in the gaming market I think in many ways indicates that they've got ... It's a crowded marketplace. They have much larger competitors and deeper pocketed, but I think the fact is they've got all the piece parts needed to be a roaring success in this new era, and they've got strong and very loyal customers I'm discovering, not discovering, I've known this all along. But, since I've rejoined the analysts' space it's been revalidated that Pentaho how strong in blue chip they are. Now that they're a new brand in a new era, they're turning themselves around fairly well. I don't think that they'll be isolated by ... Clearly, I mean, with AI ... AI right now belongs to AWS and Microsoft and Google and IBM to some degree. We have to recognize that the Hitachi Vantaras of the world right now are still a second tier in that arena. They probably have to hitch their wagon to at least one of those core cloud providers as a core partner going forward to really prevail. >> Dave: Which they can do. >> Yeah, they can do. >> Alright. Jim, thanks very much for closing with me. Thanks to you all for watching. theCUBE puts out a lot of content. You can go to SiliconAngle.com to see all the news. theCUBE.net is where we host all these videos. Wikibon.com is our research site, so check that out, as well. We've got CrowdChats going on, CrowdChat.net. It's just unbelievable. >> Unbelievable. >> Rush of content. We're all about the data, we're all about sharing, so check those sites out. Thanks very much to the crew here. Great job. And next week a lot going on. We're in New York City. We've got some stuff going on there. Want to thank our sponsor, without whom this show, this CUBE show, would not be possible, Hitachi Vantara slash Pentaho. >> Thank you to sunny Orlando. It's great and wonderful. >> This has been theCUBE at PentahoWorld 2017. We'll see you next time. Thanks for watching. (techno music)

>> Announcer: Live, from Orlando, Florida, it's TheCUBE, covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to sunny Orlando everybody. This is TheCUBE, the leader in live tech coverage. My name is Dave Vellante and this is PentahoWorld, #PWorld17. Don DeLoach here, he's the co-chair of the midwest IoT council. Thanks so much for coming on TheCUBE. >> Good to be here. >> So you've just written a new book. I got it right in my hot off the presses in my hands. The Future of IoT, leveraging the shift to a data-centric world. Can you see that okay? Alright, great, how's that, you got that? Well congratulations on getting the book done. >> Thanks. >> It's like, the closest a male can come to having a baby, I guess. But, so, it's fantastic. Let's start with sort of the premise of the book. What, why'd you write it? >> Sure, I'll give you the short version, 'cause that in and of itself could go on forever. I'm a data guy by background. And for the last five or six years, I've really been passionate about IoT. And the two converged with a focus on data, but it was kind of ahead of where most people in IoT were, because they were mostly focused on sensor technology and communications, and to a limited extent, the workflow. So I kind of developed this thesis around where I thought the market was going to go. And I would have this conversation over and over and over, but it wasn't really sticking and so I decided maybe I should write a book to talk about it and it took me forever to write the book 'cause fundamentally I didn't know what I was doing. Fortunately, I was able to eventually bring on a couple of co-authors and collectively we were able to get the book written and we published it in May of this year. >> And give us the premise, how would you summarize? >> So the central thesis of the book is that the market is going to shift from a focus on IoT enabled products like a smart refrigerator or a low-fat fryer or a turbine in a factory or a power plant or whatever. It's going to shift from the IoT enabled products to the IoT enabled enterprise. If you look at the Harvard Business Review article that Jim Heppelmann and Michael Porter did in 2014, they talked about the progression from products to smart products to smart, connected products, to product systems, to system of systems. We've largely been focused on smart, connected products, or as I would call IoT enabled products. And most of the technology vendors have focused their efforts on helping the lighting vendor or the refrigerator vendor or whatever IoT enable their product. But when that moves to mass adoption of IoT, if you're the CIO or the CEO of SeaLand or Disney or Walmart or whatever, you're not going to want to be a company that has 100,000 IoT enabled products. You're going to want to be an IoT enabled company. And the difference is really all around data primacy and how that data is treated. So, right now, most of the data goes from the IoT enabled product to the product provider. And they tell you what data you can get. But that, if you look at the progression, it's almost mathematically impossible that that is sustainable because company, organizations are going to want to take my, like let's just say we're talking about a fast food restaurant. They're going to want to take the data from the low-fat fryer and the data from the refrigerator or the shake machine or the lighting system or whatever, and they're going to want to look at it in the context of the other data. And they're going to also want to combine it with their point-of-sale or crew scheduling, or inventory and then if they're smart, they'll start to even pull in external data, like pedestrian traffic or street traffic or microweather or whatever, and they'll create a much richer signature. And then, it comes down to governance, where I want to create this enriched data set, and then propagate it to the right constituent in the right time in the right way. So you still give the product provider back the data that they want, and there's nothing that precludes you from doing that. And you give the low-fat fryer provider the data that they want, but you give your regional and corporate offices a different view of the same data, and you give the FDA or your supply chain partner, it's still the same atomic data, but what you're doing is you're separating the creation of the data from the consumption of the data, and that's where you gain maximum leverage, and that's really the thesis of the book. >> It's data, great summary by the way, so it's data in context, and the context of the low-fat fryer is going to be different than the workflow within that retail operation. >> Yeah, that's right and again, this is where, the product providers have initially kind of pushed back because they feel like they have stickiness and loyalty that's bred out of that link. But, first of all, that's going to change. So if you're Walmart or a major concern and you say, "I'm going to do a lighting RFP," and there's 10 vendors that say, "Hey, we want to compete for this," and six of 'em will allow Walmart to control the data, and four say, "No, we have to control the data," their list just went to six. They're just not going to put up with that. >> Dave: Period, the end, absolutely. >> That's right. So if the product providers are smart, they're going to get ahead of this and say, "Look, I get where the market's going. "We're going to need to give you control of the data, "but I'm going to ask for a contract that says "I'm going to get the data I'm already getting, "'cause I need to get that, and you want me to get that. "But number two, I'm going to recognize that "they can give, Walmart can give me my data back, "but enrich it and contextualize it "so I get better data back." So everybody can win, but it's all about the right architecture. >> Well and the product guys going to have the Trojan horse strategy of getting in when nobody was really looking. >> Don: That's right. >> And okay, so they've got there. Do you envision, Don, a point at which the Walmart might say, "No, that's our data "and you don't get it." >> Um, not really- >> or is there going to be a quid pro quo? >> and here's why. The argument that the product providers have made all along is, almost in a condescending way sometimes, although not intentionally condescending, it's been, look, we're selling you this low-fat fryer for your fast food restaurant. And you say you want the data, but you know, we had a team of people who are experts in this. Leave that to us, we'll analyze the data and we'll give you back what you need. Now, there's some truth to the fact that they should know their products better than anybody, and if I'm the fast food chain, I want them to get that data so that they can continually analyze and help me do my job better. They just don't have to get that data at my expense. There are ways to cooperatively work this, but again, it comes back to just the right architecture. So what we call the first receiver is in essence, setting up an abstraction close to the point of the ingestion of all this data. Upon which it's cleansed, enriched, and then propagated again to the right constituent in the right time in the right way. And by the way, I would add, with the right security considerations, and with the right data privacy considerations, 'cause like, if you look around the market now, things like GEP are in Europe and what we've seen in the US just in the wake of the elections and everything around how data is treated, privacy concerns are going to be huge. So if you don't know how to treat the data in the context of how it needs to be leveraged, you're going to lose that leverage of the data. >> Well, plus the widget guys are going to say "Look, we have to do predictive maintenance "on those devices and you want us to do that." You know, they say follow the money. Let's follow the data. So, what's the data flow look like in your mind? You got these edge devices. >> Yep, physical or virtual. Doesn't have to be a physical edge. Although, in a lot of cases, there are good reasons why you'd want a physical edge, but there's nothing technologically that says you have to have a physical edge. >> Elaborate on that, would you? What do you mean by virtual? >> Sure, so let's say I have a server inside a retail outfit. And it's collecting all of my IoT data and consolidating it and persisting it into a data store and then propagating it to a variety of constituents. That would be creating the first receiver in the physical edge. There's nothing that says that that edge device can't grab that data, but then persist it in a distributed Amazon cloud instance, or a Rackspace instance or whatever. It doesn't actually need to be persisted physically on the edge, but there's no reason it can't either. >> Okay, now I understand that now. So the guys at Wikibon, which is a sort of sister company to TheCUBE, have envisioned this three tiered data model where you've got the devices at the edge where real-time activity's going on, real-time analytics, and then you've got this sort of aggregation point, I guess call it a gateway. And then you've got, and that's as I say, aggregation of all these edge devices. And then you've got the cloud where the heavy modeling is done. It could be your private cloud or your public cloud. So does that three tier model make sense to you? >> Yeah, so what you're describing as the first tier is actually the sensor layer. The gateway layer that you're describing, in the book would be characterized as the first receiver. It's basically an edge tier that is augmented to persist and enrich the data and then apply the proper governance to it. But what I would argue is, in reality, I mean, your reference architecture is spot-on. But if you actually take that one step further, it's actually an n-tier architecture. Because there's no reason why the data doesn't go from the ten franchise stores, to the regional headquarters, to the country headquarters, to the corporate headquarters, and every step along the way, including the edge, you're going to see certain types of analytics and computational work done. I'll put a plug for my friends at Hitachi Lumada in on this, you know, there's like 700 horizontal IoT platforms out there. There aren't going to be 700 winners. There's going to be probably eight to 10, and that's only because the different specific verticals will provide for more winners than it would be if it was just one like a search engine. But, the winners are going to have to have an extensible architecture that is, will ultimately allow enterprises to do the very things I'm talking about doing. And so there are a number out there, but one of the things, and Rob Tiffany, who's the CTO of Lumada, I think has a really good handle on his team on an architecture that is really plausible for accomplishing this as the market migrates into the future. >> And that architecture's got to be very flexible, not just elastic, but sometimes we use the word plastic, plasticity, being able to go in any direction. >> Well, sure, up to and including the use of digital twins and avatars and the logic that goes along with that and the ability to spin something up and spin something down gives you that flexibility that you as an enterprise, especially the larger the enterprise, the more important that becomes, need. >> How much of the data, Don, at that edge do you think will be persisted, two part question? It's not all going to be persisted, is it? Isn't that too expensive? Is it necessary to persist all of that data? >> Well, no. So this is where, you'll hear the notion of data exhaust. What that really means is, let's just say I'm instrumenting every room in this hotel and each room has six different sensors in it and I'm taking a reading once a second. The ratio of inconsequential to consequential data is probably going to be over 99 to one. So it doesn't really make sense to persist that data and it sure as hell doesn't make sense to take that data and push it into a cloud where I spend more to reduce the value of the payload. That's just dumb. But what will happen is that, there are two things, one, I think people will see the value in locally persisting the data that has value, the consequential data, and doing that in a way that's stored at least for some period of time so you can run the type of edge analytics that might benefit from having that persisted store. The other thing that I think will happen, and this is, I don't talk much, I talk a little bit about it in the book, but there's this whole notion where when we get to the volumes of data that we really talk about where IoT will go by like 2025, it's going to push the physical limitations of how we can accommodate that. So people will begin to use techniques like developing statistical metadata models that are a highly accurate metadata representation of the entirety of the data set, but probably in about one percent of the space that's queryable and suitable for machine learning where it's going to enable you to do what you just physically couldn't do before. So that's a little bit into the future, but there are people doing some fabulous work on that right now and that'll creep into the overall lexicon over time. >> Is that a lightweight digital twin that gives you substantially the same insight? >> It could augment the digital twin in ways that allow you to stand up digital twins where you might not be able to before. The thing that, the example that most people would know about are, like in the Apache ecosystem, there are toolsets like SnappyData that are basically doing approximation, but they're doing it via sampling. And that is a step in that direction, but what you're looking for is very high value approximation that doesn't lose the outlier. So like in IoT, one of the things you normally are looking for is where am I going to pick up on anomalous behavior? Well if I'm using a sample set, and I'm only taking 15%, I by definition am going to lose a lot of that anomalous behavior. So it has to be a holistic representation of the data, but what happens is that that data is transformed into statistics that can be queryable as if it was the atomic data set, but what you're getting is a very high value approximation in a fraction of the space and time and resources. >> Ok, but that's not sampling. >> No, it's statistical metadata. There are, there's a, my last company had developed a thing that we called approximate query, and it was based on that exact set of patents around the formation of a statistical metadata model. It just so happens it's absolutely suited for where IoT is going. It's kind of, IoT isn't really there yet. People are still trying to figure out the edge in its most basic forms, but the sheer weight of the data and the progression of the market is going to force people to be innovative in how they look at some of these things. Just like, if you look at things like privacy, right now, people think in terms of anonymization. And that's, basically, I'm going to de-link data contextually where I'm going to effectively lose the linkages to the context in order to conform with data privacy. But there are techniques, like if you look at GDCAR, their techniques, within certain safe harbors, that allow you to pseudonymize the data where you can actually relink it under certain conditions. And there are some smart people out there solving these problems. That's where the market's going to go, it's just going to get there over time. And what I would also add to this equation is, at the end of the day, right now, the concepts that are in the book about the first receiver and the create, the abstraction of the creation of the data from the consumption of the data, look, it's a pretty basic thing, but it's the type of shift that is going to be required for enterprises to truly leverage the data. The things about statistical metadata and pseudonymization, pseudonymization will come before the statistical metadata. But the market forces are going to drive more and more into those areas, but you got to walk before you run. Right now, most people still have silos, which is interesting, because when you think about the whole notion of the internet of things, it infers that it's this exploitation of understanding the state of physical assets in a very broad based environment. And yet, the funny thing is, most IoT devices are silos that emulate M2M, sort of peer to peer networks just using the internet as a communication vehicle. But that'll change. >> Right, and that's really again, back to the premise of the book. We're going from these individual products, where all the data is locked into the product silo, to this digital fabric, that is an enterprise context, not a product context. >> That's right and if you go to the toolsets that Pentaho offers, the analytic toolsets. Let's just say, now that I've got this rich data set, assuming I'm following basic architectural principles so that I can leverage the maximum amount of data, that now gives me the ability to use these type of toolsets to do far better operational analytics to know what's going on, far better forensic analysis and investigative analytics to mine through the date and do root cause analysis, far better predictive analytics and prescriptive analytics to figure out what will go on, and ultimately feed the machine learning algorithms ultimately to get to in essence, the living organism, the adaptive systems that are continuously changing and adapting to circumstances. That's kind of the Holy Grail. >> You mentioned Hitachi Vantara before. I'm curious what your thoughts are on the Hitachi, you know, two years ago, we saw the acquisition, said, okay, now what? And you know, on paper it sounded good, and now it starts to come together, it starts to make more sense. You know, storage is going to the cloud. HDS says, alright, well we got this Hitachi relationship. But what do you make of that? How do you assess it, and where do you see it going? >> First of all, I actually think the moves that they've done are good. And I would not say that if I didn't think it. I'd just find a politically correct way not to say that. But I do think it's good. So they created the Hitachi Insight Group about a year and a half ago, and now that's been folded into Hitachin Vantara, alongside HDS and Pentaho and I think that it's a fairly logical set of elements coming together. I think they're going down the right path. In full disclosure, I worked for Hitachi Data Systems from '91 til '94, so it's not like I'm a recent employee of them, it's 25 years ago, but my experience with Hitachi corporate and the way they approach things has been unlike a lot of really super large companies, who may be super large, but may not be the best engineers, or may not always get everything done so well, Hitachi's a really formidable organization. And I think what they're doing with Pentaho and HDS and the Insight Group and specifically Lumada, is well thought out and I'm optimistic about where they're going. And by the way, they won't be the only winner in the equation. There's going to be eight or nine different key players, but they'll, I would not short them whatsoever. I have high hopes for them. >> The TAM is enormous. Normally, Hitachi eventually gets to where it wants to go. It's a very thoughtful company. I've been watching them for 30 years. But to a lot of people, the Pentaho and the Insight's play make a lot of sense, and then HDS, you used to work for HDS, lot of infrastructure still, lot of hardware, but a relationship with Hitachi Limited, that is quite strong, where do you see that fit, that third piece of the stool? >> So, this is where there's a few companies that have unique advantages, with Hitachi being one of them. Because if you think about IoT, IoT is the intersection of information technology and operational technology. So it's one thing to say, "I know how to build a database." or "I can build machine learning algorithms," or whatever. It's another thing to say, "I know how to build trains "or CAT scans or smart city lighting systems." And the domain expertise married with the technology delivers a set of capabilities that you can't match without that domain expertise. And, I mean, if you even just reduce it down to artificial intelligence and machine learning, you get an expert ML or AI guy, and they're only as good as the limits of their domain expertise. So that's why, and again, that's why I go back to the comparison to search engines, where there's going to be like, there's Google and maybe Yahoo. There's probably going to be more platform winners because the vertical expertise is going to be very, very important, but there's not going to be 700 of 'em. But Hitachi has an advantage that they bring to the table, 'cause they have very deep roots in energy, in medical equipment, in transportation. All of that will manifest itself in what they're doing in a big way, I think. >> Okay, so, but a lot of the things that you described, and help me understand this, are Hitachi Limited. Now of course, Hitachi Data Systems started as, National Advance Systems was a distribution arm for Hitachi IT products. >> Don: Right, good for you, not many people remember. >> I'm old. So, like I said, I had a 30 year history with this company. Do you foresee that that, and by the way, interestingly, was often criticized back when you were working for HDS, it was like, it's still a distribution hub, but in the last decade, HDS has become much more of a contributor to the innovation and the product strategy and so forth. Having said that, it seems to me advantageous if some of those things you discussed, the trains, the medical equipment, can start flowing back through HDS. I'm not sure if that's explicitly the plan. I didn't necessarily hear that, but it sort of has to, right? >> Well, I'm not privy to those discussions, so it would be conjecture on my part. >> Let's opine, but right, doesn't that make sense? >> Don: It makes perfect sense. >> Because, I mean HDS for years was just this storage silo. And then storage became a very uninteresting business, and credit to Hitachi for pivoting. But it seems to me that they could really, and they probably have a, I had Brian Householder on earlier I wish I had explored this more with him. But it just seems, the question for them is, okay, how are you going to tap those really diverse businesses. I mean, it's a business like a GE or a Siemens. I mean, it's very broad based. >> Well, again, conjecture on my part, but one way I would do it would be to start using Lumada in the various operations, the domain-specific operations right now with Hitachi. Whether they plan to do that or not, I'm not sure of. I've heard that they probably will. >> That's a data play, obviously, right? >> Well it's a platform play. And it's enabling technology that should augment what's already going on in the various elements of Hitachi. Again, I'm, this is conjecture on my part. But you asked, let's just go with this. I would say that makes a lot of sense. I'd be surprised if they don't do that. And I think in the process of doing that, you start to crosspollinate that expertise that gives you a unique advantage. It goes back to if you have unique advantages, you can choose to exploit them or not. Very few companies have the set of unique advantages that somebody like Hitachi has in terms of their engineering and massive reach into so many, you know, Hitachi, GE, Siemens, these are companies that have big reach to the extent that they exploit them or not. One of the things about Hitachi that's different than almost anybody though is they have all this domain expertise, but they've been in the technology-specific business for a long time as well, making computers. And so, they actually already have the internal expertise to crosspollinate, but you know, whether they do it or not, time will tell. >> Well, but it's interesting to watch the big whales, the horses in the track, if you will. Certainly GE has made a lot of noise, like, okay, we're a software company. And now you're seeing, wow, that's not so easy, and then again, I'm sanguine about GE. I think eventually they'll get there. And then you see IBM's got their sort of IoT division. They're bringing in people. Another company with a lot of IT expertise. Not a lot of OT expertise. And then you see Hitachi, who's actually got both. Siemens I don't know as well, but presumably, they're more OT than IT and so you would think that if you had to evaluate the companies' positions, that Hitachi's in a unique position. Certainly have a lot of software. We'll see if they can leverage that in the data play, obviously Pentaho is a key piece of that. >> One would assume, yeah for sure. No, I mean, I again, I think, I'm very optimistic about their future. I think very highly of the people I know inside that I think are playing a role here. You know, it's not like there aren't people at GE that I think highly of, but listen, you know, San Ramon was something that was spun up recently. Hitachi's been doing this for years and years and years. You know, so different players have different capabilities, but Hitachi seems to have sort of a holistic set of capabilities that they can bring together and to date, I've been very impressed with how they've been going about it. And especially with the architecture that they're bringing to bear with Lumada. >> Okay, the book is The Future of IoT, leveraging the shift to a data-centric world. Don DeLoach, and you had a co-author here as well. >> I had two co-authors. One is Wael Elrifai from Pentaho, Hitachi Vantara and the other is Emil Berthelsen, a Gartner analyst who was with Machina Research and then Gartner acquired them and Emil has stayed on with them. Both of them great guys and we wouldn't have this book if it weren't for the three of us together. I never would have pulled this off on my own, so it's a collective work. >> Don DeLoach, great having you on TheCUBE. Thanks very much for coming on. Alright, keep it right there buddy. We'll be back. This is PentahoWorld 2017, and this is TheCUBE. Be right back.

>> Announcer: Live from Midtown Manhattan it's theCUBE. Covering Big Data New York City 2017 brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Hey, welcome back everyone live here in New York City it's theCUBE's special presentation Big Data NYC. This is our fifth year doing our own event here in New York City, our eighth year covering the Hadoop World ecosystem from the beginning. Through eight years, it's had a lot evolutions, Hadoop World, Strata Conference, Strata Hadoop, now it's called Strata Data happening right around the corner. We run our own event here, talk about thought leaders and the expert CEO's, entrepreneurs. Getting the data for you, sharing that with you. I'm John Furrier co-host theCUBE with my co-host here Jim Kobielus who's the Lead Analyst at Wikibon Big Data. And Chuck Yarbough who's the Vice President at Pentaho Solutions part of Hitachi's new Vantara. A new company created just announced last week. Hitachi in a variety of their portfolio technologies into a new company, out to bring in a lot of those integrated solutions. Chuck great to see you again, theCUBE alumni. We chatted multiple times at Pentaho World, going back 2015. >> Always he always great to be at theCUBE. >> What a couple of years it's been. Give us quickly hard news, it's pretty awesome you guys have a variety of things at Pentaho you know with Hitachi, that happened, now the market's evolved, what's this new entity, this new company they're bringing together? >> Yes, so the big news Hitachi Vantara. So what that is, two years ago Hitachi Data Systems acquired Pentaho and so fast forward two years. A new company gets created from Hitachi Data Systems. Pentaho, in a third organization at Hitachi called the Insight Group so Hitachi Insight Group. Those three groups come together to form Hitachi Vantara >> What's the motivation behind that. I mean, I go connect the dots but I want to hear your perspective because it really is about pulling things together. The trend this year the show is as Jim calls it, hybrid data, integrated data. Things seem to be coming together, is that part the purpose? What's the reason behind pulling this together? >> Yeah, I think there's a lot of reasons. One of them is what we're seeing not just in our own business, but in our customers business, and that is digital transformation. Right, this this need to evolve So Hitachi Vantara is all about data and analytics. And a big focus of what we do is what Pentaho's been doing for years which is driving in all kinds of data, big data, all data. I think we're getting on the cusp of closing out the big data term, but you know, it's all data right. >> Data everywhere, every application. >> And applying analytics across the board. One of the big initiatives, part of why Pentaho was originally acquired we were actually Hitachi Data Systems was a customer of Pentaho when we got acquired, so we we knew each other pretty well. And part of the reason for that acquisition was to drive analytics in around internet of things. The IoT space, which is something that Hitachi being a very large IT and operational technology, OT, company probably does as well as anybody if not better. >> So going back couple of years, I'm just looking at my notes here from our our video index. You visited theCUBE in 2015, but really the concepts have evolved significantly. I want to just highlight a few of them. What data warehouse optimizations, we talk about that. Data refinery concepts, 360 view as applied to big data. Again that was foundational concepts that all are in play right now. >> Absolutely. >> What is the update in those areas? Because refinery, everyone talks about data refinery, you know, oil, the easy oil example but I mean, come on, data is everywhere it is most important, you can use it multiple times unlike oil, as you were pointing out. >> So interesting you bring that up. So to me data refinery in a digital transformation really in an IoT world where lots of data is is streaming through in fact, yesterday I read something by IDC that 95% of all data in the future and the data growth is dramatic it's 10x what it is today in just a few years. 95% of the that growth of data's IoT related. The question is how are you using most of that, right, and what what are you going to do with it. So that data's is streaming through, there's a lot happening, we can do things at the edge, we can apply analytics and filtering and do things. But ultimately that data is going to land somewhere and that's where that refinery, think of it as the big data center refinery, right, where I'm going to take that large amount of data and do the things that Jim does, you know and apply machine learning and deep algorithms too really. >> I had some thoughts on the IoT Jim and I were arguing, not arguing, discussing, with others in theCube about the role. >> We were bickering. >> The role of the edge because I was saying the refiner of the data can come back depending on what kind of data or you push compute to the edge, kind of known concepts, people been discussing that. But the issue is been, how do you view the edge? I'd love to get your reaction to that question because a lot of people are saying you have to think of IoT as a completely different category, than just cloud, than just data center, because the way some people are looking at IoT I know this can be semantics whether it's industrial or just straight internet of things device, or person, that is a different animal when it comes to like what you call it and how it gets put into a bucket. I mean most people put a lot of the IT bucket but. Some are saying IT edge should be completely different category of how you look at those problems. Your thoughts on how that IoT conversation shape. >> The question I always ask when I'm talking to somebody about the edge is, well what do you mean? Because it is something that can be defined a little bit differently but in an industrial IoT context I think, you know we look at it as one, you you have to know what those things are you have to really understand them. And part of understanding those things is having a digital representation of what those things are. >> A digital twin? >> A digital twin. Right, or asset avatar, as we call it at Hitachi. >> Oh I like that. >> So this idea of really managing those assets, understanding what they are and then being able to know what the current state, what the previous state, things are like that are. And then that refinery we just talked about is sort of where that information goes to so you can do other kinds of analytics right. But when you're talking about the edge, typically what we're seeing is the kinds of analytics might happen at the edge, are probably more around filtering you know, it's not quite as complex of analytics that's what we're seeing today. Now, the future I don't know. >> Sort of tiered analytics from the edge on in with more minimal, I mean, not minimal that's the wrong term, with a more narrowly scoped inference. Like predictions and so forth being handled at the edge with larger more complex models being like deep learning whatever being processed in the cloud is that it? >> Yeah that's exactly the way that I see it. Now the other thing about the edge, depends on who you're talking to, again, but what is an edge device or the the gateways or the compute right, so part of IoT is in my mind, it's not cloud, it's not on-prem or it's not, I mean it's a little bit of everything right, it depends on the use case and what you're operating. We have a customer who does trains as a service in England, in Europe, and so they don't sell the trains anymore they actually manufacture trains, and they sell the service of getting a passenger from here to there. But for them, edge is everything that happens on those trains. And tracking, as a digital representation, the train and then being able to drill down deeper and deeper, and you, know one of the things that I understand is one of the major delays for train service is doors opening and closing or being delayed, so maybe that comes down to a small part and the vibration of it and tracking that. So you've got to be able to track that appropriately. Now, on a train you might have a lot of extra space so you could put compute devices that have a lot of power. >> What's interesting you said the edge, in this context, is everything that happens on that train. In other words, it sounds like all the real world outcomes that are enabled, perhaps optimized, by embedding of the analytics in those physical devices or in that entire vehicle that is essentially. One way that you're describing the edge which is not a single device but as a complete assembly of devices that play together. Amongst themselves and in with the services in the cloud. Is that a logical sort of framework? >> That's why I said I usually ask what do we mean by edge. If you've got millions, thousands, whatever, devices out there feeding sensors whatever feeding this data, collecting, processing you know there's some some level of edge computing gateways, processes that are going to happen. >> Well, my question for ya, I'd like to get your thoughts, as we, again we're having a, we love the hyperbio we think its completely legit and it's going to be continued to be hyped because it's obvious what you see with IoT standing on the edge. But lot of customers we talked to are like, look I got a lot going on I got application development I got to break out my security got to build that up. I've got data governance issues, and now you throw in IoT over the top. They're like, I'm choking in projects. So they they come down to one of a selection criteria. How do they define a working IoT project? And the trend that we're seeing is that it has to do with their industrial equipment or something related to their business. Call it industrial IoT, because if they have something in their business, say trains, as a critical part of what they do, that's easy to say let's justify this. Everything else then tends to go on the back burner, if they don't have clear visibility of what their instrumenting. That's kind of weird do you agree with that? Do you see a pattern as well as what customers are doing by saying I'm going to bring this project in and were going to connect our IoT. >> That's exactly what I see. Industrial internet of things is where I see the biggest value today when you have trains or mining equipment or you know whatever. >> John: Whatever your business runs. >> Your manufacturing line right. and being able to a fine tune those lines to either predicts failures, maybe improve quality. Those are those are impactful and they can be done right now today and that's what we're seeing is kind of the big emerging thing. IoT's interesting to talk about, the reality is it's really digital transformation that we're seeing. Companies transforming into new business models, doing things significantly different to grow into the future. And IoT is an enabler of that. So you're not going to see IoT everywhere today. >> The low hanging fruit is where it gets to the real business. >> Yeah, but it's going to go across all verticals, right, no doubt. >> So what solutions does Pentaho have for digital twins, or managing digital twins, the objects, the data itself, within and IoT context, is this something you're engaged in already? >> So within the Hitachi Vantara, the larger company. Bigger company, we have, we have what we call our Lumada IoT Platform and in that there is this asset avatar technology that that does exactly what you're describing. Now I'm going to throw quick plug out if you don't mind. Pentaho World in a couple, in about a month. >> John: theCUBE will be there. >> theCUBE will be there, and we're excited to have theCUBE and we're going to we're going to give you complete information about asset avatar with all the right people. >> There's a movie in there somewhere I could feel it, Avatar two. There's a lot of great representations of data I want to get your thoughts on how the new firm's going to solve customer problems. Because now as the customer see this new entity from you guys, Vantara's been doing real well, we covered the acquisition and you were kind of left alone Pentaho was integrating in, but it wasn't like a radical shift. Now there's some movement, what does it mean to the customer, what's the story to the customer. >> You know I think it's great news for the customer because Pentaho's always been very customer focused. But when you look at Hitachi Vantara the wealth of technology and expertise. Everything from all of the the great IT oriented stuff that Hitachi Data Systems has done and been well known for in the past still exists. But this broader focus of taking data and processing it in a variety of ways to solve real business problems. All the way to orchestrating machine learning in applying algorithms and then with the Hitachi. >> What specifically in Hitachi is coming into this? Because again this is again a focused solution company now with data, so Hitachi Data Centers, >> Yeah, so Hitachi Data Systems, think of it as the the infrastructure company. Hitachi Insight was the really focused largely on the IoT platform development, with some Pentaho assets and then the Pentaho business. But here's the thing about Hitachi, very large company, builds everything. Mining equipment and and all kinds of stuff. So nobody understands how all those things fit together better, I believe, than Hitachi. But some of the things that we have at that organization is this idea of the Hitachi labs. And data scientists that are really doing interesting things Jim you'd love to get more embedded into what some of those things are, and making that available to customers is a huge opportunity for customers to now be able to embrace a lot of the technologies we've been talking about. I said last year that this year was going to be the year of machine learning. And if you look through the expo hall that's what everybody's talking about. Right, it's AI or machine learning. >> I'm wondering if you're commercializing R&D that's coming straight out of Hitachi labs already or whether the Vantara combination will enable that. In other words, more innovation straight out of the labs, into into the commercial arena. >> That's something that we are absolutely trying to to, right because there's great things that these lab organizations and at Hitachi they're big labs. They're really legit, I kind of joke about that. The kinds of stuff that they're able to bring about now, Pentaho is part of the engine to help actually commercialize those things. >> Chuck I know you're looking forward to Pentaho World I'll give you the final word here in this segment how you see the big data worlds evolve. Take your Pentaho hat off and put your industry guru hat on. What's happening, I mean this AI watch, that's pretty obvious, not a lot of blockchain discussion which is going to completely open up some things we getting on the decentralized application market which is going to compliment the distributed nature of how we see a date analytics flow and certainly the immutability of it's interesting. But that's kind of down the road. But here you're starting to see the swim lanes in the industry, you've seen people who've been successful and the ones who have fallen by the wayside. But now the customers, they want real solutions. They don't want more hype, they don't want another eighth year of hype, they want OK let's get into the real meat and potatoes of data impact to my organization, call it digital transformation. What's happening, what is going on the landscape. >> So you know I mentioned before and to me it's digital transformation which is a big huge thing. But that's what companies are interested in that's what they're beginning to think. If they're not thinking about those things they're falling behind, five or six, seven years ago we talked about the same exact thing with big data. It's like a big data is really you know it's a big opportunity and they're like well I don't know those that didn't adopt it aren't necessarily in a position now to transform digitally and to do some of the things that they're going to need to evolve into new business opportunities. >> And the big data examples of winner is the ones who actually made it valuable. Whether it's insight that converted to a new customer or change an outcome in a positive way, they go that wouldn't have been possible without data. The proof points kind of hit the table. >> That's right the other thing is you know, who's going to win, who's going to lose. I think people that are implementing technology for technology's sake are going to lose. People that are focused on the outcomes are going to win. That's what it is, technology enables all that but you've really got to be focused on. I want to get your quick, one more quick thing, before we go I know we got we're tight on time but I want to get thoughts on the open ecosystem. Open source going to whole other level. The projections are code will be shipping at an exponential rate, it's be a lot of onboarding of new stuff, so open obviously works, community models work, partnering is critical. So we're seeing that good partnerships, not fake deals or optical deals or Barney deals, whatever you want to call it. But real partnerships. You starting to see technology partnerships. What's your view on that, how is the new Vantara going to go forward, are you going to continue to do partnerships and what's the strategy? >> Yeah I think the opportunity with one, Hitachi Vantara is we have a breadth that can touch many different aspects. So as Pentaho we had great partnerships, very meaningful but it always comes down to what we doing for the customer. How are we changing things for customer. So I'm not a believer in those Barney kind of relationships those are nice but let's talk about what we're doing for customers. >> Yeah, real proof points. >> You guys will continue to parner. >> Yes, we will continue to do that. >> Okay great, Chuck, thank you so much. CUBE coverage Live in New York City in Manhattan it's theCUBE with Big Data NYC, out fifth year doing our own event in conjunction with Strata Data. Now bless the new name of the show. It was Strata Hadoop, Hadoop World before that. But we're still theCUBE covering eight years of the action here back with more after this short break.