 Live from Orlando, Florida. It's theCUBE, covering Pentaho World 2017. Brought to you by Hitachi Ventura. Welcome back to Orlando, everybody. This is Pentaho World, hashtag P World 17. This is theCUBE, the leader in live tech coverage. Brian Householder is here. He's the president and COO of Hitachi Ventura. Brian, thanks for taking some time out. My pleasure, thanks for having me. You're welcome. Let's start with Hitachi and Hitachi Ventura. You guys announced that just about a month or so ago. People are asking, what is Hitachi Ventura? Brings together some of the three of the key pillars of your organization, so I'll explain that to us. Yeah, so we've been doing a ton of transformation here over the last 10, 15 years for Hitachi and the original Hitachi data systems. And so really what we have been transitioning to is a data company. And frankly, today 60% of our revenue comes from software and services. And we wanted to actually then formalize that more and then create this new company for Hitachi. So basically we are the data arm for Hitachi. And so we created this company called Hitachi Ventura and that does include the Pentaho organization that includes what we call the Hitachi Insight Organization which is all of our IoT assets and includes Hitachi data systems. And so that's really the data arm. So Hitachi Ventura is the data arm for Hitachi. And so the mission of Ventura is how do we help our customers deliver what we call edged outcomes which is really wherever your data gets created, wherever environment it happens to be, if you're actually getting into IoT environments or what have you, we can actually then help you deliver the outcome that you actually need for your business. So I got to ask you about the name. Ventura, you think advantage, vantage point, insights. Where's the name come from? What's the meaning there? What are we in for? I've been through the whole branding process. So it's not an easy. So the, we ended up basically, number one, wanted to make sure that we had a suggestive name and most global companies have a suggestive name. And so Hitachi is obviously always going to be at the forefront of what we do. Ventura was a combination of a few different words. So you mentioned them. One was around advantage. So how do we actually help customers take advantage of their data? And that's really what we wanted to go do. How do you have advantage points? So how do you actually then help customers really see across their environment? And then we also wanted to give a nod to kind of a virtualization heritage as well. And that's where the V comes from. And so that's really where we came up with Hitachi Ventura. And it's exciting to have really in terms of, teaching the marketplace around more of what we do. It's ironic, you know, when I, and I have a chance to talk to companies all over the world, there's two comments that I typically hear from customers. When we talk about Hitachi and what we're doing and our social innovation strategy or any of the digital innovations that we do. Usually the first one's wow. You know, it's like, and the second one is, I didn't know you did that. And that gets into, I didn't know you did this artificial intelligence technology. I didn't know you did that around machine learning. I didn't know you actually did these kinds of solutions. And so really, this is us making sure the market understands what we're up to and making sure that we can actually let people know all the great things that Hitachi's all about. So a lot of people don't know, well, you and I have known each other since before Pentaho started, back in the late 90s, I think we met. And you've always been sort of focused on areas of innovation. You came into Hitachi, I think over a decade ago. Yeah, 14 years ago. When Hitachi was largely an infrastructure company, kind of predominantly storage company. Talk about the transformation that you and your colleagues affected at Hitachi data systems and what your mission was and how far you've come. Yeah, so I've been here over 14 years. And when we first came in, yes, Hitachi data systems back then was mainly an infrastructure company. I mean, greater than 80% of our revenue came from hardware and about 20% of our revenue came from software and services. So our job, and again, it wasn't just me, there was a number of us that kind of came on board to really help, how do we help shift this model from moving beyond infrastructure much more into a data and software type offering. And so really over the years we made some massive changes. And this gets into obviously acquisitions, Pentaho fit into that as well. So that's really kind of front and center with our data strategy. But if you start talking about the offerings that we ended up doing, you know, now Hitachi Ventaros, if you look at the combinations of the acquisitions and transformations we've done to date, including the Pentaho organization and including all the innovations we've done around IoT and Lumata, that organization is 60% of our revenue comes from software and services. That's much more of all the data solutions that we go do. So we still provide the infrastructure for companies, but it's much more around how does that infrastructure help you drive the right kind of data strategy for your organization? So you've done a lot of M&A over the years and you personally have been, I know, involved in it. You said in your keynote that you looked at all the big data companies. You chose Pentaho, executives often say that, but you did have the pick of the litter at the time. One of the things you said that you were very interested in the open source component that Pentaho brought. I want you to talk about the go-to market of open source and software and how that's different than the traditional hardware world. I mean, it kind of starts with developers, right? Maybe discuss that a little bit. Yeah, so just back on the reason why we ended up choosing that. So really our strategy is all around being open. And so I think really kind of that open culture, that open environment, having customers use what technologies they want for their environment is very critical for us. So we do talk a lot about that around how do we make sure we don't lock customers in? How do we make sure that they can actually use the technologies that they want? And we certainly saw the trend even three, four years ago around customers are going to move much more towards leveraging the open source communities and we want to do them to embrace it. So that's the reason for the Pent therapies. Yes, now a commercial open source model is different. We knew that going in. Certainly the ecosystem is radically different. The developer community is radically different. And so what we needed to do is really allow and get Pentaho to make sure that becomes a front and center kind of portion of our business when it comes to some of the new data solutions that we actually provide. And that gets into these events. This gets into how do we actually want to continue to foster the developer community? And then really how do we actually want to make sure we're adding value above and beyond what actually happens out in the open source community? And I think that gets into this whole delivering edge to outcomes for our customers. And Pentaho fits into that a little bit, but there's also a lot of other pieces around that, whether that be around IoT, around the center environment, how do you create and move from the digital to the physical worlds? And then ultimately out to what customers care about, which is really delivering the outcomes that they want for their business. So I want to translate something you just said, adding value sort of beyond what the open source world can do. I translate that into, you got to make money. And a way to make money, you know, you can have a pure open source model, but it's very, very difficult. You know, there's one example in Red Hat, but most companies struggle to do that. So you've got to have a hybrid, right? And that's really where, so maybe discuss sort of the profitability and margin model from your perspective so you can continue to fund that $3 billion in R&D. Yeah, so I think if you look at it kind of more of a matter of, if you look at our customer base, our customer base is really around the global 2000 is where we shine the most. And so a lot of the open source community stuff is amazing, but if you want to start talking about doing things at scale, that's really where we come into play. And so if you start talking about kind of, we want to scale up a Pentaho set of products or the overall Hitachi Ventura sets of products, that's really where we think we add a lot of the value. That's really where our kind of commercial piece of the equation comes into play. And that's really where we actually go out there and shine with customers. Number one, customers don't want to deploy all that open source and have to manage it. But more importantly, when they start getting into these massive scale environments, this gets into how do you actually do distributed nodes? How do you actually then scale up these environments to not these small 50 terabyte lakes, but we're talking about petabytes and petabyte type scale? That's really where we shine. And that gets into not just the software components, but a lot of the services and integration and a lot of partnerships that we do to help customers get that involved. Yeah, you do complex well. It's kind of one of the things you said in your keynote. You also made the point, and I want to push on this a little bit about, you're talking about data ownership and protection of customers data. You don't own your own cloud or maybe you do somewhere inside the giant Hitachi organization, but that's not your shtick, right? You're not AWS or Google or Azure. And so you made the point that it's your data. So I want to push at that a little bit because you also put up a slide that was very impressive about the capabilities of Hitachi Ventura. X is a service, solutions and services, data science and machine learning, et cetera. Domain expertise, so if it's the customer's data, okay, but you've got these other capabilities and you're feeding that data into models and those models get trained from the data. Essentially I have a hard time understanding where the data and the models leave off. So those models contain IP from the data. How do you ensure for your customers that the models don't go to their competitors, for example? Or do they go to the competitors and you're transparent about that? Maybe talk about that a little bit. Yeah, well we're certainly not looking to have customers IP at all go to our competitors or anything around the learnings or knowledge that we actually have there. So I think the knowledge that we learn with our customers I think hopefully adds value for them, but it's ultimately that's their domain if you will. So that's stuff that we want to go do. If you start talking about the original point around the ownership, we do want customers to own their own data, not us. And I think that there's a lot of companies out there that are actually very interested even though they won't say it that they want to actually own the customer's data. And so I think what we're looking to go do is really how do we actually help partner with our customers to make sure that they have the keys to their kingdom, to have the keys to their data wherever they want to put it. And so this is not just the Pentaho assets if you will. We have a number of other assets around content and this is called our Hitachi content platform or what have you that allows customers to put their data wherever they want it to be but make sure that they actually have control over that which really gets into more of the metadata layer, different areas that they can actually make sure that they know where all their data is, what's happening with their data if you want to actually run a bunch of models in terms of what's happening on the machine learning or what have you. Those are all things that we actually want to partner with our customers. And then the domain science and like if you talk about the data scientists and what we're actually learning from that, the knowledge around how to solve a particular problem is fine but when it comes to the algorithms and all that, that's all the customer's data. Okay, so you're not in the business obviously you're taking models and then bringing them to the competition. Cause you said, like a lot of those big internet companies will say, oh no, it's your data but you had made the point in your keynote, well you have to just look at their behavior and then judge for yourself. Yeah, exactly. Okay, let's talk about edge to outcomes. The edge is obviously an interesting area. It seems to be exploding. This notion of putting things at the edge and then everything goes to the cloud is not likely. You're going to have a lot of stuff in between. When you first acquired Pentaho, we saw the interesting vision of bringing analytics and IOT and OT together. So what's your vision for how the edge will evolve and how you guys add value there? Yeah, I mean I think if you look at the highest level, there's a big pendulum swing as we all know, right? When you go from mainframe days to kind of the open system distributed days and then much more towards a kind of a, you know, centralized cloud days to much more kind of an edge. And so I think we're moving in that direction. I think we need to. And I think the biggest thing that we look for is follow the data. And so wherever the data gets created, that's where some of the processing is going to have to occur. I mean, we all know the examples, right? Uber is not going to send information to the cloud to decide if you need to stop at a stop sign. I mean, it just doesn't happen. And so if you look at all of these edge-like devices, whether it be a car or whether it be any kind of gateway, a sensor or what have you, there is going to be some level of analytics that's going to have to occur at that edge depending on how much real-time information that you need or what you're exactly asking them to do. And that would include even analytics when it comes to video surveillance, things along those lines. And then how do you then start matching that in terms of then bringing those data points into the broader kind of ecosystem in terms of what's happening? So if you wanted to actually analyze all the cameras, let's say at this resort, you're going to have to do some things at the edge, but then centrally you can start moving those things a little bit more centrally. If you want to then start bringing those across a campus environment as well, you're going to have kind of multiple layers. But the way we look at it is follow the data. So if the data, if you've got all the data over here, you're going to have to have analytics over there. And so I think a lot of people say really, or have this belief that the data is going to move to where the analytics are. And we believe it's the exact opposite. You have to have the analytics be where the data gets created. And I think it's a fundamental shift maybe in terms of our approach relative to what others are out there. And that underscores your philosophy there. And by the way, we would agree with that. I mean, we see the edge as obviously very cost sensitive. You're going to persist only what you need to persist at the edge. And then bring pieces back maybe to some kind of aggregation point. Yep. And then up to the cloud for all the deep analysis and model training and the like. Do you agree with that sort of three tier model? Totally agree, yeah. And I think that kind of hub or gateway or what have you is going to depend on the kinds of data that you're looking at and the analysis. But you will have to have some kind of model that's going to aggregate things over time, just depending on how much data is out there, exactly what you're looking to go do. How quickly do you actually need to get the analytics into the overall deep learning model? And so I think all of those architectures will evolve. But we definitely believe you're going to have the edge. You're going to have some kind of aggregation point, some hub, some gateway or what have you. And then the overall kind of model, whether that's your cloud in the public cloud or what have you that's doing all of the aggregation and analytics across all your data points. Well, I think that's a really good point. The third tier that I'm calling the cloud is really, it's three and three A, which is public cloud and on-prem cloud. Correct. Okay, and then last question, I know you got to go. In putting together this new sort of global conglomerate, how are you spending your time? What kinds of things are you looking at when you put on the binoculars? Maybe not the telescope. You know, I thought Brian from Forester was right. You know, your three-year plan, you might as well throw it out tomorrow. But just in sort of the near to midterm, where are you spending your time? What kinds of things are you thinking about? Yeah, I mean, certainly a lot of time with customers and partners for sure. And that's why these kinds of events are great because we can actually have a number of customers come in together. That was a big event we had 30 days ago as well. Great events, certainly spent a fair amount of my time there. The other one's really around our team. And so we are changing up a lot of the leadership on our team to help us in terms of what's the next level or phase of our transformation. To your point, we've gone from this company of old 15 years ago to now a company that, we've got this data company for Hitachi, Hitachi Vintara, 60% of our revenue software services. This includes the $1.2 billion of acquisitions we've done over the last five to 10 years. All the other aspects, the team, and we talked about this earlier, but the team, the people is really where it's at. And so we have a few new leaders on our team, which are amazing. And this is around whether it be on our sales organization or product or what have you. I'm spending a fair amount of my time with our team. We'll be at an offsite all next week as well. Just making sure we're aligned on what's the next phase of executing on this strategy. Well, it's been interesting to watch the Hitachi, this portion of Hitachi evolve. You guys emphasize culture. You got a great culture and you're a great leader. I really appreciate it. I appreciate it. Thanks so much, Dave. Yeah, I appreciate it. Thank you. All right, keep it right there, buddy. We'll be back with our next guest right after this short break.