>> Announcer: Live, from Orlando Florida It's theCUBE! Covering Pentaho World 2017. Brought to you by Hitachi Ventara. >> Welcome back to theCUBE's live coverage of Pentaho World brought to you by Hitachi Ventara I'm your host Rebecca Knight along with my co-host Dave Vellante, we're joined by Brendan Aldrich he is the chief data officer at Ivy Tech which is Indiana's community college system Thanks so much for joining us. >> Thank you very much I appreciate it. >> And congratulations because I know that you've just won the Pentaho Excellence Award for the Social Impact category. At Ivy Tech you are you using the power of data to combat one of the toughest problems in education higher education drop out rate so tell us a little bit about what you're doing and how you're using data. >> Certainly, well Ivy Tech has been really one of the more innovative players in the higher education space when it comes to how we're utilizing data. Both from the work that data engineering and our chief technology officer has done to the work we're doing now from my area to make that data very useful and very usable for the organization. And we're tackling it on multiple fronts. We're using data in order to help more quickly identify students that have already completed the requirements to graduate. Or if they are close to or have already potentially completed the requirements to graduate on another major other than their declared major and starting those conversations with the students. >> And what about the drop out too so you are obviously also looking at students that are at risk. >> We've been engaged in a project called Project Early Success where we work in the first two weeks of a 16 week term to identify which students we believe are at risk for failure. And then we spend the next two weeks, weeks 3 and 4 of the term coordinating hundreds of faculty staff and administrators to reach out and try to talk to those students and see if we can move them back on track. The first term that we did that we saw a great success with, we, by mid-term were showing a 3.3 percentage point drop in our number of D's and F's being reported. For an organization our size, that meant over 3000 students, more student, who were passing their courses at mid-term as compared to failing them, compared to the year before. >> Scope of the organization? Student size? >> Ivy Tech, we are Indiana state wide community college system so we have 19 campuses, almost 9000 employees and we educate around 160 000 students per year. >> Wow. So just getting back to that college drop out, so professors are putting in the data about who's going to class, who's not going to class >> Brendan: That's right. >> The grades that their getting. And then that's all being fed in and you're finding out who the at risk people are, and it's really just reaching out to them and it's saying "Hey, what's going on?" >> Absolutely. And in fact a lot of the work was done with our engineering team to actually identify data that related to the behaviors of the students. So it's not just their attendance it's not just previous performance in similar classes. But it's really finding those data elements that relate to behaviors of the students that we believe are going to put them on a less successful track. >> Brendan I wonder if we can talk about the role of the Chief Data Officer. When we talk to CDO's in for profit organizations they always say we start with an understanding of how data can help with our monetization strategies. Now let's translate that for a community college. Is that a reasonable starting point if I frame it as how data adds value to the organization is that where you started and take us through sort of the journey of your role. >> Absolutely. Well first of all Chief Data Officers in higher education are still fairly rare. At the time Ivy Tech hired me in December of 2015 I was only the 9th Chief Data Officer working at any college or university in the country. And the first that had been appointed at a two year college. So whereas a public institution like ours is not necessarily as driven by profitability students success is something that's very high on our priority list and being sure that we were able to make data very available to everyone in the organization that was working with our students so that they could use that data to more directly target the areas that they could help the student best. Now there can be profitability components as a public institution we do receive funds from the state, performance funding for students who successfully graduate. In some ways we've been able to use data to help our registrars identify those students more quickly. Which certainly gives us a lot of opportunity not only to help the students on their own educational goals and careers but to be able to increase the amount of performance funding that Ivy Tech receives from the state as well. >> So that you brought to the other point CDO's tell us is data access, making that data accessible. And then there's a trust component too. It's got to be reliable and it's hard with all this data and all this data growth is how are you addressing kind of those challenges? >> One of the things that's really unique about how we're approaching data at Ivy Tech is this idea of a data democracy. It's more than self-service business intelligence or self-service analytics. Because instead of just providing access we wanted to make sure that once our employees had access, that the data was intuitive. That it was relevant to their responsibilities. That it was interactive. So that as their needs and challenges and questions evolved they could continue to use data to answer those questions without having to go back to a central IT team or a central research team. So the data democracy is a really unique aspect of ours that was important to us and I think at the moment we have about 4000 of our employees trained and running on our platform today. >> So everybody wants to be data driven these days your job is to actually affect that data driven initiative. Culturally, people say they're data driven but they don't necessarily act that way. They still act on gut feel and this is the way we've always done it. How have you been able to affect the cultural transformation? >> Well it's important to remember that if you can make the right data available to the people who are ready to use it, that's a transformational opportunity. For us, before we began on this project less than 2% of our employee base actually had the ability to create a report. Everyone else had to make requests wait for data to be made available it could take time and maybe that data wasn't available by the time they actually needed it. So if you think about that, moving from a place where less than 2% of our employees had access to data to a point where we're approaching 50% of our employees now having really good access to data we didn't want just a few silver bullets we feel that every one of our employees has the potential, if they have the right data available to test their ideas with data and come up with brand new, innovative ideas. So we could have thousands of silver bullets coming to rise throughout our organization. >> So give us some examples, I mean we've talked a little bit about how the data is transforming the student experience and student success rate but how, what are some of your grand ideas about how faculty and how employees can use data to test ideas and make their lives easier and make Ivy Tech more successful. >> Oh absolutely. And even if you think about Project Early Success and the idea that we were helping to identify students that we believe may be struggling behaviorally in being successful in their courses. Now if you can take that as an attribute and you can surface it through our system to the employees that are using it which includes our faculty. Our faculty members now have the ability to see very quickly which of their students may be struggling and have the chance to intervene with those students as well on a regular basis. So it's not just one phone call at the beginning of the term. It's not just Project Early Success but now what we're talking about as Project Student Success how do we continue to use that kind of information to engage the student over the entire course of the term to ensure that we're not just changing their trajectory a little bit in the beginning but that we're following that journey with them over the course of their educational goal. >> Can you talk about the regime in your organization? The reporting structure, to whom do you report is there a CIO- >> Brendan: There is. >> What's the relationship there? >> There is a CIO who I report to the Chief Technology Officer and I both report to the CIO and we had a recent change in our leadership within the organization as well. Back a year ago this last July we have a new president of the state wide organization Dr. Sue Ellspermann who was formerly our lieutenant governor for the state of Indiana. >> So that's interesting that you report to the CIO. Most Chief Data Officers, we find, I wonder if you can comment don't report to the CIO there's sort of a parallel organization for a variety of reasons. People generally believe that well, it maybe one day was the CIO's job it's sort of the CIO's job morphed into kind of keeping the lights on and the infrastructure going, but what do you see amongst your colleagues with that regard? >> You know what's important for me and I think that if you look at every organization across the country there is this data knowledge gap. This idea that you've got your IT and engineering staff that knows everything there is about how to build, support, augment and de-commission these systems but generally have not been as involved in what the data means inside those systems or what decisions are being made off that data. On the other half of that gap you've got all of the rest of your organization the people that are using data who know what it means and who are making decisions from it but generally don't know enough about how to think about structuring that data so that they could get the engineering teams to build them new tools. This is really the place where a Chief Data Officer in my mind comes to sit. Because my goal is to build those bridges between the organization so that we can help engineering learn more about what we're doing as an organization with data and then use that information to build tools that will drive the rest of the organization closer to those goals through data. >> Now you're not a bank so you've got, I'm imagining a pretty small team. >> Brendan: We do. >> So maybe you can talk about that and how you manage with such a small team. >> You know it's interesting most organizations when you think about a build versus buy scenario you think about well I don't have a lot of people I don't have a lot of bandwiths, maybe we need to buy. Now Ivy Tech went through that process and every one of the RP's that came back were too expensive We couldn't afford to do it. So as a team we had to sit down and think about how do we really rethink the way that we approach this in order to still accomplish what we need out of data and out of our data warehouse and analytic systems. Part of what I'll be speaking at the conference today is some of those entrenched data practices that we had to overcome or rethink and rewrite in order to get to where we are today. >> Well Brendan it's been so much fun having you on theCUBE, thanks so much. >> Well thank you, I appreciate it. >> I'm Rebecca Knight for Dave Vellante you are watching theCUBE, we will have more from Pentaho World in just a little bit. (electronic music)

>> Announcer: Live from Orlando, Florida, it's theCUBE covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to theCUBE's live coverage of PentahoWorld brought to you by Hitachi Vantara. I'm your host Rebecca Knight, along with my cohost Dave Vellante. We are joined by Derek Mathieson, he is the group leader at CERN. Welcome, Derek, glad to have you on the show. >> Well, glad to be here, thank you very much. >> So, CERN, which is of course the European Organization for Nuclear Research. And you know we think of it as this place of physicists and engineers working together to solve these problems. And probe the mysteries of the universe but in fact, CERN is a technology organization. >> Absolutely, I mean, I think that's the- CERN has this reputation of being exclusively physics. I mean, it is the world leading particle physics laboratory. But in fact, in the end, yeah, we're an infrastructure organization who provides all the technology, all the science. And all the scientists and engineers come to CERN to do their work. But CERN itself provides the facilities. So, our main focus, in fact, is technology. Computer science, civil engineering, construction. I mean, we built cathedral size concrete structures 400 and 50 feet underground, 17 mile long tunnels. I mean, this is civil engineering in the grand scale. And that's actually one of the major focuses. Is that CERN, although it's a physics organization, one of the difficulties we have as an organization is to explain to people, in fact, what we're looking for when we're recruiting. When we're contacting other universities. It's all about the fact that we're not looking for physicists, we're looking for engineers and technology specialists to come and work at CERN. >> So talk to us about some of the new, exciting projects that you're working on there. >> Oh, I mean, there's a lot going on. Obviously, the reason I'm here today is all about the work that we're doing with Pentaho. So we're, you know, building a new data warehouse. My group's actually responsible for the administrative computing of CERN. So basically running CERN as a business. I mean this is, there's a budget of around about one billion U.S. dollars. Going into CERN every year, in order to do all this physics research. So obviously we have a responsibility to treat, be faithfully to these tax dollars, carefully and you know spend them wisely. So a lot of my work is to make sure that we have the appropriate infrastructure, controls and proper technology there. To make sure that it's used effectively and wisely. >> So paint a picture of that infrastructure for us, if you would. What's it look like if we took a peak under the tent? Well, I mean, it's what quite nice about it is with the technology infrastructure that we have. So we have a huge computer center. There's a hundred thousand CPU's in our computer center. That's mainly used for doing physics but because we have all this infrastructure there, we can use part of it to also run the administration. Which gives us the ability to run a real world class technology stack to actually run the organization. So we have a huge data warehouse. Which gives a very rapid response to the physicists and engineers who actually want to go on and do their work. My job is to make sure that the administration of CERN doesn't get in their way. So we want to provide them the facilities so they just get on with their job and all the other things to do with actually running the organization are my problem and the team that works for me. And good examples is that CERN literally sits on the border between France and Switzerland. So we have, you know, we care about things like, there's 80 different customs forms that we have to worry about on a daily basis just as we move materials around the site. So we have such an usual organization but it's unique in the world. And that's what attracts people to work there is all these new challenges that we got. It's really a fantastic place. >> And the view is pleasant I bet. >> Oh yeah. (all giggling) >> Okay, so tell us more about the infrastructure. So you talked about this really fast data warehouse. 100,000 CPUs, is it all sort of on prem? Is it a mix sort of on prem and the Cloud? What's the data warehouse, you know, give us a sense of what that infrastructure is. 'Cause people hear data warehouse, they think you know, kind of old, clunky data warehouse. You're talking about this super high performance. >> Exactly, in fact, that's one of the challenges that we face is. We've got scientists who are used to dealing with high volumes of data with high fixation. Our particle detectors produce around 2 petabytes of data per second. So they're used to dealing with large amount of data. So immediately when they started looking at the administration of the organization of the same high expectations. They want it to be fast, they want it to process the data. Large quantities of data, very quickly indeed and give the answers (snaps) in a split second. So to do that we have to obviously put quite a lot of hardware behind it and also use good technical strength as well. We're quite big users of Oracle at CERN. We have a big Oracle database which is for the principle, where we keep most of our data. And then we use Pentaho on top of that in order to do all the deporting, the analytics, the building the Cube, so all this kind of thing. And their user base is very transient. So there's around fifteen thousand people who're actually working at CERN at any one time. Half of the world's particle physicists work at CERN. >> Rebecca: Wow. >> So, they're coming and going all the time. They don't want to worry about how to get the data. So it has to be there, has to be there right away. Has to be easy to use and easy to understand. These people live and work and breathe particle physics. They don't worry about the budget and the details about how to do all this stuff. This is something where the accountants have to get there. Get it in such a way that it's easy for them to do the right thing and make sure that we stay compliance with the various regulations. And make sure that the organization continues to function as a business while still getting on with our primary mission of particle physics research. >> And that infrastructure is primarily on premise, that correct? >> It's on premise, the vast majority of it. In fact, one of the, we have two main data centers. So there's one physically located at Cern in Geneva. And then there's another one over in the (mumbles) institute, in (snaps) >> The other place. >> The other place. (both laughing) >> Okay. >> Yep. >> And that, presume, because you've got such volumes of data. You can't just be moving that stuff around up into the Cloud. >> Right, in fact yeah, we have a lot of high speed data links between the different data centers in order to. We have a copy of quite a lot of the data in fact. The principle physics data is copied, not only at CERN, which is what's called a 2-0 site where we have all the data to start with. But we also copy it to I think it's around about seven different institutes around the world. So they have a first-line copy as well. Altogether we have a network of around a hundred computer centers working for CERN in some way or other. That's part of what we call the LHC computing grids which is (mumbles) a planetary data center in computer infrastructure to do all this processing of the LHC data. >> I'm going to ask you to go back to about the organizational structure. I mean, you described this office situated on the border of France and Switzerland. Where half the world's particle physicists work. What is the culture like? And how do you get- and as you said also the administrations job is to really get out of their way so they can do their thing. What is the culture like there? How do people work together? How do people collaborate? What do you do when there's disagreement? >> I mean this is one of the unique aspects of CERN. Is bringing people together. There's around about 90 different countries represented at CERN. Around about 100 different nationalities, all working on site. It's very much like a university environment. We have a canteen where people will come in. Their always saying that probably most of the physics and most of the science discoveries are happening within the canteen as people meet together from all over the world. We have countries, India, Pakistan, have just joined as associate members. We've got 22 member states. Mainly around Europe but now we have a policy enlargement. So we're actually trying to make the organization even larger. Touching more countries around the world. United States is an observer now within the organization. So they actually participate in the CERN council and they're also major players in some of the large LHC experiments as well. But yeah, on a day to day basis, I'll be sitting in the restaurant and there will be Nobel Prize winners. We have our director general, she will be there as well, having lunch with everyone else. So it's a very much a leveling organization where everyone feels free to speak to each other. And discuss the matters of the day and particle physics. >> So what do you guys talk about? >> (laughs) What's the canteen conversation? >> I think this is the utter geek speak usually. That's the main problem in CERN is that people are passionate about what they do. So they come to CERN, they love what they do, they talk about it all the time. So, I mean, people will be talking about the latest generation of the CPU architecture, GPU programming. How do we do simulations with petabytes of data? This is lunch time conversation. And evening and everything else. >> So you're not talking about the a football game, right? You're talking about this sort of, talking shop mostly right? >> There is a football team, there is a rugby team as well. There's real life as well at CERN but yeah, I mean, most people are there because they're passionate about what they do. >> Obviously you're listening to those conversations you must pick up a lot of it. >> Yeah, I know, I mean, I think it's if you work at Cern and you're at a dinner party, someone laughs, "Oh you work at Cern, tell me all about physics." So you pick up a bit about it of course. Everyone can speak a little bit about what we're doing at Cern and I think that's an imperative because we work there. Of course you hear about what's going on and understand a little bit about it. But I would never claim to be a physicist of course. >> Rebecca: You can fake it though. >> I have lunch with physicists, I'm not one myself. >> How 'about Pentaho? You painted the picture of the infrastructure before. Where does Pentaho fit? And how are they adding value? >> We've been using Pentaho now for the last few years. We started, I mean, what really attracted is actually this combination of open-source plus propriety software. We like the core and the open-source nature of it which it very much fits with the values of CERN as well as being an open lab. And sharing everything that we do. So we started, as I say, with Pentaho a few years ago. Now, it's a core component. It's a core strategic component of the administration and also used in other areas as well. So it's also used in some of the more technical infrastructure areas in terms of: how do we actually run the lab? Parts of the infrastructure in terms of monitoring the different parts of the accelerator complex. And even in terms of, you know, the maintenance of the buildings, all of that. So it's really, you know, core within the organization as a core component for us. >> So, CERN is an organization then as- I'll use the word insistent, if you will, on open-source as a component. So that puts pressure on companies like Pentaho to pay attention to the next project. Maybe contribute, maybe not. But it certainly integrate. Score card, how have they done on that? What would you like to see them do better in that regard? And what kind of open-source projects do you- and you may not be able to answer this. But, might your organizations see in the horizon that you want Pentaho to capture? I mean, obviously 8.0, you've heard about, Spark and bringing in Kafka and the like. But maybe you could comment. >> Absolutely, I think this is one of the eighters who's really attracted us was the open-source nature. And certainly Pentaho's movement in that direction particularly, I think, was the integration with Hitachi as well. They're seeing many other projects now being integrated within to that sort of pentacle world. This is something that was interesting to us. Of course because of our Cloud based infrastructure. The idea of scaling up and scaling out. And they're going with the open-source projects to particular and the patchy projects. Which was really interesting to us as well. Something that we've been working on a bit ourselves. And now to hear that Pentaho was doing that as well. That was great, a good piece of news for me because it was something that we have been struggling with is basically spreading out. We've got fifteen thousand users. We want to have a dynamic infrastructure where we can actually provision more service where necessary in order be able to take load when we need it. But at the same time we don't want to waste the resources when they're off doing something else. >> Over the course of last decade, let's say, has there ever been a tendency for- 'cause you've got so many alpha geeks running around. To say, "Hey, I can take these open-source components and kind of do it myself." >> Derek: Yeah. >> "I don't need the Pentaho load bouncer, I got yarn to negotiate my resources. Look what I built." And so, how do you manage that? >> No, I mean, you're absolutely right. It's a problem here there's always the risk of the naught of engineer syndrome where, "I could do it better." And we have to pressure against that. But, I mean, I think the important of the issue is take the bigger picture. If it's already done well, we don't need to do it again. Build on top of it, make something better on top of something that already exists. And that's the thing, that's the message that we can give to any of the engineers working at CERN. Is, "You can do so much more if you already use the infrastructure that's already solid." And that's part of this, you know, reuse, of course. Open-source software allows us to build on things which are already solid. We don't need to make another one of them. We'll make something on top of it. That's a primary message that we try to give. >> So here we are at Pentaho World and you're with a bunch of other practitioners. Sharing best practices, talking about how you use the product, learning from them too. What are some of the take aways? And how much are you actually talking to them versus talking to the Pentaho product people? >> We did a presentation yesterday. The focus of our presentation was managing Pentaho. So, one of the things that we've been using now for a number of years is you have to have an infrastructure to be able to actually take care of all the different artifacts, all the different reports. We have many, many different user who want to be able to use Pentaho at the same time creating their own artifacts. I mean we have to have some way of managing to actually manage all this landscape. Although Pentaho has got some tools necessary, that was one of the areas that we felt we could add some value in there. So we've been building on top of the existing Pentaho APIs. Building an infrastructure to make it easier to support for other people. And what was quite nice is we were speaking to some of the other attendees. And that's exactly the kind of thing they've been worrying about as well. And there was even some presentations of people doing a similar approach in their own organizations. On how they were actually trying to build some kind of architecture on top of Pentaho just to manage the whole thing. When you have hundred of reports and hundred of artifacts and very complicated data warehouse cubes, you need something on top of that to actually just manage the whole thing. And that's something that we've been focused on. And I see other people are doing the same kind of thing. So I can imagine that Pentaho will be taking note of this and probable incorporating some of the ideas. >> It's sending a loud and clear message to Pentaho, yes absolutely. >> How about the event? You've been to at least two or that I know of. I don't know if you were at the original. >> I've been to three altogether. >> Okay, so you've been to, I think all of them, right? >> I could have been all of them, yeah. >> I think the first one was 14, I think, I'm pretty sure. Things you've taken away? You know, interesting conversations? >> I think it's the main reason we come in. It's a long way for us to come all the way from Geneva to come here. It's really important for us to touch base with other people using the product. It is an open community, people do like to talk to each other about, you know the new things that are happening within the Pentaho community. And I think face to face contact, in the end, is very hard to beat. And we're coming to an event like this you actually get the opportunity to speak to people over lunch. Or in the evening events you can talk to them and actually find out what it's really like to use Pentaho. >> Great, well thank you so much Derek for coming on theCUBE. >> Thank you very much. >> I'm Rebecca Knight for Dave Vellante. We well have more from Pentaho World just after this.

>> Announcer: Live from Orlando, Florida it's theCUBE covering Pentaho World 2017. Brought to you by Hitachi Vantara. (upbeat techno music) (coughs) >> Welcome to Day Two of theCUBE's live coverage of Pentaho World, brought to you by Hitachi Vantara. I'm your host Rebecca Knight along with my co-host Dave Vellante. We're joined by Robert Walsh. He is the Technical Director Enterprise Business Intelligence at ZeniMax. Thanks so much for coming on the show. >> Thank you, good morning. >> Good to see ya. >> I should say congratulations is in order (laughs) because you're company, ZeniMax, has been awarded the Pentaho Excellence Award for the Big Data category. I want to talk about the award, but first tell us a little bit about ZeniMax. >> Sure, so the company itself, so most people know us by the games versus the company corporate name. We make a lot of games. We're the third biggest company for gaming in America. And we make a lot of games such as Quake, Fallout, Skyrim, Doom. We have game launching this week called Wolfenstein. And so, most people know us by the games versus the corporate entity which is ZeniMax Media. >> Okay, okay. And as you said, you're the third largest gaming company in the country. So, tell us what you do there. >> So, myself and my team, we are primarily responsible for the ingestion and the evaluation of all the data from the organization. That includes really two main buckets. So, very simplistically we have the business world. So, the traditional money, users, then the graphics, people, sales. And on the other side we have the game. That's where a lot of people see the fun in what we do, such as what people are doing in the game, where in the game they're doing it, and why they're doing it. So, get a lot of data on gameplay behavior based on our playerbase. And we try and fuse those two together for the single viewer or customer. >> And that data comes from is it the console? Does it come from the ... What's the data flow? >> Yeah, so we actually support many different platforms. So, we have games on the console. So, Microsoft, Sony, PlayStation, Xbox, as well as the PC platform. Mac's for example, Android, and iOS. We support all platforms. So, the big challenge that we have is trying to unify that ingestion of data across all these different platforms in a unified way to facilitate downstream the reporting that we do as a company. >> Okay, so who ... When it says you're playing the game on a Microsoft console, whose data is that? Is it the user's data? Is it Microsoft's data? Is it ZeniMax's data? >> I see. So, many games that we actually release have a service act component. Most of our games are actually an online world. So, if you disconnect today people are still playing in that world. It never ends. So, in that situation, we have all the servers that people connect to from their desktop, from their console. Not all but most data we generate for the game comes from the servers that people connect to. We own those. >> Dave: Oh, okay. >> Which simplifies greatly getting that data from the people. >> Dave: So, it's your data? >> Exactly. >> What is the data telling you these days? >> Oh, wow, depends on the game. I think people realize what people do in games, what games have become. So, we have one game right now called Elder Scrolls Online, and this year we released the ability to buy in-game homes. And you can buy furniture for your in-game homes. So, you can furnish them. People can come and visit. And you can buy items, and weapons, and pets, and skins. And what's really interesting is part of the reason why we exist is to look at patterns and trends based on people interact with that environment. So for example, we'll see America playerbase buy very different items compared to say the European playerbase, based on social differences. And so, that helps immensely for the people who continuously develop the game to add items and features that people want to see and want to leverage. >> That is fascinating that Americans and Europeans are buying different furniture for their online homes. So, just give us some examples of the difference that you're seeing between these two groups. >> So, it's not just the homes, it applies to everything that they purchase as well. It's quite interesting. So, when it comes to the Americans versus Europeans for example what we find is that Europeans prefer much more cosmetic, passive experiences. Whereas the Americans are much things that stand out, things that are ... I'm trying to avoid stereotypes right now. >> Right exactly. >> It is what it is. >> Americans like ostentatious stuff. >> Robert: Exactly. >> We get it. >> Europeans are a bit more passive in that regard. And so, we do see that. >> Rebecca: Understated maybe. >> Thank you, that's a much better way of putting it. But games often have to be tweaked based on the environment. A different way of looking at it is a lot of companies in career in Asia all of these games in the West and they will have to tweak the game completely before it releases in these environments. Because players will behave differently and expect different things. And these games have become global. We have people playing all over the world all at the same time. So, how do you facilitate it? How do you support these different users with different needs in this one environment? Again, that's why BI has grown substantially in the gaming industry in the past five, ten years. >> Can you talk about the evolution of how you've been able to interact and essentially affect the user behavior or response to that behavior. You mentioned BI. So, you know, go back ten years it was very reactive. Not a lot of real time stuff going on. Are you now in the position to effect the behavior in real time, in a positive way? >> We're very close to that. We're not quite there yet. So yes, that's a very good point. So, five, ten years ago most games were traditional boxes. You makes a game, you get a box, Walmart or Gamestop, and then you're finished. The relationship with the customer ends. Now, we have this concept that's used often is games as a service. We provide an online environment, a service around a game, and people will play those games for weeks, months, if not years. And so, the shift as well as from a BI tech standpoint is one item where we've been able to streamline the ingest process. So, we're not real time but we can be hourly. Which is pretty responsive. But also, the fact that these games have become these online environments has enabled us to get this information. Five years ago, when the game was in a box, on the shelf, there was no connective tissue between us and them to interact and facilitate. With the games now being online, we can leverage BI. We can be more real time. We can respond quicker. But it's also due to the fact that now games themselves have changed to facilitate that interaction. >> Can you, Robert, paint a picture of the data pipeline? We started there with sort of the different devices. And you're bringing those in as sort of a blender. But take us through the data pipeline and how you're ultimately embedding or operationalizing those analytics. >> Sure. So, the game theater, the game and the business information, game theater is most likely 90, 95% of our total data footprint. We generate a lot more game information than we do business information. It's just due to how much we can track. We can do so. And so, a lot of these games will generate various game events, game logs that we can ingest into a single data lake. And we can use Amazon S3 for that. But it's not just a game theater. So, we have databases for financial information, account users, and so we will ingest the game events as well as the databases into one single location. At that point, however, it's still very raw. It's still very basic. We enable the analysts to actually interact with that. And they can go in there and get their feet wet but it's still very raw. The next step is really taking that raw information that is disjointed and separated, and unifying that into a single model that they can use in a much more performant way. In that first step, the analysts have the burden of a lot of the ETL work, to manipulate the data, to transform it, to make it useful. Which they can do. They should be doing the analysis, not the ingesting the data. And so, the progression from there into our warehouse is the next step of that pipeline. And so in there, we create these models and structures. And they're often born out of what the analysts are seeing and using in that initial data lake stage. So, they're repeating analysis, if they're doing this on a regular basis, the company wants something that's automated and auditable and productionized, then that's a great use case for promotion into our warehouse. You've got this initial staging layer. We have a warehouse where it's structured information. And we allow the analysts into both of those environments. So, they can pick their poison in respects. Structured data over here, raw and vast over here based on their use case. >> And what are the roles ... Just one more follow up, >> Yeah. >> if I may? Who are the people that are actually doing this work? Building the models, cleaning the data, and shoring data. You've got data scientists. You've got quality engineers. You got data engineers. You got application developers. Can you describe the collaboration between those roles? >> Sure. Yeah, so we as a BI organization we have two main groups. We have our engineering team. That's the one I drive. Then we have reporting, and that's a team. Now, we are really one single unit. We work as a team but we separate those two functions. And so, in my organization we have two main groups. We have our big data team which is doing that initial ingestion. Now, we ingest billions of troves of data a day. Terabytes a data a day. And so, we have a team just dedicated to ingestion, standardization, and exposing that first stage. Then we have our second team who are the warehouse engineers, who are actually here today somewhere. And they're the ones who are doing the modeling, the structuring. I mean the data modeling, making the data usable and promoting that into the warehouse. On the reporting team, basically we are there to support them. We provide these tool sets to engage and let them do their work. And so, in that team they have a very split of people do a lot of report development, visualization, data science. A lot of the individuals there will do all those three, two of the three, one of the three. But they do also have segmentation across your day to day reporting which has to function as well as the more deep analysis for data science or predictive analysis. >> And that data warehouse is on-prem? Is it in the cloud? >> Good question. Everything that I talked about is all in the cloud. About a year and a half, two years ago, we made the leap into the cloud. We drunk the Kool-Aid. As of Q2 next year at the very latest, we'll be 100% cloud. >> And the database infrastructure is Amazon? >> Correct. We use Amazon for all the BI platforms. >> Redshift or is it... >> Robert: Yes. >> Yeah, okay. >> That's where actually I want to go because you were talking about the architecture. So, I know you've mentioned Amazon Redshift. Cloudera is another one of your solutions provider. And of course, we're here in Pentaho World, Pentaho. You've described Pentaho as the glue. Can you expand on that a little bit? >> Absolutely. So, I've been talking about these two environments, these two worlds data lake to data warehouse. They're both are different in how they're developed, but it's really a single pipeline, as you said. And so, how do we get data from this raw form into this modeled structure? And that's where Pentaho comes into play. That's the glue. If the glue between these two environments, while they're conceptually very different they provide a singular purpose. But we need a way to unify that pipeline. And so, Pentaho we use very heavily to take this raw information, to transform it, ingest it, and model it into Redshift. And we can automate, we can schedule, we can provide error handling. And so it gives us the framework. And it's self-documenting to be able to track and understand from A to B, from raw to structured how we do that. And again, Pentaho is allowing us to make that transition. >> Pentaho 8.0 just came out yesterday. >> Hmm, it did? >> What are you most excited about there? Do you see any changes? We keep hearing a lot about the ability to scale with Pentaho World. >> Exactly. So, there's three things that really appeal to me actually on 8.0. So, things that we're missing that they've actually filled in with this release. So firstly, we on the streaming component from earlier the real time piece we were missing, we're looking at using Kafka and queuing for a lot of our ingestion purposes. And Pentaho in releasing this new version the mechanism to connect to that environment. That was good timing. We need that. Also too, get into more critical detail, the logs that we ingest, the data that we handle we use Avro and Parquet. When we can. We use JSON, Avro, and Parquet. Pentaho can handle JSON today. Avro, Parquet are coming in 8.0. And then lastly, to your point you made as well is where they're going with their system, they want to go into streaming, into all this information. It's very large and it has to go big. And so, they're adding, again, the ability to add worker nodes and scale horizontally their environment. And that's really a requirement before these other things can come into play. So, those are the things we're looking for. Our data lake can scale on demand. Our Redshift environment can scale on demand. Pentaho has not been able to but with this release they should be able to. And that was something that we've been hoping for for quite some time. >> I wonder if I can get your opinion on something. A little futures-oriented. You have a choice as an organization. You could just take roll your own opensource, best of breed opensource tools, and slog through that. And if you're an internet giant or a huge bank, you can do that. >> Robert: Right. >> You can take tooling like Pentaho which is end to end data pipeline, and this dramatically simplifies things. A lot of the cloud guys, Amazon, Microsoft, I guess to a certain extent Google, they're sort of picking off pieces of the value chain. And they're trying to come up with as a service fully-integrated pipeline. Maybe not best of breed but convenient. How do you see that shaking out generally? And then specifically, is that a challenge for Pentaho from your standpoint? >> So, you're right. That why they're trying to fill these gaps in their environment. To what Pentaho does and what they're offering, there's no comparison right now. They're not there yet. They're a long way away. >> Dave: You're saying the cloud guys are not there. >> No way. >> Pentaho is just so much more functional. >> Robert: They're not close. >> Okay. >> So, that's the first step. However, though what I've been finding in the cloud, there's lots of benefits from the ease of deployment, the scaling. You use a lot of dev ops support, DBA support. But the tools that they offer right now feel pretty bare bones. They're very generic. They have a place but they're not designed for singular purpose. Redshift is the only real piece of the pipeline that is a true Amazon product, but that came from a company called Power Excel ten years ago. They licensed that from a separate company. >> Dave: What a deal that was for Amazon! (Rebecca and Dave laugh) >> Exactly. And so, we like it because of the functionality Power Excel put in many year ago. Now, they've developed upon that. And it made it easier to deploy. But that's the core reason behind it. Now, we use for our big data environment, we use Data Breaks. Data Breaks is a cloud solution. They deploy into Amazon. And so, what I've been finding more and more is companies that are specialized in application or function who have their product support cloud deployment, is to me where it's a sweet middle ground. So, Pentaho is also talking about next year looking at Amazon deployment solutioning for their tool set. So, to me it's not really about going all Amazon. Oh, let's use all Amazon products. They're cheap and cheerful. We can make it work. We can hire ten engineers and hack out a solution. I think what's more applicable is people like Pentaho, whatever people in the industry who have the expertise and are specialized in that function who can allow their products to be deployed in that environment and leverage the Amazon advantages, the Elastic Compute, storage model, the deployment methodology. That is where I see the sweet spot. So, if Pentaho can get to that point, for me that's much more appealing than looking at Amazon trying to build out some things to replace Pentaho x years down the line. >> So, their challenge, if I can summarize, they've got to stay functionally ahead. Which they're way ahead now. They got to maintain that lead. They have to curate best of breed like Spark, for example, from Databricks. >> Right. >> Whatever's next and curate that in a way that is easy to integrate. And then look at the cloud's infrastructure. >> Right. Over the years, these companies that have been looking at ways to deploy into a data center easily and efficiently. Now, the cloud is the next option. How do they support and implement into the cloud in a way where we can leverage their tool set but in a way where we can leverage the cloud ecosystem. And that's the gap. And I think that's what we look for in companies today. And Pentaho is moving towards that. >> And so, that's a lot of good advice for Pentaho? >> I think so. I hope so. Yeah. If they do that, we'll be happy. So, we'll definitely take that. >> Is it Pen-ta-ho or Pent-a-ho? >> You've been saying Pent-a-ho with your British accent! But it is Pen-ta-ho. (laughter) Thank you. >> Dave: Cheap and cheerful, I love it. >> Rebecca: I know -- >> Bless your cotton socks! >> Yes. >> I've had it-- >> Dave: Cord and Bennett. >> Rebecca: Man, okay. Well, thank you so much, Robert. It's been a lot of fun talking to you. >> You're very welcome. >> We will have more from Pen-ta-ho World (laughter) brought to you by Hitachi Vantara just after this. (upbeat techno music)

>> Announcer: Live from Orlando Florida. It's the Cube. Covering Pentaho World 2017. Brought to you by Hitachi Vantara. >> Welcome back to the Cube's live coverage of Pentaho World brought to you by Hitachi Vantara. I'm your host, Rebecca Knight along with my co-host, Jim Kobielus. We are joined by Geo Thomas. He is the director of It at Benefits Science a healthcare insurance analytics company. Thanks so much for coming on the Cube, Geo. >> Thank-you, thanks for having me. >> So Benefits Science is a company launched out of MIT, tell our viewers a little bit more about the company. >> Okay, so Benefits Science is a healthcare data analytic company which co-founded by MIT (mumbles). Doctor (mumbles) and Doctor Stephen so far and we have one more partner. We do data analytics on the healthcare side and we work with employers and the brokers to analyze the data and give them dashboards and workbooks, and so that's what we mainly do. And we, yeah. >> So, as you said, you work with employers to save them healthcare dollars. Can you get into the nitty-gritty a little bit more. >> That's exactly right, so what we do is we empower employers to manage their employee benefits. Providing them the data analytic tools and other optimization tools, and we give them a very fine clear picture of how these plans are performing, and how they can optimize their plans in the near future by giving plan optimization tools and (mumbles) algorithms and things like that. >> You refer this as a manage service for your clients or do you provide specifically licensed software that helps them do this for themselves? From their own premises. >> We are a Cloud platform, and we provide our platform as a sub-lease for our clients. So, we get the data from them and we provide data analytic tool by mashing of this data and they use our platform to see those reports and insights and things like that. >> So, healthcare data is a really special kind of complicated when it comes to data because there's so many security and privacy issues related to it, how do you go about it managing this kind of data? >> Healthcare data is a very complex, very huge and we can't expect what comes next and there a lot of regulations and there are a lot of security issues, so we take all these with upmost priority. So, our company is a SOC1, SOC2, certified company. Which covers a lot of regulations by itself. Our employee's, Benefits Science employees, are really very much aware of these heap of rules. And they are all certified. We have lots of internal an external audits and regulations throughout the place so that would cover all this compliance issues, mainly. >> From an operational standpoint, how are you managing the day-to-day, day-in and day-out, do you provide a data warehouse within which you load it and then from which you do the analysis? What's the sense for how you architected your environment and then where how Pentaho plays into the overall picture? >> We take the data. Once we get the data, we measure the data. So, how we do those, we use Pentahos, and then two and two. Because it gives us a very standardized methodology to process this data, so we identify the PHP data. We sample it, scramble it, and then we do the (mumbles). And once the data element is done, and nobody touches any of those PA jobs or the jobs which we created with Pentaho, and we run this in a very secure environment in which we put all this transformed data into a data analytical platform. >> When you say scramble, you're referring to masking and anonmyzing the data? >> Correct, yes. >> That's what I assumed, you tell me, that's required by HIPA, that you do it that way? >> Yes, that's correct, yeah, yeah. So, we don't take all the data for the development. We take only the sample data, and then we scramble it and we (mumbles) all this information. >> So, what kind of results have you seen in your company since using Pentaho? >> So, I started in almost one year back and when we started, we had 20 tenants. Now, we have 200 tenants, so that's the summary of recently of what I'm seeing because Pentaho gives us lot of flexibility to standardize and make proper checks and balances throughout the data pipeline and we had created very huge test framework which can run automatically. So, all these things would benefit us to board a client because right now, onboarding a client would take less than a week. >> When you say test run automatically what sort of test are you referring to? >> So, we create test scripts, and we created a test suit framework by using Pentaho Jobs. And we schedule that. That test suit what we do is every, whenever any tenant comes in, developers can create N number of test cases and plug that in. So, it is growing and that will run automatically. Along with the PA jobs. So, that gives us a number of outputs and checks and balances and depending on the results we board the client. >> Saving healthcare dollars, spending healthcare dollars. This is really part of the national conversation. How much does Benefits Science really feel a responsibility to weigh-in on these issues. We heard a lot from the CEO this morning about how Pentaho really views its guiding principles as doing good in the world and bettering society. >> The double bottom line. >> Very true, very true, because as Benefits Science company our vision, our motto is not to just built some software and give to customers and get some money. Our vision is to help people or employers reduce the healthcare cost, so. Our data scientists built this great plan optimization tool or (mumbles) to provide employers to look at, "Okay, these "are the large claimant details, which means we might have "to go and find out the reasons and work with them "to reduce the cost." So, we are giving all the tools for them and another thing is the data (mumbles) analyzer our users love it, because we provided a simplified cube for them to drag and drop and create the reports and they can easily drag a couple of data elements and come up with, "Okay, these are the paid amounts "which we paid last month, and this has to go down." So, they can come up with their own strategies to make it down, at least, for the next year and on. >> In terms of user's being able to, in a self-service basis define their views and their reports. Do you take that intelligence that you gained from users and then bring that back into the basic service in terms of adjusting the data model? The set of canned reports or dashboards you provide? What do you do in that regard? >> Yeah, so we have a custom insight reports. Which will give pretty good idea about what this data meant to be for the customers. Like drag dashboards or large claimants or quality measures so things like that. We also have another data science group works on this AI tools or machine-learning algorithms to provide more predictive analysis. So, that would give users a different perspective of, "Okay, if we do this, we can reduce the cost." >> Is that WECA or? >> No, we are using. That's another thing I want to go back and tell them. There is a WECA here, we probably have to start using it. So, right now, we are not, right now we are using RN Python. There's something called (mumbles). So, that's what we use. >> What are some challenges that you are facing right now? What is keeping you up at night? What do you want the next versions of Pentaho to solve for you? >> I'm Director of IT, so I care about IT more than the business. So, my challenge is always how I can board more clients within a short span of time. The scalability, the security, how we can make it compliant. So, I was listening to that ATO, what are the new things coming in ATO? One of the main thing I was looking at is the scalability that is there is something called Worker Naught, that's got announced in ATO. Which you can scale as a docker, and you can spin off as many dockers as you want, and it will work by itself. That's fantastic, I'm really looking forward to get that scalability into our system. >> So, you're saying your IT environment. Your focused now more and more on a Cloud data environment that takes the application functionality and wraps it as containers? So, that's where you're going? And then you're saying that, I don't want to put words in your mouth, what you're doing is consistent with where Pentaho's going with their overall product platform? >> We are hosting an (mumbles) Cloud with Pentaho. So, Pentaho is also going into that direction. Makes me very happy because we are really looking forward to get that working in the Cloud. The thing is the. The Worker Naught, what they're talking about? Is what we were thinking of implementing on our own. So, now they have their own Worker Naught which we can just take and put it there. So, that's very good news. >> I wanted to ask you about the talent shortage in technology because that is something that the CEO talked about, Karen Perlich talked about, too. Is this real dearth of talent in data science. There was a piece in the New York Times just the other day that talked about how data scientists just a PHD can come out and make a half a million dollars in Silicon Valley. What do you think will be the real change and will get more and more graduates into this field. It seems as though the money should be enticement enough. >> That's a million dollar question though. We are in the same boat. >> You're a Massachusetts' based company, it should be. >> Even with that, we are finding a lot of difficulties to get some good data scientists. Because the moment you pass out as data scientist they're asking half a million, so. >> Literally I saw an article the other day. A good data scientist in Silicon Valley can fetch upwards of a half a million per year, so. Imagine in other regions, and now Massachusetts has no shortage of educated, smart people, but still. >> They have that level, then yes. These tools would help, and. Building that artificial intelligence on top of these tools would help, definitely, to have some sort of, not depending on data scientists so much. That even others can do those kind of things. >> So, you might not need the talent in a way. >> I'm looking forward to that because I was listening to your session in the morning. Very impressed with that because that's where I'm also trying to see where the world is heading to. >> So, you make recommendations to your clients about how they should start structure their healthcare insurance plans or employees. Do you have a capability right now within Benefits Science to basically embed a recommendation engine of that sort to help advisors on your staff to work with clients to recommend the right set of options or approaches pulling from the data, that's already there? >> Yes, that's already there. So, we provide recommendations for clients by using these algorithms. So, we have this plan optimization tool. Which will give you, if you do such and such things this is going to go down in the next year. Or there is a plan designed data. So, whenever an enrollment happens the main thing that they look at is what plan they have to sell at for their set of employees. So, every case is unique. So, we put a lot of historical data information and we put those machine-learning algorithms in there and then we come up with. We clean that model with all this data and we predict for each tenant. So, we have that right now. >> Geo, thanks so much for coming on the Cube. It's been really fun talking to you. >> Thanks for having me. >> I'm Rebecca Knight for Jim Kobielus. We will have more from the Cube's live coverage of Pentaho World, just after this. (calm electronica music)

>> Narrator: Live from Orlando, Florida, it's the cube. Covering Pentaho World 2017. Brought to you by Hitachi Ventara >> Welcome back to The Cube's live coverage of Pentaho World brought to you by Hitachi Ventara. I'm your host Rebecca Knight. Along with my co-host Dave Vellante. We have two guests today from the Universal Service Administrative company. First Sachin Batra who is the Senior Manager, Information Architecture and Tom Nesbitt, Senior Manager, Systems and Data Analytics. Welcome, thanks so much for coming on The Cube. >> Thanks. >> Thank you. >> So, first tell our viewers a little bit what the Universal Service Administrative Company is and what it does. >> Sure USAC, Universal Service Administrative Company, was created as a result of the Telecommunications Act of 1996 so that act deregulated the telecommunications industry and opened it up for competition. Along with that, the United States Federal Government passed legislation to create the Universal Service Fund. This fund, basically, supports four programs. High costs, we have a low income program, we have rural healthcare program, we also have our E-Rate or schools and libraries program. >> Okay, so, what are you doing here are Pentaho? It's a relatively new company. How do you use Pentaho? >> We're going to share our experience and our journey to become a data driven organization and how Pentaho has helped us to achieve this mission. >> When you talk about data driven organization, that means a lot of different things, to a lot of different people. What does it meant to you guys and how does it fit into your mission? >> For me, I think the first thing is the availability of data. So, historically, a lot of business people have had a hard time getting to the data. So, Pentaho has really freed the data and made it available. For me, step one is freeing the data. From there, it's then becoming more sophisticated in terms of analyzing the data, using the data to manage your day to day operations. >> So, can you describe the before and after? Maybe, the Pentaho journey? What was life like before and how did that change? >> Sasha: Oh, you want to go ahead? >> No, I can go. So, typically, I'll just say ten years ago. You would typically have to put in a request to get data or to get a report. You want a report on the state of Texas and you would have to open up a ticket, get in a line, and wait for someone to fulfill that. Now with Pentaho, we've built self-service models. So, the user can go in themselves and just create the report on the fly. So, we're talking weeks down to minutes. >> Dave: Oh, okay. >> Just to add on to that, we also have now enterprise data warehouse available so now we can do enterprise level reporting and analytics. Rather than just doing a program level reports. >> Can you give our viewers an example of what kind of a report someone would need and what could be implemented after that reports gotten? >> Sure, a lot of our reporting is about funding. We cover products and services for telecommunications. We'll do a lot of report at the national level but we may run state reports, as well. Maybe we have an inquiry, someone wants to know how's our funding in Iowa, how many applications have we completed, what type of products and services are we covered, which schools and libraries have we funded. >> How would you describe the way in which you measure the success of the mission, and how are you doing? >> The focus is a lot about ensuring we provide the right funding to the right schools and libraries and hopefully do it quickly. It's accuracy, and it's also speed. Those are, probably, the two elements. Then, of course, it's the connectivity in the classroom. Ultimately, we're trying to ensure that our products and services lead to connectivity in the classroom as well as libraries. >> How does it work? Is it like winning the lottery? You just say, "hey good news" then somebody knocks at your door or how do you inform folks, how do you collaborate with them, what's the prerequisite on their end, or requisite, things that they have to do? Is there a give and a get? >> There's applications people have to fill out. So, each year, there's a series of applications that have to be completed. We do have a special application window for funding. It's, typically, about 75 days. All the schools and libraries across the country will go ahead and fill out their applications and it's their request of what they would like to receive funding for. So, it's a special time. (chuckles) >> So, we're hearing a lot about the social innovation piece of Pentaho and how that is really one of the real approaches that it takes to business. This double bottom-line and your organization really fulfills that principle that it's trying to make good on. How does working with Hitachi Ventara and the Pentaho product, what's that relationship like there? >> I would say with the Pentaho product, it has really helped us a lot to achieve our mission. We can do a lot more reporting, enterprise level reporting, analytics. Users have the data available at their hands. They can just quickly drag and drop and create their own reports and analytics. >> How does this change employees lives? As you've said, it used to take weeks, months, now it's minutes. >> I think if you've got an operational issue or problem you get a report, maybe there's a problem with data point, or maybe there's a certain set of applications that aren't getting processed quickly enough. We can more quickly identify that problem and respond. So, it's again, identification, and then the magnitude. Is it a small problem or a big problem? Again, by freeing the data and giving it to the managers, they can better manage their operations. And we can hopefully provide better funding, faster funding to schools and libraries across the country. >> Can you take us inside your data journey? What are the sources of data? How have those sources multiplied over time, and how you're dealing with that. >> Sure, when we started we only were thinking about the four programs. So, we wanted to start with Pentaho with the four different programs. We have extracted the data from the four different transactional db's, the four programs. Like, low-income, schools and libraries, RHC, high cost areas, and then we extract this with the help of PDI and load it into our program data marks. And on the top of that, we are making Pentaho sit and then we can report and analyze based on that. >> Maybe, talk a little bit about data quality. You have to trust the data. As the data grows, it's got to be harder and harder to maintain data quality and governance and those sort of boring but important things. >> Yeah, that's been a challenge. We obtain data from other sources. So, a lot of our data is driven by what our applicants put into our forms. So, through Pentaho and other tools, we can mine that data and find out, oh, maybe the person put down the wrong county that they live in, believe it or not. We need to correct that. We do get a lot of outside data brought in and we have to make sure it's, we can use cleaning devices to make sure it's accurate. >> So, you're kind of living the data world. You talk about data driven mission. Today you hear all this buzz about AI, and machine learning, and deep learning, and all these fancy buzzwords. Do they have meaning for you, are you thinking about applying them to your organization, and if so, why? What are the outcomes that you're hoping for? >> Sure, not that much AI but I think we are planning to go more toward the predicted analytics. So, we are going to look at that very soon. We want to be proactive rather than reactive. So we want to respond to the problem proactively. >> So, that means what? Identify areas that are in need before they inform you or anticipating other problems? Describe what problems you'd be solving. >> With our application review process we receive a large number of applications. A lot of them are very similar. So, we can hopefully, put the similar ones that are within our control points and push those through more quickly. Whereas, if we have some outliers we can then, maybe, scrutinize that a little bit more. So, some type of predictive analysis to say, hey this is within a range, it's okay, let's fund it. No, this one needs a lot more scrutiny. >> Okay, so, ensuring better outcomes really? >> Tom: Yes. >> Aligning with those is really the objective, right? Okay. Great. >> So, here at Pentaho World, there's many practitioners who are sharing best practices, learning from each other. Here's how we're using the product. What are you hearing, what are you learning, are there things that as a government agency, part of the FCC, that you are going to be able to take back home and implement? >> I think what I have seen in the last couple of presentations we can do a lot more with the Pentaho version 7.0 and 8.0. You can actually visualize the data right from, when you're extracting the data. Which, I really liked it. I'm pretty sure we're going to apply that and then make the data available in the hands of business much much early rather than later. >> And, I'd also say dashboards. There's nothing better than a slick dashboard with all the metrics right there, clean display, clear indications if your meeting your goals or not. So, I think that's a scenario we have a lot of opportunity for growth. >> Where do you expect to get the viz? Is that something that comes out of Pentaho or are you going to have to bring in other third party tools? >> I think we can do it in Pentaho with custom dashboards. >> Sure, we can do custom dashboards and we are also doing some GIS analytics that we can actually embed into Pentaho portal or even any other open-data portal. >> What did you think of this morning... Did you see the keynote this morning? >> Tom: Yep. >> How did that, I don't know if you're one of the hands that went up when they said who does business with Hitachi, probably no, most people were no. So, you have this big conglomerate, great company, known name, but not really sure exactly what it is they do. As a customer, what was your sense of the keynote, the messaging, does it matter to you, are you indifferent to that or is it meaningful? >> For me, it opened up my eyes about what the possibilities are. And the key is also to be proactive, right? You don't want to be, even though we're a government agency, we act on behalf of the government. We'd like to think we can stay at the forefront and leverage these greats tools and stay current. Because we're all dealing with so much more data and everyone's asked to do everything faster, even though there's more data. >> So what's your key take-away from this conference? >> Better use Pentaho product. (Rebecca laughs) Which we are actually using but the new versions. Apply those, the concepts, and get some more out of it. >> So, I got to ask you, When you think about the governments use of data. There's nobody more sophisticated. Of course, the guys who really use that data in sophisticated ways nobody knows what they do. You can't talk to them, I'm sure they don't expose you to their secrets. But, the government is so enormous, so, as they say, sophisticated. I mean, I'm sure there's a bell curve. But, are there ways to share best practice with non-confidential or classified information? Are you learning from your colleagues? Is there some kind of pipeline to share best practice? Or are you kind of on your own? >> We're actually sharing our practices. We collaborate with FCC and see what they are doing. Where are they in the technology and we share what our experience also. Over here there are some other common institutions, which are here at conference and we are talking to them and how they're leveraging the data, how they're leveraging the product, and how they're better using this product. >> From an enterprise grade level, you think of things like security, and compliance, and things like that. I presume that's important in your world. >> Sachin: Definitely. Absolutely. >> I would imagine that some of those can seep through different agencies and organizations. But, does the system allow for that? I guess is the question or is it just everybody's so busy kind of doing their own thing. >> Sachin: Want to take that? >> We've been getting more mandates from the government to publish our data. That's a big initiative in Washington. To make it available and it's available to the public. It's available to researchers. It's available to state agencies. So, I think there's definitely a lot of sharing of best practices in that space. >> And those are largely unfunded mandates, right? Figured out how you're going to do this and reallocate capital or is it... >> No, I think that if they give us a directive to do that they'll fund that. >> Dave: They usually provide resources to do that. >> Yeah. >> So, you're not having to rob from your mission to, alright great. >> One of the other things that we've been hearing at this conference is the enormous culture shifts that are involved in digital transformation. How would you describe the culture within your organization? Is there an understanding, that data needs to be front and center? Because there is this mission element as well. But, is it hard to bring other people along with you? >> We've been trying to do that with training. Training people how to use Pentaho, how to use data. I will say that it seems like there are some staff that, I don't know if resistance is the right word but, they're a little scared of it. I find some of the younger staff will just dive in there and start analyzing. For me, I try to do a lot of one on one sessions with people and try to individually change their approach and attitude toward data. It can be a little overwhelming. >> Great, great. Well, Tom, Sachin, thank you so much for coming on The Cube. >> Thank you very much. >> Thank you. >> Thanks, you guys. >> I'm Rebecca Knight for Dave Vellante. We will have more from Pentaho World just after this. (tech music)

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

>> Narrator: Live from Orlando, Florida, its theCUBE. Covering Pentaho World 2017. Brought to you by Hitachi Vantara. >> We are kicking off day one of Pentaho World. Brought to you, of course, by Hitachi Vantara. I'm your host, Rebecca Knight, along with my co-hosts. We have Dave Vellante and James Kobielus. Guys I'm thrilled to be here in Orlando, Florida. Kicking off Pentaho World with theCUBE. >> Hey Rebecca, twice in one week. >> I know, this is very exciting, very exciting. So we were just listening to the key notes. We heard a lot about the big three, the power of the big three. Which is internet of things, predictive analytics, big data. So the question for you both is where is Hitachi Vantara in this marketplace? And are they doing what they need to do to win? >> Well so the first big question everyone is asking is what the heck is Hitachi-Vantara? (laughing) What is that? >> Maybe we should have started there. >> We joke, some people say it sounds like a SUV, Japanese company, blah blah blah. When we talked to Brian-- >> Jim: A well engineered SUV. >> So Brian Householder told us, well you know it really is about vantage and vantage points. And when you listen to their angles on insights and data, anywhere and however you want it. So they're trying to give their customers an advantage and a vantage point on data and insights. So that's kind of interesting and cool branding. The second big, I think, point is Hitachi has undergone a massive transformation itself. Certainly Hitachi America, which is really not a brand they use anymore, but Hitachi Data Systems. Brian Householder talked in his keynote, when he came in 14 years ago, Hitachi was 80 percent hardware, and infrastructure, and storage. And they've transformed that. They're about 50/50 last year. In terms of infrastructure versus software and services. But what they've done, in my view, is taken now the next step. I think Hitachi has said, alright listen, storage is going to the cloud, Dell and EMC are knocking each others head off. China is coming in to play. Do we really want to try and dominate that business? Rather, why don't we play from our strengths? Which is devices, internet of things, the industrial internet. So they buy Pentaho two years ago, and we're going to talk more about that, bring in an analytics platform. And this sort of marrying IT and OT, information technology and operation technology, together to go attack what is a trillion dollar marketplace. >> That's it so Pentaho was a very strategic acquisition. For Hitachi, of course, Hitachi data system plus Hitachi insides, plus Pentaho equals Hitachi Vantara. Pentaho was one of the pioneering vendors more than a decade ago. In the whole open source analytics arena. If you cast your mind back to the middle millennium decade, open source was starting to come into its own. Of course, we already had Linux an so forth, but in terms of the data world, we're talking about the pre-Hadoop era, the pre-Spark era. We're talking about the pre-TensorFlow era. Pentaho, I should say at that time. Which is, by the way, now a product group within Hitachi Vantara. It's not a stand alone company. Pentaho established itself as the spearhead for open-source, predictive analytics, and data mining. They made something called Weka, which is an open-source data mining toolkit that was actually developed initially in New Zealand. The core of their offering, to market, in many ways became very much a core player in terms of analytics as a service a so forth, but very much established themselves, Pentaho, as an up and coming solution provider taking a more or less, by the book, open source approach for delivering solutions to market. But they were entering a market that was already fairly mature in terms of data mining. Because you are talking about the mid-2000's. You already had SaaS, and SPSS, and some of the others that had been in that space. And done quite well for a long time. And so cut ahead to the present day. Pentaho had evolved to incorporate some fairly robust data integration, data transformation, all ETL capabilities into their portfolio. They had become a big data player in their own right, With a strong focus on embedded analytics, as the keynoters indicated this morning. There's a certain point where in this decade it became clear that they couldn't go it any further, in terms of differentiating themselves in this space. In a space that dominated by Hadoop and Spark, and AI things like TensorFlow. Unless they are part of a more diversified solution provider that offered, especially I think the critical thing was the edge orientation of the industrial internet of things. Which is really where many of the opportunities are now for a variety of new markets that are opening up, including autonomous vehicles, which was the focus of here all-- >> Let's clarify some things a little bit. So Pentaho actually started before the whole Hadoop movement. >> Yeah, yeah. >> That's kind of interesting. You know they were young company when Hadoop just started to take off. And they said alright we can adopt these techniques and processes as well. So they weren't true legacy, right? >> Jim: No. >> So they were able to ride that sort of modern wave. But essentially they're in the business of data, I call it data management. And maybe that's not the right term. They do ingest, they're doing ETL, transformation anyway. They're embedding, they've got analytics, they're embedding analytics. Like you said, they're building on top of Weka. >> James: In the first flesh and BI as a hot topic in the market in the mid-200's, they became a fairly substantial BI player. That actually helped them to grow in terms of revenue and customers. >> So they're one of those companies that touches on a lot of different areas. >> Yes. >> So who do we sort of compare them to? Obviously, what you think of guys like Informatica. >> Yeah, yeah. >> Who do heavy ETL. >> Yes. You mentioned BI, you mentioned before. Like, guys like Saas. What about Tableau? >> Well, BBI would be like, there's Tableau, and ClickView and so forth. But there's also very much-- >> Talend. >> Cognos under IBM. And, of course, there's the business objects Portfolio under SAP. >> David: Right. And Talend would be? >> In fact I think Talend is in many ways is the closest analog >> Right. >> to Pentaho in terms of predominatly open-source, go to market approach, that involves both the robust data integration and cleansing and so forth from the back end. And also, a deep dive of open source analytics on the front end. >> So they're differentiation they sort of claim is they're sort of end to end integration. >> Jim: Yeah. >> Which is something we've been talking about at Wikibon for a while. And George is doing some work there, you probably are too. It's an age old thing in software. Do you do best-of-breed or do you do sort of an integrated suite? Now the interesting thing about Pentaho is, they don't own their own cloud. Hitachi Vantara doesn't own their own cloud. So they do a lot of, it's an integrated pipeline, but it doesn't include its own database and other tooling. >> Jim: Yeah. >> Right, and so there is an interesting dynamic occurring that we want to talk to Donna Perlik about obviously, is how they position relative to roll your own. And then how they position, sort of, in the cloud world. >> And we should ask also how are they positioning now in the world of deep learning frameworks? I mean they don't provide, near as I know, their own deep learning frameworks to compete with the likes of TensorFlow, or MXNet, or CNT or so forth. So where are they going in that regard? I'd like to know. I mean there are some others that are big players in this space, like IBM, who don't offer their own deep learning framework, but support more than one of the existing frameworks in a portfolio that includes much of the other componentry. So in other words, what I'm saying is you don't need to have your own deep learning framework, or even open-source deep learning code-based, to compete in this new marketplace. And perhaps Pentaho, or Hitachi Vantara, roadmapping, maybe they'll take an IBM like approach. Where they'll bundle support, or incorporate support, for two or more of these third party tools, or open source code bases into their solution. Weka is not theirs either. It's open source. I mean Weka is an open source tool that they've supported from the get go. And they've done very well by it. >> It's just kind of like early day machine leraning. >> David: Yeah. >> Okay, so we've heard about Hitachi's transformation internally. And then their messaging today was, of course-- >> Exactly, that's where I really wanted to go next was we're talking about it from the product and the technology standpoint. But one of the things we kept hearing about today was this idea of the double bottom line. And this is how Hitachi Vantara is really approaching the marketplace, by really focusing on better business, better outcomes, for their customers. And obviously for Hitachi Vantara, too, but also for bettering society. And that's what we're going to see on theCUBE today. We're going to have a lot of guests who will come on and talk about how they're using Pentaho to solve problems in healthcare data, in keeping kids from dropping out of college, from getting computing and other kinds of internet power to underserved areas. I think that's another really important approach that Hitachi Vantara is taking in its model. >> The fact that Hitachi Vantara, I know, received Pentaho Solution, has been on the market for so long and they have such a wide range of reference customers all over the world, in many vertical. >> Rebecca: That's a great point. >> The most vertical. Willing to go on camera and speak at some length of how they're using it inside their business and so forth. Speaks volumes about a solution provider. Meaning, they do good work. They provide good offerings. They're companies have invested a lot of money in, and are willing to vouch for them. That says a lot. >> Rebecca: Right. >> And so the acquisition was in 2015. I don't believe it was a public number. It's Hitachi Limited. I don't think they had to report it, but the number I heard was about a half a billion. >> Jim: Uh-hm >> Which for a company with the potential of Pentaho, is actually pretty cheap, believe it or not. You see a lot of unicorns, billion dollar plus companies. But the more important thing is it allows Hitachi to further is transformation and really go after this trillion dollar business. Which is really going to be interesting to see how that unfolds. Because while Hitachi has a long-term view, it always takes a long-term view, you still got to make money. It's fuzzy, how you make money in IOT these days. Obviously, you can make money selling devices. >> How do you think money, open source anything? You know, so yeah. >> But they're sort of open source, with a hybrid model, right? >> Yeah. >> And we talked to Brian about this. There's a proprietary component in there so they can make their margin. Wikibon, we see this three tier model emerging. A data model, where you've got the edge in some analytics, real time analytics at the edge, and maybe persists some of that data, but they're low cost devices. And then there's a sort of aggregation point, or a hub. I think Pentaho today called it a gateway. Maybe it was Brian from Forester. A gateway where you're sort of aggregating data, and then ultimately the third tier is the cloud. And that cloud, I think, vectors into two areas. One is Onprem and one was public cloud. What's interesting with Brian from Forester was saying that basically said that puts the nail in the coffin of Onprem analytics and Onprem big data. >> Uh-hm >> I don't buy that. >> I don't buy that either. >> No, I think the cloud is going to go to your data. Wherever the data lives. The cloud model of self-service and agile and elastic is going to go to your data. >> Couple of weeks ago, of course we Wikibon, we did a webinar for our customers all around the notion of a true private cloud. And Dave, of course, Peter Burse were on it. Explaining that hybrid clouds, of course, public and private play together. But where the cloud experience migrates to where the data is. In other words, that data will be both in public and in private clouds. But you will have the same reliability, high availability, scaleability, ease of programming, so forth, wherever you happen to put your data assets. In other words, many companies we talk to do this. They combine zonal architecture. They'll put some of their resources, like some of their analytics, will be in the private cloud for good reason. The data needs to stay there for security and so forth. But much in the public cloud where its way cheaper quite often. Also, they can improve service levels for important things. What I'm getting at is that the whole notion of a true private cloud is critically important to understand that its all datacentric. Its all gravitating to where the data is. And really analytics are gravitating to where the data is. And increasingly the data is on the edge itself. Its on those devices where its being persistent, much of it. Because there's no need to bring much of the raw data to the gateway or to the cloud. If you can do the predominate bulk of the inferrencing on that data at edge devices. And more and more the inferrencing, to drive things like face recognition from you Apple phone, is happening on the edge. Most of the data will live there, and most of the analytics will be developed centrally. And then trained centrally, and pushed to those edge devices. That's the way it's working. >> Well, it is going to be an exciting conference. I can't wait to hear more from all of our guests, and both of you, Dave Vellante and Jim Kobielus. I'm Rebecca Knight, we'll have more from theCUBE's live coverage of Pentaho World, brought to you by Hitachi Vantara just after this.

>> 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.

>> Announcer: Live from San Jose, California, it's theCUBE. Covering Big Data Silicon Valley 2017. >> Okay, welcome back everyone. Here live in Silicon Valley this is theCUBE. I'm John Furrier, covering our Big Data SV event, #BigDataSV. Our companion event to Big Data NYC, all in conjunction Strata Hadoop, the Big Data World comes together, and great to have guests come by. Donna Prlich, who's the senior VP of products and solutions at Pentaho, a Hitachi company who we've been following before Hitachi had acquired you guys. But you guys are unique in the sense that you're a company within Hitachi left alone after the acquisition. You're now running all the products. Congratulations, welcome back, great to see you. >> Yeah, thank you, good to be back. It's been a little while, but I think you've had some of our other friends on here, as well. >> Yep, and we'll be at Pentaho World, you have Orlando, I think is October. >> Yeah, October, so I'm excited about that, too, so. >> I'm sure the agenda is not yet baked for that because it's early in the year. But what's going on with Hitachi? Give us the update, because you're now, your purview into the product roadmap. The Big Data World, you guys have been very, very successful taking this approach to big data. It's been different and unique to others. >> [Donna} Yep. What's the update? >> Yeah, so, very exciting, actually. So, we've seen, especially at the show that the Big Data World, we all know that it's here. It's monetizable, it's where we, actually, where we shifted five years ago, and it's been a lot of what Pentaho's success has been based on. We're excited because the Hitachi acquisition, as you mentioned, sets us up for the next bit thing, which is IOT. And I've been hearing non-stop about machine learning, but that's the other component of it that's exciting for us. So, yeah, Hitachi, we're-- >> You guys doing a lot of machine learning, a lot of machine learning? >> So we, announced our own kind of own orchestration capabilities that really target how do you, it's less about building models, and how do you enable the data scientists and data preparers to leverage the actual kind of intellectual properties that companies have in those models they've built to transform their business. So we have our own, and then the other exciting piece on the Hitachi side is, on the products, we're now at the point where we're running as Pentaho, but we have access to these amazing labs, which there's about 25 to 50 depending on where you are, whether you're here or in Japan. And those data scientists are working on really interesting things on the R & D side, when you apply those to the kind of use cases we're solving for, that's just like a kid in a candy store with technology, so that's a great-- >> Yeah, you had a built-in customer there. But before I get into Pentaho focusing on what's unique, really happening within you guys with the product, especially with machine learning and AI, as it starts to really get some great momentum. But I want to get your take on what you see happening in the marketplace. Because you've seen the early days and as it's now, hitting a whole another step function as we approach machine learning and AI. Autonomous vehicles, sensors, everything's coming. How are enterprises in these new businesses, whether they're people supporting smart cities or a smart home or automotive, autonomous vehicles. What's the trends you are seeing that are really hitting the pavement here. >> Yeah, I think what we're seeing is, and it's been kind of Pentaho's focus for a long time now, which is it's always about the data. You know, what's the data challenge? Some of the amounts of data which everybody talks about from IOT, and then what's interesting is, it's not about kind of the concepts around AI that have been around forever, but when you start to apply some of those AI concepts to a data pipeline, for instance. We always talk about that 6data pipeline. The reason it's important is because you're really bringing together the data and the analytics. You can't separate those two things, and that's been kind of not only a Pentaho-specific, sort of bent that I've had for years, but a personal one, as well. That, hey, when you start separating it, it makes it really hard to get to any kind of value. So I think what we're doing, and what we're going to be seeing going forward, is applying AI to some of the things that, in a way, will close the gaps between the process and the people, and the data and the analytics that have been around for years. And we see those gaps closing with some of the tools that are emerging around preparing data. But really, when you start to bring some of that machine learning into that picture, and you start applying math to preparing data, that's where it gets really interesting. And I think we'll see some of that automation start to happen. >> So I got to ask you, what is unique about Pentaho? Take a minute to share with the audience some of the unique things that you guys are doing that's different in this sea of people trying to figure out big data. You guys are doing well, an6d you wrote a blog post that I referenced earlier yesterday, around these gaps. How, what's unique about Pentaho and what are you guys doing with examples that you could share? >> Yeah, so I think the big thing about Pentaho that's unique is that it's solving that analytics workflow from the data side. Always from the data. We've always believed that those two things go together. When you build a platform that's really flexible, it's based on open source technology, and you go into a world where a customer says, "I not only want to manage and have a data lake available," for instance, "I want to be able to have that thing extend over the years to support different groups of users. I don't want to deliver it to a tool, I want to deliver it to an application, I want to embed analytics." That's where having a complete end-to-end platform that can orchestrate the data and the analytics across the board is really unique. And what's happened is, it's like, the time has come. Where all we're hearing is, hey, I used to think it was throw some data over and, "here you go, here's the tools." The tools are really easy, so that's great. Now we have all kinds of people that can do analytics, but who's minding the data? With that end-to-end platform, we've always been able to solve for that. And when you move in the open source piece, that just makes it much easier when things like Spark emerge, right. Spark's amazing, right? But we know there's other things on the horizon. Flink, Beam, how are you going to deal with that without being kind of open source, so this is-- >> You guys made a good bet there, and your blog post got my attention because of the title. It wasn't click bait either, it was actually a great article, and I just shared it on Twitter. The Holy Grail of analytics is the value between data and insight. And this is interesting, it's about the data, it's in bold, data, data, data. Data's the hardest part. I get that. But I got to ask you, with cloud computing, you can see the trends of commoditization. You're renting stuff, and you got tools like Kinesis, Redshift on Amazon, and Azure's got tools, so you don't really own that, but the data, you own, right? >> Yeah, that's your intellectual property, right? >> But that's the heart of your piece here, isn't it, the Holy Grail. >> Yes, it is. >> What is that Holy Grail? >> Yeah, that Holy Grail is when you can bring those two things together. The analytics and the data, and you've got some governance, you've got the control. But you're allowing the access that lets the business derive value. For instance, we just had a customer, I think Eric might have mentioned it, but they're a really interesting customer. They're one of the largest community colleges in the country, Ivy Tech, and they won an award, actually, for their data excellence. But what's interesting about them is, they said we're going to create a data democracy. We want data to be available because we know that we see students dropping out, we can't be efficient, people can't get the data that they need, we have old school reporting. So they took Pentaho, and they really transformed the way they think about running their organization and their community colleges. Now they're adding predictive to that. So they've got this data democracy, but now they're looking at things like, "Okay we an see where certain classes are over capacity, but what if we could predict, next year, not only which classes are over capacity, what's the tendency of a particular student to drop out?" "What could we do to intervene?" That's where the kind of cool machine learning starts to apply. Well, Pentaho is what enables that data democracy across the board. I think that's where, when I look at it from a customer perspective, it's really kind of, it's only going to get more interesting. >> And with RFID and smart phones, you could have attendance tracking, too. You know, who's not showing up. >> Yeah absolutely. And you bring Hitachi into the picture, and you think about, for instance, from an IOT perspective, you might be capturing data from devices, and you've got a digital twin, right? And then you bring that data in with data that might be in a data lake, and you can set a threshold, and say, "Okay, not only do we want to be able to know where that student is," or whatever, "we want to trigger something back to that device," and say, "hey, here's a workshop for you to login to right away, so that you don't end up not passing a class." Or whatever it is, it's a simplistic model, but you can imagine where that starts to really become transformative. >> So I asked Eric a question yest6erday. It was from Dave Valante, who's in Boston, stuck in the snowstorm, but he was watching, and I'll ask you and see how it matches. He wrote it differently on Crouch, it was public, but this is in my chat, "HDS is known for main frames, historically, and storage, but Hitachi is an industrial giant. How is Pentaho leveraging the Hitachi monster?" >> Yes, that's a great way to put it. >> Or Godzilla, because it's Japan. >> We were just comparing notes. We were like, "Well, is it an $88 billion company or $90 billion. According to the yen today, it's 88. We usually say 90, but close enough, right? But yeah, it's a huge company. They're in every industry. Make all kinds of things. Pretty much, they've got the OT of the world under their belt. How we're leveraging it is number one, what that brings to the table, in terms of the transformations from a software perspective and data that we can bring to the table and the expertise. The other piece is, we've got a huge opportunity, via the Hitachi channel, which is what's seeing for us the growth that we've had over the last couple of years. It's been really significant since we were acquired. And then the next piece is how do we become part of that bigger Hitachi IOT strategy. And what's been starting to happen there is, as I mentioned before, you can kind of probably put the math together without giving anything away. But you think about capturing, being able to capture device data, being able to bring it into the digital twin, all of that. And then you think about, "Okay, and what if I added Pentaho to the mix?" That's pretty exciting. You bring those things together, and then you add a whole bunch of expertise and machine learning and you're like, okay. You could start to do, you could start to see where the IOT piece of it is where we're really going to-- >> IOT is a forcing function, would you agree? >> Yes, absolutely. >> It's really forcing IT to go, "Whoa, this is coming down fast." And AI and machine learning, and cloud, is just forcing everyone. >> Yeah, exactly. And when we came into the big data market, whatever it was, five years ago, in the early market it's always hard to kind of get in there. But one of the things that we were able to do, when it was sort of, people were still just talking about BI would say, "Have you heard about this stuff called big data, it's going to be hard." You are going to have to take advantage of this. And the same thing is happening with IOT. So the fact that we can be in these environments where customers are starting to see the value of the machine generated data, that's going to be-- >> And it's transformative for the business, like the community college example. >> Totally transformative, yeah. The other one was, I think Eric might have mentioned, the IMS, where all the sudden you're transforming the insurance industry. There's always looking at charts of, "I'm a 17-year-old kid," "Okay, you're rate should be this because you're a 17-year-old boy." And now they're starting to track the driving, and say, "Well, actually, maybe not, maybe you get a discount." >> Time for the self-driving car. >> Transforming, yeah. >> Well, Donna, I appreciate it. Give us a quick tease here, on Pentaho World coming in October. I know it's super early, but you have a roadmap on the product side, so you can see a little bit around the corner. >> Donna: Yeah. >> What is coming down the pike for Pentaho? What are the things that you guys are beavering away at inside the product group? >> Yeah, I think you're going to see some really cool innovations we're doing. I won't, on the Spark side, but with execution engines, in general, we're going to have some really interesting kind of innovative stuff coming. More on the machine learning coming out, and if you think about, if data is, you know what, is the hard part, just think about applying machine learning to the data, and I think you can think of some really cool things, we're going to come up with. >> We're going to need algorithms for the algorithms, machine learning for the machine learning, and, of course, humans to be smarter. Donna, thanks so much for sharing here inside theCUBE, appreciate it. >> Thank you. >> Pentaho, check them out. Going to be at Pentaho World in October, as well, in theCUBE, and hopefully we can get some more deep dives on, with their analyst group, for what's going on with the engines of innovation there. More CUBE coverage live from Silicon Valley for Big Data SV, in conjunction with Strata Hadoop, I'm John Furrier. Be right back with more after this short break. (techno music)