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

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

>> Announcer: Live, from 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)

>> Narrator: Live from Orlando, Florida, it's theCube covering PentahoWorld 2017 brought to you by Hitachi Ventara. >> Welcome back to theCube's live coverage of PentahoWorld brought to you by Hitachi Ventara. My name is Rebecca Knight, I'm your host along with my co-host, Dave Vellante. We're joined by Michael Weiss, he is the senior manager at NASDAQ, and Shere Saidon, who is analytics manager at NASDAQ. Thanks so much for coming back to theCube, I should say, you're Cube veterans now. >> We are, at least I am. This is his first year, this is his first time at PentahoWorld. So, excited to bring him along. >> Okay so you're a newbie but you're a veteran so. (laughing) >> Great. So, tell us a little bit about what has changed since the last time you came on, which was 2015, back then? >> So the biggest thing that's happened in the past 18 months is we've launched seven new exchanges. Integrated seven new exchanges. We bought the ISE, the International Stock Exchange, which is three options markets. We just completed that integration in August. We've also bought the Canadian, CHI-X, the Canadian Exchange, which also had three equities markets, so we integrated them, and we went live with a dark pool offering for Goldman back in June. So now we operate a dark pool for Goldman Sachs, and we're looking to kind of expand that offering at this point. >> So you're just getting bigger and bigger. So tell our viewers a little bit how Pentaho fits into this. >> So Pentaho is the engine that kind of does all our analytics behind the scenes at post trade, right. So we do a lot of traditionally TL, where we're doing batch processing. In the back-end we're doing a little bit more with the Hadoop ecosystem leveraging things like EMR, Spark, Presto, that type of stuff, And Pentaho kind of helps blend that stuff together a little bit. We use it for reporting, we do some of the BA, we're actually now looking to have the data Pentaho generates plug in a little bit of Tableau. So, we're looking to expand it and really leverage that data in other ways at this point. Even doing some things more externally, doing more data offerings via Pentaho externally. >> So I got to do a NASDAQ 101 for my 13 year-old. Came up to me the other day and said, "Daddy, what's the NASDAQ index and how does it work?" Well, give us a 20 second answer. >> Michael: On the NASDAQ index? >> Yeah, what's the NASDAQ Index and how does it work? >> Probably the wrong person to answer that one but, the index is generally just a blend of various stocks. So the S&P 500 is a blend of different stocks, much like that the cues, are NASDAQ's equivalent of the S&P, right, so, we use a different algorithm to determine the companies that make up that blend, but it's an index just like at the S&P. >> They're weighted by market cap- >> Michael: Right, yeah. >> And that determines the number at the end- >> Michael: Correct. >> And it goes up and down based on what the stock's index. >> Right, and that's how most people know NASDAQ, right. They see the S&P went up by 5 points, The Dow went down by 3 and the NASDAQ went up by a point, right. But most people don't realize that NASDAQ also operates 27 exchanges worldwide, I think it is now. So, probably a little bit more, maybe closer to 32, but... >> So you mentioned that you're doing a dark pool for Goldman >> Michael: Yes. >> So that's interesting. We were talking off camera about HFT and kind of the old days, and dark pools were criticized at the time. Now Goldman was one of the ones shown to be honest and above board, but what does that mean the dark pool for your business and how does that all tie in? >> Michael: So, dark pools are isolated markets, right, so they don't necessarily interact with the NASDAQ exchange themselves, it's all done within the pool. You interact with only people trading on that pool. What NASDAQ has done is we took our technology and we now host it for Goldman so, we have I-NETs our trading system, so we gave them I-NET, we built all the surrounding solutions, how you manage symbols, how you manage membership. Even the data, we curate their data in the AWS. We do some Pentaho transformations for them. We do some analytics for them. And that's actually going to start expanding, but yeah, we've provided them an entire solution, so now they don't have to manage their own dark pool. And now we're going to look to expand that to other potential clients. >> Dave: So that's NASDAQ as a technology >> Yes. >> Dave: Provider. Very interesting. So I was saying, earlier, the Hong Kong Stock Exchange is basically closing the facility where they house humans, again another example of machines replacing humans. So the joining, well NASDAQ, kind of, but NYSE, London Stock Exchange, Singapore, now Hong Kong... Essentially, electronic trading. So, brings us to the sort of technology underpinnings of NASDAQ. Shere, maybe you can talk a little bit about your role, and paint a picture of the technology infrastructure. >> Yeah so I focus primarily on the financial side of corporate finance. So we leverage Pentaho to do a lot of data integration, allow us to really answer our business questions. So, previously it would take days to put basic reporting together, now you've got it all automated, or we're working towards getting it mostly automated, and it just answer the questions that we need. And no longer use our gut to drive decisions, we're using hard data. And so that's helped us instrumentally in a lot of different places. >> Dave: So, talk more about the data pipeline, where the data's coming from, how you're blending it, and how you're bringing it through the pipeline and operationalizing it. >> Yeah, so we've got a lot of different billing systems, so we integrate companies, and historically we've let them keep their billings systems. So just kind of bring it all together into our core ERP, seeing how quantities...and just getting the data, and just figuring out on the basic side, how much do we make from a certain customer? What are we making from them? What happens in different scenarios if they consolidate, or if they default? And some of the pipeline there is just blending it all together, normalizing the data, making sure it's all in the same format, and then putting it in a format where our executives or business managers can actually make decisions off of it. >> Well you're talking about the decision making process, and you said it's no longer gut, you're using data to drive your decisions, to know which direction is the right direction. How big a change is that, just culturally speaking? How has that changed? >> Yeah, it's huge, at least on our side, it's making us a long more confident in the decisions we're making. We're no longer going in saying, hey this is probably how we should do it. No, the numbers are showing us that this is going to pay off, and we stick to it and look at the hard facts, rather than what do we think is going to happen? >> So, talk a little bit about what you guys are seeing here, and you're doing a lot of speaking here, we were joking earlier, you're kind of losing your voice. You're telling your story, what kind of reactions you getting? Share with us the behind the scenes at the conference. >> I think at this conference you're seeing a lot of people kind of fall in line with similar ideas that we're trying to get to. Taking advantage more instead of your traditional MPPs, or your traditional relational databases, moving more towards this Hadoop ecosystem. Leveraging Spark, Presto, Flume, all these various new technologies that have emerged over the past two to five years, and are now more viable than ever. They're easier to scale, if you look at your traditional MPPs, like we're a big Redshift user, but every time you scale it there's a cost with that, and we don't necessarily need to maintain all that data all the time, so something in the Hadoop ecosystem now lets us maintain that data without all the unnecessary cost. I see a lot of more of that than I did two years ago, a lot more people are following that trend. I think the other interesting trend I've seen this week is this idea of becoming more cloud agnostic. Where do you operate, and how do you store your data should be irrelevant to the data processing, and I think it's going to be a tough nut to crack for Pentaho, or any vendor. But if you can figure out a way to either do some type of cloud parity, where you have support across all your services, but you don't have to know which service you deploy to when you design your pipelines, I think that's going to be huge. I think we're a little ways from that, but that's been a common theme this week as well, both private and your big three cloud providers right now, your Googles, your Azures, and your AWS. >> So when I asked you said cloud agnostic, that's great, good vision and aspiration. The follow up would be, am I correct that you don't see it as data location agnostic, right, you want to bring the cloud model to your data, versus try to force your data into a cloud? Or not necessarily? >> A lot of it I think is being driven by not wanting to be vendor locked in, so they want to have the ability to, and I think this is easier said than done, the ability to move your data to different cloud providers based on pricing or offerings, right, and right now going from AWS to Google to Azure would be a very painful process. So you move petabytes of data across, it's not cost efficient and all the savings you want to realize by moving to maybe a Google in the future, are not going to be realized cause of all the effort it's going to take to get there. >> Dave: We had CERN on earlier, and they were working on that problem... >> Yeah, it's not a trivial problem to solve, but if you can crack that, and you can then say hey I wanna...even if I have a service offering, Like our operating a dark pool for Goldman. We also have a market tech side, where we sell our trading platform and various solutions to other exchanges worldwide. If we can come up with a way to be able to deploy to any cloud provider, even on an on-prem cloud, without having to do a bunch of customizations each time, that would be huge, it would revolutionize what we do. We're, as our own company, starting to look at that, and talking with Pentaho, they're also... are going to eye that as a potential way to go, with abstractions and things like that, but it's going to take some time. >> We're you guys here yesterday for the keynotes? >> Michael: Saw some of the keynotes, yes. >> The big messaging, like every conference that you go to, is be the disruptor, or you're going to get disrupted. We talked earlier off camera... Trading volumes are down, so the way you traditionally did business is changing, and made money is changing. >> Michael: Right. >> We talked earlier about you guys becoming a technology provider, I wonder if you could help us understand that a little bit, from the standpoint of NASDAQ strategy, when we hear your CEOs talk, real visionary, technology driven transformations. >> Yeah, I think Adena's coming in is definitely looking at that as a trend, right? Trading volumes are down, they've been going down, they've kind of stabilized a little bit, and we're stable able to make money in that space, but the problem is there's not a ton of growth. We acquire the ISE, we acquire the CHI-X, we're buying market share at that point. So you increase revenue, but you also increase overhead in that way. And you can only do so many major acquisitions at a time, you can only do how many one billion dollar acquisitions a year before you have to call it a day. And we can look at more strategic, smaller acquisitions for exchanges, but that doesn't necessarily bring you the transformation, the net revenue you're looking for. So what Adena has started to look at is, how do we transform to more of a technology company? We're really good at operating exchanges, how do we take that, and we already have market tech doing it, but how do we make that more scalable, not just to the financial sector, but to your other exchanges, your Ubers or your StubHubs of the world? How do you become a service provider, or a platform as a service for these other companies, to come in and use your tech? So we're looking at how do we rewrite our entire platform, from trading to the back-end, to do things like: Can we deploy to any cloud provider? Can we deploy on-prem? Can we be a little bit more technology agnostic so to speak, and offer these as services, and offer a bunch of microservices, so that if a startup comes up and wants to set up an exchange, they can do it, they can leverage our services, then build whatever other applications they want on top of it. I think that's a transformation we need to go through, I think it's good vision, and I'm looking forward to executing it. It's going to be a couple years before we see the fruits of that labor, but Adena's really doing a great job of coming in, and really driving that innovation, and Brad Peterson as well, our CIO, has really been pushing this vision, and I think it's really going to work out for us, assuming we can execute. >> Well you know what's interesting about that, if I may, is financial services is usually so secretive about their technology, right? But your business, you guys are becoming a technology provider, so you got to face the world and start marketing your capabilities now, and opening about that. It's sort of an interesting change. >> I think you'll see that starting to become more of a thing over the next year or two, as we start actually looking to build out the platform and figure it out. We do market on the market tech side, I mean it's not a small business, but we're more strategic about who we market to, cause we're still targeting your financial exchanges, more internationally than in the U.S., but there's only so many of them, again you have to start looking at rebranding, rebuilding, and rethinking how we think about exchanges in general, and not thinking of them as just a financial thing. >> Well that's what I wanted to get into, because you're talking about this rebranding, and this rebuilding, this transformation, to the backdrop within an industry that is changing rapidly, and we have sort of the threat of legislative reform, perhaps some administrative reforms coming down all the time, so how do you manage that? I mean, those are a lot of pressures there, are you constantly trying to push the envelope right up until any changes take place? Or what would you say Shere and Michael? >> Probably again not the right person to ask about this, but we're definitely trying to stay on top of the cutting edge in innovation and the technologies out there that, whether it be Blockchain, or different types of technologies. I mean we're definitely trying to make sure we're investing in them, while maintaining our core businesses. >> Right, it's trying to find that balance right now of when to make the next step in the technology food chain, and when to balance that with regulatory obligations. And if you look at it, going back to the idea of being able to launch marketplaces, I think what you're ending up seeing over the coming years is your Ubers, your StubHubs, I think they're going to become more regulated at some level. And we're good at operating more regulated markets, so I think that's where we can kind of come in and play a role, and help wade through those regulations a little bit more, and help build software to adhere to those regulations. >> Since you brought up Blockchain, Jamie Dimon craps all over Blockchain, or you know, Bitcoin, and then clarifies his remarks, saying look, technology underneath is here to stay. Thoughts on Blockchain? Obviously Financial Services is looking at it very closely, doing some really advanced stuff, what can you tell us? >> Yeah, I think there's no argument that it's definitely an innovation and a disruptive technology. I think that it's definitely in it's early stages across the board, so we're investing in it where we can, and trying to keep a close eye on it. We think that there's a lot of potential in a lot of different applications. >> As the NASDAQ transforms its business, how does that effect the sort of back-end analytics activity and infrastructure? >> The data is just growing, that's like the biggest challenge we have now. Data that used to be done in Excel, it's just no longer an option, so now in order to get the insights that we used to get just from having a couple people doing Excel transformations, you need to now invest in the infrastructure in the back-end, and so there's a lot that needs to go into building out an infrastructure to be able to ingest the data, and then also having the UI on the front-end, so that the business can actually view it the way they want. >> So skills wise, how's that affecting who you guys are hiring and training? And how's that transformation going? >> Michael: I'll let you go first. >> I think there's definitely, data analytics is a hot field. It's very new, there's definitely a big skills gap in administrative work and in the analytics side. Usually you have people could perform analytical functions just by being administrative or operational, and now it's really, we're investing in analysts, and making sure that we have the right people in place to be able to do these transformations, or pull the data and get the answers that we need from them. >> I mean from the tech side, I think what you're seeing is where we traditionally would just plug a developer in there, whether a Java developer, or an ETL developer, I think what you're seeing now is we're looking to bring more of a business minded data analyst to the tech side, right? So we're looking to bring a data engineer, so to speak, more to the tech side. So we're not looking to hire a traditional four year Computer Science degree, or Software Engineering degree, you're looking for a different breed of person, cause quite honestly because you're traditional Java dev. or C++ developer, they're not skilled or geared towards data. And when we've tried to plug that paradigm in, it just doesn't really work, so we're looking now to hiring more of an analyst, but someone who's a little bit more techie as well. They still need to have those skills to do some level of coding, and what we are finding is that skill gap is still very much... There's a gap there. There's a huge gap. And I think it's closing, but- >> And as you have to fund those for the new areas, I presume, like many companies in your business, you're trying to move away from the sort of undifferentiated low-level infrastructure deployment hassles, and the IT labor costs there, especially as we move to the cloud, presumably, so is that shift palpable? I mean, can you see that going on? >> Yeah, I think we made a lot of progress over the past couple years in doing that. We do more one button deployments, where the operation cost is a lot lower, a lot more automation around alerting, around when things go wrong, so there's not necessarily a human being sitting there watching a computer. We've invested a lot in that area to kind of reduce the costs, and make the experience better for our end user. And even from a development side, the cost of a new application is a lot less every time you have to do a release. The question is, how do you balance that with the regulations, and make sure you still have a good process in place. The idea of putting single button deployments in place is a great one, but you still have to balance that with making sure that what you push to productions been tested, well defined, and it meets the need, and you're not just arbitrarily throwing things out there. So we're still trying to hit that balance a little bit, it's more on the back-end side. The trading system is not quite there for obvious reasons, we're way more protective of what goes out there, then surrounding it a lot of the times, but I can see a future where, again going back to this idea of transforming our business, where you can stand up and do an exchange with the click of a button. I think that's a trend we're looking at. >> Rebecca: It's not too far in the future. >> No, I don't think it is. >> Last question, Pentaho report card. What are they doing really well? What do you want to see them do better? >> I think they continue to focus in the right areas, focusing more on the data processing side, and with the big data technologies, trying to fill that gap in the big data, and be the layer that you don't have to tie yourself to ike vCloud Air or MapR, you can kind of be a little bit more plug and play. I think they still need to do some improvements on there visualizations in their front-ends. I think they've been so much more focused on the data processing, that part of it, that the visualization's kind of lacked behind, so I think they need to put a little more focus into that, but all in all, they're an A, and we've been extremely happy with them as a software provider. >> Great. >> Shere: I think the visualization part is the part that allows people to understand that value being created at Pentaho. So I think being able to maybe improve a little bit on the visualization could go a far way. >> Michael, Shere, it's been so much fun having you on theCube, and having this conversation, keep that bull market coming please, do whatever you can. >> We'll do our best. >> I'm Rebecca Knight. We are here at PentahoWorld, sponsored by Hitachi Vantara. For Dave Vellante, we will have more from theCube in just a little bit.

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

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

(Upbeat music) >> Announcer: Live from Orlando Florida, It's theCUBE covering PentahoWorld 2017. Brought to you by Hitachi Vantara. >> Welcome back to theCUBE's coverage of PentahoWorld, brought to you, of course, by Hitachi Vantara, I'm your host Rebecca Knight along with my cohost James Kobielius. We're joined by Charles Gaddy, he is the Business Development Manager at Melissa Data. Thanks so much for joining us. >> Great, thank you for having me. >> So tell us, tell our viewers a little bit about Melissa Data and what you do there. >> Well, Melissa is a data quality and identity assurance company, so we have been around for 30 years. And we're a 30 year old start up you might say. Very innovative in what we do, and the way we address our problems. We are the strategic partner for Pentaho as it relates to data quality. So most of our data quality solutions are embedded and available within the Pentaho stack. So my particular role there is to facilitate global sales and alliances, and Pentaho is one of our global alliances. >> Okay, so that's the, it's a strategic alliance, and so what is your relationship now with Hitachi Vantara? >> That's a great question, because now that we're with Hitachi Vantara, one of the things we're focusing on is a strategy around data quality blue prints. Data quality blueprints are something that Pentaho brought in to that relationship, or that new company, right? And it's a powerful way that they sell their solutions, and craft the message around their solutions in a way that sounds less technical and more engaging, I think. And I'll give you a bit of an opinion there, and so we're very excited to be one of the first companies, from a partner perspective, to do a blueprint that's not strictly Pentaho based. >> Is it, you're talking about blueprints, is it a consultative marketing and sales tool? Or is it a solution accelerator template, or a bit of both? >> You stole my thunder, I was going to say I think it's a bit of both actually, yes. The nice thing that I've seen about the other ones they've done and the one that we're crafting is, you're taking a use case, effectively, and you're breaking down what you're bringing to that use case, with a sprinkle of technology, so that they know it is a technical solution, as well as a consultative sale. Then you're telling them about the problem you're going to solve with it, and the expected outcomes after you've solved that problem. So, the first use case is around customer data quality, within online retail, right. So, everything from preventing packages from being misplaced by using address verification, and geocoding in order to improve the quality of address data that you're shipping, all the way through to customer demographics, so you can understand and overlay demographic information about the customers you're targeting online. All of these solutions, we bring the data piece of that, and Pentaho brings the other elements to make that combined blueprint. >> So just in hearing you say those things, I'm thinking back to what we heard on the main stage today, about the potential of the dark side, in the sense of the models maybe being used for nefarious reasons, I mean, how do you guard against that? >> Well, you know, there's that AI component, which was very much of the Skynet comment I believe, and then there's data quality, which, having been around data quality for quite a while, there's a rules based element to that, that isn't necessarily AI based, so you don't necessarily have as much of that dark side to deal with, what you are rightfully pointing out, is the idea that you're using elements of data that represent someone's identity potentially, right. And how do you protect and safeguard that? And our 30 years in the business really gives us an insight on how to protect the data in ways that insure the quality of it, but then also insure that it's not used for nefarious purposes, like you said. >> Okay, so as you know, Pentaho co-founder James Dixon coined the term "the data lake". So how has Melissa partnered and integrated with Pentaho in that way? >> And how does data governance and quality ride upon and leverage the data lake to be effective? >> Okay, so it's a two part question. Looking at it from the perspective of what was described in the data lake, things are going in to the data lake. Well, you can take two approaches to it, I guess. You can try to boil that data lake, which is very challenging, you know. Or you can extract quality information out of it, and so, data quality, whether you're pushing data quality into the lake, or whether you're trying to extract actionable intelligence out of the lake, fits on both sides and gives you that step towards analytics and intelligence that you need. Right, otherwise it's a lake. The other side you mentioned is the governance side of it. So, our components that run, and our services that run as a part of what is offered with Pentaho, give elements of a feature like profiling, so you're able to profile the data as it's moving between these different places, see the anomalies, potentially address the anomalies, if that's something you need to do, or at least be aware of them so you know what's going on, right, and you're constantly monitoring. >> Does that involve AI or machine learning on your end to do that, the anomaly detection within the data lake? >> There's elements of our technology that leverage pieces of that for sure. I wouldn't call it full blown AI from that perspective, but there are some patents and some proprietary technology that we have, that gives us a unique approach on how to profile that data, and how to make that profiled information actionable within Pentaho. >> So, you talked about the retailer use case, and that's how we can make sure the packages are delivered to the right places, and the demographic. What are some other examples of ways that we can use Melissa Data? >> Okay, so as luck would have it, the first blueprint we're doing is the customer one I just mentioned, but we're already talking with Hitachi Vantara about the idea of doing a financial services one, right. And so in that fin tech space, not only would you be able to leverage matching deduplication, which they call more of an identity resolution in that element, but you'd also be able to leverage the elements of data that we bring to bear to say that you are who you say you are. So you bundle those together in a fin tech, or a financial services model, and you've got a different use case from customers and online retail, but you still have a very compelling joint offering as you're pushing data through. >> Which is particularly relevant in light of the Equifax breach, which will haunt us for the rest of our lives, we keep hearing about this. >> Yes, you have to be very careful with the data that you utilize, absolutely. >> One of the terms we keep hearing a lot is future proofing. What does that mean to you at Melissa Data? How do you describe your approach to future proofing your business? >> So, it's interesting because, as I mentioned, we're pretty much a 30 year old start up, so as a function of that, we future proofed ourselves. Because we've evolved and adapted, you have to be nimble, you have to be agile, as well as embracing agile concepts, which, there's two different meanings there, if you will. And so, in looking at that, you want to make sure that you've got the right technology set, and that that technology set can be easily adapted and evolve over time, right. I think those are they key things we've done as a company, with the solutions we've built, and much like, I heard today on the keynote, that Hitachi had focused to do, we've done a very similar thing, because we started in direct marketing, with a database of zip codes. And now we offer matching, and we offer these cloud solutions and identity. So we've had a very similar track to that story you heard earlier. >> You've said it a couple of times, you're a 30 year old start up. How do you stay innovative? I mean, you're a 30 year old start up that now has employees in four locations across the U.S. dealing in huge businesses. How do you keep that start up mentality? The hungry mentality, and the hack-y mentality, I guess I should say too? >> One of the real advantages we've got there, is our CEO and founder has always innovated. From the first company before Melissa, all the way up through today, he's always been one to say we need to try that next thing, right. Pentaho, five or six years ago, was that next thing that he and our VP of strategy said we should try, and now I'm sitting here with you today. There's a top down, bottom up approach, if that makes sense to you, because if you have an idea, you can bring that idea forward as well. >> You consider the next thing, and Hitachi Vantara's been saying that in spades today here at this event, it's also a Wikibon research focus, the Edge, Edge computing, Edge analytics, data, machine data coming from Edge devices, how is Melissa Data, in partnership with Pentaho, moving towards this Edge to outcome frame of reference, or frame for building innovative solutions, where does that fit with your roadmap going forward? >> So our perspective on that, much like when we first engaged with them, data was going into the data lake, let's just get it all in there, get it all in there, get it all in there, get it all in there, right. Well, eventually you have to make that data actionable. You're going to have a reverse scenario with the Edge. There's a lot of data, small amounts, small chunks, that are going to be everywhere, I think it was talked about being on cell phones, and everywhere else. The idea that you can extend the reach of data quality along with the reach of analytics, to actually make sure you're getting the best data you can, to feed those microanalytics, to feed that, that's a critical part that we see as potential. >> Looking ahead, what are some of the problems that you want to solve, just sort of in the next year, the next five years, what are some of the things that you're thinking about and keeping you up at night right now. >> We're doing some very interesting things with globally unique identifiers, I'll call them that, not a GUID in that sense, but the idea that every address on the planet could be indexed, right. And then the idea beyond that was every email and every phone and every identity around that could be indexed. Then when you're dealing with a massive amount of indexes, becomes a lot faster and a lot easier to match, to dedupe, to do other data quality tasks. So, it's one of the projects that our CEO is very interested in, is this sort of indexing or massive indexing table concept. And so that's one of the things I know we're very focused on as an organization, and how that can feed all of our other technologies. >> How would that work, I mean, I know it's a research process in motion, but >> And keep in mind I am the head of global sales and alliances, so don't bust out all the too technical a question. (laughter) >> Yeah, so this is identity resolution at a massive scale, does it involve an internet of things, almost like a, slap me on the wrist, a graph, a social graph of you and all the identities you may have running on various Edge devices? You meaning a user. >> I think there is the potential for pieces. >> Remember, I'm a geek here so. >> Yeah, yeah there's a potential for pieces of that to be used in that way. Like an example we got approached about was, someone who wanted to have a cookie that represented the address that they just captured from this particular interaction on the web, right. Well, imagine if you could use this table of addresses that was indexed, right, to get that number back, and you just store that number constantly with that cookie, you'd never have to store that address data again, you could match that index against other indexes, and the uses go on and on and on. >> James: Right. >> So it's not complete in any way, so I wouldn't want to venture to answer the implete part of your question, but the idea that you can represent things with a series of numbers is how the internet got started, effectively, right, so you could look at something similar. >> Right. >> So you're here at PentahoWorld, and you said you're a biz dev manager, what is your, what do you hope to take away from it? I mean, are you talking? >> You mean outside of business? (laughter) >> Get some deals done, exactly. But what are you learning, what are you hearing, are you sharing best practices, and how do you do that here? >> Well, we're pretty tightly connected into different elements of what is now Hitachi Vantara, right, so we work with their office in Singapore, we work with them engaged all over the world, on many different fronts, and so it's nice to be here one, so you can literally put some faces with some names, right. And as you look at some of their different initiatives, like cyber security that I've seen, over there somewhere, and some of the other initiatives they've got going, they march a bit in lock step with what we're doing, and the nice thing about being here, is the ability to sort of reconcile that and see and talk about how we can go forward together with those elements, if that makes sense. >> James: Right. >> Absolutely. Well Charles, thanks so much for coming on theCUBE, it's been a great talking to you. >> James: Yeah absolutely. >> Thank you for having me, I appreciate it. >> We will have more from theCUBE's live coverage of PentahoWorld in just a little bit. (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 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 PentahoWorld 2017. Brought to you by Hitachi Ventara. >> Welcome back to The Cube's live coverage of PentahoWorld, brought to you, of course, by Hitachi Ventara. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We're joined by Reni Waegelein, he is the IT manager of Veikkaus. Thanks so much for coming on The Cube Reni. >> Thank you for having me here. >> So, Veikkaus is the Finnish national betting agency wholly owned by the government. >> Yeah. >> Tell us more. >> Yeah we have, we used to have like three companies, now we are merged as one and we operate every money gaming thing, all the money gaming in Finland. So that includes from casino to lottery, to scratch tickets, sports betting, horse betting, whatever that is, and we gather money, of course, pay out some good winnings as well. But everything we make under the line, that goes to good causes, and I mean everything. >> And you are IT manager. >> Reni: Yeah. >> So what does, what are your responsibilities? >> Yeah, responsibilities like the developing the whole of the idea things we have, from architecture to doing the IT procurement development, and harnessing how we work. >> So the public policy on betting is, hey, let's have a single state-run monopoly. >> Reni: Yep. >> And we'll take the winnings and put it to the public good, right, makes sense. >> Reni: Yep. >> And is there any competition from internet, for example? >> Of course, yes, and the internet, well, it's like a full competition, although we are a legally-based company in Finland and we operate and sell only to Finnish people. The people itself, they have all the freedom to choose whoever they want to play with, so in that sense, it's full competition and have been so for many years. >> So you have to have great websites. >> Reni: Yep. >> Great customer experience, >> Reni: Yep. >> User experience. >> Reni: Yeah. >> Competitive rates, all that stuff. >> Reni: Yep. >> Okay so, and good analytics. (laughing) I mean that industry is obviously very data heavy. >> Reni: Yep. >> Always has been. So how do you analytics and data to compete? >> So we have been doing, like, the product analytics for quite a long time and then we established a customer-ship. So in Finland we have a 5.4 million habitats, and we sell only for the 18+ year old people, and at the moment we have more than 2 million registered customers already. So, you can imagine that we have that vast amount of data from the customer, and we use that data, for example, promoting the service, promoting games, targeting, making some recommendation. We build our own recommendation engine, for example, and utilize all of that kind of data. But, as you know, the gaming is also like a two-edged sword, that's a happy side, but there's also the dark side. So it does cause problem, so we try also to use the data so that we want to identify the bad patterns when somebody is about to lose control of gaming. So we use also the same data that we want to see, for example, for these players who want to see all the activities of marketing, for example, we don't want anybody to get into problems because of gaming. >> So that's a really interesting tension here, is that you obviously want to make money in this, but you also have to watch out for the Finnish society. And as you said, if there's a compulsive gambler or an addicted gambler, you need to act, I mean, is that? >> Yeah, yeah that's really big part of our responsibility, and if we didn't have any data or if we couldn't process it fast, we couldn't know who is problematic gambler and who is not. Since vast majorities, of course, is enjoying it, it's a nice habit. Play a game of poker every now and then or go to the casino for once or twice a month, for example. But then there's the small portion of people who we want to protect so that they don't get into the debt. That's not our intention. >> And the level of protection that you provide, is you stop marketing to them, is that right or? >> Reni: Yeah, yeah. >> It's not like you intervene in some other way. >> Yeah, of course, we want to promote that if you want, you can stop and close your account, or this kind of activities. >> So you promote cutting the cord basically? >> Yeah, yeah, yeah. So instead of marketing, we say that this might be a problem to use, so yeah. >> Let's take a break. >> You should take a break, yeah. >> So, as Dave was saying, you're really, because you are competing with private entities you really have to have a great interface, great customer experience, great rates. How much does this put Veikkaus really on the vanguard of this kind of technology, more so than what other government agencies are doing, in the sense that, you really have to stay on the cutting edge of these things. >> Yeah, we have to be like double-backed, you say. >> So how much do you then you talk to the health agency, or other government agencies about what you're doing and sharing the best practices about capturing customer attention? >> We are actually talking more to the new players out in the field who already live and breath true to data, so that's where we can learn and, I would say that we are also in to like a lottery area itself but also in quite many other industries as well. So we have been doing this for awhile, so we have had the luxury that we have already gathered some experience and opened some paths and, well, maybe learned also from the hard way how not to do it. We of course didn't succeeded in the first runs but you just have to go and have a trial and error in some areas as well. >> And you have multiple data sources obviously, maybe talk about how you're handling those data sources, are you ingesting, how you ingest those into Pentaho, what you do with it, how you're operationalizing the analytics. Where does Pentaho fit in that whole process? >> Yeah Pentaho we use, that's like ETL process, so to get this 360 view of the customer, we have like a various data sources. After the merger, we tripled the amount of different sources, and I think more than quadrupled the amount of data. So of course, just to make the data and work of the analysts easier, we need to make some transformations to the data and in that area the Pentaho has it's place. And in the future, what we are also expecting like the future versions to help us with is the tech in the more real time data. So for example, we can put in the real time data feed for the one physical place so they can see like which machines are used well, which are not, or is there any other activities that they can learn right in their place. >> So are you in the process of instrumenting the machines at this point? >> Reni: Yep. >> And so you're putting, how does that work, is it rip and replace, is it some kind of chip that you put into the machine? How do you instrument the machine? >> It's a good thing, so that we have actually we design our own slot machines, even. >> Dave: Okay, okay so. >> So we, we can like build up from the ground up. >> Dave: Design it in. >> Yeah. We designed the hardware supports like, it's, they are big IOT machines. >> Dave: Right. >> But also the software will support us. >> And then you've got connectivity, is it hard-wired? Is it physical or is it wireless connectivity? >> We use, well, whatever is available, so... >> Dave: Depends. >> Yeah, yeah. And when we are developing like a new type of games, for example, when the slot machines should have like online all the time, like jackpot available, then of course, we have to think about what's the quality of service of the network, as well. So far, we have been like using whatever is available. >> So what does the data architecture look like? I wonder if you could paint the picture, so you've got the machines, let's just use slot machines as an example. So you have the slot machines, you've instrumented those, you're doing real time analytics there, and maybe talk about what kinds of things you do there? And then where does the data go? How much data, do you persist the data? Maybe talk about that a little. >> Yeah so we get like the slot machines and other resources as well, and have like Kafka Hadoop area where we collect everything. Then there's a Pentaho doing the ETL work and we store the, all the data that goes through it to the Vertica. So we have HP's Vertica there, in that Vertica they've like lots of users, they have like a SAS analytics, use that and the Hadoop as well, so then we have some reporting, financials, finance department they also utilize it. But then we are also building up some new things like Apache's Kudu is one thing that we want to set up there just to make the life of analysts much more easier so they are the moment having little bit hard time in some areas how to utilize the data, and especially how to use like the different analyst tools from different cloud vendors for this data since we are still at the moment on premise, so everything is on premise partly because of the government requirements. >> Dave: Okay. >> So some part of the data they require that we keep it in within the Finland. >> Right so could we call that your private cloud? >> Reni: It's not private cloud yet. >> It's not, okay. >> But we're, we are going. >> Dan: Someday. >> Yeah, yeah. >> It will be a private cloud, okay, so you have edge device, which is the slot machine, and then you do you send all the data back to Vertica or no, probably not, right, I mean. >> Not yet. >> Dave: But do you want to? >> But it will be. >> Dave: Really? >> Yeah, it will be. Of course we have to make some decision like what data will be important and what is not, so not all the data is valuable, but especially when it's like connected somehow to the customer, or the retailer as well, that data we also keep like more than a year. So we are not doing all the analytics just for a short time of data but also want to seek out the long trends and make new hypothesis out of it. >> And the Vertica system is essentially your data warehouse, is that right? >> Reni: Yeah. >> Okay. And then are you doing sort of, well you mentioned recommendation engine so you're doing some >> Reni: Yeah. form of it. That's a form of AI, as far as I'm concerned. Are you doing that, where are you doing that? Is you doing that in your data center, and is that another layer of the data pipeline or is that done in the? >> Yeah, it's done partly on site but also in AVS. >> Yeah >> So we used Amazon services in some areas where we can use those, so the recommendation for example, and part of the cost of AI, that's part, some blocks are also on the AWS. >> So it's a three tier. >> Reni: Yeah. >> So there's the edge, then there's the aggregation at Vertica, and then there's the cloud modeling and training that goes on, and Pentaho plays across that data pipeline, is that right? >> Yeah, yeah, it's our one major player in our data platform in this sense so that it will take care quite a many different kind of transactions so that we have the right data in the right place. >> Dave: All right I'm done geeking out. (laughing) >> All right, so Reni before the cameras were rolling, we were talking a little bit about the difficulties of cultural change within these organizations and you were talking about something that you're working on in Finland that's not necessarily related to Veikkaus, can you tell our viewers a little bit about what you're doing? >> Yeah, we are also setting up a Teal Finland, so promoting this like next phase of organizational, well you cannot call it belief, but vision and perspective so we want to also promote these kind of activities. So I know that especially with the big data movement, you have also seen the cultural changes so not the normal organization ways of working are not, just are not efficient enough so you have to liberate today, you have to give the freedom, how to use the data, what kind of hypothesis, what kind of activities are done, and this cultural change is also with the Teal movement. It's like getting next big leap so this is, well it's a side project but it's also really heavily work related. >> And how open is the Finnish tech community to these ideas, I mean is there an adversarial relationship within the people who don't necessarily welcome the change, I mean how would you describe it? >> I believe it's a really open, we have already, I believe, a handful of companies who work and who operate by this, from this perspective and more is popping out. And we are establishing one cooperative, like to support this movement, and maybe to create new spinoffs which can be for profit. >> All right, let's get to the heart of the matter here, (laughing) how do I beat the house? >> I knew you were going there, Dave. >> Just, just between us. >> I knew it. (laughing) >> Obviously I'm kidding but different games have different odds. >> Reni: Yeah. >> Right, I mean, and those are, you're transparent about that, people know what they are, but what are the best odds? Is it slots, best chance of winning, or poker, or... >> Yeah, slots is good side and also whenever you go to Cassie you know, it has a top notch, so 90 point something, so... >> Of probabilities and, >> But of course I have to say that the house wins eventually, so yeah, yeah. >> The bookeys always win so. >> Rebecca: Right exactly. >> So the higher the probability, the lower the pay out, and reverse, presumably, right? >> Reni: Yeah, yeah. >> The lottery would be. >> Lottery you're a check out if you're yeah. >> Dave: Low odds. >> Low odds but, >> Dave: Telephone numbers if you win. >> Yeah. >> Dave: Yeah. >> But David, you can't win if you don't play, okay, just saying, just saying. >> And every week there's somebody who wins. >> Rebecca: Right! >> Yeah. So why it cannot be me, or you? (laughing) >> Or me, or me, maybe! >> So what do you do to the guys who count cards, you like break arms or you put them in jail, no? >> It's Finland, this is no, no, come on. >> Nobody does that, right? >> Reni: No, no, no. But of course, yeah that's probably something we could in future also to use data more efficiently than we use it at the moment, so that's one part like how people behave versus machines behave. So for example in the online poker, the card counting program, that's one problem I think every, for the industry. >> Dave: Right. >> Are you working with behavioral finance experts in this to sort of understand people's behavior when it comes to this? >> Yeah we work, for example, with psychologists to understand this and the same goes with problematic gambling as well so you have to know about how people behave. >> And do you have customers outside of Finland or is it pretty much exclusively? >> No, sorry, it's exclusive club, you have to move to, you know you have to move to Finland. (laughing) And then we welcome you. >> Awesome. >> He's going to immigrate, I think, any day now. Well Reni, >> Reni: But hey, it's one of the best countries. >> Thank you so much for coming on The Cube, it was a lot of fun talking to you. >> Yeah, thank you. >> I'm Rebecca Knight, for Dave Vellante, we will have more from PentahoWorld just after this.

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

>> 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)

>> Announcer: Live from Orlando, Florida. It's theCube covering PentahoWorld 2017, brought to you buy Hitachi Ventara. >> Welcome back to theCube's live coverage of PentahoWorld, brought to you of course by Hitachi Ventara. I'm your host, Rebecca Knight, along with my cohost James Kobielus. We are joined by Stephano Celati. He is a Pentaho Solutions consultant at BNova. Thanks so much for coming on theCube, Stephano. >> Thank you for having me. >> So I should say congratulations are in order because you are here to accept the Pentaho Excellence Award for the ROI category on behalf of LAZIOcrea. Tell us about the award. >> Yes, as I was saying, I'm really proud of this award because it is something that is related to public administration savings, which is a good thing, first of all for me as a citizen, let's say. This project is about healthcare spending. In Italy the National Healthcare Services allows the drugstore to sell medicines to total or partial reimbursement by NHS itself. And they also have the possibility to replace the medicine with a generic drug which normally costs less to the people and also to the health service itself. So a couple of years ago (speaks in foreign language) which is the political area to which Rome belongs just to explain, launched a new project to monitor, analyze and inspect the spending flow in drugs. So we partnered with LAZIOcrea to create a business analytics platform based on Pentaho obviously, and which collects all the data coming from the prescriptions and store it in an analytical database that is Vertica, and uses PDI/ETL tools to store this data. >> That's for Pentaho Data Integration. >> Yes, PDI is Pentaho Data Integration, good point. And after that we present the data in terms of reporting, analysis, dashboards, to all the people that are interested in this data. So we talk about regional managers, we talk about auditors, and also to local district users which are in charge of managing the expenditure for drugs. The outcome of this project was real impressive because we had an expenditure fell by 3.6%, which in a region where we have more than 200 million prescriptions every year means 34 million Euros in a years. >> Rebecca: Wow. >> So it was really huge result. We were very happy about that. And it was so simple because simply monitoring better the expenditure, monitoring how they deliver the drugs out, what kind of medicine they prescribe and targeting what pharmacies sell to the end user just gave these impressive results. And this year they are forecasting for 41 million Euros in savings more, so it's a huge result. It's something that is for us really a good result. >> So here in the U.S., I mean we have problems very similar to what you just described in Italy. And just putting the transparency around the data would be a huge revelation for the United States, too. How big a departure was it in Italy? >> Well, it was a really a big problem to start because they didn't have any system to collect all this data. So they had to set up everything from scratch, let's say, just by acquiring the paper where the physician writes the recipe, so it was not that easy to build it from scratch. But after that the region has had the opportunity to monitor this data and also to publish this data, which is something that in Italy is really relevant in this moment because we are talking about open government, we are talking about open data, and so again, the result was really impressive. >> Do you see any follow on opportunities to use this data for other purposes other than the initial application? >> Yes, we already experienced a different usage of this data because during the last major earthquake we have in 2016 in this area, those guys from LAZIOcrea were able to produce a list of mostly the drugs in that area just in a couple of hours, just by using the ETL and setting up this list that somehow help the first aid units in giving the right assistance on time. And next steps will be about hyper prescriptions because we want to monitor if there are any doctors that prescribe drugs that are not really necessary. And we also try to move our inspection also to hospitals because when you do a surgery, you get medicine, you get a lot of assistance in the hospital. So we want also to monitor that kind of the aspect, which is again in charge of the health system. >> To make sure that the right medicines are being distributed to the right regions at the right time for the intent to likely-- >> Yes, this could also lead to something that is a correlation analysis, meaning what is your pain and what are you assuming so that they can have an historical data they can use to prescribe better medicines. >> But the anecdote he was sharing about the earthquake too is really compelling too, if you think about a public health crisis and outbreak of some sort, to be able to get drugs quickly to those in needs, it's really astonishing. >> Again, this morning we were talking about data lake. This is a sort of data lake. We found several ways to use that data, to fish them back from the data, let's say from the lake, and it's really impressive what you can do if you have the right information and you know how to use it. >> How do you see the market developing over the next year, next five years? >> Yes, the problem in Italy is that the market is not so responsive to innovation like others, let's say U.S. or U.K. and Europe. So for this reason my company Bnova set up annual event which is called Big Data Tech, and the purpose of this event is to spread knowledge about big data systems, products, architecture and so on, which helps companies in knowing better what they can do with these platforms. So in the next month we see a lot of opportunities. Generically speaking data mining field, we start talking about predictive analysis, we start talking about smart cities and other stuff like that. So again, we will need maybe to enter in a new phase of let's say (mumbling) because companies like BNova and others that operate in this field of business analytics need to put to general knowledge what other innovative companies are doing. So in the next month we will for sure move to newer architectures, new technology, and we will have to support all the companies with this kind of stuff. >> In terms of the new technology you're moving to, is there a role for the internet of things, both in your plans and really in terms of the Italian market. What sort of potential applications are there for IOT related perhaps to the use of it with health data going forward in Italy? >> Yes, also for healthcare, but in Italy the IOT team is a parallel line that is growing thanks to a governmental initiative which is called Industry 4.0, which encourages the usag of interconnected machines, connected to the internet, so classical approach of the IOT field. So with this new approach and the government sustain we believe that the IOT will have a big improvement in the next years. Again, we are talking about Italy, so we are not so fast in growing. But again, we are starting to talk about smart cities for energy saving, sustainable energy and other stuff in which the IOT plays a key role. So as far as our business is concerned, that is business analytics, so on top of that we see a lot of opportunities coming from predictive analysis, which means to prevent the maintenance of a machine, for example, or to use virtual reality to simulate a laboratory test and other stuff. So with these opportunities for sure the usage of data mining tools, such Wake Up when we're talking about Pentaho Solutions, could be a great advantage because you will apply the knowledge to your data. So you will not only analyze the data, but you will also extract some sort of knowledge from the data which can help companies. >> Of course, Italy is where the renaissance began, and it just sounds like you, I mean renaissance use of analytics to help the Italian people and the Italian economy to continue to grow and innovate. >> Stephano: Yes, yes. >> So I want to see not a data lake, a data colosseum, that should be on your to do list. >> I want a data gallery with lots of data masterpieces hanging on the walls all around Italy. >> Exactly. >> You'll be the new Leonardo and Michelangelo. >> Stefano , I love it. Well, thank you so much for coming on theCube. >> Thank you for having me. >> I am Rebecca Knight for Jim Kubielus. We will have more from PentahoWorld just after this.

>> 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, of course, by Hitachi Vantara. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We have two guests today, we have Michael Becker, a senior chief inspector, and Henry Liebrenz, the police sergeant of the German Federal Police, the Bundespolizei. Welcome, gentlemen. Thanks so much for joining us. So do you want to start out by telling us, telling our viewers a little bit about Bundespolizei and what you do there? >> Okay. The Federal German Police employs about 41,000 people, and as part of Federal German Ministry of Interior, we have, the police is responsible for many demanding and varied tasks, like air control or air safety, rail patrol, water control, crime reduction, and patrol the high seas. And besides an internal task, we do many international missions, police missions all over the world and missions in the European Union for neighboring. And our job, our main job is to development specialty police software. You couldn't buy (foreign words) products, and the development was our own framework based on lamp. >> Classical open source systems plus open source databases plus PHP, it's script language, on the top of it's end. And we built our own absolute framework on this, it's exclusively for us and that's our main job, to build applications on this top. >> And besides our name, our main job we are responsible for the data warehouse and responsible for integration, data integration technologies of the Federal Police. >> So you're both within the IT organization of Bundespolizei, okay. >> Yes, we stay in the IT department that belongs to headquarter. In Germany, or in German police, we have one headquarter, we have 11 district offices, about 80 regional offices, and about 160 local offices. >> All over Germany, is it. >> So when you're thinking about your software challenges, you have a lot of different obstacles: safety, operational, security. What are some of the things that you're taking into account when you're implementing software? >> Um, what we take in account? Not so easy to (speaking in foreign language). >> What is your approach? What are the things on your mind that is keeping you up at night? >> We have two different ways. The main way is to build software. And we have in special case. In turn case we build software that bring is on the point for this case. The other way is we have a way to product data in this cases. That's the other way. What can we do with this data? That's the other case around Pentaho. We want to have more benefit with this kind of data. >> What sort of data driven application development do you do or do you oversee for Bundespolizei? Can you describe some of the applications within their specific functions? >> We have one main application is our time planning tool. So all the shifts on the agencies it's possible to plan. In one case that we build on this platform and it's exclusively for us. We have the situation that other polices in Germany ask us about. Hey, that's very a good solution. Maybe we can take it also for us. But because it's a little bit different for normal situations outside and in other companies. Because we have the situation 24 hours, seven days a week, 365 days a year to bring our services. We have a big many rules about this kind of working. The offices get some more money in the night or it's Saturday and something like this it's not so easy to implement with normal software. So we were at the case what we do. Then okay we do it ourself and that's exactly on point. >> You describe the rules, you're describing the rules that are provided from the European Union or from your government in terms of security, privacy, and so forth. Is that what you're describing? How have this whole total set of rules and policies and mandates shaped your data management strategy within your organization? How does the Pentaho set of solutions support those requirements? >> I think with Pentaho I told it yesterday also it was for us definitely the game changer. It's definitely true. Before we don't have the chance to build something like this only was two us. But now we have the big Swiss knife. We get entrance with especially with the Ketel, solution, PDI. >> With Ketel everything is possible. >> It's not possible to build your own. >> That was the entrance to build a strategy about it. Then at this point we had the solution to let the data flow wherever you want. Then we start okay, when can we have data every time at every point. So what can we do with it? What is the benefit for us? We start to come in discussion with our other departments inside what is your problem? What can we do to help you to get more benefit about it? >> How much sharing goes on between departments? >> Henry: The sharing? >> Yes, in terms of as you said, how can I help you? Oh, we are doing something over here. >> I think it's a classic job like other. (speaking in foreign language) We do it inside so we go to the other departments and have this part of discussion. We try to bring it in the right way. >> What degree of this sharing is intergovernmental? Meaning you are reaching out to your peer agencies within the European Union maybe through Interpol to other nations? Is any of that going on and is Pentaho playing a role in terms of helping you in that regard? (speaking in foreign language) >> How we have to say? >> If you don't want to say or can't say. >> Actually I think in German or in European it's not so big. I don't know why, I can't believe it. But it's also to take advantage at Pentaho that you can start at any time. You can start as a community. We work also before, two years with the (voice is muffled). And started this year with enterprise and we have only one day for integration from the community server of the new enterprise server. No problems. I think that is a great benefit. You can almost start with a small problem or data integration. >> In the past the other big companies maybe they had a little bit earlier start. Pentaho, the goal to come along the other players. I think in Europe, especially in Germany at the moment can be good. >> In Germany we have a situation over Pentaho user meeting or Pentaho community meetings but also other agencies come and ask why Pentaho and how did you do it? >> Is there an ongoing program of working with other federal agencies in Germany to share the best practices you've learned from using data at least to manage your agency's requirements? What could they learn from what you've done? >> The progress is starting now so the other come to us. We meet together and they want to take a look directly on our screens and want to see some cases. We play for them live and it's a very interesting situation. When they see eh, you have the same problems as I. It's interesting. >> And very important is also that we learned and we have learned from Pentaho that everything is possible. You need much less time for everything or for every kind of problem. We are very fast. Before we used to have another (foreign word), it's called Excel. It's crazy, it's good for statistics but we have no data quality. >> It's not possible to work with big data. (voice is muffled) >> Our data are actual, daily actual. Before we wait for one month or two months. >> Before we had exactly one day per month. At this day the data was correct only one day. And other other days we had to collect the data for the next month. >> It's a whole new world with Pentaho. Henry and Michael, thank you so much for coming on theCube. It was great having you on here. >> Thank you very much. >> We will have more from theCube's live coverage of PentahoWorld just after this. (upbeat digital music)