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

>> Announcer: Live from Orlando, Florida it's theCUBE covering 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)

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

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