(energetic music) >> Hi, and welcome to another Cube conversation, where we go in-depth with the thought leaders in the industry that are making significant changes to how we conduct digital business and the likelihood of success with digital business transformations. I'm Peter Burris. Every organization today has some experience with the power of analytics. But, they're also warning that the value of their analytics systems are, in part, constrained and determined by their access to core information. Some of the most important information that any business can start to utilize within their new advanced analytic systems, quite frankly, is that operational business information, that the business has been using to run the business on for years. Now, we've looked at that as silos and maybe it is. Although, partly, that's in response to the need to have good policy, good governance, and good certainty and practicably in how the system behaves and how secure it's going to be. So, the question is, how do we marry the new world of advanced analytics with the older, but, nonetheless, extremely valuable world of operational processing to create new types of value within digital business today? It's a great topic and we've got a great conversation. Tendu Yogurtcu is the CTO of Syncsort. Tendu, welcome back to The Cube! >> Hi Peter. It's great to be back here in The Cube. >> Excellent! So, look, let's start with the, let's start with a quick update on Syncsort. How are you doing, what's going on? >> Oh, it's been really exciting time at Syncsort. We have seen a tremendous growth in the last three years. We quadrupled our revenue, and also number of employees, through both organic innovation and growth, as well as through acquisitions. So, we now have 7,000 plus customers in over 100 countries, and, we still have the eight 40 Fortune 100, serving large enterprises. It's been a really great journey. >> Well, so, let's get into the specific distinction that you guys have. At Wikibon theCube, we've observed, we predicted that 1919, 2019 rather, 2019 was going to be the year that the enterprise assert itself in the cloud. We had seen a lot of developers drive cloud forward. We've seen a lot of analytics drive cloud forward. But, now as enterprises are entering into cloud in a big way, they're generating, or bringing with them, new types of challenges and issues that have to be addressed. So, when you think about where we are in the journey to more advanced analytics, better operational certainty, greater use of information, what do you think the chief challenges that customers face today are? >> Of course, as you mentioned, that everybody, every organization is trying to take advantage of the data. Data is the core. And, take advantage of the digital transformation to enable them for taking, getting more value out of their data. And, in doing so, they are moving into cloud, into hybrid cloud architectures. We have seen early implementations, starting with the data lake. Everybody started creating the centralized data hub, enabling advanced analytics and creating a data marketplace for their internal, or external clients. And, the early data lakes were for utilizing Hadoop on premise architectures. Now, we are also seeing data lakes, sometimes, expanding over hybrid or cloud architectures. The challenges that these organizations also started realizing is around, once I create this data marketplace, the access to the data, critical customer data, critical product data, >> Order data. >> Order data, is a bigger challenge than I thought that it would be in the pilot project. Because, these critical data sets, and core data sets, often in financial services, banking and insurance, and health care, are in environments, data platforms that these companies have invested over multiple decades. And, I'm not referring to that as legacy because definition of legacy changes. These environment's platforms have been holding this current critical data assets for decades successfully. So-- >> We call them high-value traditional applications. >> High-valude traditional sounds great. >> Because, they're traditional. We know what they do, and there's a certain operational certainty, and we've built up the organization around them to take care of those assets. >> But, they still are very very high-value. >> Exactly. And, making those applications and data available for next generation, next wave platforms, is becoming a challenge, for couple of different reasons. One, accessing this data. And, accessing this data, making sure the policies and the security, and the privacy around these data stores are preserved when the data is available for advanced analytics. Whether it's in the cloud or on premise deployments. >> So, before we go to the second one, I want to make sure I'm understanding that, because it seems very very important. >> Yes. >> That, what you're saying is, if I may, the data is not just the ones and the zeroes in the file. The data really start, needs to start being thought of as the policies, the governance, the security, and all the other attributes and elements, the metadata, if you will, has to be preserved as the data's getting used. >> Absolutely. And, there are challenges around that, because now you have to have skill sets to understand the data in those different types of stores. Relational data warehouses. Mainframe, IBMI, SQL, Oracle. Many different data owners, and different teams in the organization. And, then, you have to make sense of it and preserve the policies around each of these data assets, while bringing it to the new analytics environments. And, make sure that everybody's aligned with the access to privacy, and the policies, and the governance around that data. And also, mapping to metadata, to the target systems, right? That's a big challenge, because somebody who understands these data sets in a mainframe environment is not necessarily understanding the cloud data stores or the new data formats. So, how do you, kind of, bridge that gap, and map into the target-- >> And, vice-versa, right? >> Yes. >> So. >> Likewise, yes. >> So, this is where Syncsort starts getting really interesting. Because, as you noted, a lot of the folks in the mainframe world may not have the familiarity of how the cloud works, and a lot of the folks, at least from a data standpoint. >> Yes. >> And, a lot of the folks in the cloud that have been doing things with object stores and whatnot, may not, and Hadoop, may not have the knowledge of how the mainframe works. And, so, those two sides are seeing silos, but, the reality is, both sides have set up policies and governance models, and security regimes, and everything else, because it works for the workloads that are in place on each side. So, Syncsort's an interesting company, because, you guys have experience of crossing that divide. >> Absolutely. And, we see both the next phase, and the existing data platforms, as a moving, evolving target. Because, these challenges have existed 20 years ago, 10 years ago. It's just the platforms were different. The volume, the variety, complexity was different. However, Hadoop, five, ten years ago, was the next wave. Now, it's the cloud. Blockchain will be the next platform that we have to, still, kind of, adopt and make sure that we are advancing our data and creating value out of data. So, that's, accessing and preserving those policies is one challenge. And, then, the second challenge is that as you are making these data sets available for analytics, or machine learning, data science applications, deduplicating, standardizing, cleansing, making sure that you can deliver trusted data becomes a big challenge. Because, if you train the models with the bad data, if you create the models with the bad data, you have bad model, and then bad data inside. So, machine learning and artificial intelligence depends on the data, and the quality of the data. So, it's not just bringing all enterprise data for analytics. It's also making sure that the data is delivered in a trusted way. That's the big challenge. >> Yeah. Let me build on that, if I may, Tendu. Because, a lot of these tools involve black box belief in what the tool's performing. >> Correct. >> So, you really don't have a lot of visibility in the inner workings of how the algorithm is doing things. It's, you know, that's the way it is. So, in many respects, your only real visibility into the quality of the outcome of these tools is visibility into the quality of the data that's going into the building of these models. >> Correct. >> Have I got that right? >> Correct. And, in machine learning, the effect of bad data is, really, it multiplies. Because of the training of the model, as well as insights. And, with Blockchain, in the future, it will also become very critical because, once you load the data into Blockchain platform, it's immutable. So, data quality comes at a higher price, in some sense. That's another big challenge. >> Which is to say, that if you load bad data into a Blockchain, it's bad forever. >> Yes. That's very true. So, that's, obviously, another area that Syncsort, as we are accessing all of the enterprise data, delivering high-quality data, discovering and understanding the data, and delivering the duplicated standardized enriched data to the machine learning and AI pipeline, and analytics pipeline, is an area that we are focused with our products. And, a third challenge is that, as you are doing it, the speed starts mattering. Because, okay, I created the data lake or the data hub. The next big use case we started seeing is that, "Oh yeah, but I have 20 terabyte data, "only 10% is changing on a nightly basis. "So, how do I keep my data lake in sync? "Not only that, I want to keep my data lake in sync, "I also would like to feed that change data "and keep my downstream applications in sync. "I want to feed the change data to the microservices "in the cloud." That speed of delivery started really becoming a very critical requirement for the business. >> Speed, and the targeting of the delivery. >> Speed of the targeting, exactly. Because, I think the bottom line is, you really want to create an architecture that you can be agnostic. And, also be able to deliver at the speed the business is going to require at different times. Sometimes, it's near real-time, and at batch, sometimes it's real-time, and you have to feed the changes as quickly as possible to the consumer applications and the microservices in the cloud. >> Well, we've got a lot of CIO's who are starting to ask us questions about, especially, since they start thinking about Kubernetes, and Istio, and other types of platforms that are intended to facilitate the orchestration, and ultimately, the management of how these container-based applications work. And, we're starting to talk more about the idea of data assurance. Make sure the data's good. Make sure it's been high-quality. Make sure it's being taken care of. But, also make sure that it's targeted where it needs to be. Because, you don't want a situation where you spin up a new cluster, which you could do very quickly with Kubernetes. But, you haven't made the data available to that Kubernetes-based application, so that it can, actually, run. And, a lot of CIO's, and a lot of application development leaders, and a lot of business people, are now starting to think about that. "How do I make sure the data is where it needs to be, "so that the applications run when they need to run?" >> That's a great point. And, going back to your, kind of, comment around cloud, and taking advantage of cloud architectures. One of the things we have observed is organizations, for sure, looking at cloud, in terms of scalability, elasticity, and reducing costs. They did lift and shift of applications. And, not all applications can be taking advantage of cloud elasticity, then you do that. Most of these applications are created for the existing on-premise fixed architectures. So, they are not designed to take advantage of that. And, we are seeing a shift now. And, the shift is around, instead of, trying to, kind of, lift and shift existing applications. One, for new applications, let me try and adopt the technology assets, like you mentioned Kubernetes, that I can stay vendor-agnostic, for cloud vendors. But, more importantly, let me try to have some best practices in the organization. The new applications can be created to take advantage of the elasticity. Even though, they may not be running in the cloud yet. So, some organizations refer to this as cloud native, cloud first, some different terms. And, make the data. Because, the core asset here, is always the data. Make the data available, instead of going after the applications. Make the data from these existing on-premise and different platforms available for cloud. We are definitely seeing that the shift. >> Yeah, and make sure that it, and assure, that that data is high-quality, carries the policies, carries the governance, doesn't break in security models, all those other things. >> That is a big difference between how, actually, organizations ran into their Hadoop data lake implementations, versus the cloud architectures now. Because, when initial Hadoop data lake implementations happened, it was dump all the data. And, then, "Oh, I have to deal with the data quality now." >> It was also, "Oh, those mainframe people just would, "they're so difficult to work with." Meanwhile, you're still closing the books on a monthly basis, on a quarterly basis. You're not losing orders. Your customers aren't calling you on the phone angry. And, that, at the end of the day, is what a business has to do. You have to be able to extend what you can currently do, with a digital business approach. And, if you can replace certain elements of it, okay. But, you can't end up with less functionality as you move forward in the cloud. >> Absolutely. And, it's not just mainframe. It's IBMI, it's the Oracle, it's the teledata, it's the TDZa. It's growing rapidly, in terms of the complex stuff, that data infrastructure. And, for cloud, we are seeing now, a lot of pilots are happening with the cloud data warehouses. And, trying to see if the cloud data warehouses can accommodate some of these hybrid deployments. And, also, we are seeing, there's more focus, not after the fact, but, more focus on data quality from day one. "How am I going to ensure that "I'm delivering trusted data, and populating "the cloud data stores, or delivering trusted data "to microservices in the cloud?" There's greater focus for both governance and quality. >> So, high-quality data movement, that leads to high-quality data delivery, in ways that the business can be certain that whatever derivative work is done remains high-quality. >> Absolutely. >> Tendu Yogurtcu, thank you very much for being, once again, on The Cube. It's always great to have you here. >> Thank you Peter. It's wonderful to be here! >> Tandu Yogurtcu's the CTO of Syncsort, and once again, I want to thank you very much, for participating in this cloud, or this Cube conversation. Cloud on the mind, this Cube conversation. Until next time. (upbeat electronic music)

(upbeat music) >> Hi and welcome to another Cube Conversation, where we go in depth with the thought leaders in the industry that are making significant changes to how we conduct digital business, and the likelihood of success with digital business transformations. I'm Peter Burris. Every organization today has some experience with the power of analytics, but they're also learning that the value of their analytic systems are, in part, constrained and determined by their access to core information. Some of the most important information that any business can start to utilize within their new advanced analytic systems, quite frankly, is that operational business information that the business has been using to run the business on for years. Now, we've looked at that as silos, and maybe it is, although partly that's in response to the need to have good policy, good governance, and good certainty and predictably in how the system behaves, and how secure it's going to be. So, the question is, how do we marry the new world of advanced analytics with the older, but, nonetheless, extremely valuable world of operational processing to create new types of value within digital business today? It's a great topic and we've got a great conversation. Tendü Yogurtçu is the CTO of Syncsort. Tendü, welcome back to theCube. >> Hi Peter, it's great to be back in theCube. >> Excellent. So, look, let's start with a quick update on Syncsort. How are you doing? What's going on? >> Oh, it's been really a exciting time at Syncsort. We have seen tremendous growth in the last three years. We quadrupled our revenue and also number of employees, tripled organic innovation and growth, as well as true acquisitions. So, we now have 7,000 plus customers in over 100 countries, and we still have the 84 of Fortune 100 serving large enterprises. It's been a really great journey. >> Well, so let's get into the specific distinction that you guys have. At Wikibon theCube, we've observed, we predicted that 1919, 2019, rather, 2019 was going to be the year that the enterprise asserted itself in the cloud. We had seen a lot of developers drive cloud forward, we've seen a lot of analytics drive cloud forward, but now as enterprises are entering into cloud in a big way, they're generating or bringing with them new types of challenges and issues that have to be addressed. So, when you think about where we are in the journey to more advanced analytics, better operational certainty, greater use of information, what do you think the chief challenges that customers face today are? >> Of course, as you mentioned, that everybody, every organization is trying to take advantage of the data, data is the core, and take advantage of the digital transformation to enable them for taking, getting more value out of their data. And, in doing so, they are moving into cloud, into hybrid cloud architectures. We have seen early implementations starting with the data lake. Everybody started creating this centralized data hub enabling advanced analytics and creating a data marketplace for their internal or external clients. And the early data lakes were utilizing Hadoop on on-premise architectures, now we are also seeing data lakes sometimes expanding over hybrid or cloud architectures. The challenges that these organizations also started realizing is around once I create this data marketplace, the access to the data, critical customer data, critical product data-- >> Order data. >> Order data, is a bigger challenge that I told that it will be in the pilot project because these critical data sets and core data sets often in financial services, banking, and insurance, and healthcare are environments, data platforms that these companies have invested over multiple decades. And I'm not referring to that as legacy because definition of legacy changes, these environments, platforms have been holding these critical data assets for decades successfully. >> We call them high value traditional applications because the traditional we know what they do, there's a certain operational certainty, and we've built up, you know, the organization around them to take care of those assets, but they still are very, very high value. >> Exactly, and making those applications and data available for next generation, next wave platforms is becoming a challenge for couple of different reasons. One, accessing this data, and accessing this data making sure the policies and the security and the privacy around these data stores are preserved when the data is available for advanced analytics, whether it's in the cloud or on-premise deployments. >> So, before you go to the second one, I want to make sure I understand that because it seems very, very important, that what you're saying is, if I may, the data is not just the ones and the zeros in the file, the data really needs to start being thought of as the policies, the governance, the security, and all the other attributes and elements, the metadata, if you will, has to be preserved as the data is getting used. >> Absolutely, and there are challenges around that because now you have to have skillsets to understand the data in those different types of stores, relational data warehouses, Mainframe, IBM i, SQL, Oracle, many different data owners and different teams in the organization, and then, you have to make sense of it and preserve the policies around each of these data assets while bringing it to the new analytics environments. And make sure that everybody is aligned with the access to privacy and the policies and the governance around that data. And also, mapping the metadata to the target systems, right? That's a big challenge because somebody who understands these data sets in a Mainframe environment is not necessarily understanding the cloud data stores or the new data formats, so how do you kind of bridge that gap and map into the target environment? >> And vice versa, right? >> Likewise, yes. >> This is where Syncsort starts getting really interesting because, as you noted, a lot of the folks in the Mainframe world may not have the familiarity of how the cloud works, and a lot of the folks, at least from a data standpoint, and a lot of folks in the cloud that have been doing things with object stores and whatnot, may not, in Hadoop, may not have the knowledge of how the Mainframe works. And so those two sides are seeing silos, but the reality is both sides have set up policies and governance models and security regimes and everything else because it works for the workloads that are in place on each side. >> Absolutely. >> So Syncsort's an interesting company because you guys have experience of crossing that divide. >> Absolutely, and we see both the next wave and existing data platforms as a moving, evolving target because these challenges have existed twenty years ago, ten years ago, it's just the platforms were different, the volume, the variety, complex was different, however, Hadoop, five, ten years ago was the next wave, now it's the cloud, blockchain will be the next platform that we have to still kind of adapt and make sure that we are advancing our data and creating value out of data. So that's accessing and preserving those policies is one challenge. And then the second challenge is that as you are making these data sets available for analytics or mission learning, data science applications, you're duplicating, standardizing, cleansing, making sure that you can deliver trusted data becomes a big challenge because if you train the models with the bad data, if you create the models with the bad data you have bad model and then bad data inside. So, mission learning and artificial intelligence depends on the data and the quality of the data, so it's not just bringing all enterprise data for analytics, it's also making sure that the data is delivered in a trusted way. That's a big challenge. >> Yeah, let me build on that if I may, Tendü, because a lot of these tools involve black box belief in what the tool's performing. >> Correct. >> So you really don't have a lot of visibility in the inner workings of how the algorithm is doing things. It's, you know, that's the way it is. So, in many respects, you're only real visibility into the quality of the outcome of these tools is visibility into the quality of data that's going into the building of these models. Have I got that right? >> Correct. And in mission learning, the effect of bad data it really multiplies because of the training of the model, as well as the insights. And with blockchain, in the future, it will also become very critical because once you load the data into blockchain platform, it's immutable. So, data quality comes at a higher price in some sense. So that's another big challenge. >> Which is to say that if you load bad data into a blockchain, it's bad forever. >> Yes, that's very true. So that's obviously another area that Syncsort, as we are accessing all of the enterprise data, delivering high quality data, discovering and understanding the data, and delivering the duplicated, standardized, enriched data to the mission learning and AI pipeline and analytics pipeline is an area that we are focused with our products. And the third challenge is that as you are doing it, the speed starts mattering because, okay, I created the data lake or the data hub, the next big use case we started seeing is that oh yeah, but I have twenty terabyte data, only ten percent is changing on a nightly basis, so how do I keep my data lake in sync? Not only that, I want to keep my data lake in sync, I also would like to feed that changed data and keep my downstream applications in sync. I want to feed the changed data to the micro services in the cloud. That speed of delivery started really becoming very critical requirement for the businesses. >> Speed and the targeting of the delivery. >> Speed of the targeting grid, exactly. Because I think the bottom line is you really want to create an architecture that you can be agnostic and also be able to deliver at the speed the business is going to require at different times. Sometimes it's near real time in a batch, sometimes it's real time and you have to feed the changes as quickly as possible to the consumer applications and the micro services in the cloud. >> Well, we've got a lot of CIOs who are starting to ask us questions about, especially as they start thinking about Kubernetes and Istio and other types of platforms that are intended to facilitate the orchestration and ultimately the management of how these container-based applications work. And we're starting to talk more about the idea of data assurance. Make sure the data is good, make sure it's high quality, make sure it's being taken care of, but also make sure that it's targeted where it needs to be, because you don't want a situation where you spin up a new cluster, which you could do very quickly with Kubernetes, but you haven't made the data available to that Kubernetes based application so that they can actually run. And a lot of CIOs and a lot of application development leaders and a lot of business people are now starting to think about that. How do I make sure the data is where it needs to be so that the applications run when they need to run? >> That's a great point, and going back to your kind of comment around the cloud and taking advantage of cloud architectures, one of the things we have observed is organizations looking at cloud in terms of scalable elasticity and reducing costs, dated lift and shift of applications, and not all applications can be taking advantage of cloud elasticity when you do that. Most of these applications are created for the existing on premise fixed architectures, so they are not designed to take advantage of that. And we are seeing a shift now, and the shift is around instead of trying to kind of lift and shift the existing applications, one, for new applications, let me try to adopt the technology assets, like you mentioned Kubernetes, that I can stay vendor agnostic for cloud vendors, but, more importantly, let me try to have some best practices in organization that new applications can be created to take advantage of the elasticity, even though they may not be running in the cloud yet. So some organizations refer to this as cloud native, cloud first, some different terms. And make the data, because the core asset here is always the data, make the data available, instead of going after the applications, make the data from these existing on premise and different platforms available for cloud. We are definitely seeing that shift. >> Yeah, and make sure and assure that that data is high quality, carries the policies, carries the governance, doesn't break the security models, all those other things. >> That is a big difference between how actual organizations ran into their Hadoop data lake implementations versus the cloud architectures now, because when initial Hadoop data lake implementations happened, it was dump all the data. And then, oh, I have to deal with the data quality now. >> No, it was also, oh, those Mainframe people just would, they're so difficult to work with, meanwhile, you're still closing the books on a monthly basis, on a quarterly basis, you're not losing orders, your customers aren't calling you on the phone angry, and that, at the end of the day, is what business has to do. You have to be able to extend what you can currently do with a digital business approach, and if you can replace certain elements of it, okay. But you can't end up with less functionality as you move forward into the cloud. >> Absolutely, and it's not just Mainframe, it's IBM i, it's the Oracle, it's the teradata, it's the DTSA, it's growing rapidly in terms of the complexity of that data infrastructure. And for cloud, we are seeing now a lot of pilots are happening with the cloud data warehouses, and trying to see if the cloud data warehouses can accommodate some of these hybrid deployments, and also we are seeing there's more focus, not after the fact, but more focus on data quality from day one. How am I going to insure that I'm delivering trusted data and populating the cloud data stores, or delivering trusted data to micro services in the cloud. There is a greater focus for both governance and quality. >> So, high quality data movement that leads to high quality data delivery in ways that the business can be certain that whatever derivative of work is done, remains high quality. >> Absolutely. >> Tendü Yogurtçu, thank you very much for being once again on theCube, it's always great to have you here. >> Thank you, Peter, it's wonderful to be here. >> Tendü Yogurtçu is the CTO of Syncsort, and, once again, I want to thank you very much for participating in this cloud, or this Cube Conversation, cloud on the mind, this Cube Conversation. Until next time. (upbeat music)

(upbeat music) >> Announcer: Live from Atlanta, Georgia, it's theCUBE. Covering Citrix Synergy Atlanta 2019. Brought to you by Citrix. >> Hello, and welcome to theCUBE's coverage of Citrix Synergy 2019 from Atlanta, Georgia. I'm Lisa Martin with my co-host Keith Townsend, the CTO Advisor. Keith, it's so great to see you. >> Lisa, good to be on the show with you again. >> So we're going to geek out the next two days. >> Oh isn't it so good? >> We've been geeking out already just coming from the keynote. This is ... >> Yeah This is, it was really good there was meat, there was announcements, there was news, partnerships. Citrix is a 30 year old company, who's done a lot in the last 12/18 months, to transform. From rebranding, product names, et cetera, lots of launches and announcements. And something that really peaked my interest as a marketer this morning, is hearing the influence of consumerization. Them talking about leveraging Citrix Workspace, and the things that they have done to beef it up which we'll talk about, to deliver a stellar employee experience, to delight the users. And those are words that we hear often in the marketing space, like customer lifetime value, they talked about the employee lifetime value because employee attraction, talent attraction and retention, is critical for every business. Really meaty stuff. What was some of your take on some of the announcements on Workspace? >> So I was really interested because as I'm coming off of SAP SAPPHIRE, where I'm accustomed to hearing terms like customer experience, employee experience, you know, the kind of X-data versus O-data conversation. We heard a lot of that here today. And it's weird coming from an infrastructure company. Citrix in the past I like to put into a box, it's about VDI, application virtualization and networking, and that's pretty much the conversation, it stayed at the IT infrastructure leader perspective. Today we heard a lot that broke out of that, and it was going into the C-Suite and delivering not just technology results, but business results. There was a lot about making transformation real. >> You're right it was about making it real, and if you think at the end of the day, I think I heard a stat the other day, that by 2020, which is literally around the corner, 50% of workers are going to be remote. You and I are great examples of that, we're on the road all the time, we have multiple devices we need to have connectivity that ... to all the apps, SAS apps, mobile apps, web, that allow us to be productive from wherever we are, done in a way that our employers, are confident there is security behind this. But delivering that exceptional employee experience is absolutely business critical. They gave some stats today about the trillions of dollars that are spent, or rather work that's lost, with employees that have so many apps each day that they're working with that really distract from their actual day to day function. >> Yeah I think one of the stats that they gave from an ambitious perspective, they want to give one day back to every employee, 20% of their time, back, I think the stat you referred to some seven trillion dollars of productivity is lost from just hunting and pecking inside of applications. Both of us work remotely, you work from your tablet, I work from a tablet or my phone a lot. Because I just, you know, it's low power to, it lasts the day, but yeah I still need to edit video, I need to sign invoices, I need to create statements that worked. I need to be just as creative on the road as I am at home. It helps me to compete against larger competitors, but more importantly, offer a different customer experience and this is what Citrix was talking about today, was more than just VDIs, about picking up any device asking basic logical questions like what is the status of the latest deal, the big deal, and getting that status from Salesforce without again hunting and pecking, from whatever device you're on. >> Which is critical, especially to have that seamless experience going from desktop to mobile. I think they also said ... there was a lot of stats this morning, which I really geek out on. But that the average person is using seven to 10 apps a day and I loved the video that they showed this morning that really brought that to life. Looking at a senior marketing manager for some enterprise company, who, as she starts her day, there's 10 minutes that goes by which is lie, oh, I forgot I got to log into Workday and request my PTO, oh, one of my employees needs me to approve an expense report, and oh, my boss wants to know about this big deal that's closed. And the time that is spent logging into different applications is really as you mentioned that number seven trillion dollars lost, what they're doing with Citrix, with the intelligent, the workspace intelligence experience is bringing all of that to the end user. So it's much more an activities focus rather than an app focus experience. And I loved what you said that they're very ambitiously aiming to give each person back one day a week, yes please. I will take that. In any organization. >> So I was at a government conference a few weeks ago and they talked very much about this CFO of GSA presented to a crowd of fellow government workers, and they were talking about eliminating waste, they were talking about automating processes, taking the PDF, taking a document and scanning it into a system, and then kicking off a real workflow. And this is done, the industry's been working on this problem for the past 10 years, it's called RPA, Robotic Process Automation. One of Citrix's partners and I guess now competitors in that space just received $560,000,000 in funding, in a single round, to enable artificial intelligence to do this. What I thought was interesting, is that Citrix didn't use the term bots, I think other than one time ... >> Lisa: That's right. ... on the stage. But these are essentially bots, that take redundant processes, automates them, to ultimately add value. I'm anxious to dive in and talk about how Citrix is taking stuff like, they mentioned Mainframe, AS/400 applications, integrating that in Salesforce without having this huge multi-million dollar project to re-write these core business applications and processes. So, you know it's a really exciting time in the industry Citrix has really stepped up in saying, you know what, we won't settle for just having a good business, and this application virtualization and network space, we're going to go all in. >> So one of the things I saw in Twitter this morning, is you and I are both tweeting during the keynote, which we just came from is you talked about PRA right away on Twitter and it's something that you heard instinctively with what they were saying. What are your thoughts as to why RPA as a term wasn't discussed? Did you think it's the type of audience that's here? Is it just not a term that resonates as well as AI and machine learning, which are buzz words at every event we go to? >> And I think a good portion of that is a mix. We're at a conference that's very IT-centric. Citrix is a you know, one of the core IT infrastructure vendors. So when you throw out a term like Robotic Process Automation you constantly, you instantly think, you know, gain of productivity from me or your level maybe, but from an IT infrastructure practitioner perspective, Robotic Processing Automation has a resonance with being equal to eliminating jobs. If, you know, you're going to automate the integration between VMware VSphere and Citrix desktop virtualization and that administration piece, which these solutions definitely can do that, what's left for me to do the work on. If you're going to automate the provisioning of DNS and IP addressing and all these mundane tasks that administrators probably spend 50-60% of their day doing, you know what, that's threatening. To say that you know what, we're going to give you the same tools that we give to make the workspace available today from an application perspective and to tackle that from the concept of this is just extending that ideal and you're a what, your job and what you do today to adding true business value, I think it was smart on their part to kind of avoid the bot conversation. >> Okay, I'm glad that you shared that insight, that makes perfect sense. So, PJ Hough was up there, the Chief Product Officer, who's going to be on tomorrow, talking about what Citrix is doing to distill apps and make this experience much more personalized. And of course he was joined on stage with a big Microsoft announcement today. I think I've been to so many shows this year I've lost count but I think Satya Nadella has either been on stage, he was at Dell Technologies World with Michael Dell and Pat Gelsinger, or in a video like he was today. So the partnership with Microsoft expanding here a little bit of a teaser at Microsoft Ignite a couple of months ago. Gimme your thoughts on what Microsoft, I should say what Citrix is doing to facilitate their users being much more proficient at using Microsoft Team, which I believe the gentleman from Microsoft said there's over 300,000 active users already. Fastest growing product in Microsoft's history. >> So when you talk about collaboration, you can't collaborate without these tools, whether Teams, Slack, whatever, it's become an integral part of how we communicate, how we interact, I know a lot of friends that I have are moving from Slack to Teams, just because of the integration with Office365 they can collaborate around, and I think here on theCUBE we talk about data as being the key. You have to talk about data. One of the things that was prepared to go kind of head on with Citrix today, and tomorrow about, was about data. You know it's great to present applications, but how are you helping to help users collaborate and use and access data and the combination of RPA with the intelligent experi- intelligent, it's going to take us some time to used to this ... >> I keep wanting to say enterprise. >> Yeah enterprise >> Intelligent experience >> Experience product, with Teams, with the Azure announcement, integration with Azure and full support of the Citrix platform inside Azure will just make the employee experience at least potentially seamless, a lot more seamless, I'm super excited about, you can't tell in my voice, I haven't gotten excited about Citrix in a long time. And this is the first time they've had theCUBE at Synergy since 2011, I think it was a great time to reignite that partnership, and this coverage is going to be an interesting two days. >> It is. So we talked about digital workspace, the other two areas of Citrix's business that you touched on a little bit, security and analytics. Let's talk about the security piece first as it relates to Microsoft Teams and Azure. SD-WAN is becoming more and more absolutely critical to ensure that because as people we are the number one threat vector in any organization. Not that we're all bad actors. >> Keith: Right. >> But because we need to get things done, as frictionless or seamless, as you said, as possible, and efficiently as possible. What did you hear today with respect to security, that might really make some of those IT folks take notice? >> Well, we want to work from any device. Like, I want to be able to, ideally if I say, you know what, I want to pick up a new Surface tablet, when I go to Atlanta I don't want to pack my iPad. I want to be able to pick that up, and work. If I go to a kiosk, I want to be able to, even if it's running Windows XP, I want to be able to do my work, I want to be able to do my work from any device. This is a nightmare for system administrators to say how do I control security, while making the experience frictionless? Those two things don't seem to go together. So Citrix, whether it's with this new announcement with Microsoft with Teams, it's traditional applications around SD-WAN, enabling access from remote locations, and Citrix is kind ... this is their bread and butter, offering remote access to applications securely and fast, this is you know, Citrix is starting to formulate a really great end to end story about making applications, data and more importantly, business answers and capability available anywhere securely, so it's a great story. >> It really is. So if you're excited, you already know how excited I am. I think we're going to have a fantastic day today, and tomorrow. We've got a whole bunch of the C-Suite from Citrix on, we're also going to be talking with some partners and customers, and interestingly as a marketer this peaked my interest as well, they have the innovation awards. There are three finalists, we will be talking with all three over the next two days, and this is a customer awards program, that anybody can vote on. So I haven't seen that before, so I'm excited to understand how Citrix is enabling them to have this great employee experience which is more and more critical as the shortages and the gaps are becoming more and more prevalence. And also, how these customers are reacting to just some of the news announced today, with Microsoft, the intelligent enterprise, and how they see their employees, and attracting and retaining top talent as actually really mission critical. So we're going to have fun Keith. >> I agree. >> All right, you're watching Keith Townsend and Lisa Martin live from theCUBE, we are on the show floor at Citrix Synergy 2019 from Atlanta, Georgia. Stick around, Keith and I will be right back with our first guest after a short break. (upbeat electronic music)

(upbeat music) >> Live from Las Vegas, it's theCUBE, covering AWS re:Invent 2018, brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Well, welcome back here, as we continue our coverage at AWS re:Invent, along with Justin Warren, I'm John Walls, we are live in Las Vegas in the Sands. Day one of our coverage here, three days, with you all week. We're with Jon Masters now, who's the chief architect at Red Hat. Jon, good to see you this afternoon. >> Thank you, nice to be here. >> First off, give me your impression of what you've seen so far on the show floor, what's the feeling you've got as you come in this week? Well, it's been absolutely fabulous for me. It's my first time at re:Invent, so I've not had chance to witness firsthand the growth over the few years, but I've heard stories that we're up to 75,000 people, some very high number this year, and the growth is absolutely amazing. Very, very passionate people, it's very clear that the story of containerization and microservices is foremost this year, and yeah, it's just a fabulous experience to be here. >> Great, now yesterday, there was announcement from AWS about A1 instance, tell me a little bit about how that comes to play in a Red Hat and just your take on the release. >> Yeah, so Amazon did announce yesterday the new A1 instance type, and it's based on the Arm architecture, I think the interesting thing for me is that it's based on a processor that they themselves built called the Graviton. You know, this is really the culmination of what we've seen in the industry in the past few years. As the cloud vendors get bigger and have greater resources and greater capabilities, what they can do is they can take that self-determination aspect, and they can say, you know what, we're now big enough, and we now understand, and we're sophisticated enough that we can say we would like to deliver this to our customers, and we don't have to wait for someone to build it for us, we can just go and do it. And so what they did is they licensed an Arm design from Arm Holdings, the actual core inside the processor, and then they built the chip themselves, and contracted out to a foundry, manufactured and deployed these, and then, you know, they can snap their fingers and deploy these and, surprise, now we have Arm-based instances, so it's been very interesting. >> So I'm curious, 'cause we keep getting told that software is leading the world, and yet here we are, building hardware and customized hardware. So what is it about the Arm architecture in particular, but also the fact that you can build custom silicone, what is it that Amazon, or indeed any other cloud vendor, what benefit do they get from manufacturing their own silicone here? >> That's a very good question. Well, I think there's multiple aspects to it. At the end of the day, people tell me that the future is serverless, and I remind them that there's still servers somewhere, right? So we still need to have computers. Of course, we're going to have a smaller number of very big vendors on which we rely, I mean, we're seeing that with the adoption of public cloud, and as these vendors get bigger, they have that scale that they can invest what, for them, is a modest amount of money, for anybody else, it'd be a fortune, but a modest amount, and they can go and build a design. Now, with a traditional microprocessor design, you'd take a team of four people, and you would spend many hundreds of millions of dollars, maybe 300 million dollars over four years, to build a high-performance processor. What you can do with Arm is work with Arm Holdings, which is now a part of SoftBank, to license kind of cookie-cutter pre-made pieces, so you can license a processor core, and you can stamp it out and say, well, I'll have 16 of those in my chip. So you don't have to do the heavy lifting to design many of the building blocks, but you can integrate them together, so you get a lot of cost-efficiency there, you don't have to go and do all that design, but you can integrate building blocks. And the key piece there, I think, is the ability to choose how you want to integrate that and what you want to build. Right? And then, what we're seeing in the industry is that compute is becoming boring, right? I mean, everyone needs compute, but what are we talking about? We're talking about machine learning and GPUs and tensors and all kinds of other accelerators, right? So, the interesting thing for me is, once you've made the compute kind of so commodity that you can just license it from somebody and stamp out your own design, what opportunity does that bring later to maybe integrate various accelerators and other hardware goodies? I don't know what Amazon plan to do, but if I had a crystal ball, I would say this is probably not the end. This is kind of the beginning of a journey, and now they will have the ability to integrate some very interesting and novel hardware advances of their own as well. Okay, 'cause that does sort of lead into what my next question going to be. Which is, for a customer of Amazon, it's like, well, I don't know anything about the internals of chip design, why would I want to choose the A1 instance type over one of the other existing instance types? What's in it for me? >> Yeah, very good question. I think when Amazon announced it last night, the top line that the media picked up on first was the price benefit there, which was advertised as being 40% lower for certain workloads. Now the design that they've chosen today is not about having that top-shelf performance, that top-line performance. If you want that level of performance, clearly you're going to use one of the existing instance types. But if you want to have something that is more cost-effective for at-scale deployments, maybe where you're not using all the compute resources that you need, you're more memory-bound, or you're doing web app-serving, this kind of thing, in that case, you don't really need that level of compute. You still need the instances, and so this brings your cost down when you're doing that at-scale kind of deployment. And that seems to be where they're targeting. And in addition, they're targeting, I think, developers, and those that want to invest in the Arm ecosystem, because clearly this is the beginning of a journey, I don't know exactly where they'll go next, but one could imagine that it will continue from here. >> Okay, now you are an Arm fan. >> I am. >> But we don't actually work for Arm, you work for Red Hat, so what's the Red Hat angle here? >> Well, so I'll tell you a story. >> Okay, I like stories. (men laughing) >> Me too, so back at the end of-- >> I like stories too, Jon, go ahead. >> Well, I'll spare you the long form. The end of 2010, I was in one of my execs' offices, and I've been with Red Hat since 2006, and I had done a couple of things before that that kind of were very useful for the company but kind of dull, so they said, "All right, you choose something exciting to work on next," right? So I held up a BeagleBoard, which is a bit like a raspberry pie, and I told one of my execs, "This will be a server one day." And I walked through Moore's law and the pace of innovation and fast-forwarded and say, if these things were to happen, this technology would be in a server. Now why is that relevant to Red Hat? Well, if you look at it from Red Hat's point of view, we don't pick winners and losers, what we do is we work with customers and what they want to adopt, but we also need to be able to respond to our customers' needs, so kind of the concern was, this Arm thing looks like it could be interesting in a few years' time, what if it is? And if it is interesting, and it's kind of a zoo, as I used to call it, a free-for-all, you know, it's kind of an embedded mess, that works fine, well "fine" in quotes, if you're building cell phone widgets and so on, because it's kind of a different ecosystem there, but if you want to have a mainstream server play, we had to have a few of us in industry come in and say, all right, this looks interesting, but let's make sure that the level of standardization is there, so that if this does take off, standard operating systems and standard software can run on it, that's why we cared, was just in case it takes off. And then fans like me, of course, want to kind of promote it as well, but I think that's why Red Hat cared. >> You know, and this is kind of off-topic, but I'm just curious, because you've talked about the acceleration of change, you've talked about innovation, you've talked about new wrinkles, and Moore's law, is it possible, or do you see that the acceleration of change is so rapid that we're almost outpacing ourselves in a way? And that change is happening so dramatically and so quickly that to make a decision on a particular solution or service is difficult because you're afraid of missing the next flavor in eight months or nine months, instead of three or five years? >> That's right, and I think there's another piece there where the cloud makes even more sense, doesn't it? Because if you are a customer, or an end-user, and you're deploying an app, you could say, well, this Arm thing could be interesting, I don't know, I don't want to go and build out physical infrastructure and go and pay that tax to go and figure this out, what I want to do is I just want to try it out right now. And the fabulous thing that Amazon did yesterday, that no one had done, you know, there'd been some efforts out there to provide Arm to the mainstream, right? But Amazon put a giant rubber stamp on it and said, this is good enough for us, and it works. Now anyone who's used to a workflow in EC2, they can just use exactly the same flow to spin up one of these instances and try it out. It's a 30-second thing, just try it out, see what you think. If you like it, great, if you don't, then don't use it. And because you are able to just consume it, according to whatever you want, you don't have that commitment either, yeah. >> So a test drive? >> You can test drive it, if it works well, you can adopt it. There's no obligation, and that's, I think, key to exploring new technologies as well. >> Yeah, it does require you to have that software layer on top of it that runs, we were talking before, that Red Hat has invested a lot to actually get the Red Hat software suite to run on Arm. >> That's right. >> So I'm sure that with this announcement, there's going to be a whole lot of other people suddenly discovering how to compile to the Arm architecture. (Jon laughs) That'll be fun. >> That's right, we've invested for the last eight years in this, and what we have now is a strategy we call our multi-architecture strategy. So again, we don't pick winners and losers, we have all these different architectures that we support, obviously x86, also Power, and Mainframe, and now Arm, and all these architectures are treated equally going forward, so in RHEL 8, which we just announced the beta of RHEL 8, you'll see all these architectures treated just the same. And so the rule for our developers is, whenever they make a change, it has to run on all the architectures equally. >> Democratize it, and then make it so that it is standard across the board. >> That's right. >> Makes sense. Jon, thanks for the time. >> Oh, absolutely. >> Good to see you here at re:Invent, and wish you all the success down the road. >> Thank you very much. >> You bet. Jon Masters joining us from Red Hat. Back with more, we are here at AWS re:Invent, we're live in Las Vegas, and Justin and I'll be back in just a moment.

>> Announcer: Live from New Orleans, Louisiana, it's theCUBE covering .NEXT conference, 2018. Brought to you by Nutanix. >> Welcome back I'm Stu Miniman with my co-host Keith Townsend, and you're watching theCUBE, the leader in live tech coverage. We're at Nutanix NEXT 2018, happy to welcome to the program Brian Stevens, who's the CTO of Google Cloud, had on the program many times. Brian, always a pleasure to catch up with you. >> Thanks, glad to be here. >> Stu: And have a first time guest, Ricardo Jenez, who's the SVP of Development at Nutanix. Thank you so much for joining us. >> Well, thank you for being, thanks for being here. >> Alright, so Ricardo you've only been with Nutanix for three months. I believe this is probably your first .NEXT? >> Ricardo: Yes, it is. >> So give us a little bit about your role and what brings you to us today. >> So I'm responsible for some of the core data path and per some products. So, you know, a lot of it has to do with how do we end up delivering value to our customers and actually end up having predictable, scalable HCI solutions. So, that's really what I'm focused on and focusing on sort of improving our ability to deliver products more quickly. >> So Brian, last year Diane Greene was up on stage talking about the partnership and what was happening here, see Google at the show, obviously a tighter partnership for Nutanix, but give us the update on-- >> We're downgrading. >> Yeah? >> Slumming it. >> Not at all. Not at all. I wish we had enough time to get into the weeds on some of the stuff you're working on, but tell us what brings you here and what kind of stuff you're poking at these days. >> Geez, I think I met Sunil Potti a couple years ago, just at the very beginning of trying to find sort of the intersection between Google Cloud and Nutanix. I mean, Nutanix is largely redefining what IT looks like on premise. We believe we're doing that in cloud, and you really just want to eliminate the impedance between on-premise and public cloud, and so the work with Nutanix is all like what can we do to actually make it more seamless for users that want to use core cloud technology. >> Yeah Brian, you're one of those people that we would say have enterprise DNA in what they've done in their background. People on the outside will always say, Well you know, it's Google, it's Google-y. It's too smart for us. >> Brian: Enterprise DNA is still sexy. >> Yeah, I mean look, there's a lot of enterprises out there, and while yes, the other startups. Maybe we talk a little bit about what that means inside Google. >> Oh my gosh, yeah it was quite a pivot for Google, you know. It was amazing technology, but the customer that you were serving with Google Cloud was already inside of Google. You were serving Surge and YouTube and ads. So you end being up, a really technically, but close relationship. And so what enterprise is, a couple things, it's been a cultural transformation inside of Google, it's been obviously working with enterprise customers globally and building that go to market model and motion that you can sell, but we want a really technical engineered partnership with our customers. So it's not a vendor relationship. So building all that out, we thing we're unique with that. And then the other part I think you were alluding to early before we went on mic, was just around enterprise has a increased set of requirements on what we deliver them from a capabilities perspective, from a security aspect, from a telemetry aspect. And then it's all like how do we actually slipstream into their process, rather than just redefine everything. So to us, that's a big part of what our enterprise pivot's been, for the last three, four years. >> So Ricardo, you have some background at Google. What brought you to Nutanix? What appealed to you? >> Well you know, more than anything else, I think Nutanix has set themselves up, to basically take that experience it has in enterprise, and translate that into the cloud. So when I was at Google, I actually worked on the Google search appliance, which was Google's first-- >> I remember that. >> You remember that. >> I had that one. >> Little yellow boxes, sometimes blue boxes, and that was a great experience. So I'm really happy to hear that Brain talk about the transformation that has happened within Google. But you know, being at Nutanix, the ability to take that experience very close with the work loads that customers are running, and then being able to work with a partner like Google and actually be able to have hybrid clouds where internal private cloud plus having public cloud providers, that really ends up changing the game for a lot of enterprises. >> Yep. Brain, One of the things we've been struggling with as an industry is, you know, it's application mobility. Data, where it goes. Nutanix has been talking about really hybrid cloud from their standpoint. We've talked with you before about where Kubernetes fits into this. Application portability, you just made an acquisition, today was announced, Velostrata. Give us your state on where those things added, it's a big gnarly topic. >> It was just more friction, like public cloud offers great capability that's going to be used not necessarily completely instead of, but in companion to, you know, application services. But there was still that friction around in the early incarnation, it was like it's VMware in this environment or KVM in this environment, and it's a whole nother AMI kind of model here. So the ability to use it, there was a tax. And then there also wasn't that portability and that lightweight aspect that you'd want from an application containerization. I mean, you want what you have on your phone. You want that ability to install apps anywhere. And cloud and IT infrastructure should be exactly the same way. So that's a big part of our investment in containerization. You know Google, back when I was at Red Hat, was investing in cgroups back when there was a kernel, way back then to kind of build that first incarnation of containers in Linux. Along comes Docker to standardize that. I mean, it's an amazing gift to the world. And then Kubernetes is, we're just moving up the stack, on how do you orchestrate it. So sure, companies like Velostrata are really interesting because you have, you know, beyond having Kubernetes platform everywhere, yeah we'll say it's the de facto, but that doesn't mean everybody's running it. And so you're still running on existing systems, you know, largely kind of virtualized. And Velostrata is a technology leader in being virtualization of this type to Google Cloud or other clouds. And then even more so, the technology they have to bring that to containers. So they help you do that migration, transformation process. And I think that's really important for IT organizations. >> Ricardo, you want to comment on some of the hybrid cloud migration stuff? >> So we have our com product, which allows us to actually end up taking workload and moving it to, for instance, Google Cloud or eventually sciCloud and then moving that workload back. So having that sort of Nutanix inside and outside gives it maximum flexibility, and that's a lot of power for IT to have, right? Deciding where it's best to actually run their workloads and be as efficient as possible. >> So as we look at the com, we look at Google Cloud, just the overall pictures, if you're enterprise, you're looking at Google and you're saying man, Google runs at two different speeds. One is 12 factor, micro services, Kubernetes, functions. And then the other side is that, some people just want a VM. They just want a cloud instance and how to make that simple. So let's talk about this relationship. How does Nutanix come together with Google who runs at two different speeds, to make Google Cloud more consumable to the average enterprise? >> Well we're going to talk a little bit more about it later, but the fact that basically we're going to be able to deploy Xi within Google Cloud with nested AHV, and then allow our customers, that'll basically be doing standard workloads to migrate their jobs over to a Google Cloud offering. And as Brian will point out, that basically creates opportunities for them to be able to avail themselves of other capabilities that Google has. So it's not altogether an instant moving path to rewrite, reorient all your apps. It's an ability to kind of do that school migration, if you want to. But you have that capability of being able to go back and forth, in terms of what your workloads are. >> Yeah. >> Brian, want to get your viewpoint on just some of the changing roles that are happening in our industry. We were talking that some of the interviews we've been doing today, it's people talking about infrastructure and code. There was a big hackathon at this event for the first time in, they sold out with over 14 groups, and everything like that. This is a show that started out with people talking about storage, and now we're talking about individual data centers and clouds and all of those things. What are you seeing out in the marketplace? What are some of the challenges and opportunities you're hearing from customers these days? >> I mean it depends on which customers, right? Which region of the world and what their business looks like and I think we all know the holy grail. Infrastructure, as code, is an implementation, but I think what we know that what you really desire is the ability for reproduce ability. The ability to sort of not have state in the IT process. You want to be able to recreate things anywhere. Recreate a whole application, blueprint internally, on public cloud. Tear it down, recreate it. There's no other way to do that without code. So what sort of comes from that SRE model that Google invented, is that what it you didn't have an IT department? And what if you had software engineers that were responsible for IT function? What would that look like? And that's where all of the sudden you realize, everything's APIs and code. So I think that's interesting, and that's sort of where you want to get to, but it's then like, how do you bridge that because a lot of people aren't software developers in IT departments. >> So here's my follow up, 'cause when I go to the Kubernetes show and I talk to users there, 95% of them-- >> They're way over there. >> Had built their own stack, and why do they do that? Because they were ahead of all the platforms. And then I come to the Nutanix show and they're like oh, tensorflow and functions and all that stuff. We're going to put an easy button, and make it easy. I need to take all of these tools and open source and put it together, versus the platform and the easy button. Is this just the early adopters and the majority? >> I think that's okay. That's the open source world, right? I mean think about what's great about open source, is not just creating sort of a venue for collaboration and developers, it's creating access for end users. And so some of the best companies in the world have been built on a DIY model of people just taking open source and integrating it and making the recipe that they want. And so I think you get that whole sort of spectrum and you aren't forced down this model of, here's a COTS product, oh and it happens to be based on open source, but you always have to use technology this way. Open source gives them the freedom to do it as they want. We just need to make sure that we bridge it, so that there's not anybody left behind. That everybody should be able to use the power of Kubernetes, and that means making things super easy to use, and the integration with Nutanix we think is a huge part of making you use that technology stack in a way that's seamlessly operated for an audience like this. >> So a lot of the debate and questions around Kubernetes is how far should it go? Should it go as far as being an opinionated pass? Should it just be a container platform? Where does it start and end? >> Brian: You want my opinion? >> Yeah, opinion that would be awesome. >> Yeah, that was it. Well I think the way the industry started was obviously, there were no PASes, and then we built OpenShift to Red Hat and Google app engine in Roku. And what happened is, those are interesting, but you're right, they are overly opinionated. So you were left either picking a PAS, and you got to change everything to do it this way, and it's great because it delivers value of managed service, but not everything fit in that model. Or you got next to nothing. >> Keith: Right. >> You got a straight IS platform, and then you got to do all the rest. So what we've been doing at Google is tearing that apart and building that architecture from the ground up where you opt into the level that you want. If you want to be able to use IS and the features of IS you use that. If you want to step up and just use containers and IS, you can use containers and IS. If you want to step up and use Kubernetes orchestration, you can do that. If you want to step up and run managing everything in services, than that stacks on top of Kubernetes with STO. If you want to be full on and put in a developer workflow that always has you do deploys this way, then that stacks on top. So I think you're going to get away from this false dichotomy of a choice over here or here, and you're going to all of a sudden get this architectural layering cake that lets you opt into what you want and have IT consistency all the way through it. >> I mean, I used to have a startup that was focused on Hatuputu service, and you know one of the things was basically you didn't have this layering, right? It was, you take the whole stack or you take nothing. And I think the strategy that Google has employed with Kubernetes is just brilliant, to kind of work you way up and basically get people at different levels to be involved. You know, there is a do-it-yourself folks, and they should be allowed to and empowered to do the things that they want to do. And then there are other people who want to have more composed environment. And so we can actually bring that to them as well. And I think that's brilliant. Basically very early on, while Google used a lot of open source internally, it wasn't a strong sort of part of the open source environment. And so I've just enjoyed watching the evolution of Google, sort of leading the open source movement. So, it's been fantastic. I'm right there with you, you know, give them at every level. >> Ricardo, one of the questions coming into this week, people want to know the update of what's happening with Xi. Can you speak about where we are with that and the relationship with Google? What should we be looking for for the rest of this year? >> Well I can't really talk about that, but you know, we are working very closely with Google. And we'll talk a little bit about that at our talk later today. But I won't comment on anything to do with Xi. >> So that gives me the opportunity to ask another controversial question about Kubernetes and getting both of your opinions on it. There's religions and open sourced as religions, enterprise IT, one of which is DevOps. And you look at what companies like Netflix have done with containerizing Java applications and running those legacy Java applications in their container platform. Enterprises are looking at that stuff and thinking, you know what, can I containerize my monolithic application, put it on top of Kubernetes, and drive more efficiency out of my operations from portability to being able to stack up applications in public cloud, general things. Monolithic applications, is that a good thing, bad thing, indifferent? Wrong plate, wrong tool, wrong-- >> No, I think it's just that there's no like one size even for what a monolithic app looks like. Like we don't really have a really proper definition of what it is, but I think people do feel that all of a sudden Kubernetes needs a rewrite and containers needs a rewrite, and actually it doesn't. Because apps are usually sort of separated from the OS already. And so what they're doing is marrying the libraries of the OS, and containers allows them to do that, but just get a higher degree of portability and then with Kubernetes orchestration. So it really depends more around what's the machine resources that that monolithic app needs and are those machine resources still available in a containerized environment. In most cases, the answer is yes. Now the most interesting thing is, what's the escape hatch? Because you can't have a monolithic app that your company, say it's on Mainframe, say it's in the case of something that will not containerize and shouldn't because it's working as designed and there's no use touching it. But that should still participate in the application architecture of the future. And that's why SEO and services are so important. So even if you can't change your runtime stack, you still need to be able to put a services layer in an API in front of that monolithic service, and you'll have a visibility of a service mesh inside of that environment. So now IT sees it just looks like a black box IT service. It doesn't really matter to them that it's not running on the next generation stack because they can still depend on its' services. >> Yeah, I mean I would agree. I look at what Kubernetes offers and containers as sort of an on ramp to creating services, the on ramp to actually taking that monolithic application, assuming that they're resources, and take a step up in terms of the architectures that you can build around it and then be able to break apart that monolithic application. It doesn't have to happen all at once. It's sort of the stepping stones that you can take. So it's a very powerful model for enablement for people who have stuff that they haven't been able to make the most value out of because maybe the application's been around for a while. Now they can actually end up putting it in an environment where they can actually make the most of it and then work on how they're going to end up slowly pulling it apart and making it more service oriented. >> Alright, Ricardo and Brian, thank you so much for joining us. Appreciate the update and look forward to seeing more throughout the show and further in the year. Be sure to check out theCUBE.net where you'll not only find all of this information, but theCUBE is really excited to say that we will be at the Google Cloud show in July. So for Keith Townsend, and I'm Stu Miniman, getting towards the end of day one of two days of live coverage. Thanks so much for watching theCUBE. (upbeat music)

(techno music) >> Announcer: Live, from downtown San Francisco, it's theCUBE. Covering IBM Chief Data Officer Strategy Summit 2018. Brought to you by IBM. >> Welcome back to San Francisco everybody, this is theCUBE, the leader in live tech coverage. We're here at the Parc 55 in San Francisco covering the IBM CDO Strategy Summit. I'm here with Joel Horwitz who's the Vice President of Digital Partnerships & Offerings at IBM. Good to see you again Joel. >> Thanks, great to be here, thanks for having me. >> So I was just, you're very welcome- It was just, let's see, was it last month, at Think? >> Yeah, it's hard to keep track, right. >> And we were talking about your new role- >> It's been a busy year. >> the importance of partnerships. One of the things I want to, well let's talk about your role, but I really want to get into, it's innovation. And we talked about this at Think, because it's so critical, in my opinion anyway, that you can attract partnerships, innovation partnerships, startups, established companies, et cetera. >> Joel: Yeah. >> To really help drive that innovation, it takes a team of people, IBM can't do it on its own. >> Yeah, I mean look, IBM is the leader in innovation, as we all know. We're the market leader for patents, that we put out each year, and how you get that technology in the hands of the real innovators, the developers, the longtail ISVs, our partners out there, that's the challenging part at times, and so what we've been up to is really looking at how we make it easier for partners to partner with IBM. How we make it easier for developers to work with IBM. So we have a number of areas that we've been adding, so for example, we've added a whole IBM Code portal, so if you go to developer.ibm.com/code you can actually see hundreds of code patterns that we've created to help really any client, any partner, get started using IBM's technology, and to innovate. >> Yeah, and that's critical, I mean you're right, because to me innovation is a combination of invention, which is what you guys do really, and then it's adoption, which is what your customers are all about. You come from the data science world. We're here at the Chief Data Officer Summit, what's the intersection between data science and CDOs? What are you seeing there? >> Yeah, so when I was here last, it was about two years ago in 2015, actually, maybe three years ago, man, time flies when you're having fun. >> Dave: Yeah, the Spark Summit- >> Yeah Spark Technology Center and the Spark Summit, and we were here, I was here at the Chief Data Officer Summit. And it was great, and at that time, I think a lot of the conversation was really not that different than what I'm seeing today. Which is, how do you manage all of your data assets? I think a big part of doing good data science, which is my kind of background, is really having a good understanding of what your data governance is, what your data catalog is, so, you know we introduced the Watson Studio at Think, and actually, what's nice about that, is it brings a lot of this together. So if you look in the market, in the data market, today, you know we used to segment it by a few things, like data gravity, data movement, data science, and data governance. And those are kind of the four themes that I continue to see. And so outside of IBM, I would contend that those are relatively separate kind of tools that are disconnected, in fact Dinesh Nirmal, who's our engineer on the analytic side, Head of Development there, he wrote a great blog just recently, about how you can have some great machine learning, you have some great data, but if you can't operationalize that, then really you can't put it to use. And so it's funny to me because we've been focused on this challenge, and IBM is making the right steps, in my, I'm obviously biased, but we're making some great strides toward unifying the, this tool chain. Which is data management, to data science, to operationalizing, you know, machine learning. So that's what we're starting to see with Watson Studio. >> Well, I always push Dinesh on this and like okay, you've got a collection of tools, but are you bringing those together? And he flat-out says no, we developed this, a lot of this from scratch. Yes, we bring in the best of the knowledge that we have there, but we're not trying to just cobble together a bunch of disparate tools with a UI layer. >> Right, right. >> It's really a fundamental foundation that you're trying to build. >> Well, what's really interesting about that, that piece, is that yeah, I think a lot of folks have cobbled together a UI layer, so we formed a partnership, coming back to the partnership view, with a company called Lightbend, who's based here in San Francisco, as well as in Europe, and the reason why we did that, wasn't just because of the fact that Reactive development, if you're not familiar with Reactive, it's essentially Scala, Akka, Play, this whole framework, that basically allows developers to write once, and it kind of scales up with demand. In fact, Verizon actually used our platform with Lightbend to launch the iPhone 10. And they show dramatic improvements. Now what's exciting about Lightbend, is the fact that application developers are developing with Reactive, but if you turn around, you'll also now be able to operationalize models with Reactive as well. Because it's basically a single platform to move between these two worlds. So what we've continued to see is data science kind of separate from the application world. Really kind of, AI and cloud as different universes. The reality is that for any enterprise, or any company, to really innovate, you have to find a way to bring those two worlds together, to get the most use out of it. >> Fourier always says "Data is the new development kit". He said this I think five or six years ago, and it's barely becoming true. You guys have tried to make an attempt, and have done a pretty good job, of trying to bring those worlds together in a single platform, what do you call it? The Watson Data Platform? >> Yeah, Watson Data Platform, now Watson Studio, and I think the other, so one side of it is, us trying to, not really trying, but us actually bringing together these disparate systems. I mean we are kind of a systems company, we're IT. But not only that, but bringing our trained algorithms, and our trained models to the developers. So for example, we also did a partnership with Unity, at the end of last year, that's now just reaching some pretty good growth, in terms of bringing the Watson SDK to game developers on the Unity platform. So again, it's this idea of bringing the game developer, the application developer, in closer contact with these trained models, and these trained algorithms. And that's where you're seeing incredible things happen. So for example, Star Trek Bridge Crew, which I don't know how many Trekkies we have here at the CDO Summit. >> A few over here probably. >> Yeah, a couple? They're using our SDK in Unity, to basically allow a gamer to use voice commands through the headset, through a VR headset, to talk to other players in the virtual game. So we're going to see more, I can't really disclose too much what we're doing there, but there's some cool stuff coming out of that partnership. >> Real immersive experience driving a lot of data. Now you're part of the Digital Business Group. I like the term digital business, because we talk about it all the time. Digital business, what's the difference between a digital business and a business? What's the, how they use data. >> Joel: Yeah. >> You're a data person, what does that mean? That you're part of the Digital Business Group? Is that an internal facing thing? An external facing thing? Both? >> It's really both. So our Chief Digital Officer, Bob Lord, he has a presentation that he'll give, where he starts out, and he goes, when I tell people I'm the Chief Digital Officer they usually think I just manage the website. You know, if I tell people I'm a Chief Data Officer, it means I manage our data, in governance over here. The reality is that I think these Chief Digital Officer, Chief Data Officer, they're really responsible for business transformation. And so, if you actually look at what we're doing, I think on both sides is we're using data, we're using marketing technology, martech, like Optimizely, like Segment, like some of these great partners of ours, to really look at how we can quickly A/B test, get user feedback, to look at how we actually test different offerings and market. And so really what we're doing is we're setting up a testing platform, to bring not only our traditional offers to market, like DB2, Mainframe, et cetera, but also bring new offers to market, like blockchain, and quantum, and others, and actually figure out how we get better product-market fit. What actually, one thing, one story that comes to mind, is if you've seen the movie Hidden Figures- >> Oh yeah. >> There's this scene where Kevin Costner, I know this is going to look not great for IBM, but I'm going to say it anyways, which is Kevin Costner has like a sledgehammer, and he's like trying to break down the wall to get the mainframe in the room. That's what it feels like sometimes, 'cause we create the best technology, but we forget sometimes about the last mile. You know like, we got to break down the wall. >> Where am I going to put it? >> You know, to get it in the room! So, honestly I think that's a lot of what we're doing. We're bridging that last mile, between these different audiences. So between developers, between ISVs, between commercial buyers. Like how do we actually make this technology, not just accessible to large enterprise, which are our main clients, but also to the other ecosystems, and other audiences out there. >> Well so that's interesting Joel, because as a potential partner of IBM, they want, obviously your go-to-market, your massive company, and great distribution channel. But at the same time, you want more than that. You know you want to have a closer, IBM always focuses on partnerships that have intrinsic value. So you talked about offerings, you talked about quantum, blockchain, off-camera talking about cloud containers. >> Joel: Yeah. >> I'd say cloud and containers may be a little closer than those others, but those others are going to take a lot of market development. So what are the offerings that you guys are bringing? How do they get into the hands of your partners? >> I mean, the commonality with all of these, all the emerging offerings, if you ask me, is the distributed nature of the offering. So if you look at blockchain, it's a distributed ledger. It's a distributed transaction chain that's secure. If you look at data, really and we can hark back to say, Hadoop, right before object storage, it's distributed storage, so it's not just storing on your hard drive locally, it's storing on a distributed network of servers that are all over the world and data centers. If you look at cloud, and containers, what you're really doing is not running your application on an individual server that can go down. You're using containers because you want to distribute that application over a large network of servers, so that if one server goes down, you're not going to be hosed. And so I think the fundamental shift that you're seeing is this distributed nature, which in essence is cloud. So I think cloud is just kind of a synonym, in my opinion, for distributed nature of our business. >> That's interesting and that brings up, you're right, cloud and Big Data/Hadoop, we don't talk about Hadoop much anymore, but it kind of got it all started, with that notion of leave the data where it is. And it's the same thing with cloud. You can't just stuff your business into the public cloud. You got to bring the cloud to your data. >> Joel: That's right. >> But that brings up a whole new set of challenges, which obviously, you're in a position just to help solve. Performance, latency, physics come into play. >> Physics is a rough one. It's kind of hard to avoid that one. >> I hear your best people are working on it though. Some other partnerships that you want to sort of, elucidate. >> Yeah, no, I mean we have some really great, so I think the key kind of partnership, I would say area, that I would allude to is, one of the things, and you kind of referenced this, is a lot of our partners, big or small, want to work with our top clients. So they want to work with our top banking clients. They want, 'cause these are, if you look at for example, MaRisk and what we're doing with them around blockchain, and frankly, talk about innovation, they're innovating containers for real, not virtual containers- >> And that's a joint venture right? >> Yeah, it is, and so it's exciting because, what we're bringing to market is, I also lead our startup programs, called the Global Entrepreneurship Program, and so what I'm focused on doing, and you'll probably see more to come this quarter, is how do we actually bridge that end-to-end? How do you, if you're startup or a small business, ultimately reach that kind of global business partner level? And so kind of bridging that, that end-to-end. So we're starting to bring out a number of different incentives for partners, like co-marketing, so I'll help startups when they're early, figure out product-market fit. We'll give you free credits to use our innovative technology, and we'll also bring you into a number of clients, to basically help you not burn all of your cash on creating your own marketing channel. God knows I did that when I was at a start-up. So I think we're doing a lot to kind of bridge that end-to-end, and help any partner kind of come in, and then grow with IBM. I think that's where we're headed. >> I think that's a critical part of your job. Because I mean, obviously IBM is known for its Global 2000, big enterprise presence, but startups, again, fuel that innovation fire. So being able to attract them, which you're proving you can, providing whatever it is, access, early access to cloud services, or like you say, these other offerings that you're producing, in addition to that go-to-market, 'cause it's funny, we always talk about how efficient, capital efficient, software is, but then you have these companies raising hundreds of millions of dollars, why? Because they got to do promotion, marketing, sales, you know, go-to-market. >> Yeah, it's really expensive. I mean, you look at most startups, like their biggest ticket item is usually marketing and sales. And building channels, and so yeah, if you're, you know we're talking to a number of partners who want to work with us because of the fact that, it's not just like, the direct kind of channel, it's also, as you kind of mentioned, there's other challenges that you have to overcome when you're working with a larger company. for example, security is a big one, GDPR compliance now, is a big one, and just making sure that things don't fall over, is a big one. And so a lot of partners work with us because ultimately, a number of the decision makers in these larger enterprises are going, well, I trust IBM, and if IBM says you're good, then I believe you. And so that's where we're kind of starting to pull partners in, and pull an ecosystem towards us. Because of the fact that we can take them through that level of certification. So we have a number of free online courses. So if you go to partners, excuse me, ibm.com/partners/learn there's a number of blockchain courses that you can learn today, and will actually give you a digital certificate, that's actually certified on our own blockchain, which we're actually a first of a kind to do that, which I think is pretty slick, and it's accredited at some of the universities. So I think that's where people are looking to IBM, and other leaders in this industry, is to help them become experts in their, in this technology, and especially in this emerging technology. >> I love that blockchain actually, because it's such a growing, and interesting, and innovative field. But it needs players like IBM, that can bring credibility, enterprise-grade, whether it's security, or just, as I say, credibility. 'Cause you know, this is, so much of negative connotations associated with blockchain and crypto, but companies like IBM coming to the table, enterprise companies, and building that ecosystem out is in my view, crucial. >> Yeah, no, it takes a village. I mean, there's a lot of folks, I mean that's a big reason why I came to IBM, three, four years ago, was because when I was in start-up land, I used to work for H20, I worked for Alpine Data Labs, Datameer, back in the Hadoop days, and what I realized was that, it's an opportunity cost. So you can't really drive true global innovation, transformation, in some of these bigger companies because there's only so much that you can really kind of bite off. And so you know at IBM it's been a really rewarding experience because we have done things like for example, we partnered with Girls Who Code, Treehouse, Udacity. So there's a number of early educators that we've partnered with, to bring code to, to bring technology to, that frankly, would never have access to some of this stuff. Some of this technology, if we didn't form these alliances, and if we didn't join these partnerships. So I'm very excited about the future of IBM, and I'm very excited about the future of what our partners are doing with IBM, because, geez, you know the cloud, and everything that we're doing to make this accessible, is bar none, I mean, it's great. >> I can tell you're excited. You know, spring in your step. Always a lot of energy Joel, really appreciate you coming onto theCUBE. >> Joel: My pleasure. >> Great to see you again. >> Yeah, thanks Dave. >> You're welcome. Alright keep it right there, everybody. We'll be back. We're at the IBM CDO Strategy Summit in San Francisco. You're watching theCUBE. (techno music) (touch-tone phone beeps)

>> Announcer: Live from San Jose, it's theCUBE. Presenting data, Silicon Valley brought to you by Silicon Angle Media and it's ecosystem partners. >> Welcome back to theCUBE. We are live in San Jose at our event, Big Data SV. I'm Lisa Martin, my co-host is George Gilbert and we are down the street from the Strata Data Conference. We are at a really cool venue: Forager Eatery Tasting Room. Come down and join us, hang out with us, we've got a cocktail par-tay tonight. We also have an interesting briefing from our analysts on big data trends tomorrow morning. I want to welcome back to theCUBE now one of our CUBE VIP's and alumna Tendu Yogurtcu, the CTO at Syncsort, welcome back. >> Thank you. Hello Lisa, hi George, pleasure to be here. >> Yeah, it's our pleasure to have you back. So, what's going on at Syncsort, what are some of the big trends as CTO that you're seeing? >> In terms of the big trends that we are seeing, and Syncsort has grown a lot in the last 12 months, we actually doubled our revenue, it has been really an successful and organic growth path, and we have more than 7,000 customers now, so it's a great pool of customers that we are able to talk and see the trends and how they are trying to adapt to the digital disruption and make data as part of their core strategy. So data is no longer an enabler, and in all of the enterprise we are seeing data becoming the core strategy. This reflects in the four mega trends, they are all connected to enable business as well as operational analytics. Cloud is one, definitely. We are seeing more and more cloud adoption, even our financial services healthcare and banking customers are now, they have a couple of clusters running in the cloud, in public cloud, multiple workloads, hybrid seems to be the new standard, and it comes with also challenges. IT governance as well as date governance is a major challenge, and also scoping and planning for the workloads in the cloud continues to be a challenge, as well. Our general strategy for all of the product portfolio is to have our products following design wants and deploy any of our strategy. So whether it's a standalone environment on Linux or running on Hadoop or Spark, or running on Premise or in the Cloud, regardless of the Cloud provider, we are enabling the same education with no changes to run all of these environments, including hybrid. Then we are seeing the streaming trend, with the connected devices with the digital disruption and so much data being generated, being able to stream and process data on the age, with the Internet of things, and in order to address the use cases that Syncsort is focused on, we are really providing more on the Change Data Capture and near real-time and real-time data replication to the next generation analytics environments and big data environments. We launched last year our Change Data Capture, CDC, product offering with data integration, and we continue to strengthen that vision merger we had data replication, real-time data replication capabilities, and we are now seeing even Kafka database becoming a consumer of this data. Not just keeping the data lane fresh, but really publishing the changes from multiple, diverse set of sources and publishing into a Kafka database and making it available for applications and analytics in the data pipeline. So the third trend we are seeing is around data science, and if you noticed this morning's keynote was all about machine learning, artificial intelligence, deep learning, how to we make use of data science. And it was very interesting for me because we see everyone talking about the challenge of how do you prepare the data and how do you deliver the the trusted data for machine learning and artificial intelligence use and deep learning. Because if you are using bad data, and creating your models based on bad data, then the insights you get are also impacted. We definitely offer our products, both on the data integration and data quality side, to prepare the data, cleanse, match, and deliver the trusted data set for data scientists and make their life easier. Another area of focus for 2018 is can we also add supervised learning to this, because with the premium quality domain experts that we have now in Syncsort, we have a lot of domain experts in the field, we can infuse the machine learning algorithms and connect data profiling capabilities we have with the data quality capabilities recommending business rules for data scientists and helping them automate the mandate tasks with recommendations. And the last but not least trend is data governance, and data governance is almost a umbrella focus for everything we are doing at Syncsort because everything about the Cloud trend, the streaming, and the data science, and developing that next generation analytics environment for our customers depends on the data governance. It is, in fact, a business imperative, and the regulatory compliance use cases drives more importance today than governance. For example, General Data Protection Regulation in Europe, GDPR. >> Lisa: Just a few months away. >> Just a few months, May 2018, it is in the mind of every C-level executive. It's not just for European companies, but every enterprise has European data sourced in their environments. So compliance is a big driver of governance, and we look at governance in multiple aspects. Security and issuing data is available in a secure way is one aspect, and delivering the high quality data, cleansing, matching, the example Hilary Mason this morning gave in the keynote about half of what the context matters in terms of searches of her name was very interesting because you really want to deliver that high quality data in the enterprise, trust of data set, preparing that. Our Trillium Quality for big data, we launched Q4, that product is generally available now, and actually we are in production with very large deployment. So that's one area of focus. And the third area is how do you create visibility, the farm-to-table view of your data? >> Lisa: Yeah, that's the name of your talk! I love that. >> Yes, yes, thank you. So tomorrow I have a talk at 2:40, March 8th also, I'm so happy it's on the Women's Day that I'm talking-- >> Lisa: That's right, that's right! Get a farm-to-table view of your data is the name of your talk, track data lineage from source to analytics. Tell us a little bit more about that. >> It's all about creating more visibility, because for audit reasons, for understanding how many copies of my data is created, valued my data had been, and who accessed it, creating that visibility is very important. And the last couple of years, we saw everyone was focused on how do I create a data lake and make my data accessible, break the data silos, and liberate my data from multiple platforms, legacy platforms that the enterprise might have. Once that happened, everybody started worrying about how do I create consumable data set and how do I manage this data because data has been on the legacy platforms like Mainframe, IMBI series has been on relational data stores, it is in the Cloud, gravity of data originating in the Cloud is increasing, it's originating from mobile. Hadoop vendors like Hortonworks and Cloudera, they are creating visibility to what happens within the Hadoop framework. So we are deepening our integration with the Cloud Navigator, that was our announcement last week. We already have integration both with Hortonworks and Cloudera Navigator, this is one step further where we actually publish what happened to every single granular level of data at the field level with all of the transformations that data have been through outside of the cluster. So that visibility is now published to Navigator itself, we also publish it through the RESTful API, so governance is a very strong and critical initiative for all of the businesses. And we are playing into security aspect as well as data lineage and tracking aspect and the quality aspect. >> So this sounds like an extremely capable infrastructure service, so that it's trusted data. But can you sell that to an economic buyer alone, or do you go in in conjunction with anther solution like anti-money laundering for banks or, you know, what are the key things that they place enough value on that they would spend, you know, budget on it? >> Yes, absolutely. Usually the use cases might originate like anti-money laundering, which is very common, fraud detection, and it ties to getting a single view of an entity. Because in anti-money laundering, you want to understand the single view of your customer ultimately. So there is usually another solution that might be in the picture. We are providing the visibility of the data, as well as that single view of the entity, whether it's the customer view in this case or the product view in some of the use cases by delivering the matching capabilities and the cleansing capabilities, the duplication capabilities in addition to the accessing and integrating the data. >> When you go into a customer and, you know, recognizing that we still have tons of silos and we're realizing it's a lot harder to put everything in one repository, how do customers tell you they want to prioritize what they're bringing into the repository or even what do they want to work on that's continuously flowing in? >> So it depends on the business use case. And usually at the time that we are working with the customer, they selected that top priority use case. The risk here, and the anti-money laundering, or for insurance companies, we are seeing a trend, for example, building the data marketplace, as that tantalize data marketplace concept. So depending on the business case, many of our insurance customers in US, for example, they are creating the data marketplace and they are working with near real-time and microbatches. In Europe, Europe seems to be a bit ahead of the game in some cases, like Hadoop production was slow but certainly they went right into the streaming use cases. We are seeing more directly streaming and keeping it fresh and more utilization of the Kafka and messaging frameworks and database. >> And in that case, where they're sort of skipping the batch-oriented approach, how do they keep track of history? >> It's still, in most of the cases, microbatches, and the metadata is still associated with the data. So there is an analysis of the historical what happened to that data. The tools, like ours and the vendors coming to picture, to keep track, of that basically. >> So, in other words, by knowing what happened operationally to the data, that paints a picture of a history. >> Exactly, exactly. >> Interesting. >> And for the governance we usually also partner, for example, we partner with Collibra data platform, we partnered with ASG for creating that business rules and technical metadata and providing to the business users, not just to the IT data infrastructure, and on the Hadoop side we partner with Cloudera and Hortonworks very closely to complete that picture for the customer, because nobody is just interested in what happened to the data in Hadoop or in Mainframe or in my relational data warehouse, they are really trying to see what's happening on Premise, in the Cloud, multiple clusters, traditional environments, legacy systems, and trying to get that big picture view. >> So on that, enabling a business to have that, we'll say in marketing, 360 degree view of data, knowing that there's so much potential for data to be analyzed to drive business decisions that might open up new business models, new revenue streams, increase profit, what are you seeing as a CTO of Syncsort when you go in to meet with a customer, data silos, when you're talking to a Chief Data Officer, what's the cultural, I guess, not shift but really journey that they have to go on to start opening up other organizations of the business, to have access to data so they really have that broader, 360 degree view? What's that cultural challenge that they have to, journey that they have to go on? >> Yes, Chief Data Officers are actually very good partners for us, because usually Chief Data Officers are trying to break the silos of data and make sure that the data is liberated for the business use cases. Still most of the time the infrastructure and the cluster, whether it's the deployment in the Cloud versus on Premise, it's owned by the IT infrastructure. And the lines of business are really the consumers and the clients of that. CDO, in that sense, almost mitigates and connects to those line of businesses with the IT infrastructure with the same goals for the business, right? They have to worry about the compliance, they have to worry about creating multiple copies of data, they have to worry about the security of the data and availability of the data, so CDOs actually help. So we are actually very good partners with the CDOs in that sense, and we also usually have IT infrastructure owner in the room when we are talking with our customers because they have a big stake. They are like the gatekeepers of the data to make sure that it is accessed by the right... By the right folks in the business. >> Sounds like maybe they're in the role of like, good cop bad cop or maybe mediator. Well Tendu, I wish we had more time. Thanks so much for coming back to theCUBE and, like you said, you're speaking tomorrow at Strata Conference on International Women's Day: Get a farm-to-table view of your data. Love the title. >> Thank you. >> Good luck tomorrow, and we look forward to seeing you back on theCUBE. >> Thank you, I look forward to coming back and letting you know about more exciting both organic innovations and acquisitions. >> Alright, we look forward to that. We want to thank you for watching theCUBE, I'm Lisa Martin with my co-host George Gilbert. We are live at our event Big Data SV in San Jose. Come down and visit us, stick around, and we will be right back with our next guest after a short break. >> Tendu: Thank you. (upbeat music)

from Las Vegas accepting the signal from the noise it's the kue coverage interconnect 2016 brought to you by IBM now your host John hurry and Dave vellante okay welcome back around we are here live in Las Vegas for IBM interconnect 2016 special presentation of the cube our flagship program would go out to the events and extract the signal from the noise I'm John forreal echoes gave a lot they are next guest pricing Kohler who's the chief information technology officer and I'm saying this for the first time on the cube the weather company and IBM business welcome back to the cube thank you very much glad to be back last time you weren't an IBM business we were just the weather company were just the weather company so congratulations on your success want to say we really big fans of it but what Papa Chiana the team have done is visionary bold and very relevant so congratulations hey how's it feel it is grateful din we are really excited the opportunity with the IBM platform and you know the reach and the capabilities I mean it it really helps accelerate what we were trying to get done as the weather company you know as our own standalone business um and you know as you try to prepare and protect the entire planet all of its people and all of its businesses prepare and protect them for tomorrow which is really what the weather is company is all about finding that intersection of consumer behavior helping prepare and protect you as a in your personal life and your family but also you as a business owner how do we prepare and protect you to do better tomorrow because of the weather and the insights that we can provide fit straight into the work the Bob picciano in team have been doing with the insights you know economy with Watson and analytics with insights as a service all of that just kind of plugs together in it it really is a natural fit it's interesting to see IBM's move we were asked to guess on from IBM earlier and Jamie Thomas said it's all open source we want to get in early so this is an early bet for IBM certainly a bold move with the weather company but it's interesting the scuttlebutt as we talk to our sources inside the company close to the company have telling us that the weather companies is infiltrating and affecting the DNA IBM in a good way and you guys have always been a large scale data company and that is what all businesses are striving to digitize everything yes and so take us through that I mean one I think it's fair to say that you guys are kind of infecting I play in a positive way the mindset of being large-scale data yeah well why is that so compelling and how did you guys get here obviously whether the big data problem share some commentary around where it all came from well i think you know it's in my DNA first of all and it's in our company's DNA it's are no teams DNA you know I'm a change agent you would not want to hire me to maintain something good if you want to hire me to you know to break something and rebuild it better that's I'm your guy so you know I think when you look at the movement from you know the kind of the movement over time of IBM and you know the constant evolution that IBM goes through time is ripe when you take the cloud capabilities and you take data and you take analytics and the whole concept and capabilities of Watson Watson gets smarter as it learns more Watson can only be as smart as the data you feed it and so for Watson to continue to learn and continue to solve new problems and continue to expand its capability set we do have to feed it more data and and so you know looking at whether whether it was the original big data problem ever since the first mainframe the first you know application ever written on a mainframe was a weather forecast and ever since then everybody's been trying to figure out how to make the forecast more accurate and a lot of that comes from more data the more data you have the more accurate your forecast is going to be so we've been trying to solve this big data problem Walt and Dave talks about it was saw earlier in the opening about digital assets and in this digital transformation companies have to create more digital assets that's just dating yeah in this new model so when you look at the data aspect you say whether also is a use case where people are familiar with we were talking before we went on camera that people can understand the geekiness of whether it's different they're familiar with it but also highlights a real-life use case and the IOT Internet of Things wearables we heard you have sports guys on here tracking sensors this brings up that digital digitizing is going to be everything not just IT right it makes it real right if I think about my parents right we've been talking about IOT hey dad you're gonna have a connected refrigerator why does he care what do I need a connected refrigerator for but as you start to bring these insights to life and you make them real and you say you know what if I actually understand the humidity levels in your house and I can get that off the sensor on the air intake of your refrigerator I can now correlate that the humidity level outside of your house and I might be able to actually tweak your HVAC and I can make that run efficiently and I can now you know cut thirty percent of your cooling costs and all of these you know examples they're integrated they become real yeah and and I think weather is great because everybody checks their weather app the weather channel app or the weather underground app every day they're always looking at it and you know we get it right seventy-eight percent of the time we'd get it wrong sometimes we're constantly working to maintain our number-one position and data accuracy on weather forecasting and you know the more data we have the more accurate we can make it and so we've got any safer to you think just think about the use cases of people's lives slippery rose you know events correct I mean it's all tied in no goes back to another you know if I understand what's going on with the anti-lock braking system of a car and I already have a communication vehicle into everybody in that car which is our appt in their pocket I can alert them if the car is up ahead are having here are their abs activated and if all of the cars up ahead are having their abs activated I could alert them two miles back and say hey get ready slow down it's real it's not forecasted it's real data I'm giving you a real alert you should really take action and you know as we move from you know weather-alerts that we're looking out forward in time many hours as we're now doing rain alerts where we tell you it's going to start raining in the next seven minutes ten minutes people love those because it's right now and I can make a decision right now lightning strikes are always fascinating oh god because I gotta see crisis so last fall at IBM insight we interviewed David Kinney death your CEO and then right after I think was the week after I was watching some you know I was in Boston watching some sports program and there's bill belichick complaining about the in accuracy of whether i'll try that whether some reporter asked him about you know you factor in the weather i don't even pay attention i look at the weather forecast they're always wrong as a wait a minute I just I just interviewed David Kennedy he was bragging on the weather is the accuracy and how much it's improved so helping you mentioned seventy-eight percent of the time it's it's gotten better over time it has it still got rooms we're not perfect so so talk about that progression it is the data but how much better are you over time where is that better is it just short term or is it longer term at so color to that it's a great question and it's a fair point I think one of the biggest changes we've made in the last three years that the weather company is we've taken our forecast from what was roughly 2 million locations where we would do a forecast two million locations around the globe and today we we create a forecast for 2.2 billion locations around the globe because the weather is different at Fenway then Boston Logan it's just different than the the start time of rain the start time of a thunderstorm you know that's gonna be different now maybe five minutes but it's different the temperature the wind it's different and so as we've increased the accuracy and granularity of ours are our locations we've also done that from a time perspective as well so we used to produce a forecast every four to six hours depending upon how fast the models ran and did they run and complete successfully we now update our forecast every 15 minutes and so we we've increased the the you know all aspects of that and when you when you now think about getting your weather forecast you can no longer just type in BOS for your airport code and say i want to know what the weather is at boston logan if you're you know if you're in cambridge the boston logan forecast is not accurate for you you know five years ago every that was fine for everybody right right and so we have to retrain people to think about and make sure that when they're looking for a forecast and they're using our apps they can get a very specific forecast for where they are whatever point on the globe they are and and don't have you know Boston you know Logan as your you know favorite for your city if you're sitting in Cambridge or your you know you know it in Andover further outside where I am now where you gonna be my guess I gotta get so different you leverage the gps capabilities get that pinpoint location it will improve what the forecast is telling so I feel like this is one of those omni headed acquisition monsters for lack of a better term because when the acquisition was first announced is huh wow really interesting remember my line Dell's by an emc IBM is buying the weather company oh how intriguing it's a contrast it's all about the data the Dane is a service and then somebody whispered in my ear well you know there's like 800 Rockstar data scientists that come along with that act like wow it's all about the data scientists and then on IBM's earnings call i hear the weather company will provide the basis for our IOT platform like okay there's another one so we're take uh uh well i think IBM made a very smart move i'm slightly biased on that opinion but I think I be made a very smart move at very forward-looking move and one built on a cloud foundation not kind of a legacy foundation and when you think about IOT data sets we ingest 100 terabytes of data a day i ingest 62 different types of data at the weather company i ingest this data and then i distributed it massive volumes so what we had fundamentally built was the world's you know largest cloud-based iot data platform and you know IBM has many capabilities of their own and as we bring these things together and create a true next-gen cloud-based IOT data engine the ability for IBM to become smarter for Watson to become smarter than all of IBM's customers and clients to to become smarter with better applications better alerts better triggers and that alerts if you think about alerting my capability to alert hundreds of millions of people weather-alerts whether that's a lightning alert a rain alert a tornado warning whatever it is that's not really any different than me being able to alert a store clerk a night stock clerk at the local you know warehouse club that they need a stock you know aisle three differently put a different in cap on because we now have a new insight we have a new insight for what demand is going to be tomorrow and how do we shift what's going on that alert going down to a handheld device on the guy driving the four club yeah it's no different skoda tato yeah the capability to ingest transform store do analytics lon provide alerting on and then distribute data at massive scale that's what we do we talk about is what happened when Home Depot gets a big truck comes in a bunch of fans and say we know where this know the weather company did for you yeah we don't understand you'll understand you'll fake it later they file a big on the top of it so I OT as well as markets where people don't can't understand that some people don't know it means being like what's IOT Internet of Things I don't get it explain to them some little use cases that you guys are involved in today and some of these new areas that you're highlighting with with learning somehow see real life examples for for businesses and users there is a smarter planet kind of you know safe society kind of angle to it but it's also there's a nuts-and-bolts kind of practical if business value saving money saving lives changing you know maintenance what are some of the things share the IOT so there's there's only two things there so one is what is IOT and IOT really is is sensor data at the end of the day computers sensors electronic equipment has a sensor in it usually that sensor is there to do its job it's there to make a decision for what if it's a thermostat it has a sensor in it what's the temperature you know and so there are sensors in everything today things have become digitized and so those sensors are there as next as those next evolutions have come online those those sensors got connected to the Internet why because it was easier than to manage and monitor you know you know here we are at the mandalay bay how many thermostat sensors do you think this hotel casino complex has thousands and so you can't walk around and look at each one to understand well how's the temperature doing they all needed to be shipped back to a central room so that the in a building manager could actually do his job more efficiently those things then got connected so you could look at it on a smartphone those things they continued to get connected to make those jobs easier that first version of all of those things it was siloed that data SAT within just this hotel but now as we move forward we have the ability to take that data and merge it with other data sets there's actually a personal a Weather Underground personal weather station on the roof of the Mandalay Bay and it's actually collecting weather data every three seconds sending it back to us we have a very accurate understanding of the state of the Earth's atmosphere right atop this building having those throws is very good for the weather data but now how does the weather data impact a business that cares about the weather that has there we understand what the Sun load is on the top of this building and so we can go ahead and pre-heat your pre cool rooms get ahead of what's changing out sign that will have an impact here inside we have sensors on aircraft today that are collecting telemetry from aircraft turbulence data that helps us understand exactly what's going on with that airplane and as that's fed in real-time back down to the earth we process that and then send it back to the plane behind it and let that plane behind it know that it needs to alter it course change its flight plan automatically and update the pilots that they need to change course to a smoother altitude so gone are the days of the pilot having to radio down and fall around his body it's bumpy to get these through there anywhere machines can can can do this in real time collected and synthesize it from hundreds of aircraft that have been flying in that same route now we can actually take that and produce a better you know in flight plan for those for those machines we do that with with advertising so you know when you think about advertising you be easy the easy example is hey we know that you're going to sell more of X product when y weather condition happens that's easy but what if I also help you know when not to run an ad how do I help save you money you know if I know that there's no way for me to actually impact demand of your product up or down because we know over the course of time looking at your skew data and weather data that no matter what what we do weathers gonna have this impact on your product save your money don't run an ad tomorrow because it doesn't matter what you do you're not going to actually move your product more that's great and it's much business intelligence it's all the above its contextual data help people get insights in subjective and prescriptive analytics all rolled into one in a tool that alerts the actual person may explain to people out they were predictive versus prescriptive means a lot people get those confused what's your how would you prescriptive is you know where we want data that just tell us what to do based upon historic looking trends so i can take ten years of weather data and I can marry that up with ten years of some other data set and I can come up with you know a trend based upon the past and with that then I could prescribe what you should do in the future hey looks like general trend bring an umbrella tomorrow it's good it might rain but if I get into predictive analytics now I can start to understand by looking at forward-looking data things that haven't happened yet or new data sets that I'm merging in in real time oh wait a minute we thought that every time it rained more people went to this gas station to fill up but wait a minute today there's an accident on the road and people no matter what we do they're not going to go to that gas station because they're not even going to drive by it so being able to predict based upon feet of our real-time data but also forward-looking data the predictive analytics is really around the insights that we want to guess I got to ask you one question about the IBM situation and I want you to kind of reflect get him get you know all right philosophical for a second what's the learning that you've had over the past few weeks months post-acquisition inside IBM is there a learning that you to kind of hit you that you didn't expect there's something you'd expect what sure what was your big takeaway from this experience personally and you had some great success in the business now integrated into IBM what's the learning that cuz that's comes out of this for you I am really proud of the team at the weather company you know I I think what we have been able to accomplish as a small company you know comparative to my four hundred and sixty-eight thousand colleagues at IBM yeah what we've been able to accomplish what we've been able to do is really you know it's impressive and I've been proud of my team I'm proud of our company I'm proud of what we were able to get done as a company and you know the reflection really is as you bring that into IBM how do you make sure that you can you can now scale that to benefit such a large organization and and so while we were great at doing it for ourselves and we built an amazing business with amazing growth you know attracted lots of people that looked at buying us and obviously IBM executing on that I think that's amazing and I'm proud of that but I think my biggest reflection is that doesn't necessarily equate to success at IBM and we now have to retool and retrans form ourselves again to be able to take what we know how to do really well which is build great capabilities build big data platforms build analytics engines and inside engines and then armed a sea of developers to use our API we can't just take what we've done and go mate rest on your laurels you gotta go reinvent so I think my biggest you know real learning and take away from the kind of integration process is well we have a lot to learn and we have a lot of change we need to do so that we can actually now adapt and and continue to be us but do it in a way that works as an IBM ER and and that's that's there's there's going to be an art to this and we've got a ways to learn so I'm going in while eyes wide open around what I have to learn but I also am very reflective on on how proud I am as a leader of the team that you know has created you know such an amazing capability acquisition is done you savor it you come in you get blue washed and I hope I had a Saturday afternoon where I say okay got all like what is this gonna think so and then okay so you you wake up in the morning and you sort of described at a high level you know what you're doing but top three things that you're focused on the next you know 12 12 months so so you know the biggest thing that I'm focused on number one is making sure that we protect the weather company culture and how we know how to do and build great things and so I've got to lead us through obviously becoming integrated with IBM but not losing who we are and IBM is very supportive of that you know Bob picciano his team have been awesome and you know John Kelly and team have been awesome everybody that we have worked with has been so supportive of Bryson please make sure you find the right way through this we don't want to break you and I think that's natural for any acquisition for any yeah but you guys aren't dogmatic you were very candid saying we're gonna transform ourselves and adapt absolutely and so and so so we've got that on wrestling on my mind how do we go find immediate wins there's there's a a million different ways for us to win there's thousands of IBM sales teams that are out in front of clients it's just today with new problems how do we quickly adapt what we've been good at doing and help solve new problems very quickly so that's on my mind and then you know wrapping that in a way that becomes self service we can't I don't want to scale my team through people to solve all these problems I want to find a way to make sure that all these capabilities new data sets new insights new capabilities that we bring the life I want to do that in a self-service way I want to make sure that our technology the way we interact with developers the developer community that we bring in to kind of work on our behalf to make this happen I don't want to solve all these problems I want to enable others to solve the problems and so we're very focused on the self service aspect which i think is very new prices thank you so much taking the time out of your busy schedule to see with us in the queue good to see you again or any congratulations IOT everything's a sensor that we're a sense are here in the cube and we sense that it's time to go to SiliconANGLE DV and check out all the videos we have a purpose our sensor is to get the data to share that out with you thanks for the commentary and insight appreciate it whether company great success weather effects of song could affect stock prices all kinds of things in the real world so we had a lot of a lot of big data thank you very much look you here live in Las Vegas right back more coverage at this short break