>> >> Announcer: From around the globe, it's theCUBE, with digital coverage of IBM Think2021, brought to you by IBM. >> Hey, welcome back to theCUBE's coverage of IBM Think2021 virtual. I'm John Furrier, host of theCUBE. We're here with Madhu Kochar who's the vice president, product management for IBM Data and AI, also CUBE alumni. Great to see you, Madhu, thanks for coming on theCUBE remotely soon to be in person, I hope soon. Great to see you. >> Thanks, John. Obviously very, very happy to be here and yeah, like you said, hopefully next time face-to-face. >> Yeah, I can't, we've had many conversations in the past on theCUBE about data, machine learning. Now more than ever it's prime time. And as companies put the AI to work, they're facing more and more challenges, especially with the growing data complexity and obviously quality, what are they doing to solve it, solve these problems today? I mean, as cloud scales here, it's transformation, innovation cloud scale, still complexity. What are they doing to solve this? >> Yeah, you're right, John, right? The data complexity is just becoming overwhelming and it's threatening what I would say progress. And you would agree to that, right? As organizations are struggling to turn these complex data landscapes and to get some sustained value out of it and it's becoming costly. And I believe the worst of it is because of the rapid pace of digitization is happening right now generate, the data is just getting generated at higher velocity, all different types of data and all different touch points. And traditionally, trying to move replicate data, integrate data and bring it all into the big data store. It's just not working out, right? It's becoming costly and the stats shows that 97% of enterprise data is still not trusted or analyzed. And with all the movements happening on to the migrating of data to multiclouds is just adding a lot of complexity. Our point of view, all of us has been, we've been talking a lot about hybrid. And to me, hybrid approach is the one that truly accepts the reality, that data will be everywhere and is going to be changing by the minute. And it turned and we have to figure out how do we turn that problem into an advantage. To me, automation is going to be inevitable. And what we truly believe in is like you got to leave the data where it is, bring AI to your data and that means what's AI for business here, right? So, that truly means that you have to be able to understand the language of the business, automates workflows and experiences and truly deliver the trust and predictability in these outcomes. That is where John I firmly believe where we need to be going. >> Madhu you know that's so right on. And I think the business case is well understood, what's interesting is, is that we were talking to some other IBMers about autonomous shipping, about ships that are being powered by automation and getting all that data and integrating in all kinds of diverse sources, is also a business challenge. So autonomous vehicles, autonomous everything these days requires massive amounts of data ingestion and processing and insights and operationalizing and decision-making, all kind of coming in. This is like the Holy Grail of automation. So I have to ask you what is the IBM's perspective on managing data that exists in different forms and on across different environments in an organization? Because this is where the diversity comes in and it's actually better for the data, because AI loves diverse data 'cause the better the data, the better the AI, right? So, but it's still complicated. How are you guys looking at this perspective of managing data that exists in different forms? >> Yeah, I know, that's a great question. And I think this is where we need to be thinking about, what we are talking about here is that you need what I call an intelligent data fabric, data fabric it's a term which is just picking up in the industry and the definition, which I would say an intelligent data fabric is what weaves together and automates data and AI lifecycle over anything. Over anything means any data, any cloud anywhere, right? And what do you get with this? What you get with this is that you're able to then unlock totally new insights from unified data. You're able to democratize your trusted data usage across more people. You unleash truly productivity, right? And you reduce cost and risk and you make AI for business easier. Like I was talking about you bring your AI to the data, it's all about AI for business. You make AI for business easier, faster and more trusted. And underneath the covers, automation is what's going to help us to scale all this. So by truly bringing the intelligent data fabric together which helps us automate and view all these things is going to be the answer. >> I love noises like democratizing, trusted data usage and unifying data and getting all those insights 'cause that tries the value. I'm sold on that and I love it. And I understand it, the question I have for you and I heard this term and I'd love to get you to help me define it for the audience, the notion of distributed data life cycles. Can you describe and define what does that mean for an organization? >> So truly that would just mean, right? Like I was talking about what is intelligent data fabric. This is all about data is everywhere, right? You're, it is in your operational data stores, your data warehouses and now with spans of multiple clouds, people are moving their workloads to various clouds, it's everywhere. When you have to make a decision, you have to be able to have access to all that data, right? You have to keep in mind what are your data privacy rules? What is your data residency rules around it, right? And how do I make, how do I analyze that data? How do I categorize that data to have some business outcomes, right? And anything what you're doing right now, it's going to require AI to it. How do I apply AI to where data lives? That is where the whole aspect of the intelligent data fabric needs to come into the picture. >> Yeah, and my next question is around some of the new announcements that you guys have here at Think and I want to get this new upgrade, and new data pack announcement, because I think, I mean, Cloud Pak for Data because having data become operationalized is much more sensitive than it was in the Wild West in the early days. Like, Hey, the data is everywhere. People are concerned and there's compliance, there's risk and getting sued and different sovereignty issues. So, you guys have had this IBM Cloud Pak for Data for a few years now, helping clients with the chance, some of the data challenges. I think we've talked about it. You guys are announcing more here, the next generation. Could you explain this announcement? >> Yes, yes and yeah, you're right. Cloud Pak for Data was announced about three years ago, we launched it then, many customers in production with that, so very proud. And it really, Cloud Pak for Data in simple terms is all about your data platform and analytics platform, right? So at this think, we are announcing what I call the next generation of Cloud Pak for Data. A lot of enhancements going in but I would like to focus on three top key capabilities which we are bringing in. And it's all about how we are weaving together as part of the intelligent data fabric I was talking about earlier. So the three capabilities which I want the audience to walk away is, number one, auto privacy. What does that mean? It means how do we automate how you enforce universal data and usage policies across hybrid data and cloud ecosystems of various sources and users and how to express that to users in business terms and why? Because this is going to further simplify risk mitigation across an organization of self-serve data consumers. So that's all about auto privacy. The second key capabilities will be around auto catalog. Very, very critical. I call it the brain of it, right? This is where, how we automate, how it is, the data is discovered, how is cataloged and enriched for users, relevance of maintenance of knowledge for business ready data, right? Which is spread again across hybrid sources and multiple cloud landscapes. The third thing, very critical is what we call AutoSQL. This is how you're going to automate how you access, update and unify data spread across distributed data and cloud landscapes without the need of actually doing any data movements or replication if it's needed. So, and part of that data access is also needs to be, is it optimized for performance, right? Can I get to the petabytes of scale of data and how does the visual query building experiences look on top of that? So we are just so super excited about obviously with Cloud Pak for Data with this new enhancements, how we are reading the story around with data fabric, intelligent data fabric, with three things, right? Auto privacy, auto catalog and AutoSQL. And John, this is also on top of what we've been also talking about AutoAI for a while and this is all about AI life cycle management. There's going to be tons of enhancements coming in as to how we are simplifying federated training, right? Federated training across complex and siloed data sets is going to be important fact sheets. How do we improve the model quality and explainability that is going to be very critical for us and the time series optimizations? So all these auto stuff weaved with our intelligent data fabric, is going to be our next generation of Cloud Pak for Data. And I must say, John, lot of our clients are early adopters, early customers, been giving us the fantastic feedback. And it's like, this is what's needed. And you mentioned that earlier, right? Complexity is known, everybody wants a solution. What a great way to get these quick outcomes of the data, right? We've got to monetize the data. And that's what it's going to lead to. >> Madhu, is very exciting, great insight and congratulations. I love the auto name, autopilot, AutoAI. Just, it sounds automated. And I guess my final question for you is one that's a little bit more current around hybrid and that is the big theme that we're seeing. And we've been reporting and kind of connecting the dots here in the CUBE, through all the different interviews with you guys and all your partners is this ecosystem dynamic now with hybrid cloud is more important than ever before because cloud and cloud operations is API based. So more and more people are connected. So is this where auto privacy, auto catalog and AutoSQL and AI connect in? Is that where it's relevant? Because hybrid cloud really speaks about ecosystems and partnering 'cause no one does it alone anymore. >> Absolutely, you hit it on the nail, right? Hybrid is all not just about on-prem and one cloud is all about intra clouds and inter clouds and everywhere. And that is where the data fabric helps us, right? And that auto privacy meaning your data is spread across, how do I understand what are the policies across? Or do I respect my data residency? So exactly to the point that is what this gives us the solution. >> That's awesome. I do remember the old school inter networking was a category in the industry, connecting networks together. Now we have inter clouding, inter DataOps. It's all good, exciting. Thanks for coming on, really appreciate your insights. You're a pro and love the work you're doing over there in the product management. Great job and looking forward to hearing more. Thanks for coming on. >> Thank you, thank you John. >> Okay, Madhu Kochar, here at VP of product management IBM Data and AI, the hottest area in hybrid cloud. Of course, it's the CUBE coverage for IBM Think2021, I'm John Furrier. Thanks for watching. (upbeat music)

>>from around the globe. It's the cube >>With digital coverage of IBM think 20 >>21 brought to you >>by IBM Hey, welcome back to the cubes coverage of IBM think 2021 virtual. I'm john for a host of the cube we're here with my do Kochar, who's the vice president, Product management for IBM data and Ai also cube alumni. Great to see you do. Thanks for coming on the cube remotely. Soon to be in person. I hope soon. Great to see you. >>Thanks john obviously very, very happy to be here and yeah, like you said, hopefully next time face to face. >>Yeah, I can't, we've had many conversations to pass on the cube about data machine learning now more than ever it's prime time and as companies put the ai to work, you know, they're facing more and more challenge especially with the growing data complexity and equality. What are they doing to solve it, solve these problems today? I mean as cloud scales here, its transformation innovation, cloud scale, still complexity. What are they doing to solve this? >>Yeah, you're right, john right. The data complexity is just becoming overwhelming and it's threatening what I would say progress and you would agree to that right. As organizations are struggling to turn these complex data landscapes and to get some sustained value out of it and it's becoming costly and I believe the worst of it is because of the rapid pace of digitalization is happening right now, generate. The data is just getting generated at higher velocity, all different types of data and all different touchpoints and traditionally, you know, trying to move replicate data, integrate data and bring it all into the big data store is just not working out right? It's becoming costly And and the stats shows that 97 of enterprise data is still not trusted or analyzed and with all the movements happening onto the migrating of data to multi clouds is just adding a lot of complexity. Our point of view always has been, we've been talking a lot about hybrid and to me, hybrid approach is the one that truly accepts the reality, that data will be everywhere and it's going to be changing by the minute and it turned and we have to figure out how do we turn that problem into an advantage? Um To me, automation is going to be inevitable and what we truly believe in is like you gotta leave the data where it is bring ai to your data and that means what's aI for business here. Right. So that really means that you have to be able to understand the language of the business, automates, workflows and experiences and truly deliver the trust and predictability in these outcomes. That is where john I firmly believe that we need to be going, >>do you know? That's so right on. And I think the business cases is well understood. What's interesting is that we were talking to some other IBM Rs about autonomous shipping, about ships that are being powered by automation and getting all that data and integrating in all kinds of diverse sources um is also a business challenge. So autonomous vehicles, autonomous everything these days requires massive amounts of data, ingestion and processing and insights and and operationalize ng and decision making, all kind of coming in. This is like the holy grail of, of automation. So I have to ask you what is the iBMS perspective on managing data that exists in different forms and on across different environments in an organization? Because this is where the diversity comes in and it's actually better for the data because a I loves diverse data because the better the data the better the eye. Right? So but it's still complicated. How are you guys looking at this perspective of managing data that exists? >>Yeah, No, that's a that's a great question. And I think this is where we need to be thinking about what we are talking about here is that you need what I call an intelligent data fabric. Data fabric is a is a term which is just picking up in the industry. And the definition which I would say an intelligent data fabric is what weaves together and automates data and ai lifecycle over anything over anything means any data, any cloud, anywhere. Right? And what do you get with this? What you get with this is that you are able to then unlock totally new insights from unified data. You're able to democrat is your trusted data usage across more people. You unleash truly productivity right? And you reduce cost and risk and you make A I for business easier. Like I was talking about you bring your ai to the data, it's all about ai for business you make A I for business easier, faster and more trusted. And underneath the covers, automation is what's going to help us to scale all this. So by truly bringing the intelligent data fabric together which helps us automate and view all these things is going to be the answer. >>I'd love noses like democratizing trusted data usage and unifying data and getting all those insights because that drives the value I'm sold on that and I love it and I understand it the question I have for you and I heard this term, I'd love to get you to help me define it for the audience. The notion of distributed data life cycles, can you describe and define what does that mean for an organization? >>So truly that would just mean right? Like I was talking about, what is intelligent data fabric? This is all about data is everywhere, right? You're it is in your um operational data stores, your data warehouses and now with spans of multiple clouds, you know, people are moving their workloads to various clouds. It's everywhere. When you have to make a decision, you have to be able to have access to all that data, right? You have to keep in mind. What are your data privacy rules? What is your data residency rules around it? Right. And how do I make, how do I analyze that data? How do I categorize that data to have some business outcomes? Right. And anything what you're doing right now? It's gonna require a I to it. How do I apply ai to where data lives? That is where the whole aspect of the intelligent data fabric needs to come into the picture. >>Yeah. My next questions around some of the new announcements that you guys have here thinking I want to get this new new upgrade new data pack announcement. Because I think, I mean cloud pack for data because having data become operationalized is much more sensitive than it was in the wild west of the early days. Like hey, the data's everywhere, people are concerned, you know, and there's compliance risk and getting sued and different sovereignty issues. So, you know, you guys have had this IBM cloud pack for data for a few years now helping clients with the chance, some of the data challenges. I think we've talked about it. You guys are announcing more here the next generation. Could you explain this announcement? >>Yes, Yes and yeah, you're right. Cloud Back for data was announced about three years ago. We launched it then. Uh many customers in production with that, so very proud. And it really cloud back for data in simple terms is all about your data platform and analytics platform. Right. So at this tank we are announcing what I call the next generation of cloud back for data, a lot of enhancements going in. But I would like to focus on 33 top key capabilities which we are bringing in. And it's all about how we are weaving together as part of the intelligent data fabric. I was talking about earlier. So the three capabilities, which I want the audience to walk away is number one auto privacy. What does that mean? It means? How do we automate how you enforce universal data and usage policies across hybrid data and cloud ecosystems of various sources and users and how to express that in you to users in business terms and why? Because this is going to further simplify risk mitigation across an organization of self serve data consumers. So that's all about auto privacy. The second key capabilities will be around auto catalog. Very, very critical. I call it the brain of it, Right? This is where how we automate how it is that the data is discovered how is catalogued and enriched for users, relevance of maintenance of knowledge for business ready data, right? Which is spread again across hybrid hybrid sources and multiple cloud landscapes. The third thing very critical is what we call auto sequel. This is how you're going to automate how you access, update and unify data spread across distributed data and cloud landscapes without the need of actually doing any data movements or replication if it's needed. So. And and part of that data access is also needs to be, is it optimized for performance? Right. Can I get to the petabytes of scale of data? And how does the visual query building experiences look on top of that? So we are just so super excited about obviously with cloud back for data, with this new enhancements, how we are reading the story around with data fabric, intelligent data fabric with three things. Right? Auto privacy, auto catalog and auto sequel and john this is also on top of what we've been also talking about Auto Ai for a while and this is all about aI Lifecycle management. There's gonna be tons of enhancements coming in as to how we are simplifying federal training right? Federally training across complex and siloed data. Such is going to be important fact sheets. How do we improve the model quality and explain ability that is going to be very critical for us and the time series optimizations. So all these auto stuff weaved with our um, intelligent data fabric is going to be our next generation of cloud back for data. And I must say john a lot of our clients are early adopters, early customers been giving us the fantastic feedback and it's like this is what's needed. And you mentioned that earlier, right, complexity is known. Everybody wants a solution. What a great way to, to get this uh, quick outcomes of the data, right? We've got to monetize the data and that's what it's going to need to >>do is very exciting gradients. Congratulations. I love the auto name. Autopilot. Auto, Auto Auto A I just, it sounds automated and I guess my final question for you is, um, one that's a little bit more current around hybrid and that is the big theme that we're seeing and we've been reporting and kind of connecting the dots here in the cube through all the different interviews with you guys and and all your partners. Is this ecosystem dynamic now with hybrid cloud is more important than ever before because cloud and cloud operations is A. P. I based so more and more people are connecting. So is this where auto privacy, Auto catalog and auto sequel and A I connect in. Is that where it's relevant because Hybrid cloud really speaks about ecosystems and partnering because no one does it alone anymore. >>Absolutely. You hit it on the nail, right. Hybrid is all not just about on prem and one cloud, it's all about intra clouds and inter clouds and everywhere. And that is where the the data fabric helps us, right. And the auto privacy meaning your data is spread across. How do I understand? What are the policies across? How do I respect my data residency? So exactly to the point that is what this gives us the solution. >>Remember the old school inter networking was a category in the industry, you know, connecting networks together. Now we have inter clouding into data ops all all good, exciting. Thanks for coming on. Really appreciate your insights. Uh you're a pro and love the work you're doing over there, the product management. Great job, looking forward to hearing more. Thanks for coming on. >>Thank you. Thank you. >>Don. Okay madu Kochar here at VP of Product measure, IBM Data and ai the hottest area in hybrid cloud. Of course it's the cube coverage probably I think 2021. I'm john ferrier. Thanks for watching. Mhm. >>Mhm mm. Yeah.

hello this is mad approach our vice-president offering management in IBM data yet the shift to adopt data op is real we are seeing that about 73% of company are planning to invest in data benefit of their data are being realized in many forms infrastructure need gets reduced by 50% time savings for compliance to speed up your metadata classification to be ready for regulations by 90% data discovery is accelerated from weeks to minute through automation even within IBM our chief data office when they implemented a top methodology saw the savings of about twenty seven million in productivity that's a holistic approach to the data of food chain is critical for our success I would like to invite you to our data of crowd Chad on May 27th where you will hear very interactive discussion between IBM and our lead clients as to how they're leveraging data ops methodology and implementing the solution see you there thank you

hello this is maduko achar vice president offering management in ibm data and ai the shift to adopt data op is real we are seeing that about 73 percent of companies are planning to invest in data up benefit of data ops are being realized in many forms for example infrastructure need gets reduced by 50 time savings for compliance to speed up your metadata classification to be ready for regulations by 90 data discovery is accelerated from weeks to minute through automation even within ibm our chief data office when they implemented data op methodology saw the savings of about 27 million in productivity that's a holistic approach to the data optional chain is critical for our success so i would like to invite you to our data of crowd chat on may 27th where you will hear a very interactive discussion between ibm and our lead clients as to how they're leveraging data ops methodology and implementing the solutions see you there thank you

>> Live from Boston, it's theCUBE covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back everyone to theCUBE's live coverage of the IBM CDO Summit here in beautiful Boston, Massachusetts. I'm your host, Rebecca Knight, along with my co-host Paul Gillin. We have two guests for this segment, we have Susan Wagner, who is the VP Data Artificial Intelligence and Governance at Deutsche Telekom and Madhu Kochar, whose the Vice President Analytics Product Development at IBM. Thank you so much for coming on the show. >> Thank you. >> Happy to be here. Susan you're coming to us from Berlin, tell us a little bit about what you it's a relatively new job title and Paul was marveling before the cameras are rolling. Do you have artificial intelligence in your job title? Tell us a little bit about what you do at Deutsche Telekom. >> So we have a long history, working with data and this is a central role in the headquarter guiding the different data and artificial intelligence activities within Deutsche Telekom. So we have different countries, different business units, we have activities there. We have already use case catalog of 300,000 cases there and from a central point we are looking at it and saying, how are we able really to get the business benefit out of it. So we are looking at the different product, the different cases and looking for some help for the business units, how to scale things. For example, we have a case we implemented in one of our countries, it was about a call center to predict if someone calls the call center, if this is a problem, we would never have(laughing) at Deutsche Telekom but it could happen and then we open a ticket and we are working on it and then we're closing that ticket and but the problem is not solved, so the ticket comes again and the customer will call again and this is very bad for us bad for the customer and we did on AI project, there predicting what kind of tickets will come back in future and this we implemented in a way that we are able to use it not only in one country, but really give it to the next country. So our other business units other countries can take the code and use it in another country. That's one example. >> Wow. >> How would you define artificial intelligence? There's someone who has in your job-- (laughing) >> That's sometimes very difficult question I must admit. I'm normally if I would say from a scientific point, it's really to have a machine that works and feels and did everything like a human. If you look now at the hype, it's more about how we learn, how we do things and not about I would say it's about robotic and stuff like that but it's more how we are learning and the major benefit we are getting now out of artificial intelligence is really that we are able now to really work on data. We have great algorithm and a lot of progress there and we have the chips that develops so far that we are able to do that. It's far away from things like a little kid can do because little kid can just, you show them an apple and then it knows an apple is green. It's were-- >> A little kid can't open a support ticket. (laughing) >> Yeah, but that's very special, so in where we special areas, we are already very, very good in things, but this is an area, for example, if you have an (mumbles) who is able like we did to predict this kind of tickets this agreement is not able at the moment to say this as an apple and this is an orange, so you need another one. So we are far away from really having something like a general intelligence there. >> Madhu do I want to bring you into this conversation. (laughing) And a little bit just in terms of what Susan was saying the sort of the shiny newness of it all. Where do you think we are in terms of thinking about the data getting in the weeds of the data and then also sort of the innovations that we saw, dream about really impacting the bottom line and making the customer experience better and also the employee experience better? >> Yeah, so from IBM perspective, especially coming from data and analytics, very simple message, right? We have what we say your letter to AI. Everybody like Susan and every other company who is part of doing any digital transformation or modernization is talking about Ai. So our message is very simple, in order to get to the letter of AI, the most critical part is that you have access to data, right? You can trust your data, so this way you can start using it in terms of building models, not just predictive models but prescriptive and diagnostics. Everything needs to kind of come together, right? So that is what we are doing in data analytics. Our message is very, very simple. The innovations are coming in from the perspectives of machine learning, deep learning and making and to me that all equates to automation, right? A lot of this stuff data curation, I think you can Susan, tell how long and how manual the data curation aspects can be. Now with machine learning, getting to your latter of AI, You can do this in a matter of hours, right? And you can get to your business users, you can if your CHARM model, If your clients are not happy, your fraud, you have to detect in your bank or retail industry, it just applies to all the industry. So there is tons of innovation happening. We just actually announced a product earlier called IBM Cloud Private for Data. This is our the analytics platform which is ready with data built in governance to handle all your data curation and be building models which you can test it out, have all the DevOps and push it into production. Really, really trying to get clients like Deutsche Telekom to get their journey there faster. Very simple-- >> We've heard from many of our guests today about the importance of governance, of having good quality data before you can start building anything with it. What was that process like? How is the... what is the quality of data like at Deutsche Telekom and what work did it take to get it in that condition. >> So data quality is a major issue everywhere, because as Madhu that this is one of the essential things to really get into learning, if you want to learn, you need the data and we have in the different countries, different kind of majorities and what we are doing at the moment is that we are really doing it case by case because you cannot do everything from the beginning, so you start with one of the cases looking what to do there? How to define the quality? And then if the business asked for the next case, then you can integrate that, so you have the business impact, you have demand from the business and then you can integrate the data quality there and we are doing it really step by step because to bring it to the business from the beginning, it's very, very difficult. >> You mentioned, one of the new products that you announced just today, what are some of the-- (laughing) >> We announced it in may. >> Oh, okay, I'm sorry. >> It's okay still new. >> In terms of the other innovations in the pipeline, what I mean this is such a marvelous and exciting time for technology. What are some of the most exciting developments that you see? >> I think the most exciting, especially if I talk about what I do day out everything revolves around metadata, right? Used to be not a very sticky term, but it is becoming quite sexy all over again, right? And all the work in automatic metadata generation, understanding the lineage where the data is coming from. How easy, we can make it to the business users, then all the machine learning algorithms which we are doing in terms of our prescriptive models and predictive, right? Predictive maintenance is such a huge thing. So there's a lot of work going on there and then also one of the aspects is how do you build once and run anywhere, right? If you really look at the business data, it's behind the firewalls, Is in multicloud. How do you bring solutions which are going to be bringing all the data? Doesn't matter where it resides, right? And so there's a lot of innovation like that which we are working and bringing in onto our platform to make it really simple story make data easy access which you can trust. >> One of the remarkable things about machine learning is that the leading libraries have all been open source, Google, Facebook, eBay, others have open source their libraries. What impact do you think that has had on the speed with which machine learning is developed? >> Just amazing, right. I think that gives us that agility to quickly able to use it, enhance it, give it back to the community. That has been the one of the tenants for, I think that how everybody's out there, moving really really fast. Open source is going to play a very critical role for IBM, and we're seeing that with many of our clients as well. >> What tools are you using? >> We're using different kind of tools that depending on the departments, so the data scientists like to use our patents. (laughing) They are always use it, but we are using a lot like the Jupiter notebook, for example, to have different kind of code in there. We have in one of our countries, the classical things like thus there and the data scientists working with that one or we have the Cloud-R workbench to really bringing things into the business. We have in some business-- >> Data science experience. >> IBM, things integrated, so it it really depends a little bit on the different and that's a little bit the challenge because you really have to see how people working together and how do we really get the data, the models the sharing right. >> And then also the other challenges that all the CDOs face that we've been talking about today, the getting by in the-- >> Yes. >> The facing unrealistic expectations of what data can actually do. I mean, how would you describe how you are able to work with the business side? As a chief working in the chief data office. >> Yeah, so what I really like and what I'm always doing with the business that we are going to the business and doing really a joint approach having a workshop together like the design thinking workshop with the business and the demand has to come from the business. And then you have really the data scientists in there the data engineers best to have the operational people in there and even the controlling not all the time, but that it's really clear that all people are involved from the beginning and then you're really able to bring it into production. >> That's the term of DataOps, right? That's starting to become a big thing. DevOps was all about to agility. Now DataOps bring all these various groups together and yeah I mean that's how you we really move forward. >> So for organizations so that's both of you for organizations that are just beginning to go down the machine learning path that are excited by everything you've been hearing here. What advice would you have for them? They're just getting started. >> I think if you're just getting started to me, the long pole item is all about understanding where your data is, right? The data curation. I have seen over and over again, everybody's enthusiastic. They love the technology, but the... It just doesn't progress fast enough because of that. So invest in tooling where they have automation with machine learning where they can quickly understand it, right? Data virtualization, nobody's going to move data, right? They're sitting in bedrock systems access to that which I call dark data, is important because that is sometimes your golden nugget because that's going to help you make the decisions. So to me that's where I would focus first, everything else around it just becomes a lot easier. >> Great. >> So-- >> Do you have a best practice too? Yeah. >> Yeah. Focus on really bringing quick impact on some of the cases because they're like the management needs success, so you need some kind of quick access and then really working on the basics like Madhu said, you need to have access of the data because if you don't start work on that it will take you every time like half a year. We have some cases where we took finance department half a year to really get all that kind of data and you have to sharpen that for the future, but you need the fast equipments. You need to do both. >> Excellent advice. >> Right, well Susan and Madhu thank you so much for coming on theCUBE, it's been great having you. >> Thank you. >> Thank you. >> I'm Rebecca Knight for Paul Gillin we will have more from theCUBE's live coverage of the IBM CDO just after this. (upbeat music)

>> Live from New York City it's theCube. Covering Lenovo Transform 2.0. Brought to you by Lenovo. >> Welcome back to theCube's live coverage of Lenovo Transform, I'm your host Rebecca Knight along with my co-host Stu Miniman. We're joined by Madhu Matta; He is the VP and GM High Performance Computing and Artificial Intelligence at Lenovo and Dr. Daniel Gruner the CTO of SciNet at University of Toronto. Thanks so much for coming on the show gentlemen. >> Thank you for having us. >> Our pleasure. >> So, before the cameras were rolling, you were talking about the Lenovo mission in this area to use the power of supercomputing to help solve some of society's most pressing challenges; and that is climate change, and curing cancer. Can you talk a little bit, tell our viewers a little bit about what you do and how you see your mission. >> Yeah so, our tagline is basically, Solving humanity's greatest challenges. We're also now the number one supercomputer provider in the world as measured by the rankings of the top 500 and that comes with a lot of responsibility. One, we take that responsibility very seriously, but more importantly, we work with some of the largest research institutions, universities all over the world as they do research, and it's amazing research. Whether it's particle physics, like you saw this morning, whether it's cancer research, whether it's climate modeling. I mean, we are sitting here in New York City and our headquarters is in Raleigh, right in the path of Hurricane Florence, so the ability to predict the next anomaly, the ability to predict the next hurricane is absolutely critical to get early warning signs and a lot of survival depends on that. So we work with these institutions jointly to develop custom solutions to ensure that all this research one it's powered and second to works seamlessly, and all their researchers have access to this infrastructure twenty-four seven. >> So Danny, tell us a little bit about SciNet, too. Tell us what you do, and then I want to hear how you work together. >> And, no relation with Skynet, I've been assured? Right? >> No. Not at all. It is also no relationship with another network that's called the same, but, it doesn't matter. SciNet is an organization that's basically the University of Toronto and the associated research hospitals, and we happen to run Canada's largest supercomputer. We're one of a number of computer sites around Canada that are tasked with providing resources and support, support is the most important, to academia in Canada. So, all academics, from all the different universities, in the country, they come and use our systems. From the University of Toronto, they can also go and use the other systems, it doesn't matter. Our mission is, as I said, we provide a system or a number of systems, we run them, but we really are about helping the researchers do their research. We're all scientists. All the guys that work with me, we're all scientists initially. We turned to computers because that was the way we do the research. You can not do astrophysics other than computationally, observationally and computationally, but nothing else. Climate science is the same story, you have so much data and so much modeling to do that you need a very large computer and, of course, very good algorithms and very careful physics modeling for an extremely complex system, but ultimately it needs a lot of horsepower to be able to even do a single simulation. So, what I was showing with Madhu at that booth earlier was results of a simulation that was done just prior us going into production with our Lenovo system where people were doing ocean circulation calculations. The ocean is obviously part of the big Earth system, which is part of the climate system as well. But, they took a small patch of the ocean, a few kilometers in size in each direction, but did it at very, very high resolution, even vertically going down to the bottom of the ocean so that the topography of the ocean floor can be taken into account. That allows you to see at a much smaller scale the onset of tides, the onset of micro-tides that allow water to mix, the cold water from the bottom and the hot water from the top; The mixing of nutrients, how life goes on, the whole cycle. It's super important. Now that, of course, gets coupled with the atmosphere and with the ice and with the radiation from the sun and all that stuff. That calculation was run by a group from, the main guy was from JPL in California, and he was running on 48,000 cores. Single runs at 48,000 cores for about two- to three-weeks and produced a petabyte of data, which is still being analyzed. That's the kind of resolution that's been enabled... >> Scale. >> It gives it a sense of just exactly... >> That's the scale. >> By a system the size of the one we have. It was not possible to do that in Canada before this system. >> I tell you both, when I lived on the vendor side and as an analyst, talking to labs and universities, you love geeking out. Because first of all, you always have a need for newer, faster things because the example you just gave is like, "Oh wait." "If I can get the next generation chipset." "If the networking can be improved." You know you can take that petabyte of data and process it so much faster. >> If I could only get more money to buy a bigger one. >> We've talked to the people at CERN and JPL and things like that. - Yeah. >> And it's like this is where most companies are it's like, yeah it's a little bit better, and it might make things a little better and make things nice, but no, this is critical to move along the research. So talk a little bit more about the infrastructure and what you look for and how that connects to the research and how you help close that gap over time. >> Before you go, I just want to also highlight a point that Danny made on solving humanity's greatest challenges which is our motto. He talked about the data analysis that he just did where they are looking at the surface of the ocean, as well as, going down, what is it, 264 nautical layers underneath the ocean? To analyze that much data, to start looking at marine life and protecting marine life. As you start to understand that level of nautical depth, they can start to figure out the nutrients value and other contents that are in that water to be able to start protecting the marine life. There again, another of humanity's greatest challenge right there that he's giving you... >> Nothing happens in isolation; It's all interconnected. >> Yeah. >> When you finally got a grant, you're able to buy a computer, how do you buy the computer that's going to give you the most bang for your buck? The best computer to do the science that we're all tasked with doing? It's tough, right? We don't fancy ourselves as computer architects; we engage the computer companies who really know about architecture to help us do it. The way we did our procurement was, 'Ok vendors, we have a set pot of money, we're willing to spend every last penny of this money, you give us the biggest and the baddest for our money." Now, it has to have a certain set of criteria. You have to be able to solve a number of benchmarks, some sample calculations that we provided. The ones that give you the best performance that's a bonus. It also has to be able to do it with the least amount of power, so we don't have to heat up the world and pay through the nose with power. Those are objective criteria that anybody can understand. But then, there's also the other criteria, so, how well will it run? How is it architected? How balanced is it? Did we get the iOS sub-system for all the storage that was the one that actually meets the criteria? What other extras do we have that will help us make the system run in a much smoother way and for a wide variety of disciplines because we run the biologists together with the physicists and the engineers and the humanitarians, the humanities people. Everybody uses the system. To make a long story short, the proposal that we got from Lenovo won the bid both in terms of what we got for in terms of hardware and also the way it was put together, which was quite innovative. >> Yeah. >> I want to hear about, you said give us the biggest, the baddest, we're willing to empty our coffers for this, so then where do you go from there? How closely do you work with SciNet, how does the relationship evolve and do you work together to innovate and kind of keep going? >> Yeah. I see it as not a segment or a division. I see High Performance Computing as a practice, and with any practice, it's many pieces that come together; you have a conductor, you have the orchestra, but the end of the day the delivery of that many systems is the concert. That's the way to look at it. To deliver this, our practice starts with multiple teams; one's a benchmarking team that understands the application that Dr. Gruner and SciNet will be running because they need to tune to the application the performance of the cluster. The second team is a set of solution architects that are deep engineers and understand our portfolio. Those two work together to say against this application, "Let's build," like he said, "the biggest, baddest, best-performing solution for that particular application." So, those two teams work together. Then we have the third team that kicks in once we win the business, which is coming on site to deploy, manage, and install. When Dr. Gruner talks about the infrastructure, it's a combination of hardware and software that all comes together and the software is open-source based that we built ourselves because we just felt there weren't the right tools in the industry to manage this level of infrastructure at that scale. All this comes together to essentially rack and roll onto their site. >> Let me just add to that. It's not like we went for it in a vacuum. We had already talked to the vendors, we always do. You always go, and they come to you and 'when's your next money coming,' and it's a dog and pony show. They tell you what they have. With Lenovo, at least the team, as we know it now, used to be the IBM team, iXsystems team, who built our previous system. A lot of these guys were already known to us, and we've always interacted very well with them. They were already aware of our thinking, where we were going, and that we're also open to suggestions for things that are non-conventional. Now, this can backfire, some data centers are very square they will only prescribe what they want. We're not prescriptive at all, we said, "Give us ideas about what can make this work better." These are the intangibles in a procurement process. You also have to believe in the team. If you don't know the team or if you don't know their track record then that's a no-no, right? Or, it takes points away. >> We brought innovations like DragonFly, which Dr. Dan will talk about that, as well as, we brought in for the first time, Excelero, which is a software-defined storage vendor and it was a smart part of the bid. We were able to flex muscles and be more creative versus just the standard. >> My understanding, you've been using water cooling for about a decade now, maybe? - Yes. >> Maybe you could give us a little bit about your experiences, how it's matured over time, and then Madhu will talk and bring us up to speed on project Neptune. >> Okay. Our first procurement about 10 years ago, again, that was the model we came up with. After years of wracking our brains, we could not decide how to build a data center and what computers to buy, it was like a chicken and egg process. We ended up saying, 'Okay, this is what we're going to do. Here's the money, here's is our total cost of operation that we can support." That included the power bill, the water, the maintenance, the whole works. So much can be used for infrastructure, and the rest is for the operational part. We said to the vendors, "You guys do the work. We want, again, the biggest and the baddest that we can operate within this budget." So, obviously, it has to be energy efficient, among other things. We couldn't design a data center and then put in the systems that we didn't know existed or vice-versa. That's how it started. The initial design was built by IBM, and they designed the data center for us to use water cooling for everything. They put rear door heat exchanges on the racks as a means of avoiding the use of blowing air and trying to contain the air which is less efficient, the air, and is also much more difficult. You can flow water very efficiently. You open the door of one of these racks. >> It's amazing. >> And it's hot air coming out, but you take the heat, right there in-situ, you remove it through a radiator. It's just like your car radiator. >> Car radiator. >> It works very well. Now, it would be nice if we could do even better by doing the hot water cooling and all that, but we're not in a university environment, we're in a strip mall out in the boonies, so we couldn't reuse the heat. Places like LRZ they're reusing the heat produced by the computers to heat their buildings. >> Wow. >> Or, if we're by a hospital, that always needs hot water, then we could have done it. But, it's really interesting how the option of that design that we ended up with the most efficient data center, certainly in Canada, and one of the most efficient in North America 10 years ago. Our PUE was 1.16, that was the design point, and this is not with direct water cooling through the chip. >> Right. Right. >> All right, bring us up to speed. Project Neptune, in general? >> Yes, so Neptune, as the name suggests, is the name of the God of the Sea and we chose that to brand our entire suite of liquid cooling products. Liquid cooling products is end to end in the sense that it's not just hardware, but, it's also software. The other key part of Neptune is a lot of these, in fact, most of these, products were built, not in a vacuum, but designed and built in conjunction with key partners like Barcelona Supercomputer, LRZ in Germany, in Munich. These were real-life customers working with us jointly to design these products. Neptune essentially allows you, very simplistically put, it's an entire suite of hardware and software that allows you to run very high-performance processes at a level of power and cooling utilization that's like using a much lower processor, it dissipates that much heat. The other key part is, you know, the normal way of cooling anything is run chilled water, we don't use chilled water. You save the money of chillers. We use ambient temperature, up to 50 degrees, 90% efficiency, 50 degree goes in, 60 degree comes out. It's really amazing, the entire suite. >> It's 50 Celsius, not Fahrenheit. >> It's Celsius, correct. >> Oh. >> Dr. Bruner talked about SciNet with the rado-heat exchanger. You actually got to stand in front of it to feel the magic of this, right? As geeky as that is. You open the door and it's this hot 60-, 65-degree C air. You close the door it's this cool 20-degree air that's coming out. So, the costs of running a data center drop dramatically with either the rado-heat exchanger, our direct to node product, which we just got released the SE650, or we have something call the thermal-transfer module, which replaces a normal heat sink. Where for an air cool we bring water cool goodness to an air cool product. >> Danny, I wonder if you can give us the final word, just the climate science in general, how's the community doing? Any technological things that are holding us back right now or anything that excites you about the research right now? >> Technology holds you back by the virtual size of the calculations that you need to do, but, it's also physics that hold you back. >> Yes. Because doing the actual modeling is very difficult and you have to be able to believe that the physics models actually work. This is one of the interesting things that Dick Peltier, who happens to be our scientific director and he's also one of the top climate scientists in the world, he's proven through some of his calculations that the models are actually pretty good. The models were designed for current conditions, with current data, so that they would reproduce the evolution of the climate that we can measure today. Now, what about climate that started happening 10,000 years ago, right? The climate was going on; it's been going on forever and ever. There's been glaciations; there's been all these events. It turns out that it has been recorded in history that there are some oscillations in temperature and other quantities that happen about every 1,000 years and nobody had been able to prove why they would happen. It turns out that the same models that we use for climate calculations today, if you take them back and do what's called paleoclimate, you start with approximating the conditions that happened 10,000 years ago, and then you move it forward, these things reproduce, those oscillations, exactly. It's very encouraging that the climate models actually make sense. We're not talking in a vacuum. We're not predicting the end of the world, just because. These calculations are right. They're correct. They're predicting the temperature of the earth is climbing and it's true, we're seeing it, but it will continue unless we do something. Right? It's extremely interesting. Now he's he's beginning to apply those results of the paleoclimate to studies with anthropologists and archeologists. We're trying to understand the events that happened in the Levant in the Middle East thousands of years ago and correlate them with climate events. Now, is that cool or what? >> That's very cool. >> So, I think humanity's greatest challenge is again to... >> I know! >> He just added global warming to it. >> You have a fun job. You have a fun job. >> It's all the interdisciplinarity that now has been made possible. Before we couldn't do this. Ten years ago we couldn't run those calculations, now we can. So it's really cool. - Amazing. Great. Well, Madhu, Danny, thank you so much for coming on the show. >> Thank you for having us. >> It was really fun talking to you. >> Thanks. >> I'm Rebecca Knight for Stu Miniman. We will have more from the Lenovo Transform just after this. (tech music)

>> Announcer: From New York, it's theCUBE! Covering Blockchain Week. Now, here's John Furrier. Hello everyone this is theCUBE's exclusive coverage here in New York at Blockchain Week New York, #BlockchainNY this is theCUBE, I'm John Furrier your host. Our next guest is Madhu Kutty, who's a partner at Arcadia Crypto Ventures, thanks for joining me here in New York City. We're at the Block Party, a private event here, thanks for joining us during Blockchain Week. >> Yep. >> So you guys do a lot of deals, we had Richard on, who's the managing partner of the firm, early in the space, super early, so you're in the front wave, get all the best deals, now it's competitive, you got to read the white papers, you got to get down and dirty. Still got the pretenders, figure out what's the bad deals, the good deals, and then when you get a good deal make sure it's tailor fit, both the tech matches the economics. (laughing) which I find to be interesting, because, you can have a brilliant entrepreneur come in but their token model's off. Do you see this every day? >> Yeah, we see this a lot. Especially the last year things were much more easier, because most of the people who are coming were generally at least trying to do something good, but this year we see a lot of people who just want to make use of the bail that's happening, just get on the hype and get some quick buck, even on traditional firms that've failed are coming and trying to capture the blockchain hype. >> Yeah, so throw the hail Mary basically, let's do an ICO, and they're going, we're going under throw the hail Mary! >> Yes, that's what we see a lot happening, and then there was a lot of tech projects back in the past so it was a little more easy to evaluate, so, but this year you're seeing more real business applications coming onboard. >> I was talking with Richard about some of the growth things around crypto, and I want to get your take on it because, the internet infrastructure is changing. We see the web 1.0, I mean we hear in these, all the events I go to, similar kind of conversation, TCBIP created inter-networking and inter-operability, HTTP created a whole new way to do things: web 1.0. Now we're hearing token economics and blockchain as a new way, but yet, inter-operating with the old systems, so you have a whole seat change, and there's a real tech-enablement. What's your view on this, because some people get it wrong, they understand what the business logic means, but they want to know what the tech-enablement is. What's the tech that's driving all this new infrastructure? >> So the internet, you could think of it as a way to share information with a worldwide audience, so blockchain, for the first time ever, enables on blockchain, or the inter-crypto infrastructure, first time enables humans to transfer value over the wire. So you could represent one as one over the wire, rather than creating like a duplication of one. So you could have your own Bitcoin stored on the network, and you can access it yourself, and you can send that value across. This was never possible in the history of the human race, so that's what a blockchain enables, it's the solution to basically this general problem, and we always thought that it was not possible but for the first time ever we have a means to achieve that. >> I've also been saying at some of these cube events, that every company needs a chief economic officer, you used to have a CGO, now you need a chief economic officer. So I got to ask you, when you see a technology, you got to kind of make sure it marries the right model. So in the token world, putting security tokens aside, which I like by the way. They're very easy to deal with, utility tokens are different you have two types of utility tokens a work-like token, and a burn-mint equilibrium approach. What's your take on the two strategies there, when should some appoint a burn-mint--or a BME strategy versus say a work token which is much more of a utility classic. >> So a burn token is what has been the work, at least in the past, for building actual platforms, so that solution itself is not fully solved. So once we solve that completely, that's when we see much more utility tokens coming on board, but as this point we see more of the remittance problem that's being solved so that, we have a lot of exchanges, and transferring of currency that's being worked on. So you can think of it like the email in the internet era, though we had all these different .coms, only email worked well. So right now the transfer of value, the remittance on the exchange is the only thing that's working, but as we go forward we'll see much more business models coming out. >> So it's really going to evolve. >> Yep. I think this could be the year that's going to be-- Ethereum was moment when, there was the Ethereum moment when you really could start the next generation of the cryptocurrency movement, so I think this year we could see more business. >> And I've heard some of the conversations here at the consensus event around, a lot of people trying to force blockchain and decentralize, specifically with a centralized business model, so a lot of people are poopooing that. Which is, we just call that blockchain washing, white washing, trying to save themselves. So I got to ask you, I mean first of all, I remember having conversations back when the web started, oh my God, AOL and this 14.4 dial up is so slow, so much slower than a mini computer, technically right, mini computer was much faster than dial up modems to web, but web wasn't replacing the mini computers, replacing direct mail, direct response, analog things. So the question I want to ask you is: what is the analog displacement, what apples-to-apples comparison should we be making when people throw out these idiotic comments like, oh my God blockchain's so slow, because it is kind of slow, the web was slow too, but it replaced something old. Is it right, is blockchain replacing something old, and what is the right comparison? >> So the right comparison is that it is, it has solved, theoretically, the problems, the theoretical solution for these problems we are going to solve the decentralization and decentralizing business, theoretically we have solved it, and we have proved it practically, it's possible, but it is not really there for that-- the mass option for real business to onboard, it has not reached that scale yet. As we all know, if Netflix was in business in 1999, it could never succeed. But then a lot of infrastructure was built up on top of it, and Netflix worked, so the same thing is going to happen here. >> So you're not worried about the complains when people say it's slow? >> No, because there's more in IC, each time I go to these conferences, as you have seen, more and more smart people are jumping in, more and more money is flowing in-- >> The web grew too, more people were using the web, so growth was the key. >> Yeah, growth is the key, and more smart people coming, and they're going to figure it out. >> When you look under the hood of a company they come in and say hey I want to get funding, or I have this great business model, or I take an existing business and tokenize it. What are the things you look for in a good ICO candidate, or just someone who's tryna do token economics, with a technology trying to transition, not pivot, transform into token economics? >> So a lot of it, something people call it is conviction-based investing, so that's a lot, they have a lot in this cryptocurrency space. So we look at the technology, underlying technology, how we can solve some of the issues. We look at the broader aspect of the space, how big the space is so it can solve that, and we also look at the team, and if these three things are in good combination we believe in it can be a rival business. And also the partners, or the founders, have to be a little less greedy, like look for smaller raises that's good enough for the next two years, roughly. >> Well I think entrepreneurs can get liquid faster off token economics, I think it's actually better for the entrepreneur, in my opinion. I want to take change gears a second talk about you personally how did you get here, did you just wake up one day and say I'm going to work for Arcadia Crypto Ventures, were you scratching an itch, did you come from finance, what's your background? >> My background has been in technology and finance, worked with a bunch of Wall Street banks, then private equity firms, then I was running a technology firm when I met Richard, who's very early in the space. So we talked about it and he had a lot of interesting questions about the space so myself and one of my partners we went back and researched on it. So we came back with these answers, he had a little more insight, back in the day, more access to the detail during that time, so we worked with him, and loosely, we worked as some kind of analyst for him and we started working together and then it formalized in a bigger way, now we are working. >> When did you have that moment saying damn this is going to be good? >> I think, so it was once we totally understood the Santoshi Paper, at that point we knew that this is going to change the world. But even I didn't expect that this would be this fast. So what we are seeing today, at that time I thought maybe we'll see that in 2020, 2021, but the space exploded. Ethereum hitting a thousand, which it hit sometime this year I was thinking might happen sometime 2021 or 2022, by then. >> What sector surprised you the most, was it the trading side, the entrepreneur side, what area of the market has surprised you the most? >> What's surprising is the world wide ERD option, and how especially the new generation has kind of lost a bit of interest, not even like they are disillusioned with this other investment model, they are jumping in a big way. And I think this is even ruggedly, everyone has to look for that, these people have come in, so let's get it right for these folks so that they have a belief in the system and they can go forward. >> Madhu I want to get your thoughts on something I think is important for folks to understand, and that is there's a lot of liquidity, Richard mentioned that liquidity is an important part. >> Absolutely. >> So there's a lot of new dynamics and art and science that goes into the trading side of it, much accelerated than a classic IPO or say a hedge-fund kind of deal, where there's always kind of some stuff going on, but here you can get much earlier in on the process. Talk about, for the folks who like now know what a wallet is and might have an account on Coinbase, to the extent that that's their knowledge base. You're so much deeper on some of the trading side, what are the dynamics, how would you break down the trading situation on crypto, give us the crypto trading 101. >> So the idea is that, first of all, there are some huge exchanges, so every cryptocurrency out there wants to be on these exchanges, so these exchanges have much more trading volume, have much more liquidity, that's where you want to be. If you are doing some investment and you want to protect it, you want to be in these highly liquidized ones. And so I would stick to top 10-20 coins for the majority of the portfolio if you want to protect your investment, so that has a lot more liquidity. And then, around I would say 10-20% you would do in sectors that you are interested in, where you really have some kind of idea, that's what I call a conviction-based investment. >> So if I want to convert my crypto to Fiat currency, you're saying stay with the top trading forms, or stay with the sector, what's the advice? >> If you're a regular investor, who's not following the market 24/7 I would say, at least, put like 80% on the top 20 coins where there is much more liquidity and which, you know won't go bust tomorrow. Then you would focus maybe 10-20% of your-- this is, I'm just talking with a crypto portfolio, on something you are going to have some kind of conviction, if you, let's say you are in an automobile space, that's what you understand a lot so any crypto on that, which you think is interesting, you could put your money there. And I'm a tech person, so I would put more money on technology platforms. >> What's your favorite tech coins right now, what're the investments you're putting money into? >> Oh, we've always been long on Bitcoin, Ethereum, so there are a lot of new, exciting stuff coming, like EOS, like we are big on Tezos, it's a very community-driven project, we are very excited about that, what Bloq is bringing, Metronome, I think that's going to be huge. These are very unique in their own ways, you're also offering something as a challenger to Ethereum, Tezos, also in some form, where they're very community-focused. And Metronome, for the first time, offers the ability to do cross-platform transactions. >> Madhu, this space is attracting a lot of young kids, I say kids, coming out of business school, or from a firm like Goldman Sachs, one of these classic firms, kind of bored. They want to do something new. What's your advice to the next generation coming in, jump on the wave, fall down, learn it, get off my wave, get off my beach, I mean what's your advice for the young people? >> If I was in that spot right now, I would just jump in and go with the flow, and you'll figure out what you need to do. At least rather than stick with the traditional companies, this is something new and exciting, and at least the next two-three years, spend on-- >> Get's your hands dirty, don't lose a lot of money. Try not to lose a lot of money. (both laughing) Madhu, thanks for coming on-- >> Thank you! >> Appreciate the commentary. We're here, exclusive coverage, we're at the Block Party, here at the Blockchain Week New York, exclusive continued coverage with theCUBE, we're here in New York City to breakdown all the action inside the ropes of the industry, I'm John Furrier, thanks for watching. (bubbly music)

>> narrator: From Las Vegas it's theCUBE covering IBM Think 2018. Brought to you by IBM. >> We're back at IBM Think 2018. My name is Dave Vellante. I'm here with Peter Burris, my co-host, and you're watching theCUBE, the leader in live tech coverage. Our day two of our wall to wall coverage of IBM Think. Madhu Kochar is here. She's the Vice President of Analytics, Product Development at IBM and she's joined by Pat Maqetuka >> Close enough. She's a data officer at Nedbank. Ladies, welcome to theCUBE. You have to say your last name for me. >> Patricia Maqetuka. >> Oh, you didn't click >> I did! >> Do it again. >> Maqetuka. >> Amazing. I wish I could speak that language. Well, welcome. >> Madhu & Pat: Thank you. >> Good to see you again. >> Madhu: Thank you. >> Let's start with IBM Think. New show for you guys you consolidated, you know, six big tent events into one. There's a lot of people, there's too many people, to count I've been joking. 30, 40 thousand people, we're not quite sure, but how's the event going for you? What are clients telling you? >> Yeah, no, I mean, to your point, yes, we brought in all three big pillars together; a lot of folks here. From data and analytics perspective, an amazing, amazing event for us. Highlights from yesterday with Arvind Krishna on our research. What's happening. You know, five for five, that was really inspiring for all of us. You know, looking into the future, and it's not all about technology, was all about how we are here to help protect the world and change the world. So that as a, as a gige, as an engineer, that was just so inspiring. And as I was talking to our clients, they walk away with IBM as really a solution provider and helping, so that was really good. I think today's, Ginni's, keynote was very inspiring, as well. From our clients, we got some of our key clients, you know, Nedbank is here with us, and we've been talking a lot about our future, our strategy. We just announced, Ginni actually announced, our new product, IBM Cloud Private for Data. Everything around data, you know, where we are really bringing the power of data and analytics all together on a private cloud. So that's a huge announcement for us, and we've been talking a lot with our clients and the strategies resonating and particularly, where I come from in terms of the co-ordinance and integration space, this is definitely becoming now the "wow" factor because it helps stitch the entire solutions together and provide, you know, better insights to the data. >> Pat from your perspective, you're coming from Johannesburg so you probably like the fact that there's all IBM in one, so you don't have to come back to three or four conferences every year, but love your perspectives on that, and can you please tell us about Nedbank and your role as Chief Data Officer. >> Nedbank is one of the big five financial banks in South Africa. I've been appointed as the CDO about 18 months ago, so it's a new role in the bank per say. However, we're going through tremendous transformation in the bank and especially our IT eco-system has been transformed because we need to keep up with what is happening in the IT world. >> Are you the banks first Chief Data Officer? >> Definitely yes. I'm the first. >> Okay. So, you're a pioneer. I have to ask you then, so where did you start when you took over as the Chief Data Officer? I mean banking is one of those industries that tends to be more Chief Data Officer oriented, but it's a new role, so where did you start? >> Well we are not necessary new in the data, per say. We have had traditional data warehousing functions in the organization with traditional warehousing or data roles in the organization. However, the Chief Data role was never existent in the bank and actual fact, the bank appointed two new roles 18 months ago. One of them was the Chief Digital Officer, who is my colleague, and myself being appointed as the Chief Data Officer. >> Interesting, okay. I talked to somebody from Northern Trust yesterday and she was the lead data person and she said, "I had to start with a mission. We had to find the mission first and then we looked at the team, and then we evolved into, how we contribute to the business, how we improve data quality, who has access, what skills we needed." Does that seem like a logical progression and did you take a similar path? >> I think every bank will look at it differently or every institution. However, from a Nedbank perspective, we were given the gift by the regulator in bringing the BCBS 239 compliancy into play, so what the bank then did, how do we leverage, not just being compliant, but leveraging the data to create competitive advantage, and to create new sources of revenue. >> Okay. Let's talk about, Madhu we talked about this in New York City, you know, governance, compliance, kind of an evil word to a lot of business people. Although, your contention was "Look, it's reality. You can actually turn it into a positive." So talk about that a little bit and then we can tie it into Nedbank's experiences. >> Yeah, so I firmly believe in, you know, in the past governance 1.0 was all about compliance and regulations, very critical, but that's all we drew. I believe now, it's all about governance 2.0, where it's not just the compliance, but how do I drive insights, you know, so data is so, so critical from that perspective, and driving insights quicker to your businesses, is going to be very important, so as we engage with Nedbank and other clients as well, they are turning that because they are incumbents. They know their data, they've got a lot of data, you know, some of, they know sitting in structure, law structure land, and it's really, really important that they quickly able to assess what's in it, classify it, right, and then quickly deliver the results to the businesses, which they're looking for, so we're, I believe in lieu of governance 2.0, and compliance and regulations are always going to be with us, and we're making, actually, a lot of improvements in our technology, introducing machine learning, how we can do these things faster and quicker. >> So one of the first modern pieces of work that Peter and I did was around data classification and that seems to be, I heard this theme before, it seems to be a component or a benefit of putting governance in place. That you can automate data classification and use it to affect policy, but Pat, from your standpoint, how do you approach governance, what are the business benefits beyond "we have to do this"? >> Like I earlier on alluded to, we took the regulation as a gift and said, "How do we turn this regulation into benefits for the organization?" So in looking at the regulation we then said, "How do we then structure the approach?" So we looked at the two prompts. The first was, the right to win. The right to win meaning that we are able to utilize the right to compete approach from a regulation perspective, to create a platform and a foundation for analytics for our organization. We also created the blueprint for our enterprise data program and in the blueprint, we also came up with key nine principles of what it means to stay true to our data. I.e. you mentioned classification, you mentioned data politic, you mentioned lineage. Those are the key aspects within our principles. The other key principle we also indicated was the issue around duplication. How do we ensure that we describe data once, we ingest it once, but we use it multiple times to answer different questions, and as you are aware, in analytics, the more you mine the data, the more inquisitive you become, so it is, (clears throat) Sorry. It's not been from data to information, information to insight, and eventually insights to foresight, so looking into the future, and now you bring it back into data. >> And also some points that you've made Pat, so the concept is, one of the challenges of using the fuel example, is that governance of fuel, is still governance of a thing. You can apply it here, you can apply it there, you can't apply it to both places. Data's different and you were very, very accurate when you said "We wanted to find it once, we want to ingest it once, we want to use it multiple times". That places a very different set of conditions on the types of governance and in many respects, in the past, other types of assets where there is this sense of scarcity, it is a problem, but one of the things that I'm, and this is a question, is the opportunity, you said the regulatory opportunity, is the opportunity, because data can be shared, should we start treating governance really as a way of thinking about how to generate value out of data, and not a way of writing down the constraints of how we use it. What do you think about that? >> I think you are quite right with that because the more you give the people the opportunity to go and explore, so you unleash empowerment, you unleash freedom for them to go and explore. They will not see governance as a stick like I initially indicated, but they see it as business as usual, so it will come natural. However, it doesn't happen overnight. People need to be matured, organization is to be matured. Now, the first step you have to do is to create those policies, create awareness around the policies, and make sure that the people who are utilizing the data are trained in to what are the do's and the don'ts. We are fully aware that cyber security's one of our biggest threats, so you can also not look at how you create security around your data. People knowing that how I use my data it is an asset of the bank and not an asset of an individual. >> I know you guys have to go across the street, but I wanted to get this in. You're a global analytics global elite client; I want to understand what the relationship is. I mean, IBM, why IBM, maybe make a few comments about your relationship with the company. >> I think we as Nedbank, we are privileged, actually, to be inculcated into this global elite program of IBM. That has helped me in actual, in advancing what we need to do from a data perspective because anytime I can pick up a phone to collaborate with the IBM MaaS, I can pick up the phone whenever I need support, I need guidance. I don't have to struggle alone because they've done it with all the other clients before, so why should I reinvent the wheel, whereas someone else has done it, so let me tap into that, so that that can progress quicker than try it first. >> Alright. Madhu, we'll give you the final word. On Think and your business and your priorities. >> So, Think is amazing, you know, the opportunity to meet with all our clients and coming from product development, talking about our strategy and getting that validation is just good, you know, sharing open road maps with clients like Nedbank and our other global elites, you know. It gives us an opportunity, not just sharing of the road maps, but actually a lot of co-creation, right, to take us into the future, so I'm having a blast. I got to go run over and meet a few other clients, but thank you for having us over here. It's a pleasure. >> You're very welcome and thank you so much for coming on and telling your story, Pat, and Madhu, always a pleasure to see you. >> Thank you. >> Alright, got to get in your high horse and go. Thanks for watching everybody, we'll be right back after this short break. You're watching theCUBE live from IBM Think 2018. We'll be right back. (electronic music)

>> Announcer: Live from New York, it's theCUBE covering Machine Learning Everywhere, Build Your Ladder To AI, brought to you by IBM. (techy music playing) >> Welcome back to New York City as we continue here at IBM's Machine Learning Everywhere, Build Your Ladder To AI bringing it to you here on theCUBE, of course the rights to the broadcast of SiliconANGLE Media and Dave Vellante joins me here. Dave, good morning once again to you, sir. >> Hey, John, good to see you. >> And we're joined by Madhu Kochar, who is the Vice President of Analytics Development and Client Success at IBM, I like that, client success. Good to see you this morning, thanks for joining us. >> Yeah, thank you. >> Yeah, so let's bring up a four letter / ten letter word, governance, that some people just cringe, right, right away, but that's very much in your wheelhouse. Let's talk about that in terms of what you're having to be aware of today with data and all of a sudden these great possibilities, right, but also on the other side, you've got to be careful, and I know there's some clouds over in Europe as well, but let's just talk about your perspective on governance and how it's important to get it all under one umbrella. >> Yeah, so I lead product development for IBM analytics, governance, and integration, and like you said, right, governance has... Every time you talk that, people cringe and you think it's a dirty word, but it's not anymore, right. Especially when you want to tie your AI ladder story, right, there is no AI without information architecture, no AI without IA, and if you think about IA, what does that really mean? It means the foundation of that is data and analytics. Now, let's look deeper, what does that really mean, what is data analytics? Data is coming at us from everywhere, right, and there's records... The data shows there's about 2.5 quintillion bytes of data getting generated every single day, raw data from everywhere. How are we going to make sense out of it, right, and from that perspective it is just so important that you understand this type of data, what is the type of data, what's the classification of this means in a business. You know, when you are running your business, there's a lot of cryptic fields out there, what is the business terms assigned to it and what's the lineage of it, where did it come from. If you do have to do any analytics, if data scientists have to do any analytics on it they need to understand where did it actually originated from, can I even trust this data. Trust is really, really important here, right, and is the data clean, what is the quality of this data. The data is coming at us all raw formats from IOT sensors and such. What is the quality of this data? To me, that is the real definition of governance. Right, it's not just about what we used to think about compliance, yes, that's-- >> John: Like rolling a rag. >> Right, right. >> But it's all about being appropriate with all the data you have coming in. >> Exactly, I call it governance 2.0 or governance for insights, because that's what it needs to be all about. Right, compliance, yes indeed, with GDPR and other things coming at us it's important, but I think the most critical is that we have to change the term of governance into, like, this is that foundation for your AI ladder that is going to help us really drive the right insights, that's my perspective. >> I want to double click on that because you're right, I mean, it is kind of governance 2.0. It used to be, you know, Enron forced a lot of, you know, governance and the Federal Rules of Civil Procedure forced a lot of sort of even some artificial governance, and then I think organization, especially public companies and large organizations said, "You know what, we can't just do "this as a band-aid every time." You know, now GDPR, many companies are not ready for GDPR, we know that. Having said that, because it is, went through governance 1.0, many companies are not panicked. I mean, they're kind of panicking because May is coming, (laughs) but they've been through this before. >> Madhu: Mm-hm. >> Do you agree with that premise, that they've got at least the skillsets and the professionals to, if they focus, they can get there pretty quickly? >> Yeah, no, I agree with that, but I think our technology and tools needs to change big time here, right, because regulations are coming at us from all different angles. Everybody's looking to cut costs, right? >> Dave: Right. >> You're not going to hire more people to sit there and classify the data and say, "Hey, is this data ready for GDPR," or for Basel or for POPI, like in South Africa. I mean, there's just >> Dave: Yeah. >> Tons of things, right, so I do think the technology needs to change, and that's why, you know, in our governance portfolio, in IBM information server, we have infused machine learning in it, right, >> Dave: Hm. >> Where it's automatically you have machine learning algorithms and models understanding your data, classifying the data. You know, you don't need humans to sit there and assign terms, the business terms to it. We have compliance built into our... It's running actually on machine learning. You can feed in taxonomy for GDPR. It would automatically tag your data in your catalog and say, "Hey, this is personal data, "this is sensitive data, or this data "is needed for these type of compliance," and that's the aspect which I think we need to go focus on >> Dave: Mm-hm. >> So the companies, to your point, don't shrug every time they hear regulations, that it's kind of built in-- >> Right. >> In the DNA, but technologies have to change, the tools have to change. >> So, to me that's good news, if you're saying the technology and the tools is the gap. You know, we always talk about people, process, and technology the bromide is, but it's true, people and process are the really-- >> Madhu: Mm-hm. >> Hard pieces of it. >> Madhu: Mm-hm. >> Technology comes and goes >> Madhu: Mm-hm. >> And people kind of generally get used to that. So, I'm inferring from your comments that you feel as though governance, there's a value component of governance now >> Yeah, yeah. >> It's not just a negative risk avoidance. It can be a contributor to value. You mentioned the example of classification, which I presume is auto-classification >> Madhu: Yes. >> At the point of use or creation-- >> Madhu: Yes. >> Which has been a real nagging problem for decades, especially after FRCP, Federal Rules of Civil Procedure, where it was like, "Ugh, we can't figure "this out, we'll do email archiving." >> Madhu: Mm-hm. >> You can't do this manually, it's just too much data-- >> Yeah. >> To your point, so I wonder if you could talk a little bit about governance and its contribution to value. >> Yeah, so this is good question. I was just recently visiting some large banks, right, >> Dave: Mm-hm. >> And normally, the governance and compliance has always been an IT job, right? >> Dave: Right. >> And they figure out bunch of products, you know, you can download opensource and do other things to quickly deliver data or insights to their business groups, right, and for business to further figure out new business models and such, right. So, recently what has happened is by doing machine learning into governance, you're making your IT guys the heroes because now they can deliver stuff very quickly, and the business guys are starting to get those insights and their thoughts on data is changing, you know, and recently I was talking with these banks where they're like, "Can you come and talk to "our CFOs because I think the policies," the cultural change you referred to then, maybe the data needs to be owned by businesses. >> Dave: Hm. >> No longer an IT thing, right? So, governance I feel like, you know, governance and integration I feel like is a glue which is helping us drive that cultural change in the organizations, bringing IT and the business groups together to further drive the insights. >> So, for years we've been talking about information as a liability or an asset, and for decades it was really viewed as a liability, get rid of it if you can. You have to keep it for seven years, then get rid of it, you know. That started to change, you know, with the big data movement, >> Madhu: Yeah. >> But there was still sort of... It was hard, right, but what I'm hearing now is increasingly, especially of the businesses sort of owning the data, it's becoming viewed as an asset. >> Madhu: Yes. >> You've got to manage the liabilities, we got that, but now how do we use it to drive business value. >> Yeah, yeah, no, exactly, and that's where I think our focus in IBM analytics, with machine learning and automation, and truly driving that insights out of the data. I mean, you know, people... We've been saying data is a natural resource. >> Dave: Mm-hm. >> It's our bloodline, it's this and that. It truly is, you know, and talking to the large enterprises, everybody is in their mode of digital transformation or transforming, right? We in IBM are doing the same things. Right, we're eating our own, drinking our own champagne (laughs). >> John: Not the Kool-Aid. >> You know, yeah, yeah. >> John: Go right to the dog. >> Madhu: Yeah, exactly. >> Dave: No dog smoothie. (laughs) >> Drinking our own champagne, and truly we're seeing transformation in how we're running our own business as well. >> Now what, there are always surprises. There are always some, you know, accidents kind of waiting to happen, but in terms of the IOT, you know, have got these millions, right, of sensors-- >> Madhu: Mm-hm. >> You know, feeding data in, and what, from a governance perspective, is maybe a concern about, you know, an unexpected source or an unexpected problem or something where yeah, you have great capabilities, but with those capabilities might come a surprise or two in terms of protecting data and a machine might provide perhaps a little more insight than you might've expected. So, I mean, just looking down the road from your perspective, you know, is there anything along those lines that you're putting up flags for just to keep an eye on to see what new inputs might create new problems for you? >> Yeah, no, for sure, I mean, we're always looking at how do we further do innovation, how do we disrupt ourselves and make sure that data doesn't become our enemy, right, I mean it's... You know, as we are talking about AI, people are starting to ask a lot of questions about ethics and other things, too, right. So, very critical, so obviously when you focus on governance, the point of that is let's take the manual stuff out, make it much faster, but part of the governance is that we're protecting you, right. That's part of that security and understanding of the data, it's all about that you don't end up in jail. Right, that's the real focus in terms of our technology in terms of the way we're looking at. >> So, maybe help our audience a little bit. So, I described at our open AI is sort of the umbrella and machine learning is the math and the algorithms-- >> Madhu: Yeah. >> That you apply to train systems to do things maybe better than, maybe better than humans can do and then there's deep learning, which is, you know, neural nets and so forth, but am I understanding that you've essentially... First of all, is that sort of, I know it's rudimentary, but is it reasonable, and then it sounds like you've infused ML into your software. >> Madho: Yes. >> And so I wonder if you could comment on that and then describe from the client's standpoint what skills they need to take advantage of that, if any. >> Oh, yeah, no, so embedding ML into a software, like a packaged software which gets delivered to our client, people don't understand actually how powerful that is, because your data, your catalog, is learning. It's continuously learning from the system itself, from the data itself, right, and that's very exciting. The value to the clients really is it cuts them their cost big time. Let me give you an example, in a large organization today for example, if they have, like, maybe 22,000 some terms, normally it would take them close to six months for one application with a team of 20 to sit there and assign the terms, the right business glossary for their business to get data. (laughs) So, by now doing machine learning in our software, we can do this in days, even in hours, obviously depending on what's the quantity of the data in the organization. That's the value, so the value to the clients is cutting down that. They can take those folks and go focus on some, you know, bigger value add applications and others and take advantage of that data. >> The other huge value that I see is as the business changes, the machine can help you adapt. >> Madhu: Yeah. >> I mean, taxonomies are like cement in data classification, and while we can't, you know, move the business forward because we have this classification, can your machines adapt, you know, in real time and can they change at the speed of my business, is my question. >> Right, right, no, it is, right, and clients are not able to move on their transformation journey because they don't have data classified done right. >> Dave: Mm-hm. >> They don't, and you can't put humans to it. You're going to need the technology, you're going to need the machine learning algorithms and the AI built into your software to get that, and that will lead to, really, success of every kind. >> Broader question, one of the good things about things like GDPR is it forces, it puts a deadline on there and we all know, "Give me a deadline and I'll hit it," so it sort of forces action. >> Madhu: Mm-hm. >> And that's good, we've talked about the value that you can bring to an organization from a data perspective, but there's a whole non-governance component of data orientation. How do you see that going, can the governance initiatives catalyze sort of what I would call a... You know, people talk about a data driven organization. Most companies, they may say they are data driven but they're really not foundational. >> Mm-hm. >> Can governance initiatives catalyze that transformation to a data driven organization, and if so, how? >> Yeah, no, absolutely, right. So, the example I was sharing earlier with talking to some of the large financial institutes, where the business guys, you know, outside of IT are talking about how important it is for them to get the data really real time, right, and self-service. They don't want to be dependent on either opening a work ticket for somebody in IT to produce data for them and god forbid if somebody's out on vacation they can never get that. >> Dave: Right. >> We don't live in that world anymore, right. It's online, it's real time, it's all, you know, self-service type of aspects, which the business, the data scientists building new analytic models are looking for that. So, for that, data is the key, key core foundation in governance. The way I explained it earlier, it's not just about compliance. That is going to lead to that transformation for every client, it's the core. They will not be successful without that. >> And the attributes are changing. Not only is it self-service, it's pervasive-- >> Madhu: Yeah. >> It's embedded, it's aware, it's anticipatory. Am I overstating that? >> Madhu: No. >> I mean, is the data going to find me? >> Yeah, you know, (laughs) that's a good way to put it, you know, so no, you're at the, I think you got it. This is absolutely the right focus, and the companies and the enterprises who understand this and use the right technology to fix it that they'll win. >> So, if you have a partner that maybe, if it is contextual, I mean... >> Dave: Yeah. >> So, also make it relevant-- >> Madhu: Yes. >> To me and help me understand its relevance-- >> Madhu: Yes. >> Because maybe as a, I hate to say as a human-- >> Madhu: Yes. >> That maybe just don't have that kind of prism, but can that, does that happen as well, too? >> Madhu: Yeah, no. >> John: It can put up these white flags and say, "Yeah, this is what you need." >> Yeah, no, absolutely, so like the focus we have on our natural language processing, for example, right. If you're looking for something you don't have to always know what your SQL is going to be for a query to do it. You just type in, "Hey, I'm looking for "some customer retention data," you know, and it will go out and figure it out and say, "Hey, are you looking for churn analysis "or are you looking to do some more promotions?" It will learn, you know, and that's where this whole aspect of machine learning and natural language processing is going to give you that contextual aspect of it, because that's how the self-service models will work. >> Right, what about skills, John asked me at the open about skillsets and I want to ask a general question, but then specifically about governance. I would make the assertion that most employees don't have the multidimensional digital skills and domain expertise skills today. >> Yeah. >> Some companies they do, the big data companies, but in governance, because it's 2.0, do you feel like the skills are largely there to take advantage of the innovations that IBM is coming out with? >> I think I generally, my personal opinion is the way the technology's moving, the way we are getting driven by a lot of disruptions, which are happening around us, I think we don't have the right skills out there, right. We all have to retool, I'm sure all of us in our career have done this all the time. You know, so (laughs) to me, I don't think we have it. So, building the right tools, the right technologies and enabling the resources that the teams out there to retool themselves so they can actually focus on innovation in their own enterprises is going to be critical, and that's why I really think more burn I can take off from the IT groups, more we can make them smarter and have them do their work faster. It will help give that time to go see hey, what's their next big disruption in their organization. >> Is it fair to say that traditionally governance has been a very people-intensive activity? >> Mm-hm. >> Will governance, you know, in the next, let's say decade, become essentially automated? >> That's my desire, and with the product-- >> Dave: That's your job. >> That's my job, and I'm actually really proud of what we have done thus far and where we are heading. So, next time when we meet we will be talking maybe governance 3.0, I don't know, right. (laughs) Yeah, that's the thing, right? I mean, I think you hit it on the nail, that this is, we got to take a lot of human-intensive stuff out of our products and more automation we can do, more smarts we can build in. I coined this term like, hey, we've got to build smarter metadata, right? >> Dave: Right. >> Data needs to, metadata is all about data of your data, right? That needs to become smarter, think about having a universe where you don't have to sit there and connect the dots and say, "I want to move from here to there." System already knows it, they understand certain behaviors, they know what your applications is going to do and it kind of automatically does it for you. No more science fake, I think it can happen. (laughs) >> Do you think we'll ever have more metadata than data... (laughs) >> Actually, somebody did ask me that question, will we be figuring out here we're building data lakes, what do we do about metadata. No, I think we will not have that problem for a while, we'll make it smarter. >> Dave: Going too fast, right. >> You're right. >> But it is, it's like working within your workforce and you're telling people, you know, "You're a treasure hunter and we're going to give you a better map." >> Madhu: Yeah. >> So, governance is your better map, so trust me. >> Madhu: Hey, I like that, maybe I'll use it next time. >> Yeah, but it's true, it's like are you saying governance is your friend here-- >> Madhu: Yes. >> And we're going to fine-tune your search, we're going to make you a more efficient employee, we're going to make you a smarter person and you're going to be able to contribute in a much better way, but it's almost enforced, but let it be your friend, not your foe. >> Yes, yeah, be your differentiator, right. >> But my takeaway is it's fundamental, it's embedded. You know, you're doing this now with less thinking. Security's got to get to the same play, but for years security, "Ugh, it slows me down," but now people are like, "Help me," right, >> Madhu: Mm-hm. >> And I think the same dynamic is true here, embedded governance in my business. Not a bolt on, not an afterthought. It's fundamental and foundational to my organization. >> Madhu: Yeah, absolutely. >> Well, Madhu, thank you for the time. We mentioned on the outset by the interview if you want to say hi to your kids that's your camera right there. Do you want to say hi to your kids real quick? >> Yeah, hi Mohed, Kepa, I love you so much. (laughs) >> All right. >> Thank you. >> So, they know where mom is. (laughs) New York City at IBM's Machine Learning Everywhere, Build Your Ladder To AI. Thank you for joining us, Madhu Kochar. >> Thank you, thank you. >> Back with more here from New York in just a bit, you're watching theCUBE. (techy music playing)

>> Narrator: Live from San Jose, in the heart of Silicon Valley, it's theCUBE. Covering DataWorks Summit 2017, brought to you by Hortonworks. (digitalized music) >> Welcome back to theCUBE. We are live at day one of the DataWorks Summit, in the heart of Silicon Valley. I'm Lisa Martin with theCUBE; my co-host George Gilbert. We're very excited to be joined by our two next guests. Going to be talking about a lot of the passion and the energy that came from the keynote this morning and some big announcements. Please welcome Madhu Kochar, VP of analytics and product development and client success at IBM, and Jamie Engesser, VP of product management at Hortonworks. Welcome guys! >> Thank you. >> Glad to be here. >> First time on theCUBE, George and I are thrilled to have you. So, in the last six to eight months doing my research, there's been announcements between IBM and Hortonworks. You guys have been partners for a very long time, and announcements on technology partnerships with servers and storage, and presumably all of that gives Hortonworks Jamie, a great opportunity to tap into IBM's enterprise install base, but boy today? Socks blown off with this big announcement between IBM and Hortonworks. Jamie, kind of walk us through that, or sorry Madhu I'm going to ask you first. Walk us through this announcement today. What does it mean for the IBM-Hortonworks partnership? Oh my God, what an exciting, exciting day right? We've been working towards this one, so three main things come out of the announcement today. First is really the adoption by Hortonworks of IBM data sciences machine learning. As you heard in the announcement, we brought the machine learning to our mainframe where the most trusted data is. Now bringing that to the open source, big data on Hadoop, great right, amazing. Number two is obviously the whole aspects around our big sequel, which is bringing the complex-query analytics, where it brings all the data together from all various sources and making that as HDP and Hadoop and Hortonworks and really adopting that amazing announcement. Number three, what we gain out of this humongously, obviously from an IBM perspective is the whole platform. We've been on this journey together with Hortonworks since 2015 with ODPI, and we've been all champions in the open source, delivering a lot of that. As we start to look at it, it makes sense to merge that as a platform, and give to our clients what's most needed out there, as we take our journey towards machine learning, AI, and enhancing the enterprise data warehousing strategy. >> Awesome, Jamie from your perspective on the product management side, what is this? What's the impact and potential downstream, great implications for Hortonworks? >> I think there's two things. I think Hortonworks has always been very committed to the open source community. I think with Hortonworks and IBM partnering on this, number one is it brings a much bigger community to bear, to really push innovation on top of Hadoop. That innovation is going to come through the community, and I think that partnership drives two of the biggest contributors to the community to do more together. So I think that's number one is the community interest. The second thing is when you look at Hadoop adoption, we're seeing that people want to get more and more value out of Hadoop adoption, and they want to access more and more data sets, to number one get more and more value. We're seeing the data science platform become really fundamental to that. They're also seeing the extension to say, not only do I need data science to get and add new insights, but I need to aggregate more data. So we're also seeing the notion of, how do I use big sequel on top of Hadoop, but then I can federate data from my mainframe, which has got some very valuable data on it. DB2 instances and the rest of the data repositories out there. So now we get a better federation model, to allow our customers to access more of the data that they can make better business decisions on, and they can use data science on top of that to get new learnings from that data. >> Let me build on that. Let's say that I'm a Telco customer, and the two of you come together to me and say, we don't want to talk to you about Hadoop. We want to talk to you about solving a problem where you've got data in applications and many places, including inaccessible stuff. You have a limited number of data scientists, and the problem of cleaning all the data. Even if you build models, the challenge of integrating them with operational applications. So what do the two of you tell me the Telco customer? >> Yeah, so maybe I'll go first. So the Telco, the main use case or the main application as I've been talking to many of the largest Telco companies here in U.S. and even outside of U.S. is all about their churn rate. They want to know when the calls are dropping, why are they dropping, why are the clients going to the competition and such? There's so much data. The data is just streaming and they want to understand that. I think if you bring the data science experience and machine learning to that data. That as said, it doesn't matter now where the data resides. Hadoop, mainframes, wherever, we can bring that data. You can do a transformation of that, cleanup the data. The quality of the data is there so that you can start feeding that data into the models and that's when the models learn. More data it is, the better it is, so they train, and then you can really drive the insights out of it. Now data science the framework, which is available, it's like a team sport. You can bring in many other data scientists into the organization who could have different analyst reports to go render for or provide results into. So being a team support, being a collaboration, bringing together with that clean data, I think it's going to change the world. I think the business side can have instant value from the data they going to see. >> Let me just test the edge conditions on that. Some of that data is streaming and you might apply the analytics in real time. Some of it is, I think as you were telling us before, sort of locked up as dark data. The question is how much of that data, the streaming stuff and the dark data, how much do you have to land in a Hadoop repository versus how much do you just push the analytics out too and have it inform a decision? >> Maybe I can take a first thought on it. I think there's a couple things in that. There's the learnings, and then how do I execute the learnings? I think the first step of it is, I tend to land the data, and going to the Telecom churn model, I want to see all the touch points. So I want to see the person that came through the website. He went into the store, he called into us, so I need to aggregate all that data to get a better view of what's the chain of steps that happened for somebody to churn? Once I end up diagnosing that, go through the data science of that, to learn the models that are being executed on that data, and that's the data at rest. What I want to do is build the model out so that now I can take that model, and I can prescriptively run it in this stream of data. So I know that that customer just hung up off the phone, now he walked in the store and we can sense that he's in the store because we just registered that he's asking about his billing details. The system can now dynamically diagnose by those two activities that this is a churn high-rate, so notify that teller in the store that there's a chance of him rolling out. If you look at that, that required the machine learning and data science side to build the analytical model, and it required the data-flow management and streaming analytics to consume that model to make a real-time insight out of it, to ultimately stop the churn from happening. Let's just give the customer a discount at the end of the day. That type of stuff; so you need to marry those two. >> It's interesting, you articulated that very clearly. Although then the question I have is now not on the technical side, but on the go-to market side. You guys have to work very very closely, and this is calling at a level that I assume is not very normal for Hortonworks, and it's something that is a natural sales motion for IBM. >> So maybe I'll first speak up, and then I'll let you add some color to that. When I look at it, I think there's a lot of natural synergies. IBM and Hortonworks have been partnered since day one. We've always continued on the path. If you look at it, and I'll bring up community again and open source again, but we've worked very well in the community. I think that's incubated a really strong and fostered a really strong relationship. I think at the end of the day we both look at what's going to be the outcome for the customer and working back from that, and we tend to really engage at that level. So what's the outcome and then how do we make a better product to get to that outcome? So I think there is a lot of natural synergies in that. I think to your point, there's lots of pieces that we need to integrate better together, and we will join that over time. I think we're already starting with the data science experience. A bunch of integration touchpoints there. I think you're going to see in the information governance space, with Atlas being a key underpinning and information governance catalog on top of that, ultimately moving up to IBM's unified governance, we'll start getting more synergies there as well and on the big sequel side. I think when you look at the different pods, there's a lot of synergies that our customers will be driving and that's what the driving factors, along with the organizations are very well aligned. >> And VPF engineering, so there's a lot of integration points which were already identified, and big sequel is already working really well on the Hortonworks HDP platform. We've got good integration going, but I think more and more on the data science. I think in end of the day we end up talking to very similar clients, so going as a joined go-to market strategy, it's a win-win. Jamie and I were talking earlier. I think in this type of a partnership, A our community is winning and our clients, so really good solutions. >> And that's what it's all about. Speaking of clients, you gave a great example with Telco. When we were talking to Rob Thomas and Rob Bearden earlier on in the program today. They talked about the data science conversation is at the C-suite, so walk us through an example of whether it's a Telco or maybe a healthcare organization, what is that conversation that you're having? How is a Telco helping foster what was announced today and this partnership? >> Madhu: Do you want to take em? >> Maybe I'll start. When we look in a Telco, I think there's a natural revolution, and when we start looking at that problem of how does a Telco consume and operate data science at a larger scale? So at the C-suite it becomes a people-process discussion. There's not a lot of tools currently that really help the people and process side of it. It's kind of an artist capability today in the data science space. What we're trying to do is, I think I mentioned team sport, but also give the tooling to say there's step one, which is we need to start learning and training the right teams and the right approach. Step two is start giving them access to the right data, etcetera to work through that. And step three, giving them all the tooling to support that, and tooling becomes things like TensorFlow etcetera, things like Zeppelin, Jupiter, a bunch of the open source community evolved capabilities. So first learn and training. The second step in that is give them the access to the right data to consume it, and then third, give them the right tooling. I think those three things are helping us to drive the right capabilities out of it. But to your point, elevating up to the C-suite. It's really they think people-process, and I think giving them the right tooling for their people and the right processes to get them there. Moving data science from an art to a science, is I would argue at a top level. >> On the client success side, how instrumental though are your clients, like maybe on the Telco side, in actually fostering the development of the technology, or helping IBM make the decision to standardize on HDP as their big data platform? >> Oh, huge, huge, a lot of our clients, especially as they are looking at the big data. Many of them are actually helping us get committers into the code. They're adding, providing; feet can't move fast enough in the engineering. They are coming up and saying, "Hey we're going to help" "and code up and do some code development with you." They've been really pushing our limits. A lot of clients, actually I ended up working with on the Hadoop site is like, you know for example. My entire information integration suite is very much running on top of HDP today. So they are saying, OK what's next? We want to see better integration. So as I called a few clients yesterday saying, "Hey, under embargo this is something going to get announced." Amazing, amazing results, and they're just very excited about this. So we are starting to get a lot of push, and actually the clients who do have large development community as well. Like a lot of banks today, they write a lot of their own applications. We're starting to see them co-developing stuff with us and becoming the committers. >> Lisa: You have a question? >> Well, if I just were to jump in. How do you see over time the mix of apps starting to move from completely custom developed, sort of the way the original big data applications were all written, down to the medal-ep in MapReduce. For shops that don't have a lot of data scientists, how are we going to see applications become more self-service, more pre-packaged? >> So maybe I'll give a little bit of perspective. Right now I think IBM has got really good synergies on what I'll call vertical solutions to vertical organizations, financial, etcetera. I would say, Hortonworks has took a more horizontal approach. We're more of a platform solution. An example of one where it's kind of marrying the two, is if you move up the stack from Hortonworks as a platform to the next level up, which is Hortonworks as a solution. One of the examples that we've invested heavily in is cybersecurity, and in an Apache project called Metron. Less about Metron and more about cybersecurity. People want to solve a problem. They want to defend an attacker immediately, and what that means is we need to give them out-of-the-box models to detect a lot of common patterns. What we're doing there, is we're investing in some of the data science and pre-packaged models to identify attack vectors and then try to resolve that or at least notify you that there's a concern. It's an example where the data science behind it, pre-packaging that data science to solve a specific problem. That's in the cybersecurity space and that case happens to be horizontal where Hortonwork's strength is. I think in the IBM case, there's a lot more vertical apps that we can apply to. Fraud, adjudication, etcetera. >> So it sounds like we're really just hitting the tip of the iceberg here, with the potential. We want to thank you both for joining us on theCUBE today, sharing your excitement about this deepening, expanding partnership between Hortonworks and IBM. Madhu and Jamie, thank you so much for joining George and I today on theCUBE. >> Thank you. >> Thank you Lisa and George. >> Appreciate it. >> Thank you. >> And for my co-host George Gilbert, I am Lisa Martin. You're watching us live on theCUBE, from day one of the DataWorks Summit in Silicon Valley. Stick around, we'll be right back. (digitalized music)

>> Announcer: Live from New York, it's theCUBE, covering Machine Learning Everywhere: Build Your Ladder to AI, brought to you by IBM. >> Welcome back to New York City. theCUBE continue our coverage here at IBM's event, Machine Learning Everywhere: Build Your Ladder to AI. And with us now is Rob Thomas, who is the vice president of, or general manager, rather, of IBM analytics. Sorry about that, Rob. Good to have you with us this morning. Good to see you, sir. >> Great to see you John. Dave, great to see you as well. >> Great to see you. >> Well let's just talk about the event first. Great lineup of guests. We're looking forward to visiting with several of them here on theCUBE today. But let's talk about, first off, general theme with what you're trying to communicate and where you sit in terms of that ladder to success in the AI world. >> So, maybe start by stepping back to, we saw you guys a few times last year. Once in Munich, I recall, another one in New York, and the theme of both of those events was, data science renaissance. We started to see data science picking up steam in organizations. We also talked about machine learning. The great news is that, in that timeframe, machine learning has really become a real thing in terms of actually being implemented into organizations, and changing how companies run. And that's what today is about, is basically showcasing a bunch of examples, not only from our clients, but also from within IBM, how we're using machine learning to run our own business. And the thing I always remind clients when I talk to them is, machine learning is not going to replace managers, but I think machine learning, managers that use machine learning will replace managers that do not. And what you see today is a bunch of examples of how that's true because it gives you superpowers. If you've automated a lot of the insight, data collection, decision making, it makes you a more powerful manager, and that's going to change a lot of enterprises. >> It seems like a no-brainer, right? I mean, or a must-have. >> I think there's a, there's always that, sometimes there's a fear factor. There is a culture piece that holds people back. We're trying to make it really simple in terms of how we talk about the day, and the examples that we show, to get people comfortable, to kind of take a step onto that ladder back to the company. >> It's conceptually a no-brainer, but it's a challenge. You wrote a blog and it was really interesting. It was, one of the clients said to you, "I'm so glad I'm not in the technology industry." And you went, "Uh, hello?" (laughs) "I've got news for you, you are in the technology industry." So a lot of customers that I talk to feel like, meh, you know, in our industry, it's really not getting disrupted. That's kind of taxis and retail. We're in banking and, you know, but, digital is disrupting every industry and every industry is going to have to adopt ML, AI, whatever you want to call it. Can traditional companies close that gap? What's your take? >> I think they can, but, I'll go back to the word I used before, it starts with culture. Am I accepting that I'm a technology company, even if traditionally I've made tractors, as an example? Or if traditionally I've just been you know, selling shirts and shoes, have I embraced the role, my role as a technology company? Because if you set that culture from the top, everything else flows from there. It can't be, IT is something that we do on the side. It has to be a culture of, it's fundamental to what we do as a company. There was an MIT study that said, data-driven cultures drive productivity gains of six to 10 percent better than their competition. You can't, that stuff compounds, too. So if your competitors are doing that and you're not, not only do you fall behind in the short term but you fall woefully behind in the medium term. And so, I think companies are starting to get there but it takes a constant push to get them focused on that. >> So if you're a tractor company, you've got human expertise around making tractors and messaging and marketing tractors, and then, and data is kind of there, sort of a bolt-on, because everybody's got to be data-driven, but if you look at the top companies by market cap, you know, we were talking about it earlier. Data is foundational. It's at their core, so, that seems to me to be the hard part, Rob, I'd like you to comment in terms of that cultural shift. How do you go from sort of data in silos and, you know, not having cloud economics and, that are fundamental, to having that dynamic, and how does IBM help? >> You know, I think, to give companies credit, I think most organizations have developed some type of data practice or discipline over the last, call it five years. But most of that's historical, meaning, yeah, we'll take snapshots of history. We'll use that to guide decision making. You fast-forward to what we're talking about today, just so we're on the same page, machine learning is about, you build a model, you train a model with data, and then as new data flows in, your model is constantly updating. So your ability to make decisions improves over time. That's very different from, we're doing historical reporting on data. And so I think it's encouraging that companies have kind of embraced that data discipline in the last five years, but what we're talking about today is a big next step and what we're trying to break it down to what I call the building blocks, so, back to the point on an AI ladder, what I mean by an AI ladder is, you can't do AI without machine learning. You can't do machine learning without analytics. You can't do analytics without the right data architecture. So those become the building blocks of how you get towards a future of AI. And so what I encourage companies is, if you're not ready for that AI leading edge use case, that's okay, but you can be preparing for that future now. That's what the building blocks are about. >> You know, I think we're, I know we're ahead of, you know, Jeremiah Owyang on a little bit later, but I was reading something that he had written about gut and instinct, from the C-Suite, and how, that's how companies were run, right? You had your CEO, your president, they made decisions based on their guts or their instincts. And now, you've got this whole new objective tool out there that's gold, and it's kind of taking some of the gut and instinct out of it, in a way, and maybe there are people who still can't quite grasp that, that maybe their guts and their instincts, you know, what their gut tells them, you know, is one thing, but there's pretty objective data that might indicate something else. >> Moneyball for business. >> A little bit of a clash, I mean, is there a little bit of a clash in that respect? >> I think you'd be surprise by how much decision making is still pure opinion. I mean, I see that everywhere. But we're heading more towards what you described for sure. One of the clients talking here today, AMC Networks, think it's a great example of a company that you wouldn't think of as a technology company, primarily a content producer, they make great shows, but they've kind of gone that extra step to say, we can integrate data sources from third parties, our own data about viewer habits, we can do that to change our relationship with advertisers. Like, that's a company that's really embraced this idea of being a technology company, and you can see it in their results, and so, results are not coincidence in this world anymore. It's about a practice applied to data, leveraging machine learning, on a path towards AI. If companies are doing that, they're going to be successful. >> And we're going to have the tally from AMC on, but so there's a situation where they have embraced it, that they've dealt with that culture, and data has become foundational. Now, I'm interested as to what their journey look like. What are you seeing with clients? How they break this down, the silos of data that have been built up over decades. >> I think, so they get almost like a maturity curve. You've got, and the rule I talk about is 40-40-20, where 40% of organizations are really using data just to optimize costs right now. That's okay, but that's on the lower end of the maturity curve. 40% are saying, all right, I'm starting to get into data science. I'm starting to think about how I extend to new products, new services, using data. And then 20% are on the leading edge. And that's where I'd put AMC Networks, by the way, because they've done unique things with integrating data sets and building models so that they've automated a lot of what used to be painstakingly long processes, internal processes to do it. So you've got this 40-40-20 of organizations in terms of their maturity on this. If you're not on that curve right now, you have a problem. But I'd say most are somewhere on that curve. If you're in the first 40% and you're, right now data for you is just about optimizing cost, you're going to be behind. If you're not right now, you're going to be behind in the next year, that's a problem. So I'd kind of encourage people to think about what it takes to be in the next 40%. Ultimately you want to be in the 20% that's actually leading this transformation. >> So change it to 40-20-40. That's where you want it to go, right? You want to flip that paradigm. >> I want to ask you a question. You've done a lot of M and A in the past. You spent a lot of time in Silicon Valley and Silicon Valley obviously very, very disruptive, you know, cultures and organizations and it's always been a sort of technology disruption. It seems like there's a ... another disruption going on, not just horizontal technologies, you know, cloud or mobile or social, whatever it is, but within industries. Some industries, as we've been talking, radically disrupted. Retail, taxis, certainly advertising, et cetera et cetera. Some have not yet, the client that you talked to. Do you see, technology companies generally, Silicon Valley companies specifically, as being able to pull off a sort of disruption of not only technologies but also industries and where does IBM play there? You've made a sort of, Ginni in particular has made a deal about, hey, we're not going to compete with our customers. So talking about this sort of dual disruption agenda, one on the technology side, one within industries that Apple's getting into financial services and, you know, Amazon getting into grocery, what's your take on that and where does IBM fit in that world? >> So, I mean, IBM has been in Silicon Valley for a long time, I would say probably longer than 99.9% of the companies in Silicon Valley, so, we've got a big lab there. We do a lot of innovation out of there. So love it, I mean, the culture of the valley is great for the world because it's all about being the challenger, it's about innovation, and that's tremendous. >> No fear. >> Yeah, absolutely. So, look, we work with a lot of different partners, some who are, you know, purely based in the valley. I think they challenge us. We can learn from them, and that's great. I think the one, the one misnomer that I see right now, is there's a undertone that innovation is happening in Silicon Valley and only in Silicon Valley. And I think that's a myth. Give you an example, we just, in December, we released something called Event Store which is basically our stab at reinventing the database business that's been pretty much the same for the last 30 to 40 years. And we're now ingesting millions of rows of data a second. We're doing it in a Parquet format using a Spark engine. Like, this is an amazing innovation that will change how any type of IOT use case can manage data. Now ... people don't think of IBM when they think about innovations like that because it's not the only thing we talk about. We don't have, the IBM website isn't dedicated to that single product because IBM is a much bigger company than that. But we're innovating like crazy. A lot of that is out of what we're doing in Silicon Valley and our labs around the world and so, I'm very optimistic on what we're doing in terms of innovation. >> Yeah, in fact, I think, rephrase my question. I was, you know, you're right. I mean people think of IBM as getting disrupted. I wasn't posing it, I think of you as a disruptor. I know that may sound weird to some people but in the sense that you guys made some huge bets with things like Watson on solving some of the biggest, world's problems. And so I see you as disrupting sort of, maybe yourselves. Okay, frame that. But I don't see IBM as saying, okay, we are going to now disrupt healthcare, disrupt financial services, rather we are going to help our, like some of your comp... I don't know if you'd call them competitors. Amazon, as they say, getting into content and buying grocery, you know, food stores. You guys seems to have a different philosophy. That's what I'm trying to get to is, we're going to disrupt ourselves, okay, fine. But we're not going to go hard into healthcare, hard into financial services, other than selling technology and services to those organizations, does that make sense? >> Yeah, I mean, look, our mission is to make our clients ... better at what they do. That's our mission, we want to be essential in terms of their journey to be successful in their industry. So frankly, I love it every time I see an announcement about Amazon entering another vertical space, because all of those companies just became my clients. Because they're not going to work with Amazon when they're competing with them head to head, day in, day out, so I love that. So us working with these companies to make them better through things like Watson Health, what we're doing in healthcare, it's about making companies who have built their business in healthcare, more effective at how they perform, how they drive results, revenue, ROI for their investors. That's what we do, that's what IBM has always done. >> Yeah, so it's an interesting discussion. I mean, I tend to agree. I think Silicon Valley maybe should focus on those technology disruptions. I think that they'll have a hard time pulling off that dual disruption and maybe if you broadly define Silicon Valley as Seattle and so forth, but, but it seems like that formula has worked for decades, and will continue to work. Other thoughts on sort of the progression of ML, how it gets into organizations. You know, where you see this going, again, I was saying earlier, the parlance is changing. Big data is kind of, you know, mm. Okay, Hadoop, well, that's fine. We seem to be entering this new world that's pervasive, it's embedded, it's intelligent, it's autonomous, it's self-healing, it's all these things that, you know, we aspire to. We're now back in the early innings. We're late innings of big data, that's kind of ... But early innings of this new era, what are your thoughts on that? >> You know, I'd say the biggest restriction right now I see, we talked before about somehow, sometimes companies don't have the desire, so we have to help create the desire, create the culture to go do this. Even for the companies that have a burning desire, the issue quickly becomes a skill gap. And so we're doing a lot to try to help bridge that skill gap. Let's take data science as an example. There's two worlds of data science that I would describe. There's clickers, and there's coders. Clickers want to do drag and drop. They will use traditional tools like SPSS, which we're modernizing, that's great. We want to support them if that's how they want to work and build models and deploy models. There's also this world of coders. This is people that want to do all their data science in ML, and Python, and Scala, and R, like, that's what they want to do. And so we're supporting them through things like Data Science Experience, which is built on Apache Jupiter. It's all open source tooling, it'd designed for coders. The reason I think that's important, it goes back to the point on skill sets. There is a skill gap in most companies. So if you walk in and you say, this is the only way to do this thing, you kind of excluded half the companies because they say, I can't play in that world. So we are intentionally going after a strategy that says, there's a segmentation in skill types. In places there's a gap, we can help you fill that gap. That's how we're thinking about them. >> And who does that bode well for? If you say that you were trying to close a gap, does that bode well for, we talked about the Millennial crowd coming in and so they, you know, do they have a different approach or different mental outlook on this, or is it to the mid-range employee, you know, who is open minded, I mean, but, who is the net sweet spot, you think, that say, oh, this is a great opportunity right now? >> So just take data science as an example. The clicker coder comment I made, I would put the clicker audience as mostly people that are 20 years into their career. They've been around a while. The coder audience is all the Millennials. It's all the new audience. I think the greatest beneficiary is the people that find themselves kind of stuck in the middle, which is they're kind of interested in this ... >> That straddle both sides of the line yeah? >> But they've got the skill set and the desire to do some of the new tooling and new approaches. So I think this kind of creates an opportunity for that group in the middle to say, you know, what am I going to adopt as a platform for how I go forward and how I provide leadership in my company? >> So your advice, then, as you're talking to your clients, I mean you're also talking to their workforce. In a sense, then, your advice to them is, you know, join, jump in the wave, right? You've got your, you can't straddle, you've got to go. >> And you've got to experiment, you've got to try things. Ultimately, organizations are going to gravitate to things that they like using in terms of an approach or a methodology or a tool. But that comes with experimentation, so people need to get out there and try something. >> Maybe we could talk about developers a little bit. We were talking to Dinesh earlier and you guys of course have focused on data scientists, data engineers, obviously developers. And Dinesh was saying, look, many, if not most, of the 10 million Java developers out there, they're not, like, focused around the data. That's really the data scientist's job. But then, my colleague John Furrier says, hey, data is the new development kit. You know, somebody said recently, you know, Andreessen's comment, "software is eating the world." Well, data is eating software. So if Furrier is right and that comment is right, it seems like developers increasingly have to become more data aware, fundamentally. Blockchain developers clearly are more data focused. What's your take on the developer community, where they fit into this whole AI, machine learning space? >> I was just in Las Vegas yesterday and I did a session with a bunch of our business partners. ISVs, so software companies, mostly a developer audience, and the discussion I had with them was around, you're doing, you're building great products, you're building great applications. But your product is only as good as the data and the intelligence that you embed in your product. Because you're still putting too much of a burden on the user, as opposed to having everything happen magically, if you will. So that discussion was around, how do you embed data, embed AI, into your products and do that at the forefront versus, you deliver a product and the client has to say, all right, now I need to get my data out of this application and move it somewhere else so I can do the data science that I want to do. That's what I see happening with developers. It's kind of ... getting them to think about data as opposed to just thinking about the application development framework, because that's where most of them tend to focus. >> Mm, right. >> Well, we've talked about, well, earlier on about the governance, so just curious, with Madhu, which I'll, we'll have that interview in just a little bit here. I'm kind of curious about your take on that, is that it's a little kinder, gentler, friendlier than maybe some might look at it nowadays because of some organization that it causes, within your group and some value that's being derived from that, that more efficiency, more contextual information that's, you know, more relevant, whatever. When you talk to your clients about meeting rules, regs, GDPR, all these things, how do you get them to see that it's not a black veil of doom and gloom but it really is, really more of an opportunity for them to cash in? >> You know, my favorite question to ask when I go visit clients is I say, I say, just show of hands, how many people have all the data they need to do their job? To date, nobody has ever raised their hand. >> Not too many hands up. >> The reason I phrased it that way is, that's fundamentally a governance challenge. And so, when you think about governance, I think everybody immediately thinks about compliance, GDPR, types of things you mentioned, and that's great. But there's two use cases for governance. One is compliance, the other one is self service analytics. Because if you've done data governance, then you can make your data available to everybody in the organization because you know you've got the right rules, the right permissions set up. That will change how people do their jobs and I think sometimes governance gets painted into a compliance corner, when organizations need to think about it as, this is about making data accessible to my entire workforce. That's a big change. I don't think anybody has that today. Except for the clients that we're working with, where I think we've made good strides in that. >> What's your sort of number one, two, and three, or pick one, advice for those companies that as you blogged about, don't realize yet that they're in the software business and the technology business? For them to close the ... machine intelligence, machine learning, AI gap, where should they start? >> I do think it can be basic steps. And the reason I say that is, if you go to a company that hasn't really viewed themselves as a technology company, and you start talking about machine intelligence, AI, like, everybody like, runs away scared, like it's not interesting. So I bring it back to building blocks. For a client to be great in data, and to become a technology company, you really need three platforms for how you think about data. You need a platform for how you manage your data, so think of it as data management. You need a platform for unified governance and integration, and you need a platform for data science and business analytics. And to some extent, I don't care where you start, but you've got to start with one of those. And if you do that, you know, you'll start to create a flywheel of momentum where you'll get some small successes. Then you can go in the other area, and so I just encourage everybody, start down that path. Pick one of the three. Or you may already have something going in one of them, so then pick one where you don't have something going. Just start down the path, because, those building blocks, once you have those in place, you'll be able to scale AI and ML in the future in your organization. But without that, you're going to always be limited to kind of a use case at a time. >> Yeah, and I would add, this is, you talked about it a couple times today, is that cultural aspect, that realization that in order to be data driven, you know, buzzword, you have to embrace that and drive that through the culture. Right? >> That starts at the top, right? Which is, it's not, you know, it's not normal to have a culture of, we're going to experiment, we're going to try things, half of them may not work. And so, it starts at the top in terms of how you set the tone and set that culture. >> IBM Think, we're less than a month away. CUBE is going to be there, very excited about that. First time that you guys have done Think. You've consolidated all your big, big events. What can we expect from you guys? >> I think it's going to be an amazing show. To your point, we thought about this for a while, consolidating to a single IBM event. There's no question just based on the response and the enrollment we have so far, that was the right answer. We'll have people from all over the world. A bunch of clients, we've got some great announcements that will come out that week. And for clients that are thinking about coming, honestly the best thing about it is all the education and training. We basically build a curriculum, and think of it as a curriculum around, how do we make our clients more effective at competing with the Amazons of the world, back to the other point. And so I think we build a great curriculum and it will be a great week. >> Well, if I've heard anything today, it's about, don't be afraid to dive in at the deep end, just dive, right? Get after it and, looking forward to the rest of the day. Rob, thank you for joining us here and we'll see you in about a month! >> Sounds great. >> Right around the corner. >> All right, Rob Thomas joining us here from IBM Analytics, the GM at IBM Analytics. Back with more here on theCUBE. (upbeat music)

(upbeat techno music) >> Announcer: Good morning ladies and gentlemen. This is Lenovo Transform. Please welcome to the stage Lenovo's Rod Lappin. (upbeat instrumental) >> Alright, ladies and gentlemen. Here we go. I was out the back having a chat. A bit faster than I expected. How are you all doing this morning? (crowd cheers) >> Good? How fantastic is it to be in New York City? (crowd applauds) Excellent. So my name's Rod Lappin. I'm with the Data Center Group, obviously. I do basically anything that touches customers from our sales people, our pre-sales engineers, our architects, et cetera, all the way through to our channel partner sales engagement globally. So that's my job, but enough of that, okay? So the weather this morning, absolutely fantastic. Not a cloud in the sky, perfect. A little bit different to how it was yesterday, right? I want to thank all of you because I know a lot of you had a lot of commuting issues getting into New York yesterday with all the storms. We have a lot of people from international and domestic travel caught up in obviously the network, which blows my mind, actually, but we have a lot of people here from Europe, obviously, a lot of analysts and media people here as well as customers who were caught up in circling around the airport apparently for hours. So a big round of applause for our team from Europe. (audience applauds) Thank you for coming. We have some people who commuted a very short distance. For example, our own server general manager, Cameron (mumbles), he's out the back there. Cameron, how long did it take you to get from Raleigh to New York? An hour-and-a-half flight? >> Cameron: 17 hours. >> 17 hours, ladies and gentleman. That's a fantastic distance. I think that's amazing. But I know a lot of us, obviously, in the United States have come a long way with the storms, obviously very tough, but I'm going to call out one individual. Shaneil from Spotless. Where are you Shaneil, you're here somewhere? There he is from Australia. Shaneil how long did it take you to come in from Australia? 25 hour, ladies and gentleman. A big round of applause. That's a pretty big effort. Shaneil actually I want you to stand up, if you don't mind. I've got a seat here right next to my CEO. You've gone the longest distance. How about a big round of applause for Shaneil. We'll put him in my seat, next to YY. Honestly, Shaneil, you're doing me a favor. Okay ladies and gentlemen, we've got a big day today. Obviously, my seat now taken there, fantastic. Obviously New York City, the absolute pinnacle of globalization. I first came to New York in 1996, which was before a lot of people in the room were born, unfortunately for me these days. Was completely in awe. I obviously went to a Yankees game, had no clue what was going on, didn't understand anything to do with baseball. Then I went and saw Patrick Ewing. Some of you would remember Patrick Ewing. Saw the Knicks play basketball. Had no idea what was going on. Obviously, from Australia, and somewhat slightly height challenged, basketball was not my thing but loved it. I really left that game... That was the first game of basketball I'd ever seen. Left that game realizing that effectively the guy throws the ball up at the beginning, someone taps it, that team gets it, they run it, they put it in the basket, then the other team gets it, they put it in the basket, the other team gets it, and that's basically the entire game. So I haven't really progressed from that sort of learning or understanding of basketball since then, but for me, personally, being here in New York, and obviously presenting with all of you guys today, it's really humbling from obviously some of you would have picked my accent, I'm also from Australia. From the north shore of Sydney. To be here is just a fantastic, fantastic event. So welcome ladies and gentlemen to Transform, part of our tech world series globally in our event series and our event season here at Lenovo. So once again, big round of applause. Thank you for coming (audience applauds). Today, basically, is the culmination of what I would classify as a very large journey. Many of you have been with us on that. Customers, partners, media, analysts obviously. We've got quite a lot of our industry analysts in the room. I know Matt Eastwood yesterday was on a train because he sent a Tweet out saying there's 170 people on the WIFI network. He was obviously a bit concerned he was going to get-- Pat Moorhead, he got in at 3:30 this morning, obviously from traveling here as well with some of the challenges with the transportation, so we've got a lot of people in the room that have been giving us advice over the last two years. I think all of our employees are joining us live. All of our partners and customers through the stream. As well as everybody in this packed-out room. We're very very excited about what we're going to be talking to you all today. I want to have a special thanks obviously to our R&D team in Raleigh and around the world. They've also been very very focused on what they've delivered for us today, and it's really important for them to also see the culmination of this great event. And like I mentioned, this is really the feedback. It's not just a Lenovo launch. This is a launch based on the feedback from our partners, our customers, our employees, the analysts. We've been talking to all of you about what we want to be when we grow up from a Data Center Group, and I think you're going to hear some really exciting stuff from some of the speakers today and in the demo and breakout sessions that we have after the event. These last two years, we've really transformed the organization, and that's one of the reasons why that theme is part of our Tech World Series today. We're very very confident in our future, obviously, and where the company's going. It's really important for all of you to understand today and take every single snippet that YY, Kirk, and Christian talk about today in the main session, and then our presenters in the demo sections on what Lenovo's actually doing for its future and how we're positioning the company, obviously, for that future and how the transformation, the digital transformation, is going ahead globally. So, all right, we are now going to step into our Transform event. And I've got a quick agenda statement for you. The very first thing is we're going to hear from YY, our chairman and CEO. He's going to discuss artificial intelligence, the evolution of our society and how Lenovo is clearly positioning itself in the industry. Then, obviously, you're going to hear from Kirk Skaugen, our president of the Data Center Group, our new boss. He's going to talk about how long he's been with the company and the transformation, once again, we're making, very specifically to the Data Center Group and how much of a difference we're making to society and some of our investments. Christian Teismann, our SVP and general manager of our client business is going to talk about the 25 years of ThinkPad. This year is the 25-year anniversary of our ThinkPad product. Easily the most successful brand in our client branch or client branch globally of any vendor. Most successful brand we've had launched, and this afternoon breakout sessions, obviously, with our keynotes, fantastic sessions. Make sure you actually attend all of those after this main arena here. Now, once again, listen, ask questions, and make sure you're giving us feedback. One of the things about Lenovo that we say all the time... There is no room for arrogance in our company. Every single person in this room is a customer, partner, analyst, or an employee. We love your feedback. It's only through your feedback that we continue to improve. And it's really important that through all of the sessions where the Q&As happen, breakouts afterwards, you're giving us feedback on what you want to see from us as an organization as we go forward. All right, so what were you doing 25 years ago? I spoke about ThinkPad being 25 years old, but let me ask you this. I bet you any money that no one here knew that our x86 business is also 25 years old. So, this year, we have both our ThinkPad and our x86 anniversaries for 25 years. Let me tell you. What were you guys doing 25 years ago? There's me, 25 years ago. It's a bit scary, isn't it? It's very svelte and athletic and a lot lighter than I am today. It makes me feel a little bit conscious. And you can see the black and white shot. It shows you that even if you're really really short and you come from the wrong side of the tracks to make some extra cash, you can still do some modeling as long as no one else is in the photo to give anyone any perspective, so very important. I think I might have got one photo shoot out of that, I don't know. I had to do it, I needed the money. Let me show you another couple of photos. Very interesting, how's this guy? How cool does he look? Very svelte and athletic. I think there's no doubt. He looks much much cooler than I do. Okay, so ladies and gentlemen, without further ado, it gives me great honor to obviously introduce our very very first guest to the stage. Ladies and gentlemen, our chairman and CEO, Yuanqing Yang. or as we like to call him, YY. A big round of applause, thank you. (upbeat techno instrumental) >> Good morning everyone. Thank you, Rod, for your introduction. Actually, I didn't think I was younger than you (mumbles). I can't think of another city more fitting to host the Transform event than New York. A city that has transformed from a humble trading post 400 years ago to one of the most vibrant cities in the world today. It is a perfect symbol of transformation of our world. The rapid and the deep transformations that have propelled us from the steam engine to the Internet era in just 200 years. Looking back at 200 years ago, there was only a few companies that operated on a global scale. The total value of the world's economy was around $188 billion U.S. dollars. Today, it is only $180 for each person on earth. Today, there are thousands of independent global companies that compete to sell everything, from corn and crude oil to servers and software. They drive a robust global economy was over $75 trillion or $1,000 per person. Think about it. The global economy has multiplied almost 450 times in just two centuries. What is even more remarkable is that the economy has almost doubled every 15 years since 1950. These are significant transformation for businesses and for the world and our tiny slice of pie. This transformation is the result of the greatest advancement in technology in human history. Not one but three industrial revolutions have happened over the last 200 years. Even though those revolutions created remarkable change, they were just the beginning. Today, we are standing at the beginning of the fourth revolution. This revolution will transform how we work (mumbles) in ways that no one could imagine in the 18th century or even just 18 months ago. You are the people who will lead this revolution. Along with Lenovo, we will redefine IT. IT is no longer just information technology. It's intelligent technology, intelligent transformation. A transformation that is driven by big data called computing and artificial intelligence. Even the transition from PC Internet to mobile Internet is a big leap. Today, we are facing yet another big leap from the mobile Internet to the Smart Internet or intelligent Internet. In this Smart Internet era, Cloud enables devices, such as PCs, Smart phones, Smart speakers, Smart TVs. (mumbles) to provide the content and the services. But the evolution does not stop them. Ultimately, almost everything around us will become Smart, with building computing, storage, and networking capabilities. That's what we call the device plus Cloud transformation. These Smart devices, incorporated with various sensors, will continuously sense our environment and send data about our world to the Cloud. (mumbles) the process of this ever-increasing big data and to support the delivery of Cloud content and services, the data center infrastructure is also transforming to be more agile, flexible, and intelligent. That's what we call the infrastructure plus Cloud transformation. But most importantly, it is the human wisdom, the people learning algorithm vigorously improved by engineers that enables artificial intelligence to learn from big data and make everything around us smarter. With big data collected from Smart devices, computing power of the new infrastructure under the trend artificial intelligence, we can understand the world around us more accurately and make smarter decisions. We can make life better, work easier, and society safer and healthy. Think about what is already possible as we start this transformation. Smart Assistants can help you place orders online with a voice command. Driverless cars can run on the same road as traditional cars. (mumbles) can help troubleshoot customers problems, and the virtual doctors already diagnose basic symptoms. This list goes on and on. Like every revolution before it, intelligent transformation, will fundamentally change the nature of business. Understanding and preparing for that will be the key for the growth and the success of your business. The first industrial revolution made it possible to maximize production. Water and steam power let us go from making things by hand to making them by machine. This transformed how fast things could be produced. It drove the quantity of merchandise made and led to massive increase in trade. With this revolution, business scale expanded, and the number of customers exploded. Fifty years later, the second industrial revolution made it necessary to organize a business like the modern enterprise, electric power, and the telegraph communication made business faster and more complex, challenging businesses to become more efficient and meeting entirely new customer demands. In our own lifetimes, we have witnessed the third industrial revolution, which made it possible to digitize the enterprise. The development of computers and the Internet accelerated business beyond human speed. Now, global businesses have to deal with customers at the end of a cable, not always a handshake. While we are still dealing with the effects of a digitizing business, the fourth revolution is already here. In just the past two or three years, the growth of data and advancement in visual intelligence has been astonishing. The computing power can now process the massive amount of data about your customers, suppliers, partners, competitors, and give you insights you simply could not imagine before. Artificial intelligence can not only tell you what your customers want today but also anticipate what they will need tomorrow. This is not just about making better business decisions or creating better customer relationships. It's about making the world a better place. Ultimately, can we build a new world without diseases, war, and poverty? The power of big data and artificial intelligence may be the revolutionary technology to make that possible. Revolutions don't happen on their own. Every industrial revolution has its leaders, its visionaries, and its heroes. The master transformers of their age. The first industrial revolution was led by mechanics who designed and built power systems, machines, and factories. The heroes of the second industrial revolution were the business managers who designed and built modern organizations. The heroes of the third revolution were the engineers who designed and built the circuits and the source code that digitized our world. The master transformers of the next revolution are actually you. You are the designers and the builders of the networks and the systems. You will bring the benefits of intelligence to every corner of your enterprise and make intelligence the central asset of your business. At Lenovo, data intelligence is embedded into everything we do. How we understand our customer's true needs and develop more desirable products. How we profile our customers and market to them precisely. How we use internal and external data to balance our supply and the demand. And how we train virtual agents to provide more effective sales services. So the decisions you make today about your IT investment will determine the quality of the decisions your enterprise will make tomorrow. So I challenge each of you to seize this opportunity to become a master transformer, to join Lenovo as we work together at the forefront of the fourth industrial revolution, as leaders of the intelligent transformation. (triumphant instrumental) Today, we are launching the largest portfolio in our data center history at Lenovo. We are fully committed to the (mumbles) transformation. Thank you. (audience applauds) >> Thanks YY. All right, ladies and gentlemen. Fantastic, so how about a big round of applause for YY. (audience applauds) Obviously a great speech on the transformation that we at Lenovo are taking as well as obviously wanting to journey with our partners and customers obviously on that same journey. What I heard from him was obviously artificial intelligence, how we're leveraging that integrally as well as externally and for our customers, and the investments we're making in the transformation around IoT machine learning, obviously big data, et cetera, and obviously the Data Center Group, which is one of the key things we've got to be talking about today. So we're on the cusp of that fourth revolution, as YY just mentioned, and Lenovo is definitely leading the way and investing in those parts of the industry and our portfolio to ensure we're complimenting all of our customers and partners on what they want to be, obviously, as part of this new transformation we're seeing globally. Obviously now, ladies and gentlemen, without further ado once again, to tell us more about what's going on today, our announcements, obviously, that all of you will be reading about and seeing in the breakout and the demo sessions with our segment general managers this afternoon is our president of the data center, Mr. Kirk Skaugen. (upbeat instrumental) >> Good morning, and let me add my welcome to Transform. I just crossed my six months here at Lenovo after over 24 years at Intel Corporation, and I can tell you, we've been really busy over the last six months, and I'm more excited and enthusiastic than ever and hope to share some of that with you today. Today's event is called "Transform", and today we're announcing major new transformations in Lenovo, in the data center, but more importantly, we're celebrating the business results that these platforms are going to have on society and with international supercomputing going on in parallel in Frankfurt, some of the amazing scientific discoveries that are going to happen on some of these platforms. Lenovo has gone through some significant transformations in the last two years, since we acquired the IBM x86 business, and that's really positioning us for this next phase of growth, and we'll talk more about that later. Today, we're announcing the largest end-to-end data center portfolio in Lenovo's history, as you heard from YY, and we're really taking the best of the x86 heritage from our IBM acquisition of the x86 server business and combining that with the cost economics that we've delivered from kind of our China heritage. As we've talked to some of the analysts in the room, it's really that best of the east and best of the west is combining together in this announcement today. We're going to be announcing two new brands, building on our position as the number one x86 server vendor in both customer satisfaction and in reliability, and we're also celebrating, next month in July, a very significant milestone, which will we'll be shipping our 20 millionth x86 server into the industry. For us, it's an amazing time, and it's an inflection point to kind of look back, pause, but also share the next phase of Lenovo and the exciting vision for the future. We're also making some declarations on our vision for the future today. Again, international supercomputing's going on, and, as it turns out, we're the fastest growing supercomputer company on earth. We'll talk about that. Our goal today that we're announcing is that we plan in the next several years to become number one in supercomputing, and we're going to put the investments behind that. We're also committing to our customers that we're going to disrupt the status quo and accelerate the pace of innovation, not just in our legacy server solutions, but also in Software-Defined because what we've heard from you is that that lack of legacy, we don't have a huge router business or a huge sand business to protect. It's that lack of legacy that's enabling us to invest and get ahead of the curb on this next transition to Software-Defined. So you're going to see us doing that through building our internal IP, through some significant joint ventures, and also through some merges and acquisitions over the next several quarters. Altogether, we're driving to be the most trusted data center provider in the industry between us and our customers and our suppliers. So a quick summary of what we're going to dive into today, both in my keynote as well as in the breakout sessions. We're in this transformation to the next phase of Lenovo's data center growth. We're closing out our previous transformation. We actually, believe it or not, in the last six months or so, have renegotiated 18,000 contracts in 160 countries. We built out an entire end-to-end organization from development and architecture all the way through sales and support. This next transformation, I think, is really going to excite Lenovo shareholders. We're building the largest data center portfolio in our history. I think when IBM would be up here a couple years ago, we might have two or three servers to announce in time to market with the next Intel platform. Today, we're announcing 14 new servers, seven new storage systems, an expanded set of networking portfolios based on our legacy with Blade Network Technologies and other companies we've acquired. Two new brands that we'll talk about for both data center infrastructure and Software-Defined, a new set of premium premiere services as well as a set of engineered solutions that are going to help our customers get to market faster. We're going to be celebrating our 20 millionth x86 server, and as Rod said, 25 years in x86 server compute, and Christian will be up here talking about 25 years of ThinkPad as well. And then a new end-to-end segmentation model because all of these strategies without execution are kind of meaningless. I hope to give you some confidence in the transformation that Lenovo has gone through as well. So, having observed Lenovo from one of its largest partners, Intel, for more than a couple decades, I thought I'd just start with why we have confidence on the foundation that we're building off of as we move from a PC company into a data center provider in a much more significant way. So Lenovo today is a company of $43 billion in sales. Absolutely astonishing, it puts us at about Fortune 202 as a company, with 52,000 employees around the world. We're supporting and have service personnel, almost a little over 10,000 service personnel that service our servers and data center technologies in over 160 countries that provide onsite service and support. We have seven data center research centers. One of the reasons I came from Intel to Lenovo was that I saw that Lenovo became number one in PCs, not through cost cutting but through innovation. It was Lenovo that was partnering on the next-generation Ultrabooks and two-in-ones and tablets in the modem mods that you saw, but fundamentally, our path to number one in data center is going to be built on innovation. Lastly, we're one of the last companies that's actually building not only our own motherboards at our own motherboard factories, but also with five global data center manufacturing facilities. Today, we build about four devices a second, but we also build over 100 servers per hour, and the cost economics we get, and I just visited our Shenzhen factory, of having everything from screws to microprocessors come up through the elevator on the first floor, go left to build PCs and ThinkPads and go right to build server technology, means we have some of the world's most cost effective solutions so we can compete in things like hyperscale computing. So it's with that that I think we're excited about the foundation that we can build off of on the Data Center Group. Today, as we stated, this event is about transformation, and today, I want to talk about three things we're going to transform. Number one is the customer experience. Number two is the data center and our customer base with Software-Defined infrastructure, and then the third is talk about how we plan to execute flawlessly with a new transformation that we've had internally at Lenovo. So let's dive into it. On customer experience, really, what does it mean to transform customer experience? Industry pundits say that if you're not constantly innovating, you can fall behind. Certainly the technology industry that we're in is transforming at record speed. 42% of business leaders or CIOs say that digital first is their top priority, but less than 50% actually admit that they have a strategy to get there. So people are looking for a partner to keep pace with that innovation and change, and that's really what we're driving to at Lenovo. So today we're announcing a set of plans to take another step function in customer experience, and building off of our number one position. Just recently, Gartner shows Lenovo as the number 24 supply chains of companies over $12 billion. We're up there with Amazon, Coca-Cola, and we've now completely re-architected our supply chain in the Data Center Group from end to end. Today, we can deliver 90% of our SKUs, order to ship in less than seven days. The artificial intelligence that YY mentioned is optimizing our performance even further. In services, as we talked about, we're now in 160 countries, supporting on-site support, 50 different call centers around the world for local language support, and we're today announcing a whole set of new premiere support services that I'll get into in a second. But we're building on what's already better than 90% customer satisfaction in this space. And then in development, for all the engineers out there, we started foundationally for this new set of products, talking about being number one in reliability and the lowest downtime of any x86 server vendor on the planet, and these systems today are architected to basically extend that leadership position. So let me tell you the realities of reliability. This is ITIC, it's a reliability report. 750 CIOs and IT managers from more than 20 countries, so North America, Europe, Asia, Australia, South America, Africa. This isn't anything that's paid for with sponsorship dollars. Lenovo has been number one for four years running on x86 reliability. This is the amount of downtime, four hours or more, in mission-critical environments from the leading x86 providers. You can see relative to our top two competitors that are ahead of us, HP and Dell, you can see from ITIC why we are building foundationally off of this, and why it's foundational to how we're developing these new platforms. In customer satisfaction, we are also rated number one in x86 server customer satisfaction. This year, we're now incentivizing every single Lenovo employee on customer satisfaction and customer experience. It's been a huge mandate from myself and most importantly YY as our CEO. So you may say well what is the basis of this number one in customer satisfaction, and it's not just being number one in one category, it's actually being number one in 21 of the 22 categories that TBR talks about. So whether it's performance, support systems, online product information, parts and availability replacement, Lenovo is number one in 21 of the 22 categories and number one for six consecutive studies going back to Q1 of 2015. So this, again, as we talk about the new product introductions, it's something that we absolutely want to build on, and we're humbled by it, and we want to continue to do better. So let's start now on the new products and talk about how we're going to transform the data center. So today, we are announcing two new product offerings. Think Agile and ThinkSystem. If you think about the 25 years of ThinkPad that Christian's going to talk about, Lenovo has a continuous learning culture. We're fearless innovators, we're risk takers, we continuously learn, but, most importantly, I think we're humble and we have some humility. That when we fail, we can fail fast, we learn, and we improve. That's really what drove ThinkPad to number one. It took about eight years from the acquisition of IBM's x86 PC business before Lenovo became number one, but it was that innovation, that listening and learning, and then improving. As you look at the 25 years of ThinkPad, there were some amazing successes, but there were also some amazing failures along the way, but each and every time we learned and made things better. So this year, as Rod said, we're not just celebrating 25 years of ThinkPad, but we're celebrating 25 years of x86 server development since the original IBM PC servers in 1992. It's a significant day for Lenovo. Today, we're excited to announce two new brands. ThinkSystem and ThinkAgile. It's an important new announcement that we started almost three years ago when we acquired the x86 server business. Why don't we run a video, and we'll show you a little bit about ThinkSystem and ThinkAgile. >> Narrator: The status quo is comfortable. It gets you by, but if you think that's good enough for your data center, think again. If adoption is becoming more complicated when it should be simpler, think again. If others are selling you technology that's best for them, not for you, think again. It's time for answers that win today and tomorrow. Agile, innovative, different. Because different is better. Different embraces change and makes adoption simple. Different designs itself around you. Using 25 years of innovation and design and R&D. Different transforms, it gives you ThinkSystem. World-record performance, most reliable, easy to integrate, scales faster. Different empowers you with ThinkAgile. It redefines the experience, giving you the speed of Cloud and the control of on-premise IT. Responding faster to what your business really needs. Different defines the future. Introducing Lenovo ThinkSystem and ThinkAgile. (exciting and slightly aggressive digital instrumental) >> All right, good stuff, huh? (audience applauds) So it's built off of this 25-year history of us being in the x86 server business, the commitment we established three years ago after acquiring the x86 server business to be and have the most reliable, the most agile, and the most highest-performing data center solutions on the planet. So today we're announcing two brands. ThinkSystem is for the traditional data center infrastructure, and ThinkAgile is our brand for Software-Defined infrastructure. Again, the teams challenge themselves from the start, how do we build off this rich heritage, expanding our position as number one in customer satisfaction, reliability, and one of the world's best supply chains. So let's start and look at the next set of solutions. We have always prided ourself that little things don't mean a lot. Little things mean everything. So today, as we said on the legacy solutions, we have over 30 world-record performance benchmarks on Intel architecture, and more than actually 150 since we started tracking this back in 2001. So it's the little pieces of innovation. It's the fine tuning that we do with our partners like an Intel or a Microsoft, an SAP, VMware, and Nutanix that's enabling us to get these world-record performance benchmarks, and with this next generation of solutions we think we'll continue to certainly do that. So today we're announcing the most comprehensive portfolio ever in our data center history. There's 14 servers, seven storage devices, and five network switches. We're also announcing, which is super important to our customer base, a set of new premiere service options. That's giving you fast access directly to a level two support person. No automated response system involved. You get to pick up the phone and directly talk to a level two support person that's going to have end-to-end ownership of the customer experience for ThinkSystem. With ThinkAgile, that's going to be completely bundled with every ThinkAgile you purchase. In addition, we're having white glove service on site that will actually unbox the product for you and get it up and running. It's an entirely new set of solutions for hybrid Cloud, for big data analytics and database applications around these engineered solutions. These are like 40- to 50-page guides where we fine-tuned the most important applications around virtual desktop infrastructure and those kinds of applications, working side by side with all of our ISP partners. So significantly expanding, not just the hardware but the software solutions that, obviously, you, as our customers, are running. So if you look at ThinkSystem innovation, again, it was designed for the ultimate in flexibility, performance, and reliability. It's a single now-unified brand that combines what used to be the Lenovo Think server and the IBM System x products now into a single brand that spans server, storage, and networking. We're basically future-proofing it for the next-generation data center. It's a significantly simplified portfolio. One of the big pieces that we've heard is that the complexity of our competitors has really been overwhelming to customers. We're building a more flexible, more agile solution set that requires less work, less qualification, and more future proofing. There's a bunch of things in this that you'll see in the demos. Faster time-to-service in terms of the modularity of the systems. 12% faster service equating to almost $50 thousand per hour of reduced downtime. Some new high-density options where we have four nodes and a 2U, twice the density to improve and reduce outbacks and mission-critical workloads. And then in high-performance computing and supercomputing, we're going to spend some time on that here shortly. We're announcing new water-cooled solutions. We have some of the most premiere water-cooled solutions in the world, with more than 25 patents pending now, just in the water-cooled solutions for supercomputing. The performance that we think we're going to see out of these systems is significant. We're building off of that legacy that we have today on the existing Intel solutions. Today, we believe we have more than 50% of SAP HANA installations in the world. In fact, SAP just went public that they're running their internal SAP HANA on Lenovo hardware now. We're seeing a 59% increase in performance on SAP HANA generation on generation. We're seeing 31% lower total cost to ownership. We believe this will continue our position of having the highest level of five-nines in the x86 server industry. And all of these servers will start being available later this summer when the Intel announcements come out. We're also announcing the largest storage portfolio in our history, significantly larger than anything we've done in the past. These are all available today, including some new value class storage offerings. Our network portfolio is expanding now significantly. It was a big surprise when I came to Lenovo, seeing the hundreds of engineers we had from the acquisition of Blade Network Technologies and others with our teams in Romania, Santa Clara, really building out both the embedded portfolio but also the top racks, which is around 10 gig, 25 gig, and 100 gig. Significantly better economics, but all the performance you'd expect from the largest networking companies in the world. Those are also available today. ThinkAgile and Software-Defined, I think the one thing that has kind of overwhelmed me since coming in to Lenovo is we are being embraced by our customers because of our lack of legacy. We're not trying to sell you one more legacy SAN at 65% margins. ThinkAgile really was founded, kind of born free from the shackles of legacy thinking and legacy infrastructure. This is just the beginning of what's going to be an amazing new brand in the transformation to Software-Defined. So, for Lenovo, we're going to invest in our own internal organic IP. I'll foreshadow: There's some significant joint ventures and some mergers and acquisitions that are going to be coming in this space. And so this will be the foundation for our Software-Defined networking and storage, for IoT, and ultimately for the 5G build-out as well. This is all built for data centers of tomorrow that require fluid resources, tightly integrated software and hardware in kind of an appliance, selling at the rack level, and so we'll show you how that is going to take place here in a second. ThinkAgile, we have a few different offerings. One is around hyperconverged storage, Hybrid Cloud, and also Software-Defined storage. So we're really trying to redefine the customer experience. There's two different solutions we're having today. It's a Microsoft Azure solution and a Nutanix solution. These are going to be available both in the appliance space as well as in a full rack solution. We're really simplifying and trying to transform the entire customer experience from how you order it. We've got new capacity planning tools that used to take literally days for us to get the capacity planning done. It's now going down to literally minutes. We've got new order, delivery, deployment, administration service, something we're calling ThinkAgile Advantage, which is the white glove unboxing of the actual solutions on prem. So the whole thing when you hear about it in the breakout sessions about transforming the entire customer experience with both an HX solution and an SX solution. So again, available at the rack level for both Nutanix and for Microsoft Solutions available in just a few months. Many of you in the audience since the Microsoft Airlift event in Seattle have started using these things, and the feedback to date has been fantastic. We appreciate the early customer adoption that we've seen from people in the audience here. So next I want to bring up one of our most important partners, and certainly if you look at all of these solutions, they're based on the next-generation Intel Xeon scalable processor that's going to be announcing very very soon. I want to bring on stage Rupal Shah, who's the corporate vice president and general manager of Global Data Center Sales with Intel, so Rupal, please join me. (upbeat instrumental) So certainly I have long roots at Intel, but why don't you talk about, from Intel's perspective, why Lenovo is an important partner for Lenovo. >> Great, well first of all, thank you very much. I've had the distinct pleasure of not only working with Kirk for many many years, but also working with Lenovo for many years, so it's great to be here. Lenovo is not only a fantastic supplier and leader in the industry for Intel-based servers but also a very active partner in the Intel ecosystem. In the Intel ecosystem, specifically, in our partner programs and in our builder programs around Cloud, around the network, and around storage, I personally have had a long history in working with Lenovo, and I've seen personally that PC transformation that you talked about, Kirk, and I believe, and I know that Intel believes in Lenovo's ability to not only succeed in the data center but to actually lead in the data center. And so today, the ThinkSystem and ThinkAgile announcement is just so incredibly important. It's such a great testament to our two companies working together, and the innovation that we're able to bring to the market, and all of it based on the Intel Xeon scalable processor. >> Excellent, so tell me a little bit about why we've been collaborating, tell me a little bit about why you're excited about ThinkSystem and ThinkAgile, specifically. >> Well, there are a lot of reasons that I'm excited about the innovation, but let me talk about a few. First, both of our companies really stand behind the fact that it's increasingly a hybrid world. Our two companies offer a range of solutions now to customers to be able to address their different workload needs. ThinkSystem really brings the best, right? It brings incredible performance, flexibility in data center deployment, and industry-leading reliability that you've talked about. And, as always, Xeon has a history of being built for the data center specifically. The Intel Xeon scalable processor is really re-architected from the ground up in order to enhance compute, network, and storage data flows so that we can deliver workload optimized performance for both a wide range of traditional workloads and traditional needs but also some emerging new needs in areas like artificial intelligence. Second is when it comes to the next generation of Cloud infrastructure, the new Lenovo ThinkAgile line offers a truly integrated offering to address data center pain points, and so not only are you able to get these pretested solutions, but these pretested solutions are going to get deployed in your infrastructure faster, and they're going to be deployed in a way that's going to meet your specific needs. This is something that is new for both of us, and it's an incredible innovation in the marketplace. I think that it's a great addition to what is already a fantastic portfolio for Lenovo. >> Excellent. >> Finally, there's high-performance computing. In high-performance computing. First of all, congratulations. It's a big week, I think, for both of us. Fantastic work that we've been doing together in high-performance computing and actually bringing the best of the best to our customers, and you're going to hear a whole lot more about that. We obviously have a number of joint innovation centers together between Intel and Lenovo. Tell us about some of the key innovations that you guys are excited about. >> Well, Intel and Lenovo, we do have joint innovation labs around the world, and we have a long and strong history of very tight collaboration. This has brought a big wave of innovation to the marketplace in areas like software-defined infrastructure. Yet another area is working closely on a joint vision that I think our two companies have in artificial intelligence. Intel is very committed to the world of AI, and we're committed in making the investments required in technology development, in training, and also in R&D to be able to deliver end-to-end solutions. So with Intel's comprehensive technology portfolio and Lenovo's development and innovation expertise, it's a great combination in this space. I've already talked a little bit about HPC and so has Kirk, and we're going to hear a little bit more to come, but we're really building the fastest compute solutions for customers that are solving big problems. Finally, we often talk about processors from Intel, but it's not just about the processors. It's way beyond that. It's about engaging at the solution level for our customers, and I'm so excited about the work that we've done together with Lenovo to bring to market products like Intel Omni-Path Architecture, which is really the fabric for high-performance data centers. We've got a great showing this week with Intel Omni-Path Architecture, and I'm so grateful for all the work that we've done to be able to bring true solutions to the marketplace. I am really looking forward to our future collaboration with Lenovo as we have in the past. I want to thank you again for inviting me here today, and congratulations on a fantastic launch. >> Thank you, Rupal, very much, for the long partnership. >> Thank you. (audience applauds) >> Okay, well now let's transition and talk a little bit about how Lenovo is transforming. The first thing we've done when I came on board about six months ago is we've transformed to a truly end-to-end organization. We're looking at the market segments I think as our customers define them, and we've organized into having vice presidents and senior vice presidents in charge of each of these major groups, thinking really end to end, from architecture all the way to end of life and customer support. So the first is hyperscale infrastructure. It's about 20% on the market by 2020. We've hired a new vice president there to run that business. Given we can make money in high-volume desktop PCs, it's really the manufacturing prowess, deep engineering collaboration that's enabling us to sell into Baidu, and to Alibaba, Tencent, as well as the largest Cloud vendors on the West Coast here in the United States. We believe we can make money here by having basically a deep deep engineering engagement with our key customers and building on the PC volume economics that we have within Lenovo. On software-defined infrastructure, again, it's that lack of legacy that I think is propelling us into this space. We're not encumbered by trying to sell one more legacy SAN or router, and that's really what's exciting us here, as we transform from a hardware to a software-based company. On HPC and AI, as we said, we'll talk about this in a second. We're the fastest-growing supercomputing company on earth. We have aspirations to be the largest supercomputing company on earth, with China and the U.S. vying for number one in that position, it puts us in a good position there. We're going to bridge that into artificial intelligence in our upcoming Shanghai Tech World. The entire day is around AI. In fact, YY has committed $1.2 billion to artificial intelligence over the next few years of R&D to help us bridge that. And then on data center infrastructure, is really about moving to a solutions based infrastructure like our position with SAP HANA, where we've gone deep with engineers on site at SAP, SAP running their own infrastructure on Lenovo and building that out beyond just SAP to other solutions in the marketplace. Overall, significantly expanding our services portfolio to maintain our number one customer satisfaction rating. So given ISC, or International Supercomputing, this week in Frankfurt, and a lot of my team are actually over there, I wanted to just show you the transformation we've had at Lenovo for delivering some of the technology to solve some of the most challenging humanitarian problems on earth. Today, we are the fastest-growing supercomputer company on the planet in terms of number of systems on the Top 500 list. We've gone from zero to 92 positions in just a few short years, but IDC also positions Lenovo as the fast-growing supercomputer and HPC company overall at about 17% year on year growth overall, including all of the broad channel, the regional universities and this kind of thing, so this is an exciting place for us. I'm excited today that Sergi has come all the way from Spain to be with us today. It's an exciting time because this week we announce the fastest next-generation Intel supercomputer on the planet at Barcelona Supercomputer. Before I bring Sergi on stage, let's run a video and I'll show you why we're excited about the capabilities of these next-generation supercomputers. Run the video please. >> Narrator: Different creates one of the most powerful supercomputers for the Barcelona Supercomputer Center. A high-performance, high-capacity design to help shape tomorrow's world. Different designs what's best for you, with 25 years of end-to-end expertise delivering large-scale solutions. It integrates easily with technology from industry partners, through deep collaboration with the client to manufacture, test, configure, and install at global scale. Different achieves the impossible. The first of a new series. A more energy-efficient supercomputer yet 10 times more powerful than its predecessor. With over 3,400 Lenovo ThinkSystem servers, each performing over two trillion calculations per second, giving us 11.1 petaflop capacity. Different powers MareNostrum, a supercomputer that will help us better understand cancer, help discover disease-fighting therapies, predict the impact of climate change. MareNostrom 4.0 promises to uncover answers that will help solve humanities greatest challenges. (audience applauds) >> So please help me in welcoming operations director of the Barcelona Supercomputer Center, Sergi Girona. So welcome, and again, congratulations. It's been a big week for both of us. But I think for a long time, if you haven't been to Barcelona, this has been called the world's most beautiful computer because it's in one of the most gorgeous chapels in the world as you can see here. Congratulations, we now are number 13 on the Top500 list and the fastest next-generation Intel computer. >> Thank you very much, and congratulations to you as well. >> So maybe we can just talk a little bit about what you've done over the last few months with us. >> Sure, thank you very much. It is a pleasure for me being invited here to present to you what we've been doing with Lenovo so far and what we are planning to do in the next future. I'm representing here Barcelona Supercomputing Center. I am presenting high-performance computing services to science and industry. How we see these science services has changed the paradigm of science. We are coming from observation. We are coming from observation on the telescopes and the microscopes and the building of infrastructures, but this is not affordable anymore. This is very expensive, so it's not possible, so we need to move to simulations. So we need to understand what's happening in our environment. We need to predict behaviors only going through simulation. So, at BSC, we are devoted to provide services to industry, to science, but also we are doing our own research because we want to understand. At the same time, we are helping and developing the new engineers of the future on the IT, on HPC. So we are having four departments based on different topics. The main and big one is wiling to understand how we are doing the next supercomputers from the programming level to the performance to the EIA, so all these things, but we are having also interest on what about the climate change, what's the air quality that we are having in our cities. What is the precision medicine we need to have. How we can see that the different drugs are better for different individuals, for different humans, and of course we have an energy department, taking care of understanding what's the better optimization for a cold, how we can save energy running simulations on different topics. But, of course, the topic of today is not my research, but it's the systems we are building in Barcelona. So this is what we have been building in Barcelona so far. From left to right, you have the preparation of the facility because this is 160 square meters with 1.4 megabytes, so that means we need new piping, we need new electricity, at the same time in the center we have to install the core services of the system, so the management practices, and then on the right-hand side you have installation of the networking, the Omni-Path by Intel. Because all of the new racks have to be fully integrated and they need to come into operation rapidly. So we start deployment of the system May 15, and we've now been ending and coming in production July first. All the systems, all the (mumbles) systems from Lenovo are coming before being open and available. What we've been installing here in Barcelona is general purpose systems for our general workload of the system with 3,456 nodes. Everyone of those having 48 cores, 96 gigabytes main memory for a total capacity of about 400 terabytes memory. The objective of this is that we want to, all the system, all the processors, to work together for a single execution for running altogether, so this is an example of the platinum processors from Intel having 24 cores each. Of course, for doing this together with all the cores in the same application, we need a high-speed network, so this is Omni-Path, and of course all these cables are connecting all the nodes. Noncontention, working together, cooperating. Of course, this is a bunch of cables. They need to be properly aligned in switches. So here you have the complete presentation. Of course, this is general purpose, but we wanted to invest with our partners. We want to understand what the supercomputers we wanted to install in 2020, (mumbles) Exascale. We want to find out, we are installing as well systems with different capacities with KNH, with power, with ARM processors. We want to leverage our obligations for the future. We want to make sure that in 2020 we are ready to move our users rapidly to the new technologies. Of course, this is in total, giving us a total capacity of 13.7 petaflops that it's 12 times the capacity of the former MareNostrum four years ago. We need to provide the services to our scientists because they are helping to solve problems for humanity. That's the place we are going to go. Last is inviting you to come to Barcelona to see our place and our chapel. Thank you very much (audience applauds). >> Thank you. So now you can all go home to your spouses and significant others and say you have a formal invitation to Barcelona, Spain. So last, I want to talk about what we've done to transform Lenovo. I think we all know the history is nice but without execution, none of this is going to be possible going forward, so we have been very very busy over the last six months to a year of transforming Lenovo's data center organization. First, we moved to a dedicated end-to-end sales and marketing organization. In the past, we had people that were shared between PC and data center, now thousands of sales people around the world are 100% dedicated end to end to our data center clients. We've moved to a fully integrated and dedicated supply chain and procurement organization. A fully dedicated quality organization, 100% dedicated to expanding our data center success. We've moved to a customer-centric segment, again, bringing in significant new leaders from outside the company to look end to end at each of these segments, supercomputing being very very different than small business, being very very different than taking care of, for example, a large retailer or bank. So around hyperscale, software-defined infrastructure, HPC, AI, and supercomputing and data center solutions-led infrastructure. We've built out a whole new set of global channel programs. Last year, or a year passed, we have five different channel programs around the world. We've now got one simplified channel program for dealer registration. I think our channel is very very energized to go out to market with Lenovo technology across the board, and a whole new set of system integrator relationships. You're going to hear from one of them in Christian's discussion, but a whole new set of partnerships to build solutions together with our system integrative partners. And, again, as I mentioned, a brand new leadership team. So look forward to talking about the details of this. There's been a significant amount of transformation internal to Lenovo that's led to the success of this new product introduction today. So in conclusion, I want to talk about the news of the day. We are transforming Lenovo to the next phase of our data center growth. Again, in over 160 countries, closing on that first phase of transformation and moving forward with some unique declarations. We're launching the largest portfolio in our history, not just in servers but in storage and networking, as everything becomes kind of a software personality on top of x86 Compute. We think we're very well positioned with our scale on PCs as well as data center. Two new brands for both data center infrastructure and Software-Defined, without the legacy shackles of our competitors, enabling us to move very very quickly into Software-Defined, and, again, foreshadowing some joint ventures in M&A that are going to be coming up that will further accelerate ourselves there. New premiere support offerings, enabling you to get direct access to level two engineers and white glove unboxing services, which are going to be bundled along with ThinkAgile. And then celebrating the milestone of 25 years in x86 server compute, not just ThinkPads that you'll hear about shortly, but also our 20 million server shipping next month. So we're celebrating that legacy and looking forward to the next phase. And then making sure we have the execution engine to maintain our position and grow it, being number one in customer satisfaction and number one in quality. So, with that, thank you very much. I look forward to seeing you in the breakouts today and talking with many of you, and I'll bring Rod back up to transition us to the next section. Thank you. (audience applauds) >> All right, Kirk, thank you, sir. All right, ladies and gentlemen, what did you think of that? How about a big round of applause for ThinkAgile, ThinkSystems new brands? (audience applauds) And, obviously, with that comes a big round of applause, for Kirk Skaugen, my boss, so we've got to give him a big round of applause, please. I need to stay employed, it's very important. All right, now you just heard from Kirk about some of the new systems, the brands. How about we have a quick look at the video, which shows us the brand new DCG images. >> Narrator: Legacy thinking is dead, stuck in the past, selling the same old stuff, over and over. So then why does it seem like a data center, you know, that thing powering all our little devices and more or less everything interaction today is still stuck in legacy thinking because it's rigid, inflexible, slow, but that's not us. We don't do legacy. We do different. Because different is fearless. Different reduces Cloud deployment from days to hours. Different creates agile technology that others follow. Different is fluid. It uses water-cooling technology to save energy. It co-innovates with some of the best minds in the industry today. Different is better, smarter. Maybe that's why different already holds so many world-record benchmarks in everything. From virtualization to database and application performance or why it's number one in reliability and customer satisfaction. Legacy sells you what they want. Different builds the data center you need without locking you in. Introducing the Data Center Group at Lenovo. Different... Is better. >> All right, ladies and gentlemen, a big round of applause, once again (mumbles) DCG, fantastic. And I'm sure all of you would agree, and Kirk mentioned it a couple of times there. No legacy means a real consultative approach to our customers, and that's something that we really feel is differentiated for ourselves. We are effectively now one of the largest startups in the DCG space, and we are very much ready to disrupt. Now, here in New York City, obviously, the heart of the fashion industry, and much like fashion, as I mentioned earlier, we're different, we're disruptive, we're agile, smarter, and faster. I'd like to say that about myself, but, unfortunately, I can't. But those of you who have observed, you may have noticed that I, too, have transformed. I don't know if anyone saw that. I've transformed from the pinstripe blue, white shirt, red tie look of the, shall we say, our predecessors who owned the x86 business to now a very Lenovo look. No tie and consequently a little bit more chic New York sort of fashion look, shall I say. Nothing more than that. So anyway, a bit of a transformation. It takes a lot to get to this look, by the way. It's a lot of effort. Our next speaker, Christian Teismann, is going to talk a lot about the core business of Lenovo, which really has been, as we've mentioned today, our ThinkPad, 25-year anniversary this year. It's going to be a great celebration inside Lenovo, and as we get through the year and we get closer and closer to the day, you'll see a lot more social and digital work that engages our customers, partners, analysts, et cetera, when we get close to that birthday. Customers just generally are a lot tougher on computers. We know they are. Whether you hang onto it between meetings from the corner of the Notebook, and that's why we have magnesium chassis inside the box or whether you're just dropping it or hypothetically doing anything else like that. We do a lot of robust testing on these products, and that's why it's the number one branded Notebook in the world. So Christian talks a lot about this, but I thought instead of having him talk, I might just do a little impromptu jump back stage and I'll show you exactly what I'm talking about. So follow me for a second. I'm going to jaunt this way. I know a lot of you would have seen, obviously, the front of house here, what we call the front of house. Lots of videos, et cetera, but I don't think many of you would have seen the back of house here, so I'm going to jump through the back here. Hang on one second. You'll see us when we get here. Okay, let's see what's going on back stage right now. You can see one of the team here in the back stage is obviously working on their keyboard. Fantastic, let me tell you, this is one of the key value props of this product, obviously still working, lots of coffee all over it, spill-proof keyboard, one of the key value propositions and why this is the number one laptop brand in the world. Congratulations there, well done for that. Obviously, we test these things. Height, distances, Mil-SPEC approved, once again, fantastic product, pick that up, lovely. Absolutely resistant to any height or drops, once again, in line with our Mil-SPEC. This is Charles, our producer and director back stage for the absolute event. You can see, once again, sand, coincidentally, in Manhattan, who would have thought a snow storm was occurring here, but you can throw sand. We test these things for all of the elements. I've obviously been pretty keen on our development solutions, having lived in Japan for 12 years. We had this originally designed in 1992 by (mumbles), he's still our chief development officer still today, fantastic, congratulations, a sand-enhanced notebook, he'd love that. All right, let's get back out front and on with the show. Watch the coffee. All right, how was that? Not too bad (laughs). It wasn't very impromptu at all, was it? Not at all a set up (giggles). How many people have events and have a bag of sand sitting on the floor right next to a Notebook? I don't know. All right, now it's time, obviously, to introduce our next speaker, ladies and gentlemen, and I hope I didn't steal his thunder, obviously, in my conversations just now that you saw back stage. He's one of my best friends in Lenovo and easily is a great representative of our legendary PC products and solutions that we're putting together for all of our customers right now, and having been an ex-Pat with Lenovo in New York really calls this his second home and is continually fighting with me over the fact that he believes New York has better sushi than Tokyo, let's welcome please, Christian Teismann, our SVP, Commercial Business Segment, and PC Smart Office. Christian Teismann, come on up mate. (audience applauds) >> So Rod thank you very much for this wonderful introduction. I'm not sure how much there is to add to what you have seen already back stage, but I think there is a 25-year of history I will touch a little bit on, but also a very big transformation. But first of all, welcome to New York. As Rod said, it's my second home, but it's also a very important place for the ThinkPad, and I will come back to this later. The ThinkPad is thee industry standard of business computing. It's an industry icon. We are celebrating 25 years this year like no other PC brand has done before. But this story today is not looking back only. It's a story looking forward about the future of PC, and we see a transformation from PCs to personalized computing. I am privileged to lead the commercial PC and Smart device business for Lenovo, but much more important beyond product, I also am responsible for customer experience. And this is what really matters on an ongoing basis. But allow me to stay a little bit longer with our iconic ThinkPad and history of the last 25 years. ThinkPad has always stand for two things, and it always will be. Highest quality in the industry and technology innovation leadership that matters. That matters for you and that matters for your end users. So, now let me step back a little bit in time. As Rod was showing you, as only Rod can do, reliability is a very important part of ThinkPad story. ThinkPads have been used everywhere and done everything. They have survived fires and extreme weather, and they keep surviving your end users. For 25 years, they have been built for real business. ThinkPad also has a legacy of first innovation. There are so many firsts over the last 25 years, we could spend an hour talking about them. But I just want to cover a couple of the most important milestones. First of all, the ThinkPad 1992 has been developed and invented in Japan on the base design of a Bento box. It was designed by the famous industrial designer, Richard Sapper. Did you also know that the ThinkPad was the first commercial Notebook flying into space? In '93, we traveled with the space shuttle the first time. For two decades, ThinkPads were on every single mission. Did you know that the ThinkPad Butterfly, the iconic ThinkPad that opens the keyboard to its size, is the first and only computer showcased in the permanent collection of the Museum of Modern Art, right here in New York City? Ten years later, in 2005, IBM passed the torch to Lenovo, and the story got even better. Over the last 12 years, we sold over 100 million ThinkPads, four times the amount IBM sold in the same time. Many customers were concerned at that time, but since then, the ThinkPad has remained the best business Notebook in the industry, with even better quality, but most important, we kept innovating. In 2012, we unveiled the X1 Carbon. It was the thinnest, lightest, and still most robust business PC in the world. Using advanced composited materials like a Formula One car, for super strengths, X1 Carbon has become our ThinkPad flagship since then. We've added an X1 Carbon Yoga, a 360-degree convertible. An X1 Carbon tablet, a detachable, and many new products to come in the future. Over the last few years, many new firsts have been focused on providing the best end-user experience. The first dual-screen mobile workstation. The first Windows business tablet, and the first business PC with OLED screen technology. History is important, but a massive transformation is on the way. Future success requires us to think beyond the box. Think beyond hardware, think beyond notebooks and desktops, and to think about the future of personalized computing. Now, why is this happening? Well, because the business world is rapidly changing. Looking back on history that YY gave, and the acceleration of innovation and how it changes our everyday life in business and in personal is driving a massive change also to our industry. Most important because you are changing faster than ever before. Human capital is your most important asset. In today's generation, they want to have freedom of choice. They want to have a product that is tailored to their specific needs, every single day, every single minute, when they use it. But also IT is changing. The Cloud, constant connectivity, 5G will change everything. Artificial intelligence is adding things to the capability of an infrastructure that we just are starting to imagine. Let me talk about the workforce first because it's the most important part of what drives this. The millennials will comprise more than half of the world's workforce in 2020, three years from now. Already, one out of three millennials is prioritizing mobile work environment over salary, and for nearly 60% of all new hires in the United States, technology is a very important factor for their job search in terms of the way they work and the way they are empowered. This new generation of new employees has grown up with PCs, with Smart phones, with tablets, with touch, for their personal use and for their occupation use. They want freedom. Second, the workplace is transforming. The video you see here in the background. This is our North America headquarters in Raleigh, where we have a brand new Smart workspace. We have transformed this to attract the new generation of workers. It has fewer traditional workspaces, much more meaning and collaborative spaces, and Lenovo, like many companies, is seeing workspaces getting smaller. An average workspace per employee has decreased by 30% over the last five years. Employees are increasingly mobile, but, if they come to the office, they want to collaborate with their colleagues. The way we collaborate and communicate is changing. Investment in new collaboration technology is exploding. The market of collaboration technology is exceeding the market of personal computing today. It will grow in the future. Conference rooms are being re-imagined from a ratio of 50 employees to one large conference room. Today, we are moving into scenarios of four employees to one conference room, and these are huddle rooms, pioneer spaces. Technology is everywhere. Video, mega-screens, audio, electronic whiteboards. Adaptive technologies are popping up and change the way we work. As YY said earlier, the pace of the revolution is astonishing. So personalized computing will transform the PC we all know. There's a couple of key factors that we are integrating in our next generations of PC as we go forward. The most important trends that we see. First of all, choose your own device. We talked about this new generation of workforce. Employees who are used to choosing their own device. We have to respond and offer devices that are tailored to each end user's needs without adding complexity to how we operate them. PC is a service. Corporations increasingly are looking for on-demand computing in data center as well as in personal computing. Customers want flexibility. A tailored management solution and a services portfolio that completes the lifecycle of the device. Agile IT, even more important, corporations want to run an infrastructure that is agile, instant respond to their end-customer needs, that is self provisioning, self diagnostic, and remote software repair. Artificial intelligence. Think about artificial intelligence for you personally as your personal assistant. A personal assistant which does understand you, your schedule, your travel, your next task, an extension of yourself. We believe the PC will be the center of this mobile device universe. Mobile device synergy. Each of you have two devices or more with you. They need to work together across different operating systems, across different platforms. We believe Lenovo is uniquely positioned as the only company who has a Smart phone business, a PC business, and an infrastructure business to really seamlessly integrate all of these devices for simplicity and for efficiency. Augmented reality. We believe augmented reality will drive significantly productivity improvements in commercial business. The core will be to understand industry-specific solutions. New processes, new business challenges, to improve things like customer service and sales. Security will remain the foundation for personalized computing. Without security, without trust in the device integrity, this will not happen. One of the most important trends, I believe, is that the PC will transform, is always connected, and always on, like a Smart phone. Regardless if it's open, if it's closed, if you carry it, or if you work with it, it always is capable to respond to you and to work with you. 5G is becoming a reality, and the data capacity that will be out there is by far exceeding today's traffic imagination. Finally, Smart Office, delivering flexible and collaborative work environments regardless on where the worker sits, fully integrated and leverages all the technologies we just talked before. These are the main challenges you and all of your CIO and CTO colleagues have to face today. A changing workforce and a new set of technologies that are transforming PC into personalized computing. Let me give you a real example of a challenge. DXC was just formed by merging CSE company and HP's Enterprise services for the largest independent services company in the world. DXC is now a 25 billion IT services leader with more than 170,000 employees. The most important capital. 6,000 clients and eight million managed devices. I'd like to welcome their CIO, who has one of the most challenging workforce transformation in front of him. Erich Windmuller, please give him a round of applause. (audience applauds). >> Thank you Christian. >> Thank you. >> It's my pleasure to be here, thank you. >> So first of all, let me congratulation you to this very special time. By forming a new multi-billion-dollar enterprise, this new venture. I think it has been so far fantastically received by analysts, by the press, by customers, and we are delighted to be one of your strategic partners, and clearly we are collaborating around workforce transformation between our two companies. But let me ask you a couple of more personal questions. So by bringing these two companies together with nearly 200,00 employees, what are the first actions you are taking to make this a success, and what are your biggest challenges? >> Well, first, again, let me thank you for inviting me and for DXC Technology to be a part of this very very special event with Lenovo, so thank you. As many of you might expect, it's been a bit of a challenge over the past several months. My goal was really very simple. It was to make sure that we brought two companies together, and they could operate as one. We need to make sure that could continue to support our clients. We certainly need to make sure we could continue to sell, our sellers could sell. That we could pay our employees, that we could hire people, we could do all the basic foundational things that you might expect a company would want to do, but we really focused on three simple areas. I called it the three Cs. Connectivity, communicate, and collaborate. So we wanted to make sure that we connected our legacy data centers so we could transfer information and communicate back and forth. We certainly wanted to be sure that our employees could communicate via WIFI, whatever locations they may or may not go to. We certainly wanted to, when we talk about communicate, we need to be sure that everyone of our employees could send and receive email as a DXC employee. And that we had a single-enterprise directory and people could communicate, gain access to calendars across each of the two legacy companies, and then collaborate was also key. And so we wanted to be sure, again, that people could communicate across each other, that our legacy employees on either side could get access to many of their legacy systems, and, again, we could collaborate together as a single corporation, so it was challenging, but very very, great opportunity for all of us. And, certainly, you might expect cyber and security was a very very important topic. My chairman challenged me that we had to be at least as good as we were before from a cyber perspective, and when you bring two large companies together like that there's clearly an opportunity in this disruptive world so we wanted to be sure that we had a very very strong cyber security posture, of which Lenovo has been very very helpful in our achieving that. >> Thank you, Erich. So what does DXC consider as their critical solutions and technology for workplace transformation, both internally as well as out on the market? >> So workplace transformation, and, again, I've heard a lot of the same kinds of words that I would espouse... It's all about making our employees productive. It's giving the right tools to do their jobs. I, personally, have been focused, and you know this because Lenovo has been a very very big part of this, in working with our, we call it our My Style Workplace, it's an offering team in developing a solution and driving as much functionality as possible down to the workstation. We want to be able, for me, to avoid and eliminate other ancillary costs, audio video costs, telecommunication cost. The platform that we have, the digitized workstation that Lenovo has provided us, has just got a tremendous amount of capability. We want to streamline those solutions, as well, on top of the modern server. The modern platform, as we call it, internally. I'd like to congratulate Kirk and your team that you guys have successfully... Your hardware has been certified on our modern platform, which is a significant accomplishment between our two companies and our partnership. It was really really foundational. Lenovo is a big part of our digital workstation transformation, and you'll continue to be, so it's very very important, and I want you to know that your tools and your products have done a significant job in helping us bring two large corporations together as one. >> Thank you, Erich. Last question, what is your view on device as a service and hardware utility model? >> This is the easy question, right? So who in the room doesn't like PC or device as a service? This is a tremendous opportunity, I think, for all of us. Our corporation, like many of you in the room, we're all driven by the concept of buying devices in an Opex versus a Capex type of a world and be able to pay as you go. I think this is something that all of us would like to procure, product services and products, if you will, personal products, in this type of a mode, so I am very very eager to work with Lenovo to be sure that we bring forth a very dynamic and constructive device as a service approach. So very eager to do that with Lenovo and bring that forward for DXC Technology. >> Erich, thank you very much. It's a great pleasure to work with you, today and going forward on all sides. I think with your new company and our lineup, I think we have great things to come. Thank you very much. >> My pleasure, great pleasure, thank you very much. >> So, what's next for Lenovo PC? We already have the most comprehensive commercial portfolio in the industry. We have put the end user in the core of our portfolio to finish and going forward. Ultra mobile users, like consultants, analysts, sales and service. Heavy compute users like engineers and designers. Industry users, increasingly more understanding. Industry-specific use cases like education, healthcare, or banking. So, there are a few exciting things we have to announce today. Obviously, we don't have that broad of an announcement like our colleagues from the data center side, but there is one thing that I have that actually... Thank you Rod... Looks like a Bento box, but it's not a ThinkPad. It's a first of it's kind. It's the world's smallest professional workstation. It has the power of a tower in the Bento box. It has the newest Intel core architecture, and it's designed for a wide range of heavy duty workload. Innovation continues, not only in the ThinkPad but also in the desktops and workstations. Second, you hear much about Smart Office and workspace transformation today. I'm excited to announce that we have made a strategic decision to expand our Think portfolio into Smart Office, and we will soon have solutions on the table in conference rooms, working with strategic partners like Intel and like Microsoft. We are focused on a set of devices and a software architecture that, as an IoT architecture, unifies the management of Smart Office. We want to move fast, so our target is that we will have our first product already later this year. More to come. And finally, what gets me most excited is the upcoming 25 anniversary in October. Actually, if you go to Japan, there are many ThinkPad lovers. Actually beyond lovers, enthusiasts, who are collectors. We've been consistently asked in blogs and forums about a special anniversary edition, so let me offer you a first glimpse what we will announce in October, of something we are bring to market later this year. For the anniversary, we will introduce a limited edition product. This will include throwback features from ThinkPad's history as well as the best and most powerful features of the ThinkPad today. But we are not just making incremental adjustments to the Think product line. We are rethinking ThinkPad of the future. Well, here is what I would call a concept card. Maybe a ThinkPad without a hinge. Maybe one you can fold. What do you think? (audience applauds) but this is more than just design or look and feel. It's a new set of advanced materials and new screen technologies. It's how you can speak to it or write on it or how it speaks to you. Always connected, always on, and can communicate on multiple inputs and outputs. It will anticipate your next meeting, your next travel, your next task. And when you put it all together, it's just another part of the story, which we call personalized computing. Thank you very much. (audience applauds) Thank you, sir. >> Good on ya, mate. All right, ladies and gentlemen. We are now at the conclusion of the day, for this session anyway. I'm going to talk a little bit more about our breakouts and our demo rooms next door. But how about the power with no tower, from Christian, huh? Big round of applause. (audience applauds) And what about the concept card, the ThinkPad? Pretty good, huh? I love that as well. I tell you, it was almost like Leonardo DiCaprio was up on stage at one stage. He put that big ThinkPad concept up, and everyone's phones went straight up and took a photo, the whole audience, so let's be very selective on how we distribute that. I'm sure it's already on Twitter. I'll check it out in a second. So once again, ThinkPad brand is a core part of the organization, and together both DCG and PCSD, what we call PCSD, which is our client side of the business and Smart device side of the business, are obviously very very linked in transforming Lenovo for the future. We want to also transform the industry, obviously, and transform the way that all of us do business. Lenovo, if you look at basically a summary of the day, we are highly committed to being a top three data center provider. That is really important for us. We are the largest and fastest growing supercomputing company in the world, and Kirk actually mentioned earlier on, committed to being number one by 2020. So Madhu who is in Frankfurt at the International Supercomputing Convention, if you're watching, congratulations, your targets have gone up. There's no doubt he's going to have a lot of work to do. We're obviously very very committed to disrupting the data center. That's obviously really important for us. As we mentioned, with both the brands, the ThinkSystem, and our ThinkAgile brands now, highly focused on disrupting and ensuring that we do things differently because different is better. Thank you to our customers, our partners, media, analysts, and of course, once again, all of our employees who have been on this journey with us over the last two years that's really culminating today in the launch of all of our new products and our profile and our portfolio. It's really thanks to all of you that once again on your feedback we've been able to get to this day. And now really our journey truly begins in ensuring we are disrupting and enduring that we are bringing more value to our customers without that legacy that Kirk mentioned earlier on is really an advantage for us as we really are that large startup from a company perspective. It's an exciting time to be part of Lenovo. It's an exciting time to be associated with Lenovo, and I hope very much all of you feel that way. So a big round of applause for today, thank you very much. (audience applauds) I need to remind all of you. I don't think I'm going to have too much trouble getting you out there, because I was just looking at Christian on the streaming solutions out in the room out the back there, and there's quite a nice bit of lunch out there as well for those of you who are hungry, so at least there's some good food out there, but I think in reality all of you should be getting up into the demo sessions with our segment general managers because that's really where the rubber hits the road. You've heard from YY, you've heard from Kirk, and you've heard from Christian. All of our general managers and our specialists in our product sets are going to be out there to obviously demonstrate our technology. As we said at the very beginning of this session, this is Transform, obviously the fashion change, hopefully you remember that. Transform, we've all gone through the transformation. It's part of our season of events globally, and our next event obviously is going to be in Tech World in Shanghai on the 20th of July. I hope very much for those of you who are going to attend have a great safe travel over there. We look forward to seeing you. Hope you've had a good morning, and get into the sessions next door so you get to understand the technology. Thank you very much, ladies and gentlemen. (upbeat innovative instrumental)