(bright upbeat music) >> Welcome to The Cube. We're here for the AWS Executive Summit part of Reinvent 2021. I'm John Farrow, your host of the Cube. We've got a great segment focus here, Art of the Possible is the segment. Jas Bains, Chief Executive at Hafod and Jamie Smith, director of research and innovation and Laetitia Cailleteau who's the global lead of conversational AI at Accenture. Thanks for joining me today for this Art of the Possible segment. >> Thank you. >> So tell us a little bit about Hafod and what you guys are doing to the community 'cause this is a really compelling story of how technology in home care is kind of changing the game and putting a stake in the ground. >> Yeah, so Hafod is one of the largest not for profits in Wales. We employ about 1400 colleagues. We have three strands a service, which practices on key demographics. So people who are vulnerable and socioeconomically disadvantaged. Our three core strands of service are affordable housing, we provide several thousand homes to people in housing need across Wales. We also are an extensive provider of social provision, both residential and in the community. And then we have a third tier, which is a hybrid in between. So that supports people who are not quite ready for independent living but neither are they ready for residential care. So that's a supportive provision. I suppose what one of the things that marks Hafod out and why we're here in this conversation is that we're uniquely placed as one of the organizations that actually has a research and innovation capacity. And it's the work of the research and innovation capacity led by Jamie that brought about this collaboration with Accenture which is great in great meaning and benefits. So thousands of our customers and hopefully universal application as it develops. >> You know this is a really an interesting discussion because multiple levels, one, the pandemic accelerated this needs so, I want to get comments on that. But two, if you look at the future of work and work and home life, you seeing the convergence of where people live. And I think this idea of having this independent home and the ecosystem around it, there's a societal impact as well. So what brought this opportunity together? How did this come together with Accenture and AWS? >> We're going for Jamie and Laetitia. >> Yeah, I can start. Well, we were trying to apply for the LC Aging Grand Challenge in the U.K., so the United Kingdom recognized the need for change around independent living and run a grand challenge. And then we got together as part of this grand challenge. You know, we had some technology, we had trialed with AGK before and Hanover Housing Association. Hafod was really keen to actually start trying some of that technology with some of the resident. And we also worked with Swansea University, was doing a lot of work around social isolation and loneliness. And we came together to kind of pitch for the grand challenge. And we went quite far actually, unfortunately we didn't win but we have built such a great collaboration that we couldn't really let it be, you know, not going any further. And we decided to continue to invest in this idea. And now we here, probably 18 months on with a number of people, Hafod using the technology and a number of feedbacks and returns coming back and us having a grand ambitions to actually go much broader and scale this solution. >> Jas and Jamie, I'd love to get your reaction and commentary on this trend of tech for good because I mean, I'm sure you didn't wake up, oh, just want to do some tech for good. You guys have an environment, you have an opportunity, you have challenges you're going to turn into opportunities. But if you look at the global landscape right now, things that are jumping out at us are looking at the impact of social media on people. You got the pandemic with isolation, this is a first order problem in this new world of how do we get technology to change how people feel and make them better in their lives. >> Yeah, I think for us, the first has to be a problem to solve. There's got to be a question to be answered. And for us, that was in this instance, how do we mitigate loneliness and how do we take services that rely on person to person contact and not particularly scalable and replicate those through technology somehow. And even if we can do 10% of the job of that in-person service then for us, it's worth it because that is scalable. And there are lots of small interventions we can make using technology which is really efficient way for us to support people in the community when we just can't be everywhere at once. >> So, John, just to add, I think that we have about 1500 people living in households that are living alone and isolated. And I think the issue for us was more than just about technology because a lot of these people don't have access to basic technology features that most of us would take for granted. So far this is a two-prong journey. One is about increasing the accessibility to tech and familiarizing people so that they're comfortable with these devices technology and two importantly, make sure that we have the right means to help people reduce their loneliness and isolation. So the opportunity to try out something over the last 12 months, something that's bespoke, that's customized that will undoubtedly be tweaked as we go forward has been an absolutely marvelous opportunity. And for us, the collaboration with Accenture has been absolutely key. I think what we've seen during COVID is cross-fertilization. We've seen multi-disciplinary teams, we've got engineers, architects, manufacturers, and clinicians, and scientists, all trying to develop new solutions around COVID. And I think this probably just exemplary bias, especially as a post COVID where industry and in our case for example public sector and academia working together. >> Yeah, that's a great example and props to everyone there. And congratulations on this really, really important initiative. Let's talk about the home care solution. What does it do? How does it work? Take us through what's happening? >> Okay, so Home Care is actually a platform which is obviously running on AWS technology and this particular platform is the service offered accessible via voice through the Alexa device. We use the Echo Show to be able to use voice but also visuals to kind of make the technology more accessible for end user. On the platform itself, we have a series of services available out there. We connecting in the background a number of services from the community. So in the particular case of Hafod, we had something around shopping during the pandemic where we had people wanting to have access to their food bank. Or we also had during the pandemic, there was some need for having access to financial coaching and things like that. So we actually brought all of the service on the platform and the skills and this skill was really learning how to interact with the end user. And it was all customized for them to be able to access those things in a very easy way. It did work almost too well because some of our end users have been a kind of you know, have not been digital literate before and it was working so well, they were like, "But why can't it do pretty much anything on the planet? "Why can't it do this or that?" So the expectations were really, really high but we did manage to bring comfort to Hafod residents in a number of their daily kind of a need, some of the things during COVID 'cause people couldn't meet face to face. There was some challenge around understanding what events are running. So the coaches would publish events, you know, through the skills and people would be able to subscribe and go to the event and meet together virtually instead of physically. The number of things that really kind of brought a voice enabled experience for those end users. >> You know, you mentioned the people like the solution just before we, I'm going to get the Jamie in a second, but I want to just bring up something that you brought up. This is a digital divide evolution because digital divide, as Josh was saying, is that none about technology,, first, you have to access, you need access, right? First, then you have to bring broadband and internet access. And then you have to get the technology in the home. But then here it seems to be a whole nother level of digital divide bridging to the new heights. >> Yeah, completely, completely. And I think that's where COVID has really accelerated the digital divide before the solution was put in place for Hafod in the sense that people couldn't move and if they were not digitally literate, it was very hard to have access to services. And now we brought this solution in the comfort of their own home and they have the access to the services that they wouldn't have had otherwise on their own. So it's definitely helping, yeah. >> It's just another example of people refactoring their lives or businesses with technology. Jamie, what's your take on the innovation here and the technical aspects of the home care solutions? >> I think the fact that it's so easy to use, it's personalized, it's a digital companion for the home. It overcomes that digital divide that we talked about, which is really important. If you've got a voice you can use home care and you can interact with it in this really simple way. And what I love about it is the fact that it was based on what our customers told us they were finding difficult during this time, during the early lockdowns of the pandemic. There was 1500 so people Jas talked about who were living alone and at risk of loneliness. Now we spoke to a good number of those through a series of welfare calls and we found out exactly what it is they found challenging. >> What were some of the things that they were finding challenging? >> So tracking how they feel on a day-to-day basis. What's my mood like, what's my wellbeing like, and knowing how that changes over time. Just keeping the fridge in the pantry stocked up. What can I cook with these basic ingredients that I've got in my home? You could be signposted to basic resources to help you with that. Staying connected to the people who are really important to you but the bit that shines out for me is the interface with our services, with our neighborhood coaching service, where we can just give these little nudges, these little interventions just to mitigate and take the edge of that loneliness for people. We can see the potential of that coming up to the pandemic, where you can really encourage people to interact with one another, to be physically active and do all of those things that sort of mitigate against loneliness. >> Let me ask you a question 'cause I think a very important point. The timing of the signaling of data is super important. Could you comment on the relevance of having access to data? If you're getting something connected, when you're connected like this, I can only imagine the benefits. It's all about timing, right? Knowing that someone might be thinking some way or whether it's a tactical, in any scenario, timing of data, the right place at the right time, as they say. What's your take on that 'cause it sounds like what you're saying is that you can see things early when people are in the moment. >> Yeah, exactly. So if there's a trend beginning to emerge, for example, around some of these wellbeing, which has been on a low trajectory for a number of days, that can raise a red flag in our system and it alerts one of our neighborhood coaches just to reach out to that person and say, "Well, John, what's going on? "You haven't been out for a walk for a few days. "We know you like to walk, what's happening?" And these early warning signs are really important when we think of the long-term effects of loneliness and how getting upstream of those, preventing it reaching a point where it moves from being a problem into being a crisis. And the earlier we can detect that the more chance we've got of these negative long-term outcomes being mitigated. >> You know, one of the things we see in the cloud business is kind of separate track but it kind of relates to the real world here that you're doing, is automation and AI and machine learning bringing in a lot of value if applied properly. So how are you guys seeing, I can almost imagine that patterns are coming in, right? Do you see patterns in the data? How does AI and analytics technology improve this process especially with the wellbeing and emotional wellbeing of the elderly? >> I think one of the things we've learned through the pilot study we've done is there's not one size fits all. You know, all those people are very different individuals. They have very different habits. You know, there's some people not sleeping over the night. There's some people wanting to be out early, wanting to be social. Some people you have to put in much more. So it's definitely not one size fits all. And automation and digitalization of those kinds of services is really challenging because if they're not personalized, it doesn't really catch the interest or the need of the individuals. So for me as an IT professional being in the industry for like a 20 plus years, I think this is the time where personalization has really a true meaning. Personalization at scale for those people that are not digitally literate. But also in more vulnerable settings 'cause there's just so many different angles that can make them vulnerable. Maybe it's the body, maybe it's the economy position, their social condition, there's so many variation of all of that. So I think this is one of the use case that has to be powered by technology to complement the human side of it. If we really want to start scaling the services we provide to people in general, meaning obviously, in all the Western country now we all growing old, it's no secret. So in 20 years time the majority of everybody will be old and we obviously need people to take care of us. And at the moment we don't have that population to take care of us coming up. So really to crack on those kinds of challenges, we really need to have technology powering and just helping the human side to make it more efficient, connected than human. >> It's interesting. I just did a story where you have these bots that look at the facial recognition via cameras and can detect either in hospitals and or in care patients, how they feel. So you see where this is going. Jas I got to ask you how all this changes, the home care model and how Hafod works. Your workforce, the career's culture, the consortium you guys are bringing to the table, partners, you know this is an ecosystem now, it's a system. >> Yes John, I think that probably, it's also worth talking a little bit about the pressures on state governments around public health issues which are coming to the fore. And clearly we need to develop alternative ways that we engage with mass audiences and technology is going to be absolutely key. One of the challenges I still think that we've not resolved in the U.K. level, this is probably a global issue, is about data protection. When we're talking to cross governmental agencies, it's about sharing data and establishing protocols and we've enjoyed a few challenging conversations with colleagues around data protection. So I think those need to be set out in the context of the journey of this particular project. I think that what's interesting around COVID is that, hasn't materially changed the nature in which we do things, probably not in our focus and our work remains the same. But what we're seeing is very clear evidence of the ways, I mean, who would have thought that 12 months ago, the majority of our workforce would be working from home? So rapid mobilization to ensure that people can use, set IT home effectively. And then how does that relationship impact with people in the communities we're serving? Some of whom have got access to technology, others who haven't. So that's been, I think the biggest change, and that is a fundamental change in the design and delivery of future services that organizations like us will be providing. So I would say that overall, some things remain the same by and large but technology is having an absolutely profound change in the way that our engagement with customers will go forward. >> Well, you guys are in the front end of some massive innovation here with this, are they possible and that, you're really delivering impact. And I think this is an example of that. And you brought up the data challenges, this is something that you guys call privacy by design. This is a cutting edge issue here because there are benefits around managing privacy properly. And I think here, your solution clearly has value, right? And no one can debate that, but as these little blockers get in the way, what's your reaction to that? 'Cause this certainly is something that has to be solved. I mean, it's a problem. >> Yeah, so we designed a solution, I think we had, when we design, I co-designed with your end-users actually. We had up to 14 lawyers working with us at one point in time looking at different kinds of angles. So definitely really tackle the solution with privacy by design in mind and with end users but obviously you can't co-design with thousands of people, you have to co-design with a representative subset of a cohort. And some of the challenge we find is obviously, the media have done a lot of scaremongering around technology, AI and all of that kind of things, especially for people that are not necessarily digitally literate, people that are just not in it. And when we go and deploy the solution, people are a little bit worried. When we make them, we obviously explain to them what's going to happen if they're happy, if they want to consent and all that kind of things. But the people are scared, they're just jumping on a technology on top of it we're asking them some questions around consent. So I think it's just that the solution is super secured and we've gone over millions of hoops within Accenture but also with Hafod itself. You know, it's more that like the type of user we deploying the solution to are just not in that world and then they are little bit worried about sharing. Not only they're worried about sharing with us but you know, in home care, there there's an option as well to share some of that data with your family. And there we also see people are kind of okay to share with us but they don't want to share with their family 'cause they don't want to have too much information kind of going potentially worrying or bothering some of their family member. So there is definitely a huge education kind of angle to embracing the technology. Not only when you create the solution but when you actually deploy it with users. >> It's a fabulous project, I am so excited by this story. It's a great story, has all the elements; technology, innovation, cidal impact, data privacy, social interactions, whether it's with family members and others, internal, external. In teams themselves. You guys doing some amazing work, thank you for sharing. It's a great project, we'll keep track of it. My final question for you guys is what comes next for the home care after the trial? What are Hafod's plans and hopes for the future? >> Maybe if I just give an overview and then invite Jamie and Laetitia. So for us, without conversations, you don't create possibilities and this really is a reflection of the culture that we try to engender. So my ask of my team is to remain curious, is to continue to explore opportunities because it's home care up to today, it could be something else tomorrow. We also recognize that we live in a world of collaboration. We need more cross industrial partnerships. We love to explore more things that Accenture, Amazon, others as well. So that's principally what I will be doing is ensuring that the culture invites us and then I hand over to the clever people like Jamie and Laetitia to get on with the technology. I think for me we've already learned an awful lot about home care and there's clearly a lot more we can learn. We'd love to build on this initial small-scale trial and see how home care could work at a bigger scale. So how would it work with thousands of users? How do we scale it up from a cohort of 50 to a cohort of 5,000? How does it work when we bring different kinds of organizations into that mix? So what if, for example, we could integrate it into health care? So a variety of services can have a holistic view of an individual and interact with one another, to put that person on the right pathway and maybe keep them out of the health and care system for longer, actually reducing the costs to the system in the long run and improving that person's outcomes. That kind of evidence speaks to decision-makers and political partners and I think that's the kind of evidence we need to build. >> Yeah, financial impact is there, it's brutal. It's a great financial impact for the system. Efficiency, better care, everything. >> Yeah and we are 100% on board for whatever comes next. >> Laetitia-- >> What about you Laetitia? >> Great program you got there. A amazing story, thank you for sharing. Congratulations on this awesome project. So much to unpack here. I think this is the future. I mean, I think this is a case study of represents all the moving parts that need to be worked on, so congratulations. >> Thank you. >> Thank you. >> We are the Art of the Possible here inside the Cube, part of AWS Reinvent Executive Summit, I'm John Furrier, your host, thanks for watching. (bright upbeat music)

(bright upbeat music) >> Welcome to The Cube. We're here for the AWS Executive Summit part of Reinvent 2021. I'm John Farrow, your host of the Cube. We've got a great segment focus here, Art of the Possible is the segment. Jas Bains, Chief Executive at Hafod and Jamie Smith, director of research and innovation and Laetitia Cailleteau who's the global lead of conversational AI at Accenture. Thanks for joining me today for this Art of the Possible segment. >> Thank you. >> So tell us a little bit about Hafod and what you guys are doing to the community 'cause this is a really compelling story of how technology in home care is kind of changing the game and putting a stake in the ground. >> Yeah, so Hafod is one of the largest not for profits in Wales. We employ about 1400 colleagues. We have three strands a service, which practices on key demographics. So people who are vulnerable and socioeconomically disadvantaged. Our three core strands of service are affordable housing, we provide several thousand homes to people in housing need across Wales. We also are an extensive provider of social provision, both residential and in the community. And then we have a third tier, which is a hybrid in between. So that supports people who are not quite ready for independent living but neither are they ready for residential care. So that's a supportive provision. I suppose what one of the things that marks Hafod out and why we're here in this conversation is that we're uniquely placed as one of the organizations that actually has a research and innovation capacity. And it's the work of the research and innovation capacity led by Jamie that brought about this collaboration with Accenture which is great in great meaning and benefits. So thousands of our customers and hopefully universal application as it develops. >> You know this is a really an interesting discussion because multiple levels, one, the pandemic accelerated this needs so, I want to get comments on that. But two, if you look at the future of work and work and home life, you seeing the convergence of where people live. And I think this idea of having this independent home and the ecosystem around it, there's a societal impact as well. So what brought this opportunity together? How did this come together with Accenture and AWS? >> We're going for Jamie and Laetitia. >> Yeah, I can start. Well, we were trying to apply for the LC Aging Grand Challenge in the U.K., so the United Kingdom recognized the need for change around independent living and run a grand challenge. And then we got together as part of this grand challenge. You know, we had some technology, we had trialed with AGK before and Hanover Housing Association. Hafod was really keen to actually start trying some of that technology with some of the resident. And we also worked with Swansea University, was doing a lot of work around social isolation and loneliness. And we came together to kind of pitch for the grand challenge. And we went quite far actually, unfortunately we didn't win but we have built such a great collaboration that we couldn't really let it be, you know, not going any further. And we decided to continue to invest in this idea. And now we here, probably 18 months on with a number of people, Hafod using the technology and a number of feedbacks and returns coming back and us having a grand ambitions to actually go much broader and scale this solution. >> Jas and Jamie, I'd love to get your reaction and commentary on this trend of tech for good because I mean, I'm sure you didn't wake up, oh, just want to do some tech for good. You guys have an environment, you have an opportunity, you have challenges you're going to turn into opportunities. But if you look at the global landscape right now, things that are jumping out at us are looking at the impact of social media on people. You got the pandemic with isolation, this is a first order problem in this new world of how do we get technology to change how people feel and make them better in their lives. >> Yeah, I think for us, the first has to be a problem to solve. There's got to be a question to be answered. And for us, that was in this instance, how do we mitigate loneliness and how do we take services that rely on person to person contact and not particularly scalable and replicate those through technology somehow. And even if we can do 10% of the job of that in-person service then for us, it's worth it because that is scalable. And there are lots of small interventions we can make using technology which is really efficient way for us to support people in the community when we just can't be everywhere at once. >> So, John, just to add, I think that we have about 1500 people living in households that are living alone and isolated. And I think the issue for us was more than just about technology because a lot of these people don't have access to basic technology features that most of us would take for granted. So far this is a two-prong journey. One is about increasing the accessibility to tech and familiarizing people so that they're comfortable with these devices technology and two importantly, make sure that we have the right means to help people reduce their loneliness and isolation. So the opportunity to try out something over the last 12 months, something that's bespoke, that's customized that will undoubtedly be tweaked as we go forward has been an absolutely marvelous opportunity. And for us, the collaboration with Accenture has been absolutely key. I think what we've seen during COVID is cross-fertilization. We've seen multi-disciplinary teams, we've got engineers, architects, manufacturers, and clinicians, and scientists, all trying to develop new solutions around COVID. And I think this probably just exemplary bias, especially as a post COVID where industry and in our case for example public sector and academia working together. >> Yeah, that's a great example and props to everyone there. And congratulations on this really, really important initiative. Let's talk about the home care solution. What does it do? How does it work? Take us through what's happening? >> Okay, so Home Care is actually a platform which is obviously running on AWS technology and this particular platform is the service offered accessible via voice through the Alexa device. We use the Echo Show to be able to use voice but also visuals to kind of make the technology more accessible for end user. On the platform itself, we have a series of services available out there. We connecting in the background a number of services from the community. So in the particular case of Hafod, we had something around shopping during the pandemic where we had people wanting to have access to their food bank. Or we also had during the pandemic, there was some need for having access to financial coaching and things like that. So we actually brought all of the service on the platform and the skills and this skill was really learning how to interact with the end user. And it was all customized for them to be able to access those things in a very easy way. It did work almost too well because some of our end users have been a kind of you know, have not been digital literate before and it was working so well, they were like, "But why can't it do pretty much anything on the planet? "Why can't it do this or that?" So the expectations were really, really high but we did manage to bring comfort to Hafod residents in a number of their daily kind of a need, some of the things during COVID 'cause people couldn't meet face to face. There was some challenge around understanding what events are running. So the coaches would publish events, you know, through the skills and people would be able to subscribe and go to the event and meet together virtually instead of physically. The number of things that really kind of brought a voice enabled experience for those end users. >> You know, you mentioned the people like the solution just before we, I'm going to get the Jamie in a second, but I want to just bring up something that you brought up. This is a digital divide evolution because digital divide, as Josh was saying, is that none about technology,, first, you have to access, you need access, right? First, then you have to bring broadband and internet access. And then you have to get the technology in the home. But then here it seems to be a whole nother level of digital divide bridging to the new heights. >> Yeah, completely, completely. And I think that's where COVID has really accelerated the digital divide before the solution was put in place for Hafod in the sense that people couldn't move and if they were not digitally literate, it was very hard to have access to services. And now we brought this solution in the comfort of their own home and they have the access to the services that they wouldn't have had otherwise on their own. So it's definitely helping, yeah. >> It's just another example of people refactoring their lives or businesses with technology. Jamie, what's your take on the innovation here and the technical aspects of the home care solutions? >> I think the fact that it's so easy to use, it's personalized, it's a digital companion for the home. It overcomes that digital divide that we talked about, which is really important. If you've got a voice you can use home care and you can interact with it in this really simple way. And what I love about it is the fact that it was based on what our customers told us they were finding difficult during this time, during the early lockdowns of the pandemic. There was 1500 so people Jas talked about who were living alone and at risk of loneliness. Now we spoke to a good number of those through a series of welfare calls and we found out exactly what it is they found challenging. >> What were some of the things that they were finding challenging? >> So tracking how they feel on a day-to-day basis. What's my mood like, what's my wellbeing like, and knowing how that changes over time. Just keeping the fridge in the pantry stocked up. What can I cook with these basic ingredients that I've got in my home? You could be signposted to basic resources to help you with that. Staying connected to the people who are really important to you but the bit that shines out for me is the interface with our services, with our neighborhood coaching service, where we can just give these little nudges, these little interventions just to mitigate and take the edge of that loneliness for people. We can see the potential of that coming up to the pandemic, where you can really encourage people to interact with one another, to be physically active and do all of those things that sort of mitigate against loneliness. >> Let me ask you a question 'cause I think a very important point. The timing of the signaling of data is super important. Could you comment on the relevance of having access to data? If you're getting something connected, when you're connected like this, I can only imagine the benefits. It's all about timing, right? Knowing that someone might be thinking some way or whether it's a tactical, in any scenario, timing of data, the right place at the right time, as they say. What's your take on that 'cause it sounds like what you're saying is that you can see things early when people are in the moment. >> Yeah, exactly. So if there's a trend beginning to emerge, for example, around some of these wellbeing, which has been on a low trajectory for a number of days, that can raise a red flag in our system and it alerts one of our neighborhood coaches just to reach out to that person and say, "Well, John, what's going on? "You haven't been out for a walk for a few days. "We know you like to walk, what's happening?" And these early warning signs are really important when we think of the long-term effects of loneliness and how getting upstream of those, preventing it reaching a point where it moves from being a problem into being a crisis. And the earlier we can detect that the more chance we've got of these negative long-term outcomes being mitigated. >> You know, one of the things we see in the cloud business is kind of separate track but it kind of relates to the real world here that you're doing, is automation and AI and machine learning bringing in a lot of value if applied properly. So how are you guys seeing, I can almost imagine that patterns are coming in, right? Do you see patterns in the data? How does AI and analytics technology improve this process especially with the wellbeing and emotional wellbeing of the elderly? >> I think one of the things we've learned through the pilot study we've done is there's not one size fits all. You know, all those people are very different individuals. They have very different habits. You know, there's some people not sleeping over the night. There's some people wanting to be out early, wanting to be social. Some people you have to put in much more. So it's definitely not one size fits all. And automation and digitalization of those kinds of services is really challenging because if they're not personalized, it doesn't really catch the interest or the need of the individuals. So for me as an IT professional being in the industry for like a 20 plus years, I think this is the time where personalization has really a true meaning. Personalization at scale for those people that are not digitally literate. But also in more vulnerable settings 'cause there's just so many different angles that can make them vulnerable. Maybe it's the body, maybe it's the economy position, their social condition, there's so many variation of all of that. So I think this is one of the use case that has to be powered by technology to complement the human side of it. If we really want to start scaling the services we provide to people in general, meaning obviously, in all the Western country now we all growing old, it's no secret. So in 20 years time the majority of everybody will be old and we obviously need people to take care of us. And at the moment we don't have that population to take care of us coming up. So really to crack on those kinds of challenges, we really need to have technology powering and just helping the human side to make it more efficient, connected than human. >> It's interesting. I just did a story where you have these bots that look at the facial recognition via cameras and can detect either in hospitals and or in care patients, how they feel. So you see where this is going. Jas I got to ask you how all this changes, the home care model and how Hafod works. Your workforce, the career's culture, the consortium you guys are bringing to the table, partners, you know this is an ecosystem now, it's a system. >> Yes John, I think that probably, it's also worth talking a little bit about the pressures on state governments around public health issues which are coming to the fore. And clearly we need to develop alternative ways that we engage with mass audiences and technology is going to be absolutely key. One of the challenges I still think that we've not resolved in the U.K. level, this is probably a global issue, is about data protection. When we're talking to cross governmental agencies, it's about sharing data and establishing protocols and we've enjoyed a few challenging conversations with colleagues around data protection. So I think those need to be set out in the context of the journey of this particular project. I think that what's interesting around COVID is that, hasn't materially changed the nature in which we do things, probably not in our focus and our work remains the same. But what we're seeing is very clear evidence of the ways, I mean, who would have thought that 12 months ago, the majority of our workforce would be working from home? So rapid mobilization to ensure that people can use, set IT home effectively. And then how does that relationship impact with people in the communities we're serving? Some of whom have got access to technology, others who haven't. So that's been, I think the biggest change, and that is a fundamental change in the design and delivery of future services that organizations like us will be providing. So I would say that overall, some things remain the same by and large but technology is having an absolutely profound change in the way that our engagement with customers will go forward. >> Well, you guys are in the front end of some massive innovation here with this, are they possible and that, you're really delivering impact. And I think this is an example of that. And you brought up the data challenges, this is something that you guys call privacy by design. This is a cutting edge issue here because there are benefits around managing privacy properly. And I think here, your solution clearly has value, right? And no one can debate that, but as these little blockers get in the way, what's your reaction to that? 'Cause this certainly is something that has to be solved. I mean, it's a problem. >> Yeah, so we designed a solution, I think we had, when we design, I co-designed with your end-users actually. We had up to 14 lawyers working with us at one point in time looking at different kinds of angles. So definitely really tackle the solution with privacy by design in mind and with end users but obviously you can't co-design with thousands of people, you have to co-design with a representative subset of a cohort. And some of the challenge we find is obviously, the media have done a lot of scaremongering around technology, AI and all of that kind of things, especially for people that are not necessarily digitally literate, people that are just not in it. And when we go and deploy the solution, people are a little bit worried. When we make them, we obviously explain to them what's going to happen if they're happy, if they want to consent and all that kind of things. But the people are scared, they're just jumping on a technology on top of it we're asking them some questions around consent. So I think it's just that the solution is super secured and we've gone over millions of hoops within Accenture but also with Hafod itself. You know, it's more that like the type of user we deploying the solution to are just not in that world and then they are little bit worried about sharing. Not only they're worried about sharing with us but you know, in home care, there there's an option as well to share some of that data with your family. And there we also see people are kind of okay to share with us but they don't want to share with their family 'cause they don't want to have too much information kind of going potentially worrying or bothering some of their family member. So there is definitely a huge education kind of angle to embracing the technology. Not only when you create the solution but when you actually deploy it with users. >> It's a fabulous project, I am so excited by this story. It's a great story, has all the elements; technology, innovation, cidal impact, data privacy, social interactions, whether it's with family members and others, internal, external. In teams themselves. You guys doing some amazing work, thank you for sharing. It's a great project, we'll keep track of it. My final question for you guys is what comes next for the home care after the trial? What are Hafod's plans and hopes for the future? >> Maybe if I just give an overview and then invite Jamie and Laetitia. So for us, without conversations, you don't create possibilities and this really is a reflection of the culture that we try to engender. So my ask of my team is to remain curious, is to continue to explore opportunities because it's home care up to today, it could be something else tomorrow. We also recognize that we live in a world of collaboration. We need more cross industrial partnerships. We love to explore more things that Accenture, Amazon, others as well. So that's principally what I will be doing is ensuring that the culture invites us and then I hand over to the clever people like Jamie and Laetitia to get on with the technology. I think for me we've already learned an awful lot about home care and there's clearly a lot more we can learn. We'd love to build on this initial small-scale trial and see how home care could work at a bigger scale. So how would it work with thousands of users? How do we scale it up from a cohort of 50 to a cohort of 5,000? How does it work when we bring different kinds of organizations into that mix? So what if, for example, we could integrate it into health care? So a variety of services can have a holistic view of an individual and interact with one another, to put that person on the right pathway and maybe keep them out of the health and care system for longer, actually reducing the costs to the system in the long run and improving that person's outcomes. That kind of evidence speaks to decision-makers and political partners and I think that's the kind of evidence we need to build. >> Yeah, financial impact is there, it's brutal. It's a great financial impact for the system. Efficiency, better care, everything. >> Yeah and we are 100% on board for whatever comes next. >> Laetitia-- >> What about you Laetitia? >> Great program you got there. A amazing story, thank you for sharing. Congratulations on this awesome project. So much to unpack here. I think this is the future. I mean, I think this is a case study of represents all the moving parts that need to be worked on, so congratulations. >> Thank you. >> Thank you. >> We are the Art of the Possible here inside the Cube, part of AWS Reinvent Executive Summit, I'm John Furrier, your host, thanks for watching. (bright upbeat music)

>> Narrator: From around the globe, it's the CUBE with digital coverage of IBM Think 2021, brought to you by IBM. >> Welcome back to IBM Think 2021, the virtual edition. This is the CUBEs, continuous, deep dive coverage of the people, processes and technologies that are really changing our world. Right now, we're going to talk about modernization and what's beyond with Jamie Thomas, general manager, strategy and development, IBM Enterprise Security. Jamie, always a pleasure. Great to see you again. Thanks for coming on. >> It's great to see you, Dave. And thanks for having me on the CUBE is always a pleasure. >> Yeah, it is our pleasure. And listen, we've been hearing a lot about IBM is focused on hybrid cloud, Arvind Krishna says we must win the architectural battle for hybrid cloud. I love that. We've been hearing a lot about AI. And I wonder if you could talk about IBM Systems and how it plays into that strategy? >> Sure, well, it's a great time to have this discussion Dave. As you all know, IBM Systems Technology is used widely around the world, by many, many 1000s of clients in the context of our IBM System Z, our power systems and storage. And what we have seen is really an uptake of monetization around those workloads, if you will, driven by hybrid cloud, the hybrid cloud agenda, as well as an uptake of Red Hat OpenShift, as a vehicle for this modernization. So it's pretty exciting stuff, what we see as many clients taking advantage of OpenShift on Linux, to really modernize these environments, and then stay close, if you will, to that systems of record database and the transactions associated with it. So they're seeing a definite performance advantage to taking advantage of OpenShift. And it's really fascinating to see the things that they're doing. So if you look at financial services, for instance, there's a lot of focus on risk analytics. So things like fraud, anti money laundering, mortgage risk, types of applications being done in this context, when you look at our retail industry clients, you see also a lot of customer centricity solutions, if you will, being deployed on OpenShift. And once again, having Linux close to those traditional LPARs of AIX, I-Series, or in the context of z/OS. So those are some of the things we see happening. And it's quite real. >> Now, you didn't mention power, but I want to come back and ask you about power. Because a few weeks ago, we were prompted to dig in a little bit with the when Arvind was on with Pat Kessinger at Intel and talking about the relationship you guys have. And so we dug in a little bit, we thought originally, we said, oh, it's about quantum. But we dug in. And we realized that the POWER10 is actually the best out there and the highest performance in terms of disaggregating memory. And we see that as a future architecture for systems and actually really quite excited about it about the potential that brings not only to build beyond system on a chip and system on a package, but to start doing interesting things at the Edge. You know, what do you what's going on with power? >> Well, of course, when I talked about OpenShift, we're doing OpenShift on power Linux, as well as Z Linux, but you're exactly right in the context for a POWER10 processor. We couldn't be more we're so excited about this processor. First of all, it's our first delivery with our partner Samsung with a seven nanometer form factor. The processor itself has only 18 billion transistors. So it's got a few transistors there. But one of the cool inventions, if you will, that we have created is this expansive memory region as part of this design point, which we call memory inception, it gives us the ability to reach memory across servers, up to two petabytes of memory. Aside from that, this processor has generational improvements and core and thread performance, improved energy efficiency. And all of this, Dave is going to give us a lot of opportunity with new workloads, particularly around artificial intelligence and inferencing around artificial intelligence. I mean, that's going to be that's another critical innovation that we see here in this POWER10 processor. >> Yeah, processor performance is just exploding. We're blowing away the historical norms. I think many people don't realize that. Let's talk about some of the key announcements that you've made in quantum last time we spoke on the qubit for last year, I think we did a deeper dive on quantum. You've made some announcements around hardware and software roadmaps. Give us the update on quantum please. >> Well, there is so much that has happened since we last spoke on the quantum landscape. And the key thing that we focused on in the last six months is really an articulation of our roadmaps, so the roadmap around hardware, the roadmap around software, and we've also done quite a bit of ecosystem development. So in terms of the roadmap around hardware, we put ourselves out there we've said we were going to get to over 1000 qubit machine and in 2023, so that's our milestone. And we've got a number of steps we've outlined along that way, of course, we have to make progress, frankly, every six months in terms of innovating around the processor, the electronics and the fridge associated with these machines. So lots of exciting innovation across the board. We've also published a software roadmap, where we're articulating how we improve a circuit execution speeds. So we hope, our plan to show shortly a 100 times improvement in circuit execution speeds. And as we go forward in the future, we're modifying our Qiskit programming model to not only allow a easily easy use by all types of developers, but to improve the fidelity of the entire machine, if you will. So all of our innovations go hand in hand, our hardware roadmap, our software roadmap, are all very critical in driving the technical outcomes that we think are so important for quantum to become a reality. We've deployed, I would say, in our quantum cloud over, you know, over 20 machines over time, we never quite identify the precise number because frankly, as we put up a new generation machine, we often retire when it's older. So we're constantly updating them out there, and every machine that comes on online, and that cloud, in fact, represents a sea change and hardware and a sea change in software. So they're all the latest and greatest that our clients can have access to. >> That's key, the developer angle you got redshift running on quantum yet? >> Okay, I mean, that's a really good question, you know, as part of that software roadmap in terms of the evolution and the speed of that circuit execution is really this interesting marriage between classical processing and quantum processing and bring those closer together. And in the context of our classical operations that are interfacing with that quantum processor, we're taking advantage of OpenShift, running on that classical machine to achieve that. And once again, if, as you can imagine, that'll give us a lot of flexibility in terms of where that classical machine resides and how we continue the evolution the great marriage, I think that's going to that will exist that does exist and will exist between classical computing and quantum computing. >> I'm glad I asked it was kind of tongue in cheek. But that's a key thread to the ecosystem, which is critical to obviously, you know, such a new technology. How are you thinking about the ecosystem evolution? >> Well, the ecosystem here for quantum is infinitely important. We started day one, on this journey with free access to our systems for that reason, because we wanted to create easy entry for anyone that really wanted to participate in this quantum journey. And I can tell you, it really fascinates everyone, from high school students, to college students, to those that are PhDs. But during this journey, we have reached over 300,000 unique users, we have now over 500,000 unique downloads of our Qiskit programming model. But to really achieve that is his back plane by this ongoing educational thrust that we have. So we've created an open source textbook, around Qiskit that allows organizations around the world to take advantage of it from a curriculum perspective. We have over 200 organizations that are using our open source textbook. Last year, when we realized we couldn't do our in person programming camps, which were so exciting around the world, you can imagine doing an in person programming camp and South Africa and Asia and all those things we did in 2019. Well, we had just like you all, we had to go completely virtual, right. And we thought that we would have a few 100 people sign up for our summer school, we had over 4000 people sign up for our summer school. And so one of the things we had to do is really pedal fast to be able to support that many students in this summer school that kind of grew out of our proportions. The neat thing was once again, seeing all the kids and students around the world taking advantage of this and learning about quantum computing. And then I guess that the end of last year, Dave, to really top this off, we did something really fundamentally important. And we set up a quantum center for historically black colleges and universities, with Howard University being the anchor of this quantum center. And we're serving 23 HBCUs now, to be able to reach a new set of students, if you will, with STEM technologies, and most importantly, with quantum. And I find, you know, the neat thing about quantum is is very interdisciplinary. So we have quantum physicist, we have electrical engineers, we have engineers on the team, we have computer scientists, we have people with biology and chemistry and financial services backgrounds. So I'm pretty excited about the reach that we have with quantum into HBCUs and even beyond right I think we can do some we can have some phenomenal results and help a lot of people on this journey to quantum and you know, obviously help ourselves but help these students as well. >> What do you see in people do with quantum and maybe some of the use cases. I mean you mentioned there's sort of a connection to traditional workloads, but obviously some new territory what's exciting out there? >> Well, there's been a really a number of use cases that I think are top of mind right now. So one of the most interesting to me has been one that showed us a few months ago that we talked about in the press actually a few months ago, which is with Exxon Mobil. And they really started looking at logistics in the context of Maritime shipping, using quantum. And if you think of logistics, logistics are really, really complicated. Logistics in the face of a pandemic are even more complicated and logistics when things like the Suez Canal shuts down, are even more complicated. So think about, you know, when the Suez Canal shut down, it's kind of like the equivalent of several major airports around the world shutting down and then you have to reroute all the traffic, and that traffic and maritime shipping is has to be very precise, has to be planned the stops are plan, the routes are plan. And the interest that ExxonMobil has had in this journey is not just more effective logistics, but how do they get natural gas shipped around the world more effectively, because their goal is to bring energy to organizations into countries while reducing CO2 emissions. So they have a very grand vision that they're trying to accomplish. And this logistics operation is just one of many, then we can think of logistics, though being a being applicable to anyone that has a supply chain. So to other shipping organizations, not just Maritime shipping. And a lot of the optimization logic that we're learning from that set of work also applies to financial services. So if we look at optimization, around portfolio pricing, and everything, a lot of the similar characteristics will also go be applicable to the financial services industry. So that's one big example. And I guess our latest partnership that we announced with some fanfare, about two weeks ago, was with the Cleveland Clinic, and we're doing a special discovery acceleration activity with the Cleveland Clinic, which starts prominently with artificial intelligence, looking at chemistry and genomics, and improve speed around machine learning for all of the the critical healthcare operations that the Cleveland Clinic has embarked on but as part of that journey, they like many clients are evolving from artificial intelligence, and then learning how they can apply quantum as an accelerator in the future. And so they also indicated that they will buy the first commercial on premise quantum computer for their operations and place that in Ohio, in the the the years to come. So it's a pretty exciting relationship. These relationships show the power of the combination, once again, of classical computing, using that intelligently to solve very difficult problems. And then taking advantage of quantum for what it can uniquely do in a lot of these use cases. >> That's great description, because it is a strong connection to things that we do today. It's just going to do them better, but then it's going to open up a whole new set of opportunities. Everybody wants to know, when, you know, it's all over the place. Because some people say, oh, not for decades, other people say I think it's going to be sooner than you think. What are you guys saying about timeframe? >> We're certainly determined to make it sooner than later. Our roadmaps if you note go through 2023. And we think the 2023 is going to will be a pivotal year for us in terms of delivery around those roadmaps. But it's these kind of use cases and this intense working with these clients, 'cause when they work with us, they're giving us feedback on everything that we've done, how does this programming model really help me solve these problems? What do we need to do differently? In the case of Exxon Mobil, they've given us a lot of really great feedback on how we can better fine tune all elements of the system to improve that system. It's really allowed us to chart a course for how we think about the programming model in particular in the context of users. Just last week, in fact, we announced some new machine learning applications, which these applications are really to allow artificial intelligence users and programmers to get take advantage of quantum without being a quantum physicist or expert, right. So it's really an encapsulation of a composable elements so that they can start to use, using an interface allows them to access through PyTorch into the quantum computer, take advantage of some of the things we're doing around neural networks and things like that, once again, without having to be experts in quantum. So I think those are the kind of things we're learning how to do better, fundamentally through this co-creation and development with our quantum network. And our quantum network now is over 140 unique organizations and those are commercial, academic, national laboratories and startups that we're working with. >> The picture started become more clear, we're seeing emerging AI applications, a lot of work today in AI is in modeling. Over time, it's going to shift toward inference and real time and practical applications. Everybody talks about Moore's law being dead. Well, in fact, the yes, I guess, technically speaking, but the premise or the outcome of Moore's law is actually accelerating, we're seeing processor performance, quadrupling every two years now, when you include the GPU along with the CPU, the DSPs, the accelerators. And so that's going to take us through this decade, and then then quantum is going to power us, you know, well beyond who can even predict that. It's a very, very exciting time. Jamie, I always love talking to you. Thank you so much for coming back on the CUBE. >> Well, I appreciate the time. And I think you're exactly right, Dave, you know, we talked about POWER10, just for a few minutes there. But one of the things we've done in POWER10, as well as we've embedded AI into every core that processor, so you reduce that latency, we've got a 10 to 20 times improvement over the last generation in terms of artificial intelligence, you think about the evolution of a classical machine like that state of the art, and then combine that with quantum and what we can do in the future, I think is a really exciting time to be in computing. And I really appreciate your time today to have this dialogue with you. >> Yeah, it's always fun and it's of national importance as well. Jamie Thomas, thanks so much. This is Dave Vellante with the CUBE keep it right there our continuous coverage of IBM Think 2021 will be right back. (gentle music) (bright music)

(bright music) >> Narrator: From around the globe, it's the CUBE with digital coverage of IBM Think 2021, brought to you by IBM. >> Welcome back to IBM Think 2021, the virtual edition. This is the CUBEs, continuous, deep dive coverage of the people, processes and technologies that are really changing our world. Right now, we're going to talk about modernization and what's beyond with Jamie Thomas, general manager, strategy and development, IBM Enterprise Security. Jamie, always a pleasure. Great to see you again. Thanks for coming on. >> It's great to see you, Dave. And thanks for having me on the CUBE is always a pleasure. >> Yeah, it is our pleasure. And listen, we've been hearing a lot about IBM is focused on hybrid cloud, Arvind Krishna says we must win the architectural battle for hybrid cloud. I love that. We've been hearing a lot about AI. And I wonder if you could talk about IBM Systems and how it plays into that strategy? >> Sure, well, it's a great time to have this discussion Dave. As you all know, IBM Systems Technology is used widely around the world, by many, many 1000s of clients in the context of our IBM System Z, our power systems and storage. And what we have seen is really an uptake of monetization around those workloads, if you will, driven by hybrid cloud, the hybrid cloud agenda, as well as an uptake of Red Hat OpenShift, as a vehicle for this modernization. So it's pretty exciting stuff, what we see as many clients taking advantage of OpenShift on Linux, to really modernize these environments, and then stay close, if you will, to that systems of record database and the transactions associated with it. So they're seeing a definite performance advantage to taking advantage of OpenShift. And it's really fascinating to see the things that they're doing. So if you look at financial services, for instance, there's a lot of focus on risk analytics. So things like fraud, anti money laundering, mortgage risk, types of applications being done in this context, when you look at our retail industry clients, you see also a lot of customer centricity solutions, if you will, being deployed on OpenShift. And once again, having Linux close to those traditional LPARs of AIX, I-Series, or in the context of z/OS. So those are some of the things we see happening. And it's quite real. >> Now, you didn't mention power, but I want to come back and ask you about power. Because a few weeks ago, we were prompted to dig in a little bit with the when Arvind was on with Pat Kessinger at Intel and talking about the relationship you guys have. And so we dug in a little bit, we thought originally, we said, oh, it's about quantum. But we dug in. And we realized that the POWER10 is actually the best out there and the highest performance in terms of disaggregating memory. And we see that as a future architecture for systems and actually really quite excited about it about the potential that brings not only to build beyond system on a chip and system on a package, but to start doing interesting things at the Edge. You know, what do you what's going on with power? >> Well, of course, when I talked about OpenShift, we're doing OpenShift on power Linux, as well as Z Linux, but you're exactly right in the context for a POWER10 processor. We couldn't be more we're so excited about this processor. First of all, it's our first delivery with our partner Samsung with a seven nanometer form factor. The processor itself has only 18 billion transistors. So it's got a few transistors there. But one of the cool inventions, if you will, that we have created is this expansive memory region as part of this design point, which we call memory inception, it gives us the ability to reach memory across servers, up to two petabytes of memory. Aside from that, this processor has generational improvements and core and thread performance, improved energy efficiency. And all of this, Dave is going to give us a lot of opportunity with new workloads, particularly around artificial intelligence and inferencing around artificial intelligence. I mean, that's going to be that's another critical innovation that we see here in this POWER10 processor. >> Yeah, processor performance is just exploding. We're blowing away the historical norms. I think many people don't realize that. Let's talk about some of the key announcements that you've made in quantum last time we spoke on the qubit for last year, I think we did a deeper dive on quantum. You've made some announcements around hardware and software roadmaps. Give us the update on quantum please. >> Well, there is so much that has happened since we last spoke on the quantum landscape. And the key thing that we focused on in the last six months is really an articulation of our roadmaps, so the roadmap around hardware, the roadmap around software, and we've also done quite a bit of ecosystem development. So in terms of the roadmap around hardware, we put ourselves out there we've said we were going to get to over 1000 qubit machine and in 2023, so that's our milestone. And we've got a number of steps we've outlined along that way, of course, we have to make progress, frankly, every six months in terms of innovating around the processor, the electronics and the fridge associated with these machines. So lots of exciting innovation across the board. We've also published a software roadmap, where we're articulating how we improve a circuit execution speeds. So we hope, our plan to show shortly a 100 times improvement in circuit execution speeds. And as we go forward in the future, we're modifying our Qiskit programming model to not only allow a easily easy use by all types of developers, but to improve the fidelity of the entire machine, if you will. So all of our innovations go hand in hand, our hardware roadmap, our software roadmap, are all very critical in driving the technical outcomes that we think are so important for quantum to become a reality. We've deployed, I would say, in our quantum cloud over, you know, over 20 machines over time, we never quite identify the precise number because frankly, as we put up a new generation machine, we often retire when it's older. So we're constantly updating them out there, and every machine that comes on online, and that cloud, in fact, represents a sea change and hardware and a sea change in software. So they're all the latest and greatest that our clients can have access to. >> That's key, the developer angle you got redshift running on quantum yet? >> Okay, I mean, that's a really good question, you know, as part of that software roadmap in terms of the evolution and the speed of that circuit execution is really this interesting marriage between classical processing and quantum processing and bring those closer together. And in the context of our classical operations that are interfacing with that quantum processor, we're taking advantage of OpenShift, running on that classical machine to achieve that. And once again, if, as you can imagine, that'll give us a lot of flexibility in terms of where that classical machine resides and how we continue the evolution the great marriage, I think that's going to that will exist that does exist and will exist between classical computing and quantum computing. >> I'm glad I asked it was kind of tongue in cheek. But that's a key thread to the ecosystem, which is critical to obviously, you know, such a new technology. How are you thinking about the ecosystem evolution? >> Well, the ecosystem here for quantum is infinitely important. We started day one, on this journey with free access to our systems for that reason, because we wanted to create easy entry for anyone that really wanted to participate in this quantum journey. And I can tell you, it really fascinates everyone, from high school students, to college students, to those that are PhDs. But during this journey, we have reached over 300,000 unique users, we have now over 500,000 unique downloads of our Qiskit programming model. But to really achieve that is his back plane by this ongoing educational thrust that we have. So we've created an open source textbook, around Qiskit that allows organizations around the world to take advantage of it from a curriculum perspective. We have over 200 organizations that are using our open source textbook. Last year, when we realized we couldn't do our in person programming camps, which were so exciting around the world, you can imagine doing an in person programming camp and South Africa and Asia and all those things we did in 2019. Well, we had just like you all, we had to go completely virtual, right. And we thought that we would have a few 100 people sign up for our summer school, we had over 4000 people sign up for our summer school. And so one of the things we had to do is really pedal fast to be able to support that many students in this summer school that kind of grew out of our proportions. The neat thing was once again, seeing all the kids and students around the world taking advantage of this and learning about quantum computing. And then I guess that the end of last year, Dave, to really top this off, we did something really fundamentally important. And we set up a quantum center for historically black colleges and universities, with Howard University being the anchor of this quantum center. And we're serving 23 HBCUs now, to be able to reach a new set of students, if you will, with STEM technologies, and most importantly, with quantum. And I find, you know, the neat thing about quantum is is very interdisciplinary. So we have quantum physicist, we have electrical engineers, we have engineers on the team, we have computer scientists, we have people with biology and chemistry and financial services backgrounds. So I'm pretty excited about the reach that we have with quantum into HBCUs and even beyond right I think we can do some we can have some phenomenal results and help a lot of people on this journey to quantum and you know, obviously help ourselves but help these students as well. >> What do you see in people do with quantum and maybe some of the use cases. I mean you mentioned there's sort of a connection to traditional workloads, but obviously some new territory what's exciting out there? >> Well, there's been a really a number of use cases that I think are top of mind right now. So one of the most interesting to me has been one that showed us a few months ago that we talked about in the press actually a few months ago, which is with Exxon Mobil. And they really started looking at logistics in the context of Maritime shipping, using quantum. And if you think of logistics, logistics are really, really complicated. Logistics in the face of a pandemic are even more complicated and logistics when things like the Suez Canal shuts down, are even more complicated. So think about, you know, when the Suez Canal shut down, it's kind of like the equivalent of several major airports around the world shutting down and then you have to reroute all the traffic, and that traffic and maritime shipping is has to be very precise, has to be planned the stops are plan, the routes are plan. And the interest that ExxonMobil has had in this journey is not just more effective logistics, but how do they get natural gas shipped around the world more effectively, because their goal is to bring energy to organizations into countries while reducing CO2 emissions. So they have a very grand vision that they're trying to accomplish. And this logistics operation is just one of many, then we can think of logistics, though being a being applicable to anyone that has a supply chain. So to other shipping organizations, not just Maritime shipping. And a lot of the optimization logic that we're learning from that set of work also applies to financial services. So if we look at optimization, around portfolio pricing, and everything, a lot of the similar characteristics will also go be applicable to the financial services industry. So that's one big example. And I guess our latest partnership that we announced with some fanfare, about two weeks ago, was with the Cleveland Clinic, and we're doing a special discovery acceleration activity with the Cleveland Clinic, which starts prominently with artificial intelligence, looking at chemistry and genomics, and improve speed around machine learning for all of the the critical healthcare operations that the Cleveland Clinic has embarked on but as part of that journey, they like many clients are evolving from artificial intelligence, and then learning how they can apply quantum as an accelerator in the future. And so they also indicated that they will buy the first commercial on premise quantum computer for their operations and place that in Ohio, in the the the years to come. So it's a pretty exciting relationship. These relationships show the power of the combination, once again, of classical computing, using that intelligently to solve very difficult problems. And then taking advantage of quantum for what it can uniquely do in a lot of these use cases. >> That's great description, because it is a strong connection to things that we do today. It's just going to do them better, but then it's going to open up a whole new set of opportunities. Everybody wants to know, when, you know, it's all over the place. Because some people say, oh, not for decades, other people say I think it's going to be sooner than you think. What are you guys saying about timeframe? >> We're certainly determined to make it sooner than later. Our roadmaps if you note go through 2023. And we think the 2023 is going to will be a pivotal year for us in terms of delivery around those roadmaps. But it's these kind of use cases and this intense working with these clients, 'cause when they work with us, they're giving us feedback on everything that we've done, how does this programming model really help me solve these problems? What do we need to do differently? In the case of Exxon Mobil, they've given us a lot of really great feedback on how we can better fine tune all elements of the system to improve that system. It's really allowed us to chart a course for how we think about the programming model in particular in the context of users. Just last week, in fact, we announced some new machine learning applications, which these applications are really to allow artificial intelligence users and programmers to get take advantage of quantum without being a quantum physicist or expert, right. So it's really an encapsulation of a composable elements so that they can start to use, using an interface allows them to access through PyTorch into the quantum computer, take advantage of some of the things we're doing around neural networks and things like that, once again, without having to be experts in quantum. So I think those are the kind of things we're learning how to do better, fundamentally through this co-creation and development with our quantum network. And our quantum network now is over 140 unique organizations and those are commercial, academic, national laboratories and startups that we're working with. >> The picture started become more clear, we're seeing emerging AI applications, a lot of work today in AI is in modeling. Over time, it's going to shift toward inference and real time and practical applications. Everybody talks about Moore's law being dead. Well, in fact, the yes, I guess, technically speaking, but the premise or the outcome of Moore's law is actually accelerating, we're seeing processor performance, quadrupling every two years now, when you include the GPU along with the CPU, the DSPs, the accelerators. And so that's going to take us through this decade, and then then quantum is going to power us, you know, well beyond who can even predict that. It's a very, very exciting time. Jamie, I always love talking to you. Thank you so much for coming back on the CUBE. >> Well, I appreciate the time. And I think you're exactly right, Dave, you know, we talked about POWER10, just for a few minutes there. But one of the things we've done in POWER10, as well as we've embedded AI into every core that processor, so you reduce that latency, we've got a 10 to 20 times improvement over the last generation in terms of artificial intelligence, you think about the evolution of a classical machine like that state of the art, and then combine that with quantum and what we can do in the future, I think is a really exciting time to be in computing. And I really appreciate your time today to have this dialogue with you. >> Yeah, it's always fun and it's of national importance as well. Jamie Thomas, thanks so much. This is Dave Vellante with the CUBE keep it right there our continuous coverage of IBM Think 2021 will be right back. (gentle music) (bright music)

Narrator: From theCUBE studios in Palo Alto and Boston, it's theCUBE, covering IBM Think, brought to you by IBM. >> We're back. You're watching theCUBE and our coverage of IBM Think 2020, the digital IBM thinking. We're here with Jamie Thomas, who's the general manager of strategy and development for IBM Systems. Jamie, great to see you. >> It's great to see you as always. >> You have been knee deep in qubits, the last couple years. And we're going to talk quantum. We've talked quantum a lot in the past, but it's a really interesting field. We spoke to you last year at IBM Think about this topic. And a year in this industry is a long time, but so give us the update what's new in quantum land? >> Well, Dave first of all, I'd like to say that in this environment we find ourselves in, I think we can all appreciate why innovation of this nature is perhaps more important going forward, right? If we look at some of the opportunities to solve some of the unsolvable problems, or solve problems much more quickly, in the case of pharmaceutical research. But for us in IBM, it's been a really busy year. First of all, we worked to advance the technology, which is first and foremost in terms of this journey to quantum. We just brought online our 53 qubit computer, which also has a quantum volume of 32, which we can talk about. And we've continued to advance the software stack that's attached to the technology because you have to have both the software and the hardware thing, right rate and pace. We've advanced our new network, which you and I have spoken about, which are those individuals across the commercial enterprises, academic and startups, who are working with us to co-create around quantum to help us understand the use cases that really can be solved in the future with quantum. And we've also continued to advance our community, which is serving as well in this new digital world that we're finding ourselves in, in terms of reaching out to developers. Now, we have over 300,000 unique downloads of the programming model that represents the developers that we're touching out there every day with quantum. These developers have, in the last year, have run over 140 billion quantum circuits. So, our machines in the cloud are quite active, and the cloud model, of course, is serving us well. The data's, in addition, to all the other things that I mentioned. >> So Jamie, what metrics are you trying to optimize on? You mentioned 53 qubits I saw that actually came online, I think, last fall. So you're nearly six months in now, which is awesome. But what are you measuring? Are you measuring stability or coherence or error rates? Number of qubits? What are the things that you're trying to optimize on to measure progress? >> Well, that's a good question. So we have this metric that we've defined over the last year or two called quantum volume. And quantum volume 32, which is the capacity of our current machine really is a representation of many of the things that you mentioned. It represents the power of the quantum machine, if you will. It includes a definition of our ability to provide error correction, to maintain states, to really accomplish workloads with the computer. So there's a number of factors that go into quantum volume, which we think are important. Now, qubits and the number of qubits is just one such metric. It really depends on the coherence and the effect of error correction, to really get the value out of the machine, and that's a very important metric. >> Yeah, we love to boil things down to a single metric. It's more complicated than that >> Yeah, yeah. >> specifically with quantum. So, talk a little bit more about what clients are doing and I'm particularly interested in the ecosystem that you're forming around quantum. >> Well, as I said, the ecosystem is both the network, which are those that are really intently working with us to co-create because we found, through our long history in IBM, that co-creation is really important. And also these researchers and developers realize that some of our developers today are really researchers, but as you as you go forward you get many different types of developers that are part of this mix. But in terms of our ecosystem, we're really fundamentally focused on key problems around chemistry, material science, financial services. And over the last year, there's over 200 papers that have been written out there from our network that really embody their work with us on this journey. So we're looking at things like quadratic speed up of things like Monte Carlo simulation, which is used in the financial services arena today to quantify risk. There's papers out there around topics like trade settlements, which in the world today trade settlements is a very complex domain with very interconnected complex rules and trillions of dollars in the purview of trade settlement. So, it's just an example. Options pricing, so you see examples around options pricing from corporations like JPMC in the area of financial services. And likewise in chemistry, there's a lot of research out there focused on batteries. As you can imagine, getting everything to electric powered batteries is an important topic. But today, the way we manufacture batteries can in fact create air pollution, in terms of the process, as well as we want batteries to have more retention in life to be more effective in energy conservation. So, how do we create batteries and still protect our environment, as we all would like to do? And so we've had a lot of research around things like the next generation of electric batteries, which is a key topic. But if you can think, you know Dave, there's so many topics here around chemistry, also pharmaceuticals that could be advanced with a quantum computer. Obviously, if you look at the COVID-19 news, our supercomputer that we installed at Oak Ridge National Laboratory for instance, is being used to analyze 8000 different compounds for specifically around COVID-19 and the possibilities of using those compounds to solve COVID-19, or influence it in a positive manner. You can think of the quantum computer when it comes online as an accelerator to a supercomputer like that, helping speed up this kind of research even faster than what we're able to do with something like the Summit supercomputer. Oak Ridge is one of our prominent clients with the quantum technology, and they certainly see it that way, right, as an accelerator to the capacity they already have. So a great example that I think is very germane in the time that we find ourselves in. >> How 'about startups in this ecosystem? Are you able to-- I mean there must be startups popping up all over the place for this opportunity. Are you working with any startups or incubating any startups? Can you talk about that? >> Oh yep. Absolutely. There's about a third of our network are in VC startups and there's a long list of them out there. They're focused on many different aspects of quantum computing. Many of 'em are focused on what I would call loosely, the programming model, looking at improving algorithms across different industries, making it easier for those that are, perhaps more skilled in domains, whether that is chemistry or financial services or mathematics, to use the power of the quantum computer. Many of those startups are leveraging our Qiskit, our quantum information science open programming model that we put out there so it's open. Many of the startups are using that programming model and then adding their own secret sauce, if you will, to understand how they can help bring on users in different ways. So it depends on their domain. You see some startups that are focused on the hardware as well, of course, looking at different hardware technologies that can be used to solve quantum. I would say I feel like more of them are focused on the software programming model. >> Well Jamie, it was interesting hear you talk about what some of the clients are doing. I mean obviously in pharmaceuticals, and battery manufacturers do a lot of advanced R and D, but you mentioned financial services, you know JPMC. It's almost like they're now doing advanced R and D trying to figure out how they can apply quantum to their business down the road. >> Absolutely, and we have a number of financial institutions that we've announced as part of the network. JPMC is just one of our premiere references who have written papers about it. But I would tell you that in the world of Monte Carlo simulation, options pricing, risk management, a small change can make a big difference in dollars. So we're talking about operations that in many cases they could achieve, but not achieve in the right amount of time. The ability to use quantum as an accelerator for these kind of operations is very important. And I can tell you, even in the last few weeks, we've had a number of briefings with financial companies for five hours on this topic. Looking at what could they do and learning from the work that's already done out there. I think this kind of advanced research is going to be very important. We also had new members that we announced at the beginning of the year at the CES show. Delta Airlines joined. First Transportation Company, Amgen joined, a pharmaceutical, an example of pharmaceuticals, as well as a number of other research organizations. Georgia Tech, University of New Mexico, Anthem Insurance, just an example of the industries that are looking to take advantage of this kind of technology as it matures. >> Well, and it strikes me too, that as you start to bring machine intelligence into the equation, it's a game changer. I mean, I've been saying that it's not Moore's Law driving the industry anymore, it's this combination of data, AI, and cloud for scale, but now-- Of course there are alternative processors going on, we're seeing that, but now as you bring in quantum that actually adds to that innovation cocktail, doesn't it? >> Yes, and as you recall when you and I spoke last year about this, there are certain domains today where you really cannot get as much effective gain out of classical computing. And clearly, chemistry is one of those domains because today, with classical computers, we're really unable to model even something as simple as a caffeine molecule, which we're all so very familiar with. I have my caffeine here with me today. (laughs) But you know, clearly, to the degree we can actually apply molecular modeling and the advantages that quantum brings to those fields, we'll be able to understand so much more about materials that affect all of us around the world, about energy, how to explore energy, and create energy without creating the carbon footprint and the bad outcomes associated with energy creation, and how to obviously deal with pharmaceutical creation much more effectively. There's a real promise in a lot of these different areas. >> I wonder if you could talk a little bit about some of the landscape and I'm really interested in what IBM brings to the table that's sort of different. You're seeing a lot of companies enter this space, some big and many small, what's the unique aspect that IBM brings to the table? You've mentioned co-creating before. Are you co-creating, coopertating with some of the other big guys? Maybe you could address that. >> Well, obviously this is a very hot topic, both within the technology industry and across government entities. I think that some of the key values we bring to the table is we are the only vendor right now that has a fleet of systems available in the cloud, and we've been out there for several years, enabling clients to take advantage of our capacity. We have both free access and premium access, which is what the network is paying for because they get access to the highest fidelity machines. Clearly, we understand intently, classical computing and the ability to leverage classical with quantum for advantage across many of these different industries, which I think is unique. We understand the cloud experience that we're bringing to play here with quantum since day one, and most importantly, I think we have strong relationships. We have, in many cases, we're still running the world. I see it every day coming through my clients' port vantage point. We understand financial services. We understand healthcare. We understand many of these important domains, and we're used to solving tough problems. So, we'll bring that experience with our clients and those industries to the table here and help them on this journey. >> You mentioned your experience in sort of traditional computing, basically if I understand it correctly, you're still using traditional silicon microprocessors to read and write the data that's coming out of quantum. I don't know if they're sitting physically side by side, but you've got this big cryogenic unit, cables coming in. That's the sort of standard for some time. It reminds me, can it go back to ENIAC? And now, which is really excites me because you look at the potential to miniaturize this over the next several decades, but is that right, you're sort of side by side with traditional computing approaches? >> Right, effectively what we do with quantum today does not happen without classical computers. The front end, you're coming in on classical computers. You're storing your data on classical computers, so that is the model that we're in today, and that will continue to happen. In terms of the quantum processor itself, it is a silicon based processor, but it's a superconducting technology, in our case, that runs inside that cryogenics unit at a very cold temperature. It is powered by next-generation electronics that we in IBM have innovated around and created our own electronic stack that actually sends microwave pulses into the processor that resides in the cryogenics unit. So when you think about the components of the system, you have to be innovating around the processor, the cryogenics unit, the custom electronic stack, and the software all at the same time. And yes, we're doing that in terms of being surrounded by this classical backplane that allows our Q network, as well as the developers around the world to actually communicate with these systems. >> The other thing that I really like about this conversation is it's not just R and D for the sake of R and D, you've actually, you're working with partners to, like you said, co-create, customers, financial services, airlines, manufacturing, et cetera. I wonder if you could maybe kind of address some of the things that you see happening in the sort of near to midterm, specifically as it relates to where people start. If I'm interested in this, what do I do? Do I need new skills? Do I need-- It's in the cloud, right? >> Yeah. >> So I can spit it up there, but where do people get started? >> Well they can certainly come to the Quantum Experience, which is our cloud experience and start to try out the system. So, we have both easy ways to get started with visual composition of circuits, as well as using the programming model that I mentioned, the Qiskit programming model. We've provided extensive YouTube videos out there already. So, developers who are interested in starting to learn about quantum can go out there and subscribe to our YouTube channel. We've got over 40 assets already recorded out there, and we continue to do those. We did one last week on quantum circuits for those that are more interested in that particular domain, but I think that's a part of this journey is making sure that we have all the assets out there digitally available for those around the world that want to interact with us. We have tremendous amount of education. We're also providing education to our business partners. One of our key network members, who I'll be speaking with later, I think today, is from Accenture. Accenture's an example of an organization that's helping their clients understand this quantum journey, and of course they're providing their own assets, if you will, but once again, taking advantage of the education that we're providing to them as a business partner. >> People talk about quantum being a decade away, but I think that's the wrong way to think about it, and I'd love your thoughts on this. It feels like, almost like the return coming out of COVID-19, it's going to come in waves, and there's parts that are going to be commercialized thoroughly and it's not binary. It's not like all of a sudden one day we're going to wake, "Hey, quantum is here!" It's really going to come in layers. Your thoughts? >> Yeah, I definitely agree with that. It's very important, that thought process because if you want to be competitive in your industry, you should think about getting started now. And that's why you see so many financial services, industrial firms, and others joining to really start experimentation around some of these domain areas to understand jointly how we evolve these algorithms to solve these problems. I think that the production level characteristics will curate the rate and pace of the industry. The industry, as we know, can drive things together faster. So together, we can make this a reality faster, and certainly none of us want to say it's going to be a decade, right. I mean, we're getting advantage today, in terms of the experimentation and the understanding of these problems, and we have to expedite that, I think, in the next few years. And certainly, with this arms race that we see, that's going to continue. One of the things I didn't mention is that IBM is also working with certain countries and we have significant agreements now with the countries of Germany and Japan to put quantum computers in an IBM facility in those countries. It's in collaboration with Fraunhofer Institute or miR Scientific Organization in Germany and with the University of Tokyo in Japan. So you can see that it's not only being pushed by industry, but it's also being pushed from the vantage of countries and bringing this research and technology to their countries. >> All right, Jamie, we're going to have to leave it there. Thanks so much for coming on theCUBE and give us the update. It's always great to see you. Hopefully, next time I see you, it'll be face to face. >> That's right, I hope so too. It's great to see you guys, thank you. Bye. >> All right, you're welcome. Keep it right there everybody. This is Dave Vellante for theCUBE. Be back right after this short break. (gentle music)

>> Live from San Francisco. It's theCube covering IBM Think 2019. Brought to you by IBM. >> Welcome back to Moscone Center everybody. The new, improved Moscone Center. We're at Moscone North, stop by and see us. I'm Dave Vellante, he's Stu Miniman and Lisa Martin is here as well, John Furrier will be up tomorrow. You're watching theCube, the leader in live tech coverage. This is day zero essentially, Stu, of IBM Think. Day one, the big keynotes, start tomorrow. Chairman's keynote in the afternoon. Jamie Thomas is here. She's the general manager of IBM's Systems Strategy and Development at IBM. Great to see you again Jamie, thanks for coming on. >> Great to see you guys as usual and thanks for coming back to Think this year. >> You're very welcome. So, I love your new role. You get to put on the binoculars sometimes the telescope. Look at the road map. You have your fingers in a lot of different areas and you get some advanced visibility on some of the things that are coming down the road. So we're really excited about that. But give us the update from a year ago. You guys have been busy. >> We have been busy, and it was a phenomenal year, Dave and Stu. Last year, I guess one of the pinnacles we reached is that we were named with our technology, our technology received the number one and two supercomputer ratings in the world and this was a significant accomplishment. Rolling out the number one supercomputer in Oakridge National Laboratory and the number two supercomputer in Lawrence Livermore Laboratory. And Summit as it's called in Oakridge is really a cool system. Over 9000 CPUs about 27,000 GPUs. It does 200 petaflops at peak capacity. It has about 250 petabytes of storage attached to it at scale and to cool this guy, Summit, I guess it's a guy. I'm not sure of the denomination actually it takes about 4,000 gallons of water per minute to cool the supercomputer. So we're really pleased with the engineering that we worked on for so many years and achieving these World records, if you will, for both Summit and Sierra. >> Well it's not just bragging rights either, right, Jamie? I mean, it underscores the technical competency and the challenge that you guys face I mean, you're number one and number two, that's not easy. Not easy to sustain of course, you got to do it again. >> Right, right, it's not easy. But the good thing is the design point of these systems is that we're able to take what we created here from a technology perspective around POWER9 and of course the patnership we did with Invidia in this case and the software storage. And we're able to downsize that significantly for commercial clients. So this is the world's largest artificial intlligence supercomputer and basically we are able to take that technology that we invented in this case 'cause they ended up being one of our first clients albeit a very large client, and use that across industries to serve the needs of artificial intelligence work loads. So I think that was one of the most significant elements of what we actually did here. >> And IBM has maintained, despite you guys selling off your microelectronics division years ago, you've maintained a lot of IP in the core processing and the design. You've also reached out certainly with open power, for example, to folks. You mentioned Invidia. But having that, sort of embracing that alternative processor mode as opposed to trying to jam everything in the die. Different philosophy that IBM is taking. >> Yeah we think that the workload specific processing is still very much in demand. Workloads are going to have different dimensions and that's what we really have focused on here. I don't think that this has really changed over the last decades of computing and so we're really focused on specialized computing purpose-built computing, if you will. Obviously using that on premise and also using that in our hybrid cloud strategies for clients that want to do that as well. >> What are some of the other cool things that you guys are working on that you can talk about. >> Well I would say last year was quite an interesting year in that from a mainframe perspective we delivered our first 19 inch form factor which allows us to fit nicely on a floor tile. Obviously allows clients to scale more effectively from a data center planning perspective. Allows us to have a cloud footprint, but with all the characteristics of security that you would normally expect in a mainframe system. But really tailored toward new workloads once again. So Linux form factor and going after the new workloads that a lot of these cloud data centers really need. One of our first and foremost focus areas continues to be security around that system and tomorrow there will be some announcements that will happen around Z security. I can't say what they are right now but you'll see that we are extending security in new ways to support more of these hybrid cloud scenarios. >> It's so funny. We were talking in one of our earlier segments talking about how the path of virtualization and trying to get lots of workloads into something and goes back to the device that could manage all workloads which was the Mainframe. So we've watched for many years system Z lots of Linux on there if you want to do some cool container, you know global Z that's an option, so it's interesting to watch while the pendulum swings in IT have happened the Z system has kept up with a lot of these innovations that have been going on in the industry. >> And you're right, one of our big focuses for the platform for Z and power of course is a container-based strategy. So we've created, you know last year we talked about secure container technology and we continue to evolve secure container technology but the idea is we want to eliminate any kind of friction from a developer's perspective. So if you want to design in a container-based environment then you're more easily able to port that technology or your applications, if you will to a Z mainframe environment if that's really what your target environment is. So that's been a huge focus. The other of course major invention that we announced at the Consumer Electronics show is our Quantum System One. And this represented an evolution of our Quantum system over the last year where we now have the world's really first self-contained universal quantum computer in a single form factor where we were able to combine the Quantum processor which is living in the dilution refrigerator. You guys remember the beautiful chandelier from last year. I think it's back this year. But this is all self-contained with it's electronics in a single form factor. And that really represents the evolution of the electronics in particular over the last year where we were able to miniaturize those electronics and get them into this differentiated form factor. >> What should people know about Quantum? When you see the demos, they explain it's not a binary one or zero, it could be either, a virtually infinite set of possibilities, but what should the lay person know about Quantum and try to understand? >> Well I think really the fundamental aspect of it is in today's world with traditional computers they're very powerful but they cannot solve certain problems. So when you look at areas like material science, areas like chemistry even some financial trading scenarios, the problems can either not be solved at all or they cannot be completed in the right amount of time. Particularly in the world of financial services. But in the area of chemistry for instance molecular modeling. Today we can model simple molecules but we cannot model something even as complex as caffeine. We simply don't have the traditional compute capacity to do that. A quantum computer will allow us once it comes to maturity allow us to solve these problems that are not solvable today and you can think about all the things that we could do if were able to have more sophisticated molecular modeling. All the kinds of problems we could solve probably in the world of pharmacology, material science which affects many, many industries right? People that are developing automobiles, people that are exploring for oil. All kinds of opportunities here in this space. The technology is a little bit spooky, I guess, that's what Einstein said when he first solved some of this, right? But it really represents the state of the universe, right? How the universe behaves today. It really is happening around us but that's what quantum mechanics helps us capture and when combined with IT technology the quantum computer can bring this to life over time. >> So one of the things that people point to is potentially a new security paradigm because Quantum can flip the way in which we do security on it's head so you got to be thinking around that as well. I know security is something that is very important to IBM's Systems division. >> Right, absolutely. So the first thing that happens when someone hears about quantum computing is they ask about quantum security. And as you can imagine there's a lot of clients here that are concerned about security. So in IBM research we're also working on quantum-safe encryption. So you got one team working on a quantum computer, you got another team ensuring that the data will be protected from the quantum computer. So we do believe we can construct quantum-safe encryption algorithms based on lattice-based technology that will allow us to encrypt data today and in the future when the quantum computer does reach that kind of capacity the data will be protected. So the idea is that we would start using these new algorithms far earlier than the computer could actually achieve this result but it would mean that data created today would be quantum safe in the future. >> You're kind of in your own arm's race internally. >> But it's very important. Both aspects are very important. To be able to solve these problems that we can't solve today, which is really amazing, right? And to also be able to protect our data should it be used in inappropriate ways, right? >> Now we had Ed Bausch on earlier today. Used to run the storage division. What's going on in that world? I know you've got your hands in that pie as well. What can you tell us about what's going on there? >> Well I believe that Ed and the team have made some phenomenal innovations in the past year around flash MVME technology and fusing that across product lines state-of-the-art. The other area that I think is particularly interesting of course is their data management strategy around things like Spectrum Discover. So, today we all know that many of our clients have just huge amounts of data. I visited a client last year that interesting enough had 1 million tapes, and of course we sell tapes so that's a good thing but then how do you deal and manage all the data that is on 1 million tapes. So one of the inventions that the team has worked on is a metadata tagging capability that they've now shipped in a product called spectrum discover. And that allows a client to have a better way to have a profile of their data, data governance and understand for different use cases like data governance or compliance how do they pull back the right data and what does this data really mean to them. So have a better lexicon of their data, if you will than what they can do in today's world. So I think that's very important technology. >> That's interesting. I would imagine that metadata could sit in Flash somewhere and then inform the serial technology to maybe find stuff faster. I mean, everybody thinks tape is slow because it's sequential. But actually if you do some interesting things with metadata you can-- >> There's all kinds of things you can do I mean it's one thing to have a data ocean if you will, but then how do you really get value out of that data over a long period of time and I think we're just the tip of the spear in understanding the use cases that we can use this technology for. >> Jamie, how does IBM manage that pipeline of innovation. I think we heard very specific examples of how the super computers drive HPC architectures which everybody is going to use for their AI infrastructure. Something like quantum computing is a little bit more out there. So how do you balance kind of the research through the product and what's going to be more useful to users today. >> Yeah, well, that's an interesting question. So IBM is one of the few organizations in the world really that have an applied research organization still. And Dario Gil is here this week he manages our research organization now under Arvind Krishna. An organization like IBM Systems has a great relationship with research. Research are the folks that had people working on Quantum for decades, right? And they're the reason that we are in a position now to be able to apply this in the way that we are. The great news is that along the way we're always working on a pipeline of this next generation set of technologies and innovations. Some of them succeed and some of them don't. But without doing that we would not have things like Quantum. We would not have advanced encryption capability that we pushed all the way down into our chips. We would not have quantum-safe encryption. Things like the metadata tagging that I talked about came out of IBM research. So it's working with them on problems that we see coming down the pipe, if you will that will affect our clients and then working with them to make sure we get those into the product lines at the right amount of time. I would say that Quantum is the ultimate partnership between IBM Systems and IBM research. We have one team in this case that are working jointly on this product. Bringing the skills to bear that each of us have on this case with them having the quantum physics experts and us having the electronics experts and of course the software stacks spanning both organizations is really a great partnership. >> Is there anything you could tell us about what's going on at the edge. The edge computing you hear a lot about that today. IBM's got some activities going on there? You haven't made huge splashes there but anything going on in research that you can share with us, or any directions. >> Well I believe the edge is going to be a practical endeavor for us and what I mean by that is there are certain use cases that I think we can serve very well. So if we look at the edge as perhaps a factory environment, we are seeing opportunities for our storaging compute solutions around the data management out in some of these areas. If you look at the self-driving automobile for instance, just design something like that can easily take over a hundred petabytes of data. So being able to manage the data at the edge, being able to then to provide insight appropriately using AI technologies is something we think we can do and we see that. I own factories based on what I do and I'm starting to use AI technology. I use Power AI technology in my factories for visual inspection. Think about a lot of the challenges around provenance of parts as well as making sure that they're finally put together in the right way. Using these kind of technologies in factories is just really an easy use case that we can see. And so what we anticipate is we will work with the other parts of IBM that are focused on edge as well and understand which areas we think our technology can best serve. >> That's interesting you mention visual inspection. That's an analog use case which now you're transforming into digital. >> Yeah well Power AI vision has been very successful in the last year . So we had this power AI package of open source software that we pulled together but we drastically simplified the use of this software, if you will the ability to use it deploy it and we've added vision capability to it in the last year. And there's many use cases for this vision capability. If you think about even the case where you have a patient that is in an MRI. If you're able to decrease the amount of time they stay in the MRI in some cases by less fidelity of the picture but then you've got to be able to interpret it. So this kind of AI and then extensions of AI to vision is really important. Another example for Power AI vision is we're actually seeing use cases in advertising so the use case of maybe you're at a sporting event or even a busy place like this where you're able to use visual inspection techniques to understand the use of certain products. In the case of a sporting event it's how many times did my logo show up in this sporting event, right? Particularly our favorite one is Formula One which we usually feature the Formula One folks here a little bit at the events. So you can see how that kind of technology can be used to help advertisers understand the benefits in these cases. >> Got it. Well Jamie we always love having you on because you have visibility into so many different areas. Really thank you for coming and sharing a little taste of what's to come. Appreciate it. >> Well thank you. It's always good to see you and I know it will be an exciting week here. >> Yeah, we're very excited. Day zero here, day one and we're kicking off four days of coverage with theCube. Jamie Thomas of IBM. I'm Dave Vellante, he's Stu Miniman. We'll be right back right after this short break from IBM Think in Moscone. (upbeat music)

>> Narrator: Live from Las Vegas, it's TheCUBE! Covering IBM Think 2018. Brought to you by IBM. >> Hello everyone I'm John Furrier, we're here inside TheCUBE Studios at Think 2018. We're extracting the scene, even though it's actually our live event coverage leader, covering IBM Think. The big tent event taking six shows down to one. Big tent event. Everyone's here; the customers, developers, all the action. My next guest is Jamie Thomas, General Manager of IBM's Systems Strategy and Development. Good to see you Cube alumni, thanks for coming by. >> Good to see you, it's always one of the highlights of my parts of these meetings is getting a chance to talk with you all about what we're doing. >> We've had, I can't even remember how many, it's like eight years now, but you've been on pretty much every year, giving the update. I was just riffing on the opening about blockchain the innovation sandwich at IBM. I'm calling it the innovation sandwich, that's not what you guys are calling it. It really is about the data, and then blockchain and AI, that's the main thing with Cloud as the foundational element. You're in strategy. Systems. So you have all the underlying enabling technology with IBM and looking at that direction. Part of the innovation sandwich is systems. >> Absolutely, I think it fundamentally what we're seeing is all of the work and innovation we've invested in over the last few years is finally culminating in a really nice conclusion for us, if you will. Because if you look at the trajectory of those forces you spoke about right? Which is how do we harness the power of data? Of course, to harness that data we have to apply techniques like artificial intelligence, machine learning, deep learning to really get the value out of the data. And then we have to underpin that with a multi-cloud architecture. So we really do feel that all the innovations that we've been working on for the last few years are now coming to bear to help our clients solve these problems in really unique ways. >> We've had many conversations, we've gone down in the weeds, we've been under the hood, we've talked about business value. But I think that what I'm seeing and what TheCube is reporting over the past year and more recently is, there's now a clear line of sight for the customers. The interesting thing is the model's flipped around as we've always been seeing, but it's clear, dev ops enabled cloud to be successful where we have a programmable infrastructure. You guys have been doing software defined systems for a long time. But now with blockchain, cryptocurrency and decentralized application developers, you have inefficiencies being disrupted by making things more efficient. We're seeing the business logic be the intellectual property. So users, business users, business decision makers are looking at the business model of token economics. It's kind of at the top of the business stack that have to manage technology now. So the risk is flipped around. It used to be that technology was the risk. Technology purchase, payback period over ten plus years, more longevity to the cycle. Now you've got Agile now going real-time, this requires everything to be programmable. The data's got to be programmable, the systems have to be programmable. What's the IBM solution there? How do you guys fit that formula? Do you agree with it? Your thoughts. >> Well absolutely, I think that fundamentally you have infrastructure that can really meet the needs and characteristics of the next generation killer applications, right? So whether that's blockchain, or whether we're talking about artificial intelligence across numerous industries and every industry is looking at applying those techniques. You have to ensure that you have an architectural approach with your infrastructure that allows you to actually get the result from a client perspective. When we look at the things that we've invested in we're really investing in infrastructure that we feel allow clients to achieve those goals. If you look at what we've done with things like Power9, the ability to create a high speed interconnect with things like GPU acceleration using our partner NVIDIA's technology as an example. Those are really important characteristics of the infrastructure to be able to enable clients to then achieve the goals of something like artificial intelligence. >> What's different for the people that are now getting this, coming in, how do you summarize the past few years of strategy and development around the systems piece? Because systems programming is all about making things smaller, faster, cheaper, Moore's Law. But also having a network effect in supply chains or value chains, blockchain or whatever that is, that's the business side. What's new, how do you talk about that to the first time to someone who's now for the first time going, okay, I get it. It's clear. What's the system equation? How do you explain that to someone? >> Well I think it's a combination of focusing on both economics, but also having a keen eye on where the puck is going. In the world of hardware development, you have to have that understanding at least a year and a half, two years back, to actually culminate in a product offering that can serve the needs at the right time. So I think we've looked at both of those combinations. It's not just about economics. Is is about also being specialized, being able to serve the needs of the next generation of killer applications and therefore the programmers that support those applications. >> What's the big bet that you guys have made? If you could look back of the past three, four years, in the trials and tribulations of storage, compute, cloud, and it's been a lot of zigging and zagging. Not pivoting, because you guys have been innovating. What's the one thing, a few things you can point to, one thing or a few things and saying that was a good bet, that's now fruits coming off the tree in this new equation. >> Well, I think there's a few things and all of these things were done with a context that we believe that artificial intelligence and cloud architectures were here to stay. But if you look at the bets we made around the architecture of Power9, which was really how do we make this the best architecture in the world for artificial intelligence execution? All of those design points, all of the thought about the ecosystem around the partners, OpenPOWER, the connectivity between the GPU and the CPU that I mentioned. All of that and the software stack the investments we've made in things like PowerAI to allow developers to easily use the platform for that have been fundamentally important. Then if you look at what we did in the Z platform, it's really about this notion about pervasive encryption. Allowing developers to use encryption without forethought. Ensuring that performance would always be on. They would not have to change their applications. That's really fundamentally important for applications like blockchain. To be able to have encryption in the cloud, the kind of services we announced yesterday. So these bets of understanding that it's not just about the short term, it's about the long term and this next generation of applications. As we all know, as you and I know, you can't serve those kind of applications without having an understanding of the data map. How are you going to manage the just huge amounts of data that these organizations are dealing with? So our investments, for years now, in software defined storage, our Spectrum Storage family, and our Flash have served us well. Because now we have the mechanisms, if you will, at our fingertips to manage storage and data in these multi-cloud architectures as well as improve data latency. Access to data through the things we've done. >> So the performance is critical there? >> Yeah, absolutely, the things we've done with Flash, and the things we've done with our high end storage with the mainframe, the zHyperlink capability we've built in there between the KEK and the storage device, those are really, really important in this new world order of these kinds of next generation applications. >> Yeah, skating where the puck is is great and then sometimes you're just near there and the puck comes to you, however, whatever way you want to look at it. Take a minute to explain your role now, what specifically does systems mean? Where does it begin and where does it stop? You mentioned software stack, software defined storage, we get that piece. What's the system portfolio look like? >> We're focused on the modern infrastructure of the future. And of course that infrastructure involves hardware. It involves systems and storage. But it also fundamentally involves infrastructure-related hardware, software stacks. So we own and manage critical software stacks. The creation of things like PowerAI that work with the IBM Cloud team to ensure that IBM Cloud Private can support our platforms, Power and Z out of the box. Those are all fundamentally important initiatives. We of course still own all of the operating systems everybody loves, whether it's Linux, AIX, Z/OS, as well as the work around all the transactional systems. But first and foremost, there's a really tight tie as we all know, between hardware and then the software that needs to be brought to bear to execute against that hardware, the two have to be together, right? >> What about R&D? What's the priority on R&D? It's the continuation of some of the things you just mentioned, but is there anything on the radar that you can share in R&D that's worth noting? >> Well I think, clearly we're working on the next evolution of these systems already. The next series of Power9's we have new machines rolling out this month from a Power9 perspective. We're always working on the next generation of the mainframe of course. But I'd say that our project that's gotten a lot of note at the conferences is our Quantum project. So IBM Systems is partnering with IBM Research to create the Quantum computer. That would be the most leading edge effort that we have going on right now, so that's pretty exciting. >> Yeah, and that's always good stuff coming out. Smaller, how big is this Quantum, can you put it on your finger? Was that the big news? A lot of great action there. >> Well the Quantum computer is a very different form factor. It's truly an evolutionary, revolutionary event, if you will, from a hardware perspective, right? Because the qubit itself has to run at absolute zero. So it has to run in a very cold environment. And then we speak to it through a wave-based communications, if you will, coming in from an electronic stack. It's fundamentally a huge change in hardware architecture. >> What's that going to enable for the folks watching? Is it more throughput? More data? New things, what kind of enablement do you guys envision? >> Well first of all the Quantum computer will never replace classical computers because they're very different in terms of what they can process. There's many problems today in the world that are really not solvable. Problems around chemistry, material science, molecular modeling. There's certainly certain financial equations that really are processable but not processable in the right amount of time. So when you look at what we can do with Quantum, I think there will be problems that we can solve today that we can't even solve. As well as it will be an accelerator to a lot of the existing traditional systems if you will, to allow us to accelerate certain operations. If we think about the creation of more intelligent training models for instance, to apply against artificial intelligence problems, we could anticipate that the Quantum computer could help speed up the evolution and development of these models. There is a lot of interest in working on this evolution of hardware because it's somewhat like the 1940's era of the mainframe. We're at the very beginning stages and we all know that when we evolve the mainframe it was through significant partnerships. Helping the man get to the moon. Working with airlines on the airline's reservation system. It was these partnerships that really enabled us to understand what the power of the machine could be. I think it will be the same way with Quantum as we work with our partners on that endeavor. >> Talk about the, because performance is critical, and you know blockchain has been criticized as having performance problems, writing to the chain, if you will. So clearly there's a problem opportunity basis you can work on there. What are the problems in blockchain, is that your area? Do you work on that? Are you vectoring into blockchain? >> Well of course we're very involved in the blockchain efforts because IBM secure blockchain is running on our z14 processor. One of the things we want to take advantage there is not only the performance of the system, but also, once again, the security characteristics. The ability to just encrypt on the fly. The exploitation of the fast encryption, the cryptology module, all of that, is really key fundamental in our journey on blockchain. I also think that we have a unique perspective in IBM on blockchain because we're a consumer of blockchain. We're already using it in our CFO office. I've spoken to you guys before about supply chains, I own the supply chain manufacturing for IBM and we're also running a shadow process for blockchain where we're working on customs declarations just like Maersk was talking about yesterday. Because customs declarations is a very difficult process. Very manual, labor intensive, a lot of paper. So we're doing that as well, and we'll be a test case for IBM's blockchain work. >> And I've heard from last night that you have 100 customers already. You've heard my opening, I was ranting on the opportunity that blockchain has which is to take away inefficiencies. And supply chain, you guys no stranger to supply chain, you've been bringing technology to solve supply chain problems for generations at IBM. Blockchain brings a new opportunity. >> It does, and my team fundamentally realizes this of course, as a supply chain organization. We ship over five million pieces of stuff every year. We're shipping into 170 countries. We have a tight but dispersed manufacturing operations, so we see this everyday. We have to ship products into every country in the world. We have to work with a very dispersed network through our partners of logistics. So we see the opportunity in blockchain for things like customs declarations as a first priority, but obviously, the logistics network, there's just huge opportunities here where far too much of this is really done manually. >> You guys could really run the table on this area. I mean blockchain, supply chain, chain I mean similar concept it's just decentralized and distributed. >> Well I think supply chain is such an area ripe for this kind of application. Something that's really going to breakthrough what has been so labor intensive from a manual perspective. Even if you look at how ports are managed and Maersk talked about that yesterday. >> So you're long on blockchain? >> Well, I'm excited about it because I'm a customer of blockchain. I see the issues that occur in supply chains everyday and I fundamentally think it will be a game changer. >> Yeah, I'm biased, I mean we're trying to move our media business to the blockchain because everything's decentralized. I'm excited about the application developer movement that's starting now. You're starting to see with crytocurrency, token economics come into play around the business model innovations. Do you guys talk about that internally when you do R&D? You have to cross-connect the business model logic token economics with the technology? >> Well of course you know that's a fundamental part of what the blockchain focus on right? It's just like any new project that we embarked on. You've got to get the underlying technology right but you've always have to do that in the context of the business execution, the business deployment. So we're learning from all the engagements we're doing. And then that shapes the direction that we take the underlying technology into. >> Jamie, talk about the IBM Think 2018, it's a big event. I mean you can't multiply yourself times six. You go to all the events. This is a big event. You must be super busy. What's the focus? What's your reaction, what have you been talking about? >> Well it's kind of nice to talk to you kind of towards the end of the event. Sometimes I talk to you guys at the very beginning of the event so they all kind of have a retrospective of the things that have happened. I think it was a great event in terms of showcasing our innovation, but also having a number of key CEO's from various firms talk to us about how they're really using this technology. Great examples from RBC, from Maersk, from Verizon, from the NVIDIA CEO yesterday. And also some really pointed discussions around looking into the future. So we had a research talk about, Arvind Krishna spoke about, the next five big plays. Which are artificial intelligence, blockchain, Quantum were on that list certainly. As well as now we'll be having a Quantum keynote later today so we'll dive into Quantum a little bit more in terms of how the future will be shaped by that technology. But I think it was a nice mix of hearing about the realization of deploying some of the things that we've done in IBM, but combined with where are things going and stimulating thought with the client which is always important in these kind of meetings. It is having that strategic discussion about how we can really partner with them. >> Real conversations. >> Yeah, real conversations about how we can partner with them to be successful as they leave this conference and go back to their home offices. >> Well congratulations on a great strategy, you've been running strategy. I know we've talked in the past. You've kind of had to bring it all together into one package, into one message, but still have the ability and flexibility to manage the tech. So my final question for you is where's the puck going next? Where are you skating now strategy wise to catch that next puck? >> Well I think that what we'll see is a continued progression, if you will, and speed around some of the things that we've already talked about here. I think there's been a lot of discussion for instance, around multi-cloud architectures. But I really think we're still at the tip of the spear in fundamentally getting the value out of those architectures. That real deployment of some of those architectures as clients modernize their applications and really take advantage of Cloud, I think will drive a different utilization of storage, and it will require different characteristics out of our systems as we go forward. So I think that we're at the tip of a journey here that will inform us. >> The modernization and business model innovation, technology enablement all coming together. >> Right, we were talking about that right? So think about the primary use case of IBM Cloud Private right now is modernization of those applications. So as those clients modernize those applications and then start to deploy these new techniques in combination with that; around artificial intelligence and blockchain, there's just a huge opportunity for us to continue this infrastructure innovation journey. >> International Business Machines. The name of the company obviously, and you know my opinion on this, we're reporting that the real critical intellectual property for customers is going to be the business innovation, the business model opportunities in blockchain, AI, really accelerate that piece. >> And as Ginni said yesterday, we're here to serve our clients, to make sure that they're successful in moving from where they have been and the continuation of this journey. And so that will be where we keep our focus as we go forward. >> Well looking forward to talking about token economics. I think that's going to be a continued conversation as you guys create more speed, more performance, the business model innovations around token economics. And then decentralized application developers will probably impact IoT, will probably impact a lot of these fringe, emerging, use cases that need compute, that need power. They need network effect, they need data. >> Absolutely, so I mean there's been a lot of discussion this week about making sure that we move the processing to the data, not the data to the processing because obviously you can't move all that data around. That's why I think these and Fungible architecture and Agile architecture will give clients the ability to do that more effectively. And as you said, we always have to worry about those developers. We have to make sure that they have the modern tools and techniques that allow them to move with speed and still take advantage of a lot of those. >> And educate the business users . >> Exactly, exactly. >> Are you having fun? >> I'm having great fun, this has been a great conference. It's always great to talk with you guys. >> We really appreciate your friendship and always coming on TheCube and sharing your insights. Always great to get the data out there. Again, we're data driven, this data driven interview with Jamie Thomas, General Manager of System Strategy and Development here at IBM Think inside TheCube studios we're on the floor here in Las Vegas. I'm John Furrier. We'll be back with more after this short break.

>> 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)

>> Welcome back, everyone, we're at the Flink Forward conference, this is the user conference for the Flink community, started by Data Artisans and sponsored by Data Artisans. We're at the Kabuki Hotel in San Francisco and we have with this another special guest, Jamie Grier, who's Director of Applications Engineering at Data Artisans. Jamie, welcome. >> Thanks. >> So we've seen an incredible pace of innovation in the Apache open source community and as soon as one technology achieves mainstream acceptance, it sort of gets blown away by another one, like MapReduce and Spark. There's an energy building around Flink and help us understand where it fits relative to, not necessarily things that it's replacing so much as things that it's complementing. >> Sure. Really what Flink is is it's a real stream processor so it's a stateful stream processor. The reason that I say it's a real stream processor is because the model, the competition model, the way the engine works, the semantics of the whole thing are the continuous programming model, which means that, really, you just consume events one at a time, you can update any sort of data structures you want, which Flink manages, full tolerantly, at scale, and you can do flexible things with processing, with regards to time, scheduling things to happen at different times, when certain amounts of data are complete, et cetera, so it's not oriented strictly towards, a lot of the stream processing in the past has been oriented sort of towards analytics alone or that's the real sweet spot, whereas Flink as a technology enables you to build much more complex event- and time-driven applications in a much more flexible way. >> Okay so let me unpack that a bit. >> Sure. >> So what we've seen in the Haddud community for the last x many years was really an analytic data pipeline put the data into a data lake and the hand-offs between the services made it a batch process. We tried to start adding data science and machine learning to it, it remained pretty much a batch process, 'cause it's in the data lake, and then when we started to experiment with stream processors, their building blocks were all around analytics and so they were basically an analytic pipeline. If I'm understanding you, you handle not just the analytics but the update-oriented or the cred-oriented operations, create, read, update, delete. >> Yeah, exactly. >> That you would expect from having a database as part of an application platform. >> Yeah. I mean, that's all true, but it goes beyond that. I mean, Flink as a stateful stream processor has in a sense a micro simple database as part of the stream processor. So yeah, you can update that state, like you said, the crud operations on that state, but it's more than that, you can build any kind of logic at all that you can think of that's driven by consuming events. Consuming events, doing calculations, and emitting events. Analytics is very easily built on top of something as powerful as that, but if you drop down below these higher level analytics APIS, you truly can build anything you want that consumes events, updates state, and emits events. And especially when there's a time dimension to these things like sometimes you consume some event and it means that at some future time, you want to schedule some processing to happen. And these basic primitives really allow you to build, I tell people all the time, Flink allows you to do this consuming of events and updating data structures of your own choosing, does it full tolerantly and at scale, build whatever you want out of that. And what people are building are things that are truly not really expressible as an analytics jobs. It's more just building applications. >> Okay, so let me drill down on that. >> Sure. >> Let's take an example app, whether it's, I'll let you pick it, but one where you have to assume that you can update state and you can do analytis and they're both in the same map, which is what we've come to expect from traditional apps although they have their shared state in a database outside the application. >> So a good example is, I just got done doing a demo, literally just before this, and it's a training application, so you build a training engine, it's consuming position information from webstream systems and it's consuming quotes. Quotes are all the bids and all the offers to buy stock at a given price, we have our own positions we're holding within the firm if we're a bank, and those positions, that's our state we're talking about. So it says I own a million shares of Apple, I own this many shares of Google, this is the price I paid, et cetera, so then we have some series of complex rules that say, hey, I've been holding this position service for a certain period of time, I've been holding it for a day now and so I want to more aggressively trade out of this position and I do that by modifying my state, driven by time, so more time has gone past, I'm going to lower my ask price, now trades are streaming in as well to the system and I'm trying to more aggressively make trades by lowering the price I'm willing to trade for. So these things are all just event-driven applications, the state is your positions in the market and the time dimension is exactly that, as you've been holding the position longer, you start to change your price or change your trading strategy in order to liquidate a little bit more aggressively, none of that is in the category of, I'd say you're using analytics along the way but none of that is just what you'd think of as a typical analytics or an analytics API. You need an API that allows you to build those sorts of flexible event-driven things. >> And the persistence part or the maybe transactional part is I need to make a decision as a human or the machine and record that decision and so that's why there's benefit to having the analytics and the database, whatever term we give it, in the same. >> Co-located. >> Co-located, yeah, in the same platform. >> Yeah there's a bunch of reasons why that's good. That's one of them, another reason is because when you do things at high scale and you have high through, say in that trading system we're consuming the entire options chains worth of all the bids in asks, right? It's a load of data so you want to use a bunch of machines but you want to, you don't want to have to look up your state in some database for every single message when instead you could share the input stream and both input streams by the same key and you end up doing all of your look-up join type operations locally on one machine. So at high scale it's a huge just performance benefit. Also allows you to manage that state consistently, consistent with the input streams. If you have the data in a external database and a node fails then you need to sort of back up in the input stream a little bit, replay a little bit of the data, you have to also be able to back up your state to a consistent point with all of the inputs and if you don't manage that state, you cannot do it. So that's one of the core reasons why stream processors need to have state, so they can provide strong guarantees about correctness. >> What are some of the other popular stream processors, when they choose perhaps not to manage state to the same integrated degree that you guys do? What was their thinking in terms of, what trade-off did they make? >> It was hard. So I've also worked on previous streaming systems in the past and for a long time, actually, and managing all this state in a consistent way is difficult and so the early generation systems didn't do it for exactly that reason, let's just put it in the database but the problem with that is exactly what I just mentioned and in stream processing we tend to talk about exactly once and at least once, this is actually the source of the problem so if the database is storing your state, you can't really provide these exactly-once type guarantees because when you replace some data, you back up in the input, you also have to back up the state and that's not really a database operation that's normally available, so when you manage to state yourself in the stream processor, you can consistently manage the input in the state. So you can exactly-once semantics in the face of failure. >> And what do you trade in not having, what do you give up in not having a shared database that has 40 years of maturity and scalability behind it versus having these micro databases distributed around. Is it the shuffling of? >> You give up a robust external quarry interface, for one thing, you give up some things you don't need like the ability to have multiple writers and transactions and all that stuff, you don't need any of that because in a stream processor, for any given key there's always one writer and so you get a much simpler type of database you have to support. What else? Those are the main things you really give up but I would like to also draw a distinction here between state and storage. Databases are still obviously, Flink state is not storage, not long-term storage, it's to hold the data that's currently sort of in flight and mutable until it's no longer being mutated and then the best practice would be to emit that as some sort of event or as a sync into a database and then it's stored for the long-term, so it's really good to start to think about the difference between what is state and what is storage, does that make sense? >> I think so. >> So think of, you're accounting, you're doing distributed accounting, which is an analytics thing, you're counting by key, the count per key is your state until that window closes and I'm not going to be mutated anymore, then we're headed into the database. >> Got it. >> Right? >> Yeah. >> But that internal, that sort of in-flight state is what you need to manage in the stream process. >> Okay, so. >> So it's not a total replacement for database, it's not that. >> No no no, but this opens up another thread that I don't think we've heard enough of. Jamie, we're going to pause it here. >> Okay. >> 'Cause I hope to pick this thread up with you again, the big surprise from the last two interviews, really, is Flink is not just about being able to do low latency per event processing, it's that it's a new way of thinking about applications beyond the traditional stream processors where it manages state or data that you want to keep that's not just transient and that it becomes a new way of building micro services. >> Exactly, yeah. >> So on that note, we're going to sign off from the Data Artisans user conference, Flink Forward, we're here in San Francisco on the ground at the Kabuki Hotel. (upbeat music)

>> Announcer: Live, from Las Vegas, it's the Cube. Covering InterConnect 2017. Brought to you by, IBM. >> Okay welcome back everyone, we're here live in Las Vegas for IBM InterConnect 2017, this is the Cube coverage here, in Las Vegas for IBM's cloud and data shows. It turns out, I'm John Furrier, with my cohost Dave Vellante, next guess is Jamie Thomas, general manager of systems development and strategy at IBM, Cube Alum. Great to see you, welcome back. >> Thank you, great to see you guys as usual. >> So, huge crowds here. This is I think, the biggest show I've been to for IBM. It's got lines around the corner, just a ton of traffic online, great event. But it's the cloud show, but it's a little bit different. What's the twist here today at InterConnect? >> Well, if you saw the Keynote, I think we've definitely demonstrated that while we're focused on differentiating experience on the cloud through cloud native services, we're also interesting in bridging existing clients IT investments into that environment. So, supporting hybrid cloud scenarios, understanding how we can provide connective fabric solutions, if you will, to enable clients to run mobile applications on the cloud and take advantage of the investments they've made and their existing transactional infrastructure over a period of time. And so the Keynote really featured that combination of capabilities and what we're doing to bring those solution areas to clients and allow them to be productive. >> And the hybrid cloud is front and center, obviously. IOT on the data side, you've seen a lot of traction there. AI and machine learning, kind of powering and lifting this up, it's a systems world now, I mean this is the area that you're in. Cause you have the component pieces, the composibility of that. How are you guys facilitating the hybrid cloud journey for customers? Because now, it's not just all here it is, I might have a little bit of this and a little bit of that, so you have this component-isationer composobility that app developers are consistent with, yet the enterprises want that work load flexibility. What do you guys do to facilitate that? >> Well we absolutely believe that infrastructure innovation is critical on this hybrid cloud journey. And we're really focused on three main areas when we think about that innovation. So, integration, security, and supportive cognitive workloads. When we look at things like integration, we're focused on developers as key stake holders. We have to support the open communities and frameworks that they're leveraging, we have to support API's and allow them to tap into our infrastructure and those investments once again, and we also have to ensure that data and workload can be flexibly moved around in the future because these will allow better characteristics for developers in terms of how they're designing their applications as they move forward with this journey. >> And the insider threat, though, is a big thing too. >> Yes. >> I mean security is not only table stakes, it's a highly sensitive area. >> It's a given. And as you said, it's not just about protecting from the outside threats, it's about protecting from internal threats, even from those who may have privileged access to the systems, so that's why, with our systems infrastructure, we have protected from the chip, all the way through the levels of hardware into the software layer. You heard us talk about some of that today with the shipment of secure service containers that allow us to support the system both at install time and run time, and support the applications and the data appropriately. These systems that run Blockchain, our high security Blockchain services, LinuxONE, we have the highest certification in the industry, EAL five plus, and we're supporting FIPS 120-two, level four cryptology. So it's about protecting at all layers of the system, because our perspective is, there's not a traditional barrier, data is the new perimeter of security. So you've got to protect the data, at rest, in motion, and across the life cycle of the data. >> Let's go back to integration for a second. Give us an example of some of the integrations that you're doing that are high profile. >> Well one of the key integrations is that a lot of clients are creating new mobile applications. They're tapping back into the transactions that reside in the mainframe environment, so we've invested in ZOS Connect and this API set of capabilities to allow clients to do that. It's very prevalent in many different industries, whether it's retail banking, the retail sector, we have a lot of examples of that. It's allowing them to create new services as well. So it's not just about extending the system, but being able to create entirely new solutions. And the areas of credit card services is a good example. Some of the organizations are doing that. And it allows for developer productivity. >> And then, on the security side, where does encryption fit? You mentioned you're doing some stuff at the chip level, end to end encryption. >> Yeah it really, it's at all levels, right? From the chip level, through the firmware levels. Also, we've added encryption capability to ensure that data is encrypted at rest, as well as in motion, and we've done that in a way that encrypts these data sets that are heavily used in the main frame environment as an example, without impending on developer productivity. So that's another key aspect of how we look at this. How can we provide this data protection? But once again, not slow down the velocity of the developers. Cause if we slow down the velocity of the developers, they will be an inhibitor to achieving the end goal. >> How important is the ecosystem on that point? Because you have security, again, end to end, you guys have that fully, you're protecting the data as it moves around, so it's not just in storage, it's everywhere, moving around, in flight, as they say. But now you got ecosystem parties, cause you got API economy, you're dealing with no perimeter, but now also you have relationships as technology partners. >> Yes, well the ecosystem is really important. So if we think about it from a developer perspective, obviously supporting these open frameworks is critical. So supporting Linux and Docker and Spark and all of those things. But also, to be able to innovate at the rate and pace we need, particularly for things like cognitive workloads, that's why we created the Open Power Foundation. So we have more than 300 partners that we're able to innovate with, that allow us to create the solutions that we think we'll need for these cognitive workloads. >> What is a cognitive workload? >> So a cognitive workload is what I would call an extremely data hungry workload, the example that we can all think of is we're expecting, when we experience the world around us, we're expecting services to be brought to us, right, the digital economy understands our desires and wants and reacts immediately. So all of that is driving, that expectation is driving this growth and artificial intelligence, machine learning, deep learning type algorithms. Depending on what industry you're in, they take on a different persona, but there's so many different problems that can be solved by this, whether it's I need to have more insight into the retail offers I provide to an in consumer, to I need to be able to do fraud analytics because I'm in the financial services industry, there's so many examples of these cognitive applications. The key factors are just, tremendous amount of data, and a constrained amount of time to get business insight back to someone. >> When you do these integrations and you talk about the security investments that you're making, how do you balance the resource allocation between say, IBM platforms, mainframe, power, and the OS's, the power in those, and Linux, for example, which is such a mainstay of what you guys are doing. Are you doing those integrations on the open side as well in Linux and going deep into the core, or is it mostly focused on, sort of, IBM owned technology? >> So it really depends on what problem we're trying to solve. So, for instance, if we're trying to solve a problem where we're marrying data insight with a transaction, we're going to implement a lot of that capability on ZOS, cause we want to make sure that we're reducing data latency and how we execute the processing, if you will. If we're looking at things like new work loads and evolution of new work loads, and new things are being created, that's more naturally fit for purpose from a Linux perspective. So we have to use judgment, a lot of the new programming, the new applications, are naturally going to be done on a Linux platform, cause once again that's a platform of choice for the developer community. So, we have to think about whether we're trying to leverage existing transactions with speed, or whether we're allowing developers to create new assets, and that's a key factor in what we look at. >> Jamie, your role, is somewhat unique inside of IBM, the title of GM system's development and strategy. So what's your scope, specifically? >> So, I'm responsible for the systems development involved in our processor's mainframes, power systems, and storage. And of course, as a strategy person for a unit like that, I have responsibility for thinking about these hybrid scenarios and what do we need to do to make our clients successful on this journey? How do we take advantage of their tremendous investments they made with us over years. We have strong responsibility for those investments and making sure the clients get value. And also understanding where they need to go in the future and evolving our architecture and our strategic decisions, along those lines. >> So you influence development? >> Jamie: Yes. >> In a big way, obviously. It's a lot of roadmap work. >> Jamie: Yes. >> A lot of working with clients to figure out requirements? >> Well I have client support too, so I have to make sure things run. >> What about quantum computing? This has been a big topic, what's the road map look like? What's the evolution of that look like? Talk about that initiative. >> Well if I gave you the full road map they'd take me out of here with a hook out of this chair. >> You're too good for that, damn, almost got it from you. >> But we did announce the industries first commercial universal quantum computing project. A few weeks ago. It's called IBM Q, so we had some clever branding help, because Q makes me think of the personality in the James Bond movie who was always involved in the latest R&D research activity. And it really is the culmination of decades of research between IBM researchers and researchers around the world, to create this system that hopefully can solve problems to date, that are unsolvable today with classical computers. So, problems in areas like material science and chemistry. Last year we had announced quantum experience, which is an online access to a quantum capabilities in our Yorktown research laboratory. And over the last year, we've had more than 40,000 users access this capability. And they've actually executed a tremendous number of experiments. So we've learned from that, and now we're on this next leg of the journey. And we see a world where IBM Q could work together with our classical computers to solve really really tough problems. >> And that computing is driving a lot of the IOT, whether that's health care, to industrial, and everything in between. >> Well we're in the early stages of quantum, to be fair, but there's a lot of unique problems that we believe that it will solve. We do not believe that everything, of course, will move from classical to quantum. It will be a combination, an evolution, of the capabilities working together. But it's a very different system and it will have unique properties that allow us to do things differently. >> So, what are the basics? Why quantum computing? I presume it's performance, scale, cost, but it's not traditional, binary, computing, is that right? >> Yes. It's very, very different. In fact, if. >> Oh we just got the two minute sign. >> It's a very different computing model. It's a very different physical, computing model, right? It's built on this unit called a Q bit, and the interesting thing about a Q bit is it could be both a zero and a one at the same time. So it kind of twists our minds a little bit. But because of that, and those properties, it can solve very unique problems. But we're at the early part of the journey. So this year, our goal is to work with some organizations, learn from the commercialization of some of the first systems, which will be run in a cloud hosted model. And then we'll go from there. But, it's very promising. >> In the timeframe for commercial systems, have you guys released that? >> Well, this year, we'll start the commercial journey, but within the next few years we do plan to have a quantum computer that would then, basically, out strip the power of the largest super computers that we have today in the industry. But that's, you know, over the next few years we'll be evolving to that level. Because eventually, that's the goal, right? Is to solve the problems that we can't solve with today's classical computers. >> Talk about real quickly, in the last couple minutes, Blockchain, and where that's going, because you have a lot of banks and financial institutions looking at this as part of the messaging and the announcements here. >> Well, Blockchain is one of those workloads of course that we're optimizing with a lot that security work that I talked about earlier so. The target of our high security Blockchain services is LinuxONE, is driving a lot of encryption strategy. This week, in fact, we've seen a number of examples of Blockchain. One was talked about this morning, which was around diamond provenance, from the Everledger organization. Very clever implementation of Blockchain. We've had a number of financial institutions that are using Blockchain. And I also showed an interesting example today. Plastic Bank, which is an organization that's using Blockchain to allow ecosystem improvement, or improving our planet, if you will, by allowing communities to exchange plastic, recyclable plastic for currency. So it's really about enabling plastic to be turned into currency through the use of Blockchain. So a very novel example of a foundational research organization improving the environment and allowing communities to take advantage of that. >> Jamie thanks for stopping by the Cube, really appreciate giving the update and insight into the quantum, the Q project, and all the greatness around, all the hard work going to into the hybrid cloud, the security-osity is super important, thanks for sharing. >> It's good to see you. >> Okay we're live here, in Mandalay Bay, for IBM InterConnect 2017, stay with us for more live coverage, after this short break.

>> Announcer: Live from Las Vegas. It's theCUBE. Covering InterConnect 2017. Brought to you by IBM. >> You're a startup, growing. >> Jamie Alexander: Absolutely. >> You're working with big banks. This is not easy. >> Jamie Alexander: It is not easy. >> Normally they don't work with startups at all. >> Jamie Alexander: It's not easy at all. >> And Thintek is exploding as a very big growth area. Cloud enables this. Take us through some of the key points in your journey. As CTO, you've nailed some big wins with some big, established financial institutions, how'd you pull it off, what's the formula? >> Yeah, actually you could come and see my talk on Wednesday. I actually do that in detail. But I could give you a quick summary. So there's really, all along the way from the initial pre-sales to the pitch sessions with the customers, to the pilots, there are kind of learnings all along the way of the process and I think the number one thing is white glove service. So, typically, from a scalability perspective, startups are being trained to make it self-service, API, there's a developer portal, people can go in-- >> John: Move fast and break stuff. >> But actually, especially for the first set of customers, the white glove service is absolutely essential and really establishing the relationships at the ground level, so not just on the business side, that's a given, but also with the technical folks, the people at the banks that are doing the integrations, they can kill your projects. And so, really, giving them a bit of a taste of our culture I think, actually, really excites them. >> The white glove service, though, if I hear this correctly, it's not just being kind and holding their hand, there's some technical table stakes. >> Absolutely. >> What are those table stakes? 'Cause that seems to be the enterprise readiness matrix. >> Yeah, that's a great question. So I think the key is making tools that are very simple for developers to use, have developers love using your product, because, ultimately, it's a technical integration, and so one of the things that we did is we created an SDK both for iOS and Android and it's not just service connectivity, but it's also the full user experience around receipt capture. And what that did is it precluded the need for the banks to go and build all the screens and all the workflows. We could come in and say right away, here, we have it for you, you can customize it, configure it to make it look like your banking application to add your brand elements to it. But, ultimately, it allowed them, in a very short period of time, to bring on that new feature. The end user has no idea about Sensibill, there might be a little logo at the bottom of the receipt that says it's powered by Sensibill but other than that, it very much fits in with the existing banking application. And that's really important because receipts aren't their space, we want them to, right out of the gate, have a receipt capture application that's intuitive for end users. And this allows us to put it in their hand and just make it work for them. So that's really a big part of the success for them. >> And you've overcome that startup fear. >> Jamie: Absolutely. How have you done that? >> So I think the advantage for me is did spend my early career with IBM. So I spent about the first 13 years, >> Dave: So you were trained by IBM. So you kind of know. (laughs) >> And so I was both in software groups or working on e-commerce implementations but sort of the middle part after that, was in global services where I got to work with people in enterprise but across various sectors. And so that gave me they confidence and really allows me to think in the same way that enterprise folks think. Because we're not a startup that's selling, that has a platform where people are sharing pictures of sneakers, I mean this is serious business, and not to belittle other-- >> And their brand. Your customer's brand is on the line here. >> Absolutely, and so it really impacts everything we do. Who we hire, the culture we try to build, how we present ourselves to our customers. I mean it's across the board. Many considerations. But I think also, like me personally, I've always had that entrepreneurial spirit. So I've always been hacking things together on the side, and, actually, around 2010 when I left IBM, I had a previous startup, so this is number two for me. In fact, at IBM, I tried to, actually, do something intrapreneurial. But for me, actually, B to B, especially Business to Enterprise is for me really the sweet spot in terms of my skills and it's hard, so I like that. I like a hard problem and I would prefer that there's more barriers and it shows in the interest from our investors as well. You want a business with moats around it, and certainly financial institutions like banks, can take two years to close a deal. It's a really long sales cycle. >> John: So you're up for the challenge. >> Absolutely. >> So other than your past with IBM, what's the other IBM connection? You're running this on Bluemix, and IBM Cloud? >> Yeah, so we're running the solution on Bluemix. So we chose IBM for a number of reasons. One was their global footprints, in terms of their data centers. Our customers have certain SLAs they expect us to uphold. They require that we have disaster recovery in place. And so SoftLayer was very early, in terms of, bringing their data centers into Canada. So they recognize the opportunity there. And so we were both in Toronto and Montreal data centers. On top of that, as well, we've been part of the IBM Global Entrepreneurship Program. That's given us some mentoring around how to scale our business. Gave us some financial incentives as well. On top of that, there are other relationships that we've explored with the services business at IBM. so could, theoretically, IBM be a preferred vendor for, or integrator for our technology, and so there's a number of fronts that we're working with IBM and I think also, partly, because my former relationship, I was an employee at IBM. >> Dave: In Canada or in the U.S.? >> In Canada. So even our CEO for example, she was also at IBM. So bringing the best talents that I can find. People that want a change in their career and move from a large enterprise to a small company, we look for those people. >> And you were in the software labs up there, and then in the services group you got the financial services domain expertise and brought the software and FS together, wallah. >> Yeah and I, certainly, would not have predicted all the excitement around Thintek when I started. I'm really pleased that I, magically, threw horse shoes in luck and ended up in the right place at the right time. Even from three years ago-- >> When you tackle hard problems, usually, you end up in a good spot. >> Absolutely, yeah. >> So the hard question I want to ask you, this is a tough question, so be ready. Canadians or The Maple Leafs? >> (laughs) I'd have to say the Maple Leafs, to be honest, I'm from Toronto so. (laughing) >> Unless the Maple Leafs lose and then the Canadians over the Bruins, obviously. >> Hey, if there's a Canadian team, I'll be rooting for them. >> I love the hockey in Canada, being from the Boston area. Alright now, I want to ask you something more sentimental about the culture. You mentioned culture which you were talking about, your company culture. What's the cultural shift that you're seeing in the market place? Because we're talking about you're a start up that has cracked the code on a very hard problem with banks getting a customer. So kudos and props for that. But also, there's a whole dev ops movement that's going, now, to data. Where we heard some of the IBM execs pointing out the counter culture that's developing. The younger generation, they don't want things the old way. They're doing things much different. Can you comment about what your observations are around this cultural shift? >> Yeah, for sure. I think we've spent a bit too long, in general, paying lip service to the word innovation and I think, finally, it's, really, coming to fruition. Like real innovation not innovation just for the sake of marketing but, really, being able to innovate. Because a sub set of the millennials that are coming up, they really have, the culture of innovation has, really, been infused into their entire upbringing. And then they're, really, showing that in the work place. You see, over the last say, five, six years, the rise of hack days and these kind of things. People that are also interested in solving problems that don't just have commercial outcomes to them. What you find is, that if you can align people's passions and interests and have them understand that if you go after this thing, your career will be set. That's some of the things we try to do with our more junior resources. Is let them know that if there's something that they're interested in, a problem they want to tackle. It's aligned with where we're going from corporate objectives. Go after that because you will get what you want at Sensibill. We want those kind of people that don't just pay lip service to innovation but, really, see something and are self starting and can go after things on their own. I think there's, also, a big aspect of social awareness. There's people on our team and rightly so that are concerned about ethical use of data. So we're, at Sensibill, drafting up a policy just so, internally, we know that we can agree, collectively, on how we intend too use our data. It's, certainly, not malicious purposes. We're not selling individual user data. Now the banks do have access, the data collected through their systems is theirs. But, ultimately, in terms of how we plan to monetize the insights which is the next, really, interesting thing and things that I'm working on in 2017, really making sure it's done in an ethical way. >> That's your next moon shot is to, really, crack the code on the governance and the management of the data? >> But I think to get the right people, you also have to have to consider the social implications of using the data. People have to feel good about the work they do. There can be a lot of sensitivity around the type of data that we collect. >> Well Jamie, congratulations on the financing of your start up. Jamie Alexander, who's the co-founder and CTO of Sensibill. Check em out. If you're a big bank, not many of them, it's mostly potential customers. Congratulations on winning the big deal as a start up, that's great news. >> Thanks so much. Thanks for coming on the CUBE and sharing your start up story. I'm John Furrier with Dave Vellante. Keep watching it here. Stay with us for more coverage from Las Vegas after this short break. (lively music)

>>Um, Jamie Sharath with Liga data, I'm primarily on the delivery side of the house, but I also support our new business teams. I'd like to spend a minute really just kind of telling you about, uh, uh, legal data where basically a Silicon valley startup, uh, started in 2014 and, uh, our lead iron, our executive team, basically where the data officers at Yahoo before this, uh, we provide managed data services and we provide products that are focused on telcos. So we have some experience in non telco industry, but our focus for the last seven years or so is specifically on telco. So again, something over 200 employees, we have a global presence in north America, middle east Africa, Asia, and Europe. And we have folks in all of those places. Uh, I'd like to call your attention to the, uh, the middle really of the screen there. >>So here is where we have done some partnership with Cloudera. So if you look at that, you can see we're in Holland and, uh, Jamaica, and then a lot to throughout Africa as well. Now the data fabric is the product that we're talking about. And the data fabric is basically a big data type of data warehouse with a lot of additional functionality involved. The data fabric is comprised of, uh, some something called flare, which we'll talk about admitted below there, and then the Cloudera data platform underneath. So this is how we're partnering together. We, uh, we, we have this tool and it's, uh, it's functioning and delivering in something over and up. Oops. So flare now, flare is a piece of that. It's legal data IP. The rest is Cloudera. And what flare does is that basically pulls in data and integrates it to an event streaming, uh, platform. >>It, uh, it is the engine behind the data fabric. Uh, it's also a decisioning platform. So in real time, we're able to pull in data. We're able to run analytics on it and we're able to alert our, do whatever is needed in a real-time basis. Of course, a lot of clients at this point are still sending data in batch. So it handles that as well, but we call that a cut off picture Sanchez. Now Sacho is a very interesting app. It's an AI analytics app for executives. What it is is it runs on your mobile phone. It ties into your data. Now this could be the data fabric, but it couldn't be a standalone product. And basically it allows you to ask, you know, human type questions to say, how are my gross ads last week? How are they comparing against same time last week before that? >>And even the same time 60 days ago. So as an executive or as an analyst, I can pull it up and I can look at it instantly in a meeting or anywhere else without having to think about queries or anything like that. So that's pretty much for us legal data. Now, it really does set the context of where we are. So this is a traditional telco environment. So you see the systems of record and you see the cloud, you see OSS and BSS day. So one of the things that the next step above which calls we call the system of intelligence of the data fabric does, is it mergers that BSS and OSS data. So the longer we have any silos or anything that's separated, it's all coming into one area to allow business, to go in or allow data scientists go in and do that. >>So if you look at the bottom line, excuse me, of the, uh, of the system of intelligence, you can see that flare is the tool that pulls in the data. So it provides even screening capabilities, it preserves entity states, so that you can go back and look at it to the state at any time. It does stream analytics that is as the data is coming in, it can perform analytics on it. And it also allows real-time decisioning. So that's something that, uh, that's something that business users can go in and create a system of, uh, if them's, it looks very much like a graph database where you can create a product that will allow the user to be notified if a certain condition happens. So for instance, a bundle, so a real-time offer or user is fixing to run out of is ongoing and an offer can be sent to him right on the fly. >>And that's set up by the business user as opposed to programmers a data infrastructure. So the fabric has really three areas. That data is persistent, obviously there's the data lake. So the data lake stores that level of granularity that is very deep years and years of history, data scientists love that. And, uh, you know, for a historical record keeping and requirements from the government, that data would be stored there. Then there's also something we call the business semantics layer and the business semantics layer contains something over 650 specific telco KPIs. These are initially from PM forum, but they also are included in, uh, various, uh, uh, mobile operators that we've delivered at. And we've, we've grown that. So that's there for business. The data lake is there for data scientists, analytical stores, uh, they can be used for many different reasons. There are a lot of times RDBMS is, are still there. >>So these, this, this basically platform, this cloud they're a platform can tie into analytical data stores as well via flair access and reporting. So graphic visualizations, API APIs are a very key part of it. A third-party query tools, any kind of grid jewels can be used. And those are the, of course, the, uh, the ones that are highly optimized and allow, you know, search of billions of records. And then if you look at the top, it's the systems of engagement, then you might vote this use cases. So telco reporting, hundreds of KPIs that are, that are generated for users, segmentation, basically micro to macro segmentation, segmentation will play a key role in a use case. We talk about in a minute monetizations. So this helps telco providers monetize their specific data, but monetize it in, okay, how to do they make money off of it, but also how might you leverage this data to, in, in dates with another client? >>So for instance, in some cases where it's allowed a DPI is used and the, uh, fabric tracks exactly where each person goes each, uh, we call it a subscriber, goes within his, uh, um, uh, internet browsing for 5g and, uh, all that data is stored. Uh, whereas you can tell a lot of things where the segment, the profile that's being used and, you know, what are they propensity to buy? Did they spend a lot of time on the Coca-Cola page? There are buyers out there that find that information very valuable, and then there's sideshow. And we spoke briefly about Sacha before that sits on top of the fabric or it's it's alone. >>So, so the story really that we want to tell is, is one, this is, this is one case out of it. This is a CVM type of case. So there was a mobile operator out there that was really offering, you know, packages, whether it's a bundle or whether it's a particular tool to subscribers, they, they were offering kind of an abroad approach that it was not very focused. It was not depending on the segments that were created around the profiling earlier, uh, the subscriber usage was somewhat dated and this was causing a lot of those. Uh, a lot of those offers to be just basically not taken and not, not, uh, uh, there was limited segmentation capabilities really before the, uh, before the, uh, fabric came in. Now, one of the key things about the fabric is when you start building segments, you can build that history. >>So all of that data stored in the data lake can be used in terms of segmentation. So what did we do about that? The, the, the MDNO, the challenge, uh, we basically put the data fabric in and the data fabric was running Cloudera data platform and that, uh, and that's how we team up. Uh, we facilitated the ability to personalize campaign. So what that means is, uh, the segments that were built and that user fell within that segment, we knew exactly what his behavior most likely was. So those recommendations, those offers could be created then, and we enable this in real time. So real-time ability to even go out to the CRM system, again, their further information about that, all of these tools, again, we're running on top of the cloud data platform, uh, what was the outcome? Willie, uh, outcome was that there was a much more precise offer given to the client that is, that was accepted, you know, increase in cross sell and upsell subscriber retention. >>Uh, our clients came back to us and pointed out that, uh, it was 183% year on year revenue increase. Uh, so this is a, this is probably one of the key use cases. Now, one thing to really mention is there are hundreds and hundreds of use cases running on the fabric. And, uh, I would even say thousands. A lot of those have been migrated. So when the fabric is deployed, when they bring the, uh, Cloudera and the legal data solution in there's generally a legacy system that has many use cases. So many of those were, were migrated virtually all of them in pen, on put on the cloud. Uh, another issue is that new use cases are enabled again. So when you get this level of granularity and when you have campaigns that can now base their offers on years of history, as opposed to 30 days of history, the campaigns campaign management response systems, uh, are, are, uh, are enabled quite a bit to do all, uh, to be precise in their offers. Yeah. >>Okay. So this is a technical slide. Uh, one of the things that we normally do when we're, when we're out there talking to folks, is we talk and give an overview and that last little while, and then we give a deep technical dive on all aspects of it. So sometimes that deep dive can go a couple of hours. I'm going to do this slide and a couple of minutes. So if you look at it, you can see over on the left, this is the, uh, the sources of the data. And they go through this tool called flare that runs on the cloud. They're a data platform, uh, that can either be via cues or real-time cues, or it can be via a landing zone, or it can be a data extraction. You can take a look at the data quality that's there. So those are built in one of the things that flare does is it has out of the box ability to ingest data sources and to apply the data quality and validation for telco type sources. >>But one of the reasons this is fast to market is because throughout those 10 or 12 opcos that we've done with Cloudera, where we have already built models, so models for CCN, for air for, for most mediation systems. So there's not going to be a type of, uh, input that we haven't already seen are very rarely. So that actually speeds up deployment very quickly. Then a player does the transformation, the, uh, the metrics, continuous learning, we call it continuous decisioning, uh, API access. Uh, we, uh, you know, for, for faster response, we use distributed cash. I'm not going to go too deeply in there, but the layer and the business semantics layer again, are, are sitting top of the Cloudera data platform. You see the cough, but flu, uh, Q1 on the right as well. >>And all of that, we're calling the fabric. So the fabric is Cloudera data platform and the cloud and flair and all of this runs together. And by the way, there've been many, many, many, many hundreds of hours testing flare with Cloudera and, uh, and the whole process, the results, what are the results? Well, uh, there are, there are four I'm going to talk about, uh, we saw the one for the, it was called my pocket pocket, but it's a CDM type, uh, use case. Uh, the subscribers of that mobile operator were 14 million plus there was a use case for a 24 million plus a year on year revenue was 130%, uh, 32 million plus for 38%. These are, um, these are different CVM pipe, uh, use cases, as well as network use cases. And then there were 44%, uh, telco with 76 million subscribers. So I think that there are a lot more use cases that we could talk about, but, but in this case, this is the ones we're looking at again, 183%. This is something that we find consistently, and these figures come from our, uh, our actual end client. So how do we unlock the full potential of this? Well, I think to start is to arrange a meeting and, uh, it would be great to, to, uh, for you to reach out to me or to Anthony. Uh, we're working in conjunction on this and we can set up a, uh, we can set up a meeting and we can go through this initial meeting. And, uh, I think that's the very beginning. Uh, again, you can get additional information from Cloudera website and from the league of data website, Anthony, that's the story. Thank you. >>Oh, that's great. Jeremy, thank you so much. It's a, it's, it's wonderful to go deep. And I know that there are hundreds of use cases being deployed in MTN, um, but great to go deep on one. And like you said, it can, once you get that sort of architecture in place, you can do so many different things. The power of data is tremendous, but it's great to be able to see how you can, how you can track it end to end from collecting the data, processing it, understanding it, and then applying it in a commercial context and bringing actual revenue back into the business. So there is your ROI straightaway. Now you've got a platform that you can transform your business on. That's, that's, it's a tremendous story, Jimmy, and thank you for your partnership. So, um, that's, uh, that's, that's our story for today, like Jamie says, um, please do fleet, uh, feel free to reach out to us. Um, the, the website addresses are there and our contact details, and we'd be delighted to talk to you a little bit more about some of the other use cases, perhaps, um, and maybe about your own business and, uh, and how we might be able to make it, make it perform a little better.

>>Hi today, I'm going to talk about network analytics and what that means for, for telecommunications as we go forward. Um, thinking about, uh, 5g, what the impact that's likely to have on, on network analytics and the data requirement, not just to run the network and to understand the network a little bit better. Um, but also to, to inform the rest of the operation of the telecommunications business. Um, so as we think about where we are in terms of network analytics and what that is over the last 20 years, the telecommunications industry has evolved its management infrastructure, uh, to abstract away from some of the specific technologies in the network. So what do we mean by that? Well, uh, in the, in the initial, uh, telecommunications networks were designed, there were management systems that were built in, um, eventually fault management systems, uh, assurance systems, provisioning systems, and so on were abstracted away. >>So it didn't matter what network technology had, whether it was a Nokia technology or Erickson technology or Huawei technology or whatever it happened to be. You could just look at your fault management system, understand where false, what happened as we got into the last sort of 10, 15 years or so. Telecommunication service providers become became more sophisticated in terms of their approach to data analytics and specifically network analytics, and started asking questions about why and what if in relation to their network performance and network behavior. And so network analytics as a, as a bit of an independent function was born and over time, more and more data began to get loaded into the network analytics function. So today just about every carrier in the world has a network analytics function that deals with vast quantities of data in big data environments that are now being migrated to the cloud. >>As all telecommunications carriers are migrating as many it workloads as possible, um, to the cloud. So what are the things that are happening as we migrate to the cloud that drive, uh, uh, enhancements in use cases and enhancements and scale, uh, in telecommunications network analytics? Well, 5g is the big thing, right? So 5g, uh, it's not just another G in that sense. I mean, in some cases, in some senses, it is 5g means greater bandwidth, lower latency and all those good things. So, you know, we can watch YouTube videos with less interference and, and less sluggish bandwidth and so on and so forth. But 5g is really about the enterprise and enterprise services. Transformation, 5g is more secure, kind of a network, but 5g is also a more pervasive network 5g, a fundamentally different network topology than previous generations. So there's going to be more masts and that means that you can have more pervasive connectivity. >>Uh, so things like IOT and edge applications, autonomous cars, smart cities, these kinds of things, um, are all much better served because you've got more masks that of course means that you're going to have a lot more data as well. And we'll get to that. The second piece is immersive digital services. So with more masks, with more connectivity, with lower latency with higher man, the potential, uh, is, is, is, is immense for services innovation. And we don't know what those services are going to be. We know that technologies like augmented reality, virtual reality, things like this have great potential. Um, but we, we have yet to see where those commercial applications are going to be, but the innovation and the innovation potential for 5g is phenomenal. Um, it certainly means that we're going to have a lot more, uh, edge devices, um, uh, and that again is going to lead to an increase in the amount of data that we have available. >>And then the idea of pervasive connectivity when it comes to smart, smart cities, uh, autonomous, autonomous currents, um, uh, integrated traffic management systems, um, all of this kind of stuff, those of those kind of smart environments thrive where you've got this kind of pervasive connectivity, this persistent, uh, connection to the network. Um, again, that's going to drive, um, um, uh, more innovation. And again, because you've got these new connected devices, you're going to get even more data. So this rise, this exponential rise in data is really what's driving the change in, in network analytics. And there are four major vectors that are driving this increase in data in terms of both volume and in terms of speed. So the first is more physical elements. So we said already that 5g networks are going to have a different apology. 5g networks will have more devices, more and more masks. >>Um, and so with more physical elements in the network, you're going to get more physical data coming off those physical networks. And so that needs to be aggregated and collected and managed and stored and analyzed and understood when, so that we can, um, have a better understanding as to why things happened the way they do, why the network behaves in which they do in, in, in, in ways that it does and why devices that are connected to the network. And ultimately of course, consumers, whether they be enterprises or retail customers, um, behave in the way they do in relation to their interaction within our edge nodes and devices, we're going to have a, uh, an explosion in terms of the number of devices. We've already seen IOT devices with your different kinds of trackers and, uh, and, and sensors that are hanging off the edge of the network, whether it's to make buildings smarter car smarter, or people smarter, um, in, in terms of having the, the, the measurements and the connectivity and all that sort of stuff. >>So the numbers of devices on the agent beyond the age, um, are going to be phenomenal. One of the things that we've been trying to with as an industry over the last few years is where does the telco network end, and where does the enterprise, or even the consumer network begin. You used to be very clear that, you know, the telco network ended at the router. Um, but now it's not, it's not that clear anymore because in the enterprise space, particularly with virtualized networking, which we're going to talk about in a second, um, you start to see end to end network services being deployed. Um, uh, and so are they being those services in some instances are being managed by the service provider themselves, and in some cases by the enterprise client, um, again, the line between where the telco network ends and where the enterprise or the consumer network begins, uh, is not clear. >>Uh, so, so those edge, the, the, the proliferation of devices at the age, um, uh, in terms of, um, you know, what those devices are, what the data yield is and what the policies are, their need to govern those devices, um, in terms of security and privacy, things like that, um, that's all going to be really, really important virtualized services. We just touched on that briefly. One of the big, big trends that's happening right now is not just the shift of it operations onto the cloud, but the shift of the network onto the cloud, the virtualization of network infrastructure, and that has two major impacts. First of all, it means that you've got the agility and all of the scale, um, uh, benefits that you get from migrating workloads to the cloud, the elasticity and the growth and all that sort of stuff. But arguably more importantly for the telco, it means that with a virtualized network infrastructure, you can offer entire networks to enterprise clients. >>So if you're selling to a government department, for example, is looking to stand up a system for certification of, of, you know, export certification, something like that. Um, you can not just sell them the connectivity, but you can sell them the networking and the infrastructure in order to serve that entire end to end application. You could sentence, you could offer them in theory, an entire end-to-end communications network, um, and with 5g network slicing, they can even have their own little piece of the 5g bandwidth that's been allocated against the carrier, um, uh, and, and have a complete end to end environment. So the kinds of services that can be offered by telcos, um, given virtualize network infrastructure, uh, are, are many and varied. And it's a, it's a, it's a, um, uh, an outstanding opportunity. But what it also means is that the number of network elements virtualized in this case is also exploding. >>That means the amount of data that we're getting on, uh, informing us as to how those network elements are behaving, how they're performing, um, uh, is, is, is going to go up as well. And then finally, AI complexity. So on the demand side, um, while historically, uh, um, network analytics, big data, uh, has been, has been driven by, um, returns in terms of data monetization, uh, whether that's through cost avoidance, um, or service assurance, uh, or even revenue generation through data monetization and things like that. AI is transforming telecommunications and every other industry, the potential for autonomous operations, uh, is extremely attractive. And so understanding how the end-to-end telecommunication service delivering delivery infrastructure works, uh, is essential, uh, as a training ground for AI models that can help to automate a huge amount of telecommunications operating, um, processes. So the AI demand for data is just going through the roof. >>And so all of these things combined to mean big data is getting explosive. It is absolutely going through the roof. So that's a huge thing that's happening. So as telecommunications companies around the world are looking at their network analytics infrastructure, which was initially designed for service insurance primarily, um, and how they migrate that to the cloud. These things are impacting on those decisions because you're not just looking at migrating a workload to operate in the cloud that used to work in the, in the data center. Now you're looking at, um, uh, migrating a workload, but also expanding the use cases in that work and bear in mind, many of those, those are going to need to remain on prem. So they'll need to be within a private cloud or at best a hybrid cloud environment in order to satisfy a regulatory jurisdictional requirements. So let's talk about an example. >>So LGU plus is a Finastra fantastic service provider in Korea. Um, huge growth in that business over the last, uh, over the last 10, 15 years or so. Um, and obviously most people will be familiar with LG, the electronics brand, maybe less so with, uh, with LG plus, but they've been doing phenomenal work. And we're the first, uh, business in the world who launch commercial 5g in 2019. And so a huge milestone that they achieved. And at the same time they deploy the network real-time analytics platform or in rep, uh, from a combination of Cloudera and our partner calmer. Now, um, there were a number of things that were driving, uh, the requirement for it, for the, for the analytics platform at the time. Um, clearly the 5g launch was that was the big thing that they had in mind, but there were other things that re so within the 5g launch, um, uh, they were looking for, for visibility of services, um, and service assurance and service quality. >>So, you know, what services have been launched? How are they being taken up? What are the issues that are arising, where are the faults happening? Um, where are the problems? Because clearly when you launch a new service, but then you want to understand and be on top of the issues as they arise. Um, so that was really, really important. The second piece was, and, you know, this is not a new story to any telco in the world, right. But there are silos in operation. Uh, and so, um, taking advantage of, um, or eliminating redundancies through the process, um, of, of digital transformation, it was really important. And so particular, the two silos between wired and the wireless sides of the business come together so that there would be an integrated network management system, um, for, uh, for LGU plus, as they rolled out 5g. So eliminating redundancy and driving cost savings through the, the integration of the silos is really, really important. >>And that's a process and the people thing every bit, as much as it is a systems and a data thing. So, um, another big driver and the fourth one, you know, we've talked a little bit about some of these things, right? 5g brings huge opportunity for enterprise services, innovation. So industry 4.0 digital experience, these kinds of use cases, um, are very important in the south Korean marketing and in the, um, in the business of LGU plus. And so, uh, um, looking at AI and how can you apply AI to network management? Uh, again, there's a number of use cases, really, really exciting use cases that have gone live now, um, in LG plus since, uh, since we did this initial deployment and they're making fantastic strides there, um, big data analytics for users across LGU plus, right? So it's not just for, um, uh, it's not just for the immediate application of 5g or the support or the 5g network. >>Um, but also for other data analysts and data scientists across the LGU plus business network analytics, while primarily it's primary it's primary use case is around network management, um, LGU plus, or, or network analytics, um, has applications across the entire business, right? So, um, you know, for customer churn or next best offer for understanding customer experience and customer behavior really important there for digital advertising, for product innovation, all sorts of different use cases and departments within the business needed access to this information. So collaboration sharing across the network, the real-time network analytics platform, um, it was very important. And then finally, as I mentioned, LG group is much bigger than just LG plus it's because the electronics and other pieces, and they had launched a major group wide digital transformation program in 2019, and still being a part of that was, well, some of them, the problems that they were looking to address. >>Um, so first of all, the integration of wired and wireless data service data sources, and so getting your assurance data sources, your network, data sources, uh, and so on integrated with is really, really important scale was massive for them. Um, you know, they're talking about billions of transactions in under a minute, uh, being processed, um, and hundreds of terabytes per day. So, uh, you know, phenomenal scale, uh, that needed to be available out of the box as it were, um, real time indicators and alarms. And there was lots of KPIs and thresholds set that, you know, w to make, make it to meet certain criteria, certain standards, um, customer specific, real time analysis of 5g, particularly for the launch root cause analysis, an AI based prediction on service, uh, anomalies and service service issues was, was, was a core use case. Um, as I talked about already the provision of service of data services across the organization, and then support for 5g, uh, served the business service, uh, impact, uh, was extremely important. >>So it's not just understand well, you know, that you have an outage in a particular network element, but what is the impact on the business of LGU plus, but also what is the impact on the business of the customer, uh, from an outage or an anomaly or a problem on, on, on the network. So being able to answer those kinds of questions really, really important, too. And as I said, between Cloudera and Kamarck, uh, uh, and LGU plus, uh, really themselves an intrinsic part of the solution, um, uh, this is, this is what we, we ended up building. So a big complicated architecture space. I really don't want to go into too much detail here. Um, uh, you can see these things for yourself, but let me skip through it really quickly. So, first of all, the key data sources, um, you have all of your wireless network information, other data sources. >>This is really important because sometimes you kind of skip over this. There are other systems that are in place like the enterprise data warehouse that needed to be integrated as well, southbound and northbound interfaces. So we get our data from the network and so on, um, and network management applications through file interfaces. CAFCA no fire important technologies. And also the RDBMS systems that, uh, you know, like the enterprise data warehouse that we're able to feed that into the system. And then northbound, um, you know, we spoke already about me making network analytics services available across the enterprise. Um, so, uh, you know, uh, having both the file and the API interface available, um, for other systems and other consumers across the enterprise is very important. Um, lots of stuff going on then in the platform itself to petabytes and persistent storage, um, Cloudera HDFS, 300 nodes for the, the raw data storage, um, uh, and then, uh, could do for real time storage for real-time indicator analysis, alarm generation, um, uh, and other real time, um, processes. >>Uh, so there, that was the, the core of the solution, uh, spark processes for ETL key quality indicators and alarming, um, and also a bunch of work done around, um, data preparation, data generation for transferal to, to third party systems, um, through the northbound interfaces, um, uh, Impala, API queries, um, for real-time systems, uh, there on the right hand side, and then, um, a whole bunch of clustering classification, prediction jobs, um, through the, uh, the, the, the, the ML processes, the machine learning processes, uh, again, another key use case, and we've done a bunch of work on that. And, um, I encourage you to have a look at the Cloudera website for more detail on some of the work that we did here. Um, so this is some pretty cool stuff. Um, and then finally, just the upstream services, some of these there's lots more than, than, than simply these ones, but service assurance is really, really important. So SQM cm and SED grade. So the service quality management customer experience, autonomous controllers, uh, really, really important consumers of, of the, of the real-time analytics platform, uh, and your conventional service assurance, um, functions like faulted performance management. Uh, these things are as much consumers of the information and the network analytics platform as they are providers of data to the network, uh, analytics >>Platform. >>Um, so some of the specific use cases, uh, that, uh, have been, have been stood up and that are delivering value to this day and lots of more episodes, but these are just three that we pulled out. Um, so first of all, um, uh, sort of specific monitoring and customer quality analysis, Karen response. So again, growing from the initial 5g launch and then broadening into broader services, um, understanding where there are the, where there are issues so that when people complaining, when people have an issue, um, that, um, uh, that we can answer the, the concerns of the client, um, in a substantive way, um, uh, AI functions around root cause analysis or understanding why things went wrong when they went wrong. Um, uh, and also making recommendations as to how to avoid those occurrences in the future. Uh, so we know what preventative measures can be taken. Um, and then finally the, uh, the collaboration function across LGU plus extremely important and continues to be important to this day where data is shared throughout the enterprise, through the API Lira through file interfaces and other things, and through interface integrations with, uh, with upstream systems. >>So, um, that's kind of the, the, uh, real quick run through of LGU plus the numbers are just stave staggering. Um, you know, we've seen, uh, upwards of a billion transactions in under 40 seconds being, um, uh, being tested. Um, and, and we've gone beyond those thresholds now, already, um, and we're started and, and, and, and this isn't just a theoretical sort of a benchmarking test or something like that. We're seeing these kinds of volumes of data and not too far down the track. So, um, with those things that I mentioned earlier with the proliferation of, of, um, of network infrastructure, uh, in the 5g context with virtualized elements, with all of these other bits and pieces are driving massive volumes of data towards the, uh, the, the, the network analytics platform. So phenomenal scale. Um, this is just one example we work with, with service providers all over the world is over 80% of the top 100 telecommunication service providers run on Cloudera. >>They use Cloudera in the network, and we're seeing those customers, all migrating legacy cloud platforms now onto CDP onto the Cloudera data platform. Um, they're increasing the, the, the jobs that they do. So it's not just warehousing, not just ingestion ETL, and moving into things like machine learning. Um, and also looking at new data sources from places like NWTF the network data analytics function in 5g, or the management and orchestration layer in, in software defined networks, network, function, virtualization. So, you know, new use cases coming in all the time, new data sources coming in all the time growth in, in, in, in the application scope from, as we say, from edge to AI. Um, and so it's, it's really exciting to see how the, the, the, the footprint is growing and how, uh, the applications in telecommunications are really making a difference in, in facilitating, um, network transformation. And that's covering that. That's me covered for today. I hope you found that helpful, um, by all means, please reach out, uh, there's a couple of links here. You can follow me on Twitter. You can connect to the telecommunications page, reach out to me directly at Cloudera. I'd love to answer your questions, um, uh, and, uh, and talk to you about how big data is transforming networks, uh, and how network transformation is, is accelerating telcos, uh, throughout >>Jamie Sharath with Liga data, I'm primarily on the delivery side of the house, but I also support our new business teams. I'd like to spend a minute really just kind of telling you about the legal data, where basically a Silicon valley startup, uh, started in 2014, and, uh, our lead iron, our executive team, basically where the data officers at Yahoo before this, uh, we provide managed data services, and we provide products that are focused on telcos. So we have some experience in non telco industry, but our focus for the last seven years or so is specifically on telco. So again, something over 200 employees, we have a global presence in north America, middle east Africa, Asia, and Europe. And we have folks in all of those places, uh, I'd like to call your attention to the, uh, the middle really of the screen there. So here is where we have done some partnership with Cloudera. >>So if you look at that and you can see we're in Holland and Jamaica, and then a lot to throughout Africa as well. Now, the data fabric is the product that we're talking about. And the data fabric is basically a big data type of data warehouse with a lot of additional functionality involved. The data fabric is comprised of, uh, some something called a flare, which we'll talk about in a minute below there, and then the Cloudera data platform underneath. So this is how we're partnering together. We, uh, we, we have this tool and it's, uh, it's functioning and delivering in something over 10 up. So flare now, flare is a piece of that legal data IP. The rest is there. And what flare does is that basically pulls in data, integrates it to an event streaming platform. It's, uh, it is the engine behind the data fabric. >>Uh, it's also a decisioning platform. So in real time, we're able to pull in data. We're able to run analytics on it, and we're able to alert are, do whatever is needed in a real-time basis. Of course, a lot of clients at this point are still sending data in batch. So it handles that as well, but we call that a CA picture Sanchez. Now Sacho is a very interesting app. It's an AI analytics app for executives. What it is is it runs on your mobile phone. It ties into your data. Now this could be the data fabric, but it couldn't be a standalone product. And basically it allows you to ask, you know, human type questions to say, how are my gross ads last week? How are they comparing against same time last week before that? And even the same time 60 days ago. So as an executive or as an analyst, I can pull it up and I can look at it instantly in a meeting or anywhere else without having to think about queries or anything like that. >>So that's pretty much for us at legal data, not really to set the context of where we are. So this is a traditional telco environments. So you see the systems of record, you see the cloud, you see OSS and BSS data. So one of the things that the next step above which calls we call the system of intelligence of the data fabric does, is it mergers that BSS and OSS data. So the longer we have any silos or anything that's separated, it's all coming into one area to allow business, to go in or allow data scientists go in and do that. So if you look at the bottom line, excuse me, of the, uh, of the system of intelligence, you can see that flare is the tools that pulls in the data. So it provides even streaming capabilities. It preserves entity states, so that you can go back and look at it state at any time. >>It does stream analytics that is as the data is coming in, it can perform analytics on it. And it also allows real-time decisioning. So that's something that, uh, that's something that business users can go in and create a system of, uh, if them's, it looks very much like the graph database, where you can create a product that will allow the user to be notified if a certain condition happens. So for instance, a bundle, so a real-time offer or user is succinct to run out of is ongoing, and an offer can be sent to him right on the fly. And that's set up by the business user as opposed to programmers, uh, data infrastructure. So the fabric has really three areas. That data is persistent, obviously there's the data lake. So the data lake stores that level of granularity that is very deep years and years of history, data, scientists like that, uh, and, uh, you know, for a historical record keeping and requirements from the government, that data would be stored there. >>Then there's also something we call the business semantics layer and the business semantics layer contains something over 650 specific telco KPIs. These are initially from PM forum, but they also are included in, uh, various, uh, uh, mobile operators that we've delivered at. And we've, we've grown that. So that's there for business data lake is there for data scientists, analytical stores, uh, they can be used for many different reasons. There are a lot of times RDBMS is, are still there. So these, this, this basically platform, this cloud they're a platform can tie into analytical data stores as well via flair access and reporting. So graphic visualizations, API APIs are a very key part of it. A third-party query tools, any kind of grid tools can be used. And those are the, of course, the, uh, the ones that are highly optimized and allow, you know, search of billions of records. >>And then if you look at the top, it's the systems of engagement, then you might vote this use cases. So teleco reporting, hundreds of KPIs that are, that are generated for users, segmentation, basically micro to macro segmentation, segmentation will play a key role in a use case. We talked about in a minute monetization. So this helps teleco providers monetize their specific data, but monetize it in. Okay, how to, how do they make money off of it, but also how might you leverage this data to engage with another client? So for instance, in some where it's allowed a DPI is used, and the fabric tracks exactly where each person goes each, uh, we call it a subscriber, goes within his, uh, um, uh, internet browsing on the, on the four or 5g. And, uh, the, all that data is stored. Uh, whereas you can tell a lot of things where the segment, the profile that's being used and, you know, what are they propensity to buy? Do they spend a lot of time on the Coca-Cola page? There are buyers out there that find that information very valuable, and then there's signs of, and we spoke briefly about Sanchez before that sits on top of the fabric or it's it's alone. >>So, so the story really that we want to tell is, is one, this is, this is one case out of it. This is a CVM type of case. So there was a mobile operator out there that was really offering, you know, packages, whether it's a bundle or whether it's a particular tool to subscribers, they, they were offering kind of an abroad approach that it was not very focused. It was not depending on the segments that were created around the profiling earlier, uh, the subscriber usage was somewhat dated and this was causing a lot of those. A lot of those offers to be just basically not taken and, and not, not, uh, audited. Uh, there was limited segmentation capabilities really before the, uh, before the, uh, fabric came in. Now, one of the key things about the fabric is when you start building segments, you can build that history. >>So all of that data stored in the data lake can be used in terms of segmentation. So what did we do about that? The, the, the envy and, oh, the challenge this, uh, we basically put the data fabric in and the data fabric was running Cloudera data platform and that, uh, and that's how we team up. Uh, we facilitated the ability to personalize campaign. So what that means is, uh, the segments that were built and that user fell within that segment, we knew exactly what his behavior most likely was. So those recommendations, those offers could be created then, and we enable this in real time. So real-time ability to even go out to the CRM system and gather further information about that. All of these tools, again, we're running on top of the Cloudera data platform, uh, what was the outcome? Willie, uh, outcome was that there was a much more precise offer given to the client that is, that was accepted, no increase in cross sell and upsell subscriber retention. >>Uh, our clients came back to us and pointed out that, uh, it was 183% year on year revenue increase. Uh, so this is a, this is probably one of the key use cases. Now, one thing to really mention is there are hundreds and hundreds of use cases running on the fabric. And I would even say thousands. A lot of those have been migrated. So when the fabric is deployed, when they bring the Cloudera and the legal data solution in there's generally a legacy system that has many use cases. So many of those were, were migrated virtually all of them in pen, on put on the cloud. Uh, another issue is that new use cases are enabled again. So when you get this level of granularity and when you have campaigns that can now base their offers on years of history, as opposed to 30 days of history, the campaigns campaign management response systems, uh, are, are, uh, are enabled quite a bit to do all, uh, to be precise in their offers. Okay. >>Okay. So this is a technical slide. Uh, one of the things that we normally do when we're, when we're out there talking to folks, is we talk and give an overview and that last little while, and then we give a deep technical dive on all aspects of it. So sometimes that deep dive can go a couple of hours. I'm going to do this slide and a couple of minutes. So if you look at it, you can see over on the left, this is the, uh, the sources of the data. And they go through this tool called flare that runs on the cloud. They're a data platform, uh, that can either be via cues or real-time cues, or it can be via a landing zone, or it can be a data extraction. You can take a look at the data quality that's there. So those are built in one of the things that flare does is it has out of the box ability to ingest data sources and to apply the data quality and validation for telco type sources. >>But one of the reasons this is fast to market is because throughout those 10 or 12, uh, opcos that we've done with Cloudera, where we have already built models, so models for CCN, for air for, for most mediation systems. So there's not going to be a type of, uh, input that we haven't already seen are very rarely. So that actually speeds up deployment very quickly. Then a player does the transformations, the, uh, the metrics, continuous learning, we call it continuous decisioning, uh, API access. Uh, we, uh, you know, for, for faster response, we use distributed cash. I'm not going to go too deeply in there, but the layer in the business semantics layer again, are, are sitting on top of the Cloudera data platform. You see the Kafka CLU, uh, Q1, the right as well. >>And all of that, we're calling the fabric. So the fabric is Cloudera data platform and the cloud and flair and all of this runs together. And, and by the way, there've been many, many, many, many hundreds of hours testing flare with Cloudera and, uh, and the whole process, the results, what are the results? Well, uh, there are, there are four I'm going to talk about, uh, we saw the one for the, it was called my pocket pocket, but it's a CDM type, a use case. Uh, the subscribers of that mobile operator were 14 million plus there was a use case for 24 million plus that a year on year revenue was 130%, uh, 32 million plus for 38%. These are, um, these are different CVM pipe, uh, use cases, as well as network use cases. And then there were 44%, uh, telco with 76 million subscribers. So I think that there are a lot more use cases that we could talk about, but, but in this case, this is the ones we're looking at, uh, again, 183%. This is something that we find consistently. And these figures come from our, uh, our actual end client. How do we unlock the full potential of this? Well, I think to start is to arrange a meeting and, uh, it would be great to, to, uh, for you to reach out to me or to Anthony. Uh, we're working at the junction on this, and we can set up a, uh, we can set up a meeting and we can go through this initial meeting. And, uh, I think that's the very beginning. Uh, again, you can get additional information from Cloudera website and from the league of data website, Anthony, that's the story. Thank you. >>No, that's great. Jeremy, thank you so much. It's a, it's, it's wonderful to go deep. And I know that there are hundreds of use cases being deployed in MTN, um, but great to go deep on one. And like you said, it can, once you get that sort of architecture in place, you can do so many different things. The power of data is tremendous, but it's great to be able to see how you can, how you can track it end to end from collecting the data, processing it, understanding it, and then applying it in a commercial context and bringing actual revenue back into the business. So there is your ROI straight away. Now you've got a platform that you can transform your business on. That's, that's, it's a tremendous story, Jamie, and thank you for your part. Sure. Um, that's a, that's, that's our story for today. Like Jamie says, um, please do flee, uh, feel free to reach out to us. Um, the, the website addresses are there and our contact details, and we'd be delighted to talk to you a little bit more about some of the other use cases, perhaps, um, and maybe about your own business and, uh, and how we might be able to make it, make it perform a little better. So thank you.

(gentle music) >> Okay, thank you Adam. Thank you everyone for joining us on the main stage here, folks watching, appreciate it. I'm John Furrier, Dave Vellante co-hosts of theCube. We're here in the main stage to talk to the two main players of Totogi, Danielle Royston, CEO as of today, the big news. Congratulations. >> Danielle: Yeah. Thank you. >> And Robin Langdon the CTO, Totogi. >> Robin: Thanks. So big news, CEO news today and $100 million investment. Every wants to know where's all the action? Why is this so popular right now? (Danielle chuckles) What's going on? Give us the quick update. >> Yeah, I met the Totogi guys and they have this great product I was really excited about. They're focused purely on telco software and bringing, coupling that with the Public Cloud, which is everything that I talk about, what I've been about for so long. And I really wanted to give them enough funding so they could focus on building great products. A lot of times, telcos, startups, you know they try to get a quick win. They kind of chase the big guys and I really wanted to make sure they were focused on building a great product. #2, I really wanted to show the industry, they had the funding they needed to be a real player. This wasn't like $5 million or a couple million dollars, so that was really important. And then #3, I want to make sure that we could hire great talent and you need money for compensation. And so $100 million it is. >> $100 million is a lot of fresh fat financing as they say. I got to ask you, what's different? Because I've been researching on the refactoring aspect of with the Cloud, obviously public cloud with AWS, a big deal. What's different about the charging aspect of this? >> Yeah I mean, charging hasn't been exciting, maybe ever. I mean, it's kind of like this really sort of sleepy area, but I think what the Totogi guys are doing is they're really coupling the idea of charging and network data to bring hyper-personalization to subscribers. And I think that's where it changes from being a charging engine to become an engagement engine. Telcos know more about us than Google, which is kind of crazy to think about it. They know when we wake up, they know what apps we use. If we call or text, if we game or stream and it's time to start using that data to drive a better experience to us. And I think to Totogi is enabling that. I'm super excited to do that. >> So Robin, I wonder if you could talk about that a little bit. I mean, maybe we get into the plumbing and I don't want to go too deep in it, but I think it's important because we've seen this movie before where people take their on-prem stacks, they wrap it in containers and they shove it into the Public Cloud and they say, "Hey, we're cloud too." If reading a press release, you guys are taking advantage of things like Amazon Nitro of course, but also Graviton and Graviton2 and eventually 3, which is the underlying capabilities, give you a cloud native advantage. Can you explain that a little bit? >> Yeah, absolutely. I mean, we wanted to build this in the Cloud using all of those great cloud innovations. So Graviton2, DynamoDB and using their infrastructure, just allowing us to be able to scale out. These all available to us to use and essentially free for us to use. And it's great, so as you say, we're not shoehorning something in that's decade's old technology, wrapping it in some kind of container and pushing it in. Which is just then, you just can't use any of those great innovations. >> And you've selected DynamoDB as the database. Okay, that's fine. We don't have to get so much into why, but maybe you could explain the advantage because I saw some benchmark numbers which were, like an order of magnitude greater than the competition, like share with us, why? How you were able to get there? And maybe share those numbers. >> Yeah, no, we do. So we just launched our benchmark. So, a million transactions per second. So we just blew away everyone else out there. And that's really because we could take advantage of all that great AWS technology in there and the database side we're using DynamoDB, where we had a huge debate about using what kind of database to go and use? There's a lot of people out there probably get very religious about the kind of database technology that you should be using. And whether it should it be SQL in-memory object database type technology, but really a single table design, gives you that true scalability. You can just horizontally scale that easily, across the whole planet. >> You know, Danielle. Again, I said that we've seen this movie before. There are a lot of parallels in telco with the enterprise. And if you look at enterprise SAS pricing, a lot of it is very static, kind of lock you in, per seat pricing, kind of an old model. And you're seeing a lot of the modern SAS companies who are emerging with a consumption pricing models. How are you guys thinking about pricing? >> Yeah, I don't know of any other company in telco that's starting to price by usage. And that is a very standard offering with the cloud providers, right? Google we know, Amazon, all those guys have a price by the API, price by the transaction. So we're really excited to offer that to telcos. They've been asking for it for awhile, right? Pay for what you need, when you need it, by the use. And so we're really excited to offer that, but I think what's really cool is the idea of a free tier, right? And so I think it's smaller telcos have a trade-off to make, whether, am I going to buy the best technology and pay through the nose and maybe at an unaffordable level, or do I compromise and buy something more affordable, but not as great. And what's so great about Totogi, it's the same product just priced for what you need. And so I think a CSP it'll, below 250,000 subscribers should be able to use the Totogi absolutely for free. And that is, and it's the same product that the big guy would get. So it's not a junior version or scaled back. And so I think that's really exciting. I think we're the only ones that do it. So here we go. >> Love the freemium model. So Robin, maybe you could explain why that's so much, so important in the charging space, because you've got a lot of different options that you want to configure for the consumer. >> Yeah. >> Maybe you could talk about sort of how the old world does that, the old guard and how long it takes and how you're approaching this. >> Yeah so it's, I mean traditionally, charging design, there's as you say, there's lots of different pricing leavers you want to be able to move and change to charge different people. And these systems, even if they say they're configurable, if they normally turn into an IT project where it takes weeks, months, even years to build out the system, you know, marketing can't just go in there and configure the dials and push out your new plans and tariffs. They have to go and create a requirement specification. They hand it down to IT. Those guys go and create a big change project. And by the time they're finished, the market's moved on. They're on to their next plan, their next tariff to go and build. So we wanted to create something that was truly configurable from a marketing standpoint. You know, user-friendly, they can go in there, configure it and be live in minutes, not even days or weeks. >> No, IT necessary. >> Robin: No IT necessary. >> So you know, I've been thinking about, John and I talk about this all the time, It's that there's a data play here. And what I think you're doing is actually building a data product. I think there's a new metric emerging in the industry, which is how long does it take me to go from idea to monetization with a data product. And that's what this is. This is a data product >> Yeah. >> for your customers. >> Absolutely, what Robin was talking about is totally the way the industry works. It's weeks before you have an idea and get it out to the market. And like Robin was mentioning, the market's changed by the time you get it out there, the data's stale. And so we researched every single plan in the world from every single CSP. There is about 30,000 plans in the world, right? The bigger you are, the more plans you have. On average, a tier one telco has 40 to 50 plans. And so how many offers, I mean think about, that's how many phones to buy, plans to buy. And so we're like, let's get some insight on the plans. Let's drive it into a standardization, right? Let's make them, which ones work, which ones don't. And that's, I think you're right. I think it's a data play and putting the power back into the marketer's hands and not with IT. >> So there's a lot of data on-prem. Explain why I can't do this with my on-prem data. >> Oh, well today that, I mean, sorry if you want to jump in. Feel free to jump in, right. But today, the products are designed in a way where they're, perpetually licensed, by the subscriber, rigid systems, not API based. I mean, there might be an API, but you got to pay through the nose to use it or you got to use the provider's people to code against it. They're inflexible. They were written when voice was the primary revenue driver, not data, right? And so they've been shoehorned, right? Like Robin was saying, shoehorned to be able to move into the world that we are now. I mean, when the iPhone came about that introduced apps and data went through the roof and the systems were written for voice, not written for data. >> And that's a good point, if you think about the telco industry, it seems like it could be a glacier that just needs to just break and just like, just get modern because we all have phones. We have apps. We can delete them. And the billing plans, like either nonexistent or it has to be all free. >> Well I mean, I'll ask you. Do you know what your billing plan is? Do you know how much data you use on a monthly basis? No one knows. >> I have no clue. >> A lot. >> No one. And so what you do is you buy unlimited. >> Dave: Right. >> You overpay. And so what we're seeing in the plans is that if you actually knew how much you used, you would be able to maybe pay less, which I remember the telcos are not excited to hear that message, but it's a win for the subscriber. And if you could >> I mean it's only >> accurately predict that. >> get lower and lower. I having a conversation last night at dinner with industry analysts, we're talking about a vehicle e-commerce, commerce in your car as you're driving. You can get that kind of with a 5G. The trend is transactions everywhere, ad-hoc, ephemeral... >> Yeah. >> The new apps are going to demand this kind of subscriber billing. >> Yeah >> Do people get this? Are you guys the only ones kind of like on this? >> No I think people have been talking about it for years. I think there's vendors out there that have been trying to offer this idea of like, build your own plan and all that other stuff but I think it's more than just minutes, text and data. It's starting to really understand what subscribers are using, right? Are you a football fan? Are you a golf fan? Are you a shopper? Are you a concert goer? And couple that with how you use your phone and putting out offers that are really exciting to subscribers so that we love our telco. Like we should be loving our telco. And I don't... I don't know that people talk >> They saved us >> about loving their telco. >> from the pandemic >> They saved us during the pandemic. The internet didn't crash, we got our zoom meetings. We got everything going on. What's the technical issue on solving these problems? Is it just legacy? Is it just mindset? Robin, what's your take on that? >> I'll keep talking as long as Robin will let me. (Daniel laughing) >> So the big technical issues, you're trying to build in this flexibility so that you can have, we don't know what people are going to configure in the future. It's minutes and text messages are given away for free. They're unlimited. Data is where it's at, about charging for apps and about using all that data in the network the telcos have, which is extremely valuable and there's a wealth of information in there that can be used to be monetized and push that out. And they need a charging system on top that can manage that and we have the flexibility that you don't have to go off and then start creating programs and IT projects that are going to do that. >> Well it's funny Danielle, you say that the telcos might not like that, right? 'Cause you might pay less. But in fact, that is the kind of on-prem mindset because when you have a fixed resource, you say, okay, don't use too much because we have to buy more. Or you overbuy to your point. The cloud mindset is, I'll try it. I'll try some more, I'll try some more. I'm aligning it with business value. Oh, I'm making money. Oh, great. I'm going to keep buying more. And it's very clear. It's transparent where the business value is. So my question is when you think about your charging engine and all this data conversation, is there more than just a charging engine in this platform? >> Well, I think just going back to what Robin was talking about. I think what Totogi is doing differently is by building it on the Public Cloud gives you virtually unlimited resources, right? In a couple of different directions, certainly hardware and capacity and scalability and all those other things, right? But also as Amazon is putting out more and more product, when you build it in this new way, you can take advantage of these new services very, very easily. And that is a different mindset. It's a different way to deploy applications. And I think that's what makes Totogi really different. You couldn't build Totogi on-premise because you need the infinite scalability. You need the machine learning, you need the AI of Amazon, which they have been investing in for decades, if they now charge you by the API call. And you get to use it like you were saying. Just give it a try, don't like it, stop. And it's just a completely different way of thinking, yeah. >> If I have to ask you a question about the Public Cloud, because the theme here in Cloud City is the Public Cloud is driving innovation, which is also includes disruption. And the new brands are coming in, old brands are either reinventing themselves or falling away. What is the Public Cloud truly enabling? Is it the scale? Is it the data? Is it the refactoring capability? What is the real driver of the Public Cloud innovation? >> I think the insight that CSPs are going to have is what Jamie Dimon had in banking. Like I think he was pretty famously saying, "I'm never going to use the Public Cloud. Our data is too precious, you know, regulations and all that stuff." But I think the insight they're going to have, and I hopefully, I do a keynote and I mentioned this, which is feature velocity. The ability to put out features in a day or two. Our feature velocity in telco is months. Months, months. >> Seriously? >> Yeah, sometimes years. It's just so slow between big iterations of new capability and to be able to put out new features in minutes or days and be able to outmaneuver your competition is unheard of. So the CSPs that starts to get this, it's going to be a real big get, and then they're going to start to.. (Danielle makes swishing sound) >> We just interviewed (Dave speaking indistinctly) a venture capitalist, Dave and I last month. And he's a big investor in Snowflake, on the big deals. He said that the new strategy that's working is you go to be agile with feature acceleration. We just talked about this at lunch and you get data. And you can dismantle the bad features quickly and double down >> Yup. >> on the winners. >> Ones that are working. So what used to be feature creep now is a benefit if you play it right? >> Danielle: It's feature experimentation. >> That's essentially what you- >> It's experimentation, right? And you're like, that one worked, this one didn't, kill that one, double down on this one, go faster and faster and so feature experimentation, which you can't do in telco, because every time we ask for a feature from your current vendor, it's hundreds of thousands, if not millions of dollars. So you don't experiment. And so yeah- >> You can make features disposable. >> Correct. And I think that we just discovered that on this stage just now. (group chuckling) >> Hey look at this. Digital revolution, DR. Telco DR. >> Yeah. >> Great to have you guys. >> This is super awesome. Thanks so much. >> You guys are amazing. Congratulations. And we're looking forward to the more innovation stories again, get out there, get the momentum. Great stuff. >> Danielle: It's going to be great. >> And awesome. >> Feature experimentation. >> Yeah. >> Hashtag. >> And Dave and I are going to head back over to our Cube set here, here on the main stage. We'll toss it back to the Adam in the studio. Adam, back to you and take it from here.

>>from the cube studios in Palo alto >>in boston >>connecting with thought leaders all around the >>world. This >>is a cute conversation. Hello and welcome to today's session of the AWS Global Public sector Partner Awards. I'm your host Natalie ehrlich. Today we're going to focus on the following award for best partner transformation. I'm pleased to introduce our guests, josh door smith, vice president of public sector at Effectual and jeremy Yates, deputy technology architect at jenny May. Welcome gentlemen so glad to have you on our show. >>Hi there. Very nice to be here. Thank you so much for having me >>terrific. Well josh, I'd like to start with you. How can companies leverage cloud native solutions to deliver higher quality services? >>So Natalie, that's a great question. And in the public sector and our our government customers, we run into this all the time. It's kind of our bread and butter. What what they can do is the first thing they need to be aware of is you don't have to be afraid of the cloud as some very obscure technology that is just emerging. It's been out for 10, 11 years now, customers across government space are using it lock stock and barrel to do everything from just managing simple applications, simple websites all the way through hosting their entire infrastructure, both in production and for disaster recovery purposes as well. So the first thing to note is just don't be afraid of the cloud. Um secondly, it's, it's imperative that they select the right partner who is able to kind of be there Sherpa to go into however far they want to dip their toe into the, into the proverbial cloud waters. Um to select somebody who knows whatever it is that they need to go do. So if they want to go Aws as we are talking about today, pick a partner who has the right experience, past performance designations and competencies with the cloud that they're interested in. >>Terrific. Well, you know, Jeremy, I'd love to move to you. What does modern modernization mean to jenny May? >>Sure, Thanks Natalie, great to be here. Thanks josh as well, you know. So for jenny May, modernization is really, it's not just technology is holistic across the organization. So that includes things like the business, um not just you know, the the I. T. Division. So we're looking at the various things to modernize like our culture and structural changes within the organization. Um moving to implement some, some proven practices like def sec ops and continuous integration and continuous delivery or deployment. Uh and then, you know, our overall overarching goal is to give the best and most secure technology to the business that we can to meet the Jeannie Mai mission and the needs of our customers >>terrific. Well josh, how is Effectual planning to support jenny Maes modernization plans? >>So we have been supporting jenny May for about 14 months now. Uh and back in september of last year, we rewarded a co prime 10 year contract for Jeannie Mai to do exactly that. It's to provide all things cloud to Jeannie Mai for 10 years on AWS and that's including reselling AWS. That's including providing all sorts of professional services to them. And it's, it's providing some third party software applications to help them support their applications themselves. So what Effectual is doing is kind of a threefold. We are supporting the modernization of their process, which jeremy mentioned a moment ago and that includes in stan shih ating a cloud center of Excellence for jenny May, which enables them to modernize the way they do cloud governance while they're modernizing their technology stack. We're also providing a very expert team of cloud architects and Dempsey cops engineers to be able to, to design the Jeannie Mai environment, collaborating with our co prime uh to ensure that it meets the security requirements, the compliance requirements that jerry mentions. Uh, Jeannie Mai is a federal entity, but it also has to adhere to all the finance industry uh compliance requirements as well. So very strenuous from that perspective. And then the third thing that we're doing to help them kind of along their modernization journey is in stan shih aging infrastructure as code. So in the cloud, rather than building everything in the AWS management console, we script everything to build it automatically, so it improves consistency, it improves the customer experience regardless of which resource is working on it. And it improves disaster recovery capability as well. And also, just quite frankly, the speed by which they can actually deploy things. >>And jeremy, how is this transition helping your security really enhancing it now? >>Uh From a security perspective we're implementing a number of various tools um both, you know, a W. S based as well as other software that josh mentioned. Um So we're able to utilize those in a more scalable manner than we could previously in the traditional data center. Um we've got a number of things such as we're looking at multiple vulnerability management products like 10 of Ohio and Wallace. Um we're using uh tools such as Centra fi for our our pam or privileged access management capabilities. Um Splunk a pretty industry standard. Um software for log and data correlation and analysis um will also be using that for some system and application monitoring. Um as well as uh the Mcafee envision product for endpoint and other cloud service security. So being able to pull all those in in a more scalable and more cost efficient way as well from cloud based services. Uh, it's really helped us be able to get those services and integrate them together in a way that, you know, we may not previously been able to. >>Yeah, terrific. Well, josh, let's move back to you and talk further about compliance. You know, any insight here, how Effectual is building a modern cloud infrastructure to integrate AWS services with third party tools to really achieve compliance with the government requirements. Just any further insight on that >>front? That's a great question. Natalie and I'm gonna tag team with Jeremy on this one if you don't mind, but I'll start off so jenny may obviously I mentioned earlier has federal requirements and financial requirements so focused right now on on those federal aspects. Um, so the tools that Jeremy mentioned a moment ago, we are integrating all of them with a W. S native meaning all of the way we do log aggregation in the various tools within AWS cloudwatch cloud trail. All of those things were implementing an AWS native, integrating them with Splunk to aggregate all of that information. But then one of the key requirements that's coming up with the federal government in the very near future is tick three dot or trusted internet connection. Basically in the first iteration a decade or so ago, the government wanted to limit the amount of points of presence that they have with the public facing internet fast forward several versions to today and they're pushing that that onus back on the various entities like jenny May and like hud, which Jeannie Mai is a part of but they still want to have that kind of central log repository to where all of the, all of the security logs and vulnerability logs and things like that. Get shipped to a central repository and that will be part of DHS. So what effectual has done in partnership with jenny May is create a, a W. S native solution leveraging some of those third party tools that we mentioned earlier to get all of those logs aggregated in a central repository for Ginny MaE to inspect ingest and take action from. But then also provide the mechanism to send that to DHS to do that and correlate that information with everything coming in from feeds across the government. Now that's not required just yet. But we're future proofing jenny Maes infrastructure in order to be able to facilitate adherence to those requirements when it becomes uh required. Um, and so jeremy, I'll pass it over to you to talk a little bit further about that because I know that's one of the things that's near and dear to your sister's heart as well as jenny may overall. >>Yeah, absolutely. Thanks josh. Um, so yeah, we, as you mentioned, we have implemented um, uh, sort of a hybrid tech model right now, um, to to handle compliance on that front. Um, so we're still using a, you know, some services from the legacy or our existing T two dot x models. That that josh was mentioning things such as m tips, um, uh, the Einstein sensors, etcetera. But we're also implementing that take 30 architecture on our own. As josh mentioned that that will allow us to sort of future proof and and seamlessly really transitioned to once we make that decision or guidance comes out or, you know, mandates or such. Um, so that effort is good to future proof house from a compliance perspective. Um, also, you know, the tools that I mentioned, uh, josh reiterated, those are extremely important to our our security and compliance right. Being able to ensure, you know, the integrity and the confidentiality of of our systems and our data is extremely important. Not both, not just both on the r not only on the government side, but as josh mentioned, the finance side as well. >>Terrific. Well, I'd love to get your insight to on AWS workspaces. Um, if either one of you would like to jump in on this question, how did they empower the jenny May team to work remotely through this pandemic? >>That's a great question. I guess I'll start and then we'll throw it to jeremy. Um, so obviously uh effectual started working with jenny May about three weeks after the pandemic formally started. So perfect timing for any new technology initiative. But anyway, we, we started talking with Jeremy and with his leadership team about what is required to actually facilitate and enable our team as well as the government resources and the other contractors working for jenny May to be able to leverage the new cloud environment that we were building and the very obvious solution was to implement a virtual desktop infrastructure uh type solution. And obviously Jeannie Mai had gone all in on amazon web services, so it became the national natural fit to look first at AWS workspaces. Um, so we have implemented that solution. There are now hundreds of jenny May and jenny make contractor resources that have a WS workspaces functioning in the GovCloud regions today and that's a very novel approach to how to facilitate and enable not only our team who is actually configuring the infrastructure, but all the application developers, the security folks and the leadership on the jenny may side to be able to access, review, inspect, check log etcetera, through this remote capability. It's interesting to note that Jeannie Mai has been entirely remote since the pandemic initiated. Jeremy's coming to us from, from west Virginia today, I'm coming to us from national harbor Maryland And we are operating totally remotely with a team of 60 folks about supporting this specific initiative for the cloud, not to mention the hundreds that are supporting the applications that Jamie runs to do its day to day business. So jeremy, if you wouldn't mind talking about that day to day business that jenny may has and, and kind of what the, the mission statement of Jeannie Mai is and how us enabling these workspaces uh facilitates that mission >>or you know, so the part of the overall mission of jenny Maes to, to ensure affordable housing is, is made available to uh, the american public. Um that's hud and, and jenny may as part of that and we provide um mortgage backed securities to help enable that. Um, so we back a lot of V A. Loans, um, F H A, those sort of loans, um, workspaces has been great in that manner from a technology perspective, I think because as you mentioned, josh, it's really eliminated the need for on premise infrastructure, right? We can be geographically dispersed, We can be mobile, um, whether we're from the east coast or west coast, we can access our environment securely. Uh, and then we can, you know, administer and operate and maintain the technology that the business needs to, to fulfill the mission. Um, and because we're able to do that quickly and securely and effectively, that's really helpful for the business >>Terrific. And um, you know, I'd like to shift gears a bit and uh you know, discuss what you're looking ahead toward. What is your vision for 2021? How do you see this partnership evolving? >>Yeah, you >>Take that 1/1. >>Sure. Yeah. Um you know, definitely some of the things we look forward to in 2021 as we evolve here is we're going to continue our cloud journey um you know, through practices like Deb said cops, you realize that uh that journey has never done. It's always a continual improvement process. It's a loop to continually work towards um a few specific things or at least one specific thing that we're looking forward to in the future, as josh mentioned earlier was our arctic three Oh Initiative. Um, so with that we think will be future proofed. Um as there's been a lot of um a lot of recent cyber security activity and things like that, that's going to create um opportunities I think for the government and Jeannie Mai is really looking forward to to leading in that area. >>Mhm and josh, can you weigh in quickly on that? >>Absolutely. Uh First and foremost we're very much looking forward to receiving authority to operate with our production environment. We have been preparing for that for this last year plus. Uh but later on this summer we will achieve that 80 oh status. And we look forward to starting to migrate the applications into production for jenny May. And then for future proof, it's as jerry jerry mentioned, it's a journey and we're looking forward to cloud optimizing all of their applications to ensure that they're spending the right money in the right places uh and and ensuring that they're not spending over on any of the one given area. So we're very excited to optimize and then see what the technology that we're being able to provide to them will bring to them from an idea and a conceptual future for jenny may. >>Well thank you both so very much for your insights. It's been a really fantastic interview. Our guests josh duggar smith as well as jeremy Gates. Really appreciate it. >>Thank you very much. >>Thank you so much. >>Terrific. Well, I'm your host for the cube Natalie or like to stay tuned for more coverage. Thanks so much for watching.

>>From around the globe. It's the cube with digital coverage of IBM. Think 20, 21 brought to you by IBM, >>Welcome back to IBM. Think at 2021, the virtual edition, my name is Dave Volante and you're watching the cubes continuous coverage of think 21. And right now we're going to talk about banking and the post isolation economy. I'm very pleased to welcome our next guests. Look at Wigley's the general manager global banking financial markets at IBM and John diagonal is the global CTO and vice president and distinguished engineer for banking and financial services. Gentlemen, welcome to the cube. That's my pleasure. Look at this current economic upheaval, it's quite a bit different from the last one. Isn't it? I mean, liquidity doesn't seem to be a problem for most banks these days. I mean, if anything, they're releasing loan loss reserves that they didn't need. What's from your perspective, what's the state of banking today and hopefully as we exit this pandemic soon. Okay. >>So, so Dave, I think, like you say, it's a, it's a, it's a state in a picture that, uh, in a significantly different from what people were expecting. And I, and I think some way, in some ways you're seeing the benefits of a number of the regulations that were put into, into place after the, you know, the financial crisis last time round, right? And therefore this time, you know, a health crisis did not become a financial crisis because I think the banks were in better shape. And also, you know, governments clearly have put worldwide a lot of liquidity into the, into the system. Um, I think if you look at it though, um, maybe two or three things ready to call out, firstly, there's a, there's a massive regional variation. So if you look at the U S banking industry, uh, it's extremely buoyant and I'll come back to that in a managing the way in which it's performing. >>Uh, you know, the banks that are starting to report that first quarter results are going to show a profitability that's significantly ahead of where they were last year. And probably some of those, some of that best performance for quite a long time, if you go into Europe, it's a completely different picture. I think the banks are extremely challenged at that. And I think you're going to see a much Bleaker outlook in terms of what those banks report, as far as Asia Pacific is concerned again, you know, because they did, they have come out of the pandemic much faster that consumer businesses back into growth. Again, I think they're showing some pretty buoyant up performance as far as, as far as banking performance is concerned. I think the beast that's particularly interesting. And I think Kim is a bit of a surprise to most, uh, is, is what we've seen in the U S right? >>And in the U S what's actually happened is, uh, the investment banking side of banking businesses has been doing better than they've ever done before. There's been the most unbelievable amount of acquisition activity. You've seen a lot of what's going on with the specs that's driving the res you know, deal based fee income for the banks, the volatility in the marketplace, meaning that trading income is much, much higher than it's ever been. And therefore the banks are very much seeing a profitability on that investment banking side. That was way ahead of what I think they were, they were expecting. Consumer business is definitely down. If you look at the credit card business, it's down, if you look at, uh, you know, lending activity, that's going down, going out, it's substantially less than where it was before. There's hardly any lending growth because the economy is flat at this moment in time. >>But again, the good news that, and I think this is a worldwide, but you're not just in the us. The good news here is that because of the liquidity and some of them are special mentions that government put out that there has not been, uh, the, the level of bankruptcies that people were expecting. Right. And that for most of the provisioning that the banks did, um, in expectation of non-performing loans has been, I think, a much more, much greater than what they're going to need, which is why you're starting to supervision is being released as well, which I kind of flattering, flattering the income flattering. I think going forward though, you're going to see a different picture. >>It's the, thank you for the clarification on the regional divergence is that you're right on, I mean, European central banks are, are not the same, the same position, uh, to, to affect liquidity, but is that nuance, is that variation across the globe? Is that, uh, is that a blind spot? Is that a, is that a, a concern, uh, or the other, other greater concerns, you know, inflation and, and, and the, the, the pace of the, the return to the economy. What are your thoughts on that? >>So I think, I think the, um, the, the, the concern, um, you know, as far as the European marketplace is concerned is, um, you know, whether the, the performance that in particularly, I don't think the level of Verition in there was quite as generous as we saw in other parts of the world. And therefore, um, you know, ease the issue around non-performing loans in, in Europe going to hold the European, uh, European banks back. And are they going to, you know, therefore constrained them under lending that they put into the economy. And that then, um, you know, reduces the level of economic growth that we see in Europe. Right. I think, I think that is certainly that is certainly a concern. Um, I would be surprised and I've been looking at, you know, forecasts that have been brought forward by various people around the world around infection. >>I would be surprised if inflation starts to become a genuine problem in the, in the kind of short to medium term. I think in the industry that are going to be two or three other things that are probably going to be more, you know, going to be more issues. Right. I think the first one, which is becoming top of mind for chief executives is this whole area around operational resiliency. So, you know, regulators universally are making very, very sure that banks do not have a technical debt or a complexity of legacy systems issue. They are. And, you know, the UK has taken the lead on this and they are going so far as even requiring non-executive directors to be liable. If banks are found to not have the right policies in place, this is not being followed by other regulators around the world. Right. So, so that is very much top of mind at this moment in time. >>So I think discretionary investment is going to be, uh, you know, to watch, um, uh, solving that particular problem. I think that that's one issue. I think the other issue is what the pandemic has shown is that, and, and, and this was very evident to me. I mean, I spent the last three years out in Singapore where, you know, banks have become very digital businesses. Right. When I came into the U S in my current role, it was somewhat surprising to me as to where the U S marketplace was in terms of digitization of banking. But if you look in the last 12 months, uh, you know, I think more has been achieved in terms of banks becoming digital businesses. And they've probably done in the last two or three years. Right. And then the real acceleration of that, uh, digitalization, which is going to continue to happen. But the downside of that has been that the threat to the banking industry from essentially fintechs and big decks has exactly, you know, it's really accelerated. Right, right. I mean, just to give you an example, pay Pat is the second largest financial services institution in the us, right. So that's become a real problem of my English. The banking industry is going to have to deal with, >>I want to come back to that, but now let's bring John into the conversation. Let's talk about the tech stack. Look, it was talking about whether it was resiliency going digital. We certainly saw with the pandemic remote work, huge, huge volumes of things like PPP and, and, and, and, and mortgages and with dropping rates, et cetera. So, John, how has the tech stack been altered in the past 14 months? >>Great question, Dave and it's top of mind for almost every single financial services firm, regardless of the sector within the overall industry, every single business has been taking stock of how they handled the pandemic and the economic conditions thereafter, and all of the business needs that were driven by the pandemic. In so many situations, firms were unable to service their clients or were not competitive in serving their clients. And as a result, they've had to do very deep, uh, uh, architectural, uh, transformation and digital transformation around their core platforms, their systems of analytics and their systems, their front end systems of engagement in terms of, uh, the core processing systems that many of these institutions, some in many cases, they're 50 years old. And with any 50 year old application platform, there are inherent limitations as an inflexibility and flexibility as an inability to innovate for the future as a speed of delivery issue. In, in other words, it can be very hard to accelerate delivery of new capabilities onto an aging platform. And so in every single case, um, institutions are looking to hybrid cloud and public cloud technology, and pre-packaged AI and pre-packaged solutions from an ISV ecosystem of software vendor ecosystem to say, as long as we can crack open many of these old monolithic cores and surround them with new digitization, new user experience that spans every channel and automation from the front to back of every interaction, that's where most institutions are prioritizing. Yep. >>Banks, aren't gonna migrate. Uh, they're gonna, they're going to build a abstraction layer. I want to come back to the disruption is so interesting. You had the Coinbase IPO last month, see Tesla and micro strategy. They're putting Bitcoin on their balance sheets. Jamie diamond says traditional banks are playing a smaller role in the financial system because of the new fintechs. Look at, you mentioned PayPal, the Stripe does Robin hood. You get the Silicon Valley giants have this dual disruptive disruption agenda, Apple, Amazon, even Walmart, Facebook. The question is, are traditional banks going to lose control of the payment systems? >>Yeah, I mean, I think to a large extent that is, that is already happened, right? Because I think if you look at, if you look at the experience in Asia, right, and you look at particularly organizations like iron financial, uh, you know, in India, you look at organizations like ATM the, you know, very substantial trends, particularly on the consumer payment side has actually moved, uh, away from the banks. And I think you're starting to see that in the West as well, right. With organizations like, you know, cloud. Now that's coming out with this, um, you know, pay, you know, buying out the later type of schemes. You've got and then, so you've got PayPal. And as you said, Stripe, uh, and, and others as well, but it's not just, um, you know, in the payment side. Right. I think, I think what's starting to happen is that, that are very core part of the banking business, you know, especially things like lending, for instance, where again, you are getting a number of these, um, fintechs and big, big tech companies entering the marketplace. >>And I, and I think the threat for the banks is, and this is not going to be small chunks of market share that you're going to actually lose. Right. It's, it's, it's actually, uh, it could actually be a Kodak moment. Let me give you an example. Uh, you know, you will have just seen that grab is going to be acquired by one of these facts for about $40 billion. I mean, this organization started like the Uber in Singapore. It very rapidly got into both the payment side, right? So it actually went to all of these mom and pop shops and it offered QR based, um, go out code based payment capabilities to these very small retailers. They were charging about half or a third of what MasterCard or visa were charging to run those payment routes. They took market share overnight. You look at the remittance business, right? >>They, they went into the remittance business, they set up these wallets in 28 countries around the ICR and region. They took huge chunks of business completely away from DBS, which is the local bank out there from Western union and all of these, all of these others. So, so I, I think it's a real threat. I think Jamie Dimon is saying what the banking industry has said always, right? Which is the reason we are losing is because the playing field is not even, this is not about playing fields and even right. All of these businesses have been subject to exactly the same regulation that the bank shop subject to regulations in Singapore and India, more onerous than maybe in other parts of the world. This is around the banking business, recognizing that this is a threat. And exactly, as John was saying, you got to get to delivering the customer experience. >>That juniors are wanting at the level of pasta they're prepared to pay. And you're not going to do that by purely shorting out the channels and having a cool app on somebody's smartphone. Right? If that smartphone is 48 by arcade processes and legacy systems, where can I apply? You know, like, like today, you know, you make a payment, your payment does not clear for five days, right? Whereas in Singapore I make a payment, the payment is instantaneously cleared, right? That's where the banking system is going to have to get to in order to get to that. You need to order the whole stack. And the really good news is there are many examples where this has been done very successfully by incumbent banks. You don't have to set up a digital bank on the side to do it. An incumbent bank could do it, and it can do it in a sense of a period of time, or does sense for level of investment. A lot of IBM's business across our consulting, as well as our, our technology stack is very much trying to do that with our clients. So I am personally very bullish about what the industry >>Yeah. I mean, taking friction out of the system sometimes with the case of crypto taking the middle person out of the system. But I think you guys are savvy. You understand that, you know, like, yeah, Jamie diamonds a couple of years ago said, he'd fire anybody doing crypto Janet Yellen and says, ah, I don't really get it. You know, Warren buffet. But I think as technology people, we look at it and say, okay, wait a minute. This is an interesting Petri dish. There's, there's fundamental technology here that has massive funding that is going to inform, you know, the future. I think, you know, big bags are gonna lean in some of them and others, others. Won't, uh, John, give you the last word here, >>But for sure they're leaning in. Uh, so to just, to, to, to think about, uh, uh, something that Likud said a moment ago, the reason these startups were able to innovate fast was because they didn't have the legacy. They didn't have the spaghetti lying around. They were able to be relentlessly laser focused on building new, using the API ecosystem, going straight to public and hybrid cloud and not worrying about everything that had been built for the last 50 years or so. The benefit for existing institutions, the incumbents is that they can use all of the same techniques and tools and hybrid cloud accelerators in terms. And we're not just thinking about, um, uh, retail banking here, your question around the industry, that disruption from Bitcoin, blockchain technologies, new ways of processing securities. It is playing out in every single securities processing and capital markets organization. Right now I'm working with several organizations right now, exactly on how to build custody systems, to take advantage of these non fungible digital assets. It's a hot, hot topic around which there's, uh, incredible, uh, appetite to invest an incredible appetite to innovate. And we know that the center of all these technologies are going to be cloud forward cloud ready, AI infused data infuse technologies. >>So I want to have you back. I wish you had more time. I want to talk about specs. I want to talk about NFTs. I want to talk about technology behind all this really great conversation and really appreciate your time. I'm sorry. We got to go. >>Thank you. Thanks so much indeed, for having us. >>Oh, really? Pleasure. Was mine. Thank you for watching everybody. This is Dave Volante for IBM. Think 2021. You're watching the cube.