>> 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)

>>from around the globe. It's the Cube with digital coverage of you. I path live the release show brought to you by you. >>I path Hi. Welcome back to this special R p A drill down with support from you. I path You're watching The Cube. My name is Dave Volante and Bobby CMO. You know I passed Bobby. Good to see you again. Hope you're doing well. Thanks for coming on. >>Hi, Dave. It's great to see you as well. It's always a pleasure to be on the Cube and even in the virtual format, this is really exciting. >>So, you know, last year at forward, we talked about the possibility of a downturn. Now nobody expected this kind of downturn. But we talked about that. Automation was likely something that was going to stay strong even in the downturn. We were thinking about potential recession or an economic downturn. Stock market dropped, but nothing like this. How are you guys holding up in this posted 19 pandemic? What are you seeing in the marketplace? >>Yeah, we certainly we're not thinking of a black swan or rhino or whatever we call this, but, you know, it's been a pretty crazy couple of months for everybody. You know, when When this first started, we were like everybody else. Not sure how it impact our business. The interesting thing has been that you're in code. It actually brought a reality check through. A lot of companies and organizations realize that it's very few tools to respond quickly, right? Bond with, you know, cost pressures that we're urgent or preserving revenue, perhaps, or responding to Ah, strange resource is, you know, in all centers, or or built to support. You know, the surge in in, um, in the healthcare community. And so r p a became one of those tools that quickly waas knowledge and adopted. And so we went out two months ago to go find those 1st 1st use cases. Talk about him, then. You know, 1st 30 days we had 50 in production, right? Companies, you know, great organizations like Cleveland Clinic, right? You know where they use their parking lot? Give the first tests the swab tests, right of, uh, well, who have proven right? You know, they had a line of 88 hours by, you know, putting a robot in place in two days. They got that line down by 80 or 90% right? It is a huge hit as we see that kind of a kind of benefit all across right now in the world. Right now we have. We were featured in The Wall Street Journal recently with nurses and a large hospital system in Ireland called Matter. The nurses said in the interview that, you know they have. They were able to free up time to be a patient's right, which is what they're there for, anyway, thanks to robots during this during this emergency. So I think you know, it's it's definitely raise The awareness that that this technology is provides an amazing time to value, and that's it's pretty unprecedented in the world of B two B software. >>I want to share some data with you in our community is the first time we've we've shown this. Guys would bring up the data slide, and so this is ah, chart that e. T are produced. There's enterprise technology research. They go out of reporter. They survey CIOs and I T practitioners and a survey in different segments and the use of methodology Net score. And this is sort of how method how Net scores derived. And so what this chart shows is the percent of customers that responded there were about 125 You I path customers that responded. Are you adopting new U I path? Are you increasing spending in 2020? Are you planning on flat spending or decreasing spending? Are you replacing the platform of beacons? And so basically, we take the green, uh, subtract the read from the green, and that gives us net score. But the point is that Bobby abouts about 80% of your customers are planning to spend Maurin 2020 than they spent in 2019 and only about 6% of planning on spending less, which is fairly astounding. I mean, we've been reporting on this for a while in the heat nous in the in the automation market generally and specifically. But are you seeing this in the marketplace? And maybe you could talk about why? >>Well, we just finished our first fiscal quarter into the end of April, and we're still privately held, so we can be, uh, find some insights of our company, but yeah, the the pace of our business picked up actually in in the mark. April timeframe. Um, customer adoption, large customer adoption. Um, the number of new new companies and new logos were at a record high. And, you know, we're entering into this quarter now, and we have some 20 plus $1,000,000 deals that are like that. It closed, right? I mean, that's probably a 30% increase Versus what? How many we have today alone. Right? So our business, you know, is is now well over 400 million and air are we ended last year, 3 60 and the growth rate continues fast. I think you know what's interesting is that the pace of the recode world was already fast, right? The the luxury of time has kind of disappeared. And so people are thinking about, you know, they don't have they can't wait now, months and years for digital transformation. They have to do things in days and days and days and weeks. And and that's where our technology really comes into play. Right? And and and it actually is also coming to play well in the world of the remote workforce. Reality two of the ability for remote workers to get trained while they're home on automation to build automation pipelines to to build automation. Now, with our latest release, you can download our podcast, capture and report what you're doing, and it basically generates the process definition document and the sample files, which allow for faster implementation by our center of excellence. So what's really happening here? We see it is a sense of urgency coming out of this. Prices are coming down the curve. Hopefully, now this is of urgency that our customers are facing in terms of how they respond, you know, and respond digitally to helping their business out. And it varies a lot by industry, our state and local business was really thinking was not going to be the biggest laggard of any industry picked up in a significant way in the last couple of months, New York State, with Governor Cuomo, became a big customer of ours. There's a quote from L. A County, see Iot that I've got here. They just employed us. It's public, this quote, he said. Deputy CIO said Price is always the mother of invention. We can always carry forward the good things they're coming out of this crisis situation. He's referring to our P A is being a lesson. They learned hearing this, that they're going to carry forward. And so we see this state of Oklahoma became a customer and others. So I think that's that's what we're seeing kind of a broad based. It's worldwide. >>You're really organizations can't put it off anymore. I think you're right. It sort of brought forward the future into the present. Now you mentioned 360 million last year. We had forecast 350 million was pretty good for you guys released, so it's happy about that. But so obviously still a strong trajectory. You know, it might have been higher without without covert. We'll never know, but sort of underscores the strength of the space. Um, and February you guys, there was an article that so you're essentially Theo Dan, Daniel Hernandez was quoted. Is that on hold now? Are you guys still sort of thinking about pressing forward or too early to say right? >>Yeah. I mean, I think I think the reality is we have a very, very strong business. We've raised, you know, significant money from great investors, some of which are the leading VCs in the world. and also that the public company investors and, you know, we have, ah, aggressive plan. We have an aggressive plan to build out our platform for hyper automation to continue. The growth path is now becoming the center of companies of I, T and Digital Strategies, not on the side. Right. And so to do that, you know, we're gonna want capital to help fuel our our our ambitions and fuel Our ability to serve our customers and public markets is probably a very, very logical one. As Daniel mentioned in a in a A recent, uh, he's on Bloomberg that he definitely sees. That is ah, maybe accelerating that, You know, we're late Last year, we started focusing on sustainable growth as a company and operational regular. These are important things in addition to having strong growth that, you know, a long term company has to have in place. And I can tell you, um, I'm really excited about the fact that we, you know, we operate very much like a public company. Now, internally, we you know, we do draft earnings releases that aren't public yet, and we do mock earnings, earnings calls, and we have hired Thomas Hansen is runs our chief revenue officer with storage backgrounds. And so you're gonna interview as well. These are these are these are the best of the best, right? That joint, they're joined this company, they're joining alongside the arm Kalonzo the world that are part of this company. And so I think, Yeah, I think it's an AR It's likely. And and it's gonna We're here to be a long term leader in this decade of automation. >>Well, and one of the other things that we forecast on our breaking analysis we took a look at the total available market kind of like into it. Early days of service Now is you know, people were really not fully understanding the market and chillin C it is is quite large, so video. So when we look at the competition, you know, you guys, if I showed you the same wheel with automation anywhere, it would also look strong. You know, some of the others, maybe not a strong but still stronger than many of the segments. I mean, for instance, you know, on Prem hardware. You know, compared with that and you know the automation space in general across the board is very, very strong. So I wonder if maybe you could talk a little bit about how you guys differentiate from the competition. How you see that? >>Yeah, I think you know, we've We've come a long way in the last three years, right? In terms of becoming the market leader, having the highest market share, we're very open and transparent about our numbers with We've long had the vision of a robot. Every person, uh, and and we've been delivering on that on on that vision and ah, building out a platform that helps companies, you know, transform digitally enterprise wide. Right. So, you know, I don't see any of our competitors with a platform for hyper automation like this. We have an incredible focus on the ability to help people actually find the ideas, build the pipeline, score the pipelines and integrate those with the automation center of excellence. Right? We have the ability now with our latest release to help test automation testers now not only in the world of art A but actually take robotic robots and and architecture into doing test automation. The traditional test automation market in a much better and faster way So you know, we're innovating at a pace that that it is, I think, much faster than I don't. I don't know automation anywhere. I won't share any their numbers. You know, who knows what the numbers are. We have guesses, but I'm fairly certain that we continue to gain share on them. But you know, what's most important is customer adoption, and we've also seen a number of customers switch from some of our competitors to us. Our competitors are undercapitalized and middle. Invest in R and D. This is an investment area, really build a platform out from our competitors have architectures that are hard to upgrade, right? This has been a big source of pain for companies that have been on our competitors. Where upgrades are difficult requires them to retest every time where our upgrades are very rolling, you know, are very smooth. We have an insider program which you know, I don't think any of our competitors have. If you go inside that you had pat that your customer every single bit every single review betting, private preview, public preview and general availability, you can provide feedback on and the customers can score up new ideas. They drive our our roadmap. Right. And this is I think we operate differently. I think our growth is a is a good indication of that. And, you know, and there are new competitors like Microsoft. But I think you know, you know, medium or long term, you know, they're gonna make effort around our, um and you know, they're behind the, um, automation is really hard. The buried entry here is not it's not. Not easy. And we're going to keep me on that platform, play out, and I think that's ah, that's what makes us so different. Um and ah, you know, we have the renewal numbers, retention numbers, expansion numbers and and the revenue numbers to improve that, uh, you know, we're number one. >>Well, so I mean, there's a lot of ways to skin the cat, and you're right. You guys are really focused, you know, you automation anywhere really focused on this space, and you shared with us how you differentiate there. But as you point out Microsoft, they sort of added on I had talked to Allan, preferably the day from paga. You know, those guys don't position themselves as our PC, but they have r p A. I talked to, you know, our mutual friend Robert Young John the other day, right? They're piling onto this this trend, right? So why not? Right, It's it's ah, it's hot. But so, you know, clearly you guys are innovating there. I want to talk about your vision before we get into the latest product release two things that I would call out the term hyper automation with, I think is the Gartner term. And then it will probably stick. And then this this idea of a robot for every person How would you describe your vision? >>Yeah, I mean, we think that robots can and improve, you know, the the lives of of or pers everywhere, right? We think in every every function, every role. And we see that already, the job satisfaction and the people don't want to do the mundane, repetitive work, right? The new hires coming out of college, you know, they're gonna be excel and sequel server. We're no longer the tools of productivity. For them, it's it's your path. We have business. Schools that have committed top tier business schools have committed to deploying your path or to putting you're passing every force in the school these students are graduating with the right path is their most important skill going into companies. And they're gonna expect to be able to use robots within their companies in their daily lives. A swell. So, you know, we have customers today that are rolling out a robot for every person you know. We had Ah, Conoco Phillips on just earlier in our launch, talking about citizen developers, enabling says, developer armies of developers and growing enterprise wide. See, Intel was on as well from Singapore, the large telco. They're doing the exact same thing. So I think you know, I think this is this is this is this is about broad based digital transformation. Everybody participating And what happens is the leading companies to do this, you know, they're going to get the benefit of benefits out of it. It can reinvest that productivity, benefits and data science and analytics and serving customers and in, you know, and and, ah, new product ideas. And so, you know, this is this. You know, automation is going to fuel now the ability for companies to really differentiate and serve their customers better. And it's only needed enterprise wide view on it that you really maximizing. Take Amazon, for example, a great customer during during this prices. You know, they're trying to hire hundreds of thousands of people, right? Help in the fact that in their in their distribution centers elsewhere, this all served demand to help people who like you and I home or ordering things that we need, right? Well, they're use your path robots all throughout their HR hr on boarding HR recruiting HR administration And so helping them has been a big during this prices surge of robots is helping them actually hire workers. You know another example of Schneider Electric and amazing customer of ours. They're bringing their plants, their manufacturing facilities, implants back online faster by using robots to help manage the PPE personal protective equipment in the plant allow people workers to get back to work faster. Right? So what's happening is is, you know in that in those cases is your different examples of robots and different functions, right? In all cases, it's about helping grow a company faster. It's about helping protect workers. It's about helping getting revenue machines back up and running after Kobe is going to be critical to get back to work faster. So I'm I'm really excited about the fact that as people think about automation across the organization, the number of ideas and Aaron opportunities for improvement are are we're just starting to tap that potential. >>Well, this is why I think the vision is so important because you're talking about things that are transformative. Now, as you well know, one of the criticisms of RPS. So you have people, the suppliers and just yeah, we, you know, looking at mundane tasks, just automating mundane tasks like sometimes paving the cow path and say, you're very much aware of that criticism. But if I look at the recent announcements, you're really starting to build out that vision that you just talked about. They're really four takeaways. You sort of extending the core PAP platform, injecting AI end some or and more automation end to end automation really taken that full lifestyles lifecycle systems view and the last one is sort of putting it talks to the robot. For every person that sort of citizen automation, if you will, that sort of encompasses your product announcements. So it wasn't just sort of a point Announcement really is a underscores the platform. I wonder if you could just What do we need to know about you guys? Just that out. >>So we think about how we think about the rolls back to a division of robots person how automation can help different roles. And so this product launch $20 for this large scale launch that you just articulated, um, impacts in a fax and helps many different kinds of new roles Certainly process analysts now who examined processes, passes performance improvements. You know, they're a user of our process mining solution in our past. Find a solution that helps speed on our way. Arpaio engine, no testers and quality engineers. Now they can actually use studio pro and actually used test robots are brand new, and our new test manager is sort of the orchestration and management of test executions. Now they can participate in in leveraged power of robots and what they do as well. And we kind of think about that, you know, kind of across the board in our organization across the platform. They can use tools like you have path insights in Europe. If you're an analyst or your, uh ah. B I, this intelligence person really know what's going on with robots in terms of our wife for my organization and provide that up to the, you know, sea levels in the board of directors in real time. So I think that's that's the big part. Here is we're bringing, and we're helping bring in many, many different kinds of roles different kinds of people. Data scientist. You mentioned AI. Now data scientists can build a model. The models applied to ai fabric an orchestrator. It's drag and drop by our developer in studio, and now you can turn, you know, a a mundane, rules based task right into an experience based ones where a robot can help make a decision right. Based on experience and data, they can tweak and tune that model and data scientists can interact, you know, with the automation is flowing through your path. So I think that's how we think about it, right? You know, one of the great new capabilities, as well as the ability to engage line workers, dispatch out workers If you're a telco or or retail story retail store workers you know the robots can work with humans out in the field. We've got one real large manufacturer with 18,000 drivers in a DST direct store delivery scenario. And you know the ability for them to interact with robots and help them do their job in the field. Our customers better after the list data entry and data manipulation, multiple systems. So I this is this makes us very unique in our vision and in our execution. And again, I don't I have not heard of a single ah example by competitors that has any kind of a vision or articulation to be able to help a company enterprise wide and, you know, with the speed and the and the full, full vision that we have. >>Okay, so you're not worried about downturns. You can't control black swans Anyway, you're not worried about the competition. It feels like you know, you're worried about what you're worried about. You want about growing too fast. Additionally, deploying the the capital that you've raised. What worries you? >>Yeah. You know, we're paranoid or paranoid company, right? And when it comes to the market and and trying to drive, I think we've done a lot to help actually push the rock up the hill in terms of really, really driving our market, building the market, and we want to continue that right and not let up. So there's this kind of desire to never let up, right? Well, we always remind ourselves we must work harder, must work harder. We must work harder. And that's that's That's sort of this this mentality around ourselves, by the smartest people. Hire the smartest people you work with our customers, our customers are priority. Do that with really high excellence and really high sincerity that it comes through and everything that we do, you know, to build a world class operation to be, you know, Daniel DNS. When I first met him, he said, You know, I really want to be the enemy of the great news ecology company that serve customers really well. And it was amazing things for society, and and, you know, we're on that track, but we've got, you know, we're in the in the in the early innings. So, you know, making sure that we also run our business in a way that, um, you know, uh, is ready to be Ah, you know, publicly successful company on being able to raise new sources of capital to fund our ambitions and our ideas. I mean, you saw the number of announcements from our 24 release. It reminded me of an AWS re invent conference, where it's just innovation, innovation, innovation, innovation. And these are very real. They're not made up mythical announcements that some of our competitors do about launching some kind of discovery box doesn't exist, right? These are very real with real customers behind them, and and so you know, just doing that with the same level of tenacity. But being, you know, old, fast, immersed and humble, which are four core culture values along the way and not losing that Azeri grow. That's that's something we talk about maintaining that culture that's super critical to us. >>Everybody's talking about Okay, What What's gonna be permanent? Postpone it. I was just listening to Julie Sweet, CEO of Accenture, and she was saying that, you know, prior to Covic, they had data that showed that the top 25% of companies that have leaned into digital transformation were outperforming. You know, the balance of their peers, and I know question now that the the rest of that base really is going to be focused on automation. Automation is is really going to be one of those things that is high, high priority now and really for the next decade and beyond. So, Bobby, thanks so much for coming on the Cube and supporting us in this in this r p. A drill down. Really appreciate it, >>Dave. It's always a pleasure as always. Great to see you. Thank you. >>Alright. And thank you for watching everybody. Dave Volante. We'll be right back right after this short break. You're watching the cube. >>Yeah, yeah, yeah, yeah.

>> Live, from Boston, it's theCUBE, covering IBM Chief Data Officer Summit. Brought to you by IBM. >> Welcome back everyone to theCUBE's live coverage of the IBM CDO Summit here in Boston, Massachusetts. I'm your host Rebecca Knight, along with my co-host Paul Gillin. We're joined by Dr. Prakriteswar Santikary known as Dr Santi. He is the Vice President and Global Chief Data Officer at eResearch Technology. Thank you so much for coming back on theCUBE. >> Yeah, thank you for inviting me. >> So Dr Santi tell our viewers a little bit about eResearch Technology. You're based in Marlborough... >> Yeah, so we're in Boston, but ERT has been around since 1977 and we are a data and technology company that minimizes risks and uncertainties within clinical trial space and our customers are pharmaceutical companies, biotechnology companies, medical device companies, and where they really trust us in terms of running their clinical trials on our platform. So we have been around over 40 years, so we have seen a thing or two in the space. It's a very complex domain a very highly regulated as you know, because it's dealing with patients lives. So we take huge pride in what we do. >> We know how involved clinical trials can be long, very expensive, how are the new tools, big data impacting the cost? >> Well, that has been an age old problem within the clinical trials, usually a drug takes about eight to 12 years and costs about $2 billion from start to commercialization. So it's a very lengthy, manual and arduous process. So there are lots going on in this clinical trial domain that's tries to shorten the timeline and employing of big data technologies, modern data platform to expedite data processing, data collection from mobile devices and health technologies and all these. Artificial intelligence is playing a big role in terms of disrupting some of these domains, particularly if you see the protocol development down to patient selection, down to study design, then study monitoring. So you need to do all those things and each takes long long long time, so AI with the big data technologies is they're really making a difference. >> In what ways? >> For example, patient selection is one of the huge pin points in any clinical trial, because without patients there are no clinical trials. Particularly when you try to launch a drug, you will have to identify the patients, select the patients and not only select the patients, you have to make sure those patients stay with the clinical trials throughout the duration of the trial. So patient engagement is also a big deal. So with these big data technologies, like now you can see all this mobile health devices that patients are wearing using which you can monitor them. You can remind, send them a reminder, take your drug or you can send a text saying that there will be a clinical visit at that site come at seven o'clock, don't come at nine o'clock. So these kind of encouragement and constant feedback loop is really helping patients stay engaged. That is critical. Then matching patients with the given clinical trials is a very manual and arduous process, so that's where the algorithms is helping. So they are just cranking up real world evidence data for example claims data, prescription data and other type of genomic data and they're matching patients and the clinical trial needs. Instead of just fishing around in a big pond and find out, okay I need three patients. So go and fish around the world to get the three patients. That's why current process is very manual and these AI techniques and behind technologies and big data technologies are really disrupting this industry. >> So are the pharmaceutical companies finding that clinical trials are better today because patients are more engaged and they are getting as you said this constant reminder, take your drug, stay with us. Do you think that they are, in fact, giving them better insights into the efficacy of the drug? >> Yes because you will see their compliance rate is increasing, so because remember when they have to fill out all these diaries, like morning diaries evening diaries, when they are taking which medicine, when they are not taking. It used to be all manual paper driven, so they would forget and particularly think about a terminally ill patient, each day is so critical for them. So they don't have patience, nor do they have time to really maintain a manual diary. >> Nor do their caregivers have the time. Right. >> So this kind of automation is really helping and that is also encouraging them as well, that yeah somebody is really caring about me. We are not just a number, patient is not a number that somebody is really relating to them. So patient engagement, we have a product that specifically focuses around patient engagement. So we do all these phase one through phase four trials, one, two, three, four and then forced marketing, obviously, but through the entire process, we also do patient engagement, so that we help our customers like pharmaceutical companies and biotechnology companies so that they can run their trials with confidence. >> How about analyzing the data that you collect from the trials, are you using new techniques to gain insights more quickly? >> Yes, we are. We just recently launched a modern data platform, a data lake while we are consolidating all the data and anonymizing it and then really applying AI techniques on top of it and also it is giving us real time information for study monitoring. Like which side is not complying, with patients or not complying, so if the data quality is a big deal in clinical trials, because if the quality is good, then FDA approval, there is a chance that FDA may approve, but if the data quality is bad, forget about it, so that's why I think the quality of the data and monitoring of that trial real time to minimize any risks before they become risks. So you have to be preempted, so that's why this predictive algorithms are really helping, so that you can monitor the site, you can monitor individual patient through mHealth devices and all these and really pinpoint that, hey, your clinical trials are not going to end on time nor on budget. Because here you see the actual situation here, so, do something instead of waiting 10 years to find that out. So huge cost saving and efficiency gain. >> I want to ask about data in healthcare in general because one of the big tensions that we've talked about today is sort of what the data is saying versus what people's gut is saying and then in industry, it's the business person's gut but in healthcare it is the doctor, the caregivers' gut. So how are you, how have you seen data or how is data perceived and is that changing in terms of what the data shows that the physician about the patient's condition and what the patient needs right then and there, versus what the doctors gut is telling him that the patient needs? >> Yeah and that's where that augmentation and complementary nature, right? So AI and doctors, they're like complementing each other, So predictive algorithm is not replacing doctors the expertise, so you still need that. What AI and predictive algorithm is playing a big role is in expediting that process, so instead of sifting through manual document so sifting through this much amount of document, they would only need to do this much of document. So then that way it's minimizing that time horizon. It's all about efficiency again, so AI is not going to be replacing doctors anytime soon. We still need doctors, because remember a site is run by a primary investigator and primary investigator owns that site. That's the doctor, that's not a machine. That's not an AI algorithm, so his or her approval is the final approval. But it's all about efficiency cost cutting and bringing the drugs to the market faster. If you can cut down these 12 years by half, think about that not only are you saving lots of money, you are also helping patients because those drugs are going to get to the market six year earlier. So you're saving lots of patients in that regard as well. >> One thing that technologies like Watson can do is sort through, read millions of documents lab reports and medical journals and derive insights from them, is that helping in the process of perhaps avoiding some clinical trials or anticipating outputs earlier? >> Yes, because if you see Watson run a clinical study with Cleveland Clinic recently or Mayo Clinic I think or maybe both. While they reduce the patient recruitment time by 80%, 80%. >> How so? >> Because they sweep through all those documents, EMR results, claims data, all this data they combined-- >> Filter down-- >> Filter down and then say, for this clinical trial, here are the 10 patients you need. It's not going to recommend to who those 10 patients are but it will just tell you that, the goal is the average locations, this that, so that you just focus on getting those 10 patients quickly instead of wasting nine months to research on those 10 patients and that's a huge, huge deal. >> And how can you trust that, that is right? I mean I think that's another question that we have here, it's a big challenge. >> It is a challenge because AI is all about math and algorithm, right? So when you, so it's like, input black box, output. So that output may be more accurate than what you perceive it to be. >> But that black box is what is tripping me up here. >> So what is happening is sometimes, oftentimes, if it is a deep learning technique, so that kind of lower level AI techniques. It's very hard to interpret that results, so people will keep coming back to you and say, how did you arrive at that results? And that's where most of the, there are techniques like Machine Learning techniques that are easily interpretable. So you can convince FDA folks or other folks that here is how we've got to it, but there are a deep learning techniques that Watson uses for example, people will come and, how did you, how did you arrive at that? And it's very hard because those neural networks are multi-layers and all about math, but as I said, output may be way more accurate, but it's very hard to decipher. >> Right, exactly. >> That's the challenge. So that's a trust issue in that regard. >> Right, well, Dr. Santi, thank you so much for coming on theCUBE. It was great talking to you. >> Okay, thank you very much. Thanks for inviting. >> I'm Rebecca Knight for Paul Gillin we will have more from the IBM CDO Summit in just a little bit. (upbeat music)