(light upbeat music) (mouse clicks) >> Hey, everyone. Welcome back to the program. Lisa Martin here. I've got two guests joining me. Please welcome back to The Cube, Paula Hansen, the chief revenue officer and president at Alteryx. And Jacqui Van der Leij - Greyling joins us as well, the global head of tax technology at eBay. They're going to share with you how Alteryx is helping eBay innovate with analytics. Ladies, welcome. It's great to have you both on the program. >> Thank you, Lisa. >> Thank you, Lisa. >> It's great to be here. >> Yeah, Paula. We're going to start with you. In this program, we've heard from Jason Klein, we've heard from Alan Jacobson, they talked about the need to democratize analytics across any organization to really drive innovation. With analytics as they talked about at the forefront of software investments, how's Alteryx helping its customers to develop roadmaps for success with analytics? >> Well, thank you, Lisa. It absolutely is about our customer's success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts, of course, with our innovative technology and platform but ultimately, we help our customers to create a culture of data literacy and analytics from the top of the organization, starting with the C-suite. And we partner with our customers to build their roadmaps for scaling that culture of analytics through things like enablement programs, skills assessments, hackathons, setting up centers of excellence to help their organizations scale and drive governance of this analytics capability across the enterprise. So at the end of the day, it's really about helping our customers to move up their analytics maturity curve with proven technologies and best practices so they can make better business decisions and compete in their respective industries. >> Excellent. Sounds like a very strategic program. We're going to unpack that. Jacqui let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the IDC report that showed that 93% of organizations are not utilizing the analytics skills of their employees, but then there's eBay. How, Jacqui, did eBay become one of the 7% of organizations who's really maturing and how are you using analytics across the organization at eBay? >> So I think the main thing for us is just when we started out was, is that, you know, our, especially in finance they became spreadsheet professionals, instead of the things that we really want our employees to add value to. And we realized we had to address that. And we also knew we couldn't wait for all our data to be centralized until we actually start using the data or start automating and be more effective. So ultimately, we really started very, very actively embedding analytics in our people and our data and our processes. >> Starting with people is really critical. Jacqui, continuing with you, what were some of the roadblocks to analytics adoption that you faced and how did you overcome them? >> So I think, you know, eBay is a very data driven company. We have a lot of data. I think we are 27 years around this year so we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and just finding those data sources and finding ways to connect to them to move forward. The other thing is, is that you know, people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals, right? And there was no, we're not independent. You couldn't move forward. You would've been dependent on somebody else's roadmap to get to data and to get the information you wanted. So really finding something that everybody could access analytics or access data. And finally, we have to realize is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy and that is not so daunting on somebody who's brand new to the field? And I would call those out as your major roadblocks because you always have, not always, but most of the times you have support from the top in our case, we have, but in the end of the day, it's our people that need to actually really embrace it and making that accessible for them, I would say is definitely not per se, a roadblock but basically some, a block you want to be able to move. >> It's really all about putting people first. Question for both of you, and Paula will start with you, and then Jacqui will go to you. I think the message in this program that the audience is watching with us is very clear. Analytics is for everyone, should be for everyone. Let's talk now about how both of your organizations are empowering people those in the organization that may not have technical expertise to be able to leverage data so that they can actually be data driven? Paula? >> Yes. Well, we leverage our platform across all of our business functions here at Alteryx. And just like Jacqui explained at eBay finance is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jacqui mentioned, we have this huge amount of data flowing through our enterprise and the opportunity to leverage that into insights and analytics is really endless. So our CFO, Kevin Rubin has been a key sponsor for using our own technology. We use Alteryx for forecasting, all of our key performance metrics for business planning across our audit function to help with compliance and regulatory requirements, tax and even to close our books at the end of each quarter so it's really remained across our business. And at the end of the day, it comes to how do you train users? How do you engage users to lean into this analytic opportunity to discover use cases. And so one of the other things that we've seen many companies do is to gamify that process to build a game that brings users into the experience for training and to work with each other, to problem solve, and along the way, maybe earn badges depending on the capabilities and trainings that they take. And just have a little healthy competition as an employee base around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jacqui mentioned, it's really about ensuring that people feel comfortable, that they feel supported that they have access to the training that they need. And ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >> That confidence is key. Jacqui, talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >> Yeah, I think it means to what Paula has said in terms of you know, getting people excited about it but it's also understanding that this is a journey. And everybody is the different place in their journey. You have folks that's already really advanced who has done this every day, and then you have really some folks that this is brand new and, or maybe somewhere in between. And it's about how you could get everybody in their different phases to get to the initial destination. I say initially, because I believe the journey is never really complete. What we have done is that we decided to invest in a... We build a proof of concepts and we got our CFO to sponsor a hackathon. We opened it up to everybody in finance in the middle of the pandemic. So everybody was on Zoom. And we told people, "Listen, we're going to teach you this tool, super easy. And let's just see what you can do." We ended up having 70 entries. We had only three weeks. So, and these are people that has... They do not have a background. They are not engineers, they're not data scientists. And we ended up with a 25,000 hour savings at the end of that hackathon. From the 70 entries with people that have never, ever done anything like this before and there you had the result. And then it just went from there. It was people had a proof of concept, they knew that it worked, and they overcame that initial barrier of change. And that's where we are seeing things really, really picking up now. >> That's fantastic. And the business outcome that you mentioned there, the business impact is massive helping folks get that confidence to be able to overcome sometimes the cultural barriers is key here. I think another thing that this program has really highlighted is there is a clear demand for data literacy in the job market, regardless of organization. Can each of you share more about how you're empowering the next generation of data workers? Paula will start with you. >> Absolutely. And Jacqui says it so well, which is that it really is a journey that organizations are on. And we, as people in society are on in terms of upskilling our capabilities. So one of the things that we're doing here at Alteryx to help address this skillset gap on a global level is through a program that we call SparkED, which is essentially a no-cost analytics education program that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay, and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this program's really developed to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with SparkED, we started last May, but we currently have over 850 educational institutions globally engaged across 47 countries. And we're going to continue to invest here because there's so much opportunity for people, for society and for enterprises, when we close gap and empower more people with the necessary analytics skills to solve all the problems that data can help solve. >> So SparkED just made a really big impact in such a short time period. It's going to be fun to watch the progress of that. Jacqui let's go over to you now. Talk about some of the things that eBay is doing to empower the next generation of data workers. >> So we basically wanted to make sure that we kicked that momentum from the hackathon. Like we don't lose that excitement, right? So we just launched a program called eBay Masterminds. And what it basically is, it's an inclusive innovation initiative, where we firmly believe that innovation is for upscaling for all analytics role. So it doesn't matter your background, doesn't matter which function you are in, come and participate in this, where we really focus on innovation, introducing new technologies and upscaling our people. We are... Apart from that, we also said... Well, we should just keep it to inside eBay. We have to share this innovation with the community. So we are actually working on developing an analytics high school program, which we hope to pilot by the end of this year, where we will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, but also how to use alter Alteryx. And we're working with actually, we're working with SparkED and they're helping us develop that program. And we really hope that, let us say, by the end of the year have a pilot and then also next, was hoping to roll it out in multiple locations, in multiple countries, and really, really focus on that whole concept of analytics role. >> Analytics role, sounds like Alteryx and eBay have a great synergistic relationship there, that is jointly aimed at, especially, kind of, going down the stuff and getting people when they're younger interested and understanding how they can be empowered with data across any industry. Paula let's go back to you. You were recently on The Cube's Supercloud event just a couple of weeks ago. And you talked about the challenges the companies are facing as they're navigating what is by default a multi-cloud world? How does the Alteryx Analytics Cloud platform enable CIOs to democratize analytics across their organization? >> Yes, business leaders and CIOs across all industries are realizing that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organizations. Last, I check there was 2 million data scientists in the world. So that's woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. (Paula clears throat) So what we're seeing now with CIOs, with business leaders is that they're integrating data analysis and the skillset of data analysis into virtually every job function. And that is what we think of when we think of analytics for all. And so our mission with Alteryx Analytics Cloud, is to empower all of those people in every job function regardless of their skillset. As Jacqui pointed out from people that would, you know are just getting started all the way to the most sophisticated of technical users. Every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organizations. So that's our goal with Alteryx Analytics Cloud and it operates in a multi-cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyze and report out so that we can break down data silos across the enterprise and help drive real business outcomes as a result of unlocking the potential of data. >> As well as really lessening that skills gap as you were saying, there's only 2 million data scientists. You don't need to be a data scientist. That's the beauty of what Alteryx is enabling and eBay is a great example of that. Jacqui let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where Alteryx fits in as that analytics maturity journey continues. And what are some of the things that you are most excited about as analytics truly gets democratized across eBay? >> When we started about getting excited about things when it comes to analytics, I can go on all day but I'll keep it short and sweet for you. I do think we are on the topic full of data scientists. And I really feel that that is your next step, for us anyways, it's just that, how do we get folks to not see data scientists as this big thing, like a rocket scientist, it's something completely different. And it's something that is in everybody in a certain extent. So again, partnering with Alteryx would just release the AI/ML solution, allowing, you know, folks to not have a data scientist program but actually build models and be able to solve problems that way. So we have engaged with Alteryx and we purchased the licenses quite a few. And right now, through our mastermind program we're actually running a four-months program for all skill levels. Teaching them AI/ML and machine learning and how they can build their own models. We are really excited about that. We have over 50 participants without the background from all over the organization. We have members from our customer services, we have even some of our engineers, are actually participating in the program. We just kicked it off. And I really believe that that is our next step. I want to give you a quick example of the beauty of this is where we actually just allow people to go out and think about ideas and come up with things. And one of the people in our team who doesn't have a data scientist background at all was able to develop a solution where, you know, there is a checkout feedback, checkout functionality on the eBay site, where sellers or buyers can verbatim add information. And she build a model to be able to determine what relates to tax specific, what is the type of problem, and even predict how that problem can be solved before we, as a human even step in. And now instead of us or somebody going to the bay to try to figure out what's going on there, we can focus on fixing the error versus actually just reading through things and not adding any value. And it's a beautiful tool, and I'm very impressed when you saw the demo and they've been developing that further. >> That sounds fantastic. And I think just the one word that keeps coming to mind and we've said this a number of times in the program today is, empowerment. What you're actually really doing to truly empower people across the organization with varying degrees of skill level going down to the high school level, really exciting. We'll have to stay tuned to see what some of the great things are that come from this continued partnership. Ladies, I want to thank you so much for joining me on the program today and talking about how Alteryx and eBay are really partnering together to democratize analytics and to facilitate its maturity. It's been great talking to you >> Thank you, Lisa. >> Thank you so much. (light upbeat music) >> As you heard over the course of our program, organizations where more people are using analytics who have deeper capabilities in each of the four E's that's, everyone, everything, everywhere and easy analytics. Those organizations achieve more ROI from their respective investments in analytics and automation than those who don't. We also heard a great story from eBay, great example of an enterprise that is truly democratizing analytics across its organization. It's enabling an empowering line of business users to use analytics. Not only focused on key aspects of their job, but develop new skills rather than doing the same repetitive tasks. We want to thank you so much for watching the program today. Remember you can find all of the content on thecube.net. You can find all of the news from today on siliconangle.com, and of course alteryx.com. We also want to thank Alteryx for making this program possible and for sponsoring The Cube. For all of my guests, I'm Lisa Martin. We want to thank you for watching and bye for now. (light upbeat music)

>>Hey everyone. Welcome back to the program. Lisa Martin here, I've got two guests joining me, please. Welcome back to the cube. Paula Hansen, the chief revenue officer and president at Al alters and Jackie Vander lake grayling joins us as well. The global head of tax technology at eBay. They're gonna share with you how an alter Ricks is helping eBay innovate with analytics. Ladies. Welcome. It's great to have you both on the program. >>Thank you, Lisa. It's great to be here. >>Yeah, Paula, we're gonna start with you in this program. We've heard from Jason Klein, we've heard from Alan Jacobson, they talked about the need to democratize analytics across any organization to really drive innovation with analytics. As they talked about at the forefront of software investments, how's alters helping its customers to develop roadmaps for success with analytics. >>Well, thank you, Lisa. It absolutely is about our customer's success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts of course, with our innovative technology and platform, but ultimately we help our customers to create a culture of data literacy and analytics from the top of the organization, starting with the C-suite. And we partner with our customers to build their roadmaps for scaling that culture of analytics through things like enablement programs, skills, assessments, hackathons, setting up centers of excellence to help their organizations scale and drive governance of this analytics capability across the enterprise. So at the end of the day, it's really about helping our customers to move up their analytics, maturity curve with proven technologies and best practices so they can make better business decisions and compete in their respective industries. >>Excellent. Sounds like a very strategic program. We're gonna unpack that Jackie, let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the IDC report that showed that 93% of organizations are not utilizing the analytics skills of their employees, but then there's eBay. How Jackie did eBay become one of the 7% of organizations who's really maturing and how are you using analytics across the organization at eBay? >>So I think the main thing for us is just when we started out was is that, you know, our, especially in finance, they became spreadsheet professionals instead of the things that we really want our employees to add value to. And we realized we had to address that. And we also knew we couldn't wait for all our data to be centralized until we actually start using the data or start automating and be more effective. So ultimately we really started very, very actively embedding analytics in our people and our data and our processes, >>Starting with people is really critical. Jackie, continuing with you, what were some of the roadblocks to analytics adoption that you faced and how did you overcome them? >>So I think, you know, eBay is a very data driven company. We have a lot of data. I think we are 27 years around this year, so we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and, and just finding those data sources and finding ways to connect to them to move forward. The other thing is, is that, you know, people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals, right? And we, there was no, we're not independent. You couldn't move forward. You would've opinion on somebody else's roadmap to get to data and to get the information you wanted. So really finding something that everybody could access analytics or access data. >>And finally we have to realize is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy? And that is not so daunting on somebody who's brand new to the field. And I would, I would call those out as your, as your major roadblocks, because you always have not always, but most of the times you have support from the top in our case, we have, but in the end of the day, it's, it's our people that need to actually really embrace it and, and making that accessible for them, I would say is definitely not per se, a roadblock, but basically some, a block you wanna be able to move. >>It's really all about putting people. First question for both of you and Paula will start with you. And then Jackie will go to you. I think the message in this program that the audience is watching with us is very clear. Analytics is for everyone should be for everyone. Let's talk now about how both of your organizations are empowering people, those in the organization that may not have technical expertise to be able to leverage data so that they can actually be data driven Paula. >>Yes. Well, we leverage our platform across all of our business functions here at Altrix and just like Jackie explained it, eBay finances is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jackie mentioned, we have this huge amount of data flowing through our enterprise and the opportunity to leverage that into insights and analytics is really endless. So our CFO, Kevin Rubin has been a, a key sponsor for using our own technology. We use Altrix for forecasting, all of our key performance metrics for business planning across our audit function, to help with compliance and regulatory requirements tax, and even to close our books at the end of each quarter. So it's really remain across our business. And at the end of the day, it comes to how do you train users? How do you engage users to lean into this analytic opportunity to discover use cases? >>And so one of the other things that we've seen many companies do is to gamify that process, to build a game that brings users into the experience for training and to work with each other, to problem solve and along the way, maybe earn badges depending on the capabilities and trainings that they take. And just have a little healthy competition as an employee base around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jackie mentioned, it's really about ensuring that people feel comfortable, that they feel supported, that they have access to the training that they need. And ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >>That confidence is key. Jackie, talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >>Yeah, I think it means to what Paula has said in terms of, you know, you know, getting people excited about it, but it's also understanding that this is a journey and everybody's the different place in their journey. You have folks that's already really advanced who has done this every day. And then you have really some folks that this is brand new and, or maybe somewhere in between. And it's about how you put, get everybody in their different phases to get to the, the initial destination. I say initially, because I believe the journey is never really complete. What we have done is, is that we decided to invest in an Ebola group of concept. And we got our CFO to sponsor a hackathon. We opened it up to everybody in finance, in the middle of the pandemic. So everybody was on zoom and we had, and we told people, listen, we're gonna teach you this tool super easy. >>And let's just see what you can do. We ended up having 70 entries. We had only three weeks. So, and these are people that has N that do not have a background. They are not engineers, they're not data scientists. And we ended up with a 25,000 hour savings at the end of that hackathon from the 70 inches with people that have never, ever done anything like this before and there you had the result. And then it just went from there. It was, people had a proof of concept. They, they knew that it worked and they overcame the initial barrier of change. And that's where we are seeing things really, really picking up. Now >>That's fantastic. And the, the business outcome that you mentioned there, the business impact is massive helping folks get that confidence to be able to overcome. Sometimes the, the cultural barriers is key. I think another thing that this program has really highlighted is there is a clear demand for data literacy in the job market, regardless of organization. Can each of you share more about how you are empowering the next generation of data workers, Paula will start with you? >>Absolutely. And, and Jackie says it so well, which is that it really is a journey that organizations are on. And, and we, as people in society are on in terms of upskilling our capabilities. So one of the things that we're doing here at Altrix to help address this skillset gap on a global level is through a program that we call sparked, which is essentially a, no-cost a no cost analytics education program that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this program's really developed just to, to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with sparked. We started last may, but we currently have over 850 educational institutions globally engaged across 47 countries. And we're gonna continue to invest here because there's so much opportunity for people, for society and for enterprises, when we close gap and empower more people within necessary analytics skills to solve all the problems that data can help solve. >>So spark has made a really big impact in such a short time period. And it's gonna be fun to watch the progress of that. Jackie, let's go over to you now talk about some of the things that eBay is doing to empower the next generation of data workers. >>So we basically wanted to make sure that we keep that momentum from the hackathon that we don't lose that excitement, right? So we just launched a program called Ebo masterminds. And what it basically is, it's an inclusive innovation initiative where we firmly believe that innovation is all up scaling for all analytics for. So it doesn't matter. Your background doesn't matter which function you are in, come and participate in, in this where we really focus on innovation, introducing new technologies and upskilling our people. We are apart from that, we also say, well, we should just keep it to inside eBay. We, we have to share this innovation with the community. So we are actually working on developing an analytics high school program, which we hope to pilot by the end of this year, where we will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, but also how to use alter alter. And we're working with actually, we're working with spark and they're helping us develop that program. And we really hope that as a say, by the end of the year, have a pilot and then also make you, so we roll it out in multiple locations in multiple countries and really, really focus on, on that whole concept of analytics, role >>Analytics for all sounds like ultra and eBay have a great synergistic relationship there that is jointly aimed at, especially kind of going down the staff and getting people when they're younger, interested, and understanding how they can be empowered with data across any industry. Paula, let's go back to you. You were recently on the Cube's super cloud event just a couple of weeks ago. And you talked about the challenges the companies are facing as they're navigating. What is by default a multi-cloud world? How does the alters analytics cloud platform enable CIOs to democratize analytics across their organization? >>Yes, business leaders and CIOs across all industries are realizing that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organizations. Last I check there was 2 million data scientists in the world. So that's woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. So what we're seeing now with CIOs with business leaders is that they're integrating data analysis and the skill of data analysis into virtually every job function. And that is what we think of when we think of analytics for all. And so our mission with Altrics analytics cloud is to empower all of those people in every job function, regardless of their skillset. As Jackie pointed out from people that would, you know, are just getting started all the way to the most sophisticated of technical users. Every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organizations. So that's our goal with Altrics analytics cloud, and it operates in a multi cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyze, and report out so that we can break down data silos across the enterprise and drive real business outcomes. As a result of unlocking the potential of data, >>As well as really re lessening that skill gap. As you were saying, there's only 2 million data scientists. You don't need to be a data scientist. That's the, the beauty of what Altrics is enabling. And, and eBay is a great example of that. Jackie, let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where alters fits in on as that analytics maturity journey continues and what are some of the things that you are most excited about as analytics truly gets democratized across eBay? >>When we start about getting excited about things, when it comes to analytics, I can go on all day, but I I'll keep it short and sweet for you. I do think we are on the topic full of, of, of data scientists. And I really feel that that is your next step for us anyways, is that, how do we get folks to not see data scientists as this big thing, like a rocket scientist, it's, it's something completely different. And it's something that, that is in everybody to a certain extent. So again, partner with three X would just released the AI ML solution, allowing, you know, folks to not have a data scientist program, but actually build models and be able to solve problems that way. So we have engaged with alters and we, we purchased a license, this quite a few. And right now through our mastermind program, we're actually running a four months program for all skill levels, teaching, teaching them AI ML and machine learning and how they can build their own models. >>We are really excited about that. We have over 50 participants without the background from all over the organization. We have members from our customer services. We have even some of our engineers are actually participating in the program. We just kicked it off. And I really believe that that is our next step. I wanna give you a quick example of, of the beauty of this is where we actually just allow people to go out and think about ideas and come up with things. And one of the people in our team who doesn't have a data scientist background at all, was able to develop a solution where, you know, there is a checkout feedback checkout functionality on the eBay site where sellers or buyers can verbatim add information. And she build a model to be able to determine what relates to tax specific, what is the type of problem, and even predict how that problem can be solved before we, as a human even step in, and now instead of us or somebody going to verbatim and try to figure out what's going on there, we can focus on fixing the error versus actually just reading through things and not adding any value. >>And it's a beautiful tool and very impressed. You saw the demo and they developing that further. >>That sounds fantastic. And I think just the one word that keeps coming to mind, and we've said this a number of times in the program today is empowerment. What you're actually really doing to truly empower people across the organization with, with varying degrees of skill level, going down to the high school level, really exciting, we'll have to stay tuned to see what some of the great things are that come from this continued partnership. Ladies, I wanna thank you so much for joining me on the program today and talking about how alters and eBay are really partnering together to democratize analytics and to facilitate its maturity. It's been great talking to you. >>Thank you. >>As you heard over the course of our program organizations, where more people are using analytics who have the deeper capabilities in each of the four E's, that's, everyone, everything everywhere and easy analytics, those organizations achieve more ROI from their respective investments in analytics and automation than those who don't. We also heard a great story from eBay, great example of an enterprise that is truly democratizing analytics across its organization. It's enabling an empowering line of business users to use analytics, not only focused on key aspects of their job, but develop new skills rather than doing the same repetitive tasks. We wanna thank you so much for watching the program today. Remember you can find all of the content on the cue.net. You can find all of the news from today on Silicon angle.com and of course, alter.com. We also wanna thank alt alters for making this program possible and for sponsored in the queue for all of my guests. I'm Lisa Martin. We wanna thank you for watching and bye for now.

>> It's no surprise that 73% of organizations indicate analytics spend will outpace other software investments in the next 12 to 18 months. After all as we know, data is changing the world and the world is changing with it. But is everyone's spending resulting in the same ROI? This is Lisa Martin. Welcome to "theCUBE"'s presentation of democratizing analytics across the enterprise, made possible by Alteryx. An Alteryx commissioned IDC info brief entitled, "Four Ways to Unlock Transformative Business Outcomes from Analytics Investments" found that 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. On this special "CUBE" presentation, Jason Klein, product marketing director of Alteryx, will join me to share key findings from the new Alteryx commissioned IDC brief and uncover how enterprises can derive more value from their data. In our second segment, we'll hear from Alan Jacobson, chief data and analytics officer at Alteryx. He's going to discuss how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. And then in our final segment, Paula Hansen, who is the president and chief revenue officer of Alteryx, and Jacqui Van der Leij Greyling, who is the global head of tax technology at eBay, they'll join me. They're going to share how Alteryx is helping the global eCommerce company innovate with analytics. Let's get the show started. (upbeat music) Jason Klein joins me next, product marketing director at Alteryx. Jason, welcome to the program. >> Hello, nice to be here. >> Excited to talk with you. What can you tell me about the new Alteryx IDC research, which spoke with about 1500 leaders, what nuggets were in there? >> Well, as the business landscape changes over the next 12 to 18 months, we're going to see that analytics is going to be a key component to navigating this change. 73% of the orgs indicated that analytics spend will outpace other software investments. But just putting more money towards technology, it isn't going to solve everything. And this is why everyone's spending is resulting in different ROIs. And one of the reasons for this gap is because 93% of organizations, they're still not fully using the analytics skills of their employees, and this widening analytics gap, it's threatening operational progress by wasting workers' time, harming business productivity and introducing costly errors. So in this research, we developed a framework of enterprise analytics proficiency that helps organizations reap greater benefits from their investments. And we based this framework on the behaviors of organizations that saw big improvements across financial, customer, and employee metrics, and we're able to focus on the behaviors driving higher ROI. >> So the info brief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the info brief that nearly three quarters plan on spending more on analytics than any other software. And can you unpack, what's driving this demand, this need for analytics across organizations? >> Sure, well first there's more data than ever before, the data's changing the world, and the world is changing data. Enterprises across the world, they're accelerating digital transformation to capitalize on new opportunities, to grow revenue, to increase margins and to improve customer experiences. And analytics along with automation and AI is what's making digital transformation possible. They're providing the fuel to new digitally enabled lines of business. >> One of the things that the study also showed was that not all analytics spending is resulting in the same ROI. What are some of the discrepancies that the info brief uncovered with respect to the changes in ROI that organizations are achieving? >> Our research with IDC revealed significant roadblocks across people, processes, and technologies. They're preventing companies from reaping greater benefits from their investments. So for example, on the people side, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% from our survey, are still not using the full breadth of data types available. Yet data's never been this prolific, it's going to continue to grow, and orgs should be using it to their advantage. And lastly organizations, they need to provide the right analytics tools to help everyone unlock the power of data. >> So they- >> They instead rely on outdated spreadsheet technology. In our survey, nine out of 10 respondents said less than half of their knowledge workers are active users of analytics software beyond spreadsheets. But true analytic transformation can't happen for an organization in a few select pockets or silos. We believe everyone regardless of skill level should be able to participate in the data and analytics process and be driving value. >> Should we retake that, since I started talking over Jason accidentally? >> Yep, absolutely we can do so. We'll just go, yep, we'll go back to Lisa's question. Let's just, let's do the, retake the question and the answer, that'll be able to. >> It'll be not all analytics spending results in the same ROI, what are some of the discrepancies? >> Yes, Lisa, so we'll go from your ISO, just so we get that clean question and answer. >> Okay. >> Thank you for that. On your ISO, we're still speeding, Lisa, so give it a beat in your head and then on you. >> Yet not all analytics spending is resulting in the same ROI. So what are some of the discrepancies that the info brief uncovered with respect to ROI? >> Well, our research with IDC revealed significant roadblocks across people, processes, and technologies, all preventing companies from reaping greater benefits from their investments. So on the people side, for example, only one out of five organizations reported a commensurate investment in upskilling for analytics and data literacy as compared to the technology itself. And next, while data is everywhere, most organizations, 63% in our survey, are still not using the full breadth of data types available. Data has never been this prolific. It's going to continue to grow and orgs should be using it to their advantage. And lastly, organizations, they need to provide the right analytic tools to help everyone unlock the power of data, yet instead they're relying on outdated spreadsheet technology. Nine of 10 survey respondents said that less than half of their knowledge workers are active users of analytics software. True analytics transformation can't happen for an organization in a few select pockets or silos. We believe everyone regardless of skill level should be able to participate in the data and analytics process and drive value. >> So if I look at this holistically, then what would you say organizations need to do to make sure that they're really deriving value from their investments in analytics? >> Yeah, sure. So overall, the enterprises that derive more value from their data and analytics and achieve more ROI, they invested more aggressively in the four dimensions of enterprise analytics proficiency. So they've invested in the comprehensiveness of analytics across all data sources and data types, meaning they're applying analytics to everything. They've invested in the flexibility of analytics across deployment scenarios and departments, meaning they're putting analytics everywhere. They've invested in the ubiquity of analytics and insights for every skill level, meaning they're making analytics for everyone. And they've invested in the usability of analytics software, meaning they're prioritizing easy technology to accelerate analytics democratization. >> So very strategic investments. Did the survey uncover any specific areas where most companies are falling short, like any black holes that organizations need to be aware of at the outset? >> It did, it did. So organizations, they need to build a data-centric culture. And this begins with people. But what the survey told us is that the people aspect of analytics is the most heavily skewed towards low proficiency. In order to maximize ROI, organizations need to make sure everyone in the organization has access to the data and analytics technology they need. And then the organizations also have to align their investments with upskilling in data literacy to enjoy that higher ROI. Companies who did so experience higher ROI than companies who underinvested in analytics literacy. So among the high ROI achievers, 78% have a good or great alignment between analytics investment and workforce upskilling compared to only 64% among those without positive ROI. And as more orgs adopt cloud data warehouses or cloud data lakes, in order to manage the massively increasing workloads- Can I start that one over. >> Sure. >> Can I redo this one? >> Yeah. >> Of course, stand by. >> Tongue tied. >> Yep, no worries. >> One second. >> If we could do the same, Lisa, just have a clean break, we'll go your question. >> Yep, yeah. >> On you Lisa. Just give that a count and whenever you're ready. Here, I'm going to give us a little break. On you Lisa. >> So are there any specific areas that the survey uncovered where most companies are falling short? Like any black holes organizations need to be aware of from the outset? >> It did. You need to build a data-centric culture and this begins with people, but we found that the people aspect of analytics is most heavily skewed towards low proficiency. In order to maximize ROI organizations need to make sure everyone has access to the data and analytics technology they need. Organizations that align their analytics investments with upskilling enjoy higher ROI than orgs that are less aligned. For example, among the high ROI achievers in our survey, 78% had good or great alignment between analytics investments and workforce upskilling, compared to only 64% among those without positive ROI. And as more enterprises adopt cloud data warehouses or cloud data lakes to manage increasingly massive data sets, analytics needs to exist everywhere, especially for those cloud environments. And what we found is organizations that use more data types and more data sources generate higher ROI from their analytics investments. Among those with improved customer metrics, 90% were good or great at utilizing all data sources, compared to only 67% among the ROI laggards. >> So interesting that you mentioned people, I'm glad that you mentioned people. Data scientists, everybody talks about data scientists. They're in high demand, we know that, but there aren't enough to meet the needs of all enterprises. So given that discrepancy, how can organizations fill the gap and really maximize the investments that they're making in analytics? >> Right, so analytics democratization, it's no longer optional, but it doesn't have to be complex. So we at Alteryx, we're democratizing analytics by empowering every organization to upskill every worker into a data worker. And the data from this survey shows this is the optimal approach. Organizations with a higher percentage of knowledge workers who are actively using analytics software enjoy higher returns from their analytics investment than orgs still stuck on spreadsheets. Among those with improved financial metrics, AKA the high ROI achievers, nearly 70% say that at least a quarter of their knowledge workers are using analytics software other than spreadsheets compared to only 56% in the low ROI group. Also among the high ROI performers, 63% said data and analytic workers collaborate well or extremely well compared to only 51% in the low ROI group. The data from the survey shows that supporting more business domains with analytics and providing cross-functional analytics correlates with higher ROI. So to maximize ROI, orgs should be transitioning workers from spreadsheets to analytics software. They should be letting them collaborate effectively and letting them do so cross-functionally. >> Yeah, that cross-functional collaboration is essential for anyone in any organization and in any discipline. Another key thing that jumped out from the survey was around shadow IT. The business side is using more data science tools than the IT side. And it's expected to spend more on analytics than other IT. What risks does this present to the overall organization, if IT and the lines of business guys and gals aren't really aligned? >> Well, there needs to be better collaboration and alignment between IT and the line of business. The data from the survey, however, shows that business managers, they're expected to spend more on analytics and use more analytics tools than IT is aware of. And this isn't because the lines of business have recognized the value of analytics and plan to invest accordingly, but a lack of alignment between IT and business. This will negatively impact governance, which ultimately impedes democratization and hence ROI. >> So Jason, where can organizations that are maybe at the outset of their analytics journey, or maybe they're in environments where there's multiple analytics tools across shadow IT, where can they go to Alteryx to learn more about how they can really simplify, streamline, and dial up the value on their investment? >> Well, they can learn more on our website. I also encourage them to explore the Alteryx community, which has lots of best practices, not just in terms of how you do the analytics, but how you stand up in Alteryx environment, but also to take a look at your analytics stack and prioritize technologies that can snap to and enhance your organization's governance posture. It doesn't have to change it, but it should be able to align to and enhance it. >> And of course, as you mentioned, it's about people, process, and technologies. Jason, thank you so much for joining me today, unpacking the IDC info brief and the great nuggets in there. Lots that organizations can learn and really become empowered to maximize their analytics investments. We appreciate your time. >> Thank you, it's been a pleasure. >> In a moment, Alan Jacobson, who's the chief data and analytics officer at Alteryx is going to join me. He's going to be here to talk about how organizations across all industries can accelerate their analytic maturity to drive transformational business outcomes. You're watching "theCUBE", the leader in tech enterprise coverage. >> Somehow many have come to believe that data analytics is for the few, for the scientists, the PhDs, the MBAs. Well, it is for them, but that's not all. You don't have to have an advanced degree to do amazing things with data. You don't even have to be a numbers person. You can be just about anything. A titan of industry or a future titan of industry. You could be working to change the world, your neighborhood, or the course of your business. You can be saving lives or just looking to save a little time. The power of data analytics shouldn't be limited to certain job titles or industries or organizations because when more people are doing more things with data, more incredible things happen. Analytics makes us smarter and faster and better at what we do. It's practically a superpower. That's why we believe analytics is for everyone, and everything, and should be everywhere. That's why we believe in analytics for all. (upbeat music) >> Hey, everyone. Welcome back to "Accelerating Analytics Maturity". I'm your host, Lisa Martin. Alan Jacobson joins me next. The chief of data and analytics officer at Alteryx. Alan, it's great to have you on the program. >> Thanks, Lisa. >> So Alan, as we know, everyone knows that being data driven is very important. It's a household term these days, but 93% of organizations are not utilizing the analytics skills of their employees, which is creating a widening analytics gap. What's your advice, your recommendations for organizations who are just starting out with analytics? >> You're spot on, many organizations really aren't leveraging the full capability of their knowledge workers. And really the first step is probably assessing where you are on the journey, whether that's you personally, or your organization as a whole. We just launched an assessment tool on our website that we built with the International Institute of Analytics, that in a very short period of time, in about 15 minutes, you can go on and answer some questions and understand where you sit versus your peer set versus competitors and kind of where you are on the journey. >> So when people talk about data analytics, they often think, ah, this is for data science experts like people like you. So why should people in the lines of business like the finance folks, the marketing folks, why should they learn analytics? >> So domain experts are really in the best position. They know where the gold is buried in their companies. They know where the inefficiencies are. And it is so much easier and faster to teach a domain expert a bit about how to automate a process or how to use analytics than it is to take a data scientist and try to teach them to have the knowledge of a 20 year accounting professional or a logistics expert of your company. Much harder to do that. And really, if you think about it, the world has changed dramatically in a very short period of time. If you were a marketing professional 30 years ago, you likely didn't need to know anything about the internet, but today, do you know what you would call that marketing professional if they didn't know anything about the internet, probably unemployed or retired. And so knowledge workers are having to learn more and more skills to really keep up with their professions. And analytics is really no exception. Pretty much in every profession, people are needing to learn analytics to stay current and be capable for their companies. And companies need people who can do that. >> Absolutely, it seems like it's table stakes these days. Let's look at different industries now. Are there differences in how you see analytics in automation being employed in different industries? I know Alteryx is being used across a lot of different types of organizations from government to retail. I also see you're now with some of the leading sports teams. Any differences in industries? >> Yeah, there's an incredible actually commonality between the domains industry to industry. So if you look at what an HR professional is doing, maybe attrition analysis, it's probably quite similar, whether they're in oil and gas or in a high tech software company. And so really the similarities are much larger than you might think. And even on the sports front, we see many of the analytics that sports teams perform are very similar. So McLaren is one of the great partners that we work with and they use Alteryx across many areas of their business from finance to production, extreme sports, logistics, wind tunnel engineering, the marketing team analyzes social media data, all using Alteryx, and if I take as an example, the finance team, the finance team is trying to optimize the budget to make sure that they can hit the very stringent targets that F1 Sports has, and I don't see a ton of difference between the optimization that they're doing to hit their budget numbers and what I see Fortune 500 finance departments doing to optimize their budget, and so really the commonality is very high, even across industries. >> I bet every Fortune 500 or even every company would love to be compared to the same department within McLaren F1. Just to know that wow, what they're doing is so incredibly important as is what we're doing. >> So talk- >> Absolutely. >> About lessons learned, what lessons can business leaders take from those organizations like McLaren, who are the most analytically mature? >> Probably first and foremost, is that the ROI with analytics and automation is incredibly high. Companies are having a ton of success. It's becoming an existential threat to some degree, if your company isn't going on this journey and your competition is, it can be a huge problem. IDC just did a recent study about how companies are unlocking the ROI using analytics. And the data was really clear, organizations that have a higher percentage of their workforce using analytics are enjoying a much higher return from their analytic investment, and so it's not about hiring two double PhD statisticians from Oxford. It really is how widely you can bring your workforce on this journey, can they all get 10% more capable? And that's having incredible results at businesses all over the world. An another key finding that they had is that the majority of them said that when they had many folks using analytics, they were going on the journey faster than companies that didn't. And so picking technologies that'll help everyone do this and do this fast and do it easily. Having an approachable piece of software that everyone can use is really a key. >> So faster, able to move faster, higher ROI. I also imagine analytics across the organization is a big competitive advantage for organizations in any industry. >> Absolutely the IDC, or not the IDC, the International Institute of Analytics showed huge correlation between companies that were more analytically mature versus ones that were not. They showed correlation to growth of the company, they showed correlation to revenue and they showed correlation to shareholder values. So across really all of the key measures of business, the more analytically mature companies simply outperformed their competition. >> And that's key these days, is to be able to outperform your competition. You know, one of the things that we hear so often, Alan, is people talking about democratizing data and analytics. You talked about the line of business workers, but I got to ask you, is it really that easy for the line of business workers who aren't trained in data science to be able to jump in, look at data, uncover and extract business insights to make decisions? >> So in many ways, it really is that easy. I have a 14 and 16 year old kid. Both of them have learned Alteryx, they're Alteryx certified and it was quite easy. It took 'em about 20 hours and they were off to the races, but there can be some hard parts. The hard parts have more to do with change management. I mean, if you're an accountant that's been doing the best accounting work in your company for the last 20 years, and all you happen to know is a spreadsheet for those 20 years, are you ready to learn some new skills? And I would suggest you probably need to, if you want to keep up with your profession. The big four accounting firms have trained over a hundred thousand people in Alteryx. Just one firm has trained over a hundred thousand. You can't be an accountant or an auditor at some of these places without knowing Alteryx. And so the hard part, really in the end, isn't the technology and learning analytics and data science, the harder part is this change management, change is hard. I should probably eat better and exercise more, but it's hard to always do that. And so companies are finding that that's the hard part. They need to help people go on the journey, help people with the change management to help them become the digitally enabled accountant of the future, the logistics professional that is E enabled, that's the challenge. >> That's a huge challenge. Cultural shift is a challenge, as you said, change management. How do you advise customers if you might be talking with someone who might be early in their analytics journey, but really need to get up to speed and mature to be competitive, how do you guide them or give them recommendations on being able to facilitate that change management? >> Yeah, that's a great question. So people entering into the workforce today, many of them are starting to have these skills. Alteryx is used in over 800 universities around the globe to teach finance and to teach marketing and to teach logistics. And so some of this is happening naturally as new workers are entering the workforce, but for all of those who are already in the workforce, have already started their careers, learning in place becomes really important. And so we work with companies to put on programmatic approaches to help their workers do this. And so it's, again, not simply putting a box of tools in the corner and saying free, take one. We put on hackathons and analytic days, and it can be great fun. We have a great time with many of the customers that we work with, helping them do this, helping them go on the journey, and the ROI, as I said, is fantastic. And not only does it sometimes affect the bottom line, it can really make societal changes. We've seen companies have breakthroughs that have really made great impact to society as a whole. >> Isn't that so fantastic, to see the difference that that can make. It sounds like you guys are doing a great job of democratizing access to Alteryx to everybody. We talked about the line of business folks and the incredible importance of enabling them and the ROI, the speed, the competitive advantage. Can you share some specific examples that you think of Alteryx customers that really show data breakthroughs by the lines of business using the technology? >> Yeah, absolutely, so many to choose from. I'll give you two examples quickly. One is Armor Express. They manufacture life saving equipment, defensive equipments, like armor plated vests, and they were needing to optimize their supply chain, like many companies through the pandemic. We see how important the supply chain is. And so adjusting supply to match demand is really vital. And so they've used Alteryx to model some of their supply and demand signals and built a predictive model to optimize the supply chain. And it certainly helped out from a dollar standpoint. They cut over a half a million dollars of inventory in the first year, but more importantly, by matching that demand and supply signal, you're able to better meet customer demand. And so when people have orders and are looking to pick up a vest, they don't want to wait. And it becomes really important to get that right. Another great example is British Telecom. They're a company that services the public sector. They have very strict reporting regulations that they have to meet and they had, and this is crazy to think about, over 140 legacy spreadsheet models that they had to run to comply with these regulatory processes and report, and obviously running 140 legacy models that had to be done in a certain order and length, incredibly challenging. It took them over four weeks each time that they had to go through that process. And so to save time and have more efficiency in doing that, they trained 50 employees over just a two week period to start using Alteryx and learn Alteryx. And they implemented an all new reporting process that saw a 75% reduction in the number of man hours it took to run in a 60% run time performance. And so, again, a huge improvement. I can imagine it probably had better quality as well, because now that it was automated, you don't have people copying and pasting data into a spreadsheet. And that was just one project that this group of folks were able to accomplish that had huge ROI, but now those people are moving on and automating other processes and performing analytics in other areas. So you can imagine the impact by the end of the year that they will have on their business, potentially millions upon millions of dollars. And this is what we see again and again, company after company, government agency after government agency, is how analytics are really transforming the way work is being done. >> That was the word that came to mind when you were describing the all three customer examples, transformation, this is transformative. The ability to leverage Alteryx, to truly democratize data and analytics, give access to the lines of business is transformative for every organization. And also the business outcome you mentioned, those are substantial metrics based business outcomes. So the ROI in leveraging a technology like Alteryx seems to be right there, sitting in front of you. >> That's right, and to be honest, it's not only important for these businesses. It's important for the knowledge workers themselves. I mean, we hear it from people that they discover Alteryx, they automate a process, they finally get to get home for dinner with their families, which is fantastic, but it leads to new career paths. And so knowledge workers that have these added skills have so much larger opportunity. And I think it's great when the needs of businesses to become more analytic and automate processes actually matches the needs of the employees, and they too want to learn these skills and become more advanced in their capabilities. >> Huge value there for the business, for the employees themselves to expand their skillset, to really open up so many opportunities for not only the business to meet the demands of the demanding customer, but the employees to be able to really have that breadth and depth in their field of service. Great opportunities there, Alan. Is there anywhere that you want to point the audience to go to learn more about how they can get started? >> Yeah, so one of the things that we're really excited about is how fast and easy it is to learn these tools. So any of the listeners who want to experience Alteryx, they can go to the website, there's a free download on the website. You can take our analytic maturity assessment, as we talked about at the beginning, and see where you are on the journey and just reach out. We'd love to work with you and your organization to see how we can help you accelerate your journey on analytics and automation. >> Alan, it was a pleasure talking to you about democratizing data and analytics, the power in it for organizations across every industry. We appreciate your insights and your time. >> Thank you so much. >> In a moment, Paula Hansen, who is the president and chief revenue officer of Alteryx, and Jacqui Van der Leij Greyling, who's the global head of tax technology at eBay, will join me. You're watching "theCUBE", the leader in high tech enterprise coverage. >> 1200 hours of wind tunnel testing, 30 million race simulations, 2.4 second pit stops. >> Make that 2.3. >> Sector times out the wazoo. >> Way too much of this. >> Velocities, pressures, temperatures, 80,000 components generating 11.8 billion data points and one analytics platform to make sense of it all. When McLaren needs to turn complex data into winning insights, they turn to Alteryx. Alteryx, analytics automation. (upbeat music) >> Hey, everyone, welcome back to the program. Lisa Martin here, I've got two guests joining me. Please welcome back to "theCUBE" Paula Hansen, the chief revenue officer and president at Alteryx, and Jacqui Van der Leij Greyling joins us as well, the global head of tax technology at eBay. They're going to share with you how Alteryx is helping eBay innovate with analytics. Ladies, welcome, it's great to have you both on the program. >> Thank you, Lisa, it's great to be here. >> Yeah, Paula, we're going to start with you. In this program, we've heard from Jason Klein, we've heard from Alan Jacobson. They talked about the need to democratize analytics across any organization to really drive innovation. With analytics, as they talked about, at the forefront of software investments, how's Alteryx helping its customers to develop roadmaps for success with analytics? >> Well, thank you, Lisa. It absolutely is about our customers' success. And we partner really closely with our customers to develop a holistic approach to their analytics success. And it starts of course with our innovative technology and platform, but ultimately we help our customers to create a culture of data literacy and analytics from the top of the organization, starting with the C-suite. And we partner with our customers to build their roadmaps for scaling that culture of analytics, through things like enablement programs, skills assessments, hackathons, setting up centers of excellence to help their organization scale and drive governance of this analytics capability across the enterprise. So at the end of the day, it's really about helping our customers to move up their analytics maturity curve with proven technologies and best practices, so they can make better business decisions and compete in their respective industries. >> Excellent, sounds like a very strategic program, we're going to unpack that. Jacqui, let's bring you into the conversation. Speaking of analytics maturity, one of the things that we talked about in this event is the IDC report that showed that 93% of organizations are not utilizing the analytics skills of their employees, but then there's eBay. How Jacqui did eBay become one of the 7% of organizations who's really maturing and how are you using analytics across the organization at eBay? >> So I think the main thing for us is when we started out was is that, our, especially in finance, they became spreadsheet professionals instead of the things that we really want our employees to add value to. And we realized we had to address that. And we also knew we couldn't wait for all our data to be centralized until we actually start using the data or start automating and being more effective. So ultimately we really started very, very actively embedding analytics in our people and our data and our processes. >> Starting with people is really critical. Jacqui, continuing with you, what were some of the roadblocks to analytics adoption that you faced and how did you overcome them? >> So I think eBay is a very data driven company. We have a lot of data. I think we are 27 years around this year, so we have the data, but it is everywhere. And how do you use that data? How do you use it efficiently? How do you get to the data? And I believe that that is definitely one of our biggest roadblocks when we started out and just finding those data sources and finding ways to connect to them to move forward. The other thing is that people were experiencing a lot of frustration. I mentioned before about the spreadsheet professionals. And there was no, we were not independent. You couldn't move forward, you would've put it on somebody else's roadmap to get the data and to get the information if you want it. So really finding something that everybody could access analytics or access data. And finally we have to realize is that this is uncharted territory. This is not exactly something that everybody is used to working with every day. So how do you find something that is easy, and that is not so daunting on somebody who's brand new to the field? And I would call those out as your major roadblocks, because you always have, not always, but most of the times you have support from the top, and in our case we have, but at the end of the day, it's our people that need to actually really embrace it, and making that accessible for them, I would say is definitely not per se, a roadblock, but basically a block you want to be able to move. >> It's really all about putting people first. Question for both of you, and Paula we'll start with you, and then Jacqui we'll go to you. I think the message in this program that the audience is watching with us is very clear. Analytics is for everyone, should be for everyone. Let's talk now about how both of your organizations are empowering people, those in the organization that may not have technical expertise to be able to leverage data, so that they can actually be data driven. Paula. >> Yes, well, we leverage our platform across all of our business functions here at Alteryx. And just like Jacqui explained, at eBay finance is probably one of the best examples of how we leverage our own platform to improve our business performance. So just like Jacqui mentioned, we have this huge amount of data flowing through our enterprise and the opportunity to leverage that into insights and analytics is really endless. So our CFO Kevin Rubin has been a key sponsor for using our own technology. We use Alteryx for forecasting all of our key performance metrics, for business planning, across our audit function, to help with compliance and regulatory requirements, tax, and even to close our books at the end of each quarter. So it's really going to remain across our business. And at the end of the day, it comes to how do you train users? How do you engage users to lean into this analytic opportunity to discover use cases? And so one of the other things that we've seen many companies do is to gamify that process, to build a game that brings users into the experience for training and to work with each other, to problem solve and along the way, maybe earn badges depending on the capabilities and trainings that they take. And just have a little healthy competition as an employee base around who can become more sophisticated in their analytic capability. So I think there's a lot of different ways to do it. And as Jacqui mentioned, it's really about ensuring that people feel comfortable, that they feel supported, that they have access to the training that they need, and ultimately that they are given both the skills and the confidence to be able to be a part of this great opportunity of analytics. >> That confidence is key. Jacqui, talk about some of the ways that you're empowering folks without that technical expertise to really be data driven. >> Yeah, I think it means to what Paula has said in terms of getting people excited about it, but it's also understanding that this is a journey and everybody is at a different place in their journey. You have folks that's already really advanced who has done this every day. And then you have really some folks that this is brand new or maybe somewhere in between. And it's about how you get everybody in their different phases to get to the initial destination. I say initial, because I believe a journey is never really complete. What we have done is that we decided to invest, and built a proof of concept, and we got our CFO to sponsor a hackathon. We opened it up to everybody in finance in the middle of the pandemic. So everybody was on Zoom and we told people, listen, we're going to teach you this tool, it's super easy, and let's just see what you can do. We ended up having 70 entries. We had only three weeks. So and these are people that do not have a background. They are not engineers, they're not data scientists. And we ended up with a 25,000 hour savings at the end of that hackathon from the 70 entries with people that have never, ever done anything like this before. And there you have the result. And then it just went from there. People had a proof of concept. They knew that it worked and they overcame the initial barrier of change. And that's where we are seeing things really, really picking up now. >> That's fantastic. And the business outcome that you mentioned there, the business impact is massive, helping folks get that confidence to be able to overcome sometimes the cultural barriers is key here. I think another thing that this program has really highlighted is there is a clear demand for data literacy in the job market, regardless of organization. Can each of you share more about how you're empowering the next generation of data workers? Paula, we'll start with you. >> Absolutely, and Jacqui says it so well, which is that it really is a journey that organizations are on and we as people in society are on in terms of upskilling our capabilities. So one of the things that we're doing here at Alteryx to help address this skillset gap on a global level is through a program that we call SparkED, which is essentially a no-cost analytics education program that we take to universities and colleges globally to help build the next generation of data workers. When we talk to our customers like eBay and many others, they say that it's difficult to find the skills that they want when they're hiring people into the job market. And so this program's really developed just to do just that, to close that gap and to work hand in hand with students and educators to improve data literacy for the next generation. So we're just getting started with SparkED. We started last May, but we currently have over 850 educational institutions globally engaged across 47 countries, and we're going to continue to invest here because there's so much opportunity for people, for society and for enterprises, when we close the gap and empower more people with the necessary analytics skills to solve all the problems that data can help solve. >> So SparkED has made a really big impact in such a short time period. It's going to be fun to watch the progress of that. Jacqui, let's go over to you now. Talk about some of the things that eBay is doing to empower the next generation of data workers. >> So we basically wanted to make sure that we kept that momentum from the hackathon, that we don't lose that excitement. So we just launched the program called eBay Masterminds. And what it basically is, is it's an inclusive innovation in each other, where we firmly believe that innovation is for upskilling for all analytics roles. So it doesn't matter your background, doesn't matter which function you are in, come and participate in in this where we really focus on innovation, introducing new technologies and upskilling our people. We are, apart from that, we also said, well, we shouldn't just keep it to inside eBay. We have to share this innovation with the community. So we are actually working on developing an analytics high school program, which we hope to pilot by the end of this year, where we will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, but also how to use Alteryx. And we're working with, actually, we're working with SparkED and they're helping us develop that program. And we really hope that at, say, by the end of the year, we have a pilot and then also next year, we want to roll it out in multiple locations in multiple countries and really, really focus on that whole concept of analytics for all. >> Analytics for all, sounds like Alteryx and eBay have a great synergistic relationship there that is jointly aimed at especially going down the stuff and getting people when they're younger interested, and understanding how they can be empowered with data across any industry. Paula, let's go back to you, you were recently on "theCUBE"'s Supercloud event just a couple of weeks ago. And you talked about the challenges the companies are facing as they're navigating what is by default a multi-cloud world. How does the Alteryx Analytics Cloud platform enable CIOs to democratize analytics across their organization? >> Yes, business leaders and CIOs across all industries are realizing that there just aren't enough data scientists in the world to be able to make sense of the massive amounts of data that are flowing through organizations. Last I checked, there was 2 million data scientists in the world, so that's woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. So what we're seeing now with CIOs, with business leaders is that they're integrating data analysis and the skillset of data analysis into virtually every job function, and that is what we think of when we think of analytics for all. And so our mission with Alteryx Analytics Cloud is to empower all of those people in every job function, regardless of their skillset, as Jacqui pointed out from people that are just getting started all the way to the most sophisticated of technical users. Every worker across that spectrum can have a meaningful role in the opportunity to unlock the potential of the data for their company and their organizations. So that's our goal with Alteryx Analytics Cloud, and it operates in a multi cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyze, and report out so that we can break down data silos across the enterprise and help drive real business outcomes as a result of unlocking the potential of data. >> As well as really lessening that skill gap. As you were saying, there's only 2 million data scientists. You don't need to be a data scientist, that's the beauty of what Alteryx is enabling and eBay is a great example of that. Jacqui, let's go ahead and wrap things with you. You talked a great deal about the analytics maturity that you have fostered at eBay. It obviously has the right culture to adapt to that. Can you talk a little bit and take us out here in terms of where Alteryx fits in as that analytics maturity journey continues and what are some of the things that you are most excited about as analytics truly gets democratized across eBay? >> When we're starting up and getting excited about things when it comes to analytics, I can go on all day, but I'll keep it short and sweet for you. I do think we are on the top of the pool of data scientists. And I really feel that that is your next step, for us anyways, is that how do we get folks to not see data scientists as this big thing, like a rocket scientist, it's something completely different. And it's something that is in everybody in a certain extent. So again, partnering with Alteryx who just released the AI ML solution, allowing folks to not have a data scientist program, but actually build models and be able to solve problems that way. So we have engaged with Alteryx and we purchased the licenses, quite a few. And right now through our Masterminds program, we're actually running a four month program for all skill levels, teaching them AI ML and machine learning and how they can build their own models. We are really excited about that. We have over 50 participants without a background from all over the organization. We have members from our customer services. We have even some of our engineers are actually participating in the program. We just kicked it off. And I really believe that that is our next step. I want to give you a quick example of the beauty of this is where we actually just allow people to go out and think about ideas and come up with things. And one of the people in our team who doesn't have a data scientist background at all, was able to develop a solution where there is a checkout feedback functionality on the eBay side where sellers or buyers can verbatim add information. And she built a model to be able to determine what relates to tax specific, what is the type of problem, and even predict how that problem can be solved before we as a human even step in, and now instead of us or somebody going to verbatim and try to figure out what's going on there, we can focus on fixing the error versus actually just reading through things and not adding any value, and it's a beautiful tool and I was very impressed when I saw the demo and definitely developing that sort of thing. >> That sounds fantastic. And I think just the one word that keeps coming to mind, and we've said this a number of times in the program today is empowerment. What you're actually really doing to truly empower people across the organization with varying degrees of skill level, going down to the high school level, really exciting. We'll have to stay tuned to see what some of the great things are that come from this continued partnership. Ladies, I want to thank you so much for joining me on the program today and talking about how Alteryx and eBay are really partnering together to democratize analytics and to facilitate its maturity. It's been great talking to you. >> Thank you, Lisa. >> Thank you so much. (cheerful electronic music) >> As you heard over the course of our program, organizations where more people are using analytics who have deeper capabilities in each of the four Es, that's everyone, everything, everywhere, and easy analytics, those organizations achieve more ROI from their respective investments in analytics and automation than those who don't. We also heard a great story from eBay, great example of an enterprise that is truly democratizing analytics across its organization. It's enabling and empowering line of business users to use analytics, not only focused on key aspects of their job, but develop new skills rather than doing the same repetitive tasks. We want to thank you so much for watching the program today. Remember you can find all of the content on thecube.net. You can find all of the news from today on siliconangle.com and of course alteryx.com. We also want to thank Alteryx for making this program possible and for sponsoring "theCUBE". For all of my guests, I'm Lisa Martin. We want to thank you for watching and bye for now. (upbeat music)

(upbeat music) >> Hi everyone, and welcome back to managing risk across your extended attack service area with Armis Asset Intelligence Platform. I'm John Furrier, your host. We're here with the CISO Perspective. Alex Schuchman, who is the CISO of Colgate-Palmolive Company. Alex, thanks for coming on. >> Thanks for having me. >> You know, unified visibility across the enterprise service area is about knowing what you got to protect. You can't protect what you can't see. Tell me more about how you guys are able to centralize your view with network assets with Armis. >> Yeah, I think the most important part of any security program is really visibility. And that's one of the building blocks when you're building a security program. You need to understand what's in your environment, what you can control, what is being introduced new into the environment, and that's really what, any solution that gives you full visibility to your infrastructure, to your environment, to all the assets that are there, that's really one of your bread and butter pieces to your security program. >> What's been the impact on your business? >> You know, I think from an IT point of view, running the security program, you know, our key thing is really enabling the business to do their job better. So if we can give them visibility into all the assets that are available in their individual environments, and we're doing that in an automated fashion with no manual collection, you know, that's yet another thing that they don't have to worry about, and then we're delivering. Because really IT is an enabler for the business. And then they can focus really on what their job is, which is to deliver product. >> Yeah, and a lot of changes in their network. You got infrastructure, you got IOT devices, OT devices. So vulnerability management becomes more important. It's been around for a while, but it's not just IT devices anymore. There are gaps in vulnerability across the OT network. What can you tell us about Colgate's use of Armis' vulnerability management? What can you see now? What couldn't you see before? Can you share your thoughts on this? >> Yeah, I think what's really interesting about the kind of manufacturing environments today is, if you look back a number of years, most of the manufacturing equipment was really disconnected from the internet. It was really running in silos. So it was very easy to protect equipment that isn't internet-connected. You could put a firewall, you could segment it off. And it was really on an island on its own. Nowadays, you have a lot of IOT devices. you have a lot of internet-connected devices, sensors providing information to multiple different suppliers or vendor solutions. And you have to really then open up your ecosystem more, which, of course, means you have to change your security posture, and you really have to embrace if there's a vulnerability with one of those suppliers then how do you mitigate the risk associated to that vulnerability? Armis really helps us get a lot of information so that we can then make a decision with our business teams. >> That whole operational aspect of criticality is huge, on the assets knowing what's key. How has that changed the security workload for you guys? >> You know, for us, I mean, it's all about being efficient. If we can have the visibility across our manufacturing environments, then my team can easily consume that information. You know, if we spend a lot of time trying to digest the information, trying to process it, trying to prioritize it, that really hurts our efficiency as a team or as a function. What we really like is being able to use technology to help us do that work. We're not an IT shop. We're a manufacturing shop, but we're a very technical shop so we like to drive everything through automation and not be a bottleneck for any of the actions that take place. >> You know the old expression, is the juice worth the squeeze? It comes up a lot when people are buying tools around vulnerability management, and point for all this stuff. So SaaS solution is key with no agents to deploy. They have that. Talk about how you operationalize Armis in your environment. How quickly did it achieve time to value? Take us through that consumption of the product, and what was the experience like? >> Yeah, I'll definitely say in the security ecosystem, that's one of the biggest promises you hear across the industry. And when we started with Armis, we started with a very small deployment, and we wanted to make sure if it was really worth the lift, to your point. We implemented the first set of plants very quickly, actually even quicker than we had put in our project plan, which is not typical for implementing complex security solutions. And then we were so successful with that, we expanded to cover more of our manufacturing plants, and we were able to get really true visibility across our entire manufacturing organization in the first year, with the ability to also say that we extended that information, that visibility to our manufacturing organization, and they could also consume it just as easily as we could. >> That's awesome. How many assets did you guys discover? Just curious on the numbers? >> Oh, that's the really interesting part. You know, before we started this project we would've had to do a manual audit of our plants, which is typical in our industry. You know, when we started this project and we put in estimates, we really didn't have a great handle on what we were going to find. And what's really nice about the Armis solution is it's truly giving you full visibility. So you're actually seeing, besides the servers, and the PLCs, and all the equipment that you're familiar with, you're also connecting it to your wireless access points. You're connecting it to see any of those IOT devices as well. And then you're really getting full visibility through all the integrations that they offer. You're amazed how many devices you're actually seeing across your entire ecosystem. >> It's like Google maps for your infrastructure. You know, the street view. You want to look at it. You get the, you know, fake tree in there, whatever, but it gives you the picture. That's key. >> Correct. And with a nice visualization and an easy search engine, similar to your Google analogy, you know, everything is really at your fingertips. If you want to find something, you just go to the search bar, click a couple entries and boom, you get your list of the associated devices or the the associated locations devices. >> Well, Alex, I appreciate your time. I know you're super busy at CSIG a lot of your plate. Thanks for coming on sharing. Appreciate it. >> No problem, John. Thanks for having me. >> Okay. In a moment, Bryan Inman, a sales engineer at Armis will be joining me. You're watching theCUBE, the leader in high tech coverage. Thanks for watching. (upbeat music)

>>Hello, Ron. Welcome back to the manage risk across your extended attack service area with Armas asset intelligence platform. I'm Sean furier host we're here at the CSO perspective, Alex Chuck bin, who is the CSO of Colgate Colgate Palm mall of company. Alex, thanks for coming on. >>Thanks for having >>Me, you know, unified visibility across the enterprise surface area is about knowing what you gotta protect. You can't protect what you can't see. Tell me more about how you guys are able to centralize your view with network assets with Armas. >>Yeah, I think the, the most important part of any security program is really visibility. And, and that's one of, kind of the building blocks. When you're building a security program, you need to understand what's in your environment. What's what you control, what is being introduced new into the environment. And that's really what any solution that gives you full visibility to your infrastructure, to your environment, to all the assets that are there, that that's really one of your bread and butter pieces to your security program. >>What's been the impact on your business? >>You know, I, I think from, from an it point of view, running the security program, you know, our key thing is really enabling the business to do their job better. So if we can give them visibility into all the assets that are available in their individual environments, and we're doing that in an automated fashion with no manual collection, you know, that's yet another thing that they don't have to worry about. And then we're delivering because really it is an enabler for the business. And then they can focus really on what their job is, which is to, to deliver product. >>Yeah. And a lot of changes in their network. You got infrastructure, you got OT devices, OT devices. So vulnerability management becomes more important. It's been around for a while, but it's not just it devices anymore. There are gaps in vulnerability across the OT network. What can you tell us about Colgate's use of Armas as vulnerability management? What can you, can you see now what you couldn't you see before? Can you share your thoughts on this? >>Yeah, I, I think what's really interesting about the, the kind of manufacturing environments today is if you look back a number of years, most of the manufacturing equipment was really disconnected from the internet. It was really running in silos. So it was very easy to protect equipment that, that isn't internet connected. You could put a firewall, you could segment it off. And it was, it was really on an island on its own. Nowadays you have a lot of IOT devices. You have a lot of internet connected devices, sensors providing information to multiple different suppliers or vendor solutions. And you have to really then open up your ecosystem more, which of course means you have to change your security posture and you really have to embrace. If there's a vulnerability with one of those suppliers, then how do you mitigate the risk associated to vulnerability? Armas really helps us get a lot of information so that we can then make a decision with our business teams. >>That whole operational aspect of criticality is huge. How on the assets knowing what's what's key? How has that changed your, the, the security workload for you guys? >>Yeah, for us, I mean, it, it's all about being efficient. If we can have the, the visibility across our manufacturing environments, then, then my team can easily consume that information. You know, if we spend a lot of time trying to digest the information, trying to process it, trying to prioritize it, that, that, that really hurts our efficiency as, as a team where as a function, what we really like is being able to use technology to help us do that work. We're, we're not an it shop. We're a manufacturing shop, but we're a very technical shop so that we like to drive everything through automation and not be a bottleneck for any of the, the actions that take place. >>You know, the old expression is the juice worth. The squeeze. It comes up a lot when people are buying tools around vulnerability management and point, all this stuff. So SAS solution is key with no agents to deploy. They have that talk about how you operationalize Armas in your environment, how quickly did it AC achieve time to value, take us through that, that consumption of the product. And, and, and what was the experience like? >>Yeah, I I'll definitely say a in, in the security ecosystem that that's one of the, the biggest promises you hear across the industry. And when, when we started with Armas, we started with a very small deployment and we wanted to make sure if, if it was really worth the lift to your point, we implemented the, the first set of plants very quickly, actually, even quicker than we had put in our project plan, which is, is not typical for implementing complex security solutions. And then we were so successful with that. We expanded to cover more of our manufacturing plants, and we were able to get really true visibility across our entire manufacturing organization in the first year with the ability to also say that we extended that, that information, that visibility to our manufacturing organization, and they could also consume it just as easily as we could. >>That's awesome. How many assets did you guys discover? Just curious on the numbers? >>Oh, that, that's the really interesting part, you know, before we started this project, we would've had to do a, a manual audit of, of our plants, which is typical in, in our industry. You know, when, when we started this project and, and we put in estimates, we really, really didn't have a great handle on what we were gonna find. And what's really nice about the Arma solution is it it's truly giving you full visibility. So you're actually seeing, besides the servers and the PLCs and all the equipment that you're familiar with, you're also connecting it to your wireless access points. You're connecting it to see any of those IOT devices as well. And then you're really getting full visibility through all the integrations that they offer. You're amazed how many devices you're actually seeing across your entire ecosystem. >>It's like Google maps for your infrastructure. You get little street view. You wanna look at it, you get the, you know, fake tree in there, whatever, but it gives you the picture that's key, >>Correct. And with a nice visualization and an easy search engine, similar to your, your Google analogy, you know, everything is, is, is really at your fingertips. If you wanna find something, you just go to the search bar, click a couple entries and, and boom, you get your, your list of the associated devices or the, the associated locations devices. >>Well, I appreciate your time. I know you're super busy at CSIG a lot of your plate. Thanks for coming on sharing. Appreciate it. >>No problem, John. Thanks for having me. >>Okay. In a moment, Brian Inman, a sales engineer at Armas will be joining me. You're watching the cube, the leader in high tech coverage. Thanks for watching.

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020, sponsored by Intel and AWS. >>Welcome back to the cubes coverage of AWS reinvent 2020 I'm Lisa Martin. Joining me next is one of our cube alumni. Breton McMillan is back the director of us, federal for AWS. Right. It's great to see you glad that you're safe and well. >>Great. It's great to be back. Uh, I think last year when we did the cube, we were on the convention floor. It feels very different this year here at reinvent, it's gone virtual and yet it's still true to how reinvent always been. It's a learning conference and we're releasing a lot of new products and services for our customers. >>Yes. A lot of content, as you say, the one thing I think I would say about this reinvent, one of the things that's different, it's so quiet around us. Normally we're talking loudly over tens of thousands of people on the showroom floor, but great. That AWS is still able to connect in such an actually an even bigger way with its customers. So during Theresa Carlson's keynote, want to get your opinion on this or some info. She talked about the AWS open data sponsorship program, and that you guys are going to be hosting the national institutes of health, NIH sequence, read archive data, the biologist, and may former gets really excited about that. Talk to us about that because especially during the global health crisis that we're in, that sounds really promising >>Very much is I am so happy that we're working with NIH on this and multiple other initiatives. So the secret greed archive or SRA, essentially what it is, it's a very large data set of sequenced genomic data. And it's a wide variety of judge you gnomic data, and it's got a knowledge human genetic thing, but all life forms or all branches of life, um, is in a SRA to include viruses. And that's really important here during the pandemic. Um, it's one of the largest and oldest, um, gen sequence genomic data sets are out there and yet it's very modern. It has been designed for next generation sequencing. So it's growing, it's modern and it's well used. It's one of the more important ones that it's out there. One of the reasons this is so important is that we know to find cures for what a human ailments and disease and death, but by studying the gem genomic code, we can come up with the answers of these or the scientists can come up with answer for that. And that's what Amazon is doing is we're putting in the hands of the scientists, the tools so that they can help cure heart disease and diabetes and cancer and, um, depression and yes, even, um, uh, viruses that can cause pandemics. >>So making this data, sorry, I'm just going to making this data available to those scientists. Worldwide is incredibly important. Talk to us about that. >>Yeah, it is. And so, um, within NIH, we're working with, um, the, um, NCBI when you're dealing with NIH, there's a lot of acronyms, uh, and uh, at NIH, it's the national center for, um, file type technology information. And so we're working with them to make this available as an open data set. Why, why this is important is it's all about increasing the speed for scientific discovery. I personally think that in the fullness of time, the scientists will come up with cures for just about all of the human ailments that are out there. And it's our job at AWS to put into the hands of the scientists, the tools they need to make things happen quickly or in our lifetime. And I'm really excited to be working with NIH on that. When we start talking about it, there's multiple things. The scientists needs. One is access to these data sets and SRA. >>It's a very large data set. It's 45 petabytes and it's growing. I personally believe that it's going to double every year, year and a half. So it's a very large data set and it's hard to move that data around. It's so much easier if you just go into the cloud, compute against it and do your research there in the cloud. And so it's super important. 45 petabytes, give you an idea if it were all human data, that's equivalent to have a seven and a half million people or put another way 90% of everybody living in New York city. So that's how big this is. But then also what AWS is doing is we're bringing compute. So in the cloud, you can scale up your compute, scale it down, and then kind of the third they're. The third leg of the tool of the stool is giving the scientists easy access to the specialized tool sets they need. >>And we're doing that in a few different ways. One that the people would design these toolsets design a lot of them on AWS, but then we also make them available through something called AWS marketplace. So they can just go into marketplace, get a catalog, go in there and say, I want to launch this resolve work and launches the infrastructure underneath. And it speeds the ability for those scientists to come up with the cures that they need. So SRA is stored in Amazon S3, which is a very popular object store, not just in the scientific community, but virtually every industry uses S3. And by making this available on these public data sets, we're giving the scientists the ability to speed up their research. >>One of the things that Springs jumps out to me too, is it's in addition to enabling them to speed up research, it's also facilitating collaboration globally because now you've got the cloud to drive all of this, which allows researchers and completely different parts of the world to be working together almost in real time. So I can imagine the incredible power that this is going to, to provide to that community. So I have to ask you though, you talked about this being all life forms, including viruses COVID-19, what are some of the things that you think we can see? I expect this to facilitate. Yeah. >>So earlier in the year we took the, um, uh, genetic code or NIH took the genetic code and they, um, put it in an SRA like format and that's now available on AWS and, and here's, what's great about it is that you can now make it so anybody in the world can go to this open data set and start doing their research. One of our goals here is build back to a democratization of research. So it used to be that, um, get, for example, the very first, um, vaccine that came out was a small part. It's a vaccine that was done by our rural country doctor using essentially test tubes in a microscope. It's gotten hard to do that because data sets are so large, you need so much computer by using the power of the cloud. We've really democratized it and now anybody can do it. So for example, um, with the SRE data set that was done by NIH, um, organizations like the university of British Columbia, their, um, cloud innovation center is, um, doing research. And so what they've done is they've scanned, they, um, SRA database think about it. They scanned out 11 million entries for, uh, coronavirus sequencing. And that's really hard to do in a typical on-premise data center. Who's relatively easy to do on AWS. So by making this available, we can have a larger number of scientists working on the problems that we need to have solved. >>Well, and as the, as we all know in the U S operation warp speed, that warp speed alone term really signifies how quickly we all need this to be progressing forward. But this is not the first partnership that AWS has had with the NIH. Talk to me about what you guys, what some of the other things are that you're doing together. >>We've been working with NIH for a very long time. Um, back in 2012, we worked with NIH on, um, which was called the a thousand genome data set. This is another really important, um, data set and it's a large number of, uh, against sequence human genomes. And we moved that into, again, an open dataset on AWS and what's happened in the last eight years is many scientists have been able to compute about on it. And the other, the wonderful power of the cloud is over time. We continue to bring out tools to make it easier for people to work. So what they're not they're computing using our, um, our instance types. We call it elastic cloud computing. whether they're doing that, or they were doing some high performance computing using, um, uh, EMR elastic MapReduce, they can do that. And then we've brought up new things that really take it to the next layer, like level like, uh, Amazon SageMaker. >>And this is a, um, uh, makes it really easy for, um, the scientists to launch machine learning algorithms on AWS. So we've done the thousand genome, uh, dataset. Um, there's a number of other areas within NIH that we've been working on. So for example, um, over at national cancer Institute, we've been providing some expert guidance on best practices to how, how you can architect and work on these COVID related workloads. Um, NIH does things with, um, collaboration with many different universities, um, over 2,500, um, academic institutions. And, um, and they do that through grants. And so we've been working with doc office of director and they run their grant management applications in the RFA on AWS, and that allows it to scale up and to work very efficiently. Um, and then we entered in with, um, uh, NIH into this program called strides strides as a program for knowing NIH, but also all these other institutions that work within NIH to use the power of the cloud use commercial cloud for scientific discovery. And when we started that back in July of 2018, long before COVID happened, it was so great that we had that up and running because now we're able to help them out through the strides program. >>Right. Can you imagine if, uh, let's not even go there? I was going to say, um, but so, okay. So the SRA data is available through the AWS open data sponsorship program. You talked about strides. What are some of the other ways that AWS system? >>Yeah, no. So strides, uh, is, uh, you know, wide ranging through multiple different institutes. So, um, for example, over at, uh, the national heart lung and blood Institute, uh, do di NHL BI. I said, there's a lot of acronyms and I gel BI. Um, they've been working on, um, harmonizing, uh, genomic data. And so working with the university of Michigan, they've been analyzing through a program that they call top of med. Um, we've also been working with a NIH on, um, establishing best practices, making sure everything's secure. So we've been providing, um, AWS professional services that are showing them how to do this. So one portion of strides is getting the right data set and the right compute in the right tools, in the hands of the scientists. The other areas that we've been working on is making sure the scientists know how to use it. And so we've been developing these cloud learning pathways, and we started this quite a while back, and it's been so helpful here during the code. So, um, scientists can now go on and they can do self-paced online courses, which we've been really helping here during the, during the pandemic. And they can learn how to maximize their use of cloud technologies through these pathways that we've developed for them. >>Well, not education is imperative. I mean, there, you think about all of the knowledge that they have with within their scientific discipline and being able to leverage technology in a way that's easy is absolutely imperative to the timing. So, so, um, let's talk about other data sets that are available. So you've got the SRA is available. Uh, what are their data sets are available through this program? >>What about along a wide range of data sets that we're, um, uh, doing open data sets and in general, um, these data sets are, um, improving the human condition or improving the, um, the world in which we live in. And so, um, I've talked about a few things. There's a few more, uh, things. So for example, um, there's the cancer genomic Atlas that we've been working with, um, national cancer Institute, as well as the national human genomic research Institute. And, um, that's a very important data set that being computed against, um, uh, throughout the world, uh, commonly within the scientific community, that data set is called TCGA. Um, then we also have some, uh, uh, datasets are focused on certain groups. So for example, kids first is a data set. That's looking at a lot of the, um, challenges, uh, in diseases that kids get every kind of thing from very rare pediatric cancer as to heart defects, et cetera. >>And so we're working with them, but it's not just in the, um, uh, medical side. We have open data sets, um, with, uh, for example, uh, NOAA national ocean open national oceanic and atmospheric administration, um, to understand what's happening better with climate change and to slow the rate of climate change within the department of interior, they have a Landsat database that is looking at pictures of their birth cell, like pictures of the earth, so we can better understand the MCO world we live in. Uh, similarly, uh, NASA has, um, a lot of data that we put out there and, um, over in the department of energy, uh, there's data sets there, um, that we're researching against, or that the scientists are researching against to make sure that we have better clean, renewable energy sources, but it's not just government agencies that we work with when we find a dataset that's important. >>We also work with, um, nonprofit organizations, nonprofit organizations are also in, they're not flush with cash and they're trying to make every dollar work. And so we've worked with them, um, organizations like the child mind Institute or the Allen Institute for brain science. And these are largely like neuro imaging, um, data. And we made that available, um, via, um, our open data set, um, program. So there's a wide range of things that we're doing. And what's great about it is when we do it, you democratize science and you allowed many, many more science scientists to work on these problems. They're so critical for us. >>The availability is, is incredible, but also the, the breadth and depth of what you just spoke. It's not just government, for example, you've got about 30 seconds left. I'm going to ask you to summarize some of the announcements that you think are really, really critical for federal customers to be paying attention to from reinvent 2020. >>Yeah. So, um, one of the things that these federal government customers have been coming to us on is they've had to have new ways to communicate with their customer, with the public. And so we have a product that we've had for a while called on AWS connect, and it's been used very extensively throughout government customers. And it's used in industry too. We've had a number of, um, of announcements this weekend. Jasmine made multiple announcements on enhancement, say AWS connect or additional services, everything from helping to verify that that's the right person from AWS connect ID to making sure that that customer's gets a good customer experience to connect wisdom or making sure that the managers of these call centers can manage the call centers better. And so I'm really excited that we're putting in the hands of both government and industry, a cloud based solution to make their connections to the public better. >>It's all about connections these days, but I wish we had more time, cause I know we can unpack so much more with you, but thank you for joining me on the queue today, sharing some of the insights, some of the impacts and availability that AWS is enabling the scientific and other federal communities. It's incredibly important. And we appreciate your time. Thank you, Lisa, for Brett McMillan. I'm Lisa Martin. You're watching the cubes coverage of AWS reinvent 2020.

>>Hello. My name is Isaac twang and I am on the faculty at MIT in electrical engineering and computer science and in physics. And it is a pleasure for me to be presenting at today's NTT research symposium of 2020 to share a little bit with you about programmable quantum simulators theory and practice the simulation of physical systems as described by their Hamiltonian. It's a fundamental problem which Richard Fineman identified early on as one of the most promising applications of a hypothetical quantum computer. The real world around us, especially at the molecular level is described by Hamiltonians, which captured the interaction of electrons and nuclei. What we desire to understand from Hamiltonian simulation is properties of complex molecules, such as this iron molded to them. Cofactor an important catalyst. We desire there are ground States, reaction rates, reaction dynamics, and other chemical properties, among many things for a molecule of N Adams, a classical simulation must scale exponentially within, but for a quantum simulation, there is a potential for this simulation to scale polynomials instead. >>And this would be a significant advantage if realizable. So where are we today in realizing such a quantum advantage today? I would like to share with you a story about two things in this quest first, a theoretical optimal quantum simulation, awkward them, which achieves the best possible runtime for generic Hamiltonian. Second, let me share with you experimental results from a quantum simulation implemented using available quantum computing hardware today with a hardware efficient model that goes beyond what is utilized by today's algorithms. I will begin with the theoretically optimal quantum simulation uncle rhythm in principle. The goal of quantum simulation is to take a time independent Hamiltonian age and solve Schrodinger's equation has given here. This problem is as hard as the hardest quantum computation. It is known as being BQ P complete a simplification, which is physically reasonable and important in practice is to assume that the Hamiltonian is a sum over terms which are local. >>For example, due to allow to structure these local terms, typically do not commute, but their locality means that each term is reasonably small, therefore, as was first shown by Seth Lloyd in 1996, one way to compute the time evolution that is the exponentiation of H with time is to use the lead product formula, which involves a successive approximation by repetitive small time steps. The cost of this charterization procedure is a number of elementary steps, which scales quadratically with the time desired and inverse with the error desired for the simulation output here then is the number of local terms in the Hamiltonian. And T is the desired simulation time where Epsilon is the desired simulation error. Today. We know that for special systems and higher or expansions of this formula, a better result can be obtained such as scaling as N squared, but as synthetically linear in time, this however is for a special case, the latest Hamiltonians and it would be desirable to scale generally with time T for a order T time simulation. >>So how could such an optimal quantum simulation be constructed? An important ingredient is to transform the quantum simulation into a quantum walk. This was done over 12 years ago, Andrew trials showing that for sparse Hamiltonians with around de non-zero entries per row, such as shown in this graphic here, one can do a quantum walk very much like a classical walk, but in a superposition of right and left shown here in this quantum circuit, where the H stands for a hazard market in this particular circuit, the head Mar turns the zero into a superposition of zero and one, which then activate the left. And the right walk in superposition to graph of the walk is defined by the Hamiltonian age. And in doing so Childs and collaborators were able to show the walk, produces a unitary transform, which goes as E to the minus arc co-sign of H times time. >>So this comes close, but it still has this transcendental function of age, instead of just simply age. This can be fixed with some effort, which results in an algorithm, which scales approximately as towel log one over Epsilon with how is proportional to the sparsity of the Hamiltonian and the simulation time. But again, the scaling here is a multiplicative product rather than an additive one, an interesting insight into the dynamics of a cubit. The simplest component of a quantum computer provides a way to improve upon this single cubits evolve as rotations in a sphere. For example, here is shown a rotation operator, which rotates around the axis fi in the X, Y plane by angle theta. If one, the result of this rotation as a projection along the Z axis, the result is a co-sign squared function. That is well-known as a Ravi oscillation. On the other hand, if a cubit is rotated around multiple angles in the X Y plane, say around the fee equals zero fee equals 1.5 and fee equals zero access again, then the resulting response function looks like a flat top. >>And in fact, generalizing this to five or more pulses gives not just flattered hops, but in fact, arbitrary functions such as the Chevy chef polynomial shown here, which gets transplants like bullying or, and majority functions remarkably. If one does rotations by angle theta about D different angles in the X Y plane, the result is a response function, which is a polynomial of order T in co-sign furthermore, as captured by this theorem, given a nearly arbitrary degree polynomial there exists angles fi such that one can achieve the desired polynomial. This is the result that derives from the Remez exchange algorithm used in classical discreet time signal processing. So how does this relate to quantum simulation? Well recall that a quantum walk essentially embeds a Hamiltonian insight, the unitary transform of a quantum circuit, this embedding generalize might be called and it involves the use of a cubit acting as a projector to control the application of H if we generalize the quantum walk to include a rotation about access fee in the X Y plane, it turns out that one obtains a polynomial transform of H itself. >>And this it's the same as the polynomial in the quantum signal processing theorem. This is a remarkable result known as the quantum synchrony value transformed theorem from contrast Julian and Nathan weep published last year. This provides a quantum simulation auger them using quantum signal processing. For example, can start with the quantum walk result and then apply quantum signal processing to undo the arc co-sign transformation and therefore obtain the ideal expected Hamiltonian evolution E to the minus I H T the resulting algorithm costs a number of elementary steps, which scales as just the sum of the evolution time and the log of one over the error desired this saturates, the known lower bound, and thus is the optimal quantum simulation algorithm. This table from a recent review article summarizes a comparison of the query complexities of the known major quantum simulation algorithms showing that the cubitus station and quantum sequel processing algorithm is indeed optimal. >>Of course, this optimality is a theoretical result. What does one do in practice? Let me now share with you the story of a hardware efficient realization of a quantum simulation on actual hardware. The promise of quantum computation traditionally rests on a circuit model, such as the one we just used with quantum circuits, acting on cubits in contrast, consider a real physical problem from quantum chemistry, finding the structure of a molecule. The starting point is the point Oppenheimer separation of the electronic and vibrational States. For example, to connect it, nuclei, share a vibrational mode, the potential energy of this nonlinear spring, maybe model as a harmonic oscillator since the spring's energy is determined by the electronic structure. When the molecule becomes electronically excited, this vibrational mode changes one obtains, a different frequency and different equilibrium positions for the nuclei. This corresponds to a change in the spring, constant as well as a displacement of the nuclear positions. >>And we may write down a full Hamiltonian for this system. The interesting quantum chemistry question is known as the Frank Condon problem. What is the probability of transition between the original ground state and a given vibrational state in the excited state spectrum of the molecule, the Frank content factor, which gives this transition probability is foundational to quantum chemistry and a very hard and generic question to answer, which may be amiable to solution on a quantum computer in particular and natural quantum computer to use might be one which already has harmonic oscillators rather than one, which has just cubits. This has provided any Sonic quantum processors, such as the superconducting cubits system shown here. This processor has both cubits as embodied by the Joseph's injunctions shown here, and a harmonic oscillator as embodied by the resonant mode of the transmission cavity. Given here more over the output of this planar superconducting circuit can be connected to three dimensional cavities instead of using cubit Gates. >>One may perform direct transformations on the bull's Arctic state using for example, beam splitters, phase shifters, displacement, and squeezing operators, and the harmonic oscillator, and may be initialized and manipulated directly. The availability of the cubit allows photon number resolve counting for simulating a tri atomic two mode, Frank Condon factor problem. This superconducting cubits system with 3d cavities was to resonators cavity a and cavity B represent the breathing and wiggling modes of a Triumeq molecule. As depicted here. The coupling of these moles was mediated by a superconducting cubit and read out was accomplished by two additional superconducting cubits, coupled to each one of the cavities due to the superconducting resonators used each one of the cavities had a, a long coherence time while resonator States could be prepared and measured using these strong coupling of cubits to the cavity. And Posana quantum operations could be realized by modulating the coupling cubit in between the two cavities, the cavities are holes drilled into pure aluminum, kept superconducting by millikelvin scale. >>Temperatures microfiber, KT chips with superconducting cubits are inserted into ports to couple via a antenna to the microwave cavities. Each of the cavities has a quality factor so high that the coherence times can reach milliseconds. A coupling cubit chip is inserted into the port in between the cavities and the readout and preparation cubit chips are inserted into ports on the sides. For sake of brevity, I will skip the experimental details and present just the results shown here is the fibrotic spectrum obtained for a water molecule using the Pulsonix superconducting processor. This is a typical Frank content spectrum giving the intensity of lions versus frequency in wave number where the solid line depicts the theoretically expected result and the purple and red dots show two sets of experimental data. One taken quickly and another taken with exhaustive statistics. In both cases, the experimental results have good agreement with the theoretical expectations. >>The programmability of this system is demonstrated by showing how it can easily calculate the Frank Condon spectrum for a wide variety of molecules. Here's another one, the ozone and ion. Again, we see that the experimental data shown in points agrees well with the theoretical expectation shown as a solid line. Let me emphasize that this quantum simulation result was obtained not by using a quantum computer with cubits, but rather one with resonators, one resonator representing each one of the modes of vibration in this trial, atomic molecule. This approach represents a far more efficient utilization of hardware resources compared with the standard cubit model because of the natural match of the resonators with the physical system being simulated in comparison, if cubit Gates had been utilized to perform the same simulation on the order of a thousand cubit Gates would have been required compared with the order of 10 operations, which were performed for this post Sonic realization. >>As in topically, the Cupid motto would have required significantly more operations because of the need to retire each one of the harmonic oscillators into some max Hilbert space size compared with the optimal quantum simulation auger rhythms shown in the first half of this talk, we see that there is a significant gap between available quantum computing hardware can perform and what optimal quantum simulations demand in terms of the number of Gates required for a simulation. Nevertheless, many of the techniques that are used for optimal quantum simulation algorithms may become useful, especially if they are adapted to available hardware, moving for the future, holds some interesting challenges for this field. Real physical systems are not cubits, rather they are composed from bolt-ons and from yawns and from yawns need global anti-Semitism nation. This is a huge challenge for electronic structure calculation in molecules, real physical systems also have symmetries, but current quantum simulation algorithms are largely governed by a theorem, which says that the number of times steps required is proportional to the simulation time. Desired. Finally, real physical systems are not purely quantum or purely classical, but rather have many messy quantum classical boundaries. In fact, perhaps the most important systems to simulate are really open quantum systems. And these dynamics are described by a mixture of quantum and classical evolution and the desired results are often thermal and statistical properties. >>I hope this presentation of the theory and practice of quantum simulation has been interesting and worthwhile. Thank you.

( bright upbeat music) >> Announcer: From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a Cube conversation. >> Hey, welcome back. You're ready, Jeff Frick here with theCUBE, we are still getting through COVID. It's a hot August day here in San Francisco Bay Area. It is 99, somebody said in the city that's hot, but we're still getting through it. We're still reaching out to the community, we're still talking to leaders in all the areas that we cover. And one of the really interesting areas is natural language processing. And it's a small kind of subset. We'll get into it a little bit more detail, we are very specific place within the applied AI world. And one of my very good friends and Cube alumni, who's really an expert in the space, he's coming back for his second startup in the space. And we're joined by Ben, he's Ben Chung, the Co-founder of Ogmagod. Did I get that right Ben, Ogmagod. >> That's correct. That's right. >> Great to see you again. >> Thank you for inviting to the show. >> Well, I love it. One of the topics that we've been covering a lot Ben is applied AI. 'Cause there's just so much kind of conversation about artificial intelligence, the machine learning is kind of this global big thing. And it kind of reminds me of kind of big data or cloud, in the generic it's interesting but it's really not that interesting, 'cause that's really not where it gets applied. Where I think what's much more interesting and why I wanted to have you back on is, where is it actually being applied in applications? And where are we seeing it in solutions? And where is it actually changing people's lives, changing people's days, changing people's behavior, and you seem to have a propensity for this stuff. It was five years ago, I looked July five years ago, we had you on and you had found Genie, which was a natural anti processing company focused on scheduling. Successful exit, sold that to Microsoft and they baked it into who knows, there probably baked in all over the place. Left there now you've done it again. So before we get into it. What so intriguing to you about natural language processing for all the different kind of opportunities that you might go after from an AI perspective? What's special about this realm that keeps drawing you back? >> Yeah, sure, I mean it, to be honest it was not anything premeditated, I kind of stumbled on it. I before this, I was more like an infrastructure guy spent a number of years at VMware and had a blast there and learned a lot. Then I kind of just stumble on it. Because when we started doing the startup, we didn't intend it to be a AI startup or anything like that. We just had a problem that my co-founder Charles Lee and I really wanted to solve, which is to help people, solve people's scheduling problem. But very shortly after getting into and start looking at some use cases, we thought that the easiest way is to communicate with people like humans do to help them do the scheduling. And that's kind of how I stumbled on it. And it wasn't until that I stumbled on it that I realized that it has a lot of attraction to me, because I throughout my whole life, I'm always very interested in the human emotions of it, how humans relate to each other. And that's always been the hidden side project thing, I do traveling to figure out stuff and get a little bit of that. But once I start getting into this field, I realized that there's a lot about it, about humanity and how humans communicate that it was kind of like a hidden interest for me. That now suddenly coming out and it kind of just got me hooked. >> Right, that's awesome. So one of the things and we'll just get into it is people are a little bit familiar with natural language processing, probably from Siri and from Google and from Alexa and increasingly some of these tools but I think, you kind of rapidly find out beyond what's the weather and play a song and tell me a joke that the functionality is relatively limited. So when people think about natural language and they have that as a reference point, how do you help them see that it's a lot more than, asking Siri for the weather. >> Yeah, there are a lot of capability but also hopefully not offensive to some of the tech visionaries. Just as a guy who is dealing with it every day, there are also lots of limitation is not nearly to the degree of refinements. Like what might being preach out there saying that the machines are going to take over everything in one day, we have a lot of struggles that are very basic stuff with machines. However, there has been definitely a lot of breakthroughs in the last few years and that's why I'm dedicating my life and my time into this area because I think that it just, there's going to be huge amount of innovation continuously going in this area. So that's at the high level, but if you talk about, in terms of artificial intelligence and in general, I think, I have my own understanding, I'm more like an apply guy, lot of academics so what I'm going to say might make some academics cringe because I'm more like a everyday practical guy and try to re conciliate these concepts myself. The way that I view is that artificial intelligence has really tried to help mimic some human capabilities that originally thought that is the domain of human, only humans are able to do it, but machines now try to demonstrate that machine can do it, like as though the humans could. So and then usually people get that mixed up with machine learning, to me is actually quite different thing. Artificial intelligence just like what I mentioned, machine learning is just a technique or a science or way of applying like to leverage this capability, machine learning capability in solving these artificial intelligent problems, to make it more achievable to raise the bar on it. So I don't think we should use them interchangeably, artificial intelligence and machine learning. Because today machine learning is the big deal that are making the progress wise, tomorrow might be something else to help improve artificial intelligence. And in the past, it was something else before machine learning. So it's a progression, the machine learning is the very powerful and popular technique right now to being used. Now within artificial intelligence, I think you mentioned that there are various different domains and topics, there is like object recognition deals with image processing, there's speech detection, there's a video and what I would call action or situation detection. And then there's natural language processing, which is the domain that I'm in that is really in that stage of where we seeing quite a bit of break through, but it's not quite there yet. Whereas versus speech detection and image processing actually has done a tremendous progress in the past. So and in you can say that like the innovation there is not as obvious or as leap frogging as the natural language processing. >> Right, so some of the other examples that we know about that are shared often for machine learning or say, the visual thing, can you identify a chihuahua from the blueberry muffin, which sounds kind of funny until you see the pictures, they actually look very, very similar. And the noise stated that Google and their Google Photos, right, has so many pictures such a huge and diverse data set in which to train the machines to identify a chihuahua versus a blueberry muffin. Or you take the case in Tesla, if you've watched any of their autonomous vehicle stuff and their computer vision process and they have the fleet, hundreds of thousands of cars that are recording across many, many cameras reporting back every night. With natural language processing you don't have that kind of a data set. So when you think about training the machine to the way that I speak, which is different than the way you speak and the little nuances, even if we're trying to say the same thing, I would imagine that the variety in the data set is so much higher and the quantity of the data set is so much lower that's got to be a kind of special machine learning challenge. >> Yes, it is. I think the people say that there is, we are at the cusp of, being able to understand language in general, I don't believe that we are very far away from that. And even if when you narrow scope to say, like focus on one single language like English, even within that, we still very far from it. So I think the reality, at least for me, speaking from the ground level, kind of person tried to make use of these capabilities is that you really have to narrow it to a very narrow domain to focus on and bound it. And my previous startup is really that our assistant to help you schedule meetings, that assistant doesn't understand anything else other than scheduling, we were only able to train it to really focus on doing scheduling, if you try to ask it about joke or ask anything else, it wouldn't be able to understand that. So, I think the reality on the ground at least from what I see of a practical application and being successful at it, you really need to like have a very narrow domain in which you apply these capabilities. And then in terms of technology being used broadly in natural language processing in my view there are two parts of it, one is the input, which is sometimes call natural language understanding. And then that part is actually very good progress. And then the other part is the natural language generation, meaning that the machine knows how to compose sentences and generate back to you, that is still very, very early days. So there is that break up and then if you go further, I don't want to bore you Jeff here with all these different nuances, but when you look at natural language understanding, there are a lot of areas like what we call topic extraction or entity extraction, event extraction. So that's to extract the right things and understand those things from the sentences, there is sentimental analysis knowing that where some a sentence expresses somebody angry or some different kinds of emotions, there is summarization, meaning that I can take sets of texts or paragraphs of text and summarize with fewer words for you. So and then there is like dialogue management, which manages the dialogue with the person. So they're like these various different fields within it. So the deeper you look, there's like the more stuff within it and there's more challenges. So it's not like a blanket statement, say like, "Hey, we could conquer on this." And if you digging deep there's some good progress in certain this area. But some areas like it's really just getting started. >> Right, well we talked about in getting ready for this call and kind of reviewing some of the high level concepts of and you brought up, what is the vocab? So first you have to just learn what is the vocabulary, which a lot of people probably think it stops there. But really then what is the meaning of the vocabulary, but even more important is the intent, right, which is all driven by context. And so the complexity, beyond vocabulary is super high and extremely nuanced. So how do you start to approach algorithmically, to start to call out these things like intent or I mean, people talk about sentiment all the time, that's kind of an old marketing thing, but when you're talking about specific details, to drive a conversation, and you're also oh, by the way, converting back and forth between voice and text to run the algorithms in a text based system, I assume inside the computer, not a voice system. How do you start to identify and programmatically define intent and context? >> Yeah, just to share a little anecdote, like one of the most interesting part of, since I started this journey six years ago and also interesting was a very frustrating part is that, especially when I was doing the scheduling system, is that how sloppy people are with their communication and how little that they say they communicate to you and expect you to understand. And when we were doing the scheduling assistant, we're constantly challenged by somebody telling us certain things and we look at it's like, well, what do they mean exactly? For example, like one of the simple thing that we used to talk a lot with new people coming on the team about is that when people say they want to schedule next week, they don't necessarily mean next week, what they mean is not this week. So it doesn't, if you like take it literally and you say, "Oh, sorry, Jeff, there is no time available next week." And actually Jeff probably not even remember that he told you to schedule next week, to what he remember, what he told you not to schedule it this week. So when you come back to them and say, "Jeff, you have nothing available this week or next week." And Jeff might say like, while your assistant is kind of dumb, like, why are you asking me this question? If there was nothing available next week, just scheduled the week after next week. But the problem is that you literally said next week, so if we took you literally, we would cause unhappiness for you. But we kind of have to guess like what exactly you mean. So don't like this a good example where they're like lot of sloppiness and lot of contextual things that we have to take into account when we communicate what humans, or when we try to understand what they say. So yeah, is exactly your point is not like mathematics is not simple logic. There are a lot of things to it. So the way that I look at it, there are really two parts of it. There's the science part and then there's art part to it. The science part is like what people normally talk about and I mentioned earlier, you have to narrow your domain to a very narrow domain. Because you cannot, you don't have the luxury of collecting infinite data set like Google does. You as a startup, or any team within a corporation, you cannot expect to have that kind of data set that Google or Microsoft or Facebook has. So without the data set, huge data set, so you want to deliver something with a smaller data set. So you have to narrow your domain. So that's one of the science part. The other part is I think people talk about all the time to be very disciplined about data collection and creating training data sets so that you have a very clean and good training data set. So these two are very important on the science part and that's expected. But I think a lot of people don't realize this, what I would call the art part of it, is really there are two parts to that. One is exactly like what you said Jeff is to narrow your domain or make some assumption within the domain, so that you can make some guesses about the context because the user is not giving it to you verbally or giving you to you into text. A lot of us we find out visually by looking at the person as we communicate with them. Or even harder we have some kind of empathetic understanding or situational understanding, meaning that there is some knowledge that we know that Jeff is in this situation, therefore, I understand what he's saying right now means this or that Jeff is a tech guy like me, therefore, he's saying certain thing, I have the empathetic understanding that he meant this as a tech guy. So that's a really hard kind of part of it to capture or make some good guesses about the context. So that's one part. The other part is that you can only guess so much. So you have to really design the user experience, you have to be very careful how you design the user experience to try what you don't know. So that it's not frustrating to the user or to put guardrails in place such that the user doesn't go out of balm and start going to the place where you are not trained for that you don't have to understand it. >> Right, because it's so interesting, 'cause we talked about that before that so much of communication, it's not hard to know that communication is really hard, emails are horrible. We have a hard time as humans, unless we're looking at each other and pick up all these nonverbal cues that add additional context and am I being heard, am I being understood? Does this person seem to understand what I'm trying to say? Is it not getting in? I mean, there's so many these kind of nonverbal cues as you've expressed, that really support the communication of ideas beyond simply the words in which we speak. So and then the other thing you got to worry about too, as you said, ultimately, it's user experience if the user experience sucks, for instance, if you're just super slow, 'cause you're not ready to make some guesses on context and it just takes for a long time, people are not going to to use the thing. So I'm curious on the presentation of the results, right? Lots of different ways that that can happen. Lots of different ways to screw it up. But how do you do it in such a way that it's actually adding value to some specific task or job and maybe this is a good segue to talk about what you're doing now at Ogmagod, I'm sorry I have to look again. I haven't memorized that yet. 'Cause what you're also doing if I recall is you're taking out an additional group of data and additional datasets in beyond simply this conversational flow. But ultimately, you've got to suck it in, as you said, you've got to do the analysis on it. But at the end of the day, it's really about effective presentation of that data in a way that people can do something with it. So tell us a little bit about what you're doing now beyond scheduling in the old days. >> Sure, yeah, I left Microsoft late last year and started a new startup. It's called Ogmagod. And what we do is to help salespeople to be more effective, understand the customer better so that they have higher probability of winning the deal or to be able to shorten the sales cycle. And oftentimes, a lot of the sales cycle got LinkedIn is because of the lack of understanding and there's also, I say, we focus on B2B sales. So for B2B salespeople, the world's really changed a lot since the internet came about. In the old days is really about, tell it to explain what your product is and so that your customer understand your product, but the new days is really about not explaining your product because the customer can find out everything about your product by looking at your website or maybe your marketing people did do such a good job, they already communicated to the customer exactly what your product does. But really to win out against other people you really like almost like a consultant to go to your customer and say, like, I have done your job, almost like I've done your job before I know about your company. And let me try to help you to fix this problem. And our product fit in as part of that, but our focus is let's fix this problem. So how would you be able to talk like that, like you've done this job before? Like you worked at this company before? How do you get at the level of information that you can present yourself that way to the customer and differentiate yourself against all the other people who try to get their attention, all the people sending them email every day automatically, how do you differentiate that? So we felt that the way that you do it is really have the depth of understanding where your customer that is unrivaled by anybody else. Now sure, you can do that, you can Google your customer all day, reorder news report, know all the leadership, could follow them on social media-- >> Right, they're supposed to be doing all this stuff, right Ben, they're supposed to be doing all this stuff and with Google and the internet there's no excuse anymore. It's like, how did you not do your homework? You just have to get the Yellow Pages. >> Yeah, why didn't you do your work? Yes, people get beat up by their management saying like, "Oh, how come you miss this? "It's right there go on Google." But the truth is that you have to be empathetic to a salesperson. A lot of people don't realize that for a salesperson, every salesperson, you might own 300 accounts in your territory. And a lot of times in terms of companies, there might be thousands of companies in your territory. Are you going to spend seven hours, follow all these 300 companies and read all tweet. Check out the thousands of employees in each of these company, their LinkedIn profiles, look at their job listings, look at all the news articles. It's impossible to do as a human, as a person. If you do that you'll be sitting in your computer all day and you never even get to the door to have a conversation with the customer. So that is the challenge so I felt like salespeople really put up impossible tasks, because all this information out there, you're expected it to know. And if you screwed up because you didn't check, then it's your fault. But then on the same time, how can they check all 300 accounts and be on top of everything? So, what we thought is that like, "Hey, we made a lot of progress "on natural language processing "and natural language understanding." And salespeople what they look for is a quite narrow domain. They are looking for some very specific thing related to what they selling, and very specific projects, pinpoints budget related to what they're selling. So it's a very narrow domain, we felt like it's not super narrow. It's a little bit broader than I would say scheduling. But it's still very narrow the kind of things that they're looking for. They're looking for those buying triggers. They're looking for problem statements within the customers that relate to what they selling. So we think that we can use, develop a bunch of machine learning models and use what's available in terms of the web. What's out there on the web, the type of information out there. And to be able to say, like, salesperson, you don't need to go and keep up and scan, all the tweets and all the news and everything else for these 300 companies that you cover, we'll scan all of them, we will put them into our machine learning pipeline and filter out all the junk, because there are lots of junk out there, like Nike, that's like, I don't know, hundreds of news release probably per week. And most of them are not relevant to you. It doesn't make sense for you to read all of those. So but how about we read all of them and we extract out, we it's difficult topic extraction, we extract out the topic that you're looking for and then we organize it and present to you. Not just we extracting out the topic. Once we get the topic how about we look up all the people that are related to that topic in the company for you so that you can call on them. So you know what you want to talk to them about, which is this topic or this pin point. And you know who to talk to, these are the people. So that's what we do. That's that's really interesting. It's been a tagline around here for a long time, right separating the signal from the noise. And I think what you have identified, right is, as you said, now we live in the age where all the information is out there. In fact, there's too much information. So you should be able to find what you're looking for. But to your point, there's too much. So how do you find the filter? How do you find the trusted kind of conduit for information so that you're not just simply overwhelmed that what you're talking about, if I hear you right, is you're actually querying publicly available data for particular types of I imagine phrases, keywords, sentences, digital transformation initiative, blah, blah, blah. And then basically then coalescing the ecosystem around that particular data point. And then how do you then present that back to the salesperson who's trying to figure out what he's going to work on today. >> B2B salespeople, they start with an opportunity. So opportunity is actually a very concrete word at least in the tech B2B sales-- >> We know, we see the 60 stories in downtown San Francisco will validate statement. (laughs) >> Yes, so yeah, so it starts with the word opportunity. So the output is a set of potential opportunity. So it speaks to the salespersons language and say, when you use us, we don't just say "Hey, Jeff, there's this news article about Twilio and you cover Twilio, that's interesting to you." "Oh, there's a guy at Twilio that matches the kind of persona that you sell into." We don't start with that, we start with, "Jeff, there are six Opportunities for you at Twilio. "Let's explain what those things are." And then explain the people behind these opportunities so that you can start qualify them. So get you started, right way in your vocabulary in a package that you understand. So that I think that's what differentiates us. >> Right, and at some point in time, would you potentially just thinking logically down the road, you have some type of Salesforce API. So it just pumps into whatever their existing system is. That they're working every day. And then it describes based on the algorithm, why the system identified this opportunity, what the attributes are that flagged it, who are the right people, et cetera. Awesome, so what kind of data are you requiring-- >> Yes, you are designing our product wise. >> (laughs) Since Dave and John, watch this. They're going to want to talk to you, I'm sure. But what type of data sets are you querying? >> There are lots of them. We learned most of it by through the process working for salespeople, meaning that we work for salespeople, we may be quote, unquote, stand behind their back and see what they're searching. They're searching LinkedIn. They're searching jobs. They're searching endless court transcripts, they're looking at 10K 10Q's, they dig up various, some people are very, very creative, digging out various parts of the web and find really good information. The challenge is that they can't do this to scale. They can't do it for 300 accounts, 'cause we're doing for one accounts very is laborious. So there are various different places that we can find information. And in terms of the pattern that we're looking for. It's not just keyword, it's really concepts. We call it a topic. We really looking for very specific topics that the salesperson looks for. And that's not just a word, because sometimes words is very misleading. For example, I tell you one of the common words in tech is called Jenkins. Jenkins is a very popular technologies, continuous delivery technologies step but Jenkins is also happens to be a very common last name for people. >> (laughs) Well, I'm always reminded of our Intel days with all the acronyms, but my favorite is ASP 'Cause you could use ASP twice in the same sentence and mean two different things, right? Average selling price or application service provider back in the days before we call them clouds, but yeah, so the nuances is so tricky. So within kind of what you're doing then and as you described working within defined data sets and keeping the UX and user experience pretty dialed in and within the rails, are there particular types of opportunities in terms of B2B types of opportunities that fit better that have kind of a richer data set, a higher efficacy in the returns what do you kind of seeing in terms of great opportunities for you guys. >> We're still early, so I can't tell you that like from a global view because we are like less than one year old experience, quite honestly. But so far we are being led by the customer. So meaning that there is an interesting customer, they ask us to look for certain topics or certain things. And we always find it to my surprise, because and that really is, like, I'm constantly surprised by how much is there out in the web, like what you were saying, like customer ask us to look for something. And I thought for sure, this thing we couldn't do it, we can find it. And we gave it a try and low and behold, there it is. It's out there. So, to be honest, I can't tell you at this point, because I have not run into any limits. But that is because we are still a very young startup. And we are not like Google. We're not trying to be all encompassing looking for everything and looking over everything. We're just looking over everything that a salesperson wants, that's it. >> So I'm going to make you jump up a couple levels. Since you've been thinking about this and working on this for a long time, there's a lot of conversation about machines taking everybody's jobs, then there's the whole kind of sidetrack launch to that, which is no, it's all about helping people do better jobs and helping people do more higher value work and less drudgery. I mean, that sounds so consistent with what you're talking about, I wonder as somebody down in the weeds of artificial intelligence, if you can kind of tell us your vision of how this is going to unfold over the next several years, is it just going to be many, many, many little applications that slowly before we know it are going to have moved, along many fronts very far, or do you still see it's such a fundamental human thing in terms of the communication that the these machines will get better at learning, but ultimately, they can kind of fulfill this promise of taking care of the drudgery and freeing up the people to make what are actually much harder decisions from a computer's point of view than maybe the things that we think about that a three year old could ascertain with very little extra effort. >> Yeah, if you take a look at what we do and hopefully it didn't sound like we're underselling our startup but a lot of it really is we taking away to time consumer and also grunt work process of the data collection and cleaning up the data. The humans, the real human intelligence should be focused on data analysis to be able to derive lots of insights of the data. So and to be able to formulate a strategy, how to win the account, how to win the deal. That's what's the human intelligence should be focused on. The other part by struggling with doing the Google search and in return 300 entries, in 30 different pages and you have to click through each one and then give up the first week, that kind of data collection data hunting work, we are really, it should not, I don't think it's worthy, quite honestly, for a very educated person to deal with. And we can invest it back in helping the human to do what the humans are really good at is that, how do I talk to Jeff? And I'm going to get a deal out to Jeff, how can I help and through helping him solving his problem, how can I take the burden of solving the problem from Jeff's head and solve the problem for him? That's what human intelligence for me as a salesperson, I would prefer to do that instead of sitting in front of my desk and doing googling, so net net what I'm trying to say using ourselves as an example is that we're not taking over the job of a salesperson, there was no way that we can close a deal for you. But what we're doing is that we're empowering you so that you look like you're on top of 300 accounts and you talk to any of those accounts, you'll be able to talk to the people, your customer, their particular customer, like you know them inside out. And without you being the superhuman to be able to do all this stuff, but as far as that customer is concerned, sounds like you were on top of all this stuff all day and that's all you do, you have no other customers, they're the only customer. In fact, you on top of 300 customers. So that's kind of the value that we see, to provide to the human is to allow you to scale by removing these grunt work that are preventing you from scaling or living up to your potential how you wanted to present yourself, how you want to deliver yourself. There's no way that we can be smarter than human, no way. I just don't see it not in my lifetime. >> I just love, we've had a lot of conversations over the years and you talking about the difficulty in training the computers on some really nuanced kind of human things versus the things that they're very very good at and keeping the AI in the right guard wheel is probably just as important as keeping the user interface in the right lane as well to make sure that it's a mutually beneficial exchange and one doesn't go off and completely miss the benefit to the other. Well, Ben, it's a great story. Really exciting place to dedicate yourself and we are just digging watching the story and we're going to enjoy watching this one unfold. So thanks for taking a few minutes in sharing your insight on natural language processing and this applied machine learning techniques. >> Thank you, Jeff. It's always a pleasure. >> Yep, all right. He's Ben, am Jeff, you're watching theCUBE. Thanks for watching. We'll see you next time. (bright upbeat music)

>> Paige: Hello, everybody and thank you for joining us today on the Virtual Vertica BDC 2020. Today's breakout session is entitled A Technical Overview of the Vertica Architecture. I'm Paige Roberts, Open Source Relations Manager at Vertica and I'll be your host for this webinar. Now joining me is Ryan Role-kuh? Did I say that right? (laughs) He's a Vertica Senior Software Engineer. >> Ryan: So it's Roelke. (laughs) >> Paige: Roelke, okay, I got it, all right. Ryan Roelke. And before we begin, I want to be sure and encourage you guys to submit your questions or your comments during the virtual session while Ryan is talking as you think of them as you go along. You don't have to wait to the end, just type in your question or your comment in the question box below the slides and click submit. There'll be a Q and A at the end of the presentation and we'll answer as many questions as we're able to during that time. Any questions that we don't address, we'll do our best to get back to you offline. Now, alternatively, you can visit the Vertica forums to post your question there after the session as well. Our engineering team is planning to join the forums to keep the conversation going, so you can have a chat afterwards with the engineer, just like any other conference. Now also, you can maximize your screen by clicking the double arrow button in the lower right corner of the slides and before you ask, yes, this virtual session is being recorded and it will be available to view on demand this week. We'll send you a notification as soon as it's ready. Now, let's get started. Over to you, Ryan. >> Ryan: Thanks, Paige. Good afternoon, everybody. My name is Ryan and I'm a Senior Software Engineer on Vertica's Development Team. I primarily work on improving Vertica's query execution engine, so usually in the space of making things faster. Today, I'm here to talk about something that's more general than that, so we're going to go through a technical overview of the Vertica architecture. So the intent of this talk, essentially, is to just explain some of the basic aspects of how Vertica works and what makes it such a great database software and to explain what makes a query execute so fast in Vertica, we'll provide some background to explain why other databases don't keep up. And we'll use that as a starting point to discuss an academic database that paved the way for Vertica. And then we'll explain how Vertica design builds upon that academic database to be the great software that it is today. I want to start by sharing somebody's approximation of an internet minute at some point in 2019. All of the data on this slide is generated by thousands or even millions of users and that's a huge amount of activity. Most of the applications depicted here are backed by one or more databases. Most of this activity will eventually result in changes to those databases. For the most part, we can categorize the way these databases are used into one of two paradigms. First up, we have online transaction processing or OLTP. OLTP workloads usually operate on single entries in a database, so an update to a retail inventory or a change in a bank account balance are both great examples of OLTP operations. Updates to these data sets must be visible immediately and there could be many transactions occurring concurrently from many different users. OLTP queries are usually key value queries. The key uniquely identifies the single entry in a database for reading or writing. Early databases and applications were probably designed for OLTP workloads. This example on the slide is typical of an OLTP workload. We have a table, accounts, such as for a bank, which tracks information for each of the bank's clients. An update query, like the one depicted here, might be run whenever a user deposits $10 into their bank account. Our second category is online analytical processing or OLAP which is more about using your data for decision making. If you have a hardware device which periodically records how it's doing, you could analyze trends of all your devices over time to observe what data patterns are likely to lead to failure or if you're Google, you might log user search activity to identify which links helped your users find the answer. Analytical processing has always been around but with the advent of the internet, it happened at scales that were unimaginable, even just 20 years ago. This SQL example is something you might see in an OLAP workload. We have a table, searches, logging user activity. We will eventually see one row in this table for each query submitted by users. If we want to find out what time of day our users are most active, then we could write a query like this one on the slide which counts the number of unique users running searches for each hour of the day. So now let's rewind to 2005. We don't have a picture of an internet minute in 2005, we don't have the data for that. We also don't have the data for a lot of other things. The term Big Data is not quite yet on anyone's radar and The Cloud is also not quite there or it's just starting to be. So if you have a database serving your application, it's probably optimized for OLTP workloads. OLAP workloads just aren't mainstream yet and database engineers probably don't have them in mind. So let's innovate. It's still 2005 and we want to try something new with our database. Let's take a look at what happens when we do run an analytic workload in 2005. Let's use as a motivating example a table of stock prices over time. In our table, the symbol column identifies the stock that was traded, the price column identifies the new price and the timestamp column indicates when the price changed. We have several other columns which, we should know that they're there, but we're not going to use them in any example queries. This table is designed for analytic queries. We're probably not going to make any updates or look at individual rows since we're logging historical data and want to analyze changes in stock price over time. Our database system is built to serve OLTP use cases, so it's probably going to store the table on disk in a single file like this one. Notice that each row contains all of the columns of our data in row major order. There's probably an index somewhere in the memory of the system which will help us to point lookups. Maybe our system expects that we will use the stock symbol and the trade time as lookup keys. So an index will provide quick lookups for those columns to the position of the whole row in the file. If we did have an update to a single row, then this representation would work great. We would seek to the row that we're interested in, finding it would probably be very fast using the in-memory index. And then we would update the file in place with our new value. On the other hand, if we ran an analytic query like we want to, the data access pattern is very different. The index is not helpful because we're looking up a whole range of rows, not just a single row. As a result, the only way to find the rows that we actually need for this query is to scan the entire file. We're going to end up scanning a lot of data that we don't need and that won't just be the rows that we don't need, there's many other columns in this table. Many information about who made the transaction, and we'll also be scanning through those columns for every single row in this table. That could be a very serious problem once we consider the scale of this file. Stocks change a lot, we probably have thousands or millions or maybe even billions of rows that are going to be stored in this file and we're going to scan all of these extra columns for every single row. If we tried out our stocks use case behind the desk for the Fortune 500 company, then we're probably going to be pretty disappointed. Our queries will eventually finish, but it might take so long that we don't even care about the answer anymore by the time that they do. Our database is not built for the task we want to use it for. Around the same time, a team of researchers in the North East have become aware of this problem and they decided to dedicate their time and research to it. These researchers weren't just anybody. The fruits of their labor, which we now like to call the C-Store Paper, was published by eventual Turing Award winner, Mike Stonebraker, along with several other researchers from elite universities. This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. That sounds exactly like what we want for our stocks use case. Reasoning about what makes our queries executions so slow brought our researchers to the Memory Hierarchy, which essentially is a visualization of the relative speeds of different parts of a computer. At the top of the hierarchy, we have the fastest data units, which are, of course, also the most expensive to produce. As we move down the hierarchy, components get slower but also much cheaper and thus you can have more of them. Our OLTP databases data is stored in a file on the hard disk. We scanned the entirety of this file, even though we didn't need most of the data and now it turns out, that is just about the slowest thing that our query could possibly be doing by over two orders of magnitude. It should be clear, based on that, that the best thing we can do to optimize our query's execution is to avoid reading unnecessary data from the disk and that's what the C-Store researchers decided to look at. The key innovation of the C-Store paper does exactly that. Instead of storing data in a row major order, in a large file on disk, they transposed the data and stored each column in its own file. Now, if we run the same select query, we read only the relevant columns. The unnamed columns don't factor into the table scan at all since we don't even open the files. Zooming out to an internet scale sized data set, we can appreciate the savings here a lot more. But we still have to read a lot of data that we don't need to answer this particular query. Remember, we had two predicates, one on the symbol column and one on the timestamp column. Our query is only interested in AAPL stock, but we're still reading rows for all of the other stocks. So what can we do to optimize our disk read even more? Let's first partition our data set into different files based on the timestamp date. This means that we will keep separate files for each date. When we query the stocks table, the database knows all of the files we have to open. If we have a simple predicate on the timestamp column, as our sample query does, then the database can use it to figure out which files we don't have to look at at all. So now all of our disk reads that we have to do to answer our query will produce rows that pass the timestamp predicate. This eliminates a lot of wasteful disk reads. But not all of them. We do have another predicate on the symbol column where symbol equals AAPL. We'd like to avoid disk reads of rows that don't satisfy that predicate either. And we can avoid those disk reads by clustering all the rows that match the symbol predicate together. If all of the AAPL rows are adjacent, then as soon as we see something different, we can stop reading the file. We won't see any more rows that can pass the predicate. Then we can use the positions of the rows we did find to identify which pieces of the other columns we need to read. One technique that we can use to cluster the rows is sorting. So we'll use the symbol column as a sort key for all of the columns. And that way we can reconstruct a whole row by seeking to the same row position in each file. It turns out, having sorted all of the rows, we can do a bit more. We don't have any more wasted disk reads but we can still be more efficient with how we're using the disk. We've clustered all of the rows with the same symbol together so we don't really need to bother repeating the symbol so many times in the same file. Let's just write the value once and say how many rows we have. This one length encoding technique can compress large numbers of rows into a small amount of space. In this example, we do de-duplicate just a few rows but you can imagine de-duplicating many thousands of rows instead. This encoding is great for reducing the amounts of disk we need to read at query time, but it also has the additional benefit of reducing the total size of our stored data. Now our query requires substantially fewer disk reads than it did when we started. Let's recap what the C-Store paper did to achieve that. First, we transposed our data to store each column in its own file. Now, queries only have to read the columns used in the query. Second, we partitioned the data into multiple file sets so that all rows in a file have the same value for the partition column. Now, a predicate on the partition column can skip non-matching file sets entirely. Third, we selected a column of our data to use as a sort key. Now rows with the same value for that column are clustered together, which allows our query to stop reading data once it finds non-matching rows. Finally, sorting the data this way enables high compression ratios, using one length encoding which minimizes the size of the data stored on the disk. The C-Store system combined each of these innovative ideas to produce an academically significant result. And if you used it behind the desk of a Fortune 500 company in 2005, you probably would've been pretty pleased. But it's not 2005 anymore and the requirements of a modern database system are much stricter. So let's take a look at how C-Store fairs in 2020. First of all, we have designed the storage layer of our database to optimize a single query in a single application. Our design optimizes the heck out of that query and probably some similar ones but if we want to do anything else with our data, we might be in a bit of trouble. What if we just decide we want to ask a different question? For example, in our stock example, what if we want to plot all the trade made by a single user over a large window of time? How do our optimizations for the previous query measure up here? Well, our data's partitioned on the trade date, that could still be useful, depending on our new query. If we want to look at a trader's activity over a long period of time, we would have to open a lot of files. But if we're still interested in just a day's worth of data, then this optimization is still an optimization. Within each file, our data is ordered on the stock symbol. That's probably not too useful anymore, the rows for a single trader aren't going to be clustered together so we will have to scan all of the rows in order to figure out which ones match. You could imagine a worse design but as it becomes crucial to optimize this new type of query, then we might have to go as far as reconfiguring the whole database. The next problem of one of scale. One server is probably not good enough to serve a database in 2020. C-Store, as described, runs on a single server and stores lots of files. What if the data overwhelms this small system? We could imagine exhausting the file system's inodes limit with lots of small files due to our partitioning scheme. Or we could imagine something simpler, just filling up the disk with huge volumes of data. But there's an even simpler problem than that. What if something goes wrong and C-Store crashes? Then our data is no longer available to us until the single server is brought back up. A third concern, another one of scalability, is that one deployment does not really suit all possible things and use cases we could imagine. We haven't really said anything about being flexible. A contemporary database system has to integrate with many other applications, which might themselves have pretty restricted deployment options. Or the demands imposed by our workloads have changed and the setup you had before doesn't suit what you need now. C-Store doesn't do anything to address these concerns. What the C-Store paper did do was lead very quickly to the founding of Vertica. Vertica's architecture and design are essentially all about bringing the C-Store designs into an enterprise software system. The C-Store paper was just an academic exercise so it didn't really need to address any of the hard problems that we just talked about. But Vertica, the first commercial database built upon the ideas of the C-Store paper would definitely have to. This brings us back to the present to look at how an analytic query runs in 2020 on the Vertica Analytic Database. Vertica takes the key idea from the paper, can we significantly improve query performance by changing the way our data is stored and give its users the tools to customize their storage layer in order to heavily optimize really important or commonly wrong queries. On top of that, Vertica is a distributed system which allows it to scale up to internet-sized data sets, as well as have better reliability and uptime. We'll now take a brief look at what Vertica does to address the three inadequacies of the C-Store system that we mentioned. To avoid locking into a single database design, Vertica provides tools for the database user to customize the way their data is stored. To address the shortcomings of a single node system, Vertica coordinates processing among multiple nodes. To acknowledge the large variety of desirable deployments, Vertica does not require any specialized hardware and has many features which smoothly integrate it with a Cloud computing environment. First, we'll look at the database design problem. We're a SQL database, so our users are writing SQL and describing their data in SQL way, the Create Table statement. Create Table is a logical description of what your data looks like but it doesn't specify the way that it has to be stored, For a single Create Table, we could imagine a lot of different storage layouts. Vertica adds some extensions to SQL so that users can go even further than Create Table and describe the way that they want the data to be stored. Using terminology from the C-Store paper, we provide the Create Projection statement. Create Projection specifies how table data should be laid out, including column encoding and sort order. A table can have multiple projections, each of which could be ordered on different columns. When you query a table, Vertica will answer the query using the projection which it determines to be the best match. Referring back to our stock example, here's a sample Create Table and Create Projection statement. Let's focus on our heavily optimized example query, which had predicates on the stock symbol and date. We specify that the table data is to be partitioned by date. The Create Projection Statement here is excellent for this query. We specify using the order by clause that the data should be ordered according to our predicates. We'll use the timestamp as a secondary sort key. Each projection stores a copy of the table data. If you don't expect to need a particular column in a projection, then you can leave it out. Our average price query didn't care about who did the trading, so maybe our projection design for this query can leave the trader column out entirely. If the question we want to ask ever does change, maybe we already have a suitable projection, but if we don't, then we can create another one. This example shows another projection which would be much better at identifying trends of traders, rather than identifying trends for a particular stock. Next, let's take a look at our second problem, that one, or excuse me, so how should you decide what design is best for your queries? Well, you could spend a lot of time figuring it out on your own, or you could use Vertica's Database Designer tool which will help you by automatically analyzing your queries and spitting out a design which it thinks is going to work really well. If you want to learn more about the Database Designer Tool, then you should attend the session Vertica Database Designer- Today and Tomorrow which will tell you a lot about what the Database Designer does and some recent improvements that we have made. Okay, now we'll move to our next problem. (laughs) The challenge that one server does not fit all. In 2020, we have several orders of magnitude more data than we had in 2005. And you need a lot more hardware to crunch it. It's not tractable to keep multiple petabytes of data in a system with a single server. So Vertica doesn't try. Vertica is a distributed system so will deploy multiple severs which work together to maintain such a high data volume. In a traditional Vertica deployment, each node keeps some of the data in its own locally-attached storage. Data is replicated so that there is a redundant copy somewhere else in the system. If any one node goes down, then the data that it served is still available on a different node. We'll also have it so that in the system, there's no special node with extra duties. All nodes are created equal. This ensures that there is no single point of failure. Rather than replicate all of your data, Vertica divvies it up amongst all of the nodes in your system. We call this segmentation. The way data is segmented is another parameter of storage customization and it can definitely have an impact upon query performance. A common way to segment data is by using a hash expression, which essentially randomizes the node that a row of data belongs to. But with a guarantee that the same data will always end up in the same place. Describing the way data is segmented is another part of the Create Projection Statement, as seen in this example. Here we segment on the hash of the symbol column so all rows with the same symbol will end up on the same node. For each row that we load into the system, we'll apply our segmentation expression. The result determines which segment the row belongs to and then we'll send the row to each node which holds the copy of that segment. In this example, our projection is marked KSAFE 1, so we will keep one redundant copy of each segment. When we load a row, we might find that its segment had copied on Node One and Node Three, so we'll send a copy of the row to each of those nodes. If Node One is temporarily disconnected from the network, then Node Three can serve the other copy of the segment so that the whole system remains available. The last challenge we brought up from the C-Store design was that one deployment does not fit all. Vertica's cluster design neatly addressed many of our concerns here. Our use of segmentation to distribute data means that a Vertica system can scale to any size of deployment. And since we lack any special hardware or nodes with special purposes, Vertica servers can run anywhere, on premise or in the Cloud. But let's suppose you need to scale out your cluster to rise to the demands of a higher workload. Suppose you want to add another node. This changes the division of the segmentation space. We'll have to re-segment every row in the database to find its new home and then we'll have to move around any data that belongs to a different segment. This is a very expensive operation, not something you want to be doing all that often. Traditional Vertica doesn't solve that problem especially well, but Vertica Eon Mode definitely does. Vertica's Eon Mode is a large set of features which are designed with a Cloud computing environment in mind. One feature of this design is elastic throughput scaling, which is the idea that you can smoothly change your cluster size without having to pay the expenses of shuffling your entire database. Vertica Eon Mode had an entire session dedicated to it this morning. I won't say any more about it here, but maybe you already attended that session or if you haven't, then I definitely encourage you to listen to the recording. If you'd like to learn more about the Vertica architecture, then you'll find on this slide links to several of the academic conference publications. These four papers here, as well as Vertica Seven Years Later paper which describes some of the Vertica designs seven years after the founding and also a paper about the innovations of Eon Mode and of course, the Vertica documentation is an excellent resource for learning more about what's going on in a Vertica system. I hope you enjoyed learning about the Vertica architecture. I would be very happy to take all of your questions now. Thank you for attending this session.

(exciting music) >> From our studios, in the heart of Silicon Valley, Palo Alto, California. This is a CUBE conversation. >> Hello and welcome to this special CUBE Conversation, here in Palo Alto, CA CUBE Studios. I'm John Furrier your host of the CUBE. We're here with James Segil President and Co-Founder of Openpath Security. Hot start-up in a very cutting edge area that everyone can relate to physical security. But as that grows with the internet, the convergence of physical security with how people work online. It's been a huge issue, we've been covering IOT, we've been covering cloud security, we've been covering internet security. James, thanks for joining me today. >> It's great to be here, John. >> So, you guys are a young company in a very hot area. Great investors, you have a great background, we interviewed in the CUBE before, CUBE Alumni. Before we get into it, this is a super important area, I wanted you to take a minute to explain what you guys do. How long you've been around, what is Openpath? >> Sure, so you know, my partners and I are serial tech entrepreneurs out of L.A. this is our fourth company together over the last twenty years. You interviewed me when we were running EdgeCast. So, it's great to be back. You know, Openpath came from our own frustration. We're an Access Control company so we allow folks to enter office buildings, physical space, work space, using a security tool. That is not a badge. So, this is how we used to enter our prior buildings. So, this is actually my business partners badge pack just to get in and out of our offices, and we were basically tired of wearing dog tags or dog collar, however you want to call it, right? The whole idea was you can use your phone, your phone is your key. So, the credential to get into the office, into the building is on your phone, and mobile was a technology that hadn't really been introduced into the physical, sort of, property technology space before. And by bringing mobile to Bear as well as cloud technology, 'cause all the software's in the cloud. We were able to improve this value proposition and offer a cool solution. >> So, just quickly how, how long have you guys been out with the product and when was the company founded? >> So, we started the company three years ago and launched commercially about a year ago. You know, we spent two years building the technology, getting our patents, really getting everything, figured out. We have software and hardware, it's part of our solution. And so, when we launched a year ago, it was kind of like drinking from a fire hose. We literally had people coming and saying, finally, somebody figured out how to get rid of the badge and use my phone just so it will let me in. And since then we've raised a good amount of money and have been, you know just selling to basically everyone, yeah. >> Congratulations, this is a hot story, so I want to get into it. So, the origination story is, obviously you had to be a successful entrepreneur in the past. Being a serial entrepreneur has it's ups and downs, but you know, with the cloud, everyone thinks, Oh, Security is just a cloud problem. You guys are attacking a physical property, physical security, kind of bringing a DevOps ethos to this. I mean, when you hold those badges up, reminds me of the old janitor key ring. This is the digital ring. You know, all your access. So, clearly an opportunity to automate. >> Yeah. >> So clearly, kind of, obviously, the cloud mentality here. But, your impact is to, kind of, the kind of older industry. Explain this trend of property technology. I mean, most people can relate to their office space. >> Yeah. >> You know, waving the badge to get in, maybe VDI on the desktop or whatever's happening. I mean, talk about the the market place and the trend. >> So, you know, buildings, real estate for the most part, are very slow to move in adopting new technology. And I think, you've seen that in a lot of different industries. Certainly in real estate, there was a sort of slowness or unwillingness to move on past old techs. So, this works, it's an RFID badge. And you can use it and people are comfortable with it. It's worked for forty years. Prop-tech, Property Technology, is really a focus around innovating how you work with, interact with, and spend time at work, in office buildings. But it extends well beyond office, it extends into multi-family residential, health care, any building you really go to. And so, there is a lot money and there is a lot of entrepreneurs who are focused on, how do I improve the quality of every experience we have? When I go into an apartment building, when I got into a hospital, when I go to school, when I go to work and that's really what were focused. We're sort of thinking about that whole experience and reducing the friction in every step of how you interact with that building. >> You know, this used to be an IT problem, if your with big company you sign in, you on board, you get your laptop, you get your badge, someone probably enters your name into a database. And then if you leave it has to be deleted. Is you guys addressing that area? Talk about that piece of it because I think this is more real time, more person without the phone, for instance, your bridging the physical and the logical. Talk about the IT versus the old way of doing it. >> Yeah, so, you know, typically in the real estate world, there's an office manager, a facilities person, maybe, a physical security person, or even like real estate person and they're in charge, at least within the enterprise, of thinking about physical security. But what's happened is, there is a lot of exposure that we have to our data, to our personal safety, to everything really in the office. If you don't protect the physical space, from the thieves or bad actors who want to steal your data or hurt you. And so, all this money has gone into Cyber Security, the chief security officer, the IT department, they have unlimited budgets to go out and solve that problem, to protect the network. But they are literally leaving the front door open. And so, a lot of what is happening today in the enterprise is that the CISO, the Chief Security Team, the IT Team is starting to really gain denomination over this real estate and facilities space, and sort of say, hey, these systems need to work together. If I have a single source of truth to hold all my users and my employees in a single database, I want that to connect, not just to my salesforce.com instance but I want it to connect my Access Control system and how people enter the the building. >> Access Control also an IOT problem, Industrial IOT, we hear that area. Clearly a use case for that opportunity so clearly why you got some funding and I want to cover that in a second on origination story. But the question I have for you is, when you guys started the company and now that you are in market with customers, what's the main problem that you solve? What's like, I mean, you have to solve that one problem, what problem do you solve and where is the growth from there? >> So, I have two groups of sort of customers who I talk to. The first group are tenants or enterprise customers, and these folks who need to move into an office, and most of the choice around when to buy Access Control comes because you're building out space or your moving into an office. You need Access Control. It's not on the list of nice to haves, you need to be able to lock the door. So, when you move into a new office, you need to have internet connectivity, alright, you need to have Access Control, maybe an alarm system, sparkletts water or whatever it's going to be. And we're on that list. So, when people are investing in that capital infrastructure. They're going to future proof that investment, they're are going to choose Openpath. The second group we talk to are folks that are building buildings or renovating buildings. And that's asset managers, developers, property managers, landlords. And those constituents are looking to build a physical space that's both safe but allows them to attract folks to their building as tenants. And so, if you offer amenities, you offer a gym, a cool, sort of, you know, work space, and Access Control Technology it becomes an incentive for folks to want to come and office in your space. >> So, you know, you and I are techies. We love to buy that shinny new toy. The property type tech world, they not as innovative or have a propensity to just at the next thing because, they're about security, they're about that, locking doors. So, I got to ask you, what are some of the things, and they're getting more savvy now, I can see that, so it's clear. You can see most of the digital amenities. First, a start with WIFI, we don't have WIFI, you're done. Now, you're starting to see much more app, centric things happening on these locations. What are some of the areas that people are gravitating in terms that they like, in terms of features with Access Control? What is it enabling from a value stand point? Is it differentiate services, is it access to certain amenities, you mentioned some of that. What is some of the new things that are being created? >> Well, I think the first thing is that we're reducing some level of friction in interacting with you workspace. So, the fact that you can basically, keep your phone in your pocket or keep talking on your phone or keep it in your purse and just walk up to the door and have the door unlock because it knows you're there. That's not just kind of cool that's really just helping out the quality of your day to day experience. You know, ever since 9/11 when we upgraded the security experience almost everywhere. Whether you're entering an arena, a plane or a building that friction is something we are used to now and there is a push back that people want a little bit less friction even though they want that higher level of security. >> Not that I want to get doom day scenario. You mentioned 9/11, they were told to stay in their buildings when they could have been evacuated, everyone in New York knows that tragic story. Huge active shooter environment right now, it's just my kids went to an event in San Francisco. Literally, what is on the mind of people is, oh my God, is there going to be an active shooter? These are examples of things that could go wrong and in security this becomes an Apocalypse scenario that we've been talking about it takes that to get people to take action. So, can you help in those scenarios? How do you help someone either thwart those kinds of security attacks or help them get through them if somethings happening? Let's just say an active shooter comes into a building? >> Yeah, so we've thought a lot about that. And we have kids in schools and we actually have a lot of schools and houses of worship that are buying and installing our system. So, we have a couple different capabilities, lockdown is our latest release. And this is the capability from anyone, anywhere on any mobile phone in that building to enable a lockdown procedure. What I think is particularly valuable here is that if you're basically no where near the fire alarm which is where the lock down button might be as well, and you're stuck in a closet and or hidden away tryna to make sure you're not going to get shot. If you have your phone on you can enable a lockdown and because our plans are kind customized, you can enable a lockdown that let's say locks all the doors in the zone. But lifts up the garage gate so that first responders can get there. And we've seen proven the faster the first responders can get to the problem, whether it's, you know, an EMS person that's tryna to stem the bleeding on someone who is injured or whether it's a SWAT team-- >> Well that's actually proven you saw Gilroy, you saw the response in Dayton. Literally minutes taking those active shooter. >> Well, every second counts, so being able to have a lockdown that works fast, that's effective and that allows people to get through and the bad guys to sort of be isolated is important. The second thing is, we actually have integration with video systems, so you can send a live video feed instantly of every door that's locked down to the first responders. And they can actually see it right there on their iPhone where the bad guy is, what he is doing, real time, from the video systems. They can take over the video system, so it's a pretty-- >> So, it augments the security environment for good and bad scenarios. So, let's get a kind of more realistic scenario. Doomsday scenarios is kind of depressing, but it's real. Our people are planning and are protecting around that. One basic concept, and I got reprimanded at VMware was, I've been at the VMware campuses since they've been building it. But recently I was going to a meeting, and I knew it was building number four, or whatever it was. And I'm sitting there waiting at the door. Someone comes out and I went in and they call it tailgating. Turns out I didn't have a badge and the new person who was there really kind of got in my face and said, You tailgated, I'm like, I do it all the time, I'm like, okay, stop. So, okay, you don't tailgate a VMware anymore and I now know that. But this happens all the time. This is another common problem, I could be stealing laptops, I could be getting the plans at VMworld. I mean, whatever's going on. And this, bad things are happening with tailgating. That's a big thing isn't it? >> It is a big thing. Security experts are telling us it is one of the top three physical security challenges that enterprise CISO's are running into, tailgating. And what's happening is, people just like you, are well meaning are sneaking in. But, there's some bad actors that are sneaking in as well. So, we've got technology that have deployed with partners that actually count the people that are coming in through the door. And if there's two entries when you're only supposed to have one, we can actually track that and instantly make the meter go beep, beep, beep, beep and send an email alert to a security desk or to the individual themself with a video and a picture of the person who snuck in behind you. >> That is a great example, and I mentioned VMware in all seriousness. That actually had happened. There's a huge campus and the reason why, I just didn't want to go to the front I parked at the wrong garage and I didn't want to walk five buildings over. A little bit lazy but that's the point of the large buildings, where the security access comes in. For large campuses, whether it's Universities or corporate, that's the big challenge, right? Not just Access Control but management. >> It's management and so the idea, of sort giving and empowering people to be able to really quickly change, configure and access places. The fact that from your phone you can actually, as a manager change access privileges and give someone who's visiting a temporary pass. That's not one of these, but it's actually a virtual pass on your phone. That's really empowering. So, if you were coming to visit me at VMware, I'd send you a guest pass that gives you one hour access to five different doors and so that you wouldn't have to sneak in. You would basically be able to just use your phone to get in as a visitor for one hour. And after an hour you're not going to be able to get in. >> All right, so let's talk about the company. Openpath Security, you guys obviously targeting the physical space, Access Control, logical physical coming together seamless frictionless environment. Business model? How much funding did you get? What kind of investors do you have? Employee count? Product shipping status? Give us through the numbers. Give us the data. >> Sure, so we started the company three years ago, we came out a stealth mode a year ago and launched commercially, we had actually done our series A internally, we led that ourselves as the founders. And then, when we came out of stealth mode, we had a lot of great attention in the space. Emergence Capital is our lead investor in our series B. We raised $27 millions total. We've got a great team of folks, just under 16 employees. We are based in Los Angeles but we have offices in Indianapolis as well 'cause why not? It's the best place to be. And we're growing fast. We actually sell focused on commercial real estate, but have expanded to multi-family residential. Also, to schools, churches, houses of worship. And we are here in the U.S. now and we're growing internationally over the next two or three years. >> And the product is the a SaaS, managed service, physical? What's the story of the product? >> Yeah, so there's a combination of physical hardware but there is a 100% attached software to it. So, you install a reader at the door, a panel in the IT closet and it's wired as most traditional Access Control systems are but our software is all hosted in the cloud. As well, as the credential that is on the phone. And so, we sort of sell the hardware upfront and then you buy sort of a recurring annual fee associated with the number of doors you own. >> And so you get on the spec that be on the new building, so you do a little go to, you go to market as it is, getting on the design side, suppliers to the building. >> Yup, so, there's the developers, the architects, who put us into the spec. There the system integrators, these are the folks who are low voltage electricians, security system integrators who go out and actually deploy all the wiring you have in this building. They'll go ahead and do the WIFI network, the CCTV camera system, the alarm system and the Access Control system. And so, we have a national network of certified installers who go out, and that's actually how we go to market. We sell through them. >> And you have the software, it's a nice margin. And is there a cloud play here too? Is data stored in the cloud? >> Yeah. >> How are you guys handling some of the backend stuff? >> So, yeah, all the information is stored in the cloud. What's kind of important in a life safety environment is that you have a cloud system that runs it but that you can work if the internet is down. 'Cause imagine if the Internet's down and you can't even get into the office to fix the internet. So, our system works offline as well as online. We store all the credentials locally. >> I remember interviewing Ring's founder at an Amazon event. Simple concept use the cloud. Same thing for you? Not a simple concept but you're in the spec use the cloud with a hundred percent attach rate. >> Exactly. >> All right, so what's the coolest thing that you see happening in this market for you guys? What's going on that you would say that's notable that you would think is important that people should pay attention to. >> There is a number of big trends. You know, we talked about one, right? Which is the whole change of, you know, combining physical security with cyber security and having those two really come together. I'd say the transition of IOT from just the home into the workspace is another big trend we are watching. People are just used to having an NEST on their wall or a Ring on their doorbell and the want Openpath on their door at work. And that's something else that we've seen as a big transition. People are getting used to having an easier experience and I think the final thing is how people use the workspace, right? People work all over the space now. It's not just at their cubicle and that's impacting. >> I got to get some commentary and understanding around the name Openpath because most people in these kind of areas that you're in have closed systems. You know, the HVAC system, I'm running an IOT like an operational technology. Information technology is a protocol based OSI model, open source. So, those worlds are colliding, we're covering that in the whole IOT, industrial IOT trend. Openpath Security? If it's open can I hack it, what's going the Openpath name? Tell us why Openpath? How are you open? Tell us the story behind the name? >> I'm really glad you asked. We were really frustrated when we analyzed the space, as investors and entrepreneurs in this category. We saw that all the systems that are out there, are incredibly closed. Their proprietary systems, they work on old protocols and they're not open. Ours is open. It's built on open API's. Every element of our technology can be connected to, right? And we have tons of developers who are integrating, just like they do in the web, with Openpath. And that's something you can't really do in the old physical Access Control World. So open is just correlating that. >> So, you that's from an ecosystems stand point, you guys enabling others to build on top of your stuff. >> Oh yeah, we've got Envoy the visitor management company. They've got an integration with our Access Control. Density, which is a really cool people counting tool. We've got Camo, a video integration tool. All these folks are integrating with us because it's open and it's really easy to do. >> Okay, so I got to ask the question. I'm now, I'm a building person designing the specs for the new campus, open? That sounds insecure. How do you guarantee that you're going to to be secure? I'm worried about security. How can a hacker get in, take over the physical space, shut it down, that's my concern. How do you address that? >> Yeah, no it's legit. So, what I often say to people is, let's see. You can have a badge, like this, right? And you can pick up my badge and find it anywhere you want, right? And now you're James, right? You can go take that, and you can get in anywhere you want. But I challenge you to try to use my phone. Try to unlock right now, right? >> There it is. (laughs) >> That super computer is encrypted, there's no way you're going to break that. This is the most secure way to enter anywhere. >> But if I get, that's an iPhone but with an Android I'd get some Malware on there. >> But the Malware that you get on your Android isn't necessarily going to allow you to authenticate our system. >> So, you're content, even though you might be on an open device, you guys are containing the app, security app on the device. >> Yeah, so the same protocols that we use on the internet to have secure HTTPS communication between any kind of client, your computer and a website. We're using that same hand off. Where we have rotating security certificates on this, as well as in the cloud, as well as on the panel. So, everything is fully encrypted end to end. And that gives us a level of security that's unmatched and unrivaled actually, in the Access Control space. >> James, thanks for coming on theCUBE, final just give a plug for the company. What's new, what's happening? What's going on Openpath? What's next for you guys? >> Well, if it's a plug openpath.com that's an easy one. But, I think for us, we're really growing in a way that people are excited about. I want to change the work day experience. So, everybody who's out there, who's tired of using a keycard and a badge, I want them to go to their boss and say, why can't we upgrade to Openpath? Go to your landlord and say, hey, I'm negotiating this into my tenant improvement. I want Openpath as a part of how I sort of access the building. The trends that we're really excited about, this lockdown technology, the Anti-Tailgating Technology. Those are really cool, sort of advantages that we give the enterprise and we're just excited to be helping people improve the quality of the workday. >> And what's the reason why you're winning deals? What's the one factor or two factors? Ease of use, open-ness, convince features? What's your-- >> I love it, you're selling my product for me. It's ease of use, it's the fact that it reduces a number of steps in the friction you experience personally everyday. And that the enterprise or the landlord experiencing managing a system, is less expensive and more secure. Kind of all the things you want. Plus, I mean, how much sense does it make that you don't have to carry around ten badges that you can actually just have it all on your phone. It just makes sense. >> Soon series C funding around the corner. (laughs) >> If you're interested, we should have a conversation. >> TheCUBE fund's not yet setup but when we get theCUBE venture capital fund will be in. >> That's good, you let me invest in your company, I'll let you invest in mine. >> We'll talk. James Segil, entrepreneur President, Co-Founder Openpath Security, hot start up here inside theCUBE. Featured startup here. Thanks for watching. I'm John Furrier. (exciting music)

Right, welcome to the studios here in Palo Alto. I'm John Three cohost in the queue. We here for Special News Conversation with Prakash, dodgy general manager of the flash array business at pure storage, some exciting cloud news for pure storage. Great to see you prakash. Thanks for coming in. Thanks for having us. So you guys got some big news. So I'm excited by this because I've been ranting and raving about how cloud native has been impacting the enterprise. It's pretty well documented that everyone's going going cloud operations. You guys are announcing a kind of a historic milestone for pure storage in that you guys had been doing great on the storage side within covering new since inception, but now as you guys continue to grow, you now have a new offering that's in the cloud. This is new for you guys. Talk about this announcement. What does it mean? You're an on premises storage, has done great to grow. Has Been Amazing gun public that now with the cloud growth you have a cloud offering. What's going on? Well, interestingly people were looking at storage for performance, cost and reliability reasons. That's kind of the three holy grails that you know, everyone expects out of storage. We added a fourth dimension in simplicity. Storage didn't need to be hard, and that's kind of the brand of pier and as we took a look, there was a fifth dimension that we realized was somewhat missing. While we made things simple. We didn't have the agility that public cloud offered. So as we were taking a liquid like, okay, public cloud brings to this instant available capacity, agility model, but do you have to trade off on the other dimensions? Performance costs for liability or simplicity, and our goal to bring customer value was to avoid tradeoffs. So why would you have to trade off on any of those dimensions? And then the second piece was why do you have to choose? Why do you have to choose between on premises or public cloud? And if you make the wrong choice, how do you have freedom to move? So the problem set that we were trying to address was that unification across all those dimensions, the onboarding of agility and frankly the ability to avoid people having to choose between them and use the best of what's available. Where know, I think you nailed something important that I want to get into the why you guys are doing this little bit deeper, but this notion of tradeoffs is an old it kind of philosophy. I got to trade this off to get that. Whether it's, you know, I want compute and stability or flexibility, agility, but with cloud and cloud operations, the operating model now is you choose, as you said, so this cloud operations on premises and cloud has to look the same. This is what we're hearing from Ceos and practitioners, cloud architects, they're re architecting their enterprises now because you know, the, the three main of it, storage, networking and compute never go away. It's just changing. This is a critical, fundamental piece of the architecture of it operations. Why now? Why cloud was the customer demand? Was it a natural progression for you guys? Explain. Explaining the why now. Well, I'll start with not the storage computer networking, but what they're used for and fundamentally the world's using those three dimensions for long, one of two sites, either building applications or building automation. That's kind of the two major trends in the industry. Now if we take a look, if you are running an application, primarily you would choose am I running it on premises or in the public cloud and as as the journeys emerged like public cloud probably introduction of as around 15 years ago, but initially there was this enamored. Everything's going there and then people settled down to some things will go here and some things will go here, but we believe that's a middle state where people are actually trying to do is deliver applications that solve problems and we believe that future is a hybrid application. Now, what is a hybrid application today? If you've got an on premises finance system, should you be able to use ai algorithms from Google's cloud? They book journal Entries for month end close. Let me, because it's now not a choice of am I using pads for the. That doesn't mean the whole application needs to sit in platform as a service. You should be able to use the best capabilities of what's available, where the same way today, anyone who is selling anything and using salesforce crm needs to ensure that what you've sold is booked in a finance system. That could be an sap finance system on premises, so what is the APP? It's an APP without borders now and these are modern day hybrid applications now coming and bringing that down to compute storage and networking. Trying to bring that together and actually deliver that in a consistent and operational way is difficult. It's a difficult across your application architectures. There are different. It's different across your management, even your consumption and how you bill cap ex versus Opex, but the big difference is that the storage layer, because the application architecture on premises relies on your storage for your reliability, but in the cloud they've actually moved that reliability characteristic to the middle tier. You're sharding and doing scaleout distributed application because you can't rely on the same characteristics out of your storage and we found this as an opportunity to bring these two worlds together. We call it the cloud divide. Talk about the cloud device. I think that's important because one of the things we talked with a lot of the end user customers, your customers and others, their challenges again, to focus on the outcomes that they want, the application that's going to drive their and and the value, not so much what the infrastructure, they have them create an infrastructure to enable that. What is this cloud divide when it comes to storage? In your mind, what did you guys discover? What were the key pain points? What were the, what was the customer's telling you around what and what is the cloud divide? No. Uh, the cloud divided, coming back to it is how you deal with applications, how you deal with management and how you deal with storage different between the enterprise in the cloud. We like to say the enterprise is not very cloudy, meaning you don't have instant available capacity in the cloud is not very enterprisey. Now what does that mean? What do we call enterprise? And there's a how it works with the rest of my landscape, what the API is our, uh, what the reliability characteristics, our performance and cost characteristics are also different. So if you want to adopt public cloud, you have to go ahead and say, I got to do a hard left, right? Because you're kind of going down this way and you got to choose a different path. And if you choose that hard left, you're now stuck on that road. It's a one way road. And we're trying to do is say, you know what, what if we could bridge these environments, like let's dig into the application architecture on the cloud divide. Pretty much people are using scale up or scale out as application architectures and then they're deciding, you know, vms or containers yet a, that those are common application development paradigms. What if you could use either one anywhere, right? Those technologies. Now, if you look at what vm ware is doing with Vm ware cloud and you look at what kubernetes is doing across on premise and cloud, there is now a unification happening at application architecture across management. What if you could have a seamless api in a seamless pane of glass around how you manage your applications? That's emerging, but as we looked around, no one was unifying the storage paradigm and actually that was the hardest we we thought that to unify the hardware or the storage paradigm, you have to build a data centric architecture and that's what we've been focused on doing. We've introduced our concept of data centric architecture a year ago and we're now extending that concept to the public cloud. What I like about what you guys are doing here, and I want to get your thoughts on this because this is. I think the trend that's really big in here is that you guys have been great storage provider since again, since inception can been following you guys and you have hardware and hardware has been a rack and stack kind of enterprise paradigm enterprises. We've got gear, we protected, we secure, but now with public cloud becoming more secure and more mainstream and with the Dev ops application environment developing. You mentioned the ems, the containers and Guth Coobernetti's. You're now having an operating model that's changing. You guys are doing software, so it's not a boxer. You're not shipped boxes to Amazon. They have stores. You got s three right out of the services. You're now extending the software component of your business. I want you to take a minute to explain for the people that might not know the extent of the software business at pure and specifically the cloud component software piece. It's not hardware and software, but it works with on premises. Talk about that dynamic of software in the cloud and the impact of the on premise piece of it. Well, I'll rewind a little back intel. What peers been known for peers been known as kind of this all flash company, but if you unwind that. When I took a look at it as I've joined pier actually about six months ago, what I realized is the unique skill that peer has is software engineering. To get the best out of any infrastructure that you give it. The medium happened to be flash initially, so what we've built with our direct flash and NBME and a lot of the advancements in our software has been to deal with the flash medium, but the core skill in Ip we have is software development to get the best out of a medium. What we've introduced is another medium. This medium is infrastructure as a service. We treat that as another medium and we believe that we're uniquely qualified to get the most out of that medium, which is the cloud. Alright, so I want to get into the infrastructure piece. You guys are well known for being a cloud, a data infrastructure component or data infrastructure. You mentioned the history of flash storage has been a great place to store data on premises. When you get into the cloud, you guys call this cloud data services and I'm going to get in another video on that, on the details of that, but when you hear about cloud data services, but pops in my mind is more is coming. You need to store it somewhere. You have to manage that data for applications, hybrid applications. You need to store the protect that data. You need to make that data available. They'll be able to recover all the same things that get done with data in the past on storage has to happen at a whole nother level. Describe what is cloud data services mean? What does that mean to you guys at pure and what does it mean to your customers? If you back up a little bit where we started and where a lot of our initial customers were at where sas customers and what we delivered to them was what we called cloud data infrastructure. That cloud data infrastructure allowed some of the largest sas companies, either consumer or enterprise to go ahead and use peer to build their sass applications. Companies like service now workday, those types of companies, but what was missing was how do you get that same value on infrastructure as a service environments, aws, Azure and GCP. So what we realized was the consistency model was not the same. The apis were not the same and you had to choose or not. And so our cloud data services are a set of services that give you, for example, the same block storage that you had on premises in the public cloud, gives you the same Api. And from a management and operations standpoint, we have pure one which is a cloud data management solution where all of your data, wherever it sits, because as you said, data is growing. You can see all of your Ras. It was interesting as we built the software, uh, when we first built it internally, we realized that hey, we went into pier one and we see all these storage volumes, but we didn't know which ones were on premises or cloud because our software is the same. We actually had to do some engineering to make it look different. Like, Hey, let's color the cloud volumes different. Or we had to actually think about that because we started from the place of driving consistency. And then we've extended the cloud data services dead. Go ahead and say not only can we allow you to run in either place, but how do you extend that to data protection? Because today, as you mentioned earlier on premises, people have workflows for backup and data protection and initially those workflows could have been disk to disk to tape to truck and we see that there's now a more modern way where you can do flash to flash to cloud where you can have your primary mission critical applications and flash and if you want one hop for backup, people looked at backup as an insurance policy. What happens if something goes wrong, but what's really important is when something goes wrong, how quickly can you recover? So providing flash in that second medium and then third, extending the step for cost optimization by leveraging public cloud in s three allows us to drive a consistency model and we can drive that same workflow on premises or in the public cloud. So the consistency to me, I should maybe put you on the spot here. So and consistency. Are we talking about if I'm a pure customer and I'm running pure on premises and I'm using, I'm using all the management something pure one, all this other great stuff and I want to use cloud. Does my job change at all? Does it look the same? So as a dashboard into the storage and the data because I want, I want, I want persistent data, I want ai and I want analytics now. Now I've got cloud going on. There's a lot of things out there, sage maker, tensorflow on the AI side. Lot of things. Goodness out there. What changes for me or does it change and how do you guys solve that problem? Because what I don't want is I don't want to have to hire developers to go do an integration with Amazon and Azure and Google cloud. I want to have a single consistent environment. Do you guys provide that from a data standpoint? We do. So this is a journey because when you start, you need to ensure that your data consistency and management across all of those environments, aws, azure and Google and on premises is the same. So we're introducing our solution cloud data services on Amazon first, but we are planning on extending that to the azure and Google environments in the cloud standpoint. So let's take Amazon. So I said, hey, I want to use some of that cloud. I just go to Amazon. It's extensible, fully extensible as if I'm using pure cloud formation template on Amazon. You just go in, it's there, you can pick it up, you could choose it, use it, and then what really is the difference is your platform services at a higher layer, maybe a little bit different because some of the things you mentioned and the pads are Amazon specific. Yeah. So if you start using pads services, it could impact your application development architecture, but the good news is if your goal is to drive the ability to use, what's the best thing that's available where as you take a look at evolutions in Vm ware, cloud and Coobernetti's combined with our cloud data services, you're now able to put together a use. Best of what available wherever you guys. I mean that's the. That's the application side. So you guys are providing a consistent layer for the data and the storage. Absolutely. That's going to. If I'm building an Amazon as a developer, I'm going to use those anyway. So it's not like it's a dependency per se, it's just you're going to allow for those hybrid apps to run across premises and in cloud and all the data takes care of itself. Right? It's like they get that, right? Yeah, and what's great about it is we've learned some things along the way. For example, we've been trying to get the best out of the flash medium in the past by enhancing performance characteristics or efficiency characteristics for cost optimization. We can bring some of those same value props to the Amazon world. So if you need to aggregate iops, we can do that. If you need to go ahead and drive efficiency, we have techniques to drive efficiency around thin provisioning. Those types of opens up more use cases for the customer to add more policy based things to their application. It makes data programmable. Well, it's interesting. There was one customer that we were speaking to a as part of our alpha usage and it's a online education company. They do curriculum development and that type of thing and they brought this use case to us. They have their APP that they've built for their curriculum on Amazon and then they want to take a lot of snapshots. So what they. One of the technologies, we have his space saving snapshots so they're like, oh, that'd be great if I could use your cloud block store data service on Amazon that way. But then they thought about it and they're like, well, every time we develop a new curriculum we have to send a snapshot out to a different location and site and what we could do is set up a your hardware in a direct attached way to Amazon because your software is the same. And we have active synchronous replication technology where we can now synchronously replicated between the public cloud and this private hosted direct attached diversion. And then they can do work here or even take snapshots from here. And the reason they were doing it was go ahead and say, use that space saving snapshot to reduce their overall cost profile on exports. That's a great example of cloudifying being cloudified, but more options. This brings up the question about competition. How do you guys compare to the competition? So you guys are. It's the first move for you guys in the cloud, within this operating model, which is consistent, you know, pure on premises and the cloud, get the consistency, loved the agility of the ability for applications and get all that goodness. What about the competition? How do you guys stand versus the competition? Well, when we take a look at what was going on, I think a lot of people wanted to check the box on cloud. So let's throw something out there and you know, see how people use. As we've done this market introduction, we've been very careful about that because peer has a certain brand reputation around when we say we're going to deliver some of these characteristics, we deliver and deliver those characteristics. And we didn't want to lose the value proposition of simplicity and agility. So as we launched this, we didn't just say let's throw it out there and see what happens. We did it with the deliberate intent of saying we want to provide agility is a characteristic that people could use and we want to deliver that agility with the same simplicity that they've come to know and love with peer. So those are the principles that we're focused on and as we take a look at the competition, you know, they've thrown their software out there but we don't see that it's been broadly adopted and then they're still the tradeoffs of should I go on premises or public cloud so they're stuck in the divide and that they're in the storage or the cloud, divide on premise, different operating models. And our goal is to really enable that replicates those guys are stuck on the divide. Yeah, and if you think about these hybrid applications that we see the world moving to think about it this way, the world's evolving where you're going to have more application to application integration. Gone is the days where you're going to have one monolithic application doing everything. So what's evolved is the application to application integration is exponentially growing. Now, if you assume that if you need to do a production to Dev test copy, do you need to do it for one app or for that entire set of apps that you treat as one monolithic entity because now they're all connected. He otherwise you have to decide, okay, I'm snapshotting this one and then I got to choose this one and I got to choose that one. So you, there's now a need to go ahead and consolidate a lot of application workloads and treat the management and operations of that as a unique entity. So hybrid apps are actually making you rethink how you deal with management of compute networking and storage. Yeah, I think that's a great example. I think application to application integration and totally agree with you is going to be happening at a much accelerated rate, but it changed the role of data. The role of data is central to that because as you mentioned, that other example, if you're doing a financial app and you want to use some ai from a cloud over here, the best tool for the job needs to be integrated in seamlessly and storage. Should they be part of that conversation? It should just be stored somewhere. That's what you guys are doing with this announcement and you guys are bringing that to the table. Um, so I got to get. I guess I'll ask you the final question here because it's exciting news. You guys are cloudified it made it. He bridged the divide on the storage cloud storage divide. What's the bottom line for this announcement? As you look at this impact to customers, what's the impact to pure customers and what does it mean for prospects that aren't yet your customers? What's the bottom line? This announcement? Well, I'll give it to you. For me, each perspective for our existing customers, this adds the agility tool set to their bag of tricks they've got and it does it in a way where they can start, get that instant available capacity and if they want, they can go ahead and now start benchmarking across both environments without having to re architect because the kpis are the same and for net new customers and prospects. It's interesting. As we speak to customers, we find that people are on a different educated education journey in the public cloud. Some are already using the public cloud and as we've been discussing this with them, they're like, hey, this could improve on some of these characteristics. Either I have performance challenges, cost challenges, reliability or manageability challenges. So we find that the customers or the prospects that are most educated or the ones that have already leaped, right? They've jumped in the pool and now they realize, hey, you know what? The water's cold and I need something, and there's another set of customers that are still haven't jumped in that pool. And what we're saying is for those customers, you have to make a choice. Right now you have to decide between multiple public clouds, you have to decide between on premise and what we're doing is we're de-risking that choice by allowing them to get the best of what's available where and most importantly ensuring that if they've chosen, if they've chosen something but one of the other choices evolves or matures to be a better option for them, they have the ability to move and I think also the focus we hear from the practitioners that they are investing more and more of their time and energy on building applications, hybrid applications as you're calling them, ones that are going to be a in the cloud or on premises, but solving a problem. They want to shift their resources and attention from mundane storage admin like maintenance problems and make the storage invisible to them. So the developer that they said, I know my thing's working great in the cloud. One of my apps are productive. My developers are programming and the storage resources are invisible and it's never a headache. That's kind of what you guys are getting at here. You're making storage pervasive and important to the developers and the it so that it kind of goes away in their mind, isn't it the sleep better at night, Kinda well, take Kubernetes, for example, um, a lot of application developers using it, but storage is not necessarily transparent. We, six months ago we introduced a pure service orchestrator that made storage transparent, so you have a block file object interface you, you just call and use storage, spin it up, use it as you need and let go, but you should not have to worry about, let me go phone someone creative volume decider either. So you need that transparent and elasticity and we've been focused on delivering that and now few modernize were kind of application development is going, we can provide that. It's always on. It always works. It's globally consistent, it shared, and it's easy to manage from wherever you're saying progress. Thanks for coming in and sharing the news on the new hybrid cloud applications that are hitting the market. Of course, having the right solutions and having the cloud data services available from pure storage. I'm here percussion, just general manager of the flash of rapists and pure storage. This is a special cube conversation. I'm John Furrier. Thanks for watching.

[Music] [Music] [Music] [Music] [Music] [Music] that will be successful in the 21st century [Music] being open is really important because it comes with a lot of trust the open-source community now has matured so much and that contribution from the community is really driving innovation [Music] but what's really exciting is the change that we've seen in our teams not only the way they collaborate but the way they operate in the way they work [Music] I think idea is everything ideas can change the way you see things open-source is more than a license it's actually a way of operating [Music] ladies and gentlemen please welcome Red Hat president and chief executive officer Jim Whitehurst [Music] all right well welcome to day two at the Red Hat summit I'm amazed to see this many people here at 8:30 in the morning given the number of people I saw pretty late last night out and about so thank you for being here and have to give a shout out speaking of power participation that DJ is was Mike Walker who is our global director of open innovation labs so really enjoyed that this morning was great to have him doing that so hey so day one yesterday we had some phenomenal announcements both around Red Hat products and things that we're doing as well as some great partner announcements which we found exciting I hope they were interesting to you and I hope you had a chance to learn a little more about that and enjoy the breakout sessions that we had yesterday so yesterday was a lot about the what with these announcements and partnerships today I wanted to spin this morning talking a little bit more about the how right how do we actually survive and thrive in this digitally transformed world and to some extent the easy parts identifying the problem we all know that we have to be able to move more quickly we all know that we have to be able to react to change faster and we all know that we need to innovate more effectively all right so the problem is easy but how do you actually go about solving that right the problem is that's not a product that you can buy off the shelf right it is a capability that you have to build and certainly it's technology enabled but it's also depends on process culture a whole bunch of things to figure out how we actually do that and the answer is likely to be different in different organizations with different objective functions and different starting points right so this is a challenge that we all need to feel our way to an answer on and so I want to spend some time today talking about what we've seen in the market and how people are working to address that and it's one of the reasons that the summit this year the theme is ideas worth it lorring to take us back on a little history lesson so two years ago here at Moscone the theme of the summit was the power of participation and then I talked a lot about the power of groups of people working together and participating are able to solve problems much more quickly and much more effectively than individuals or even individual organizations working by themselves and some of the largest problems that we face in technology but more broadly in the world will ultimately only be solved if we effectively participate and work together then last year the theme of the summit was the impact of the individual and we took this concept of participation a bit further and we talked about how participation has to be active right it's a this isn't something where you can be passive that you can sit back you have to be involved because the problem in a more participative type community is that there is no road map right you can't sit back and wait for an edict on high or some central planning or some central authority to tell you what to do you have to take initiative you have to get involved right this is a active participation sport now one of the things that I talked about as part of that was that planning was dead and it was kind of a key my I think my keynote was actually titled planning is dead and the concept was that in a world that's less knowable when we're solving problems in a more organic bottom-up way our ability to effectively plan into the future it's much less than it was in the past and this idea that you're gonna be able to plan for success and then build to it it really is being replaced by a more bottom-up participative approach now aside from my whole strategic planning team kind of being up in arms saying what are you saying planning is dead I have multiple times had people say to me well I get that point but I still need to prepare for the future how do I prepare my organization for the future isn't that planning and so I wanted to spend a couple minutes talk a little more detail about what I meant by that but importantly taking our own advice we spent a lot of time this past year looking around at what our customers are doing because what a better place to learn then from large companies and small companies around the world information technology organizations having to work to solve these problems for their organizations and so our ability to learn from each other take the power of participation an individual initiative that people and organizations have taken there are just so many great learnings this year that I want to get a chance to share I also thought rather than listening to me do that that we could actually highlight some of the people who are doing this and so I do want to spend about five minutes kind of contextualizing what we're going to go through over the next hour or so and some of the lessons learned but then we want to share some real-world stories of how organizations are attacking some of these problems under this how do we be successful in a world of constant change in uncertainty so just going back a little bit more to last year talking about planning was dead when I said planning it's kind of a planning writ large and so that's if you think about the way traditional organizations work to solve problems and ultimately execute you start off planning so what's a position you want to get to in X years and whether that's a competitive strategy in a position of competitive advantage or a certain position you want an organizational function to reach you kind of lay out a plan to get there you then typically a senior leaders or a planning team prescribes the sets of activities and the organization structure and the other components required to get there and then ultimately execution is about driving compliance against that plan and you look at you say well that's all logical right we plan for something we then figure out how we're gonna get there we go execute to get there and you know in a traditional world that was easy and still some of this makes sense I don't say throw out all of this but you have to recognize in a more uncertain volatile world where you can be blindsided by orthogonal competitors coming in and you the term uber eyes you have to recognize that you can't always plan or know what the future is and so if you don't well then what replaces the traditional model or certainly how do you augment the traditional model to be successful in a world that you knows ambiguous well what we've heard from customers and what you'll see examples of this through the course of this morning planning is can be replaced by configuring so you can configure for a constant rate of change without necessarily having to know what that change is this idea of prescription of here's the activities people need to perform and let's lay these out very very crisply job descriptions what organizations are going to do can be replaced by a greater degree of enablement right so this idea of how do you enable people with the knowledge and things that they need to be able to make the right decisions and then ultimately this idea of execution as compliance can be replaced by a greater level of engagement of people across the organization to ultimately be able to react at a faster speed to the changes that happen so just double clicking in each of those for a couple minutes so what I mean by configure for constant change so again we don't know exactly what the change is going to be but we know it's going to happen and last year I talked a little bit about a process solution to that problem I called it that you have to try learn modify and what that model try learn modify was for anybody in the app dev space it was basically taking the principles of agile and DevOps and applying those more broadly to business processes in technology organizations and ultimately organizations broadly this idea of you don't have to know what your ultimate destination is but you can try and experiment you can learn from those things and you can move forward and so that I do think in technology organizations we've seen tremendous progress even over the last year as organizations are adopting agile endeavor and so that still continues to be I think a great way for people to to configure their processes for change but this year we've seen some great examples of organizations taking a different tack to that problem and that's literally building modularity into their structures themselves right actually building the idea that change is going to happen into how you're laying out your technology architectures right we've all seen the reverse of that when you build these optimized systems for you know kind of one environment you kind of flip over two years later what was the optimized system it's now called a legacy system that needs to be migrated that's an optimized system that now has to be moved to a new environment because the world has changed so again you'll see a great example of that in a few minutes here on stage next this concept of enabled double-clicking on that a little bit so much of what we've done in technology over the past few years has been around automation how do we actually replace things that people were doing with technology or augmenting what people are doing with technology and that's incredibly important and that's work that can continue to go forward it needs to happen it's not really what I'm talking about here though enablement in this case it's much more around how do you make sure individuals are getting the context they need how are you making sure that they're getting the information they need how are you making sure they're getting the tools they need to make decisions on the spot so it's less about automating what people are doing and more about how can you better enable people with tools and technology now from a leadership perspective that's around making sure people understand the strategy of the company the context in which they're working in making sure you've set the appropriate values etc etc from a technology perspective that's ensuring that you're building the right systems that allow the right information the right tools at the right time to the right people now to some extent even that might not be hard but when the world is constantly changing that gets to be even harder and I think that's one of the reasons we see a lot of traction and open source to solve these problems to use flexible systems to help enterprises be able to enable their people not just in it today but to be flexible going forward and again we'll see some great examples of that and finally engagement so again if execution can't be around driving compliance to a plan because you no longer have this kind of Cris plan well what do leaders do how do organizations operate and so you know I'll broadly use the term engagement several of our customers have used this term and this is really saying well how do you engage your people in real-time to make the right decisions how do you accelerate a pace of cadence how do you operate at a different speed so you can react to change and take advantage of opportunities as they arise and everywhere we look IT is a key enabler of this right in the past IT was often seen as an inhibitor to this because the IT systems move slower than the business might want to move but we are seeing with some of these new technologies that literally IT is becoming the enabler and driving the pace of change back on to the business and you'll again see some great examples of that as well so again rather than listen to me sit here and theoretically talk about these things or refer to what we've seen others doing I thought it'd be much more interesting to bring some of our partners and our customers up here to specifically talk about what they're doing so I'm really excited to have a great group of customers who have agreed to stand in front of 7,500 people or however many here this morning and talk a little bit more about what they're doing so really excited to have them here and really appreciate all them agreeing to be a part of this and so to start I want to start with tee systems we have the CEO of tee systems here and I think this is a great story because they're really two parts to it right because he has two perspectives one is as the CEO of a global company itself having to navigate its way through digital disruption and as a global cloud service provider obviously helping its customers through this same type of change so I'm really thrilled to have a del hasta li join me on stage to talk a little bit about T systems and what they're doing and what we're doing jointly together so Adelle [Music] Jim took to see you Adele thank you for being here you for having me please join me I love to DJ when that fantastic we may have to hire him no more events for events where's well employed he's well employed though here that team do not give him mics activation it's great to have you here really do appreciate it well you're the CEO of a large organization that's going through this disruption in the same way we are I'd love to hear a little bit how for your company you're thinking about you know navigating this change that we're going through great well you know key systems as an ICT service provider we've been around for decades I'm not different to many of our clients we had to change the whole disruption of the cloud and digitization and new skills and new capability and agility it's something we had to face as well so over the last five years and especially in the last three years we invested heavily invested over a billion euros in building new capabilities building new offerings new infrastructures to support our clients so to be very disruptive for us as well and so and then with your customers themselves they're going through this set of change and you're working to help them how are you working to help enable your your customers as they're going through this change well you know all of them you know in this journey of changing the way they run their business leveraging IT much more to drive business results digitization and they're all looking for new skills new ideas they're looking for platforms that take them away from traditional waterfall development that takes a year or a year and a half before they see any results to processes and ways of bringing applications in a week in a month etcetera so it's it's we are part of that journey with them helping them for that and speaking of that I know we're working together and to help our joint customers with that can you talk a little bit more about what we're doing together sure well you know our relationship goes back years and years with with the Enterprise Linux but over the last few years we've invested heavily in OpenShift and OpenStack to build peope as layers to build you know flexible infrastructure for our clients and we've been working with you we tested many different technology in the marketplace and been more successful with Red Hat and the stack there and I'll give you an applique an example several large European car manufacturers who have connected cars now as a given have been accelerating the applications that needed to be in the car and in the past it took them years if not you know scores to get an application into the car and today we're using open shift as the past layer to develop to enable these DevOps for these companies and they bring applications in less than a month and it's a huge change in the dynamics of the competitiveness in the marketplace and we rely on your team and in helping us drive that capability to our clients yeah do you find it fascinating so many of the stories that you hear and that we've talked about with with our customers is this need for speed and this ability to accelerate and enable a greater degree of innovation by simply accelerating what what we're seeing with our customers absolutely with that plus you know the speed is important agility is really critical but doing it securely doing it doing it in a way that is not gonna destabilize the you know the broader ecosystem is really critical and things like GDP are which is a new security standard in Europe is something that a lot of our customers worry about they need help with and we're one of the partners that know what that really is all about and how to navigate within that and use not prevent them from using the new technologies yeah I will say it isn't just the speed of the external but the security and the regulation especially GDR we have spent an hour on that with our board this week there you go he said well thank you so much for being here really to appreciate the work that we're doing together and look forward to continued same here thank you thank you [Applause] we've had a great partnership with tea systems over the years and we've really taken it to the next level and what's really exciting about that is you know we've moved beyond just helping kind of host systems for our customers we really are jointly enabling their success and it's really exciting and we're really excited about what we're able to to jointly accomplish so next i'm really excited that we have our innovation award winners here and we'll have on stage with us our innovation award winners this year our BBVA dnm IAG lasat Lufthansa Technik and UPS and yet they're all working in one for specific technology initiatives that they're doing that really really stand out and are really really exciting you'll have a chance to learn a lot more about those through the course of the event over the next couple of days but in this context what I found fascinating is they were each addressing a different point of this configure enable engage and I thought it would be really great for you all to hear about how they're experimenting and working to solve these problems you know real-time large organizations you know happening now let's start with the video to see what they think about when they think about innovation I define innovation is something that's changing the model changing the way of thinking not just a step change improvement not just making something better but actually taking a look at what already exists and then putting them together in new and exciting lives innovation is about to build something nobody has done before historically we had a statement that business drives technology we flip that equation around an IT is now demonstrating to the business at power of technology innovation desde el punto de vista de la tecnología supone salir de plataform as proprietary as ADA Madero cloud basado an open source it's a possibility the open source que no parameter no sir Kamala and I think way that for me open-source stands for flexibility speed security the community and that contribution from the community is really driving innovation innovation at a pace that I don't think our one individual organization could actually do ourselves right so first I'd like to talk with BBVA I love this story because as you know Financial Services is going through a massive set of transformations and BBVA really is at the leading edge of thinking about how to deploy a hybrid cloud strategy and kind of modular layered architecture to be successful regardless of what happens in the future so with that I'd like to welcome on stage Jose Maria Rosetta from BBVA [Music] thank you for being here and congratulations on your innovation award it's been a pleasure to be here with you it's great to have you hi everybody so Josemaria for those who might not be familiar with BBVA can you give us a little bit of background on your company yeah a brief description BBVA is is a bank as a financial institution with diversified business model and that provides well financial services to more than 73 million of customers in more than 20 countries great and I know we've worked with you for a long time so we appreciate that the partnership with you so I thought I'd start with a really easy question for you how will blockchain you know impact financial services in the next five years I've gotten no idea but if someone knows the answer I've got a job for him for him up a pretty good job indeed you know oh all right well let me go a little easier then so how will the global payments industry change in the next you know four or five years five years well I think you need a a Weezer well I tried to make my best prediction means that in five years just probably will be five years older good answer I like that I always abstract up I hope so I hope so yah-yah-yah hope so good point so you know immediately that's the obvious question you have a massive technology infrastructure is a global bank how do you prepare yourself to enable the organization to be successful when you really don't know what the future is gonna be well global banks and wealth BBBS a global gam Bank a certain component foundations you know today I would like to talk about risk and efficiency so World Bank's deal with risk with the market great the operational reputational risk and so on so risk control is part of all or DNA you know and when you've got millions of customers you know efficiency efficiency is a must so I think there's no problem with all these foundations they problem the problem analyze the problems appears when when banks translate these foundations is valued into technology so risk control or risk management avoid risk usually means by the most expensive proprietary technology in the market you know from one of the biggest software companies in the world you know so probably all of you there are so those people in the room were glad to hear you say that yeah probably my guess the name of those companies around San Francisco most of them and efficiency usually means a savory business unit as every department or country has his own specific needs by a specific solution for them so imagine yourself working in a data center full of silos with many different Hardware operating systems different languages and complex interfaces to communicate among them you know not always documented what really never documented so your life your life in is not easy you know in this scenario are well there's no room for innovation so what's been or or strategy be BES ready to move forward in this new digital world well we've chosen a different approach which is quite simple is to replace all local proprietary system by a global platform based on on open source with three main goals you know the first one is reduce the average transaction cost to one-third the second one is increase or developers productivity five times you know and the third is enable or delete the business be able to deliver solutions of three times faster so you're not quite easy Wow and everything with the same reliability as on security standards as we've got today Wow that is an extraordinary set of objectives and I will say their world on the path of making that successful which is just amazing yeah okay this is a long journey sometimes a tough journey you know to be honest so we decided to partnership with the with the best companies in there in the world and world record we think rate cut is one of these companies so we think or your values and your knowledge is critical for BBVA and well as I mentioned before our collaboration started some time ago you know and just an example in today in BBVA a Spain being one of the biggest banks in in the country you know and using red hat technology of course our firm and fronting architecture you know for mobile and internet channels runs the ninety five percent of our customers request this is approximately 3,000 requests per second and our back in architecture execute 70 millions of business transactions a day this is almost a 50% of total online transactions executed in the country so it's all running yes running I hope so you check for you came on stage it's I'll be flying you know okay good there's no wood up here to knock on it's been a really great partnership it's been a pleasure yeah thank you so much for being here thank you thank you [Applause] I do love that story because again so much of what we talk about when we when we talk about preparing for digital is a processed solution and again things like agile and DevOps and modular izing components of work but this idea of thinking about platforms broadly and how they can run anywhere and actually delivering it delivering at a scale it's just a phenomenal project and experience and in the progress they've made it's a great team so next up we have two organizations that have done an exceptional job of enabling their people with the right information and the tools they need to be successful you know in both of these cases these are organizations who are under constant change and so leveraging the power of open-source to help them build these tools to enable and you'll see it the size and the scale of these in two very very different contexts it's great to see and so I'd like to welcome on stage Oh smart alza' with dnm and David Abraham's with IAG [Music] Oh smart welcome thank you so much for being here Dave great to see you thank you appreciate you being here and congratulations to you both on winning the Innovation Awards thank you so Omar I really found your story fascinating and how you're able to enable your people with data which is just significantly accelerated the pace with which they can make decisions and accelerate your ability to to act could you tell us a little more about the project and then what you're doing Jim and Tina when the muchisimas gracias por ever say interesado pono true projecto [Music] encargado registry controller las entradas a leda's persona por la Frontera argentina yo sé de dos siento treinta siete puestos de contrôle tienen lo largo de la Frontera tanto area the restreamer it EEMA e if looool in dilute ammonia shame or cinta me Jonas the tránsito sacra he trod on in another Fronteras dingus idea idea de la Magneto la cual estamos hablando la Frontera cantina tienen extension the kin same in kilo metros esto es el gada mint a maje or allege Estancia kaeun a poor carretera a la co de mexico con el akka a direction emulation s 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calidad de vida de atras de mettre personas SI y meet our que el delito perform a trois Natura from Dana's Argentine sigue siendo en favor de esto SI temes uno de los países mess Alberto's Allah immigration en Latin America yah hora con una plataforma mas segunda first of all I want to thank you for the interest is played for our project the National migration administration or diem records the entry and exit of people on the Argentine territory it grants residents permits to foreigners who wish to live in our country through 237 entry points land air border sea and river ways Jim dnm registered over 80 million transits throughout last year Argentine borders cover about 15,000 kilometers just our just to give you an idea of the magnitude of our borders this is greater than the distance on a highway between Mexico City and Alaska our department applies the mechanisms that prevent the entry and residents of people involved in crimes like terrorism trafficking of persons weapons drugs and others in 2016 we shifted to a more preventive and predictive paradigm that is how Sam's the system for migration analysis was created with red hats great assistance and support this allowed us to tackle the challenge of integrating multiple and varied issues legal issues police databases national and international security organizations like Interpol API advanced passenger information and PNR passenger name record this involved starting private cloud with OpenShift Rev data virtualization cloud forms and fuse that were the basis to develop Sam and implementing machine learning models and artificial intelligence our analysts consulted a number of systems and other manual files before 2016 4 days for each person entering or leaving the country so this has allowed us to optimize our decisions making them in real time each time Sam is consulted it processes patterns of over two billion data entries Sam's aim is to improve the quality of life of our citizens and visitors making sure that crime doesn't pierce our borders in an environment of analytic evolution and constant improvement in essence Sam contributes toward Argentina being one of the leaders in Latin America in terms of immigration with our new system great thank you and and so Dave tell us a little more about the insurance industry and the challenges in the EU face yeah sure so you know in the insurance industry it's a it's been a bit sort of insulated from a lot of major change in disruption just purely from the fact that it's highly regulated and the cost of so that the barrier to entry is quite high in fact if you think about insurance you know you have to have capital reserves to protect against those major events like floods bush fires and so on but the whole thing is a lot of change there's come in a really rapid pace I'm also in the areas of customer expectations you know customers and now looking and expecting for the same levels of flexibility and convenience that they would experience with more modern and new startups they're expecting out of the older institutions like banks and insurance companies like us so definitely expecting the industry to to be a lot more adaptable and to better meet their needs I think the other aspect of it really is in the data the data area where I think that the donor is now creating a much more significant connection between organizations in a car summers especially when you think about the level of devices that are now enabled and the sheer growth of data that's that that's growing at exponential rates so so that the impact then is that the systems that we used to rely on are the technology we used to rely on to be able to handle that kind of growth no longer keeps up and is able to to you know build for the future so we need to sort of change that so what I G's really doing is transform transforming the organization to become a lot more efficient focus more on customers and and really set ourselves up to be agile and adaptive and so ya know as part of your Innovation Award that the specific set of projects you tied a huge amount of different disparate systems together and with M&A and other you have a lot to do there to you tell us a little more about kind of how you're able to better respond to customer needs by being able to do that yeah no you're right so we've we've we're nearly a hundred year old company that's grown from lots of merger and acquisition and just as a result of that that means that data's been sort of spread out and fragmented across multiple brands and multiple products and so the number one sort of issue and problem that we were hearing was that it was too hard to get access to data and it's highly complicated which is not great from a company from our perspective really because because we are a data company right that's what we do we we collect data about people what they what's important to them what they value and the environment in which they live so that we can understand that risk and better manage and protect those people so what we're doing is we're trying to make and what we have been doing is making data more open and accessible and and by that I mean making data more of easily available for people to use it to make decisions in their day-to-day activity and to do that what we've done is built a single data platform across the group that unifies the data into a single source of truth that we can then build on top of that single views of customers for example that puts the right information into the into the hands of the people that need it the most and so now why does open source play such a big part in doing that I know there are a lot of different solutions that could get you there sure well firstly I think I've been sauce has been k2 these and really it's been key because we've basically started started from scratch to build this this new next-generation data platform based on entirely open-source you know using great components like Kafka and Postgres and airflow and and and and and then fundamentally building on top of red Red Hat OpenStack right to power all that and they give us the flexibility that we need to be able to make things happen much faster for example we were just talking to the pivotal guys earlier this week here and some of the stuff that we're doing they're they're things quite interesting innovative writes even sort of maybe first in the world where we've taken the older sort of appliance and dedicated sort of massive parallel processing unit and ported that over onto red Red Hat OpenStack right which is now giving us a lot more flexibility for scale in a much more efficient way but you're right though that we've come from in the past a more traditional approach to to using vendor based technology right which was good back then when you know technology solutions could last for around 10 years or so on and and that was fine but now that we need to move much faster we've had to rethink that and and so our focus has been on using you know more commoditized open source technology built by communities to give us that adaptability and sort of remove the locking in there any entrenchment of technology so that's really helped us but but I think that the last point that's been really critical to us is is answering that that concern and question about ongoing support and maintenance right so you know in a regular environment the regulator is really concerned about anything that could fundamentally impact business operation and and so the question is always about what happens when something goes wrong who's going to be there to support you which is where the value of the the partnership we have with Red Hat has really come into its own right and what what it's done is is it's actually giving us the best of both worlds a means that we can we can leverage and use and and and you know take some of the technology that's being developed by great communities in the open source way but also partner with a trusted partner in red had to say you know they're going to stand behind that community and provide that support when we needed the most so that's been the kind of the real value out of that partnership okay well I appreciate I love the story it's how do you move quickly leverage the power community but do it in a safe secure way and I love the idea of your literally empowering people with machine learning and AI at the moment when they need it it's just an incredible story so thank you so much for being here appreciate it thank you [Applause] you know again you see in these the the importance of enabling people with data and in an old-world was so much data was created with a system in mind versus data is a separate asset that needs to be available real time to anyone is a theme we hear over and over and over again and so you know really looking at open source solutions that allow that flexibility and keep data from getting locked into proprietary silos you know is a theme that we've I've heard over and over over the past year with many of our customers so I love logistics I'm a geek that way I come from that background in the past and I know that running large complex operations requires flawless execution and that requires great data and we have two great examples today around how to engage own organizations in new and more effective ways in the case of lufthansa technik literally IT became the business so it wasn't enabling the business it became the business offering and importantly went from idea to delivery to customers in a hundred days and so this theme of speed and the importance of speed it's a it's a great story you'll hear more about and then also at UPS UPS again I talked a little earlier about IT used to be kind of the long pole in the tent the thing that was slow moving because of the technology but UPS is showing that IT can actually drive the business and the cadence of business even faster by demonstrating the power and potential of technology to engage in this case hundreds of thousands of people to make decisions real-time in the face of obviously constant change around weather mechanicals and all the different things that can happen in a large logistics operation like that so I'd like to welcome on stage to be us more from Lufthansa Technik and Nick Castillo from ups to be us welcome thank you for being here Nick thank you thank you Jim and congratulations on your Innovation Awards oh thank you it's a great honor so to be us let's start with you can you tell us a little bit more about what a viet are is yeah avatars are a digital platform offering features like aircraft condition analytics reliability management and predictive maintenance and it helps airlines worldwide to digitize and improve their operations so all of the features work and can be used separately or generate even more where you burn combined and finally we decided to set up a viet as an open platform that means that we avoid the whole aviation industry to join the community and develop ideas on our platform and to be as one of things i found really fascinating about this is that you had a mandate to do this at a hundred days and you ultimately delivered on it you tell us a little bit about that i mean nothing in aviation moves that fast yeah that's been a big challenge so in the beginning of our story the Lufthansa bot asked us to develop somehow digital to win of an aircraft within just hundred days and to deliver something of value within 100 days means you cannot spend much time and producing specifications in terms of paper etc so for us it was pretty clear that we should go for an angel approach and immediately start and developing ideas so we put the best experts we know just in one room and let them start to work and on day 2 I think we already had the first scribbles for the UI on day 5 we wrote the first lines of code and we were able to do that because it has been a major advantage for us to already have four technologies taken place it's based on open source and especially rated solutions because we did not have to waste any time setting up the infrastructure and since we wanted to get feedback very fast we were certainly visited an airline from the Lufthansa group already on day 30 and showed them the first results and got a lot of feedback and because from the very beginning customer centricity has been an important aspect for us and changing the direction based on customer feedback has become quite normal for us over time yeah it's an interesting story not only engaging the people internally but be able to engage with a with that with a launch customer like that and get feedback along the way as it's great thing how is it going overall since launch yeah since the launch last year in April we generated much interest in the industry as well from Airlines as from competitors and in the following month we focused on a few Airlines which had been open minded and already advanced in digital activities and we've got a lot of feedback by working with them and we're able to improve our products by developing new features for example we learned that data integration can become quite complex in the industry and therefore we developed a new feature called quick boarding allowing Airlines to integrate into the via table platform within one day using a self-service so and currently we're heading for the next steps beyond predictive maintenance working on process automation and prescriptive prescriptive maintenance because we believe prediction without fulfillment still isn't enough it really is a great example of even once you're out there quickly continuing to innovate change react it's great to see so Nick I mean we all know ups I'm still always blown away by the size and scale of the company and the logistics operations that you run you tell us a little more about the project and what we're doing together yeah sure Jim and you know first of all I think I didn't get the sportcoat memo I think I'm the first one up here today with a sport coat but you know first on you know on behalf of the 430,000 ups was around the world and our just world-class talented team of 5,000 IT professionals I have to tell you we're humbled to be one of this year's red hat Innovation Award recipients so we really appreciate that you know as a global logistics provider we deliver about 20 million packages each day and we've got a portfolio of technologies both operational and customer tech and another customer facing side the power what we call the UPS smart logistics network and I gotta tell you innovations in our DNA technology is at the core of everything we do you know from the ever familiar first and industry mobile platform that a lot of you see when you get delivered a package which we call the diad which believe it or not we delivered in 1992 my choice a data-driven solution that drives over 40 million of our my choice customers I'm whatever you know what this is great he loves logistics he's a my choice customer you could be one too by the way there's a free app in the App Store but it provides unmatched visibility and really controls that last mile delivery experience so now today we're gonna talk about the solution that we're recognized for which is called site which is part of a much greater platform that we call edge which is transforming how our package delivery teams operate providing them real-time insights into our operations you know this allows them to make decisions based on data from 32 disparate data sources and these insights help us to optimize our operations but more importantly they help us improve the delivery experience for our customers just like you Jim you know on the on the back end is Big Data and it's on a large scale our systems are crunching billions of events to render those insights on an easy-to-use mobile platform in real time I got to tell you placing that information in our operators hands makes ups agile and being agile being able to react to changing conditions as you know is the name of the game in logistics now we built edge in our private cloud where Red Hat technologies play a very important role as part of our overage overarching cloud strategy and our migration to agile and DevOps so it's it's amazing it's amazing the size and scale so so you have this technology vision around engaging people in a more effect way those are my word not yours but but I'd be at that's how it certainly feels and so tell us a little more about how that enables the hundreds of thousands people to make better decisions every day yep so you know we're a people company and the edge platform is really the latest in a series of solutions to really empower our people and really power that smart logistics network you know we've been deploying technology believe it or not since we founded the company in 1907 we'll be a hundred and eleven years old this August it's just a phenomenal story now prior to edge and specifically the syphon ishutin firm ation from a number of disparate systems and reports they then need to manually look across these various data sources and and frankly it was inefficient and prone to inaccuracy and it wasn't really real-time at all now edge consumes data as I mentioned earlier from 32 disparate systems it allows our operators to make decisions on staffing equipment the flow of packages through the buildings in real time the ability to give our people on the ground the most up-to-date data allows them to make informed decisions now that's incredibly empowering because not only are they influencing their local operations but frankly they're influencing the entire global network it's truly extraordinary and so why open source and open shift in particular as part of that solution yeah you know so as I mentioned Red Hat and Red Hat technology you know specifically open shift there's really core to our cloud strategy and to our DevOps strategy the tools and environments that we've partnered with Red Hat to put in place truly are foundational and they've fundamentally changed the way we develop and deploy our systems you know I heard Jose talk earlier you know we had complex solutions that used to take 12 to 18 months to develop and deliver to market today we deliver those same solutions same level of complexity in months and even weeks now openshift enables us to container raise our workloads that run in our private cloud during normal operating periods but as we scale our business during our holiday peak season which is a very sure window about five weeks during the year last year as a matter of fact we delivered seven hundred and sixty-two million packages in that small window and our transactions our systems they just spiked dramatically during that period we think that having open shift will allow us in those peak periods to seamlessly move workloads to the public cloud so we can take advantage of burst capacity economically when needed and I have to tell you having this flexibility I think is key because you know ultimately it's going to allow us to react quickly to customer demands when needed dial back capacity when we don't need that capacity and I have to say it's a really great story of UPS and red hat working you together it really is a great story is just amazing again the size and scope but both stories here a lot speed speed speed getting to market quickly being able to try things it's great lessons learned for all of us the importance of being able to operate at a fundamentally different clock speed so thank you all for being here very much appreciated congratulate thank you [Applause] [Music] alright so while it's great to hear from our Innovation Award winners and it should be no surprise that they're leading and experimenting in some really interesting areas its scale so I hope that you got a chance to learn something from these interviews you'll have an opportunity to learn more about them you'll also have an opportunity to vote on the innovator of the year you can do that on the Red Hat summit mobile app or on the Red Hat Innovation Awards homepage you can learn even more about their stories and you'll have a chance to vote and I'll be back tomorrow to announce the the summit winner so next I like to spend a few minutes on talking about how Red Hat is working to catalyze our customers efforts Marko bill Peter our senior vice president of customer experience and engagement and John Alessio our vice president of global services will both describe areas in how we are working to configure our own organization to effectively engage with our customers to use open source to help drive their success so with that I'd like to welcome marquel on stage [Music] good morning good morning thank you Jim so I want to spend a few minutes to talk about how we are configured how we are configured towards your success how we enable internally as well to work towards your success and actually engage as well you know Paul yesterday talked about the open source culture and our open source development net model you know there's a lot of attributes that we have like transparency meritocracy collaboration those are the key of our culture they made RedHat what it is today and what it will be in the future but we also added our passion for customer success to that let me tell you this is kind of the configuration from a cultural perspective let me tell you a little bit on what that means so if you heard the name my organization is customer experience and engagement right in the past we talked a lot about support it's an important part of the Red Hat right and how we are configured we are configured probably very uniquely in the industry we put support together we have product security in there we add a documentation we add a quality engineering into an organization you think there's like wow why are they doing it we're also running actually the IT team for actually the product teams why are we doing that now you can imagine right we want to go through what you see as well right and I'll give you a few examples on how what's coming out of this configuration we invest more and more in testing integration and use cases which you are applying so you can see it between the support team experiencing a lot what you do and actually changing our test structure that makes a lot of sense we are investing more and more testing outside the boundaries so not exactly how things must fall by product management or engineering but also how does it really run in an environment that you operate we run complex setups internally right taking openshift putting in OpenStack using software-defined storage underneath managing it with cloud forms managing it if inside we do that we want to see how that works right we are reshaping documentation console to kind of help you better instead of just documenting features and knobs as in how can how do you want to achieve things now part of this is the configuration that are the big part of the configuration is the voice of the customer to listen to what you say I've been here at Red Hat a few years and one of my passion has always been really hearing from customers how they do it I travel constantly in the world and meet with customers because I want to know what is really going on we use channels like support we use channels like getting from salespeople the interaction from customers we do surveys we do you know we interact with our people to really hear what you do what we also do what maybe not many know and it's also very unique in the industry we have a webpage called you asked reacted we show very transparently you told us this is an area for improvement and it's not just in support it's across the company right build us a better web store build us this we're very transparent about Hades improvements we want to do with you now if you want to be part of the process today go to the feedback zone on the next floor down and talk to my team I might be there as well hit me up we want to hear the feedback this is how we talk about configuration of the organization how we are configured let me go to let me go to another part which is innovation innovation every day and that in my opinion the enable section right we gotta constantly innovate ourselves how do we work with you how do we actually provide better value how do we provide faster responses in support this is what we would I say is is our you know commitment to innovation which is the enabling that Jim talked about and I give you a few examples which I'm really happy and it kind of shows the open source culture at Red Hat our commitment is for innovation I'll give you good example right if you have a few thousand engineers and you empower them you kind of set the business framework as hey this is an area we got to do something you get a lot of good IDs you get a lot of IDs and you got a shape an inter an area that hey this is really something that brings now a few years ago we kind of said or I say is like based on a lot of feedback is we got to get more and more proactive if you customers and so I shaped my team and and I shaped it around how can we be more proactive it started very simple as in like from kbase articles or knowledgebase articles in getting started guys then we started a a tool that we put out called labs you've probably seen them if you're on the technical side really taking small applications out for you to kind of validate is this configured correctly stat configure there was the start then out of that the ideas came and they took different turns and one of the turns that we came out was right at insights that we launched a few years ago and did you see the demo yesterday that in Paul's keynote that they showed how something was broken with one the data centers how it was applied to fix and how has changed this is how innovation really came from the ground up from the support side and turned into something really a being a cornerstone of our strategy and we're keeping it married from the day to day work right you don't want to separate this you want to actually keep that the data that's coming from the support goes in that because that's the power that we saw yesterday in the demo now innovation doesn't stop when you set the challenge so we did the labs we did the insights we just launched a solution engine called solution engine another thing that came out of that challenge is in how do we break complex issues down that it's easier for you to find a solution quicker it's one example but we're also experimenting with AI so insights uses AI as you probably heard yesterday we also use it internally to actually drive faster resolution we did in one case with a a our I bought basically that we get to 25% faster resolution on challenges that you have the beauty for you obviously it's well this is much faster 10% of all our support cases today are supported and assisted by an AI now I'll give you another example of just trying to tell you the innovation that comes out if you configure and enable the team correctly kbase articles are knowledgebase articles we q8 thousands and thousands every year and then I get feedback as and while they're good but they're in English as you can tell my English is perfect so it's not no issue for that but for many of you is maybe like even here even I read it in Japanese so we actually did machine translation because it's too many that we can do manually the using machine translation I can tell it's a funny example two weeks ago I tried it I tried something from English to German I looked at it the German looked really bad I went back but the English was bad so it really translates one to one actually what it does but it's really cool this is innovation that you can apply and the team actually worked on this and really proud on that now the real innovation there is not these tools the real innovation is that you can actually shape it in a way that the innovation comes that you empower the people that's the configure and enable and what I think is all it's important this don't reinvent the plumbing don't start from scratch use systems like containers on open shift to actually build the innovation in a smaller way without reinventing the plumbing you save a lot of issues on security a lot of issues on reinventing the wheel focus on that that's what we do as well if you want to hear more details again go in the second floor now let's talk about the engage that Jim mentioned before what I translate that engage is actually engaging you as a customer towards your success now what does commitment to success really mean and I want to reflect on that on a traditional IT company shows up with you talk the salesperson solution architect works with you consulting implements solution it comes over to support and trust me in a very traditional way the support guy has no clue what actually was sold early on it's what happens right and this is actually I think that red had better that we're not so silent we don't show our internal silos or internal organization that much today we engage in a way it doesn't matter from which team it comes we have a better flow than that you deserve how the sausage is made but we can never forget what was your business objective early on now how is Red Hat different in this and we are very strong in my opinion you might disagree but we are very strong in a virtual accounting right really putting you in the middle and actually having a solution architect work directly with support or consulting involved and driving that together you can also help us in actually really embracing that model if that's also other partners or system integrators integrate put yourself in the middle be around that's how we want to make sure that we don't lose sight of the original business problem trust me reducing the hierarchy or getting rid of hierarchy and bureaucracy goes a long way now this is how we configured this is how we engage and this is how we are committed to your success with that I'm going to introduce you to John Alessio that talks more about some of the innovation done with customers thank you [Music] good morning I'm John Alessio I'm the vice president of Global Services and I'm delighted to be with you here today I'd like to talk to you about a couple of things as it relates to what we've been doing since the last summit in the services organization at the core of everything we did it's very similar to what Marco talked to you about our number one priority is driving our customer success with red hat technology and as you see here on the screen we have a number of different offerings and capabilities all the way from training certification open innovation labs consulting really pairing those capabilities together with what you just heard from Marco in the support or cee organization really that's the journey you all go through from the beginning of discovering what your business challenge is all the way through designing those solutions and deploying them with red hat now the highlight like to highlight a few things of what we've been up to over the last year so if I start with the training and certification team they've been very busy over the last year really updating enhancing our curriculum if you haven't stopped by the booth there's a preview for new capability around our learning community which is a new way of learning and really driving that enable meant in the community because 70% of what you need to know you learned from your peers and so it's a very key part of our learning strategy and in fact we take customer satisfaction with our training and certification business very seriously we survey all of our students coming out of training 93% of our students tell us they're better prepared because of red hat training and certification after Weeds they've completed the course we've updated the courses and we've trained well over a hundred and fifty thousand people over the last two years so it's a very very key part of our strategy and that combined with innovation labs and the consulting operation really drive that overall journey now we've been equally busy in enhancing the system of enablement and support for our business partners another very very key initiative is building out the ecosystem we've enhanced our open platform which is online partner enablement network we've added new capability and in fact much of the training and enablement that we do for our internal consultants our deal is delivered through the open platform now what I'm really impressed with and thankful for our partners is how they are consuming and leveraging this material we train and enable for sales for pre-sales and for delivery and we're up over 70% year in year in our partners that are enabled on RedHat technology let's give our business partners a round of applause now one of our offerings Red Hat open innovation labs I'd like to talk a bit more about and take you through a case study open innovation labs was created two years ago it's really there to help you on your journey in adopting open source technology it's an immersive experience where your team will work side-by-side with Red Hatters to really propel your journey forward in adopting open source technology and in fact we've been very busy since the summit in Boston as you'll see coming up on the screen we've completed dozens of engagements leveraging our methods tools and processes for open innovation labs as you can see we've worked with large and small accounts in fact if you remember summit last year we had a European customer easier AG on stage which was a startup and we worked with them at the very beginning of their business to create capabilities in a very short four-week engagement but over the last year we've also worked with very large customers such as Optim and Delta Airlines here in North America as well as Motability operations in the European arena one of the accounts I want to spend a little bit more time on is Heritage Bank heritage Bank is a community owned bank in Toowoomba Australia their challenge was not just on creating new innovative technology but their challenge was also around cultural transformation how to get people to work together across the silos within their organization we worked with them at all levels of the organization to create a new capability the first engagement went so well that they asked us to come in into a second engagement so I'd like to do now is run a video with Peter lock the chief executive officer of Heritage Bank so he can take you through their experience Heritage Bank is one of the country's oldest financial institutions we have to be smarter we have to be more innovative we have to be more agile we had to change we had to find people to help us make that change the Red Hat lab is the only one that truly helps drive that change with a business problem the change within the team is very visible from the start to now we've gone from being separated to very single goal minded seeing people that I only ever seen before in their cubicles in the room made me smile programmers in their thinking I'm now understanding how the whole process fits together the productivity of IT will change and that is good for our business that's really the value that were looking for the Red Hat innovation labs for us were a really great experience I'm not interested in running an organization I'm interested in making a great organization to say I was pleasantly surprised by it is an understatement I was delighted I love the quote I was delighted makes my heart warm every time I see that video you know since we were at summit for those of you who are with us in Boston some of you went on our hardhat tours we've opened three physical facilities here at Red Hat where we can conduct red head open Innovation Lab engagements Singapore London and Boston were all opened within the last physical year and in fact our site in Boston is paired with our world-class executive briefing center as well so if you haven't been there please do check it out I'd like to now talk to you a bit about a very special engagement that we just recently completed we just recently completed an engagement with UNICEF the United Nations Children's Fund and the the purpose behind this engagement was really to help UNICEF create an open-source platform that marries big data with social good the idea is UNICEF needs to be better prepared to respond to emergency situations and as you can imagine emergency situations are by nature unpredictable you can't really plan for them they can happen anytime anywhere and so we worked with them on a project that we called school mapping and the idea was to provide more insights so that when emergency situations arise UNICEF could do a much better job in helping the children in the region and so we leveraged our Red Hat open innovation lab methods tools processes that you've heard about just like we did at Heritage Bank and the other accounts I mentioned but then we also leveraged Red Hat software technologies so we leveraged OpenShift container platform we leveraged ansible automation we helped the client with a more agile development approach so they could have releases much more frequently and continue to update this over time we created a continuous integration continuous deployment pipeline we worked on containers and container in the application etc with that we've been able to provide a platform that is going to allow for their growth to better respond to these emergency situations let's watch a short video on UNICEF mission of UNICEF innovation is to apply technology to the world's most pressing problems facing children data is changing the landscape of what we do at UNICEF this means that we can figure out what's happening now on the ground who it's happening to and actually respond to it in much more of a real-time manner than we used to be able to do we love working with open source communities because of their commitment that we should be doing good for the world we're actually with red hat building a sandbox where universities or other researchers or data scientists can connect and help us with our work if you want to use data for social good there's so many groups out there that really need your help and there's so many ways to get involved [Music] so let's give a very very warm red hat summit welcome to Erica kochi co-founder of unicef innovation well Erica first of all welcome to Red Hat summit thanks for having me here it's our pleasure and thank you for joining us so Erica I've just talked a bit about kind of what we've been up to and Red Hat services over the last year we talked a bit about our open innovation labs and we did this project the school mapping project together our two teams and I thought the audience might find it interesting from your point of view on why the approach we use in innovation labs was such a good fit for the school mapping project yeah it was a great fit for for two reasons the first is values everything that we do at UNICEF innovation we use open source technology and that's for a couple of reasons because we can take it from one place and very easily move it to other countries around the world we work in 190 countries so that's really important for us not to be able to scale things also because it makes sense we can get we can get more communities involved in this and look not just try to do everything by ourselves but look much open much more openly towards the open source communities out there to help us with our work we can't do it alone yeah and then the second thing is methodology you know the labs are really looking at taking this agile approach to prototyping things trying things failing trying again and that's really necessary when you're developing something new and trying to do something new like mapping every school in the world yeah very challenging work think about it 190 countries Wow and so the open source platform really works well and then the the rapid prototyping was really a good fit so I think the audience might find it interesting on how this application and this platform will help children in Latin America so in a lot of countries in Latin America and many countries throughout the world that UNICEF works in are coming out of either decades of conflict or are are subject to natural disasters and not great infrastructure so it's really important to a for us to know where schools are where communities are well where help is needed what's connected what's not and using a overlay of various sources of data from poverty mapping to satellite imagery to other sources we can really figure out what's happening where resources are where they aren't and so we can plan better to respond to emergencies and to and to really invest in areas that are needed that need that investment excellent excellent it's quite powerful what we were able to do in a relatively short eight or nine week engagement that our two teams did together now many of your colleagues in the audience are using open source today looking to expand their use of open source and I thought you might have some recommendations for them on how they kind of go through that journey and expanding their use of open source since your experience at that yeah for us it was it was very much based on what's this gonna cost we have limited resources and what's how is this gonna spread as quickly as possible mm-hmm and so we really asked ourselves those two questions you know about 10 years ago and what we realized is if we are going to be recommending technologies that governments are going to be using it really needs to be open source they need to have control over it yeah and they need to be working with communities not developing it themselves yeah excellent excellent so I got really inspired with what we were doing here in this project it's one of those you know every customer project is really interesting to me this one kind of pulls a little bit at your heartstrings on what the real impact could be here and so I know some of our colleagues here in the audience may want to get involved how can they get involved well there's many ways to get involved with the other UNICEF or other groups out there you can search for our work on github and there are tasks that you can do right now if and if you're looking for to do she's got work for you and if you want sort of a more a longer engagement or a bigger engagement you can check out our website UNICEF stories org and you can look at the areas you might be interested in and contact us we're always open to collaboration excellent well Erica thank you for being with us here today thank you for the great project we worked on together and have a great summer thank you for being give her a round of applause all right well I hope that's been helpful to you to give you a bit of an update on what we've been focused on in global services the message I'll leave with you is our top priority is customer success as you heard through the story from UNICEF from Heritage Bank and others we can help you innovate where you are today I hope you have a great summit and I'll call out Jim Whitehurst thank you John and thank you Erica that's really an inspiring story we have so many great examples of how individuals and organizations are stepping up to transform in the face of digital disruption I'd like to spend my last few minutes with one real-world example that brings a lot of this together and truly with life-saving impact how many times do you think you can solve a problem which is going to allow a clinician to now save the life I think the challenge all of his physicians are dealing with is data overload I probably look at over 100,000 images in a day and that's just gonna get worse what if it was possible for some computer program to look at these images with them and automatically flag images that might deserve better attention Chris on the surface seems pretty simple but underneath Chris has a lot going on in the past year I've seen Chris Foreman community and a space usually dominated by proprietary software I think Chris can change medicine as we know it today [Music] all right with that I'd like to invite on stage dr. Ellen grant from Boston Children's Hospital dr. grant welcome thank you for being here so dr. grant tell me who is Chris Chris does a lot of work for us and I think Chris is making me or has definitely the potential to make me a better doctor Chris helps us take data from our archives in the hospital and port it to wrap the fastback ends like the mass up and cloud to do rapid data processing and provide it back to me in any format on a desktop an iPad or an iPhone so it it basically brings high-end data analysis right to me at the bedside and that's been a barrier that I struggled with years ago to try to break down so that's where we started with Chris is to to break that barrier between research that occurred on a timeline of days to weeks to months to clinical practice which occurs in the timeline of seconds to minutes well one of things I found really fascinating about this story RedHat in case you can't tell we're really passionate about user driven innovation is this is an example of user driven innovation not directly at a technology company but in medicine excuse me can you tell us just a little bit about the genesis of Chris and how I got started yeah Chris got started when I was running a clinical division and I was very frustrated with not having the latest image analysis tools at my fingertips while I was on clinical practice and I would have to on the research so I could go over and you know do line code and do the data analysis but if I'm always over in clinical I kept forgetting how to do those things and I wanted to have all those innovations that my fingertips and not have to remember all the computer science because I'm a physician not like a better scientist so I wanted to build a platform that gave me easy access to that back-end without having to remember all the details and so that's what Chris does for us is brings allowed me to go into the PAC's grab a dataset send it to a computer and back in to do the analysis and bring it back to me without having to worry about where it was or how it got there that's all involved in the in the platform Chris and why not just go to a vendor and ask them to write a piece of software for you to do that yeah we thought about that and we do a lot of technical innovations and we always work with the experts so we wanted to work with if I'm going to be able to say an optical device I'm going to work with the optical engineers or an EM our system I'm going to work with em our engineers so we wanted to work with people who really knew or the plumbers so to speak of the software in industry so we ended up working with the massive point cloud for the platform and the distributed systems in Red Hat as the infrastructure that's starting to support Chris and that's been actually a really incredible journey for us because medical ready medical softwares not typically been a community process and that's something that working with dan from Red Hat we learned a lot about how to participate in an open community and I think our team has grown a lot as a result of that collaboration and I know you we've talked about in the past that getting this data locked into a proprietary system you may not be able to get out there's a real issue can you talk about the importance of open and how that's worked in the process yeah and I think for the medical community and I find this resonates with other physicians as well too is that it's medical data we want to continue to own and we feel very awkward about giving it to industry so we would rather have our data sitting in an open cloud like the mass open cloud where we can have a data consortium that oversees the data governance so that we're not giving our data way to somebody else but have a platform that we can still keep a control of our own data and I think it's going to be the future because we're running of a space in the hospital we generate so much data and it's just going to get worse as I was mentioning and all the systems run faster we get new devices so the amount of data that we have to filter through is just astronomically increasing so we need to have resources to store and compute on such large databases and so thinking about where this could go I mean this is a classic feels like an open-source project it started really really small with a originally modest set of goals and it's just kind of continue to grow and grow and grow it's a lot like if yes leanest torval Linux would be in 1995 you probably wouldn't think it would be where it is now so if you dream with me a little bit where do you think this could possibly go in the next five years ten years what I hope it'll do is allow us to break down the silos within the hospital because to do the best job at what we physicians do not only do we have to talk and collaborate together as individuals we have to take the data each each community develops and be able to bring it together so in other words I need to be able to bring in information from vital monitors from mr scans from optical devices from genetic tests electronic health record and be able to analyze on all that data combined so ideally this would be a platform that breaks down those information barriers in a hospital and also allows us to collaborate across multiple institutions because many disorders you only see a few in each hospital so we really have to work as teams in the medical community to combine our data together and also I'm hoping that and we even have discussions with people in the developing world because they have systems to generate or to got to create data or say for example an M R system they can't create data but they don't have the resources to analyze on it so this would be a portable for them to participate in this growing data analysis world without having to have the infrastructure there and be a portal into our back-end and we could provide the infrastructure to do the data analysis it really is truly amazing to see how it's just continued to grow and grow and expand it really is it's a phenomenal story thank you so much for being here appreciate it thank you [Applause] I really do love that story it's a great example of user driven innovation you know in a different industry than in technology and you know recognizing that a clinicians need for real-time information is very different than a researchers need you know in projects that can last weeks and months and so rather than trying to get an industry to pivot and change it's a great opportunity to use a user driven approach to directly meet those needs so we still have a long way to go we have two more days of the summit and as I said yesterday you know we're not here to give you all the answers we're here to convene the conversation so I hope you will have an opportunity today and tomorrow to meet some new people to share some ideas we're really really excited about what we can all do when we work together so I hope you found today valuable we still have a lot more happening on the main stage as well this afternoon please join us back for the general session it's a really amazing lineup you'll hear from the women and opensource Award winners you'll also hear more about our collab program which is really cool it's getting middle school girls interested in open sourcing coding and so you'll have an opportunity to see some people involved in that you'll also hear from the open source Story speakers and you'll including in that you will see a demo done by a technologist who happens to be 11 years old so really cool you don't want to miss that so I look forward to seeing you then this afternoon thank you [Applause]

>> Announcer: Live from Las Vegas, it's The Cube, covering HPE Discover, 2017 brought to you by Hewlett Packard Enterprise. >> Okay, welcome back everyone. We're here live in Las Vegas for our exclusive three day coverage from The Cube Silicon Angle media's flagship program. We go out to events, talk to the smartest people we can find CEOs, entrepreneurs, R&D lab managers and of course we're here at HPE Discover 2017 our next two guests, Andrew Wheeler, the Fellow, VP, Deputy Director, Hewlett Packard Labs and Kirk Bresniker, Fellow and VP, Chief Architect of HP Labs, was on yesterday. Welcome back, welcome to The Cube. Hewlett Packard Labs well known you guys doing great research, Meg Whitman really staying with a focused message and one of the comments she mentioned at our press analyst meeting yesterday was focusing on the lab. So I want ask you where is that range in the labs? In terms of what you guys, when does something go outside the lines if you will? >> Andrew: Yeah good question. So, if you think about Hewlett Packard Labs and really our charter role within the company we're really kind of tasked for looking at things that will disrupt our current business or looking for kind of those new opportunities. So for us we have something we call an innovation horizon and you know it's like any other portfolio that you have where you've got maybe things that are more kind of near term, maybe you know one to three years out, things that are easily kind of transferred or the timing is right. And then we have kind of another bucket that says well maybe it's more of a three to five year kind of in that advanced development category where it needs a little more incubation but you know it needs a little more time. And then you know we reserve probably you know a smaller pocket that's for more kind of pure research. Things that are further out, higher risk. It's a bigger bet but you know we do want to have kind of a complete portfolio of those, and you know over time throughout our history you know we've got really success stories in all of those. So it's always finding kind of that right blend. But you know there's clearly a focus around the advanced development piece now that we've had a lot of things come from that research point and really one of the... >> John: You're looking for breakthroughs. I mean that's what you're... Some-- >> Andrew: Clearly. >> Internal improvement, simplify IT all that good stuff, you guys still have your eyes on some breakthroughs. >> That's right. Breakthroughs, how do we differentiate what we're doing so but yeah clearly, clearly looking for those breakthrough opportunities. >> John: And one of the things that's come up really big in this show is the security and chip thing was pretty hot, very hot, and actually wiki bonds public, true public cloud report that they put out sizing up on prem the cloud mark. >> Dave: True private cloud. >> True private cloud I'm sorry. And that's not including hybrids of $265 billion tam but the notable thing that I want to get your thoughts on is the point they pushed was over 10 years $150 billion is going to shift out of IT on premise into other differentiated services. >> Andrew: Out of labor. >> Out of labor. So this, and I asked them what that means, as he said that means it's going to shift to vendor R&D meaning the suppliers have to do more work. So that the customers don't have to do the R&D. Which we see a lot in cloud where there's a lot of R&D going on. That's your job. So you guys are HP Labs, what's happening in that R&D area that's going to off load that labor so they can move to some other high yield tasks. >> Sure. Take first. >> John: Go ahead take a stab at it. >> When we've been looking at some of the concepts we had in the memory driven computing research and advanced development programs the machine program, you know one of the things that was the kick off for me back in 2003 we looked at what we had in the unix market, we had advanced virtualization technologies, we had great management of resources technologies, we had memory fabric technologies. But they're all kind of proprietary. But Silicon is thinking and back then we were saying how does risk unix compete with industry standards service? This new methodology, new wave, exciting changing cost structures. And for us it was that it was a chance to explore those ideas and understand how they would affect our maintaining the kind of rich set of customer experiences, mission criticality, security, all of these elements. And it's kind of funny that we're sort of just coming back to the future again and we're saying okay we have this move we want to see these things happen on the cloud and we're seeing those same technologies, the composable infrastructure we have in synergy and looking forward to see the research we've done on the machine advanced development program and how will that intersect hardware composability, converged infrastructure so that you can actually have that shift, those technologies coming in taking on more of that burden to allow you freedom of choice, so you can make sure that you end up with that right mix. The right part on a full public cloud, the right mix on a full private cloud, the right mixing on that intelligent edge. But still having the ability to have all of those great software development methodologies that agile methodology, the only thing the kids know how to do out of school is open source and agile now. So you want to make sure that you can embrace that and make sure regardless of where the right spot is for a particular application in your entire enterprise portfolio that you have this common set of experiences and tools. And some of the research and development we're doing will enable us to drive that into that existing, conventional, enterprise market as well as this intelligent edge. Making a continuum, a continuum from the core to the intelligent edge. And something that modern computer science graduates will find completely comfortable. >> One attracting them is going to be the key, I think the edge is kind of intoxicating if you think about all the possibilities that are out there in terms of what you know just from a business model disruption and also technology. I mean wearables are edge, brain implants in the future will be edge, you know the singularities here as Ray Kersewile would say... >> Yeah. >> I mean but, this is the truth. This is what's happened. This is real right now. >> Oh absolutely. You know we think of all that data and right now we're just scratching the surface. I remember it was 1994 the first time I fired up a web server inside of my development team. So I could begin thinning out design information on prototype products inside of HP, and it was a novelty. People would say "What is that thing "you just sent me an email, W W whatever?" And suddenly we went from, like almost overnight, from a novelty to a business necessity, to then it transformed the way that we created the applications for the... >> John: A lot of people don't know this but since you brought it up this historical trivia, HP Labs, Hewlett Packard Labs had scientists who actually invented the web with Tim Berners-Lee, I think HTML founder was an HP Labs scientist. Pretty notable trivia. A lot of people don't know that so congratulations. >> And so I look at just what you're saying there and we see this new edge thing is it's going to be similarly transformative. Now today it's a little gimmicky perhaps it's sort of scratching the surface. It's taking security and it can be problematic at times but that will transform, because there is so much possibility for economic transformation. Right now almost all that data on the edge is thrown away. If you, the first person who understands okay I'm going to get 1% more of that data and turn it into real time intelligence, real time action... That will unmake industries and it will remake new industries. >> John: Andrew this the applied research vision, you got to apply R&D to the problem... >> Andrew: Correct. >> That's what he's getting at but you got to also think differently. You got to bring in talent. The young guns. How are you guys bringing in the young guns? What's the, what's the honeypot? >> Well I think you know for us it's, the sell for us, obviously is just the tradition of Hewlett Packard to begin with right? You know we have recognition on that level even it's not just Hewlett Packard Labs as well it's you know just R&D in general right? Kind of it you know the DNA being an engineering company so... But it's you know I think it is creating kind of these opportunities and whether it's internship programs you know just the various things that we're doing whether it's enterprise related, high performance computing... I think this edge opportunity is a really interesting one as a bridge because if you think about all the things that we hear about in enterprise in terms of "Oh you know I need this deep analytics "capability," or you know even a lot of the in memories things that we're talking about, real time response, driving information, right? All of that needs to happen at the edge as well for various opportunities so it's got a lot of the young graduates excited. We host you know hundreds of interns every year and it's real exciting to see kind of the ideas they come in with and you know they're all excited to work in this space. >> Dave: So Kirk you have your machine button, three, of course you got the logo. And then the machine... >> I got the labs logo, I got the machine logo. >> So when I first entered you talked about in the early 1980s. When I first got in the business I remembered Gene Emdall. "The best IO is no IO." (laughter) >> Yeah that's right. >> We're here again with this sort of memory semantics, centric computing. So in terms of the three that Andrew laid out the three types of sort of projects you guys pursue... Where does the machine fit? IS it sort of in all three? Or maybe you could talk about that a little bit. >> Kirk: I think it is, so we see those technologies that over the last three years we have brought so much new and it was, the critical thing about this is I think it's also sort of the prototyping of the overall approach our leaning in approach here... >> Andrew: That's right. >> It wasn't just researchers. Right? Those 500 people who made that 160 terabyte monster machine possible weren't just from labs. It was engineering teams from across Hewlett Packard Enterprise. It was our supply chain team. It was our services team telling us how these things fit together for real. Now we've had incredible technology experiences, incredible technologist experiences, and what we're seeing is that we have intercepts on conventional platforms where there's the photonics, the persistent memories. Those will make our existing DCIG and SDCG products better almost immediately. But then we also have now these whole cloth applications and as we take all of our learnings, drive them into open source software, drive them into the genesys consortium and we'll see you know probably 18, 24 months from now some of those first optimized silicon designs pop out of that ecosystem then we'll be right there to assemble those again, into conventional systems as well as more expansive, exo-scale computing, intelligent edge with large persistent memories and application specific processing as that next generation of gateways, I think we can see these intercept points at every category Andrew talked about. >> Andrew: And another good point there that kind of magnifies the model we were talking about, if we were sitting here five years ago, we would talking about things like photonics and non-volatile memory as being those big R projects. Those higher risk, longer term things, that right? As those mature, we make more progress innovation happens, right? It gets pulled into that shorter time frame that becomes advanced development. >> Dave: And Meg has talked about that... >> Yeah. >> Wanting to get more productivity out of the labs. And she's also pointed out you guys have spent more on R&D in the last several years. But even as we talked about the other day you want to see a little more D and keep the R going. So my question is, when you get to that point, of being able to support DCIG... Where do you, is it a hand off? Are you guys intimately involved? When you're making decisions about okay so member stir for example, okay this is great, that's still in the R phase then you bring it in. But now you got to commercialize this and you got 3D nan coming out and okay let's use that, that fits into our framework. So how much do you guys get involved in that handoff? You know the commercialization of this stuff? >> We get very involved. So it's at the point where when we think have something that hey we think you know maybe this could get into a product or let's see if there's good intercept here. We work jointly at that point. It's lab engineers, it's the product managers out of the group, engineers out of the business group, they essentially work collectively then on getting it to that next step. So it's kind of just one big R&D effort at that point. >> Dave: And so specifically as it relates to the machine, where do you see in the next in the near term, let's call near term next three years, or five years even, what do you see that looking like? Is it this combination of memory width capacitors or flash extensions? What does that look like in terms of commercial terms that we can expect? >> Kirk: So I really think the palette is pretty broad here. That I can see these going into existing rack and tower products to allow them to have memory that's composable down to the individual module level. To be able to take that facility to have just the right resources applied at just the right time with that API that we have in one view. Extend down to composing the hardware itself. I think we look at those edge line systems and want to have just the right kind of analytic capability, large persistent memories at that edge so we can handle those zeta bytes and zeta bytes of data in full fidelity analyzed at the edge sending back that intelligence to the core but also taking action at the edge in a timeframe that matters. I also see it coming out and being the basis of our exoscale high performance computing. You know when you want to have a exoscale system that has all of the combined capacity of the top 500 systems today but 1/20th of their power that is going to take rather novel technologies and everything we've been working on is exactly what's feeding that research and soon to be advanced development and then soon to be production in supply chain. >> Dave: Great. >> John: So the question I have is obviously we saw some really awesome Gen 10 stuff here at this show you guys are seeing that obviously you're on stage talking about a lot of the cool R&D, but really the reality is that's multiple years in the works some of this root of trust silicon technology that's pretty, getting the show buzzed up everyone's psyched about it. Dreamworks Animation's talking about how inorganic opportunities is helping their business and they got the security with the root of trust NIST certified and compliant. Pretty impressive. What's next? What else are you working on because this is where the R&D is on your shoulders for that next level of innovation. Where, what do you guys see that? Because security is a huge deal. That's that great example of how you guys innovated. Cause that'll stop the vector of a tax in the service area of IOT if you can get the servers to lock down and you have firmware that's secure, makes a lot of sense. That's probably the tip of the iceberg. What else is happening with security? >> Kirk: So when we think about security and our efforts on advanced development research around the machine what you're seeing here with the proliance is making the machines more secure. The inherent platform more secure. But the other thing I would point to you is the application we're running on the prototype. Large scale graph inference. And this is security because you have a platform like the machine. Able to digest hundreds and hundreds of tera bytes worth of log data to look for that fingerprint, that subtle clue that you have a system that has been compromised. And these are not blatant let's just blast everything out to some dot dot x x x sub domain, this is an advanced persistent thread by a very capable adversary who is very subtle in their reach out from a system that has been compromised to that command and control server. The signs are there if you can look at the data holistically. If you can look at that DNS log, graph of billions of entries everyday, constantly changing, if you can look at that as a graph in totality in a timeframe that matters then that's an empowering thing for a cyber defense team and I think that's one of the interesting things that we're adding to this discussion. Not only protect, detect and recover, but giving offensive weapons to our cyber defense team so they can hunt, they can hunt for those events for system threats. >> John: One of the things, Andrew I'll get your thoughts and reaction to this because Ill make an observation and you guys can comment and tell me I'm all wet, fell off the deep end or what not. Last year HP had great marketing around the machine. I love that Star Trek ad. It was beautiful and it was just... A machine is very, a great marketing technique. I mean use the machine... So a lot of people set expectations on the machine You saw articles being written maybe these people didn't understand it. Little bit pulled back, almost dampered down a little bit in terms of the marketing of the machine, other than the bin. Is that because you don't yet know what it's going to look like? Or there's so many broader possibilities where you're trying to set expectations? Cause the machine certainly has a lot of range and it's almost as if I could read your minds you don't want to post the position too early on what it could do. And that's my observation. Why the pullback? I mean certainly as a marketer I'd be all over that. >> Andrew: Yeah, I think part of it has been intentional just on how the ecosystem, we need the ecosystem developed kind of around this at the same time. Meaning, there are a lot of kind of moving parts to it whether it's around the open source community and kind of getting their head wrapped around what is this new architecture look like. We've got things like you know the Jin Zee Consortium where we're pouring a lot of our understanding and knowledge into that. And so we need a lot of partners, we know we're in a day and an age where look there's no single one company that's going to do every piece and part themselves. So part of it is kind of enough to get out there, to get the buzz, get the excitement to get other people then on board and now we have been heads down especially this last six months of... >> John: Jamming hard on it. >> Getting it all together. You know you think about what we showed first essentially first booted the thing in November and now you know we've got it running at this scale, that's really been the focus. But we needed a lot of that early engagement, interaction to get a lot of the other, members of the ecosystem kind of on board and starting to contribute. And really that's where we're at today. >> John: It's almost you want it let it take its own course organically because you mentioned just on the cyber surveillance opportunity around the crunching, you kind of don't know yet what the killer app is right? >> And that's the great thing of where we're at today now that we have kind of the prototype running at scale like this, it is allowing us to move beyond, look we've had the simulators to work with, we've had kind of emulation vehicles now you've got the real thing to run actual workloads on. You know we had the announcement around DZ and E as kind of an early early example, but it really now will allow us to do some refinement that allows us to get to those product concepts. >> Dave: I want to just ask the closing question. So I've had this screen here, it's like the theater, and I've been seeing these great things coming up and one was "Moore's Law is dead." >> Oh that was my session this morning. >> Another one was block chain. And unfortunately I couldn't hear it but I could see the tease. So when you guys come to work in the morning what's kind of the driving set of assumptions for you? Is it just the technology is limitless and we're going to go figure it out or are there things that sort of frame your raison d'etre? That drive your activities and thinking? And what are the fundamental assumptions that you guys use to drive your actions? >> Kirk: So what's been driving me for the last couple years is this exponential growth of information that we create as a species. That seems to have no upper bounding function that tamps it down. At the same time, the timeframe we want to get from information, from raw information to insight that we can take action on seems to be shrinking from days, weeks, minutes... Now it's down to micro seconds. If I want to have an intelligent power grid, intelligent 3G communication, I have to have micro seconds. So if you look at those two things and at the same time we just have to be the lucky few who are sitting in these seats right when Moore's Law is slowing down and will eventually flatten out. And so all the skills that we've had over the last 28 years of my career you look at those technologies and you say "Those aren't the ones that are going "to take us forward." This is an opportunity for us to really look and examine every piece of this, because if was something we could of just can't we just dot dot dot do one thing? We would do it, right? We can't just do one thing. We have to be more holistic if we're going to create the next 20, 30, 40 years of innovation. And that's really what I'm looking at. How do we get back exponential scaling on supply to meet this unending exponential demand? >> Dave: So technically I would imagine, that's a very hard thing to balance because the former says that we're going to have more data than we've ever seen. The latter says we've got to act on it fast which is a great trend for memory but the economics are going to be such a challenge to meet, to balance that. >> Kirk: We have to be able to afford the energy, and we have to be able to afford the material cost, and we have to be able to afford the business processes that do all these things. So yeah, you need breakthroughs. And that's really what we've been doing. And I think that's why we're so fortunate at Hewlett Packard Enterprise to have the labs team but also that world class engineering and that world class supply chain and a services team that can get us introduced to every interesting customer around the world who has those challenging problems and can give us that partnership and that insight to get those kind of breakthroughs. >> Dave: And I wonder if there will be a tipping point, if the tipping point will be, and I'm sure you've thought about this, a change in the application development model that drives so much value and so much productivity that it offsets some of the potential cost issues of changing the development paradigm. >> And I think you're seeing hints of that. Now we saw this when we went from systems of record, OLTP systems, to systems of engagement, mobile systems, and suddenly new ways to develop it. I think now the interesting thing is we move over to systems of action and we're moving from programmatic to training. And this is this interesting thing if you have those data bytes of data you can't have a pair of human eyeballs in front of that, you have to have a machine learning algorithm. That's the only thing that's voracious enough to consume this data in a timely enough fashion to get us answers, but you can't program it. We saw those old approaches in old school A.I., old school autonomous vehicle programs, they go about 10 feet, boom, and they'd flip over, right? Now you know they're on our streets and they are functioning. They're a little bit raw right now but that improvement cycle is fantastic because they're training, they're not programming. >> Great opportunity to your point about Moore's Law but also all this new functionality that has yet been defined, is right on the doorstep. Andrew, Kirk thank you so much for sharing. >> Andrew: Thank you >> Great insight, love Hewlett Packard Labs love the R&D conversation. Gets us a chance to go play in the wild and dream about the future you guys are out creating it congratulations and thanks for spending the time on The Cube, appreciate it. >> Thanks. >> The Cube coverage will continue here live at Las Vegas for HPE Discover 2017, Hewlett Packard Enterprises annual event. We'll be right back with more, stay with us. (bright music)