>>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.

(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)

>> 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 the Cube's presentation of "Democratizing Analytics Across the Enterprise," made possible by Alteryx. An Alteryx-commissioned IDC InfoBrief 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 InfoBrief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the InfoBrief 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. >> Yet not all analytics spending is resulting in the same ROI. So, what are some of the discrepancies that the InfoBrief 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 out 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 achieved 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 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 is 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 is 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, you know, 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 an 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 InfoBrief 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 the Cube, the leader in tech enterprise coverage. (gentle music)

>> 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 the Cube's presentation of "Democratizing Analytics Across the Enterprise," made possible by Alteryx. An Alteryx-commissioned IDC InfoBrief 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 InfoBrief also revealed that nearly all organizations are planning to increase their analytics spend. And it looks like from the InfoBrief 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 InfoBrief uncovered with respect to the 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. They instead rely on outdated spreadsheet technology. In our survey, 9 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, you can do so. Yep, we'll go back to Lisa's question. Let's retake the question and the answer. >> That'll be not all analog spending results in the same ROI. What are some of the discrepancies? >> Yes, Lisa, so we'll go from your ISO, just so we can 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 InfoBrief 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 out 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 achieved 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? Can I redo this one? >> Sure. >> Yeah >> Of course. Stand by. >> Tongue tied. >> Yep. No worries. >> One second. >> If we could get, if we could do the same, Lisa, just have a clean break. We'll go to 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 is 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 is 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, you know, 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 an 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 InfoBrief 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 the Cube, the leader in tech enterprise coverage. (gentle music)

>> 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)

>>Hey, everyone, welcome back to the programme. Lisa Martin here. I've got two guests joining me. Please welcome back to the Q. Paula Hanson, the chief Revenue officer and president at all tricks. And Jackie Vanderlei Grayling joins us as well. The global head of tax technology at eBay. They're gonna share with you how an all tricks is helping eBay innovate with analytics. Ladies, welcome. It's great to have you both on the programme. >>Thank you, Lisa. Not great to be >>here. >>Yeah, Paula, we're gonna start with you in this programme. We've heard from Jason Klein. We've heard from Allan Jacobsen. They talked about the need to democratise analytics across any organisation to really drive innovation with analytics as they talked about at the forefront of software investments. House all tricks, helping its customers to develop roadmaps for success with analytics. >>Well, thank you, Lisa. 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 organisation starting with the C suite and we partner with our customers to build their road maps for scaling that culture of analytics through things like enablement programmes, skills assessments, hackathons, uh, setting up centres of excellence to help their organisation scale and drive governance of this, uh, 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 practises so they can make better business decisions and compete in their respective industries. >>Excellent. Sounds like a very strategic programme. 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 I. D. C report that showed that 93% of organisations are not utilising the analytic skills of their employees. But then there's eBay. How Jackie did eBay become one of the 7% of organisations who's really maturing and how are you using analytics across the organisation at bay? >>So I think the main thing for us is 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 influence to add value to. And we realised we have to address that. And we also knew we couldn't wait for all our data to be centralised until we actually start using the data or start automating and be more effective. Um, 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 jacket continuing with you. What was in the roadblocks to analytics adoption that you faced and how did you overcome them? >>So I think you know, Eva 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, um, to move forward. The other thing 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're dependent on somebody else's roadmap to get to data to get the information you want it. So really finding something that everybody could access analytics or access data. And finally we have to realise, 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 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 making that accessible for them. I would say it's not to say a road block a block you want to be able to do. >>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 programme 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 organisations are empowering people, those in the organisation that may not have technical expertise to be able to leverage data so that they can actually be data driven colour. >>Yes, well, we leverage our platform across all of our business functions here at all tricks. And just like Jackie explained that eBay finance 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, uh, flowing through our enterprise, and the opportunity to leverage that into insights and analytics is really endless. So our CFO, Kevin Ruben has been a key sponsor for using our own technology. We use all tricks for forecasting all of our key performance metrics for business planning across our audit function, uh, 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, Uh, as an employee based 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 supportive, 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. >>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's done this every day. And then you have really some folks that this is brand new and, um, or maybe somewhere in between. And it's about how you could get everybody in their different phases to get to the the initial destination. And I say initial because I believe the journey is never really complete. Um, what we have done is that we decided to invest in a group of concept when we got our CFO to sponsor a hackathon. Um, we open it up to everybody in finance, um, in the middle of the pandemic. So everybody was on Zoom, um, and we had and we told people, Listen, we're gonna teach you this tool. It's super easy, and let's just see what you can do. We ended up having 70 injuries. We had only three weeks. So these are people that that do not have a background. They are not engineers and not data scientists and we ended up with 25,000 our savings at the end of the hackathon. Um, from the 70 countries with people that I've never, ever done anything like this before. And there you have the results. And they just went from there because people had a proof of concept. They knew that it worked and they overcame the initial barrier of change. Um, and that's what we are seeing things really, really picking up now >>that's fantastic. And the business outcome that you mentioned that the business impact is massive, helping folks get that confidence to be able to overcome. Sometimes the cultural barriers is key there. I think another thing that this programme has really highlighted is there is a clear demand for data literacy in the job market, regardless of organisation. Can each of you share more about how your empowering the next generation of data workers Paula will start with you? >>Absolutely. And Jackie says it so well, which is that it really is a journey that organisations are on and we, as people in society, are on in terms of up skilling our capabilities. Uh, so one of the things that we're doing here at all tricks to help address the skill set gap on a global level is through a programme that we call Sparked, which is essentially a no cost analyst education programme 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 programme is 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 gap and empower more people with the necessary analytic skills to solve all the problems that data can help solve. >>So >>I just made a really big impact in such a short time period is 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 definitely wanted to make sure that we kept implemented from the hackathon that we don't lose that excitement life. So we just launched a programme for evil masterminds and what it basically is. It's an inclusive innovation initiative where we firmly believe that innovation is all 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 these technologies and upscaling of people. Um, we are apart from that. We also said, Well, we should just keep it to inside the way we have to share this innovation with the community. So we are actually working on developing an analytics high school programme which we hope to pilot by the end of this year. We will actually have high schoolers come in and teach them data essentials, the soft skills around analytics, But also, um, how to use all tricks and we're working with Actually, we're working with spark and they're helping us develop that programme. And we really hope that it is said by the end of the year, have a pilot and then also makes you must have been rolled out in multiple locations in multiple countries and really, really, uh, focused on that whole concept of analytic school >>analytics. Girl sounds like ultra and everybody have a great synergistic relationship there that is jointly aimed at especially kind of going down the stock 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 cubes Super Cloud event just a couple of weeks ago and you talked about the challenges the companies are facing as they are navigating what is by default, a multi cloud world. How does the all tricks analytics cloud platform enable CEO s to democratise analytics across their organisation? >>Yes, business leaders and CEO s across all industries are realising 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 organisations. Last I checked, there was two million data scientists in the world. So that's, uh, woefully underrepresented in terms of the opportunity for people to be a part of the analytics solution. So what we're seeing now with CEO s with business leaders is that they are integrating data analysis and the skill set of data analysis into virtually every job function. Uh, and that is what we think of when we think of analytics for all. And so our mission with all tricks analytics cloud is to empower all of those people in every job function, regardless of their skill set, as Jackie pointed out, from people that would are just getting started all the way to the most sophisticated of technical users. Um, 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 organisations. So that's our goal with all tricks, analytics cloud and it operates in a multi cloud world and really helps across all sizes of data sets to blend, cleanse, shape, analyse and report out so that we can break down data silos across the Enterprise and Dr Real Business Outcomes. As a result, of unlocking the potential of data >>as well as really listening that skills gap. As you were saying, There's only two million data scientists. You don't need to be a data scientist. That's the beauty of what all tricks is enabling. 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 all tricks fits in as that analytics maturity journey continues. And what are some of the things that you're most excited about as analytics truly gets democratised across eBay >>when we start 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. Um, I do think we're on the topic full of data scientists, and I really feel that that is your next step for us, anyway. Is that how do we get folks to not see data scientist 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, um, game partnering with all tricks to just release uh, ai ml um, solution allowing. You know, folks do not have a data scientist programme but actually build models and be able to solve problems that way. So we have engaged with all turrets and we purchase the licence is quite a few. And right now, through our masterminds programme, we're actually running a four months programme. Um, for all skill levels, um, teaching them ai ml and machine learning and how they can build their own models. Um, we are really excited about that. We have over 50 participants without the background from all over the organisation. We have members from our customer services. We have even some of our engineers are actually participating in the programme will just kick it off. And I really believe that that is our next step. Um, I want to give you a quick example of the beauty of this is where we actually, um, 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, um, you know there is a checkout feedback checkout functionality on the eBay side, There's sellers or buyers can pervade them at 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 human, even stepped in. And now, instead of us or somebody going to debate and try to figure out what's going on there, we can focus on fixing their versus, um, actually just reading through things and not adding any value and its a beautiful tool. And I'm very impressed when we 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 programme. Today's empowerment, what you're actually really doing to truly empower people across the organisation with with varying degrees of skill level, going down to the high school level really exciting. We'll have so 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 programme today and talking about how all tricks and eBay are really partnering together to democratise analytics and to facilitate its maturity. It's been great talking to you. >>Thank you. >>Thank you so much.

>> Presenter: From around the globe, it's theCUBE. Presenting Adaptive Data Governance, brought to you by Io-Tahoe. >> Our next segment here is an interesting panel, you're going to hear from three gentlemen, about Adaptive Data Governance. We're going to talk a lot about that. Please welcome Yusef Khan, the global director of data services for Io-Tahoe. We also have Santiago Castor, the chief data officer at the First Bank of Nigeria, and Gudron Van Der Wal, Oracle's senior manager of digital transformation and industries. Gentlemen, it's great to have you joining us in this panel. (indistinct) >> All right, Santiago, we're going to start with you. Can you talk to the audience a little bit about the First Bank of Nigeria and its scale? This is beyond Nigeria, talk to us about that. >> Yes. So First Bank of Nigeria was created 125 years ago, it's one of the oldest, if not the oldest bank in Africa. And because of the history, it grew, everywhere in the region, and beyond the region. I'm currently based in London, where it's kind of the European headquarters. And it really promotes trade finance, institutional banking, corporate banking, private banking around the world, in particular in relationship to Africa. We are also in Asia, in the Middle East. And yes, and is a very kind of active bank in all these regions. >> So Santiago, talk to me about what adaptive data governance means to you, and how does it helps the First Bank of Nigeria to be able to innovate faster with the data that you have. >> Yes I like that concept of adaptive data governance, because it's kind of, I would say, an approach that can really happen today with the new technology before it was much more difficult to implement. So just to give you a little bit of context, I used to work in consulting for 16-17 years before joining the First Bank of Nigeria. And I saw many organizations trying to apply different type of approaches in data governance. And the beginning, early days was really kind of (indistinct), where you top down approach, where data governance was seen as implement a set of rules, policies and procedures, but really from the top down. And is important, it's important to have the battle of your sea level, of your director, whatever is, so just that way it fails, you really need to have a complimentary approach, I often say both amount, and actually, as a CEO I'm really trying to decentralized data governance, really instead of imposing a framework that some people in the business don't understand or don't care about it. It really needs to come from them. So what I'm trying to say is that, data basically support business objectives. And what you need to do is every business area needs information on particular decisions to actually be able to be more efficient, create value, et cetera. Now, depending on the business questions they have to show, they will need certain data sets. So they need actually to be able to have data quality for their own, 'çause now when they understand that, they become the stewards naturally of their own data sets. And that is where my bottom line is meeting my top down. You can guide them from the top, but they need themselves to be also in power and be actually in a way flexible to adapt the different questions that they have in order to be able to respond to the business needs. And I think that is where these adaptive data governance starts. Because if you want, I'll give you an example. In the bank, we work, imagine a Venn diagram. So we have information that is provided to finance, and all information to risk, or information for business development. And in this Venn diagram, there is going to be part of that every circle that are going to kind of intersect with each other. So what you want as a data governance is to help providing what is in common, and then let them do their own analysis to what is really related to their own area as an example, nationality. You will say in a bank that will open an account is the nationality of your customer, that's fine for final when they want to do a balance sheet an accounting or a P&L, but for risk, they want that type of analysis plus the net nationality of exposure, meaning where you are actually exposed as a risk, you can have a customer that are on hold in the UK, but then trade with Africa, and in Africa they're exposing their credit. So what I'm trying to say is they have these pieces in common and pieces that are different. Now I cannot impose a definition for everyone. I need them to adapt and to bring their answers to their own business questions. That is adaptive data governance. And all that is possible because we have and I was saying at the very beginning, just to finalize the point, we have new technologies that allow you to do these metadata classification in a very sophisticated way that you can actually create analytics of your metadata. You can understand your different data sources, in order to be able to create those classifications like nationalities and way of classifying your customers, your products, et cetera. But you will need to understand which areas need, what type nationality or classification, which others will mean that all the time. And the more you create that understanding, that intelligence about how your people are using your data you create in a way building blocks like a label, if you want. Where you provide them with those definitions, those catalogs you understand how they are used or you let them compose like Lego. They would play their way to build their analysis. And they will be adaptive. And I think the new technologies are allowing that. And this is a real game changer. I will say that over and over. >> So one of the things that you just said Santiago kind of struck me in to enable the users to be adaptive, they probably don't want to be logging in support ticket. So how do you support that sort of self service to meet the demand of the user so that they can be adaptive? >> Yeah, that's a really good question. And that goes along with that type of approach. I was saying in a way more and more business users want autonomy, and they want to basically be able to grab the data and answers their question. Now, when you have that, that's great, because then you have demand. The business is asking for data. They're asking for the insight. So how do you actually support that? I will say there is a changing culture that is happening more and more. I would say even the current pandemic has helped a lot into that because you have had, in a way, of course, technology is one of the biggest winners without technology we couldn't have been working remotely. Without this technology, where people can actually log in from their homes and still have a market data marketplaces where they self serve their information. But even beyond that, data is a big winner. Data because the pandemic has shown us that crisis happened, but we cannot predict everything and that we are actually facing a new kind of situation out of our comfort zone, where we need to explore and we need to adapt and we need to be flexible. How do we do that? With data. As a good example this, every country, every government, is publishing everyday data stats of what's happening in the countries with the COVID and the pandemic so they can understand how to react because this is new. So you need facts in order to learn and adapt. Now, the companies that are the same. Every single company either saw the revenue going down, or the revenue going very up for those companies that are very digital already now, it changed the reality. So they needed to adapt, but for that they needed information in order to think and innovate and try to create responses. So that type of self service of data, (indistinct) for data in order to be able to understand what's happening when the construct is changing, is something that is becoming more of the topic today because of the pandemic, because of the new capabilities of technologies that allow that. And then, you then are allowed to basically help, your data citizens, I call them in organization. People that know their business and can actually start playing and answer their own questions. So these technologies that gives more accessibility to the data, that gives some cataloging so we can understand where to go or where to find lineage and relationships. All this is basically the new type of platforms or tools that allow you to create what I call a data marketplace. So once you create that marketplace, they can play with it. And I was talking about new culture. And I'm going to finish with that idea. I think these new tools are really strong because they are now allowing for people that are not technology or IT people to be able to play with data because it comes in the digital world they are useful. I'll give you an example with all your stuff where you have a very interesting search functionality, where you want to find your data and you want to self serve, you go there in that search, and you actually go and look for your data. Everybody knows how to search in Google, everybody searching the internet. So this is part of the data culture, the digital culture, they know how to use those tools. Now similarly, that data marketplace is in Io-Tahoe, you can for example, see which data sources are mostly used. So when I'm doing an analysis, I see that police in my area are also using these sources so I trust those sources. We are a little bit like Amazon, when you might suggest you what next to buy, again this is the digital kind of culture where people very easily will understand. Similarly, you can actually like some type of data sets that are working, that's Facebook. So what I'm trying to say is you have some very easy user friendly technologies that allows you to understand how to interact with them. And then within the type of digital knowledge that you have, be able to self serve, play, collaborate with your peers, collaborate with the data query analysis. So its really enabling very easily that transition to become a data savvy without actually needing too much knowledge of IT, or coding, et cetera, et cetera. And I think that is a game changer as well. >> And enabling that speed that we're all demanding today during these unprecedented times. Gudron I wanted to go to you, as we talk about in the spirit of evolution, technology's changing. Talk to us a little bit about Oracle Digital. What are you guys doing there? >> Yeah, thank you. Well, Oracle Digital is a business unit at Oracle EMEA. And we focus on emerging countries, as well as low end enterprises in the mid market in more developed countries. And four years ago, they started with the idea to engage digital with our customers via central hubs across EMEA. That means engaging with video having conference calls, having a wall, agreeing wall, where we stand in front and engage with our customers. No one at that time could have foreseen how this is the situation today. And this helps us to engage with our customers in the way we're already doing. And then about my team. The focus of my team is to have early stage conversations with our customers on digital transformation and innovation. And we also have a team of industry experts who engage with our customers and share expertise across EMEA. And we we inspire our customers. The outcome of these conversations for Oracle is a deep understanding of our customer needs, which is very important. So we can help the customer and for the customer means that we will help them with our technology and our resources to achieve their goals. >> It's all about outcomes. Right Gudron? So in terms of automation, what are some of the things Oracle is doing there to help your clients leverage automation to improve agility so that they can innovate faster? Which on these interesting times it's demanding. >> Yeah. Thank you. Well, traditionally, Oracle is known for their databases, which has been innovated year over year since the first launch. And the latest innovation is the autonomous database and autonomous data warehouse. For our customers, this means a reduction in operational costs by 90%, with a multimodal converged database, and machine learning based automation for full lifecycle management. Our database is self driving. This means we automate database provisioning, tuning and scaling. The database is self securing. This means ultimate data protection and security and itself repairing the ultimate failure detection, failover and repair. And the question is for our customers, what does it mean? It means they can focus on their business instead of maintaining their infrastructure and their operations. >> That's absolutely critical. Yusef, I want to go over to you now. Some of the things that we've talked about, just the massive progression and technology, the evolution of that, but we know that whether we're talking about data management, or digital transformation. A one size fits all approach doesn't work to address the challenges that the business has. That the IT folks have. As you are looking to the industry, with what Santiago told us about First Bank of Nigeria, what are some of the changes that you're seeing that Io-Tahoe has seen throughout the industry? >> Well, Lisa, I think the first way I'd characterize it is to say, the traditional kind of top down approach to data, where you have almost a data policeman who tells you what you can and cannot do just doesn't work anymore. It's too slow, it's too result intensive. Data Management, data governance, digital transformation itself, it has to be collaborative. And it has to be an element of personalization today to users. In the environment we find ourselves in now, it has to be about enabling self service as well. A one size fits all model when it comes to those things around data doesn't work. As Santiago was saying, it needs to be adaptive to how the data is used and who is using it. And in order to do this, companies, enterprises, organizations really need to know their data. They need to understand what data they hold, where it is, and what the sensitivity of it is. They can then in a more agile way, apply appropriate controls and access so that people themselves are in groups within businesses are agile and can innovate. Otherwise, everything grinds to a halt, and you risk falling behind your competitors. >> Yet a one size fits all terms doesn't apply when you're talking about adaptive and agility. So we heard from Santiago about some of the impact that they're making with First Bank of Nigeria. Yusef, talk to us about some of the business outcomes that you're seeing other customers make leveraging automation that they could not do before. >> I guess one of the key ones is around. Just it's automatically being able to classify terabytes of data or even petabytes of data across different sources to find duplicates, which you can then remediate and delete. Now, with the capabilities that Io-Tahoe offers, and Oracle offers, you can do things not just with a five times or 10 times improvement, but it actually enables you to do project for stock that otherwise would fail, or you would just not be able to do. Classifying multi terabyte and multi petabyte estates across different sources, formats, very large volumes of data. In many scenarios, you just can't do that manually. We've worked with government departments. And the issues there as you'd expect are the result of fragmented data. There's a lot of different sources, there's a lot of different formats. And without these newer technologies to address it, with automation and machine learning, the project isn't doable. But now it is. And that could lead to a revolution in some of these businesses organizations. >> To enable that revolution now, there's got to be the right cultural mindset. And one, when Santiago was talking about those really kind of adopting that and I think, I always call that getting comfortably uncomfortable. But that's hard for organizations to do. The technology is here to enable that. But when you're talking with customers, how do you help them build the trust and the confidence that the new technologies and a new approaches can deliver what they need? How do you help drive that kind of attack in the culture? >> It's really good question, because it can be quite scary. I think the first thing we'd start with is to say, look, the technology is here, with businesses like Io-Tahoe, unlike Oracle, it's already arrived. What you need to be comfortable doing is experimenting, being agile around it and trying new ways of doing things. If you don't want to get left behind. And Santiago, and the team at FBN, are a great example of embracing it, testing it on a small scale and then scaling up. At Io-Tahoe we offer what we call a data health check, which can actually be done very quickly in a matter of a few weeks. So we'll work with the customer, pick a use case, install the application, analyze data, drive out some some quick wins. So we worked in the last few weeks of a large energy supplier. And in about 20 days, we were able to give them an accurate understanding of their critical data elements, help them apply data protection policies, minimize copies of the data, and work out what data they needed to delete to reduce their infrastructure spend. So it's about experimenting on that small scale, being agile, and then scaling up in a in a kind of very modern way. >> Great advice. Santiago, I'd like to go back to you. Is we kind of look at, again, that topic of culture, and the need to get that mindset there to facilitate these rapid changes. I want to understand kind of last question for you about how you're doing that. From a digital transformation perspective, we know everything is accelerating in 2020. So how are you building resilience into your data architecture and also driving that cultural change that can help everyone in this shift to remote working and a lot of the the digital challenges that we're all going through? >> That's a really interesting transition, I would say. I was mentioning, just going back to some of the points before to transition these I said that the new technologies allowed us to discover the data in a new way to blog and see very quickly information, to have new models of (indistinct) data, we are talking about data (indistinct), and giving autonomy to our different data units. Well, from that autonomy, they can then compose and innovate their own ways. So for me now we're talking about resilience. Because, in a way autonomy and flexibility in our organization, in our data structure, we platform gives you resilience. The organizations and the business units that I have experienced in the pandemic, are working well, are those that actually, because they're not physically present anymore in the office, you need to give them their autonomy and let them actually engage on their own side and do their own job and trust them in a away. And as you give them that they start innovating, and they start having a really interesting idea. So autonomy and flexibility, I think, is a key component of the new infrastructure where you get the new reality that pandemic shows that yes, we used to be very kind of structure, policies, procedures, as they're important, but now we learn flexibility and adaptability at the same site. Now, when you have that, a key other components of resiliency is speed, of course, people want to access the data and access it fast and decide fast, especially changes are changing so quickly nowadays, that you need to be able to, interact and iterate with your information to answer your questions quickly. And coming back maybe to where Yusef was saying, I completely agree is about experimenting, and iterate. You will not get it right the first time, especially that the world is changing too fast. And we don't have answers already set for everything. So we need to just go play and have ideas fail, fail fast, and then learn and then go for the next. So, technology that allows you to be flexible, iterate, and in a very fast agile way continue will allow you to actually be resilient in the way because you're flexible, you adapt, you are agile and you continue answering questions as they come without having everything said in a stroke that is too hard. Now coming back to your idea about the culture is changing in employees and in customers. Our employees, our customers are more and more digital service. And in a way the pandemic has accelerated that. We had many branches of the bank that people used to go to ask for things now they cannot go. You need to, here in Europe with the lockdown you physically cannot be going to the branches and the shops that have been closed. So they had to use our mobile apps. And we have to go into the internet banking, which is great, because that was the acceleration we wanted. Similarly, our employees needed to work remotely. So they needed to engage with a digital platform. Now what that means, and this is, I think the really strong point for the cultural change for resilience is that more and more we have two type of connectivity that is happening with data. And I call it employees connecting to data. The session we're talking about, employees connecting with each other, the collaboration that Yusef was talking about, which is allowing people to share ideas, learn and innovate. Because the more you have platforms where people can actually find themselves and play with the data, they can bring ideas to the analysis. And then employees actually connecting to algorithms. And this is the other part that is really interesting. We also are a partner of Oracle. And Oracle (indistinct) is great, they have embedded within the transactional system, many algorithms that are allowing us to calculate as the transactions happen. What happened there is that when our customers engage with algorithms, and again, with Io-Tahoe as well, the machine learning that is there for speeding the automation of how you find your data allows you to create an alliance with the machine. The machine is there to actually in a way be your best friend, to actually have more volume of data calculated faster in a way to discover more variety. And then, we couldn't cope without being connected to these algorithms. And then, we'll finally get to the last connection I was saying is, the customers themselves engaging with the connecting with the data. I was saying they're more and more engaging with our app and our website and they're digitally serving. The expectation of the customer has changed. I work in a bank where the industry is completely challenged. You used to have people going to a branch, as I was saying, they cannot not only not go there, but they're even going from branch to digital to ask to now even wanting to have business services actually in every single app that they are using. So the data becomes a service for them. The data they want to see how they spend their money and the data of their transactions will tell them what is actually their spending is going well with their lifestyle. For example, we talk about a normal healthy person. I want to see that I'm standing, eating good food and the right handle, healthy environment where I'm mentally engaged. Now all these is metadata is knowing how to classify your data according to my values, my lifestyle, is algorithms I'm coming back to my three connections, is the algorithms that allow me to very quickly analyze that metadata. And actually my staff in the background, creating those understanding of the customer journey to give them service that they expect on a digital channel, which is actually allowing them to understand how they are engaging with financial services. >> Engagement is absolutely critical Santiago. Thank you for sharing that. I do want to wrap really quickly. Gudron one last question for you. Santiago talked about Oracle, you've talked about it a little bit. As we look at digital resilience, talk to us a little bit in the last minute about the evolution of Oracle, what you guys are doing there to help your customers get the resilience that they have to have to be. To not just survive, but thrive. >> Yeah. Well, Oracle has a cloud offering for infrastructure, database, platform service, and the complete solutions offered at SaaS. And as Santiago also mentioned, we are using AI across our entire portfolio, and by this will help our customers to focus on their business innovation and capitalize on data by enabling your business models. And Oracle has a global coverage with our cloud regions. It's massively investing in innovating and expanding their cloud. And by offering cloud as public cloud in our data centers, and also as private clouds with clouded customer, we can meet every sovereignty and security requirement. And then this way, we help people to see data in new ways. We discover insights and unlock endless possibilities. And maybe one one of my takeaways is, if I speak with customers, I always tell them, you better start collecting your data now. We enable this by this like Io-Tahoe help us as well. If you collect your data now you are ready for tomorrow. You can never collect your data backwards. So that is my takeaway for today. >> You can't collect your data backwards. Excellent Gudron. Gentlemen, thank you for sharing all of your insights, very informative conversation. All right. This is theCUBE, the leader in live digital tech coverage. (upbeat music)

>> Female Voice: From San Francisco, it's theCUBE, covering VMware RADIO 2019, brought to you by VMware. >> Welcome to theCUBE's exclusive coverage of VMware RADIO 2019, Lisa Martin with John Furrier in San Francisco, talking all sorts of innovation in this innovation long history culture at VMware, welcoming back to theCUBE, Mornay Van Der Walt, VP of R&D in the Explorer Group. Mornay, thank you for joining John and me on theCUBE today. >> Thank you for having me. >> So, I got to start with Explorer Group. Super cool name. >> Yeah. >> What is that within R&D? >> So the origins of the Explorer Group. I've had many roles at VMware, and I've been fortunate enough to do a little bit of everything. Technical marketing; product development; business development; one of the big things I did before the Explorer group was created was actually EVO:RAIL. I was the founder of that, pitched that idea. Raghu and Ray and Pat were very supportive. We took that to market, took it to (inaudible), handed that off to Dell EMC, the rest is history, right? And then was, "what's next?" So Ray and me look at some special projects, go and look at IoT, go and look at Telemetry, and did some orders for them, and then said "Alright, why don't you look at all our innovation programs." Because beyond RADIO, we actually have four other programs. And everyone, was -- RADIO gets a lot of airtime and press, but it's really the collective. It's the power of those other four programs that support RADIO that allow us to take an idea from inception to an impactful outcome. So hence the name, the Explorer Group. We're going out there, we're exploring for new ideas, new technologies, what's happening in the market. >> Talk about the R&D management style. You've actually got all these-- RADIO's one-- kind of a celebration, it's kind of the best of the best come together, with papers and submissions. Kind of a symposium meets kind of a, you know, successive end for all the top engineers. There's more, as you've mentioned. How does all of it work? Because, in this modern era of distributed teams, decentralization, decisions around business, decisions on allocating to the portfolio, what gets invested, money, spend, how do you organize? Give a quick minute to explain how R&D is structured. >> So, obviously, we have the BUs structured-- well there's PCS, Raghu and Rajeev head that up. And then we've got the OCTO organization, which Ray O'Farrell heads up. And the business, you know, it's innovating every day to get products out the door, right, and that's something that we've got to be mindful of because, I mean, that's ultimately what's allowing us to get products into the hands of our customers, solving tough problems. But then in addition to that, we want to give our engineers an avenue to go and explore, and, you know, tinker on something that's maybe related to their day job, or completely off, unrelated to their day job. The other thing that's important is, we also want to give, because we're such a global R&D, you know, our setup globally, we want to give teams the opportunity to work together, collaborate together, get that diversity of thought going, and so a lot of times, if we do a Hackathon, which we call a Borathon, we actually give bonus points if teams pull from outside of their business units. So you've got an idea, well, let's make it a diverse idea in terms of thought and perspective. If you're from the storage business unit, bring in folks from the network business unit. Bring in folks from the cloud business unit. Maybe you've partnered with some folks that are in IT. It's very, you know, sometimes engineers will go, "Ah, it's just R&D that's innovating." But in reality, there's great innovation coming out of our IT department. There's great innovation coming out of our global support organization. Our SEs that are on the front lines, sometimes are seeing the customers' pain points firsthand, and then they bring that back, and some of that makes it into the product. >> How much of R&D is applied R&D, which is kind of business unit aligned, or somewhat aligned, versus the wacky, crazy ideas: "Go solve a big, hairy problem", that's out there, that's not, kind of, related to the current product sets? >> Ah, that's tough to put an actual number on it, >> John: Well ballpark, I mean. >> But if I just say, like, if I had to just think about budgets and that, it's probably ten to fifteen percent is the wacky stuff, that's, you know, not tied to a roadmap, that's why we call it "off-road innovation", and the five programs that my Explorer Group ultimately leads is all about driving that off-road innovation. And eventually you want to find an on-ramp, >> Yeah. >> to a roadmap, you know, that's aligned to a business unit, or a new emerging, you know, technology. >> How does someone come up with an idea and say, "Hey, you know, I want to do this"? Do they submit, like, a form? Is there a proposal? Who approves it? I mean, do you get involved? How does that process work? >> So that's a good question. It really depends on the engineer, right? You take someone who's just a new college grad, straight out of, you know, college. That's why we have these five programs. Because some of these folks, they've got a good idea, but they don't really know how to frame it, pitch it. And so if you've got a good idea, and let's say, this is your first rodeo, so to speak, We have a program called TechTalks where it allows you to actually go and pitch your idea; get some feedback. And that's sometimes where you get the best feedback, because you go and, you know, present your idea, and somebody will come back and say, "Well, you know, have you met, you know, Johnny and Sue over there, in this group? They're actually working on something similar. You should go and talk to them, maybe you guys can bring your ideas together." Folks that are, you know, more seasoned, you know, longer tenure, sometimes they just come up, and-- "I'm going to pitch an idea to xLabs," and for xLabs, for example --that's an internal incubator-- there is, like, a submissions process. We want to obviously make sure, that, you know, your idea's timing in the market's correct, we've got limited funding there so we're going to make sure we're really investing on the right, you know, type of ideas. But if you don't want to go and pitch your idea and get feedback, go and do a Borathon. Turn an idea into a little prototype. And we see a lot of that happening, and some of the greatest ideas are coming from our Borathons, you know? And it's also about tracking the journey. So, we have RADIO here today, we have mentioned xLabs, TechTalks, we have another program called Flings. Some of our engineers are shipping product, and they've got an idea to augment the product. They put it out as a Fling, and our customers and the ecosystem download these, and it augments the product. And then we get great feedback. And then that makes it back into the product roadmap. So there's a lot of different ways to do it, and RADIO, the process for RADIO, there's a lot of rigor in it. It's, like, it's run as a research program. >> Lisa: It's a call for papers, right? >> Call for papers, you know, there's a strict format, it's got to be, you know, this many pages; if you go over about one line, you're sort of, disqualified, so to speak. And then once you've got those papers, like this year we had 560 papers be submitted, out of those 560, 31 made it onto mainstage, and another 61 made it as posters, as you can see in the room we're sitting in. >> I have an idea. Machine learning should get all those papers. (laughs) I mean, that's-- >> Funny you say that. We actually have, one of our engineers, Josh Simons, is actually using machine learning to go back in time and look at all the submissions. So idea harvesting is something we're paying a lot of attention to, because you submit an idea, >> Interesting. >> the market may not be right for it, or reality is, I just don't have a budget to fund it if it's an xLab. >> John: So it's like a Google search for your, kind of, the indexing all those workers. >> Internally, yeah, and sometimes it's-- there's a great idea here, you merge that with another idea from another group or another geo, and then you can actually go and fund something. >> Well, that's important because timing is critical, in these early-- most stuff can be early in just incubation, gestation period for that tech or concept, could be in play because the computer-- all the new things, right? >> Correct. And, do you actually have the time? You're an engineer working on a release, the priority is getting that release out the door, right? >> (laughs) >> So, put the idea on the back burner, come off the release, and then, you know, get a couple of colleagues together and maybe there's a Borathon being held and you go and move that idea forward that way. Or, it's time for RADIO submissions, get a couple of colleagues together and submit a RADIO paper. So we want to have different platforms for our engineers to submit ideas outside of their day job. >> And it sounds like, the different programs that you're talking about: Flings, xLab, Borathon, RADIO, what it sounds like is, there isn't necessarily a hierarchy that ideas have to go through. It really depends on the teams that have the ideas, that are collaborating, and they can put them forward to any of these programs, >> Correct, yeah. >> and one might get, say, rejected for RADIO, but might be great for a Borathon or a Fling? >> Correct. >> So they've got options there, and there's multiple committees, I imagine? Is that spearheaded out of Ray's OCTO group, >> Yep. >> that's helping to make the selections? Tell us a little bit about that process. >> Sure, so. That's a great point, right? To get an idea out the door, you don't always have to take the same pathway. And so one thing we started tracking was these innovation journeys that all take different pathways. We just published an impact report on innovation for FY19, and we've got the vSAN story in there, right? It was an idea. A group of engineers had an idea, like, in 2009, and they worked on their idea a little bit-- it first made it to RADIO in 2011. And then they came back in 2013, and, sort of, the rest is history, you know. vSAN launched in 2014. We had a press release this week for Carbon Avoidance Meter. It was an idea that actually started as a calculator many years ago. Was used, and then sort of died on the vine, so to speak? One of our SEs said, "You know, this is a good idea. I want to evolve this a little bit further." Came and pitched an xLabs idea, and we said, "Alright, we're going to fund this as an xLabs Lite. Three to six months project, limited funding, work on one objective --you're still doing your day job-- move the project forward a little bit." Then Nicola Acutt, our Sustainability VP, got involved, wanted to move the idea a little bit further along, came back for another round of funding through an xLabs Lite, and then GSS, with their Skyline platform, picked it up, and that's going to be integrated in the coming months into Skyline, and we're going to be able to give our customers a carbon, sort of, readout of their data center. And then they'll be able to, you know, map that, and get a bigger picture, because obviously, it's not just the servers that are virtualized, there's cooling in the data center plants, and all these other factors that you've got to, you know, take into account when you want to look at your carbon footprint for your facility. So, we have lots of examples of how these innovation pathways take different turns, and sometimes it's Team A starting with an idea, Team B joins in, and then there's this convergence at a particular point, and then it goes nowhere for a couple of months, and then, a business unit picks it up. >> One of the things that's come out-- Pat Gelsinger mentioned that a theme outside of the normal product stuff is how people do work. There's been some actual R&D around it, because you guys have a lot of distributed, decentralized operations in R&D because of the global nature. >> Yeah. >> How should companies and R&D be run when the reality is that developers could be anywhere? They could be at a coffee shop, they could be overseas, they could be in any geography, how do you create an environment where you have that kind of innovation? Can you just share some of the best practices that you guys have found? >> I'm not sure if there's 'best practices', per se, but to make sure that the programs are open and inclusive to everybody on the planet. So, I'll give you some stats. For example, when RADIO started in the early days, we were founded in Palo Alto. It was a very Palo Alto-centric company. And for the first few years, if you looked at the percentage of attendees, it was probably over 75% were coming from Palo Alto. We've now over the years shifted that, to where Palo Alto probably represents about 44%, 16% is the rest of North America, and then the balance is from across the globe. And so that shift has been deliberate, obviously that impacts the budget a little bit, but in our programs, like a Borathon, you can hack from anywhere. We've got a lot of folks that are remote office workers, using, you know, collaborative tools, they can be part of a team. If the Borathon's happening in China, it doesn't stop somebody in Palo Alto or in Israel or in Bulgaria, participating. And, you know, that's the beautiful nature of being global, right? If you think about how products get out of the door, sometimes you've got teams and you are literally following the sun, and you're doing handoff, you know, from Team A to B to C, but at the end of the day you're delivering one product. And so that's just part of our culture, I mean, everybody's open to that, we don't say, "Oh, we can't work with those guys because they're in that geo-location." It's pretty open. >> This is also, really, an essential driver, and I think I saw last year's RADIO, there were participants from 25+ countries. But this is an essential-- not only is VMware a global company, but many of your customers are as well, and they have very similar operating models. So that thought diversity, to be able to build that into the R&D process is critical. >> Absolutely. And also, think about, you know, when you're going to Europe. Smaller borders, countries, you deploy technology differently. And so, you want to have that diversity in thought as well, because you don't just want to be thinking, "Alright, we're going to deploy a disaster recovery product in North America where they can fail over from, you know, East Coast to West Coast. You go to Europe, and typically you're failing over from, you know, site A to site B, and they're literally three or four miles apart. And so, just having that perspective as well, is very important. And we see that, you know, when we release certain products, you'll get, you know, better uptick in a certain geo, and then, "Why is it stalling over here?" well it's, sometimes it's cultural, right? How do you deploy that technology? Just because it works in the US, doesn't mean it's going to work in Europe or in APJ. >> How was your team involved in the commercialization? You mentioned vSAN and the history of that, but I'm just wondering, looking at it from an investment standpoint of deciding which projects to invest in, and then there's also the-- if they're ready to go to market, the balance of "How much do we need to invest in sales and marketing to be able to get this great idea-- because if we can't market it and sell it, you know, then there's obviously no point." So what's that balance like, within your organization, about, "how do we commercialize this effectively, at scale"? >> So that is ultimately not the responsibility of my group. We'll incubate ideas, like, for example, through an xLabs project. And, you know, sometimes we'll get to a point and we'll work, collaborate with a business unit, and we'll say, "Alright, we feel this project's probably a 24 months project", if it's an xLabs Full. So these folks are truly giving up their day job. But at the end of the day, you want to have an exit and when we say exit, what does that exit mean? Is that an exit into a business unit? Are you exiting the xLabs project because we're now out of funding? You know, think about a VC, I'm going to fund you to, you know, to a particular point; if there is no market traction, >> Right. >> we may, you know, sunset the project. And, you know, so our goal is to get these ideas, select which ones we want to invest in, and then find a sort of off-ramp into a business unit. And sometimes there'll be an off-ramp into a business unit, and the project goes on for a couple of months, and then we make a decision, right? And it's not a personal decision, it's like, "Well we funded that as an xLabs; we're now going to shut it down because, you know, we're going to go and make an acquisition in this space. And with the talent that's going to come onboard, the talent that was working on this xLab project, we can push the agenda forward." >> John: You have a lot of action going on so you move people around. >> Exactly. >> Kind of like the cloud, elastic resource, yeah? (laughs) >> So, then, some of these things, because xLabs is only a two-year-old, you know, we haven't had things exit yet that are, you know, running within a business unit that we're seeing this material impact. You know, from a revenue point of view. So that's why tracking the journeys is very important. And, you know, stay tuned, maybe in about three or four years we'll have this, similar, you know, interview, and I'll be able to say, "Yeah, you know, that started as an xLab, and now it's three years into the market, and look at the run rate. >> So there's 31-- last question for you-- there's 31 projects that were presented on mainstage. Are there any that you could kind of see, early on, "ooh", you know, those top five? Anything that really kind of sticks out-- you don't have to explain it in detail, but I'm just curious, can you see some of that opportunity in advance? >> Absolutely. There's been some great papers up on mainstage. And covering, you know, things on the networking side, there's a lot of innovation going in on the storage side. If you think about data, right, the explosion of data because of edge computing, how are you going to manage that data? How are you going to take, you know, make informed decisions on that data? How can you manipulate that data? What are you going to have to do from a dedupe point of view, or a replication point of view, because you want to get that to many locations, quickly? So, I saw some really good papers on data orchestration, manipulation, get it out to many places, it can take an informed decision. I saw great-- there was a great paper on, you know, you want to go and put something in AWS. There's a bull that you get at the end of the month, right? Sometimes those bulls can be a little bit frightening, right? You know, what can you do to make sure that you manage those bulls correctly? And sometimes, the innovation has got nothing to do with the product per se, but it has to do with how we're going to develop. So we have some innovation on the floor here where an engineer has looked at a different way of, basically, creating an application. And so, there's a ton of these ideas, so after RADIO, it doesn't stop there. Now the idea harvesting starts, right? So yes, there were 31 papers that made it onto mainstage, 61 that are posters here. During that review process, and you asked that question earlier and I apologize, I didn't answer it-- you know, when we look at the papers, there's a team of over 100 folks from across the globe that are reviewing these papers. During that review process, they'll flag things like "This is not going to make it onto mainstage, but the idea here is very novel; we should send this off to our IP team," you know. So this year at RADIO, there were 250 papers that were flagged for further followup with our IP team, so, do we go and then file an IDF, Invention Disclosure Form, do those then become patents, you know? So if we look at the data last year, it was 210. Out of those 210, 74 patents were filed. So there's a lot of work that now will happen post-RADIO. Some of these papers come in, they don't make it onto mainstage; they might become a poster. But at the same time they're getting flagged for a business unit. So from last year, there were 39 ideas that were submitted that are now being mapped to roadmap across the BUs. Some of these papers are great for academic research programs, so David Tennenhouse's research group will take these papers and then, you know, evolve them a little bit more, and then go and present them at academic conferences around the world. So there's a lot of, like, the "what's next?" aspect of RADIO has become a really big deal for us. >> The potential is massive. Well, Mornay, thank you so much for joining John and me, >> Thank you. >> and I've got to follow xLabs, there's just a lot of >> (laughs) >> really, really, innovative things that are so collaborative, coming forward. We thank you for your time. >> Thank you. >> For John Furrier, I'm Lisa Martin; you're watching theCUBE, exclusive coverage of VMware RADIO 2019, from San Francisco. Thanks for watching.

(energetic music) >> [Narrator] From San Francisco, it's theCUBE, covering Radio 2018. Brought to you by VMware. >> Hello everyone. Welcome to the special CUBE coverage here in San Francisco, California for VMware's Radio 2018 event. This is their R&D big event kickoff. It's like a sales kickoff for engineers, as Steve Herrod said on stage. Out next guest is Mornay Van Der Walt, VP of the Explore Group, Office of the CTO. Also, program chair of the Event Today Conference, working for the collective of people within VMware on a rigorous selection committee for a high bar here at your event. Welcome to theCUBE. Thanks for joining me. >> Thank you. >> Talk about the event, because I know a lot of work went into it. Congratulations, the talks were amazing. I see the schedule. We have Pat Gelsinger coming on later today. We just had Ray O'Farrell on. This is like the, I don't want to say, Burning Man of Vmware, but this is really a recognition, but also really important innovation. Take a minute to talk about the process that you go through to put this together. It's a fantastic event. The smartest minds, the cream rises to the top. It's hard, it's challenging, it's a team effort, but yet you gotta ride the right waves. >> Right. So, RADIO: R&D Innovation Offsite. And as you said, it is tough because we've got this huge R&D community and they've all got amazing ideas. So they get the opportunity to submit ideas. I think this this year we have over 1,700 ideas submitted, and at the end of the day we're only going to showcase 226 of those ideas across research programs, posters, breakout sessions, Just-In-Time BOFs, Birds Of a Feather. You know, so, the bar is high. we've got a finite amount of time, but what's amazing is we take these ideas, and we don't just showcase them at RADIO. We have four other programs that give us the ability to take those ideas to the next level. So when we think about the innovation programs that come out of OCTO, this is really to drive what we call "Off-Road Map Innovation." So Raghu and Rajiv, with our Product Cloud Services Division, are driving road map, zero to three years out the stuff that you can buy from sales, >> [Furrier] Customer centric? >> Customer centric, yeah. OCTO is providing an innovation program structure, these five programs: Tech Talks, Flings, Borathons, RADIO, and xLabs, and as a collective, they are focused on off-road map innovation. Maybe something that's-- >> Give me an example of what that means, Off-Road Map. >> Sure. So last year at RADIO we did a paper that was showcased on functions as a service. So you think of AWS Lambda, right. [Furrier] Yep, yep >> VM was uniquely positioned, with the substrate, to manage and orchestrate VM's containers and whynot functions. So this radio paper was submitted, I then, as the xLabs group, said we're going to fund this, but given where we are in this market, we said, "Alright, we'll fund this for 12 months." So, we're incubating functions as a service. In July/August time frame, that'll actually exit xLabs into the Cloud Native business. >> It's a real rapid innovation. >> Very rapid. >> Within a 12 month period, we're gonna get something into a BU that they can take it to market. >> Yeah, and also I would say that this also I've seen from the talks here, there's also off-road map hard problems that need to kind of get the concepts, building blocks, or architecture... >> [Van Der Walt] Correct. >> With the confluence of hitting, whatever, its IOT or whatever, blockchains, seeing things like that. >> [Van Der Walt] Yeah. Correct. >> Is that also accurate too? >> Very true. And, you know, Ray had a great slide in his keynote this morning, you know, we spoke about how we started in 2003, when he joined the company, it was all about computer virtualization. Fast-forward 15 years, and you look at our strategy today, it's any Cloud, any device, any app, right? Then, you gotta look to the future, beyond there, what we're doing today, what are the next twenty years going to look like? Obviously, there's things like, you know, blockchain, VR, edge computing, you know, AIML... >> [Furrier] Service meshes? >> Services meshes, adaptive security. And, you know, people say, "Oh, AIML, that's a hot topic right now, but if you look back at VM ware, we've been doing that since 2006. Distributed resource scheduler: a great example of something that, at the core of the product, was already using ML techniques, you know, to load-balance a data center. And now, you can load-balance across Clouds. >> It's interesting how buzzwords can become industry verticals. We saw that with Hadoop; it didn't really happen, although it became important in big data as it integrates in. I mean, I find that you guys, really from the ecosystem we look at, you guys have a really interesting challenge. You started out as "inside the box," if you will. I saw your old t-shirt there from the 14 year history you guys have been doing this event. Great collection of t-shirts behind me if you can't see it. It's really cool. But infrastructures, on premise, you buy, it's data center, growth, all that stuff happened. Cloud comes in. Big data comes in. Now you got blockchain. These are big markers now, but the intersection of all these are all kind of touching each other. >> [Van Der Walt] Correct. >> IOT...so it's really that integration. I also find that you guys do a great job of fostering innovation, and always amazed at the VM world with some great either bechmarks or labs that show the good stuff. How do you do it? Walk me through the steps because you have this Explorer program, which is working. >> [Van Der Walt] Yeah >> It's almost a ladder, or a reverse ladder. Start with tech talks, get it out to the marketplace... >> [Van Der Walt] Do a hackathon. >> Hackathon. Take us through the process. So there's four things: tech talks, borathons, which is the meaning behind the name, flings, and xLabs. >> Correct >> Take us through that progression. >> ... and RADIO, of course. >> And RADIO, of course, the big tent event. Bring it all together. >> So, I'm an engineer. I have a great idea. I wanna socialize it; I wanna get some feedback. So, at VMWare, we offer a tech talk platform. You come, you present your idea. It's live. There'll be engineers in the audience. We also record those, and then those get replayed, and engineers will say, "You know, have you thought about this?" or "Have you met up with Johnny and Mary?" They're actually working on something very similar. Why don't you go and, you know, compare ideas? I can actually make that very real. I was in India in November, and we were doing a shark tank for our xLabs incubator, and this one team presented an idea on an augmented reality desktop. We went over to another office, actually the air watch office, and we did another shark tank there. Another team pitched the exact same idea, so I looked at my host, and I said, "Do these two teams know each other?" and the guy goes, "Absolutely not," so what did we do? We made the connection point. Their ideas were virtually identical. They were 25 kilometers apart. Never met. >> [Furrier] Wow. >> You know, so when, that's one of the challenges when your company becomes so big, you've got this vast R&D organization that's truly global, in one country 25 kilometers apart, you had two teams with the same idea that had never met. So part of the challenge is also bringing these ideas together because, you know, the sum of the parts makes for a greater whole. >> And they can then collectively come together then present to RADIO one single paper or idea. >> [Van Der Walt] Absolutely, or go ahead and say, you know what, let's take this to the next step, which would be a borathon, so borathons are heckathons. >> Explain the name because borathon sounds like heckathon, so it is, but there's a meaning behind the name borathon. What is the meaning? >> Sure. So, our very first build repository was named after Bora Bora, and so we paid homage to that, and so, instead of saying a heckathon, we called it a borathon. And one of our senior engineers apparently came up with that name, and it stuck, and it's great. >> So it's got history, okay. So, borathons is like ... okay, so you do tech talks, you collaborate, you socialize the idea via verbal or presentation that gets the seeds of innovation kinda planted. Borathon is okay, lets attack it. >> Turn it into a prototype. >> Prototype. >> And it gets judged, so then you get even more feedback from your most senior engineers. In fact ... >> And there's a process for all this that you guys run? >> Yeah, so the Explorer groups run these five innovation programs. We just recently, in Palo Alto, did a theme borathon. Our fellows and PE's came together. Decided the theme should be sustainability, and we mixed it up a little bit. So, normally, at a borathon, teams come with ideas that they've already been developing. For this one, the teams had no idea what the theme was going to be, so we announced the theme. Then, they showed up on the day to learn what the five challenges were going to be, and some of those challenges, one of them was quite interesting. It was using distributed ledger to manage microgrids, and that's a ... >> A blockchain limitation >> Well, it's a project that's, you know, is near and dear to us at VMWare. We're actually going to be setting up a microgrid on campus, and if you think about microgrids, and Nicola Acutt can talk more to this, we're gonna be looking at, you know, how can we give power back to the city of Palo Alto? Well, imagine that becoming a mesh network. >> [Furrier] With token economics. >> How do you start tracking this, right? A blockchain would be a perfect way to do this, right? So, then, you take your ideas at a borathon, get them into a prototype, get some more feedback, and now you might have enough critical mass to say, "Alright, I'm going to present a RADIO paper next year." So, then, you work as a team; get that into the system. >> [Furrier] And, certainly, in India and these third-world countries now becoming large, growing middle-class, these are important technologies to build on top of, say, mobile... >> [Van Der Walt] Absolutely. >> And with solar and power coming in, it's a natural evolution, so that's good use case. Okay, so, now I do the borathon. I've got a product. Flings? >> It's a prototype, right, so now ... >> You can socialize it, you have a fling, you throw it out there, you fling it out there What happens? >> Yeah, so, I've done something at a borathon. It's like, I want to get some actual feedback from the ecosystem: our customers and partners. That example I used with vSAN. You know, vSAN launched. We wanted to get some health analytics. The release managers were doing their job. The products got a ship on the state. Senior engineers on the team got a health analytics tool out as a fling. It got incredible feedback from the community. Made it into the next release. We did the same with the HTML clients, right? And that's been in the press lately because, you know, we've got Rotoflex. Now, there's HTML, but that actually started - two teams started working on that. One team just did HTML >> a very small portion of the HTML client, presented a RADIO paper. Two years later, another team, started the work, and now we have a full-fledged HTML client that's embedded into the VIS via product. >> [Furrier] So, the fling brings in a community dynamic, it brings in new ideas, or diversity, if you will. All kinds of diverse ideas melting together. Now, xLabs, I'm assuming that's an incubator. That brings it together. What is xLabs? Is that an incubator? You fund it? What happens there? >> So with an xLabs, the real way to think about it, it's truly an incubator. I don't want to use the word "start-up" there because you've clearly got the protection of the larger VMware organization, so you're not being a scrappy start-up, but you've got a great idea, we see there's merit ... >> [Furrier] Go build a real product. >> We see it more being on the disruptive side, and so we offer two tracks in the xLabs. There's a light track, which typically runs three to six months, and you're still doing your day job. You know, so you're basically doing two jobs. You know, we fund you with a level of funding that allows you to bring on extra contracting, resources, developers, etc., and you're typically delivering one objective. The larger xLab is the full-track, so functions as a service. Full-track, we showcased it as a RADIO paper last year. We said, "Alright, we're going to fund this. We're going to give it 12 months worth of funding, and then it needs to exit into a business unit," and we got lucky with that one because we were already doing a lot of work with containers, the PKS, the pivotal. >> [Furrier] Do the people have to quit their day job, not quit their day job, but move their resource over? >> [Van Der Walt] Absolutely. >> The full-track is go for it, green light >> Yep >> Run as fast as you can, take it to this business unit. Is the business unit known as the end point in time? Is it kind of tracked there, or is it more flexible still. >> Not all the time. You know so sometimes, with functions it was easier, right? So, we know we've got pull for zone heading up Cloud native apps. The Cloud native business unit is doing all the partnerships with PKS. That one makes sense. >> [Furrier] Yeah. >> We're actually doing one right now, another xLabs full, called network slicing, and it's going to play into the Telco space. We've obviously got NFV being led by Shekar and team, but we don't know if network slicing, when it exits, and this one is probably going to have a longer time arise and probably 24-36 months. Does it go into the NFV business unit, or does it become its own business unit. >> [Furrier] That's awesome. So, you got great tracks, end to end, so you have a good process. I gotta ask you the question that's on my mind. I think everyone would look at this, and some people might look at Vmware as, and most people do, at least I do, as kind of a cutting-edge tier one company. You guys always are a great place to work. Voted as, get awards for that, but you take seriously innovation and organic growth in community and engineering. Engineering and community are two really important things. How do you bring the foster culture because engineers can be really pissed off. "Oh my god! They're idiots that make the selection!" because you don't want engineers to be pissed cuz they're proud, and they're inventing. >> Yep, yep. >> So, how to manage the team approach? What's the cultural secret in the DNA that makes this so successful over 14 years? >> So, before I answer that question, I think it's important to take a step back. So, when we think about innovation, we call this thing the Vmware "innovation engine." It's really three parts to it, right? If you think about innovation at its core: sustaining, disruptive, internal, external, And, so, we've got product Cloud Services group, Raghu and Rajiv, we've got OCTO, headed up by Ray, we've got corp dev headed up by Shekar. Think of it as it's a three-legged stool. You take one of those legs away, the stool falls over. So, it's a balancing act, right? And we need to be collaborating. >> [Furrier] And they're talking to each other all the time. >> We're talking to each other all the time, right? Build or buy? Are we gonna do something internal, or we gonna go external, right? You think something about acquisitions like Nicira, right? We didn't build that; we bought it. You think about Airwatch, right? Airwatch put us into the top right quadrant from Gartner, right? So, these are very strategic decision that get made. Petchist presented at Dell emc world, Dell Technologies world. He had a slide on there that showed, it was the Nicira acquisition, and then it sort of was this arc leading all the way up to VeloCloud, and when you saw it on one slide, it made perfect sense. As an outsider looking in, you might have thought, "Why were they doing all these things? Why was that acquisition made? But there's always a plan, and that plan involves us all talking across. >> [Furrier] Strategic plan around what to move faster on. >> Correct >> Because there's always the challenge on M&A, if they're not talking to each other, is the buy/build is, you kinda, may miss a core competency. They always ... what's the core competency of the company? And should you outsource a core competency, or should you build it internally? Sometimes, you might even accelerate that, so I think Airwatch and Nicira, I would say, was kinda on the edges of core competency, but together with the synergies ... >> [Van Der Walt] Helped us accelerate. >> And I think that's your message. >> [Van Der Walt] Yep. >> Okay, so that's the culture. How do you make, what's the secret sauce of making all this work? I mean, cuz you have to kinda create an open, collaborative, but it's competitive. >> [Van Der Walt] Absolutely. >> So how do you balance that? >> You know, so clearly, there's a ton of innovation going on within the prior Cloud services division. The stuff that's on the truck that our customers can buy today, alright? We also know we gotta look ahead, and we gotta start looking at solving problems that aren't on the truck today, alright? And, so, having these five programs and the collective is really what allows us to do that. But at the same time, we need to have open channels of communication back into corp dev as well. I can give you examples of, you know, Shekar and his team might be looking at Company X. We're doing some exploratory work, IOT, I did an ordered foray. IOT is gonna be massive; everybody knows that, but you know what's going to be even more massive is all the data at the edge, and what do you do with that data? How do you turn that data into something actionable, right? So, if you think about a jet engine on a big plane, right? When it's operating correctly, you know what all the good levels are, the metrics, the telemetry coming off it. Why do I need to collect that and throw it away? You're interested in the anomalies, right? As we start thinking about IOT, and we start thinking all this data at the edge, we're going to need a different type of analytics engine that can do real-time analytics but not looking at the norm, looking at the deviations, and report back on that, so you can take action on that, you know? So, we started identifying some companies like PubNub, Mulesoft, too, just got acquired, right? Shekar and his team were looking at the same companies, and was like, "These companies are interesting because they're starting to attack the problem in a different way. We do that at Vmware all the time. You think about Appdefense. We've taken a completely different approach to security. You know what the good state is, but if you have a deviation, attack that, you know? And then you can use things like ... >> It's re-imagining, almost flipping everything upside-down. >> Yeah, challenging the status quo. >> Yeah, great stuff, great program. I gotta ask you a final question since it's your show here. Great content program, by the way. Got the competition, got the papers, which is deep, technical coolness, but the show is great content, great event. Thanks for inviting us. What's trending? What's rising up? Have you heard or kind of point at something you see getting some buzz, that you thought might get buzz, or it didn't get buzz? What's rising of the topics of interest here? What's kind of popping out for you; what's trending if I had to a Twitter feed, not Twitter feed, but like top three trending items here. >> Well, I'll take it back to that last borathon that we did on sustainability. We set out the five challenges. The challenge that got the most attention was the blockchain microgrid. So, blockchain is definitely trending, and, you know, the challenge we have with blockchain today is it's not ready for the enterprise. So, David Tennenhouse and his research group is actually looking at how do you make blockchain enterprise ready? And that is a difficult problem to solve. So, there's a ton of interest in watching ... >> [Furrier] Well, we have an opinion. Don't use the public block chain. (both laugh) >> So, you know, that's one that's definitely trending. We have a great program called Propel, where we basically attract the brightest of the brightest, you know, new college grads coming into the company, and they actually come through OCTO first and do a sort of onboarding process. What are they interested in? They're not really interested in working for a particular BU, but, you know, when we share with them, "You're gonna have the ability to work on blockchain, AI, VR, augmented reality, distributed systems, new ways of doing analytics >> that's what attracts them. >> [Furrier] And they have the options to go test and put the toe in the water or jump in deep with xLabs. >> Absolutely >> So, I mean, this is like catnip for engineers. It draws a lot of people in. >> Absolutely, and, you know, we need to do that to be competitive in the valley. I mean, it's a very hard marketplace. >> Great place to work. >> You guys have a great engineering team. >> Congratulations for a great event, Mornay, and thanks for coming on theCUBE. We're here in San Francisco for theCUBE coverage of RADIO 2018. I'm John Furrier. Be back with more coverage after this break. Thanks for watching. (upbeat techno music)

>> Announcer: From San Francisco, it's theCUBE covering OSIsoft PI World 2018, brought to you by OSIsoft. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco at OSIsoft's PI World. It's been going on for 28 year, I think. I saw some 15 year pins. It's my first year pin, but I just heard that 28 years, 68 people. This year 3,000 people talking about the industrial internet, the internet of things, and it's happening here. A lot of places talk about it's coming, it's happening here. We're really excited to have two guests on from OSIsoft Richard Beeson. He's a CTO. Richard, great to see you. >> Yeah, thank you. >> And Michael Van Der Veeken, he's a senior developer. Welcome. So first off, impressions on this year's PI World compared to when you started out 28 years ago. >> Yeah, you said it. We started in San Francisco in 1990 at a small hotel down by Fisherman's Wharf, and we had 68 of our closest friends. And it's just been an amazing journey, an amazing journey to see the customer base just continue to appreciate the message, appreciate the value and the consistency that we've bene bring, and most recently just seeing this incredible explosion around the value of information in operations, in IoT and the time-space. >> It's funny because we usually cover it from the IT side and a lot of the IT players are excited now to be bringing IT and connecting it with OT and, in fact, I can show you very formal handshakes and exchanges of pleasantries around that. But you guys have been coming at it from the OT side for a very long time, before there was IP sensors on all these machines, before there was 5G, before there was saduke, before there was all these kind of enabling technologies for what people are talking about now for the industrial internet, but you guys have been doing it for a very long time with the existing infrastructure that was already in place at these places >> Yeah, it is kind of funny. Sometimes we'll say, hey, we've been doing this IoT or industrial IoT for the last 30 years. It's what process control engineers have been doing. You need to get the data from the sensors, from the operation to be able to control it. So the act of control, the act of optimization, the act of running a plant, of running any kind of operation requires that. >> Jeff: Right. >> The big shift has just been fundamentally in the scale, the cost point and just the general availability of that kind of information. It's really changing the game. >> Right. >> And a lot of the same principles still apply. And we've had experience here for 30 years now. And with the whole IoT boom, a lot of the same principles still apply to streaming data, to real-time data, and the PI system is able to support that. >> Right, but it's interesting because now you have a whole new level of computer horsepower that you did have many years ago. You've have a whole new level of networking speed which is even going to go up again with 5G on the mobile side shortly which is going to give massive amounts of more data, and the, of course, to store and everything else just gets cheaper, cheaper and cheaper so you're kind of enabling technologies under the cover or probably just allowing you to explore and expand dramatically the value that you guys are able to generate. >> Yeah, on one had it changes how we do what we do, but, fundamentally, you go back to the original proposition. For our customers, it's all about getting all of the information into the system, no matter where it's coming from, traditionally DCSs, now IoT devices and beyond. And it then becomes all about making that data available in the way, in the place, in the form that they will value it, and there's a myriad. One of the beautiful things about this conference is we see our partners, we see our customers. We see hundreds and thousands of different technologies and applications built around this information. That hasn't changed. It think that's one of the things Michael was eluding to. >> Yeah and you mentioned more available computing power and things like that, but what we see is that using that, people can get much more actionable information out of their data, things or types of analyses that were previously, we were unable to do that because we didn't have the right technology or the right computing power. >> Jeff: Right. >> But now we do. And especially if you can combine different sources of data and people are starting to share that data, you can get way more value out of that raw data that comes from those sensors. >> Right, but now we're going to talk about kind of the next thing, one of the next things. There's always the next thing. And that's blockchain. A lot of talk about blockchains. There's talk about bitcoin and cryptocurrencies. We're going to just put that on the side for now, and really talk about the fundamental technology under the covers which is this blockchain. We see IBM making big investments in it. We hear about it all the time. What are you guys doing in blockchain? And what do you kind of see as an opportunity that you hope that you eventually you'll be able to execute on using blockchain technology? >> Right so we have been researching blockchain for a little while now, and we're still kind of in exploration phase. We first wanted to really get a good understanding of the technology. Mainly to be able to separate the hype from the hope. There is a big hype around everything that is blockchain. But we really want to start looking at where does it actually make sense. Where does it actually add value? Are there situations where a centralized system might actually make much more sense? Or are there actually situations where this decentralized shared ecosystem makes more sense. So I think we have a decent understanding of the technology now, and we're starting to have those conversations with customers. Where should this make sense to you? So this week at PI World, we had our first conversations about that. We had our first session The session was very well attended. There was very good feedback. We'll have a more of a deep dive session this Thursday. And, yeah, we're really looking for those different use cases and to identify patterns within those different use cases across our different industries basically. >> And are you getting pull from the industries. Are they asking you for you guys to do this? Do they see either the curiosity or the opportunity or, I don't want to say hope, that's not a good word, to use blockchain in this distributed, trusted, non-centralized transaction engine to take care of some big issues that are out there right now. >> When I get out and I talk to executives around our customer base, I'm hearing at least three things, multiple times. It's a bit of a pattern. One is how could we use or would it be possible to use blockchain or some other technology in protecting or verifying the consumption or the use or the sharing of data, so kind of the outbound field. Another thing that I'm hearing frequently is most of our customers have very complex supply chains, very complex distribution chains, and as materials that they either depend on or create flow through these supply chains, there's often data around the conditions or the volumes or the paths that they take. And as that information transitions across various ownerships, various boundaries, how do they guarantee the authenticity, the availability and where that information can go in conjunction with that product. And then another one I've been hearing recently which was, I guess, not surprising, but it was novel when I first heard it is one of the activities in operations that every operator goes through is they send instructions or commands or settings or operational conditions down into their factory. How do you know if you can trust the instruction that has been delegated down? How do you know who did it? How do you know how long that instruction is valid for? All different aspects around that. So those are just three very, very significant challenges that our customers are surfacing for which this may be a solution. >> Right. >> And that's some of the fun, I think in going to this research path that we're going down. >> And I want to add to that the whole concept of the exchange of value within a blockchain network also makes the monetization of data very possible. People are starting to realize that the data they're collecting or the information they collected out of that data actually has value to other people. So can we find an easy way for them to monetize on that so see the data as an asset. And that's something that, you know, there are a number of startup projects that focus around that, and they're really looking into that, okay, would that make sense for our customers and how could we potentially tie into that or make that available to our customers. >> Right, the balance sheet value of data is an interesting topic because, you know, before data was just expensive because we had to store it and we had to keep it and we threw most of it away because we had to buy servers and machines to store it. Now, obviously, on the consumer side, you see the valuation of the data with companies like Google and Facebook whose valuation is a function of the value of that data even though its not reflected on their balance sheet and it's an interesting concept. How do you not only monetize it, but eventually get it on the balance sheet so that there is all the benefits that come by having that on the balance sheet with the value of that data. And that's the first time I've ever heard of using blockchain potentially as a way to capture, track and extract that value from that data. >> Exactly, and there are many different applications. It could be, for instance, a renewable company that has a wind farm that is monitoring the environment or monitoring the weather. That data is something that they use. But that data could potentially be very interesting to other companies or maybe to local governments as well. So is that data that they can monetize on? Another aspect could be, for instance, in autonomous vehicles where you're driving past somewhere and you want to get information about what are the gas prices or where can I get something to eat or things like that. So those could be really quick even microsecond transactions >> Jeff: Right. or interactions between a vehicle and whatever is in its environment. But maybe there are some way to do some quick micropayments of that data because that is valuable to that vehicle, and, in turn, that vehicle could also sell some of the data that it is collecting about the weather, about the road conditions, about traffic. So, in general, potentially we could see this whole economy around data arising. >> Right. >> And there's also a lot of cost in validating the trust now. We talked to some of the shipping lines and like 50% of the cost of shipping is the processing of the paperwork that basically does the validation that you just kind of outlined. Is it what it's supposed to be? Did it come from where it's supposed to be coming from? It is going to where it's supposed to be going to? And literally it's like 50% of the cost of shipments is processing this paper. So not only does it provide value, but it unlocks another whole set of value that currently is just getting eating up by super inefficient, still paper-based not even Excel, right. They probably still have copy machines. >> Transportation is one of the worse. (Jeff laughs) >> But you look at that scenario and a number of these others, immediately you go to this notion of data ownership. You eluded to it. Philosophically and practically, OSI is firmly committed to all of the information that we manage for our customers is our customer's data. They own that. But even as they get into these complex landscapes, then there really is that question. As materials flow through these supply chains, who owns the data associated with that. So this is going to be an interesting frontier >> Right. where these things have to get resolved and understood. And most of our customers consider the 10, 20, 30 years of operational data that they've preserved one of their more valuable IP assets. It's both an amazing frontier and amazing opportunity and something that's going to stir up some emotions as well. >> Right. And then you got the geopolitics of it as well because of the disparate laws all over the place about data, data treatment and exactly where was the data generated. That's always one of my favorite things when you really dig down as to where was that data actually generated. And it's not necessarily an easy thing to determine. So here we are 2018, what are you guys working on this year? If we come back a year from now, what are we going to be talking about? >> So right now, we are starting the conversation. We are starting to have this discussion. We have some assumptions where blockchain might make sense to us as a company especially to our customers. So this year, we really want to use this year to validate some of those assumptions, to really work with our customers but also with academia to find out where does this actually make sense. How can we get the most value out of this amazing new technology that has a lot of promise. And maybe we'll see us starting prototyping some of these solutions together with our customers. >> You going with that? >> Yeah, I'm going with that. >> All right, Richard's going with Michael, all right. So we're going to leave it there. And thanks for taking a few minutes and congratulations. I don't know if you've been here for all 28 years, Michael. >> Seven years. >> Seven years, pretty good. But what a great story, what a great success and really happy to come here and learn some of the story. >> Yeah, I'm honored every year. It just blows me away what I get to see and listen to and the people I get to meet so thank you. >> Thank you. All right, and he's Richard. >> Thank you. >> And he's Michael, I'm Jeff. You're watching theCUBE from OSIsoft PI World 2018 in downtown San Francisco. Thanks for watching. (upbeat music)