>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle. Okay, we're now going >>to explore what it's like to be the CEO of a fast paced growth company in Silicon Valley. And how the cloud, however, you wanted to find the cloud public cloud on Prem Hybrid, etcetera. How it supported that growth. And with me is Kathy Southwick, who is the CEO of pure storage. Kathy is really deep experience. Managing technology organizations spent a number of years overseeing A T and T s cloud planning and engineering and another few years overseeing a team of a Couple 1000 network and I T engineers working to break the physical stranglehold of fossilized telco networks, implementing network functions, virtualization and a software defined methodology for the company. And, of course, you spent the last couple of years is the CEO of Pure. So Cathy, it's great to see you again. Thank you for coming on the program. >>Thanks for having me. It's good to be here. >>You're very welcome. And so so >>given your >>experience with cloud, you know, dating back to really the early part of last decade. How did you look at cloud back then and how How is it evolved from your point of view? >>You know, it's Ah, it's an interesting question because I think that we've there's some things that have moved very fast and there's some some things that are very much the same as they were even a decade ago. I think that all companies are very focused on How do you think about Cloud? Do you think about it as on Prem? And when I started, we really were focused on an on Prem solution, and I'm in building an on Prem private cloud to help modernize our business. So I think that, you know, with that all companies are still in that same mindset of how do I want to think about Cloud? And how do I want to think about that on Prem versus Public versus, you know, combination or some type of hybrid solution? So I think all of us around that journey, it just seems like it's taken. It's probably a bit longer than most of us probably thought from beginning. >>So as a CEO thinking about that evolution, how has that informed the way you think about applying specifically the public cloud to pure business. >>You know, I think that we've been a for pure ourselves. I think we're in a really unique position. We were essentially born in the cloud. So we're, you know, company. That's 10 11 years old. And if I If I give the contrast of that of 18 t being, you know, 130 year old company Onda having a lot of applications that have, you know, lived historically on prim. There's very different issues and challenges that you have pure has had that. I think the advantage just like many other companies that were born in the cloud who have can see what advantages are very quickly. And we made decisions early on that said that we were gonna actually do both. We were gonna look to say, How do I put those applications in that in that data, whether it was on public or in on Prem and be able to do that both in the i t. Side as well as within the product side? So how we build our products now, >>as I mentioned up front, you have obviously a lot of experience managing large technology teams. My question is. When you first saw the emergence of the modern cloud, how did you communicate with your team members? I mean, you mentioned you were kind of building your own private cloud, so I guess that's less threatening to people. But what was it like? You know, Was there a concern? You know, with the eager to jump in? What was that dynamic like? And how did you manage >>it? You know, it's really it's a different depending on the different part of the organization. So I'll give you kind of two things I learned one of them was that our teams in the operation side, they saw it as a huge advantage. They saw it as an opportunity to really modernized to really get themselves both their own individual skill sets advanced, as well as provide a better level of service for our internal, you know, customer, so to speak. Our application in our data partners that we had to work with, um, they thought is an opportunity to bring agility to their applications quicker speed to market, um, or currency of their applications. So they actually got some benefits that they weren't. Actually, I'll call planning for they were they had the opportunity toe get investment in their applications without having to put the that investment on themselves. I would tell you the thing I learned from the teams, this is probably might be a little bit surprised. But often, you know, leaders believe like, you gotta have all the answers. You're gonna drive everything you're gonna let make sure everyone knows what needs to get done and what I actually found. This was actually one of my big moments, I think, was our Our individuals are employees are teams. They're so brilliant and so bright on driving change. And a lot of times leaders, I think, get in the way that so for cloud and adoption, it was really about me getting out of the way. It was really about setting that north star for where we want to go from the ability to deliver fast and quick for our business. And they get out of the way and let our teams actually drive. So it was a great, um, it was we actually actually saw the reverse. I saw more employees wanting to drive, and I needed to, like, back out and just say, Here's what we need to go. Let them drive us there. >>Alright, So I gotta ask you don't Please don't hate me for asking this question, but was your your gender and advantage was at a disadvantage. It wasn't really irrelevant in that regard. >>It was a relevant um, I think that it was I actually I truly believe it's irrelevant. I think it was literally recognizing that leaders need to set vision and what we want to achieve and let our letter of teams help us drive to get there. And I think that that is, you know, gender neutral. I think it's really about, you know, kind of checking your ego and everything else out to the side. And it's really about empowering people in our teams. Thio help drive us there. >>So thinking about that that learning specifically are there any similar tectonic shifts that you're you're seeing today where you can apply that experience? I'm just like, for instance, new modes of application development and requiring new skill sets are, or maybe another that you can think of. >>Yeah, I think I think honestly, it traverse is everything that we that we have to do as a you know, as a leader of a technology team, and whether you're in a high growth company like Pure or you're in a company that's trying to take costs out of your business or trying to, you know, do things. I think that it, um it really is a matter of leaders needing to set the stage. And so if we're trying to drive, you know, changing the business, it's really making sure that we're doing I'll calm or more empowering of our employees and they because they will see the way that we can get there. It's just a matter of, you know, letting them have that ability to do it. >>So you joined pure around two years ago and obviously growing very quickly. I love pandemic has changed the trajectory of that growth, but still good outlook. Um, but Silicon Valley fast paced company, you know, I kind of put it in the camp of the the work days, and the service now is that could have similar similar cultural patterns there. So you talked a little bit about this, but I wonder if we could come back and more specifically how you're leveraging cloud, how you're thinking about it, you know, on Prem Hybrid, Now the edge. And how did that contribute Thio Puros growth? >>Yeah, that za great question because I think that why I shared earlier, you know, we were essentially born in the cloud. I think that what it's really driven us is to be thinking more forward about where customers were going and what their challenges are. So whether it's for the I t. Teams on what we're trying to do to deliver for our business and, you know, innovation, they're obviously trying to make sure they can hit their revenue goals and all those things that important that every business deals with. But we also have that same mindset on how we develop our products. So it's really all driven by where the customer is going that they need data mobility. They need application mobility. They need really portability so that the moment that you have that ability where you can kind of control your destiny and define it, and you only could get that by having, you know, applications that are portable and data that is mobile and secure, that you have that kind of flexibility. So I think for pure we've been definitely in a great position to drive for our customers or drive where our customers are going. And so we have to find our entire product set. So not just how we operate as a business and run our business. But then how we define for our customers Same mindset is if our customers are going to the cloud that we need, have products that can help them to be in the cloud or be, you know, on print and let them decide what that looks like. Well, >>it's interesting you mentioned that and I hearken back to the The Port Works acquisition, which is an attempt to really change the way application development has done is another sort of approach Thio in a sort of modern data architecture, you, as the CEO of a technology company, most CEO, is that I know inside the tech companies that they're sort of the dog Fuding or champagne drinking, you know, testing. So So had you already started to sort of use that tech? Are you starting to, you know, Does it support that vision that you just put forth? Maybe you could talk a little bit about that. >>Yeah, It does. So we eso We had not been using port works as a za product. We were just starting down that path of looking at How do we do container ization for the applications that we do have on Prem? That's both in our engineering side as well as within I t. And so But we quickly have recognized, just like you know, And part of that acquisition is applications or companies won't have the ability to have that portability of their applications and have that flexibility that they're all striving for unless they've done things like containerized or applications made them that they're able to move them across different cloud environments, whether that's on Prem or off Prem or some hybrid eso for ourselves. You know, Port Works was a really critical acquisition, will help us on our own journey of doing the application, modernization and putting that keep those capabilities in place. But it will also enable our customers to have that same flexibility. So, again, going back to the we've adopt, these things aren't like a this is for this group, and this is for you know, this customer. It's really about how we operate both internally and then what we are providing for our customers so that portability and being able to have control of your own destiny, that's that's really to me what hybrid cloud is all about. And you can't really achieve that If you don't have some of these capabilities within your, you know, within kind of your toolbox. >>Great. Thank you for that. So I'm interested in is the head of, ah technology group at a tech company? And what are the meaningful differences? I mean, a lot of differences, but relative to CEO of a large telco or or other incumbent, you know, what are some of the good, the bad? And, uh, you know, the ugly, the differences. >>Yeah, you know, it's I meet with a lot of CEOs across Silicon Valley and we kind of joked that when you are working in a company that is a technology based company, you know, everybody knows how to dio, you know, because you do you have a brilliant engineers and and that they do know. I think the difference that you start to see is that you know, I t is, um is required to make sure that availability is their inherent in what you're doing on immediate roll out with like, you know, an application that's occurring. That's very different than how you do product lifecycle management. Um, what what we've what I've seen, actually, though, is more similarities. I know that's probably surprised to you, but coming out of a T and T, what I have been working on those last couple of years was actually doing the combination of engineering and I t into one organization and that you do have a lot of benefits for, for how you can then develop, how you can manage and the skill sets. There's a lot of similarities. So there's there's actually probably more similarities between companies and on what they're trying to achieve than than you would probably think there would be just because we're all trying to make sure that we can develop quickly. How about is >>it relates to cloud Cathy? I mean, I remember the early days of cloud, a lot of the big banks that we could build our own cloud. We can essentially compete at scale with with Amazon, where you know the big bank on. Then I think they quickly realized well, the economics actually don't favor us necessarily. Do you think there's a different perception about the use of cloud between sort of traditional incumbents and a tech company in Silicon Valley? And if so, how? >>So now I think that the if you are, you know, a bank is you refer to, and having it really is where you're starting from. If you have a very large infrastructure footprint and application footprint, your applications probably not born in the cloud. There's a lot of modernization that has to be done with those applications so that they could operate as efficiently in a public cloud as an example. And I think that's something that sometimes gets overlooked is there are enormous benefits going to public cloud. But there's also cost if your applications or your data doesn't really fit as well in that type of environment. So I think that for large enterprises like the banks, some of the telcos they've got very large footprints of infrastructure. Already, those investments have been made, and what they're really looking for is how doe I increase my ability to, you know, whether it's agility or its speed, or it's lower cost or it's all those things, and I think that's the That's a different path of different journey that they're on. So they're trying to balance all those equations of, you know, the economics as well as the ability to have, you know, no more investment or minimal investment in that infrastructure. For companies like Pure, where we started off of those investments are decision and kind of. The decision tree that we use is if it makes sense. And I don't have to make that investment on Prem for whatever reason, that I should go ahead and make that investment in a public cloud strategy or a hybrid cloud strategy kind. Differentiate that because I think that it's different depending on the company. You are, um, and so it really kind of depends on where you're starting from then. It also depends on what you're trying to achieve if you're just trying to achieve an economic solution. If you're trying to achieve a strategic solution, if you're trying to get agility. Andi, I think it is different for companies, and it's different depending where you're at in your kind of journey. So for a Silicon Valley company whose you know hyper growth, you know, one. We're very focused on abilities. You know everything from scale, because we've got to scale quickly. And those are things that we don't wanna have to start going and building all these data centers to go do that. We don't have those embedded investments. So it's Ah, it's a real difference in where your starting point is. And I think there I think there's value in in all those different type of approaches, >>right? And it's a real advantage for you that you don't have to shell out all that cap ex on Data Center. >>That's right. Um, as you look >>back at the last 10 years of cloud, you know, it was largely about eliminating the heavy lift of infrastructure deployment and SAS if I ng you know the business, what do you see? Going forward? What do you think the was gonna unfold in the 2020 is? Is it gonna be more of the same? Or do you expect meaningful differences? >>I think that we're going to get better as, um as you know, technology leaders on how to quickly make decisions. Um, and not its have it less political. And I think Kobe is actually taught us a lot about that around companies more willing to make. I'll call it a A you know, a faster decision and remove some of the red tape. I've heard this from many of my peers that things that might have taken them months and months to get approved. Um, it's nowadays if even if they even have to go get approval. So I think that what we're going to see is we'll see the continuance of, um, you know, a public and I'll call really hybrid cloud type of solutions. And I think it will be more purposeful about what goes there and how. How that can help us toe, you know, I'll call it enable us much faster than we've been able to do it before. I think that's been our challenges. We've, you know, we get mired into some of the you know, the details of some of these things that maybe it would be easier for us to just make the decision to move forward than Thio. Keep going around around on what's the right way to do it. Yeah, >>so that's interesting. You're saying about the fast decisions? I felt like, ah, lot of 2020 was very tactical. Okay, go deal with the work from home, etcetera. Although you you definitely see I t spending, uh, suppressed in 2020. Our forecast was minus 4% but we're saying it's gonna grow. We actually see a decent snapback. You know, what are you seeing? Generally, Not even necessarily pure. But when you talk to some of your colleagues, you obviously in the technology business, it's good to be in the technology business these days. But to use do you see spending, you know, generally coming back And maybe the timing first half, maybe a little soft second. What are you seeing >>there? Yeah, almost identical wage that. I think that we'll see, you know, a little bit of, ah tendency toe, not really hold back, but really kind of see what's happening in the first quarter of the year. There's a lot, you know, going on with companies and everyone's having to kind of balance at what that looks like. I do see. And what I'm hearing from several of my peers is that, you know, it's not necessarily budget cuts. It might be budget re directions. It might be rude prioritization, but definitely technology investments are still there, and it's still important for businesses to keep on their journeys on. But we do see that even at pure as a way to differentiate ourselves in the market as well, do you? What >>about the work from home piece? I mean, prior to co vid, I think the average was about 15 or 16% of employees work from home. You know, now it's gotta be, you know, well, over in the high seventies, Onda CEO is that we've talked to suggest that, you know, that's gonna come down in the first half, maybe down toe, still pretty high 50 60%. But then eventually is gonna settle at a higher rate than it was pre pre covert. Maybe double that rate may be in the 30 35 maybe even 40%. You know? What are you expecting >>Something probably very similar. I think that what companies have recognized and I actually tell you CEO have thought this many of them for many years that there is a huge value value and having some type of hybrid model. There's value in having, you know, both from a business perspective as well as a personal perspective. So employees work life balance and trying to balance that. So I think that, you know, we a pure and myself, As you know the CEO hugely expect that we will see some type of you know, I'll call leveling off, figure out what's the right for the right group. And I think what we don't want to get into is, you know, Chris prescriptive that says, You know, this is what the company will look like as a whole. I think it really is going to come down to certain certain types of work are more conducive to a more work, remote environment others need to have. And I always kind of uses term of individual, you know, productivity versus team. You know, productivity. We've seen, you know, great advances and or individual productivity. A team productivity is still a challenge when you're still trying to do very collaborative, you know, brainstorming sessions. And so we are looking at capabilities to be able to enable our employees to do that. But there there's some things you just can't replace. The human interaction and ability to very quickly inter actively, you know, five minutes catch someone to do that. So I think we'll see. We'll see both. We'll see some leveling off, and I think we'll see some areas of businesses that have once thought You can't do that remote. They might actually say, Hey, that is work that commute remote So I think we'll see a combination of both. That's an >>interesting perspective on productivity. And what's the What's the old saying is You could go go faster alone. But further as a team and and not a lot of folks have been talking about that team productivity, we we clearly saw the hit the positive hit on productivity, especially in the in the technology business. So So my question then is so you expect? You know H Q doesn't go away. Maybe it gets, you know, maybe it gets smaller, Uh, but so is their pent up demand for technology spending at the headquarters. Because you've been you've been, you know, pushing tech out out to the edge out to the remote workers. Securing those remote workers figuring out better ways to collaborate is their pent up demand at H. Q. >>Um, absolutely. We've been, you know, we've been actually exploring different technologies. We've been uh, looking at what are things that you know could help create a different kind of experience, eh? So I do think it will be some different types of technology. Those would be the things that maybe aren't even out there developed yet on Have you create some of those comparable experiences. So I think that the notion of you know individuals will continue to thrive, but we've got to start working on How do we continue to enhance that? That team, um, collaborative productivity environment that looks and feels different than what it might look like today. Yeah. >>They got to leave it there. Great as always. Having you in the Cube. Thanks so much for participating in Cuban Cloud. >>Great. It's great to be here. Thank you. >>Keep it right there. Back more content right after this short break. >>Yeah.

>> Okay, we're now going to explore what it's like to be the CIO of a fast-paced growth company in Silicon Valley, and how the cloud, however you want to define the cloud, public cloud, on-prem, hybrid, et cetera, how it's supported that growth, and with me is Cathy Southwick, who is the CIO of Pure Storage. Kathy has really deep experience managing technology organizations. She spent a number of years overseeing AT&T's cloud planning and engineering, and another few years overseeing a team of a couple thousand network and IT engineers, working to break the physical stranglehold of fossilized telco networks, implementing network function virtualization and a software-defined methodology for the company, and of course, she's spent the last couple of years as the CIO of Pure, so Cathy, it's great to see you again. Thank you for coming on the program. >> Thanks for having me. It's good to be here. >> You're very welcome. And so, given your experience with cloud, you know, dating back to really the early part of last decade, how did you look at cloud back then, and how has it evolved, from your point of view? >> You know, it's an interesting question, 'cause I think that there's some things that have moved very fast, and there's some things that are very much the same as they were even a decade ago. I think that all companies are very focused on how do you think about cloud? Do you think about it as on-prem, and when I started, we really were focused on an on-prem solution, and building an on-prem private cloud to help modernize our business, so I think that with that, all companies are still in that same mindset of how do I want to think about cloud, and how do I want to think about that on-prem versus public, versus a combination or some type of hybrid solution? So, I mean, all of us are on that journey. It just seems like it's taken us probably a little bit longer than most of us probably thought from the beginning. >> So as a CIO, thinking about that evolution, how has that informed the way you think about applying specifically the public cloud to Pure's business. >> You know, I think that we've been a-- For Pure ourself, I think we're in a really unique position. We were essentially born in the cloud, so we're a company that's 10, 11 years old, and if I give the contrast of that of AT&T being you know, 130 year old company, and having a lot of applications that have, you know, lived historically on-prem, there's very different issues and challenges that you have. Pure has had, I think, the advantage, just like many other companies that are born in the cloud, who can see what the advantages are very quickly and we made decisions early on that said that we were going to actually do both. We were going to look to say how do I put those applications and that data, whether it was on public or on-prem, and be able to do that both in the IT side as well as within the product side, so how we develop our products. >> Now, as I mentioned up front, you have obviously a lot of experience managing large technology teams. My question is when you first saw the emergence of the modern cloud, how did you communicate with your team members? I mean, you mentioned you were kind of building your own private cloud, so I guess that's less threatening to people, but what was it like? Was there a concern? Were they eager to jump in? What was that dynamic like, and how did you manage it? >> You know, it's really, it's different depending on the different part of the organization, so I'll give you kind of two things I learned. One of them was that our teams on the operation side, they saw it as a huge advantage. They saw it as an opportunity to really modernize, to really get themselves, both their own, individual skill sets advanced, as well as provide a better level of service for our internal customer, so to speak. Our application and our data partners that we had to work with, they saw it as an opportunity to bring agility to their applications, quicker speed to market, and more currency of their applications, so they actually got some benefits that they weren't actually I'll call planning for. They had the opportunity to get investment in their applications without having to put that investment on themselves. I would tell you the thing I learned from the teams, this is probably, might be a little bit of a surprise, but often, you know, leaders believe you got to have all the answers, you got to drive everything. You're going to make sure everyone knows what needs to get done. What I actually found, this was actually one of my big moments, I think, was our individuals, our employees, our teams, they are so brilliant and so bright on driving change, and a lot of times leaders, I think, get in the way of it. So for cloud and adoption, it was really about me getting out of the way. It was really about setting that north star for where we want to go, from the ability to deliver fast and quick for our business, and then get out of the way and let our teams actually drive. So it was a great-- I actually saw the reverse. I saw more employees wanting to drive, and I needed to back out and just say here's where we need to go. Let them drive us there. >> All right, so I got to ask you. Please don't hate me for asking this question, but was your gender an advantage? Was it a disadvantage, or was it really irrelevant in that regard? >> I think it was irrelevant. I think that it was-- Actually, I truly believe it's irrelevant. I think it was literally recognizing that leaders need to set vision in what we want to achieve, and let our teams help us drive to get there, and I think that that is gender neutral. I think it's really about kind of chucking your ego and everything else out to the side, and it's really about empowering people and our teams to help drive us there. >> So thinking about that learning specifically, are there any similar tectonic shifts that you're seeing today, where you can apply that experience? Like for instance, new modes of application development, and acquiring new skill sets, or maybe another that you can think of. >> Yeah, I think honestly, it traverses everything that we have to do as a leader of a technology team, and whether you're in a high growth company like Pure, or you're in a company that's trying to take costs out of your business, or trying to do things, I think that it really is a matter of leaders needing to set the stage, and so if we're trying to drive you know, change in a business, it's really making sure that we're doing, I'll call it more empowering of our employees, 'cause they will see the way that we can get there. It's just a matter of letting them have that ability to do it. >> So you joined Pure around two years ago, and obviously growing very quickly. I know the pandemic has changed the trajectory of that growth, but still, a good outlook. But Silicon Valley, fast-paced company. You know, I kind of put it in the camp of the Workday and the ServiceNow. It's kind of similar cultural patterns there, so you talked a little bit about this, but I wonder if we could come back and more specifically, how you're leveraging cloud, how you're thinking about it, you know, on-prem, hybrid, now the edge, and how did that contribute to Pure's growth? >> You know, it's a great question because I think that-- Well, I shared earlier, we were essentially born in the cloud. I think that what it's really driven us is to be thinking more forward about where customers are going and what their challenges are. So whether it's for the IT teams on what we're trying to do to deliver for our business and innovation, they're obviously trying to make sure they can hit their revenue goals, and all of those things that are important that every business deals with, but we also have that same mindset on how we develop our products. So it's really all driven by where the customer is going, that they need data mobility, they need application mobility, they need really portability, so that the moment that you have that ability where you can kind of control your destiny and define it, and you only can get that by having applications that are portable and data that is mobile and secure, that you have that kind of flexibility. So I think for Pure, we have been definitely in a great position to drive for our customers, or drive where our customers are going, and so we've defined our entire product set, so not just how we operate as a business and run our business, but then how we define for our customers, same mindset is if our customers are going to the cloud, then we need to have products that can help them to be in the cloud, or be on-prem, and let them decide what that looks like. >> Well, it's interesting you mention that, and I hearken back to the Portworx acquisition, which is an attempt to really change the way application development is done. It's another sort of approach to sort of modern data architecture. As the CIO of a technology company, most CIOs that I know inside of tech companies, they're sort of the dog-fooding or champagne drinking, testing, so had you already started to use that tech? Are you starting to? Does it support that vision that you just put forth? Maybe you could talk a little bit about that. >> Yeah, it does. So we had not been using Portworx as a product. We were just starting down that path of looking at how do we do containerization for the applications that we do have on-prem? That's both on our engineering side as well as within IT. But we quickly have recognized, just like you know, and part of that acquisition is applications, or companies, won't have the ability to have that portability of their applications and have that flexibility that they're all striving for unless they've done things like containerize their applications, made them that they're able to move them across different cloud environments, whether that's on-prem or off-prem, or some hybrid. So for ourselves, you know, Portworx was a really critical acquisition that will help us on our own journey of doing the application modernization and putting those capabilities in place, but it will also enable our customers to have that same flexibility, so again going back to the-- These things aren't like this is for this group and this is for this customer. It's really about how we operate, both internally and then what we are providing for our customers. So that portability and being able to have control of your own destiny, that's really, to me, what hybrid cloud is all about, and you can't really achieve that if you don't have some of these capabilities within your toolbox. >> Great, thank you for that. So I'm interested, as the head of a technology group at a tech company, and what are the meaningful differences? I mean there are a lot of differences, but relative to CIO of a large telco, or other incumbent, you know, what are some of the good, the bad, and the ugly of the differences? >> Yeah, you know, it's-- I meet with a lot of CIOs across Silicon Valley, and we kind of joke that when you are working in a company that is a technology based company, you know, everybody knows how to do-- Because you do, you have brilliant engineers, and they do know. I think the difference that you start to see is that IT is required to make sure that availability is there inherent in what you're doing on immediate rollout with like, you know, an application that's occurring. That's very different than how you do product life cycle management. What I've seen actually though, is more similarities. I know that's probably a surprise to you, but coming out of AT&T, what I had been working on those last couple of years was actually doing the combination of engineering and IT into one organization, and that you do have a lot of benefits for how you can then develop, how you can manage, and the skillsets. There's a lot of similarities, so there's actually probably more similarities between companies on what they're trying to achieve than you would probably think there would be, just because we're all trying to make sure that we can develop quickly. >> How about as it relates to cloud, Cathy? I mean, I remember in the early days of cloud, a lot of the big banks said we can build our own cloud. We can essentially compete at scale with Amazon. We're the big bank. And then I think they quickly realized well, the economics actually don't favor us necessarily. Do you think there's a different perception about the use of cloud between sort of traditional incumbents and a tech company in Silicon Valley, and if so, how so? >> I think that the-- If you are a bank, as you refer to, and having-- It really is where you're starting from. If you have a very large infrastructure footprint and application footprint, your applications probably are not born in the cloud. There's a lot of modernization that has to be done with those applications so that they can operate as efficiently in a public cloud, as an example. And I think that's something that sometimes gets overlooked, is there are enormous benefits with going to public cloud, but there's also costs if your applications or your data doesn't really fit as well in that type of environment. So I think that for large enterprises like the banks, some of the telcos, they've got very large footprints of infrastructure already. Those investments have been made, and what they're really looking for is how do I increase my ability to, whether it's agility or it's speed, or it's lower costs, or it's all those things, and I think they've got the different path, a different journey that they're on, so they're trying to balance all those equations of the economics, as well as the ability to have no more investment or minimal investment in that infrastructure. For companies like Pure, where we started off with those investments, our decision, and kind of the decision tree that we used is if it makes sense and I don't have to make that investment on-prem for whatever reason, then I should go ahead and make that investment in a public cloud strategy or a hybrid cloud strategy, and I'll kind of differentiate that, because I think that it's different depending on the company you are. And so, it really kind of depends on where you're starting from, and then it also depends on what you're trying to achieve, if you're just trying to achieve an economic solution, if you're trying to achieve a strategic solution, if you're trying to get agility, and I think it's different for companies and it's different depending where you're at in your journey. So for a Silicon Valley company who's hyper-growth, you know, one, we're very focused on agility, everything from scale, because we've got to scale quickly, and those are things that we don't want to have to start going and building all these data centers to go do that. We don't have those embedded investments, so it's a real difference in where your starting point is, and I think there's value in all those different type of approaches. >> Right, and it's a real advantage for you that you don't have to shell out all that cap-ex on data centers. >> That's right. >> As you look back at the last 10 years of cloud, it was largely about eliminating the heavy lift of infrastructure deployment, and SaaSifying the business. What do you see going forward? What do you think is going to unfold in the 2020s? Is it going to be more of the same, or do you expect meaningful differences? >> I think that we're going to get better as technology leaders on how to quickly make decisions and have it less political, and I think COVID's actually taught us a lot about that around companies more willing to make, I'll call it a faster decision, and remove some of the red tape. I've heard this from many of my peers, that things that might have taken them months and months to get approved, it's now days, even if they even have to go get approval, so I think that what we're going to see is, we'll see the continuance of public and I'll call it really hybrid cloud type of solutions, and I think it will be more purposeful about what goes there and how that can help us to you know, I'll call it enable us much faster than we've been able to do it before. I think that's been our challenge is we've-- You know, we get mired into some of the details of some of these things that maybe it would be easier for us to just make the decision and move forward than to keep going round and round on what's the right way to do it. >> Yeah, so that's interesting, what you're saying about the fast decisions. I felt like a lot of 2020 was you know, very tactical. Okay, go deal with the work from home, et cetera, although you definitely see IT spending suppressed in 2020. Our forecast was -4%, but we're saying it's going to grow. We actually see a decent snap back. You know, what are you seeing generally, not even necessarily Pure, but when you talk to some of your colleagues? You're obviously in the technology business. It's good to be in the technology business these days, but do you see spending generally coming back, and maybe the timing? First half maybe a little soft, second half-- What are you seeing there? >> Yeah, almost identical to what you said. I think that we'll see a little bit of a tendency to not really hold back, but really kind of see what's happening in the first quarter of the year. There's a lot going on with companies, and everyone's having to kind of balance that and what that looks like. I do see, and what I'm hearing from several of my peers is that you know, it's not necessarily budget cuts. It might be budget redirections, it might be reprioritization, but definitely technology investments are still there, and it's still important for businesses to keep on their journeys, and we do see that even at Pure, as a way to differentiate ourselves in the market as well. >> What about the work from home piece? I mean, prior to COVID, I think the average was about 15 or 16% of employees worked from home. You know, now it's got to be well over in the high 70s, and the CIOs that we've talked to suggest that that's going to come down in the first half, maybe down to still pretty high, 50, 60%, but then eventually it's going to settle at a higher rate than it was pre-COVID, maybe double that rate, maybe in the 30, 35, maybe even 40%. What are you expecting? >> Something probably very similar. I think that what companies have recognized, and I actually tell you, CIOs have thought this, many of them for many years, that there's a huge value in having some type of hybrid model. There's value in having, both from a business perspective as well as a personal perspective, so employees' work-life balance and trying to balance that. So I think that we at Pure, and myself as the CIO, hugely expect that we will see some type of I'll call it leveling off, figuring out what's the right for the right group, and I think what we don't want to get into is a prescriptive that says this is what the company will look like as a whole. I think it really is going to come down to certain types of work are more conducive to a more work remote environment. Others need to have, and I always kind of use this term of individual productivity versus team productivity. We've seen great advances in individual productivity. Team productivity is still a challenge when you're still trying to do very collaborative brainstorming sessions, and so we are looking at capabilities to be able to enable our employees to do that, but there's some things you just can't replace the human interaction and the ability to very quickly, interactively, you know, five minutes, catch someone and do that. So I think we'll see both. We'll see some leveling off, and I think we'll see some areas of businesses that had once thought you can't do that remote, they might actually say hey, that is work that can be remote, so I think we'll see a combination of both. >> That's an interesting perspective on productivity, and what's the old saying, is you can go faster alone, but further as a team. And not a lot of folks have been talking about that team productivity. We clearly saw the hit, the positive hit on productivity, especially in the technology business, so my question then is, so you expect, you know, HQ doesn't go away. Maybe it gets smaller, but so is there pent-up demand for technology spending at the headquarters? 'Cause you've been pushing tech out to the edge, out to the remote workers, securing those remote workers, figuring out better ways to collaborate. Is there pent-up demand at HQ? >> Absolutely, we've been-- You know, we've been actually exploring different technologies. We've been looking at what are things that could help create a different kind of experience? And it'll be some different types of technology. Those will be the things that maybe aren't even out there developed yet, on how do you create some of those comparable experiences? So I think that the notion of individuals will continue to thrive, but we've got to start working on how do we continue to enhance that team, collaborative productivity environment that looks and feels different than what it might look like today. >> Cathy, got to leave it there. Great, as always, having you on the CUBE. Thanks so much for participating in CUBE on Cloud. >> Great, it was great to be here, thank you. >> All right, keep it right there. Back with more content right after this short break.

>> Announcer: From the CUBE studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. >> Hello, everybody. Welcome to this CUBE Conversation. This is Dave Vellante, and I've been running the last several weeks a CXO series, where I've talked to CEOs, CIOs, CISOs, to really try to understand the impact of COVID-19. As you know, we've really dug into the budget outlook, et cetera. Well, Cathy Southwick is here. She's a CUBE alum and the CIO of Pure Storage. Cathy, it's great to see you again. Thanks so much for taking the time to come on the CUBE. >> Yeah, it's great to see you again, Dave. Thanks for having me. >> You're very welcome. So my first question to leaders is, when you started to get visibility that there was going to be this crisis, what was your first move? >> The first move for probably most organizations and especially for ours was to really assess current state of our organization. And how we support the business, so we went into a lot of pre planning and a lot of looking out and saying if things were to happen in a quarter and two quarters, three quarters what would we need to be prepared for? So we spend a lot of time going and looking at our BCP our DR plans and say what would we need to execute on? We actually started similar time that you met with Mike Fitzgerald last week. My supply chain actually started pulling triggers to get ourselves prepared and look across both our application side as how we evaluate availability, our partners that we use both, on prem and off-Prem. And we started to also look at just kind of the current state of how our employees currently work. And we do have a fair amount of our employees who are very productive and successful working remote. So we knew we need to make sure that we continue to provide that level of service across the board. There was really a lot of pre planning a lot of looking at what are we currently doing. And what do we need to make sure that we're prepared for in the unknown that we were all facing >> And I want to ask you about the kind of work from home piece of that. But before I do, help us understand, you're right, I did talk to Mike Fitzgerald. And it sounded like you guys maybe had an early warning system because you obviously have a supply chain, you're sourcing components from all over the world. Do you feel like you had kind of an early warning to this and maybe more than many organizations? >> I think we probably have an equivalent to others. I think the difference was that we are a small nimble organization our IT is very lean. And we immediately move towards kind of executing plan so there isn't a lot of approval cycles or red tape to go through. We're really empowered at the organization level to make the decisions that we think are necessary to help our business be successful. So that was really the the forefront was, see what needs to happen and then actually start doing. so don't just think about it, plan, do the analysis over and over, but actually start executing. And I think you would have seen that across all of Pure. that we immediately started mobilizing teams, our supply chain, both from an internal What do we consume, whether it's laptops, devices for employees, et cetera. Or it was the products that we need to provide our customers and what they need to do. So I think it has to do with more of how our company culture is and how we operate very much you make it happen for our customers. And that means that all of us need to be constantly prepared for what needs to happen in within our respective teams to support our customers. So I want to break this talk down to people, process, and tech. We always talk about the big three. Let's start with the people. I mean, obviously your first concern was the health and well being and safety of your people. But as Sanjay Gupta told me, once we figured that out, it's like, get to work, you know, we got to be productive. So maybe talking about the people situation, California was kind of hit early. But I imagine you've got a remote workforce as well. Paint a picture of the people resource if you would. >> That's a great point that Sanjay made. You're absolutely right. The people was it's going to be look at this is its people the safety and health, and then, what do we have to do to provide the necessary tools for our customers. And so we immediately from the employee lens, we immediately start doing things like interactions with our employees. I'll say on a very regular basis. So we do a daily set up and IT at the leadership level, that each of the organizations, they're also holding those. And a big part of that is not you know, how's your work going? It's, you know, how are you doing? How's your family doing? How are the things that you need to have in your own personal space to be able to be productive. Because if you don't have those things, then people actually can't do the work. So try to be very flexible, and we've got people who are working different shifts, meaning that they normally will be in the office Monday through Friday, but they're trying to balance those home things with homeschooling children now, and all those different aspects. So it's really been about, take care of the people first we've done some fun things like, doing little exercises where we have a challenge every day. Some type of challenge that we're asking our employees to do just to kind of keep the health and mind part of it healthy and then go right into the work. And you're right. You going to get to that, okay. I think our employees have settled down to this, whatever this new norm is. And people are not becoming very focused on how do I make sure I hit my deliverables. They're so focused on helping our business that they are, it's literally about, we actually had to tell some people to kind of back off, take some time off, because 24/7 became 24/7 for everyone instead of having some rotations because people just didn't want others to have to wait for responses. >> Right Cathy, well let's turn to the next P, which is the process. So what kind of changes did you have to make in order to support this whole work from home notion? >> You know, we're we're very fortunate, as I had mentioned earlier, we already have a fair amount of our employees that were very successful working remote. So we had already the tools in place, the capabilities in place, everything from, what we really did was a lot of validation of what we had was it going to really be sufficient. So things like checking, do we have the right VPN, do we have enough network bandwidth connections, et cetera. So for the most part, all of that has worked pretty well knock on wood, but it's gone pretty well. And we've had some challenges with some of the new hires coming in, because we still are onboarding new employees. And there are some locations around the world where there are some logistical challenges of going to certain countries. But other than that, we've actually, the plans, a lot of the planning the team had done has really paid off for them as we move forward. So we've been very successful from that perspective. >> And I definitely want to ask you about the technology but before I do, I want to talk about the macros. So we've been reporting that when we came into this year, the consensus for IT spending was a plus 4%. Through COVID and other research and surveys that we've done with our data partner ETR, we've kind of settled in at a negative 4% for the year. which would be a lot worse were it not for the work from home offset. And about 20% of the organizations that we talked to said they're increasing spending about 35% said Hey, no change. And then of course, there are a lot of industries, airlines, others affected by supply chains, et cetera, which are way way down the hospitality, et cetera. Now, that's the macro. Pure is not a bellwether for the macro because you guys are a high growth company. But, what's your budget situation kind of coming into the year and has there been any change? >> I feel very fortunate. We've been very supported from our business. We actually made some changes to I'll say double down on some investments in IT this year. So our budget coming into this year was already increased. And that was to deal with just opportunities that we saw are better aligned with the business, Better strategic partnerships in different areas of our business, how we support them from the customer side. And then also how do we want to think about areas of like our employee experience. So we actually have made this an investment year for IT. Right now, we're still on track for that. I would say that we've done a little bit of some re-prioritization of some initiatives that we wanted to do versus things that we're now wanting to make sure we care for. So think about like collaboration tools, do we want to expand any of that portfolio that we currently have? So it's more of, I feel very fortunate that we came in pretty strong, and we're still at that point. But we are doing some, I'll call, reassessing some of our priorities, say, are these the right things we should still continue to do? Or should we alter some of that. So at this point, that's what we're looking at. >> Yeah, I've talked to a number of technology leaders that have said, Look, this is a sort of, shift in priorities as you were mentioning. For instance, one said yesterday that we had this kind of hard network And now we've got this distributed workforce. So we're really kind of rethinking our network priorities. We've got to secure those remote workers. It's not just video conferencing tools. It's our VPN. It's security. It's our network bandwidth, it's maybe things like VDI, et cetera. Have you had to shuffle some of your priorities? >> So I would say the ours have been more of augmenting capabilities. So we've had a pretty successful strategy around what we're doing on the remote workers and how we secure them et cetera. We are absolutely like everybody else. I participate in several CIO forums, we talked about this. Do we need to go back and revalidate some of the decisions that you might have made in the past? And so we're doing that from ensuring that we have the right security around our enterprise. But the other is that it's also looking at are there things that we could do that would help us from productivity capabilities. And those are things like we have some tools that allow us to do Like you said video conferencing. But they're also, do you want to have capabilities to actually do white boarding or sessions that allow you to feel like you're in the room with someone. So looking at some of those new opportunities. >> So I wonder if you could talk a little bit about some of the things that some of your suppliers are doing. Obviously, Pure is a big supplier of yours. I presume they're treating you like gold. But I've had some CIOs that I've talked to say that they really were overwhelmed and pleased with the way in which their vendor partners have treated them. Maybe deferring payments for them and some of these hard hit industries and the like. What is the relationship in with your providers? >> I would characterize it very similar. We've had great partnerships with many of our major suppliers of technology. And for both SaaS providers as well as some of the other areas that we work with. And I would say that everyone has made the extra effort to either reach out, whether it's at the CEO level, it's at, different layers within their supply chain. A lot of communication we do, We've had a lot of communication from the, I'll say, the supplier community, which has been really great and very responsive. So when we are seeing issues or have questions, it's not I will get back to you at some time, or you're not important. It's about what we'll do to help you. So I think we're seeing similar to what you've heard from others. We're experiencing that great kind of ecosystem that says, hey, we got to make sure everyone is successful not just an individual company. So that's been the experience we've had as well. It's been really nice to see it. >> Tn that CIO and CISO Roundtable that I mentioned from ETR. We had thought that, especially in some of the harder hit industries that they'd be less likely or more reticent to work with startups, but it was just the opposite. In fact, one individual said, you know, you hear, the other guy says, hey, I picked three from the upper right in the Magic Quadrant, I vet them. He said, I always pick two from the upper right, and then one from the lower left. Because I'm trying to find that next diamond in the rough. And he mentioned a couple of success stories. They don't all pan out. But, they want to try new things, and get some competitive advantage. What's your take? I mean, you guys were in this position. That the challenge or the disrupter. But what's your take on working with startups in these times of crisis? >> I think it's important to, because they're seeing and they're nimble. And so I think that was one of the benefits that Pure had. We're very nimble, very quick to make decisions. You're hyper focused on the customer. And I know that a lot of companies will comment that they're very focused on their customer first, but coming into Pure you see it firsthand. And so you often see that from startups, they're very curious on what problem are you trying to solve? So they're not necessarily trying to sell you a widget of some sort. They're really trying to solve a business issue that you're facing. So I would tell you that we've had success as well over the years. Pure has worked with startups over the years. We participate with different communities whether they're the VC community who's doing round tables and discussions around new startups and the opportunities that they're bringing. Will they solve problems? And I would tend to agree. I think you need to have something that, you know, understand your business enough that can help you to scale. Because as a company like Pure, we're continuing to grow. You got to able to scale your business. And so some startups might not have some of those capabilities. But there's actually quite a few that, they can solve the problems a different way. And so, scale looks different than maybe what a traditional way to approach it. So, I think it's really important to keep them in the mix. And to actually just keep anyone who's trying to solve problems. Because the if you aren't looking at it from the lens of like, what are we trying to do to support our business. And a lot of the CIOs were in the same boat. You're trying to figure out how do I help the business be faster, better, et cetera. You're stuck with the status quo, and none of us want to be in that position. And none of our businesses can actually afford that either. >> But it sounds like your business continuance, business resilience, well, beyond disaster recovery, it sounds like you are in pretty good shape. But I'll ask you, what do you feel as though your biggest challenge has been since COVID-19 hit? >> It's funny, I thought, I've been thinking a lot about this. Because we're always trying to figure like, how do we do this better next time? What's that look like? So certainly, I think it's the education part with our employee community. We've spent an enormous amount of time with our HR partners on doing or best practice sessions? How do you use this? What's the best way to do this way? So I think that our key learning is that most users only touch the surface of capabilities in any of the tools that you provide. Whether it's a laptop, to their home networks, to what they're doing on software tools, collaboration tools. and everyone kind of just does enough to get themself started. But when you move into an environment where we're all remote, you have to rely on all those tools so much more. So I found it to be that we spend more time on what I'll call the education side, and the awareness side we are still running like some of our normal campaigns from like security awareness. So those things can't stop. But I found that we've spent more in that lane, than we have on, I'll say, dealing with, or challenge with some of the just technical capabilities to keep our teams productive. So it's been more of the people side, when you asked the question earlier. More of the people side that we've been focused on. >> So technology infrastructure, pretty solid, It's really sort of educating people. I always said bad user behavior prompts good security every time. But I'll follow up with somebody who was joking the other day that work from home infrastructure is the new hand sanitizer and you can't get a lot of it. But no, no challenges with securing laptops, or other sort of components that you might need. >> No. I think in some of our initiatives we're still very focused on, I would say some of the areas that we're trying to get a little bit further on is what I'll call complete touchless remote capabilities. So similar to like what our customers expect of Pure, where you want, remote installs, you want to be able to do it touchless, you don't want people coming in. It's the same thing in the IT space. So we're trying to look at can we adopt some of what we're doing in the what I'd call the market side? Can we do more of that with our employee base? And I think that's where we're going to spend more of our time. Do we need to expand the collaboration capabilities? And then do we need to expand more of our capabilities on the remote side when people are not physically in the office or can't come into an office very quickly. What can we do to be more remote assist for those users? And we've spent a fair amount on that. But I would call it more of the basic capabilities. I think we need to look at, do we expand and really kind of to strengthen some of what we've done today. >> I mean, I got to say, I'm not super surprised about Your posture here. I mean Pure, you don't have a lot of legacy baggage. You've always been a very fast moving company kind of forward thinking. But I wonder if we could just ask you generally. kind of my last question here is when you think about Pure, but specifically, but also your peers? What portions of kind of post COVID-19? Do you think you're going to be permanent You know, a lot of people we're kind of on the wrong side of history in terms of work from home, forcing people to the create the beehive. Understandably, but that obviously, is going to change the whole use of collaborations. What's your sense of the top things that might remain permanent in a post COVID world? >> That's a great question. Actually one of the top questions that we're trying to grapple with right now is, What does reintegration look like? And what do you do both from a technical perspective, the people perspective and what are we going to do? So? I would say that there's still a lot of questions that are unanswered right now. I think that like all companies we're open for business right now. We're trying to understand what's happening from government controls, et cetera. And what will those need at the state, country level, et cetera,, for what we would have to differ with our employees. And I would tell you that our position as a company has been very much that we're going to have to continue to expand how we thought about, the beehive concept versus do we have more work from home. I think you will see that as a general you'll see more work from home across the board. And I also think that we'll look for some creative things that we've never done as companies before, whether that is staggering times when people are in the office. While we kind of do that today, just based on employee needs. We've got people who come in at six in the morning. we have people who come in at 10 or 11 in the morning. so, I think that we might have to be a little bit more prescribed about so many people in the office at certain times. So, I think it's going to be a combination of, I think the technical will all continue to work that. I think the people side of reintegration is going to be the how do we want to balance the working from home or the remote working? And then do we have more staggered schedules? All those things are going to be I think the bigger challenges for us to address. As just kind of as the economy in general. >> Okay, it's been great having you on, as you know, the cube we love face to face, but this is safe to safe. So thanks so much for coming on and sharing your perspectives. Great stuff. >> Great. It's great to see you Dave. >> All right, and stay safe. And thank you for watching everybody. This is Dave Vellante for the cube, and the cube conversation. We'll see you next time. (music playing)

>> Narrator: Live from Las Vegas, it's theCUBE. Covering UiPath Forward Americas, 2019. Brought to you by UiPath. >> Welcome back everyone to theCUBE's live coverage of UiPath Forward here at the Bellagio in Las Vegas, Nevada. I'm your host, Rebecca Knight, co-hosting alongside of Dave Vellante. We're joined by Cathy Tornbohm, she is the distinguished VP Analyst at Gartner. Thank you so much for coming on theCUBE. >> Very welcome, nice to be here. >> And Guy Kirkwood, he is the Chief Evangelist at UiPath. Thank you so much. >> Thanks Rebecca. >> So, we're hearing so much of these mantras, these catchphrases of UiPath. "automation first", "a robot for every person", "we're re-booting work", these are the theme's that Guy was touting up on the main stage, Cathy. Beyond that, I'd like to hear from you a little bit about what you're seeing in the RPA space at the moment. What are the trends and the themes that you think are most salient? >> I think the most fascinating thing about RPA right now is that it's really highlighting the problems the organizations have. All their accidents of history are really being brought up by RPA. And then you've got these digital darlings that they're trying to compete with, the Greenfield site kind of people. And some of those don't have beautiful back offices, but let's not go there for a minute. So, it, RPA is an opportunity for companies to link their digital dreams with their existing legacy nightmares. >> And those legacy nightmares include all of the things that Guy was talking about today: the drudgery, the dreariness, those mundane tasks that take up so much of our time. >> Absolutely, and really, if you think about it, in organizations, typically less than 15% of the applications that they're using have got some sort of application programming interface. So if you don't have a way of linking them, you end up with this long turn of applications that aren't linked together, with people literally being swivel-chair integration between the applications. >> Well, why can't you just string a bunch of API's together and automate that way? >> Well, in fact, there's a guy called Ian Barkin who works for Symphony, one of their organizations, it was set up to create automations for organizations. So one of the services businesses since been acquired by Sykes. And he describes it as process sediment, and it builds up in businesses in the same way that sedimentary rock builds up over millions of years. And digging through that, so that you can actually become more efficient is very difficult to do. So doing it on API level means you got to join up all those things individually. Whereas, using RPA, if system 'A' has a user interface, and system 'B' has a user interface, you can just use RPA. >> So, Cathy, you've been following process automation as a category for a number of years. Why RPA, why is it so hot, and why now? We've heard that it's the number one software category... >> Cathy: Fastest growing, yeah. Fastest growing, from Gartner. We've seen spending data that confirms that. Why now? (sighing) >> It's the digital competition that companies are facing, and the recognition that they cannot continue to be quite as bad at some of the things that they are bad at. So it's really that business transformation story back again, business process re-engineering, the same story that we had with BPO like ten years ago, but now, with robots instead. >> Yeah, it's interesting, I was at a, we had a show last weekend, it was the CEO of Suze, Suze... How do ya say it? Anyway, Suze, she said to me, "Well, you know, digital transformation's really about business transformation." And you kind of said the same thing. I mean, thoughts on that? >> I mean, you look at the start of the outsourcing market, the BPA market, twenty years ago. The very first deals were actually IT outsourcing deals that then transformed the business using IT as the enabler. So the first deal that I got involved with ever, in the outsourcing market, was Perot Systems with a British and Asian company. And we were putting in business process re-engineering consultants who actually transformed the business using IT as the enabler for that. There is no difference now, in fact one of the, one of the partners here, one of our original customers, actually put together a plan where we did the implementation, you know, soup to nuts, so that we could find out how we fit in to that whole transformation piece. And our team put together a whole package on all the learnings that we got out of that. And I had to laugh, because they're exactly the same things that every transformation program has had for the last thirty years. >> You know, if you look at kind of the history of certain segments, and I wonder if, Cathy, if you see RPA as one of them, like if you could've figured out who was implementing ERP the best, you didn't know SAP was going to become the leader, but if you could've figured out who was adopting ERP, you could've made a lot of money in the stock market, 'cause those companies had a huge productivity boost. Kind of same thing with Big Data, nobody really made any money in Big Data, so-called 'Big Data', a dupe. But the guys who applied it probably did pretty well. Do you see RPA as similar where the practitioners are going to actually be the ones that add more value to the industry than the new, the newly minted billionaires? >> It's almost the opposite. So the more RPA a company needs, it means the worse they did at managing their ERP in the first place. >> So they're kind of a mess? >> Yeah, yes. That need to be cleaned up, yeah. >> Yes, if you've got a hundred and twenty four ERP's that don't talk to each other, and you want to close your books in any kind of reasonable time frame, you're going to be a massive adopter of RPA, which basically means the more rubbish you are and activity, the more opportunity there is to automate more of it. >> So, what are the metrics that matter when you talk to your clients? >> Well, what I try and encourage clients to do is to really focus on business outcomes. So, much as Guy probably doesn't want me to say this, I don't really care how many 'scripts', aka robots, you've built, or how many run times you've deployed. What I care about is the business impact that you've managed to achieve. So, whatever KPI's are important to you, so are you managing to collect more revenue? Are you managing to make your customers happier because you're managing to decrease average handle times? or increase right first time activities. So anything that you're doing that actually improves the good old business metrics, is just going to be fantastic. So those are the sort of metrics that, really, companies should be focusing on. Not how many scripts they've built, that's absolutely pointless. >> I mean, are they focusing on that? I mean, when you... >> Yeah, lots of people are. >> Yeah? >> Yeah. >> In terms of ROI, we hear from customers that it has had them more accurate, they're more efficient, they're cost saving on human hours of the mundane tasks. But, when you were up on the main stage talking about how we're rebooting work, we're changing this moment, is it sparking the creativity, the imagination, the time spent on strategy in the more higher-level things? Is that, I mean that seems like that's the goal of return on investment. >> It is, within those organizations that are the most mature. So, what we're seeing, is the bifurcation, really, of the market between those organizations that are just starting and scaling up what they can, internal senses of excellence. Those organizations that are using the partners behind us. Those organizations that are using external parties to help them develop that. So Delight, for instance, they are sort of a managed service business. And instead of using people, they're using automation. So, Delight, by accident, has a BPA business in Spain, but then they'll turn that into an automation-heavy business and then providing that managed service. And then, the smartest customers, including SNBC, who we heard from yesterday, are actually turning their back office cost operations into a front office of revenue generator. Now, that is radically different from what we've seen prior. >> So Cathy, I got to ask you, when I was on a plane out here, somebody texted me a picture of the latest hype cycle. And they said, they knew I was going to UiPath, they said, "RPA has entered the trough of disillusionment." I said, "Oh, awesome, Gartner's, Cathy's coming on, and I can ask her about that." Well, what's your take on that? >> I think as Guy says, some people have already sailed through the trough, they've already gone through the challenges, or some of the challenges, and they've already found these fantastic productive things. I mean, we're estimating that people will save close to a million dollars for a large company, and just not having to do re-work of getting it wrong first time with re-keying that data. So, where there's some fantastic savings available, that you know, some of the ones have gone through the trough and done that, a lot of the other ones, they kind of, they don't understand the limitations of RPA and all those other partner tools that they need to put with it. So, don't understand it, can't handle unstructured data by itself. It needs a sister tool, so, what Gartner's talking about right now is this concept of hyper automation where you look across all the different activities that you would need to, sort of replace a person. So the people that are heading into the trough as sort of this second wave of adopters that Guy talked about, that will really struggle because they didn't understand the limitations in the first place. >> Well then, you know the, sometimes, things like the Magic Quadrant, and the trough of disillusionment, they're somewhat misunderstood sometimes, people, you know they see 'em, Gartner's very clever with the way it works things, but, so how should we think about that hype cycle? It's actually, in a way it's progress, isn't it? For an industry where they start... Entering that trough. >> Its, what Gartner says, is all industries have to go through that type of growing pains. And I think that we're seeing that, UiPath's expanded massively, and that's always a challenge for companies as they grow very rapidly. And as companies try and, as they say, take these wrong metrics. So I think things like UiPath buying ProcessGold is fantastic, it's a really, really good move for them. And I expect to see a lot of other process mining companies acquired, brought in to the RPA fold, because, there's four reasons why companies are going to go into this disillusionment, right? These are the main challenges with companies trying to use RPA properly. One is, they don't know what the processes are. So ProcessGold will give you a really good indication, they don't know about the microscopic level, and they don't know about the macro level. So things like digital twins will be something else that we would expect to see very closely partnered with companies like UiPath. And they don't know how to orchestrate their resources. So, other companies, like Innate, that can help you figure out how to do that will become... So its kind of like we're sort of breaking down a lot of what happened in other software categories and re-building them all up, in the way that the business can actually adopt them, hence, the AI Fabric sort of idea. So they don't know the processes, politics, people will lie to you about what they do all day, so they can sabotage your process, and there's a lot of silos within organizations that hate each other and throw things over the wall. So that all needs streamlining, and the more you can do across silos, the more successful any automation project would be. Then you've got, when you take a person out of a process, you take their eyes, their ears, the mouth, the nose. How are you going to replace that when you're trying to take them out, because you've got the keyboard fingers thing with the RPA tool? You need all these other activities replaced, replicated, supported. And then you've got the economics of production, so actually making sure that the scripts that you've built are actually worthwhile and are going to be cost-effective. It's something that we're studying at the moment. So you've got all these, all these different barriers, from all these different angles that are really going to push this thing into the trough for a little bit. And that's why it's great that RPA companies are looking at ways to mitigate that for their customers. >> Now, remember we said, as the understandings. So RPA is really good at dealing with structured data. Rule-spaced activities, deterministic things. That's why in regulatory, highly regulated environments, it's very effective, and the regulators love this sort of stuff. Because it's deterministic. When you look at AI, then we look at it in four ways. So you've got process understanding, which is the ProcessGold acquisition, you look at conversational understanding, 'cause ultimately robots are going to be controlled by voice. So you have to understand, the system has to understand that, let's say you're sitting in a bank, and the robot doesn't understand something, you say, "Okay, robot, stick that in the Well's account." It has to understand that Well's, in this case, means Well's Fargo. It does not mean a hole in the ground, water at the bottom, or a town in Somerset, in the UK, 'cause they're well's. So getting those ontologies correct is so important. So, that's conversational understanding. Document understanding. Because, as Cathy said, companies are still wading around in paper. So, understanding what those different documents are and how to action them is going to be really important. And finally, you're looking at visual understanding. So understanding and viewing things on the screen exactly the same way that humans do. So it's getting that combination right. >> So for RPA to live up to the hype, and there's a lot of hype, and it's a good thing, it's fun to track. It's got to go presumably beyond cleaning up the crime scene, if you will, to this new vision that you and Guy just laid out. What is the distance between, I dunno, sometimes I say 'paving the cow path', which gives you a nice hit, but as you say, it's 'cause companies... Ya know, they're messed up, to this vision of this, actually the guy from Pepsi today talked about it, this fabric of automation across the organization. How big of a gap is that? >> It's very different by every different company on the planet, really, in terms of their accidents of history, what their IT application landscape looks like, and what their business landscape looks like. And when you try and put the two things together, that's where you find the opportunities for any type of automation. >> Well come on, that's such an 'it depends' answer. (laughing) At the macro, will... In your expert opinion, will RPA live up to the hype? So many trends haven't, enterprise data warehousing, Big Data, Doob, all that stuff. You think RPA has the potential to crack through that. >> You mentioned a very good point. I think the most successful companies are the ones that actually will take the person that's managing the data and analytics of how their process is performing, and doing that with their automation strategy. And there are very few companies that've actually worked that out. They've still got totally two walls and they just meet up here at the CEO. So, unless companies actually take a more active business outcomes approach, and look at their end-to-end processes of order to cash and source to pay, these problems will carry on for some time. >> Well that's a great point, I mean, so it's data, it's machine intelligence, I guess Cloud for scale, you guys made a SAS announcement today, it's "automation first", to use your buzz word. >> Cathy: You need it all to come together. >> And it's really developing those best practices in your role as Chief Evangelist in helping understand what the most successful companies do, and then making sure that's implemented. >> Well that's why I spend more of my time listening than I do talking. Because the very nature of being a Chief Evangelist is the best job and the worst job title in the world. It's the best job because I spend my entire time talking to people like Cathy who know about what's happening within the market, and then feeding it back into our organization so we can make the right bets, so we can make the right acquisitions, but develop the right things. The bad thing about the job, is that I keep getting an inordinate number of people on LinkedIn saying, "So pleased that Jesus has entered your life." And I'm not that type of evangelist. (laughing) >> It's in the title. >> You know there's always this age-old debate in the industry of best of breed versus kind of a sweet approach. You see in SAP, for instance, acquired an RPA company, In Four talks about it. And then you get the specialist, UiPath. How do you see that shaking out, as the industry gets kind of more consolidated, how do you see a company like UiPath thriving, continuing to thrive? >> Gartner's going to predict coming in our new prediction series, but... Roughly 20 to 30% of enterprise adoption of AI, machine learning activities for process-based activities, will go through the RPA market. So, and with the IBPMS market, sort of combined together, that process management, because RPA has managed, cleverly, to capture the imagination of the business person. So, actually, there's a lot of IT departments that are talking to us about, how do we, how do we enshrine this activity, foreshadow IT, that's happening in the business, and make it successful, put governance plans in place so it will actually be successful in the way that it's actually now dealing with its own crime scene... (laughing) (mumbling) Its own rubbish, in a much better way. And I think that responsibility of business to understand how it can automate things and how it can manage things will really help a lot. So, I think the RPA players are well-placed to either be acquired into that bigger set of the established, large... Software providers, all to kind of keep blazing a trail for independence of the business. I'm not so sure about this idea that everybody should be programming their own scripts, I think that's a challenge. And I think the new interfaces will help mitigate some of the problems that we've seen with that approach, that hasn't been, haven't been very well done historically, so that's another area that will probably be a bit trough of disillusionment, but, actually, well-managed RPA projects have actually got a really good chance of delivering back very interesting benefits for businesses. >> Yeah, as a discreet innovation category, it does kind of feel that way, and often times, those markets are winner take most, the winner makes a ton of dough, number two makes a little bit of money, number three kind of breaks even, and everybody else gets consolidated or goes out of business, so, you guys go big or go home. That's kind of... Your posture. >> Tomorrow morning I'm doing, I'm doing my predictions for next year, and one of them is that the challenger RPA vendors, and indeed the service organizations that are small, are going to continue to consolidate and get acquired next year. So that's the 2020 prediction for us. >> Great. Well, Guy and Cathy, thank you both so much for coming on theCUBE. It was a great conversation. >> Oh, good, thank you. >> Thank you very much, indeed. Thanks Rebecca. >> Dave: Thanks you guys. >> I'm Rebecca Knight for Dave Vellante, stay tuned for more of theCUBES live coverage of UiPath. (techno music)

>> from Austin, Texas. It's Theo Cube, covering your storage. Accelerate 2019. Brought to you by pure storage. >> Hey, welcome back to the cubes. Coverage day to appear. Storage your accelerate 2019. I'm Lisa Martin Day. Volante is my co host, and we're very pleased to welcome for the first time to the Cube. Kathy Southwark. This C I O at pure Cathy. Welcome. Thank you. Glad to be here. You have a great story. This is not only your fear. Your first your accelerate. You been at the company less than a year. You were not only a pure customer before, but in a completely different industry. So your first your accelerate. Here we are in Delhi Technologies backyard. Give us your perspective on appears business from your previous customer role. >> Yes. So I spent. I've been here just under a year, she said, and I spent the almost 22 years 18 t and coming into a company. It's completely different. Different size company, different size technology issues. Everything we do is looks very different. But there's a lot of similarities that, you know, you're trying to as any company trying to innovate and trying to stay on the cutting edge and you're trying to make sure you have the right teams in place and all that, so it's a lot of fun. It's great to see the energy and the excitement here, so that's been a lot of fun to come in and to see orange everywhere painted or so it's been a lot of fun coming on >> and you're complying with your orders. >> I got the memo. I said, You know, it's hard because orange is not one of my better colors toe where but no happy toe, happy to wear orange and proud to be part of such a company who's really looking at? How do we take care of the customer? >> Right. So you were sold on pure as a customer when you were with age and T. What was it about the technology that when you were in that prayer roll that really differentiated it from its competition? It was really >> interesting. I was sharing folks earlier today that here was very different, smaller company coming into a very large organization. We started working with them back in 18 t in 2013 so they were a very small company, very early on, but they were so bullish they had this completely different attitude about storage. And it wasn't really necessarily about this storage. It's about what we're gonna do to help you change your business. So for us, you know, I really looked at when you're in a very large company, you tend to not look so much at the particular like storage or computer or what you're really looking at, How many enabling my business and with the limited dollars that you have. And resource is etcetera, you're always trying to balance and prioritize. So for us when they came in, they made this proposition and said, Hey, we can show you this in two weeks and it'll, um And you know, when you're also big enterprise, you don't have time. Thio look a technology for weeks and months on end and then have to test it. And so we brought pure end. They they were tested out the products within two weeks, and we saw more than what we're expecting. And I think that was what changed for us is it wasn't just about we could do, you know, compression. We could do the deed if we could do. It was that all of a sudden was all these other capabilities been planned for So it really was. It was pretty pretty dramatic for us because we hadn't seen other providers to come in with a story that sounded different and not just the technology. Like I'm gonna save you a dollar. It's about now I'm going to enable your business to do something different faster. And we saw it firsthand. >> I was the role of C i o at a technology company. Different from you were in a c i o N a t. But you you had kind of an engineering roll. If I think it's a solution Engineering, how is the role different in terms of how you spend your time and what you care about? >> Yeah. So, you know, in 18 t, the CEOs were focused on the application delivery sites of specific applications at pure and so an 82. My role is centered around all the infrastructure for I t. As well as our network engineering. So what we did for the Service Writer network coming into pure, you have, you know, the whole spectrum. But we're a different kind of company. and that really 10 years old. Our technical debt looks very different. We use a lot of sass products, so we use a lot of hosted solutions from our partners and providers, and we do someone premise well. But it's a very different kind of landscape, so the opportunity is you don't have as much technical debt. You also have the ability to to try things because you are smaller and you can try things much quicker and be able to say, Well, this working isn't good enough and not have to have maybe things as gold plated. As you know, a regulated telecom would have versus a product technology product company that it's trying to be very agile to produce things and change for their customers. >> So essentially you were. I'll call you the C i o of of infrastructure at AT and T with infrastructure that had to support, like you said, highly regulated in a very diverse I'm sure application portfolio. Extremely there. Thousands of systems, probably >> thousands of applications and very complex business models. They, you know, they're ah, it's not a one. So the interesting is 80. >> She's >> not a one entity business, you know they've got their media business. They've got there mobility business. We've got their wireline business. So when you have people often think of 18 t as a company, But there's actually it's a very complex business model supporting multiple products. So it's just that those air, you know, multi $1,000,000,000 product portfolios versus coming into pure where you know we're still, you know, 1,000,000,000 have company building and growing our product portfolio. >> So what's your technology strategy of pure and how are you enabling business outcomes for the company? >> That's a great, great question. So, you know, really, a business strategy here has been that I t has to really evolve and scale differently than it had in the past. The organization before was really centered around Some of the end user capabilities wasn't as centered around business outcomes, and we've taken on a different role. So as I've come on to the organization, our opportunity and our challenge is that we now have different responsibilities, were taking on things like, How do we want to think about data across the enterprise, not just within each individual domain, and so as a start up company, you often are very focused on your R and D investments in your sales and marketing investments, and you do a lot of things to get it done. And that means that individual teams will do work. But you tend to not think about what the full life cycle is of, you know, of something that you're working on. So for our opportunity now this is take a step back, be able to look across and say it worked great for that period of time. Now we have the opportunity to rethink how we want to think about the customer experience from the time product is developed all the way through and, you know, a quote to a customer through its life cycle through delivery and then the support for that customer >> so so technology, the support that sort of workflow >> the ecosystem instead of within individual areas. And so that's really there are focuses. How do we help our business to become even faster? How do we get more focused on the customer from ah whole ecosystem? And that we think about the customer from the whole ecosystem instead of each individual area? >> Sounds like that horizontal view that Charlie Giancarlo talks about you know, with storage being so vertical in the past and cures wanting to revolutionize that and make that horizontal, ensuring that any type of business, whether we're talking about yours, business or ah retailer or our airline, every function in an organization has access to share. That data exactly struck business value to lower costs to find new revenue streams, new routes to market, et cetera. >> And we're no different as a business. We need to do those same things to make sure that we can. We can deliver those for business, so that's a big part of a lot of >> times we'll talk to C. I ose that technology companies and their large established technology companies that I think Cisco S A P. They've been around a long time. They have a lot of technical debt. They look a lot like your customers, frankly, many of your customers yours ever. But my question is a lot of these c I ose that I've just mentioned, sort of generically there come wine tasters, right? You know, they used to be dog food or his drink your own champagne, but But they they are like the first line of defense verse beta customer, and they give feedback to the product groups. Do you play that role as well? >> Way do we not probably to the extent, because we're a smaller company. So we tend Thio, as with our product announcements we've made will go out to a wide set of our customers, you know? So I think we had 16 1 of the bait is that was just done. What we do with an I T. Is because we have a smaller footprint just the size we do have flash ray with a flash blade with you do use pure one. We do it Maur of ah, from how would a a smaller customer look at it, Think about it and use it. And so that's tends to be the I'll say, the lens that we look through. I think that the role I've played coming in is the bringing a perspective from a larger enterprise on how does a larger enterprise an I t. Think about it and it's again. It's not just your helping me with storage. You're actually helping me to solve a business problem. So there's s oh, there's some other and some of the leaders that we've brought in. They also come from outside industry. Some have used pure, some have not, and so have that different kind of lens of what you know we would expect to see from our product seems, but they're also extremely open. Thio. What do you think? What is I t thinking about how you were thinking about these product ideas? What what's the input from I T. So there's a lot of what we're very small from a nightie organization. I think that the two way communication is what it's gonna you know, what will help, >> what are some of the innovations? And I know you've only had a short tenure there, but one of the things I read in the Q two earnings but that we're just released last month in August was seven. That new customers added per business safer pierce of 450 or so, plus customers at it in that quarter but also a 50% increase in multimillion dollar deals. So, enterprise, any innovations that you can share since you've been on board that your team has helped cure, understood to be able to go after those large enterprise multimillion dollar deals directly. >> Well, certainly from, um, you know, from a you know, a personal understanding of the product and what here could do it scale is, you know, I certainly have that perspective to share with our customers and bringing that confidence and credibility that, you know, if you are looking at a large enterprise customer in the opportunity, they have a lot of questions about. So how exactly did 18 t do it? It's not like they run a few arrays. They run hundreds and hundreds of rays and hundreds and hundreds of petabytes. So there's It's not like it's a proof of concept or a pilot. And it's been years of doing upgrades, non disruptive Lee over the years, with all the pure upgrades that have come into play. So I can certainly bring that to the table with helping the customers to get it, you know, a little bit of confidence, but also just an understanding about how pure is approaching it with these other large customers. So and as you've talked to other customers, there's there's enough customers out there that are, you know, very, >> very eager to >> share because they're so excited about what it's done for their business. We've >> heard. Sorry, David, I was going to say on the customer front we've, what 6600 plus customers pure now has in its 1st 10 years. And the customers we've spoken to the last two days, Dave and I have noticed that a common theme is they're talking about their overall experience with the technology. They're not talking about boxes and array names, and all these specifics are talking about how they are able to one customer from, ah, legal firm, I think in Florida didn't even do a PC had appear. That was a pure customer. And from that piers advice. I got it right on board and was really talking about the experience and all of the things to your point on the business side that they're able to to influence with the technology, not talking about speeds and feeds and arrange drives and things like that. So it's very, very different conversation. >> It's S O. It's interesting because and the role that I had, I had the teams that did the architecture, planning, design and through implementation. So the operation teams one of the most unique things I've said I share with customers is when you are in a technology and you're in a large enterprise, you tend to have a challenge with introducing new technology because you don't want more technical debt. It doesn't matter what you just don't want more technical debt. So typically your operation teams are >> doing a little >> bit of pushback on you. No, no, we don't need something new. No, we don't need unless they're having significant outages or incidents that they're trying to solve for what I found. And even to this day, there is some of the folks there actually around the floor here. The folks that were in operations, they were literally coming and saying, We want more pure And so when you're in a technology organisation that typically doesn't happen. It's S o it wasn't And it wasn't like we want more of like you said the array, it was we just want we don't wanna have to worry about. And I just took a reduction of my head count. So I want I find you have to take on more data and I am. You take on more support for the business. I don't have to worry about it. And so to have that. That's a very different. And we had the same experience of their application team saying, Hey, I just got lower latents. So they didn't actually know why. They just knew that when I was trying to do my work on the application side, working within a database, all the sudden I had all this improvement and, um and so what? We allowed them to sit. Okay, well, we'll give you more capabilities, more future functionality. And that doesn't happen. Before, those were things were like, really like operations and application teams are gonna work as a team together. Very different. I'm experience. >> So if I were a pure sales rep, I would say, >> Kathy, can you come tell my customers my prospect that >> story to the sales reps have access to your calendar? How much of your time? How much time you spend, you know, sales folks wanting you to tell stories like I got >> so the I have no the company that long. So I have I have spent a fair amount of my time talking to customers. But, you know, we also have a lot of work with an I t. And so are you know it's there just is incentive to have me work with an i. T. Because I can understand what we need to do to help our field as well. And that's one of our objectives is what are we gonna do an I t. To make it that much easier and better for not just our sales teams but the manufacturing teams. The support teams are hardware, teams, all the teams that takes a deliver. And so, you know, in fairness, I have joked with some that have stopped me and said, Hey, we need to I said, Remember, we also want to deliver for you so that to make your jobs easier So there's a balance >> that it's different. A technology company writes kind of encouraged that the C I. O goes out and evangelize is >> Yeah, it's actually a lot of fun. I, uh I I do joke that when I go out to talkto the other CEOs, I mean, they're my people there, too. I know it's It's the challenges that we have to deal with. The you know, you're dealing with the technology, those very specific items, then you're dealing with that. How do we help my business and then you're dealing with. I want to make sure I'm doing the right things for people development and all those so and you have a lens across the entire enterprise. So it's not like you're just looking at sales or you're just looking at ops for your You're kind of looking at everything to say, Well, how do I help all the teams to be that much better? Because the better we are, you know, be cliche. You know, collectively, that just got is gonna enable pure toe to do more fun. >> So what's on the minds of your peers in these days? >> You know, I feel so fortunate to be in the Bay Area, and there are amazing CEOs that get together, talk very openly, share strategies, actually eagerly and openly reach out to say, How can I help you? Um, and that's I think that's a unique as part of the CIA, a community that there's this willingness to say, Look, we're all in this together from a technology perspective. I mean, look, we all want to do well for our companies, but you're also trying to figure out how to make technology team stronger and you know it's a lot of the the same issues. It's how do I change the focus of and the perception of where I t fits into a business that it's not just a back office? It's not these systems, but it's actually becoming a very strategic, you know, Enabler, advisor, participant Helping to help, you know, can provide input. You can be that one of the first you know, Betas for your company if you're in a technology area and that's a change. There's a lot of companies who have always fascinated where it's like if you're a product and you have an I T. You're selling to those people, so pitch to them. If you can't sell to them, you're not gonna be successful. So I think it's just changing, evolving. You know some of those relationships and and that's a big deal and and you know, that's from the how you run your organization. There's that, you know, how do we make sure that the technologies were were all investing in our somewhat future proof and that they can evolve with us, not become inhibitors or, you know, box you into something that you can't kind of navigate through >> well, actually deliver on future proof. It's one of those marketing terms that is used by so many organizations delivering whatever kind of product. Same is with simple and seamless says We talk about this all the time. We did hear from customers wherever Green is concerned. You know, I said, non disruptive is how much of that goes from a marketing to reality and consistently heard about Piers ability to deliver their. But it's interesting and it's a refreshing, I think, to hear that you've experienced the changing role of the CEO to be collaborative versus he knows a lot of competition. And in tech, that's a refreshing The deer And I have an idea for you since you're so you're in such a habit to D'oh, it's good. What? You're gonna like this. I have an idea. Hash tag. Help Cathy Scale. Give them this video. Just so many pure customers all across the globe. >> Thank you. I will do that. I would. That's great advice. >> That's it. Easy to d'oh! D'oh! Well, Cathy's been great having you on the Cube. Thank you for sharing your perspective as there newish. See Io and how you went from here customer to running their i t. And congratulations on being part of the next decade of pure success. Thank you. Thank you for having our pleasure for day. Volante. I'm Lisa Martin. You're watching the Cube.

>> Welcome to this Special Cube conversation. We're here in Mountain View, California. Pure storages headquarters on cash for street were here in the arcade of the main building, one of six buildings here in downtown where Pure has their contingent of offices. On joint cab itself was a C i o a pure formerly many, many years of it and running T operations and right of other work clothes. Great to see you. Thanks for From the time >> Great. Thanks for having me. So we're in the >> arcade here. All the old school stand up video games, but our generation, when we have to play videogames standing up but kind of speaks to the culture of pure What's your role of pure What do you do and how long you've been here? >> Okay, so I've been here appear for just about five months and and it's been great. I came on board is the CEO and, as you know, all companies air facing their challenges, going forward, way have ours for scaling is this Business continues to grow so super excited to be here and spent a lot of fun so far. >> So about your career before pure, where were you and how long you worked there. >> I you know, I spent a better part of my current ATT and T Amazing Company and was there for over twenty one years. Had a variety of rules, primarily always in the technology sides. So everything from the from application side two operations infrastructure, Teo, Project Management, Teo Technology, Innovation And then the last few years was was spent working on a lot of our network strategy. So what are we doing as a, um, as a business to kind of transform a teen tea service provider network? And how does that a line from a cloud and a technology for what we have been doing some cases on on the side >> and you've been on both side of the table. You you were a customer of pure. Now you work. If you were running well, I t Here's a CEO. Try and try and keep this transformation going. What's your take on the industry right now? Because it's interesting times as you know it is transforming. You got security front center roles were changing. Got skills, gaps. You get the cloud with scale, a lot of change, >> you know, and it's interesting. That happens that both big companies and smaller companies, so the transition had eighty having those same challenges. They look different here, appear because we are a little over nine year old company, and as you start to look at, we have the real benefit of being a little bit younger on the tech front and being so close to, you know, obviously, being in Silicon Valley, you're so close to all the VCs and the startups. You get to have a little bit of different flavor, but, you know, it's a huge transition. I think that all of us in the industry are really faced with the challenges of not just trying to transform your teams and the work and what they're doing, but then also enable technology that's going to bridges from what we have to do with today, and where do we think we're going to go? So I don't think that is any different on any of the companies. I think we're all in that same boat of saying, How do you make sure I have technology that's gonna live longer than you know, a year, three years? And then how do we have a workforce that can continue to grow and develop because, you know, we want to be able to have our talent stay with us and make those journeys with us >> and one things we to a lot of Cuban Aries over the past ten years. Certainly in it changes been constant theme. But what's interesting is his economic changed and look of skill gas. But economics have changed, and then the time to value the big long projects used to take months and months years right now, shorten those solo cycles have been accelerated down two months and days. Sometimes Frank, can you come and reactive economics and then time to value. >> You know, I think economics are. They're always in the forefront of every company, especially publicly traded companies that, you know you want to make sure we're turning the right value to our shareholders, and that's an important aspect. But I think the more the more important part of it is just trying to make sure that you can make decisions that can outlive kind of a shorter economic window they maybe had would have done in the past. So I think that's where all of us in in the space of CIA rolls or trying to really evaluate. How do you do that? How do you make sure that you could make those transitions and not have next year on Leigh Foundation, But be part of it to help you make some of that shift >> and the evidence on workload. I've heard the word workload was a tag cloud. I'd probably say workload would be the biggest font because, you know, workloads would you mean applications that have been around for a while? But more and more applications are coming. The migrating workload. So the cloud on premise So a lot of emphasis on workloads these days >> is that putting >> pressure on it is putting pressure on the operations. How do you see that? That whole workload thing evolving? >> Yeah, you know, I definitely do. So, you know, one of the one of big initiatives I ran eighteen t was migrating a thousand of our strategic applications onto ah nonprime private cloud. And it was all about not just the economics, but also the efficiency and the enablement for the business to move faster, you know, at a lower cost point. So that always tends to be your kind of bottom line part, but I think is I've come into pure and has You're trying to figure out how do you evolve our workloads of very different. We are very in our applications, like very different. So companies have different profiles of whether it's an application or workload. I think the other is It's a hyper focused around the user experience with, you know, so not just the end customer, but also the employees experience and what happens So, you know, when you talk about workloads, it's not just applications that heir business functions. They're also about. How do you make sure that our employees are having that great experience because you want to have that so that they can help to, you know, grow us as well and be productive in their roles? >> I wantto askyou one of the talk tracks I have on my notes here about pure specifically. But more generically, workloads are dominating conversation, but also technology selection and personnel selection are also tied to workloads, and some have said to me pick the right cloud for the right work. We'LL pick the right tool for the job. You hear that a lot. >> You did. What's your >> thoughts on that? Because this seems a kind of model of wars. Little bit, because the old school was Here's my suppliers. You pick them, they're all stand in the hall, come in with this general purpose. But now, with customization mohr agility, it seems to be that workloads and selection of tech and people are tied together. So >> yeah, no, I think you're right. I think that, um you know, part of our challenge is figuring out and this is me, her. A lot of us don't get yourself locked in And to that old notion of, you know, what you would have seen, you know, back in the day is you Did you pick a vendor and you kind of right that through whatever the challenge he had, I think the vendor community has also recognized that's not really the model they want to be in either. They really want to be a partner. So now what's about figuring out what I consider enablement? So can I use you to work on or to be optimized for a certain type of function? But can I put my work load somewhere else on DH? Do it So that's when one of things I've been surprised, that's probably more rapid shift is it's not just about Can I do it all myself or on Prem or with these set of vendors? It's to say I want to be able to actually move across. So can I have the flexibility and being realistic? But she can't have you? No total flexibility, everything. But you can't start to be prescriptive about certain areas and saying these type of applications are these functions or these workloads. I could get the largest amount of flexibility, but the's I'm actually okay saying most optimized should go here, whether it's on Prem off Prem hybrid. I think that's what we'LL start to see. A lot of >> we see that the cloud conversation. We're going to talk with him, your folks, about this. But no one cloud could be great for a workload for another cloud for another workload. And that's multi cloud because you have a couple clouds, right? And that's the train that we're seeing. You dude >> absolutely saw it at a T. Same thing here, a pure we do some on Prem and we do some hybrid. We also do some hosted where we have our SAS provider host are the applications well and that actually then starts to get you into some other challenges that we have a night that you start to say. So what happens with my data, and what does that look like? Where is it going? How is it secure all those things that are so important as a business to make sure your customer in your employee's data is, you know, corn centric to >> final question for going to the talk trash. I've gotta ask you, being a veteran in the business. What is it, crazier now, then it wass ten, fifteen years ago in terms of work operations. Is it faster? What's your take on it if you look back the old way into the new ways that you know more of the same but just different kind of product and technologies, which you're >> probably in the unique role because I think it's super fun, I think that Theobald litt e to be able to transform your business and have the flexibility. I'm certainly being here in in this roll and, you know, nine half year old company. There's lots of opportunity to be completely flexible, and I think that part is really fun. I think that the challenge for some larger you know, companies who've been around hundred plus year old companies as those companies you know, have a challenge with saying I've got such a large embedded base and trying to be, you know, interoperable around, what what exists and where they want to go. I think a lot of us that were, you know, in these companies that are, like, pure we have, Ah, you know, kind of. I think it is a gift to be able to say Hey, this is really something we should be able to do How do we go do it and have the support to actually do it? So it's Ah, I think it probably depends on the part of the industry that you're in. There's definitely some challenges, and I think privacy is definitely, you know, kind of a backdrop. But I think is you think about that. There's workable solutions for as all cos they're trying to go through. I think it's just a matter of making it. You know, that commitment to say you know you can can be flexible and you can make the progress you're looking for. >> It seems to be more of a builder culture as well as your operational calls. That's right. You can build and operate, build, operate kind of a new kind of flywheel. >> Yeah, I think that's the That's the exciting part for it is, I think we've we have transitioned or we're in that mode that time period where, instead of just being a pure enablement for the business, it's really turning into How do you become a strategic partner? How how do you have that seat at the table where you're helping to say, How do we help your business? It's not just about paying out these applications. Here's our availability hears. I mean those air, what I consider table stakes. You gotta be able to do those things. Now it's about how can we help you? Actually, you know, improve what? Your trying Teo, you know, in the business side of it. So that's the That's the part I think is unique and different is that focus on helping Teo and you're not just enabled, but be that strategic partner to help. He had changed business. >> That's awesome. Couple talk trash. I want to get your thoughts on one is accelerating Conference, which is pure We've been following. The company was founded. Scott Deaton. First interview, I think, was the way he found company Washington success. Now they have a big customer conference. We have the sixteenth in September sixteen September that week. Check it out your first conference, you guys, we're introducing some new things. What's the buzz? What are you planning on for the conference >> yet? So you know, it's interesting cause such as someone who's coming out of, you know, the industry side of it. The thing that's hard is as the CIA or was trying figure out what's going to be the biggest bank for my my time, cause I can't can't go to everything. So I'm super excited. Babel Teo to attend the event. I think the uniqueness is it's focused on the customers so existing customers, but also prospects customers who are considering pure thie. Other unique thing that's happening this year is there is a very specific track around the executive side so that having the sea level conversations, you know, with some of our key leaders in our business and innovative thinkers and so It's kind of running the spectrum of be able to say, If you're coming on and you should all come if you're coming, you're going to be able to have the conversations that you're expecting out of sea level. That might look a little different than maybe someone who's trying to do innovation and in your team and what they're looking for. So whether it's you know, demos or workshops and thinks that you get your hands, you know, hands dirty on, I think that that's the you know, the excitement of all of it is it's it's kind of a multifaceted and it's, ah, it's a great opportunity connects with your peers and with other companies, be able to say, What are you doing? How do we learn from you? We do a lot of those kinds of things, I think in general, but I think when you can get focused and have a peer group that's in, ah, you know smaller type of venue where and it's not thousands at a you know, major major conference that's existing somewhere in the in the U. S. Or u know worldwide, then you can actually have those meaningful conversations with your peers to say, Here's the things I'm working on. How ve you done it? What are you doing so well, I think we're gonna enable all those type of conversations to take place. So I'm excited to be a lot of fuss. >> The objective of the sea level trackers. It's just CEOs, is it? See? So says that CX ohs. What's the focus? What's the objective? >> It is all of that. One of its so interesting is my CEO is actually going to be quizzing me and talking to me about what is they actually expected of a CEO? Because I think that as a zany sea level position, your you know, we have expectations of you know what we need to deliver. But there's also how you contribute to the business. So it's kind of all all facets of it. It's everything from, you know, understanding what the expectations are to Some also thought leadership around where technology's going trends, those type of conversations and being would have some round table conversations. Maybe industry peers, eso all those kind of aspect. So but all all those areas they're covered >> should be great event. Looking forward to it. >> Yeah, a lot of fine >> Cuban be there. Of course we'LL check us out We'Ll be broadcasting live. Okay, Second talk track Women Tech You're a woman Takes years and years in the business in a big focus over the past years, Accused men have a lot of interviews with the great women in tech. >> Where do you >> see this state of the sticks? The needles doesn't seem to be moving on the percentages, but there seems to be great mo mentum in real pros. Lot of mentoring, a lot of networking. You seeing women, VC firms evolving very rapidly seeing cohorts together. What's your take on women in Tech, where we are, What challenges was opportunities? >> Yeah, you know, So we actually, in the Silicon Valley we actually have. There's several forms that go on for women CEOs and events that were to be able to have some of those conversations. And what do we do? And I think it starts with all of us, you know, individually and a in our organization, so organically is to figure out, you know, how do you make sure everyone feels that they belong, whether it's, you know, women or it's any other diverse group of employees. We have to figure out how to make people feel connected and part of the team, and I think it starts with that. And that's for kind of every discipline. And you know that you can think of in a business in text. Specifically, I think the challenge for women is you tend to not want to be identified. As, you know, a woman in Tech. It's like I want to be evaluated for my compensate what I bring to the table, my thought leadership, my perspective on and I don't think that's you mean to women. I think that's just unique to people that we all want to be valued for what we contribute. I definitely think that is a, um, a general kind of population and technology. I have seen where it used to be that I was the only female for many, many years and meetings, whether it was that fender briefings or it was in different company forms. Uh, I've had some unique opportunity. Eighteen t was hugely focused on women in tech women and engineering all those disciplines coming to pure, super exciting that we're we also so small of a company relatively sized eighteen t We have, you know, women heir Geez, employee resource groups. We have women in engineering. We have limited night. So we have kind of the ability to get that mentoring in that coaching the support within the company. And I think that's really valuable. But to your point, I think we have to still do Mohr of connecting outside of our company, figuring out whether that's through, you know, the different universities to make sure that we're getting the pipeline coming in and then retaining. I think that's the other challenges. The number's probably won't change much because we still see a significant amount of women leaving the workforce at a certain point. Er there were staging their career, and we need to figure out, you know what? What's that draw? Why is that happening? So >> what's the technology impact? Because as technology becomes consumer, I just seeing Data Analytics to arm or big a range of topics and confidence is not just computer science or probably or whatever Lim Maura broader perspective that helping at all do you see that evolving, that getting any lift, increasing the population and competency levels, >> you know it's a great question. I think we've had a pretty strong I'LL say, run at women. Being in computer science, we haven't seen enough women going into leadership positions. I think this just kind of industry, you know, generic kind of comment. I think it definitely helps. The more that you have a broader range of skills and capabilities. I think it's what is more fascinating is we need more women in those roles because as you think about the problems that all of our businesses air trying to solve their, it's not one dimension. So if we only have attack our problems with one dimension, one skill set, we just start going to be prepared to be, Oh, it's gonna take us longer And are all of us want to be able to quickly solve the issues that are >> of a personal question put you on the spot? What's the big learnings for you? Looking back now that you've seen that you can share as a woman attack and you put your twenty three year old hat on, what would you do differently? If anything, if you're living in today's world, >> you know eso it's interesting at has asked this question before actually came to pure as I talked to a number of companies in the Valley and it was like, What would you tell your younger self? And I said one of them is not to be afraid, and I think that's so so many of us. Whether you know, male or female. Sometimes you get into a routine and you don't necessarily break out of it or change. And so you tend to maybe take a safer path or a safer direction. And I think if I was to think back you No one is. Don't be afraid and the other, I think, is I probably would have. I was probably naive Tio not realize that I was sometimes the only female, and so I just kind of worked as I didn't think that was a different shade IRT it mattered. And when I think about it now, I probably should have done more to do some of the networking that we're doing today. That might have helped. You know, we talk a lot about the difference between mentoring and sponsoring, and it really gets into that. There needs to be enough, you know, sponsors both male and and female who can help to, you know, not just developed but have the conversations, you know, Make sure that people are included. Those kind of having a voice at the table. And I was very fortunate. I worked under some amazing leaders, both male and female, who who made sure that I had a voice. But I you know, I'm not a timid flower anyway, So I wouldn't have you know, I'm not going to sit there and to sit back and not do it and not to speak up. But I think that's something that not everyone is this comfortable was speaking up and being okay. That maybe I'm not right or so I think that I would tell my younger son Don't be afraid. And the second is to doom or Teo help get other others who maybe don't feel as if they belonged much. Teo, be able to have that. That same voice >> possible. Congratulations. You're awesome. And I'm excited for the event with you, >> you have to be a lot of fun. >> Okay, Next talk track. You were a customer of pure before you joined the company. Yes, you're t you were You know you have the keys to the kingdom. All the vendors pitching you You have big infrastructure, run tons and tons of work loads on DH. This is what, six, seven years ago here was in the growth phase. Now they're public company and much larger experience. But back then you took a risk on a technology. Tell us about that story because you made a big bet. Did it? Work actually worked out. You're here. I'm sure that he still has pure detail. The story? >> Yes. So first it starts with, You know, I had amazing team at a great team of folks who didn't want to accept the status quo, what was happening in the stores storage industry. And so as a CZ, we were hearing, you know, like you said, the pitches about what's due. What's different? Um, they were willing to stand up and say, Hey, you know what I think we need to look at this company, and, you know, it is hard when you are, you know, kind of that time I would say Pierre was somewhat of a unicorn in the sense that you try to have a somebody who's that small, non private, probably health company toe work with a big behemoth like tea. There's a lot of different things, whether it's contractual, you know, the legal, that decency is all that's having put aside the, you know, all of the technology. It's all of that That's really hard for companies to navigate. Pure had an amazing technology, and what happened is they came in and they said, Hey, this is what we can do. We can transform your business is not just about the economics will prove you that part, but we can actually help you to deliver faster for your application teams. We can help you with all these areas and and we could do it all within like two weeks. So the key was being able to stand up and say I'm going to do this and then prove it in this very small window because when you're in a large enterprise, you often you don't have unlimited resource is very constrained of. It's not a different that it started, but you're very limited. Resource is welcome to try to run big scale and so they were able to prove out everything they said, and then plus more. It was things like we started seeing efficiencies in the data center. We started to see that things like that where we thought we're going to have to expand and buy, you know, additional ports. We were able to not have to do it. So there was a lot of these, like side benefits that we weren't expecting. We all those Plus we asked for. So we did. We took a bet on Pure. They were a great, innovative team to work with. And, you know, he's had it, you know, their legacy is ah, very much innovation. And so it kind of was that match to say we need to re and companies who can help us to continue to innovate. >> We little skeptical at first, but we can do in two weeks. >> Yes. So it's almost like a bet. You go. All right, let's get you to do it. We'Ll see how it works in two weeks. So and that's s o they came in. It was all proved out. So we way actually, you know, move forward And you know, today a t and T is you know, hundreds of race, which is, you know, a very large footprint for any company to have with size. And it spans, you know, production application, Tier one applications to things that are specific use cases. So it kind of spans a large. >> Not a lot of war stories around. Critical failures, either. You said you had some successes with them before you came on camera. Yeah. Share that story is the storage. Is one those things where he was going to have something that might go down? The question is, how severe is the problem? What was some of the experiences you have? >> Yeah. You know, I can say that. You know, I left a team t last last summer. So up through that point, we had not had any several announces. So when you think of a large company, it's not unusual to have incidents outages. I mean, that that tends to happen just with the size of your footprint. Um, t was very successful, working with pure on, having essentially having a product that had big stability. And we didn't see those outages and not just on running it, but actually doing the non disruptive upgrades. So the ability to actually take the technology, do the next generation and not have any outages. That's pretty unusual for for any company tau experience. And so I looked at us from a scale highly unusual, but that's that was success. >> Great success. Okay, finally, you're here pure now, your CEO not as big as a T and T was still public company and they have a lot of employees. They're maturing as a company. You're running pure at your house. You can't unless you're doing a bake off internal assessment. How using pure. Now, how's it going? What's the share? Some of the architectural details without giving away any secrets. What's it like? And what you guys doing this innovative. >> So, you know, we are. We are much smaller organization, obviously. Then you know where I live just left. But it's really important for us to have that same innovation and capabilities we actually use pure we. But we use both flash array. We use flash blades of both of our you know, I'll say premiere products. We also use pure one, which is kind of the the telemetry visibility allows you to do what if analysis looked to see how you're doing for capacity perspective. So we actually use, um are you know, three of our primary products to actually run our daily our data warehouse, and then we are doing some of it just to be able to do some of our security. So we actually run a splunk in tableaux and using those type of tools, those capabilities, we run them on our environments and were able to do a lot of things that the feedback that we're getting is like, Oh, my gosh, we can't believe that you guys were able to do this. We have a very, very lean ight organization. So to do some of the things we're doing from a security analytics and, you know, threat, detections and all that, those are things that aren't very common for a lot of companies are we're all trying to be better on it and were able to use our own technology to kind of help substantiate what we're trying to solve for that's so super exciting. >> That's awesome. Final point on the CEO perspective. Great to have you and get the CEO perspective again. Bullets a customer and then working up your CEO is out there right now are challenged with transformation. Digital transmissions like buzz word that's been kicked around for years. But now you starting to see the robber hitting the road. Really? Development pressure, modernisation, run app, development. See, I see the pipe lining to multi cloud hybrid cloud. All this is now pretty much got some visibility into architectural decisions. What do you think is the bigger It's callous facing CEOs today in terms of, you know, thinking about the holistic, you know, five ten year horizon as they start to make investments and think about either aging out or contain arising preexisting workloads to cloud native APS and on premise giving me all your thoughts. >> Yeah. You know, I think that the kind of boils down to a couple aspects. One of them is, you know, module ization of your applications. That's why containers ations become such a big deal. Being able to do things like, you know, have your data separate from your application and not have everything so integrated at that level where you then are getting somewhat confined. You have issues with in I have to have this application running in this location. I also need to have the data has to be, you know, coexisting with it and so you run with all these constraints. So I think that for depending on the age of your organisation, that the first challenge is trying to figure out how do I start, Tio, you almost break apart my application of iron, my infrastructure. So I have more ability to have more modularity between what needs to happen and where it needs to happen. So I think to me, that's the That's one of the biggest aspects were, you know, super fortunate, because because we're a big sash shot. Most of our applications were dependent on our venders for the US Ask providers to have kind of worked through some of those issues, but that's that's one aspect. I think the second is the ability to navigate between, you know, on permanent prints. So the hybrids solution is really I don't see that going away. I think that all of us are struggling with the whole notion of whether it's the economics. It's the ability to like you, said move workload to the right location for the right optimization, the right tooley, et cetera. And so I think it's that flexibility. You can't get any of that if you don't have the first part done. And then when you start talking about your like your digital strategy, none of that works when you start wanting to get into, like, a A and m o until you have some of those things done and you put that data strategy in place. So you then have that ability have the threat across your whole, you know, ecosystem. And I think that's what our challenge, >> an automation, is key. But you gotta automate manual test. You have people to do it. Then you got a strange way to make that. So the skill gap stills always gonna be there. Right? Kathy, Thanks for Spend the time sharing your insight here on the cube conversation. Really appreciated. Absolutely. Thank you for >> having me >> here. Cube culture here. Pure storages headquarters in Mountain View, California. John Korea. Thanks for watching

>> Hi, everyone; I am thrilled to personally invite you to a special event created and hosted by "theCUBE." On January 21st, we're holding "theCUBE on Cloud," our first editorial event of the year. We have lined up a fantastic guest list of experts in their respective fields, talking about CIOs, COOs, CEOs, and technologists, analysts, and practitioners. We're going to share their vision of Cloud in the coming decade. Of course, we also have guests from the big three Cloud companies, who are going to sit down with our hosts and have unscripted conversations that "theCUBE" is known for. For example, Mahlon Thompson Bukovec is the head of AWS's storage business, and she'll talk about the future of infrastructure in the Cloud. Amit Zavery is one of Thomas Kurian's lieutenants at Google, and he'll share a vision of the future of application development and how Google plans to compete in Cloud. And J.G. Chirapurath leads Microsoft's data and analytics business. He's going to address our questions about how Microsoft plans to simplify the complexity of tools in the Azure ecosystem and compete broadly with the other Cloud players. But this event, it's not just about the big three Cloud players. It's about how to take advantage of the biggest trends in Cloud, and, of course, data in the coming decade, because those two superpowers along with AI are going to create trillions of dollars in value, and not just for sellers, but for practitioners who apply technology to their businesses. For example, one of our guests, Zhamak Dehghani, lays out her vision of a new data architecture that breaks the decade-long failures of so-called big data architectures and data warehouse and data lakes. And she puts forth a model of a data mesh, not a centralized, monolithic data architecture, but a distributed data model. Now that dovetails into an interview we do with the CEO of Fungible, who will talk about the emergence of the DPU, the data processing unit, and that's a new class of alternative processors that's going to support these massively distributed systems. We also have a number of CXOs who are going to bring practical knowledge and experience to the program. Allen Nance, he led technology transformation for Phillips. Dan Sheehan is a CIO, COO, and CTO and has led teams at Dunkin' brands, Modell's Sporting Goods and other firms. Cathy Southwick has been a CIO at a large firm like AT&T and now is moving at the pace of Silicon Valley at Pure Storage. Automation in the Cloud is another theme we'll hit on with Daniel Dines, who founded and heads the top RPA company. And of course, we'll have a focus on developers in the Cloud with Rachel Stevens of RedMonk. That's a leading edge analyst firm focused exclusively on the developer community. And much more that I just don't have time to go into here, but rest assured, John Furrier and I will be bringing our thoughts, our hard-hitting opinions, along with some special guests that you don't want to miss. So click on the link below and register for this free event, "theCUBE on Cloud." Join us and join the conversation. We'll see you there.

>> Narrator: Live. From Atlanta Georgia. It's theCUBE. Covering, Citrix Synergy, Atlanta 2019. Brought to you by Citrix. >> Hey, Welcome back to theCUBE. Lisa Martin with Keith Townsend and we're comin' to you Live from Atlanta, Georgia. The Showfloor from Citrix Synergy 2019. We're excited to welcome to theCUBE for the first time Simon Bray. Principle and Head of Leadership and Culture at Vega Factor. Simon, welcome to theCUBE. >> Thanks very much. Great to be here. >> So, I was doing some stalkin' of you online as I do for every guest. >> Simon: Thank you. >> Yup. And I read you're a culture Agent based in New York City. >> Simon: Yes. >> Then you come on set and I'm like , you're not originally from New York City. >> Simon: That is correct. >> You're a transplant and you hail from Brooklyn. >> Simon: Yes, I do. >> Which is where my mom Cathy Dally is from. >> Simon: Very nice, very nice. >> She will love you automatically because you live in Brooklyn. >> Keith: (laughter) >> I consider myself to be a New Yorker. I moved to New York in early 2006. So 14 years, and I think I can make that claim. Although, I am originally form London. >> I would say so. Although your accent is still identifiable. >> Simon: I keep-- I try to keep the accent. >> I mistaked it for a Texas accent, so that's--. >> Texas? >> Yeah. >> Yeah, yeah. >> Got confused? Oh there you go. >> I get that a lot. >> I bet. Yeah. Well, regardless of that you're a Culture Agent. >> Simon: Yes. >> What is that? >> Simon: Okay. >> Because we talk about Cultural Transformation and Corporate Cultures and how employee experience is essential. But Culture Agent, I just thought that was very interesting. >> Yes, so my job is to help organizations create great cultures. Essentially, our theory at Vega Factor is that to create a great culture-- First of all you need to make sure that you build an adaptive organization. So one that is able to change, is able to flex is able to innovate. So that's kind of part one. So my role is really to help organizations become more adaptive. The way that I do that is by trying to -- create an operating model in an organization that is highly motivating. We've developed a way of measuring motivation. We call it TOMO. Where, essentially you look at trying to create a system which drives up the Play, Purpose and Potential the individuals feel. Play is enjoying the work. Purpose is when people feel like they're makin' a difference. And Potential, is when people feel like they're developing and growing. And minimizing the emotional pressure, economic pressure and inertia people feel. So were tyin' to design an operating model that drives that up in order to help people become more adaptive. So my work is to work with leaders and organizations all over the place to help them apply the science of that approach to become more adaptive. >> Lisa: That's awesome. So I've heard of FOMO. >> Okay. >> Fear of missing out. Which I imagine -- everybody that's not here at Citrix Synergy has FOMO. >> Well, of course. It's incredible. >> I know JOMO, I don't have that right now goin' on. >> (giggles). >> TOMO. >> Yes. >> Total, motivation? >> Total motivation. Yeah, so we were asking ourselves the question how do you get people to turn up and do their best work? You know, how do you get people to be motivated not just to do what it says on their job description. To actually lean into their role. To be constantly thinking about how to improve it. About how to innovate, about to problem solve. About how to collaborate. When times are tough. And really what it boiled down to is really simple insight. Which is why we work, determines how well we work. So we started lookin' at the psychology behind motivation. That's where we developed this frame work we call TOMO or total motivation. Which is where we're lookin' at trying to get people to turn up and be excited for themselves. Versus doing things because they're being pressured to do it by external forces. >> Keith: So a lot-- a lot of great conversation on stage this mornin' talking about employee experience, employee's not getting disenfranchised or feelin' unmotivated about their jobs. What are some of the largest factors you see, did what Citrix shared this morning correlate with what you're seeing when you're talkin' to clients? >> Simon: 100%, Yeah. I think what was interesting this morning was the focus on not just the technology but the peoples side of things. And making sure that there's a close partnership between people and technology. And really seeing that through the lens of the overall employee experience. The way that we see the employee experience is everything that affects somebody's motivation in an organization is part of the employee experience. So, everything from the way that you frame up your purpose and identity as an organization. To the way that you organize yourselves and design roles and teams. To the way that you work together as a team. To the way that you set up governance, planning, the talent and performance systems. All of these things can be designed poorly. And therefore create disengagement and lack of motivation. They can also be designed really well. To drive that play, purpose and potential that I talked about earlier on. And as I said before, our work is all about helping organizations design the system within which people work to maximize TOMO. And that's our answer to some of the issues that were raised this morning about employees being disengaged. Our view is that that's just not good enough. You need to make sure that you're really focusing on how to make sure that people turn up and do their best work. And then the technology side of it is making sure that they're equipped with the tools, of course, that they need to do that. >> So, where do you start when organizations come to you in the Vega Factor and say, Hey guys, whether it's a younger organization that I would think on one hand might have an advantage being younger, maybe less kind of cultural biases built in. Versus an organization that might be a competitor with Citrix. Who's been around for decades and has a very, probably I don't want to say static culture. But probably a lot of cultural elements really locked in. What's the starting point? For that fresher organization versus a legacy organization? Are there any overlaps with where you guys recommend, all right this where we got to go. >> Yeah, I think the same overall framework applies. But just in a slightly different way. So, the way we start our conversations often is by defining performance as being, having two parts. The first part of performance is tactical performance. Which is all about strategy, planning and execution. And doing that as efficiently as possible. So tactical performance is really important. And then there's also the adaptive performance. Adaptive performance is how effectively can you diverge from the plan. Reacting to context changes, innovating, problem solving, solving issues. And those two types of performance are both important but they're opposing. So if you have too much tactical performance, you end up being very rigid. And it kills your ability to be adaptive. If you have too much adaptive performance you end up reinventing things all of the time and having no tactical performance and being a bit chaotic. I share that because when we look at two different types of company. Call it a legacy organization and a high growth start up. We often find that that high growth start ups have too much adaptive performance and that eats their ability to be tactical performance. People go crazy, because they're reinventing themselves all the time. And they haven't got the processes and systems so a lot of times with those organizations it's all about getting clear, on some of the basics. What are the guard rails within which we operate. What is our purpose, and identity? How do we want to organize? So that's all about helping a highly adaptive organization improve their tactical performance. Then the other side of it is a legacy organization. You know, think of any big mega organization. Where, actually as they've grown they've shifted from being really adaptive into being really tactical. They put in place processes and policies and structures, to help them manage their scale. The issue is, is that in doing that they can often lose their ability to be adaptive, By becoming bureaucratic. So, with those organizations a lot of our work is how can you, without losing that tactical performance create the space, and autonomy for teams to be able to be adaptive at the same time? So that's kind of where we come. Same framework, but actually a start point that's really quite different. >> So, let's talk about scale. Small organizations, start ups, I can see how that approach can be very deliberate. You can, spend a percentage of your time building culture. Let's talk about the big battle ships. When your goin' into a large organization. How does a large organization where it's very difficult to impact change and culture change. How do large organization's tackle this challenge? >> That's a great question. You're right, it is much harder to work within a big organization to affect change. I think the way that we would typically approach it is first of all, not to try to change a big organization at the same time. You know, it's hard to change the behavior of a thousands of people quickly. And so what we try to do is to start by taking a small part of the organization. To actually, show using that organization what good looks like. To, and then build from that. So, show the rest of the organization how this can work, how you can manage both tactical and adaptive performance. How you can create a high TOMO way of working. Then we find, that people gradually follow on from there. What's really important about that is, one you're not trying to boil the ocean. But secondly, you're showing people what good looks like and you're giving them the opportunity to opt in. And I found that when people opt into change and they start pulling for it that's a much better way to affect change versus operate in a situation where change is something that's done to people. Where people are told what to do. >> Lisa: It's being pushed on them. >> Yeah exactly. >> Lisa: Right, naturally, you're going to get resistance there. >> Yeah. For sure. >> Some of the stats that we heard this morning and Keith and I are both living this. I think I heard, maybe it was within the last week that by 2020, which is literally around the corner that 50% of the work force is going to be remote. >> Simon: Yeah. >> And I think they were saying this morning that in the next few years there's going to be 65 billion connected devices. With each person having about eight different connected devices. Keith and I are here with out different devices. Where do you see, the necessity of delivering mobile experiences. But also, for cultural impact for businesses, small or large to enable workers to be remote and give them access? Rather than forcing that they sit on a train for an hour, or in the car, and be in the office. Where is that conversation in the Vega platform? >> Yeah, it's an interesting one. So, one of the things that we spend quite a lot of time doing, is working with individual teams to help them operate more as a team. One of the things I found quite surprising in my work with different types of organizations is that often teams are connected by a common manager, but essentially are a collection of individual contributors who aren't actually working on shared issues. So, one of the first things that we like to do is to help teams re-orient themselves around the shared purpose. And, set themselves challenges for things that they want to solve together to improve their collective performance. So that's a foundational piece. Once we've done that, then the next question becomes, to answer your question, how do you get teams to problem solve together effectively. And there's two different times when teams problem solve. One is what we would call synchronously. Synchronicity is where teams need to get together physically, and actually brain storm and kick around a problem. And there is a time and a place to do that. That's why its still important to have get-togethers and to create that human connection. But actually more and more we find that teams need to also be able to solve problems in parallel asynchronously. And I think that's where technology comes in. Technology allows teams to work together on problems but not all be in the same place at the same time. To be able to do that in parallel and whenever they want to. It's when you get that asynchronous problem solving and synchronous problem solving that you can get teams to generally work together and that way we find people perform at a much higher level than if they're essentially just focusing on the job that they have. >> So obviously, you work across industries, groups and type of functions. Can you share with us some correlation between TOMO, the rise in kind of this-- employee experience measure and performance? Like, what are some of the key indicators that culture is improving performance? >> That's a great question as well. So, one of the things that we spend a lot of time doing in our early research is trying to quantify culture. Because you know, most of us would agree that culture is important but it becomes something that can easily get shunted down the list. Or seen as a nice to have (mumbles) especially if times are tough. So we spend a lot of time trying to measure culture and then to be able to to show a correlation between that measure and the performance of a business. So, the first thing that we did is say well how do you measure culture? And our way of measuring culture is the degree to which people are motivated. So this comes back to TOMO. The way that we calculate TOMO is by adding up the play, purpose and potential that somebody feels. And subtracting the emotional pressure, economic pressure and inertia they feel. And these are weighted according to their proximity to the work. And they end up giving us a nice neat score between minus 100 and positive 100. Like a net promoters score. We get that number by asking them six multiple choice questions. So it's two or three minutes. That gives us an individual score but also of course, we can measure that organizationally. We then looked at the correlation between that score and the performances of business across a range of different industries. And we show a straight line correlation between TOMO and business performance. So as an example, we looked at the airline industry. And we looked at the TOMO of most of the U.S airlines. And we found that the highest TOMO airline -- Can you guess what the highest TOMO airline is? >> Lisa: JetBlue? Southwest? >> Southwest. >> Keith: I was going to say SouthWest. >> JetBlue is second. So we found that when you walk on a Southwest or JetBlue airplane you feel great. It feels different. Because the employees, the flight crew, turn up in a different way. That's because their TOMO is higher. And as a result the performance of Southwest particularly around elements like Customer Experience, Customer Satisfaction is significantly higher than the rest. The lowest TOMO airline in our data set is United. Now-- >> Lisa: I was going to guess that. >> Keith: (laughter) >> Now, if you think of TOMO low TOMO tends to be when people either have low play, purpose and potential. They're not enjoying their job they don't feel like their makin' a difference or they're not learning. Or the system they're in is very high pressured. High emotional pressure, economic pressure. That creates a lower business performance. It can also have a negative effects from a behavioral point of view. As an example, if you saw the story a couple of years ago where United had a big scandal where someone was pulled, man-handled off one of their planes. That's a predictable affect of low TOMO. Our data, our research was done two years before that happened. And we would've been able to predict that United would have issues based upon their low TOMO score. To try to explain, and I wasn't there of course. But to try to explain what happened through the lens of TOMO. If you create a very high pressure system where the ground staff, are being essentially measured on their ability to get the plane to take off on time. When a passenger sits, and refuses to move, because of the pressure the ground crew are under they forget what the right thing to do is. Because of their low TOMO and pressure they ended up man-handling the passenger off. United lost a billion dollars in their share price. And that's a predictable, what we call a cobra affects. Which, is where people kind of feel like they have to cheat or short cut the system, that we are predicting is a result of low TOMO. >> So, we're almost out of time here but I'm so curious, how do you -- what the incentives are, for United airlines to really look at that kind of experience that goes viral on social media and these things happen, I don't want to say all the time. But that was not an isolated incident. >> Simon: Absolutely not. >> So for a company like that that's making money hand over fist flights are always sold out. They're not hurting for business. What incentivizes a business like that to flip that TOMO scale? >> Yeah, it's a great question. I think it's really difficult to make any kind of change happen when you're doing okay. First of all. And that's difficult. But the reality is is that, if you had measured the TOMO of United, and there are many other examples I am not just trying to pick on them. You would've been able to guess that this would happen. And so, our advise to organizations is regardless of how successful you are regardless of how well your business results are in the short term. You need to be thinking long term about culture. You need to be thinking about what is the operating system that you're putting your employees into. And getting ahead of what could be consequences that happen down the stream as a result of well intended moves that you make now to improve your tactical performance. >> Awesome, Simon. This has been so interesting. I learned a new acronym >> Together: TOMO. >> It's some great stuff. >> Lisa: I want to have TOMO everyday, and I'm going to really work on that. Being on theCUBE it's not hard to achieve that. >> Simon: This is great. >> Well Simon, we, Keith and I so appreciate you comin' by and sharing what you guys are doin' at Vega. And really, really interesting. TOMO. >> Thank you very much. Thanks guys. >> Lisa: Our pleasure. For Keith Townsend, I am Lisa Martin. You're watching theCUBE Live on the show floor of Citrix Synergy 2019, from Atlanta Georgia. Thanks for watching.

>> Live from San Jose, in the heart of Silicon Valley, it's theCUBE, covering DataWorks Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We're joined by Stephanie McReynolds. She is the Vice President of Marketing at Alation. Thanks so much for, for returning to theCUBE, Stephanie. >> Thank you for having me again. >> So, before the cameras were rolling, we were talking about Kevin Slavin's talk on the main stage this morning, and talking about, well really, a background to sort of this concern about AI and automation coming to take people's jobs, but really, his overarching point was that we really, we shouldn't, we shouldn't let the algorithms take over, and that humans actually are an integral piece of this loop. So, riff on that a little bit. >> Yeah, what I found fascinating about what he presented were actual examples where having a human in the loop of AI decision-making had a more positive impact than just letting the algorithms decide for you, and turning it into kind of a black, a black box. And the issue is not so much that, you know, there's very few cases where the algorithms make the wrong decision. What happens the majority of the time is that the algorithms actually can't be understood by human. So if you have to roll back >> They're opaque, yeah. >> in your decision-making, or uncover it, >> I mean, who can crack what a convolutional neural network does, at a layer by layer, nobody can. >> Right, right. And so, his point was, if we want to avoid not just poor outcomes, but also make sure that the robots don't take over the world, right, which is where every like, media person goes first, right? (Rebecca and James laugh) That you really need a human in the loop of this process. And a really interesting example he gave was what happened with the 2015 storm, and he talked about 16 different algorithms that do weather predictions, and only one algorithm predicted, mis-predicted that there would be a huge weather storm on the east coast. So if there had been a human in the loop, we wouldn't have, you know, caused all this crisis, right? The human could've >> And this is the storm >> Easily seen. >> That shut down the subway system, >> That's right. That's right. >> And really canceled New York City for a few days there, yeah. >> That's right. So I find this pretty meaningful, because Alation is in the data cataloging space, and we have a lot of opportunity to take technical metadata and automate the collection of technical and business metadata and do all this stuff behind the scenes. >> And you make the discovery of it, and the analysis of it. >> We do the discovery of this, and leading to actual recommendations to users of data, that you could turn into automated analyses or automated recommendations. >> Algorithmic, algorithmically augmented human judgment is what it's all about, the way I see it. What do you think? >> Yeah, but I think there's a deeper insight that he was sharing, is it's not just human judgment that is required, but for humans to actually be in the loop of the analysis as it moves from stage to stage, that we can try to influence or at least understand what's happening with that algorithm. And I think that's a really interesting point. You know, there's a number of data cataloging vendors, you know, some analysts will say there's anywhere from 10 to 30 different vendors in the data cataloging space, and as vendors, we kind of have this debate. Some vendors have more advanced AI and machine learning capabilities, and other vendors haven't automated at all. And I think that the answer, if you really want humans to adopt analytics, and to be comfortable with the decision-making of those algorithms, you need to have a human in the loop, in the middle of that process, of not only making the decision, but actually managing the data that flows through these systems. >> Well, algorithmic transparency and accountability is an increasing requirement. It's a requirement for GDPR compliance, for example. >> That's right. >> That I don't see yet with Wiki, but we don't see a lot of solution providers offering solutions to enable more of an automated roll-up of a narrative of an algorithmic decision path. But that clearly is a capability as it comes along, and it will. That will absolutely depend on a big data catalog managing the data, the metadata, but also helping to manage the tracking of what models were used to drive what decision, >> That's right. >> And what scenario. So that, that plays into what Alation >> So we talk, >> And others in your space do. >> We call that data catalog, almost as if the data's the only thing that we're tracking, but in addition to that, that metadata or the data itself, you also need to track the business semantics, how the business is using or applying that data and that algorithmic logic, so that might be logic that's just being used to transform that data, or it might be logic to actually make and automate decision, like what they're talking about GDPR. >> It's a data artifact catalog. These are all artifacts that, they are derived in many ways, or supplement and complement the data. >> That's right. >> They're all, it's all the logic, like you said. >> And what we talk about is, how do you create transparency into all those artifacts, right? So, a catalog starts with this inventory that creates a foundation for transparency, but if you don't make those artifacts accessible to a business person, who might not understand what is metadata, what is a transformation script. If you can't make that, those artifacts accessible to a, what I consider a real, or normal human being, right, (James laughs) I love to geek out, but, (all laugh) at some point, not everyone is going to understand. >> She's the normal human being in this team. >> I'm normal. I'm normal. >> I'm the abnormal human being among the questioners here. >> So, yeah, most people in the business are just getting our arms around how do we trust the output of analytics, how do we understand enough statistics and know what to apply to solve a business problem or not, and then we give them this like, hairball of technical artifacts and say, oh, go at it. You know, here's your transparency. >> Well, I want to ask about that, that human that we're talking about, that needs to be in the loop at every stage. What, that, surely, we can make the data more accessible, and, but it also requires a specialized skill set, and I want to ask you about the talent, because I noticed on your LinkedIn, you said, hey, we're hiring, so let me know. >> That's right, we're always hiring. We're a startup, growing well. >> So I want to know from you, I mean, are you having difficulty with filling roles? I mean, what is at the pipeline here? Are people getting the skills that they need? >> Yeah, I mean, there's a wide, what I think is a misnomer is there's actually a wide variety of skills, and I think we're adding new positions to this pool of skills. So I think what we're starting to see is an expectation that true business people, if you are in a finance organization, or you're in a marketing organization, or you're in a sales organization, you're going to see a higher level of data literacy be expected of that, that business person, and that's, that doesn't mean that they have to go take a Python course and learn how to be a data scientist. It means that they have to understand statistics enough to realize what the output of an algorithm is, and how they should be able to apply that. So, we have some great customers, who have formally kicked off internal training programs that are data literacy programs. Munich Re Insurance is a good example. They spoke with James a couple of months ago in Berlin. >> Yeah, this conference in Berlin, yeah. >> That's right, that's right, and their chief data officer has kicked off a formal data literacy training program for their employees, so that they can get business people comfortable enough and trusting the data, and-- >> It's a business culture transformation initiative that's very impressive. >> Yeah. >> How serious they are, and how comprehensive they are. >> But I think we're going to see that become much more common. Pfizer has taken, who's another customer of ours, has taken on a similar initiative, and how do they make all of their employees be able to have access to data, but then also know when to apply it to particular decision-making use cases. And so, we're seeing this need for business people to get a little bit of training, and then for new roles, like information stewards, or data stewards, to come online, folks who can curate the data and the data assets, and help be kind of translators in the organization. >> Stephanie, will there be a need for a algorithm curator, or a model curator, to, you know, like a model whisperer, to explain how these AI, convolutional, recurrent, >> Yeah. >> Whatever, all these neural, how, what they actually do, you know. Would there be a need for that going forward? Another as a normal human being, who can somehow be bilingual in neural net and in standard language? >> I think, I think so. I mean, I think we've put this pressure on data scientists to be that person. >> Oh my gosh, they're so busy doing their job. How can we expect them to explain, and I mean, >> Right. >> And to spend 100% of their time explaining it to the rest of us? >> And this is the challenge with some of the regulations like GDPR. We aren't set up yet, as organizations, to accommodate this complexity of understanding, and I think that this part of the market is going to move very quickly, so as vendors, one of the things that we can do is continue to help by building out applications that make it easy for information stewardship. How do you lower the barrier for these specialist roles and make it easy for them to do their job by using AI and machine learning, where appropriate, to help scale the manual work, but keeping a human in the loop to certify that data asset, or to add additional explanation and then taking their work and using AI, machine learning, and automation to propagate that work out throughout the organization, so that everyone then has access to those explanations. So you're no longer requiring the data scientists to hold like, I know other organizations that hold office hours, and the data scientist like sits at a desk, like you did in college, and people can come in and ask them questions about neural nets. That's just not going to scale at today's pace of business. >> Right, right. >> You know, the term that I used just now, the algorithm or model whisperer, you know, the recommend-er function that is built into your environment, in similar data catalog, is a key piece of infrastructure to rank the relevance rank, you know, the outputs of the catalog or responses to queries that human beings might make. You know, the recommendation ranking is critically important to help human beings assess the, you know, what's going on in the system, and give them some advice about how to, what avenues to explore, I think, so. >> Yeah, yeah. And that's part of our definition of data catalog. It's not just this inventory of technical metadata. >> That would be boring, and dry, and useless. >> But that's where, >> For most human beings. >> That's where a lot of vendor solutions start, right? >> Yeah. >> And that's an important foundation. >> Yeah, for people who don't live 100% of their work day inside the big data catalog. I hear what you're saying, you know. >> Yeah, so people who want a data catalog, how you make that relevant to the business is you connect those technical assets, that technical metadata with how is the business actually using this in practice, and how can we have proactive recommendation or the recommendation engines, and certifications, and this information steward then communicating through this platform to others in the organization about how do you interpret this data and how do you use it to actually make business decisions. And I think that's how we're going to close the gap between technology adoption and actual data-driven decision-making, which we're not quite seeing yet. We're only seeing about 30, when they survey, only about 36% of companies are actually confident they're making data-driven decisions, even though there have been, you know, millions, if not billions of dollars that have gone into the data analytics market and investments, and it's because as a manager, I don't quite have the data literacy yet, and I don't quite have the transparency across the rest of the organization to close that trust gap on analytics. >> Here's my feeling, in terms of cultural transformations across businesses in general. I think the legal staff of every company is going to need to get real savvy on using those kinds of tools, like your catalog, with recommendation engines, to support e-discovery, or discovery of the algorithmic decision paths that were taken by their company's products, 'cause they're going to be called by judges and juries, under a subpoena and so forth, and so on, to explain all this, and they're human beings who've got law degrees, but who don't know data, and they need the data environment to help them frame up a case for what we did, and you know, so, we being the company that's involved. >> Yeah, and our politicians. I mean, anyone who's read Cathy's book, Weapons of Math Destruction, there are some great use cases of where, >> Math, M-A-T-H, yeah. >> Yes, M-A-T-H. But there are some great examples of where algorithms can go wrong, and many of our politicians and our representatives in government aren't quite ready to have that conversation. I think anyone who watched the Zuckerberg hearings you know, in congress saw the gap of knowledge that exists between >> Oh my gosh. >> The legal community, and you know, and the tech community today. So there's a lot of work to be done to get ready for this new future. >> But just getting back to the cultural transformation needed to be, to make data-driven decisions, one of the things you were talking about is getting the managers to trust the data, and we're hearing about what are the best practices to have that happen in the sense, of starting small, be willing to experiment, get out of the lab, try to get to insight right away. What are, what would your best advice be, to gain trust in the data? >> Yeah, I think the biggest gap is this issue of transparency. How do you make sure that everyone understands each step of the process and has access to be able to dig into that. If you have a foundation of transparency, it's a lot easier to trust, rather than, you know, right now, we have kind of like the high priesthood of analytics going on, right? (Rebecca laughs) And some believers will believe, but a lot of folks won't, and, you know, the origin story of Alation is really about taking these concepts of the scientific revolution and scientific process and how can we support, for data analysis, those same steps of scientific evaluation of a finding. That means that you need to publish your data set, you need to allow others to rework that data, and come up with their own findings, and you have to be open and foster conversations around data in your organization. One other customer of ours, Meijer, who's a grocery store in the mid-west, and if you're west coast or east coast-based, you might not have heard of them-- >> Oh, Meijers, thrifty acres. I'm from Michigan, and I know them, yeah. >> Gigantic. >> Yeah, there you go. Gigantic grocery chain in the mid-west, and, Joe Oppenheimer there actually introduced a program that he calls the social contract for analytics, and before anyone gets their license to use Tableau, or MicroStrategy, or SaaS, or any of the tools internally, he asks those individuals to sign a social contract, which basically says that I'll make my work transparent, I will document what I'm doing so that it's shareable, I'll use certain standards on how I format the data, so that if I come up with a, with a really insightful finding, it can be easily put into production throughout the rest of the organization. So this is a really simple example. His inspiration for that social contract was his high school freshman. He was entering high school and had to sign a social contract, that he wouldn't make fun of the teachers, or the students, you know, >> I love it. >> Very simple basics. >> Yeah, right, right, right. >> I wouldn't make fun of the teacher. >> We all need social contract. >> Oh my gosh, you have to make fun of the teacher. >> I think it was a little more formal than that, in the language, but that was the concept. >> That's violating your civil rights as a student. I'm sorry. (Stephanie laughs) >> Stephanie, always so much fun to have you here. Thank you so much for coming on. >> Thank you. It's a pleasure to be here. >> I'm Rebecca Knight, for James Kobielus. We'll have more of theCUBE's live coverage of DataWorks just after this.

>> Announcer: Live from New York City it's The Cube, covering IBM data science for all. Brought to you by IBM. (light digital music) >> Welcome back to data science for all. It's a whole new game. And it is a whole new game. >> Dave Vellante, John Walls here. We've got quite a distinguished panel. So it is a new game-- >> Well we're in the game, I'm just happy to be-- (both laugh) Have a swing at the pitch. >> Well let's what we have here. Five distinguished members of our panel. It'll take me a minute to get through the introductions, but believe me they're worth it. Jennifer Shin joins us. Jennifer's the founder of 8 Path Solutions, the director of the data science of Comcast and part of the faculty at UC Berkeley and NYU. Jennifer, nice to have you with us, we appreciate the time. Joe McKendrick an analyst and contributor of Forbes and ZDNet, Joe, thank you for being here at well. Another ZDNetter next to him, Dion Hinchcliffe, who is a vice president and principal analyst of Constellation Research and also contributes to ZDNet. Good to see you, sir. To the back row, but that doesn't mean anything about the quality of the participation here. Bob Hayes with a killer Batman shirt on by the way, which we'll get to explain in just a little bit. He runs the Business over Broadway. And Joe Caserta, who the founder of Caserta Concepts. Welcome to all of you. Thanks for taking the time to be with us. Jennifer, let me just begin with you. Obviously as a practitioner you're very involved in the industry, you're on the academic side as well. We mentioned Berkeley, NYU, steep experience. So I want you to kind of take your foot in both worlds and tell me about data science. I mean where do we stand now from those two perspectives? How have we evolved to where we are? And how would you describe, I guess the state of data science? >> Yeah so I think that's a really interesting question. There's a lot of changes happening. In part because data science has now become much more established, both in the academic side as well as in industry. So now you see some of the bigger problems coming out. People have managed to have data pipelines set up. But now there are these questions about models and accuracy and data integration. So the really cool stuff from the data science standpoint. We get to get really into the details of the data. And I think on the academic side you now see undergraduate programs, not just graduate programs, but undergraduate programs being involved. UC Berkeley just did a big initiative that they're going to offer data science to undergrads. So that's a huge news for the university. So I think there's a lot of interest from the academic side to continue data science as a major, as a field. But I think in industry one of the difficulties you're now having is businesses are now asking that question of ROI, right? What do I actually get in return in the initial years? So I think there's a lot of work to be done and just a lot of opportunity. It's great because people now understand better with data sciences, but I think data sciences have to really think about that seriously and take it seriously and really think about how am I actually getting a return, or adding a value to the business? >> And there's lot to be said is there not, just in terms of increasing the workforce, the acumen, the training that's required now. It's a still relatively new discipline. So is there a shortage issue? Or is there just a great need? Is the opportunity there? I mean how would you look at that? >> Well I always think there's opportunity to be smart. If you can be smarter, you know it's always better. It gives you advantages in the workplace, it gets you an advantage in academia. The question is, can you actually do the work? The work's really hard, right? You have to learn all these different disciplines, you have to be able to technically understand data. Then you have to understand it conceptually. You have to be able to model with it, you have to be able to explain it. There's a lot of aspects that you're not going to pick up overnight. So I think part of it is endurance. Like are people going to feel motivated enough and dedicate enough time to it to get very good at that skill set. And also of course, you know in terms of industry, will there be enough interest in the long term that there will be a financial motivation. For people to keep staying in the field, right? So I think it's definitely a lot of opportunity. But that's always been there. Like I tell people I think of myself as a scientist and data science happens to be my day job. That's just the job title. But if you are a scientist and you work with data you'll always want to work with data. I think that's just an inherent need. It's kind of a compulsion, you just kind of can't help yourself, but dig a little bit deeper, ask the questions, you can't not think about it. So I think that will always exist. Whether or not it's an industry job in the way that we see it today, and like five years from now, or 10 years from now. I think that's something that's up for debate. >> So all of you have watched the evolution of data and how it effects organizations for a number of years now. If you go back to the days when data warehouse was king, we had a lot of promises about 360 degree views of the customer and how we were going to be more anticipatory in terms and more responsive. In many ways the decision support systems and the data warehousing world didn't live up to those promises. They solved other problems for sure. And so everybody was looking for big data to solve those problems. And they've begun to attack many of them. We talked earlier in The Cube today about fraud detection, it's gotten much, much better. Certainly retargeting of advertising has gotten better. But I wonder if you could comment, you know maybe start with Joe. As to the effect that data and data sciences had on organizations in terms of fulfilling that vision of a 360 degree view of customers and anticipating customer needs. >> So. Data warehousing, I wouldn't say failed. But I think it was unfinished in order to achieve what we need done today. At the time I think it did a pretty good job. I think it was the only place where we were able to collect data from all these different systems, have it in a single place for analytics. The big difference between what I think, between data warehousing and data science is data warehouses were primarily made for the consumer to human beings. To be able to have people look through some tool and be able to analyze data manually. That really doesn't work anymore, there's just too much data to do that. So that's why we need to build a science around it so that we can actually have machines actually doing the analytics for us. And I think that's the biggest stride in the evolution over the past couple of years, that now we're actually able to do that, right? It used to be very, you know you go back to when data warehouses started, you had to be a deep technologist in order to be able to collect the data, write the programs to clean the data. But now you're average causal IT person can do that. Right now I think we're back in data science where you have to be a fairly sophisticated programmer, analyst, scientist, statistician, engineer, in order to do what we need to do, in order to make machines actually understand the data. But I think part of the evolution, we're just in the forefront. We're going to see over the next, not even years, within the next year I think a lot of new innovation where the average person within business and definitely the average person within IT will be able to do as easily say, "What are my sales going to be next year?" As easy as it is to say, "What were my sales last year." Where now it's a big deal. Right now in order to do that you have to build some algorithms, you have to be a specialist on predictive analytics. And I think, you know as the tools mature, as people using data matures, and as the technology ecosystem for data matures, it's going to be easier and more accessible. >> So it's still too hard. (laughs) That's something-- >> Joe C.: Today it is yes. >> You've written about and talked about. >> Yeah no question about it. We see this citizen data scientist. You know we talked about the democratization of data science but the way we talk about analytics and warehousing and all the tools we had before, they generated a lot of insights and views on the information, but they didn't really give us the science part. And that's, I think that what's missing is the forming of the hypothesis, the closing of the loop of. We now have use of this data, but are are changing, are we thinking about it strategically? Are we learning from it and then feeding that back into the process. I think that's the big difference between data science and the analytics side. But, you know just like Google made search available to everyone, not just people who had highly specialized indexers or crawlers. Now we can have tools that make these capabilities available to anyone. You know going back to what Joe said I think the key thing is we now have tools that can look at all the data and ask all the questions. 'Cause we can't possibly do it all ourselves. Our organizations are increasingly awash in data. Which is the life blood of our organizations, but we're not using it, you know this a whole concept of dark data. And so I think the concept, or the promise of opening these tools up for everyone to be able to access those insights and activate them, I think that, you know, that's where it's headed. >> This is kind of where the T shirt comes in right? So Bob if you would, so you've got this Batman shirt on. We talked a little bit about it earlier, but it plays right into what Dion's talking about. About tools and, I don't want to spoil it, but you go ahead (laughs) and tell me about it. >> Right, so. Batman is a super hero, but he doesn't have any supernatural powers, right? He can't fly on his own, he can't become invisible on his own. But the thing is he has the utility belt and he has these tools he can use to help him solve problems. For example he as the bat ring when he's confronted with a building that he wants to get over, right? So he pulls it out and uses that. So as data professionals we have all these tools now that these vendors are making. We have IBM SPSS, we have data science experience. IMB Watson that these data pros can now use it as part of their utility belt and solve problems that they're confronted with. So if you''re ever confronted with like a Churn problem and you have somebody who has access to that data they can put that into IBM Watson, ask a question and it'll tell you what's the key driver of Churn. So it's not that you have to be a superhuman to be a data scientist, but these tools will help you solve certain problems and help your business go forward. >> Joe McKendrick, do you have a comment? >> Does that make the Batmobile the Watson? (everyone laughs) Analogy? >> I was just going to add that, you know all of the billionaires in the world today and none of them decided to become Batman yet. It's very disappointing. >> Yeah. (Joe laughs) >> Go ahead Joe. >> And I just want to add some thoughts to our discussion about what happened with data warehousing. I think it's important to point out as well that data warehousing, as it existed, was fairly successful but for larger companies. Data warehousing is a very expensive proposition it remains a expensive proposition. Something that's in the domain of the Fortune 500. But today's economy is based on a very entrepreneurial model. The Fortune 500s are out there of course it's ever shifting. But you have a lot of smaller companies a lot of people with start ups. You have people within divisions of larger companies that want to innovate and not be tied to the corporate balance sheet. They want to be able to go through, they want to innovate and experiment without having to go through finance and the finance department. So there's all these open source tools available. There's cloud resources as well as open source tools. Hadoop of course being a prime example where you can work with the data and experiment with the data and practice data science at a very low cost. >> Dion mentioned the C word, citizen data scientist last year at the panel. We had a conversation about that. And the data scientists on the panel generally were like, "Stop." Okay, we're not all of a sudden going to turn everybody into data scientists however, what we want to do is get people thinking about data, more focused on data, becoming a data driven organization. I mean as a data scientist I wonder if you could comment on that. >> Well I think so the other side of that is, you know there are also many people who maybe didn't, you know follow through with science, 'cause it's also expensive. A PhD takes a lot of time. And you know if you don't get funding it's a lot of money. And for very little security if you think about how hard it is to get a teaching job that's going to give you enough of a pay off to pay that back. Right, the time that you took off, the investment that you made. So I think the other side of that is by making data more accessible, you allow people who could have been great in science, have an opportunity to be great data scientists. And so I think for me the idea of citizen data scientist, that's where the opportunity is. I think in terms of democratizing data and making it available for everyone, I feel as though it's something similar to the way we didn't really know what KPIs were, maybe 20 years ago. People didn't use it as readily, didn't teach it in schools. I think maybe 10, 20 years from now, some of the things that we're building today from data science, hopefully more people will understand how to use these tools. They'll have a better understanding of working with data and what that means, and just data literacy right? Just being able to use these tools and be able to understand what data's saying and actually what it's not saying. Which is the thing that most people don't think about. But you can also say that data doesn't say anything. There's a lot of noise in it. There's too much noise to be able to say that there is a result. So I think that's the other side of it. So yeah I guess in terms for me, in terms of data a serious data scientist, I think it's a great idea to have that, right? But at the same time of course everyone kind of emphasized you don't want everyone out there going, "I can be a data scientist without education, "without statistics, without math," without understanding of how to implement the process. I've seen a lot of companies implement the same sort of process from 10, 20 years ago just on Hadoop instead of SQL. Right and it's very inefficient. And the only difference is that you can build more tables wrong than they could before. (everyone laughs) Which is I guess >> For less. it's an accomplishment and for less, it's cheaper, yeah. >> It is cheaper. >> Otherwise we're like I'm not a data scientist but I did stay at a Holiday Inn Express last night, right? >> Yeah. (panelists laugh) And there's like a little bit of pride that like they used 2,000, you know they used 2,000 computers to do it. Like a little bit of pride about that, but you know of course maybe not a great way to go. I think 20 years we couldn't do that, right? One computer was already an accomplishment to have that resource. So I think you have to think about the fact that if you're doing it wrong, you're going to just make that mistake bigger, which his also the other side of working with data. >> Sure, Bob. >> Yeah I have a comment about that. I've never liked the term citizen data scientist or citizen scientist. I get the point of it and I think employees within companies can help in the data analytics problem by maybe being a data collector or something. I mean I would never have just somebody become a scientist based on a few classes here she takes. It's like saying like, "Oh I'm going to be a citizen lawyer." And so you come to me with your legal problems, or a citizen surgeon. Like you need training to be good at something. You can't just be good at something just 'cause you want to be. >> John: Joe you wanted to say something too on that. >> Since we're in New York City I'd like to use the analogy of a real scientist versus a data scientist. So real scientist requires tools, right? And the tools are not new, like microscopes and a laboratory and a clean room. And these tools have evolved over years and years, and since we're in New York we could walk within a 10 block radius and buy any of those tools. It doesn't make us a scientist because we use those tools. I think with data, you know making, making the tools evolve and become easier to use, you know like Bob was saying, it doesn't make you a better data scientist, it just makes the data more accessible. You know we can go buy a microscope, we can go buy Hadoop, we can buy any kind of tool in a data ecosystem, but it doesn't really make you a scientist. I'm very involved in the NYU data science program and the Columbia data science program, like these kids are brilliant. You know these kids are not someone who is, you know just trying to run a day to day job, you know in corporate America. I think the people who are running the day to day job in corporate America are going to be the recipients of data science. Just like people who take drugs, right? As a result of a smart data scientist coming up with a formula that can help people, I think we're going to make it easier to distribute the data that can help people with all the new tools. But it doesn't really make it, you know the access to the data and tools available doesn't really make you a better data scientist. Without, like Bob was saying, without better training and education. >> So how-- I'm sorry, how do you then, if it's not for everybody, but yet I'm the user at the end of the day at my company and I've got these reams of data before me, how do you make it make better sense to me then? So that's where machine learning comes in or artificial intelligence and all this stuff. So how at the end of the day, Dion? How do you make it relevant and usable, actionable to somebody who might not be as practiced as you would like? >> I agree with Joe that many of us will be the recipients of data science. Just like you had to be a computer science at one point to develop programs for a computer, now we can get the programs. You don't need to be a computer scientist to get a lot of value out of our IT systems. The same thing's going to happen with data science. There's far more demand for data science than there ever could be produced by, you know having an ivory tower filled with data scientists. Which we need those guys, too, don't get me wrong. But we need to have, productize it and make it available in packages such that it can be consumed. The outputs and even some of the inputs can be provided by mere mortals, whether that's machine learning or artificial intelligence or bots that go off and run the hypotheses and select the algorithms maybe with some human help. We have to productize it. This is a constant of data scientist of service, which is becoming a thing now. It's, "I need this, I need this capability at scale. "I need it fast and I need it cheap." The commoditization of data science is going to happen. >> That goes back to what I was saying about, the recipient also of data science is also machines, right? Because I think the other thing that's happening now in the evolution of data is that, you know the data is, it's so tightly coupled. Back when you were talking about data warehousing you have all the business transactions then you take the data out of those systems, you put them in a warehouse for analysis, right? Maybe they'll make a decision to change that system at some point. Now the analytics platform and the business application is very tightly coupled. They become dependent upon one another. So you know people who are using the applications are now be able to take advantage of the insights of data analytics and data science, just through the app. Which never really existed before. >> I have one comment on that. You were talking about how do you get the end user more involved, well like we said earlier data science is not easy, right? As an end user, I encourage you to take a stats course, just a basic stats course, understanding what a mean is, variability, regression analysis, just basic stuff. So you as an end user can get more, or glean more insight from the reports that you're given, right? If you go to France and don't know French, then people can speak really slowly to you in French, you're not going to get it. You need to understand the language of data to get value from the technology we have available to us. >> Incidentally French is one of the languages that you have the option of learning if you're a mathematicians. So math PhDs are required to learn a second language. France being the country of algebra, that's one of the languages you could actually learn. Anyway tangent. But going back to the point. So statistics courses, definitely encourage it. I teach statistics. And one of the things that I'm finding as I go through the process of teaching it I'm actually bringing in my experience. And by bringing in my experience I'm actually kind of making the students think about the data differently. So the other thing people don't think about is the fact that like statisticians typically were expected to do, you know, just basic sort of tasks. In a sense that they're knowledge is specialized, right? But the day to day operations was they ran some data, you know they ran a test on some data, looked at the results, interpret the results based on what they were taught in school. They didn't develop that model a lot of times they just understand what the tests were saying, especially in the medical field. So when you when think about things like, we have words like population, census. Which is when you take data from every single, you have every single data point versus a sample, which is a subset. It's a very different story now that we're collecting faster than it used to be. It used to be the idea that you could collect information from everyone. Like it happens once every 10 years, we built that in. But nowadays you know, you know here about Facebook, for instance, I think they claimed earlier this year that their data was more accurate than the census data. So now there are these claims being made about which data source is more accurate. And I think the other side of this is now statisticians are expected to know data in a different way than they were before. So it's not just changing as a field in data science, but I think the sciences that are using data are also changing their fields as well. >> Dave: So is sampling dead? >> Well no, because-- >> Should it be? (laughs) >> Well if you're sampling wrong, yes. That's really the question. >> Okay. You know it's been said that the data doesn't lie, people do. Organizations are very political. Oftentimes you know, lies, damned lies and statistics, Benjamin Israeli. Are you seeing a change in the way in which organizations are using data in the context of the politics. So, some strong P&L manager say gets data and crafts it in a way that he or she can advance their agenda. Or they'll maybe attack a data set that is, probably should drive them in a different direction, but might be antithetical to their agenda. Are you seeing data, you know we talked about democratizing data, are you seeing that reduce the politics inside of organizations? >> So you know we've always used data to tell stories at the top level of an organization that's what it's all about. And I still see very much that no matter how much data science or, the access to the truth through looking at the numbers that story telling is still the political filter through which all that data still passes, right? But it's the advent of things like Block Chain, more and more corporate records and corporate information is going to end up in these open and shared repositories where there is not alternate truth. It'll come back to whoever tells the best stories at the end of the day. So I still see the organizations are very political. We are seeing now more open data though. Open data initiatives are a big thing, both in government and in the private sector. It is having an effect, but it's slow and steady. So that's what I see. >> Um, um, go ahead. >> I was just going to say as well. Ultimately I think data driven decision making is a great thing. And it's especially useful at the lower tiers of the organization where you have the routine day to day's decisions that could be automated through machine learning and deep learning. The algorithms can be improved on a constant basis. On the upper levels, you know that's why you pay executives the big bucks in the upper levels to make the strategic decisions. And data can help them, but ultimately, data, IT, technology alone will not create new markets, it will not drive new businesses, it's up to human beings to do that. The technology is the tool to help them make those decisions. But creating businesses, growing businesses, is very much a human activity. And that's something I don't see ever getting replaced. Technology might replace many other parts of the organization, but not that part. >> I tend to be a foolish optimist when it comes to this stuff. >> You do. (laughs) >> I do believe that data will make the world better. I do believe that data doesn't lie people lie. You know I think as we start, I'm already seeing trends in industries, all different industries where, you know conventional wisdom is starting to get trumped by analytics. You know I think it's still up to the human being today to ignore the facts and go with what they think in their gut and sometimes they win, sometimes they lose. But generally if they lose the data will tell them that they should have gone the other way. I think as we start relying more on data and trusting data through artificial intelligence, as we start making our lives a little bit easier, as we start using smart cars for safety, before replacement of humans. AS we start, you know, using data really and analytics and data science really as the bumpers, instead of the vehicle, eventually we're going to start to trust it as the vehicle itself. And then it's going to make lying a little bit harder. >> Okay, so great, excellent. Optimism, I love it. (John laughs) So I'm going to play devil's advocate here a little bit. There's a couple elephant in the room topics that I want to, to explore a little bit. >> Here it comes. >> There was an article today in Wired. And it was called, Why AI is Still Waiting for It's Ethics Transplant. And, I will just read a little segment from there. It says, new ethical frameworks for AI need to move beyond individual responsibility to hold powerful industrial, government and military interests accountable as they design and employ AI. When tech giants build AI products, too often user consent, privacy and transparency are overlooked in favor of frictionless functionality that supports profit driven business models based on aggregate data profiles. This is from Kate Crawford and Meredith Whittaker who founded AI Now. And they're calling for sort of, almost clinical trials on AI, if I could use that analogy. Before you go to market you've got to test the human impact, the social impact. Thoughts. >> And also have the ability for a human to intervene at some point in the process. This goes way back. Is everybody familiar with the name Stanislav Petrov? He's the Soviet officer who back in 1983, it was in the control room, I guess somewhere outside of Moscow in the control room, which detected a nuclear missile attack against the Soviet Union coming out of the United States. Ordinarily I think if this was an entirely AI driven process we wouldn't be sitting here right now talking about it. But this gentlemen looked at what was going on on the screen and, I'm sure he's accountable to his authorities in the Soviet Union. He probably got in a lot of trouble for this, but he decided to ignore the signals, ignore the data coming out of, from the Soviet satellites. And as it turned out, of course he was right. The Soviet satellites were seeing glints of the sun and they were interpreting those glints as missile launches. And I think that's a great example why, you know every situation of course doesn't mean the end of the world, (laughs) it was in this case. But it's a great example why there needs to be a human component, a human ability for human intervention at some point in the process. >> So other thoughts. I mean organizations are driving AI hard for profit. Best minds of our generation are trying to figure out how to get people to click on ads. Jeff Hammerbacher is famous for saying it. >> You can use data for a lot of things, data analytics, you can solve, you can cure cancer. You can make customers click on more ads. It depends on what you're goal is. But, there are ethical considerations we need to think about. When we have data that will have a racial bias against blacks and have them have higher prison sentences or so forth or worse credit scores, so forth. That has an impact on a broad group of people. And as a society we need to address that. And as scientists we need to consider how are we going to fix that problem? Cathy O'Neil in her book, Weapons of Math Destruction, excellent book, I highly recommend that your listeners read that book. And she talks about these issues about if AI, if algorithms have a widespread impact, if they adversely impact protected group. And I forget the last criteria, but like we need to really think about these things as a people, as a country. >> So always think the idea of ethics is interesting. So I had this conversation come up a lot of times when I talk to data scientists. I think as a concept, right as an idea, yes you want things to be ethical. The question I always pose to them is, "Well in the business setting "how are you actually going to do this?" 'Cause I find the most difficult thing working as a data scientist, is to be able to make the day to day decision of when someone says, "I don't like that number," how do you actually get around that. If that's the right data to be showing someone or if that's accurate. And say the business decides, "Well we don't like that number." Many people feel pressured to then change the data, change, or change what the data shows. So I think being able to educate people to be able to find ways to say what the data is saying, but not going past some line where it's a lie, where it's unethical. 'Cause you can also say what data doesn't say. You don't always have to say what the data does say. You can leave it as, "Here's what we do know, "but here's what we don't know." There's a don't know part that many people will omit when they talk about data. So I think, you know especially when it comes to things like AI it's tricky, right? Because I always tell people I don't know everyone thinks AI's going to be so amazing. I started an industry by fixing problems with computers that people didn't realize computers had. For instance when you have a system, a lot of bugs, we all have bug reports that we've probably submitted. I mean really it's no where near the point where it's going to start dominating our lives and taking over all the jobs. Because frankly it's not that advanced. It's still run by people, still fixed by people, still managed by people. I think with ethics, you know a lot of it has to do with the regulations, what the laws say. That's really going to be what's involved in terms of what people are willing to do. A lot of businesses, they want to make money. If there's no rules that says they can't do certain things to make money, then there's no restriction. I think the other thing to think about is we as consumers, like everyday in our lives, we shouldn't separate the idea of data as a business. We think of it as a business person, from our day to day consumer lives. Meaning, yes I work with data. Incidentally I also always opt out of my credit card, you know when they send you that information, they make you actually mail them, like old school mail, snail mail like a document that says, okay I don't want to be part of this data collection process. Which I always do. It's a little bit more work, but I go through that step of doing that. Now if more people did that, perhaps companies would feel more incentivized to pay you for your data, or give you more control of your data. Or at least you know, if a company's going to collect information, I'd want you to be certain processes in place to ensure that it doesn't just get sold, right? For instance if a start up gets acquired what happens with that data they have on you? You agree to give it to start up. But I mean what are the rules on that? So I think we have to really think about the ethics from not just, you know, someone who's going to implement something but as consumers what control we have for our own data. 'Cause that's going to directly impact what businesses can do with our data. >> You know you mentioned data collection. So slightly on that subject. All these great new capabilities we have coming. We talked about what's going to happen with media in the future and what 5G technology's going to do to mobile and these great bandwidth opportunities. The internet of things and the internet of everywhere. And all these great inputs, right? Do we have an arms race like are we keeping up with the capabilities to make sense of all the new data that's going to be coming in? And how do those things square up in this? Because the potential is fantastic, right? But are we keeping up with the ability to make it make sense and to put it to use, Joe? >> So I think data ingestion and data integration is probably one of the biggest challenges. I think, especially as the world is starting to become more dependent on data. I think you know, just because we're dependent on numbers we've come up with GAAP, which is generally accepted accounting principles that can be audited and proven whether it's true or false. I think in our lifetime we will see something similar to that we will we have formal checks and balances of data that we use that can be audited. Getting back to you know what Dave was saying earlier about, I personally would trust a machine that was programmed to do the right thing, than to trust a politician or some leader that may have their own agenda. And I think the other thing about machines is that they are auditable. You know you can look at the code and see exactly what it's doing and how it's doing it. Human beings not so much. So I think getting to the truth, even if the truth isn't the answer that we want, I think is a positive thing. It's something that we can't do today that once we start relying on machines to do we'll be able to get there. >> Yeah I was just going to add that we live in exponential times. And the challenge is that the way that we're structured traditionally as organizations is not allowing us to absorb advances exponentially, it's linear at best. Everyone talks about change management and how are we going to do digital transformation. Evidence shows that technology's forcing the leaders and the laggards apart. There's a few leading organizations that are eating the world and they seem to be somehow rolling out new things. I don't know how Amazon rolls out all this stuff. There's all this artificial intelligence and the IOT devices, Alexa, natural language processing and that's just a fraction, it's just a tip of what they're releasing. So it just shows that there are some organizations that have path found the way. Most of the Fortune 500 from the year 2000 are gone already, right? The disruption is happening. And so we are trying, have to find someway to adopt these new capabilities and deploy them effectively or the writing is on the wall. I spent a lot of time exploring this topic, how are we going to get there and all of us have a lot of hard work is the short answer. >> I read that there's going to be more data, or it was predicted, more data created in this year than in the past, I think it was five, 5,000 years. >> Forever. (laughs) >> And that to mix the statistics that we're analyzing currently less than 1% of the data. To taking those numbers and hear what you're all saying it's like, we're not keeping up, it seems like we're, it's not even linear. I mean that gap is just going to grow and grow and grow. How do we close that? >> There's a guy out there named Chris Dancy, he's known as the human cyborg. He has 700 hundred sensors all over his body. And his theory is that data's not new, having access to the data is new. You know we've always had a blood pressure, we've always had a sugar level. But we were never able to actually capture it in real time before. So now that we can capture and harness it, now we can be smarter about it. So I think that being able to use this information is really incredible like, this is something that over our lifetime we've never had and now we can do it. Which hence the big explosion in data. But I think how we use it and have it governed I think is the challenge right now. It's kind of cowboys and indians out there right now. And without proper governance and without rigorous regulation I think we are going to have some bumps in the road along the way. >> The data's in the oil is the question how are we actually going to operationalize around it? >> Or find it. Go ahead. >> I will say the other side of it is, so if you think about information, we always have the same amount of information right? What we choose to record however, is a different story. Now if you want wanted to know things about the Olympics, but you decide to collect information every day for years instead of just the Olympic year, yes you have a lot of data, but did you need all of that data? For that question about the Olympics, you don't need to collect data during years there are no Olympics, right? Unless of course you're comparing it relative. But I think that's another thing to think about. Just 'cause you collect more data does not mean that data will produce more statistically significant results, it does not mean it'll improve your model. You can be collecting data about your shoe size trying to get information about your hair. I mean it really does depend on what you're trying to measure, what your goals are, and what the data's going to be used for. If you don't factor the real world context into it, then yeah you can collect data, you know an infinite amount of data, but you'll never process it. Because you have no question to ask you're not looking to model anything. There is no universal truth about everything, that just doesn't exist out there. >> I think she's spot on. It comes down to what kind of questions are you trying to ask of your data? You can have one given database that has 100 variables in it, right? And you can ask it five different questions, all valid questions and that data may have those variables that'll tell you what's the best predictor of Churn, what's the best predictor of cancer treatment outcome. And if you can ask the right question of the data you have then that'll give you some insight. Just data for data's sake, that's just hype. We have a lot of data but it may not lead to anything if we don't ask it the right questions. >> Joe. >> I agree but I just want to add one thing. This is where the science in data science comes in. Scientists often will look at data that's already been in existence for years, weather forecasts, weather data, climate change data for example that go back to data charts and so forth going back centuries if that data is available. And they reformat, they reconfigure it, they get new uses out of it. And the potential I see with the data we're collecting is it may not be of use to us today, because we haven't thought of ways to use it, but maybe 10, 20, even 100 years from now someone's going to think of a way to leverage the data, to look at it in new ways and to come up with new ideas. That's just my thought on the science aspect. >> Knowing what you know about data science, why did Facebook miss Russia and the fake news trend? They came out and admitted it. You know, we miss it, why? Could they have, is it because they were focused elsewhere? Could they have solved that problem? (crosstalk) >> It's what you said which is are you asking the right questions and if you're not looking for that problem in exactly the way that it occurred you might not be able to find it. >> I thought the ads were paid in rubles. Shouldn't that be your first clue (panelists laugh) that something's amiss? >> You know red flag, so to speak. >> Yes. >> I mean Bitcoin maybe it could have hidden it. >> Bob: Right, exactly. >> I would think too that what happened last year is actually was the end of an age of optimism. I'll bring up the Soviet Union again, (chuckles). It collapsed back in 1991, 1990, 1991, Russia was reborn in. And think there was a general feeling of optimism in the '90s through the 2000s that Russia is now being well integrated into the world economy as other nations all over the globe, all continents are being integrated into the global economy thanks to technology. And technology is lifting entire continents out of poverty and ensuring more connectedness for people. Across Africa, India, Asia, we're seeing those economies that very different countries than 20 years ago and that extended into Russia as well. Russia is part of the global economy. We're able to communicate as a global, a global network. I think as a result we kind of overlook the dark side that occurred. >> John: Joe? >> Again, the foolish optimist here. But I think that... It shouldn't be the question like how did we miss it? It's do we have the ability now to catch it? And I think without data science without machine learning, without being able to train machines to look for patterns that involve corruption or result in corruption, I think we'd be out of luck. But now we have those tools. And now hopefully, optimistically, by the next election we'll be able to detect these things before they become public. >> It's a loaded question because my premise was Facebook had the ability and the tools and the knowledge and the data science expertise if in fact they wanted to solve that problem, but they were focused on other problems, which is how do I get people to click on ads? >> Right they had the ability to train the machines, but they were giving the machines the wrong training. >> Looking under the wrong rock. >> (laughs) That's right. >> It is easy to play armchair quarterback. Another topic I wanted to ask the panel about is, IBM Watson. You guys spend time in the Valley, I spend time in the Valley. People in the Valley poo-poo Watson. Ah, Google, Facebook, Amazon they've got the best AI. Watson, and some of that's fair criticism. Watson's a heavy lift, very services oriented, you just got to apply it in a very focused. At the same time Google's trying to get you to click on Ads, as is Facebook, Amazon's trying to get you to buy stuff. IBM's trying to solve cancer. Your thoughts on that sort of juxtaposition of the different AI suppliers and there may be others. Oh, nobody wants to touch this one, come on. I told you elephant in the room questions. >> Well I mean you're looking at two different, very different types of organizations. One which is really spent decades in applying technology to business and these other companies are ones that are primarily into the consumer, right? When we talk about things like IBM Watson you're looking at a very different type of solution. You used to be able to buy IT and once you installed it you pretty much could get it to work and store your records or you know, do whatever it is you needed it to do. But these types of tools, like Watson actually tries to learn your business. And it needs to spend time doing that watching the data and having its models tuned. And so you don't get the results right away. And I think that's been kind of the challenge that organizations like IBM has had. Like this is a different type of technology solution, one that has to actually learn first before it can provide value. And so I think you know you have organizations like IBM that are much better at applying technology to business, and then they have the further hurdle of having to try to apply these tools that work in very different ways. There's education too on the side of the buyer. >> I'd have to say that you know I think there's plenty of businesses out there also trying to solve very significant, meaningful problems. You know with Microsoft AI and Google AI and IBM Watson, I think it's not really the tool that matters, like we were saying earlier. A fool with a tool is still a fool. And regardless of who the manufacturer of that tool is. And I think you know having, a thoughtful, intelligent, trained, educated data scientist using any of these tools can be equally effective. >> So do you not see core AI competence and I left out Microsoft, as a strategic advantage for these companies? Is it going to be so ubiquitous and available that virtually anybody can apply it? Or is all the investment in R&D and AI going to pay off for these guys? >> Yeah, so I think there's different levels of AI, right? So there's AI where you can actually improve the model. I remember when I was invited when Watson was kind of first out by IBM to a private, sort of presentation. And my question was, "Okay, so when do I get "to access the corpus?" The corpus being sort of the foundation of NLP, which is natural language processing. So it's what you use as almost like a dictionary. Like how you're actually going to measure things, or things up. And they said, "Oh you can't." "What do you mean I can't?" It's like, "We do that." "So you're telling me as a data scientist "you're expecting me to rely on the fact "that you did it better than me and I should rely on that." I think over the years after that IBM started opening it up and offering different ways of being able to access the corpus and work with that data. But I remember at the first Watson hackathon there was only two corpus available. It was either the travel or medicine. There was no other foundational data available. So I think one of the difficulties was, you know IBM being a little bit more on the forefront of it they kind of had that burden of having to develop these systems and learning kind of the hard way that if you don't have the right models and you don't have the right data and you don't have the right access, that's going to be a huge limiter. I think with things like medical, medical information that's an extremely difficult data to start with. Partly because you know anything that you do find or don't find, the impact is significant. If I'm looking at things like what people clicked on the impact of using that data wrong, it's minimal. You might lose some money. If you do that with healthcare data, if you do that with medical data, people may die, like this is a much more difficult data set to start with. So I think from a scientific standpoint it's great to have any information about a new technology, new process. That's the nice that is that IBM's obviously invested in it and collected information. I think the difficulty there though is just 'cause you have it you can't solve everything. And if feel like from someone who works in technology, I think in general when you appeal to developers you try not to market. And with Watson it's very heavily marketed, which tends to turn off people who are more from the technical side. Because I think they don't like it when it's gimmicky in part because they do the opposite of that. They're always trying to build up the technical components of it. They don't like it when you're trying to convince them that you're selling them something when you could just give them the specs and look at it. So it could be something as simple as communication. But I do think it is valuable to have had a company who leads on the forefront of that and try to do so we can actually learn from what IBM has learned from this process. >> But you're an optimist. (John laughs) All right, good. >> Just one more thought. >> Joe go ahead first. >> Joe: I want to see how Alexa or Siri do on Jeopardy. (panelists laugh) >> All right. Going to go around a final thought, give you a second. Let's just think about like your 12 month crystal ball. In terms of either challenges that need to be met in the near term or opportunities you think will be realized. 12, 18 month horizon. Bob you've got the microphone headed up, so I'll let you lead off and let's just go around. >> I think a big challenge for business, for society is getting people educated on data and analytics. There's a study that was just released I think last month by Service Now, I think, or some vendor, or Click. They found that only 17% of the employees in Europe have the ability to use data in their job. Think about that. >> 17. >> 17. Less than 20%. So these people don't have the ability to understand or use data intelligently to improve their work performance. That says a lot about the state we're in today. And that's Europe. It's probably a lot worse in the United States. So that's a big challenge I think. To educate the masses. >> John: Joe. >> I think we probably have a better chance of improving technology over training people. I think using data needs to be iPhone easy. And I think, you know which means that a lot of innovation is in the years to come. I do think that a keyboard is going to be a thing of the past for the average user. We are going to start using voice a lot more. I think augmented reality is going to be things that becomes a real reality. Where we can hold our phone in front of an object and it will have an overlay of prices where it's available, if it's a person. I think that we will see within an organization holding a camera up to someone and being able to see what is their salary, what sales did they do last year, some key performance indicators. I hope that we are beyond the days of everyone around the world walking around like this and we start actually becoming more social as human beings through augmented reality. I think, it has to happen. I think we're going through kind of foolish times at the moment in order to get to the greater good. And I think the greater good is using technology in a very, very smart way. Which means that you shouldn't have to be, sorry to contradict, but maybe it's good to counterpoint. I don't think you need to have a PhD in SQL to use data. Like I think that's 1990. I think as we evolve it's going to become easier for the average person. Which means people like the brain trust here needs to get smarter and start innovating. I think the innovation around data is really at the tip of the iceberg, we're going to see a lot more of it in the years to come. >> Dion why don't you go ahead, then we'll come down the line here. >> Yeah so I think over that time frame two things are likely to happen. One is somebody's going to crack the consumerization of machine learning and AI, such that it really is available to the masses and we can do much more advanced things than we could. We see the industries tend to reach an inflection point and then there's an explosion. No one's quite cracked the code on how to really bring this to everyone, but somebody will. And that could happen in that time frame. And then the other thing that I think that almost has to happen is that the forces for openness, open data, data sharing, open data initiatives things like Block Chain are going to run headlong into data protection, data privacy, customer privacy laws and regulations that have to come down and protect us. Because the industry's not doing it, the government is stepping in and it's going to re-silo a lot of our data. It's going to make it recede and make it less accessible, making data science harder for a lot of the most meaningful types of activities. Patient data for example is already all locked down. We could do so much more with it, but health start ups are really constrained about what they can do. 'Cause they can't access the data. We can't even access our own health care records, right? So I think that's the challenge is we have to have that battle next to be able to go and take the next step. >> Well I see, with the growth of data a lot of it's coming through IOT, internet of things. I think that's a big source. And we're going to see a lot of innovation. A new types of Ubers or Air BnBs. Uber's so 2013 though, right? We're going to see new companies with new ideas, new innovations, they're going to be looking at the ways this data can be leveraged all this big data. Or data coming in from the IOT can be leveraged. You know there's some examples out there. There's a company for example that is outfitting tools, putting sensors in the tools. Industrial sites can therefore track where the tools are at any given time. This is an expensive, time consuming process, constantly loosing tools, trying to locate tools. Assessing whether the tool's being applied to the production line or the right tool is at the right torque and so forth. With the sensors implanted in these tools, it's now possible to be more efficient. And there's going to be innovations like that. Maybe small start up type things or smaller innovations. We're going to see a lot of new ideas and new types of approaches to handling all this data. There's going to be new business ideas. The next Uber, we may be hearing about it a year from now whatever that may be. And that Uber is going to be applying data, probably IOT type data in some, new innovative way. >> Jennifer, final word. >> Yeah so I think with data, you know it's interesting, right, for one thing I think on of the things that's made data more available and just people we open to the idea, has been start ups. But what's interesting about this is a lot of start ups have been acquired. And a lot of people at start ups that got acquired now these people work at bigger corporations. Which was the way it was maybe 10 years ago, data wasn't available and open, companies kept it very proprietary, you had to sign NDAs. It was like within the last 10 years that open source all of that initiatives became much more popular, much more open, a acceptable sort of way to look at data. I think that what I'm kind of interested in seeing is what people do within the corporate environment. Right, 'cause they have resources. They have funding that start ups don't have. And they have backing, right? Presumably if you're acquired you went in at a higher title in the corporate structure whereas if you had started there you probably wouldn't be at that title at that point. So I think you have an opportunity where people who have done innovative things and have proven that they can build really cool stuff, can now be in that corporate environment. I think part of it's going to be whether or not they can really adjust to sort of the corporate, you know the corporate landscape, the politics of it or the bureaucracy. I think every organization has that. Being able to navigate that is a difficult thing in part 'cause it's a human skill set, it's a people skill, it's a soft skill. It's not the same thing as just being able to code something and sell it. So you know it's going to really come down to people. I think if people can figure out for instance, what people want to buy, what people think, in general that's where the money comes from. You know you make money 'cause someone gave you money. So if you can find a way to look at a data or even look at technology and understand what people are doing, aren't doing, what they're happy about, unhappy about, there's always opportunity in collecting the data in that way and being able to leverage that. So you build cooler things, and offer things that haven't been thought of yet. So it's a very interesting time I think with the corporate resources available if you can do that. You know who knows what we'll have in like a year. >> I'll add one. >> Please. >> The majority of companies in the S&P 500 have a market cap that's greater than their revenue. The reason is 'cause they have IP related to data that's of value. But most of those companies, most companies, the vast majority of companies don't have any way to measure the value of that data. There's no GAAP accounting standard. So they don't understand the value contribution of their data in terms of how it helps them monetize. Not the data itself necessarily, but how it contributes to the monetization of the company. And I think that's a big gap. If you don't understand the value of the data that means you don't understand how to refine it, if data is the new oil and how to protect it and so forth and secure it. So that to me is a big gap that needs to get closed before we can actually say we live in a data driven world. >> So you're saying I've got an asset, I don't know if it's worth this or this. And they're missing that great opportunity. >> So devolve to what I know best. >> Great discussion. Really, really enjoyed the, the time as flown by. Joe if you get that augmented reality thing to work on the salary, point it toward that guy not this guy, okay? (everyone laughs) It's much more impressive if you point it over there. But Joe thank you, Dion, Joe and Jennifer and Batman. We appreciate and Bob Hayes, thanks for being with us. >> Thanks you guys. >> Really enjoyed >> Great stuff. >> the conversation. >> And a reminder coming up a the top of the hour, six o'clock Eastern time, IBMgo.com featuring the live keynote which is being set up just about 50 feet from us right now. Nick Silver is one of the headliners there, John Thomas is well, or rather Rob Thomas. John Thomas we had on earlier on The Cube. But a panel discussion as well coming up at six o'clock on IBMgo.com, six to 7:15. Be sure to join that live stream. That's it from The Cube. We certainly appreciate the time. Glad to have you along here in New York. And until the next time, take care. (bright digital music)