>>Hello >>Everyone and welcome back to Dallas where we're live from, Super computing. My name is Savannah Peterson, joined with my co-host David, and we have a rocket of a show for you this afternoon. The doctors are in the house and we are joined by nasa, ladies and gentlemen. So excited. Please welcome Dr. Dan Duffy and Dr. Bill Putman. Thank you so much for being here, guys. I know this is kind of last minute. How's it to be on the show floor? What's it like being NASA here? >>What's exciting? We haven't, we haven't been here for three years, so this is actually really exciting to come back and see everybody, to see the showroom floor, see the innovations that have happened over the last three years. It's pretty exciting. >>Yeah, it's great. And, and so, because your jobs are so cool, and I don't wanna even remotely give even too little of the picture or, or not do it justice, could you give the audience a little bit of background on what you do as I think you have one of the coolest jobs ever. YouTube bill. >>I, I appreciate that. I, I, I run high Performance Computing Center at NASA Goddard for science. It's high performance information technology. So we do everything from networking to security, to high performance computing, to data sciences, artificial intelligence and machine learning is huge for us now. Yeah, large amounts of data, big data sets, but we also do scientific visualizations and then cloud and commercial cloud computing, as well as on premises cloud computing. And quite frankly, we support a lot of what Bill and his team does. >>Bill, why don't you tell us what your team >>Does? Yeah, so I'm a, I'm an earth scientist. I work as the associate chief at the global modeling assimilation office. And our job is to really, you know, maximize the use of all the observations that NASA takes from space and build that into a coherent, consistent physical system of the earth. Right? And we're focused on utilizing the HC that, that Dan and the folks at the nccs provide to us, to the best of our abilities to integrate those observations, you know, on time scales from hours, days to, to seasonal to to monthly time scales. That's, that's the essence of our focus at the GMA o >>Casual modeling, all of NASA's earth data. That, that in itself as a sentence is pretty wild. I imagine you're dealing with a ton of data. >>Oh, massive amounts of data. Yes, >>Probably, I mean, as much as one probably could, now that I'm thinking about it. I mean, and especially with how far things have to travel. Bill, sticking with you, just to open us up, what technology here excites you the most about the future and that will make your job easier? Let's put it that way. >>To me, it's the accelerator technologies, right? So there's the limited, the limiting factor for, for us as scientists is how fast we can get an answer. And if we can get our answer faster through accelerated technologies, you know, with the support of the, of the nccs and the computing centers, but also the software engineers enabling that for us, then we can do more, right. And push the questions even further, you know, so once we've gotten fast enough to do what we want to do, there's always something next that we wanna look for. So, >>I mean, at nasa you have to exercise such patience, whether that be data, coming back, images from a rover, doesn't matter what it is. Sometimes there's a lot of time, days, hours, years, depending on the situation. Right? I really, I really admire that. What about you, Dan? What's got you really excited about the future here? So >>Bill talked about the, the accelerated technology, which is absolutely true and, and, and is needed to get us not to only to the point where we have the compute resources to do the simulations that Bill wants to do, and also do it in a energy efficient way. But it's really the software frameworks that go around that and the software frameworks, the technology that dealing with how to use those in an energy efficient and and most efficient way is extremely important. And that's some of the, you know, that's what I'm really here to try to understand better about is how can I support these scientists with not just the hardware, but the software frameworks by which they can be successful. >>Yeah. We've, we've had a lot of kind of philosophical discussion about this, the difference between the quantitative increases in power in computing that we're seeing versus the question of whether or not we need truly qualitative changes moving forward. Where do you see the limits of, of, of, you know, if you, if you're looking at the ability to gather more data and process more data more quickly, what you can do with that data changes when you're getting updates every second versus every month seems pretty obvious. Is there a, is there, but is there, is there a near term target that you have specifically where once you reach that target, if you weren't thinking ahead of that target, you'd kind of be going, Okay, well we solved that problem, we're getting the data in so fast that you can, you can ask me, what is the temperature in this area? And you can go, Oh, well, huh, an hour ago the data said this. Beyond that, do you need a qualitative change in our ability to process information and tease insight into out of chaos? Or do you just need more quantity to be able to get to the point where you can do things like predict weather six months in advance? What are, what are your thoughts on that? Yeah, >>It's an interesting question, right? And, and you ended it with predicting whether six months in advance, and actually I was thinking the other way, right? I was thinking going to finer and finer scales and shorter time scales when you talk about having data more frequently, right? So one of the things that I'm excited about as a modeler is going to hire resolution and representing smaller scale processes at nasa, we're, we're interested in observations that are global. So our models are global and we'd like to push those to as fine a resolution as possible to do things like severe storm predictions and so forth. So the faster we can get the data, the more data we can have, and that area would improve our ability to do that as well. So, >>And your background is in meteorology, right? >>Yes, I'm a meteorologist. >>Excellent. Okay. Yeah, yeah, >>Yeah. So, so I have to ask a question, and I'm sure all the audience cares about this. And I went through this when I was talking about the ghost satellites as well. What, what is it about weather that makes it so hard to predict? >>Oh, it's the classic chaos problem. The, the butterfly effects problem, and it's just true. You know, you always hear the story of a butterfly in Africa flaps, its rings and wings, and the weather changes in, in New York City, and it's just, computers are an excellent example of that, right? So we have a model of the earth, we can run it two times in a row and get the exact same answer, but if we flip a bit somewhere, then the answer changes 10 days later significantly. So it's a, it's a really interesting problem. So, >>Yeah. So do you have any issue with the fact that your colleague believes that butterflies are responsible for weather? No, I does that, does that, is it responsible for climate? Does that bother you at all? >>No, it doesn't. As a matter of fact, they actually run those butterfly like experi experiments within the systems where they do actually flip some bits and see what the uncertainties are that happen out 7, 8, 9 days out in advance to understand exactly what he's saying, to understand the uncertainties, but also the sensitivity with respect to the observations that they're taking. So >>Yeah, it's fascinating. It is. >>That is fascinating. Sticking with you for a second, Dan. So you're at the Center for Climate Simulation. Is that the center that's gonna help us navigate what happens over the next decade? >>Okay, so I, no one center is gonna help us navigate what's gonna happen over the next decade or the next 50 or a hundred years, right. It's gonna be everybody together. And I think NASA's role in that is really to pioneer the, the, the models that that bill and others are doing to understand what's gonna happen in not just the seasonal sub, but we also work with G, which is the God Institute for Space Studies. Yeah. Which does the decatal and, and the century long studies. Our, our job is to really help that research, understand what's happening with the client, but then feed that back into what observations we need to make next in order to better understand and better quantify the risks that we have to better quantify the mitigations that we can make to understand how and, and, and affect how the climate is gonna go for the future. So that's really what we trying to do. We're trying to do that research to understand the climate, understand what mitigations we can have, but also feedback into what observations we can make for the future. >>Yeah. And and what's the partnership ecosystem around that? You mentioned that it's gonna take all of us, I assume you work with a lot of >>Partners, Probably both of you. I mean, obviously the, the, the federal agencies work huge amounts together. Nasa, Noah is our huge partnerships. Sgs, a huge partnerships doe we've talked to doe several times this, so this, this this week already. So there's huge partnerships that go across the federal agency. We, we work also with Europeans as much as we can given the, the, the, you know, sort of the barriers of the countries and the financials. But we do collaborate as much as we can with, And the nice thing about NASA, I would say is the, all the observations that we take are public, they're paid for by the public. They're public, everybody can down them, anybody can down around the world. So that's also, and they're global measurements as Bill said, they're not just regional. >>Do you have, do you have specific, when you think about improving your ability to gain insights from data that that's being gathered? Yeah. Do you set out specific milestones that you're looking for? Like, you know, I hope by June of next year we will have achieved a place where we are able to accomplish X. Yeah. Do you, do you, Yeah. Bill, do you put, what, >>What milestones do we have here? So, yeah, I mean, do you have >>Yeah. Are, are you, are you sort of kept track of that way? Do you think of things like that? Like very specific things? Or is it just so fluid that as long as you're making progress towards the future, you feel okay? >>No, I would say we absolutely have milestones that we like to keep in track, especially from the modeling side of things, right? So whether it's observations that exist now that we want to use in our system, milestones to getting those observations integrated in, but also thinking even further ahead to the observations that we don't have yet. So we can use the models that we have today to simulate those kind of observations that we might want in the future that can help us do things that we can do right now. So those missions are, are aided by the work that we do at the GBO and, and the nccs, but, >>Okay, so if we, if we extrapolate really to the, to the what if future is really trying to understand the entire earth system as best as we can. So all the observations coming in, like you said, in in near real time, feeding that into an earth system model and to be able to predict short term, midterm or even long term predictions with, with some degree of certainty. And that may be things like climate change or it may be even more important, shorter term effects of, of severe weather. Yeah. Which is very important. And so we are trying to work towards that high resolution, immediate impact model that we can, that we can, you know, really share with the world and share those results as best, as best we can. >>Yeah. I, I have a quick, I have a quick follow up on that. I I bet we both did. >>So, so if you think about AI and ml, artificial intelligence and machine learning, something that, you know, people, people talk about a lot. Yeah. There's the concept of teaching a machine to go look for things, call it machine learning. A lot of it's machine teaching we're saying, you know, hit, you know, hit the rack on this side with a stick or the other side with the stick to get it to, to kind of go back and forth. Do you think that humans will be able to guide these systems moving forward enough to tease out the insights that we want? Or do you think we're gonna have to rely on what people think of as artificial intelligence to be able to go in with this massive amount of information with an almost infinite amount of variables and have the AI figure out that, you know what, it was the butterfly, It really was the butterfly. We all did models with it, but, but you understand the nuance that I'm saying. It's like we, we, we think we know what all the variables are and that it's chaotic because there's so many variables and there's so much data, but maybe there's something we're not taking into >>A account. Yeah, I I, I'm, I'm, I'm sure that's absolutely the case. And I'll, I'll start and let Bill, Bill jump in here. Yeah, there's a lot of nuances with a aiml. And so the, the, the, the real approach to get to where we want to be with this earth system model approach is a combination of both AI ML train models as best as we can and as unbiased way as we can. And there's a, there's a big conversation we have around that, but also with a physics or physical based model as well, Those two combined with the humans or the experts in the loop, we're not just gonna ask the artificial intelligence to predict anything and everything. The experts need to be in the loop to guide the training in as best as we, as, as we can in an unbiased, equitable way, but also interpret the results and not just give over to the ai. But that's the combination of that earth system model that we really wanna see. The future's a combination of AI l with physics based, >>But there's, there's a, there's an obvious place for a AI and ML in the modeling world that is in the parameterizations of the estimations that we have to do in our systems, right? So when we think about the earth system and modeling the earth system, there are many things like the equations of motions and thermodynamics that have fixed equations that we know how to solve on a computer. But there's a lot of things that happen physically in the atmosphere that we don't have equations for, and we have to estimate them. And machine learning through the use of high resolution models or observations in training the models to understand and, and represent that, yeah, that that's the place where it's really useful >>For us. There's so many factors, but >>We have to, but we have to make sure that we have the physics in that machine learning in those, in those training. So physics informed training isn't very important. So we're not just gonna go and let a model go off and do whatever it wants. It has to be constrained within physical constraints that the, that the experts know. >>Yeah. And with the wild amount of variables that affect our, our earth, quite frankly. Yeah, yeah. Which is geez. Which is insane. My god. So what's, what, what technology or what advancement needs to happen for your jobs to get easier, faster for our ability to predict to be even more successful than it is currently? >>You know, I think for me, the vision that I have for the future is that at some point, you know, all data is centrally located, essentially shared. We have our applications are then services that sit around all that data. I don't have to sit as a user and worry about, oh, is this all this data in place before I run my application? It's already there, it's already ready for me. My service is prepared and I just launch it out on that service. But that coupled with the performance that I need to get the result that I want in time. And I don't know when that's gonna happen, but at some point it might, you know, I don't know rooting for you, but that's, >>So there are, there are a lot of technologies we can talk about. What I'd like to mention is, is open science. So NASA is really trying to make a push and transformation towards open science. 2023 is gonna be the year of open science for nasa. And what does that mean? It means a lot of what Bill just said is that we have equity and fairness and accessibility and you can find the data, it's findability, it's fair data, you know, a fair findability accessibility reproducibility, and I forget what the eye stands for, but these are, these are tools and, and, and things that we need to, as, as a computing centers and including all the HC centers here, as well as the scientists need to support, to be as transparent as possible with the data sets and the, and the research that we're doing. And that's where I think is gonna be the best thing is if we can get this data out there that anybody can use in an equitable way and as transparent as possible, that's gonna eliminate, in my opinion, the bias over time because mistakes will be found and mistakes will be corrected over time. >>I love that. Yeah. The open source science end of this. No, it's great. And the more people that have access people I find in the academic world, especially people don't know what's going on in the private sector and vice versa. And so I love that you just brought that up. Closing question for you, because I suspect there might be some members of our audience who maybe have fantasized about working at nasa. You've both been working there for over a decade. Is it as cool as we all think of it? It is on the outside. >>I mean, it's, it's definitely pretty cool. >>You don't have to be modest about it, you know, >>I mean, just being at Goddard and being at the center where they build the James web web telescope and you can go to that clean room and see it, it's just fascinating. So it, it's really an amazing opportunity. >>Yeah. So NASA Goddard as a, as a center has, you know, information technologist, It has engineers, it has scientists, it has support staff, support team members. We have built more things, more instruments that have flown in this space than any other place in the world. The James Lab, we were part of that, part of a huge group of people that worked on James. We and James, we came through and was assembled in our, our, our clean room. It's one of the biggest clean rooms in, in, in the world. And we all took opportunities to go over and take selfies with this as they put those loveness mirrors on them. Yeah, it was awesome. It was amazing. And to see what the James we has done in such a short amount of time, the successes that they've gone through is just incredible. Now, I'm not a, I'm not a part of the James web team, but to be a, to be at the same center, to to listen to scientists like Bill talk about their work, to listen to scientists that, that talk about James, we, that's what's inspiring. And, and we get that all the time. >>And to have the opportunity to work with the astronauts that service the, the Hubble Telescope, you know, these things are, >>That's literally giving me goosebumps right now. I'm sitting over >>Here just, just an amazing opportunity. And woo. >>Well, Dan, Bill, thank you both so much for being on the show. I know it was a bit last minute, but I can guarantee we all got a lot out of it. David and I both, I know I speak for us in the whole cube audience, so thank you. We'll have you, anytime you wanna come talk science on the cube. Thank you all for tuning into our supercomputing footage here, live in Dallas. My name is Savannah Peterson. I feel cooler having sat next to these two gentlemen for the last 15 minutes and I hope you did too. We'll see you again soon.

>>The Cube presents UI Path Forward five. Brought to you by UI Path. >>Hi everybody. We're back at UI path forward to five. This is Dave Ante with Dave Nicholson. Derek Mu is here. He's automation product line lead for Merck. Thank you, by the way, for, you know, all you guys do, and thank you Dave for having in the, in the, in the vaccine area, saving our butts. And Bill Engel is back on the cube. He's the director at cgi. Guys, good to see you again. >>Good to see you. Thank >>You. So Merrick, Wow, it's been quite a few years for you guys. Take us through Derek, what's happening in sort of your world that's informing your automation strategy? >>Well, Dave, I mean as you know, we just came out of the pandemic. We actually have quite a few products like Gabriel Antiviral Pill. Obviously we worked, you know, continue to drive our products through a difficult time. But, you know, is during these can last few years that, you know, we've accelerated our journey in automation. We're about four years plus in our journey, you know, so just like the theme of this conference we're we're trying to move towards, you know, bigger automations, transformational change, continue to drive digital transformation in our company. >>Now Bill, you've been on before, but CGI tell people about the firm. It's not computer graphics imaging. >>Sure. No, it's, it's definitely not. So cgi, we're a global consultancy about 90,000 folks across the world. We're a, we're both a product company and a services company. So we have a lot of different, you know, software products that we deliver to our clients, such as CGI Advantage, which is a state local government EER P platform. And so outside of that, we, my team does automation and so we wrap automation around R IP and deliver that to our clients. >>So you guys are automation pros, implementation partners, right? So, so let's go back. Yep. Derek said four years I think. Yep. Right, You're in. So take us through what was the catalyst, how did you get started? Obviously it was pre pandemic, so it's interesting, a lot of companies pre pandemic gave lip service to digital transformation. Sounds like you guys already started your journey, but I'll come back to that. But take us back to the Catalyst four years ago. Why automation? We'll get into why UI path, >>Right. So I, I would say it started pretty niche in our company. Started first in our finance area. Of course, you know, we were looking in technology evaluating different companies, Blue Prism, ui P. Ultimately we chose UI p did it on-prem to start to use automation in sort of our invoice processing, sort of our financial processes, right? And then from there, after it was really when the pandemic hit, that's when sort of we all went to remote work. That's when the team, the COE continued to scale up, especially during pandemic. We were trying to automate more and more processes given the fact that more and more of our workers are remote, they reprocesses. How, how do you do events? You know, part of our livelihood is, is meeting with engaging with customers. Customers in this case is, are doctors and physicians, right? How do you engage with them digitally? How do you, you know, you know, a lot of the face to face contact now have to kind of shift to more digital, digital way. And so automation was a way to kind of help accelerate that, help facilitate that. >>You, you, I think you mentioned COE as in center of excellence. Yep. So, so describe your approach to implementing automation. It's, that sounds like when you say center, it sounds like something is centralized as, as opposed to a bunch of what we've been hearing a lot about citizen developers. What does that interaction >>Look like? We do have both. I would say in the beginning was more decentralized, but over time we, over the few years as, as we built more and more bots, we're now at maybe somewhere between four to 500 bots. We now have sort of internal to the company functional verticals, right? So there's an animal health, we have an animal health function. So there's, there's a team building engaging with the animal health business to build animal health box. There's human health, which is what I work on as well as hr, finance, manufacturing, research. And so internally there's engagement leads, one of the engagement leads that interact with the business. Then when there's an engineering squads that help build and design, develop and support and maintain those as well as sort of a DevOps team that supports the platform and maintains all the bot infrastructure. >>So you started in finance common story, right? I'm sure you hear this a lot Belt, How did you decide what to target? Was it, was it process driven decision? Was it, was it data oriented? Like some kind of combination? How did you decide, Do you remember? Or do you, could you take >>Us back to Oh yeah. So for, for cgi how we started to engage with MER is, you know, we, we do a lot of other business with Merck. We work on all their different business lines and we, we understand the business process. So we, we knew where there was potential for automation. So we brought those ideas to Merck and, and really kind of landed there and helped them realize the value from automation from that standpoint. And then from there the journey just continued to expand, you know, looking for those use cases that, that, you know, fit the mold for, for, for RPA to start. And now the evolution is to go to broader hyper automation. >>And, and was it CFO led into the finance department and then, or was it sort of more bottoms >>Up? Yeah, so, so I think it started in, in finance and, and, but we actually really started out in the business line. So out in regulatory clinical, that's, that's where we, we have the life science expertise that are embedded. And so I partnered with them to come up with, hey, here's a real solution we could do to help streamline, say submission archiving. So when, when submissions come back from the fda, they need to be archived into, you know, the, their system of record. So that's, those are the types of use cases that, that we helped automate. >>Okay. Cause you're saying a human had to sort physically archive that and you were able to sort of replicate that. Okay. And you started with software robots, obviously rpa and now you're expanding into, we we're hearing from UI this the platform message. How does that coincide Derek, with what you guys are doing? Are you sort of adding platform? What aspects of the platform are, are you adding? >>Yeah, no, I mean we are, we are on-premise, right? So we have the platform, but some of the cool things we just had, another colleague of mine presented earlier today. Some of the cool things we're, we're doing ephemeral infrastructure. So infrastructure as code, which essentially means instead of having all these dedicated bot machines, that that, you know, cuz these bots only in some cases run 10 minutes and they're done. So we're, we're soon of doing all on demand, you know, start up a server, run the bot when it's finished, you know, kill the server. So we only pay for the servers that we use, which allows us to save a whole >>Lot of money. Serverless bots. So you, but you're doing that OnPrem, so you >>No, >>No, but >>That's >>Cloud. We, >>We, we we're doing it OnPrem, but our, our bot machines that actually run the, let's say SAP process, right? We spin that machine up, it's on the cloud, it runs it finish, Let's say it's processed in one hour and then when it's done, we kill that machine. So we only play for that one hour usage of that bot machine. >>Okay. So you mentioned SAP earlier you mentioned Blue Prism when you probably looked at other competitors too. You pull the Gartner Magic quadrant, blah, blah, you know, with the way people, you know, evaluate technology, but SAP's got a product. Why UI path mean? Is it that a company like SAP two narrow for their only sap you wanted to apply it other ways? Maybe they weren't even in the business that back then four years ago they probably weren't. Right? But I'm curious as to how the decision was made for UiPath. >>Well, I think you hit it right on the nail. You know, SAP sort of came on a little later and they're specific to sort of their function, right? So UiPath for us is the most flexible tool can interact by UI to our sales and marketing systems, to, to workday, to service Now. It's, it cuts across every function that we have in the company as well as you're the most mature. I mean, you're the market leader, right? So Right. Definitely you, you continue to build upon those capabilities and we are exploring the new capabilities, especially being announced today. >>And what do you see Bill in the marketplace? Are you, are you kind of automation tool agnostic? Are you more sort of all in on? I >>Would say we are, we are agnostic as a company, but obviously as part of a, as an automation practice lead, you know, I want to deliver solutions to my clients that are gonna benefit them as a whole. So looking at UI path, you know, that this platform is, it covers the end to end spectrum of, of automation. So I can go really into any use case and be able to provide a solution that, that delivers value. And so that's, that's where I see the value in UI path and that's why CGI is, is a customer as well. We automate our internal processes. We actually have, we just launched probably SALT in the, in the market last week, expanded partnership with UiPath. We launched CGI, Excel 360. That's our fully managed service around automation. We host our clients whole UI path infrastructure and bots. It's completely hands off to them and they just get the value outta >>Automation. Nice, nice. Love >>It. Derek, you mentioned, you mentioned this ephemeral infrastructure. Yeah. Sounds like it's also ethereal possibility possibly you're saying, you, you're saying you have processes that are running on premises, right? But then you reach out to have an automation process run that's happening off pre and you're, and you're sort of, >>It's on the cloud, so, so yeah, so we have a in-house orchestrator, so we don't, we're not using your sort of on the cloud orchestrator. So, so we brought it in-house for security reasons. Okay. But we use, you know, so inside the vpn, you know, we have these cloud machines that run these automations. So, so that's, that's the ephemeral side of the, of the >>Infrastructure. But is there a financial angle to that in terms of when you're spinning these things up, are you, is it a, is it a pay by the drink or by the, by the CPU >>Hours, if you can imagine like we, you know, like I mentioned where somewhere between four to 500 bots and every bot has a time slot to run and takes a certain amount of time. And so that's hundreds and hundreds of bot machines that we in the old days have to have to buy and procure and, you know, staff and support and maintain. So in this new model, and we're just beginning to kind of move from pilot into implementation, we're moving all, all of bots this in ephemeral infrastructure, right? So these, okay, these machines, these bot machines are, you know, spun up. They run the, they, they run their automation and then they spin >>Down. But just to be clear, they're being spun up on physical infrastructure that is in your >>Purview and they spun up on aws. Yeah. Okay. And then they spin down. Okay, got >>It. Got it. Interesting. Four >>To 500 bots. You know, Daniel one point play out this vision of a bot chicken in every pot, I called it a bot for every employee. Is that where you're headed or is that kind of in this new ephemeral world, not necessary, it's like maybe every employee has access to an ephemeral bot. How, how are you thinking about that? >>That's a good question. So obviously the, the four to 500 is a mix of unattended bonds versus attended bonds, right? That, that we also have a citizen developer, sort of a group team. We support that as well from a coe. So, you know, we see the future as a mix. There's, there's a spectrum of, we are the professional development team. There's also, we support and nurture the personal automation and we provide the resources to help them build smaller scale automations that help, you know, reduce the, you know, the mundaneness and the hours of their own tasks. But you know, for us, we want to focus more and more on building bigger and bigger transfer transformational automations that really drive process efficiencies and, and savings. >>And what's the, what's the business impact been? You mentioned savings and maybe there's other sort of productivity. How do you measure the benefit, the ROI and, and >>Quantify that we, you know, I, I don't, I don't profess I don't think we have all the right answers, but yeah, simple metrics like number of hours saved or other sort of excitement sort of in like an nps, internal NPS between the different groups that we engage. But we definitely see automation demand coming from our, our functional teams going up, driving up. So it's, it's continued to be a hot area and hopefully we, we can, you know, like, like what the key message and theme of this, of this conference. Essentially we want to take and build upon the, the good work that we've done in terms of rpa and we want to drive it more towards digital transformation. >>So Bill, what are you seeing across the, your customer base in terms of, of, of roi? I'm not looking for percentages there. I'm sure they're off the charts, but in terms of, you know, you can optimize for fast payback, you know, maybe lower the denominator, you know, or you can optimize for, you know, net benefit over time, right? You know, what are you seeing? What are customers after they want fast payback and little quick hits? Or are they looking for sort of a bigger enterprise wide impact? >>Yeah, I think it's, it's the latter. It's that larger impact, right? Obviously they, you know, they want an roi and just depending upon the use case, that's gonna vary in terms of the, the benefits delivered. And a lot of our clients, depending on the industry, so in in life sciences it may be around, you know, compliance like GXP compliance is huge. And so that may may not be much of a time saver, but it ensures that they're, they're running their processes and they're being compliant with, you know, federal standards. So that's, that's one aspect to it. But you know, to, you know, a bank, they're looking to reduce their overall costs and and so on. But yeah, I think, I think the other, the other part of it is, you know, impacting broader business processes. So taking that top down approach versus kind of bottom up, you know, doing ta you know, the ones you choose the tasks is not as impactful as looking at broader across the entire business process and seeing how we can impact >>It. Now, Derek, when you guys support a citizen developer, how does that work? So, hey, I got this task I want to automate, I'm gonna go write a, you know, software robot. I'm gonna go do an automation. Do I just do it and then throw her to the defense? You guys, you guys send me a video on how to do it. Hold my hand. How's that work? >>Yeah, I mean, good question. So, so we obviously direct them to the UI path Academy, get some training. We also have some internal training materials to how to build a bot sort of internal inside Merck. We, we go through, we have writeups and SOPs on using the right framework for automations, using the right documentation, PDD kind of materials, and then ultimately how do we deploy bot inside the MER ecosystem. But I, I, maybe I'll just add, I think you asked the point about ROI before. Yeah. I'll also say because we're, we're a pharmaceutical company. I think one of the other key metrics is actually time saved, right? So if, if, if we have a bot that helps us get through the clinical process or even the getting a, a label approved faster, even if it's eight days saved, that's eight days of a product that can get out to the market faster to, to our patients and, and healthcare professionals. And that's, that, that's immeasurable benefit. >>Yeah, I bet if you compress that ELAP time of, of getting approval and so forth. All right guys, we've gotta go. Thanks so much. Congratulations on all the success and appreciate you sharing your story. Thank >>You so much. Appreciate it. You're welcome. >>Appreciate it. All right. Thank you for watching this Dave Ante for Dave Nicholson, The cubes coverage, two day coverage. We're here in day one, UI path forward, five. We'll be right back right after the short break. Awesome. >>Great.

(ethereal music) >> Good morning, everyone, and welcome to theCUBE's day-two coverage of Snowflake Summit '22. Lisa Martin here with Dave Vellante. We are live in Las Vegas at Caesar's Forum, looking forward to an action-packed day here on theCUBE. Our first guest joins us, Bill Stratton, the global industry lead, media, entertainment and advertising at Snowflake. Bill, great to have you on the program talking about industry specifics. >> Glad to be here, excited to have a conversation. >> Yeah, the media and entertainment industry has been keeping a lot of us alive the last couple of years, probably more of a dependence on it than we've seen stuck at home. Talk to us about the data culture in the media, entertainment and advertising landscape, how is data being used today? >> Sure. Well, let's start with what you just mentioned, these last couple of years, I think, coming out of the pandemic, a lot of trends and impact to the media industry. I think there were some things happening prior to COVID, right? Streaming services were starting to accelerate. And obviously, Netflix was an early mover. Disney launched their streaming service right before the pandemic, Disney+, with ESPN+ as well. I think then, as the pandemic occurred these last two years, the acceleration of consumers' habits, obviously, of not just unbundling their cable subscription, but then choosing, you know, what services they want to subscribe to, right? I mean, I think we all sort of grew up in this era of, okay, the bundle was the bundle, you had sports, you had news, you had entertainment, whether you watched the channel or not, you had the bundle. And what the pandemic has accelerated is what I call, and I think a lot of folks call, the golden age of content. And really, the golden age of content is about the consumer. They're in control now, they pick and choose what services they want, what they watch, when they watch it. And I think that has extremely, sort of accelerated this adoption on the consumer side, and then it's creating this data ecosystem, as a result of companies like Disney having a direct-to-consumer relationship for the first time. It used to be a Disney or an NBC was a wholesaler, and the cable or satellite company had the consumer data and relationship. Now, the companies that are producing the content have the data and the consumer relationships. It's a fascinating time. >> And they're still coming over the top on the Telco networks, right? >> Absolutely right. >> Telco's playing in this game? >> Yeah, Telco is, I think what the interesting dynamic with Telco is, how do you bundle access, high speed, everybody still needs high speed at their home, with content? And so I think it's a similar bundle, but it takes on a different characteristic, because the cable and Telcos are not taking the content risk. AT&T sold Warner Media recently, and I think they looked at it and said, we're going to stay with the infrastructure, let somebody else do the content. >> And I think I heard, did I hear this right the other day, that Roku is now getting into the content business? >> Roku is getting into it. And they were early mover, right? They said the TVs aren't, the operating system in the television is not changing fast enough for content. So their dongle that you would slide into a TV was a great way to get content on connected televisions, which is the fastest growing platform. >> I was going to say, what are the economics like in this business? Because the bundles were sort of a limiting factor, in terms of the TAM. >> Yeah. >> And now, we get great content, all right, to watch "Better Call Saul", I have to get AMC+ or whatever. >> You know, your comment, your question about the economics and the TAM is an interesting one, because I think we're still working through it. One of the things, I think, that's coming to the forefront is that you have to have a subscription revenue stream. Okay? Netflix had a subscription revenue stream for the last six, eight, 10 years, significantly, but I think you even see with Netflix that they have to go to a second revenue model, which is going to be an ad-supported model, right? We see it in the press these last couple days with Reid Hastings. So I think you're going to see, obviously subscription, obviously ad-supported, but the biggest thing, back to the consumer, is that the consumer's not going to sit through two minutes of advertising to watch a 22 minute show. >> Dave: No way. >> Right? So what's then going to happen is that the content companies want to know what's relevant to you, in terms of advertising. So if I have relevancy in my ad experience, then it doesn't quite feel, it's not intrusive, and it's relevant to my experience. >> And the other vector in the TAM, just one last follow-up, is you see Amazon, with Prime, going consumption. >> Bill: That's right. >> You get it with Prime, it's sort of there, and the movies aren't the best in the world, but you can buy pretty much any movie you want on a consumption basis. >> Yeah. Just to your last quick point, there is, we saw last week, the Boston Red Sox are bundling tickets, season tickets, with a subscription to their streaming service. >> NESN+, I think it is, yeah. So just like Prime, NESN+- >> And it's like 30 bucks a month. >> -just like Prime bundling with your delivery service, you're going to start to see all kinds of bundles happen. >> Dave: Interesting. >> Man, the sky is the limit, it's like it just keeps going and proliferating. >> Bill: It does. >> You talk about, on the ad side for a second, you mentioned the relevance, and we expect that as consumers, we're so demanding, (clears throat) excuse me, we don't have the patience, one of the things I think that was in short supply during COVID, and probably still is, is patience. >> That's right. >> I think with all of us, but we expect that brands know us enough to surf up the content that they think we watched, we watched "Breaking Bad", "Better Call Saul", don't show me other things that aren't relevant to the patterns I've been showing you, the content creators have to adapt quickly to the rising and changing demands of the consumer. >> That's right. Some people even think, as you go forward and consumers have this expectation, like you just mentioned, that brands not only need to understand their own view of the consumer, and this is going to come into the Snowflake points that we talk about in a minute, but the larger view that a brand has about a consumer, not just their own view, but how they consume content, where they consume it, what other brands they even like, that all builds that picture of making it relevant for the consumer and viewer. >> Where does privacy come into the mix? So we want it to be relevant and personalized in a non-creepy way. Talk to us about the data clean rooms that Snowflake launched, >> Bill: That's right. >> and how is that facilitating from a PII perspective, or is it? >> Yeah. Great question. So I think the other major development, in addition to the pandemic, driving people watching all these shows is the fact that privacy legislation is increasing. So we started with California with the CCPA, we had GDPR in Europe, and what we're starting to see is state by state roll out different privacy legislations. At some point, it may be true that we have a federal privacy legislation, and there are some bills that are working through the legislature right now. Hard to tell what's going to happen. But to your question, the importance of privacy, and respecting privacy, is exactly happening at the same time that media companies and publishers need to piece together all the viewing habits that you have. You've probably watched, already this morning, on your PC, on your phone, and in order to bring that experience together a media company has to be able to tie that together, right? Collaborate. So you have collaboration on one side, and then you have privacy on the other, and they're not necessarily, normally, go together, Right? They're opposing forces. So now though, with Snowflake, and our data clean room, we like to call it a data collaboration platform, okay? It's not really what a data warehouse function traditionally has been, right? So if I can take data collaboration, and our clean room, what it does is it brings privacy controls to the participants. So if I'm an advertiser, and I'm a publisher, and I want to collaborate to create an advertising campaign, they both can design how they want to do that privacy-based collaboration, Because it's interesting, one company might have a different perspective of privacy, on a risk profile, than another company. So it's very hard to say one size is going to fit all. So what we at Snowflake do, with our infrastructure, is let you design how you create your own clean room. >> Is that a differentiator for Snowflake, the clean rooms? >> It's absolutely a very big differentiator. Two reasons, or probably two, three reasons, really. One is, it's cross cloud. So all the advertisers aren't going to be in the same cloud, all the publishers aren't going to be in the same cloud. One big differentiator there. Second big differentiator is, we want to be able to bring applications to the data, so our clean room can enable you to create measurement against an ad campaign without moving your data. So bringing measurement to the data, versus sending data to applications then improves the privacy. And then the third one is, frankly, our pricing model. You only pay for Snowflake what you use. So in the advertising world, there's what's called an ad tech tax, there is no ad tech tax for Snowflake, because we're simply a pay-as-you-go service. So it's a very interesting dynamic. >> So what's that stack look like, in your world? So I've pulled up Frank's chart, I took a picture of his, he's called it the new, modern data stack, I think he called it, but it had infrastructure in the bottom, okay, that's AWS, Google, Azure, and then a lot of you, live data, that would be the media data cloud, the workload execution, the specific workload here is media and entertainment, and then application development, that's a new layer of value that you're bringing in, marketplace, which is the whole ecosystem, and then monetization comes from building on top. >> Bill: Yes. >> So I got AWS in there, and other clouds, you got a big chunk of that, where do your customers add value on top of that? >> Yeah. So the way you described it, I think, with Frank's point, is right on. You have the infrastructure. We know that a lot of advertisers, for example, aren't going to use Amazon, because the retailer competes with Amazon, So they want to might be in Google or Azure. And then sort of as you go up the stack, for the data layer that is Snowflake, especially what we call first-party data, is sitting in that Snowflake environment, right? But that Snowflake environment is a distributed environment, so a Disney, who was on stage with me yesterday, she talked about, Jaya talked about their first-party datas in Snowflake, their advertisers' datas in their own Snowflake account, in their own infrastructure. And then what's interesting is is that application layer is coming to the data, and so what we're really seeing is an acceleration of companies building that application natively on Snowflake to do measurement, to do targeting, to do activation. And so, that growth of that final application layer is what we're seeing as the acceleration in the stack. >> So the more data that's in that massive distributed data cloud, the more value your customers can get out of it. And I would imagine you're just looking to tick things off that where customers are going outside of the Snowflake data cloud, let's attack that so they don't have to. >> Yeah, I think these partners, (clears throat) excuse me, and customers, it's an interesting dynamic, because they're customers of ours. But now, because anybody who is already in Snowflake can be their customer, then they're becoming our partner. So it's an interesting dynamic, because we're bringing advertisers to a Disney or an NBCU, because they already have their data in Snowflake. So the network effect that's getting created because of this layer that's being built is accelerated. >> In 2013, right after the second reinvent, I wrote a piece called "How to Compete with the Amazon Gorilla." And it seemed to us pretty obvious at the time, you're not going to win an infrastructure again, you got to build on top of it, you got to build ecosystems within industries, and the data, the connection points, that network effect that you just talked about, it's actually quite thrilling to see you guys building that. >> Well, and I think you know this too, I mean, Amazon's a great partner of ours as well, right? So they're part of our media data cloud, as Amazon, right? So we're making it easier and easier for companies to be able to spin up a clean room in places like AWS, so that they get the privacy controls and the governance that's required as well. >> What do you advise to, say, the next generation of media and advertising companies who may be really early in the data journey? Obviously, there's competition right here in the rear view mirror, but we've seen services that launch and fail, what do you advise to those folks that maybe are early in the journey and how can Snowflake help them accelerate that to be able to launch services they can monetize, and get those consumers watching? >> I think the first thing for a lot of these brands is that they need to really own their data. And what I mean by that is, they need to understand the consumer relationship that they have, they need to take the privacy and the governance very seriously, and they need to start building that muscle. It's almost, it's a routine and a muscle that they just need to continue to kind of build up, because if you think about it, a media company spends two, three hours a day with their customer. You might watch two hours of a streaming show, but how much time do you spend with a single brand a day? Maybe 30 seconds, maybe 10 seconds, right? And so, their need to build the muscle, to be able to collect the data in a privacy-compliant way, build the intelligence off of that, and then leverage the intelligence. We talked about it a few days ago, and you look at a retailer, as a really good example, a retailer is using Snowflake and the retail data cloud to optimize their supply chain. Okay? But their supply chain extends beyond their own infrastructure to the advertising and marketing community, because if I can't predict demand, how do I then connect it to my supply chain? So our media data cloud is helping retailers and consumer product goods companies actually drive demand into their reconstructed supply chain. So they both work together. >> So you have a big focus, obviously, on the monetization piece, of course, that's a great place to start. Where do you see the media data cloud going? >> Yeah. I think we'll start to expand beyond advertising and beyond marketing. There's really important sub-segments of media. Gaming is one. You talk about the pandemic and teenagers playing games on their phones. So we'll have an emphasis around gaming. We'll have an emphasis in sports. Sports is going through a big change in an ecosystem. And there's a big opportunity to connect the dots in those ecosystems as well. And then I think, to what we were just talking about, I think connecting commerce and media is a very important area. And I think the two are still very loosely connected today. It used to be, could I buy the Jennifer Aniston sweater from "Friends", right? That was always the analogy. Now, media and social media, and TikTok and everything else, are combining media and commerce very closely. So I think we'll start to see more focus around that as well. So that adds to your monetization. >> Right, right. And you can NFT that. (Lisa laughs) >> Bill: That's right, there you go, you can mint an NFT on that. >> It's the tip of the iceberg. >> Absolutely. >> There's so much more potential to go. Bill, thank you so much for joining us bright and early this morning, talking about what snowflake is doing in media, entertainment and advertising. Exciting stuff, relevant to all of us, we appreciate your insights and your forward-looking statements. >> Thank you for having me. I enjoyed it. >> Our pleasure. >> Thank you. >> Good >> Bill: Bye now. >> For our guest and Dave Vellante, I'm Lisa Martin, you're up early with us watching theCUBE's day-two coverage of Snowflake Summit '22. We'll be back in a moment with our next guest. (upbeat music)

(upbeat music) >> We're back at VeeamON 2022. We're here at the Aria in Las Vegas Dave Vellante with Dave Nicholson. Bill Andrews is here. He's the president and CEO of ExaGrid, mass boy. Bill, thanks for coming on theCUBE. >> Thanks for having me. >> So I hear a lot about obviously data protection, cyber resiliency, what's the big picture trends that you're seeing when you talk to customers? >> Well, I think clearly we were talking just a few minutes ago, data's growing like crazy, right This morning, I think they said it was 28% growth a year, right? So data's doubling almost just a little less than every three years. And then you get the attacks on the data which was the keynote speech this morning as well, right. All about the ransomware attacks. So we've got more and more data, and that data is more and more under attack. So I think those are the two big themes. >> So ExaGrid as a company been around for a long time. You've kind of been the steady kind of Eddy, if you will. Tell us about ExaGrid, maybe share with us some of the differentiators that you share with customers. >> Sure, so specifically, let's say in the Veeam world you're backing up your data, and you really only have two choices. You can back that up to disc. So some primary storage disc from a Dell, or a Hewlett Packard, or an NetApp or somebody, or you're going to back it up to what's called an inline deduplication appliance maybe a Dell Data Domain or an HPE StoreOnce, right? So what ExaGrid does is we've taken the best of both those but not the challenges of both those and put 'em together. So with disc, you're going to get fast backups and fast restores, but because in backup you keep weekly's, monthly's, yearly retention, the cost of this becomes exorbitant. If you go to a deduplication appliance, and let's say the Dell or the HPs, the data comes in, has to be deduplicated, compare one backup to the next to reduce that storage, which lowers the cost. So fixes that problem, but the fact that they do it inline slows the backups down dramatically. All the data is deduplicated so the restores are slow, and then the backup window keeps growing as the data grows 'cause they're all scale up technologies. >> And the restores are slow 'cause you got to rehydrate. >> You got to rehydrate every time. So what we did is we said, you got to have both. So our appliances have a front end disc cache landing zone. So you're right directed to the disc., Nothing else happens to it, whatever speed the backup app could write at that's the speed we take it in at. And then we keep the most recent backups in that landing zone ready to go. So you want to boot a VM, it's not an hour like a deduplication appliance it's a minute or two. Secondly, we then deduplicate the data into a second tier which is a repository tier, but we have all the deduplicated data for the long term retention, which gets the cost down. And on top of that, we're scale out. Every appliance has networking processor memory end disc. So if you double, triple, quadruple the data you double, triple, quadruple everything. And if the backup window is six hours at 100 terabyte it's six hours at 200 terabyte, 500 terabyte, a petabyte it doesn't matter. >> 'Cause you scale out. >> Right, and then lastly, our repository tier is non-network facing. We're the only ones in the industry with this. So that under a ransomware attack, if you get hold of a rogue server or you hack the media server, get to the backup storage whether it's disc or deduplication appliance, you can wipe out all the backup data. So you have nothing to recover from. In our case, you wipe it out, our landing zone will be wiped out. We're no different than anything else that's network facing. However, the only thing that talks to our repository tier is our object code. And we've set up security policies as to how long before you want us to delete data, let's say 10 days. So if you have an attack on Monday that data doesn't get deleted till like a week from Thursday, let's say. So you can freeze the system at any time and do restores. And then we have immutable data objects and all the other stuff. But the culmination of a non-network facing tier and the fact that we do the delayed deletes makes us the only one in the industry that can actually truly recover. And that's accelerating our growth, of course. >> Wow, great description. So that disc cache layer is a memory, it's a flash? >> It's disc, it's spinning disc. >> Spinning disc, okay. >> Yeah, no different than any other disc. >> And then the tiered is what, less expensive spinning disc? >> No, it's still the same. It's all SaaS disc 'cause you want the quality, right? So it's all SaaS, and so we use Western Digital or Seagate drives just like everybody else. The difference is that we're not doing any deduplication coming in or out of that landing zone to have fast backups and fast restores. So think of it like this, you've got disc and you say, boy it's too expensive. What I really want to do then is put maybe a deduplication appliance behind it to lower the cost or reverse it. I've got a deduplication appliance, ugh, it's too slow for backups and restores. I really want to throw this in front of it to have fast backups first. Basically, that's what we did. >> So where does the cost savings, Bill come in though, on the tier? >> The cost savings comes in the fact that we got deduplication in that repository. So only the most recent backup >> Ah okay, so I get it. >> are the duplicated data. But let's say you had 40 copies of retention. You know, 10 weekly's, 36 monthly's, a few yearly. All of that's deduplicated >> Okay, so you're deduping the stuff that's not as current. >> Right. >> Okay. >> And only a handful of us deduplicate at the layer we do. In other words, deduplication could be anywhere from two to one, up to 50 to one. I mean it's all over the place depending on the algorithm. Now it's what everybody's algorithms do. Some backup apps do two to one, some do five to one, we do 20 to one as well as much as 50 to one depending on the data types. >> Yeah, so the workload is going to largely determine the combination >> The content type, right. with the algos, right? >> Yeah, the content type. >> So the part of the environment that's behind the illogical air gap, if you will, is deduped data. >> Yes. >> So in this case, is it fair to say that you're trading a positive economic value for a little bit longer restore from that environment? >> No, because if you think about backup 95% of the customers restores are from the most recent data. >> From the disc cache. >> 95% of the time 'cause you think about why do you need fast restores? Somebody deleted a file, somebody overwrote a file. They can't go work, they can't open a file. It's encrypted, it's corrupted. That's what IT people are trying to keep users productive. When do you go for longer-term retention data? It's an SEC audit. It's a HIPAA audit. It's a legal discovery, you don't need that data right away. You have days and weeks to get that ready for that legal discovery or that audit. So we found that boundary where you keep users productive by keeping the most recent data in the disc cache landing zone, but anything that's long term. And by the way, everyone else is long term, at that point. >> Yeah, so the economics are comparable to the dedupe upfront. Are they better, obviously get the performance advance? >> So we would be a lot looped. The thing we replaced the most believe it or not is disc, we're a lot less expensive than the disc. I was meeting with some Veeam folks this morning and we were up against Cisco 3260 disc at a children's hospital. And on our quote was $500,000. The disc was 1.4 million. Just to give you an example of the savings. On a Data Domain we're typically about half the price of a Data Domain. >> Really now? >> The reason why is their front end control are so expensive. They need the fastest trip on the planet 'cause they're trying to do inline deduplication. >> Yeah, so they're chasing >> They need the fastest memory >> on the planet. >> this chips all the time. They need SSD on data to move in and out of the hash table. In order to keep up with inline, they've got to throw so much compute at it that it drives their cost up. >> But now in the case of ransomware attack, are you saying that the landing zone is still available for recovery in some circumstances? Or are you expecting that that disc landing zone would be encrypted by the attacker? >> Those are two different things. One is deletion, one is encryption. So let's do the first scenario. >> I'm talking about malicious encryption. >> Yeah, absolutely. So the first scenario is the threat actor encrypts all your primary data. What's does he go for next? The backup data. 'Cause he knows that's your belt and suspend is to not pay the ransom. If it's disc he's going to go in and put delete commands at the disc, wipe out the disc. If it's a data domain or HPE StoreOnce, it's all going to be gone 'cause it's one tier. He's going to go after our landing zone, it's going to be gone too. It's going to wipe out our landing zone. Except behind that we have the most recent backup deduplicate in the repository as well as all the other backups. So what'll happen is they'll freeze the system 'cause we weren't going to delete anything in the repository for X days 'cause you set up a policy, and then you restore the most recent backup into the landing zone or we can restore it directly to your primary storage area, right? >> Because that tier is not network facing. >> That's right. >> It's fenced off essentially. >> People call us every day of the week saying, you saved me, you saved me again. People are coming up to me here, you saved me, you saved me. >> Tell us a story about that, I mean don't give me the names but how so. >> I'll actually do a funnier story, 'cause these are the ones that our vendors like to tell. 'Cause I'm self-serving as the CEO that's good of course, a little humor. >> It's your 15 minutes of job. >> That is my 15 minutes of fame. So we had one international company who had one ExaGrid at one location, 19 Data Domains at the other locations. Ransomware attack guess what? 19 Data Domains wiped out. The one ExaGrid, the only place they could restore. So now all 20 locations of course are ExaGrids, China, Russia, Mexico, Germany, US, et cetera. They rolled us out worldwide. So it's very common for that to occur. And think about why that is, everyone who's network facing you can get to the storage. You can say all the media servers are buttoned up, but I can find a rogue server and snake my way over the storage, I can. Now, we also of course support the Veeam Data Mover. So let's talk about that since we're at a Veeam conference. We were the first company to ever integrate the Veeam Data Mover. So we were the first actually ever integration with Veeam. And so that Veeam Data Mover is a protocol that goes from Veeam to the ExaGrid, and we run it on both ends. So that's a more secure protocol 'cause it's not an open format protocol like SaaS. So with running the Veeam Data Mover we get about 30% more performance, but you do have a more secure protocol layer. So if you don't get through Veeam but you get through the protocol, boom, we've got a stronger protocol. If you make it through that somehow, or you get to it from a rogue server somewhere else we still have the repository. So we have all these layers so that you can't get at it. >> So you guys have been at this for a while, I mean decade and a half plus. And you've raised a fair amount of money but in today's terms, not really. So you've just had really strong growth, sequential growth. I understand it, and double digit growth year on year. >> Yeah, about 25% a year right now >> 25%, what's your global strategy? >> So we have sales offices in about 30 countries already. So we have three sales teams in Brazil, and three in Germany, and three in the UK, and two in France, and a lot of individual countries, Chile, Argentina, Columbia, Mexico, South Africa, Saudi, Czech Republic, Poland, Dubai, Hong Kong, Australia, Singapore, et cetera. We've just added two sales territories in Japan. We're adding two in India. And we're installed in over 50 countries. So we've been international all along the way. The goal of the company is we're growing nicely. We have not raised money in almost 10 years. >> So you're self-funding. You're cash positive. >> We are cash positive and self-funded and people say, how have you done that for 10 years? >> You know what's interesting is I remember, Dave Scott, Dave Scott was the CEO of 3PAR, and he told me when he came into that job, he told the VCs, they wanted to give him 30 million. He said, I need 80 million. I think he might have raised closer to a hundred which is right around what you guys have raised. But like you said, you haven't raised it in a long time. And in today's terms, that's nothing, right? >> 100 is 500 in today's terms. >> Yeah, right, exactly. And so the thing that really hurt 3PAR, they were public companies so you could see all this stuff is they couldn't expand internationally. It was just too damn expensive to set up the channels, and somehow you guys have figured that out. >> 40% of our business comes out of international. We're growing faster internationally than we are domestically. >> What was the formula there, Bill, was that just slow and steady or? >> It's a great question. >> No, so what we did, we said let's build ExaGrid like a McDonald's franchise, nobody's ever done that before in high tech. So what does that mean? That means you have to have the same product worldwide. You have to have the same spares model worldwide. You have to have the same support model worldwide. So we early on built the installation. So we do 100% of our installs remotely. 100% of our support remotely, yet we're in large enterprises. Customers racks and stacks the appliances we get on with them. We do the entire install on 30 minutes to about three hours. And we've been developing that into the product since day one. So we can remotely install anywhere in the world. We keep spares depots all over the world. We can bring 'em up really quick. Our support model is we have in theater support people. So they're in Europe, they're in APAC, they're in the US, et cetera. And we assign customers to the support people. So they deal with the same support person all the time. So everything is scalable. So right now we're going to open up India. It's the same way we've opened up every other country. Once you've got the McDonald's formula we just stamp it all over the world. >> That's amazing. >> Same pricing, same product same model, same everything. >> So what was the inspiration for that? I mean, you've done this since day one, which is what like 15, 16 years ago. Or just you do engineering or? >> No, so our whole thought was, first of all you can't survive anymore in this world without being an international company. 'Cause if you're going to go after large companies they have offices all over the world. We have companies now that have 17, 18, 20, 30 locations. And there were in every country in the world, you can't go into this business without being able to ship anywhere in the world and support it for a single customer. You're not going into Singapore because of that. You're going to Singapore because some company in Germany has offices in the U.S, Mexico Singapore and Australia. You have to be international. It's a must now. So that was the initial thing is that, our goal is to become a billion dollar company. And we're on path to do that, right. >> You can see a billion. >> Well, I can absolutely see a billion. And we're bigger than everybody thinks. Everybody guesses our revenue always guesses low. So we're bigger than you think. The reason why we don't talk about it is we don't need to. >> That's the headline for our writers, ExaGrid is a billion dollar company and nobody's know about it. >> Million dollar company. >> On its way to a billion. >> That's right. >> You're not disclosing. (Bill laughing) But that's awesome. I mean, that's a great story. I mean, you kind of are a well kept secret, aren't you? >> Well, I dunno if it's a well kept secret. You know, smaller companies never have their awareness of big companies, right? The Dells of the world are a hundred billion. IBM is 70 billion, Cisco is 60 billion. Easy to have awareness, right? If you're under a billion, I got to give a funny story then I think we got to close out here. >> Oh go ahead please. >> So there's one funny story. So I was talking to the CIO of a super large Fortune 500 company. And I said to him, "Just so who do you use?" "I use IBM Db2, and I use, Cisco routers, and I use EMC primary storage, et cetera. And I use all these big." And I said, "Would you ever switch from Db2?" "Oh no, the switching costs would kill me. I could never go to Oracle." So I said to him, "Look would you ever use like a Pure Storage, right. A couple billion dollar company." He says, "Who?" >> Huh, interesting. >> I said to him, all right so skip that. I said, "VMware, would you ever think about going with Nutanix?" "Who?" Those are billion dollar plus companies. And he was saying who? >> Public companies. >> And he was saying who? That's not uncommon when I talk to CIOs. They see the big 30 and that's it. >> Oh, that's interesting. What about your partnership with Veeam? Tell us more about that. >> Yeah, so I would actually, and I'm going to be bold when I say this 'cause I think you can ask anybody here at the conference. We're probably closer first of all, to the Veeam sales force than any company there is. You talk to any Veeam sales rep, they work closer with ExaGrid than any other. Yeah, we are very tight in the field and have been for a long time. We're integrated with the Veeam Data Boomer. We're integrated with SOBR. We're integrated with all the integrations or with the product as well. We have a lot of joint customers. We actually do a lot of selling together, where we go in as Veeam ExaGrid 'cause it's a great end to end story. Especially when we're replacing, let's say a Dell Avamar to Dell Data Domain or a Dell Network with a Dell Data Domain, very commonly Veeam ExaGrid go in together on those types of sales. So we do a lot of co-selling together. We constantly train their systems engineers around the world, every given week we're training either inside sales teams, and we've trained their customer support teams in Columbus and Prague. So we're very tight with 'em we've been tight for over a decade. >> Is your head count public? Can you share that with us? >> So we're just over 300 employees. >> Really, wow. >> We have 70 open positions, so. >> Yeah, what are you looking for? Yeah, everything, right? >> We are looking for engineers. We are looking for customer support people. We're looking for marketing people. We're looking for inside sales people, field people. And we've been hiring, as of late, major account reps that just focus on the Fortune 500. So we've separated that out now. >> When you hire engineers, I mean I think I saw you were long time ago, DG, right? Is that true? >> Yeah, way back in the '80s. >> But systems guy. >> That's how old I am. >> Right, systems guy. I mean, I remember them well Eddie Castro and company. >> Tom West. >> EMV series. >> Tom West was the hero of course. >> The EMV 4000, the EMV 20,000, right? >> When were kids, "The Soul of a New Machine" was the inspirational book but anyway, >> Yeah Tracy Kidder, it was great. >> Are you looking for systems people, what kind of talent are you looking for in engineering? >> So it's a lot of Linux programming type stuff in the product 'cause we run on a Linux space. So it's a lot of Linux programs so its people in those storage. >> Yeah, cool, Bill, hey, thanks for coming on to theCUBE. Well learned a lot, great story. >> It's a pleasure. >> That was fun. >> Congratulations. >> Thanks. >> And good luck. >> All right, thank you. >> All right, and thank you for watching theCUBE's coverage of VeeamON 2022, Dave Vellante for Dave Nicholson. We'll be right back right after this short break, stay with us. (soft beat music)

>>From the Bellagio hotel in Las Vegas, it's the cube covering UI path forward for brought to you by UI path. >>Welcome back to Las Vegas. Lisa Martin, with Dave Vellante at UI path forward for, we have had it all today. Lots of great guests. We've had weather, we've had rain. We are outside and lots of great conversations going on. Next up, we're going to be talking about automation at healthcare giant. Merck. Joining us from merch is Dan Boyd automation leader, and from CGI partner of UI paths, bill angles, senior automation architect, guys, welcome to the program. >>Thanks for having us. >>So Dan, we'll go ahead and start with you. Let's talk about Merck and the implement and the adoption of automation, such a history company. >>Yeah. Thank you. Um, our journey started about two years ago and started with the small team and has evolved ever since we started just the handful of folks we've evolved, uh, from the size of our team, matured, operationally and expanded our capabilities along that journey to where we are today. And it continues to evolve as the technology changes. And it's been exciting to see the adoption at Merck over, you know, across the enterprise. Um, it's been an educational process, but it's been exciting just to see that understanding of the power that automation can deliver to them. And they see the value in making it real to them has been key. Um, then once it's real and they get excited and the word spreads and they appreciate the value right before their eyes and bill, are you, >>Uh, industry specialized or more automation specialist? >>Yeah. Yeah. So I'm more, uh, automation specialized, but uh, you know, CGI, we partner with our industry experts to identify use cases for automation and I help kind of, you know, solution the best approach to automation. Uh, and you know, so I actually started, you know, with, with Merck a little bit earlier before it was really formalized and, uh, just CGI is a large partner of merch and embedded within various areas of business. And, you know, I, I ended up educating, uh, CGI on automation and here's what to look for, you know, in a, in a, in a great use case for automation and, you know, really, we started to drum up some internal excitement and then came up with some actual real use cases within Merck, proved it out early. And then we began to partner with, uh, Dan and his team. >>Can you share a little bit about some of those use cases? Yes. >>So, you know, the ones that, uh, we've worked on are really specific within, uh, various areas, uh, within the division. So Dan, you want to talk about some of the >>You're working on yeah, I'll share one use case within a specific market of merch, and it's a commercial area where they were embarking on a revision in their customer engaged engagement approach in this market and where the, they had a problem. They, they needed to get the invoices out of SAP for customers. So that was on the one side of the process on the other was a customer portal where the customers needed access near real time to those invoices. So when they came to us, they had the invoices kind of set up to be emailed out of SAP. So they had that process set up. The problem is how do they get them over here into this customer portal? Say the backup plan was to have a temporary workers come on and do that manually handle the open emails with the invoices attachments and get them loaded. >>So we came in, uh, they called us in, in the 11th hour and we were able to, fortunately that the process was straightforward, uh, whereas invoices were coming through, uh, an email attachment and that was set up. So basically we automated the reading of the emails, the processing of the PDF attachments and saved them into a shared drive where there was another process to load them into SAP. So the volume was really large on a daily basis. Initially it was estimated at approximately 2,500 emails per day with these invoices. Um, so that would estimate it would take about 125 hours of people time to do that manually. Um, so that's what we automated. And in the end it was the averages it's over 3000 a day. So, um, the solution really came in and, and we were able to deliver that. And it's been a really, they were, they were static with what they could do, and then they saw the art of the possible with, with this automation. So it's a good success story. And, um, it's exciting to see, and they were thrilled >>And it's not an uncommon story, right. Where you're automating mundane tasks that was pushing a lot of paper, a lot of copy and pasting. Um, do you see how far away, and maybe we're there already? You think about mark it's it's uh, in a, in a unique industry, we've got, got highly skilled scientists too in serious R and D high risk trials. You got partners, you do some organic, some inorganic, you've got the manufacturing components. So a lot of different parts to the business. And when you think about saving time, as you think about some of the, the scientists that are working on various pipeline products, highly paid, if you can save more of their time, wow. That even drops more to the bottom line. Are we at that point yet? We heard the stats this morning. It was 2% or some single digit percentage of our processes are automated. How far away are we from attacking those types of automations? Are we there today? >>Uh, we do automations for all the, all the functions across Merck. Um, in some places adoption is farther along than others in their journey, but yeah, um, from the shop floor and the manufacturing sites, we found opportunities to, to introduce automation there. And even in the, in the labs in various capacities, see the use cases continue to grow and the adoption continue. We see that growing as well. >>Do you find that the, the highly skilled, uh, automations targeted at highly skilled folks are, are harder to sort of get your hands around, but they give you bigger ROI? Or is it not the case? Is it all sort of earn and burn? >>Yeah, from my perspective, I think it's, you know, use case by use case. Like if it's a, a complex use case, it requires, you know, more advanced capabilities, uh, you know, machine learning models, you know, leveraging, uh, you know, AI center within UI path, uh, you know, those they can, you know, provide, you know, fairly sizeable ROI, but I think is for those highly skilled workers, I'll give one example is, you know, out in, out in the labs, we, we helped, you know, automate some things that, you know, just made their life easier, right. Uh, you know, tests running overnight, if something failed, uh, with, with a test that was happening, then, you know, they, they wouldn't know about it and they lose critical data for, for these early tests that they're doing in, in the, in the preclinical cycle. So we actually put in a UI path robots to, to monitor and send alerts and provide recovery to make their lives a lot easier. Uh, so they don't have to worry about things, you know, failing in the middle of the night, you have a UI path robot, you know, supporting them in that map, that aspect, >>What's an automation, architecture look like we, where do we start architecting automation? >>Well, I think the journey, uh, so where do you start with an automation? Right. It's really understanding the use case. It comes down to what is the, the end to end process, and then where, where can we automate, uh, within that process and what is the right set of automation capabilities? So, you know, RPA is great for, you know, um, where we get, where we need to interact with user interfaces. But if we can, uh, you know, interact with API APIs, we would do that. You know, preferably over a UI is, is to keep, keep it more of a seamless integration. But I think it's about understanding the process, laying out the right solution, uh, if there's an opportunity to improve the process prior to automating it, you know, if there's, if there is that ability, then we'll look to do that. And we've done that. We may change that process, uh, up a little bit, just to make automation more efficient, more effective. Uh, and so, and then it just, we built it and we deploy it and they start to realize the value >>Hard. Is it dental prove the, on the versus just automating what's, what's known. In other words, you've got dependencies and there are complexities there w what's your experience in terms of how you approached it >>From my experience and what we found to be best practice and bill touched on it. But every use case is of course different than the, the corresponding process. Very, very varied, but really what's key, I think, is to right upfront, understand the end to end process. And a lot of cases, my team it's new to us, right. But the process owners, they live it every day. So understanding, partnering with them to really understand the end to end solution in the form of like a process map. So you can kind of echo back your understanding of their process and get that nod of the head from them and say, yes, you understand that this is an accurate representation. Then we can with the spirit of trying to get it right the first time. And, but it really, I think is incumbent upon us to really get that in-depth understanding upfront. And a lot of cases, if there's time sensitivity in the end, it's just more efficient and saves a lot of rework. So, >>So working backwards, sorry, at least working backwards from the known existing process and then implementing an automation is probably the best starting point, as opposed to trying to work backwards from some kind of the outcome that you envision. But, but I would think there's attractiveness in the, in the ladder. Right. So that you're not just repeating a process that may be outdated. >>Yeah. So your, uh, it comes down to a couple of things. So when you're initially looking at a process, you know, should we automate this or not? And how complex is it? You need to understand what is the potential benefit. So, you know, how much, uh, you know, how much time am I able to, uh, you know, have those workers reinvest into other areas of work, right. Or what other, what are some other benefits? Uh, you know, there, there may be some, uh, you know, compliance fines that were experienced through automation, we're able to, you know, to make sure we're meeting SLS and so on. Uh, so you is a lot to, you know, defining the benefits, the automation, putting a value to that. And then the process of going through the actual process, understand the complexity, right? And then you can come up with, you know, here's, here's what it's gonna take to build this thing. Here's the potential value. And then we have ways where we track, you know, what's, how has that ROI trending once it's in production? Uh, so we'll be, that gives us more insight. >>Dan, I've got a question for you. One of the conversations that Dave and I had earlier on the program was about automation as a boardroom topic. I'd love to get your perspectives. Merck is a history organization, been around for a long time. Cultural change is incredibly challenging, but I'd love to get your perspective on where is automation at Merck's board. Is that something that is really key to transformation? >>I'd say automation falls under our strategic initiative, just around digital digital transformation, right? So it's a sub pillar of that. So that is a strategic imperative and very important. And just being a more efficient and, and leveraging technology effectively, um, just to make merch more efficient and, and, and optimized and RPA and automation plays a part in that. I mean, >>That's what I suspected Lisa this morning when we have in that conversation, it seems to me that you wouldn't necessarily create an automation stove pipe at the board meeting. You might want to report on how these automations have affected, whether it's the income statement or the health of the company, et cetera. But it seems to me to be a fundamental part of the digital transformation, um, which involves a lot of different things, data and cloud and strategy and it et cetera. So is that pretty >>Common bill? Yeah, I, yes, it is. I mean, when, when an organization is looking to automate there's, you know, various angles are coming out, they're coming from the top-down approach where, you know, management saying, Hey, we need to, we need to automate what's, let's look across all the divisions and, and figure out where, where we should go. But then it's also, you know, bottom up where, you know, folks out in, out within the various lines of business know, they, they know the problems. They know, they know the business processes. So there's a couple of different angles where, you know, you you're able to discover new opportunities to automate. Uh, but those also those smaller ones opened the door to understanding, you know, much larger processes where we can look, you know, automate more of the upstream or downstream in that process. Are there variations of the process? So >>Was, was merch more bottom-up or top-down or middle out? I wouldn't say it's >>Started bottoms up. That's really out there. It came from the top-down. So as bill touched on, I think it's really key that we do have, uh, from, from this coming from the top, from our leadership is endorsing it and advocating it, but also we're on the, on the ground floor and educating. So the people with the hands-on doing the process, they understand it and the word is spreading. They see we've, we've made it real for them. Now it's real for them, and they can appreciate the value. And they're happy to be able to do more, to be freed up from the tedious tasks and do more interesting work. >>So we did start in the department, there was a champion with a budget who said, Hey, I'm going to try this and then look what I got. Yeah, >>Yeah. You definitely need the champion. So part of that is creating champions out in the different business lines to truly own the pipeline and understand the opportunities are out there and say, yeah, this is a good opportunity. This, this one let's look at it later. So you definitely have to have those folks out there that, that understand the technology, but also understand the business. >>How has that changed in the last 18 months with healthcare care undergoing such? I mean, my goodness, the things that have happened in a healthcare organization, how has that accelerated the need for things like automation, Christian, for both of you and for mark as well? Yeah. >>Yeah. So mark initiated, uh, like most companies that digital transformation, three, three plus years ago, and this just became an extension of that. And, and it's, it's a, it's a must, right? Just to stay up with the, the digital transformation and everything that's happening in this world. And, and obviously, uh, COVID accelerated, helped accelerate it in certain areas and made it real for a lot of people and appreciate the value and the need for it. >>Yeah. W within CGI, just across all of our clients, it's automation is really towards the top of the list of strategic priorities. So it's, so we've seen this massive just acceleration of, of needing to automate more and more and more, you know, which is, which is great. >>What's it like inside a merch these days, you guys must be really excited with all that. I mean, I know it's early days and nothing has been fully blessed yet, but I mean, you know, some of the big has got a lot of headlines and obviously, you know, we've been taking jabs, et cetera, but, but now here's Merck in the headlines. It's, it's gotta be an exciting time for you guys. >>Yeah. It's, it's great to be part of a company whose mission is to save and improve lives and right. It's um, with today, it's, it's really becoming real and more relevant, uh, of that mission and vision. So it's exciting. >>There were any gotchas when you go into this, I'm sure there are into this automation journey. What kinds of things would you advise people, Hey, make sure that you deal with these, whether it's an audit scope, consideration or things that you definitely don't want to do, or do you want to do? >>Yeah. It just comes down to the, you know, choosing the right use case to start with. Right. Making sure that you, if you're just starting out in your automation journey, you know, start with those use cases that you can quickly prove value for and then tackle the more complex ones. That's good >>For folks to know where to start, especially when there's still such a tumultuous environment that we're living in. Dan and bill. Thank you for joining Dave and Manet, talking about automation, the innovation that you're doing at Merck partnering with CGI really appreciate >>Your time. Thanks for having us >>For Dave Volante. I'm Lisa Martin, coming to you from windy, chilly Las Vegas. We are at UI path forward for stick around Dave and I will be right back with our next guest.

>>VM is one of the more interesting stories we've covered in the past decade. Born in 2006 with a simple premise to make backing up virtualized systems easy, fast, consistent and cost effective. The company's timing was perfect as it rode the coattails of the virtualization trend with a laser focus on developing great products that just work. That's the tagline fast forward to 2021 and the team has surpassed the billion dollar mark in revenues and is transformed into a leading the, leading the number one independent pure play company for backup and data protection. Software company is expanding its tam extending from its on prem routes into cloud, cloud data backup containers and SAS data protection and with me to talk about its progress and how it thinks about the future of Bill Largent is the Ceo and chairman of VM and Jim Krueger. The CMO at the company. Gentlemen welcome, Great to see you again. >>Hey, great to see you again. Dave. >>Thank you, Dave, Great to be here. >>It's great, great introduction but I appreciate that set up >>following you guys for a long time but you know, you don't sit still so give us the business update. I mean you guys are cranking you just put out a press release on your progress. You're pretty transparent for a private company. We love that. But give us the update bill. >>Yeah, thanks very much. I'll do that really. We're pretty excited about the press release. We said Q one how we're 25% up air our growth, we've made a great transition with our team um super strong on our performance, our economic performance. We like being transparent were transparent with Clearly our owners now insight venture partners and that's been a big transaction that occurred in the last 15, 14 months. And we're we're transparent with our customers, our partners, our distributors, so and our vendors. So we're just that way we like to make sure we're good partners with all those along the line and we care quite a bit about him. So I think that transparency helps us with our customer base and where we're going with our product and what we've had to offer. We're now over 4500, employees strong around the globe. 40 plus countries operating uh, well, in all those jurisdictions with the covid being coming out of, I would say, coming out of hopefully coming out of the rough time period from from the geographical geopolitical covid issues. So we believe we've done extremely well through then businesses continued to grow for us. So we're really excited about where things are, insight has done a great job and helping us advance our business and thinking about looking at new tams to go after. And we're pretty excited about it. So. Well >>I had 13 consecutive quarters of double-digit growth for a company of of of your age and size is is very impressive. You don't see that often. >>Yeah we've we work pretty hard at that. It's I'd say we really focus on that year to year even though our quarters just keep popping along very nicely for us. You know, starting back in oh six, having been around since then we did start at absolute zero. We didn't have a didn't have a bit in the bank at that time. Uh Now it's a whole different story with our second year over a billion dollars. And when you look at A. T. C. V. Kind of calculation and us transforming into an A. R. R. A. Heavy perpetual base. So we've transitioned with new product offering being all subscription base. So that's uh that's making us um making us even more competitive than than we have been passed. >>I like how you couch that because you don't want to be trapped into that, you know, the quarterly shot clock, but still it's quite impressive performance. And with the A. R. R. That sort of smooth things out, jim I want to ask you about vermin again, virtual this year, second year in a row. Uh You know, it's been it's been frustrating because we can't be face to face, but well, what have you learned about the sort of virtual events? How is it performed for you? Do you see that continuing in some way, shape or form when you get back to physical? How are you thinking about that? >>Yeah, we had, we had two major virtual events last year. And so to your point, we we did learn a lot in terms of what works, what doesn't work, and we've continued to refine refine the plans. Uh One thing that it does is it just really opens the doors for a much broader audience. Typically we get about 2000 ISH people Uh that attend in person for vermin. Uh and uh and last year we had actually about 28,000 people representing 150 countries that registered for the event, and we had over 11,000 that actually attended. Uh and the engagement was, was really good and we, we were our scores in terms of satisfaction, we're, you know, four plus out of five. And so I think we did a pretty good job, but we continue to learn one of the things that you need to do is you need to sort of shorten the agenda because the attention span is not not very long. So all of our sessions are 30 minutes or less and we've actually uh put it over two days where it's about 3.5 hours each day, so we try to keep it a little bit lighter. We still have 30 different breakout sessions. We have keynotes, um, uh, some great speakers that are coming, some great customers that are coming, but you have to integrate some fun, some incentives, things of that sort. So we've been creating a lot of buzz leading up to Vermont with um, a lot of social activity. We have some some new uh, VM Nike kicks that people can win, which has generated a lot of excitement uh, and uh, and we'll be doing a lot of incentives and prizes and things as a part of the event. Uh, so, so that that's a key learning that we, we, we know that people love and just creates a lot of us in addition to of course the great content >>VM has good swag too, is an analyst. I always appreciate that VM and pure storage as you guys are at the top of the living >>area and a >>big thanks for that. But I gotta ask you, so one of the hard parts because you guys are all channel, 100% channel have been from day zero. That's some, that's gotta be tough because we've been in this business a long time doing hybrid for a long time and the sellers love to be belly to belly. They never wanted sort of hybrid. So it's gotta be tougher for the partners as well who are big channel. So how do you integrate the partners? What can we expect, you know, in these types of things going forward? >>Yeah. So I can start with just with the man bill and then we can talk a little bit more, but for the moment we actually have a really strong partner registration, so partners are attending Vermont as well, we have separate sort of cordoned off content specifically for our partners, so they're definitely a key part of that. We have 38 uh sponsors uh that uh you know a part of our ecosystem uh and uh so you know, partners kind of overall are a really key part of iman in addition to to customers attending as well. Uh So that's one way that we continue to connect with with our partners >>bill, that's part of the tam expansion, obviously it's leverage. Right? >>Absolutely. It is and you know, we'll stay with that two tier distribution system that we have affected with those partners and distributors as to how we work through them. Uh Partners are absolutely key key for us all around the globe. Different kinds of partners in the U. S. A rather large wind sell a lot of small ones uh same in the media and same in A P. J. So we'll keep that network going. Why? Because they're they're an extension of the VM arm of our internal sales group that numbers well over 2000 of our people that focus on the selling. But the partners are key. They've adapted nicely I believe, to a lot of virtual events like we're going through now and they to sound like they're as excited as we are about getting things back in uh in person. >>So I always been fascinated by tam expansion. It's part of the Ceos job you have. You know, I love the fact that that incite the board promoted from within somebody. They tapped a Ceo who knew the business. You have to do a reset your at $5 billion Mantra. And so when you're thinking about your tam expansion, cast an acquisition cloud, uh, you guys have been into the SAS backup world for for a bit now it's starting to produce. How are you thinking about the future and opportunities ahead. >>We've got great opportunities with cast and so into the cooper Nettie space. That's a huge uh, market expansion for us in an evolving marketplace. So our goal was, let's be that thought leader. Let's get the best technology that's out there and then you'll see us execute really well. This on the sales side. This uh, this year also 2021 here and beyond the public cloud. Peace with azure with aws with our Google offering. So we're continuing to bring those up the up to up to speed in a sense of size because there's this tremendous future there, that's a big expansion of our team. Also I think Jim might talk a little bit about jumping into that security space a little bit. That is going to be big, It's big for us, It's a selling um selling feature that I think has come a long way. So it is about expanding that tam and our focus is always long term. It is, yeah, evaluation is important, but more important is that, you know, that long term customer service uh meaning new products for them like Office 3 65 that we brought out that has had tremendous growth for us. Uh so we're really excited about the growth, but again, our focus is on for that customer over the long term. It's uh it's years out, it's not the quarters and it's not about that valuation, it's really about how we keep them going in their business. >>So jim talk about that positioning, I mean, you're not a security company, you're not like building firewalls and and perimeters and so forth. But the notion of security and data protection plays in there. I mean, you think about these ransomware attacks, if I can actually recover from a ransomware attack, I got way more leverage and you're part of that recovery process. So those lines are blurring. How do you think about it? >>Yeah, that's that's definitely a key benefit that our solution provides in terms of protection from that. Uh And as we've had new releases into the market v 10 last year, the 11 this year, we've continued to up the ante relative to the protection from ransomware that's obviously a hot topic in the marketplace. And uh, and and we have some some great differentiators that we brought to market. And I think that that's part of why we're seeing the, you know, the strong growth, Uh, B 11, you know, Focus on ransomware, but over 200 new features and capabilities that we have brought to market to make it easier for our customers. We have kind of three key pillars around simplicity, flexibility, reliability, uh, and also just super powerful with the features and capabilities that we bring the market. So, uh, so we're seeing great traction there. Uh, and uh, and that's definitely an area that we're focused on. Uh, as again, we're not a security company, but we do protect from that. And some of the capabilities, you know, give you, if you do run into that situation, the capability to recover quickly and not have to pay a ransom And keep your business running, which is a key focus for us across all of our 400,000 plus customers. >>What about cloud? Cloud is a lot of people, you know, traditionally on prem vendors, they're like threatened by cloud. But clouds a gift for wien I mean it's like the internet is a gift is this big platform that's been built out, you can build on top of it. So how are you guys thinking about the cloud opportunity and the SAS data protection? >>Yeah, we want to go and >>Yeah, absolutely. Sorry, Jim. Yeah, it is a tremendous market opportunity for us. We've evolved as, you know, get back to our roots and 06, it was about um protecting those virtual workloads and we've evolved from there with evolved from there maintaining that that on print businesses is not going away. The public cloud businesses skyrocketed the private cloud business the same way. And we want to have products for all um all of those and the ability to move and move data's move, move workloads around, move those datas around, move that data around. So big piece for us, the casting piece, I keep getting back in how that might evolve with the SAs offering or not. Well, we'll tell over time period here as we work on that product roadmap. So, absolutely tremendous opportunity for us. >>Well, when you think about hybrid jim, I mean, the first thing that people say is for developers, containers, first thing you do is containerized the app and then you don't care where run anywhere right at once. Run it anywhere. That's a big opportunity. >>Yeah, so, so we're definitely focused on building a single platform for all environments. Uh So with the storage integration with the top three hyper scale ear's uh moving into kubernetes and containers, uh backup and protection. Uh So that that's a key part of our strategy. It's again to make it easy for customers To be able to manage all of their backup from one single console. Uh So so that's a key strategy and their own, you know, the research that we've done, I think, you know, due to Covid, we're actually seeing an increase in the move to the cloud, uh and we play in in in two key areas, ones with the hyper sailors and providing, you know, the backup and recovery within those environments, but then also with our service providers and as a service we're seeing really strong growth within that market and that ecosystem. So uh you know, working to to partner with them closely and continue to build that ecosystem and support them and their efforts to to uh to drive growth in the market as well. >>All right I'm gonna let you guys go the last question bill is so what's life like with insight? I know these guys are players. We've we've seen the moves they make is what's the future like? Is I. P. O. Still on the table? What can you tell us about life with insight? >>It's a great question. Well keep in mind we worked with insight in our prior entity uh starting in I think in 2002 we brought them in. So and our partner that's on our account has been with us since that time even though after we sold the business they were out of him. So uh No I'm really well they've been a big help their operations operationally efficiency, efficiently focused. I'd say they're helping us bring new ideas back to the M. And A activity back to your I. P. O. Question. I think that's an avenue for us. We clearly are pushing in that direction to get set up that way. What's it do for us? It gives us that bigger currency versus just cash. It gives you that stock currency to use on M. And A activity that you know I think you saw us do that with Kasten. Uh in the sense of acquiring M. And A. That's been different for vein. We build it from within that was a buy from outside. You'll see us do both both of those in the future. Very important I think uh you know without being too predictive I would say hopefully that's something you see in the I. P. O. Side that in the next 12 24 months a run like that. >>Well, Hey, uh, as an upside of observing the street wants growth, they want execution, they want consistency and you guys bring all those guys. Thanks so much for coming back in the Cube. Always a pleasure. And look forward to seeing you face to face. Hopefully before 22. >>It would be nice. >>All right. Thank you. Thanks. >>Dave. All right. >>You're welcome. Alright, keep it right, everybody. This is Dave Volonte for the Cubes, continuous coverage of them on 2021, the virtual edition, We're right back.

>> Announcer: From around the globe it's theCUBE with digital coverage of IBM Think 2021. Brought to you by IBM. >> And welcome back here on theCUBE. John Walls, your host with you as we continue our IBM Think 2021 initiative. Been talking a lot about IBM's assistance in terms of what it's doing for its client-base. We're going to talk about partnerships today, a little bit with Bill Patterson who is the EVP and General Manager of CRM Applications at Salesforce who has a really good partnership in great practice right now, with IBM. And Bill, thanks for the time today. Lookin' forward to spending some time with you, here. >> Yeah, thank you John, thanks for having me today. >> You bet. Well, let's just jump right in. First off, let's share with the viewers about your core responsibilities at Salesforce. We talked about CRM, what your engagement is there, but if you would just kind of of give us an idea of the kind of things that you're handling on a day-to-day basis. >> Well, I am responsible for our CRM applications here, at Salesforce, which are our sales cloud technologies to help organizations get back to growth, our service cloud technologies which are really helping organizations to take care of their customers, you know, through all moments of the digital lifecycle, our small business solutions, so to help growing organizations thrive, and our Work.com and vaccine management solutions which are helping the economy safely reopen through the crisis modes that we've all been living in. So broad range responsibilities and my day-to-day is nothing like it was a year ago. >> Yeah and I could only imagine, especially when you throw that last component in, COVID, which hopefully, we'll have time to talk about just because, I think, people are so are taken to the subject now and obviously it's impacting business on so many different levels. But let's talk, first off, about IBM and your partnership with them, kind of the genesis of that, how that came about, and maybe how you're working together. How are you integrated these days with IBM? >> Well, you know, one of the things at Salesforce that are key value as an organization is is to establish trust around the transformation of organizations across the world. And when you think about brands that you can trust to drive transformations with, IBM and Salesforce really stand apart. So IBM is an incredible partner for us on the technology side, on a service delivery side, and in an innovation side for us to create new solutions to help our clients really go in this from-to state of how their businesses used to operate to how they need to operate in the future. I loved working with the IBM team. We have a lot of great values that are shared across our two organizations. But most fundamentally, those values are deeply rooted in customer success. And I think that that is one of the things that really draws me too, working with such a great partner here. >> Go into the process a little bit, if you will. So if I'm a prospective client of yours and I come to you with some cloud needs, you know, again, whether it's storage or whether it's applications or whether it's Edge, whatever it is I'm coming to you for, how do you then translate that to IBM and how does IBM come into play, where do the boundaries kind of start and stop or do they? Or is it a complete mesh? >> Yeah, well I think one of the things that's sort of unique about today's climate is people aren't just looking to solve technology problems, they're looking to solve business problems. And what we really do at Salesforce is lead with the business transformation opportunity and deeply partner with IBM on a number of fronts to really go help those opportunities become realized. The first is in the services line. IBM has great partnerships with Salesforce around the transformation about core business processes, configuration, integration services. That's one of the dimensions that we work together on. We also work together on areas of artificial intelligence and how we help businesses become smart in their operations every day to empower their workforce to really achieve more. And finally, you know that you mentioned about core technology, you know, oftentimes, the business requirements translate into great technology transformation. And that's what we do deeply with the IBM team is really outlining a blueprint and a roadmap for modernizing the technical infrastructure to help organizations move fast, increase their operational agility, and run at such scale and safely in today in the modern world that we all operate in on. So through all those facets of the lifecycle, IBM continues to be one of our leading partners, globally, to help clients, you know, not just here, in the United States, but around the world to think about how they need to maximize their transformational abilities. >> Yeah, and you touched on this at the outset of the interview. We were talking about IBM and the impact and obviously, the great association relationship that you have with them and the value in that. I'd like you to amplify on that a little bit more in terms of, specifically, what are you getting out of it you think, from a Salesforce perspective to have kind of the power and the weight and the bench, basically, that IBM provides. >> Well you think about transformation and you know, you read a lot about digital transformation online, that means so many different things to so many different businesses. Businesses, not just, like I said, here in one country, but globally, the transformational needs really need to come with incredible bench and domain expertise by industry, by geography, even by some micro-regions in those geographies given what we've been experiencing here, in the public sector in the United States with this COVID response activity we've been doing with the IBM team. And so when you talk about the deep bench, what I love about working with IBM on is, again, commanding just great industry insights and knowledge of where industries are heading and also cross-industry insights so that you can really bring great best practices from say, one industry to another. Second is that real understanding of the global nature of business today. And I don't think the one thing that's fascinating about digital, it is not a sovereign identity, today. Digital really means that you need to understand how to operate in every country, every region, every location, you know, safely. And so IBM has incredible depth in bench of experiences to help our clients truly transform those areas. Maybe another area that I really have appreciated working with IBM on is that deep technical understanding and deep technical domain of excellence maybe in the area of artificial intelligence. And our partnership is quite unique between Salesforce and IBM. Not only do we work together for external clients but inside of IBM, IBM is using Salesforce today to run a lot of your core operations. And so the partnership we work with, not only IBM as a kind of delivery excellence, but internally as a customer, is really helping IBM transform its operations from service to sales to marketing all around the world. So I think this partnership is one that is deeply rooted in working together and really, like I mentioned before, finding the right path to drive the outcomes of tomorrow. >> You know, you mentioned COVID and so we'd like to touch on that. But I assume that's a big part of your current relationship, if you will, in terms of the partnership goes. What, specifically, are you doing with IBM in that space and what have you done, and then what are you continuing to do as we go through now, the vaccination process and the variant identification processes and all these things? So maybe you can share with our viewers a little bit about the kinds of things that you have been working on together and the kind of progress that you've been making. >> Well, back a year ago, you know, when the world was really at a standstill, Salesforce created a solution called Work.com which was to engineer new technologies to help businesses kind of deal with the reality of a hard shutdown to business in the, say, private sector and then in the public sector, to really create new innovation around key solutions like contact tracing that you might have needed to track, you know, kind of outbreak and the rate of progression of the virus. And what we did with the IBM team, working with clients around the world first was work together to deploy those technologies rapidly into the hands of our customers. Through those moments of opportunity and realization, you know, working with our clients, we also started to hear of, you know, kind of about where we find ourselves today, this mass vaccination wave of where our citizens and societies are kind of on the recovery journey. And the work that we did with IBM was to start to plan out the next wave of recovery options around vaccine managements, Salesforce creating a core vaccine scheduling, distribution, and administration management services and IBM focusing on more of that credentialing and vaccination state of how someone has gone from receiving a shot in arm to now having a trusted profile of which vaccines, when did you receive them, are they still accurate and valid around those solutions. So where we're working with the IBM team most acutely on COVID now is in the vaccine credential management side through Watson Health. >> Well, can you give us an idea now, let's see if we can dig in a little deeper on some of those other things you talked about to about core technologies, you talked about, I mentioned Edge, you know, and that's people tryin' to figure out how they integrate these Edge technologies into their primary systems, now. So can you give us some examples, some specific examples of some things that you're actually collaborating on today in those areas or maybe another that comes to mind? >> Yeah, Edge computing is probably one of the other more exciting things that we're doing with the IBM team and I think you find that really working with our field service business and IBM cloud services, you know, globally speaking. On the Edge, as devices become smarter and more digital, they have a lot of signals that organizations can now tap into, not only for real-time intelligence but also fault intelligence when a device is starting to need repair or preventative maintenance around the solutions that kind of need to be administered. And the work that we're doing to really broker this connected, not just enterprise, but connected sort of experiences with IBM, super powerful here, because the IBM Edge services are now helping us get into anomaly detection. Those anomaly detections are automatically routing to workers who use the Salesforce field service capabilities, and now we can help organizations stay running safely and with continuity which is really all our customers are asking us for. So the ability for us to be creative and understand, you know, our parts of the picture together are really the things that I think are most exciting for what we're doing for clients around the world. >> Yeah, you mentioned continuity, kind of a cousin to that, I think, is security in a way because you're-- >> Absolutely. >> So what are you hearing from your customer-base these days with regard to security? You know, a lot of high profile instances certainly from bad state actors, as we well know. But what are you hearing in terms of security that you're looking at and maybe cooperating or collaborating with IBM on to make sure that those concerns are being addressed? >> Yeah, you know, I think, well, first off, security is on the top of minds for all decision-makers, executives, today. It's the number one threat that a lot of companies are really needed to respond to given what we've seen in the geo-political world that we're in. And security isn't just about securing your servers, it's also about securing every operational touchpoint that you might have with, you know, your every end-user or even every customer that's inter-operating with your services that you project as an organization. And what I love about working with the IBM team is, as we mentioned, you know, such great insights across all parts of technology infrastructure to really help understand both the threat level, how to contain that threat level, and more importantly, how to engineer with, you know, great solutions all the way into the hands of customers so they become safe and easy for all actors in your environment to really operate with. And that's where, again, you know, you think about a solution like mobile sales professionals, they're out traveling around the world on mobile devices, sometimes, their AG even brought their own personal devices into the enterprise. And so IBM is a great partner for ours just to help us understand the overall threat level of every device every moment that an employee might have within their organizational data, and really help create great solutions to help keep organizations running safely. >> Yeah, I think it's interesting you tell about people bringing their own devices on, back when, I remember that acronym, BYOB was like a huge thing, right? (chuckling) And this major problem or conundrum and now it's almost like an afterthought, you've got it solved, you've got it well taken care of. >> Well you think about, again, devices in the enterprise and how much we've been able to achieve with the BYOB becoming commonplace and norm, even today, the workman place from home kind of environment that we're in. I mean, who would have thought a year ago that most of our operations can be conducted safely from our home offices, not just our regional or corporate offices? And again, that's the kind of thing that working with IBM has been such a great value for our clients because no one could have forecasted that the contact center would've had to moved to your kitchen last year. And yet, we had to really go achieve that in this time and working with great partners like IBM, it became not just a conversation but real practice. >> By the way, I think I said BYOB. I meant BYOD, so you know where my mind's at, right? (chuckling) >> I wasn't going to correct you. >> Hey thanks, Bill, I appreciate that. It just kind of hit me. I think that that just, that was a Freudian slip, certainly. Hey Bill, thanks for the time. I certainly do appreciate and thanks for shining a light on this really good partnership between Salesforce and IBM. And we wish you continued success down the road with that, as well. >> Yeah, thanks again. And again, love being your partner and love the impact we're having together. >> Great, thank you very much. Bill Patterson joining us, the EVP work in CRM at Salesforce talking about IBM and that relationship that they're putting into practice for their client-base. John Walls reporting here, on theCUBE. Thanks for joining us with more on IBM Think. (soft music) ♪ Dah de dah ♪ ♪ Dah ♪

(upbeat music) >> Welcome back to Dell Technologies World 2021, the virtual version. My name is Dave Vellante and this is theCUBE I'm pleased to welcome Bill Wavro, the president and GM, Dell Financial Services at Dell Technologies. Bill. Great to see you. Thanks for coming on. >> Yeah, thanks Dave. It's really great to be here with you. >> So we heard in Alison Doos keynote, Dell Technologies all in on as a service APEX. She's the executive lead. So really passionate about that. Talk about DFS in that context, you've always had a way to help people shift CapEx to OPEX but what's your specific role regarding APEX? >> Yeah, well, thanks Dave. Look, we're really excited about APEX in DFS we think APEX solutions gives customers access to the broadest range of infrastructured solutions in the industry if you combine that with the Dell Technologies portfolio of ISG technology and solutions, that's a great combination for customers but you asked about DFS and in DFS we've been delivering consumption solutions for over 15 years. So taking a look at the APEX portfolio you can broadly divided into two categories, turnkey, APEX turnkey solutions, like data storage services and then APEX custom solutions like data center utility and Flex on Demand and those custom solutions are with DFS plays. And as I said we've been offering those for over 15 years. We have a lot of experience with customers, what customers are looking for? We have tens of thousands of assets out there today with hundreds of customers that are being metered on a regular basis that we're billing monthly. So we're getting a lot of feedback from customers. And I think the APEX solutions announcements that you heard here at Dell Technologies World are a further expansion of that. They're built on the backbone of what DFS has been delivering for a long time. So we've taken I think the best of those solutions and we've listened to customers, what are they looking for? What do they want? And we've got even new and improved offerings in the current announcement that we made. So, I think when you talk to customers they want a couple of things. They want simplicity. They want to be able to understand how they're going to acquire it and how those payment solutions work and they want choice. And I think the APEX solutions gives customers both of those things. And the good news is they're available on all sorts of ISD hardware be it servers storage, hyper-converged, converged infrastructures. Customers can choose what technology they're looking for and they can create their own demand environment. And the last thing I'll do is a little plug for DFS. We've been in the payment solution business for over 25 years. So we not only offer consumption solutions but we offer traditional leases and financing. And so when you add all that together and you go talk to a customer about technology and how they want to pay for it we think we've got the broadest range in the industry. And we're really proud of that. >> Okay, cool. So you have the two areas that you simplified it. That which is great. You've got that standard off the shelf and then you've got the custom solutions. The standard stuff is like console data storage and then cloud services that breaks down even further. And then the custom, I got my notes, APEX Flex on Demand and APEX data center utility, how do APEX custom solutions differ from those turnkey offerings. >> Yeah. Well, the beautiful thing about APEX is it gives customers choice. So as you mentioned, you have turnkey. So if you think data storage services that's one of our new turnkey type offerings and those turnkey offerings are outcome-based. So think about it as an outcome. What is a customer looking for? Do they want file versus block storage? What sort of capacity do they need? What performance level are they looking for? But it's thinking of those outcomes. And Dell Technology is going to take care of the rest, right? A customer can go to the APEX console and they can review those choices. They can make their selections and then they can turn it over to Dell and we'll deploy that technology, we'll manage it, we'll upgrade it and we'll service it over the life of the term. So the customer can focus on outcomes versus on acquiring and managing technology. So that's the turnkey solution and that's probably the biggest and newest part of our announcement of APEX solutions. And then we have the custom, the custom, as you mentioned data center utility Flex on-demand. That's what DFS has been delivering for many years now. And that's for our customer who wants to select the product. So think of it as a product-based solution where a customer wants to select the technology. They may want to manage it themselves. They may want to have a partner manage it. They may want to include different services. So they're able to put that together in a custom way and satisfy whatever problem they're trying to solve. So we found that many customers are going to want to select that custom solution because they're in a part of their data transformation journey where they still want to control some of that technology and others will want to go the turnkey route. So again, it kind of goes back to the customer choice in allowing them to acquire it the way that they want to acquire it. >> Okay. So like an example might be, I'm just making this up. I'm a financial institution, I'm a big VMAX customer. I got some kind of special process that I use, that I wrote that gives me competitive differentiation because I can get a millisecond faster than my competitor speed or whatever. And I want that I'm not going to take it. That's not part of your turnkey solution but that's part of my value add, I want that but I want your help in sort of customizing that and making it as a service. Is that like reasonable example? >> That's a great example. And so let me talk about that a little bit. So let me give you a couple of examples of use cases and what the products provide. So think about, let's talk about data center utility first because when you think of the data center utility think data center, right? These are the large deployments. They're big customers. They they're most likely a global customer and they want to get out of the data center business, right? They want to get out of the day-to-day management of that and be able to focus on, hey, how do we as I as a CIO deliver value to my business? I want to make a difference in that business strategy. CIO is more and more being asked to help the business and enable that business strategy. And so many of them want to get out of the data center management business and this is where a data center utility product can come in. It allows us to go in as Dell and help that customer manage that data center. So it has the most flexibility in terms of, custom building, custom reporting, very low if any minimum commitments. And one of the best features is we have a delivery manager who's assigned to every account who can help that customer procure assets, manage assets, deal with capacity management. So we really can take over the management of that data center and allow the customers IT group to focus on delivering value to the business. So we think that's a really important aspect of it. And it allows us to manage even existing assets that are sitting out at the customer as well as new purchases. And then back to the kind of example that you gave, where a customer really wants that high performance they have specific hardware in mind. They can also use a Flex On Demand type product so that the customer is able to pick the hardware whether it be servers, storage, converged infrastructure, hyper-converged and they can select the technology that they want to use. They can sign up for a very flexible period of time. So they can go from one year to five years. They may only need this hardware for a limited period of time. Maybe they're working on a project where they're going to need additional storage capacity for the next couple of years, so they can sign up for a two year contract if they want, they can sign up for the commitment level that they want to use. So, one of the great things that customers are looking for is they want that cloudlike operating model. They want to pay for that technology as they utilize it. And they don't want to be locked in to having to purchase a large amount of data if potentially they're not going to use it. So Flex On Demand gives them that flexibility. They could sign up for 50% of a storage arrays capacity and only pay for usage above and beyond that 50%. So it offers customers a lot of choice and a lot of ability to get the technology they want and be a very flexible utilization method as they go forward. >> So, I don't think a lot of people realize that you said very low or no minimum commitments. And so maybe you could explain that a little bit and who owns the asset? >> When we talk about Flex on Demand it is still owned by Dell. So Dell owns that asset and the customer can commit. And we think we have the broadest range of commitment levels in the industry. So if you think about a Flex on Demand type offering and let's say you want to purchase a storage array you can sign up for a 50% minimum commitment. So again, you've got flexibility on the term. So you can go one to five years you can sign up for 50% commitment. So you're going to get a bill for 50% of that storage usage every month but you're not going to get a bill for anything more unless you utilize it. Now, let's say one month you go to 60%, right? So you're going to pay that extra 10% only when you use it. If you go back down to 50% the following month, you don't pay. And let me point something out on this because I think this is where we differ from a lot of our competition. The rate you pay is the same. So it's the same for the minimum commitment level of 50% as it is for the incremental 10% or 20% that you use above that. Some of our competitors have surge pricing. So basically once you go above your commitment you're paying a premium. We don't do that. We've heard from customers. They don't like that. They want it simple. They want to pay one rate per gigabyte throughout the life of that contract. And so we do that. Another unique feature which we kind of just implemented recently is that the max that you can pay is 85% on that storage usage. So if you sign up for a 50% minimum commitment and let's say you use 90% of it so you've got a pretty significant increase off your minimum. We will only charge you up to 85%. And that's a new feature that we added to all our Flex on Demand products recently for future customers as well as previous customer. So you've actually gone back to all our current customers and said, this applies to you even though that wasn't part of the original offering we're going to cap you at that 85% level. And the reason we did that is because a lot of customers love, pay for things as they use them. They're a little uncomfortable on uncertainty of maybe paying too much, right? So we put this in to help protect customers that they wouldn't have to worry about paying more than they expected to. So we think that's a pretty cool feature of what we offer. >> Yeah. So to summarize the features, I got the portfolio, I got the whole portfolio I have access to, I get the flexibility that you just described in great detail and then pricing transparency or certainty. And then the other piece of that is the value the 85% cap. So that's pretty cool. >> Yeah. >> Okay. So you've been doing this for a while. You have data and experience with real customers, with different types of customs solutions. Maybe you could share some of the business impacts and benefits that customers have seen. >> Yeah. Well, let me give you example. So data center utility like we had a large multinational manufacturer who again as I mentioned earlier they wanted to get out of the data center environment. They don't want to manage that anymore. They had a complex data center. It was managed with about 50% of their own internal IT department and 50% with a third-party service provider. They had multiple hardware vendors, multiple third parties providing services. It was very very complex. So they came to Dell Technologies. We took that data center put it into a data center and utility contract. And we took over management of the data center. So it freed up that 50% of their own IT staff to work on other things. It provided value to the business. And then we were able to take that consolidate vendors make it a lot simpler and improve the efficiency of the data center. And that's an important thing. So it's not just about how you pay for it, that you pay for it in a variable manner. It's how efficient can you make that data center? And no one knows Dell Technologies assets and hardware better than us, right? So we were able to create tremendous value by utilizing those assets more efficiently. So they were getting more productivity out of the underlying IT assets. We simplified it for them. And we were able to take their team out of that day-to-day management which is what they were looking for. So there's a great example of a win-win on both sides. And what we've found with these customers is once they go to this kind of model, they stay with it. They like it. And we actually ended up expanding our relationship with these customers. So it's good for us in a lot of ways. And one of the things we didn't talk about earlier is another benefit that maybe isn't so obvious, particularly when you look at a Flex on Demand type product. So let's think about Flex on Demand. One of the things customers are struggling with is how to predict how much data storage they're going to need in the future. There's this huge data explosion going on in the world. We've talked about that many times. And CIO's often have trouble trying to predict how much capacity they're going to need in the future, right? They don't know exactly where it's going to go. And so one of the struggles that they have is when they have a need for that data it takes time to get it available. So if you think about a CIO that all of a sudden has additional capacity they need to add to their infrastructure, it could take 90 days to get that out on the floor. They've got to go through their internal requisition process. They've got to select a vendor, they've got to acquire the hardware to get it all set up. This all takes time. 90 days later is too late. One of the things that Flex on Demand offers is that you're able to get that capacity on the floor and at the push of the button you can have it up and running. So you're not paying for it until you use it but once you do need it you can have it available really quickly. And that's one of the benefits that maybe people don't expect when they use this Flex on Demand product but provides tremendous value for customers. So we've seen that kind of time and time again with Flex on Demand we had a large pharmaceutical customer who went to one of these, had a mandate that they wanted to go to a cloud operating model for all their IT purchases. So they wanted to free up cash flow that was kind of a directive from the top, free up cashflow, let's get out of the CapEx business and Flex on Demand was a perfect answer to that because it freed up the cashflow. They paid for the technology over time and they were able to have this capacity available whenever they needed it. So we've seen that as a tremendous benefit of the custom Flex on Demand model. >> I've done a lot of TCO studies Bill and I'll tell you that the upfront planning, the capacity planning, the asset management, the procurement, these are a big chunk of the labor cost associated with total costs. Okay. So we've got the turnkey with the three components. We've got the custom with the two components. There was a fifth bucket in my notes here which is the partners in the channel. And I'm really interested in how the channel is transforming, no longer can you just be a box seller in the channel. Those days are gone. Made a lot of money doing that, good deal. But, now you got to add value. The cloud has really changed everything there. And of course it's all about the margin and profitability. So talk about the channel. You've always had relationships and how APEX fits with channel partners. >> Yeah, well look Dave, we've had great experience working with our partners. I mean, partners are hearing the same thing from customers that were here, they want a cloud-based type operating model. They want to pay for their technology as they use it. So partners are looking to provide the same value to customers. And we found that working with partners expands our reach tremendously and they have a lot of expertise. So the APEX custom solutions are designed to work with partners. Partners can either sell those for themselves so they can sort of resell our solution to them or they can just refer the business to us. And they're going to get a 20% uplift on the committed contract value for those contracts. So this is really financially attractive to our channel partners. And it allows us to work with those partners to get to more customers, right? And as I mentioned earlier these custom solutions can include services from the partners so we can provide the hardware piece to them. They can add on their services on top of that and they can be the primary relationship with a customer or again they can refer it over to us. So we found that to be a really good value proposition for partners. And we think they're pretty happy about it. I mean, we have an example, RelateCare as a company that supports healthcare organizations around the world. So helping with patient communication appointment scheduling tele-health which is a really big area right now as you know with the pandemic still going across the world this is an area for medics growth. So RelateCare was working with one of our partners, Arc Fire and they needed more flexibility in the data center. And so these APEX Flex on Demand was a perfect solution for that. It allowed them to deliver a secure flexible data center and work with their partner to really improve the service level that was occurring in that relationship. And if you think about it, one of the things we didn't mention is particularly for a lot of healthcare companies and companies and customers who have a high sensitivity around data security and where that data resides. One of the advantages of on-prem solutions like APEX Flex on Demand or data center utility is know where your data is. The public cloud can move the data around often without you knowing about it. And so that security of that assurance that you know exactly where your data is, is really important to a lot of our customers. So its kind of another feature that has been official for customers. And again, we're seeing partners adopt this more and more. And I think over time that is going to continue to grow. >> Yeah. So 20 points on the uplift plus additional services that I can bring in because they have a tighter relationship, in this model, right? The renewal starts when you sign the contract. So it's a much, much deeper relationship. Can the partners, can they white label the service? Is it co-branded? Is it all Dell branded? >> Yeah. Well, they have the choice. I mean, the partner can financially take on the billing and relationship primarily if that's what they would like to do. And again, so that's one of the offerings or they can refer it to us. Many partners they don't have the back end or the infrastructure to do all that billing and collecting themselves. So they prefer just to refer it to us. Another part is at large they do have those capabilities and they want to take on the primary relationships. So we can work with them both ways and we have worked with them both ways. >> Nice. We're out of time, Bill, but give us the bottom line. You've touched on some of this but why APEX over the competition? >> Yeah. Well, look, I think it goes back. The first thing is Dell Technologies, right? Dell Technologies has the broadest selection of products and services means. So you combine that with APEX solutions you've got a win-win, it's unbeatable in my opinion. We also I think have the best range of flexibility in those payments solutions. So you can go from a minimum commitment of 40% all the way up to 85%, you can go one to five years, no surcharges, right? The rate is the rate. The rate goes from the minimum all the way to the maximum. We have the storage cap that I talked about, HCI cap at 85%. So you're going to be capped. So you don't have unexpected costs that you didn't forecast and you can flex up and down, right? So you flex up, you could flex down, some of our competition once flex up you can't flex back down. And that's a real negative in my opinion. And we've got 15 years of experience of doing this. So that's really important. We work with a lot of customers. We've learned a lot during those journeys and we think we're the best equipped to provide you with consumption solutions and as a service solutions that really work with customers. >> Financial flexibility, asset management, the really key part of IT that we don't spend enough time talking about. Bill, thanks so much for coming on theCUBE and sharing your insights. >> Thanks, Dave. Really glad to be here. Thank you. >> All right. And thank you for watching everybody. This is Dave Vellante for theCUBE's continuous coverage of Dell Tech World 2021 the virtual edition. We'll be right back right after this short break. (upbeat music)

>>from around the globe, >>it's the cube >>With digital coverage of IBM think 2021 brought to you by >>IBM. Welcome back here on the cube, jOHn wall is your host with you as we continue our IBM think initiative. Been talking a lot about IBM s assistance in terms of what it's doing for its client base. We're gonna talk about partnerships today a little bit with Bill Patterson who is the VP and general manager of Crm applications at Salesforce, who has a really good partnership in great practice right now with IBM and Bill, thanks for the time to you looking forward to spending some time with you hear, >>thank you Jon. Thanks for having me today. >>You bet. Let's just jump right in first off, let's share with the viewers about your core responsibilities that sales for us. We're talking about Crm what your engagement is there, but if you would just kind of give us an idea of the kind of things that you're handling on a day to day basis, >>Well I am responsible for our crm applications here at Salesforce, which are our sales cloud technologies to help organizations get back to growth. Our service cloud technologies which are really helping organizations to take care of their customers through all moments of the digital lifecycle are small business solutions. So to help growing organizations thrive uh and our work dot common vaccine management solutions which are helping economies safely reopen through the crisis modes that we've all been living in. So broad range of responsibilities in my day to day is nothing like it was a year ago. >>I could only imagine, especially when you throw that last component in Covid, which hopefully will have some time to talk about just because they people are so are taken to the subject now and obviously it's impacting business on so many different levels. But let's talk first off about IBM and your partnership with them kind of the genesis of that, how that came about and maybe how you're working together, how are you integrated these days with IBM? >>Well you know one of the things that Salesforce that are key value as an organization is is to establish trust around the transformation of organizations across the world and when you think about brands that you can trust to drive transformation with IBM and Salesforce really stand apart. Uh so IBM is an incredible partner for us, on the technology side, on a service delivery side and an innovation side for us to create new solutions to help our clients really go in this from two state of how their businesses used to operate to how they need to operate in the future. Um I love working with the IBM team, we have a lot of great values that are shared across our two organizations, but most fundamentally those values are deeply rooted in customer success. And I think that that is one of the things that really draws me to working with such a great partner here. >>Yeah. Go into the process a little bit if you will. So if I'm a prospective client of yours and I come to you with some cloud needs, you know, again, whether it's uh, you know, storage or whether it's applications or whether it's edge, whatever it is, you know, I'm coming to you for um how do you then translate that to IBM and how does IBM come into play? Where does where do the boundaries kind of start and stop or do they? Is it a complete mesh? >>Yeah. Well, I think one of the things that's sort of unique about today's climate is people aren't just looking to solve technology problems. They're looking to solve business problems and what we really do, you know, at Salesforce's lead with the business transformation opportunity uh, and deeply partnered with IBM on a number of fronts to really go help those opportunities become realized. The first is in the services line. IBM has great partnerships with Salesforce around the transformation about core business processes, configuration integration services. That's one of the dimensions that we work together on. We also worked together on the areas of artificial intelligence and how we help businesses become smart in their operations every day to empower their workforce to really achieve more. And finally, you know that you mentioned about core technology oftentimes the business requirements translate into great technology transformation and that's what we do deeply with the IBM team is really outlined a blueprint and a road map for modernizing the technical infrastructure to help organizations move fast, increase their operational agility and run at such scale and safely today in the modern world that we all operate in on so through all those facets of the life cycle, IBM continues to be one of our leading partners globally to help clients, you know, not just here in the United States but around the world think about how they need to maximize their transformational abilities. >>And you touched on this at the outset of the interview, we were talking about IBM and the impact and and obviously the great association relationship that you have with them and the value in that I'd like you to amplify on that a little bit more in terms of specifically what are you getting out of it you think from a sales force perspective to have kind of the power and the weight and the bench basically that IBM provides. >>Yeah, well you think about transformation and you know, you read a lot about digital transformation online, that means so many different things to so many different businesses, businesses not just like I said here in one country, but globally the transformational needs, you really need to come with incredible bench and domain expertise by industry, by geography. Even by, you know, some micro regions in those geography has given what we've been experiencing here in the public sector in the United States with the scope of response activity we're doing with the IBM team. And so when you talk about the deep bench, what I love about working with IBM on is again commanding just great industry insights and knowledge of where industries are heading uh and also cross industry insights so that you can really bring great best practices from say one industry to another Um second is that real understanding of the global nature of business today. And I don't think the one thing that's really fasting about digital, it is not a sovereign identity today, a digital really means that you need to understand how to operate in every country, every region, every location uh you know, safely and so IBM has incredible depth and bench of experiences to help our clients truly transform those areas. Maybe another area that I really have appreciated working with IBM on is that deep technical understanding and deep technical domain of excellence, you know, maybe in the area of artificial intelligence and our partnership is quite unique between Salesforce and IBM, not only do we work together for external clients, but inside of IBM, IBM is using Salesforce today to run a lot of your core operations. And so the partnership we work with not only IBM as a kind of delivery excellence, but internally as a customer is really helping IBM transform its operations from service to sales to marketing all around the world. So I think this partnership is one that is deeply rooted in in working together and really like I mentioned before, finding the right path to drive the outcomes >>of tomorrow, you know, you mentioned Covid, um and so I would like to touch on that, but I assume that's you know, a big part of of your current relationship, if you will in service of the partnership goes, what specifically are you doing with IBM in that space of what have you done? And then what are you continuing to do as we go through now, the vaccination process and the variant identification processes and all these things. So maybe you can share with our viewers a little bit about the kinds of things that you have been working on together and the kind of progress that you've been making. >>Yeah, well back, you know, a year ago, uh you know when the world was really at a standstill, uh sales first created a solution called work dot com, which was to engineer new technologies to help businesses kind of deal with the reality of a hard shutdown to business in the say prime um private sector and in the public sector to really create new innovation around key solutions like contact tracing that you might have needed to track, you know, kind of outbreak and uh you know, the rate of progression of the virus. And what we did with the IBM team, working with uh clients around the world, first was work together to deploy those technologies rapidly into the hands of our customers through those moments of opportunity and realization. You know, working with our clients, we also started here, you know, kind of about, you know, where we find ourselves today. This mass vaccination wave of where our citizens and societies are kind of on the recovery journey. And the work that we did with IBM was to start to plan out the next wave of recovery options around vaccine management sales force, creating the core vaccine scheduling, distribution in administration management services in IBM focusing on more of that credentialing and vaccination state of how someone has gone from receiving a shot and arm to now having a trusted profile of which vaccines. When did you receive them? Are they still accurate, valid? Uh, around those solutions. So where we're working with the IBM tape most acutely on covid now is in the vaccine credential management side through Watson health. >>Mm Well, can you give us an idea now? Let's if we can dig in a little deeper on some other things you talk about? Talk about core technologies, we talked about, I mentioned Edge you know, that's when people are trying to figure out how to integrate, you know, these edge technologies into their, into their primary systems now. So, um can you give us some examples, some specific examples of some things that you're actually collaborating on today in those areas or maybe another that comes to mind? >>Yeah. Edge computing is probably one of the other more exciting things that we're doing with the IBM team. And I think you find that really working with our field service business and IBM cloud services, you know, globally speaking on the edge, you know, as devices become smarter and more digital. Um, they have a lot of signals that organizations can now tap into not only for real time intelligence, but also fault intelligence. When a device actually is starting to need repair or preventative maintenance around the solutions that kind of need to be administered and the work that we're doing to really broker this connected, not just enterprise, but connected set of experiences, but by IBM super powerful here because the IBM edge services are now helping us get into anomaly detection, those anomaly detection czar automatically routing to workers who use the sales force field service capabilities. And now we can help organizations stay running uh, you know, safely and and with continuity, which is really all our customers are asking us for. So the ability for us to be creative and understand our parts of the picture together are really the things that I think are most exciting um for what we're doing for clients around >>the world. Now, you mentioned continuity, kind of a cousin of that and a security right, in a way, because, you know, um so what are you hearing from your your customer base these days with regard to security? You know, a lot of very high profile instances, certainly from bad state actors, as we will know. But what are you hearing in terms of security that you're looking at and maybe cooperating and collaborating with IBM on to make sure that those concerns are being addressed? >>Yeah. You know, I think, well, first off security is on the top of mines, you know, for all decision makers, executives today, it's the number one threat that a lot of companies are really needing to respond to, given what we've seen, uh you know, in the geopolitical world that we're in. Um and security isn't just about securing your servers, it's also about securing every operational touch point that you might have with, you know, your um uh every end user or even every customer that's inter operating with your services that you project as an organization. And what I love about working with the IBM team as we mentioned, you know, just such great insights across all parts of technology infrastructure to really help understand both the threat level, uh how to contain that threat level, and more importantly how to engineer, you know, with great solutions all the way into the hands of customers so that becomes safe and easy for all actors in your environment to really operate with. Um And that's where, you know, again, you think about a solution like mobile sales professionals, you know, they're out traveling around the world on mobile devices, sometimes they're even brought their own personal devices into the enterprise. And so IBM is a great partner for ours just to help us understand the overall threat level of every device, every moment that an employee might have within their organizational data and really helped create great solutions to keep organizations running >>safely. Yeah, I think it's uh interesting you talk about people bringing their own devices on back when I remember that from B y O B was like a huge thing, right? And this major problem or a conundrum and now it's, it's almost like an afterthought right? You got it solved, we got it well, taken care of, >>oh, you think about again, devices in the enterprise and how much we've been able to achieve with the will be becoming commonplace in norm even today, the working place from home kind of environment that we're in, I mean, who would have thought a year ago that most of our operations conducted safely from her home office is not just our regional and corporate offices. And again, that's the kind of thing that working with IBM has been such a great value for our clients because you know, no one could have forecasted that the context center would have had to move to your kitchen last year. Uh and yet, you know, we had to really go achieve that in this time and working great partners like IBM, it became not just a conversation but real practice. >>Right by the way, I think I said, B Y O. B. I met B Y O. D. And so you know where my mind's at? Uh >>I wasn't gonna correct you. I appreciate that. It's >>Just kind of hit me. I think that just that was a 40 and slip. Certainly. >>Hey Bill, thanks for >>the time. I certainly do appreciate it. Thanks for shining light on this really good partnership between Salesforce and IBM. And we wish you continued success down the road with that as well. >>Yeah. Thanks again and again. Love being your partner and love the impact we're having together. >>Great! Thank you very much. Bill Patterson joining us, the VP working Crm at Salesforce, talking about IBM and that relationship that they're putting into practice for their client base. John Wall's reporting here on the cube. Thanks for joining us with more on IBM thing. Yeah.

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital Experience Brought to You by Dell Technologies. >>Welcome to the Cubes Coverage of Dell Technologies World 2020. The digital coverage Find Lisa Martin And then we started to be talking with one of Dell Technologies customers. Earth Camp. Joining Me is built sharp, the senior VP of product development and strategy from Earth Camp Phil, Welcome to the Cube. >>Thank you so much. >>So talk to me a little bit. About what Earth Cam does this very interesting Web can technology? You guys have tens of thousands of cameras and sensors all over the globe give her audience and understanding of what you guys are all about. >>Sure thing. The world's leading provider of Webcam technologies and mentioned content services were leaders and live streaming time lapse imaging primary focus in the vertical construction. So a lot of these, the most ambitious, largest construction projects around the world, you see, these amazing time lapse movies were capturing all of that imagery. You know, basically, around the clock of these cameras are are sending all of that image content to us when we're generating these time lapse movies from it. >>You guys, you're headquartered in New Jersey and I was commenting before we went live about your great background. So you're actually getting to be on site today? >>Yes, Yes, that's where lives from our headquarters in Upper Saddle River, New Jersey. >>Excellent. So in terms of the types of information that you're capturing. So I was looking at the website and see from a construction perspective or some of the big projects you guys have done the Hudson Yards, the Panama Canal expansion, the 9 11 Museum. But you talked about one of the biggest focus is that you have is in the construction industry in terms of what type of data you're capturing from all of these thousands of edge devices give us a little bit of insight into how much data you're capturing high per day, how it gets from the edge, presumably back to your court data center for editing. >>Sure, and it's not just construction were also in travel, hospitality, tourism, security, architectural engineering, basically, any any industry that that need high resolution visualization of their their projects or their their performance or of their, you know, product flow. So it's it's high resolution documentation is basically our business. There are billions of files in the isil on system right now. We are ingesting millions of images a month. We are also creating very high resolution panoramic imagery where we're taking hundreds and sometimes multiple hundreds of images, very high resolution images and stitching these together to make panoramas that air up to 30 giga pixel, sometimes typically around 1 to 2 giga pixel. But that composite imagery Eyes represents millions of images per per month coming into the storage system and then being, uh, stitched together to those those composites >>the millions of images coming in every month. You mentioned Isil on talk to me a little bit about before you were working with Delhi, EMC and Power Scale. How are you managing this massive volume of data? >>Sure we had. We've used a number of other enterprise storage systems. It was really nothing was as easy to manage Azazel on really is there was there was a lot of a lot of problems with overhead, the amount of time necessary from a systems administrator resource standpoint, you to manage that, uh, and and it's interesting with the amount of data that we handle. This is being billions of relatively small files there there, you know, half a megabyte to a couple of megabytes each. It's an interesting data profile, which, which isil on really is well suited for. >>So if we think about some of the massive changes that we've all been through the last in 2020 what are some of the changes that that Earth Kemp has seen with respect to the needs for organizations? Or you mentioned other industries, like travel hospitality? Since none of us could get to these great travel destinations, Have you seen a big drive up in the demand and the need to process data more data faster? >>Yeah, that's an injury interesting point with with the Pandemic. Obviously we had to pivot and move a lot of people toe working from home, which we were able to do pretty quickly. But there's also an interesting opportunity that arose from this, where so many of our customers and other people also have to do the same. And there is an increased demand for our our technology so people can remotely collaborate. They can. They can work at a distance. They can stay at home and see what's going on in these projects sites. So we really so kind of an uptick in the in the need for our products and services. And we've also created Cem basically virtual travel applications. We have an application on the Amazon Fire TV, which is the number one app in the travel platform of people can kind of virtually travel when they can't really get out there. So it's, uh, we've been doing kind of giving back Thio to people that are having having some issues with being able to travel around. We've done the fireworks of the Washington Mall around the Statue of Liberty for the July 4th, and this year will be Webcasting and New Year's in Times Square for our 25th year, actually. So again, helping people travel virtually and be, uh, maintain can be collectivity with with each other and with their projects, >>which is so essential during these times, where for the last 67 months everyone is trying to get a sense of community, and most of us just have the Internet. So I also heard you guys were available on Apple TV, someone to fire that up later and maybe virtually travel. Um, but tell me a little bit about how working in conjunction with Delta Technologies and Power Cell How is that enabled you to manage this massive volume change you've experienced this year? Because, as you said, it's also about facilitating collaboration, which is largely online these days. >>Yeah, I mean, the the great things they're working with Dell has been just our confidence in this infrastructure. Like I said, the other systems we worked with in the past we've always found ourselves kind of second guessing. Obviously, resolutions are increasing. The camera performance is increasing. Streaming video is everything is is constantly getting bigger and better, faster. Maurits And we're always innovating. We found ourselves on previous storage platforms having to really kind of go back and look at the second guess we're at with it With with this, this did L infrastructure. That's been it's been fantastic. We don't really have to think about that as much. We just continue innovating everything scales as we needed to dio. It's it's much easier to work with, >>so you've got power scale at your core data center in New Jersey. Tell me a little bit about how data gets from thes tens of thousands of devices at the edge, back to your editors for editing and how power scale facilitates faster editing, for example. >>Basically, you imagine every one of these cameras on It's not just camera. We have mobile applications. We have fixed position of robotic cameras. There's all these different data acquisition systems were integrating with weather sensors and different types of telemetry. All of that data is coming back to us over the Internet, so these are all endpoints in our network. Eso that's that's constantly being ingested into our network and say WTO. I salon the big the big thing that's really been a timesaver Working with the video editors is, instead of having to take that content, move it into an editing environment where we have we have a whole team of award winning video editors. Creating these time lapse is we don't need to keep moving that around. We're working natively on Iselin clusters. They're doing their editing, their subsequent edits. Anytime we have to update or change these movies as a project evolves, that's all it happened right there on that live environment on the retention. Is there if we have to go back later on all of our customers, data is really kept within that 11 area. It's consolidated, its secure. >>I was looking at the Del Tech website. There's a case study that you guys did earth campaign with Deltek saying that the video processing time has been reduced 20%. So that's a pretty significant increase. I could imagine what the volumes changing so much now but on Li not only is huge for your business, but to the demands that your customers have as well, depending on where there's demands are coming from >>absolutely and and just being able to do that a lot faster and be more nimble allows us to scale. We've added actually against speaking on this pandemic, we've actually added person who we've been hiring people. A lot of those people are working remotely, as as we've stated before on it's just with the increase in business. We have to continue to keep building on that on this storage environments been been great. >>Tell me about what you guys really kind of think about with respect to power scale in terms of data management, not storage management and what that difference means to your business. >>Well, again, I mean number number one was was really eliminating the amount of resource is amount of time we have to spend managing it. We've almost eliminated any downtime of any of any kind. We have greater storage density, were able to have better visualization on how our data is being used, how it's being access so as thes as thes things, a revolving. We really have good visibility on how the how the storage system is being used in both our production and our and also in our backup environments. It's really, really easy for us Thio to make our business decisions as we innovate and change processes, having that continual visibility and really knowing where we stand. >>And you mentioned hiring folks during the pandemic, which is fantastic but also being able to do things much in a much more streamlined way with respect to managing all of this data. But I am curious in terms of of innovation and new product development. What have you been able to achieve because you've got more resource is presumably to focus on being more innovative rather than managing storage >>well again? It's were always really pushing the envelope of what the technology can do. As I mentioned before, we're getting things into, you know, 20 and 30 Giga pixel. You know, people are talking about megapixel images were stitching hundreds of these together. We've we're just really changing the way imagery is used, uh, both in the time lapse and also just in archival process. Ah, lot of these things we've done with the interior. You know, we have this virtual reality product where you can you can walk through and see in the 3 60 bubble. We're taking that imagery, and we're combining it with with these been models who are actually taking the three D models of the construction site and combining it with the imagery. And we can start doing things to visualize progress and different things that are happening on the site. Look for clashes or things that aren't built like they're supposed to be built, things that maybe aren't done on the proper schedule or things that are maybe ahead of schedule, doing a lot of things to save people, time and money on these construction sites. We've also introduced a I machine learning applications into directly into the workflow in this in the storage environment. So we're detecting equipment and people and activities in the site where a lot of that would have been difficult with our previous infrastructure, it really is seamless and working with YSL on now. >>Imagine, by being able to infuse AI and machine learning, you're able to get insight faster to be ableto either respond faster to those construction customers, for example, or alert them. If perhaps something isn't going according to plan. >>A lot of it's about schedule. It's about saving money about saving time and again, with not as many people traveling to the sites, they really just have have constant visualization of what's going on. Day to day, we're detecting things like different types of construction equipment and things that are happening on the side. We're partnering with people that are doing safety analytics and things of that nature. So these these are all things that are very important to construction sites. >>What are some of the things as we are rounding out the calendar year 2020? What are some of the things that you're excited about going forward in 2021? That Earth cam is going to be able to get into and to deliver >>it, just MAWR and more people really, finally seeing the value. I mean, I've been doing this for 20 years, and it's just it's it's It's amazing how we're constantly seeing new applications and more people understanding how valuable these visual tools are. That's just a fantastic thing for us because we're really trying to create better lives through visual information. We're really helping people with things they can do with this imagery. That's what we're all about that's really exciting to us in a very challenging environment right now is that people are are recognizing the need for this technology and really starting to put it on a lot more projects. >>Well, it's You can kind of consider an essential service, whether or not it's a construction company that needs to manage and oversee their projects, making sure they're on budget on schedule, as you said, Or maybe even just the essential nous of helping folks from any country in the world connect with a favorite favorite travel location or sending the right to help. From an emotional perspective, I think the essential nous of what you guys are delivering is probably even more impactful now, don't you think? >>Absolutely and again about connecting people and when they're at home. And recently we we webcast the president's speech from the Flight 93 9 11 observation from the memorial. There was something where the only the immediate families were allowed to travel there. We webcast that so people could see that around the world we have documented again some of the biggest construction projects out there. The new rate years greater stadium was one of the recent ones, uh, is delivering this kind of flagship content. Wall Street Journal is to use some of our content recently to really show the things that have happened during the pandemic in Times Square's. We have these cameras around the world. So again, it's really bringing awareness of letting people virtually travel and share and really remain connected during this this challenging time on and again, we're seeing a really increase demand in the traffic in those areas as well. >>I can imagine some of these things that you're doing that you're achieving now are going to become permanent, not necessarily artifacts of Cove in 19 as you now have the opportunity to reach so many more people and probably the opportunity to help industries that might not have seen the value off this type of video to be able to reach consumers that they probably could never reach before. >>Yeah, I think the whole nature of business and communication and travel on everything is really going to be changed from this point forward. It's really people are looking at things very, very differently and again, seeing the technology really can help with so many different areas that, uh, that it's just it's gonna be a different kind of landscape out there we feel on that's really, you know, continuing to be seen on the uptick in our business and how many people are adopting this technology. We're developing a lot more. Partnerships with other companies were expanding into new industries on again. You know, we're confident that the current platform is going to keep up with us and help us, you know, really scale and evolved as thes needs air growing. >>It sounds to me like you have the foundation with Dell Technologies with power scale to be able to facilitate the massive growth that you're saying and the skill in the future like you've got that foundation. You're ready to go? >>Yeah, we've been We've been We've been using the system for five years already. We've already added capacity. We can add capacity on the fly, Really haven't hit any limits. And what we can do, It's It's almost infinitely scalable, highly redundant. Gives everyone a real sense of security on our side. And, you know, we could just keep innovating, which is what we do without hitting any any technological limits with with our partnership. >>Excellent. Well, Bill, I'm gonna let you get back to innovating for Earth camp. It's been a pleasure talking to you. Thank you so much for your time today. >>Thank you so much. It's been a pleasure >>for Bill Sharp and Lisa Martin. You're watching the cubes. Digital coverage of Dell Technologies World 2020. Thanks for watching. Yeah,

>> Announcer: From around the globe, it's theCUBE! With digital coverage of Dell Technologies World, digital experience. Brought to you by Dell Technologies. >> Welcome to theCUBE's coverage of Dell Technologies World 2020, the digital coverage. I'm Lisa Martin, and I'm excited to be talking with one of Dell Technologies' customers EarthCam. Joining me is Bill Sharp, the senior VP of product development and strategy from EarthCam. Bill, welcome to theCUBE. >> Thank you so much. >> So talk to me a little bit about what EarthCam does. This is very interesting webcam technology. You guys have tens of thousands of cameras and sensors all over the globe. Give our audience an understanding of what you guys are all about. >> Sure thing. The world's leading provider of webcam technologies, you mentioned content and services, we're leaders in live streaming, time-lapse imaging, primary focus in the vertical construction. So with a lot of these, the most ambitious, largest construction projects around the world that you see these amazing time-lapse movies, we're capturing all of that imagery basically around the clock, these cameras are sending all of that image content to us and we're generating these time-lapse movies from it. >> You guys are headquartered in New Jersey. I was commenting before we went live about your great background. So you're actually getting to be onsite today? >> Yes, yes. We're live from our headquarters in upper Saddle River, New Jersey. >> Excellent, so in terms of the types of information that you're capturing, so I was looking at the website, and see from a construction perspective, some of the big projects you guys have done, the Hudson Yards, the Panama Canal expansion, the 9/11 museum. But you talked about one of the biggest focuses that you have is in the construction industry. In terms of what type of data you're capturing from all of these thousands of edge devices, give us a little bit of an insight into how much data you're capturing per day, how it gets from the edge, presumably, back to your core data center for editing. >> Sure, and it's not just construction. We're also in travel, hospitality, tourism, security, architecture, engineering, basically any industry that need high resolution visualization of their projects or their performance or their product flow. So it's high resolution documentation is basically our business. There are billions of files in the Isilon system right now. We are ingesting millions of images a month. We are also creating very high resolution panoramic imagery where we're taking hundreds and sometimes multiple hundreds of images, very high resolution images and stitching these together to make panoramas that are up to 30 gigapixel sometimes. Typically around one to two gigapixel but that composite imagery represents millions of images per month coming into the storage system and then being stitched together to those composites. >> So millions of images coming in every month, you mentioned Isilon. Talk to me a little bit about before you were working with Dell EMC and PowerScale, how were you managing this massive volume of data? >> Sure, we've used a number of other enterprise storage systems. It was really nothing was as easy to manage as Isilon really is. There was a lot of problems with overhead, the amount of time necessary from a systems administrator resource standpoint, to manage that. And it's interesting with the amount of data that we handle, being billions of relatively small files. They're, you know, a half a megabyte to a couple of megabytes each. It's an interesting data profile which Isilon really is well suited for. >> So if we think about some of the massive changes that we've all been through in the last, in 2020, what are some of the changes that EarthCam hasn't seen with respect to the needs for organizations, or you mentioned other industries like travel, hospitality, since none of us can get to these great travel destinations, have you seen a big drive up in the demand and the need to process more data faster? >> Yeah, that's an interesting point with the pandemic. I mean, obviously we had to pivot and move a lot of people to working from home, which we were able to do pretty quickly, but there's also an interesting opportunity that arose from this where so many of our customers and other people also have to do the same. And there is an increased demand for our technology. So people can remotely collaborate. They can work at a distance, they can stay at home and see what's going on in these project sites. So we really saw kind of an uptick in the need for our products and services. And we've also created some basically virtual travel applications. We have an application on the Amazon Fire TV which is the number one app in the travel platform, and people can kind of virtually travel when they can't really get out there. So it's, we've been doing kind of giving back to people that are having some issues with being able to travel around. We've done the fireworks at the Washington Mall around the Statue of Liberty for July 4th. And this year we'll be webcasting New Years in Times Square for our 25th year, actually. So again, helping people travel virtually and maintain connectivity with each other, and with their projects. >> Which is so essential during these times where for the last six, seven months, everyone is trying to get a sense of community and most of us just have the internet. So I also heard you guys were available on the Apple TV, someone should fire that up later and maybe virtually travel. But tell me a little bit about how working in conjunction with Dell Technologies and PowerScale. How has that enabled you to manage this massive volume change that you've experienced this year? Because as you said, it's also about facilitating collaboration which is largely online these days. >> Yeah, and I mean, the great things of working with Dell has been just our confidence in this infrastructure. Like I said, the other systems we've worked with in the past we've always found ourselves kind of second guessing. We're constantly innovating. Obviously resolutions are increasing. The camera performance is increasing, streaming video is, everything is constantly getting bigger and better, faster, more, and we're always innovating. We found ourselves on previous storage platforms having to really kind of go back and look at them, second guess where we're at with it. With the Dell infrastructure it's been fantastic. We don't really have to think about that as much. We just continue innovating, everything scales as we need it to do. It's much easier to work with. >> So you've got PowerScale at your core data center in New Jersey. Tell me a little bit about how data gets from these tens of thousands of devices at the edge, back to your editors for editing, and how PowerScale facilitates faster editing, for example. >> Well, basically you can imagine every one of these cameras, and it's not just cameras. It's also, you know, we have 360 virtual reality kind of bubble cameras. We have mobile applications, we have fixed position and robotic cameras. There's all these different data acquisition systems we're integrating with weather sensors and different types of telemetry. All of that data is coming back to us over the internet. So these are all endpoints in our network. So that's constantly being ingested into our network and saved to Isilon. The big thing that's really been a time saver working with the video editors is instead of having to take that content, move it into an editing environment where we have a whole team of award-winning video editors creating these time lapses. We don't need to keep moving that around. We're working natively on Isilon clusters. They're doing their editing there, and subsequent edits. Anytime we have to update or change these movies as a project evolves, that's all, can happen right there on that live environment. And the retention is there. If we have to go back later on, all of our customers' data is really kept within that one area, it's consolidated and it's secure. >> I was looking at the Dell Tech website, and there's a case study that you guys did, EarthCam did with Dell Tech saying that the video processing time has been reduced 20%. So that's a pretty significant increase. I can imagine with the volumes changing so much now, not only is huge to your business but to the demands that your customers have as well, depending on where those demands are coming from. >> Absolutely. And just being able to do that a lot faster and be more nimble allows us to scale. We've added actually, again, speaking of during this pandemic, we've actually added personnel, we've been hiring people. A lot of those people are working remotely as we've stated before. And it's just with the increase in business, we have to continue to keep building on that, and this storage environment's been great. >> Tell me about what you guys really kind of think about with respect to PowerScale in terms of data management, not storage management, and what that difference means to your business. >> Well, again, I mean, number one was really eliminating the amount of resources. The amount of time we have to spend managing it. We've almost eliminated any downtime of any kind. We have greater storage density, we're able to have better visualization on how our data is being used, how it's being accessed. So as these things are evolving, we really have good visibility on how the storage system is being used in both our production and also in our backup environments. It's really, really easy for us to make our business decisions as we innovate and change processes, having that continual visibility and really knowing where we stand. >> And you mentioned hiring folks during the pandemic, which is fantastic, but also being able to do things in a much more streamlined way with respect to managing all of this data. But I am curious in terms of innovation and new product development, what have you been able to achieve? Because you've got more resources presumably to focus on being more innovative rather than managing storage. >> Well, again, it's, we're always really pushing the envelope of what the technology can do. As I mentioned before, we're getting things into, you know, 20 and 30 gigapixels, people are talking about megapixel images, we're stitching hundreds of these together. We're just really changing the way imagery is used both in the time lapse and also just in archival process. A lot of these things we've done with the interior, we have this virtual reality product where you can walk through and see in a 360 bubble, we're taking that imagery and we're combining it with these BIM models. So we're actually taking the 3D models of the construction site and combining it with the imagery. And we can start doing things to visualize progress, and different things that are happening on the site, look for clashes or things that aren't built like they're supposed to be built, things that maybe aren't done on the proper schedule or things that are maybe ahead of schedule, doing a lot of things to save people time and money on these construction sites. We've also introduced AI and machine learning applications directly into the workflow in the storage environment. So we're detecting equipment and people and activities in the site where a lot of that would have been difficult with our previous infrastructure. It really is seamless and working with Isilon now. >> I imagine by being able to infuse AI and machine learning, you're able to get insights faster, to be able to either respond faster to those construction customers, for example, or alert them if perhaps something isn't going according to plan. >> Yeah, a lot of it's about schedule, it's about saving money, about saving time. And again, with not as many people traveling to these sites, they really just have to have constant visualization of what's going on day to day. We're detecting things like different types of construction equipment and things that are happening on the site. We're partnering with people that are doing safety analytics and things of that nature. So these are all things that are very important to construction sites. >> What are some of the things as we are rounding out the calendar year 2020, what are some of the things that you're excited about going forward in 2021, that EarthCam is going to be able to get into and to deliver? >> Just more and more people really finally seeing the value. I mean I've been doing this for 20 years and it's just, it's amazing how we're constantly seeing new applications and more people understanding how valuable these visual tools are. That's just a fantastic thing for us because we're really trying to create better lives through visual information. We're really helping people with the things they can do with this imagery. That's what we're all about. And that's really exciting to us in a very challenging environment right now is that people are recognizing the need for this technology and really starting to put it on a lot more projects. >> Well, you can kind of consider it an essential service whether or not it's a construction company that needs to manage and oversee their projects, making sure they're on budget, on schedule, as you said, or maybe even just the essentialness of helping folks from any country in the world connect with a favorite travel location, or (indistinct) to help from an emotional perspective. I think the essentialness of what you guys are delivering is probably even more impactful now, don't you think? >> Absolutely. And again about connecting people when they're at home, and recently we webcast the president's speech from the Flight 93 9/11 observation from the memorial, there was something where only the immediate families were allowed to travel there. We webcast that so people could see that around the world. We've documented, again, some of the biggest construction projects out there, the new Raiders stadium was one of the recent ones, just delivering this kind of flagship content. Wall Street Journal has used some of our content recently to really show the things that have happened during the pandemic in Times Square. We have these cameras around the world. So again, it's really bringing awareness. So letting people virtually travel and share and really remain connected during this challenging time. And again, we're seeing a real increased demand in the traffic in those areas as well. >> I can imagine some of these things that you're doing that you're achieving now are going to become permanent not necessarily artifacts of COVID-19, as you now have the opportunity to reach so many more people and probably the opportunity to help industries that might not have seen the value of this type of video to be able to reach consumers that they probably could never reach before. >> Yeah, I think the whole nature of business and communication and travel and everything is really going to be changed from this point forward. It's really, people are looking at things very, very differently. And again, seeing that the technology really can help with so many different areas that it's just, it's going to be a different kind of landscape out there we feel. And that's really continuing to be seen as on the uptick in our business and how many people are adopting this technology. We're developing a lot more partnerships with other companies, we're expanding into new industries. And again, you know, we're confident that the current platform is going to keep up with us and help us really scale and evolve as these needs are growing. >> It sounds to me like you have the foundation with Dell Technologies, with PowerScale, to be able to facilitate the massive growth that you were saying and the scale in the future, you've got that foundation, you're ready to go. >> Yeah, we've been using the system for five years already. We've already added capacity. We can add capacity on the fly, really haven't hit any limits in what we can do. It's almost infinitely scalable, highly redundant. It gives everyone a real sense of security on our side. And you know, we can just keep innovating, which is what we do, without hitting any technological limits with our partnership. >> Excellent, well, Bill, I'm going to let you get back to innovating for EarthCam. It's been a pleasure talking to you. Thank you so much for your time today. >> Thank you so much. It's been a pleasure. >> For Bill Sharp, I'm Lisa Martin, you're watching theCUBE's digital coverage of Dell Technologies World 2020. Thanks for watching. (calm music)

>> Narrator: From around the globe it's theCUBE with digital coverage of VMworld 2020 brought to you by VMware and its ecosystem partners. >> Welcome back, I'm Stu Miniman and this is theCUBE's coverage of VMworld 2020 hard to believe our 11th year covering the event. Of course, the first year it is a global virtual event. So happy to welcome to the program. We're going to be talking about some of the multicloud networking. So first from Equinix, Bill Long, he's the senior vice president of product management and joining him the founder of VeloCloud Sanjay Uppal, he's the senior vice president and general manager for a VeloCloud. Now part of VMware, Bill and Sanjay thanks, thank you so much for joining us. >> Happy to be here. >> All right so Bill, we had Sanjay on the program last year, hard to believe, you know, Sanjay and I were talking, it's been over two years since the acquisition of VeloCloud, of course, SD-WAN has been real growth. Equinix has gone through, you know, changes, you have an acquisition I want to talk about a little bit later, but since we're at VMworld, Bill, want to start with that partnership, of course every, I think most people know Equinix. You've got data centers around the globe you've got direct connects into the big public clouds and obviously a long partnership with VMware. So bring us up to date. >> Yeah, so obviously with our data centers, there's tons of virtual machines running, running in our data centers today but as we, you know, Equinix is a bit special because our specialty is, is the place where networks connect to each other. So as people are moving more to a distributed hybrid multicloud type of architecture, especially even with the work from home, with COVID, they wanted to be able to get their infrastructure deployed out further, closer to their users. I mean previously to do that they had to actually physically deploy hardware. So the exciting sort of evolution, the partnership with VMware is what we're doing with VeloCloud, where you can actually deploy the SD-WAN infrastructure on a virtualized basis, spin up the VeloCloud infrastructure and have your SD-WAN solution without actually having to ship physical equipment. So what it's doing for our customers, it's allowing them to really move to that distributed hybrid multicloud architecture without a lot of the requirements that were previously there for shipping and appliance, installing appliance, wiring it up. So we've sort of predeployed all of that and made the buying experience much more fluid, which was super fortuitous timing given sort of the environment that we're all in today, where you can't just hop on a plane and put it, ship an appliance somewhere and install it. So it's a longterm relationship and we're super thrilled to have him on the network edge platform today so that it can be enabled virtually not just physical. >> Yeah, so Sanjay. I want to follow up on what Bill was just talking about there, of course, being acquired by VMware, being a software solution makes a lot of sense in that vision that we've had for SDN for NFV was I want to get rid of the physical appliances. We want to make it much easier to be able to deliver those services. So, you know, I'd love to hear a little bit, especially here in 2020, the architecture of your service just makes it easy to deploy, easy to help customers do what they need to do. >> Yes Stu and Bill and it's all about the simplicity and automation. This whole thing about software defined WAN or SD-WAN came about because customers found it really difficult to have stacks of these physical appliances that they had to deploy at every endpoint in their network and then in a hub and spoke model where the hubs were there to deploy another set of appliances. So the software defined part of this, right from the get go for VeloCloud now at VMware was to make this really be simple and automated and how do you get simplicity and automation? Well, you take most of the complexity out. And the way that we took the complexity out was to collapse all of that hardware at the end point and make it one very simple, easy to deploy appliance and then the other end, which is the one that Bill was just talking about for the collocated entities, where we had our software defined, ran gateways, there was physical infrastructure that was required and Equinix has been a partner, right from 2013 to get those gateways deployed at the SD-WAN points of presence. The great thing now, and you've heard the announcements at VMworld. The great thing now is that you don't have to deploy all that hardware at the hub end points. All you do is use Equinix's network edge. You get our SD-WAN gateways deploy them as software, and now you can also scale it out and get your SASE or secure access service edge deployed in exactly the same way. >> Bill there's often just discussion of hybrid and multi-cloud, and sometimes it's a little hard to wrap our brains around what customers are doing. I know when I tour a data center, like one of yours, it really, you can understand what's happening. what I'm curious about is, in some of your data centers connecting between clouds could just be, I'm connecting a cable over a couple of rows, or I've got an interconnect to AWS to Azure, or the light. Does SD-WAN fit into those environments where we're connecting close is where does, and maybe, maybe there's some areas that SD-WAN doesn't fit in. 'Cause we know SD-WAN has been really that interconnected fabric, helping customers do their multicloud. I'm just curious in your world kind of the parameters. >> Yeah, I think we, as Sanjay did a great job of describing it as the hub and spoke where you have the hub that might be like a branch office location, where you want to bring that traffic in to the spoke would be the end officer in the hub. You want to bring that hub in and then that traffic might want to go out to a cloud as you were talking about the use case you just described, but what's important is you want that hub to be the place where the most ISPs or internet service providers are such that you're aggregating that traffic efficiently locally and then you want to make sure you're getting that traffic over to a cloud as efficiently as possible. So luckily Equinix, you know, locations, what's so special about those is not just, it's not just a data center It's, who's in the data centers that are with you. So in the locations where we have the VeloCloud gateway and the network edge solution, they're all located together. So you have, it's a local place where the ISP networks terminate so you can originate that traffic from a home office worker or any kind of branch office location. You can then get it into the VeloCloud edge and since, and then out to the cloud and most of the time those cloud edge nodes are actually physically in an Equinix data center. So if you look for solution, has the least amount of hops can have the least amount of latency, highest performance, fewest amounts of points of failure. It's really a very efficient architecture to be there and of course, with less sort of networks to traverse, it's also a pretty cost effective as well. >> Yeah, absolutely. You know, as, as Bill pointed out, SD-WAN solves two problems. So it is, it used to start off as a branch office solution, but it's the name doesn't connote anything that is specific only to branch offices when you get to multi-cloud and you have information that needs to go, let's say from one of the public cloud providers, Amazon to another one Azure Equinix is a perfect location to do that because both those cloud providers and others meet at Equinix, and then also the network providers meet at Equinix. Now we are the software layer that directs the traffic and that steers it between cloud providers, from branches to cloud providers, from remote users to cloud providers and that Equinix is the perfect meeting point. We are that SD-WAN software layer. That's kind of like the traffic cop, that steers the traffic around. >> Wonderful well, Bill, Sanjay was talking earlier about the secure access service edge, SASE. I keep having to go through my head. It's not a self addressed stamped envelope anymore, but Bill help us understand, you know, what Equinix, how Equinix helps deliver this environment. We know you've got a great footprint on not only the cloud partnerships, but really locations that help us get to the, you know, the customer's edge. >> Yeah, it's very similar to what we're talking about earlier, where if you're looking for an efficient way to basically originate traffic closest to the user, such as the highest performance being, the fact that we're in over 200 data centers spread out around the world means that we can be local to where the actual traffic wants to be. So, and we're connected to all the ISPs that are local so you bring that traffic in and then you can provide a security perimeter around that today. Like what the edge instance and what the Velocloud solution has but then also when you want to connect that out to the rest of your infrastructure, whether that's one of the, 10,000 enterprise customers that happens to live within Equinix today, or if you need to connect to your MPLS provider, that's all there as well. So it really acts as that hub for bringing you in to be able to efficiently originate that traffic into one of these SASE platforms, and then, do the types of things that Sanjay was talking about with the software layer on top to be able to secure it and really do special routing with the traffic, such that it has the highest performance and is more secure. So making sure that it's distributed, efficient, aggregation of local traffic, but then also having the option to be able to connect out to whoever you want to connect out to. Some of the ways that we've seen that evolve as Sanjay will talk about the, it works not only for branch offices, but from work from home users as well. The fact that people were local in our data center and as traffic we saw earlier this year, as much as a 40% increase in some locations in traffic, in order to scale that it was a matter of just setting up a couple of cross connects instead of having to procure multiple hundred gig circuits from a network service provider. So having all of those pieces co-resident in a single location really helps unlock the potential of the SASE architecture. >> Sanjay one of the items that caught everybody's attention in the keynote is project Monterey. We went the cloud native with project Pacific last year now really going deep with Monterey for AI and edge. There's some similarities between the branch technology that you said we started with with VeloCloud. Does SD-WAN fit into this Monterey discussion when we talk about edge? And then Bill, I expect this as somewhere where packet fits in. So definitely want to hear your commentary on it too. >> Yeah, absolutely. I think the bigger picture here is traffic comes from all these locations. So whether it is remote users, branch office users, campus users, and those are the entry points into the network. Then they come and they meet at the meeting point. One of the best meeting points is Equinix and then at that meeting point you can analyze the traffic. Obviously you want to keep privacy and security in mind, you can add additional security services as you go, which is the whole idea behind SASE. If you're going to aggregate this traffic and bring it to those meeting points, the meeting points are the perfect places to add the additional security, whether it is web-based security, or it is firewall security, you add that there and then the traffic goes on to its final destination as Bill was talking about and that final destination may be a cloud provider. It may be through another carrier, an MPLS provider, or maybe just back to the enterprise's data center. Now the whole idea here is as you collect the traffic, you can also analyze parts of it again, keeping privacy in mind and this is where AI comes in because you can have specific algorithms going in and figure it out when something goes wrong in the network, how to recognize that and how to self-heal. So self-healing is one of those areas that we are working on, but the infrastructure that we're putting in place between Equinix and us is a necessary building block to be able to get to that self-healing end state that we're all looking forward to. >> Great, Bill on the edge piece. Am I right that this is something that packet's going to help with? >> Absolutely. So I mentioned earlier where the model forever, the value of Equinix has been, as Sanjay was highlighting the ability to connect to the ecosystems you want to care about, but that, as I mentioned, it previously required, even if you want to have VMs, you still had to have physical servers that those VMs had to be on and that was, sort of a barrier to people adopting the hybrid multicloud architecture. So what we've done with the acquisition of packet is we're basically predeploying infrastructure that you can then load whatever VMs you want on top of that, such that you can use a SASE type of architecture to originate and secure your traffic and then connect that directly into a set of predeployed infrastructure and Equinix but to be very clear, what we're trying to do is we're still sort of the place that you put your workloads, but instead of having to ship physical equipment, we're just predeploying the infrastructure for you. So our goal is to really have infrastructure at software speed, but we still will continue to stop really at the software layer. So what that, the packet acquisition was really about was removing one of those barriers of having to physically deploy equipment, such that people can, deploy their infrastructure much more quickly and without the constraints and time and Capex requirements of actually having to put, to deploy physical equipment. >> Yeah, yeah, definitely and that's why, you know, I've heard quite a bit about packet at the Amazon shows the last couple of years, so exciting and it seems like a solid fit, not only for Equinix, but to expand the VMware partnership. Bill I'm wondering, you know, long partnership, any specific customer examples that you might be able to share, even if it's anonymized, as you know, how SD-WAN is really helping customers along with their business challenges. >> Yeah. we've got a couple of great proofs of concept coming along. Right now there's a manufacturing company that we're working with who needs to be able to localize traffic from one of their, both their headquarters and a couple of their factories and so they're looking to be able to, aggregate that traffic locally and then connect it to their, to their infrastructure that's actually physically in Equinix. So this is a, this is a GA solution. We've got a couple of great proofs of concepts going, and this is it's ready to scale. >> Wonderful. Sanjay I want to give you and Bill final words, final takeaways you want people to have with regarding the partnership and VMworld 2020. >> Yeah, sure. So, you know, we've been partners, like I said, since 2013, and we really value this relationship. It started off by us saying right in the beginning, the cloud is the network. That was our mantra, right from the get go. Now the cloud being the network, one of the buffet meeting places for the cloud was Equinix. So we located our SD-WAN gateways there. It did require hardware infrastructure to be put in, but of course people would much rather have a beer or drink a latte instead of rolling out hardware and so Bill came along and said, Hey, I have this perfectly good thing called network edge. Doesn't require to put all your hardware in. You can get your SD-WAN software deployed and so we made that gateway into a virtual edge on the context of a specific enterprise and now literally at the click of a button, you can deploy a virtual edges at Equinix locations, don't have to roll out any specific hardware and from that meeting point, you can get to any cloud provider, you know, many of the MPLS providers and then get access in low latency. Latency is now the criteria everybody's looking at and so between what Bill is doing and what we are doing, it is solving that enterprise's problem, using the latency, getting all their entry points at this meeting place. So thank you Bill. >> And thank you, Sanjay. I think you summed it up really well. I think, as Sanjay highlighted the value has been there for a long time of what it means to have a great SD-WAN solution in the right place and I think now with what we've done together, making that available on a point and click, we really have the easy button and so I know from customer conversations that I've had, I get requested for VeloCloud being available network edge. Now that it's there, I'm confident there's going to be a lot of unlock now that we have the easy button. So I think it's a great combination of a value made easy and I think the partnership is going to get stronger from here. >> Well, definitely lowering the latency to be able to deploy those solutions faster is what customers need. Sanjay, Bill thank you so much for joining us, congrats on the progress and look forward to hear more in the future. >> Thanks Stu. >> Stick with us for more coverage from VMworld 2020. I'm Stu Miniman and thank you for watching theCUBE. 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>> Narrator: From theCUBE studios in Palo Alto in Boston, connecting with our leaders all around the world. This is theCUBE conversation. >> Welcome back everybody. Jeff Frick here with theCUBE we are in our Palo Alto studios today. We're still getting through COVID, thankfully media was a necessary industry, so we've been able to come in and keep a small COVID crew, but we can still reach out to the community and through the magic of the internet and camera's on laptops, we can reach out and touch base with our friends. So we're excited to have somebody who's talking about and working on kind of the next big edge, the next big cutting thing going on in technology. And that's the internet of things you've heard about it the industrial Internet of Things. There's a lot of different words for it. But the foundation of it is this company it's Intel. We're happy to have joined us Bill Pearson. He is the Vice President of Internet of Things often said IoT for Intel, Bill, great to see you. >> Same Jeff. Nice to be here. >> Yeah, absolutely. So I just was teasing getting ready for this interview, doing a little homework and I saw you talking about Internet of Things in a 2015 interview, actually referencing a 2014 interview. So you've been at this for a while. So before we jump into where we are today, I wonder if you can share, you know, kind of a little bit of a perspective of what's happened over the last five or six years. >> I mean, I think data has really grown at a tremendous pace, which has changed the perception of what IoT is going to do for us. And the other thing that's been really interesting is the rise of AI. And of course we need it to be able to make sense of all that data. So, you know, one thing that's different is today where we're really focused on how do we take that data that is being produced at this rapid rate and really make sense of it so that people can get better business outcomes from that. >> Right, right. But the thing that's so interesting on the things part of the Internet of Things and even though people are things too, is that the scale and the pace of data that's coming off, kind of machine generated activity versus people generated is orders of magnitude higher in terms of the frequency, the variety, and all kind of your classic big data meme. So that's a very different challenge then, you know, kind of the growth of data that we had before and the types of data, 'cause it's really gone kind of exponential across every single vector. >> Absolutely. It has, I mean, we've seen estimates that data is going to increase by about five times as much as it is today, over the next, just a couple years. So it's exponential as you said. >> Right. The other thing that's happened is Cloud. And so, you know, kind of breaking the mold of the old mold roar, all the compute was either in your mini computer or data center or mainframe or on your laptop. Now, you know, with Cloud and instant connectivity, you know, it opens up a lot of different opportunities. So now we're coming to the edge and Internet of Things. So when you look at kind of edge in Internet of Things, kind of now folding into this ecosystem, you know, what are some of the tremendous benefits that we can get by leveraging those things that we couldn't with kind of the old infrastructure and our old way kind of gathering and storing and acting on data? >> Yeah. So one of the things we're doing today with the edge is really bringing the compute much closer to where all the data is being generated. So these sensors and devices are generating tons and tons of data and for a variety of reasons, we can't send it somewhere else to get processed. You know, there may be latency requirements for that control loop that you're running in your factory or there's bandwidth constraints that you have, or there's just security or privacy reasons to keep it onsite. And so you've got to process a lot of this data onsite and maybe some estimates or maybe half of the data is going to remain onsite here. And when you look at that, you know, that's where you need compute. And so the edge is all about taking compute, bringing it to where the data is, and then being able to use the intelligence, the AI and analytics to make sense of that data and take actions in real time. >> Right, right. But it's a complicated situation, right? 'Cause depending on where that edge is, what the device is, does it have power? Does it not have power? Does it have good connectivity? Does it not have good connectivity? Does it have even the ability to run those types of algorithms or does it have to send it to some interim step, even if it doesn't have, you know, kind of the ability to send it all the way back to the Cloud or all the way back to the data center for latency. So as you kind of slice and dice all these pieces of the chain, where do you see the great opportunity for Intel, where's a good kind of sweet spot where you can start to bring in some compute horsepower and you can start to bring in some algorithmic processing and actually do things between just the itty-bitty sensor at the itty-bitty end of the chain versus the data center that's way, way upstream and far, far away. >> Yeah. Our business is really high performance compute and it's this idea of taking all of these workloads and bringing them in to this high performance compute to be able to run multiple software defined workloads on single boxes, to be able to then process and analyze and store all that data that's being created at the edge, do it in a high performance way. And whether that's a retail smart shelf, for example, that we can do realtime inventory on that shelf, as things are coming and going, or whether it's a factory and somebody's doing, you know,real time defect detection of something moving across their textile line. So all of that comes down to being able to have the compute horsepower, to make sense of the data and do something with it. >> Right, right. So you wouldn't necessarily like in your shelf example that the compute might be done there at the local store or some aggregation point beyond just that actual, you know, kind of sensor that's underneath that one box of tide, if you will. >> Absolutely. Yeah, you could have that on-prem, a big box that does multiple shelves, for example. >> Okay, great. So there's a great example and you guys have the software development kit, you have a lot of resources for developers and in one of the case studies that I just wanted to highlight before we jump into the dev side was I think Audi was the customer. And it really illustrates a point that we talked about a lot in kind of the big data meme, which is, you know, people used to take action on a sample of data after the fact. And I think this case here we're talking about running 1,000 cars a day through this factory, they're doing so many welds, 5 million welds a day, and they would pull one at the end of the day, sample a couple welds and did we have a good day or not? Versus what they're doing now with your technology is actually testing each and every weld as it's being welded, based on data that's coming off the welding machine and they're inspecting every single weld. So I just love you've been at this for a long time. When you talk to customers about what is possible from a business point of view, when you go from after the fact with a sample of data, to in real time with all the data, how that completely changes your view and ability to react to your business. >> Yeah. I mean, it makes people be able to make better decisions in real time. You know, as you've got cameras on things like textile manufacturers or footwear manufacturers, or even these realtime inventory examples you mentioned, people are going to be able to make and can make decisions in real time about how to stock that shelf, what to order about what to pull off the line, am I getting a good product or not? And this has really changed, as you said, we don't have to go back and sample anymore. You can tell right now as that part is passing through your manufacturing line, or as that item is sitting on your shelf, what's happening to it. It's really incredible. >> So let's talk about developers. So you've got a lot of resources available for developers and everyone knows Intel obviously historically in PCs and data centers. And you would do what they call design wins back when I was there, many moons ago, right? You try to get a design win and then, you know, they're going to put your microprocessors and a bunch of other components in a device. When you're trying to work with, kind of Cutting Edge Developers in kind of new fields and new areas, this feels like a much more direct touch to the actual people building the applications than the people that are really just designing the systems of which Intel becomes a core part of. I wonder if you could talk about, you know, the role developers and really Intel's outreach to developers and how you're trying to help them, you know, kind of move forward in this new crazy world. >> Yeah, developers are essential to our business. They're essential to IoT. Developers, as you said, create the applications that are going to really make the business possible. And so we know the value of developers and want to make sure that they have the tools and resources that they need to use our products most effectively. We've done some things around OpenVINO toolkit as an example, to really try and simplify, democratize AI application so that more developers can take advantage of this and, you know, take the ambitions that they have to do something really interesting for their business, and then go put it into action. And the whole, you know, our whole purpose is making sure we can actually accomplish that. >> Right. So let's talk about OPenVINO. It's an interesting topic. So I actually found out what OpeVINO means, Open Visual Inference and Neural Optimization toolkit,. So it's a lot about computer vision. So I will, you know, and computer vision is an interesting early AI application that I think a lot of people are familiar with through Google photos or other things where, you know, suddenly they're putting together little or a highlight movies for you, or they're pulling together all the photos of a particular person or a particular place. So the computer vision is pretty interesting. Inference is a special subset of AI. So I wonder, you know, you guys are way behind OpenVINO. Where do you see the opportunities in visualization? What are some of the instances that you're seeing with the developers out there doing innovative things around computer vision? >> Yeah, there's a whole variety of used cases with computer vision. You know, one that we talked about earlier here was looking at defect detection. There's a company that we work with that has a 360 degree view. They use cameras all around their manufacturing line. And from there, they didn't know what a good part looks like and using inference and OpenVINO, they can tell when a bad part goes through or there's a defect in their line and they can go and pull that and make corrections as needed. We've also seen, you know, use cases like smart shopping, where there's a point of sale fraud detection. We call it, you know, is the item being scanned the same as the item that is actually going through the line. And so we can be much smarter about understanding retail. One example that I saw was a customer who was trying to detect if it was a vodka or potatoes that was being scanned in an automated checkout system. And again, using cameras and OpenVINO, they can tell the difference. >> We haven't talked about a computer testing yet. We're still sticking with computer vision and the natural language processing. I know one of the areas you're interested in and it's going to only increase in importance is education. Especially with what's going on, I keep waiting for someone to start rolling out some national, you know, best practice education courses for kindergartens and third graders and sixth graders. And you know, all these poor teachers that are learning to teach on the fly from home, you guys are doing a lot of work in education. I wonder if you can share, I think your work doing some work with Udacity. What are you doing? Where do you see the opportunity to apply some of this AI and IoT in education? >> Yeah, we launched the Nanodegree with Udacity, and it's all about OpenVINO and Edge AI and the idea is, again, get more developers educated on this technology, take a leader like your Udacity, partner with them to make the coursework available and get more developers understanding using and building things using Edge AI. And so we partnered with them as part of their million developer goal. We're trying to get as many developers as possible through that. >> Okay. And I would be remiss if we talked about IoT and I didn't throw 5G into the conversation. So 5G is a really big deal. I know Intel has put a ton of resources behind it and have been talking about it for a long, long time. You know, I think the huge value in 5G is a lot around IoT as opposed to my handset going faster, which is funny that they're actually releasing 5G handsets out there. But when you look at 5G combined with the other capabilities in IoT, again, how do you see 5G being this kind of step function in ability to do real time analysis and make real time business decisions? >> Well, I think it brings more connectivity certainly and bandwidth and reduces latency. But the cool thing about it is when you look at the applications of it, you know, we talked about factories. A lot of those factors may want to have a private 5G networks that are running inside that factory, running all the machines or robots or things in there. And so, you know, it brings capabilities that actually make a difference in the world of IoT and the things that developers are trying to build. >> That's great. So before I let you go, you've been at this for a while. You've been at Intel for a while. You've seen a lot of big sweeping changes, kind of come through the industry, you know, as you sit back with a little bit of perspective, and it's funny, even IoT, like you said, you've been talking about it for five years and 5G we've been been waiting for it, but the waves keep coming, right? That's kind of the fun of being in this business. As you sit there where you are today, you know, kind of looking forward the next couple of years, couple of four or five years, you know, what has just surprised you beyond compare and what are you still kind of surprised that's it's still a little bit lagging that you would have expected to see a little bit more progress at this point. >> You know, to me the incredible thing about the computing industry is just the insatiable demand that the world has for compute. It seems like we always come up with, our customers always come up with more and more uses for this compute power. You know, as we've talked about data and the exponential growth of data and now we need to process and analyze and store that data. It's impressive to see developers just constantly thinking about new ways to apply their craft and, you know, new ways to use all that available computing power. And, you know, I'm delighted 'cause I've been at this for a while, as you said, and I just see this continuing to go far as far as the eye can see. >> Yeah, yeah. I think you're right. There's no shortage of opportunity. I mean, the data explosion is kind of funny. The data has always been there, we just weren't keeping track of it before. And the other thing that as I look at Jira, Internet of Things, kind of toolkit, you guys have such a broad portfolio now where a lot of times people think of Intel pretty much as a CPU company, but as you mentioned, you got to FPGAs and VPUs and Vision Solutions, stretch applications Intel has really done a good job in terms of broadening the portfolio to go after, you know, kind of this disparate or kind of sharding, if you will, of all these different types of computer applications have very different demands in terms of power and bandwidth and crunching utilization to technical (indistinct). >> Yeah. Absolutely the various computer architectures really just to help our customers with the needs, whether it's high power or low performance, a mixture of both, being able to use all of those heterogeneous architectures with a tool like OpenVINO, so you can program once, right once and then run your application across any of those architectures, help simplify the life of our developers, but also gives them the compute performance, the way that they need it. >> Alright Bill, well keep at it. Thank you for all your hard work. And hopefully it won't be five years before we're checking in to see how far this IoT thing is going. >> Hopefully not, thanks Jeff. >> Alright Bill. Thanks a lot. He's bill, I'm Jeff. You're watching theCUBE. Thanks for watching, we'll see you next time. (upbeat music)

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is a CUBE conversation. >> Hey, welcome back, you're ready. Jeff Frick here with theCUBE. We are still getting through the year of 2020. It's still the year of COVID and there's no end in sight I think until we get to a vaccine. That said, we're really excited to have one of our favorite guests. We haven't had him on for a while. I haven't talked to him for a long time. He used to I think have the record for the most CUBE appearances of probably any CUBE alumni. We're excited to have him joining us from his house in Palo Alto. Bill Schmarzo, you know him as the Dean of Big Data, he's got more titles. He's the chief innovation officer at Hitachi Vantara. He's also, we used to call him the Dean of Big Data, kind of for fun. Well, Bill goes out and writes a bunch of books. And now he teaches at the University of San Francisco, School of Management as an executive fellow. He's an honorary professor at NUI Galway. I think he's just, he likes to go that side of the pond and a many time author now, go check him out. His author profile on Amazon, the "Big Data MBA," "The Art of Thinking Like A Data Scientist" and another Big Data, kind of a workbook. Bill, great to see you. >> Thanks, Jeff, you know, I miss my time on theCUBE. These conversations have always been great. We've always kind of poked around the edges of things. A lot of our conversations have always been I thought, very leading edge and the title Dean of Big Data is courtesy of theCUBE. You guys were the first ones to give me that name out of one of the very first Strata Conferences where you dubbed me the Dean of Big Data, because I taught a class there called the Big Data MBA and look what's happened since then. >> I love it. >> It's all on you guys. >> I love it, and we've outlasted Strata, Strata doesn't exist as a conference anymore. So, you know, part of that I think is because Big Data is now everywhere, right? It's not the standalone thing. But there's a topic, and I'm holding in my hands a paper that you worked on with a colleague, Dr. Sidaoui, talking about what is the value of data? What is the economic value of data? And this is a topic that's been thrown around quite a bit. I think you list a total of 28 reference sources in this document. So it's a well researched piece of material, but it's a really challenging problem. So before we kind of get into the details, you know, from your position, having done this for a long time, and I don't know what you're doing today, you used to travel every single week to go out and visit customers and actually do implementations and really help people think these through. When you think about the value, the economic value, how did you start to kind of frame that to make sense and make it kind of a manageable problem to attack? >> So, Jeff, the research project was eyeopening for me. And one of the advantages of being a professor is, you have access to all these very smart, very motivated, very free research sources. And one of the problems that I've wrestled with as long as I've been in this industry is, how do you figure out what is data worth? And so what I did is I took these research students and I stick them on this problem. I said, "I want you to do some research. Let me understand what is the value of data?" I've seen all these different papers and analysts and consulting firms talk about it, but nobody's really got this thing clicked. And so we launched this research project at USF, professor Mouwafac Sidaoui and I together, and we were bumping along the same old path that everyone else got, which was inched on, how do we get data on our balance sheet? That was always the motivation, because as a company we're worth so much more because our data is so valuable, and how do I get it on the balance sheet? So we're headed down that path and trying to figure out how do you get it on the balance sheet? And then one of my research students, she comes up to me and she says, "Professor Schmarzo," she goes, "Data is kind of an unusual asset." I said, "Well, what do you mean?" She goes, "Well, you think about data as an asset. It never depletes, it never wears out. And the same dataset can be used across an unlimited number of use cases at a marginal cost equal to zero." And when she said that, it's like, "Holy crap." The light bulb went off. It's like, "Wait a second. I've been thinking about this entirely wrong for the last 30 some years of my life in this space. I've had the wrong frame. I keep thinking about this as an act, as an accounting conversation. An accounting determines valuation based on what somebody is willing to pay for." So if you go back to Adam Smith, 1776, "Wealth of Nations," he talks about valuation techniques. And one of the valuation techniques he talks about is valuation and exchange. That is the value of an asset is what someone's willing to pay you for it. So the value of this bottle of water is what someone's willing to pay you for it. So everybody fixates on this asset, valuation in exchange methodology. That's how you put it on balance sheet. That's how you run depreciation schedules, that dictates everything. But Adam Smith also talked about in that book, another valuation methodology, which is valuation in use, which is an economics conversation, not an accounting conversation. And when I realized that my frame was wrong, yeah, I had the right book. I had Adam Smith, I had "Wealth of Nations." I had all that good stuff, but I hadn't read the whole book. I had missed this whole concept about the economic value, where value is determined by not how much someone's willing to pay you for it, but the value you can drive by using it. So, Jeff, when that person made that comment, the entire research project, and I got to tell you, my entire life did a total 180, right? Just total of 180 degree change of how I was thinking about data as an asset. >> Right, well, Bill, it's funny though, that's kind of captured, I always think of kind of finance versus accounting, right? And then you're right on accounting. And we learn a lot of things in accounting. Basically we learn more that we don't know, but it's really hard to put it in an accounting framework, because as you said, it's not like a regular asset. You can use it a lot of times, you can use it across lots of use cases, it doesn't degradate over time. In fact, it used to be a liability. 'cause you had to buy all this hardware and software to maintain it. But if you look at the finance side, if you look at the pure play internet companies like Google, like Facebook, like Amazon, and you look at their valuation, right? We used to have this thing, we still have this thing called Goodwill, which was kind of this capture between what the market established the value of the company to be. But wasn't reflected when you summed up all the assets on the balance sheet and you had this leftover thing, you could just plug in goodwill. And I would hypothesize that for these big giant tech companies, the market has baked in the value of the data, has kind of put in that present value on that for a long period of time over multiple projects. And we see it captured probably in goodwill, versus being kind of called out as an individual balance sheet item. >> So I don't think it's, I don't know accounting. I'm not an accountant, thank God, right? And I know that goodwill is one of those things if I remember from my MBA program is something that when you buy a company and you look at the value you paid versus what it was worth, it stuck into this category called goodwill, because no one knew how to figure it out. So the company at book value was a billion dollars, but you paid five billion for it. Well, you're not an idiot, so that four billion extra you paid must be in goodwill and they'd stick it in goodwill. And I think there's actually a way that goodwill gets depreciated as well. So it could be that, but I'm totally away from the accounting framework. I think that's distracting, trying to work within the gap rules is more of an inhibitor. And we talk about the Googles of the world and the Facebooks of the world and the Netflix of the world and the Amazons and companies that are great at monetizing data. Well, they're great at monetizing it because they're not selling it, they're using it. Google is using their data to dominate search, right? Netflix is using it to be the leader in on-demand videos. And it's how they use all the data, how they use the insights about their customers, their products, and their operations to really drive new sources of value. So to me, it's this, when you start thinking about from an economics perspective, for example, why is the same car that I buy and an Uber driver buys, why is that car more valuable to an Uber driver than it is to me? Well, the bottom line is, Uber drivers are going to use that car to generate value, right? That $40,000, that car they bought is worth a lot more, because they're going to use that to generate value. For me it sits in the driveway and the birds poop on it. So, right, so it's this value in use concept. And when organizations can make that, by the way, most organizations really struggle with this. They struggle with this value in use concept. They want to, when you talk to them about data monetization and say, "Well, I'm thinking about the chief data officer, try not to trying to sell data, knocking on doors, shaking their tin cup, saying, 'Buy my data.'" No, no one wants your data. Your data is more valuable for how you use it to drive your operations then it's a sell to somebody else. >> Right, right. Well, on of the other things that's really important from an economics concept is scarcity, right? And a whole lot of economics is driven around scarcity. And how do you price for scarcity so that the market evens out and the price matches up to the supply? What's interesting about the data concept is, there is no scarcity anymore. And you know, you've outlined and everyone has giant numbers going up into the right, in terms of the quantity of the data and how much data there is and is going to be. But what you point out very eloquently in this paper is the scarcity is around the resources to actually do the work on the data to get the value out of the data. And I think there's just this interesting step function between just raw data, which has really no value in and of itself, right? Until you start to apply some concepts to it, you start to analyze it. And most importantly, that you have some context by which you're doing all this analysis to then drive that value. And I thought it was really an interesting part of this paper, which is get beyond the arguing that we're kind of discussing here and get into some specifics where you can measure value around a specific business objective. And not only that, but then now the investment of the resources on top of the data to be able to extract the value to then drive your business process for it. So it's a really different way to think about scarcity, not on the data per se, but on the ability to do something with it. >> You're spot on, Jeff, because organizations don't fail because of a lack of use cases. They fail because they have too many. So how do you prioritize? Now that scarcity is not an issue on the data side, but it is this issue on the people resources side, you don't have unlimited data scientists, right? So how do you prioritize and focus on those opportunities that are most important? I'll tell you, that's not a data science conversation, that's a business conversation, right? And figuring out how you align organizations to identify and focus on those use cases that are most important. Like in the paper we go through several different use cases using Chipotle as an example. The reason why I picked Chipotle is because, well, I like Chipotle. So I could go there and I could write it off as research. But there's a, think about the number of use cases where a company like Chipotle or any other company can leverage your data to drive their key business initiatives and their key operational use cases. It's almost unbounded, which by the way, is a huge challenge. In fact, I think part of the problem we see with a lot of organizations is because they do such a poor job of prioritizing and focusing, they try to solve the entire problem with one big fell swoop, right? It's slightly the old ERP big bang projects. Well, I'm just going to spend $20 million to buy this analytic capability from company X and I'm going to install it and then magic is going to happen. And then magic is going to happen, right? And then magic is going to happen, right? And magic never happens. We get crickets instead, because the biggest challenge isn't around how do I leverage the data, it's about where do I start? What problems do I go after? And how do I make sure the organization is bought in to basically use case by use case, build out your data and analytics architecture and capabilities. >> Yeah, and you start backwards from really specific business objectives in the use cases that you outline here, right? I want to increase my average ticket by X. I want to increase my frequency of visits by X. I want to increase the amount of items per order from X to 1.2 X, or 1.3 X. So from there you get a nice kind of big revenue hit that you can plan around and then work backwards into the amount of effort that it takes and then you can come up, "Is this a good investment or not?" So it's a really different way to get back to the value of the data. And more importantly, the analytics and the work to actually call out the information. >> The technologies, the data and analytic technologies available to us. The very composable nature of these allow us to take this use case by use case approach. I can build out my data lake one use case at a time. I don't need to stuff 25 data sources into my data lake and hope there's someone more valuable. I can use the first use case to say, "Oh, I need these three data sources to solve that use case. I'm going to put those three data sources in the data lake. I'm going to go through the entire curation process of making sure the data has been transformed and cleansed and aligned and enriched and met of, all the other governance, all that kind of stuff this goes on. But I'm going to do that use case by use case, 'cause a use case can tell me which data sources are most important for that given situation. And I can build up my data lake and I can build up my analytics then one use case at a time. And there is a huge impact then, huge impact when I build out use case by use case. That does not happen. Let me throw something that's not really covered in the paper, but it is very much covered in my new book that I'm working on, which is, in knowledge-based industries, the economies of learning are more powerful than the economies of scale. Now think about that for a second. >> Say that again, say that again. >> Yeah, the economies of learning are more powerful than the economies of scale. And what that means is what I learned on the first use case that I build out, I can apply that learning to the second use case, to the third use case, to the fourth use case. So when I put my data into my data lake for my first use case, and the paper covers this, well, once it's in my data lake, the cost of reusing that data in a second, third and fourth use cases is basically, you know marginal cost is zero. So I get this ability to learn about what data sets are most important and to reapply that across the organization. So this learning concept, I learn use case by use case, I don't have to do a big economies of scale approach and start with 25 datasets of which only three or four might be useful. But I'm incurring the overhead for all those other non-important data sets because I didn't take the time to go through and figure out what are my most important use cases and what data do I need to support those use cases. >> I mean, should people even think of the data per se or should they really readjust their thinking around the application of the data? Because the data in and of itself means nothing, right? 55, is that fast or slow? Is that old or young? Well, it depends on a whole lot of things. Am I walking or am I in a brand new Corvette? So it just, it's funny to me that the data in and of itself really doesn't have any value and doesn't really provide any direction into a decision or a higher order, predictive analytics until you start to manipulate the data. So is it even the wrong discussion? Is data the right discussion? Or should we really be talking about the capabilities to do stuff within and really get people focused on that? >> So Jeff, there's so many points to hit on there. So the application of data is what's the value, and the queue of you guys used to be famous for saying, "Separating noise from the signal." >> Signal from the noise. Signal from a noise, right. Well, how do you know in your dataset what's signal and what's noise? Well, the use case will tell you. If you don't know the use case and you have no way of figuring out what's important. One of the things I use, I still rail against, and it happens still. Somebody will walk up my data science team and say, "Here's some data, tell me what's interesting in it." Well, how do you separate signal from noise if I don't know the use case? So I think you're spot on, Jeff. The way to think about this is, don't become data-driven, become value-driven and value is driven from the use case or the application or the use of the data to solve that particular use case. So organizations that get fixated on being data-driven, I hate the term data-driven. It's like as if there's some sort of frigging magic from having data. No, data has no value. It's how you use it to derive customer product and operational insights that drive value,. >> Right, so there's an interesting step function, and we talk about it all the time. You're out in the weeds, working with Chipotle lately, and increase their average ticket by 1.2 X. We talk more here, kind of conceptually. And one of the great kind of conceptual holy grails within a data-driven economy is kind of working up this step function. And you've talked about it here. It's from descriptive, to diagnostic, to predictive. And then the Holy grail prescriptive, we're way ahead of the curve. This comes into tons of stuff around unscheduled maintenance. And you know, there's a lot of specific applications, but do you think we spend too much time kind of shooting for the fourth order of greatness impact, instead of kind of focusing on the small wins? >> Well, you certainly have to build your way there. I don't think you can get to prescriptive without doing predictive, and you can't do predictive without doing descriptive and such. But let me throw a really one at you, Jeff, I think there's even one beyond prescriptive. One we're talking more and more about, autonomous, a ton of analytics, right? And one of the things that paper talked about that didn't click with me at the time was this idea of orphaned analytics. You and I kind of talked about this before the call here. And one thing we noticed in the research was that a lot of these very mature organizations who had advanced from the retrospective analytics of BI to the descriptive, to the predicted, to the prescriptive, they were building one off analytics to solve a problem and getting value from it, but never reusing this analytics over and over again. They were done one off and then they were thrown away and these organizations were so good at data science and analytics, that it was easier for them to just build from scratch than to try to dig around and try to find something that was never actually ever built to be reused. And so I have this whole idea of orphaned analytics, right? It didn't really occur to me. It didn't make any sense into me until I read this quote from Elon Musk, and Elon Musk made this statement. He says, " I believe that when you buy a Tesla, you're buying an asset that appreciates in value, not depreciates through usage." I was thinking, "Wait a second, what does that mean?" He didn't actually say it, "Through usage." He said, "He believes you're buying an asset that appreciates not depreciates in value." And of course the first response I had was, "Oh, it's like a 1964 and a half Mustang. It's rare, so everybody is going to want these things. So buy one, stick it in your garage. And 20 years later, you're bringing it out and it's worth more money." No, no, there's 600,000 of these things roaming around the streets, they're not rare. What he meant is that he is building an autonomous asset. That the more that it's used, the more valuable it's getting, the more reliable, the more efficient, the more predictive, the more safe this asset's getting. So there is this level beyond prescriptive where we can think about, "How do we leverage artificial intelligence, reinforcement, learning, deep learning, to build these assets that the more that they are used, the smarter they get." That's beyond prescriptive. That's an environment where these things are learning. In many cases, they're learning with minimal or no human intervention. That's the real aha moment. That's what I miss with orphaned analytics and why it's important to build analytics that can be reused over and over again. Because every time you use these analytics in a different use case, they get smarter, they get more valuable, they get more predictive. To me that's the aha moment that blew my mind. I realized I had missed that in the paper entirely. And it took me basically two years later to realize, dough, I missed the most important part of the paper. >> Right, well, it's an interesting take really on why the valuation I would argue is reflected in Tesla, which is a function of the data. And there's a phenomenal video if you've never seen it, where they have autonomous vehicle day, it might be a year or so old. And he's got his number one engineer from, I think the Microprocessor Group, The Computer Vision Group, as well as the autonomous driving group. And there's a couple of really great concepts I want to follow up on what you said. One is that they have this thing called The Fleet. To your point, there's hundreds of thousands of these things, if they haven't hit a million, that are calling home reporting home every day as to exactly how everyone took the Northbound 101 on-ramp off of University Avenue. How fast did they go? What line did they take? What G-forces did they take? And every one of those cars feeds into the system, so that when they do the autonomous update, not only are they using all their regular things that they would use to map out that 101 Northbound entry, but they've got all the data from all the cars that have been doing it. And you know, when that other car, the autonomous car couple years ago hit the pedestrian, I think in Phoenix, which is not good, sad, killed a person, dark tough situation. But you know, we are doing an autonomous vehicle show and the guy who made a really interesting point, right? That when something like that happens, typically if I was in a car wreck or you're in a car wreck, hopefully not, I learned the person that we hit learns and maybe a couple of witnesses learn, maybe the inspector. >> But nobody else learns. >> But nobody else learns. But now with the autonomy, every single person can learn from every single experience with every vehicle contributing data within that fleet. To your point, it's just an order of magnitude, different way to think about things. >> Think about a 1% improvement compounded 365 times, equals I think 38 X improvement. The power of 1% improvements over these 600,000 plus cars that are learning. By the way, even when the autonomous FSD, the full self-driving mode module isn't turned on, even when it's not turned on, it runs in shadow mode. So it's learning from the human drivers, the human overlords, it's constantly learning. And by the way, not only they're collecting all this data, I did a little research, I pulled out some of their job search ads and they've built a giant simulator, right? And they're there basically every night, simulating billions and billions of more driven miles because of the simulator. They are building, he's going to have a simulator, not only for driving, but think about all the data he's capturing as these cars are riding down the road. By the way, they don't use Lidar, they use video, right? So he's driving by malls. He knows how many cars are in the mall. He's driving down roads, he knows how old the cars are and which ones should be replaced. I mean, he has this, he's sitting on this incredible wealth of data. If anybody could simulate what's going on in the world and figure out how to get out of this COVID problem, it's probably Elon Musk and the data he's captured, be courtesy of all those cars. >> Yeah, yeah, it's really interesting, and we're seeing it now. There's a new autonomous drone out, the Skydio, and they just announced their commercial product. And again, it completely changes the way you think about how you use that tool, because you've just eliminated the complexity of driving. I don't want to drive that, I want to tell it what to do. And so you're saying, this whole application of air force and companies around things like measuring piles of coal and measuring these huge assets that are volume metric measured, that these things can go and map out and farming, et cetera, et cetera. So the autonomy piece, that's really insightful. I want to shift gears a little bit, Bill, and talk about, you had some theories in here about thinking of data as an asset, data as a currency, data as monetization. I mean, how should people think of it? 'Cause I don't think currency is very good. It's really not kind of an exchange of value that we're doing this kind of classic asset. I think the data as oil is horrible, right? To your point, it doesn't get burned up once and can't be used again. It can be used over and over and over. It's basically like feedstock for all kinds of stuff, but the feedstock never goes away. So again, or is it that even the right way to think about, do we really need to shift our conversation and get past the idea of data and get much more into the idea of information and actionable information and useful information that, oh, by the way, happens to be powered by data under the covers? >> Yeah, good question, Jeff. Data is an asset in the same way that a human is an asset. But just having humans in your company doesn't drive value, it's how you use those humans. And so it's really again the application of the data around the use cases. So I still think data is an asset, but I don't want to, I'm not fixated on, put it on my balance sheet. That nice talk about put it on a balance sheet, I immediately put the blinders on. It inhibits what I can do. I want to think about this as an asset that I can use to drive value, value to my customers. So I'm trying to learn more about my customer's tendencies and propensities and interests and passions, and try to learn the same thing about my car's behaviors and tendencies and my operations have tendencies. And so I do think data is an asset, but it's a latent asset in the sense that it has potential value, but it actually has no value per se, inputting it into a balance sheet. So I think it's an asset. I worry about the accounting concept medially hijacking what we can do with it. To me the value of data becomes and how it interacts with, maybe with other assets. So maybe data itself is not so much an asset as it's fuel for driving the value of assets. So, you know, it fuels my use cases. It fuels my ability to retain and get more out of my customers. It fuels ability to predict what my products are going to break down and even have products who self-monitor, self-diagnosis and self-heal. So, data is an asset, but it's only a latent asset in the sense that it sits there and it doesn't have any value until you actually put something to it and shock it into action. >> So let's shift gears a little bit and start talking about the data and talk about the human factors. 'Cause you said, one of the challenges is people trying to bite off more than they can chew. And we have the role of chief data officer now. And to your point, maybe that mucks things up more than it helps. But in all the customer cases that you've worked on, is there a consistent kind of pattern of behavior, personality, types of projects that enables some people to grab those resources to apply to their data to have successful projects, because to your point there's too much data and there's too many projects and you talk a lot about prioritization. But there's a lot of assumptions in the prioritization model that you can, that you know a whole lot of things, especially if you're comparing project A over in group A with project B, with group B and the two may not really know the economics across that. But from an individual person who sees the potential, what advice do you give them? What kind of characteristics do you see, either in the type of the project, the type of the boss, the type of the individual that really lends itself to a higher probability of a successful outcome? >> So first off you need to find somebody who has a vision for how they want to use the data, and not just collect it. But how they're going to try to change the fortunes of the organization. So it always takes a visionary, may not be the CEO, might be somebody who's a head of marketing or the head of logistics, or it could be a CIO, it could be a chief data officer as well. But you've got to find somebody who says, "We have this latent asset we could be doing more with, and we have a series of organizational problem challenges against which I could apply this asset. And I need to be the matchmaker that brings these together." Now the tool that I think is the most powerful tool in marrying the latent capabilities of data with all the revenue generating opportunities in the application side, because there's a countless number, the most important tool that I found doing that is design thinking. Now, the reason why I think design thinking is so important, because one of the things that design thinking does a great job is it gives everybody a voice in the process of identifying, validating, valuing, and prioritizing use cases you're going to go after. Let me say that again. The challenge organizations have is identifying, validating, valuing, and prioritizing the use cases they want to go after. Design thinking is a marvelous tool for driving organizational alignment around where we're going to start and what's going to be next and why we're going to start there and how we're going to bring everybody together. Big data and data science projects don't die because of technology failure. Most of them die because of passive aggressive behaviors in the organization that you didn't bring everybody into the process. Everybody's voice didn't get a chance to be heard. And that one person who's voice didn't get a chance to get heard, they're going to get you. They may own a certain piece of data. They may own something, but they're just waiting and lay, they're just laying there waiting for their chance to come up and snag it. So what you got to do is you got to proactively bring these people together. We call this, this is part of our value engineering process. We have a value engineering process around envisioning where we bring all these people together. We help them to understand how data in itself is a latent asset, but how it can be used from an economics perspective, drive all those value. We get them all fired up on how these can solve any one of these use cases. But you got to start with one, and you've got to embrace this idea that I can build out my data and analytic capabilities, one use case at a time. And the first use case I go after and solve, makes my second one easier, makes my third one easier, right? It has this ability that when you start going use case by use case two really magical things happen. Number one, your marginal cost flatten. That is because you're building out your data lake one use case at a time, and you're bringing all the important data lake, that data lake one use case at a time. At some point in time, you've got most of the important data you need, and the ability that you don't need to add another data source. You got what you need, so your marginal costs start to flatten. And by the way, if you build your analytics as composable, reusable, continuous learning analytic assets, not as orphaned analytics, pretty soon you have all the analytics you need as well. So your marginal cost flatten, but effect number two is that you've, because you've have the data and the analytics, I can accelerate time to value, and I can de-risked projects as I go use case by use case. And so then the biggest challenge becomes not in the data and the analytics, it's getting the all the business stakeholders to agree on, here's a roadmap we're going to go after. This one's first, and this one is going first because it helps to drive the value of the second and third one. And then this one drives this, and you create a whole roadmap of rippling through of how the data and analytics are driving this value to across all these use cases at a marginal cost approaching zero. >> So should we have chief design thinking officers instead of chief data officers that really actually move the data process along? I mean, I first heard about design thinking years ago, actually interviewing Dan Gordon from Gordon Biersch, and they were, he had just hired a couple of Stanford grads, I think is where they pioneered it, and they were doing some work about introducing, I think it was a a new apple-based alcoholic beverage, apple cider, and they talked a lot about it. And it's pretty interesting, but I mean, are you seeing design thinking proliferate into the organizations that you work with? Either formally as design thinking or as some derivation of it that pulls some of those attributes that you highlighted that are so key to success? >> So I think we're seeing the birth of this new role that's marrying capabilities of design thinking with the capabilities of data and analytics. And they're calling this dude or dudette the chief innovation officer. Surprise. >> Title for someone we know. >> And I got to tell a little story. So I have a very experienced design thinker on my team. All of our data science projects have a design thinker on them. Every one of our data science projects has a design thinker, because the nature of how you build and successfully execute a data science project, models almost exactly how design thinking works. I've written several papers on it, and it's a marvelous way. Design thinking and data science are different sides of the same coin. But my respect for data science or for design thinking took a major shot in the arm, major boost when my design thinking person on my team, whose name is John Morley introduced me to a senior data scientist at Google. And I was bottom coffee. I said, "No," this is back in, before I even joined Hitachi Vantara, and I said, "So tell me the secret to Google's data science success? You guys are marvelous, you're doing things that no one else was even contemplating, and what's your key to success?" And he giggles and laughs and he goes, "Design thinking." I go, "What the hell is that? Design thinking, I've never even heard of the stupid thing before." He goes, "I'd make a deal with you, Friday afternoon let's pop over to Stanford's B school and I'll teach you about design thinking." So I went with him on a Friday to the d.school, Design School over at Stanford and I was blown away, not just in how design thinking was used to ideate and bring and to explore. But I was blown away about how powerful that concept is when you marry it with data science. What is data science in its simplest sense? Data science is about identifying the variables and metrics that might be better predictors of performance. It's that might phrase that's the real key. And who are the people who have the best insights into what values or metrics or KPIs you might want to test? It ain't the data scientists, it's the subject matter experts on the business side. And when you use design thinking to bring this subject matter experts with the data scientists together, all kinds of magic stuff happens. It's unbelievable how well it works. And all of our projects leverage design thinking. Our whole value engineering process is built around marrying design thinking with data science, around this prioritization, around these concepts of, all ideas are worthy of consideration and all voices need to be heard. And the idea how you embrace ambiguity and diversity of perspectives to drive innovation, it's marvelous. But I feel like I'm a lone voice out in the wilderness, crying out, "Yeah, Tesla gets it, Google gets it, Apple gets it, Facebook gets it." But you know, most other organizations in the world, they don't think like that. They think design thinking is this Wufoo thing. Oh yeah, you're going to bring people together and sing Kumbaya. It's like, "No, I'm not singing Kumbaya. I'm picking their brains because they're going to help make their data science team much more effective and knowing what problems we're going to go after and how I'm going to measure success and progress. >> Maybe that's the next Dean for the next 10 years, the Dean of design thinking instead of data science, and who knew they're one and the same? Well, Bill, that's a super insightful, I mean, it's so, is validated and supported by the trends that we see all over the place, just in terms of democratization, right? Democratization of the tools, more people having access to data, more opinions, more perspective, more people that have the ability to manipulate the data and basically experiment, does drive better business outcomes. And it's so consistent. >> If I could add one thing, Jeff, I think that what's really powerful about design thinking is when I think about what's happening with artificial intelligence or AI, there's all these conversations about, "Oh, AI is going to wipe out all these jobs. Is going to take all these jobs away." And what we're actually finding is that if we think about machine learning, driven by AI and human empowerment, driven by design thinking, we're seeing the opportunity to exploit these economies of learning at the front lines where every customer engagement, every operational execution is an opportunity to gather not only more data, but to gather more learnings, to empower the humans at the front lines of the organization to constantly be seeking, to try different things, to explore and to learn from each of these engagements. I think it's, AI to me is incredibly powerful. And I think about it as a source of driving more learning, a continuous learning and continuously adapting an organization where it's not just the machines that are doing this, but it's the humans who've been empowered to do that. And my chapter nine in my new book, Jeff, is all about team empowerment, because nothing you do with AI is going to matter of squat if you don't have empowered teams who know how to take and leverage that continuous learning opportunity at the front lines of customer and operational engagement. >> Bill, I couldn't set a better, I think we'll leave it there. That's a great close, when is the next book coming out? >> So today I do my second to last final review. Then it goes back to the editor and he does a review and we start looking at formatting. So I think we're probably four to six weeks out. >> Okay, well, thank you so much, congratulations on all the success. I just love how the Dean is really the Dean now, teaching all over the world, sharing the knowledge and attacking some of these big problems. And like all great economics problems, often the answer is not economics at all. It's completely really twist the lens and don't think of it in that, all that construct. >> Exactly. >> All right, Bill. Thanks again and have a great week. >> Thanks, Jeff. >> All right. He's Bill Schmarzo, I'm Jeff Frick. You're watching theCUBE. Thanks for watching, we'll see you next time. (gentle music)

(upbeat music) >> Narrator: From the Cube Studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is the Cube Conversation. >> Welcome to this Cube Conversation. I'm Lisa Martin, excited to talk to the CEO of Styra, Bill Mann today. Bill, welcome to the Cube. >> Hi Lisa, how are you doing? >> I'm doing well. I should say welcome back. You've been on the Cube at a previous company, but we're excited to talk to you today about Styra, what's going on? So let's go ahead and start informing our audience who Styra is and what you do? >> Sure, so who Styra is and what do we do? So Styra is a company that's focused on reinventing policy and authorization in the cloud native stack. We're the company that created an open source project called Open Policy Agent, it's part of CNCF. And on top of Open Policy Agent, we built a control plane, a management plane to help organizations really put OPA into production and operationalized OPA. >> An OPA is Open Policy Agent. That's what the company actually developed with CNCF, correct? >> So, we actually founded Open Policy Agent and then we contributed Open Policy Agent to CNCF. And the real goal of contributing the Open Policy Agent to CNCF was we believe that we want to get authorization defacto in the market, right? And the only way to get something out there that everybody uses is to put it into the open source and having an entity like the CNCF supporting the project. So, really it's about getting everybody, all enterprises and vendors to use Open Policy Agent as a way of solving authorization for the cloud native environment. >> So you say Styra is reinventing policy and authorization for cloud native applications, your target audience, security folks, developer folks, what changes has cloud native brought to security and development teams? >> Sure, so what changes has cloud native brought to security and development teams? So fundamentally there've been three changes in the marketplace. One, as you know we're shifting from this monolithic architecture of building applications to now this new distributed architectures of kubernetes, microservices and Deep-coupled architecture. So fundamentally the way we build applications is fundamentally changed because everybody wants to have scale up and scale down and so forth. Second, the way we actually developed software, we've moved now to a DevOps model where we're doing more things earlier on in the cycle so we can innovate faster and we're producing code on an hourly basis versus when I joined the industry which was probably three releases a year. And then thirdly which is kind of a major topic that all of us kind of understand is our focus on privacy and security is higher than it's been before. And if these applications are going to be way more complex and more distributed and we're going to innovate faster than the way we focus on security and privacy has to be done differently as well. And if we don't do it differently, then we're going to have to all the breaches that we had in the previous generation of the app stack. >> And we don't want that, but you're right privacy and security are increasing concerns in any environment. How do you help address those and also with the thought of privacy and security are going to be concerned for quite a long time? >> Yeah, so let me take a step back. So how do we address privacy and security? So, at a fundamental level, authorization is a foundational part of security and authorization has never really been solved or re-imagined ever for the last 50 years or so. Every application developer or security vendor has built authorization into their own stack and done it in a very proprietary way. And it's been locked away within these applications and these stacks and so forth. So what happens now when you've got a highly distributed environment is that you've got so many moving parts, you still need to apply authorization. So, the way we've tackled it is by building Open Policy Agent. And there's three fundamental kind of tenants around Open Policy Agent that make it really ideal for this cloud native environment. Number one, it's policy as code and everything in the market now, everything is as code. You buy infrastructure as code. So this is now policy as code. So you can describe in a declarative model, how you want the policy for a system to be developed and you can use the language called Rego to do that. Second is the fact that all the cloud native projects out there which are all developed based upon open source technologies, kubernetes, microservices, envoy, SDO, cafco, all these kinds of buzzwords you hear in the marketplace, they all integrate with Open Policy Agent already. And then thirdly the architecture of Open Policy Agent is that it's distributed, which means that it's ideally suited for this distributed architecture for cloud native. And those are the three kind of characteristics of Open Policy Agent leading to developers loving it. And when I say they love it, we've got hundreds and thousands of users of Open Policy Agent. When you go to the CNCF shows co op con earlier this year and there's two more coming this year. There's many, many talks on it. You've got cloud vendors like Google and Microsoft adopting Open Policy Agent, got a lot of enterprises adopting Open Policy Agent. So, that's really fundamentally what we've built is we've built an authorization architecture for this new world to really address the security and privacy concerns, which have always existed and I'm going to be more exponential in this new world. >> And I think you've also built a community around OPA. Can you share a little bit of information about that and how they help with the co-development and even some of the other things that you're commercializing? >> Sure, yeah. So, now what have we done in from a community point of view with Open Policy Agents? So yeah, the community is a integral part of any open source project and we're lucky to have a great community. We've got a great community of enterprise users of Open Policy Agents and vendors as well, vendors like Microsoft and Google who are now contributing to OPA and building it up. And for me, the most important part of a community is that you learn how enterprises are using your software and they share ideas and they share use cases and you're able to innovate really, really fast. And what we've learned from that is the use cases that they use Open Policy Agent for, for instance, one of the major use cases for Open Policy Agent is for kubernetes Admission Control. So, essentially we can test the configuration of an application which is described in a file called YAML before it goes into production. So, think of it as pre-production tests, but companies are using it for microservices and applications and data and so forth. So, it helps us understand what they're using it for, but also we use it to help us develop our commercial product, which is the management control plane for OPA. So, we learn about what they're missing in the open source project that we can use to build our commercial product >> which is ready for enterprise use. >> So you've had a lot of success with OPA. Talk to me about Styra DAS and why the need for that? >> Sure, so why do we need Styra DAS recognizing that OPA is very, very successful. So, the fundamental difference is OPA is a very focused on developers and it's very focused on an environment for an individual node or cluster, but it doesn't have all the enterprise features necessary for a real enterprise to go into production. So what we notice is companies use OPA for pre-production, but when they want to go into production, they need a user interface. They need a way to author policies, distribute policies, monitor policies, do impact analysis and a whole bunch of other features and capabilities that are needed for enterprise deployments and so forth. So that's a fundamental difference between OPA and the commercial product. The commercial product is really operationalizing in OPA for an enterprise deployment. >> So the relationship between Styra and OPA seems very collaborative to me that what you just described with the commercial product of Styra DAS is really one that was developed based on what the OPA community and Styra have learned together? >> Correct, Yes. So, OPA was created by the CTO, the founders of the company saw early on several years ago, the need for distributed architectures and the need for unified policy so they left and created OPA. And from day one they wanted to get OPA into everybody's hands. That's why they contributed it to open source as part of CNCF. And then the next kind of strategy is to focus on the control apps aspects, the enterprise aspect. So yes, the same team that created OPA is the same team that's creating the Styra DAS commercial offering as well. >> So from the enterprise perspective, talk to me about some of the companies that you're talking to. I imagine any organization that's focused on cloud native, but any industry in particular that you see is really kind of leading edge right now? >> Yeah, so which industries are we talking to in terms of using Styra DAS and OPA? What we've actually found it's across the board. And we've seen in the early days that financial services and high tech were using OPA, but now it's really across the board. So it's all verticals really. And what we've noticed is any organization which is going through a cloud transformation project where they're either building new applications based upon cloud native app stacks like kubernetes and microservices and so forth or shift to the cloud are the companies that are also adopting OPA and the Styra DAS product, right? Because it's all part of the same solution set. And what we're noticing now and this is a fundamental difference is platform architects and developers are kind of prime to use these technologies. They learn about these technologies by going to the conferences and unlike the past which was very much top down selling from the sea level down, this is very much bottomed up. So developers learn about OPA from going to the conferences. They use it within their own environment and then they tell their management that, "Look, we're using OPA already. "We're missing these capabilities," or they come to us and we educate them about the Styra DAS product and so forth. So it's a very different sales model as well and that's why it's very important for ourselves and any open source company to really keep developers happy and provide a solution, that's meeting their requirements. >> On that side with so many of us and developers included working from home for the past nearly four months. We now are doing things like this virtual conversations, virtual events, how is Styra helping to continue to feed and educate those developers so that they can understand how you can impact their job functions and how they can then elevate you guys up the stack. >> Sure, so what's changed over the last three months or so in the market as a consequence of COVID-19 and from an educational point of view. So, what we've seen is fundamentally in the early days of COVID-19 everybody was kind of get the head around how to work from home and so forth, but what we've seen across the all verticals is developers have now really focused on educating themselves and just as a data point and the audience that we get to the OPA website is as high as it's ever been for the last three months. And what we're doing as a company is a lot of training sessions, video content, write-ups, blogs and so forth, right? And really helping the community learn about OPA and how to solve these kind of fundamental problems around policy and authorization within the environment. We've also been helped by the community as well. So there's been talks about a number of companies, Microsoft, Google, Palo Alto had a talk and many many companies are talking about OPA now and I love it because ultimately being an open source company and building a project which we want to become defacto, we want to raise the bar for security across the world, right? And if we can do that then it's going to be an achievement for us and it's very gratifying knowing that we're really fixing security problems for organizations because ultimately we always want to be able to use an application or a banking service and not worry about privacy and security concerns and that's ultimately what we're all after. But this is such a fundamental component that once we want to have developers learn this now because if they can incorporate this into the DevOps app stack then in future years when these applications are built and they're exposed there'll be more secure. >> And so it sounds like maybe there's even more engagement now during COVID when everybody is at home. Tell me about some of the things that are coming down the pipe for Styra in light of all of this exciting collaboration with the community. >> Sure, yeah. There's definitely been way more collaboration as a consequence of COVID-19. People are at home and they're focusing and they're going through learning sessions and browsing the website going through the video content and so forth. So what we're engaging as much as we have ever been, in fact I would argue that we're engaging even more so now, because it's just a different environment to work in. And what we're focused on now is really adding more features to the Styra DAS product, just to step back for a second, Open Policy Agent works across the cloud native stack and Styra DAS has been focused first on the kubernetes use case and now it also supports microservices as well. And then what we're continuing to do is add more of those enterprise features into Styra DAS and move up and up across the stack. But it is all driven by developers that we're talking to on a daily basis and that's leading to where the project is moving forward and the development for the roadmap and so forth. >> And Styra DAS was only launched in 2019, is that correct? >> 2019 yes, that's correct. That's correct. Yes, time flies, right? So, yes. >> A lot of change and a lot of development in a short period of time. >> That's right and 2019 was a big year for us, right? We started last 2019 with a soft launch at the RSA conference and we finished 2019 with series a funding led by Xcel. And yeah, it's great to see how the commercial product has been gaining traction in the marketplace as well as OPA as well and I think it's a combination of events. One, the fact that cloud native is now really well understood. Second, the fact that kubernetes at the beginning of 2019, it was still, "What does kubernetes mean, "is it going into production?" Now kubernetes is absolutely going into production and there's such a desire for organizations to make sure that security and policy and compliance are resolved before applications go into production otherwise we're going to have the same kind of challenges we had with previous app stacks. >> Well, the momentum is certainly with you. I can definitely hear that in your voice bell. Thank you so much for joining me talking about Styra, how you're reinventing policy and authorization for cloud native applications. >> Thank you, Lisa. >> For my guest Bill Mann, I'm Lisa Martin. You're watching the Cube Conversation. Thanks for your time. (upbeat music)

(upbeat music) >> Narrator: From the Cube Studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is the Cube Conversation. >> Welcome to this Cube Conversation. I'm Lisa Martin, excited to talk to the CEO of Styra, Bill Mann today. Bill, welcome to the Cube. >> Hi Lisa, how are you doing? >> I'm doing well. I should say welcome back. You've been on the Cube at a previous company, but we're excited to talk to you today about Styra, what's going on? So let's go ahead and start informing our audience who Styra is and what you do? >> Sure, so who Styra is and what do we do? So Styra is a company that's focused on reinventing policy and authorization in the cloud native stack. We're the company that created an open source project called Open Policy Agent, it's part of CNCF. And on top of Open Policy Agent, we built a control flame, a management plane to help organizations really put OPA into production and operationalized OPA. >> An OPA is Open Policy Agent. That's what the company actually developed with CNCF, correct? >> So, we actually founded Open Policy Agent and then we contributed Open Policy Agent to CNCF. And the real goal of contributing the Open Policy Agent to CNCF was we believe that we want to get authorization defacto in the market, right? And the only way to get something out there that everybody uses is to put it into the open source and having an entity like the CNCF supporting the project. So, really it's about getting everybody, all enterprises and vendors to use Open Policy Agent as a way of solving authorization for the cloud native environment. >> So you say Styra is reinventing policy and authorization for cloud native applications, your target audience, security folks, developer folks, what changes has cloud native brought to security and development teams? >> Sure, so what changes has cloud native brought to security and development teams? So fundamentally there've been three changes in the marketplace. One, as you know we're shifting from this monolithic architecture of building applications to now this new distributed architectures of kubernetes, microservices and Deep-coupled architecture. So fundamentally the way we build applications is fundamentally changed because everybody wants to have scale up and scale down and so forth. Second, the way we actually developed software, we've moved now to a DevOps model where we're doing more things earlier on in the cycle so we can innovate faster and we're producing code on an hourly basis versus when I joined the industry which was probably three releases a year. And then thirdly which is kind of a major topic that all of us kind of understand is our focus on privacy and security is higher than it's been before. And if these applications are going to be way more complex and more distributed and we're going to innovate faster than the way we focus on security and privacy has to be done differently as well. And if we don't do it differently, then we're going to have to all the breaches that we had in the previous generation of the app stack. >> And we don't want that, but you're right privacy and security are increasing concerns in any environment. How do you help address those and also with the thought of privacy and security are going to be concerned for quite a long time? >> Yeah, so let me take a step back. So how do we address privacy and security? So, at a fundamental level, authorization is a foundational part of security and authorization has never really been solved or re-imagined ever for the last 50 years or so. Every application developer or security vendor has built authorization into their own stack and done it in a very proprietary way. And it's been locked away within these applications and these stacks and so forth. So what happens now when you've got a highly distributed environment is that you've got so many moving parts, you still need to apply authorization. So, the way we've tackled it is by building Open Policy Agent. And there's three fundamental kind of tenants around Open Policy Agent that make it really ideal for this cloud native environment. Number one, it's policy as code and everything in the market now, everything is as code. You buy infrastructure as code. So this is now policy as code. So you can describe in a declarative model, how you want the policy for a system to be developed and you can use the language called Rego to do that. Second is the fact that all the cloud native projects out there which are all developed based upon open source technologies, kubernetes, microservices, envoy, SDO, cafco, all these kinds of buzzwords you hear in the marketplace, they all integrate with Open Policy Agent already. And then thirdly the architecture of Open Policy Agent is that it's distributed, which means that it's ideally suited for this distributed architecture for cloud native. And those are the three kind of characteristics of Open Policy Agent leading to developers loving it. And when I say they love it, we've got hundreds and thousands of users of Open Policy Agent. When you go to the CNCF shows co op con earlier this year and there's two more coming this year. There's many, many talks on it. You've got cloud vendors like Google and Microsoft adopting Open Policy Agent, got a lot of enterprises adopting Open Policy Agent. So, that's really fundamentally what we've built is we've built an authorization architecture for this new world to really address the security and privacy concerns, which have always existed and I'm going to be more exponential in this new world. >> And I think you've also built a community around OPA. Can you share a little bit of information about that and how they help with the co-development and even some of the other things that you're commercializing? >> Sure, yeah. So, now what have we done in from a community point of view with Open Policy Agents? So yeah, the community is a integral part of any open source project and we're lucky to have a great community. We've got a great community of enterprise users of Open Policy Agents and vendors as well, vendors like Microsoft and Google who are now contributing to OPA and building it up. And for me, the most important part of a community is that you learn how enterprises are using your software and they share ideas and they share use cases and you're able to innovate really, really fast. And what we've learned from that is the use cases that they use Open Policy Agent for, for instance, one of the major use cases for Open Policy Agent is for kubernetes Admission Control. So, essentially we can test the configuration of an application which is described in a file called Yammer before it goes into production. So, think of it as pre-production tests, but companies are using it for microservices and applications and data and so forth. So, it helps us understand what they're using it for, but also we use it to help us develop our commercial product, which is the management control plane for OPA. So, we learn about what they're missing in the open source project that we can use to build our commercial product which is ready for enterprise use. >> So you've had a lot of success with OPA. Talk to me about Styra DAS and why the need for that? >> Sure, so why do we need Styra DAS recognizing that OPA is very, very successful. So, the fundamental difference is OPA is a very focused on developers and it's very focused on an environment for an individual node or cluster, but it doesn't have all the enterprise features necessary for a real enterprise to go into production. So what we notice is companies use OPA for pre-production, but when they want to go into production, they need a user interface. They need a way to author policies, distribute policies, monitor policies, do impact analysis and a whole bunch of other features and capabilities that are needed for enterprise deployments and so forth. So that's a fundamental difference between OPA and the commercial product. The commercial product is really operationalizing in OPA for an enterprise deployment. >> So the relationship between Styra and OPA seems very collaborative to me that what you just described with the commercial product of Styra DAS is really one that was developed based on what the OPA community and Styra have learned together? >> Correct, Yes. So, OPA was created by the CTO, the founders of the company when the team was actually part of Nicira and they left Nicira which got acquired by VMware and so on early on several years ago, the need for distributed architectures and the need for unified policy so they left and created OPA. And from day one they wanted to get over into everybody's hands. That's why they contributed it to open source as part of CNCF. And then the next kind of strategy is to focus on the control apps aspects, the enterprise aspect. So yes, the same team that created OPA is the same team that's creating the Styra DAS commercial offering as well. >> So from the enterprise perspective, talk to me about some of the companies that you're talking to. I imagine any organization that's focused on cloud native, but any industry in particular that you see is really kind of leading edge right now? >> Yeah, so which industries are we talking to in terms of using Styra DAS and OPA? What we've actually found it's across the board. And we've seen in the early days that financial services and high tech were using OPA, but now it's really across the board. So it's all verticals really. And what we've noticed is any organization which is going through a cloud transformation project where they're either building new applications based upon cloud native app stacks like kubernetes and microservices and so forth or shift to the cloud are the companies that are also adopting OPA and the Styra DAS product, right? Because it's all part of the same solution set. And what we're noticing now and this is a fundamental difference is platform architects and developers are kind of prime to use these technologies. They learn about these technologies by going to the conferences and unlike the past which was very much top down selling from the sea level down, this is very much bottomed up. So developers learn about OPA from going to the conferences. They use it within their own environment and then they tell their management that, "Look, we're using OPA already. "We're missing these capabilities," or they come to us and we educate them about the Styra DAS product and so forth. So it's a very different sales model as well and that's why it's very important for ourselves and any open source company to really keep developers happy and provide a solution, that's meeting their requirements. >> On that side with so many of us and developers included working from home for the past nearly four months. We now are doing things like this virtual conversations, virtual events, how is Styra helping to continue to feed and educate those developers so that they can understand how you can impact their job functions and how they can then elevate you guys up the stack. >> Sure, so what's changed over the last three months or so in the market as a consequence of COVID-19 and from an educational point of view. So, what we've seen is fundamentally in the early days of COVID-19 everybody was kind of get the head around how to work from home and so forth, but what we've seen across the all verticals is developers have now really focused on educating themselves and just as a data point and the audience that we get to the OPA website is as high as it's ever been for the last three months. And what we're doing as a company is a lot of training sessions, video content, write-ups, blogs and so forth, right? And really helping the community learn about OPA and how to solve these kind of fundamental problems around policy and authorization within the environment. We've also been helped by the community as well. So there's been talks about a number of companies, Microsoft, Google, Palo Alto had a talk and many many companies are talking about OPA now and I love it because ultimately being an open source company and building a project which we want to become defacto, we want to raise the bar for security across the world, right? And if we can do that then it's going to be an achievement for us and it's very gratifying knowing that we're really fixing security problems for organizations because ultimately we always want to be able to use an application or a banking service and not worry about privacy and security concerns and that's ultimately what we're all after. But this is such a fundamental component that once we want to have developers learn this now because if they can incorporate this into the DevOps app stack then in future years when these applications are built and they're exposed there'll be more secure. >> And so it sounds like maybe there's even more engagement now during COVID when everybody is at home. Tell me about some of the things that are coming down the pipe for Styra in light of all of this exciting collaboration with the community. >> Sure, yeah. There's definitely been way more collaboration as a consequence of COVID-19. People are at home and they're focusing and they're going through learning sessions and browsing the website going through the video content and so forth. So what we're engaging as much as we have ever been, in fact I would argue that we're engaging even more so now, because it's just a different environment to work in. And what we're focused on now is really adding more features to the Styra DAS product, just to step back for a second, Open Policy Agent works across the cloud native stack and Styra DAS has been focused first on the kubernetes use case and now it also supports microservices as well. And then what we're continuing to do is add more of those enterprise features into Styra DAS and move up and up across the stack. But it is all driven by developers that we're talking to on a daily basis and that's leading to where the project is moving forward and the development for the roadmap and so forth. >> And Styra DAS was only launched in 2019, is that correct? >> 2019 yes, that's correct. That's correct. Yes, time flies, right? So, yes. >> A lot of change and a lot of development in a short period of time. >> That's right and 2019 was a big year for us, right? We started last 2019 with a soft launch at the RSA conference and we finished 2019 with series a funding led by Xcel. And yeah, it's great to see how the commercial product has been gaining traction in the marketplace as well as OPA as well and I think it's a combination of events. One, the fact that cloud native is now really well understood. Second, the fact that kubernetes at the beginning of 2019, it was still, "What does kubernetes mean, "is it going into production?" Now kubernetes is absolutely going into production and there's such a desire for organizations to make sure that security and policy and compliance are resolved before applications go into production otherwise we're going to have the same kind of challenges we had with previous app stacks. >> Well, the momentum is certainly with you. I can definitely hear that in your voice bell. Thank you so much for joining me talking about Styra, how you're reinventing policy and authorization for cloud native applications. >> Thank you, Lisa. >> For my guest Bill Mann, I'm Lisa Martin. You're watching the Cube Conversation. Thanks for your time. (upbeat music)