>>from Las Vegas. It's the Q covering. Next work. 2019 America's Do You buy Juniper Networks? Come back already. Jeffrey here with the Cube were in Las Vegas at Caesar's at the Juniper. Next work event. About 1000 people kind of going over a lot of new cool things. 400 gigs. Who knew that was coming out of new information for me? But that's not what we're here today. We're here for the fourth installment of around the Cube unpacking. I were happy to have all the winners of the three previous rounds here at the same place. We don't have to do it over the phone s so we're happy to have him. Let's jump into it. So winner of Round one was Bob Friday. He is the VP and CTO at Missed the Juniper Company. Bob, Great to see you. Good to be back. Absolutely. All the way from Seattle. Sharna Parky. She's a VP applied scientist at Tech CEO could see Sharna and, uh, from Google. We know a lot of a I happen to Google. Rajan's chef. He is the V p ay ay >>product management on Google. Welcome. Thank you, Christy. Here >>All right, so let's jump into it. So just warm everybody up and we'll start with you. Bob, What are some When you're talking to someone at a cocktail party Friday night talking to your mom And they say, What is a I What >>do you >>give him? A Zen examples of where a eyes of packing our lives today? >>Well, I think we all know the examples of the south driving car, you know? Aye, aye. Starting to help our health care industry being diagnosed cancer for me. Personally, I had kind of a weird experience last week at a retail technology event where basically had these new digital mirrors doing facial recognition. Right? And basically, you start to have little mirrors were gonna be a skeevy start guessing. Hey, you have a beard, you have some glasses, and they start calling >>me old. So this is kind >>of very personal. I have a something for >>you, Camille, but eh? I go walking >>down a mall with a bunch of mirrors, calling me old. >>That's a little Illinois. Did it bring you out like a cane or a walker? You know, you start getting some advertising's >>that were like Okay, you guys, this is a little bit over the top. >>Alright, Charlotte, what about you? What's your favorite example? Share with people? >>Yeah, E think one of my favorite examples of a I is, um, kind of accessible in on your phone where the photos you take on an iPhone. The photos you put in Google photos, they're automatically detecting the faces and their labeling them for you. They're like, Here's selfies. Here's your family. Here's your Children. And you know, that's the most successful one of the ones that I think people don't really think about a lot or things like getting loan applications right. We actually have a I deciding whether or not we get loans. And that one is is probably the most interesting one to be right now. >>Roger. So I think the father's example is probably my favorite as well. And what's interesting to me is that really a I is actually not about the Yeah, it's about the user experience that you can create as a result of a I. What's cool about Google photos is that and my entire family uses Google photos and they don't even know actually that the underlying in some of the most powerful a I in the world. But what they know is they confined every picture of our kids on the beach whenever they whenever they want to. Or, you know, we had a great example where we were with our kids. Every time they like something in the store, we take a picture of it, Um, and we can look up toy and actually find everything that they've taken picture. >>It's interesting because I think most people don't even know the power that they have. Because if you search for beach in your Google photos or you search for, uh, I was looking for an old bug picture from my high school there it came right up until you kind of explore. You know, it's pretty tricky, Raja, you know, I think a lot of conversation about A They always focus the general purpose general purpose, general purpose machines and robots and computers. But people don't really talk about the applied A that's happening all around. Why do you think that? >>So it's a good question. There's there's a lot more talk about kind of general purpose, but the reality of where this has an impact right now is, though, are those specific use cases. And so, for example, things like personalizing customer interaction or, ah, spotting trends that did that you wouldn't have spotted for turning unstructured data like documents into structure data. That's where a eyes actually having an impact right now. And I think it really boils down to getting to the right use cases where a I right? >>Sharon, I want ask you. You know, there's a lot of conversation. Always has A I replace people or is it an augmentation for people? And we had Gary Kasparov on a couple years ago, and he talked about, you know, it was the combination if he plus the computer made the best chess player, but that quickly went away. Now the computer is actually better than Garry Kasparov. Plus the computer. How should people think about a I as an augmentation tool versus a replacement tool? And is it just gonna be specific to the application? And how do you kind of think about those? >>Yeah, I would say >>that any application where you're making life and death decisions where you're making financial decisions that disadvantage people anything where you know you've got u A. V s and you're deciding whether or not to actually dropped the bomb like you need a human in the loop. If you're trying to change the words that you are using to get a different group of people to apply for jobs, you need a human in the loop because it turns out that for the example of beach, you type sheep into your phone and you might get just a field, a green field and a I doesn't know that, uh, you know, if it's always seen sheep in a field that when the sheep aren't there, that that isn't a sheep like it doesn't have that kind of recognition to it. So anything were we making decisions about parole or financial? Anything like that needs to have human in the loop because those types of decisions are changing fundamentally the way we live. >>Great. So shift gears. The team are Jeff Saunders. Okay, team, your mind may have been the liquid on my bell, so I'll be more active on the bell. Sorry about that. Everyone's even. We're starting a zero again, so I want to shift gears and talk about data sets. Um Bob, you're up on stage. Demo ing some some of your technology, the Miss Technology and really, you know, it's interesting combination of data sets A I and its current form needs a lot of data again. Kind of the classic Chihuahua on blue buried and photos. You got to run a lot of them through. How do you think about data sets? In terms of having the right data in a complete data set to drive an algorithm >>E. I think we all know data sets with one The tipping points for a I to become more real right along with cloud computing storage. But data is really one of the key points of making a I really write my example on stage was wine, right? Great wine starts a great grape street. Aye, aye. Starts a great data for us personally. L s t M is an example in our networking space where we have data for the last three months from our customers and rule using the last 30 days really trained these l s t m algorithms to really get that tsunami detection the point where we don't have false positives. >>How much of the training is done. Once you once you've gone through the data a couple times in a just versus when you first started, you're not really sure how it's gonna shake out in the algorithm. >>Yeah. So in our case right now, right, training happens every night. So every night, we're basically retraining those models, basically, to be able to predict if there's gonna be an anomaly or network, you know? And this is really an example. Where you looking all these other cat image thinks this is where these neural networks there really were one of the transformational things that really moved a I into the reality calling. And it's starting to impact all our different energy. Whether it's text imaging in the networking world is an example where even a I and deep learnings ruling starting to impact our networking customers. >>Sure, I want to go to you. What do you do if you don't have a big data set? You don't have a lot of pictures of chihuahuas and blackberries, and I want to apply some machine intelligence to the problem. >>I mean, so you need to have the right data set. You know, Big is a relative term on, and it depends on what you're using it for, right? So you can have a massive amount of data that represents solar flares, and then you're trying to detect some anomaly, right? If you train and I what normal is based upon a massive amount of data and you don't have enough examples of that anomaly you're trying to detect, then it's never going to say there's an anomaly there, so you actually need to over sample. You have to create a population of data that allows you to detect images you can't say, Um oh, >>I'm going to reflect in my data set the percentage of black women >>in Seattle, which is something below 6% and say it's fair. It's not right. You have to be able thio over sample things that you need, and in some ways you can get this through surveys. You can get it through, um, actually going to different sources. But you have to boot, strap it in some way, and then you have to refresh it, because if you leave that data set static like Bob mentioned like you, people are changing the way they do attacks and networks all the time, and so you may have been able to find the one yesterday. But today it's a completely different ball game >>project to you, which comes first, the chicken or the egg. You start with the data, and I say this is a ripe opportunity to apply some. Aye, aye. Or do you have some May I objectives that you want to achieve? And I got to go out and find the >>data. So I actually think what starts where it starts is the business problem you're trying to solve. And then from there, you need to have the right data. What's interesting about this is that you can actually have starting points. And so, for example, there's techniques around transfer, learning where you're able to take an an algorithm that's already been trained on a bunch of data and training a little bit further with with your data on DSO, we've seen that such that people that may have, for example, only 100 images of something, but they could use a model that's trained on millions of images and only use those 100 thio create something that's actually quite accurate. >>So that's a great segue. Wait, give me a ring on now. And it's a great Segway into talking about applying on one algorithm that was built around one data set and then applying it to a different data set. Is that appropriate? Is that correct? Is air you risking all kinds of interesting problems by taking that and applying it here, especially in light of when people are gonna go to outweigh the marketplace, is because I've got a date. A scientist. I couldn't go get one in the marketplace and apply to my data. How should people be careful not to make >>a bad decision based on that? So I think it really depends. And it depends on the type of machine learning that you're doing and what type of data you're talking about. So, for example, with images, they're they're they're well known techniques to be able to do this, but with other things, there aren't really and so it really depends. But then the other inter, the other really important thing is that no matter what at the end, you need to test and generate based on your based on your data sets and on based on sample data to see if it's accurate or not, and then that's gonna guide everything. Ultimately, >>Sharon has got to go to you. You brought up something in the preliminary rounds and about open A I and kind of this. We can't have this black box where stuff goes into the algorithm. That stuff comes out and we're not sure what the result was. Sounds really important. Is that Is that even plausible? Is it feasible? This is crazy statistics, Crazy math. You talked about the business objective that someone's trying to achieve. I go to the data scientist. Here's my data. You're telling this is the output. How kind of where's the line between the Lehman and the business person and the hard core data science to bring together the knowledge of Here's what's making the algorithm say this. >>Yeah, there's a lot of names for this, whether it's explainable. Aye, aye. Or interpret a belay. I are opening the black box. Things like that. Um, the algorithms that you use determine whether or not they're inspect herbal. Um, and the deeper your neural network gets, the harder it is to inspect, actually. Right. So, to your point, every time you take an aye aye and you use it in a different scenario than what it was built for. For example, um, there is a police precinct in New York that had a facial recognition software, and, uh, victim said, Oh, it looked like this actor. This person looked like Bill Cosby or something like that, and you were never supposed to take an image of an actor and put it in there to find people that look like them. But that's how people were using it. So the Russians point yes, like it. You can transfer learning to other a eyes, but it's actually the humans that are using it in ways that are unintended that we have to be more careful about, right? Um, even if you're a, I is explainable, and somebody tries to use it in a way that it was never intended to be used. The risk is much higher >>now. I think maybe I had, You know, if you look at Marvis kind of what we're building for the networking community Ah, good examples. When Marvis tries to do estimate your throughput right, your Internet throughput. That's what we usually call decision tree algorithm. And that's a very interpretive algorithm. and we predict low throughput. We know how we got to that answer, right? We know what features God, is there? No. But when we're doing something like a NAMI detection, that's a neural network. That black box it tells us yes, there's a problem. There's some anomaly, but that doesn't know what caused the anomaly. But that's a case where we actually used neural networks, actually find the anomie, and then we're using something else to find the root cause, eh? So it really depends on the use case and where the night you're going to use an interpreter of model or a neural network which is more of a black box model. T tell her you've got a cat or you've got a problem >>somewhere. So, Bob, that's really interested. So can you not unpacking? Neural network is just the nature of the way that the communication and the data flows and the inferences are made that you can't go in and unpack it, that you have to have the >>separate kind of process too. Get to the root cause. >>Yeah, assigned is always hard to say. Never. But inherently s neural networks are very complicated. Saito set of weights, right? It's basically usually a supervised training model, and we're feeding a bunch of data and trying to train it to detect a certain features, sir, an output. But that is where they're powerful, right? And that's why they basically doing such good, Because they are mimicking the brain, right? That neural network is a very complex thing. Can't like your brain, right? We really don't understand how your brain works right now when you have a problem, it's really trialling there. We try to figure out >>right going right. So I want to stay with you, bought for a minute. So what about when you change what you're optimizing? Four? So you just said you're optimizing for throughput of the network. You're looking for problems. Now, let's just say it's, uh, into the end of the quarter. Some other reason we're not. You're changing your changing what you're optimizing for, Can you? You have to write separate algorithm. Can you have dynamic movement inside that algorithm? How do you approach a problem? Because you're not always optimizing for the same things, depending on the market conditions. >>Yeah, I mean, I think a good example, you know, again, with Marvis is really with what we call reinforcement. Learning right in reinforcement. Learning is a model we use for, like, radio resource management. And there were really trying to optimize for the user experience in trying to balance the reward, the models trying to reward whether or not we have a good balance between the network and the user. Right, that reward could be changed. So that algorithm is basically reinforcement. You can finally change hell that Algren works by changing the reward you give the algorithm >>great. Um, Rajan back to you. A couple of huge things that have come into into play in the marketplace and get your take one is open source, you know, kind of. What's the impact of open source generally on the availability, desire and more applications and then to cloud and soon to be edge? You know, the current next stop. How do you guys incorporate that opportunity? How does it change what you can do? How does it open up the lens of >>a I Yeah, I think open source is really important because I think one thing that's interesting about a I is that it's a very nascent field and the more that there's open source, the more that people could build on top of each other and be able to utilize what what others others have done. And it's similar to how we've seen open source impact operating systems, the Internet, things like things like that with Cloud. I think one of the big things with cloud is now you have the processing power and the ability to access lots of data to be able to t create these thes networks. And so the capacity for data and the capacity for compute is much higher. Edge is gonna be a very important thing, especially going into next few years. You're seeing Maur things incorporated on the edge and one exciting development is around Federated learning where you can train on the edge and then combine some of those aspects into a cloud side model. And so that I think will actually make EJ even more powerful. >>But it's got to be so dynamic, right? Because the fundamental problem used to always be the move, the computer, the data or the date of the computer. Well, now you've got on these edge devices. You've got Tanya data right sensor data all kinds of machining data. You've got potentially nasty hostile conditions. You're not in a nice, pristine data center where the environmental conditions are in the connective ity issues. So when you think about that problem yet, there's still great information. There you got latent issues. Some I might have to be processed close to home. How do you incorporate that age old thing of the speed of light to still break the break up? The problem to give you a step up? Well, we see a lot >>of customers do is they do a lot of training on the cloud, but then inference on the on the edge. And so that way they're able to create the model that they want. But then they get fast response time by moving the model to the edge. The other thing is that, like you said, lots of data is coming into the edge. So one way to do it is to efficiently move that to the cloud. But the other way to do is filter. And to try to figure out what data you want to send to the clouds that you can create the next days. >>Shawna, back to you let's shift gears into ethics. This pesky, pesky issue that's not not a technological issue at all, but right. We see it often, especially in tech. Just cause you should just cause you can doesn't mean that you should. Um so and this is not a stem issue, right? There's a lot of different things that happened. So how should people be thinking about ethics? How should they incorporate ethics? Um, how should they make sure that they've got kind of a, you know, a standard kind of overlooking kind of what they're doing? The decisions are being made. >>Yeah, One of the more approachable ways that I have found to explain this is with behavioral science methodologies. So ethics is a massive field of study, and not everyone shares the same ethics. However, if you try and bring it closer to behavior change because every product that we're building is seeking to change of behavior. We need to ask questions like, What is the gap between the person's intention and the goal we have for them? Would they choose that goal for themselves or not? If they wouldn't, then you have an ethical problem, right? And this this can be true of the intention, goal gap or the intention action up. We can see when we regulated for cigarettes. What? We can't just make it look cool without telling them what the cigarettes are doing to them, right so we can apply the same principles moving forward. And they're pretty accessible without having to know. Oh, this philosopher and that philosopher in this ethicist said these things, it can be pretty human. The challenge with this is that most people building these algorithms are not. They're not trained in this way of thinking, and especially when you're working at a start up right, you don't have access to massive teams of people to guide you down this journey, so you need to build it in from the beginning, and you need to be open and based upon principles. Um, and it's going to touch every component. It should touch your data, your algorithm, the people that you're using to build the product. If you only have white men building the product, you have a problem you need to pull in other people. Otherwise, there are just blind spots that you are not going to think of in order to still that product for a wider audience, but it seems like >>they were on such a razor sharp edge. Right with Coca Cola wants you to buy Coca Cola and they show ads for Coca Cola, and they appeal to your let's all sing together on the hillside and be one right. But it feels like with a I that that is now you can cheat. Right now you can use behavioral biases that are hardwired into my brain is a biological creature against me. And so where is where is the fine line between just trying to get you to buy Coke? Which somewhat argues Probably Justus Bad is Jule cause you get diabetes and all these other issues, but that's acceptable. But cigarettes are not. And now we're seeing this stuff on Facebook with, you know, they're coming out. So >>we know that this is that and Coke isn't just selling Coke anymore. They're also selling vitamin water so they're they're play isn't to have a single product that you can purchase, but it is to have a suite of products that if you weren't that coke, you can buy it. But if you want that vitamin water you can have that >>shouldn't get vitamin water and a smile that only comes with the coat. Five. You want to jump in? >>I think we're going to see ethics really break into two different discussions, right? I mean, ethics is already, like human behavior that you're already doing right, doing bad behavior, like discriminatory hiring, training, that behavior. And today I is gonna be wrong. It's wrong in the human world is gonna be wrong in the eye world. I think the other component to this ethics discussion is really round privacy and data. It's like that mirror example, right? No. Who gave that mirror the right to basically tell me I'm old and actually do something with that data right now. Is that my data? Or is that the mirrors data that basically recognized me and basically did something with it? Right. You know, that's the Facebook. For example. When I get the email, tell me, look at that picture and someone's take me in the pictures Like, where was that? Where did that come from? Right? >>What? I'm curious about to fall upon that as social norms change. We talked about it a little bit for we turn the cameras on, right? It used to be okay. Toe have no black people drinking out of a fountain or coming in the side door of a restaurant. Not that long ago, right in the 60. So if someone had built an algorithm, then that would have incorporated probably that social norm. But social norms change. So how should we, you know, kind of try to stay ahead of that or at least go back reflectively after the fact and say kind of back to the black box, That's no longer acceptable. We need to tweak this. I >>would have said in that example, that was wrong. 50 years ago. >>Okay, it was wrong. But if you ask somebody in Alabama, you know, at the University of Alabama, Matt Department who have been born Red born, bred in that culture as well, they probably would have not necessarily agreed. But so generally, though, again, assuming things change, how should we make sure to go back and make sure that we're not again carrying four things that are no longer the right thing to do? >>Well, I think I mean, as I said, I think you know what? What we know is wrong, you know is gonna be wrong in the eye world. I think the more subtle thing is when we start relying on these Aye. Aye. To make decisions like no shit in my car, hit the pedestrian or save my life. You know, those are tough decisions to let a machine take off or your balls decision. Right when we start letting the machines Or is it okay for Marvis to give this D I ps preference over other people, right? You know, those type of decisions are kind of the ethical decision, you know, whether right or wrong, the human world, I think the same thing will apply in the eye world. I do think it will start to see more regulation. Just like we see regulation happen in our hiring. No, that regulation is going to be applied into our A I >>right solutions. We're gonna come back to regulation a minute. But, Roger, I want to follow up with you in your earlier session. You you made an interesting comment. You said, you know, 10% is clearly, you know, good. 10% is clearly bad, but it's a soft, squishy middle at 80% that aren't necessarily super clear, good or bad. So how should people, you know, kind of make judgments in this this big gray area in the middle? >>Yeah, and I think that is the toughest part. And so the approach that we've taken is to set us set out a set of AI ai principles on DDE. What we did is actually wrote down seven things that we will that we think I should do and four things that we should not do that we will not do. And we now have to actually look at everything that we're doing against those Aye aye principles. And so part of that is coming up with that governance process because ultimately it boils down to doing this over and over, seeing lots of cases and figuring out what what you should do and so that governments process is something we're doing. But I think it's something that every company is going to need to do. >>Sharon, I want to come back to you, so we'll shift gears to talk a little bit about about law. We've all seen Zuckerberg, unfortunately for him has been, you know, stuck in these congressional hearings over and over and over again. A little bit of a deer in a headlight. You made an interesting comment on your prior show that he's almost like he's asking for regulation. You know, he stumbled into some really big Harry nasty areas that were never necessarily intended when they launched Facebook out of his dorm room many, many moons ago. So what is the role of the law? Because the other thing that we've seen, unfortunately, a lot of those hearings is a lot of our elected officials are way, way, way behind there, still printing their e mails, right? So what is the role of the law? How should we think about it? What shall we What should we invite from fromthe law to help sort some of this stuff out? >>I think as an individual, right, I would like for each company not to make up their own set of principles. I would like to have a shared set of principles that were following the challenge. Right, is that with between governments, that's impossible. China is never gonna come up with same regulations that we will. They have a different privacy standards than we D'oh. Um, but we are seeing locally like the state of Washington has created a future of work task force. And they're coming into the private sector and asking companies like text you and like Google and Microsoft to actually advise them on what should we be regulating? We don't know. We're not the technologists, but they know how to regulate. And they know how to move policies through the government. What will find us if we don't advise regulators on what we should be regulating? They're going to regulate it in some way, just like they regulated the tobacco industry. Just like they regulated. Sort of, um, monopolies that tech is big enough. Now there is enough money in it now that it will be regularly. So we need to start advising them on what we should regulate because just like Mark, he said. While everyone else was doing it, my competitors were doing it. So if you >>don't want me to do it, make us all stop. What >>can I do? A negative bell and that would not for you, but for Mark's responsibly. That's crazy. So So bob old man at the mall. It's actually a little bit more codified right, There's GDP are which came through May of last year and now the newness to California Extra Gatorade, California Consumer Protection Act, which goes into effect January 1. And you know it's interesting is that the hardest part of the implementation of that I think I haven't implemented it is the right to be for gotten because, as we all know, computers, air, really good recording information and cloud. It's recorded everywhere. There's no there there. So when these types of regulations, how does that impact? Aye, aye, because if I've got an algorithm built on a data set in in person, you know, item number 472 decides they want to be forgotten How that too I deal with that. >>Well, I mean, I think with Facebook, I can see that as I think. I suspect Mark knows what's right and wrong. He's just kicking ball down tires like >>I want you guys. >>It's your problem, you know. Please tell me what to do. I see a ice kind of like any other new technology, you know, it could be abused and used in the wrong waste. I think legally we have a constitution that protects our rights. And I think we're going to see the lawyers treat a I just like any other constitutional things and people who are building products using a I just like me build medical products or other products and actually harmful people. You're gonna have to make sure that you're a I product does not harm people. You're a product does not include no promote discriminatory results. So I >>think we're going >>to see our constitutional thing is going applied A I just like we've seen other technologies work. >>And it's gonna create jobs because of that, right? Because >>it will be a whole new set of lawyers >>the holdings of lawyers and testers, even because otherwise of an individual company is saying. But we tested. It >>works. Trust us. Like, how are you gonna get the independent third party verification of that? So we're gonna start to see a whole terrorist proliferation of that type of fields that never had to exist before. >>Yeah, one of my favorite doctor room. A child. Grief from a center. If you don't follow her on Twitter Follower. She's fantastic and a great lady. So I want to stick with you for a minute, Bob, because the next topic is autonomous. And Rahman up on the keynote this morning, talked about missed and and really, this kind of shifting workload of fixing things into an autonomous set up where the system now is, is finding problems, diagnosing problems, fixing problems up to, I think, he said, even generating return authorizations for broken gear, which is amazing. But autonomy opens up all kinds of crazy, scary things. Robert Gates, we interviewed said, You know, the only guns that are that are autonomous in the entire U. S. Military are the ones on the border of North Korea. Every single other one has to run through a person when you think about autonomy and when you can actually grant this this a I the autonomy of the agency toe act. What are some of the things to think about in the word of the things to keep from just doing something bad, really, really fast and efficiently? >>Yeah. I mean, I think that what we discussed, right? I mean, I think Pakal purposes we're far, you know, there is a tipping point. I think eventually we will get to the CP 30 Terminator day where we actually build something is on par with the human. But for the purposes right now, we're really looking at tools that we're going to help businesses, doctors, self driving cars and those tools are gonna be used by our customers to basically allow them to do more productive things with their time. You know, whether it's doctor that's using a tool to actually use a I to predict help bank better predictions. They're still gonna be a human involved, you know, And what Romney talked about this morning and networking is really allowing our I T customers focus more on their business problems where they don't have to spend their time finding bad hard were bad software and making better experiences for the people. They're actually trying to serve >>right, trying to get your take on on autonomy because because it's a different level of trust that we're giving to the machine when we actually let it do things based on its own. But >>there's there's a lot that goes into this decision of whether or not to allow autonomy. There's an example I read. There's a book that just came out. Oh, what's the title? You look like a thing. And I love you. It was a book named by an A I, um if you want to learn a lot about a I, um and you don't know much about it, Get it? It's really funny. Um, so in there there is in China. Ah, factory where the Aye Aye. Is optimizing um, output of cockroaches now they just They want more cockroaches now. Why do they want that? They want to grind them up and put them in a lotion. It's one of their secret ingredients now. It depends on what parameters you allow that I to change, right? If you decide Thio let the way I flood the container, and then the cockroaches get out through the vents and then they get to the kitchen to get food, and then they reproduce the parameters in which you let them be autonomous. Over is the challenge. So when we're working with very narrow Ai ai, when use hell the Aye. Aye. You can change these three things and you can't just change anything. Then it's a lot easier to make that autonomous decision. Um and then the last part of it is that you want to know what is the results of a negative outcome, right? There was the result of a positive outcome. And are those results something that we can take actually? >>Right, Right. Roger, don't give you the last word on the time. Because kind of the next order of step is where that machines actually write their own algorithms, right? They start to write their own code, so they kind of take this next order of thought and agency, if you will. How do you guys think about that? You guys are way out ahead in the space, you have huge data set. You got great technology. Got tensorflow. When will the machines start writing their own A their own out rhythms? Well, and actually >>it's already starting there that, you know, for example, we have we have a product called Google Cloud. Ottawa. Mel Village basically takes in a data set, and then we find the best model to be able to match that data set. And so things like that that that are there already, but it's still very nascent. There's a lot more than that that can happen. And I think ultimately with with how it's used I think part of it is you have to start. Always look at the downside of automation. And what is what is the downside of a bad decision, whether it's the wrong algorithm that you create or a bad decision in that model? And so if the downside is really big, that's where you need to start to apply Human in the loop. And so, for example, in medicine. Hey, I could do amazing things to detect diseases, but you would want a doctor in the loop to be able to actually diagnose. And so you need tohave have that place in many situations to make sure that it's being applied well. >>But is that just today? Or is that tomorrow? Because, you know, with with exponential growth and and as fast as these things are growing, will there be a day where you don't necessarily need maybe need the doctor to communicate the news? Maybe there's some second order impacts in terms of how you deal with the family and, you know, kind of pros and cons of treatment options that are more emotional than necessarily mechanical, because it seems like eventually that the doctor has a role. But it isn't necessarily in accurately diagnosing a problem. >>I think >>I think for some things, absolutely over time the algorithms will get better and better, and you can rely on them and trust them more and more. But again, I think you have to look at the downside consequence that if there's a bad decision, what happens and how is that compared to what happens today? And so that's really where, where that is. So, for example, self driving cars, we will get to the point where cars are driving by themselves. There will be accidents, but the accident rate is gonna be much lower than what's there with humans today, and so that will get there. But it will take time. >>And there was a day when will be illegal for you to drive. You have manslaughter, right? >>I I believe absolutely there will be in and and I don't think it's that far off. Actually, >>wait for the day when I have my car take me up to Northern California with me. Sleepy. I've only lived that long. >>That's right. And work while you're while you're sleeping, right? Well, I want to thank everybody Aton for being on this panel. This has been super fun and these air really big issues. So I want to give you the final word will just give everyone kind of a final say and I just want to throw out their Mars law. People talk about Moore's law all the time. But tomorrow's law, which Gardner stolen made into the hype cycle, you know, is that we tend to overestimate in the short term, which is why you get the hype cycle and we turn. Tend to underestimate, in the long term the impacts of technology. So I just want it is you look forward in the future won't put a year number on it, you know, kind of. How do you see this rolling out? What do you excited about? What are you scared about? What should we be thinking about? We'll start with you, Bob. >>Yeah, you know, for me and, you know, the day of the terminus Heathrow. I don't know if it's 100 years or 1000 years. That day is coming. We will eventually build something that's in part of the human. I think the mission about the book, you know, you look like a thing and I love >>you. >>Type of thing that was written by someone who tried to train a I to basically pick up lines. Right? Cheesy pickup lines. Yeah, I'm not for sure. I'm gonna trust a I to help me in my pickup lines yet. You know I love you. Look at your thing. I love you. I don't know if they work. >>Yeah, but who would? Who would have guessed online dating is is what it is if you had asked, you know, 15 years ago. But I >>think yes, I think overall, yes, we will see the Terminator Cp through It was probably not in our lifetime, but it is in the future somewhere. A. I is definitely gonna be on par with the Internet cell phone, radio. It's gonna be a technology that's gonna be accelerating if you look where technology's been over last. Is this amazing to watch how fast things have changed in our lifetime alone, right? Yeah, we're just on this curve of technology accelerations. This in the >>exponential curves China. >>Yeah, I think the thing I'm most excited about for a I right now is the addition of creativity to a lot of our jobs. So ah, lot of we build an augmented writing product. And what we do is we look at the words that have happened in the world and their outcomes. And we tell you what words have impacted people in the past. Now, with that information, when you augment humans in that way, they get to be more creative. They get to use language that have never been used before. To communicate an idea. You can do this with any field you can do with composition of music. You can if you can have access as an individual, thio the data of a bunch of cultures the way that we evolved can change. So I'm most excited about that. I think I'm most concerned currently about the products that we're building Thio Give a I to people that don't understand how to use it or how to make sure they're making an ethical decision. So it is extremely easy right now to go on the Internet to build a model on a data set. And I'm not a specialist in data, right? And so I have no idea if I'm adding bias in or not, um and so it's It's an interesting time because we're in that middle area. Um, and >>it's getting loud, all right, Roger will throw with you before we have to cut out, or we're not gonna be able to hear anything. So I actually start every presentation out with a picture of the Mosaic browser, because what's interesting is I think that's where >>a eyes today compared to kind of weather when the Internet was around 1994 >>were just starting to see how a I can actually impact the average person. As a result, there's a lot of hype, but what I'm actually finding is that 70% of the company's I talked to the first question is, Why should I be using this? And what benefit does it give me? Why 70% ask you why? Yeah, and and what's interesting with that is that I think people are still trying to figure out what is this stuff good for? But to your point about the long >>run, and we underestimate the longer I think that every company out there and every product will be fundamentally transformed by eye over the course of the next decade, and it's actually gonna have a bigger impact on the Internet itself. And so that's really what we have to look forward to. >>All right again. Thank you everybody for participating. There was a ton of fun. Hope you had fun. And I look at the score sheet here. We've got Bob coming in and the bronze at 15 points. Rajan, it's 17 in our gold medal winner for the silver Bell. Is Sharna at 20 points. Again. Thank you. Uh, thank you so much and look forward to our next conversation. Thank Jeffrey Ake signing out from Caesar's Juniper. Next word unpacking. I Thanks for watching.

>>live from Miami, Florida. It's the Q covering. IBM is data in a I forum brought to you by IBM. >>Welcome back to the port of Miami, Everybody. You're watching the Cube, the leader in live tech coverage. We're here covering the IBM data and a I form. Rob Thomas is here. He's the general manager for data in A I and I'd be great to see again. >>Right. Great to see you here in Miami. Beautiful week here on the beach area. It's >>nice. Yeah. This is quite an event. I mean, I had thought it was gonna be, like, roughly 1000 people. It's over. Sold or 17. More than 1700 people here. This is a learning event, right? I mean, people here, they're here to absorb best practice, you know, learn technical hands on presentations. Tell us a little bit more about how this event has evolved. >>It started as a really small training event, like you said, which goes back five years. And what we saw those people, they weren't looking for the normal kind of conference. They wanted to be hands on. They want to build something. They want to come here and leave with something they didn't have when they arrived. So started as a little small builder conference and now somehow continues to grow every year, which were very thankful for. And we continue to kind of expand at sessions. We've had to add hotels this year, so it's really taken off >>you and your title has two of the three superpowers data. And of course, Cloud is the third superpower, which is part of IBMs portfolio. But people want to apply those superpowers, and you use that metaphor in your your keynote today to really transform their business. But you pointed out that only about a eyes only 4 to 10% penetrated within organizations, and you talked about some of the barriers that, but this is a real appetite toe. Learn isn't there. >>There is. Let's go talk about the superpower for a bit. A. I does give employees superpowers because they can do things now. They couldn't do before, but you think about superheroes. They all have an origin story. They always have somewhere where they started and applying a I an organization. It's actually not about doing something completely different. It's about extenuating. What you already d'oh doing something massively better. That's kind of in your DNA already. So we're encouraging all of our clients this week like use the time to understand what you're great at, what your value proposition is. And then how do you use a I to accentuate that? Because your superpower is only gonna last if it's starts with who you are as a company or as a >>person who was your favorite superhero is a kid. Let's see. I was >>kind of into the whole Hall of Justice. Super Superman, that kind of thing. That was probably my cartoon. >>I was a Batman guy. And the reason I love that movie because all the combination of tech, it's kind of reminds me, is what's happening here today. In the marketplace, people are taking data. They're taking a I. They're applying machine intelligence to that data to create new insights, which they couldn't have before. But to your point, there's a There's an issue with the quality of data and and there's a there's a skills gap as well. So let's let's start with the data quality problem described that problem and how are you guys attacking it? >>You're a I is only as good as your data. I'd say that's the fundamental problem and organization we worked with. 80% of the projects get slowed down or they get stopped because the company has a date. A problem. That's why we introduce this idea of the A i ladder, which is all of the steps that a company has to think about for how they get to a level of data maturity that supports a I. So how they collect their data, organize their data, analyze their data and ultimately begin to infuse a I into business processes soap. Every organization needs to climb that ladder, and they're all different spots. So for someone might be, we gotta focus on organization a data catalogue. For others, it might be we got do a better job of data collection data management. That's for every organization to figure out. But you need a methodical approach to how you attack the data problem. >>So I wanna ask you about the Aye aye ladder so you could have these verbs, the verbs overlay on building blocks. I went back to some of my notes in the original Ai ai ladder conversation that you introduced a while back. It was data and information architecture at the at the base and then building on that analytics machine learning. Aye, aye, aye. And then now you've added the verbs, collect, organized, analyze and infused. Should we think of this as a maturity model or building blocks and verbs that you can apply depending on where you are in that maturity model, >>I would think of it as building blocks and the methodology, which is you got to decide. Do wish we focus on our data collection and doing that right? Is that our weakness or is a data organization or is it the sexy stuff? The Aye. Aye. The data science stuff. We just This is just a tool to help organizations organize themselves on what's important. I asked every company I visit. Do you have a date? A strategy? You wouldn't believe the looks you get when you ask that question, you get either. Well, she's got one. He's got one. So we got seven or you get No, we've never had one. Or Hey, we just hired a CDO. So we hope to have one. But we use the eye ladder just as a tool to encourage companies to think about your data strategy >>should do you think in the context I want follow up on that data strategy because you see a lot of tactical data strategies? Well, we use Data Thio for this initiative of that initiative. Maybe in sales or marketing, or maybe in R and D. Increasingly, our organization's developing. And should they develop a holistic data strategy, or should they trying to just get kind of quick wins? What are you seeing in the marketplace? >>It depends on where you are in your maturity cycle. I do think it behooves every company to say We understand where we are and we understand where we want to go. That could be the high level data strategy. What are our focus and priorities gonna be? Once you understand focus and priorities, the best way to get things into production is through a bunch of small experiments to your point. So I don't think it's an either or, but I think it's really valuable tohave an overarching data strategy, and I recommended companies think about a hub and spokes model for this. Have a centralized chief date officer, but your business units also need a cheap date officer. So strategy and one place execution in another. There's a best practice to going about this >>the next you ask the question. What is a I? You get that question a lot, and you said it's about predicting, automating and optimizing. Can we unpack that a little bit? What's behind those three items? >>People? People overreact a hype on topics like II. And they think, Well, I'm not ready for robots or I'm not ready for self driving Vehicles like those Mayor may not happen. Don't know. But a eyes. Let's think more basic it's about can we make better predictions of the business? Every company wants to see a future. They want the proverbial crystal ball. A. I helped you make better predictions. If you have the data to do that, it helps you automate tasks, automate the things that you don't want to do. There's a lot of work that has to happen every day that nobody really wants to do you software to automate that there's about optimization. How do you optimize processes to drive greater productivity? So this is not black magic. This is not some far off thing. We're talking about basics better predictions, better automation, better optimization. >>Now interestingly, use the term black magic because because a lot of a I is black box and IBM is always made a point of we're trying to make a I transparent. You talk a lot about taking the bias out, or at least understanding when bias makes sense. When it doesn't make sense, Talk about the black box problem and how you're addressing. >>That starts with one simple idea. A eyes, not magic. I say that over and over again. This is just computer science. Then you have to look at what are the components inside the proverbial black box. With Watson, we have a few things. We've got tools for clients that want to build their own. Aye, aye, to think of it as a tool box you can choose. Do you want a hammer and you want a screwdriver? You wanna nail you go build your own, aye, aye. Using Watson. We also have applications, so it's basically an end user application that puts a I into practice things like Watson assistant to virtually no create a virtual agent for customer service or Watson Discovery or things like open pages with Watson for governance, risk and compliance. So, aye, aye, for Watson is about tools. You want to build your own applications if you want to consume an application, but we've also got in bed today. I capability so you can pick up Watson and put it inside of any software product in the >>world. He also mentioned that Watson was built with a lot of of of, of open source components, which a lot of people might not know. What's behind Watson. >>85% of the work that happens and Watson today is open source. Most people don't know that it's Python. It's our it's deploying into tensorflow. What we've done, where we focused our efforts, is how do you make a I easier to use? So we've introduced Auto Way. I had to watch the studio, So if you're building models and python, you can use auto. I tow automate things like feature engineering algorithm, selection, the kind of thing that's hard for a lot of data scientists. So we're not trying to create our own language. We're using open source, but then we make that better so that a data scientist could do their job better >>so again come back to a adoption. We talked about three things. Quality, trust and skills. We talked about the data quality piece we talked about the black box, you know, challenge. It's not about skills you mention. There's a 250,000 person Gap data science skills. How is IBM approaching how our customers and IBM approaching closing that gap? >>So think of that. But this in basic economic terms. So we have a supply demand mismatch. Massive demand for data scientists, not enough supply. The way that we address that is twofold. One is we've created a team called Data Science Elite. They've done a lot of work for the clients that were on stage with me, who helped a client get to their first big win with a I. It's that simple. We go in for 4 to 6 weeks. It's an elite team. It's not a long project we're gonna get you do for your success. Second piece is the other way to solve demand and supply mismatch is through automation. So I talked about auto. Aye, aye. But we also do things like using a eye for building data catalogs, metadata creation data matching so making that data prep process automated through A. I can also help that supply demand. Miss Max. The way that you solve this is we put skills on the field, help clients, and we do a lot of automation in software. That's how we can help clients navigate this. So the >>data science elite team. I love that concept because way first picked up on a couple of years ago. At least it's one of the best freebies in the business. But of course you're doing it with the customers that you want to have deeper relationships with, and I'm sure it leads toe follow on business. What are some of the things that you're most proud of from the data science elite team that you might be able to share with us? >>The clients stories are amazing. I talked in the keynote about origin stories, Roll Bank of Scotland, automating 40% of their customer service. Now customer SATs going up 20% because they put their customer service reps on those hardest problems. That's data science, a lead helping them get to a first success. Now they scale it out at Wonderman Thompson on stage, part of big W P p big advertising agency. They're using a I to comb through customer records they're using auto Way I. That's the data science elite team that went in for literally four weeks and gave them the confidence that they could then do this on their own. Once we left, we got countless examples where this team has gone in for very short periods of time. And clients don't talk about this because they have to talk about it cause they're like, we can't believe what this team did. So we're really excited by the >>interesting thing about the RVs example to me, Rob was that you basically applied a I to remove a lot of these mundane tasks that weren't really driving value for the organization. And an R B s was able to shift the skill sets. It's a more strategic areas. We always talk about that, but But I love the example C. Can you talk a little bit more about really, where, where that ship was, What what did they will go from and what did they apply to and how it impacted their businesses? A improvement? I think it was 20% improvement in NPS but >>realizes the inquiry's they had coming in were two categories. There were ones that were really easy. There were when they were really hard and they were spreading those equally among their employees. So what you get is a lot of unhappy customers. And then once they said, we can automate all the easy stuff, we can put all of our people in the hardest things customer sat shot through the roof. Now what is a virtual agent do? Let's decompose that a bit. We have a thing called intent classifications as part of Watson assistant, which is, it's a model that understands customer a tent, and it's trained based on the data from Royal Bank of Scotland. So this model, after 30 days is not very good. After 90 days, it's really good. After 180 days, it's excellent, because at the core of this is we understand the intent of customers engaging with them. We use natural language processing. It really becomes a virtual agent that's done all in software, and you can only do that with things like a I. >>And what is the role of the human element in that? How does it interact with that virtual agent. Is it a Is it sort of unattended agent or is it unattended? What is that like? >>So it's two pieces. So for the easiest stuff no humans needed, we just go do that in software for the harder stuff. We've now given the RVs, customer service agents, superpowers because they've got Watson assistant at their fingertips. The hardest thing for a customer service agent is only finding the right data to solve a problem. Watson Discovery is embedded and Watson assistant so they can basically comb through all the data in the bank to answer a question. So we're giving their employees superpowers. So on one hand, it's augmenting the humans. In another case, we're just automating the stuff the humans don't want to do in the first place. >>I'm gonna shift gears a little bit. Talk about, uh, red hat in open shift. Obviously huge acquisition last year. $34 billion Next chapter, kind of in IBM strategy. A couple of things you're doing with open shift. Watson is now available on open shifts. So that means you're bringing Watson to the data. I want to talk about that and then cloudpack for data also on open shifts. So what has that Red had acquisition done for? You obviously know a lot about M and A but now you're in the position of you've got to take advantage of that. And you are taking advantage of this. So give us an update on what you're doing there. >>So look at the cloud market for a moment. You've got around $600 million of opportunity of traditional I t. On premise, you got another 600 billion. That's public clouds, dedicated clouds. And you got about 400 billion. That's private cloud. So the cloud market is fragmented between public, private and traditional. I t. The opportunity we saw was, if we can help clients integrate across all of those clouds, that's a great opportunity for us. What red at open shift is It's a liberator. It says right. Your application once deployed them anywhere because you build them on red hot, open shift. Now we've brought cloudpack for data. Our data platform on the red hot open shift certified on that Watson now runs on red had open shift. What that means is you could have the best data platform. The best Aye, Aye. And you can run it on Google. Eight of us, Azure, Your own private cloud. You get the best, Aye. Aye. With Watson from IBM and run it in any of those places. So the >>reason why that's so powerful because you're able to bring those capabilities to the data without having to move the date around It was Jennifer showed an example or no, maybe was tail >>whenever he was showing Burt analyzing the data. >>And so the beauty of that is I don't have to move any any data, talk about the importance of not having Thio move that data. And I want I want to understand what the client prerequisite is. They really take advantage of that. This one >>of the greatest inventions out of IBM research in the last 10 years, that hasn't gotten a lot attention, which is data virtualization. Data federation. Traditional federation's been around forever. The issue is it doesn't perform our data virtualization performance 500% faster than anything else in the market. So what Jennifer showed that demo was I'm training a model, and I'm gonna virtualized a data set from Red shift on AWS and on premise repositories a my sequel database. We don't have to move the data. We just virtualized those data sets into cloudpack for data and then we can train the model in one place like this is actually breaking down data silos that exist in every organization. And it's really unique. >>It was a very cool demo because what she did is she was pulling data from different data stores doing joins. It was a health care application, really trying to understand where the bias was peeling the onion, right? You know, it is it is bias, sometimes biases. Okay, you just got to know whether or not it's actionable. And so that was that was very cool without having to move any of the data. What is the prerequisite for clients? What do they have to do to take advantage of this? >>Start using cloudpack for data. We've got something on the Web called cloudpack experiences. Anybody can go try this in less than two minutes. I just say go try it. Because cloudpack for data will just insert right onto any public cloud you're running or in your private cloud environment. You just point to the sources and it will instantly begin to start to create what we call scheme a folding. So a skiing version of the schema from your source writing compact for data. This is like instant access to your data. >>It sounds like magic. OK, last question. One of the big takeaways You want people to leave this event with? >>We are trying to inspire clients to give a I shot. Adoption is 4 to 10% for what is the largest economic opportunity we will ever see in our lives. That's not an acceptable rate of adoption. So we're encouraging everybody Go try things. Don't do one, eh? I experiment. Do Ah, 100. Aye, aye. Experiments in the next year. If you do, 150 of them probably won't work. This is where you have to change the cultural idea. Ask that comes into it, be prepared that half of them are gonna work. But then for the 52 that do work, then you double down. Then you triple down. Everybody will be successful. They I if they had this iterative mindset >>and with cloud it's very inexpensive to actually do those experiments. Rob Thomas. Thanks so much for coming on. The Cuban great to see you. Great to see you. All right, Keep right, everybody. We'll be back with our next guest. Right after this short break, we'll hear from Miami at the IBM A I A data form right back.

>> from Austin, Texas. It's Theo Cube, covering pure storage. Accelerate 2019. Brought to you by pure storage. >> Welcome to the Cube. The leader in live tech coverage covering up your accelerate 2019. Lisa Martin with Dave Ilan in Austin, Texas, this year. Pleased to welcome a couple of guests to the program. Please meet Charlie Boyle, VP and GM of DJ X Systems at N Video. Hey, Charlie, welcome back to the Cube, but in a long time ago and we have Brian Schwartz, VP of product management and development at your brain. Welcome. >> Thanks for having me. >> Here we are Day one of the event. Lots of News This morning here is just about to celebrate its 10th anniversary. A lot of innovation and 10 years. Nvidia partnerships. About two is two and 1/2 years old or so. Brian, let's start with you. Give us a little bit of an overview about where pure and and video are, and then let's dig into this news about the Aye aye data hub. >> Cool, it's It's been a good partnership for a couple of years now, and it really was born out of work with mutual customers. You know we brought out the flash blade product, obviously in video was in the market with DJ X is for a I, and we really started to see overlap in a bunch of initial deployments. And we really realized that there was a lot of wisdom to be gained off some of these early I deployments of capturing some of that knowledge and wisdom from those early practitioners and being able to share it with the with the wider community. So that's really kind of where the partnership was born going for a couple of years now, I've got a couple of chapters behind us and many more in the future. And obviously the eye data hub is the piece that we really talked about at this year's accelerate. >> Yeah, areas about been in the market for what? About a year and 1/2 or so Almost >> two years. >> Two years? All right, tell us a little bit about the adoption. What what customers were able to dio with this a ready infrastructure >> and point out the reason we started the partnership was our early customers that were buying dejected product from us. They were buying pure stored. Both leaders and high performance. And as they were trying to put them together, they're like, How should we do this? What's the optimal settings? They've been using storage for years. I was kind of new to them and they needed that recipe. So that's, you know, the early customer experiences turned into airy the solution, and, you know, the whole point of this to simplify. I sounds kind of scary to a lot of folks and the data scientists really just need to be productive. They don't care about infrastructure, but I t s to support this. So I t was very familiar with pure storage. They used them for years for high performance data and as they brought in the Nvidia Compute toe work with that, you know, having a solution that we both supported was super important to the I T practitioners because they knew it worked. They knew we both supported it. We stood behind it and they could get up and running in a matter of days or weeks versus 6 to 9 months if they built it >> themselves. >> You look at companies that you talk to customers. Let's let's narrow it down to those that have data scientists least one day to scientists and ask him where they are in their maturity model, if one is planning to was early threes, they got multiple use cases and four is their enterprise wide. How do you see the landscape? Are you seeing pretty aggressive adoption in those as I couched it, or is it still early? >> I mean so every customers in a different point. So there's definitely a lot of people that are still early, but we've seen a lot of production use cases. You know, everyone talks about self driving cars, but that's, you know, there's a lot behind that. But real world use cases say medicals got a ton? You know, we've got partner companies that you are looking at a reconstruction of MRI's and CT scans cutting the scan time down by 75%. You know, that's real patient outcome. You know, we've got industrial inspection, we're in Texas. People fly drones around and have a eye. Models that are built in their data center on the drone and the field operators get to re program the drones based on what they see and what is happening. Real time and re trains every night. So depending on the industry really depends on where people are in the maturity her. But you know, really, our message out to the enterprises are start now. You know, whether you've got one data scientist, you've got some community data scientists. There's no reason to wait on a because there's a use case that work somewhere in your inner. >> So so one of the key considerations to getting started. What would you say? >> So one thing I would say is, look any to your stages of maturity. Any good investment is done through some creation of business value, right? And an understanding of kind of what problem you're trying to solve and making sure it's compelling. Problem is an important one, and some industries air farther along. Like you know, one of the ones that most everybody's familiar with is the tech industry itself. Every recommendation engine you've probably ever seen on the Internet is backed by some form of a I behind it because they wanted to be super fast and, you know, customized to you as a user. So I think understanding the business value creation problem is is a really important step of it and many people go through an early stage of experimentation, data modeling really kind of, say, a prototyping stage before they go into a mass production use case. It's a very classic i t adoption curve. Just add a comment to the earlier kind of trend is it's a megatrend. Yes, not everybody is doing it in massive wide scale production today. There's some industries that are farther ahead. If you look forward over the next 15 to 20 years, there's a massive amount of Ai ai coming, and it's a It is a new form of computing, the GPU driven computing and the whole point about areas getting the ingredients right. Thio have this new set of infrastructure have storage network compute on the software stack all kind of package together to make it easier to adopt, to allow people to adopt it faster because some industries are far along and others are still in the earlier stages, >> right? So how do you help for those customers and industries that aren't self driving cards of the drones that you talked about where we use case, we all understand it and are excited about it. But for other customers in different industries. How do you help them even understand the A pipeline? And where did they start? I'm sure that varies very >> a lot. But, you know, the key point is starting a I project. You have a desired outcome from Not everything's gonna be successful, but you know Aye, aye. Projects aren't something that it's not a six month I t project or a big you know, C r m. Refresh it. Something that you could take One of our classes that we have, we do a lot of end user customer training are Deep Learning Institute. You can take 1/2 day class and actually do a deep learning project that day. And so a lot of it is understanding your data, you know, and that's where your and the data hub comes in, understanding the data that you have and then formulating a question like, What could I do if I knew this thing? That's all about a I and deep learning. It's coming up with insights that aren't natural. When you just stare at the data, how can the system understand what you want? And then what are the things that you didn't expect defined that A. I is showing you about your data, and that's really a lot of where the business value comes. And how do you know more about your customer? How do you help that customer better, eh? I can unlock things that you may not have pondered yourself. >> The other thing. I'm a huge fan of analogies when you're trying to describe a new concept of people. And there's a good analogy about Ai ai data pipelines that predates, Aye aye around data warehousing like there's been industry around, extract transformers load E T L Systems for a very long period of time. It's a very common thing for many, many people in the I T industry, and I do think there's when you think about a pipeline in a I pipeline. There's an analogy there, which you have data coming in ingress data. You're cleansing it, you're cleaning it. You're essentially trying to get some value out of it. How you do that in a eyes quite a bit different, cause it's GP use and you're looking, you know, for turning unstructured data into more structure date. It's a little different than data. Warehousing traditionally was running reports, but there's a big analogy, I think, to be used about a pipeline that is familiar to people as a way to understand the new concept. >> So that's good. I like the pipeline concept. One of the one of the counters to that would be that you know, when you think about e. T ells complicated process enterprise data warehouses that were cumbersome Do you feel like automation in the A I Pipeline? When we look back 10 years from now, we'll have maybe better things to say than we do about E D W A R e g l. >> And I think one of the things that we've seen, You know, obviously we've done a ton of work in traditional. Aye, aye, But we've also done a lot in accelerated machine learning because that's a little closer to your traditional Data analytics and one of the biggest kind of ah ha moments that I've seen customers in the past year or so. It's just how quickly, by using GPU computing, they can actually look at their data, do something useful with it, and then move on to the next thing so that rapid experimentation is all you know, what a I is about. It's not a eyes, not a one and done thing. Lots of people think Oh, I have to have a recommend er engine. And then I'm done. No, you have to keep retraining it day in and day out so that it gets better. And that's before you had accelerated. Aye, aye pipeline. Before you had accelerated data pipelines that we've been doing with cheap use. It just took too long so people didn't run those experiments. Now we're seeing people exploring Maur trying different things because when your experiment takes 10 minutes, two minutes versus two days or 10 days, you can try out your cycle time. Shorter businesses could doom or and sure, you're gonna discard a lot of results. But you're gonna find those hidden gems that weren't possible before because you just didn't have the time to do >> it. Isn't a key operational izing it as well? I mean again, one of the challenges with the analogy that you gave a needy W is fine reporting. You can operationalize it for reporting, and but the use cases weren't is rich robust, and I feel as though machine intelligence is I mean, you're not gonna help but run into it. It's gonna be part of your everyday life, your thoughts. >> It's definitely part of our everyday lives. When you talk about, you know, consumer applications of everything we all use every day just don't know it's it's, you know, the voice recognition system getting your answer right the first time. You know there's a huge investments in natural language speech right now to the point that you can ask your phone a question. It's going through searching the Web for you, getting the right answer, combining that answer, reading it back to you and giving you the Web page all in less than a second. You know, before you know that be like you talked to an I. V R system. Wait, then you go to an operator. Now people are getting such a better user experience out of a I back systems that, you know over the next few years, I think end users will start preferring to deal with those based systems rather than waiting on line for human, because it'll just get it right. It'll get you the answer you need and you're done. You save time. The company save time and you've got a better outcome. >> So there's definitely some barriers to adoption skills. Is one obvious one the other. And I wonder if Puritan video attack this problem. I'm sure you have, but I'd like some color on it. His traditional companies, which a lot of your customers, their data is in pockets. It's not at the core. You look at the aye aye leaders, you know, the Big Five data their data cos it's at the core. They're applying machine intelligence to that data. How has this modern storage that we heard about this morning affected that customers abilities to really put data at their core? >> You know, it's It's a great question, Dave and I think one of the real opportunities, particularly with Flash, is to consolidate data into a smaller number off larger kind of islands of data, because that's where you could really drive the insights. And historically, in a district in world, you would never try to consolidate your data because there was too many bad performance implications of trying to do that. So people had all these pockets, and even if you could, you probably wouldn't actually want to put the date on the same system at the same time. The difference with flashes as so much performance at the at the core of it at the foundation of it. So the concept of having a very large scale system, like 150 blade system we announced this morning is a way to put a lot of the year and be able to access it. And to Charlie's point, a lot of people they're doing constant experiment, experimentation and modeling of the data. You don't know that how the date is gonna be consumed and you need a very fast kind of wide platform to do that, Which is why it's been a good fit for us to work together >> now fall upon that. Dated by its very nature. However, Brian is distributed and we heard this morning is you're attacking that problem through in a P I framework that you don't care where it is. Cloud on Prem hybrid edge. At some point in time, your thoughts on that >> well, in again the data t be used for a I I wouldn't say it's gonna be every single piece of data inside an organization is gonna be put into the eye pipeline in a lot of cases, you could break it down again. Thio What is the problem? I'm trying to solve the business value and what is the type of data that's gonna be the best fit for it? There are a lot of common patterns for consumption in a I AA speech recognition image recognition places where you have a lot of unstructured data or it's unstructured to a computer. It's not unstructured to you. When you look at a picture, you see a lot of things in it that a computer can't see right, because you recognize what the patterns are and the whole point about a eyes. It's gonna help us get structure out of these unstructured data sets so the computer can recognize more things. You know, the speech and emotions that we as humans just take for granted. It's about having computers, being able to process and respond to that in a way that they're not really people doing today. >> Hot dog, not a hot dog. Silicon Valley >> Street light. Which one of these is not a street lights and prove you're not about to ask you about distributed environments. You know customers have so much choice for everything these days on Prem hosted SAS Public Cloud. What are some of the trends that you're seeing? I always thought that to really be able to extract a tremendous amount of value from data and to deliver a I from it you needed the cloud because you needed a massive volumes of data. Appears legacy of on print. What are some of the things that you're seeing there and how is and video you're coming together to help customers wherever this data is to really dry Valley business value from these workloads, >> I have to put comments and I'll turn over to Charlie. So one is we get asked this question a lot. Like where should I run my eye? The first thing I always tell people is, Where's your data? Gravity moving these days? That's a very large tens of terror by its hundreds of terabytes petabytes of data moving very large. That's the data is actually still ah, hard challenge today. So running your A II where your date is being generated is a good first principle. And for a lot of folks they still have a lot on premise data. That's where their systems are they're generating the systems, or it's a consolidation point from the edge or other other opportunities to run it there. So that's where your date is. Run your A I there. The second thing is about giving people flexibility. We've both made pretty big investments in the world of containerized software applications. Those things are things that can run on grammar in the cloud. So trying to use a consistent set of infrastructure and software and tooling that allows people to migrate and change over time, I think, is an important strategy not only for us but also for the end users that gives them flexibility. >> So, ideally, on Prem versus Cloud implementations shouldn't be. That shouldn't be different. Be great. It would be identical. But are they today? >> So at the lowest level, there's always technical differences, but at the layers that customers are using it, we run one software stack no matter where you're running. So if it's on one of our combined R E systems, whether it's in a cloud provider, it's the same in video software stack from our lowest end consumer of rage. He views, too. The big £350 dejected too you see back there? You know, we've got one software stack runs everywhere, And when the riders making you know, it's really Renee I where your data is And while a lot of people, if you are cloud native company, if you started that way, I'm gonna tell you to run in the cloud all day long. But most enterprises, they're some of their most valuable data is still sitting on premise. They've got decades of customer experience. They've got decades of product information that's all running in systems on Prem. And when you look at speech, speech is the biggest thing you know. They've got, you know, years of call center data that's all sitting in some offline record. What am I gonna do with that? That stuff's not in the cloud. And so you want to move the processing to that because it's impossible to move that data somewhere else and transform it because you're only gonna actually use a small fraction of that data to produce your model. But at the same time, you don't want to spend a year moving that data somewhere to process it back the truck up, put some DJ X is in front of it. And you're good to go. >> Someone's gonna beat you to finding those insides. Right? So there is no time. >> So you have another question. >> I have the last question. So you got >> so in video, you gotta be Switzerland in this game. So I'm not gonna ask you this question. But, Brian, I will ask you what? Why? You're different. I know you were first. He raced out. You got the press release out first. But now that you've been in the market for a while what up? Yours? Competitive differentiators. >> You know, there's there's really two out netted out for flash played on why we think it's a great fit for an A i N A. I use case. One is the flexibility of the performance. We call multi dimensional performance, small files, large files, meditated intensive workloads. Flash blade can do them all. It's a it's a ground up design. It's super flexible on performance. And but also more importantly, I would argue simplicity is a really hallmark of who we are. It's part of the modern date experience that we're talking about this morning. You can think about the systems. They are miniaturized supercomputers And yes, you could always build a supercomputer. People have been doing it for decades. Use Ph. D's to do it and, like most people, don't want to happen. People focused on that level of infrastructure, so we've tried to give incredible kind of capabilities in a really simple to consume platform. I joke with people. We have storage PhDs like literally people. Be cheese for storage so customers don't have to. >> Charlie, feel free to chime in on your favorite child if you want. I >> need a lot of it comes from our customers. That's how we first started with pure is our joint customers saying we need this stuff to work really fast. They're making a massive investment with us and compute. And so if you're gonna run those systems at 100% you need storage. The confusion, you know, pure is our first in there. There are longest partner in this space, and it's really our joint customers that put us together and, you know, to some extent, yes, we are Switzerland. You know, we love all of our partners, but, you know, we do incredible work with these guys all up and down the stack and that's the point to make it simple. If the customer has data we wanted to make be a simplest possible for them to run a ay, whether it's with my stuff with our cloud stuff, all of our partners, but having that deep level of integration and having some of the same shared beliefs to just make stuff simple so people can actually get value out of the data have I t get out of the way so Data scientists could just get their work done. That's what's really powerful about the partnership. >> And I imagine you know, we're out of time, but I imagine to be able to do this at the accelerated pace accelerated, I'm gonna say pun intended it wasn't but, um, cultural fed has to be pretty align. We know Piers culture is bold. Last question, Brian and we bring it home here. Talk to us about how the cultural cultures appearing and video are stars I lining to be able to enable how quickly you guys are developing together. >> Way mentioned the simplicity piece of it. The other piece that I think has been a really strong cultural fit between the companies. It's just the sheer desire to innovate and change the world to be a better place. You know, our hallmark. Our mission is to make the make the world a better place with data. And it really fits with the level of innovation that obviously the video does so like to Silicon Valley companies with wicked smart folks trying to make the world a better place, It's It's really been a good partnership. >> Echo that. That's just, you know, the rate of innovation in a I changes monthly. So if you're gonna be a good partner to your customers, you gotta change Justus fast. So our partnership has been great in that space. >> Awesome. Next time, we're out of time, But next time, come back, talk to a customer, really wanna understand it, gonna dig into some of the great things that they're extracting from you guys. So, Charlie Brian, thank you for joining David me on the Cube this afternoon. Thanks. Thanks. Thanks for David. Dante. I'm Lisa Martin. You're watching the Cube. Y'all from pure accelerate in Austin, Texas.

>> Hey, welcome back already, Jeffrey. Here with the Cube, we are high top San Francisco in the Salesforce Tower in the newest center offices. It's really beautiful and is part of that. They have their San Francisco innovation hubs, so it's five floors of maker's labs and three D printing and all kinds of test facilities and best practices Innovation theater and in this studio, which is really fun to be at. So we're talking about hybrid cloud in the development of cloud and multi cloud. And, you know, we're, you know, continuing on this path. Not only your customers on this path, but everyone's kind of on this path is the same kind of evolved and transformed. We're excited. Have a couple experts in the field. We got Larry Soccer. He's the global managing director of Intelligent Cloud Infrastructure Service's growth and strategy at a center. Very good to see you again. Great to be here. And the Jay Patel. He's the senior vice president and general manager, cloud provider, software business unit, being where enemies of the people are nice. Well, so, uh so first off, how you like the digs appear >> beautiful place and the fact we're part of the innovation team. Thank you for that. It's so let's just >> dive into it. So a lot of crazy stuff happening in the market place a lot of conversations about hybrid cloud, multi cloud, different cloud, public cloud movement of Back and forth from Cloud. Just wanted. Get your perspective a day. You guys have been in the Middle East for a while. Where are we in this kind of evolution? It still kind of feeling themselves out. Is it? We're kind of past the first inning, so now things are settling down. How do you kind of you. Evolution is a great >> question, and I think that was a really nice job of defining the two definitions. What's hybrid worse is multi and simply put hybrid. We look at hybrid as when you have consistent infrastructure. It's the same infrastructure, regardless of location. Multi is when you have disparate infrastructure. We're using them in a collective. So just from a level setting perspective, the taxonomy starting to get standardized industry starting to recognize hybrid is a reality. It's not a step in the long journey. It is an operating model that's gonna be exists for a long time, so it's no longer about location. It's a lot harder. You operate in a multi cloud and a hybrid cloud world and together, right extension BM would have a unique opportunity. Also, the technology provider Accenture, as a top leader in helping customers figure out where best to land their workload in this hybrid multicolored world, because workloads are driving decisions right and one of the year in this hybrid medical world for many years to come. But >> do I need another layer of abstraction? Cause I probably have some stuff that's in hybrid. I probably have some stuff in multi, right, because those were probably not much in >> the way we talked a lot about this, and Larry and I were >> chatting as well about this. And the reality is, the reason you choose a specific cloud is for those native different share capability. Abstraction should be just enough so you can make were close portable, really use the caper berry natively as possible right, and by fact, that we now with being where have a native VM we're running on every major hyper scaler, right? And on. Prem gives you that flexibility. You want off not having to abstract away the goodness off the cloud while having a common and consistent infrastructure. What tapping into the innovations that the public cloud brings. So it is a evolution of what we've been doing together from a private cloud perspective to extend that beyond the data center to really make it operating model. That's independent location, right? >> Solarium cures your perspective. When you work with customers, how do you help them frame this? I mean, I always feel so sorry for corporate CEOs. I mean, they got >> complexities on the doors are already going on >> like crazy that GDP are now, I think, right, The California regs. That'll probably go national. They have so many things to be worried about. They got to keep up on the latest technology. What's happening in containers away. I thought it was Dr Knight. Tell me it's kubernetes. I mean, it's really tough. So how >> do you help them? Kind of. It's got a shot with the foundation. >> I mean, you look at cloud, you look at infrastructure more broadly. I mean, it's there to serve the applications, and it's the applications that really drive business value. So I think the starting point has to be application lead. So we start off. We have are intelligent. Engineering guys are platform guys. You really come in and look And do you know an application modernisation strategy? So they'll do an assessment. You know, most of our clients, given their scale and complexity, usually have from 520,000 applications, very large estates, and they got to start to freak out. Okay, what's my current application's? You know, you're a lot of times I use the six R's methodology, and they say, OK, what is it that I I'm gonna retire. This I'm no longer needed no longer is business value, or I'm gonna, you know, replace this with sass. Well, you know, Yeah, if I move it to sales force, for example, or service now mattress. Ah, and then they're gonna start to look at their their workloads and say OK, you know, I don't need to re factor reform at this, you know, re hosted. You know, when one and things obviously be Emily has done a fantastic job is allowing you to re hosted using their softer to find a data center in the hyper scale er's environments >> that we called it just, you know, my great and then modernized. But >> the modern eyes can't be missed. I think that's where a lot of times you see clients kind of getting the trap Hammer's gonna migrate and then figure it out. You need to start tohave a modernisation strategy and then because that's ultimately going to dictate your multi and your hybrid cloud approaches, is how they're zaps evolve and, you know, they know the dispositions of those abs to figure out How do they get replaced? What data sets need to be adjacent to each other? So >> right, so a j you know, we were there when when Pat was with Andy and talking about, you know, Veum, Where on AWS. And then, you know, Sanjay has shown up, but everybody else's conferences a Google cloud talking about you know, Veum. Where? On Google Cloud. I'm sure there was a Microsoft show I probably missed. You guys were probably there to know it. It's kind of interesting, right from the outside looking in You guys are not a public cloud per se. And yet you've come up with this great strategy to give customers the options to adopt being We're in a public hot. And then now we're seeing where even the public cloud providers are saying here, stick this box in your data center and Frank, this little it's like a little piece of our cloud of floating around in your data center. So talk about the evolution of the strategy is kind of what you guys are thinking about because you know, you're cleared in a leadership position, making a lot of interesting acquisitions. How are you guys see this evolving? And how are you placing your bets? >> You know, that has been always consistent about this. Annie. Any strategy, whether it's any cloud, was any device, you know, any workload if you will, or application. And as we started to think about it, right, one of the big things be focused on was meeting the customer where he's out on its journey. Depending on the customer, let me simply be trying to figure out looking at the data center all the way to how the drive in digital transformation effort in a partner like Accenture, who has the breadth and depth and something, the vertical expertise and the insight. That's what customers looking for. Help me figure out in my journey. First tell me where, Matt, Where am I going and how I make that happen? And what we've done in a clever way, in many ways is we've created the market. We've demonstrated that VM where's the omen? Consistent infrastructure that you can bet on and leverage the benefits of the private or public cloud. And I You know, I often say hybrids a two way street. Now, which is you're bringing Maur more hybrid Cloud service is on Prem. And where is he? On Premise now the edge. I was talking to the centering folks and they were saying the mitral edge. So you're starting to see the workloads, And I think you said almost 40 plus percent off future workers that are gonna be in the central cloud. >> Yeah, actually, is an interesting stat out there. 20 years 2020 to 70% of data will be produced and processed outside the cloud. So I mean, the the edges about, you know, as we were on the tipping point of, you know, I ot finally taking off beyond, you know, smart meters. You know, we're gonna see a huge amount of data proliferate out there. So, I mean, the lines between public and private income literary output you look at, you know, Anthony, you know, as your staff for ages. So you know, And that's where you know, I think I am where strategy is coming to fruition >> sometime. It's great, >> you know, when you have a point of view and you stick with it >> against a conventional wisdom, suddenly end up together and then all of a sudden everyone's falling to hurt and you're like, This is great, but I >> hit on the point about the vertical ization. Every one of our client wth e different industries have very different has there and to the meeting that you know the customer, you know, where they're on their journey. I mean, if you talk to a pharmaceutical, you know, geekspeak compliance. Big private cloud started to dip their toes into public. You know, you go to minds and they're being very aggressive public. So >> every manufacturing with EJ boat back in >> the back, coming to it really varies by industry. >> And that's, you know, that's a very interesting here. Like if you look at all the ot environment. So the manufacturing we started see a lot of end of life of environment. So what's that? Next generation, you know, of control system's gonna run on >> interesting on the edge >> because and you've brought of networking a couple times where we've been talking it, you know, and as as, ah, potential gate right when I was still in the gates. But we're seeing Maura where we're at a cool event Churchill Club, when they had Xilinx micron and arm talking about, you know, shifting Maur that compute and store on these edge devices ti to accommodate, which you said, you know, how much of that stuff can you do at the adverse is putting in. But what I think is interesting is how are you going to manage that? There is a whole different level of management complexity when now you've got this different level of you're looting and security times many, many thousands of these devices all over the place. >> You might have heard >> recent announcements from being where around the carbon black acquisition right that combined with our work space one and the pulse I ot well, >> I'm now >> giving you a management framework with It's what people for things or devices and that consistency. Security on the client tied with the network security with NSX all the way to the data center, security were signed. A look at what we call intrinsic security. How do we bake and securing the platform and start solving these end to end and have a park. My rec center helped design these next generation application architectures are distributed by design. Where >> do you put a fence? You're you could put a fence around your data center, >> but your APP is using service now. Another SAS service is so hard to talk to an application boundary in the sea security model around that. It's a very interesting time. >> You hear a lot of you hear a >> lot about a partnership around softer to find data center on networking with Bello and NSX. But we're actually been spending a lot of time with the i o. T. Team and really looking at and a lot of our vision, the lines. I mean, you actually looked that they've been work similarly, agent technology with Leo where you know, ultimately the edge computing for io ti is gonna have to be containerized because you can need multiple middleware stacks supporting different vertical applications, right? We're actually you know what we're working with with one mind where we started off doing video analytics for predictive, you know, maintenance on tires for tractors, which are really expensive. The shovels, It's after we started pushing the data stream up it with a video stream up into azure. But the network became a bottleneck looking into fidelity. So we gotta process there. They're not looking autonomous vehicles which need eight megabits low laden C band with, you know, sitting at the the edge. Those two applications will need to co exist. And you know why we may have as your edge running, you know, in a container down, you know, doing the video analytics. If Caterpillar chooses, you know, Green Grass or Jasper that's going to co exist. So you see how the whole container ization that were started seeing the data center push out there on the other side of the pulse of the management of the edge is gonna be very difficult. I >> need a whole new frontier, absolutely >> moving forward. And with five g and telco. And they're trying to provide evaluated service is So what does that mean from an infrastructure perspective. Right? Right, Right. When do you stay on the five g radio network? Worse is jumping on the back line. And when do you move data? Where's his process? On the edge. Those all business decisions that need to be doing to some framework. >> You guys were going, >> we could go on. Go on, go. But I want to Don't fall upon your Segway from containers because containers were such an important part of this story and an enabler to the story. And, you know, you guys been aggressive. Move with hefty Oh, we've had Craig McCloskey, honor. He was still at Google and Dan great guys, but it's kind of funny, right? Cause three years ago, everyone's going to Dr Khan, right? I was like that were about shows that was hot show. Now doctors kind of faded and and kubernetes has really taken off. Why, for people that aren't familiar with kubernetes, they probably here to cocktail parties. If they live in the Bay Area, why's containers such an important enabler? And what's so special about Coburn? 80 specifically. >> Do you wanna go >> on the way? Don't talk about my products. I mean, if you >> look at the world is getting much more dynamics on the, you know, particularly you start to get more digitally to couple applications you started. You know, we've gone from a world where a virtual machine might have been up for months or years. Toe, You know, obviously you have containers that are much more dynamic, allowed to scale quickly, and then they need to be orchestrated. That's essential. Kubernetes does is just really starts to orchestrate that. And as we get more distributed workloads, you need to coordinate them. You need to be able to scale up as you need it for performance, etcetera. So kubernetes an incredible technology that allows you really to optimize, you know, the placement of that. So just like the virtual machine changed, how we compute containers now gives us a much more flexible portable. You know that, you know you can run on anything infrastructure, any location, you know, closer to the data, et cetera. To do that. And I >> think the bold movie >> made is, you know, we finally, after working with customers and partners like century, we have a very comprehensive strategy. We announced Project Enzo, a philosophy in world and Project tansy really focused on three aspects of containers. How do you build applications, which is pivotal in that mansion? People's driven around. How do we run these arm? A robust enterprise class run time. And what if you could take every V sphere SX out there and make it a container platform? Now we have half a million customers. 70 million be EMS, all of sudden that run time. We're continue enabling with the Project Pacific Soviets. Year seven becomes a commonplace for running containers, and I am so that debate of'em czar containers done gone well, one place or just spin up containers and resource is. And then the more important part is How do I manage this? You said, becoming more of a platform not just an orchestration technology, but a platform for how do I manage applications where I deploy them where it makes most sense, right? Have decoupled. My application needs from the resource is, and Coburn is becoming the platform that allows me to port of Lee. I'm the old job Web logic guy, right? >> So this is like distributed Rabb logic job on steroids, running across clouds. Pretty exciting for a middle where guy This is the next generation and the way you just said, >> And two, that's the enabling infrastructure that will allow it to roll into future things like devices. Because now you've got that connection >> with the fabric, and that's working. Becomes a key part of one of the key >> things, and this is gonna be the hard part is optimization. So how do we optimize across particularly performance, but even costs? >> You're rewiring secure, exact unavailability, >> Right? So still, I think my all time favorite business book is Clayton Christians. An innovator's dilemma. And in one of the most important lessons in that book is What are you optimizing four. And by rule, you can't optimize for everything equally you have to you have to rank order. But what I find really interesting in this conversation in where we're going in the complexity of the throughput, the complexity of the size of the data sets the complexity of what am I optimizing for now? Just begs for applied a I or this is not This is not a people problem to solve. This is this >> is gonna be all right. So you look at >> that, you know, kind of opportunity to now apply A I over the top of this thing opens up tremendous opportunity. >> Standardize infrastructural auditory allows you to >> get more metrics that allows you to build models to optimize infrastructure over time. >> And humans >> just can't get their head around me because you do have to optimize across multiple mentions. His performances cost, but then that performances gets compute. It's the network, I mean. In fact, the network's always gonna be the bottlenecks. You look at it even with five G, which is an order of magnitude, more bandwidth from throughput, the network will still lag. I mean, you go back to Moore's Law, right? It's Ah, even though it's extended to 24 months, price performance doubles. The amount of data potentially can kick in and you know exponentially grow on. Networks don't keep pays, so that optimization is constantly going to be tuned. And as we get even with increases in network, we have to keep balancing that right. >> But it's also the business >> optimization beyond the infrastructure optimization. For instance, if you're running a big power generation field of a bunch of turbines, right, you may wanna optimize for maintenance because things were running at some steady state. But maybe there's oil crisis or this or that. Suddenly the price, right? You're like, forget the maintenance. Right now we've got you know, we >> got a radio controlled you start about other >> than a dynamic industry. How do I really time change the behavior, right? Right. And more and more policy driven. Where the infrastructure smart enough to react based on the policy change you made. >> That's the world we >> want to get to. And we're far away from that, right? >> Yeah. I mean, I think so. Ultimately, I think the Cuban honeys controller gets an A I overlay and the operators of the future of tuning the Aye aye engines that optimizing, >> right? Right. And then we run into the whole thing, which we've talked about many times in this building with Dr Room, A child re from a center. Then you got the whole ethics overlay on top of the thing. That's a whole different conversation from their day. So before we wrap kind of just want to give you kind of last thoughts. Um, as you know, customers Aaron, all different stages of their journey. Hopefully, most of them are at least at least off the first square, I would imagine on the monopoly board What does you know, kind of just top level things that you would tell people that they really need just to keep always at the top is they're starting to make these considerations, starting to make these investments starting to move workloads around that they should always have kind of top >> of mind. For me, it's very simple. It's really about focused on the business outcome. Leverage the best resource for the right need and design. Architectures are flexible that give you a choice. You're not locked in and look for strategic partners with this technology partners or service's partners that alive you to guide because the complexities too high the number of choices that too high. You need someone with the breath in depth to give you that platform in which you can operate on. So we want to be the digital kind of the ubiquitous platform. From a software perspective, Neck Centuries wants to be that single partner who can help them guide on the journey. So I think that would be my ask. It's not thinking about who are your strategic partners. What is your architecture and the choices you're making that gave you that flexibility to evolve. Because this is a dynamic market. What should make decisions today? I mean, I'll be the one you need >> six months even. Yeah. And And it's And that that dynamic that dynamics is, um is accelerating if you look at it. I mean, we've all seen change in the industry of decades in the industry, but the rate of change now the pace, you know, things are moving so quickly. >> I mean, little >> respond competitive or business or in our industry regulations, right. You have to be prepared for >> Yeah. Well, gentlemen, thanks for taking a few minutes and ah, great conversation. Clearly, you're in a very good space because it's not getting any less complicated in >> Thank you. Thank you. All right. Thanks, Larry. Ajay, I'm Jeff. You're watching the Cube. >> We are top of San Francisco in the Salesforce Tower at the center Innovation hub. Thanks for watching. We'll see next time. Quick

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back >> to M I. T. Everybody watching the Cube, The leader in live tech coverage. My name is Dave Volonte here with Paul Guillen. My co host, Tom Davenport, is here is the president's distinguished professor at Babson College. Huebel? Um, good to see again, Tom. Thanks for coming on. Glad to be here. So, yeah, this is, uh let's see. The 13th annual M I t. Cdo lucky. >> Yeah, sure. As this year. Our seventh. I >> think so. Really? Maybe we'll offset. So you gave a talk earlier? She would be afraid of the machines, Or should we embrace them? I think we should embrace them, because so far, they are not capable of replacing us. I mean, you know, when we hit the singularity, which I'm not sure we'll ever happen, But it's certainly not going happen anytime soon. We'll have a different answer. But now good at small, narrow task. Not so good at doing a lot of the things that we do. So I think we're fine. Although as I said in my talk, I have some survey data suggesting that large U. S. Corporations, their senior executives, a substantial number of them more than half would liketo automate as many jobs as possible. They say. So that's a little scary. But unfortunately for us human something, it's gonna be >> a while before they succeed. Way had a case last year where McDonald's employees were agitating for increasing the minimum wage and tThe e management used the threat of wrote of robotics sizing, hamburger making process, which can be done right to thio. Get them to back down. Are you think we're going to Seymour of four that were maybe a eyes used as a threat? >> Well, I haven't heard too many other examples. I think for those highly structured, relatively low level task, it's quite possible, particularly if if we do end up raising the minimum wage beyond a point where it's economical, pay humans to do the work. Um, but I would like to think that, you know, if we gave humans the opportunity, they could do Maur than they're doing now in many cases, and one of the things I was saying is that I think companies are. Generally, there's some exceptions, but most companies they're not starting to retrain their workers. Amazon recently announced they're going to spend 700,000,000 to retrain their workers to do things that a I and robots can't. But that's pretty rare. Certainly that level of commitment is very rare. So I think it's time for the companies to start stepping up and saying, How can we develop a better combination of humans and machines? >> The work by, you know, brain Nelson and McAfee, which is a little dated now. But it definitely suggests that there's some things to be concerned about. Of course, ultimately there prescription was one of an optimist and education, and yeah, on and so forth. But you know, the key point there is the machines have always replace humans, but now, in terms of cognitive functions, but you see it everywhere you drive to the airport. Now it's Elektronik billboards. It's not some person putting up the kiosks, etcetera, but you know, is you know, you've you've used >> the term, you know, paid the cow path. We don't want to protect the past from the future. All right, so, to >> your point, retraining education I mean, that's the opportunity here, isn't it? And the potential is enormous. Well, and, you know, let's face it, we haven't had much in the way of productivity improvements in the U. S. Or any other advanced economy lately. So we need some guests, you know, replacement of humans by machines. But my argument has always been You can handle innovation better. You can avoid sort of race to the bottom at automation sometimes leads to, if you think creatively about humans and machines working as colleagues. In many cases, you remember in the PC boom, I forget it with a Fed chairman was it might have been, Greenspan said, You can see progress everywhere except in the product. That was an M. I. T. Professor Robert Solow. >> OK, right, and then >> won the Nobel Prize. But then, shortly thereafter, there was a huge productivity boom. So I mean is there may be a pent up Well, God knows. I mean, um, everybody's wondering. We've been spending literally trillions on I t. And you would think that it would have led toe productivity, But you know, certain things like social media, I think reduced productivity in the workplace and you know, we're all chatting and talking and slacking and sewing all over the place. Maybe that's is not conducive to getting work done. It depends what you >> do with that social media here in our business. It's actually it's phenomenal to see political coverage these days, which is almost entirely consist of reprinting politicians. Tweets >> Exactly. I guess it's made life easier for for them all people reporters sitting in the White House waiting for a press conference. They're not >> doing well. There are many reporters left. Where do you see in your consulting work your academic work? Where do you see a I being used most effectively in organizations right now? And where do you think that's gonna be three years from now? >> Well, I mean, the general category of activity of use case is the sort of someone's calling boring I. It's data integration. One thing that's being discussed a lot of this conference, it's connecting your invoices to your contracts to see Did we actually get the stuff that we contracted for its ah, doing a little bit better job of identifying fraud and doing it faster so all of those things are quite feasible. They're just not that exciting. What we're not seeing are curing cancer, creating fully autonomous vehicles. You know, the really aggressive moonshots that we've been trying for a while just haven't succeeded at what if we kind of expand a I is gonna The rumor, trawlers. New cool stuff that's coming out. So considering all these new checks with detective Aye, aye, Blockchain new security approaches. When do you think that machines will be able to make better diagnoses than doctors? Well, I think you know, in a very narrow sense in some cases, that could do it now. But the thing is, first of all, take a radiologist, which is one of the doctors I think most at risk from this because they don't typically meet with patients and they spend a lot of time looking at images. It turns out that the lab experiments that say you know, these air better than human radiologist say I tend to be very narrow, and what one lab does is different from another lab. So it's just it's gonna take a very long time to make it into, you know, production deployment in the physician's office. We'll probably have to have some regulatory approval of it. You know, the lab research is great. It's just getting it into day to day. Reality is the problem. Okay, So staying in this context of digital a sort of umbrella topic, do you think large retail stores roll largely disappeared? >> Uh, >> some sectors more than others for things that you don't need toe, touch and feel, And soon before you're to them. Certainly even that obviously, it's happening more and more on commerce. What people are saying will disappear. Next is the human at the point of sale. And we've been talking about that for a while. In In grocery, Not so not achieve so much yet in the U. S. Amazon Go is a really interesting experiment where every time I go in there, I tried to shoplift. I took a while, and now they have 12 stores. It's not huge yet, but I think if you're in one of those jobs that a substantial chunk of it is automata ble, then you really want to start looking around thinking, What else can I do to add value to these machines? Do you think traditional banks will lose control of the payment system? Uh, No, I don't because the Finn techs that you see thus far keep getting bought by traditional bank. So my guess is that people will want that certainty. And you know, the funny thing about Blockchain way say in principle it's more secure because it's spread across a lot of different ledgers. But people keep hacking into Bitcoin, so it makes you wonder. I think Blockchain is gonna take longer than way thought as well. So, you know, in my latest book, which is called the Aye Aye Advantage, I start out talking by about Tamara's Law, This guy Roy Amara, who was a futurist, not nearly as well known as Moore's Law. But it said, You know, for every new technology, we tend to overestimate its impact in the short run and underestimated Long, long Ryan. And so I think a I will end up doing great things. We may have sort of tuned it out of the time. It actually happens way finally have autonomous vehicles. We've been talking about it for 50 years. Last one. So one of the Democratic candidates of the 75 Democratic ended last night mentioned the chief manufacturing officer Well, do you see that automation will actually swing the pendulum and bring back manufacturing to the U. S. I think it could if we were really aggressive about using digital technologies in manufacturing, doing three D manufacturing doing, um, digital twins of every device and so on. But we are not being as aggressive as we ought to be. And manufacturing companies have been kind of slow. And, um, I think somewhat delinquent and embracing these things. So they're gonna think, lose the ability to compete. We have to really go at it in a big way to >> bring it. Bring it all back. Just we've got an election coming up. There are a lot of concern following the last election about the potential of a I chatbots Twitter chat bots, deep fakes, technologies that obscure or alter reality. Are you worried about what's coming in the next year? And that that >> could never happen? Paul. We could never see anything deep fakes I'm quite worried about. We don't seem. I know there's some organizations working on how we would certify, you know, an image as being really But we're not there yet. My guess is, certainly by the time the election happens, we're going to have all sorts of political candidates saying things that they never really said through deep fakes and image manipulation. Scary? What do you think about the call to break up? Big check. What's your position on that? I think that sell a self inflicted wound. You know, we just saw, for example, that the automobile manufacturers decided to get together. Even though the federal government isn't asking for better mileage, they said, We'll do it. We'll work with you in union of states that are more advanced. If Big Tak had said, we're gonna work together to develop standards of ethical behavior and privacy and data and so on, they could've prevented some of this unless they change their attitude really quickly. I've seen some of it sales force. People are talking about the need for data standard data protection standards, I must say, change quickly. I think they're going to get legislation imposed and maybe get broken up. It's gonna take awhile. Depends on the next administration, but they're not being smart >> about it. You look it. I'm sure you see a lot of demos of advanced A I type technology over the last year, what is really impressed you. >> You know, I think the biggest advances have clearly been in image recognition looking the other day. It's a big problem with that is you need a lot of label data. It's one of the reasons why Google was able to identify cat photos on the Internet is we had a lot of labeled cat images and the Image net open source database. But the ability to start generating images to do synthetic label data, I think, could really make a big difference in how rapidly image recognition works. >> What even synthetic? I'm sorry >> where we would actually create. We wouldn't have to have somebody go around taking pictures of cats. We create a bunch of different cat photos, label them as cat photos have variations in them, you know, unless we have a lot of variation and images. That's one of the reasons why we can't use autonomous vehicles yet because images differ in the rain and the snow. And so we're gonna have to have synthetic snow synthetic rain to identify those images. So, you know, the GPU chip still realizes that's a pedestrian walking across there, even though it's kind of buzzed up right now. Just a little bit of various ation. The image can throw off the recognition altogether. Tom. Hey, thanks so much for coming in. The Cube is great to see you. We gotta go play Catch. You're welcome. Keep right. Everybody will be back from M I t CDO I Q In Cambridge, Massachusetts. Stable, aren't they? Paul Gillis, You're watching the Cube?

>> from the Silicon Angle Media Office in Boston, Massachusetts. It's the cue. Here's your host. Still Minutemen. >> Hi, I'm Stew Minutemen. And welcome to a special cute conversation here in our Boston Areas studio where in spring 2019 whole lot of shows where the cubes gonna be on going to lots of events so many different technologies were covering on one of the areas we always love to be able to dig into is what's happening with the users. Many of these shows, we go to our user conferences as well as the community. Really happy to Boca Burger. Believe first time on the program. Steve Methodists famous. Who is the newly elected president of the mug s. So I think most of Ronan should know the V mug organization to the VM where User group. We've done cube events at, you know, the most related events. Absolute talked about the mug we've had, you know, the CEO of the mug on the program. And of course, the VM were Community 2019 will be the 10th year of the Cube at VM World. Still figuring out if we should do a party and stuff like that. We know all the ins and outs of what happened at that show. But you know the V mugs itself? I've attended many. Your Boston V mug is one that I've been, too. But before we get into the mug stuff, Steve could just give us a little bit of your back, because you are. You're practicing your user yourself. >> Yeah, well, first thanks for having me. You know what? I've been watching the cube for years, and it's ah, it's great to be on this side of the of the screen, right? So, yes. So I'm Steve. I think I, you know, show up every day as the associate chief information officer of the University of Massachusetts. Little just for 95 here, and that's my day job. That's my career, right? But what? You know what? I'm excited to be here to talk about what I'm excited in general with the mug is it's a community organization. And so it's a volunteer gig, and that's true of all of our leadership, right? So the from the president of the board of directors to our local leaders around the world, they're all volunteers, and that's I think, what makes it special is We're doing this because we're excited about it. We're passionate about it. >> Yeah, you know the mugs, It's, you know, created by users for user's. You go to them, talk a little bit. It's evolved a lot, you know, It started as just a bunch of independent little events. Is now you know, my Twitter feed. I feel like constantly every day. It's like, Oh, wait, who is at the St Louis? The Wisconsin one? I'll get like ads for like, it's like a weight is the Northeast one. I'm like, Oh, is that here in New England that I don't know about? No, no, no. It's in the UK on things like that. So I get ads and friends around the world and I love seeing the community. So, boy, how do you guys keep it all straight? Man, is that allow both the organic nature as well as some of the coordination and understanding of what's going on. How do you balance that? >> Yeah, that's a great question. And you know, So I was a V mug member for many, many years before I ever got interested in becoming a leader, and you're right it when it started, it was 10 of us would get around with a six pack of beer and a box of pizza, right? And we'd be talking shop and that, you know, that was awesome. And that's what would that was, how it started. But you get to a certain scale when you start talking about having 50,000 now, over 125,000 members around the world. You gotta coordinate that somehow you're right on the money with that. And so that's why you know, we have, you know, a strong, um, coordination effort that is our offices down in Nashville, Tennessee, and their their role is to enable our leaders to give back to their community and take the burden out of running these things. You know, sourcing venues and, you know, working with hotels and stuff. That is effort that not everybody wants to do all the time. And so to do that for them lets them focus on the really cool stuff which is the tech and connecting users. >> Yeah. Can you speak a little bit too? You know what were some of the speeds and feed to the event? How many do you have How much growing, you know, Like I'm signed up. I get the newsletter for activities as well as you know, lots of weapons. I've spoken on some of the webinars too. >> Yeah, well, first thanks for that s o. We have over 30 user cons around the world on three continents. >> In fact, what's the user cough? >> Great questions. So user kind is user conference, you know, consolidated into user Connery. And those are hundreds of end users getting together around the world were on three continents. In fact, I was fortunate enough in March, I went to Australia and I spoke at Sydney and Melbourne on That was awesome, getting to meet users literally, almost a sw far away from Boston. As you can get having the same challenges in the office day today, solving the same business problems with technology. So that was exciting. And so we've got those all over. We also have local meetings which are, you know, smaller in scope and often more focused on content. We've got 235 or Maur local chapters around the world. They're talking about this, and so we're really engaged at multiple levels with this and like you talk about. We have the online events which are global in scope. And we do those, you know, we time so that people in our time zone here in the States could get to them as well as folks in, you know, e m b A and a factory. >> Yeah, and I have to imagine the attendees have to vary. I mean, is it primarily for, you know, Sylvie, um, where admin is the primary title there up to, you know, people that are CEOs or one of the CEOs? >> Yes. So that actually we've seen that change over the past couple years, which is exciting for me being in the role that I'm in is you're right historically was vey Sphere admits, right? And we're all getting together. We're talking about how do we partition our lungs appropriately, right? And now it has switched. We see a lot more architect titles. We Seymour director titles coming in because, you know, I said the other day I was in Charlotte talking and I said, You know, business is being written in code, right? And so there's a lot more emphasis on what it's happening with V m wearing his VM worth portfolio expands. We've got a lot of new type of members coming into the group, which is exciting. >> Yeah, And what about the contents out? How much of it is user generated content versus VM were content and then, you know, I understand sponsorships or part of it vendors. The vendor ecosystem, which vm where has a robust ecosystem? Yes, you know, help make sure that it's financially viable for things to happen and as well as participate in the contest. >> Yes, I feel like I almost planted that question because it's such a good one. So, you know, in 2018 we started putting a strong emphasis on community content because we were, you know, we heard from remembers that awesome VM were content, awesome partner content. But we're starting to miss some of the user to user from the trenches, battle war stories, right? And so we put an emphasis on getting that back in and 2018 we've doubled down in 2019 in a big way, so if you've been to a user kind yet in 2019 but we've limited the number of sponsors sessions that we have, right so that we have more room for community content. We're actually able to get people from around the world to these events. So again, me and a couple folks from the States went toe Australia to share our story and then user story, right? And at the end of the day, we used to have sponsored sessions to sort of close it out. Now we have a community, our right, and Sophie Mug provides food and beverages and a chance to get together a network. And so that is a great community. Our and you know, I was at one recently and I was able to watch Ah, couple folks get to them. We're talking about different problems. They're having this and let me get your card so we can touch base on this later, which at the end of the day, that's what gets me motivated. That's what >> it's about. It's Steve. I won't touch on that for a second. You know what? Get you motivated. You've been doing this for years. You're, you know, putting your time in your president. I know. When I attended your Boston V mark the end of the day, it was a good community member talking about career and got some real good, you know, somebody we both know and it really gets you pumped up in something very, a little bit different from there. So talk a little bit without kind of your goals. For a CZ president of Emma, >> Sure eso I get excited about Vima because it's a community organization, right? And because, you know, I've said this a bunch of times. But for me, what excites me is it's a community of people with similar interests growing together right and reinforcing each other. I know for a fact that I can call ah whole bunch of people around the world and say, Hey, I'm having a problem technically or hey, I'm looking for some career advice or hey, one of my buddies is looking for work. Do you know of any opening somewhere? And that's really powerful, right? Because of the end of the day, I think the mug is about names and people and not logos, right? And so that's what it motivates me is seeing the change and the transformation of people and their career growth that V mug can provide. In fact, I know ah ton of people from Boston. In fact, several of them have. You know, they were administrators at a local organization. Maybe they moved into partners. Maybe they moved into vendors. Maybe they stay where they are, and they kept accelerating their growth. But I've seen tons of career growth and that that gets me excited watching people take the next step to be ableto to build a >> career, I tell you, most conferences, I go to the kind of jobs take boards, especially if you're kind of in the hot, cool new space they're all trying to hire. But especially when you go to a local on the smaller events, it's so much about the networking and the people. When I go to a local user, event it. Hey, what kind of jobs you hiring for who you're looking for and who do I know that's looking for those kind of things and trying to help connect? You know, people in cos cause I mean, you know, we all sometime in our career, you know we'll need help alone those lines that I have, something that's personally that you know, I always love to help >> you. I have a friend who said it. I think best, and I can't take credit for this, right? But it's It can be easy to get dismissed from your day job, right? One errant click could be the career limiting click. It is nigh impossible to be fired from the community, right? And that that, to me, is a powerful differentiator for folks that are plugged into a community versus those that are trying to go it >> alone. Yeah, there are some community guidelines that if you don't follow, you might be checking for sure, but no, if if we're there in good faith and we're doing everything like out, tell me it's speaking. You know, this is such, you know, change. Is this the constant in our world? You know, I've been around in the interview long enough. That's like, you know, I remember what the, um where was this tiny little company that had, you know, once a week, they had a barbecue for everybody in the company because they were, like, 100 of them. And, you know, you know, desktop was what they started working on first. And, you know, we also hear stories about when we first heard about the emotion and the like. But, you know, today you know Veum world is so many different aspects. The community is, you know, in many ways fragmented through so many different pieces. What are some of the hot, interesting things? How does seem a deal with the Oh, hey, I want the Aye Aye or the Dev Ops or the you know where where's the vmc cloud versus all these various flavors? How do you balance all that out? All these different pieces of the community? >> Yeah, it's an interesting question. And to be fair with you, I think that's an area that were still getting better at. And we're still adapting to write. You know, if you look at V mug Five years ago, we were the V's fear, sort of first, last and always right. And now you know, especially is VM. Where's portfolio keeps increasing and they keep moving into new areas. That's new areas for us, too. And so, you know, we've got a big, uh, initiative over the next year to really reach out and and see where we can connect with, you know, the kubernetes environment, right? Cause that the hefty oh acquisition is a really big deal. and I think fundamentally changes or potential community, right? And so you know, we've launched a bunch of special interest groups over the span of the past couple years, and I think that's a big piece of it, which is, if you're really interested in networking and security, here's an area that you can connect in and folks that are like minded. If you're really interested in and user computing, here's what you can connect into. And so I think, you know, as we continue to grow and you know, we're, you know, hundreds of thousands of people now around the world so that you can be a challenge. But I think it's It's also a huge opportunity for us to be ableto keep building that connection with folks and saying, Hey, you know, as you continue to move through your career, it's not always gonna be this. You're right. Change is constant. So hey, what's on the horizon for >> you? When I look at like the field organization for being where boy, I wonder when we're gonna have the sand and NSX user groups just because there's such a strong emphasis on the pieces, the business right now? Yeah, All right, Steve, let's change that for a second. Sure said, You know, you're you got CEO is part of your title, their eyes, what you're doing. Tell me about your life these days and you know the stresses and strains And what what's changing these days and what's exciting? You >> sure? So you know, it's exciting to have moved for my career because I'm an old school admin, right? I mean, that's my background. Uh, so, you know, as I've progressed, you know, I keep getting different things in my portfolio, right? So it started out as I was, you know, I was the admin, and then I was managing the systems engineering team. And then they added desktop support that was out of necessity was like, I'm not really a dustup person, right? So something new you need to learn. But then you start seeing where these synergies are, right? Not to hate, like the words energies. But the reality is that's where we launched our VD. I project at U Mass. Lowell, and that has been transformative for how we deliver education. And it has been a lot of ways. Reduced barriers to students to get access to things they couldn't before. So we had engineering students that would have to go out and finance a 3 $4000 laptop to get the horsepower to do their work. Now, that can use a chromebook, right? They don't have to have that because we do that for them and just they have to have any device t get access via via where horizon. Right, So that happened, and then, you know, then they moved in. Our service is operation, right? So what I'm interested now is how do we deliver applications seamlessly to users to give them the best possible experience without needing to think about it? Because if you and I have been around long enough that it used to be a hassle to figure out okay, I need to get this done. That means they need to get this new applications I have to go to I t there and I have my laptop. Now it's the expectation is just like you and I really want to pull out my phone now and go to the APP store and get it right. So how do we enable that to make it very seamless and remove any friction to people getting their work >> done? Yeah, absolutely. That the enterprise app store is something we've talked about is not just the Amazon marketplace these days. >> In some ways, it is so not all applications rate. Some applications are more specific to platforms. And so that's a challenge, which is, you know, I'm a professor. I really like my iPad. Well, how do I get S P ss on that? Okay, well, let me come up with some solutions. >> Yeah, it's interesting. I'm curious if you have any thoughts just from the education standpoint, how that ties into i t. Personally myself, I think I was in my second job out of school before I realized I was in the i t industry because I studied engineering they didn't teach us about. Oh, well, here's the industry's You're working. I knew tech, and I knew various pieces of it and, you know, was learning networking and all these various pieces there. But, you know, the industry viewpoint as a technology person wasn't something. I spend a lot of time. I was just in a conference this week and they were talking about, you know, some of the machine learning pieces. There was an analyst got up on stage is like here I have a life hack for you, he said. What you need to do is get a summer intern that's been at least a junior in college that studied this stuff, and they can educate you on all these cool new things because those of us have been here a while that there's only tools and they're teaching them at the universities. And therefore that's one of those areas that even if you have years, well, if you need to get that retraining and they can help with that >> no, that's that to me is one of most exciting parts about working in education is that our faculty are constantly pushing us in new directions that we haven't even contemplated yet. So we were buying GPU raise in order to start doing a I. Before I even knew why we were doing and there was like, Hey, I need this and I was like, Are you doing like a quake server? Like they were mining Bitcoins? I don't think so, but it was, you know, that was that was that was an area for us and now we're old. Had it this stuff, right? And so that is a exciting thing to be able to partner with people that are on the bleeding edge of innovation and hear about the work that they're doing and not just in in the tech field, but how technology is enabling Other drew some groundbreaking research in, you know, the life sciences space that the technology is enabling in a way that it wasn't possible before. In fact, I had one faculty member tell me, Geez, maybe six months ago. That said, the laboratory of the past is beakers and Silla scopes, right? The laboratory of the future is how many cores can you get? >> Yeah, all right, So next week is Del Technologies world. So you know the show. The combination of what used to be A M, C World and Del World put together a big show expecting around 15,000 people in Las Vegas to be the 10th year actually of what used to be M. C world. We actually did a bunch of dead worlds together. For me personally, it's like 17 or 18 of the M C world that I've been, too, just because disclaimer former emcee employees. So V mugs there on dhe, Maybe explain. You know, the mugs roll there. What you're looking to accomplish what you get out of a show like that. >> Sure. So V mug is a part of the affiliation of del Technologies user communities. Right? And what I love about user communities is they're not mutually exclusive, right? You absolutely can. Being a converged and Avi mug and a data protection user group. It's all about what fits your needs and what you're doing back in the office. And, you know, we're excited to be there because there's a ton of the move members that are coming to Deltek World, right? And so we're there to support our community and be a resource for them. And that's exciting for us because, you know, Del Del Technologies World is a whole bunch of really cool attack that were that were seeing people run vm were on Ray. We're seeing via more partner with, and so that's exciting for us. >> Yeah, and it's a try. Hadn't realized because, like, I've been to one of the converted user group events before, didn't realize that there was kind of an affiliation between those but makes all the sense in the world. >> Yeah, right. And it's, you know, again, it's an open hand thing, right? Beaten and one being the other. You realize them both. For what? They're what They're great at connecting with people that are doing the same thing. There's a ton of people running VM wear on. Ah, myriad. Like you talked about earlier VM Where's partner? Ecosystem is massive, right? But many, many, many in fact, I would say a huge majority of converged folks are running VM we're >> on it. All right. So, Steve want to give you the final word? What's the call to action? Understand? A lot of people in the community, but always looking from or always, ways for people to get involved. So where do they go? What? What would you recommend? >> Yeah, thanks. So if if you are not plugged into user community now, when you're in the tech field, I would strongly encourage you to do so. Right? V mug, obviously, is the one that's closest to my heart, right? If you're in that space, we'd love to have you as part of our community. And it's really easy. Go to V mug. dot com and sign up and see where the next meet up is and go there, right? If you're not into the VM where space and I know you have lots of folks that air, they're doing different things. Go check out your community, right? But I tell you, the career advantages to being in a user community are immense, and I frankly was able to track my career growth from admin to manager to director to associate CEO, right alongside my community involvement. And so it's something I'm passionate about, and I would encourage everybody to check out. >> Yeah, it's Steve. Thank you so much for joining us. Yeah, I give a personal plug on this. There are a lot of communities out there, the virtual ization community, especially the VM. One specifically is, you know, a little bit special from the rest. You know, I've seen it's not the only one, but is definitely Maur of. It's definitely welcoming. They're always looking for feedback, and it's a good collaborative environment. I've done surveys in the group that you get way better feedback than I do in certain other sectors in just so many people that are looking to get involved. So it's one that you know, I'm not only interviewing, but, you know, I can personally vouch for its steeple. Thank you. Thank you so much. Always a pleasure to see you. >> Thanks for having me. >> Alright. And be sure to check out the cube dot net. Of course, we've got dealt technologies world in the immediate future. Not that long until we get to the end of summer. And vm World 2019 back in San Francisco, the Q will be there. Double set. So for both del world del Technologies world and VM World. So come find us in Las Vegas. If you're Adele or Mosconi West in the lobby is where will be for the emerald 2019 and lots and lots of other shows. So thank you so much for watching. Thank you.

>> from Washington, D. C. It's the queue covering science logic. Symposium twenty nineteen. Brought to you by Science Logic. >> Hi, I'm student men and this is the Cubes coverage of Science Logic. Symposium twenty nineteen here at the Ritz Carlton in Washington, D. C. Been four hundred sixty. People here just finished the afternoon Kino, and they've actually gone off to the evening event. It's thie yet to be finished. Spy Museum. They get a good three sixty view of Washington D. C. So the hallways are a little echoing in quiet but really excited to have on the final guest of the day. Eric Gordon, who's the vice president of business development and alliances as science logic. Erik, thanks so much for joining me, >> thanks to you. Great to be here. >> All right, so busy. Dev and Alliances. I've talked to a number of your partner's. I've gone through a lot of things, but you wear, I think, just like your CEO. A few different hats. Ah, and your old let's let's get into what your role is that the company? >> Yeah, it's actually changed over time, but for the most part I've to court responsibilities. One is I'm looking after our ecosystem of technology partners. And so we have from key strategic CE that we work with in the marketplace, in the cloud space on the data center, all across the ecosystem, a lot of different technologies. But we also have products that we resell input on our priceless that combined to create a solution for our customers in the second half of what my responsible is really focused on. What is our product strategy around integration? Automation? Because those Air Corps components to our platform and I look after that with several different teams. >> So let's talk about that the ecosystem pit person, the alliances. Because I got a lot of shows. I talked to a lot of companies, and it's all too easy for companies to be like, Oh, we're we're the best and we do so many different things. And when I first heard about the space in a ops, it's like, Oh, well, I I Ops is replacing a lot of waves and, you know, your average customer replaces fourteen tools. I heard there's one customer who replaces fifty tools, but at the same time, there was a strong focus about integrations in deeper even some of the products that you say, Yeah, there's overlap in that competitive, you know, you're working with those environments, so give us a little bit of the philosophy, how you balance that, you know, we want to do it all and help our customers to do lots of different things. And especially when you get to big customers and service providers, we understand that it's a big world and there never is that, you know, mythical single pane of glass. >> Yeah, no, totally agree. And we hear this a lot. You know, I've got a tool for this. I got a tool for that and or I had to Vendor come in and say that they could do it all. And you know, really, At the end of the day, if there's there's no one vendor on DH, you know the Venn diagrams of functionalities, air overlapping. That's the nature of the industry. And when we saw this on the early days of it with the big monopolies. But I think right now it's it's around. How do we saw the customer problem? Mohr effectively, From our perspective, we look at the combination of things. First is is what solutions out there give us good data data that we can use data that we can enrich, how we can leverage that to help drive better insights from other types of data that we collect so that theirs is where integration is a keep part of this on DH. What we know is that ultimately in our space, we're doing about monitoring a core collection. We're goingto have to click with everybody, so we're gonna have to integrate with any partner that might have some form of I. P are connected through an I p address to some sort of a p I. We need that data. So we have partnerships on that side. I think really, what's interesting is when we think about things like workflow or orchestration or types of mediation, we might integrate with other technologies to enrich that data further. So we look for partners that ultimately our customers air using things that we can do consolidation and drive better outcome with that enrich date experience. >> Yes, so let's drill down one little bit if you talk about like, you know a PM and SM tools out there some recent announcements and and you digging deeper on there. What what are some of the highlights? So one >> thing is, if you already have, like, agents are often come up, Our customs says, Well, I've got an A P M. Agent that's already doing some things. Well, that's great. We can leverage that, that there's some good insight that we can gather from either to apologies or other metrics or like in user experience. But we also go deeper on other aspects, like on the network side or on the infrastructure side, or on the the cloud service aside. So, you know, ultimately, it's a conversation of say, what? What can we leverage? What, what's accurate, what's in real time? And if there's things that we can, you know, gather, then that's our primary strategy. So I you know, I do think the ecosystem plays a key role in a i ops, but really, to do that, it's run automation because anything that we do, we have to do with scale and we have to do with security. We have to do it with the intent of driving some form of outcome. And so, you know, those are the key principles behind selecting technology partners. >> Okay, Let's talk some about that automation. It was a big discussion in the keynote this morning. Really talking about the maturity model. One of the analysts up there says you really want to make sure you separate things like, you know, the machine learning piece of it with the automation. The observation I've made a couple of times is, you know, yes. We all know you can automate a really bad process. And so I need toe, you know, make sure, you know, do I have good data And, you know, how am I making automation make me better Not just, you know, to change things. >> Yeah, well, I think it's Science Lodge that we look at. Automation is in every part of what we do within the product. From the from the collection of how we automate it scale how we consolidate that data. And then we're doing a lot of the data preparation using automation technologies. And then when we start to analyze and enrich that data, we're also using it Other algorithmic approaches, for example, topology and context. So if we know that some things connected weaken Dr An automation to make an inference and that data then feeds into the final step, which is around how we action on that. So we drive automation in the classic sense to say trigger workflow or, let's say, update another system of record or system of truth like a C M G B or a notification. And so one of things that we did hear from Garden this morning is engaging in an SM process. Is a core part of AI ai ops as muchas data collection and driving other forms of automation. >> All right, Do you have some examples of you know how automation you're helping your customers love any customer stories you've got along that line? >> Well, >> really. You know, there's so many stories we're hearing the halls of Symposium, and so it's it's it's hard to pick one, but, you know, I think all ten times what we say is, what what's driving your service desk time like you've got people you know, looking at all of these different dispirit systems, and we can look at it. Let's say a top end of your most sort of frequented events or alerts, or even look at your top service desk incidents and say, How could we automate that, you know. And some of that automation could be at the technology level, you know, simplest as restarting a service or prove you re provisioning of'Em. Or it could be clearing a log or even maybe shutting down an event because it's irrelevant. So there's There's several different examples in the cloud as well. Terms of how things air provisioning attached. And if we see something out of a policy, we can alarm that say, hey, maybe my storage costs are going to accelerate because someone made a bad change. So there's different ways that we can apply automation during the life cycle. But I think enhancing the service management component perhaps is one of the most impactful ones, >> you know. So, Eric, we azan industry automation been something we've been talking about for quite a while now, and they're they're sometimes pushback of, you know, from the end, users especially, you know, some of the practitioners out there as you know. Well, I could do it better. You know, the fear that you're going to lose your job. How are you seeing that progressing and you know, how were things different today? Both from a technology standpoint, as well as from your customers. Can't wait. >> I think if you asked any enterprise CIA already service provider, service delivery manager, they'd always say, I'd love to operate as much as I can when you get down on the practitioner level. You know, obviously I think there's some sort. Like I I do my job, Thank you very much. I have my favorite wit, my process. So I think there's a conversation depending on. You know, if we're saying hey from the practitioner side, is there set of data that you need or set of scripts? Or are things that you're doing manually that we can put into a workflow? And at the at the business layer, it's like, Do you feel like you're getting the value from some of the investments you've made? And is, how is automation? Help you realize that an example there is. We see oftentimes is around the quality of data that's going into the C, M. D. B and from AA AA. Lot of times we see that their investment in technology is like service now, and other platforms is fairly high expense, and they want to optimize that, and they want to realize the power of automation at the at the service level. So if we can, if we can convince, if you will, through a set of really concrete use cases that the data coming from science logic at the speed and the quality can actually improve the seemed to be to >> the level of >> really efficient automation. All of a sudden, people start to see that as a change as an opportunity. And that's where I think a I Ops is helping change the narrative, to say how automation Khun B really, really applied rather than just being this mystical concept that is hard to do. And, you know, people don't liketo think that a robot's taking their job. I think what's gonna happen is that machine learning algorithms are going to make jobs easier and, you know, ultimately were far, far from the point where a ized doing something and some sort of, you know, crazy automata way. But I think it's the deep learning, moving a machine learning to you. No good quality data sets that dr meaningful insights that's giving us a lot better view until where automation could play in the >> future. Yeah, absolutely. It's our belief that you know, automation. There's certain things that you probably don't want to do because repetitive, it's boring or mistake prone on DH. Therefore, you know automation can really help those environments move forward. You could move up the stack. You can manage those environment. There's definitely some retraining that that needs to happen often. But you know that the danger is if you're if you're doing now what you were doing five years ago, chances are your competition is moving along and, you know, finding a better way to do it. >> You know, just a point on this soup is really around the velocity of data that's coming in. So we're seeing, you know, we talked about the three bees. You know, the volume of data. You have to use automation to be able to manage that huge amount of different data sources, the variety. There's no human that can process the amount of machine information from the amount of technologies that you have on DH that you know. Obviously it's speed, right. The velocity and that is that is clearly not going to be something that any human could be capable of doing. And so there's a relationship here between technology and human processes and science logics and a really interesting position right now to really kind of help with that process. But more importantly, accelerate the value by being all to process it and make it intelligent. >> Wait, Erica, you're saying I'm not neo from the Matrix and I can't, you know, read through everything and be able to move faster than physics allows. Give >> yourself maybe fifteen, twenty years. We might be. You know that that you know, I don't think that that many people can really predict the impact of the you know, we'LL say machinery, evolving toe, artificial intelligence and there's it's going to be very used, case specific. But we do know one thing is that algorithms? Air helping. But algorithms are dependent on that clean data stack, right? And And if you can't handle the scale, then obviously there's going. It's going to be minimized in terms. Is total utility >> alright? Well, Eric, I get the good to let you give us that the final word from science logic from Symposium twenty nineteen on the Cube. >> So you know, the first thing is is this is there's two things that we learned from this event. The first thing is, is how our customers you're evolving in this dynamic space. And what we know is that if if you don't change, it's going to be a problem. Because the only consistent thing is change and change is happening faster on it. And we call that disruption. And so what we want to do is we want to understand how science AJ is a technology company. I can really help that customer go through that transition with confidence. And then, more importantly, is what could we do? Delivering better, more enrich solutions to our customers that actually are changing the way the game is played. And so we feel like we're a disrupter in the A ops market. We are. Certainly Forrester has helped us recognize that. But But we're not done work. We're continuing on this journey. >> All right, Well, Eric, routine. Thank you so much for sharing your insights and the journey towards Aye, Aye, Ops. Thanks so much to. All right. Well, that comes to an end of what we're doing here at science Logic. Symposium twenty nineteen. I know. I learned a lot. I hope you did too. I'm stew Minutemen. Thanks so much from our whole crew. Here it's Silicon Angle Media's The Cube. Check out the cube dot net for all the videos from this show, as well as where we'LL be in the future. Reach out if you have any questions and once again, thanks for joining us.

>> fly from San Francisco. It's the Cube covering Google Cloud. Next nineteen, right Tio by Google Cloud and its ecosystem partners. >> Welcome back to the cues live coverage Here in San Francisco, this is day two of Google Cloud. Next twenty nineteen cubes. Exclusive coverage. We're in the middle of the show floor. All the action Aquino's are still going on a little bit over. I'm John for David Law student and kicking off, breaking down the keynote analysis. Also breaking down Post Day one. All the action in the evening, where all the parties are all the action on alway conversations. Dave's to picking off day to day one was setting the table. New CEO on stage Date date. You gets into the into the products really about data data. I machine learning's all aboutthe data cloud data, and we're seeing a machine learning data management. Smart analytics say Aye and machine learning and collaborations. The four themes of Today Google. Clearly using data has a key value proposition. Big table, Big Queary machine learning the G A support for auto ml for tables, big announcements, your thoughts >> Yes. Oh, John, I think answering some of the things that we brought up yesterday is when When Google puts out their vision of why they should be your partner of choice, like customers choose way thought that data and I and M l would be let read upfront. So they kind of buried the lead a little bit. And, you know, question we had coming this week is and they reclaim that really thought leadership that, you know, a couple years ago, You know, data. You know, they really that G technical science stuff is what Google was really good at. So I thought they laid out some really good things. I think everybody was, you know, impressed. To see there was good diversity of customers as well as all the Google me. There were a lot of the women of Google that you've written about John here showing their sewing their chops here. So a lot of pieces to go through and everything from the G sweetened the chromebooks and sick security and privacy is something I like to talk a little bit about when we get into it here. But quite quite a lot of use that day. Today I at the center of it >> and one of the power Women dipped to use the big table you see and think we're all that stuff, Dave with >> big steam Us on the Kino also was B I with a II B. I think we've covered that do space going back to our ten years of doing the tube. It's the promise of Do Remember those days. Do came from Google about Eric. The emergent Borden works and do this kind of small little sliver of the ecosystem into Google's now showing what was once the promise. Big data. They're giving demos democratizing. Bring in for the masses. Wait stories on silicon engels dot com outlining this, But the reality is there. Now remember hitting the road with promise of big data? Now, with Cloud really changed the game? Your bosses, you've been covering this from Day one? >> Well, I think that there's no question that this is a date, a game, WeII said early on John on the Cube. That big data war was going to be one in the cloud. Data was going to reside in the cloud. And having now machine intelligence applied >> to that data is what's giving companies competitive >> advantage at scale and economics I was struck by the stats that Google gave >> at the beginning of the Kino today. Google in the last three years has spent forty seven billion dollars >> capital expenditures. This year to date alone, they've spent thirteen billion dollars in Cap Xidan Data Centers. Thirteen billion. It would take IBM three and a half years to spend that much in cap back there would take Oracle six years. So from an economic standpoint, in the scale standpoint, Google, Microsoft, Amazon are gonna win that game. There's no question in my mind. So, John, you know it is a game of scale and data and I What do you think? First >> of all, Google, they got the Cuban aunties two of the white paper. They wrote that they did commercialized communities in a way that I thought was really excellent, well executed. I like a Jew where they left out on the side of the road. You got picked up by a Cloudera Michaels and memorable Jeff. I'm a Wagner. We saw what happened do communities. It is true that up. They basically put it out there in the open source system, the way they get behind Ciencia really positive there. On the data front, Google's got so much in the tool shed all across Google from day one. Their legacy is data data driven, large scale. They built software and systems to manage data at scale at a hole on president. Well, I think that they have their well ahead of the marketplace on the technology that our inside Google proper Google Cloud will be proper alphabet, whatever you wanna call it. Self driving cars question for Google is, Can they bring it to get there? They >> need to hire a team of people, just >> go out and just get it all >> together, pull the jewels together and put it into a coherent platform. That's kind of the tea leaves that I see that we're reading here. Is that Curry and pointed down the keynote. We got tons of technology. The question is, can they pull it together in a package and make a consumable addressable programmable programing, FBI's? We've seen that movie that's happening right now. The next level of innovation for Google is, can they make data programmable? This is going to be a ten year opportunity. If they get that right, they will win. Big move the ball down the field to see Amazon going big on stage maker. It's all about data data, analytics at scale, auto machine learning. These are the tell signs do data program ability. They got all the things. Can >> they bring it to bear? >> Yeah, Well, John, one of the things I saw it got a lot of people excited is if I have, You know, I'm a G sweet. Customers were geese sweet customers, and I'm using spreadsheets. Now I can use Big Query with that. So the power of analytics and big data be able to plug that right in, make it really easy. And what's interesting is trying to squint through. You know what was kind of the Google consumer side of the house that many of us know. And if used for for lots of years versus the Enterprise G sweet chromebooks and mobile? Well, you know, under Diane Green, it was Google Enterprise, and now it's all part of Google Cloud. Just when we talk about Microsoft, it's like, Well, is it azure or is it au three sixty five? Well, it was a G sweet words. Is it Google and one that I want to, you know, get get your guys comment on is they talk about privacy way. No, Google as a whole alphabet is You know what, ninety five percent plus ad revenue and they were very strong out here is that we do not own your data. We will not sell it to a third party. Privacy, privacy, privacy. And it's great to hear them say that. But way all interacted work with Google. We know all the cloud providers. The data is an important thing. When I do Aye aye and ml type activities. I need to be able to anonymous isat and leverage it train on it. So data privacy issue is still something that, you know, I heard what they said, but you know, there's got to be some concerns. >> There is another angle here that I'd like to talk about, and that's the database. Google, Amazon, Microsoft, Oracle, IBM, Mike Attention, Alibaba. All the big cloud guys. They want your data. That's why Amazon spending so much effort on the database market. That's why you don't see Oracle having such a dominant position in database. You like Google's announcement yesterday they were basically doing a backhanded slap but Amazon, saying, We're more open. They didn't deal with Mongo. There's a lot of discussion in the community of software community about how how Amazon, obviously Bogart's open source. But But if you if you look, it's something that's true if you look at Amazon, they basically taken a lot of open source products. It built their own databases. But if you look at Google, Google's got relational databases. They got non relational databases. They got operational databases. So I wonder out loud, Is this a Trojan horse strategy? Because they need to own your data that databases so important now that I think that is I talked to one noise that yesterday was a executive VP at Oracle, and he said to me that the cloud providers basically looked at the data base as another application to run on top of servers in virtual machines, >> he said, Were Oracle we integrate, you know, they do all the exit data stuff, etcetera. So my point is, database is the war to be won. That's where it starts. And if you're going to go away, I you want to have the data proximate to the application. Well, >> I mean there's two ways to look at that day. I would say that what might take on >> the database war or a position in the stack is you look out from the old way the new way the old way would be an oracle. Well, we got to preserve the database. We license that we have the license agreements. The new way is to change the game with automation. Like what? Google showing where all this stuff is gonna be done on behalf of the customer. So the business model of how database and the impact of data is being used well dictated my opinion, the monetization. And that's the question that everyone that I've talked to on the show floor offline on email, on direct messages, how we're gonna make money with containers, how we're gonna make money with Cooper Netease. How am I going to make money with data? This is the fundamental question. Now, if you look at the success pattern of the partner ecosystem, moneymaking is about new economics, new price points and new services. So if you're Deloitte or you're a censure, you're saying wow of goo could automate all the stuff that used to be really hard to do, like data migration, moving application were close around. That was once a high profit yield activity for this system integrators or selling databases like Oracle. That's the old way. The smart partners are essential, saying, OK, I'LL take the new economics where all that cost is distracted away by the automation. And I'll lower my price point but still capture the margin margin. Opportunity for cloud is significant, and this is where the smart money is going. The smart monetization schemes are around leveraging what Google and Amazon are doing at scale and shifting their business model. Take advantage of the lower cost but then lowering the price not as much, so they still capture the margin. So this's the immigration, and these are things that were like months and months project going. Data migrations to Melrose projects are like could be months. So smart money is saying Okay, how dowe I make money on this. It's not the old way. So this classic you know what side his treaty on old way or new way that's going to define who wins and who loses >> weight. By the way, I mean it. Sue Ellen >> license selling database license, for instance, is an old way. Well, essentially, it was Ramadan. Amazon does databases of service. What is the license by as you go? But you don't have, You >> know, the Oracle sells a zit buys you go to mean they play that same game. To me, it's more about when it comes to database. It's more about workloads. How much of the world needs acid property databases? Because that's oracles game versus how much of the world needs you no less database data store for for Lex structure data. And that's really I think, what Google and to a certain extent, Amazon are betting on. Although both companies, especially Amazon, is making a bet on both transactional data bases and non relationship, I >> mean in the ideal world database would be free from the margin get shifted to another spot. That's not clear yet, but still it can make money on database but lower caught in lower price. So Google makes money at scale, so with clouds scale, they can lower the price of the database like this, whether it's it's a service or some fee. But it's the people implementing, like the integrators and the people that are building applications as they build that agility. And how are they going to monetize? How does a company out in this floor make money? >> I just remember data stacks and probably like twenty twelve. I was talking to Billy Bob's worth the CEO about the merits of being in the US marketplace, and he said, You know, I'm a little nervous about that. What do you think, Dave? Do you think? Do you think they're gonna like, own me at some point in time and compete with me? So And that's what Google's announcement yesterday said is, You know, you're our friends, we're not going. They don't really come out and say, We're not going to compete with you They just basically said We are more open than aided us without mentioning a W S >> s. So it's interesting, you know, I've only had a little bit of a chance to walk around, but it's a different ecosystem, then Amazon. I remember six years ago, when we first went to Amazon. It was like game developers and all these weird start ups that I couldn't understand what they do. And now it's like, you know, like VM world, but bigger with just that. A broad ecosystem here, you know, there's a big section on collaboration. I went toe Enterprise connect a couple of weeks ago, talking about contact centers and see a lot of the same companies here heard five nines mentioned on stage zooms. Here, you know howto they plug into Google Cloud hurt sales force talking very devout Contact center. So it's a diverse ecosystem, but it's different than than Amazon, and there's not and Amazon. There's always that underlying, you know thing. Oh, is Amazon going to take over this business here? You know, I haven't heard that concern at this show. Well, >> I mean, the bottom line is that there's a shift in the economics and his model technology back in the database. Question. The fact that Mongo D. B. Was once forecast to go out of business. Oh, Amazon's going kill Mongo Devi that dynamo d B. Google's got databases. The fact the matter is, there's no one database anymore. Every application at some level has a database. So if you think about that, then you're gonna have a a new model where everything's has a database and the database is going be characterises on the workload in application. So I do agree with that point. Question is, it's not mutually exclusive one database license for all versus databases everywhere. So if databases air everywhere, then the connective tissue becomes the opportunity. That's where I think you see somebody's data playing technologies with Cloud very compelling, because I can move data very quickly around, and that's where the machine learning really shines. That's going to be a latent see question that's going to be a data integrity question. This is the new model. This is what horizontal scale ability means in the cloud, not by Oracle database. And we're good. This is It's kind of that game is that game is slowly moving into the oblivion. >> Well, I think you know, I think Amazon would say, Hey, if you're a database vendor, you gotta innovate or because we're not going to stop innovating. Whereas I think Google's message to the database vendors is somewhat different is, you know we want to partner with you, and maybe that's because they're not coming from a position of enterprise strength. But that ice I'm sensing, too, apparently different strategies. I just don't know what the end game is. And I believe the endgame is on the data. >> The tell sign on the databases of the developer, right? If I want to run a document store because that's best for my Jason or my my feeds from using Sage, eh, John? A lot of drama script. I'LL use document store. I want to use a relational database. I'll use a relational David So the ideal world does not have to develop are forced into a tooling and database decision that data >> mongo changed its licensing policy as a direct result of what Amazon was doing. So they made their community edition Ah, licence terms more restrictive if you follow that. So what? They said anybody, any cloud service provider that distributes the our community edition has to open source their entire software stack associated with distributing that, or they got to pay us. So basically saying you have to pay an open source tax or you gonna pay us we'LL be looking very interesting change in their database. One of >> the one the announcements here on the day two was the data fusion thing, which essentially means tell sign as well that fusion data moving data integrating Data's a critical thing. Pray ay, ay, ay and machine machine learning in a eyes only as good as the data that it's working with. So the data is, if his missing data saying a retail transaction, you potentially missing out on an opportunity to better user experience. So address ability of data. Having that accessible is a critical feature for machine learning, an a I and again, it's garbage in garbage out relatives of the data equation. High quality data gets high quality machine learning. High quality machine learning is high quality. I. So let's do that's that's kind of cloud offers with large compute large horizontal scale ability. >> Well, I said yes, and I said yesterday was kind of disappointed. It wasn't of talk about a I will. Google certainly made up for that today, didn't they? Still, >> Yeah, sorry was their questions >> were what was your favorite keynote moment today? >> Look, it was it was good when they actually let a couple of customers go up there and talk was that was a little bit disappointed that, you know, some of the sessions field a little bit too scripted for my take, but they laid out a lot of pieces there It takes a little wild, uh, you know, squint through all of the adjustment, you know, and all the changes that they have their I'm still digging through, like on the Antos. We talked about it quite a bit yesterday, but, you know, had some good conversations afterwards. They've got the cloud run announcement that's coming out this afternoon. But But, you know, digging into that open source discussion that you were just talking about from the database is something that I have a lot of interested. I'm glad we're actually right had on today will get their opinion as to, you know, they know a thing or two about open source and communities. And how does something like open shift fit with aunt those? They can work together, but it's not a owe it. Everything works back and forth If I'm p k s if I'm open shift or from you know, the geek based Antos, it's not seamless, and it sure ain't free you >> for not customers so weird from UPS. Scotiabank Baker Hughes McCasland heard from Cole's yesterday. So it's pretty high level senior people from the customer side speaking on stage, which is progress in the C e >> o of ups. I thought was great. He really laid out, You know, the scale of their business and how they grow. >> All right, guys, we got dates. You were kicking off here on the show floor here in San Francisco for Google Cloud next twenty nineteen. They never got it all day. And every day, two of three days, a live coverage. Stay with us as we kick off a full day of great interviews. Executives, entrepreneurs and ecosystem parties here at Google next stay with us for more after this short break.

>> Live from Orlando, Florida It's the que Covering Enterprise Connect twenty nineteen. Brought to you by five nine. >> Welcome to the Cube. Lisa Martin from Orlando. Lots going on on the keeps. That obviously is. You could just tell him with Student a man we're at Enterprise Connect twenty nineteen For Day two. You can hear all the buzz in the Expo Hall behind the hundred forty. Vendors exhibiting new products and services were joined by service. Now. Thank you, Subramanian had a product management and customer service funky. Welcome to the Cube. >> Thank you. >> So service. Now give us a little bit of info about your role and some of the announcements that have come out from this week. >> We'LL Absolutely, yeah, so So it's now. I think all of your family, I'd say one of the leading cloud software vendors are sore purposes. Digitizing work flows in the cloud, and we do that for various different parts of an enterprise like the workflow employees, experience and customers. My role in service now is I need the product management for one off our product of another business units, which is customer service management that is focused on providing companies with the tools and technologies required for them to provide a great customer experience for their end customers. So in that role, my responsible for defying the product vision, the roadmap on working with the engineering teams to release the product and capabilities that our customers love to use. >> So, Frankie, we we've heard in the keynote this morning we heard service. Now come up. Tell us a little bit about at the show Some of the partnerships you're working with on you know, it's pretty diverse spectrum of activities going on. So where service now has important place? >> Absolutely. So So it's not like you mentioned place in multiple different areas on DH. Helps enterprise deliver great employees in customer experience is so in that sense, this is a very properly it show for us to be at where we're connecting different parts of the organization to collaborate and to tell you a great experiences and deliver outcomes for employees and customers. Way have several of our partners, including five nine right here and you know, we partner on various different areas like collaboration is a key area. Focus for us and you heard us mention Microsoft keynote earlier today we partner with them on integrating their teams and other products with our product. Portfolio. Five nine actually serves a different part, different purpose for us, where they enable contact centers to operate optimally. And they connect that with our customer service management, which actually covered combines both the customer engagement aspects and the customer service on the customer workflow aspects that beeper white. >> Let's dig into that a little bit more, thank you, because the last day or so students have been talking a lot about the customer experience and table stakes for any business because, as consumers were, we're so empowered. Weaken churn easily. There's always another provider that's going to be able to deliver something, and if we're unhappy, we have that opportunity. So see, access table stakes. Talk to us about why companies should make customer service part of those table stakes. >> So absolutely, Yeah, so if you look at the evolution off, you know how customer. So this is evolved over several years, it started off as a key component of customer relationship management software and customer relationship started with managing the customer records, a customer data so that companies can make sense of who their customers are and how to >> sell them, served >> them optimally. The second stage of evolution added several engagement capabilities and the customer experience layer on top. So how do we make sense of all of this data and intelligence that we collected about customers to provide contextual personalize experience to those and customers by customer service is not just about engagement and experience, right? Ultimately, customers are looking for outcomes. They want their services to be delicate, uninterrupted. For them, things like that. And that is where way of looking at the third stage of evolution, if you will wear connecting that customer, engaged one of the customer experience layer with different parts of the organization that needs to work together on a single platform to be able to deliver effortless customer experiences and delivered to the results and the outcomes that your customers come to expect. >> Thank you. Wonder if you could drill down a little bit. Do you have a customer example you future of that? Or, you know, just some specifics, Understand? Is how we're cutting across silos, helping have the business actors the whole toe improve that customer experience? >> Absolutely, absolutely. I can mention a couple of names. I mean, be drink our own champagne. So we are our customer as well. So it's now uses our own software, our solutions to actually deliver customer service and customer experiences. One of the other customers, a reference customer for us, is nice. I believe they're probably at the show as well. And if you look at what they have done, they have been able to connect their cloud data center operations, the product organization, the product engineering and are in the organization on customer service on a single platform so that when customers report issues, they're able to reduce the effort for customers But great self service experience that contextualized personalized. They're able to identify issues and drive all the way to root, cause the resolution on then provide that information back to customers so that it's not just about answering questions faster. It's about reducing call volumes. It's about eliminating the root cause of the issue so that the next customer does not face that and then have to call you again. >> So in terms of that integration, it's critical right for all of the key constituents interacting with a customer tohave the data the right time to be able to make the right be empowered to make the right decision. But that integration is challenging example of maybe, uh, an old guard company that has to transform to stay relevant and to be competitive. How do they undergo that Those process And maybe it's more of a cultural change to facilitate that integration. So ultimately they can deliver that personalized customer experience. You're saying that one more we demand as consumers. >> Yeah. I mean, there are many examples, but I mean most off it actually starts with the realization that we need to transform right on with more and more services products getting enable through technology and technology forward services. That is not an option for companies anymore, so it really starts with the realization it starts with driving the change top down. A lot of it is really driving the change management throughout the organization. It involves identifying your customer journeys, mapping them out and identifying in a water they touch points. It also is a huge challenge for many customer service executives in a lot of those companies where they still are in that traditional mode of operation where they find it difficult to hold the other parts of the organization responsible. Right customer service is not an island. Customer service is not just a responsibility off a single department within the company. It is a thinking that needs to. It's a mindset that needs to actually get partly down to every part of the organization. So, really, for me, that is where it starts. And that is where I think organization started transplant. And then it's about, you know, deploying the right tools and technologies to really make it happen. >> So I think a couple of themes that we've been digging into out this show is how cloud and A I are transforming a lot of this space is I don't think we even need to talk about the cloud peace when it comes to service now, because because that is a given. But from an aye aye standpoint, where does Aye, aye and ml fit into the solutions that you're building. >> That's a great question. And you know, we cannot have a conversation about customer service, our enterprise collaboration without mentioning the eye there. So if you go back to what I said a little earlier about, you know the third phase of evolution where we are now able to connect the different parts of the company. Different parts of the process is on a single platform. A lot of that actually ends up providing a lot of insights. Right lot of data you need to convert those daytime too inside, and that is really where it comes in. And then you need to people the surface. Those insights at the right points of consumption to be able to eliminate reparative mundane tasks on to provide value added capabilities for agents and for customers because nobody wants to waste their time doing the same thing over and over again, right? If you talked about customer service agent, what they really feel excited about is the ability to serve the customers, not being able to write down tons of notes and capturing all the interaction details. So that's something that they have to do. So if we can help them with those aspects with with automation with intelligence, that is what makes them more productive. And ultimately that results in a direct impact on customer experience. Positive >> when you're out in the field talking with customers as I imagined as the head of product management. Ur where do you find service now? Coming in and kind of educating the customers on the opportunities and the enabler. Is that a I can deliver to them? Are they still sort of on the fence about this, or where are you from? Maybe a consul Tate of perspective, >> right? Right. No, I think we're past that face where people are kind of questioning relevance off area where I think they passed that stage. Everybody understands the value that it delivers in different points are different points of consumption for different people. I think we're at the stage where people are now trying to understand how fast they can move with this, how they can apply this, how they can adopt these technologies within. And this is where service now is trying to really be a an enabler in that process. Right? So we don't want a adoption on air initiative within a company to be a science project. We don't want it to be in somewhere somewhere in the back office with, you know, a number of you know, geek scientists and all that we really want to bring it to the forefront and the way we are doing that is by embedding AI capabilities directly into the experience on also by product izing a lot of those solutions so that our customers don't have to start from the very basics. So we're not asking our customers to go and define their own data sets and, you know, bring a number of data scientists to identify features and things like that. What we're saying is we have already done the heavy lifting for our customers way have identified key scenarios that we can enable that can be covered with the eye, your product izing that we're building that into our product directly on bring those innovations into the market. So if you just one more point Just earlier this month, March sixth actually be announced, our latest version ofthe our product that we released two in market. It's called the Madrid release on my release. If you go and look at it, it's packed with a lot of those innovations. For example, customer service were able to identify when a customer service agent is working on a case way. We're able to identify similar issues that other people might have already reported something that might be already resolved on. The agents completely used that information and resolve this particular case that they're working on, or being able to identify an issue that might be impacting made in multiple customers. >> Yeah, I wonder if you could give us a little bit insight as just a changing role of the agents and some of the stresses and strains on them. They're some concern is like Okay, wait, do your customers look at automation is something that will displace agents, make their lives better. And you know, how much do they worry about that agent age retention and how happy their agents are >> right? I think that's a huge priority for most customer service organizations. I would say it should be a priority for all customer service organizations. Reason is very simple, right? A lot of these simple, easy capabilities are offered through self service. As a customer, I'm sure you don't want to. Our first option will not be to pick up a phone and call and talk to an agent that be probably a few steps down. The line on that experience should definitely be enabled and should be easy. But when issues show about agents desk. They're much more complex than what it used to be. And the expectation is that, you know, I don't want to be handed over to somebody else. The last thing I want to hear is Oh, wait, let me hand, You know, an expert. So that's where these agents need to be up skills. They need to be empowered with tools and technology that I think the term that we hear Houston the industrious they need to be super agents, right? They're not the people who sit and answer calling and pass it on to an expert for the other people who can actually take a column is all the issue all at the same point at the first time engagement? >> And if I understand it, it's some of the solutions and products that you're helping to build that take that agent and give them their superpowers. >> Exactly. Exactly. Yeah, that's our goal. So we have interfaces that we actually design and build specifically for that persona, Andi augment several of those experiences with applications off area and technology on. We also never hit a lot of partnerships in that process. For example, the ability for an agent to seamlessly look at the call coming in to be able to identify who the customer is. What is the issue? They might be calling about previous interactions. I've seen all of that stuff in a single pane of glass and that are, you know, optimizing accidents. That's a priority for us. That is something that we take into our products. >> And how is it that that agents want to be trained these days? And one of the gentlemen in the customer panel this morning was talking about, I think, from Continental Eiji that they identified about twenty different ways that internal users, whether their agents don't want to be trained if it's send me an E mail should be a video sent you a YouTube link. What are you guys finding as you're looking at these different personas, any sort of no top five training mechanisms boiling up to the top that you are going to be consistently delivering? >> I mean training and ups. Killing is a huge priority, because if I just look at you now, how do we make an agent a super agent? They need to be provided with the right kind of trainings and upscaling opportunities. There are various different ways. I mean, I'm not probably an expert on the training methodologies itself. One thing that we can all realizes it has to be relevant. It has to be provided at the point of consumption. And it also should be something that is captured back today, that learning other than the knowledge that gets created in the process of researching and resolving an issue, it gets institutionalized, get actually put back into a system that is leveraged by everyone else in the organization. So those capabilities that I think should be important for everyone. >> Last question for you is we're here, it Enterprise connect. What are some of the exciting things that people can see and feel in touch with service now, at this event >> at this event. So first, I would say we have a boot way are showing our product demonstrations and you can talk to several SAR experts who are here at the end. Even I have a small speaking assignment later, later today. So I have a session that I will be talking at what you will actually see some of our latest innovations that were bringing to the market with the new release. So you will see how we can expand or extend the customer self service too. Not just there, but also the mobile. They're releasing that mobile capabilities for agents, which can also be explained about your customers. You will see a brand new agent interface that I just talked about. How we are packaging some of the intelligence machine learning capabilities into that. And you will also see a lot of our powerful workflow platform. You know how you can apply that for orchestrating? Optimizing process is >> a lot to learn. A lot of knowledge to be gleaned. Thank you. Thank you so much for joining me on the cute this afternoon. We appreciate your time. >> Thank you for talking to you >> or student a man. I am Lisa Martin. You're watching the Cube.

>> Live from San Francisco. It's the cube covering IBM thing twenty nineteen brought to you by IBM. >> Welcome back to Mosconi North here in San Francisco, California. I'm student of my co host, A Volante. We're in day three of four days live. Walter. Wall coverage here at IBM think happened. Welcome back to the program. Talk about one of our favorite topics. Cube alarm. Steve Hill, who's the global head of innovation. That topic I mentioned from KPMG, Steve, welcome back to the program. >> Seems to have made good to see you. >> All right. So, you know, we know that the the only constant in our industry is change. And, you know, it's one of those things. You know, I look at my career, it's like innovation. Is it a buzz word? You know? Has innovation stalled out of the industry? But you know, you're living it. You you're you're swimming in it. Talkinto a lot of people on it. KPMG has lots of tools, so give us the update from from last year. >> Well, I think you know, we talked about several things last year, but innovation was a key theme. And and when I would share with you, is that I think across all industries, innovation as a capability has become more mature and more accepted, still not widely adopted across all industries and all competitors and all kinds of companies. But the reality is, innovation used to be kind of one person's job off in the closet today. I think a lot of organizations or realizing you have to have corporate muscle that is as engaged as in changing the status quo as the production muscle is in maintaining the status quo has >> become a cultural. >> It's become part of culture, and so I think innovation really is part of the evolution of corporate governance as far as I'm >> concerned. What one thing I worry about a little bit is, you know, I see a company like IBM. They have a long history of research that throws off innovation over the years. You know, I grew up, you know, in the backyard of Bell Labs and think about the innovation a drove today, the culture you know, faster, faster, faster and sometimes innovation. He does sit back. I need to be able to think longer, You know? How does how does an innovation culture fit into the ever changing, fast paced you? No need to deliver ninety day shot clock of reality of today. >> Well, I think innovation has to be smart, meaning you have to be able to feed the engines of growth. So your horizon one, if you will, of investments and your attention and efforts have to pay off the short term. But you also can't be strategically stupid and build yourself into an alleyway or to our corner, because you're just too short term thought through. Right? So you need to have a portfolio of what we call Horizon three blended with Horizon one and Horizon two types investment. So your short term, your middle term and your longer term needs are being met. Of course, if you think about it like a portfolio of investments, you're going tohave. Probably a smaller number of investments that air further out, more experimental and a larger proportion of them going to be helping you grow. You could say, almost tactically or sort of adjacent to where you are today, incrementally. But some of those disruptive things that you work on an H three could actually change your industry. Maybe you think about today where we are. Azan Economy intangibles are starting to creep into this notion of value ways we've never seen before. Today, the top five companies in terms of net worth all fundamentally rely on intangibles for their worth. Five years ago, it was one or two, and I would argue that the notion of intangibles, particularly data we'll drive a lot of very transformative types of investments for organizations going forward. So you've got to be careful not to starve a lot of those longer term investments, >> right? And it's almost become bromide. Large companies can innovate, but those five companies just mentioned well alluded to Amazon. Google, etcetera Facebook of Apple, Microsoft there, innovators, right? So absolutely and large companies innovate. >> Yes, clearly, yeah, but you have to have muscle, but it doesn't happen by accident, and you do put discipline and process and rigor and tools and leadership around innovation. But it's a different kind of discipline than you need in the operation, so I'll make him a ratio that makes sense. Maybe ninety five percent production, five percent innovation in an organization. That innovation engine is always challenging that ninety five percent Are you good enough? Are you relevant enough? Are you fast enough? Are you agile enough? You need that in every corporate organization in terms of governance to stay healthy and relevant overtime. >> So it's interesting. You know, I was in a session that Jack Welch talk wants, and he's like, I hear big companies can innovate is like big companies made up of people. People are the things that can innovate absolute. But, you know, I've worked in large organizations. We understand that the fossilization process and the goto market that you have, you know, will often kill, you know, those new flowers that are blooming, what separates the people that can drive innovation on DH? You know, put those positive place and kind of the also rans that, you know get left behind window disruption. >> Well, there's several. There's a couple things that I would highlight of a longer list, one of them we culture. I mean, I think innovation has been part of a culture. People in the institution have value innovation and want to be part of it. And there is, you know, a role that everyone can play. Just because you're in operations, if you will, doesn't mean you ignore change or you ignore the opportunity to improve the status quo. But you still have you get paid to operate what I find that is related to culture that gets a lot of people, you know, slow down or or roadblock is the disconnect between the operating part of the business and the innovative part of the business. If you try, if you build them to separately, what happens is you have a disconnection. And if you innovate the best idea in the world over here. But you can't scale it with production, you lose. So you have to make sure that, as as a leader overall, the entire enterprise you build those connections, rotations, leadership, You know, How do you engage the production, you know, engine into the innovation engine? It's to be very collaborative. It should be seamless. You know, everyone likes to say that, but that word, but relative seamlessness is, is heavy architecture. You've gotto build that, you know, collaboration into your model of of how you innovate >> and >> don't innovate in the vacuum. >> And it comes back to the cultural aspects we're talking about. Do you mentioned the ninety day shot? Clocks were here in the Bay Area. Silicon Valley. The most innovative place in the world. They've lived along the ninety day shot clock forever, and it seems to have not heard that so called short term thinking. Why is that? >> Well, there's so much start up here. I mean, at the end of the day, there is so much churn of new thinking and start up in V C. And there's so much activity that it's almost a microcosm, right? Not every place in the world smells, feels, looks like Silicon Valley, right? And the reason for it is in part because there's just so much innovation in what happens here. And these things change me. If you think about, uh, these unicorns that we have today. Today there's about three hundred ninety one unicorns. Just five years ago, there were one hundred sixty globally on before that. Hardly people didn't know they were hardly recognized. But that's all coming from pockets of innovation like Silicon Valley. So I'd argue that what you have here is an interesting amalgamation of culture being part of a macro environment region that that really rewards innovation and demonstrates that in in market valuations in capital raises, I mean, today one hundred million dollars capital raise is pretty common, especially for unicorns. Five, ten years ago. You never see me. It was very difficult to get a hundred million dollars capital, right? >> You mean you're seeing billion dollar companies do half a billion dollars raises today? I mean, it's >> all day, right? And some of them don't make a profit. Which is I mean, and that's kind of the irony, Which is, Are those companies? What did they get that the rest of us, you know, there was that live on Wall Street right out of in New York. What do we not see? Is that some secret that downstream there will be some massive inflow? Hard to say. I mean, look at Amazon is an example. They've used an intangible to take industries out that they were never in before they started selling books, and they leverage customer behavior data to move into other spaces. And this is kind of the intangible dynamic. And the infection >> data was the fuel for the digital disruption to travel around the world. You see that folks outside of Silicon Valley are really sort of maybe creating new innovation recipes? >> Yes. I think that what you see here is starting to go viral right on DH way that KPMG likes to share a holistic way to look at this for our clients. What is what we call the twenty first century enterprise. So the things that we used to do in the twentieth century to be successful, hire people, build more machines, right? You know, buy more assets, hard, durable assets. Those things don't necessarily give you the recipe for success in the twenty first century. And if you look at that and you think about the intangibles work that's been well written about there's there's all kinds of press on this today. You'll start to realize that the recipe for success in this new century is different, and you can't look at it in a silo to say, Okay, so I've gotta change my department or I've got a I've got to go change, You know, my widgets. What you've got to think is that your entire enterprise and so are construct called the twenty first Century prize. Looks at four things. Actually, it's five, and the fifth one is the technologies to enable change in the other four. And those technologies we talk about here and I have made him think which are, you know, cloud data, smart computers or a blockchain, etcetera. But those four pillars our first customer. How do you think about your customer experience today? How do you rethink your customer experience tomorrow? I think the customer dynamic, whether it's generational or it's technologically driven, change is happening more rapidly today than ever. And looking at that front office and the customer dementia, it is really important. The second is looking at your acid base. The value of your assets are changing, and intangibles are big category of that change. But do your do your hard assets make the difference today and forward. Or all these intangibles. Companies that don't have a date a strategy today are at peril of falling victim to competitors who will use data to come through a flank. And Amazons done that with groceries, right? The third category is as a service capabilities. So if you're growing contracting going into new markets are opening new channels. How do you build that capability to serve that? Well, there's a phenomenon today that we know is, you know, I think, very practised, but usually in functions called as a service by capability on the drink instead of going out and doing big BPO deals. Think about a pea eye's. Think about other kinds of ways of get access to build and scale very fucks Pierre your capabilities and in the last category, which actually is extremely important for any change you make elsewhere is your workforce. Um, culture is part of that, right? And a lot of organizations air bringing on chief culture officers. We and KPMG did the same thing, but that workforce is changing. It's not just people you hire into your four walls today. You've got contingent workforce. You have gig economy, workforce a lot of organizations. They're leveraging platform business models to bring on employees to either help customers with help. Dex needs or build code for problems that they like to solve for free. So when you talk about productivity, which we talked about last year and you start thinking about what's separating the leaders from a practical standpoint from the laggers from practically standpoint, a lot of those attributes of changing customer value of assets as a service growth and workforce are driving growth and productivity for that subset of our community and many injured. >> So when you look at the firm level you're seeing some real productivity gains versus just paying attention to the macro >> Correct, any macro way think proactive is relatively flat, and that's not untrue. It's because the bottom portion the laggards aren't growing. In fact, productivity is in many ways falling off, but the ones that are the frontier of those top ten percent fifteen hundred global clients we've looked at, uh, you know, you see that CD study show that they're actually driving growth and productivity substantially, and the chasm is getting larger. >> So, Steve, Steve, it's curious what this means for competition. I think about if I'm using external workforces in open source communities, you know, Cloud and I, you know, changes in the environment. A supposed toe I used to kind of have my internal innovation. Now I'm out in these communities s O You know, we're here than IBM show. You know, I think back the word Coop petition. I first heard in context of talking about how IBM works with their ecosystem. So how did those dynamics change of competition and innovation in this? You know, the gig. Economy with open source and cloud. May I? Everywhere. >> Big implications. I mean, I I think you know, and this is the funny point you made is nontraditional competitors, because I think most of our clients and ourselves recognized that we haven't incredible amount of nontraditional competitors entering our space in professional services. We have companies that are not overtly going after our space, but are creating capabilities for our clients to do for themselves what we used to do for them. Data collection, for example, is one of those areas where clients used to spend money for consultants coming in to gather data into aggregate data with tools today that's ah, a very short process, and they do it themselves. So that's a disintermediation or on bundling of our business. But every business has these types of competitive non Trish competitive threats, and what we're seeing is that those same principles that we talked about earlier of the twenty first century surprise applies, right? How are they leveraging there the base and how they leveraging their workforce? Are they? Do they have a data strategy to think through? Okay, what happens if somebody else knows more about my customers than I do? Right? What does that do to make those kinds of questions need to be asked an innovation as a capability I think is a good partner and driving that nothing I would say, is that eco systems and you made you mention that word, and I want to pick up on that. I mean, I think eco systems air becoming a force in competitive protection and competitive potential going forward. If you think about a lot of you know, household names relative Teo data, you know Amazon's one of them. They are involved in the back office in the middle ofthis have so many organizations they're in integrated in those supply chains. Value change, I think services firms, and particularly to be thinking about how do they integrate into the supply chains of their customers so that they transcend the boars of, you know, their four walls, those eco systems and IBM was We consider KPMG considers IBM to be part of our ecosystem, right? Um, as well as other technology. >> So they're one of one of the things we're hearing from IBM. Jenny talked about it yesterday, and her keynote was doubling down on trust. Essentially one. Could you be implying that trust is a barrier to ay? Ay adoption is that. Is that true? Is that what your data show? >> We we we see that very much in spades. In fact, um, you know, I I if you think about it quite frankly, our oppa has driven a lot of people to class to class three. Amalgamation czar opportunities. But what's happening is we're seeing a slowdown because the price of some of these initials were big. But trust, culture and trust are big issues. In fact, we just released recently. Aye, Aye. And control framework, which includes methods and tools assessments to help our clients that were working with the city of Amsterdam today on a system for their citizens that helped them have accountability. Make sure there's no bias in their systems. As a I systems learn and importantly, explain ability. Imagine, you know. Ah, newlywed couple going into a bank to get a house note and having the banker sit back and have his Aye, aye, driven. You know, assessment for mortgage applicability. Come up moored. Recommend air saying no. You Ugh. I can't offer you a mortgage because my data shows you guys going to be divorced, right? We don't want to tell it to a newlywed couple, right? So explain ability about why it's doing what it's doing and put it in terms that relate to customer service. I mean, that's a pretty it's a silly example, but it's a true example of the day. There's a lot of there's a lack of explain ability in terms of how a eyes coming up with some of its conclusions. Lockbox, right? So a trusted A I is a big issue. >> All right, Steve, Framework that you just talked about the twenty first century enterprise. Is there a book or their papers? So I just go to the website, Or do I need to be a client? Read more about, >> you know, absolutely. You can go to our website, kpmg dot com and you can get all the della you want on the twenty first century enterprise. It talks to how we connect our customers front to middle toe back offices. How they think about those those pillars, the technologies we can help them with. Make change happen there, etcetera. So I appreciate it that >> we'll check it out that way. Don't be left in the twentieth century. Come on. >> No, you can't use twentieth century answers to solve twenty first century challenges, right? >> Well, Steve, he'll really appreciate giving us the twenty first century update for day. Volante on student will be back with our next guest here. IBM think twenty nineteen. Thanks for watching you.

>> Live from San Francisco. It's the cube covering IBM thing twenty nineteen brought to you by IBM. >> Okay. Welcome back, everyone. He live in San Francisco. Here on Mosconi St for the cubes. Exclusive coverage of IBM. Think twenty nineteen. I'm Jeffrey David Long. Four days of coverage bringing on all the action talking. The top executives, entrepreneurs, ecosystem partners and everyone who can bring the signal from the noise here on the Q and excuses. Rob Thomas, general manager, IBM Data and a I with an IBM Cube Alumni. Great to see you again. >> Great. There you go. >> You read a >> book yet? This year we've written ten books on a data. Your general manager. There's >> too much work. Not enough time >> for that's. Good sign. It means you're working hard. Okay. Give us give us the data here because a I anywhere in the center of the announcements we have a story up on. Slick earnings have been reported on CNBC. John Ford was here earlier talking to Ginny. This is a course centerpiece of it. Aye, aye. On any cloud. This highlights the data conversation you've been part of. Now, I think what seven years seems like more. But this is now happening. Give us your thoughts. >> Go back to basics. I've shared this with you before. There's no AI without IA, meaning you need an information architecture to support what you want to do in AI. We started looking into that. Our thesis became so clients are buying into that idea. The problem is their data is everywhere onpremise, private cloud, multiple public clouds. So our thesis became very simple. If we can bring AI to the data, it will make Watson the leading AI platform. So what we announced wtih Watson Anywhere is you could now have it wherever your data is public, private, any public cloud, build the models, run them where you want. I think it's gonna be amazing >> data everywhere and anywhere. So containers are big role in This is a little bit of a deb ops. The world you've been living in convergence of data cloud. How does that set for clients up? What are they need to know about this announcement? Was the impact of them if any >> way that we enable Multi Cloud and Watson anywhere is through IBM cloud private for data? That's our data Micro services architectural writing on Cooper Netease that gives you the portability so that it can run anywhere because, in addition Teo, I'd say, Aye, aye, ambitions. The other big client ambition is around how we modernize to cloud native architectures. Mohr compose herbal services, so the combination gets delivered. Is part of this. >> So this notion of you can't have a eye without a it's It's obviously a great tagline. You use it a lot, but it's super important because there's a gap between those who sort of have a I chops and those who don't. And if I understand what you're doing is you're closing that gap by allowing you to bring you call that a eye to the data is it's sort of a silo buster in regard. Er yeah, >> the model we use. I called the eye ladder. So they give it as all the levels of sophistication an organization needs to think about. From how you collect data, how you organize data, analyze data and then infused data with a I. That's kind of the model that we used to talk about. Talk to clients about that. What we're able to do here is same. You don't have to move your data. The biggest problem Modi projects is the first task is OK move a bunch of data that takes a lot of time. That takes a lot of money. We say you don't need to do that. Leave your data wherever it is. With Cloud private for data, we can virtualized data from any source. That's kind of the ah ha moment people have when they see that. So we're making that piece really >> easy. What's the impact this year and IBM? Think to the part product portfolio. You You had data products in the past. Now you got a eye products. Any changes? How should people live in the latter schism? A kind of a rubric or a view of where they fit into it? But what's up with the products and he changes? People should know about? >> Well, we've brought together the analytics and I units and IBM into this new organization we call Dayton ay, ay, that's a reflection of us. Seen that as two sides of the same coin. I really couldn't really keep them separate. We've really simplified how we're going to market with the Watson products. It's about how you build run Manager II watching studio Watson Machine Learning Watson Open scale. That's for clients that want to build their own. Aye, aye. For clients that wants something out of the box. They want an application. We've got Watson assistant for customer service. Watson Discovery, Watson Health Outset. So we've made it really easy to consume Watson. Whether you want to build your own or you want an application designed for the line of business and then up and down the data, stack a bunch of different announcements. We're bringing out big sequel on Cloudera as part of our evolving partnership with the new Cloudera Horn Works entity. Virtual Data Pipeline is a partnership that we've built with active fio, so we're doing things at all layers of the last. >> You're simplifying the consumption from a client, your customer perspective. It's all data. It's all Watson's, the umbrella for brand for everything underneath that from a tizzy, right? >> Yeah, Watson is the Aye, aye, brand. It is a technology that's having an impact. We have amazing clients on stage with this this week talking about, Hey, Eyes No longer. I'd like to say I was not magic. It's no longer this mystical thing. We have clients that are getting real outcomes. Who they II today we've got Rollback of Scotland talking about how they've automated and augmented forty percent of their customer service with watching the system. So we've got great clients talking about other using >> I today. You seen any patterns, rob in terms of those customers you mentioned, some customers want to do their own. Aye, aye. Some customers wanted out of the box. What? The patterns that you're seeing in terms of who wants to do their own. Aye. Aye. Why do they want to do their own, eh? I do. They get some kind of competitive advantage. So they have additional skill sets that they need. >> It's a >> It's a maker's mark. It is how I would describe it. There's a lot of people that want to make their own and try their own. Ugh. I think most organizations, they're gonna end up with hundreds of different tools for building for running. This is why we introduced Watson Open Scale at the end of last year. That's How would you manage all of your A II environments? What did they come from? IBM or not? Because you got the and the organization has to have this manageable. Understandable, regardless of which tool they're using. I would say the biggest impact that we see is when we pick a customer problem. That is widespread, and the number one right now is customer service. Every organization, regardless of industry, wants to do a better job of serving clients. That's why Watson assistant is taking off >> this's. Where? Data The value of real time data. Historical data kind of horizontally. Scaleable data, not silo data. We've talked us in the past. How important is to date a quality piece of this? Because you have real time and you have a historical date and everything in between that you had to bring to bear at low ladened psi applications. Now we're gonna have data embedded in them as a feature. Right. How does this change? The workloads? The makeup of you? Major customer services? One piece, the low hanging fruit. I get that. But this is a key thing. The data architecture more than anything, isn't it? >> It is. Now remember, there's there's two rungs at the bottom of the ladder on data collection. We have to build a collect data in any form in any type. That's why you've seen us do relationships with Mongo. D B. Were they ship? Obviously with Claude Era? We've got her own data warehouse, so we integrate all of that through our sequel engine. That thing gets to your point around. Are you gonna organize the data? How are you going to curate it? We've got data catalogue. Every client will have a data catalogue for many dollar data across. Clouds were now doing automated metadata creation using a I and machine learning So the organization peace. Once you've collected it than the organization, peace become most important. Certainly, if you want to get to self service analytics, you want to make data available to data scientists around the organization. You have to have those governance pieces. >> Talk about the ecosystem. One of the things that's been impressive IBM of the years is your partnerships. You've done good partners. Partnership of relationships now in an ecosystem is a lot of building blocks. There's more complexity requires software to distract him away. We get that. What's opportunities for you to create new relationships? Where are the upper opportunities for someone a developer or accompanied to engage with you guys? Where's the white spaces? Where is someone? Take advantage of your momentum and you're you're a vision. >> I am dying for partners that air doing domain specific industry specific applications to come have them run on IBM cloud private for data, which unleashes all the data they need to be a valuable application. We've already got a few of those data mirrors. One sensing is another one that air running now as industry applications on top of IBM Club private for data. I'd like to have a thousand of these. So all comers there. We announced a partnership with Red Hat back in May. Eventually, that became more than just a partnership. But that was about enabling Cloud Private, for data on red had open shift, So we're partnered at all layers of the stack. But the greatest customer need is give me an industry solution, leveraging the best of my data. That's why I'm really looking for Eyes V. Partners to run on Ivan clubs. >> What's your pitch to those guys? Why, why I should be going. >> There is no other data platform that will connect to all your data sources, whether they're on eight of us as your Google Cloud on premise. So if you believe data is important to your application. There's simply no better place to run than IBM. Claude Private for data >> in terms of functionality, breath o r. Everything >> well, integrating with all your data. Normally they have to have the application in five different places. We integrate with all the data we build the data catalogue. So the data's organized. So the ingestion of the data becomes very easy for the Iast V. And by the way, thirdly, IBM has got a pretty good reach. Globally, one hundred seventy countries, business partners, resellers all over the world, sales people all over the world. We will help you get your product to market. That's a pretty good value >> today. We talk about this in the Cube all the time. When the cloud came, one of the best things about the cloud wasn't allowed. People to put applications go there really quickly. Stand them up. Startups did that. But now, in this domain world of of data with the clouds scale, I think you're right. I think domain X expertise is the top of the stack where you need specially special ism expertise and you don't build the bottom half out. What you're getting at is of Europe. If you know how to create innovation in the business model, you could come in and innovate quickly >> and vertical APS don't scale enough for me. So that's why focus on horizontal things like customer service. But if you go talk to a bank, sometimes customer service is not in office. I want to do something in loan origination or you're in insurance company. I want to use their own underwriting those air, the solutions that will get a lot of value out of running on an integrated data start >> a thousand flowers. Bloom is kind of ecosystem opportunity. Looking forward to checking in on that. Thoughts on on gaps. For that you guys want to make you want to do em in a on or areas that you think you want to double down on. That might need some help, either organic innovation or emanate what areas you looking at. Can you share a little bit of direction on that? >> We have, >> ah, a unique benefit. And IBM because we have IBM research. One of their big announcement this week is what we call Auto Way I, which is basically automating the process of feature engineering algorithm selection, bringing that into Watson Studio and Watson Machine learning. I am spending most of my time figure out howto I continue to bring great technology out of IBM research and put in the hand of clients through our products. You guys solve the debaters stuff yesterday. We're just getting started with that. We've got some pretty exciting organic innovation happen in IBM. >> It's awesome. Great news for startups. Final question for you. For the folks watching who aren't here in San Francisco, what's the big story here? And IBM think here in San Francisco. Big event closing down the streets here in Howard Street. It's huge. What's the big story? What's the most important things happening? >> The most important thing to me and the customer stories >> here >> are unbelievable. I think we've gotten past this point of a eyes, some idea for the future we have. Hundreds of clients were talking about how they did an A I project, and here's the outcome they got. It's really encouraging to see what I encourage. All clients, though, is so build your strategy off of one big guy. Project company should be doing hundreds of Aye, aye projects. So in twenty nineteen do one hundred projects. Half of them will probably fail. That's okay. The one's that work will more than make up for the ones that don't work. So we're really encouraging mass experimentation. And I think the clients that air here are, you know, creating an aspirational thing for things >> just anecdotally you mentioned earlier. Customer service is a low hanging fruit. Other use cases that are great low hanging fruit opportunities for a >> data discovery data curation these air really hard manual task. Today you can start to automate some of that. That has a really big impact. >> Rob Thomas, general manager of the data and a I groupie with an IBM now part of a bigger portfolio. Watson Rob. Great to see you conventionally on all your success. But following you from the beginning. Great momentum on the right way. Thanks. Gradually. More cute coverage here. Live in San Francisco from Mosconi North. I'm John for Dave A lot. They stay with us for more coverage after this short break

>> Live from San Francisco. It's the cube covering IBM thing twenty nineteen brought to you by IBM. >> Welcome back to Moscow and everybody. The new, improved, shiny Mosconi Center. I'm Dave Lamont with Student of Men. This is Day one of IBM thinking you're watching the Cube, the leader and live tech coverage. Ed Walsh is here. He's the general manager of of IBM Storage and Software to find it. Great to see you again. Always. Pleasure. Thanks for coming on. I love the venue, You know, I agree. It's a saddle. Las Vegas. No offense to our friend. Thie shy. It's been a long time coming, Oscar, honey, but it looks really good. I agree. Three thousand people expected. So you must be excited. >> No, I think we have a lot of things that you're going to see announcements. Also, I think you're going to see some refinement of the overall message. I think it's going to exciting week. So it's kind of I'm I'm talking to before all the keynotes. But it's it's an interesting week, for sure. So >> so tease a little bit. What can you tell us is >> Okay, so you know my background, I've been outside IBM, coming to run storage,  I've said a couple of different times. My strategy is to drive the overall storage but also get more aligned with what we're trying to overall at IBM, because that's the strength of IBM, right? Really help the clients move forward And infrastructure matters, and what you're seeing is I think the market's coming our direction at IBM. And I'll give you a couple of things. You're gonna hear a lot about, you know, hybrid multicloud. Say AI at scale, right, and you're going to see that messaging, but that's where the markets come at us. You saw Red Hat talk about it, but all of our competitors are now doing that as well. But when it comes to once, you start a hybrid multicloud and we think it's like we're going to talk about that chapter two right? Chapter one was the first twenty percent of workloads and was all about these application driven events. And you know think about Office 365, etc. But the eighty percent of the workloads were still on premises, and there's a reason they're on premises. But what now is that people that next phase going leaving by the the organizations that have mission, critical data and how to do that? And there's a role for hybrid, which plays perfectly for us. >> And can you help help connect the >> dots for us because we've launched, you know, software to find wave come through through storage, but still many people on the outside, they'd be like, Okay, well, storage is a bunch of boxes sitting in my data center on all my new After being built here. I'm sass ify ing things. They're there, and it feels like death by a thousand cuts, too. The traditional storage markets help explain kind of what the modern storage market is. Data is at the center of everything, so we know that that's a huge thing that elwin for storage but help bring us inside your business. >> So I think in general everyone's trying to data driven, and it's easy to say hard to do and everyone at the platform going do that. It's a hybrid multi cloud and hybrid being the reason we're using the terminology, the industry. But also there is a rule for on premises. And how do you easily connect but getting the same, you know, agility and performance and cost benefit on Prem in an extended the right time for Cloud. So where you see us, we're looking, we focus on. We deal with a lot of clients somewhat advanced and some would say more laggards. And so we share loss stories where people are being successful being dad driven and we see it fall in tow. Kind of three. What I'll say is we try to success. Criteria are areas, right? So one is people just modernizing, going from traditional to go private clouds, making sure they extend use of benefits of public cloud and be more data driven. So some people are spending money to save money, and some people are spending money to make money. But even in traditional environments, you see the CEO having a bigger voice and we need to push him and show him how to do that right way. Also see another section, People really driving a I in general. We see that and definitely not hybrid people to cloud environment where we see a large on premises. But you're always looking for the different data. Sets are going to be in the multi cloud that they need. Bring it together and we see that being a very interesting and affected. How do you start with someone doing? We believe we have the best storage for a I, but we can scale from the smallest to the biggest super computer in the world. Right? Driving, You know, one point five terribly. It's a second, you know, huge monsters, but you can start small. But what we find is people are just getting started with the meets clients where they are so part of it in these different areas, meet them where they are, and there are different parts of the journey on AI is having them get going. But then really, how do you scale? You're gonna hear a lot about this. How do you scale AI? Everyone has these random acts of AI or machine learning, but they can't scale them for the business, let alone across the enterprise, which is where everyone needs to get to. And that's where we're really focused on the offering set. So from a storage that would be where we do the AI. And of course, the third one is just containers in general. Which, by the way, they intersect because a lot of things going to in containers going intersect with a especially when you go multicloud, but there's a whole different Hey, let me modernize my application infrastructure, and it's a different conversation with client. So we see people being very successful, and that's where you're seeing from a storage development. Investments is were going into that direction, helping clients those different. >> Why is a scale so difficult? Is it the silo data silo problem? >> So first of all, there's a coldness about a I everyone. It's a black box is mysterious. It's it's really just computer science. I mean, it's a process between time I'm eating their own deep learning. It is. You're you're doing stats, you're just driving you being in a river or that using custom, I silica do a faster, like abuse. Uh, the other one is it's easy, and it's not so in infrastructure matters. So when you get going, what we see is people just give a developed You give a particular data scientists on environments. You say You bring the data and you need and we'll help you with the governance of strategy, but still getting something to just be valuable to the business. Then what happens is they see another random act of a ay or another area that it becomes data silo, but they're trying their best to stay away from the data scientist. Given the right support, which I think the right thing is, the biggest thing is not having all controlled and centralized. You wantto let the business units drive. But then what happens is you have that almost like data warehousing in the past. You have these islands, and now you don't have any trusted, true source of the truth. And you don't have ability to get you force everyone to do the hard bit. The hard bit is actually having the right data. Do all the eighty percent of cleansing that dead guy having the right governance and security about that? Andi, you're adding to overtime. What if you do a couple applications and it's not on the first one? You do. But once you do the second third, maybe fifth deadly by the eighteenth application, you want to bring that together in a shared platform. And that's where IBM storage plays. And that's where we're truly differentiated. Compare the storage industry so we have no assets that no one else has. Like what we do. A spectrum scale where we can, Luli scale up from just individual server toe half rack and we can take the same environment into largest, eh? I submit computers in the world, but the key thing is, you need. You can have a without a information architecture, and that's on the software side. But it definitely has to be in the infrastructure, and we're doing a cross hybrid multi class. We're doing that on Prem. But trust me, eyes absolutely in the cloud so we can extend those environments and run the same thing in any of the public clouds. And >> what's the storage enabler eyes? It is its software, defined as you mentioned architecture. What is the linchpin there? >> Well, so one it is a softer to find. So in this particular area, where we're helping people is our file system called spectrum scale. But it allows us to do from the very small toe largest environments, right that allows you to scale, and it's also runs all different asset. So it's unstructured, be able to run a dupe Native spark. But you have the file you're able to block, able to bring it together, able, start small, but you're able to scale and keep up the thing about a eyes you go from, I want to collect that information. Get after it. You also need metadata. So we have products like Spectrum Discover to show you the metadata so we can actually track it, you know, So it doesn't become too junk drawer in the sky that we've seen with that a lot of data lease. But then it's interesting. You have to go in tow, actually do the training, and that's for using custom silicon. You know, G pews, and that dramatically changes a performance you require. So thes GP is used to run it sixty gigabytes a second. How they're one hundred fifty gigabytes a second. We no storage store. Traditional stories that you get from, you know, the environment we might get from pure net after emcee. They don't run it, run it sub twenty gigabytes a second. So how do you do this? It's a different architectures, actually, based upon a true scale out what we've seen in the largest super computers in the world. But you're able to bring that environment so we can actually do bring in all the data works, get under one governance and strategy. But then you can actually keep up with the performance of the true influencing and driving GPS that once you have a trusted source, you can scale us out. Lily, the largest, super covers the world so we can We can show you scale on the exact same components started half rack and goto. The biggest thing is in the world, but the key thing is right. You need to actually have a performance. So if you have this data back, plaintiff, you call that Now people still take a lot of the data. They'll bring it into the servers in the crunch it with GPS. They say, Well, okay, your stories doesn't need tohave that performance. But what you find is once you have a common back plane, which, how IBM did it. Now you have different business units almost hub and spoke, grabbing the data. But they have one true source of data they will get after it. They're able to get their other data that other groups are looking for. But now they're able to now scale it into the enterprise that because something is just a I call a pike, also your doing applications and they just want to have a P. I called in the same environment, and those have to be fast, cause now you're influencing, so it's sub seconds. But you need that performance. So what you need is by bring it all together. You can either do data silos, which are easy up front. But by the time you the second third, you do all that same work over and over, and you don't trust its source. You gotta bring it together. But you can't bring it together in any storage, and we don't bring it together on the same storage we do for of'em, where environment to others is different storage. And it's made specifically for this environment. And it's something that, actually IBM is no leaps and bounds above everyone supercomputer. They do these type of analysis, and we're like two x our best competitors benchmarks, by the way, that computer use spectrum scale so But it is a different architecture. But if you don't put together on, and I would also say that when you get started, no one starts with the big, in fact, that that's almost a mistake. What you want to do is let the data scientists have the creative driving get business outcome, but they need to be thinking ahead. How do you bring it together? So you have a shared because again, The way you're really gonna drive across enterprise. All that processes is actually having soon AP I calls come in and which are not going to their own environment altogether. Makes sense. >> Yeah, you've mentioned a couple times infrastructure matters, and I wantto wanna tie that into the eighty percent Sure it was at this very venue in two thousand nine when Paul Marat said is the CEO of the M where we're going to run any workload. Any application? Virtualized and a lot of people were skeptical, and I remember the time thinking about mainframes. I kind of did that. Um, >> and you can say that I can >> How you're talking about the eighty percent and, you know, Veum, where I think largely proved that that you could run that at high performance. Att. Least adequate performance. Now you're seeing a similar discussion around cloud. But it's somewhat different because of some of the things that you were just mentioning it. What does that world look like? Obviously, hybrid fits in. You mentioned the red Hat acquisition. That's key. Part of idea mes go forward. You know, multi cloud strategy. So should we think about what you know what similar and what's different than a sort of V M wear virtual ization, mainframe virtual ization days. >> Okay, that's interesting. And then you're tying into the cloud adoption as well, right? So and we do think about twenty in this idea. See, about twenty percent of workers have gone, but eighty percent are waiting, and that's that I never thought of that way. It's a good analogy. Virtual ization. That easy stuff went first, and then what you have to do is have the databases. Remember, that was a big issue. You couldn't do that for years, but then also, you move. It's like, Why would you do it? But what's happening is what we're seeing. Is this the mission? Critical workloads, And they're either regulated industries, but it's for different reasons. They're running in different places. But it might be a security concern. It might be scaled that he might be regulated industries, but there's reasons. Or maybe after reef, actually applications. Actually one cloud native because lifted shift was it the same economics as you thought. So what we think is the next eighty percent is not going to lead by the application, you know, or things like Office three sixty five we actually think is going to be people putting the real mission critical workloads. And that's a different conversation. That's where we think the market's complaints what we do at I BM and infrastructure, where you need to have the technology, but also the expertise and industry moving on. Then security becomes key concern. >> So way mentioned red hat here and you can't say too much, but we know about kind of the cloud native modern, you know, multi cloud stuff. But Red had also has, you know, quite a bit of a storage portfolio, you know, seven cluster acquisitions, open source. Wondering what you can just, you know, as an IBM or tell us about what you think of that portfolio. >> So you know, we can talk about what's gonna happen afterwards, but also I think we made it very clear we believe redheads and used the inn where I think it's a good analogy. We're going to keep it. They're going to be independent. They serve a world we're not going to change it. And I think that's a very important part of the message. But we can't talk about the assets. And I did my own kickoff to my team. My partners and I used a kid around. That was a storage acquisition, you know? So it was my thirty four billion dollars right? So but I think it has a good play. It's completely complimentary to what we do. They have some great to two technologies, but also we bring things to them. They're interesting, but a conversation when someone strikes, they were my going and for strategy. We believe containers are critical. We believe Lennox is critical. If you look at what people are doing on the cloud, it is theirs. It's already cross. It's mostly Lennox and the enterprises have chosen red hat. Now, if you think of what we could do with that particular environment, tohave the conversation make relevance about what we could do to help you on Prim. But now you can run the same thing on prime. You can put it literally anywhere. Now that's a strength of red hat would bring us on a story side. They have great assets. I'm kind of salivating to help him out with that now what they don't do with some of things that we can add to them. Right? So I don't think we're commenting any road map, I think, but they haven't. What we have is, to be honest, complimentary, if that makes >> sense. Well, and I think you you're familiar with our old troop private cloud nomenclature. It's evolving to true hybrid cloud, and what you just described is true hybrid cloud. Run it wherever you want. You're agnostic to where it runs, but don't want that cloud operating model yet to be the underpinning of the experience. >> You don't want me locked in, so that's where I think if you look a hybrid, you want to make sure. And I think it allows us to that. I think IBM is synergistic to it. I think we can bring a lot of how do you bring the integration capabilities that IBM brings on applications and help these mission critical environments that have need some industry expertise. To do that? Bring them to the cloud itself. And it doesn't matter where in the cloud. >> Yeah, at one of the things we were commenting on the open is, you know, the hybrid multicolored world. It's complicated, and IBM has a, you know, strong history with services to help drive that gives a little bit of your insight is toe what IBM brings tea. Kind of that multi cloud environment. >> It's almost too much. Right? So what we're doing is really working on the overall. How do we simplify? So we're going to meet the client where they are, So everyone's at different. You have to almost find out where they are on the journey and then even a particular client. You'll find different business units on different parts of journey so we can help him anywhere from helping figure out, you know, architect, where they would go. We could have moved to the cloud. We can actually help them manage a cloud, and they're also going to meet them where they are. So we have. If you think about what we could do with a I, we have full Aye aye stacks enterprise capabilities. But some people choose to just use their own open source and we can help them. In fact, are you see, our multi club manager allows you to manage, regardless of what you've young for your build environment. So what we're gonna do is meet clients where they are and help them do the last mile. And then we're servers and support were ableto, You know, if you look at what we do, gts were the largest red hat support organization. You look what we do with GPS. We can help people build up their own platforms and given overall struck shen and how to go drive a ay at scale in their environments. So I think I think it does play to us. And I think the red acquisition just ads. I think one of these three. >> Well, and I think, you know, we were talking about in our open that just even IBM giant application, modernization opportunity, right? I mean, because we tend to think about, you know, a I and leading edge, but there's just so much modernization opportunity and a lot of guys, you know, they don't want to go it alone with open source. The fact that IBM is there, you know, with that big blue blanket I called It's. Okay, we're gonna help you through your modernization initiatives. You know, we think a big deal. You know, we're excited about that chapter. Think about Red >> Hat that does a lot of consulting, but they have been discipline. They help you get rail going once you start Doc and open stack rights to be open shifts. Now, that is a whole different way. Tau. Look at your environment, your applications, and that takes a higher level. So it's like one and one is three. They don't do a lot of that now, right so >> well, and it's it's instant developers to That's the other thing. We said just that, while how many developers ready with a million? We're talking about Sisko before with a half a million, which is great. But you're talking eight million. Andi, you know, despite IBM's efforts around, you know, blue mix. So that was a heavy slog. Now, all of a sudden, you got eight million developers. It is. Look, Red hat. Lennox is running the Internet. You know we know this, so that's exciting. I want My last question is you've made a career early part of your career in and taking startups and getting them to a point where they could be acquired very, very successful career there. Then you joined IBM, spent some time at M. I t. You know, getting even smarter when you sit back and look at the industry, the storage industry in particular somehow that despite the trend toward, you know, cloud and bigger is better. You still see the specialist, you know, popping up all the place. You see guys like pure. You see, guys like Nutanix and you know, we saw that early with the comm Palance and the three powers and the ice Alonso, it's okay. Maybe this is the last way. But somehow, storage innovation and it continues to occur. Do you think we're seeing sort of the end of that sort of storage? Startup crazed can can independent storage companies continue to survive? What do your thoughts as an industry observer? >> So I think, is more difficult. But there's plenty of innovation. So you're seeing it as we just help people get to their journey. You're going to see different technologies that even IBM against your portfolio changed rather dramatically to keep up with the trends. And you need to do. It s Oh, I don't think it's over with, so I never want to quoted that I think there's no innovation left and there's a role for you saw storage, you know, grow last year, right? So it was. There's always been growth areas, but it's been flat also really took off on a lot of that is because we're doing >> on a I, >> which is not your average, is definitely not what pure does as faras for storage, right? But you're going to see, I think I'm going to see innovation. I think you're going to see that continue, but I think it's harder and harder for these independent firms, mostly when they scale. I think it's the innovation piece, and I think you're seeing guys like us and I am seeing others innovate very quickly and you can tell innovation is speeding up with investment cause we have to >> get our clients are demanding it, and the VCs keep pouring money in. I mean, you're seeing that in the data protection space and >> data protection isn't now cool, right? So Waseem all time. I think in general, if you look at data protection becomes now your secret weapon. When you talk about being dad driven in a classical environment, you can get copies your data at the AP Eyes anywhere in environment. So I think it's a really big play, so >> well and it opens up new opportunities beyond just back up right for whether it's Dev ops or maybe disaster recovery ransom, where even analytics? Because the backup Corpus is, you know, has all the data, and it's a lot of possibilities. Thanks so much for coming. I think we're going to see you also on Wednesday, right? And looking forward to that. So thank you. All right. Keep it right to everybody. We'll be back with our next guest. You're watching the Cube from day one. IBM thinking Mosconi right back.

>> Live from Barcelona, Spain. It's the cue covering Sisqo. Live Europe, Brought to you by Cisco and its ecosystem partners. >> Welcome back to Barcelona, Everybody watching the Cube, the leader in live tech coverage. My name is Dave Valentin here with my co host Student events. Do Myself and John for Be here all week. Eric Hurt, Saugus Here Long time Cuba Long friend. Great to see you again. He's the CMO of IBM IBM Storage division. He's joined by James Amy's, who's the head of networks at advance. The service provider Guys, Welcome to the Cube. Good to see again. >> Great. Thanks for having us loved being on the cute. >> So we love having you So, James, let's start with you. Tell us a little bit about advanced to want to dig into some of the networking trends. We're hearing a lot about it here. It's just go live. >> Yeah, I think so. Advanced are a manage service provider software software company based in the UK, one of the largest software companies in the UK, providing interim solutions for lots of different Marchal market verticals, including healthcare, local government, regional government, national infrastructure projects we've got involved with as well as charity sector legal sector. A lot of education work we do is real diverse portfolio of products we offer on with the manage services piece. We also offer complete outsourcing. So this is desktop support. Telephony support, printer support all the >> way back into integration with public cloud platforms and private cloud platforms, the majority of >> which is our in. >> So so Eric advanced are both a customer and a partner, right? Right, right. And so you love you. Love versus Stack. These guys are presumed versus stack customers. Well >> stacked customer in the Versace tack, as you know, Integrate. Cisco, UCS, Cisco Networking Infrastructure, IBM Storage of all types entry products up into the fastest all flash raise with our software spectrum virtualized spectrum, Accelerate Family and James's company is using versus tax is part of their infrastructure, which they then offer, as you know, to a service toe and uses. James just described. >> So let's talk about some of the big trends that you guys are seeing and how you're both responding to customers and you're responding to your customer. So we're seeing two hearing today. Lot about multi cloud. We've been hearing that for a while the network is flattening your network expert love to get your your thoughts on that. Security, obviously, is a huge topic. End end management, another big topic, something that IBM is focused on. So so James, what 1 of the big mega trends that you're seeing that a driving your business decisions and your customer's activity? One >> of the big changes we're seeing is a change from large scare enterprise scale deployments off a particular type of technology on customers are now choosing because they're informed the best fit for a particular application or particular service on that may be coming to a service provider like ourselves to offer our services products to them. Uh, or they're looking for us to run in infrastructure service for them or integrate with a public cloud offering. So the competition of the public cloud for service providers is key on DH. I think people were looking around a few years ago thinking, How do we compete to this well, with partnerships that we have in our Francisco? It gives us a very compelling competitive offering. But we can turn around and say, Well, we can give you a like for like, but we can give you a slightly better service because we could give you guaranteed availability. We give you guaranteed price point on, and this is all backed with key vendor certified designs. So we're not talking about going out on developing a solution that takes maybe eighteen months to take to market. This is understanding a requirement for a quick, you know, Q and A with a customer a line that, too a reference architecture that we can literally just pick up off the shelf, deploy into our data centers using the standard building rocks that we use across the business. So Nexus nine K seven k's or our standard bread and butter inside the data center environment. As Eric pointed out, Cisco UCS is our our key Intel computer platform that we used on DH. The store wise IBM product has been a real true success story for us. So we started off being a a mixed then the house where we would align storage requirement paste with what we could find in the market. That was, that was a good fit. But the store was products is basically just allowed us to standardize on the speed of deployment is one of the key things. So we started out with a very lengthy lead time tio service ready, which is when we start charging for revenue on if we want a ninety day build. Well, we've got a lot of special service time, A lot of engineering time getting that ready Teo, Teo and take to the customer and then we turn it on. We can start seeing revenue from that platform with versus Stack. This enabled us to accelerate how quickly we can turn that on. And we've seen that drop, too. They're literally days through standardisation elements of automation as well. Many of our environments are bespoke because we have such a wide arrange off different types of customers with different needs, but it allows us to take those standing building blocks, align them to their needs and deliver that service. >> James James, we found the peas are often in the middle of those discussions that customers are having on multi clouds. You talked a lot about the services you build. Are they also coming to you? If if you tie into the public Cloud services or yes, maybe you can help explain a little bit on how that worked Five years ago, it was the public loud there are going to kill them and service providers. And what we see is customers can't sort out half of what's going on. They've got to be able to turn two partners like you to be able to figure this out. >> Yeah, that's a fantastic question. I think three years ago we'd be talking to our customers and they were I am going to this public cloud or I am going to build this infrastructure. Where is now? They're They're making Mohr informed select decisions based on the drive to the hosted office and voice platforms offered by Microsoft. There's a big driving. Many of our customers are going in that direction, but it's how we integrate that with legacy applications. Some of the solutions that some of our customers use have have have had millions of pounds of investment into them, and that's not something I can just turn off the water away from overnight. So it is how we're integrating that. We're doing that at the network level, so it's how we're appearing with different service providers, bringing that in integrating that, I'm offering it to them as a solution. What we try tio, we try to try position ourselves is really it's the same experience, regardless of where we're placing it. Consumption. Workload doesn't know whether it's inside our data centers, whether we're talking one of the public cloud platforms or even on premise. So we have quite a few customers that still have significant presence on premises because that's right for their business, depending on on what they're doing, especially some of the research scientists. >> So you've got to deliver flexibility in your architecture, and you talk a lot about software to find you guys made a big move to software to find, you know, a couple years ago, actually, maybe discuss how that fits in to how you're servicing advanced another client? >> Sure. So you know, IBM Storage has embraced multi Cloud for several years. So our solutions. While, of course, they work with IBM, Cloud and IBM cloud private work with Amazon. They work with azure Google Cloud and in fact, some are products. For example, the versus stack not only is advanced using it, but we've got pry forty or fifty public, small, medium sized cloud providers that our public references for the vs Tag and Spectrum Protect you Know which is our backup product Number one in the Enterprise. Back up space Expect from detectives Got at least three hundred cloud providers. Medium, small and big. Who offered the engine underneath for their backup is a service is spectrum protect, So we make sure that weather PR transparent cloud tearing our cyber resiliency technology. What we doing? Backup archive object storage works with essentially all cloud providers. That way, someone like James A. CSP MSP can leverage our products. And we, like I said, we have tons of public records around versus Stack for that, but so can an enterprise. And in fact, I saw survey recently that it was done in Europe and in North America that when you look at a roughly two billion US size revenue and up the average company of that sizing up, we use five different public cloud riders at one time. Where that it be due to legal reasons whether that be procurement. You know, the Web is really the Internet. And, yeah, Cloud is really just It's been around for twenty some years. So in bigger accounts, guess what is now involved Procurement Well, we love that you did that deal with IBM club, but you are going to get a competitive quote now from Amazon and Microsoft, right? So that's driven it legal's driven it. Certain countries, right? The data needs to stay in that country, even if your cloud if eyeing it, it's so to speak. So if the clap water doesn't have a data center there, guess what? Another geographer used different. And then you, of course, still have some large entities that still allow regional buying pattern so they'll have three or four different cloud providers that air quote certified by corporate. And then you could use whichever one you want, so we make sure that we could take advantage of that. Wade and IBM. We ride the wave, We don't fight the way. >> So you've got in that situation. You these multi cloud you got different AP eyes, You get different frameworks potty, you abstract all that complexity you got, Francisco coming at it from a networking standpoint, I b m. Now with Red Hat is good. Be a big player in that that world. VM where What do you guys do? James, in terms of of simplifying all that multi cloud complexity >> for people. I think some of it is actually the mystifying on its engaging with our partners to understand what the proposition is on, how we can develop that on a line, that to mind your own business, but more importantly, to the needs of our customers. We've got some really, really talented technicians worked within within advance, and we've got a number of different forums that allow them to feed back their ideas. But we've got the alignments between those partners and and some of those communities, so that we can have an open discussion on drive. Some of that thinking forward about ultimately see engaging with customers. So the customers feedback is key on how we shape and deliver no need service to them, but also to the service to other customers. We have a number of customers that are very similar, but they may work in different spaces, some somewhere even competitive. So we have to tread that line very safe, very carefully and safely. But it is. It's a good one to one relationship between the client service managers, technical technicians. We have inside business having that to complete three sixty communication is key, but that's that's that's really the bottom takes. Its creation >> came like youto dig into security for us a little bit. You know, I think we surpassed a couple of years ago. I'm not going to go to the cloud to it because it's not secure to Oh, I understand it's time for me to least reevaluate meant security and, most likely, you know, manage service fighters. Public clouds are probably more secure than what I had in my data center, but if I've got multiple environment, there's a lot of complexity there. So how do you traverse that? Make sure that you've got a comprehensive security practice, not just all these point solutions for security all over the place. >> Ah, so that's that comes onto visibility. So its visibility understanding where all the control points are within a given infrastructure on how the landscape looks. So we were working quite closely with a number actually of key Cisco and IBM partners, as well as IBM and Cisco themselves directly tohave a comprehensive offering that allows us to position to our customers. You used to once upon a time you had one game, right? So we need it is from good security on your Internet. Facing viable For now, you might have a ten. Twenty, thirty of those. We need tohave consistent policies across those. We need to understand how they're performing, but also potentially, if there's any attempt attack vector on one of them. How that how someone is trying to looking to compromise that so centralized intelligence on That's where we start to look at my eye operations to gather all that information. The long gone are the days where you have twenty people sharing a room just reading streams. Those twenty people now need thio. See reams and reams of information instantly. Something needs to be called up to them. They could make a decision quickly on Active planet on DH. That's really where we we're positioning ourselves in the market to differentiate. I'm working with key part, Mr >> Never talk about your announcement cadence. Good idea as a big show. Think coming up in a couple weeks cubes gonna be there. Of course. What can we expect from from you guys? >> So we're actually gonna announce on the fifth before things way, want to drive end users and our business partners to storage campus, which is one of the largest campuses at IBM, think we'll have over fifteen pedestals of demo and actually multiple demos because we have such a broad portfolio, from the all flash arrays to our versus stack offering to a whole set of modern data protection management control for storage, which manages in control storage, that's not ours, right? Our competitors storage as well, and, of course, our software to find storage. So we're going to do a big announcement. The focus of that will be around our storage solutions. These air solutions blueprints reference architectures is Jane, you mentioned that use our software and our storage systems that allow reseller or end user to configure systems easily. Think of it as the ultimate wrestling recipe for that German chocolate cake. But it's the perfect recipe. It's tried. It's true, it's tested. It's been on the Food Channel twenty seven times and everybody loves it. That's what we do with our our solutions. Blueprints. We'll have some announcements around modern data protection, and obviously a big focus of IBM. Storage is been in the space. So both storage as an Aye aye platform for aye aye, applications are workloads but also the incorporation of technology into our own storage systems and software. So be having announcements around that on February fifth going into think, which will then be the week after in San Francisco. >> Great. So I'm here and trusted data protection plays into that. Aye, aye. Intelligence machine intelligence. And I'm also hearing header of Geneti multiple platforms. Whether it's your storage, you said our competitors now does that also include sort of the clouds? Fear we're not announcing anything. But you guys have you know, you've seen your pictures. That's azure itt's a w a s. I mean, that continues >> so absolutely so. Whether it be what we do from backup in archive, right, let's take the easy one. So we support not only the protocol of IBM clad object storage which we acquired and allows you to have object storage either on premise or in a cloud in stance e ation. But we also support the s three protocol. So, for example, our spectrum scale software giant scale out. In fact, the two fastest supercomputers world you spectrum scale over four hundred fifty petabytes running on spectrum scale, and they continue their to an object store that supports us three. Or it can tear toe IBM clad object stories through that IBM clad object storage customer. That's great for using the S three protocol. You, Khun, Tear to that as well. That's just one example. Same thing we do for cyber resiliency. So from a cyber resents me perspective, we could do things with any cloud vendor oven air cat air gap, right? And so you could do that, eh? With tape. But you could also do that with the clouds. So if your cloud is your backup archive replication repository, then you can always roll back to a known good copy. You don't have to pay the ransom writer. When you clean up the malware, you can roll back to a known good copy, and we provide that across all of the platforms in a number of ways. Our protect family, our new products, a safeguard copy for the main friend that we announced October. So all that allows us to be multi cloud resiliency as well as how do we connect a multi cloud backup archive automated tearing all kinds of clouds, whether the IBM cloud and, of course, I'm a shareholder. So I love that, but at the same time were realistic. Lots of people use Amazon Google Azar. And like I said, there's thousands of mid two small cloud providers all over the world, and we support them, too. We engage with everyone. >> What about SAS? You know, that's one of the questions we've been trying to squint through and understand is because when you talk about five cloud providers is obviously infrastructures of service. And then there's their service providers like like Advanced. And then there's like a gazillion SAS Companies >> write a lot of data >> in there and a lot of data in there. How should we think about, you know, protecting that data? Securing that data is that sort of up to the SAS vendor, and thou shalt not touch. Or should that be part of the scope of AH, storage company? Well, so what we do >> is we engage with the SAS vendor, so we have a number of different sass coming is, in fact, one of them was on the Cube two years ago with us. They were startup in the cyber security space and all of its delivered over SAS. So what they do is in that case, the use our flash system product line, they get the performance they need to deliver south. They want no bottlenecks because obviously you have to go over the network when you're doing SAS Andi. Also, what they do is data encryption at rest. So when the data is brought in because we have on our flash arrays capability and most of our product line especially the flash systems to have no performance hit on encrypt their decrypt because its hardware embedded, they're able to have the data at rest encrypted for all their customers. That gives them a level of security when it's at rest on their site. At the same time, we've given the right performance. They need tohave soft reserve, so we engage with all we pry have three hundred, four hundred different SAS companies who are the actual software vendor and their deployment model. This software's interest, by the way, we do that as well as I mentioned, over three hundred cloud providers today have a backup is a service and the engine ease their spectrum. Protect or spectrum protect. Plus, but they may call it something else. In fact, we just had a public reference out from Silver String, which is out in the UK, and all they do is cyber resiliency. Backup in archive. That's their service. They have their own product, but then spectrum Protect and Spectrum Check plus is the engine underneath their Prada. So that's an example. In this case, the backup is a service, which, I would argue is not infrastructure, but more of an application. But then true what you call real application providers like cyber security vendors, we have a vendor who in fact, does something for all of the universities and colleges. United States. They have about eight thousand of them, including the junior colleges, and they run all their bookstores. So when you place an order, all their air NPR, everything they do is from this SAS vendor that's based in there in the Northeast. And they've got, like I said, about a thousand colleges and universities in the U. S. And Canada, and they offer this if you will bookstore as a sass service and the students use it. University uses it. And, of course, the bookstores are designed to, you know, make a little money for the university, and they all use that so that's another example. And they use are flash systems as well. And then they back up that data internally with spectrum protectors. They obviously it's the financial data as well as the inventory of all of these book stores all over the United States at the collegiate >> level right now. James Way gotta wrap, but just sort of give you the final word. UK specialist, right? So Brexit really doesn't affect you. Is that a fair statement? >> Uh, we'll do? Yes. >> How so? >> I think it's too early to tell. No one really knows. I think that's all the debates are about. AJ's trying to understand that on DH for us. We're just watching and observing. >> Staying focused on your customers, obviously. So no predictions as to what's going to happen. I was not from a weeks ago. I got hurt both sides. You know, it's definitely gonna happen, All right, Not happen, but okay, again give you the last word. You know? What's your focus? Over the next twelve eighteen months? >> Eso all our focus is really about visibility, So they they they've touched on that. We're talking about security for customers. Understanding whether data is whether exposure point saw. That's our keep. Keep focusing on DH versus stack on dh thie IBM store wise product underpin all of those offerings that we have on. That will continue to be to be so forward. >> Guys. Great to see you. Thanks so much for coming on the Cube and our pleasure hosting you. Thanks. Appreciate, Really welcome. Alright, Keep right, everybody. We'll be back. Day Volante was stew Minutemen from Cisco live in Barcelona. >> No.

>> Live from Barcelona, Spain. It's the cue covering Sisqo. Live Europe, Brought to you by Cisco and its ecosystem partners. >> Welcome back to Barcelona, Everybody watching the Cube, the leader in live tech coverage. My name is Dave Valentin here with my co host Stew, Minuteman Stew Myself and John for Be here all week. Eric Hurt, Saugus Here Long time Cuba Long friend. Great to see you again. He's the CMO of IBM IBM storage division. He's joined by James Amy's, who's the head of networks at Advanced the service provider. Guys, Welcome to the Cube. Good to see again. >> Great. Thanks for having us loved being on the cute. >> So we love having you So, James, let's start with you. Tell us a little bit about advanced to want to dig into some of the networking trends. We're hearing a lot about it here. It's just go live. >> Thanks. So Advanced are a managed service provider software company based in the UK, one of the largest software companies in the UK, providing interim solutions for lots of different market verticals, including healthcare, local government, regional government, national infrastructure projects we get involved with. As well as charity sector, legal sector. A lot of education work we do, so it's real diverse portfolio of products we offer. I'm with the managed services piece. We also offer complete IT outsourcing. So this is desktop support, telephony support, printer support all the way back into integration with public cloud platforms and private cloud platforms, the majority of which is our own >> So so Eric advanced are both a customer and a partner, right? Right, right. And so you love versus stack, these guys I presume a versus stack customers as well. >> Versus stack and customer in the Versace tack, as you know, integrates Cisco, UCS, Cisco Networking Infrastructure, IBM storage of all types entry products up into the fastest all flash raise with our software spectrum, virtualized spectrum, Accelerate Family and James's company is using versus tax is part of their infrastructure, which they then offer, as you know, to a service toe and uses. James just described. >> So let's talk about some of the big trends that you guys are seeing and how you're both responding to customers and you're responding to your customer. So we're seeing two hearing today. Lot about multi cloud. We've been hearing that for a while the network is flattening your network expert love to get your your thoughts on that. Security, obviously, is a huge topic. End end management, another big topic, something that IBM is focused on. So so James, what 1 of the big mega trends that you're seeing that a driving your business decisions and your customer's activity? One >> of the big changes we're seeing is a change from large scare enterprise scale deployments off a particular type of technology on customers are now choosing because they're informed the best fit for a particular application or particular service on that may be coming to a service provider like ourselves to offer our services products to them. Uh, or they're looking for us to roam in infrastructure service for them or integrate with a public cloud offering. So the competition of the public cloud for service providers is key. Andi, I think people were looking around a few years ago thinking, How do we compete to this well, with partnerships that we have an IBM and Cisco gives us a very compelling competitive offering, but we can turn around and say, Well, we could give you a like for like, but we can give you slightly better service because we could give you guaranteed little give you guaranteed price point on. And this is all backed with key vendor certified designs. So we're not talking about going out on developing a solution that takes maybe eighteen months to take to market. This is understanding a requirement for a quick, you know, Q and A with a customer. Our line that, too, a reference architecture that we can literally just pick up off the shelf, deploy into our data centers using the standard building brought that we use across the business so Nexus nine k seven k's or our standard bread and butter inside the data center environment. As Eric pointed out, Cisco UCS is our our key Intel computer platform that we used on DH. The store wise IBM product has been a real true success story for us. So we started off being a a mixed then the house where we would align storage requirement based with what we could find in the market. That was, that was a good fit for the store. Waste products is basically just allowed us to standardize on the speed of deployment is one of the key things. So we started out with a very lengthy lead time tio service ready, which is when we start charging for revenue on If we want a ninety day build, Well, we've got a lot of special service time, A lot of engineering time getting that ready Teo, Teo and take to the customer and then we turn it on. We can start seeing revenue from that platform with versus Stack. This enabled us to accelerate how quickly we can turn that on. And we've seen that drop, too. They're literally days through standardisation elements of automation as well. Many of our environments are bespoke because we have such a wide arrange off different types of customers with different needs, but it allows us to take those standing building blocks, align them to their needs and deliver that service. >> James James, we found the peas are often in the middle of those discussions that customers are having on multi clouds. You talked a lot about the services you build. Are they also coming to you? If if you tie into the public Cloud services or yes, maybe you can help expand a little bit on how that worked. Five years ago, it was the public loves are all going to kill the man and service providers, and what we see is customers can sort out half of what's going on. They've got to be able to turn two partners like you to be able to figure this out. >> Yeah, that's a fantastic question. I think three years ago we'd be talking to our customers and they were I am going to this public cloud or I am going to build this infrastructure. Whereas now they're making more informed select decisions based on what's right. The drive to the hosted office and voice platforms offered by Microsoft.  There's a big drive and many of our ITO customers are going in that direction, but it's how we integrate that with their legacy applications. Some of the ERP solutions that some of our customers use have had millions of pounds of investment into them, and that's not something they can just turn off and walk away  from over night. So it's how we're integrating that. We're doing that at the network level, so it's how we're pairing with different service providers, bringing that in integrating that I'm offering it to them as a solution on what we try to, we try to try and position ourselves is really it's the same experience. Regardless of where we're placing IT consumptional workload, it doesn't matter whether it's inside our data centers, whether we're talking one of the public cloud platforms or even on premise. So we have quite a few customers that still have significant presence on premise, because that's right for their business, depending on what they're doing, especially some of the research scientists. >> So you've got to deliver flexibility in your architecture, and you talk a lot about software to find you guys made it big. You move to software to find, you know, a couple years ago, actually, maybe discuss how that fits in to how you're servicing advanced another client? >> Sure. So you know, IBM Storage has embraced multi cloud for several years. No, our solutions. While, of course, they work with IBM, Cloud and IBM cloud private work with Amazon. They work with Azure Google Cloud and in fact, some are products. For example, the versus Stack not only is advanced using it, but we've got pry forty or fifty public, small, medium sized cloud providers that our public references for the versus stack and spectrum protect Now, which is our backup product number one in the Enterprise. Back up space expect from detectives Got at least three hundred cloud providers. Medium, small and big. Who offered the engine underneath for their backup is a service is spectrum protect, so we make sure that weather PR transparent cloud tearing our cyber resiliency technology. What we doing? Backup archive object storage works with essentially all cloud providers. That way, someone like James A. CSP MSP can leverage our products. And we, like I said, we have tons of public records around versus Stack for that, but so can an enterprise. And in fact, I saw survey recently, and it was done in Europe and in North America that when you look at a roughly two billion US size revenue and up the average company of that sizing up, we'll use five different public cloud riders at one time. Where that it be due to legal reasons whether that be procurement. You know, the Web is really the Internet, and go Cloud is really just It's been around for twenty some years. So in bigger accounts, guess what is now involved procurement. Well, we love that you did that deal with IBM club, but you are going to get a competitive quote now from Amazon and Microsoft, right? So that's driven it legal's driven it. Certain countries, right? The data needs to stay in that country, even if your cloud if eyeing it, it's so to speak. So the clap water doesn't have a data center there. Guess what another geographer used different. And then you, of course, still have some large entities that still allow regional buying pattern so they'll have three or four different cloud providers that air quote certified by corporate. And then you could use whichever one you want, So we make sure that we could take advantage of that. Wade and IBM We ride the wave. We don't fight the way. >> So you've got in that situation. You these multi cloud you got different AP eyes, you get different frameworks. How d'you abstract all that complexity you got, Francisco coming at it From a networking standpoint, I b m. Now with Red Hat is good. Be a big player in that that world. VM where What do you guys do? James, in terms of of simplifying all that multi cloud complexity >> for people. I think some of it is actually demystifying on DH. It's engaging with our partners to understand what the proposition is on, how we can develop that on a line that to learn their own business but more importantly, to the needs of our customers. We've got some really, really talented technicians worked within within advance, and we've got a number of different forums that allow them to feed back their ideas. We've got thie alignment between those partners on DH, some of those communities, so that we can have an open discussion and drive. Some of that thinking forward about ultimately see engaging with customers so the customers feedback is key on how we shape and deliver only in service to them, but also to the service to other customers. We have a number of customers that are very similar, but they may work in different spaces, some somewhere even competitive. So we have to tread that line very safe, very carefully and safely. But it is. It's a good one to one relationship between the client service managers technical so that the technicians we have inside business having that complete three sixty. Communication is key. That's that's that's really the bottom takes its creation >> came like youto dig into security for us a little bit. You know, I think we surpassed a couple of years ago. I'm not going to go to the cloud to it because it's not secure to Oh, I understand it's time for me to least reevaluate my security and most likely, no manage service fighters. Public clouds are probably more secure than what I had in my data center, but if I've got multiple environment, there's a lot of complexity there. So how do you traverse that? Make sure that you've got a comprehensive security practice, not just all these point solutions for security all over the place. >> Yeah, so that's that comes onto visibility. So its visibility understanding where all the control points are within a given infrastructure on how the landscape looks. So we were working quite closely with a number actually Key Cisco and IBM partners, as well as IBM and Cisco themselves directly to have a comprehensive offering that allows us to position to our customers. You used to once upon a time you had one guy, right? So we need It is from good security on your Internet. Facing viable For now, you may have a ten. Twenty, thirty of those. We need tohave consistent policies across those. We need to understand how they're performing, but also potentially, if there's any attack, attack vector on one of them. How that how someone is trying to looking to compromise that so centralized intelligence on That's where we start to look at my eye operations to gather all that information. The long gone are the days where you have twenty people sharing a room just reading streams. Those twenty people now need thio. See reams and reams of information instantly. Something needs to be called up to them. They could make a decision quickly on Active planet on DH. That's really where we were. We're We're positioning ourselves in the market to differentiate. I'm working with key partners. We have >> to do that. >> Eric, talk about your announcement cadence. That idea has a big show. Think coming up in a couple weeks. Cubes going be here? Of course. What can we expect from from you guys? >> So we're actually gonna announce on the fifth before things we want to drive end users and our business partners to storage Campus, which is one of the largest campuses at IBM, think we'll have over fifteen pedestals of demo and actually multiple demos because we have such a broad portfolio, from the all flash arrays to our versus stack offering to a whole set of modern data protection management control for storage, which manages in control storage, that's not ours, right? Our competitors storage as well. And, of course, our software to find story. So we're going to do a big announcement. The focus of that will be around our storage solutions. These air solutions blueprints reference architectures is Jane, you mentioned that use our software and our storage systems that allow reseller or end user to configure systems easily. Think of it as the ultimate wrestling recipe for that German chocolate cake. But it's the perfect recipe. It's tried. It's true, it's tested. It's been on the Food Channel twenty seven times and everybody loves it. That's what we do with our our solutions. Blueprints. We'll have some announcements around modern data protection, and obviously a big focus of IBM. Storage is been in the space. So both storage as an Aye aye platform for Aye, aye. Applications are workloads, but also the incorporation of technology into our own storage systems and software. So be having announcements around that on February fifth going into think, which will then be the week after in San Francisco. >> Great. So I'm here and trusted data protection plays into that. Aye, aye. Intelligence Machine Intelligence. And I'm also hearing header of Geneti multiple platforms. Whether it's your storage, you said our competitors now does that also include sort of the clouds Fear without announcing anything. But you guys have you know, you've seen your pictures. That's azure itt's a W a s. I mean, that continues >> so absolutely so. Whether it be what we do from backup in archive, right, let's take the easy one. So we support not only the protocol of IBM clad object storage which we acquired and allows you to have object storage either on premise or in a cloud in stance E ation. Well, we also support the s three protocol. So, for example, our spectrum scale software giant scale out. In fact, the two fastest supercomputers world use spectrum scale over four hundred fifty petabytes running on spectrum scale, and they continue ear to an object store that supports US three. Or it can tear toe IBM clad object stories through that IBM clad object storage customer. That's great using the S three protocol. You, Khun, Tear to that as well. So that's just one example. Same thing we do for cyber resiliency. So from a cyber resents me perspective, we could do things with any cloud vendor oven air cat air gap, right? And so you could do that, eh? With tape. But you could also do that with the clouds. So if your cloud is your backup archive replication repository, then you can always roll back to a known good copy. You don't have to pay the ransom writer. When you clean up the malware, you can roll back to a known good copy, and we provide that across all of the platforms in a number of ways. Our Protect family, our new products say safeguard copy for the main friend that we announced October. So all that allows us to be multi cloud resiliency as well as how do we connect a multi cloud backup archive automated tearing all kinds of clouds, whether the IBM cloud and of course, I'm a shareholder, so I love that. But at the same time, we're realistic. Lots of people use Amazon Google Azar. And like I said, there's thousands of mid two small cloud providers all over the world, and we support them, too. We engage with everyone. >> What about SAS? You know, that's one of the questions we've been trying to squint through and understand is because when you talk about five cloud providers is obviously infrastructures of service. And then there's their service providers like like Advanced. And then there's like a Brazilian SAS companies >> write a lot of data in >> there and a lot of data in there. How should we think about, you know, protecting that data? Securing that data is that sort of up to the SAS vendor, and thou shalt not touch. Or should that be part of the scope of AH, storage company? Well, so what we do >> is we engage with the SAS vendor, so we have a number of different sass coming is, in fact, one of them was on the Cube two years ago with us. They were startup in the cyber security space and all of its delivered over SAS So what they do is, in that case, the use our flash system Roddick line. They get the performance they need to deliver South. They want no bottlenecks because obviously you have to go over the network when you're doing SAS on DH, then also, what they do is data encryption at rest. So when the data is brought in because we have on our flash arrays capability and most of our product line especially the flash systems to have no performance hit on encrypt their decrypt because its hardware embedded, they're able to have the data at rest encrypted for all their customers. That gives them a level of security when it's at rest on their site. At the same time, we've given the right performance. They need tohave soft reserve, so we engage with all we pry have three hundred, four hundred different SAS companies who are the actual software vendor and their deployment model. This software's interest, by the way, we do that as well as I mentioned, over three hundred cloud providers today have a backup is a service and the engine ease their spectrum. Protect or spectrum protect. Plus, but they may call it something else. In fact, we just had a public reference out from Silver String, which is out in the UK, and all they do is cyber resiliency. Backup in archives. That's their service. They have their own product, but then spectrum Protect and Spectrum Check plus is the engine underneath their product. So that's an example. In this case, the backup is a service, which, I would argue is not infrastructure, but more of an application. But then true what you call real application providers like cyber security vendors, we have a vendor who in fact, does something for all of the universities and colleges. United States. They have about eight thousand of them, including the junior colleges, and they run all their bookstores. So when you place an order, all their air NPR, everything they do is from this SAS vendor that's based in there in the Northeast. And they've got, like I said, about a thousand colleges and universities in the U. S. And Canada, and they offer this if you will bookstore as a sass service and the students use it. University uses it. And, of course, the bookstores are designed to, you know, make a little money for the university, and they all use that. So that's another example. And they use are flash systems as well. And then they back up that data internally with spectrum protectors. They obviously it's the financial data as well as the inventory of all of these book stores all over the United States at the collegiate >> level right now. James Way gotta wrap, but just sort of give you the final word. UK specialist, right? So Brexit really doesn't affect you. Is that a fair statement >> will do? Yes. >> How so? >> I think it's too early to tell. No one really knows. I think that's that's what all the debates are about. AJ's trying to understand that on DH for us. We're just watching and observing. >> Staying focused on your customers, obviously. So no predictions as to what's going to happen. I was not from a weeks ago. I got hurt both sides. You know, it's definitely gonna happen. All right, Not happen, but up. Okay, again give you the last word. You know? What's your focus? Over the next twelve eighteen months? >> Eso all our focus is really about visibility, So they they they've touched on that. We're talking about the security for customers understanding whether data is whether exposure point saw that's all Keep keep focusing on DH versus stack on dh thie IBM store wise product underpin all of those offerings that we have on. That will continue to be to be so forward. >> Guys. Great to see you. Thanks so much for coming on the Cube and, uh, our pleasure hosting you. Thanks. Appreciate, Really welcome. All right. Keep it right there, everybody. We'll be back. Day Volante was stew Minutemen from Cisco live in Barcelona.