(upbeat music) >> We're back. This is Dave Vellante of theCUBE, and we're here with Ray Wang, who just wrote a book reminiscent of the famous Tears for Fears song, Everybody Wants to Rule the World: Surviving and Thriving in a World of Digital Giants. Ray, great to see again, man. >> What's going on, man, how are you? >> Oh great, thanks for coming on. You know, it was crazy, been crazy, but it's good to see you face-to-face. >> Ray: This is, we're in the flesh, it's live, we're having conversations, and the information that we're getting is cut right. >> Dave: Yeah, so why did you write this book and how did you find the time? >> Hey, we're in the middle of pandemic. No, I wrote the book because what was happening was digital transformation efforts, they're starting to pop up, but companies weren't always succeeding. And something was happening with digital giants that was very different. They were winning in the marketplace. And never in the form of, if you think about extreme capitalism, if we think about capitalism in general, never in the history of capitalism have we seen growth of large companies. They get large, they fall apart, they don't have anything to build, they can't scale. Their organizations are in shambles. But what happened? If you look at 2017, the combined market cap of the FAANGs and Microsoft was 2 trillion. Today, it is almost 10.2 trillion. It's quintupled. That's never happened. And there's something behind that business model that they put into place that others have copied, from the Airbnbs to the Robloxes to what's going to happen with like a Starlink, and of course, the Robinhoods and you know, Robinhoods and Coinbases of the world. >> And the fundamental premise is all around data, right? Putting data at the core, if you don't do that, you're going to fly blind. >> It is and the secret behind that is the long-term platforms called data-driven digital networks. These platforms take the ability, large memberships, our large devices, they look at that effect. Then they look at figuring out how to actually win on data supremacy. And then of course, they monetize off that data. And that's really the secret behind that is you've got to build that capability and what they do really well is they dis-intermediate customer account control. They take the relationships, aggregate them together. So food delivery app companies are great example of that. You know, small businesses are out there that hundreds and thousands of customers. Today, what happens? Well, they've been aggregated. Millions of customers together into food delivery app. >> Well, I think, you know, this is really interesting what you're saying, because if you think about how we deal with Netflix, we don't call the Netflix sales department or the marketing department of the service, just one interface, the Netflix. So they've been able to put data at their core. Can incumbents do that? How can they do that? >> Incumbents can definitely do that. And it's really about figuring out how to automate that capture. What you really want to do is you start in the cloud, you bring the data together, and you start putting the three A's, analytics, automation, and AI are what you have to be able to put into place. And when you do do that, you now have the ability to go out and figure out how to create that flywheel effect inside those data-driven digital networks. These DDDNS are important. So in Netflix, what are they capturing? They're looking at sentiment, they're looking at context. Like why did you interact with, you know, one title versus another? Did you watch Ted Lasso? Did you switch out of Apple TV to Netflix? Well, I want to know why, right? Did you actually jump into another category? You switched into genres. After 10:00 p.m., what are you watching? Maybe something very different than what you're watching at 2:00 p.m.. How many members are in the home, right? All these questions are being answered and that's the business graph behind all this. >> How much of this is kind of related to the way organizations or companies are organized? In other words, you think about, historically, they would maybe put the process at the core or the, in a bottling plant, the manufacturing facility at the core and the data's all dispersed. Everybody talks about silos. So will AI be the answer to that? Will some new database, Snowflake? Is that the answer? What's the answer to sort of bringing that data together and how do you deal with the organizational inertia? >> Well, the trick to it is really to have a single plane to be able to access that data. I don't care where the data sits, whether it's on premise, whether it's in the cloud, whether it's in the edge, it makes no difference. That's really what you want to be able to do is bring that information together. But the glue is the context. What time was it? What's the weather outside? What location are you in? What's your heart rate? Are you smiling, right? All of those factors come into play. And what we're trying to do is take a user, right? So it could be a customer, a supplier, a partner, or an employee. And how do they interact with an order doc, an invoice, an incident, and then apply the context. And what we're doing is mining that context and information. Now, the more, back to your other point on self service and automation, the more you can actually collect those data points, the more you can capture that context, the more you're able to get to refine that information. >> Context, that's interesting, because if you think about our operational systems, we've contextualized most of them, whether it's sales, marketing, logistics, but we haven't really contextualized our data systems, our data architecture. It's generally run by a technical group. They don't necessarily have the line of business context. You see what HPE is doing today is trying to be inclusive of data on prem. I mentioned Snowflake, they're saying no way. Frank Slootman says we're not going on prem. So that's kind of interesting. So how do you see sort of context evolving with the actually the business line? Not only who has the context actually can, I hate to use the word, but I'm going to, own the data. >> You have to have a data to decisions pathway. That data decisions pathway is you start with all types of data, structured, unstructured, semi-structured, you align it to a business process as an issue, issue to resolution, order to cash, procure to pay, hire to retire. You bring that together, and then you start mining and figuring out what patterns exist. Once you have the patterns, you can then figure out the next best action. And when you get the next best action, you can compete on decisions. And that becomes a very important part. That decision piece, that's going to be automated. And when we think about that, you and I make a decision one per second, how long does it get out of management committee? Could be a week, two weeks, a quarter, a year. It takes forever to get anything out of management committee. But these new systems, if you think about machines, can make decisions a hundred times per second, a thousand times per second. And that's what we're competing against. That asymmetry is the decision velocity. How quickly you can make decisions will be a competitive weapon. >> Is there a dissonance between the fact that you just mentioned, speed, compressing, that sort of time to decision, and the flip side of that coin, quality, security, governance. How do you see squaring that circle? >> Well, that's really why we're going to have to make that, that's the automated, that's the AI piece. Just like we have all types of data, we got to spew up automated ontologies, we got to spit them up, we got to be using, we've got to put them back into play, and then we got to be able to take back into action. And so you want enterprise class capabilities. That's your data quality. That's your security. That's the data governance. That's the ability to actually take that data and understand time series, and actually make sure that the integrity of that data is there. >> What do you think about this sort of notion that increasingly, people are going to be building data products and services that can be monetized? And that's kind of goes back to context, the business lines kind of being responsible for their own data, not having to get permission to add another data source. Do you see that trend? Do you see that decentralization trend? Two-part question. And where do you see HPE fitting into that? >> I see, one, that that trend is definitely going to exist. I'll give you an example. I can actually destroy the top two television manufacturers in the world in less than five years. I could take them out of the business and I'll show you how to do it. So I'm going to make you an offer. $15 per month for the next five years. I'm going to give you a 72 inch, is it 74? 75 inch, 75 inch smart TV, 4k, big TV, right? And it comes with a warranty. And if anything breaks, I'm going to return it to you in 48 hours or less with a brand new one. I don't want your personal information. I'm only going to monitor performance data. I want to know the operations. I want to know which supplier lied to me, which components are working, what features you use. I don't need to know your personal viewing habits, okay? Would you take that deal? >> TV is a service, sure, of course I would. >> 15 bucks and I'm going to make you a better deal. For $25 a month, you get to make an upgrade anytime during that five-year period. What would happen to the two largest TV manufacturers if I did that? >> Yeah, they'd be disrupted. Now, you obviously have a pile of VC money that you're going to do that. Will you ever make money at that model? >> Well, here's why I'll get there and I'll explain. What's going to happen is I lock them out of the market for four to five years. I'm going to take 50 to 60% of the market. Yes, I got to raise $10 billion to figure out how to do that. But that's not really what happens at the end. I become a data company because I have warranty data. I'm going to buy a company that does, you know, insurance like in Asurion. I'm going to get break/fix data from like a Best Buy or a company like that. I'm going to get at safety data from an underwriter's lab. It's a competition for data. And suddenly, I know those habits better than anyone else. I'm going to go do other things more than the TV. I'm not done with the TV. I'm going to do your entire kitchen. For $100 a month, I'll do a mid range. For like $500 a month, I'm going to take your dish washer, your washer, your dryer, your refrigerator, your range. And I'll do like Miele, Gaggenau, right? If you want to go down Viking, Wolf, I'll do it for $450 a month for the next 10 years. By year five, I have better insurance information than the insurance companies from warranty. And I can even make that deal portable. You see where we're going? >> Yeah so each of those are, I see them as data products. So you've got your TV service products, you've got your kitchen products, you've got your maintenance, you know, data products. All those can be monetized. >> And I went from TV manufacturer to underwriter overnight. I'm competing on data, on insurance, and underwriting. And more importantly, here's the green initiative. Here's why someone would give me $10 billion to do it. I now control 50% of all power consumption in North America because I'm also going to do HVAC units, right? And I can actually engineer the green capabilities in there to actually do better power purchase consumption, better monitoring, and of course, smart capabilities in those, in those appliances. And that's how you actually build a model like that. And that's how you can win on a data model. Now, where does HPE fit into that? Their job is to bring that data together at the edge. They bring that together in the middle. Then they have the ability to manage that on a remote basis and actually deliver those services in the cloud so that someone else can consume it. >> All right, so if you, you're hitting on something that some people have have talked about, but it's, I don't think it's widely sort of discussed. And that is, historically, if you're in an industry, you're in that industry's vertical stack, the sales, the marketing, the manufacturing, the R&D. You become an expert in insurance or financial services or whatever, you know, automobile manufacturing or radio and television, et cetera. Obviously, you're seeing the big internet giants, those 10 trillion, you know, some of the market caps, they're using data to traverse industries. We've never seen this before. Amazon in content, you're seeing Apple in finance, others going into the healthcare. So they're technology companies that are able to traverse industries. Never seen this before, and it's because of data. >> And it's the collapsing value chains. Their data value chains are collapsing. Comms, media, entertainment, tech, same business. Whether you sell me a live stream TV, a book, a video game, or some enterprise software, it's the same data value stream on multi-sided networks. And once you understand that, you can see retail, right? Distribution, manufacturing collapsed in the same kind of way. >> So Silicon Valley broadly defined, if I can include, you know, Microsoft and Amazon in there, they seem to have a dual disruption agenda, right? One is on the technology front, disrupting, you know, the traditional enterprise business. The other is they're disrupting industries. How do you see that playing out? >> Well the problem is, they're never going to be able to get into new industries going forward because of the monopoly power that people believe they have, and that's what's going on, but they're going to invest in creating joint venture startups in other industries, as they power the tools to enable other industries to jump and leap frog from where they are. So healthcare, for example, we're going to have AI in monitoring in ways that we never seen before. You can see devices enter healthcare, but you see joint venture partnerships between a big hyperscaler and some of the healthcare providers. >> So HPE transforming into a cloud company as a service, do you see them getting into insurance as you just described in your little digital example? >> No, but I see them powering the folks that are in insurance, right? >> They're not going to compete with their customers maybe the way that Amazon did. >> No, that's actually why you would go to them as opposed to a hyperscale that might compete with you, right? So is Google going to get into the insurance business? Probably not. Would Amazon? Maybe. Is Tesla in the business? Yeah, they're definitely in insurance. >> Yeah, big time, right. So, okay. So tell me more about your book. How's it being received? What's the reaction? What's your next book? >> So the book is doing well. We're really excited. We did a 20 city book tour. We had chances to meet everybody across the board. Clients we couldn't see in a while, partners we didn't see in a while. And that was fun. The reaction is, if you read the book carefully, there are $3 trillion market cap opportunities, $1000 billion unicorns that can be built right there. >> Is, do you have a copy for me that's signed? (audience laughing) >> Ray: Sorry (coughs) I'm choking on my makeup. I can get one actually, do you want one? >> Dave: I do, I want, I want one. >> Can someone bring my book bag? I actually have one, I can sign it right here. >> Dave: Yeah, you know what? If we have a book, I'd love to hold it. >> Ray: Do you have any here as well? >> So it's obviously you know, Everybody Wants to Rule the World: Surviving and Thriving in a world of Digital Giants, available, you know, wherever you buy books. >> Yeah, so, oh, are we still going? >> Dave: Yeah, yeah, we're going. >> Okay. >> Dave: What's the next book? >> Next book? Well, it's about disrupting those digital giants and it's going to happen in the metaverse economy. If we think about where the metaverse is, not just the hardware platforms, not just the engines, not just what's going on with the platforms around defy decentralization and the content producers, we see those as four different parts today. What we're going to actually see is a whole comp, it's a confluence of events that's going to happen where we actually bring in the metaverse economy and the stuff that Neal Stephenson was writing about ages ago in Snow Crash is going to come out real. >> So, okay. So you're laying out a scenario that the big guys, the disruptors, could get disrupted. It sounds like crypto is possibly a force in that disruption. >> Ray: Decentralized currencies, crypto plays a role, but it's the value exchange mechanisms in an Algorand, in an Ether, right, in a Cardano, that actually enables that to happen because the value exchange in the smart contracts power that capability, and what we're actually seeing is the reinvention of the internet. So you think, see things like SIOM pop-up, which actually is creating the new set of the internet standards, and when those things come together, what we're actually going to move from is the seller is completely transparent, the buyer's completely anonymous and it's in a trust framework that actually allows you to do that. >> Well, you think about those protocols, the internet protocols that were invented whenever, 30 years ago, maybe more, TCP/IP, wow. I mean, okay. And they've been co-opted by the internet giants. It's the crypto guys, some of the guys you've mentioned that are actually innovating and putting, putting down new innovation really and have been well-funded to do so. >> I mean, I'll give you another example of how this could happen. About four years ago, five years ago, I wanted to buy Air Canada's mileage program, $400 million, 10 million users, 40 bucks a user. What do I want them in a mileage program? Well think about it. It's funded, a penny per mile. It's redeemed at 1.6 cents a mile. It's 2 cents if you buy magazines, 2 1/2 cents if you want, you know, electronics, jewelry, or sporting equipment. You don't lose money on these. CFOs hate them, they're just like (groans) liability on the books, but they mortgage the crap out of them in the middle of an ish problem and banks pay millions of dollars a year pour those mileage points. But I don't want it for the 10 million flyers in Canada. What I really want is the access to 762 million people in Star Alliance. What would happen if I turned that airline mileage program into cryptocurrency? One, I would be the world's largest cryptocurrency on day one. What would happen on day two? I'd be the world's largest ad network. Cookie apocalypse, go away. We don't need that anymore. And more importantly, on day three, what would I do? My ESG here? 2.2 billion people are unbanked in the world. All you need is a mobile device and a connection, now you have a currency without any government regulation around, you know, crayon banking, intermediaries, a whole bunch of people like taking cuts, loansharking, that all goes away. You suddenly have people that are now banked and you've unbanked, you've banked the unbanked. And that creates a whole very different environment. >> Not a lot of people thinking about how the big giants get disintermediated. Get the book, look into it, big ideas. Ray Wang, great to see you, man. >> Ray: Hey man, thanks a lot. >> Hey, thank you. All right and thank you for watching. Keep it right there for more great content from HPE's big GreenLake announcements. Be right back. (bright music)

(upbeat music) >> Welcome back to HPE Discover 2021, theCUBE's virtual coverage, continuous coverage of HPE's Annual Customer Event. My name is Dave Vellante, and we're going to dive into the intersection of high-performance computing, data and AI with Doctor Eng Lim Goh, who's a Senior Vice President and CTO for AI at Hewlett Packard Enterprise. Doctor Goh, great to see you again. Welcome back to theCUBE. >> Hello, Dave, great to talk to you again. >> You might remember last year we talked a lot about Swarm intelligence and how AI is evolving. Of course, you hosted the Day 2 Keynotes here at Discover. And you talked about thriving in the age of insights, and how to craft a data-centric strategy. And you addressed some of the biggest problems, I think organizations face with data. That's, you've got a, data is plentiful, but insights, they're harder to come by. >> Yeah. >> And you really dug into some great examples in retail, banking, in medicine, healthcare and media. But stepping back a little bit we zoomed out on Discover '21. What do you make of the events so far and some of your big takeaways? >> Hmm, well, we started with the insightful question, right, yeah? Data is everywhere then, but we lack the insight. That's also part of the reason why, that's a main reason why Antonio on day one focused and talked about the fact that we are in the now in the age of insight, right? And how to try thrive in that age, in this new age? What I then did on a Day 2 Keynote following Antonio is to talk about the challenges that we need to overcome in order to thrive in this new age. >> So, maybe we could talk a little bit about some of the things that you took away in terms of, I'm specifically interested in some of the barriers to achieving insights. You know customers are drowning in data. What do you hear from customers? What were your takeaway from some of the ones you talked about today? >> Oh, very pertinent question, Dave. You know the two challenges I spoke about, that we need to overcome in order to thrive in this new age. The first one is the current challenge. And that current challenge is, you know, stated is now barriers to insight, when we are awash with data. So that's a statement on how do you overcome those barriers? What are the barriers to insight when we are awash in data? In the Day 2 Keynote, I spoke about three main things. Three main areas that we receive from customers. The first one, the first barrier is in many, with many of our customers, data is siloed, all right. You know, like in a big corporation, you've got data siloed by sales, finance, engineering, manufacturing and so on supply chain and so on. And there's a major effort ongoing in many corporations to build a federation layer above all those silos so that when you build applications above, they can be more intelligent. They can have access to all the different silos of data to get better intelligence and more intelligent applications built. So that was the first barrier we spoke about, you know? Barriers to insight when we are awash with data. The second barrier is that we see amongst our customers is that data is raw and disperse when they are stored. And you know, it's tough to get at, to tough to get a value out of them, right? And in that case, I use the example of, you know, the May 6, 2010 event where the stock market dropped a trillion dollars in terms of minutes. We all know those who are financially attuned with know about this incident but that this is not the only incident. There are many of them out there. And for that particular May 6 event, you know, it took a long time to get insight. Months, yeah, before we, for months we had no insight as to what happened. Why it happened? Right, and there were many other incidences like this and the regulators were looking for that one rule that could mitigate many of these incidences. One of our customers decided to take the hard road they go with the tough data, right? Because data is raw and dispersed. So they went into all the different feeds of financial transaction information, took the tough, you know, took a tough road. And analyze that data took a long time to assemble. And they discovered that there was caught stuffing, right? That people were sending a lot of trades in and then canceling them almost immediately. You have to manipulate the market. And why didn't we see it immediately? Well, the reason is the process reports that everybody sees, the rule in there that says, all trades less than a hundred shares don't need to report in there. And so what people did was sending a lot of less than a hundred shares trades to fly under the radar to do this manipulation. So here is the second barrier, right? Data could be raw and dispersed. Sometimes it's just have to take the hard road and to get insight. And this is one great example. And then the last barrier has to do with sometimes when you start a project to get insight, to get answers and insight, you realize that all the data's around you, but you don't seem to find the right ones to get what you need. You don't seem to get the right ones, yeah? Here we have three quick examples of customers. One was a great example, right? Where they were trying to build a language translator or machine language translator between two languages, right? By not do that, they need to get hundreds of millions of word pairs. You know of one language compare with the corresponding other. Hundreds of millions of them. They say, well, I'm going to get all these word pairs. Someone creative thought of a willing source and a huge, it was a United Nations. You see? So sometimes you think you don't have the right data with you, but there might be another source and a willing one that could give you that data, right? The second one has to do with, there was the sometimes you may just have to generate that data. Interesting one, we had an autonomous car customer that collects all these data from their their cars, right? Massive amounts of data, lots of sensors, collect lots of data. And, you know, but sometimes they don't have the data they need even after collection. For example, they may have collected the data with a car in fine weather and collected the car driving on this highway in rain and also in snow. But never had the opportunity to collect the car in hill because that's a rare occurrence. So instead of waiting for a time where the car can drive in hill, they build a simulation by having the car collected in snow and simulated him. So these are some of the examples where we have customers working to overcome barriers, right? You have barriers that is associated. In fact, that data silo, they federated it. Virus associated with data, that's tough to get at. They just took the hard road, right? And sometimes thirdly, you just have to be creative to get the right data you need. >> Wow! I tell you, I have about a hundred questions based on what you just said, you know? (Dave chuckles) And as a great example, the Flash Crash. In fact, Michael Lewis, wrote about this in his book, the Flash Boys. And essentially, right, it was high frequency traders trying to front run the market and sending into small block trades (Dave chuckles) trying to get sort of front ended. So that's, and they chalked it up to a glitch. Like you said, for months, nobody really knew what it was. So technology got us into this problem. (Dave chuckles) I guess my question is can technology help us get out of the problem? And that maybe is where AI fits in? >> Yes, yes. In fact, a lot of analytics work went in to go back to the raw data that is highly dispersed from different sources, right? Assembled them to see if you can find a material trend, right? You can see lots of trends, right? Like, no, we, if humans look at things that we tend to see patterns in Clouds, right? So sometimes you need to apply statistical analysis math to be sure that what the model is seeing is real, right? And that required, well, that's one area. The second area is you know, when this, there are times when you just need to go through that tough approach to find the answer. Now, the issue comes to mind now is that humans put in the rules to decide what goes into a report that everybody sees. Now, in this case, before the change in the rules, right? But by the way, after the discovery, the authorities changed the rules and all shares, all trades of different any sizes it has to be reported. >> Right. >> Right, yeah? But the rule was applied, you know, I say earlier that shares under a hundred, trades under a hundred shares need not be reported. So, sometimes you just have to understand that reports were decided by humans and for understandable reasons. I mean, they probably didn't wanted a various reasons not to put everything in there. So that people could still read it in a reasonable amount of time. But we need to understand that rules were being put in by humans for the reports we read. And as such, there are times we just need to go back to the raw data. >> I want to ask you... >> Oh, it could be, that it's going to be tough, yeah. >> Yeah, I want to ask you a question about AI as obviously it's in your title and it's something you know a lot about but. And I'm going to make a statement, you tell me if it's on point or off point. So seems that most of the AI going on in the enterprise is modeling data science applied to, you know, troves of data. But there's also a lot of AI going on in consumer. Whether it's, you know, fingerprint technology or facial recognition or natural language processing. Well, two part question will the consumer market, as it has so often in the enterprise sort of inform us is sort of first part. And then, there'll be a shift from sort of modeling if you will to more, you mentioned the autonomous vehicles, more AI inferencing in real time, especially with the Edge. Could you help us understand that better? >> Yeah, this is a great question, right? There are three stages to just simplify. I mean, you know, it's probably more sophisticated than that. But let's just simplify that three stages, right? To building an AI system that ultimately can predict, make a prediction, right? Or to assist you in decision-making. I have an outcome. So you start with the data, massive amounts of data that you have to decide what to feed the machine with. So you feed the machine with this massive chunk of data, and the machine starts to evolve a model based on all the data it's seeing. It starts to evolve, right? To a point that using a test set of data that you have separately kept aside that you know the answer for. Then you test the model, you know? After you've trained it with all that data to see whether its prediction accuracy is high enough. And once you are satisfied with it, you then deploy the model to make the decision. And that's the inference, right? So a lot of times, depending on what we are focusing on, we in data science are, are we working hard on assembling the right data to feed the machine with? That's the data preparation organization work. And then after which you build your models you have to pick the right models for the decisions and prediction you need to make. You pick the right models. And then you start feeding the data with it. Sometimes you pick one model and a prediction isn't that robust. It is good, but then it is not consistent, right? Now what you do is you try another model. So sometimes it gets keep trying different models until you get the right kind, yeah? That gives you a good robust decision-making and prediction. Now, after which, if it's tested well, QA, you will then take that model and deploy it at the Edge. Yeah, and then at the Edge is essentially just looking at new data, applying it to the model that you have trained. And then that model will give you a prediction or a decision, right? So it is these three stages, yeah. But more and more, your question reminds me that more and more people are thinking as the Edge become more and more powerful. Can you also do learning at the Edge? >> Right. >> That's the reason why we spoke about Swarm Learning the last time. Learning at the Edge as a Swarm, right? Because maybe individually, they may not have enough power to do so. But as a Swarm, they may. >> Is that learning from the Edge or learning at the Edge? In other words, is that... >> Yes. >> Yeah. You do understand my question. >> Yes. >> Yeah. (Dave chuckles) >> That's a great question. That's a great question, right? So the quick answer is learning at the Edge, right? And also from the Edge, but the main goal, right? The goal is to learn at the Edge so that you don't have to move the data that Edge sees first back to the Cloud or the Call to do the learning. Because that would be the reason, one of the main reasons why you want to learn at the Edge. Right? So that you don't need to have to send all that data back and assemble it back from all the different Edge devices. Assemble it back to the Cloud Site to do the learning, right? Some on you can learn it and keep the data at the Edge and learn at that point, yeah. >> And then maybe only selectively send. >> Yeah. >> The autonomous vehicle, example you gave is great. 'Cause maybe they're, you know, there may be only persisting. They're not persisting data that is an inclement weather, or when a deer runs across the front. And then maybe they do that and then they send that smaller data setback and maybe that's where it's modeling done but the rest can be done at the Edge. It's a new world that's coming through. Let me ask you a question. Is there a limit to what data should be collected and how it should be collected? >> That's a great question again, yeah. Well, today full of these insightful questions. (Dr. Eng chuckles) That actually touches on the the second challenge, right? How do we, in order to thrive in this new age of insight? The second challenge is our future challenge, right? What do we do for our future? And in there is the statement we make is we have to focus on collecting data strategically for the future of our enterprise. And within that, I talked about what to collect, right? When to organize it when you collect? And then where will your data be going forward that you are collecting from? So what, when, and where? For what data to collect? That was the question you asked, it's a question that different industries have to ask themselves because it will vary, right? Let me give you the, you use the autonomous car example. Let me use that. And we do have this customer collecting massive amounts of data. You know, we're talking about 10 petabytes a day from a fleet of their cars. And these are not production autonomous cars, right? These are training autonomous cars, collecting data so they can train and eventually deploy commercial cars, right? Also this data collection cars, they collect 10, as a fleet of them collect 10 petabytes a day. And then when they came to us, building a storage system you know, to store all of that data, they realized they don't want to afford to store all of it. Now here comes the dilemma, right? What should I, after I spent so much effort building all this cars and sensors and collecting data, I've now decide what to delete. That's a dilemma, right? Now in working with them on this process of trimming down what they collected, you know, I'm constantly reminded of the 60s and 70s, right? To remind myself 60s and 70s, we called a large part of our DNA, junk DNA. >> Yeah. (Dave chuckles) >> Ah! Today, we realized that a large part of that what we call junk has function as valuable function. They are not genes but they regulate the function of genes. You know? So what's junk in yesterday could be valuable today. Or what's junk today could be valuable tomorrow, right? So, there's this tension going on, right? Between you deciding not wanting to afford to store everything that you can get your hands on. But on the other hand, you worry, you ignore the wrong ones, right? You can see this tension in our customers, right? And then it depends on industry here, right? In healthcare they say, I have no choice. I want it all, right? Oh, one very insightful point brought up by one healthcare provider that really touched me was you know, we don't only care. Of course we care a lot. We care a lot about the people we are caring for, right? But who also care for the people we are not caring for? How do we find them? >> Uh-huh. >> Right, and that definitely, they did not just need to collect data that they have with from their patients. They also need to reach out, right? To outside data so that they can figure out who they are not caring for, right? So they want it all. So I asked them, so what do you do with funding if you want it all? They say they have no choice but to figure out a way to fund it and perhaps monetization of what they have now is the way to come around and fund that. Of course, they also come back to us rightfully, that you know we have to then work out a way to help them build a system, you know? So that's healthcare, right? And if you go to other industries like banking, they say they can afford to keep them all. >> Yeah. >> But they are regulated, seemed like healthcare, they are regulated as to privacy and such like. So many examples different industries having different needs but different approaches to what they collect. But there is this constant tension between you perhaps deciding not wanting to fund all of that, all that you can install, right? But on the other hand, you know if you kind of don't want to afford it and decide not to start some. Maybe those some become highly valuable in the future, right? (Dr. Eng chuckles) You worry. >> Well, we can make some assumptions about the future. Can't we? I mean, we know there's going to be a lot more data than we've ever seen before. We know that. We know, well, not withstanding supply constraints and things like NAND. We know the prices of storage is going to continue to decline. We also know and not a lot of people are really talking about this, but the processing power, but the says, Moore's law is dead. Okay, it's waning, but the processing power when you combine the CPUs and NPUs, and GPUs and accelerators and so forth actually is increasing. And so when you think about these use cases at the Edge you're going to have much more processing power. You're going to have cheaper storage and it's going to be less expensive processing. And so as an AI practitioner, what can you do with that? >> Yeah, it's a highly, again, another insightful question that we touched on our Keynote. And that goes up to the why, uh, to the where? Where will your data be? Right? We have one estimate that says that by next year there will be 55 billion connected devices out there, right? 55 billion, right? What's the population of the world? Well, of the other 10 billion? But this thing is 55 billion. (Dave chuckles) Right? And many of them, most of them can collect data. So what do you do? Right? So the amount of data that's going to come in, it's going to way exceed, right? Drop in storage costs are increasing compute power. >> Right. >> Right. So what's the answer, right? So the answer must be knowing that we don't, and even a drop in price and increase in bandwidth, it will overwhelm the, 5G, it will overwhelm 5G, right? Given the amount of 55 billion of them collecting. So the answer must be that there needs to be a balance between you needing to bring all of that data from the 55 billion devices of the data back to a central, as a bunch of central cost. Because you may not be able to afford to do that. Firstly bandwidth, even with 5G and as the, when you'll still be too expensive given the number of devices out there. You know given storage costs dropping is still be too expensive to try and install them all. So the answer must be to start, at least to mitigate from to, some leave most a lot of the data out there, right? And only send back the pertinent ones, as you said before. But then if you did that then how are we going to do machine learning at the Core and the Cloud Site, if you don't have all the data? You want rich data to train with, right? Sometimes you want to mix up the positive type data and the negative type data. So you can train the machine in a more balanced way. So the answer must be eventually, right? As we move forward with these huge number of devices all at the Edge to do machine learning at the Edge. Today we don't even have power, right? The Edge typically is characterized by a lower energy capability and therefore lower compute power. But soon, you know? Even with low energy, they can do more with compute power improving in energy efficiency, right? So learning at the Edge, today we do inference at the Edge. So we data, model, deploy and you do inference there is. That's what we do today. But more and more, I believe given a massive amount of data at the Edge, you have to start doing machine learning at the Edge. And when you don't have enough power then you aggregate multiple devices, compute power into a Swarm and learn as a Swarm, yeah. >> Oh, interesting. So now of course, if I were sitting and fly on the wall and the HPE board meeting I said, okay, HPE is a leading provider of compute. How do you take advantage of that? I mean, we're going, I know it's future but you must be thinking about that and participating in those markets. I know today you are, you have, you know, Edge line and other products. But there's, it seems to me that it's not the general purpose that we've known in the past. It's a new type of specialized computing. How are you thinking about participating in that opportunity for the customers? >> Hmm, the wall will have to have a balance, right? Where today the default, well, the more common mode is to collect the data from the Edge and train at some centralized location or number of centralized location. Going forward, given the proliferation of the Edge devices, we'll need a balance, we need both. We need capability at the Cloud Site, right? And it has to be hybrid. And then we need capability on the Edge side that we need to build systems that on one hand is an Edge adapter, right? Meaning they environmentally adapted because the Edge differently are on it, a lot of times on the outside. They need to be packaging adapted and also power adapted, right? Because typically many of these devices are battery powered. Right? So you have to build systems that adapts to it. But at the same time, they must not be custom. That's my belief. It must be using standard processes and standard operating system so that they can run a rich set of applications. So yes, that's also the insight for that Antonio announced in 2018. For the next four years from 2018, right? $4 billion invested to strengthen our Edge portfolio. >> Uh-huh. >> Edge product lines. >> Right. >> Uh-huh, Edge solutions. >> I could, Doctor Goh, I could go on for hours with you. You're just such a great guest. Let's close. What are you most excited about in the future of, certainly HPE, but the industry in general? >> Yeah, I think the excitement is the customers, right? The diversity of customers and the diversity in the way they have approached different problems of data strategy. So the excitement is around data strategy, right? Just like, you know, the statement made for us was so was profound, right? And Antonio said, we are in the age of insight powered by data. That's the first line, right? The line that comes after that is as such we are becoming more and more data centric with data that currency. Now the next step is even more profound. That is, you know, we are going as far as saying that, you know, data should not be treated as cost anymore. No, right? But instead as an investment in a new asset class called data with value on our balance sheet. This is a step change, right? Right, in thinking that is going to change the way we look at data, the way we value it. So that's a statement. (Dr. Eng chuckles) This is the exciting thing, because for me a CTO of AI, right? A machine is only as intelligent as the data you feed it with. Data is a source of the machine learning to be intelligent. Right? (Dr. Eng chuckles) So, that's why when the people start to value data, right? And say that it is an investment when we collect it it is very positive for AI. Because an AI system gets intelligent, get more intelligence because it has huge amounts of data and a diversity of data. >> Yeah. >> So it'd be great, if the community values data. >> Well, you certainly see it in the valuations of many companies these days. And I think increasingly you see it on the income statement. You know data products and people monetizing data services. And yeah, maybe eventually you'll see it in the balance sheet. I know Doug Laney, when he was at Gartner Group, wrote a book about this and a lot of people are thinking about it. That's a big change, isn't it? >> Yeah, yeah. >> Dr. Goh... (Dave chuckles) >> The question is the process and methods in valuation. Right? >> Yeah, right. >> But I believe we will get there. We need to get started. And then we'll get there. I believe, yeah. >> Doctor Goh, it's always my pleasure. >> And then the AI will benefit greatly from it. >> Oh, yeah, no doubt. People will better understand how to align, you know some of these technology investments. Dr. Goh, great to see you again. Thanks so much for coming back in theCUBE. It's been a real pleasure. >> Yes, a system is only as smart as the data you feed it with. (Dave chuckles) (Dr. Eng laughs) >> Excellent. We'll leave it there. Thank you for spending some time with us and keep it right there for more great interviews from HPE Discover 21. This is Dave Vellante for theCUBE, the leader in Enterprise Tech Coverage. We'll be right back. (upbeat music)

>> Announcer: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. (smooth music) >> Hi everybody, this is Dave Vellante, and welcome. As you know, I've been interviewing a number of CEOs throughout the COVID-19 pandemic. I'm really excited to have Alan Trefler here. He's the founder and CEO of Pegasystems. Alan, thanks for being part of the program. >> Oh, thanks for having me, Dave. >> So let's get into it. I mean when you were 27 years old, 37 years ago, 1983, you started Pega. Now you've seen a lot of cycles. Never seen anything like this, I know, but certainly there was the '87 crash. You saw, you know, the banking crisis in the late '80s, early '90s, the dotcom bubble, 2008, 2009, and now this. I want to ask you sort of how have you responded to crises in the past? I mean the hallmark of your company, the book you wrote is being able to manage through change. How did you manage through this one? What was your first move? >> Well, you know, what I'll tell you is from the inception we've always been a scrappy company. You know, we never took any venture capital. We bootstrapped this firm. I went public in the late '90s, and you know, we've now got a firm that will do over a billion in revenue this year and has 5,400 staff. So we've built, I think, a cohesive team around a set of principles that really have matched the way the software and technology have evolved, but has still taken a pretty radically different approach to how it should be used by businesses and business users. >> Well, so talk about some of the big waves that you've been riding over the years. I mean you set out to help business people really communicate better with IT. You laid out in your book some of the challenges there, and as you've pointed out many, many times, it gets more complex, people try to understand the customer better with terminology like customer relationship management. People don't necessarily want a relationship, right? Talk about some of the observations that you've made around customer behavior and channels, and how you've approached things a little bit differently as an entrepreneur. >> So I think organizations, when they think about how they want to engage with their customers, typically make a couple of serious mistakes. One is they say they want to do a good job for their clients, but especially if they're a big company they then devolve into actually doing the work in their channel. I mean they have a mobile group that builds the mobile app, which is different than the group that handles the call center, which is different than the group that handles the website, and all this business logic, it's baked into that. And that just destroys their ability to implement change rapidly, which particularly in this COVID era is so important for the organizations that are going to be successful. Now on the other side sometimes they get overly focused on their backend system. They're worrying about how they're going to put in the perfect ERP system or accounting system, that that will somehow support customer engagement better, but frankly it never does. We think you need to think about your business from the center out. How do I apply AI to what I want to do for and with my client? And then how do I apply workflow and work management capability to ensure that those decisions are done optimally and effectively? That's what Pega worked on from our inception, and now as we've gone into our fifth complete generation of software I think we've really crossed some boundaries that are pretty remarkable. >> So I mean, well what you've built is actually quite amazing. Since you've written your book the stock's exploded. I don't know if that's cause and effect, but nonetheless some of the things that you talk about again in the book, you talk about, you know, people looking at data the wrong way. What's impressed me is you've always taken a systems view. You're not trying to optimize, to your point, on one little either technology or maybe optimizing on cost. If you look at the whole system and think about outcomes, that is going to, you know, yield ultimately better businesses. And so I want to ask you-- >> Well, thinking about an end to end way of understanding how the technology should be applied is exactly what we've always believed, but the key is to be able to do this incrementally, iteratively, not monolithically, because no businesses can afford to rip out things. So you need to be able to do this what we call, say, "One microjourney at a time." One set of things that are good for a customer. In today's era it might send, as we do with many of our clients now under stress, we help them help their customers around things like loan forbearing. How do you give people a payment plan because they just don't have the money to pay their loan today. >> Right. >> And how do you do that while you keep them as customers, as opposed to, well, situations that could be far worse. >> Let's talk a little bit about machine intelligence. When you started Pega it was the same year I started in the industry at IDC, and AI was all the rage, and then, you know, it just never happened. You had the very long AI winter, but now it's, you know, starting to come back. You're seeing, you know, obviously there are certain technical capabilities, the amount of data, the processing power, et cetera, and the cost are much more aligned. You're seeing trends like AI. You're seeing things like RPA, you know, which you've brought in to your platform. Talk a little bit about that sort of incremental change that you're adding in to your platform and how you go about doing that. And I want to ask you about some of your thoughts on those trends. >> Certainly. Well, AI has been, from our point of view, a really big thing for the last decade. There was a set of false starts, and we actually saw that they were false starts, so we didn't get sucked into it, but come around 2010 we made an enormous push to bring machine learning and decisioning into our work management platform, and it's in there beautifully and it's doing amazing things. You know, I just saw one of our customers, Commonwealth Bank in Australia, their CEO in his quarterly earnings announcement led by talking about what the Pegasystem, what our system, which they call a customer engagement engine, is doing for them. During the fires that earlier this year were ravaging Australia, they used that to send personalized, not just messages, but also relief to people whose homes were burned out, so they weren't going to be able to pay their credit card bill. They didn't have to call the contact center. We reached out through the brilliant work that they did using our technology, reached out to preemptively make those customers feel great, and now with the COVID epidemic that organization is doing the same types of things, which really both endears them with their customers but also gets tied into that efficiency layer because you stop doing needless work because you're being smart as a result of using AI to figure out what to do, and to learn from the outcomes that come from that. >> So we've seen, you know, the playbook of you see, you know, startups, they get out, they're well-funded, and they point to the large established companies and they say, "Oh, that's an old stack." They can't respond, innovator's dilemma, et cetera, et cetera, et cetera. One of the things about Pega is you've been able to transform yourselves over the years. You know, build for change, I guess. An example, for instance, going from perpetual to an ARR type of model, which you very successfully have done, and now, you know, as I said, bringing in RPA, but I want to ask you about RPA. A lot of competitors out there, big valuations kind of pointing at you guys as the incumbent. You have RPA, but what do you see within that space specifically? >> I see a lot of delusional behavior. The ability to put robots in to do little pieces of task work can make sense in some situations, particularly if you don't have a good API, a good application programming interface, to get data in and out. A robot in that sort of situation can be a very, very helpful stopgap, but you really need an engine driven by AI and driven by process, process automation, that has to be at the heart. That's the dog to the robotic process automation tail, and a lot of these RPA vendors are running around saying, "You know, all you need's the tail." I'll tell you that in the last week two of the "biggest leaders" have both had massive layoffs. A little google work you can find out exactly who they are, and it's because their stuff isn't working well. >> I want to ask you about entrepreneurship during and coming out of a pandemic. Is it a good time to do a startup? Not that you're thinking about doing a startup, but you know, advice to entrepreneurs. >> Well, I think it's a terrific time to have a startup mentality. You know, part of why I think we've been able to reinvent our technology literally five times over our years is that we're always prepared to look from a new angle and apply that sort of entrepreneurial thinking and scrappiness. However, in terms of starting something right now, it's a very uncertain time. It's uncertain as to when customers will be back in the market. It's uncertain as to exactly how hard certain industry segments would be hit. And so whereas I think that even during recessions it can be a fine time to launch a startup, and in fact that's when I launched Pega was during a time when the economy was not doing that great, I would wait a little bit right now to see exactly when things are going to stabilize. I think that it's just a little too uncertain, but that time will emerge again. >> So I want to ask you, so again, in your book you talked about big data, big problems. I always joke to my friends who have little kids, little kids, little problems, (chuckling) and so little companies, little problems. You're now a billion dollar company, and you're bringing in new talent. You've set your sights on becoming a multibillion dollar company. You've got a great track record. I want you to talk about sort of how you see the future and what your aspirations are. You don't have to give specific numbers, but just frame that for us. >> Well, first of all, just to be clear the numbers in terms of a billion, that's an actual revenue number, as opposed to some of these valuations which we've seen with companies like WeWork might be a little bit tentative. What we see as being central to our growth and value prop are a couple of things. First, we've made our software tremendously easier to use, particularly our last release, which came out about six months ago, really, really straightforward for business people even to take ownership of their projects and work really collaboratively with IT. So that's one aspect of how we grow and want to accelerate the growth. The second aspect is Pega Cloud. Last year Pega Cloud grew enormously. It's now more than half our business, and for people to come on Pega Cloud where we do all of the database work, we do all the heavy lifting for them from a technology point of view, also provides a route to growth, though we also support what we call client cloud, which is where one of our customers wants to run it on their own cloud. And I think the third thing that we're doing that we're hoping is going to allow us to accelerate our growth is to broaden our go-to-market function, make our go-to-market function just larger by continuing to hire, and by the way, this is a great time for a company with a half a billion dollars of cash in the bank to be out looking to hire talent. Looking to hire and broaden and deepen our go-to-market and how we work, especially with those awesome customers, some of whom are suffering but are going to come back, and they're going to increasingly need to change their digital infrastructure. Their digital transformation, we think, is going to benefit from platforms like ours in unique ways. >> Well, Alan, I love the story. As you just pointed out, you just tapped the credit market. You've got a fantastic balance sheet. You've got a lot of tailwinds, you know, despite this pandemic, and as we often say, you've got a founder as the CEO and we've seen how that really culturally makes huge differences at companies. Alan, thanks so much for coming on our CEO series. Really appreciate your time. >> Thank you, Dave. It's been a real pleasure. >> All right. And thank you for watching, everybody. This is Dave Vellante for theCUBE. We'll see you next time. (smooth music)

>> Announcer: Live from Atlanta, Georgia, it's theCUBE, covering IFS World Conference 2018. Brought to you by IFS. >> Welcome back to theCUBE's live coverage of IFS World Conference here in Atalanta, Georgia. I'm Rebecca Knight, your host along with my co-host, Jeff Frick. It is late in the day here, the reception is about to start, the drinks are flowing, but we are still interviewing guests, and we've got a great panel right now. Joining us is Scott Helmer. He is the Senior Vice President at the Aviation and Business Defense Unit at IFS, and Henry Canaday, who is a contributing editor at Aviation Week. Thank you both so much for joining us. >> Thanks for having us. >> I wonder if you could walk our viewers a little bit through the idea, where does aviation and defense sit within the IFS business strategy? >> I'm happy to answer that. I think our new CEO of IFS, Darren Roos, has been very clear that there are three things that IFS will be best at. Number one, we will be best at mid-market ERP in those vertical markets that we care about. We will be number one in field service management. And we will be number one in maintenance management solutions in aviation and defense. So aviation and defense is one of the pillars on which IFS's strategy is currently based, and we have formed a global business unit inside of IFS that is specifically responsible, it's a 300 person strong team that is responsible for distributing a comprehensive portfolio of A and D solutions to the A and D market globally. >> What are the some of the biggest challenges that you're setting out to solve for your customers? >> Also a good question. We address the full range of management solution capability across A and D. So whether you're an operator in commercial or defense sector, or whether you're an inservice support provider, we provide solutions and support, all of your MRO capabilities, some of your performance-based logistics requirements, some of your supply chain requirements. Basically leveraging the core processes that IFS is differentiated around. Those being manufacturing, asset and service management, supply chain and project management. >> What's special about aviation and defense that's not been marketed or service delivery, which captures a lot of industry verticals, but the fact that you guys got carved out as a separate vertical, what are some of those unique challenges? >> What is chiefly unique about aviation and defense is the overall complexity in the marketplace. You're talking about very very complex capital intense of mobile assets, where managing the maintenance obligations in order to maintain the availability of the aircraft is under the scrutiny of compliance and is required to be done efficiently, without compromising safety. >> Not to mention the fact, your assets are flying all over the world, so they might not necessarily be able just to roll into the maintenance yard at the end of a bad day. >> And they're large and expensive, that's for sure. >> (laughs) Large and expensive. >> Henry, you've been covering the aviation industry for more than 20 years now. What do you see as the biggest trends, biggest concerns that a company like IFS is trying to grapple with right now, in terms of servicing its clients? >> Well the interesting thing about the airline industry is that it technically in many areas it's extremely advanced and very fast moving industry. In selling tickets, the industry has been going through a continual IT revolution for the last 20 years. Things like giving you notices about when your planes arrive and stuff like that. Very fast moving, changing all the time. But this is stuff, it's just money. There's no safety involved, so they can take chances, if they get it 99% right, they make enough money, they can solve the one percent errors. The problem with maintenance is it's messy, it's complex as Scott says. It's also safety critical. They can't screw it up one tenth of one percent of the time. They've been very, very cautious and very, very slow, and they look sluggish and stagnant on the maintenance side. But fortunately, now, especially the U.S. airlines are making some good money, so there's actually an opportunity for companies like IFS to come in here and really reform the maintenance program. >> We cover a lot of autonomous vehicle shows. Autonomous vehicles are coming. Obviously, a big element of autonomous vehicles will ultimately be safety. One of the things that comes up over and over again, if you look at the number of accidents, the fatalities that happen on our streets, compared to what happens in aviation, if a week on the streets happened at a week in the aviation industry, the planes would be shut down. >> Scott: There'd be no aviation. >> The threshold that you guys have to achieve in terms of safety is second to none. I don't know if there's anything even close, especially in terms of volume of people, and then, oh by the way, everyone globally is getting richer, so the amount of passenger flow. I don't know if you can speak to that in terms of the growth of passenger miles, I imagine is the metric, continues to explode. >> You've had basically 18 straight years without a fatal crash by a major American airline. That's unheard of, that's unheard of. We used to have one crash a year up till around 2000. Every time somebody annoys me with customer service in an airline, I think of this, they're doing the important stuff right, so I don't care. (laughs) >> Very well. >> Right. >> And, then do you think the efficiency, right? At least here domestically, I always think of Southwest, 'cause they were the first ones that really had fast turns, and they raced to the gate, they raced back out of the gate, in terms of really trying to get the maximum efficiency out of those assets. The pressure there, in translating to the other airlines is pretty significant to make sure you're really getting a high ROI. >> That's absolutely right. Again one of the levels of complexity that we were discussing. Certainly airlines are being forced to finally introduce some change into their maintenance operations, as the increasingly complex assets are part of the re-fleeting, as that faster traffic continues to grow. It's about both achieving greater efficiency in maintenance operations, not only without compromising safety, but ensuring the availability of that asset. Because revenue dollars still matter greatly, and those assets are your revenue producing assets that an airline has. >> Can you describe your approach in terms of of how you work together with your clients, the airlines, in terms of developing new products and new features. >> One of the unique characteristics about aviation and defense is not only the size of the client, but the length and duration of the relationships. So, we have a long and rich history, both at IFS and through the acquired MXI technologies, of working with our partners in their programs over the very long term. As much as we have domain expertise and a sizable team of domain experts inside of our business, we're able to recognize our partners that are visionaries in the industry, and we have established multiple levels of collaboration to involve them in the shaping of solution capability to support their businesses going forward. We are just launching today two new planning applications that were not only being launched with American Airlines and LATAM Airlines respectively, but were co-developed with subject matter experts at each. So they're tremendously valuable inputs into shaping our vision of what solutions are going to best drive business value for our customers over a very long relationship horizon. >> So, what have you unpack at MXI acquisition, what did that give you that you didn't have before and what's the total solution now? >> Certainly, I joined IFS through the MXI acquisition. I was previously it's Chief Operating Officer. MXI was focused on best of breed MRO capability for both defense and service port providers, as well as commercial airlines. In combining with IFS, that had a rich history in A and D, we now have the most comprehensive solution portfolio available on the market today. We are the only vendor that can provide best of breed capability, integrated into an end to end enterprise landscape, and we've got the team of subject matter experts or domain experts that are capable of delivering that value, not just the product, but the solution to the customers across all the segments of A and D. >> Just to be clear, your defense is more than aviation. I saw a military truck over on the expo hall, so it's assets beyond just airplanes when it comes to defense. >> Correct, we support on the defense side of things. We support multiple platforms, whether they're fighter jets, whether they're cargo carriers, whether they tanks, whether they're ships, we support for the operators, the offset optimization, performance based logistics, security, et cetera. For the in-service port providers, we similarly support supply chain requirements, MRO requirements, et cetera. >> Henry, as you look forward, you've been covering this space for a while, what are some big, new things coming down the road in the aviation industry that we should be looking for, 'cause we haven't seen a lot of big things from the outside looking in. I guess we had the next generation fighter planes, and then we had obviously the A380 and the 787 on the commercial side. What's new and coming that you're excited about? >> Well, technology changes slowly in commercial aviation, because of the safety aspect. The big, new things are the new aircraft, the 787 and the A350. They are really new generation aircraft, lot more composites, plastics if you will. They're using that instead of aluminum. The other things that's happening is additive manufacturing, this whole printing parts. That's real big, and I've been telling everybody the new Boeing 787 has two printed parts, one made by GE, $120 billion a year. The other made by a company called Norsk Titanium, with 140 people coming out of Norway, which is not exactly the center of innovation in aerospace programs. >> Jeff: With a printed part, like a 3D printed part? >> Yeah a printed part. Those are the two big changes in the aircraft. I mean, customers aren't going to see it, but these planes are now made largely of plastics and the metal parts are going to be more and more printed. Much more efficient way, lighter aircraft, less fuel use, more efficient, less environmental effects, etc. That's a big deal. More important than a huge airplane. >> Right, well I can imagine, we hear about the impacts of 3D printing. I haven't really seen it yet, but this vision where your ability to print parts on demand will have significant impacts on supply chains and inventory and huge, huge impacts down the road. >> And the airline industry is the most demanding. They've go to go through really massive proofs of concept and proof of materials, and it's starting to happen. >> Henry, what would you say is the most important area that IFS should focus on. If they can solve one problem in the airline industry, what do you think it should be? >> Availability would be one. Just aircraft availability, that's what. The airlines are concerned about two things. Dollar cost per flight hour to maintain and what they call a technical dispatch reliability. They want to get that plane launched 99.99% of the time. Get rid of the unpredictive maintenance problems. Schedule everything, make it quick, I want to get the planes off on time. >> It's amazing that unscheduled maintenance, regardless of industry, still continues to be such a bug-a-boo to productivity and profitability. It's one of these things that just has huge impact. >> I would completely agree with Henry. I think asset availability is the number one focus for commercial operators. Our focus has certainly been around trying to remove the impacts of unscheduled maintenance. One of the applications that we launched today allows you to react very, very quickly to unplanned or unscheduled maintenance events, and to do some what-if modeling, so that you can implement the best plan for your fleet, in order to maximize the availability of that asset. Not just in terms of bolstering or producing a better plan. We're attempting to do that even with line planning, where we're adjusting the traditional planning perimeters away from what must be done to what should be done in order to maximize the availability of that aircraft. Of course, as Henry said, everybody's focused on faster, tighter turnaround times. All of our software is designed to try and drive tighter turnaround times and greater efficiency. >> What percentage is scheduled versus predictive versus prescriptive? Maintenance. >> I think it varies by airline. The great majority of maintenance is scheduled, I mean, there's no doubt about that. They put these aircraft down for a week or a month. It's a massive amount of money. It's not the amount of maintenance, it's when unscheduled maintenance happens, it really throws things off. It may only be one or two percent of the maintenance tasks are unscheduled, but that's what throws the aircraft off the schedule. That's what leaves passengers sitting in the departure lounges, ticked off. Not getting there till the next day or the next week, whenever, so it's a very, very small percentage, these unscheduled maintenance events, but it's crucial to the airlines' economics. >> Exactly. Crucial to our itineraries, as well, as the economics. Exactly. >> Making sure that the airlines continue to do what they do best, which is get us from place A to place B. >> Precisely. Well, Scott Henry, thank you so much, it's been a really fun conversation. >> I enjoyed being here, thank you. >> Jeff: Thank you. >> Thanks, Henry. >> Thanks. >> We will have more from theCUBE's live coverage of IFS World Conference just after this. (digital music)

>> Wait. Welcome back to Boston, everybody. This is the Cube. We're live on a special presentation of BMC Day atop of sixty State Street in Boston, Massachusetts. Beautiful view of Boston Harbor. Evelyn Ehrlich is here. She's the vice president and research director for service delivery at Force that we're going to talk about job control, language and cobalt. No, I'm just kidding. We're talking about service delivery. Who'd Evelyn? Yes. So you have a really deep background in it, And I know what J C l stands for, So I had to make that joke. So anyway, uh, welcome to the cubes. Great to see you gave a fantastic presentation today. Who doesn't need better service delivery? It's an imperative for the digital transformation. So, again, welcome to the Cube. Thank you. So tell us a little bit about what you do at Forrester, what your area is, and I want to get into your presentation today. >> Sure. So service delivery. Basically, when the application development team is ready to hand us something, whatever that issa Web service and application a service, we actually make sure that that gets to the work force or to the customer. So anything from Police Management Service Management, the front end relative to the service desk. Tell them anything around management after a performance of the applications operations. Anything like that is all about service delivery. >> And they were two. Two pieces of your talk really struck out to me on Dino. No George for a long time. So two things to majorities that you don't like to use one is users, right end users use it, and then the other really was. So talk about it. Why those terms don't make sense in this digital economy. And what does make sense? >> Yeah, so your users, it almost seems like to me, it is something where people are putting folks into a box that they are that they can like addicts. You know, user. Like I said, in a camp in the drug industry, we have users because they're addicts way have to somehow keep them at bay. We have to somehow keep them low and our engagement with them. It's no, it's not going to be enjoyable. It's not going to be fun, and it's not going to be actually effective. Unfortunately, these users today those are our workforce. There's our employees There's our partners and customers. They have other places to go. They don't need us and technology. So if we don't shift that thinking into that, their customers, so that we can actually enable them, we're might be able to lose our jobs. Because there's outsourcers service providers to workplace services, for example, as many companies out there who provide the service desk who provide of VD I who provide the services cheaper, faster and better. But what we have been lost or what if that's gonna happen? We are losing the understanding of the business for losing the connection to the business, and is that that could be a strategic conversation right? There should be a strategic conversations, not justa cost conversation. And when we think about user, it's all about cost. If you think about customer, its value and relevancy, >> okay, And of course, that leads to not its business. There's no such thing as a project. >> No, there isn't because anything we do if we think of information technology is anything almost like in the back room. It's something which is hidden in a data center somewhere in a storage or a server or in a device and it doesn't really add any value. >> Boiler, the boy, the room >> Exactly and way have done that. We have massaged it, what with whatever way measured the heck out of it. We measure meantime to repair. Well, who cares? It's time to business impact. This what we need to think about. So if we start thinking about customers to empty, TR becomes time to business impact. We're now thinking outside in and the same is true with I t. If we just use it for technology sake to Dr Information, we're not connected if the business, because it is about business technology, is dear to win, retain and sustain our customers. If we don't do that, we become borders. We become the, you know, the companies who all have not focused on the winning technology to make them successful. >> You had a really nice graph, simple sort of digital failing digital masters, and I were in between talked a little about things like I Till and Deb ops, and they feel sometimes like counter counter to each other. Once one's fast one feels home. As you talk to customer, you talk to customers. What can they expect? How long might these transformations take? Or what of the one of those key stepping stones you talked about? It being a journey? >> How do you >> will think about all this change? >> But that that's a good question. It's a very difficult question to have an answer to, and I think it has to. It has to be a little bit more compartmentalized. We have to start thinking a little bit more in smaller boxes, off influences or or areas where we can make some progress. So let's take, for example, Dev Ops and Vital and connect the process release, which is an I told process into this notion. If we combine Deaf ops and Tyto release, we're starting to see that the police management process. It's now a process which is done very agile very much. There is a lot more things behind that process and a lot more collaboration between a D and D and I, you know, to make the process of faster process. So we're now married, I told release management with the journey of Death, Bob's as we're starting to see release cycles off one day. Lookit, lookit Amazon. What they do I mean again, Amazon is a very extreme. Not everybody needs a police processes Amazon has, because it's just not that not every pieces is in the Amazon business. Maybe in ten years, who knows? Maybe in five, but those kinds of things that marriage happens through, more off for design thinking. And I think that's the practical way. Let's not adopt a Iittle blandly and say, All right, we're going to just redo our entire twenty six processes. Let's look at where is the problem? What, where? Where's the pain? What is the ninety day journey to solve that pain? Where's the six months? Nine months, twelve months, twenty four months? And if twenty four months is too far out, which I believe it's staying a twelve month road map and start adjusting it that way and measure it, measure where you are. Measure where you want to go and prove that you have done to Delta. Because if I don't measure that, I won't get funding for support, right? I think that's key. >> Devlin. You talked about the, you know, pray or a predator, right? That's good of a common theme that you hear conferences like this isn't a zero sum game, is is the taxi drivers. You know, the taxi companies screwed is, you know, the hotels in big trouble. I mean, Ken, cos you know who are sort of caught flat footed transform and begin to grow again. Talk about that zero sum game nous. >> Yeah, I think I think there is. There is hope. So I hope it's what dies last week saying right. But there is hope, hope if customers of organizations he's enterprise to see that there's a challenger out there. And if they don't necessarily stand up to fight that challenges start innovating in either copying or leveraging or ten. Gently do something else. Let me give you an example. When about two years we had a two years ago with an event in London and stuff I got Square was completely blocked off by the taxi drivers because uber was there were striking against uber or they were going on. It wasn't really a real strike was in London. It's a little bit of a challenge with unions, but anyway, instead, off going on a strike, why did they not embrace whatever they needed to and example is in the cab At that time, you could not use American Express or discover credit card uber. I never have tipple any money out of my pocket because that's a convenience. It's easy. It's enjoyable. >> Love it, >> We love it. It's simple. So why don't these other companies this cos the taxi cannot? Why don't the equip that technology in such a way? They can at least start adopting some of those innovations to make it a even part right. Some of the other things, maybe they will never get there, because there whatever limitations are there. And so that's what that's what I think needs to happen. These innovators will challenge all these other companies and those who want to stay alive. I mean, they want to because they have for street is forcing them to stay alive. They are the ones who will hopefully create a differentiation because of that >> essay, really invention required. It's applying technology and process that's well established. >> Thinking outside in thinking of you and him and me as >> customers, it becomes, you know, who just does the incumbent get innovation before the the challenger gets distribution? Exactly. You know, Huber, lots of cars. I don't have to buy them, but somebody like Tesla isn't necessarily disrupting forward because they don't have the men. They can't distribute it faster than you know. It depends where you are in the distribution versus innovation. So it's in the brief time. We have love to talk about the landscape. So and that's particularly the transformation of beings. BMC Public Company to private They were under a lot of fire, you know, kind of flattish revenues. Wall Street pound. You got companies like service now picking away at the established SM players. We're talking off camera, saying that's begun to change. Give us the narrative on that that sequence and where we are today. Yeah, we're going. >> Yeah, so if you go back, maybe me way back seven years ago or so you know, it started service now they had a fairly easy game because BMC with a very old platform, it wasn't really it wasn't. There was no fight. Um, and I think they were the enterprises. We're ready for something new, and it is always some new vendor out there is a new shiny object, and I have teenagers, so they always spent the next latest iPhone or whatever. I would >> sort of wave >> so So. And and it kept going in the other vendors into space hp, cia, IBM really had no challenge had no, no, didn't give service now a challenge either because the SAS cloud, the adoption of the cloud in this space was absolutely important. And service now was the first one to be on the cloud. BMC was not really doing much with remedy force at the time. Itis them on demand was in an A S P model. Not really an itis, um, and so service not just took names and numbers and that just grew and grew and steamrolled. Really? All of them and customers just were like, Oh, my God, this is easy. I loved it. Looks it loves it looks beautiful. It's exciting >> over for the >> same thing that innovation, right, That challenge, they served the customers. Then suddenly what happened is service now grew faster than native. You experienced some growing pains Customer saying my account rep. I haven't seen him for a while. They changed the pricing model a little bit too started to blow up their solution. And now board nebula, which is the ninety operations management solution der extending into financials and they're bolstering themselves into more of an enterprise solution, which is where BMC already has been. But they lost the connection to the customer. BMC did not love the customers at that time. Now, through some executive changes to really starting to realize that the install base they need to hug them, they're back in the game >> and watching >> service now. And they're going private. As you were asking the question earlier, try about giving them the funding to invest in R and D. >> It's so necessary if I want to give me your take on icy service now. Is someone on the collision course with sales Force? In a way, where does BMC go for to expand their their tam and to grow? >> Yeah, I said, I think so. So on the first comment Sales force and service. Now, absolutely now the CEO of service now does not think that sales force is his target off competition. I think it has to. He has to, because it is about business applications, everything. It's everything exactly So sales force and service now in I don't know. Is that the year you know, wherever Chris >> No, no, no, >> no. But they will there will collapse. Deborah Crash or you'LL see a fight. I think BMC should stay and really extend in this digital performance management in this operational management and really make it intelligent, intelligent decisions for operation for operations to become automated. To have a staff of eighty eight PM solution the application dependency mapping solution happening to be one of the best, really one of the best in the market. And customers love it. Tying that into two side intelligence, giving them the ability to understand before it happens not when it happens or after and then drive intelligence into different organizations to cmo the CEO, the CFO. Because that's what basis technology is all about. It's not about the journey anymore. They have that capability with products where service now does not have that >> great insight from a sharp analyst. Evan are like Evelyn Evelyn Ehrlich. Thanks very much for coming on the Cube. Forced to research wicked, we find more about the research that you do force the dotcom, obviously, but anything new for you, any upcoming events that we should know about where people should watch >> you go into Crystal Rica, Nicaragua >> mochi ice all right. We'LL leave you alone for a while, right, Evelyn? Great to meet you. Thanks for coming on. I keep right there, buddy. We're back with our next guest Is the Q ber live from BMC Day in Boston right back.