(upbeat music) >> Hello everyone and welcome to the special Cube remote conversation. I'm John Furrier, the host of theCUBE. We're here in our Palo Alto studio. We do all of our digital events. We do all of our content, original content here in studio. Of course, we can reach anyone around the world through our remote technology. And we like to bring experts in, to talk about some of the cutting edge issues and one of the most important things that I've been really doing a lot of thinking on lately and putting it into practice is, the role of individuals in groups in digital and since we're using software, this is becoming a really critical dynamic for the concept of engagement. Which is the holy grail of digital marketing. And now with the Coronavirus you're going to see a lot of events being canceled. You're going to see new norms being formed around how people engage, how they bond and ultimately, how they get work done. So we got a great guest J Metz, who has got a Ph.D in Communications Technology. Dr. Jay Metz, thank you for taking the time to jump on our remote interview. >> Hello. >> So I got to ask you, you know, we were talking before you came on you've got your doctorate and going back thirty years ago, you were doing a lot of pioneering work with others in academic circles around group behavior, software and you know, for us old guys but you rolled back the clock back then, you're talking message groups, you're talking about, you know, online tech systems. But the world is pretty similar evolved evolution in terms of those same concepts. Now more than ever, you're seeing Facebook breaking democracy, the government wants to try and create an e-democracy model. How do you do voting? All these things now are cutting edge issues and certainly with the Coronavirus, you're looking at people wanting to take content and posting it on the internet. It sounds so easy but it's now, it's going to be different. So, I got to ask you, you know, how do you see this world because you've done a lot of thinking on this? You know networks, you know technology and digital. How do you see the role of content and people and groups forming on the internet? >> Well I think that the role of technology hasn't really changed all that much when it comes to slowest moving piece, which is human nature. When we were, as you pointed out we were talking about this a long time ago way back before there were pretty pictures to look at on computers, you know. We had, we had IRC Chat, we had Bitnet Relay, we had Minitel in France, we've had different places had different forms of communicating through the use of computer. And at the time, they were really curious as to what was going to wind up happening. Were you going to get, you know, a bunch of freaks running things? Or are you going to get people you know, effectively isolated from society? All these questions that we're kind of asking nowadays. We still had them back then and we don't have a new answer. The same problem exists, even if there are prettier pictures to look at on the screen. >> David Vellante put and I put out a post, he actually did an interview, an article, where we talk about digital events and his advice was, "Don't just think about the software, "think about the outcome." So I have to ask you, when you start looking at digital interactions and human behavior, you're looking at stuff from whether it's visualization, Sigchi did a ton of work going back to the 80s to today. You're seeing, you're getting group theory coming in. The outcome is just people either getting something done, finding what they're looking for, making new friends and connecting. Digital is not just the software, there's a human component. Can you share your view about you know, the role of engagement, how content in groups, group social formation, social capital, social organizations can emerge from this new dynamic that is going to be forced upon us as we start thinking about virtual and work remotely and everything else. >> Well, I always felt that engagement was sort of a misnomer, to be honest. I always felt that engagement really had to do with the way that participation was counted. And participation is not necessarily an indication of how closely somebody feels to somebody else. How much of a part of the group they actually feel. And we start to look at group dynamics, as we start to look at the communication part, we look at the actual points on the graph as individual elements of participation, as if that's a good thing or tells us something. It doesn't tell us where the vector spin is going, right? Is it going in a positive way? Is it going in a negative way? And the reality as I've been able to find out over the last several decades and I can't believe I said that out loud. But the reality over time and this was always back to you know, before the radio even, I mean. This is a common theme in human nature. How people form groups through the use of technology is relatively consistent. And it has to go through the nature of the medium as it pertains to making our conversations either delayed by time or increased by time. So that synchronicity makes a huge difference as to what we call engagement and what kind of meaning we can apply to it. >> I want to get with you on asynchronous versus synchronous. Now that's an important concept and you know, the cloud native technologies are all asynchronous and horizontally scalable. These are the benefits of large scale systems now. But i get to your point about participation, you mentioned about engagement. Conventional wisdom says that, "Hey I need a lot of "people in the funnel. "I want more people, "what are the numbers? "We have a million views?." You're kind of saying it's kind of going the other way. That's actually not good engagement in digital or in these kind of group formations. Can you explain that? >> Well I mean, we just don't know. So when I was, when I was doing way back when I was doing my dissertation, I thought the same thing. I thought that if I could find out how much somebody participated in a group, I would be able to determine how closely affiliated they feel to that group. And it turns out, that's just not the case. What I found out especially in the short term, was the participation inside of a group usually was indicating that they disagree with the group, not agree. So if you only stop there, you won't get the full story. And what we'll find out is over time, there is an evolutionary approach to this, more about a fractal way of recursively coming in and an iterative approach to being part of a group, bringing yourself into it, letting the group accept you, that kind of a thing. And it simply isn't true that because I have X number of views or this level of rewatches on my videos, that that means that they were either each participating or even affiliated with what I got. Are they part of my group or not? I can't tell simply by the number of views. That's what I mean. >> Yeah, great great stuff. I want to get your thoughts on, and we, I saw your comments on my LinkedIn post, I just posted on my plane ride back from Washington, D.C. But I want to get your reaction to a couple edits here. So I wrote, "In the age of digitals, "not the individual that makes a change, "it's the group or mob. "Often groups are where "individuals voices are processed, "refined, and validated as a collective. "And then, "New social constructs "emerge in digital, "where we interact as "individuals within groups." With digital now pervasive, and certainly everyone working at home, this is going to be highlighted a lot. Can you comment and your reaction to those statements? What's your thoughts? >> Well think about the way we start conversations digitally versus in person, right? So, our idea and this goes to what you said you want to get to regarding synchronicity. So, when we have conversations in a group, in a face to face environment, it is a lot more dynamic, it is a lot more chaotic. There's a lot more complexity and the adaptive system of the group emerges in it's own particular pattern. That same adaptive system does exist inside of the digital world but it is highly regulated. It's regulated by the kind of software and platform that we use. So we will get different types of that group evolution based upon what the actual software will allow us to do. Just like Twitter has a different engagement level. And I use that in a sense of how we interact. It has a different interaction level than the way LinkedIn does. So for example, I could not have responded to you on LinkedIn the way we did because you couldn't have even posted the message on Twitter the way you did on LinkedIn. And the way that we handle the individualism is going to be handled in such a way that we have a more paced turn taking approach to doing things. So, it's not going to be a complete collective and it's not going to be complete individualistic approach depending upon which platform we're using for communication. >> Yeah, one of the beautiful things about the internet is you've seen the evolution, there has been pros and cons. A lot of value has been created. You got the website, you can self-serve yourself. Social networks, you meet some friends, you get some connections. But as we start to see more digital connection, people being connected together or individually if you will, the progression of learning has been somewhat nonlinear. You go to Google type something in, you pop to a webpage or you and I see each other on Twitter, I jump into Discord, talk to my gaming friends, next thing I'm on LinkedIn. I'm kind of popping around in a very nonlinear way. Creates for a very asynchronous kind of consumption or communication pattern. Could you talk about the difference between or the value or the pros and cons between asynchronous communication and consumption of that content and synchronous. >> Well, I think that ultimately, the concept of time is an underrated approach to evaluating how successful something is or is not. So, the time between the way that we communicate and our expectations of it makes a huge difference. If I were to have a, even a slice of a five second delay between your question and my answer, like we are doing some sort of satellite messaging, it would be very disruptive to our flow, right? We would not be able to bond in quite a way. And yet, if I write something that's five seconds after you posted, wow, that's amazing, right? So, our expectations for how time plays a role in the development of our relationship makes a huge difference. But you also sort of talking about the idea of multitasking and the content switching that we do from place to place whether it be gaming in Discord and whether it be in storage or it's, you know, my background or whether it be networking or whether it be medicine or whatever the concept that we have to involve, that probability to content switch even with the same people in the room, the "digital room", that still winds up being a place inside of our head because we've conceptualized those time elements quite differently based upon where we're actually having the conversation. And so ultimately, at the very end of the day, it's a complex system that we tend to forget that we're even doing naturally. We just, we just do. >> It's interesting. You may talk earlier about adaptive and what not. I was talking with a friend this past weekend, we're talking about the difference in organism and a mechanism. You know, organisms are self healing, they repair. You don't have, if people act as a group, there's kind of that, kind of group feel like a social organism versus a mechanism. Software today feels like a mechanism. I got a chat window open, you can't see me. You're like "Hey you're there?", and I'm like, I could be making coffee, doing whatever. I'm not really present. So, you start to see what organism and mechanism concepts and then the notion of presence and commitment. If I'm face to face, that's value and time matters, and presence matters. I'm looking over there, talking to you. So presence and commitment are also concepts. So talk about those two things. You got being an organism, a social organism, social being versus a mechanism, it's like a software and then, you know, the commitment and presence dynamic. What's your view of those things? >> So you brought up the idea of linearity earlier and non-linearity. And when you look at something called, Complex Adaptive Systems, we take very static rules and they don't have to be a lot of rules, just a couple rules and just like the mechanisms that you're talking about. They can be very simple but, you know, in a stasis way and the human nature is to work around it. So our organistic, (laughs), you know what I mean. >> Yeah. >> That element that we bring to the table, tends to wind up working within that rule set or without that rule set and depending on what our particular needs are. But what happens in that parlance is called emergence. In other words, the process is called, autopoiesis, a technical term that means a pattern self-emerges from the mixture of a static mechanical element, those rules of communication and the way that we dynamically as organisms tend to work within and without those rules. And a pattern will emerge as a result. >> I want to get your thoughts on a digital event building out with the next generation kind of constructs for, how people can actually use the digital network, Zoom, Keynotes, Breakouts and then the other community aspect of it. But I want to get your thoughts on the role within groups, online groups. One is a group that self forms, has roles and responsibilities, there's decision making, there's group interaction, there's a dynamic kind of organism feel to it. Versus a mob, people just kind of gather up, grass roots Where it's just more free and loose. Can you talk about how you see those differences 'cuz you, you know, people could just gather publicly and chat. It could be self governed in some way but there's no real roles, no decision making, it's more mob like. And then, social constructs around decision making and group formation and decision making. >> In reality, it doesn't, if all things were being equal in terms of amount of time they had spent, the human element of forming groups does not change. The social development of groups has been something that's been studied since the 1840s in academia. And when you look back at that, those as basic ground rules of how groups form, they really haven't changed all that much. The facilitation of that may have changed. But have you ever gone to a group where the first meeting will have all whole bunch of people show up, and the second, people a lot less and by the third time it's already dead? You know, that game of life that we're familiar with with the whole, you know, software program, well that's very true. That's a good metaphor for the way that humans form groups in the first place. Just because it doesn't necessarily form in a digital way, it doesn't mean that was the nature of that particular way. It means that, that particular group itself, that participation, that affiliation, didn't happen in the timeframe necessary to keep it going. And I really think and I really believe that understanding the nature of the people involved, the marriage with the content that they are for and the medium that provides that facilitation is what will provide the idea of whether or not the entire group digital or other wise lives or dies. >> That's great insight J. I really appreciate that. You know, final question on this whole digital shift. The Coronavirus is forcing people to stay at home, events are being canceled. And you've been following theCUBE, you kind of know what SiliconANGLE and theCUBE have been doing. We would go to events where people would be there, physical spaces, and we would interview people in our authentic way and face to face and bond and but gather the data from the guests and distribute it digitally to our audiences. We've been doing that for 10 years. Now what's interesting though is the worlds now changed. There's no more venue. But the people running these events still want to take content value but now they got to digitally distribute it to where the people are in digital streams or digital space. Okay, or cyber space. So, this has been a real challenge for us people that are used to relying on the venue to handle a lot of the structural things. Decisions, stage, boom, breakouts, areas for hallway tracks, happy hour, networking. So, the venue handled all that. Now you have a flip of the script where it's still content value but the distribution to digital is chaotic and distributed. There's a group challenge, right? So, the question I have for you based on your expertise, how should people be thinking about the complexity to do a digital event 'cuz you got to have content, you have a digital stage, you need distribution, but you need to have the humans involved because they are the consumers and the actors. What your view of this and if we run a team together trying to figure this out, what we would say to people to help them along? >> I think, so there's a short game and there's long game here. And the long game is that, there are elements to digital forms of communication in asynchronous method just to use the terminology we've been doing. There are realities that cannot be met, the same way that you met in a face to face. And those are ages ago they used to be called, social context cues. But effectively the richness of a face to face just simply cannot be held in an asynchronous format for long. So, the long term game here, the long game is that, this is temporary setback because you still need to be able to do things that you can't normally do just through you know, watch pre-recorded content. Even if it winds up being a recorded content that will be a pre-recorded at some point. You're watching it live, you're still going to view it that way, right? If I watch a webinar live, hell, I have produced dozens and dozens of these things. I'm always aware that this is basically being viewed as if it were a, you know, pre-recorded content. On the short term now to answer your question, what has to happen is that, we have to look at a multi-pronged communication approach. How do I get that synchronicity of communication? How do I get people to feel like they've been heard? That's the problem. When your in a face to face situation in conferences you know you've been heard. In the hallways, in the walkways you would stand up and you would do a question. You know that. That's is one of the biggest problems we have to solve digitally because ultimately, I'm broadcasting something to you and it's a very different communication style than if we're having a interpersonal communication. >> Yeah, and you know one of the things over the years with the internet, the content acquisition which was the primary use case of an event. You go and learn. That can be done online. So, we've seen the progression of the networking peaks, the face to face value, meeting new people. My friends is there, I haven't seen him in a while. Or we work remotely and we see each other and we have beers together or we're bonding. So, is that's just really hard to replicate in software. It really really is. It can be a big challenge. >> Oh, without question. I mean, but at the same time think about reality of how much time you spend with people that you don't normally spend time with at the conferences. Entire friendships had been based on 30 minute conversations spread out over three conferences, right? I mean, you'll go and you'll dinner with one group of people, one conference and then you won't see them again for another three conferences. You go back to that fourth conference and, "Hey we're back "to where we left off "and we're good friends and "that'll never really change." So, we're able to kind of fill in the blanks mentally and emotionally in that sense. The question is, can we do that through the use of a digital technology or to your other point that you mentioned earlier, do other forms whether it be the politics that come out on Twitter or the you know, the other groups we're associated with, but they are worthy, God help us the cancel culture that's coming up. Will that affect everything digitally, that you can skip over when you're actually in a face to face situation. Those are questions that I don't have an answer to. >> Yeah I mean, we're looking at it hard. We think content is key, content value. And again, timing is critical. I like your perspective on timing. There's a time series involved. And there's asynchronous, right? So, it being there. You know, content with, you know, people who are heard or participating, contribution, forming bonds, and interacting. The digital venue that has to facilitate a community loop in, right? So, it's a really complicated but new emerging trend. We're really watching this closely and we really appreciate your insight. Thanks for taking the time. >> My best. >> J Metz, Dr. J Metz here, helping me unpack and sitting back in looking at the philosophy but really the practice of 30 years of internet and online research and sociology around the role of people, individuals in context to groups, this is a big discussion as people start to figure out and operationalize what is the right mix for digital and virtual with physical of spaces. And certainly, we think events will come back soon. But J, thanks for giving us the time and we'll talk to you later. >> Thanks for the invite. >> I'm John Furrier here at theCUBE studios for remote interview with Dr. J Metz talking about the social theory around digital groups. Thanks for watching. (upbeat music)

>> Narrator: Live from Washington D.C. It's theCUBE. Covering Boomi World '19. Brought to you by Boomi. >> Welcome to theCUBE. Lisa Martin with John Ferrier. We are in Washington D.C., at Boomi World '19. John and I have been here now for two days, and we're pleased welcome another CUBE alumni back to our program, Michael Morton, the CTO of Boomi, Michael J. Morton. >> Thank you! It's so great to be back with you guys. >> Great to see you. >> I love this. This is great. >> So we were geeking out the last day and a half, John and I were, with all of our guests and realized Booomi World 2018 was only 11 months ago. >> John: Yup. >> So here we are in D.C. Lots of news around fed rant marketplace certification. But in such a short period of time, Boomi has scaled to 9,000 plus customers in over 80 countries. Your partner ecosystem is now over 580. All in 11 months. And 11 months ago, one of the things that was very clear from all of the Boomi execs is we're going to redefine the i in iPaaS to be intelligence. Now here we are, fast track a few months later, we're going to be talking about, Boomi is talking about, redefining that i to be intelligent insights. Cool stuff. Talk to us about the insights. >> Okay, so let's talk about intelligence first. So everybody's intelligence happy of course, but we've been very disciplined of actually being articulate about what does intelligence mean, not just the label. So we have a history of intelligence being how can you facilitate customers building solutions on Boomi faster. That's our legacy. And so we'll always continue to add new features to the product, but we had an opportunity that we realized we kept in our back pocket for a little while, right? And that's around insights. So we knew that the way the world uses Boomi is to integrate data. They connect the things. They move data. But now we're kind of shifting a little bit and saying it defines what your business is doing, not what your data's doing. Right? So now comes insights, the first for any iPaaS to do, is now we can intelligently tell you what is your business doing. So now we had to make a decision. We can't just advertise it and say we do this, right? And hey, wave our hands. So we said we're going to pick a business challenge, not a very common one. Just kidding, of course. What's a business challenge that every business has? Data privacy. So we chose the insights to say we want to help customers address a business challenge of data privacy. It makes perfect sense. If Boomi is the traffic to running your business about moving data, what's data privacy? It's about getting your arms around the movement of your data. So it just was a perfect fit, for an integration platform as a service, to expose, in a much different way, where is the data about your business actually coming and going? >> Is it going to be part of the product, chargeable, free? How're you guys thinking about these insights? Is it going to be a module? Is it going to be a connector? How do you guys think about the insights piece of it from a consumption stand point, from a customer stand point. >> Okay, so I'll take it one step at a time. I will just be honest and say we have yet to decide is it a charge for feature? We're still evolving it, but consumption's a very important question, so today what we're doing is we have this capability working today. We talked about it on stage, very comfortable about speaking about it, because we're working with a set of customers that gave us real feedback about what's important and what's not important. The consumption's a very interesting question, because depending on the role, right? If you are a chief security officer, what do you want to see? Do you want to see PDFs? Do you want to see reports? Or do you want APIs to get the data to consume into something else? So, one of our to do's is consumption. How do you want to receive this information? So this is actually in the works. >> So, I can see policy and AI being helpful there. You mentioned privacy. I want to get to that in a second. But why not security? That's the number one problem, too. Data, privacy, and security. Is it just too elusive? Or is it too hard? >> Michael: To me, they go together. >> Okay, so explain. What's going on, how does security fit in to this? >> Yep. I mean, I think there's many aspects of security obviously. But I mean security from an access standpoint, all right? So I'll take the position of access. One of the reasons why customers buy Boomi today is they want to expose a certain amount of data to consumers, either from monetization or to an application or to a consumer or to a website, right? And so one type of security is how do you limit the data that you get access to? And so today I'll go back to intelligence or insights. >> (chuckling) Exactly, same. >> It is not out of the realm of possibility that we actually show you who's accessing the data. >> Yeah, I mean we've seen this moving around. That's when the thieves are also moving around, too, and the bad actors. That's a good observation opportunity. And that's kind of where this comes from, right? This whole ability to observe, observability. >> That's right. Observe access. I mean, impersonations is a very popular thing, you can impersonate people, but the whole ability to observe inbound requests, right? I mean, there's always traffic controls on API gateways and things like that, which we'll fully support. But security? I mean, it comes with access. >> I want to get your thoughts on a couple things while you're here. Observability remind me of this cloud 2.0 conversation we've been having on theCUBE. And we're kind of goofing on web 2.0, cloud 2.0. Cloud 1.0, Amazon storage, computes, scale up, everyone's born there, loves it, no problem, no issues, just grow and buy as you go. It's great stuff. At some point when you're an enterprise, it's not that easy. >> Michael: Right. >> So, from cloud 2.0, observability has really taken network management to a whole 'nother level. And it's a data problem. So people going public, SignalFx got acquired, it's a whole industry now. Automation is evolving out of the configuration management area. RPA has got some AI in it. So if you connect the dots here, I can see you guys know where I'm going with this. >> Yep, yep. >> Observability is data. Automation is about making things easier. >> Michael: Yep. >> How do you see those components fitting into the Boomi world? Because architecturally they're now building blocks for either conversational AI or some sort of insights and intelligence. What is, what's the framework, what's the building blocks to make all this data value come to life? How would you talk about that? >> Well, I mean, you're asking, I broke down your whole tirade there into many sections already. >> John: Tirade, good word. That's a great word. >> So let's talk about, in relationship to Boomi, you used the word infrastructure. You used the word network. You threw a lot of things in there. >> John: Tirade, that's for sure. >> And it's like, okay, now I have a soup. So I'll just try to pick pieces out of the soup that I think are relevant. So, again I'll tie back to intelligence a little bit. Boomi, when you use the product, there's an engine that you run. It's a container, right? So you build in the cloud and Boomi, and then you choose where you want to run, right? And part of our efforts around intelligence is to keep that run time environment healthy and maybe scaling, all right? So automation for Boomi will be, let me look at the workloads that you are using to run on Boomi, and predict when I need to scale your environment. Automation. You'll see slowly even more automation capabilities to make it easier for scaling, sizing. So that's one aspect of hopefully answering what you're asking and trying to dissect a little bit about automation. So one will be automation for ourselves. I mean to help basic, just don't think about your moving around time anymore. It's just going to work. It's just going to scale. So we are planning to get to that point where it's fully automated. >> And that's efficiency for you. Creates value. >> Michael: Yeah, correct. >> Deploy resources to other areas. >> Yes, but here's something else to consider is it also saves our support organization the call. That's the most important thing, is the company when you scale, is you have to put in your company cultures. You build the product. What can you do to avoid that service call coming in? So I do want to talk about culture a little bit, even for intelligence. And I like to give a very simple example about how does a product like Boomi change their culture about building in intelligence into the product. And I have a great example. So let's say I'm a developer that's been assigned to put a new feature in Boomi. And it has five configuration parameters that you need to ask the customer to configure before you can use it. Why? Why five? Can't I just tell the customer what they need for three of those? And now there's only two? And it gets people thinking, oh yeah, I guess I could have gone back into their metadata. They already did this once. So why don't I grab that value that they already did? And that's an interesting mindshift when you think about it is instead of five, I challenge you to get down to two. Get it down to two. So, intelligence is not just an outward facing customer feature. It's a development culture. >> You talk about operating systems. It's really a great conversation, because you know when you look at data, and then and what you're talking about, back to the demo and the privacy conversation that you guys are talking about, is if you think about data holistically, as a system, not as a isolated thing, 'cause that's what you're getting at. It's a systems approach. >> Michael: It is. >> The data's somewhere. Why you have another form? You get it, pull it in, automation. But as you did the demo, people were buzzing about mind blowing, whoa! Look what's flying around! What was the purpose behind the demo? What was your main point? What were you trying to get across in that demo that you wanted people to walk away with? Was it that there's threats out there that's an issue or their problems are going to be solved? Or is this cool? What was the main driver behind the demo and the privacy as the first step? >> That's a very good question. And so I'll give you the first thing that comes to mind. The company and data is a living ecosystem. It never stops. It's always in motion. It's harder to manage. It's harder to observe. Boomi is meant to basically build the engine of your living ecosystem. All right? How can you possibly as a human get insight into that ecosystem? It's impossible. But with a product like Boomi, we're giving you insights into the living part of your business. That's the really the theme. Now applying to, you said threats. Good word. Threats to what? In this case, it's threats to being fined by GDPR. It's not necessarily a security breach. But fines are real now. I mean there's monetary loss. And so that's the message. >> What have some of the, you mentioned the word mindshift in your demo this morning, you mentioned it a minute ago, when you've been working with some of these customers helping you evaluate this intelligent insight capability, what has been the mindshift there, in terms of exposing this information? What are some of the things these customers have been really like whoa, really surprised that this intelligent insights can show them, that they just have no idea about with respect to their business? >> Yep, great question. Because I gauge success on the reaction, all right? And in this case the human reaction is actually seeing a map between countries with lines. It's actually that simple, to visually be able to see as a human, the flow of data. Then on top of that, the flow of private data. >> It's like an x-ray. It's like looking at the bloodstream. >> Ah, that's a good analogy. >> Yeah, I mean the blood's flowing, all aspects. >> Right, you can't see your blood. I can't see it, right? I know it's there. >> John: (laughing) Yeah, I think so. It's red. >> I hope so. >> That's like Superman. You can see through the data points to get into what you want because the data's flowing. You guys make that observable. Now what about the data that's not in the Boomi platform? Connectors, how would people, I mean so obviously not, Boomi's not everywhere, you've got 9,000 customers, not 900,000 customers. So there's a lot of other businesses that aren't using Boomi. Can I leverage it with other platforms? How do you think about that? >> Again I'm going to interpret what you're asking. There's many other sources of data of course that people are not using Boomi to access. But if, this may be a bit of a salesman opinion, the more you use Boomi, the more insights you're going to get. So why wouldn't you connect to those things? >> So but connecting means I can just connect to those things. I'll give you a hypothetical, real world example. We have so much data on these CUBE interviews. In fact, after this CUBE interview's done, your words will be transcribed into a transcript, will be linked to the video. We can make clips out of it. It's a big data set. When people will share those clips, we know who's sharing the data. So we are there, a lot of good data. So I would be like hey, I'd like to tap into that Boomi. Why build it? I can just connect. So do I connect all my applications into Boomi or just my data? >> That's actually interesting. Now, of course, I'm the CTO of the business. I'm going to invent stuff on the fly 'cause that's what I do, right? You have metadata about, you have metadata about these files? >> We have APIs, metadata, all kinds of stuff, yeah. >> What we would expect would be this. You would need to, if you're looking for other insights, all right, you're going to now see start combining data. So analytics is really about taking multiple sources of data, putting it in one place, and mining it for new insights because of correlating things together. >> And that validates your point about being that sales rep, because more data, the better data. Look it, we just did a master class here. Master and student. Real time, on the fly. >> This is the second master class you guys have done. At Dell Technologies World, there was a master class on block chain I sat in between you two. >> I got to say, that's a new format we should look at, this real time invention. >> Michael: I love it. >> Well, Michael, thank you so much for joining John and me on theCUBE. It's been really exciting to see, in 11 months, what's transpired for Boomi. We can't wait for next Boomi World. I can't wait to hear how this double i intelligent-- >> Maybe another i? >> Insights. I cubed? I three? All right, all right. Won't quote you on that, but we appreciate it. >> Great to see you. >> Very cool stuff. For John Ferrier, I'm Lisa Martin. You're watching theCUBE from Boomi World '19. Thanks for watching. (upbeat music)

why from Las Vegas it's the cube covering informatica world 2018 bacio by inform Attica hey welcome back it runs the cubes exclusive coverage of informatica world 2018 we're here at the Venetian in Las Vegas live I'm John for your co-host with Peterborough's coasting and head of analyst said we keep on insulating all the cube our next guest is jammed the dalla who's the enterprise analytics architecture engineer sire pharmaceutical and some named director of the enterprise analytic solutions lead at sire as well great to have you guys thanks for joining us thank you so love getting the practitioner view of kind of the reality right of what's going on off see dramatic has their show you guys are a customer you're looking at some of their products take a minute first to talk about what you guys do first see Pharma got some stuff going on Davies involved privacy's involves you're in Europe in the u.s. GDP ours here think I'm gonna talk about what you guys do sure so char Pharmaceuticals is a global leader in rare diseases so there's about 350 million patients who are effective remedies is today and so art group with NIT enterprise analytics so we're focused on making sure we bring the right technologies and capabilities around bi and analytics to the organization so we look at products tools figure out how they fit into our our ecosystem of bi stack of tools and make that available to our RIT colleagues as well as our business colleagues so rare disease can you just explain kind of categorically what that is cuz I'm assuming this fits rare is not a lot of data on it or there's data you got to figure out what is that how do you guys categorize that so rare disease you know majority the rare disease affected by affected children so that's a kind of a critical aspect of what we do you know rare disease could be in immunology it could be in oncology GI I mean there's very disease typically you know people who are affected affected probably less than a thousand or 2,000 I think one of our drugs the population is around 5,000 people and these are chronic diseases typically their chronic diseases so they're they're they're diseases that affect the quality of life of an individual so what you guys are doing is identifying what is it about the genealogy etc the genome associated with the disease but then providing treatments that will allow especially kids an opportunity to have live a better life over extensive time yeah and what do you guys do there in terms the data side can you explain what your roles are yeah so like I said we're you're in the enterprise analytics so we're focused on bringing technologies and capabilities around bi and analytics spaces so how do we bring data in and ingest it how do we curate the data how do we do if data visualizations how do we do data discovery advanced analytics so all of those kind of capabilities and we're responsible for so what's your architecture today you have some on premises their cloud involved you just kind of lay out kind of the environment as much as you can share I know maybe some confidential information but for the most part what's the current landscape internally for you guys what are you dealing with the data sure so we fill out a new a new next generation analytics we called it our marketplace or the analytics marketplace we're leveraging both on Prem as well as cloud technologies so we're leveraging Microsoft Azure hdinsight for Hadoop the Big Data technologies as well as informatica for data ingestion and bringing data and transform or transforming yet but there are many tools involved in that one so it's like the whole ecosystem we call does marketplace which is backbone for shared enterprise analytics strategy and future you guys put a policy around what tools people can bring to work so to speak and we're seeing a proliferation of tools there's a tool vendor everywhere we look around the big data it's right I got a tool for this I got a tool for wrangling I've seen everything how do you guys deal with that onslaught of tools coming in do you guys look at it more from a platform respective how are you guys handling that right so look at a platform perspective and we try to bring tools in and make that a standard within the organization we look at you know the security is it enterprise grade technology and yeah it's a challenge I mean they're basically certified you kick the tires give it a pace test through its paces and then we have our own operations team so we can support that that tool set the platform itself so and what are your customers do with the data they doing self service or they data scientists are they like just business analysts what's the profile of the users of your customers of your we have all set of users they have like a technical folks which they want to use the data like traditional ETL reality so there are folks from the business they want to do like self-serve and unless they want to do analysis on the data so we have all the capabilities in our marketplace so some tools enable those guys to get the data for the selves or like the tools we have and dalibor does their own stuff like the eld talk a little bit about the one of the key challenges associated with pharmaceuticals especially in the types of rare disease chronic young people types of things that you guys are mainly focused on a big challenge has always been that people when they start taking a drug that can significantly improve their lives they start to feel better and when they start to feel better they stop taking it so how are you using big data to or using analytics to identify people help describe potential treatments for them help keep them on the regimen how do you do are you first of all are you doing those things and as you do it how are you ensuring that you are compliant with basic ethical and privacy laws and what types of tools are you using to do that it's a big question yeah yeah so we are doing some of that you know we have looked at things around persistence and adherence and understanding kind of you know what what combination of drugs may work best for certain individuals or groups of people yeah and definitely you know some compliance is a big factor in that so when I'm working close with a compliance group understanding how we're allowed to use that data in between which parts of the organization do you anticipate that you'll have a direct relationship as some of these customers or is there an optimist in other words does analytics provide you an opportunity to start to alter the way that you engage the core users of your products and services like I believe so you know I think one thing that we're looking at which strategic standpoint is um how do we diagnose people sooner a lot of these chronic diseases you know they go through 2-3 years of undiagnosed so they'll jump around from you know doctor a doctor if I understand what you know what the issue is so I think one thing we're looking at is how do we use data and AI to to more quickly be able to diagnose patients has a 360 view helped you guys of data you guys have a 360 view how do you cuz we'll look at that in terms of a channel selling a product and serving because we have a different perspective what's the 360 view benefit that you guys are getting yeah so we have a kind of a customer care model which is kind of a 360 for our customer so understanding you know around just drug manufacturing to making sure they have the right you know they have the right supply to understand is it working for the patient's so we've always been talking about the role a big day you mentioned had to do that Hadoop supposed to be this whole industry now it's a feature of data right so there's a variety of you know infrastructure as a service platform as a service some say I pass and Big Data how are you guys looking at that as as as builders of IT next-generation IT the role of I pass and Big Data we see it as a role in a blur you know I think what cloud brings us in the past type solutions is agility you know we as the market is so evolving so quickly and there's new versions of new software coming out so quickly that you wanna be able to embrace that and leverage that give it benefit of like give it some sort of a comparison old way versus a cloud like is there been some immediate benefits that just pop out yeah that a lot more benefits with doing the world way and the cloud way because with the cloud that brings a lot more scalability in in all India's to get like 10 servers you need to work with the infrastructure team I get it like it takes three months or two months again it with the cloud based one you've worked out you can scale up or scale down so that's one thing because it's so you're talking about Big Data yeah you're getting the volume of data you're getting you need to scale up your storage or your any compute you either JMS and compute bring data to the table and then you gotta have the custom tooling for the visualization yeah how that kind of together right you talk about them from your perspective the balance that you have to have guys have to deal with every day like you got to deal with the current situation NIT you got cloud you got an electrical customers personas of people using the product but you got to stay in the cutting edge it's like what's next cuz we going down the cloud road you're looking at containers kubernetes service meshes you need a lot more stuff coming down the pike if you will coming down the road for you guys how are you guys looking at that and how are you managing it you have some greenfield projects do you do a little you know Rd you integrated in how are you dealing with this new cloud native set of technologies yeah definitely a balancing act you know I think we do a lot of pocs and we actually work with our business and IT counterparts to see hey if there's a new use case that is coming down you know how do we solve that use case with some of the newer technologies and we try a POC may bring in a product to just see if it works and then see how do we then do we then take that to the enterprise so I got one final question for you guys and maybe you do as well John but but in life and death businesses like pharmaceuticals is a life and death business the quality of the data is really really important getting it wrong has major implications the fidelity of the system is really crucial you say using informatica for for example ingest and other types of services how has that choice made the business feel more certain about the quality of their data that you're using in your analytic systems into standardization so you know if between MDM round mastering our data - ingesting data transforming our data just having that data lineage having that standard around how that data gets transformed is that fundamentally a feature of the services that you're providing is you not only were you you know the ability to do visualization on data but actually providing your scientists and your businesspeople and your legal staff explicit knowledge about where this data came from and how trustworthy it is and whether they should be making these kind of free complex very real hardcore human level decisions on is that is that all helping yes because it seems like it would be a really crucial determination of what tools you guys would use right it is yeah and absolutely I think also as we move more towards self-service and having these people having data scientists do their things on their own being able to have the tools that can do that kind of audit and data lineage is crucial great to have you guys on we had a wrap I want to ask one more question here you guys were an innovation award e informática congratulations any advice for your peers out there want to unleash the power data and be on the cutting edge and potentially be an honoree yeah I would say just definitely think outside the box seem to try new things try puce you know do POCs is there so much new technologies coming down so quickly that it's hard to keep up Jam cuz it's like a moving target you need to chase your movie target and based on B was it that gets you like what you want it to do you know siding yeah get out front don't keep your eye on the prize yeah focus on task at hand bring in the new technologies guys thanks so much for coming on great to hear the practitioners reality from the trenches certainly front lines you know life-or-death situations of quality of the data matter scaling is important cloud era of data I'm John for a Peterborough's more live coverage after the short break

>> Announcer: Live from Madrid Spain. It's theCUBE covering HPE Discover Madrid 2017. Brought to you by Hewlett Packard Enterprise. >> Welcome back to Madrid Spain everybody. This is theCUBE, the leader in live tech coverage. We're here covering HPE Discover Madrid. I'm Dave Vellante with my co-host Peter Burris. Vaibhav Parmar is here. He's a partner with PwC. Good to see you again. >> Good afternoon thank you for having me here. >> Yeah you're welcome. So PwC obviously people know it as top consultancy, global capabilities, deep industry expertise but give us the update on sort of your role and where you're focused. >> Sure so I represent our technology consulting organization at PwC. And basically we work with our clients and our business partners in bridging the gap between the business solution and the business outcome that are intended and all of the enabling technologies that get to the outcome. And so our role is to design solutions, help implement and get them deployed, and make sure they work properly all with the objective of this technology has to be aligned to the outcome, the business results that are being intended. So we provide that traceability from the beginning to the end and we are the part of the consulting organization that focuses on the use of these new and emerging technologies for bringing that business realization. >> So anybody who's followed the tech business obviously has been following cloud and the impact that cloud has had on customers, on industry competition, on how companies partner, go to market, et cetera. And get and achieve outcomes. So hybrid cloud is all the rage. Hybrid IT, what does that mean to you? You hear it from Hewlett Packard Enterprise. What does it mean to PwC? >> Sure so I think the word hybrid has become very pervasive now as we think about technology, as we think about IT and there's several definitions to the word hybrid or the terminology hybrid IT. First it's the use of multiple types of technology environments to deliver the outcome. It could be a public cloud provided by one of the major cloud providers. It could be all of the on premise traditional data center technologies and infrastructure. It could be a combination of both using some third party intermediary in a co-location or in a hosted manner. But that's one part of the hybrid definition. The second part of it is actually being able to access services and capabilities that are built and innovated by the providers. So if you look at where Microsoft is going with Azure and all of the platform as a service capabilities that they've introduced into the marketplace, how do we take advantage of that so that we're not building individual lego blocks we're actually taking lego blocks that have been built and assembling them for some type of a business outcome. So hybrid is also taking advantage of the services that have been prebuilt and predefined so that we can fuse them together to get the result that we're looking for. And the third part of hybrid is what we're now starting to take advantage of with the public cloud providers which is we use what we need and only pay for what we need. In the traditional model, we buy this much and we may be used to using this much at some point in time, this much at some point in time. I think in the new and emerging model it's... We only want to pay for what we need and what we use whether it's in a public cloud manner or it's in our own data center manner. So hybrid is also that flexible consumption and economic model associated with all of this technology and the infrastructure. >> So what's the predominant conversation going on with customers today? Are they saying Vaibhav help me get to the public cloud. I don't want to have all this infrastructure in house or are they saying look I can't move everything to the cloud, but I want that cloud experience in my own operations. >> Yeah. >> Hybrid, hybrid IT obviously is the latter, but what's the dominant discussion with customers? >> So I think it starts often as the first because customers hear that hey everybody's talking about the public cloud. Maybe I need to be there. But then very quickly we come to the conclusion that a public cloud by itself may not be enough or adequate and we actually need to think about the hybrid model for a number of reasons. It could be for data governance and compliance reasons. There are jurisdictions and municipalities that have data residency requirements which make public cloud more difficult to use. It could be for performance reasons where a lot of the applications and the use cases are of a nature that an on premise environment will continue to make more sense. So very quickly we move to the point of view that a hybrid model is the more probable and practical model. But the conversation also is not so much that I want to be in the public cloud or I need a hybrid cloud, it's more so around here's the outcome and the business result that I'm looking to achieve. It could be that I want to get more features out to the market faster. It could be that I want to take advantage of innovation that's available to me so that I don't have to invest in that myself. It could be that my developers are antsy for all of this new technology that's available and they don't want to you know be encumbered by more traditional life cycle activities. All of that then leads to the decision or the discussion on okay well this sounds like a cloud oriented mindset. Let's talk about what is available to us to get more flexibility, to get more agility, to get the developers all the tools that they're looking for. And that's where we quickly start saying okay the public cloud has all of these offerings but again it may not be adequate enough and we need to complement that with an on premise environment and the hybrid model becomes a natural landing point. >> So the technologies and means to that outcome end. Okay so in thinking about the realities of hybrid cloud, hybrid IT whatever you want to call it, how do you actually... You named some attributes and some parameters. How do you actually bring those to the clients? How do you make it real? >> Sure. So I think we have to think about what is it that we need to make it real? Again if we think about it from a developer point of view they want quick access to environments so they can do testing. They can do code reviews, right. And the environment has to be stood up in a very timely manner. Today it takes time to request the environment, to do all of the configurations, make sure all the security processes are in place. But the developers want quick access because they want to turn that code over very quickly. So that's one part of what is needed to make this real. Let's think about the developers. The second part of it is around operations. We want this to be safe, we want this to be secure. We want this to be aligned to our corporate policies on regulation, on use, whatever it might be. And so we have to think about okay well what are all of those elements that make this compliant? That make this highly aligned to our security policies? The third part is the economic part of the equation. Again it's we want to only pay for what we want to use. How do we make that a reality in an on premise world where we're used to traditionally spending a lot of money at one time and then we're just amortizing that over time. Well in this new world we need that economic model as well. The ability to only pay for what we need yet still have the capacity that we're looking for. So when we think about what is needed to make this a reality, I think it's the infrastructure in an on premise environment has to look and feel very much like the infrastructure that's in the public cloud. Highly accessible, you can turn it on and turn it off when you need it. You can spin up these environments when you need it. And then you turn 'em off when you don't need them. It's the consumption model and the economic model. Again I'm only going to pay for what I need and not for excess capacity. So that capability has to be there as well. And then the third is the fact that developers are highly motivated by these innovative capabilities that are available in the public cloud model. So you know firewall as a service, storage as a service, how do we make that equally available in an on premise environment? The on premise world and the cloud world have to look very much alike from an infrastructure point of view, from an application and services point of view, and from an economic and sourcing point of view. That's what's needed to make it a reality. >> Is the delta so... That all sounds great and it's kind of consistent with what we see in our research. But when you start to peel the onion on what exactly goes in on prem, it isn't exactly like the cloud, but maybe it doesn't have to be. Does it just have to be substantially better than what was there before? And substantially mimic the cloud? With some other attributes that the cloud can't deliver. For instance data residency. >> Correct. >> And other locality stuff. Maybe governance, maybe not. I mean that's one you've got to really think through. >> Vaibhav: Sure. >> Some of the cloud guys would say hey we've got great security and great governance. >> Vaibhav: Sure. >> But does it map to my edicts as an organization? So there's some nuance there. But I guess my question is how close do you actually have to get to satisfy this sort of customer demand? How close to that cloud model? >> So again I think I would say we can try to match it from a... A CPU to CPU point of view or from a storage terabytes petabytes point of view. But again our observation and our experience in working with our clients is it's not so much at that level, it's more so at the, the behavior level. Again if I'm a developer, I don't want to wait two, three weeks for somebody to stand up an environment. I want to be able to stand it up in 30 minutes or an hour because my code is a microcode. It's not a big monolithic, waterfall oriented code anymore. So if I have a microcode that I want to quickly test, I want to be able to have the environment that's quickly accessible and when I'm done with it, it can go away. So it's those functional attributes that we have to match. How we technically match them, you know we can go in many different directions. I think one of the things that we're excited about in where HPE is going as an example, is with Azure Stack, right. The availability of Azure Stack in collaboration with Microsoft in an on premise environment looks and feels very much like the public cloud environment. With the ability to get the microservices that we're looking for. With the ability to spin up and spin down the environments in the timely manners that we're looking for. Those are the attributes that I think are what we see as our clients looking for. And behind the scenes, how we technologically enable them, it doesn't have to be a like for like match. >> Dave: Right. >> So the exciting thing is we're seeing the evolution take place. We're seeing the availability now. And it's time to start taking advantage of it. >> So we talked about hybrid cloud, but in many respects we're really talking about multi-cloud. That's really going to be the challenge. So I want to take you back a few years. And build a scenario and you tell me if you think this is where things are going to be. So a number of years ago, we had a lot of mini computer companies with a lot of specialized networks. And if you were running a plant, you would have all these little mini computers. And you'd say I want a single network for everything. And they'd say yeah, bridge my network, gateway my network, all this other stuff. And along came TCP/IP and flattened everything. >> Vaibhav: Sure. >> And that drove a lot of the mini computer companies into... I mean HP survived because they were one of the first ones to address and adopt TCP/IP in a big way. >> Vaibhav: Right. >> So here's the question. We're going to have hybrid cloud. But it's inevitable we're going to have multi-cloud. >> Correct. >> Because we're going to have Saas, we're going to have Iaas, we're going to have on premise, we're going to have Edge. And all these have to share attributes as you're saying of the cloud experience. >> Vaibhav: Right. >> How are companies going to start thinking about flattening all those different cloud options so they are not recreating the legacy of having to manage all this stuff but adding the transaction cost of having to do that through a contractual arrangement? >> Vaibhav: Sure. >> Where's this going to end up? >> Sure. So I think there's a couple of ways to think about that. If we think about the cloud model, you mentioned those terms infrastructure as a service, you have platform as a service, and you have software as a service. I think we're making a lot of progress on the infrastructure as a service layer becoming more and more flatter. And more and more consistent across the different providers. Consistent across your private cloud providers. Consistent across your public cloud providers. And what I mean by that. >> Give us an example. >> So an example would be as we look at where technologies like Open Stack and Open Shift are going. >> Peter: Okay. >> You know you're essentially turning all of this infrastructure into virtual elements, right. I want a computing resource whether it's this company's CPU or this company's CPU, it shouldn't matter. I'm looking for storage. Whether it's this company's storage or that company's storage, it shouldn't matter. I just need high speed storage, which might be flash, or I need more traditional storage because it's all static data which may be more disc based storage. We're becoming more and more flatter from that point of view. And behind the scenes, again, it could be a white box. It could be a branded box. It doesn't really matter to us anymore. So we're making good progress at the infrastructure layer and on top of it we've got good control mechanisms to be able to access that infrastructure function. Again through Open Stack type technologies that provide this control mechanism so you can provision, you can source, and you can manage and operate. As you get to the next level which is platform as a service, this becomes a little tricky because this is where you're getting into the application layer that's taking advantage of that infrastructure. And here you do have differences between the cloud providers. You know one cloud provider may choose to offer a set of platform services that are built one way whereas somebody else may do it a little bit differently. And so that does require some contemplation, right. Are we going to go down this path or go down this path? Because getting a firewall function from this provider... It still gives us a firewall function that this provider provides, but it's done a little bit differently. And that may not be always... You may not be able to make it flat. Because that's the nature of how the application layer is being built up. As you move to the software as a service layer, this is purely at the application layer now right? And there you will always have differences. Software as a service coming from one CRM provider or HR services provider will be different than another one. But what they've done is completely abstracted all of the underlying requirements. You don't need to worry about infrastructure. You don't need to worry about platform. You're purely sourcing the software function. So I think as, going back to your question of where will we get to when it comes to flattening all of these things, I think at the infrastructure layer we will get there because it's happening. At the platform layer, we may not get there because it's the nature of the business and the functions that these providers are developing and making available. And at the software layer, we won't get there because it doesn't make sense, right? We're going to focus on the business outcome and not necessarily the software itself. >> Well how about at the governance layer? My last question, at the governance layer across all these multi-clouds, the Saas, the platform as a service, the infrastructure, can we get to a common governance model? And why is your approach, you know talk to me as a customer, how can you help me get to that common governance model? Why is that approach better than sort of doing it on my own or doing it with a cloud provider? >> Sure so you bring up a great point. That despite the fact that we will have variances in the cloud providers and the cloud capabilities, yeah we do need some way to properly manage, run, orchestrate, operate, and govern all of these different multi-cloud scenarios that we will be in as a large enterprise. And so governance becomes a key tenant of how do we make this successful? And governance has a number of different definitions behind it. It's the operating model and the policies by why you provide access to the cloud services. So how do we make sure that a developer isn't just clicking a button, and then spinning up environments without the right controls behind it? Whether it's in this cloud provider or in this cloud provider. So putting in the policies and controls in place is one element of governance. The second part is having a consistent mechanism for connecting into those cloud providers through the right APIs and interfaces so that when somebody says I need additional storage, you're not having to create bespoke processes and technical interfaces between one provider and a different provider. You almost need an abstraction layer that provides all of the transparency in the back end so you can plug to provider A, provider B, but on the front end to the developers, to the architects it looks like I want more storage, right. And the machine will do all of the translation behind the scenes. So that's the second part of governance. The third part is on the operating model, on the operations itself. How do we make sure that the cloud provider is holding up to their commitments in terms of reliability, availability, throughput, all of these commitments that they've made so that we can get the performance results that we're looking for. Well provider A is going to have a different set of mechanisms to run and operate their environment compared to provider B. So governance also includes this element of looking at all of those different mechanisms by which the environments are being monitored and operated and giving us that consistent view so we know that yep, we're getting the services and the performance and the throughput that we're looking for and we've got a common set of processes and tools that allow us to interface into each of those providers on the back end. So we're not having to do all of this bespoke tools development, we can do it in a common way. And the fourth part to governance is ongoing controls. So how do we make sure that what we're expecting to use, what we're expecting to consume is aligned to the forecast. And that we're not deviating. If we are deviating, there's a good rationale behind it. Right, if the developers say over the next six to 12 months I'm expecting to use this much storage and this much compute because it's aligned to these business objectives, how do we make sure that's what ends up happening and that the developers aren't spinning up extra environments without the right discipline behind it? So that's a part of governance as well. >> Excellent. Well great framework Vaibhav. Thanks very much we've got to leave it there. We appreciate you coming on theCUBE and sharing your thoughts. >> Absolutely. It was my pleasure thank you David. Thank you Peter. >> You're welcome. Alright keep it right there everybody we'll be back with our next guest. We're live from HPE Discover Madrid 2017. This is theCUBE. (upbeat music)

>> Announcer: Live from the Javits Center in New York City It's theCube covering Inforum 2017. Brought to you by Inforum. >> Welcome back to theCube's coverage of Inforum 2017 here in New York City. I'm your host Rebecca Knight along with my co-host Dave Ballante. We're joined by Mike Rodgers. He is the CSIO of Pilot Flying J. Thanks so much for coming on theCube. >> Thanks for having me. >> So tell our viewers a little bit about Pilot Flying J and your relationship with Inforum. >> So Pilot Flying J is a travel center. We cater to basically over the road truckers and we do have a big gas business too. We operate about 700 locations. Most of them are owned fully by Pilot Flying J. Some of them are dealers where they have a relationship with us. They're in our network but we don't know them. So we run the majority of the locations and we own about 40% of the overall road diesel market. >> Rebecca: In the US and Canada? >> In the US and Canada. >> Okay and talk about your relationship with Inforum. >> So our relationship with Inforum really goes back to Lawson. I've been with the company for about two years. We run Lawson. David Clo-thy will tell you probably 25 years. The company has very rapidly. Started off as a small little Tennessee company. Well now it's a rather large company and we felt we knew we had to make a change relative to our human capital management and our financial systems is because we basically outgrew it. And we like to write a lot of things so we wrote a lot of applications out of our desperate sylo. And of course it's a lot of technical debt that goes along with them. So when I start with the company. We started on valuation process and picked for as the partner to replace all of our financial systems, and all of our human capital management systems. >> And so you migrating from traditional legacy lawson to the cloud suite. >> Pretty much, I would characterize it as a migration but we had very little in the vein of human capital management. And what we did have, we wrote ourselves. For example, we wrote our own applicant tracking system, which we'll of course have to integrate into lawson. So we have an integration layer that we have to support there and that's just one. There was a slide put up this morning that showed that we're going to eliminate 26 systems that we either bought as the best of breed type of application or we wrote ourselves. >> So how painful is that? Is that why you-- >> It's extremely painful. >> They brought you in for this task and you obviously knew this coming in or just-- >> Oh I knew this coming in. >> Dave: No surprise. >> No surprise and by the way, pilot is no different than a lot of other retailers in other companies out there. We've got a lot of technical there and I will tell you the more I see about Inforum. The more I think we made the right decision. I really like the cloud strategy. I'd like the integration associated with all the different functions specifically within the HCM suite. It's not a roll up like some of the other guys have rolled up. They bought but whether it's PeopleSoft or whatever and they many talk about it being integrated, bit it's not as integrated as the Inforum suite. >> So if I may, sorry. We want to stay on the migrations for a second because it's non-trivial and people. The conundrum of migrations is nobody wants to do them because it's just such a heavy lift. But the longer you wait, the more technical debt you accrue. >> I use to say you have to get off the treadmill. You have to stop and say we're not going to keep digging ourself in this ditch and it's going to be painful. It's going to be expensive. It's going to be disruptive and I use to say the (indistinct speaking) usually get fired. That really is, I might say that laughingly but-- >> Dave: You got a got attitude about-- >> It's hard, okay. It's a hard thing not just for the IT guys. It's a hard thing for the organization with respect to change management. >> So incredible amount of planning obviously. You knew your freezing code. >> Pretty much because why would we continue to develop something. I wouldn't say we were 100% frozen. Things come out especially in HR where there's a regulation thing. >> Dave: Compliance, right. >> Right compliance and you got to do it so we got pretty good at saying we're not going to, we're going to wait for Inforum. And we've got a lot of it implemented. We're continuing. We got a nice plan. An iterative plan, we're not trying to blow the ocean and convert everything all at once. Very good engagement from the business. We have a lot of business partners here with us. Like the IT representation at this conference. It's the smallest compared to the business. >> So I would think a key there though is because when you freeze code. It slows your business down, but then when you actually go to the new platform. You want to be able to move faster and leap frog your competition. >> I would argue that really, because we really didn't have much. It really hasn't slow much down. Where we had to do something from a compliance perspective, we've done it. But it hasn't really slowed us down. The leap frog that we're going to do when we implement the whole cloud suite is going to be enormous. >> Sorry about. >> I wanted you to step back a little bit and tell our viewers about some of the specific HCM challenges you have and what you, talk about the pain, I guess is what I want you to describe. >> We run travel service. We're open 365 days a year, 24/7. They never close. They're all on food operations. >> Rebecca: Of the three quick services food operations. >> It could be up to three. If we don't have three in every stores someone said that. We may have one in every store plus a deli operation that we run ourselves and we actually create the food. Whether it's pizza, meatloaf whatever the truck drivers really want with respect to our food offering. They want something different, more variety. So yeah, it's a very complex business. It's hard and we're very spread out throughout the country. We're not necessarily in a big cities like New York. you're not going to see a pilot in New York City. You're going to see a pilot or a flying J on major interstates throughout the country. So there were spread out. So connecting with our team members has been a challenge for us. And our owner Jimmy Haslam will tell you that we probably have not any give himself a vibe. And we are connecting with the team member so we're doing a lot to facilitate that connection. We'd actually partner with the Disney Institute to help us with that. And we've actually called Inforum for project connect. So it's going to provide that connection platform to those team members that are spread throughout the country and Canada for that matter. That we don't get to see that very often, if ever. >> We're hearing a lot at the keynote retail has been highlighted a lot and Pilot J is a form of retail in that sense. And talking about how important it is for the customer experience. The trucker themselves who come in to apply at Pilot Flying J. >> Our strategy is focused on making it a great place to work. In other words, doing the right things for our team member and the investment at Inforum is really going to provide that platform. The other part is making it a great place to shop, and we want our customer to come back. Okay we sell a commodity, let's face it. We sell diesel. You can buy it down the road. We want the experience when they come into our store. We want to take care of our guest like nobody else takes care of them. We got a truck driver. There was an article written in New York Times but you don't throw away people. These guys, you got it, you're wearing it. Your tie, your shirt, whatever came on a truck, and these guys, they're great people. I've talked to a million of them. We want to be the place where they come that feels like home and we want to make a better day for the truck or the driver. It's a tough job. They work hard. They're waking their families. When they come into a pilot. It should feel like somewhat of an oasis. >> Right so, it's super clean I understand. >> Yeah, we try to make them clean. Remember If you're a truck driver and you're away for week's on end. You're going to shower at our locations and so the showers are cleaned and maintained after every shower. Nobody gets in a dirty shower. The rest it's challenging. We have 3000 people come through our doors every day at every location so it's challenging to keep the rest rooms in particular clean. But the showers are cleaned before anybody gets in them. >> And you own the real estate or you lease it? >> We own. >> Dave: Really. >> I'm sure we lease some of this. I've got a question for Dave. We own most of our-- >> But your in the real estate business too. >> Oh yeah. We're definitely in the real estate business. >> What about the data? How is the way in which you use data evolving? >> It's evolving very rapidly and we are a data rich company especially with respect to the professional driver which is the majority of our profitable business. They scan their loyalty card whenever they come. We have a 92% swipe rate and that's because they use those points to buy food, buy showers. >> Rebecca: They're rewarded. >> They're rewarded and it's lucrative to them. They're managing a business so they use that as currency. So that data provides us with the ability to solve. We needed utility along the customer journey. For example, we may know when a guy needs a shower and we may have a fuel buying advantage at a certain location. Offer them a free shower if he fuels at location X because it's beneficial for him and us. Okay we're going to give him a free shower or a free slice of pizza if we feel we have an advantage with respect to purchasing petroleum. >> You're building loyalty. >> Right and builds loyalty so that's on the customer side. >> Rebecca: That's the nudge they need to walk in-- >> To be able to use our digital platforms, our digital properties to take the data and drive behavior, and loyalty. It's really about loyalty. We want to give good things to our loyal customers, take good care of them and solve the problems they have. 'Cause they'll come back. And Jimmy says we want them to come back. He says it and we do things that are going to solve the problem they have. They're going to come back because it's the least friction. >> Are you using data for the logistics in any way, for these truckers in other ways? >> Yeah, that's not Inforum, however well for the truckers. We're using logistics with respect to how we procure petroleum. And I'm probably not going to get into a lot of that because we feel it's a competitive thing there with respect to how we do it. And we are investing a good bit of money into how we procure and manage how we distribute petroleum to our various locations. >> That's a data lever. You got advantage better than-- >> That's where a lot of data reach and we can use data very effectively. >> So data literally is oil. We had a guest on. >> Well data is abundant insights aren't necessarily so that's where you're making money. You've mentioned before Mike that you said you are more confident after you go through this migration, but Inforum was the right decision. What gives you that confidence? Can you double click on that? >> Yeah, it's a couple of things. Number one, and we talked about the technical debt right. So lifting everything to the cloud give me a unique opportunity to eliminate the technical debt 'cause we're not going to write it. We're going to stay current on the latest release of the software. Whereas if you looked around here, everybody will tell you they're behind releases, releases, releases on enterprise software that they've purchased from somebody else that's not in the cloud. So number one elimination of technical debt and staying current on the existing platforms. You really can't customize it. You can customize it within the tool so with the customization or configuration or extensibility carries along as they operate the software. That's the biggest events and I think being in the cloud. I was showing some data to my boss the other day regarding how our infrastructure investment has gone up. Really been able to manage the actual investment with the number of servers, VMware and all that we're running has grown exponentially. That's 'cause we hadn't retire anything. We're going to, with Inforum we're retire 26 platforms. They're going away. They'll be out of the infrastructure and it will be in the cloud. I don't have to manage anymore. >> You're getting rid of stuff, wow. >> Mike: Getting rid of it. >> GRS recall, that never happens in IT. >> I took personal responsibility for the decommissioning aspect of the project. >> I'm going to ask you another IT question is that latest release because you're in the cloud and you're multi-tenet, you have to go essentially into the next release. Does that create down stream problems for you. How do you plan for that? >> Well we're new into it, okay. We're working with Inforum on that and it's perfect now but they get it. We got to be careful when we make the release so we can be prepared for it. So far there have been upgrades and it's been nerve racking. A new release of code that we hadn't really tested or whatever but I think we'll get that route resolved. I said it's new, we got to become efficient in how that happens. We need a little bit of prior notice. >> Dave: Forced agile. >> Yeah, forced agile. Here it comes. (laughing) >> There's a lot of buzz about artificial intelligence here at Inforum. Where would you say Pilot Flying J is with regard to using artificial intelligence as part of your workforce. Giving your workers access to it and also more tools to make the right decision at the right time. >> I think it's at the stage now where it's really cool and it's somewhat of a buzz thing. AI when machine learning. I think it's going to be very relevant and probably not the too distant future. It's not on my immediate road map to worry about artificial intelligence. We thought about doing a project with IBM on fuel procurement and pricing with Lawson. It's just really not quite ready yet. What we can develop is deep insights with the data we have to make better decisions, and put power in the hands of our pricing team or our logistics team to make really good decisions. I think that's for us. Let's get that perfected and then we talked about the voice recognition that we heard yesterday. That I think is imminent and I think it's important for us and it's going to be on our road map because as a truck driver. I'm driving and if I can have the ability to ask questions of our app and purvey information back to that driver, without him having to touch his phone. There's a value of that. Most that has to be architected through the right type of data. How we structure our data to be able to access via natural speech but it is something that is on our road map. >> How large is your IT organization? Roughly. >> In number of people? >> Dave: Yeah. We have about 250 people in our IT organization but we do have a significant use of partners. >> And they're distributed or? >> No, they're in Tennessee. And for the notes popping now we use offshore resources with certain integration partners. We have a couple primary integration partners that we're using. >> So reason I'm asking so as you move to this cloud sass platform. How are you thinking about protecting your data and is it changing. >> It's a good question. And all of a sudden, for awhile there I think we do a great as securing it. We invested a significant amount of money protecting our data. I think I'd be naive to say that we could do a better job than Amazon web services. >> Dave: I would agree, no offense. >> And I think one of the gentleman was speaking yesterday said the same thing. And one of my guys looked at me says that's what we've been saying. I think there's always a risk. Security is a big deal especially with what's happened with one-acry and the subsequent problem. There's going to be more. I think that Amazon could be on top of it. I think together we can do a good job on security. It doesn't worry me anymore than it worries me everyday with respect to my own infrastructure. And it does worry me just not anymore. >> Great, well Mike, thanks so much for joining us. It's been a really enlightening conversation. >> Okay, thank you. >> I'm Rebecca Knight for Dave Ballante. We'll have more from Inforum in a little bit. (uptempo piano music)

>> Announcer: Live from the Javits Center in New York City, it's the theCUBE, covering Inforum 2017. Brought to you by Infor. (bright electronic music) >> We are back with theCUBE's coverage of Inforum 2017. I'm your host, Rebecca Knight, along with my cohost, Dave Vellante. We're joined by Barig Siraj and Nasser Bayram. They are both of the Zahid Group, out of Saudi Arabia. Thank you both so much for joining us. >> Good to be here, thank you for having us. >> So I want you to start out by just explaining to our viewers a little bit about what Zahid Group and Zahid Tractor, what you do. >> We are a large group based in Saudi Arabia. We're very diversified. We are mainly in heavy equipment, capital equipment business. We are the importer of Caterpillar machinery and Volvo trucks, Renault trucks, and many other products. More than 40 franchises. We have locations in more than 40 locations, or branches, more than 40 locations for their area, and we have about 4,000 employees, and we mainly focus on providing sales and after-sales services in the kingdom, with a big focus on after-sales. We pride ourselves to be the second to none when it comes down to after-sales services, and we strongly believe in technology and in digital transformation that is sweeping the world of business, and thus far, we embarked on this journey five years ago. >> So what does that digital transformation mean for your business, and generally, and then specifically for IT. Maybe you can start, Nasser. >> Well, first, we have to agree. The business model has changed. There are new business models that has disrupted every single industry landscape out there, and you have to be ready to change and accept that transformation, otherwise, you'll be left behind. The digital transformation takes you beyond managing an organization introducing an IT platform or technology. You have to change the way you think and your readiness to be able to manage where the future is going. If we look, we just attended this session, 52% of Fortune 500 companies in year 2000 no longer exist. They went out of business. In 2015, 55% of Fortune 500 companies lost money. There was no economic crisis or downfall. It simply missed the boat, or they did not, they were not very innovative in their digital strategy or thinking ahead, allowing their industry to be disrupted by people like Uber, Amazon, Alibaba, Souq, an other new entrants with very great innovative ideas and technologies. The old business model of cutting cost or restructuring an organization no longer works. You need to think differently and act differently, and hence, digital transformation becomes critical for your organization, and implementing an ERP platform, standardizing rationalization of your ERP platform, if you have more than one, like in our case, we have more than one, you have to have one standardized platform, one standardized processes, business processes, so that we have one source of data in order to be ready for the future where you can mine that data, have it be by analytics or business intelligence, in order to be able to better serve your customers and learning on about their behavior, about their trends, and how you can better position production services for them in the future to buy, and for you to remain profitable. >> So Barig, okay so now, that's, what Nasser just described, I'm inferring, is much more real-time, much faster, and more data. Your ability to analyze that data wherever it is, how do you, and the processes and people behind that as we talked about technology's the easy part even though some technology's even more complicated than ever. So what does that mean for the IT organization? >> Well for IT organization, we had, and we still have a legacy application built over 30 years. Now, and there we could not reap the benefits of the data mining, the standardization, even that just from AI capabilities on top of that. We cannot reap that until we have that standardized ERP Platform across all our companies. So basically, that's the tall order that was put on our plate, and what we have done, we started the journey. We're partly through it. We went live with two of our companies. We still have three more to go, and we've done it with lesser volume, allowing us to learn and therefore, once we reach our biggest volume company, we would have learned as an organization, not just applying the technology that even the personnel, the change management, the resistant pockets have to deal with all of that. >> Can you give an example of what you've learned along the way, becoming, as we said, it is so much about change management, and it's about getting people over this fear of change. Can you give an example of what you've learned, of what you're doing differently for the companies that have yet to have the rollout? >> The biggest learning experience we had, we just went live with one of our companies, called EJAR, which is a rental company. The success there of the learning, the success is a learning experience. We have a long journey for to go live with five companies, and this is the first one to go live. What we learned by doing that company first is the challenges of change management, how to support on live, challenge of data migration, data cleansing, readiness of the organization, not simply from change management perspective, but also from IT, legal, readiness of your documentation, the contracts, et cetera. It's a vast learning curve to overcome, and we're very happy that we took the strategic decision to go live once more company, so that we gain that experience, and that is the real success we got out of this project now. Now we better we feel we are in better position for the new companies to go forward with, when we go live, we learn so much about change management, where we failed and where we succeeded, we learn better about our readiness, whether it is Zahid Tractor, or Infor, or our IT, our infrastructure, our training program, our after go-live support, the war room was set up to support the go-live, and go in production. We've been two month in production. We're still having some challenges, but nothing that, there are no showstoppers, however, more and more every day, we learn more and more, and we are better positioned to go live with a bing bang on the big company. >> Nasser, as the executive in sort of leading this transformation, do you look for and demand new metrics, new types of KPIs that you want to see? >> Well, definitely, you do the whole thing because of the new metrics. The new metrics have to have built into it, not simply the traditional KPIs of your GPs and revenue and discount and so on, you need to look at customer behavior, customer analytics, pricing positioning, where you are going forward. In the old days, everybody would sit down around the table, say, "Hey, we're number one, okay?" That doesn't hold water anymore. You're number one in what? It's about number one in responding to customer requirements on that customer behavior. Today, with Amazon.com, many retail businesses are challenged, they're going out of business. How do you stop that business model? You can't. So how do you compete? You can. To do that, you have to have the right data in place, the right organization in place, and the right mindset to be able to lead your organization to compete in the new market space. >> Can you give our viewers some examples of the kind of data that you are deriving, in terms of this business analytics, in terms of understanding and deepening your understanding of customer behavior, and what customers want, and how it's changing, how you approach your customers and what you do for them. >> I'll give you a comparison. When we have a legacy systems, what you do at end of day, you extract your data, you transform it and you load it up to your data mart or data warehouse, and then you run your report, and if you're lucky, you have savvy users who can create their own reports on the fly, but with the way we're going with an integrated ERP solution and one standardized platform, we do hope we have the right analytics in place, and business intelligence in place, that we give our management the right data to make decisions, ready to make decisions. Not filtered data, not reports designed, and that takes me straight into your question on IT and ability to IT to deliver. There is no way for any IT organization to cope with the changes. Nowadays, when Amazon went live recently with Whole Food, it took them three to six, three to four months to deal with legal, to deal with retail, with pricing, with the announcement, the whole nine yards of marketing. How did they have their IT ready? That's a challenge. How can you do that in four to six month? That is the challenge in the future. If you don't have the right platform to do that, you will never be able to compete, and data analytics are critical for you to respond or predict the behavior of customer, so before a customer comes next time to the counter, you already have certain statistics that tell you what that person is ready for, and that takes you straight also into IoT. Your products, or our products now, are connected to the Internet. If you don't have IoT in place, connected to your back end, and your analytics, you won't be able to compete, and that would be the differentiator in the future. Those who could do that versus those who will continue to follow the old brick and mortar business model, restructuring and cost-cutting and whatnot. >> So your instrumenting your heavy equipment in the field, presumably, and that's, you're well down the road with that. That changes the data model, it changes the analytics model so I wonder if you could describe that a little bit. I mean, obviously you're processing data at the edge. How much data stays at the edge versus comes back to your central location, maybe you could add some color to that whole equation. >> Well the devices that are put on the machines, there are several ways of putting. The older models, you have, actually the PSSR has to actually go with his laptop, hook it up, suck the data, and bring it back for analytics. The newer models are more, are sending it to, directly to us, and enabling our, what I call tower, to do equipment monitoring, and be able to anticipate, we call up the customer and saying, "By the way." Actually tell the salesmen to call up the customer and saying, "You need to bring your machine in "because it's, you might face a failure "in so amount of time." So improving the customer side, that is, that is that part, but coming back to the organization change issue, we went from a legacy application that the branch managers waited until the end of the month to get the truth, to now being able to, seeing the performance on a daily basis, because they're seeing the truth because everything is connected, whereas before, whatever they did, they don't, their piece of the puzzle, they have a lot missing, and they, information that they waited until it show, send them back there, a report. >> And none of this takes place in the public cloud, is that right? >> No, it does, to add to that, the data is stored in the cloud. Customers have access to it, along with our SOS lab, which is oil sampling lab. They have access to the data to see what is happening, like predictive analysis of their machine performance, and as a result of analyzing the oil, plus any data collected from these machines. We do have cloud implementation. We just went live with our treasury management system. It is on the cloud, and it was our first deployment on the cloud, though the implementation of Infor today is still on-premise. Long-term, down the road, we may be looking at the cloud. >> I got to ask you, we hear Infor messaging about microspecialization, that last mile, all the hard stuff that nobody else wants to do. Is that something that you take advantage of in your industry, or is it? >> I'll give you an example. We utilize the implementation accelerator from Infor for the rental, and it's 77% of our processes map directly into that, so we, that enabled us, that, to have EJAR, which is a rental company, go much smoother. Now, we're working with Infor to enhance their equipment implementation accelerator, and it will be partly the same ratio, around 70% of the processes that we're going to go live with, are the standard processes in the product, out of the box, for the equipment rental, for the equipment business space. >> Our objective is to reduce customization as much as possible, go out of the box, or native, out of the box, as much as possible, but you have to accept the fact, depending on your business environment and some localization requirement, you have to do some customization. However we do have a governance in place, to make sure it's to the minimal. Otherwise, long-term, you'll be challenged with release management and change management and so on, and when you speak of the cloud, if you ever elect to go to the cloud, you can kiss customization goodbye. (Dave laughs) You have to be ready to adopt and adapt. >> And how about your security regime, as a result of the edge and IoT and now, cloud, how is that evolving? >> That's close to my heart. (laughs) >> Yeah, I'll bet, and probably the board's. >> Actually, well, (laughs) actually, interesting enough, many organization, like ourselves included, we invested so much money in building firewalls and security systems to protect what's behind the wall. Now with the cloud, well your most important data is no longer behind the wall. >> Rebecca: It's right there. >> It's outside the wall, so you have to have some kind of a hybrid security system, and you really have to pick the right partner who is hosting your cloud application, leasing your cloud application to you, so the challenge or the perspective of security, cybersecurity, changes drastically and totally, and your understanding of it has to change, otherwise, you just stay behind your own wall and guess what? You can end up locking yourself behind the wall, and you're going to miss the boat, but this does not mean that you'll let down your guard. You have to maintain your security awareness, you have to maintain your security diligence, and you should not underestimate the threats out there, because even if you are on the cloud, the biggest threat nowadays is through phishing. That's what we call the human firewall. Relegating the right awareness, the right education to your organization from within, to understand the threats and the danger of such a threat, otherwise, your password, that's how you access the cloud, you'll end up be compromised and guess what? So will be your data. >> Yes, so, Barig, Nasser, thank you so much for joining us. It's been great to have you on the program. >> Our pleasure. >> Thank you. >> Nasser: Thank you for hosting us, thank you. >> See you guys again, great, thank you. >> I'm Rebecca Knight, for Dave Vellante, we will have more from Inforum after this. (bright electronic music) (bright instrumental music)

(upbeat music) >> Live from Las Vegas it's theCUBE. Covering Oracle modern customer experience 2017. Brought to you by Oracle. (electronic music fades away) Welcome back everyone. We are here live in Las Vegas at the Mandalay Bay for Oracle's Modern Customer Experience Conference. I'm John Furrier with my co-host Peter Burris with theCUBE and our next guest is Brian Curran, Vice President of Strategy and Design with Oracle Cloud. Great to have you on theCUBE. Welcome to theCUBE. >> Thanks for having me. >> So you're a design and strategy person you've been the art and science of designing experiences. Not so much in the technology, Vasas Port, Vasas Server and yet innovation is the number one thing people talk about in the digital transformation certainly that's happening. But it's hard when you have all those Legacy stuff process, people, well that guy does that, that's his job over there and this guy runs it over there, so that's all coming together as we were talking about on our intro, Peter and I were talking about that. How do you get at the innovation when you engage with customers? When you walk in the door? >> It's funny. It's still a dirty word in companies innovation, right? I mean people are scared of it because the fancy word is innovation. The real word is change. And now you want to make changes in an organization and it's scary for people. And what I really do is I try to spend time with them trying to get them to understand that this is an art and a science. The science part is usually where you start first because I'm trying to get them to kind of go through the discipline of what it takes to do that. And it's really about getting the right people involved in that. And so I really try to spend my time saying look, let's find something really small to go work on, let's find a little problem that maybe you have and let me show you the art and science of getting from that understanding of the customer need all the way to hey I've got to wait to actually solve that will drive results for your business. >> It's interesting, the psychology of the customer is under a lot of stress, because as you said, it's a dirty word, innovation, because it means change but now it's interesting with Cloud you're seeing some of these technologies out there there's more pressure on top of that, it'speed they have to do it faster, so now you have a speed game going on and then agility and all these things people are seeing as use cases that okay, people are getting things right, but what do I do? And this is a lot of pressure for them. How does that add to the complication when you get to come in and say okay, we've got to change we've got to do it fast. You're roles might change. How do you take that? How do you walk through that? >> Well, first of all when you talk about the trends and the changes, what it's driving is these increased expectations so customers are dealing with an Amazon then they're coming back to another brand saying hey, how come you're experience is not like Amazon? So companies feel that pressure right now and they realize they can't wait six months, 12 months to go make the change, they've got to make it in six weeks or 12 weeks and so one of the things I'm a big believer is in rapid prototyping, get your point to a test where you can actually get it out in the market. So how do you frame something to really understand it in a couple days? How do you ideate in a couple days? How do you get everyone to understand what you're trying to do in a couple days and eventually get to a point where maybe five or six weeks, boy you're driving at that point. But the old days of going, hey, let's have a big strategy session and then we'll come through stuff in three to six months and nine months from now by then you're out of business. So we're really focused on trying to get people to understand it is about speed, it is about understanding and get to that point. >> But it comes back to the customer. Ultimately the design point is what experience do you want the customer to have with you. >> Correct. So in many respects the challenge is the customer does things and in a B2B setting they do things with a lot of other folks in their organization and they do things with the seller. In a B2C sense it's by themselves but they do things and they move through different context, what they do together. How do you help companies get focused on that singular element, it's what the customer is trying to do and how you want them to invite you to do it with them? >> To me, there's adjurity, right? There's a step-by-step process that the customer goes through in order to fulfill their need. And so it is about understanding that interaction that engagement and determining whether you're actually meeting the customer's need at that moment. Do you understand the context, do you understand the expectations, do you have all of the things that you need in order to understand that moment? But once you've chosen that moment, now what you're really focused on is the value equation. How do I fulfill that need in a way, drive that experience, that perception, that changes the customer's attitude so they think differently. That ultimately drives a different behavior from the customer that leads to a result that's different for the business. So businesses need to understand that value equation. Your job, number one job, is to fulfill customers needs. And I'm not talking about just the end need, but the need at every single moment along that life-cycle. And if you can understand and fulfill that need you can understand how to deliver results. Then it's just about plugging that formula in to get that done. >> So the question that I have for a lot of design folks, and it's kind of a big question but it ties back into some of the trends we're talking about. The Cloud, which is this thing that presumably allows companies to be in a lot of different places with at least a digital presence has been instrumental in presenting services to the communities to a lot of communities in new ways. >> Brian: Yes. >> To what degree do you think The Cloud and design thinking are reinforcing each other. By that I mean design thinking gets the business to focus on what's the value in use and The Cloud is presented as a service, not as a product. So is the design thinking The Cloud helping to move us from thinking about products to the services that they provide overall? >> Yeah, I would say design thinking first came out to actually drive product design but now it's starting to drive experiential design. The thing about The Cloud is that I can quickly go from rapid prototyping to putting it right in front of customers where before, using Legacy, armed premise capability, it would take me months to stand up something that I wanted to go do. So I think we're at the beautiful time for design, right? Is that all the disciplines around design the ability to really understand the customer to have that empathetic understanding to actually design experiences that are very relevant to that customer. But now to be able to actually take that experience and go multi-variant, AB tested immediately, not months from now but days from now and to get that learning, because part of great design thinking is not just the first generation, when I think design thinking I'm also thinking service design, lean, agile so I get the ability to take my minimal viable experience, not minimal viable product, get it in the market very quickly, get the learning from that, come back and make that iteration, put it back out on the market again So The Cloud allows you to do that on the fly where before you couldn't drive at that kind of speed. >> Talk about the commitment level, because that's a commit they have to make organizationally to iterate >> To fail? Well, to be ready for the iteration because you're throwing something out there that's also, I mean some people just got to get over, hey, the parachute will open. >> Brian: Yes. Kind of get over that fear and then once they're there they have to commit, they can't just leave it there. How do you walk through that with the customer because that to me, I think, is the trend that I see. Maybe it's different across different customers but the same organizational commitment. >> You've got to stop thinking about projects and you've got to start thinking about learning and engaging and so for me the process is really about going, hey, can I design something, can I actually test it very quickly, can I learn, and learn to me is fail. I mean I was involved in building the first Apple store. I will tell you the first Apple store was a complete failure (laughing) and it was the best learning that Apple could ever get in order to be able to use to build the next store, which was a much more successful piece. You have to build that in your DNA that says, if I'm fast then I can actually reduce my risk I can get to a point where I actually, be able to >> Yeah. learn very quickly and that I can go make that change come into place. >> That's great. I've got to ask you a question in terms of the customers because this is awesome you have a lot of experience with the customers. What's the pattern that emerges as you go out and look at the transformational heroes out there that are taking the transformation from the evolution of that? Is there a pattern that emerges, they kind of get nervous at first, then they snap in line here, and then things kind of happen. Can you share what you've seen as a pattern? >> So the pattern for innovators is usually they're just a little off-center and they have a little less fear than the rest of us about losing their job the next day and they're so passionate about what they want to do, they're willing to actually kind of push the envelope. What I find is that's the innovator. That's the guy. And by the way, usually not up high, usually down around the middle of the company. Now when they run into someone who will, on high also, is passionate about the change but not sure how to do it when the two of them come into combination, that innovator whose passionate, and that leader who understands they need to build that DNA, what I find is when those two come together, that is the pattern for success. So bottom's up, top's down innovation is really what works the best. I also find that the people who actually embrace discipline, embrace design thinking, embrace all of those aspects, but also have the arty kind of, hey, let's try some new things, let's be willing to kind of put our nose out there >> Yeah. I find the stodgy people who are not willing to make the change are the ones who actually just get stuck and we've seen those companies all go out of business, right? So the people who are willing to be leading-edge what's great is, though, if you see really great leaders, >> John: Yeah. they're also willing to be credible and authentic and get in front of audiences to say, "I designed this, it was a failure. >> Yeah. "I'm willing to actually now go do the next thing." And we see this from great leaders >> Yeah. from Starbucks on, that way I tried to do a bar in Starbucks and actually it didn't work, so we're going to go on to something else. >> Doesn't it also, I mean I agree with you totally, Brian having studied this a lot myself over the years. But it also means data. That you have to build measurement into everything >> Yes. Because the innovator doesn't get acknowledged or recognized by the leader if there isn't some data that >> Correct transmits message. You don't realize you're failing if you don't have data that alerts you early, before you double-down and triple-down, and quadruple-down on a bad idea. So how does the science of design thinking come into play here, because it's the designing-in, the measurements, the changes that become so crucial to actually moving us from just a good idea into something that actually manifests change. >> To me, the value equation is the first thing you work on, right? Which is the math. I need to understand the customer's needs and I need to understand the results that you're getting to. So I need to understand the attitudinal, the behavioral, the operational, the executional, all of those measurements so financial measurements, customer measurements, all those pieces. That data's crucial. I don't start, by the way, on any innovation projects until we have current-state understanding of that. The design is actually about how do I get that moving? How do I get that attitudinal, behavioral, operational, executional, financial movement by the design of what I'm doing. So data actually becomes more crucial. What's great too, about The Cloud, is that I actually have more access to data that I didn't have access to before and the data's in the hands of the innovator, not some other group I don't have to wait >> Right. a long time for analysis so I can literally go, here's our current state, let me go do A, B, multi-variant testing, wow, I got this change right here. Look at the pattern of behavior that I'm getting from customers. Now I say, okay, that's working, we will eventually get the results. And the fear for businesses in some cases, they need the financial result immediately, but now what we can say is actually, if you watch this track of behavior, you'll eventually get to the results. So if you're getting the behavioral change, you're actually >> With risk management to headed in the right direction. >> To your other point so there's also a piece of don't just jump to where's the ROI? >> Correct. (laughs) >> To, no, you're going to get there. >> Well we're talking about things like advocacy and retention and loyalty, well these are long-term behavioral things so you actually have to even go even further up and start measuring attitudinal, am I getting the movement for customers of how they talk about our brand and how they talk about engagement. That will eventually lead to the behaviors that I want, will eventually lead to results. So there is a leap of faith here >> Yes. that says if you understand the formula you should be able to actually drive the outcome by understanding the pieces across the formula. >> Well the good news is that by doing a better job of measurement, by having a discipline approach and think about design, how it leads innovation and getting leadership in place, you actually look at risk management as a way of thinking about what options am I going to buy in the future by failing now. >> Brian: Right. So I've learned something that says, well so now that group of options we're pairing-off. We still have this group of options. Let's pursue this group of options and when something didn't work, let's pair these options off >> Brian: Correct. And each time the risk of movement, of action goes down. >> Well the speed of it does too. >> Peter: Exactly. So time actually costs money, right? >> Right. And so if I can make quick bets, I can test them very quickly and I can determine what I should scale and what I should not scale. It's actually cheaper to de-risk that piece that way. >> Yeah, this is an interesting point you guys bring up the psychology and the DNA of the innovator. Whether it's the person in the trenches, who gets the data and makes the discovery and the innovation to the executive. But one area that we've seen is, and certainly this is always talked about at the conferences and stages, the No Manager. They're looking for ways to say no. >> Brian: Right. Then there's the guy who's looking to get to the yes. >> Brian: Yes. Take me through your experience on that, because you have to get to yes. >> Correct You have to find that person that's looking for yes. >> Correct. (laughs) In our process, by the way, we go from framing to ideating to share. And in share we believe that showcasing is really important. The ability to actually put your idea in front of someone the right way. But when people say, "No." They spell it N-O and I always spell it K-N-O-W, right? Most cases a leader is saying no because they don't actually have enough information. >> Yes. So if you framed, you really understand the customer and you've done a really good job of ideating, and you're really putting some proof of concepts together and getting them validated internally and externally and you've done the disciplined work >> John: Yeah. by the time you get to a decision, you should be able to give enough of that K-N-O-W >> Yeah to get that leader to move in the direction. >> John: Yeah, because they're looking for information, they're looking to learn. >> Peter: Which means you want an informed yes. >> Brian: Correct. Because if you don't get the informed yes, you're not getting the leader. You're really not getting the leader >> Brian: Correct. You're getting rubber-stamping >> But leaders ask great questions, right? >> and that's not what you want. >> Peter: That's right. >> And they're looking for other people to have the answers and they want to make sure that they went through the process, so when you bring me and ROI model, I want to say, well how did you put this together? How do you know that actually is going to get increased? And I back them up to well, wait a minute, here's the customer's attitude and here's the behavior and here's how I measured them. Okay, how do you know it's going to cost this much? I went through every activity, resource, partner, I've determined what I believe it's going to take. If you're doing the disciplined work, along with the artwork, you have a much better chance of actually getting things done. The other piece too, is that by the time you go to execute, even if you were wrong, you had so many measurements in place, that you're able to make those tweaks and iterations or decide to kill the innovation quick enough. So for leaders I'm saying don't make scale-decisions. Make test-decisions. Make very small, little bets, very quick, rapid prototyping and then make scale-decisions based-off of those tests. Now you've de-risked the whole process. >> Well you get clear visibility on what will the fly-wheel be for the scale, get the visibility on the metrics and unit economics or whatever >> Exactly. Alright, so final question since we have to wrap-up is what's the coolest thing that you've seen or been involved with of a customer? It could be an ah-ha moment, it could be you walked into a train wreck and you cleaned it up, or a big discovery or a big innovation. >> So I try not to share too many of the individual customers that I'm working with but I'll give you a story, it was in the Middle East, a customer that I'm working with, they were looking at, it's a communications company, they were looking at their bundling process of how do I sell wireless and broadband at the same time. So after going through the whole customer ethnography work and framing it, they realized that what they were doing is actually selling two silos that didn't make any sense. The customer just wanted connectivity. They didn't care whether it was broadband or wireless or anything, so they started thinking differently, which was maybe we should step back from this and actually stop trying to bundle or special-pricing based-off of the bundle, let's just sell connectivity. Let's just do away with the whole thought process, that it's actually two different things. >> John: And it worked? >> They're in the process of actually >> so they simplify it. going through that design. >> I thought you might say, "Well, here's how the American companies do it. Do it the exact opposite." (laughter) >> Yeah, because let's face it the process is not right but they actually got to the point, and by the way, we didn't come in with, okay, here's the idea that you should go do >> yeah they came to a conclusion that said, it's not unified billing, it's unified delivery of fulfilling the need. The customer's need is not broadband and wireless. The customer's need is connectivity. >> John: Yeah. If that's the need, we should be fulfilling that and not thinking about the duck below the water, whether that's broadband or this and that. >> That's a great point. A lot of companies just stay in their product lanes and say, "Buy the products." not what they want. >> Brian: Correct. >> Peter: Focus on the service. Alright. >> Brian: Correct. Alright, Brian Curran here inside theCUBE really laying-out some great insight into the design thinking, the role of the innovator, the role of organization. Congratulations on all your work, great insight here on theCUBE, appreciate it. Thanks for sharing the data, we learned a lot >> thanks for having me. We're going to iterate more with great interviews coming up from Oracle Modern Customer Experience after this short break. (electronic music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. >>Hello and welcome to the Cube virtual here with coverage of aws reinvent 2020. I'm your host, Justin Warren. And today I'm joined by J. Snyder, who is the chief chief customer officer at New Relic J. Welcome to the Cube. >>It is fantastic. Me back with the Cube. One of my favorite things to do has been for years. So I appreciate you having me. >>Yes, a bit of a cube veteran. Been on many times. So it's great to have you with us here again. Eso you've got some news about new relic and and Amazon away W s strategic collaboration agreement. I believe so. Maybe tell us a bit more about what that actually is and what it means. >>Yes. So we've been partners with AWS for years, but most recently in the last two weeks, we've just announced a five year strategic partnership that really expands on the relationship that we already had. We had a number of integrations and competencies already in place, but this is a big deal to us. and and we believe a big deal. Teoh A W s Aziz Well, so really takes all the work we've done to what I'll call the next level. It's joint technology development where were initially gonna be embedding new relic one right into the AWS management console for ease of use and really agility for anyone who's developing and implementing Ah cloud strategy, uh, big news as well from an adoption relative to purchase power so you can purchase straight through the AWS marketplace and leverage your existing AWS spend. And then we're gonna really be able to tap into the AWS premier partner ecosystem. So we get more skills, more scale as we look to drive consulting and skills development in any implementation for faster value realization and overall success in the cloud. So that's the high level. Happy to get into a more detailed level if you're interested around what I think it means to companies but just setting the stage, we're really excited about it as a company. In fact, I just left a call with a W S to join this call as we start to build out the execution plan for the next five years look like >>fantastic. So for those who might be new to new relic and aren't particularly across the sort of field of observe ability, could you just give us a quick overview of what new relic does? And and then maybe talk about what the strategic partnership means for for the nature of new relics business? >>Yes, so when I think about observe ability and what it means to us as opposed to the market at large, I would say our vision around observe ability is around one word, and that word is simplification. So, you know, I talked to a lot of customers. That's what I do all the time. And every time I do, I would say that there's three themes that come up over and over. It's the need to deliver a customer experience with improved up time and ever improving importance. It's the need to move more quickly to public cloud to embrace the scale and efficiency public cloud services have to offer. And then it's the need to improve the efficiency and speed of their own engineering teams so they can deliver innovation through software more quickly. And if you think about all those challenges And what observe ability is it's the one common thread that cuts across all those right. It's taking all of the operational data that your system admits it helps you measure improve the customer, experience your ability to move to public cloud and compare that experience before you start to after you get there. The effectiveness of your team before you deploy toe after you get there. And it's all the processes around that right, it helps you be almost able to be there before your there there. I mean, if that makes sense right, you'll be able to troubleshoot before the event actually happens or occurs. So our vision for this is like I talked about earlier is all about simply simplification. And we've broken this down into literally three piece parts, right? Three products. That's all we are. The first is about having a much data as you possibly can. I talked about admitting that transactional telemetry data, so we've created a telemetry data platform which rides on the world's most powerful database, and we believe that if we can take all of that data, all that infrastructure and application data and bring it into that database, including open source data and allow you to query it, analyze it and take action against it. Um, that's incredibly powerful, but that's only part one. Further, we have a really strong point of view that anybody who has the ability to break production should have the ability to fix production. And for us, that's giving them full stack observe ability. So it's the ability to action against all of that data that sits in the data platform. And then finally, we believe that you need to have applied intelligence because there's so many things that are happening in these complex environments. You wanna be able to cut through the noise and reduce it to find those insights and take action in a way that leverages machine learning. And that, for us, is a i ops. So really for us. Observe ability. When I talked about simplification, we've simplified what is a pretty large market with a whole bunch of products, just down to three simple things. A data platform, the ability to operationalize in action against that data and then layer on top in the third layer, that cake machine learning so it could be smarter than you can be so it sees problems before they occur. And that And that's what that's what I would say observe, ability is to us, and it's the ability to do that horizontally and vertically across your entire infrastructure in your entire stack. I hope that makes sense. >>Yeah, there's a lot of dig into there, So let's let's start with some of that operational side of things because I've long been a big believer in the idea of cloud is being a state of mind rather than a particular location on. A lot of people have been embracing Cloud Way Know that for we're about 10 or so years. And the and the size of reinvent is proven out how popular cloud could be. Eso some of those operational aspects that you were talking about there about the ability to react are particularly like that. You you were saying that anyone who could break production should be able to fix production. That's a very different way of working than what many organizations would be used to. So how is new relic helping customers to understand what they need to change about how they operate their business as they adopt some of these methods. >>Well, it's a great question. There's a couple of things we do. So we have an observe ability, maturity framework by which we employ deploy and that, and I don't want to bore the audience here. But needless to say, it's been built over the last year, year and a half by using hundreds of customers as a test case to determine effectively that there is a process that most companies go through to get to benefits realization. And we break those benefit categories into two different areas, one around operational efficiency and agility. The other is around innovation and digital experience. So you were talking about operational efficiency, and in there we have effectively three or four different ways and what I call boxes on how we would double, click and triple click into a set of actions that would lead you to an operational outcome. So we have learned over time and apply to methodology and approach to measure that. So depending on what you're trying to do, whether it's meantime to recover or meantime, to detect, or if you've got hundreds of developers and you're finding that they're ineffective or inefficient and you want to figure out how to deploy those resource is to different parts of the environment so you can get them to better use their time. It all depends on what your business outcome and business objective is. We have a way to measure that current state your effectiveness ply rigor to it and the design a process by using new relic one to fill in those gaps. And it can take on the burden of a lot of those people. E hate to say it because I'm not looking to replace any individual. It's really about freeing up their time to allow them to go do something in a more effective and more effective, efficient manner. So I don't know if that's answering the question perfectly, but >>e don't think there is a perfect answer to its. Every customer is a bit different. >>S So this is exactly why we developed the methodology because every customer is a little different. The rationale, though, is yeah, So the rationale there's a lot of common I was gonna say there's a lot of common themes, So what we've been able to develop over time with this framework is that we've built a catalog of use cases and experiences that we can apply against you. So depending on what your business objectives are and what you're trying to achieve, were able to determine and really auger in there and assess you. What is your maturity level of being able to deliver against these? Are you even using the platform to the level of maturity that would allow you to gain this benefit realization? And that's where we're adding a massive amount of value. And we see that every single day with our customers who are actually quite surprised by the power of the platform. I mean, if you think traditionally back not too far, two or even three years. People thought of new relic as an a P M. Company. And I think with the launch this summer, this past July with new relic one, we've really pivoted to a platform company. So while a lot of companies love new relic for a PM, they're now starting to see the power of the platform and what we can do for them by operationally operationalize ing. Those use cases around agility and effectiveness to drive cost and make people b'more useful and purposeful with their time so they can create better software. >>Yeah, I think that's something that people are realizing a lot more lately than they were previously. I think that there was a lot of TC analysis that was done on a replacement of FTE basis, but I think many organizations have realized that well, actually, that doesn't mean that those people go away. They get re tasked to do new things. So any of these efficiency, you start with efficiency. And it turns out actually being about business agility about doing new things with the same sort with the same people that you have who now don't have to do some of these more manual and fairly boring tasks. >>Yeah, just e Justin. If this if this cube interview thing doesn't work out for you were hiring some value engineers Right now it sounds like you've got the talk track down perfectly, because that's exactly what we're seeing in the market place. So I agree. >>So give us some examples, if you can, of maybe one or two off things that you've seen that customers have have used new relic where they've stripped out some of that make work or the things that they don't really need to be doing. And then they're turning that into new agility and have created something new, something more individual. Have you got an example you could share with us? >>You know, it's it's funny way were just I just finished doing our global customer advisory boards, which is, you know, rough and tough about 100 customers around the world. So we break it into the three theaters, and we just we were just talking with a particular customer. I don't want to give their name, but the session was called way broke the sessions into two different buckets, and I think every customer buys products like New Relic for two reasons. One is to either help them save money or to help them make money. So we actually split the sessions into those two areas and e think you're talking about how do we help them? How do we help them save money? And this particular company that was in the media industry talked at great length about the fact that they are a massive news conglomerate. They have a whole bunch of individual business units. They were decentralized and non standardized as it related to understanding how their software was getting created, how they were defining and, um, determining meantime to recover performance metrics. All these things were happening around them in a highly complex environment, just like we see with a lot of our customers, right? The complexity of the environments today are really driving the need for observe ability. So one of the things we did with them is we came in and we apply the same type of approach that we just discussed. We did a maturity assessment for them, and we find a found a variety of areas where they were very immature and using capabilities that existed within the platform. So we're able to light up a variety of things around. Insights were able to take more data in from a logging perspective. And again, I'm probably getting a little bit into the weeds for this particular session. But needless to say, way looked at the full gamut of metrics, events, logs and traces which was wasn't really being done in observe, ability, strategy, manner, and deploy that across the entire enterprise so created a standard platform for all the data in this particular environment. Across 5th, 14 different business units and as a byproduct, they were able to do a variety of things. One, the up time for a lot of their customer facing media applications improved greatly. We actually started to pivot from actually driving cost to showing how they could quote unquote make money, because the digital experience they were creating for a lot of their customers reduced the time to glass, if you will, for clicking the button and how quickly they could see the next page, the next page or whatever online app they were looking to get dramatically. So as a byproduct of this, they were about the repurpose to the point you made Justin. Dozens of resource is off of what was traditionally maintenance mode and fighting fires in a reactive capability towards building new code and driving new innovation in the marketplace. And they gave a couple of examples of new applications that they were able to bring to market without actually having to hire any net New resource is so again, I don't want to give away the name, the company, it maybe it was a little too high level, but it actually plays perfectly into exactly what what you're describing, Um, >>that is a good example of one of those that one of the it's always nice to have a specific concrete customer doing one of these kinds of things that you you describe in generic terms. Okay. No, this is this is being applied very specifically to one customer. So we're seeing those sorts of things more and more. >>Yeah, and I was gonna give you, you know, I thought about in advance of this session. You know, what is a really good example of what's happening in the world around us today? And I thought of particular company that we just recently worked with, which is check. I don't know if you're familiar with keg, if you've heard of them. But their education technology company based in California and they do digital and physical textbook rentals. They do online tutoring an online customer services. So, Justin, if you're like me or the rest of the world and you have kids who are learning at home right now, think about the amount of pressure and strain that's now being put on this poor company Check to keep their platform operational 24 77 days a week. So that students can learn at pace and keep up right. And it's an unbelievable success story for us and one that I love, because it touches me personally because I have three kids all doing online, learning in a variety of different manners right now. And, you know, we talked about it earlier. The complexity of some of the environments today, this is a company that you would never gas, but they run 500 micro services and highly complex, uh, technical architectural right. So we had to come in and help these folks, and we're able to produce their meantime to recover because they were having a lot of issues with their ability to provide a seamless performance experience. Because you could imagine the volume of folks hitting them these days on. Reduce that meantime to recover by five X. So it's just another example we're able to say, you know, it's a real world example. Were you able to actually reduce the time to recover, to provide a better experience and whether or not you want to say that saving money or making money? What I know for sure is is giving an incredible experience so that folks in the next generation of great minds aren't focused on learning instead of waiting to learn right, So very cool. >>That is very cool. And yes, and I have gone through the whole teaching kids >>about on >>which is, uh, which it was. It was disruptive, not necessarily in a good way, but we all we adapted and learned how to do it in a new way, which is, uh, it was a lot easier towards the end than it was at the beginning. >>I'd say we're still getting there at the Snyder household. Justin, we're still getting >>was practice makes perfect eso for organizations like check that who might be looking at JAG and thinking that that sounds like a bit of a success story. I want to learn more about how new relic might be able to help me. How should they start? >>Well, there's a lot of ways they can start. I mean, one of the most exciting things about our launch in July was that we have a new free tier. So for anybody who's interested in understanding the power of observe ability, you could go right to our website and you can sign up for free and you can start to play with new relic one. I think once you start playing for, we're gonna find the same thing that happens to most of the folks to do that. They're gonna play more and more and more, and they're gonna start Thio really embrace the power. And there's an incredible new relic university that has fantastic training online. So as you start to dabble in that free tier, start to see with the power and the potential is you'll probably sign up for some classes. Next thing you know, you're often running, so that is one of the easiest ways to get exposed to it. So certainly check us out at our website and you can find out all about that free tier. And what observe ability could potentially mean to you or your business. >>And as part of the AWS reinvent experience, are they able to engage with you in some way? >>It could definitely come by our booth, check us out, virtually see what we have to say. We'd love to talk to them, and we'd be happy to talk to you about all the powerful things we're doing with A. W. S. in the marketplace to help meet you wherever you are in your cloud journey, whether it's pre migration during migration, post migration or even optimization. We've got some incredible statistics on how we can help you maximize and leverage your investment in AWS. And we're really excited to be a strategic partner with them. And, you know, it's funny. It's, uh, for me to see how observe ability this platform can really touch every single facet of that cloud migration journey. And, you know, I was thinking originally, as I got exposed to this, it would be really useful for identity Met entity relationship management at the pre migration phase and then possibly at the post migration flays is you try to baseline and measure results. But what I've come to learn through our own process, of moving our own business to the AWS cloud, that there's tremendous value everywhere along that journey. That's incredibly exciting. So not only are we a great partner, but I'm excited that we will be what I call first and best customer of AWS ourselves new relic as we make our own journey to the cloud >>or fantastic and I'm I encourage any customers who might be interested in new relic Thio definitely gone and check you out as part of the show. Thank you. J. J. Snyder from New Relic. You've been watching the Cube virtual and our coverage of AWS reinvent 2020. Make sure that you check out all the rest of the cube coverage of AWS reinvent on your desktop laptop your phone wherever you are. I've been your host, Justin Warren, and I look forward to seeing you again soon.

>>don't talk mhm, >>Okay, thing is my presentation on coherent nonlinear dynamics and combinatorial optimization. This is going to be a talk to introduce an approach we're taking to the analysis of the performance of coherent using machines. So let me start with a brief introduction to easing optimization. The easing model represents a set of interacting magnetic moments or spins the total energy given by the expression shown at the bottom left of this slide. Here, the signal variables are meditate binary values. The Matrix element J. I. J. Represents the interaction, strength and signed between any pair of spins. I. J and A Chive represents a possible local magnetic field acting on each thing. The easing ground state problem is to find an assignment of binary spin values that achieves the lowest possible value of total energy. And an instance of the easing problem is specified by giving numerical values for the Matrix J in Vector H. Although the easy model originates in physics, we understand the ground state problem to correspond to what would be called quadratic binary optimization in the field of operations research and in fact, in terms of computational complexity theory, it could be established that the easing ground state problem is np complete. Qualitatively speaking, this makes the easing problem a representative sort of hard optimization problem, for which it is expected that the runtime required by any computational algorithm to find exact solutions should, as anatomically scale exponentially with the number of spends and for worst case instances at each end. Of course, there's no reason to believe that the problem instances that actually arrives in practical optimization scenarios are going to be worst case instances. And it's also not generally the case in practical optimization scenarios that we demand absolute optimum solutions. Usually we're more interested in just getting the best solution we can within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for a computation. This focuses great interest on so called heuristic algorithms for the easing problem in other NP complete problems which generally get very good but not guaranteed optimum solutions and run much faster than algorithms that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem for which extensive compilations of benchmarking data may be found online. A recent study found that the best known TSP solver required median run times across the Library of Problem instances That scaled is a very steep route exponential for end up to approximately 4500. This gives some indication of the change in runtime scaling for generic as opposed the worst case problem instances. Some of the instances considered in this study were taken from a public library of T SPS derived from real world Veil aside design data. This feels I TSP Library includes instances within ranging from 131 to 744,710 instances from this library with end between 6880 13,584 were first solved just a few years ago in 2017 requiring days of run time and a 48 core to King hurts cluster, while instances with and greater than or equal to 14,233 remain unsolved exactly by any means. Approximate solutions, however, have been found by heuristic methods for all instances in the VLS i TSP library with, for example, a solution within 0.14% of a no lower bound, having been discovered, for instance, with an equal 19,289 requiring approximately two days of run time on a single core of 2.4 gigahertz. Now, if we simple mindedly extrapolate the root exponential scaling from the study up to an equal 4500, we might expect that an exact solver would require something more like a year of run time on the 48 core cluster used for the N equals 13,580 for instance, which shows how much a very small concession on the quality of the solution makes it possible to tackle much larger instances with much lower cost. At the extreme end, the largest TSP ever solved exactly has an equal 85,900. This is an instance derived from 19 eighties VLSI design, and it's required 136 CPU. Years of computation normalized to a single cord, 2.4 gigahertz. But the 24 larger so called world TSP benchmark instance within equals 1,904,711 has been solved approximately within ophthalmology. Gap bounded below 0.474%. Coming back to the general. Practical concerns have applied optimization. We may note that a recent meta study analyzed the performance of no fewer than 37 heuristic algorithms for Max cut and quadratic pioneer optimization problems and found the performance sort and found that different heuristics work best for different problem instances selected from a large scale heterogeneous test bed with some evidence but cryptic structure in terms of what types of problem instances were best solved by any given heuristic. Indeed, their their reasons to believe that these results from Mexico and quadratic binary optimization reflected general principle of performance complementarity among heuristic optimization algorithms in the practice of solving heart optimization problems there. The cerise is a critical pre processing issue of trying to guess which of a number of available good heuristic algorithms should be chosen to tackle a given problem. Instance, assuming that any one of them would incur high costs to run on a large problem, instances incidence, making an astute choice of heuristic is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This has certainly been pinpointed by researchers in the field is a circumstance that must be addressed. So adding this all up, we see that a critical frontier for cutting edge academic research involves both the development of novel heuristic algorithms that deliver better performance, with lower cost on classes of problem instances that are underserved by existing approaches, as well as fundamental research to provide deep conceptual insight into what makes a given problem in, since easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law and speculate about a so called second quantum revolution, it's natural to talk not only about novel algorithms for conventional CPUs but also about highly customized special purpose hardware architectures on which we may run entirely unconventional algorithms for combinatorial optimization such as easing problem. So against that backdrop, I'd like to use my remaining time to introduce our work on analysis of coherent using machine architectures and associate ID optimization algorithms. These machines, in general, are a novel class of information processing architectures for solving combinatorial optimization problems by embedding them in the dynamics of analog, physical or cyber physical systems, in contrast to both MAWR traditional engineering approaches that build using machines using conventional electron ICS and more radical proposals that would require large scale quantum entanglement. The emerging paradigm of coherent easing machines leverages coherent nonlinear dynamics in photonic or Opto electronic platforms to enable near term construction of large scale prototypes that leverage post Simoes information dynamics, the general structure of of current CM systems has shown in the figure on the right. The role of the easing spins is played by a train of optical pulses circulating around a fiber optical storage ring. A beam splitter inserted in the ring is used to periodically sample the amplitude of every optical pulse, and the measurement results are continually read into a refugee A, which uses them to compute perturbations to be applied to each pulse by a synchronized optical injections. These perturbations, air engineered to implement the spin, spin coupling and local magnetic field terms of the easing Hamiltonian, corresponding to a linear part of the CME Dynamics, a synchronously pumped parametric amplifier denoted here as PPL and Wave Guide adds a crucial nonlinear component to the CIA and Dynamics as well. In the basic CM algorithm, the pump power starts very low and has gradually increased at low pump powers. The amplitude of the easing spin pulses behaviors continuous, complex variables. Who Israel parts which can be positive or negative, play the role of play the role of soft or perhaps mean field spins once the pump, our crosses the threshold for parametric self oscillation. In the optical fiber ring, however, the attitudes of the easing spin pulses become effectively Qantas ized into binary values while the pump power is being ramped up. The F P J subsystem continuously applies its measurement based feedback. Implementation of the using Hamiltonian terms, the interplay of the linear rised using dynamics implemented by the F P G A and the threshold conversation dynamics provided by the sink pumped Parametric amplifier result in the final state of the optical optical pulse amplitude at the end of the pump ramp that could be read as a binary strain, giving a proposed solution of the easing ground state problem. This method of solving easing problem seems quite different from a conventional algorithm that runs entirely on a digital computer as a crucial aspect of the computation is performed physically by the analog, continuous, coherent, nonlinear dynamics of the optical degrees of freedom. In our efforts to analyze CIA and performance, we have therefore turned to the tools of dynamical systems theory, namely, a study of modifications, the evolution of critical points and apologies of hetero clinic orbits and basins of attraction. We conjecture that such analysis can provide fundamental insight into what makes certain optimization instances hard or easy for coherent using machines and hope that our approach can lead to both improvements of the course, the AM algorithm and a pre processing rubric for rapidly assessing the CME suitability of new instances. Okay, to provide a bit of intuition about how this all works, it may help to consider the threshold dynamics of just one or two optical parametric oscillators in the CME architecture just described. We can think of each of the pulse time slots circulating around the fiber ring, as are presenting an independent Opio. We can think of a single Opio degree of freedom as a single, resonant optical node that experiences linear dissipation, do toe out coupling loss and gain in a pump. Nonlinear crystal has shown in the diagram on the upper left of this slide as the pump power is increased from zero. As in the CME algorithm, the non linear game is initially to low toe overcome linear dissipation, and the Opio field remains in a near vacuum state at a critical threshold. Value gain. Equal participation in the Popeo undergoes a sort of lazing transition, and the study states of the OPIO above this threshold are essentially coherent states. There are actually two possible values of the Opio career in amplitude and any given above threshold pump power which are equal in magnitude but opposite in phase when the OPI across the special diet basically chooses one of the two possible phases randomly, resulting in the generation of a single bit of information. If we consider to uncoupled, Opio has shown in the upper right diagram pumped it exactly the same power at all times. Then, as the pump power has increased through threshold, each Opio will independently choose the phase and thus to random bits are generated for any number of uncoupled. Oppose the threshold power per opio is unchanged from the single Opio case. Now, however, consider a scenario in which the two appeals air, coupled to each other by a mutual injection of their out coupled fields has shown in the diagram on the lower right. One can imagine that depending on the sign of the coupling parameter Alfa, when one Opio is lazing, it will inject a perturbation into the other that may interfere either constructively or destructively, with the feel that it is trying to generate by its own lazing process. As a result, when came easily showed that for Alfa positive, there's an effective ferro magnetic coupling between the two Opio fields and their collective oscillation threshold is lowered from that of the independent Opio case. But on Lee for the two collective oscillation modes in which the two Opio phases are the same for Alfa Negative, the collective oscillation threshold is lowered on Lee for the configurations in which the Opio phases air opposite. So then, looking at how Alfa is related to the J. I. J matrix of the easing spin coupling Hamiltonian, it follows that we could use this simplistic to a p o. C. I am to solve the ground state problem of a fair magnetic or anti ferro magnetic ankles to easing model simply by increasing the pump power from zero and observing what phase relation occurs as the two appeals first start delays. Clearly, we can imagine generalizing this story toe larger, and however the story doesn't stay is clean and simple for all larger problem instances. And to find a more complicated example, we only need to go to n equals four for some choices of J J for n equals, for the story remains simple. Like the n equals two case. The figure on the upper left of this slide shows the energy of various critical points for a non frustrated and equals, for instance, in which the first bifurcated critical point that is the one that I forget to the lowest pump value a. Uh, this first bifurcated critical point flows as symptomatically into the lowest energy easing solution and the figure on the upper right. However, the first bifurcated critical point flows to a very good but sub optimal minimum at large pump power. The global minimum is actually given by a distinct critical critical point that first appears at a higher pump power and is not automatically connected to the origin. The basic C am algorithm is thus not able to find this global minimum. Such non ideal behaviors needs to become more confident. Larger end for the n equals 20 instance, showing the lower plots where the lower right plot is just a zoom into a region of the lower left lot. It can be seen that the global minimum corresponds to a critical point that first appears out of pump parameter, a around 0.16 at some distance from the idiomatic trajectory of the origin. That's curious to note that in both of these small and examples, however, the critical point corresponding to the global minimum appears relatively close to the idiomatic projector of the origin as compared to the most of the other local minima that appear. We're currently working to characterize the face portrait topology between the global minimum in the antibiotic trajectory of the origin, taking clues as to how the basic C am algorithm could be generalized to search for non idiomatic trajectories that jump to the global minimum during the pump ramp. Of course, n equals 20 is still too small to be of interest for practical optimization applications. But the advantage of beginning with the study of small instances is that we're able reliably to determine their global minima and to see how they relate to the 80 about trajectory of the origin in the basic C am algorithm. In the smaller and limit, we can also analyze fully quantum mechanical models of Syrian dynamics. But that's a topic for future talks. Um, existing large scale prototypes are pushing into the range of in equals 10 to the 4 10 to 5 to six. So our ultimate objective in theoretical analysis really has to be to try to say something about CIA and dynamics and regime of much larger in our initial approach to characterizing CIA and behavior in the large in regime relies on the use of random matrix theory, and this connects to prior research on spin classes, SK models and the tap equations etcetera. At present, we're focusing on statistical characterization of the CIA ingredient descent landscape, including the evolution of critical points in their Eigen value spectra. As the pump power is gradually increased. We're investigating, for example, whether there could be some way to exploit differences in the relative stability of the global minimum versus other local minima. We're also working to understand the deleterious or potentially beneficial effects of non ideologies, such as a symmetry in the implemented these and couplings. Looking one step ahead, we plan to move next in the direction of considering more realistic classes of problem instances such as quadratic, binary optimization with constraints. Eso In closing, I should acknowledge people who did the hard work on these things that I've shown eso. My group, including graduate students Ed winning, Daniel Wennberg, Tatsuya Nagamoto and Atsushi Yamamura, have been working in close collaboration with Syria Ganguly, Marty Fair and Amir Safarini Nini, all of us within the Department of Applied Physics at Stanford University. On also in collaboration with the Oshima Moto over at NTT 55 research labs, Onda should acknowledge funding support from the NSF by the Coherent Easing Machines Expedition in computing, also from NTT five research labs, Army Research Office and Exxon Mobil. Uh, that's it. Thanks very much. >>Mhm e >>t research and the Oshie for putting together this program and also the opportunity to speak here. My name is Al Gore ism or Andy and I'm from Caltech, and today I'm going to tell you about the work that we have been doing on networks off optical parametric oscillators and how we have been using them for icing machines and how we're pushing them toward Cornum photonics to acknowledge my team at Caltech, which is now eight graduate students and five researcher and postdocs as well as collaborators from all over the world, including entity research and also the funding from different places, including entity. So this talk is primarily about networks of resonate er's, and these networks are everywhere from nature. For instance, the brain, which is a network of oscillators all the way to optics and photonics and some of the biggest examples or metal materials, which is an array of small resonate er's. And we're recently the field of technological photonics, which is trying thio implement a lot of the technological behaviors of models in the condensed matter, physics in photonics and if you want to extend it even further, some of the implementations off quantum computing are technically networks of quantum oscillators. So we started thinking about these things in the context of icing machines, which is based on the icing problem, which is based on the icing model, which is the simple summation over the spins and spins can be their upward down and the couplings is given by the JJ. And the icing problem is, if you know J I J. What is the spin configuration that gives you the ground state? And this problem is shown to be an MP high problem. So it's computational e important because it's a representative of the MP problems on NPR. Problems are important because first, their heart and standard computers if you use a brute force algorithm and they're everywhere on the application side. That's why there is this demand for making a machine that can target these problems, and hopefully it can provide some meaningful computational benefit compared to the standard digital computers. So I've been building these icing machines based on this building block, which is a degenerate optical parametric. Oscillator on what it is is resonator with non linearity in it, and we pump these resonate er's and we generate the signal at half the frequency of the pump. One vote on a pump splits into two identical photons of signal, and they have some very interesting phase of frequency locking behaviors. And if you look at the phase locking behavior, you realize that you can actually have two possible phase states as the escalation result of these Opio which are off by pie, and that's one of the important characteristics of them. So I want to emphasize a little more on that and I have this mechanical analogy which are basically two simple pendulum. But there are parametric oscillators because I'm going to modulate the parameter of them in this video, which is the length of the string on by that modulation, which is that will make a pump. I'm gonna make a muscular. That'll make a signal which is half the frequency of the pump. And I have two of them to show you that they can acquire these face states so they're still facing frequency lock to the pump. But it can also lead in either the zero pie face states on. The idea is to use this binary phase to represent the binary icing spin. So each opio is going to represent spin, which can be either is your pie or up or down. And to implement the network of these resonate er's, we use the time off blood scheme, and the idea is that we put impulses in the cavity. These pulses air separated by the repetition period that you put in or t r. And you can think about these pulses in one resonator, xaz and temporarily separated synthetic resonate Er's if you want a couple of these resonator is to each other, and now you can introduce these delays, each of which is a multiple of TR. If you look at the shortest delay it couples resonator wanted to 2 to 3 and so on. If you look at the second delay, which is two times a rotation period, the couple's 123 and so on. And if you have and minus one delay lines, then you can have any potential couplings among these synthetic resonate er's. And if I can introduce these modulators in those delay lines so that I can strength, I can control the strength and the phase of these couplings at the right time. Then I can have a program will all toe all connected network in this time off like scheme, and the whole physical size of the system scales linearly with the number of pulses. So the idea of opium based icing machine is didn't having these o pos, each of them can be either zero pie and I can arbitrarily connect them to each other. And then I start with programming this machine to a given icing problem by just setting the couplings and setting the controllers in each of those delight lines. So now I have a network which represents an icing problem. Then the icing problem maps to finding the face state that satisfy maximum number of coupling constraints. And the way it happens is that the icing Hamiltonian maps to the linear loss of the network. And if I start adding gain by just putting pump into the network, then the OPI ohs are expected to oscillate in the lowest, lowest lost state. And, uh and we have been doing these in the past, uh, six or seven years and I'm just going to quickly show you the transition, especially what happened in the first implementation, which was using a free space optical system and then the guided wave implementation in 2016 and the measurement feedback idea which led to increasing the size and doing actual computation with these machines. So I just want to make this distinction here that, um, the first implementation was an all optical interaction. We also had an unequal 16 implementation. And then we transition to this measurement feedback idea, which I'll tell you quickly what it iss on. There's still a lot of ongoing work, especially on the entity side, to make larger machines using the measurement feedback. But I'm gonna mostly focused on the all optical networks and how we're using all optical networks to go beyond simulation of icing Hamiltonian both in the linear and non linear side and also how we're working on miniaturization of these Opio networks. So the first experiment, which was the four opium machine, it was a free space implementation and this is the actual picture off the machine and we implemented a small and it calls for Mexico problem on the machine. So one problem for one experiment and we ran the machine 1000 times, we looked at the state and we always saw it oscillate in one of these, um, ground states of the icing laboratoria. So then the measurement feedback idea was to replace those couplings and the controller with the simulator. So we basically simulated all those coherent interactions on on FB g. A. And we replicated the coherent pulse with respect to all those measurements. And then we injected it back into the cavity and on the near to you still remain. So it still is a non. They're dynamical system, but the linear side is all simulated. So there are lots of questions about if this system is preserving important information or not, or if it's gonna behave better. Computational wars. And that's still ah, lot of ongoing studies. But nevertheless, the reason that this implementation was very interesting is that you don't need the end minus one delight lines so you can just use one. Then you can implement a large machine, and then you can run several thousands of problems in the machine, and then you can compare the performance from the computational perspective Looks so I'm gonna split this idea of opium based icing machine into two parts. One is the linear part, which is if you take out the non linearity out of the resonator and just think about the connections. You can think about this as a simple matrix multiplication scheme. And that's basically what gives you the icing Hambletonian modeling. So the optical laws of this network corresponds to the icing Hamiltonian. And if I just want to show you the example of the n equals for experiment on all those face states and the history Graham that we saw, you can actually calculate the laws of each of those states because all those interferences in the beam splitters and the delay lines are going to give you a different losses. And then you will see that the ground states corresponds to the lowest laws of the actual optical network. If you add the non linearity, the simple way of thinking about what the non linearity does is that it provides to gain, and then you start bringing up the gain so that it hits the loss. Then you go through the game saturation or the threshold which is going to give you this phase bifurcation. So you go either to zero the pie face state. And the expectation is that Theis, the network oscillates in the lowest possible state, the lowest possible loss state. There are some challenges associated with this intensity Durban face transition, which I'm going to briefly talk about. I'm also going to tell you about other types of non aerodynamics that we're looking at on the non air side of these networks. So if you just think about the linear network, we're actually interested in looking at some technological behaviors in these networks. And the difference between looking at the technological behaviors and the icing uh, machine is that now, First of all, we're looking at the type of Hamilton Ian's that are a little different than the icing Hamilton. And one of the biggest difference is is that most of these technological Hamilton Ian's that require breaking the time reversal symmetry, meaning that you go from one spin to in the one side to another side and you get one phase. And if you go back where you get a different phase, and the other thing is that we're not just interested in finding the ground state, we're actually now interesting and looking at all sorts of states and looking at the dynamics and the behaviors of all these states in the network. So we started with the simplest implementation, of course, which is a one d chain of thes resonate, er's, which corresponds to a so called ssh model. In the technological work, we get the similar energy to los mapping and now we can actually look at the band structure on. This is an actual measurement that we get with this associate model and you see how it reasonably how How? Well, it actually follows the prediction and the theory. One of the interesting things about the time multiplexing implementation is that now you have the flexibility of changing the network as you are running the machine. And that's something unique about this time multiplex implementation so that we can actually look at the dynamics. And one example that we have looked at is we can actually go through the transition off going from top A logical to the to the standard nontrivial. I'm sorry to the trivial behavior of the network. You can then look at the edge states and you can also see the trivial and states and the technological at states actually showing up in this network. We have just recently implement on a two D, uh, network with Harper Hofstadter model and when you don't have the results here. But we're one of the other important characteristic of time multiplexing is that you can go to higher and higher dimensions and keeping that flexibility and dynamics, and we can also think about adding non linearity both in a classical and quantum regimes, which is going to give us a lot of exotic, no classical and quantum, non innate behaviors in these networks. Yeah, So I told you about the linear side. Mostly let me just switch gears and talk about the nonlinear side of the network. And the biggest thing that I talked about so far in the icing machine is this face transition that threshold. So the low threshold we have squeezed state in these. Oh, pios, if you increase the pump, we go through this intensity driven phase transition and then we got the face stays above threshold. And this is basically the mechanism off the computation in these O pos, which is through this phase transition below to above threshold. So one of the characteristics of this phase transition is that below threshold, you expect to see quantum states above threshold. You expect to see more classical states or coherent states, and that's basically corresponding to the intensity off the driving pump. So it's really hard to imagine that it can go above threshold. Or you can have this friends transition happen in the all in the quantum regime. And there are also some challenges associated with the intensity homogeneity off the network, which, for example, is if one opioid starts oscillating and then its intensity goes really high. Then it's going to ruin this collective decision making off the network because of the intensity driven face transition nature. So So the question is, can we look at other phase transitions? Can we utilize them for both computing? And also can we bring them to the quantum regime on? I'm going to specifically talk about the face transition in the spectral domain, which is the transition from the so called degenerate regime, which is what I mostly talked about to the non degenerate regime, which happens by just tuning the phase of the cavity. And what is interesting is that this phase transition corresponds to a distinct phase noise behavior. So in the degenerate regime, which we call it the order state, you're gonna have the phase being locked to the phase of the pump. As I talked about non degenerate regime. However, the phase is the phase is mostly dominated by the quantum diffusion. Off the off the phase, which is limited by the so called shallow towns limit, and you can see that transition from the general to non degenerate, which also has distinct symmetry differences. And this transition corresponds to a symmetry breaking in the non degenerate case. The signal can acquire any of those phases on the circle, so it has a you one symmetry. Okay, and if you go to the degenerate case, then that symmetry is broken and you only have zero pie face days I will look at. So now the question is can utilize this phase transition, which is a face driven phase transition, and can we use it for similar computational scheme? So that's one of the questions that were also thinking about. And it's not just this face transition is not just important for computing. It's also interesting from the sensing potentials and this face transition, you can easily bring it below threshold and just operated in the quantum regime. Either Gaussian or non Gaussian. If you make a network of Opio is now, we can see all sorts off more complicated and more interesting phase transitions in the spectral domain. One of them is the first order phase transition, which you get by just coupling to Opio, and that's a very abrupt face transition and compared to the to the single Opio phase transition. And if you do the couplings right, you can actually get a lot of non her mission dynamics and exceptional points, which are actually very interesting to explore both in the classical and quantum regime. And I should also mention that you can think about the cup links to be also nonlinear couplings. And that's another behavior that you can see, especially in the nonlinear in the non degenerate regime. So with that, I basically told you about these Opio networks, how we can think about the linear scheme and the linear behaviors and how we can think about the rich, nonlinear dynamics and non linear behaviors both in the classical and quantum regime. I want to switch gear and tell you a little bit about the miniaturization of these Opio networks. And of course, the motivation is if you look at the electron ICS and what we had 60 or 70 years ago with vacuum tube and how we transition from relatively small scale computers in the order of thousands of nonlinear elements to billions of non elements where we are now with the optics is probably very similar to 70 years ago, which is a table talk implementation. And the question is, how can we utilize nano photonics? I'm gonna just briefly show you the two directions on that which we're working on. One is based on lithium Diabate, and the other is based on even a smaller resonate er's could you? So the work on Nana Photonic lithium naive. It was started in collaboration with Harvard Marko Loncar, and also might affair at Stanford. And, uh, we could show that you can do the periodic polling in the phenomenon of it and get all sorts of very highly nonlinear processes happening in this net. Photonic periodically polls if, um Diabate. And now we're working on building. Opio was based on that kind of photonic the film Diabate. And these air some some examples of the devices that we have been building in the past few months, which I'm not gonna tell you more about. But the O. P. O. S. And the Opio Networks are in the works. And that's not the only way of making large networks. Um, but also I want to point out that The reason that these Nana photonic goblins are actually exciting is not just because you can make a large networks and it can make him compact in a in a small footprint. They also provide some opportunities in terms of the operation regime. On one of them is about making cat states and Opio, which is, can we have the quantum superposition of the zero pie states that I talked about and the Net a photonic within? I've It provides some opportunities to actually get closer to that regime because of the spatial temporal confinement that you can get in these wave guides. So we're doing some theory on that. We're confident that the type of non linearity two losses that it can get with these platforms are actually much higher than what you can get with other platform their existing platforms and to go even smaller. We have been asking the question off. What is the smallest possible Opio that you can make? Then you can think about really wavelength scale type, resonate er's and adding the chi to non linearity and see how and when you can get the Opio to operate. And recently, in collaboration with us see, we have been actually USC and Creole. We have demonstrated that you can use nano lasers and get some spin Hamilton and implementations on those networks. So if you can build the a P. O s, we know that there is a path for implementing Opio Networks on on such a nano scale. So we have looked at these calculations and we try to estimate the threshold of a pos. Let's say for me resonator and it turns out that it can actually be even lower than the type of bulk Pip Llano Pos that we have been building in the past 50 years or so. So we're working on the experiments and we're hoping that we can actually make even larger and larger scale Opio networks. So let me summarize the talk I told you about the opium networks and our work that has been going on on icing machines and the measurement feedback. And I told you about the ongoing work on the all optical implementations both on the linear side and also on the nonlinear behaviors. And I also told you a little bit about the efforts on miniaturization and going to the to the Nano scale. So with that, I would like Thio >>three from the University of Tokyo. Before I thought that would like to thank you showing all the stuff of entity for the invitation and the organization of this online meeting and also would like to say that it has been very exciting to see the growth of this new film lab. And I'm happy to share with you today of some of the recent works that have been done either by me or by character of Hong Kong. Honest Group indicates the title of my talk is a neuro more fic in silica simulator for the communities in machine. And here is the outline I would like to make the case that the simulation in digital Tektronix of the CME can be useful for the better understanding or improving its function principles by new job introducing some ideas from neural networks. This is what I will discuss in the first part and then it will show some proof of concept of the game and performance that can be obtained using dissimulation in the second part and the protection of the performance that can be achieved using a very large chaos simulator in the third part and finally talk about future plans. So first, let me start by comparing recently proposed izing machines using this table there is elected from recent natural tronics paper from the village Park hard people, and this comparison shows that there's always a trade off between energy efficiency, speed and scalability that depends on the physical implementation. So in red, here are the limitation of each of the servers hardware on, interestingly, the F p G, a based systems such as a producer, digital, another uh Toshiba beautification machine or a recently proposed restricted Bozeman machine, FPD A by a group in Berkeley. They offer a good compromise between speed and scalability. And this is why, despite the unique advantage that some of these older hardware have trust as the currency proposition in Fox, CBS or the energy efficiency off memory Sisters uh P. J. O are still an attractive platform for building large organizing machines in the near future. The reason for the good performance of Refugee A is not so much that they operate at the high frequency. No, there are particular in use, efficient, but rather that the physical wiring off its elements can be reconfigured in a way that limits the funding human bottleneck, larger, funny and phenols and the long propagation video information within the system. In this respect, the LPGA is They are interesting from the perspective off the physics off complex systems, but then the physics of the actions on the photos. So to put the performance of these various hardware and perspective, we can look at the competition of bringing the brain the brain complete, using billions of neurons using only 20 watts of power and operates. It's a very theoretically slow, if we can see and so this impressive characteristic, they motivate us to try to investigate. What kind of new inspired principles be useful for designing better izing machines? The idea of this research project in the future collaboration it's to temporary alleviates the limitations that are intrinsic to the realization of an optical cortex in machine shown in the top panel here. By designing a large care simulator in silicone in the bottom here that can be used for digesting the better organization principles of the CIA and this talk, I will talk about three neuro inspired principles that are the symmetry of connections, neural dynamics orphan chaotic because of symmetry, is interconnectivity the infrastructure? No. Next talks are not composed of the reputation of always the same types of non environments of the neurons, but there is a local structure that is repeated. So here's the schematic of the micro column in the cortex. And lastly, the Iraqi co organization of connectivity connectivity is organizing a tree structure in the brain. So here you see a representation of the Iraqi and organization of the monkey cerebral cortex. So how can these principles we used to improve the performance of the icing machines? And it's in sequence stimulation. So, first about the two of principles of the estimate Trian Rico structure. We know that the classical approximation of the car testing machine, which is the ground toe, the rate based on your networks. So in the case of the icing machines, uh, the okay, Scott approximation can be obtained using the trump active in your position, for example, so the times of both of the system they are, they can be described by the following ordinary differential equations on in which, in case of see, I am the X, I represent the in phase component of one GOP Oh, Theo f represents the monitor optical parts, the district optical Parametric amplification and some of the good I JoJo extra represent the coupling, which is done in the case of the measure of feedback coupling cm using oh, more than detection and refugee A and then injection off the cooking time and eso this dynamics in both cases of CNN in your networks, they can be written as the grand set of a potential function V, and this written here, and this potential functionally includes the rising Maccagnan. So this is why it's natural to use this type of, uh, dynamics to solve the icing problem in which the Omega I J or the eyes in coping and the H is the extension of the icing and attorney in India and expect so. Not that this potential function can only be defined if the Omega I j. R. A. Symmetric. So the well known problem of this approach is that this potential function V that we obtain is very non convicts at low temperature, and also one strategy is to gradually deformed this landscape, using so many in process. But there is no theorem. Unfortunately, that granted conventions to the global minimum of There's even Tony and using this approach. And so this is why we propose, uh, to introduce a macro structures of the system where one analog spin or one D O. P. O is replaced by a pair off one another spin and one error, according viable. And the addition of this chemical structure introduces a symmetry in the system, which in terms induces chaotic dynamics, a chaotic search rather than a learning process for searching for the ground state of the icing. Every 20 within this massacre structure the role of the er variable eyes to control the amplitude off the analog spins toe force. The amplitude of the expense toe become equal to certain target amplitude a uh and, uh, and this is done by modulating the strength off the icing complaints or see the the error variable E I multiply the icing complaint here in the dynamics off air d o p. O. On then the dynamics. The whole dynamics described by this coupled equations because the e I do not necessarily take away the same value for the different. I thesis introduces a symmetry in the system, which in turn creates security dynamics, which I'm sure here for solving certain current size off, um, escape problem, Uh, in which the X I are shown here and the i r from here and the value of the icing energy showing the bottom plots. You see this Celtics search that visit various local minima of the as Newtonian and eventually finds the global minimum? Um, it can be shown that this modulation off the target opportunity can be used to destabilize all the local minima off the icing evertonians so that we're gonna do not get stuck in any of them. On more over the other types of attractors I can eventually appear, such as limits I contractors, Okot contractors. They can also be destabilized using the motivation of the target and Batuta. And so we have proposed in the past two different moderation of the target amateur. The first one is a modulation that ensure the uh 100 reproduction rate of the system to become positive on this forbids the creation off any nontrivial tractors. And but in this work, I will talk about another moderation or arrested moderation which is given here. That works, uh, as well as this first uh, moderation, but is easy to be implemented on refugee. So this couple of the question that represent becoming the stimulation of the cortex in machine with some error correction they can be implemented especially efficiently on an F B. G. And here I show the time that it takes to simulate three system and also in red. You see, at the time that it takes to simulate the X I term the EI term, the dot product and the rising Hamiltonian for a system with 500 spins and Iraq Spain's equivalent to 500 g. O. P. S. So >>in >>f b d a. The nonlinear dynamics which, according to the digital optical Parametric amplification that the Opa off the CME can be computed in only 13 clock cycles at 300 yards. So which corresponds to about 0.1 microseconds. And this is Toby, uh, compared to what can be achieved in the measurements back O C. M. In which, if we want to get 500 timer chip Xia Pios with the one she got repetition rate through the obstacle nine narrative. Uh, then way would require 0.5 microseconds toe do this so the submission in F B J can be at least as fast as ah one g repression. Uh, replicate pulsed laser CIA Um, then the DOT product that appears in this differential equation can be completed in 43 clock cycles. That's to say, one microseconds at 15 years. So I pieced for pouring sizes that are larger than 500 speeds. The dot product becomes clearly the bottleneck, and this can be seen by looking at the the skating off the time the numbers of clock cycles a text to compute either the non in your optical parts or the dog products, respect to the problem size. And And if we had infinite amount of resources and PGA to simulate the dynamics, then the non illogical post can could be done in the old one. On the mattress Vector product could be done in the low carrot off, located off scales as a look at it off and and while the guide off end. Because computing the dot product involves assuming all the terms in the product, which is done by a nephew, GE by another tree, which heights scarce logarithmic any with the size of the system. But This is in the case if we had an infinite amount of resources on the LPGA food, but for dealing for larger problems off more than 100 spins. Usually we need to decompose the metrics into ah, smaller blocks with the block side that are not you here. And then the scaling becomes funny, non inner parts linear in the end, over you and for the products in the end of EU square eso typically for low NF pdf cheap PGA you the block size off this matrix is typically about 100. So clearly way want to make you as large as possible in order to maintain this scanning in a log event for the numbers of clock cycles needed to compute the product rather than this and square that occurs if we decompose the metrics into smaller blocks. But the difficulty in, uh, having this larger blocks eyes that having another tree very large Haider tree introduces a large finding and finance and long distance start a path within the refugee. So the solution to get higher performance for a simulator of the contest in machine eyes to get rid of this bottleneck for the dot product by increasing the size of this at the tree. And this can be done by organizing your critique the electrical components within the LPGA in order which is shown here in this, uh, right panel here in order to minimize the finding finance of the system and to minimize the long distance that a path in the in the fpt So I'm not going to the details of how this is implemented LPGA. But just to give you a idea off why the Iraqi Yahiko organization off the system becomes the extremely important toe get good performance for similar organizing machine. So instead of instead of getting into the details of the mpg implementation, I would like to give some few benchmark results off this simulator, uh, off the that that was used as a proof of concept for this idea which is can be found in this archive paper here and here. I should results for solving escape problems. Free connected person, randomly person minus one spring last problems and we sure, as we use as a metric the numbers of the mattress Victor products since it's the bottleneck of the computation, uh, to get the optimal solution of this escape problem with the Nina successful BT against the problem size here and and in red here, this propose FDJ implementation and in ah blue is the numbers of retrospective product that are necessary for the C. I am without error correction to solve this escape programs and in green here for noisy means in an evening which is, uh, behavior with similar to the Cartesian mission. Uh, and so clearly you see that the scaring off the numbers of matrix vector product necessary to solve this problem scales with a better exponents than this other approaches. So So So that's interesting feature of the system and next we can see what is the real time to solution to solve this SK instances eso in the last six years, the time institution in seconds to find a grand state of risk. Instances remain answers probability for different state of the art hardware. So in red is the F B g. A presentation proposing this paper and then the other curve represent Ah, brick a local search in in orange and silver lining in purple, for example. And so you see that the scaring off this purpose simulator is is rather good, and that for larger plant sizes we can get orders of magnitude faster than the state of the art approaches. Moreover, the relatively good scanning off the time to search in respect to problem size uh, they indicate that the FPD implementation would be faster than risk. Other recently proposed izing machine, such as the hope you know, natural complimented on memories distance that is very fast for small problem size in blue here, which is very fast for small problem size. But which scanning is not good on the same thing for the restricted Bosman machine. Implementing a PGA proposed by some group in Broken Recently Again, which is very fast for small parliament sizes but which canning is bad so that a dis worse than the proposed approach so that we can expect that for programs size is larger than 1000 spins. The proposed, of course, would be the faster one. Let me jump toe this other slide and another confirmation that the scheme scales well that you can find the maximum cut values off benchmark sets. The G sets better candidates that have been previously found by any other algorithms, so they are the best known could values to best of our knowledge. And, um or so which is shown in this paper table here in particular, the instances, uh, 14 and 15 of this G set can be We can find better converse than previously known, and we can find this can vary is 100 times faster than the state of the art algorithm and CP to do this which is a very common Kasich. It s not that getting this a good result on the G sets, they do not require ah, particular hard tuning of the parameters. So the tuning issuing here is very simple. It it just depends on the degree off connectivity within each graph. And so this good results on the set indicate that the proposed approach would be a good not only at solving escape problems in this problems, but all the types off graph sizing problems on Mexican province in communities. So given that the performance off the design depends on the height of this other tree, we can try to maximize the height of this other tree on a large F p g a onda and carefully routing the components within the P G A and and we can draw some projections of what type of performance we can achieve in the near future based on the, uh, implementation that we are currently working. So here you see projection for the time to solution way, then next property for solving this escape programs respect to the prime assize. And here, compared to different with such publicizing machines, particularly the digital. And, you know, 42 is shown in the green here, the green line without that's and, uh and we should two different, uh, hypothesis for this productions either that the time to solution scales as exponential off n or that the time of social skills as expression of square root off. So it seems, according to the data, that time solution scares more as an expression of square root of and also we can be sure on this and this production show that we probably can solve prime escape problem of science 2000 spins, uh, to find the rial ground state of this problem with 99 success ability in about 10 seconds, which is much faster than all the other proposed approaches. So one of the future plans for this current is in machine simulator. So the first thing is that we would like to make dissimulation closer to the rial, uh, GOP oh, optical system in particular for a first step to get closer to the system of a measurement back. See, I am. And to do this what is, uh, simulate Herbal on the p a is this quantum, uh, condoms Goshen model that is proposed described in this paper and proposed by people in the in the Entity group. And so the idea of this model is that instead of having the very simple or these and have shown previously, it includes paired all these that take into account on me the mean off the awesome leverage off the, uh, European face component, but also their violence s so that we can take into account more quantum effects off the g o p. O, such as the squeezing. And then we plan toe, make the simulator open access for the members to run their instances on the system. There will be a first version in September that will be just based on the simple common line access for the simulator and in which will have just a classic or approximation of the system. We don't know Sturm, binary weights and museum in term, but then will propose a second version that would extend the current arising machine to Iraq off F p g. A, in which we will add the more refined models truncated, ignoring the bottom Goshen model they just talked about on the support in which he valued waits for the rising problems and support the cement. So we will announce later when this is available and and far right is working >>hard comes from Universal down today in physics department, and I'd like to thank the organizers for their kind invitation to participate in this very interesting and promising workshop. Also like to say that I look forward to collaborations with with a file lab and Yoshi and collaborators on the topics of this world. So today I'll briefly talk about our attempt to understand the fundamental limits off another continues time computing, at least from the point off you off bullion satisfy ability, problem solving, using ordinary differential equations. But I think the issues that we raise, um, during this occasion actually apply to other other approaches on a log approaches as well and into other problems as well. I think everyone here knows what Dorien satisfy ability. Problems are, um, you have boolean variables. You have em clauses. Each of disjunction of collaterals literally is a variable, or it's, uh, negation. And the goal is to find an assignment to the variable, such that order clauses are true. This is a decision type problem from the MP class, which means you can checking polynomial time for satisfy ability off any assignment. And the three set is empty, complete with K three a larger, which means an efficient trees. That's over, uh, implies an efficient source for all the problems in the empty class, because all the problems in the empty class can be reduced in Polian on real time to reset. As a matter of fact, you can reduce the NP complete problems into each other. You can go from three set to set backing or two maximum dependent set, which is a set packing in graph theoretic notions or terms toe the icing graphs. A problem decision version. This is useful, and you're comparing different approaches, working on different kinds of problems when not all the closest can be satisfied. You're looking at the accusation version offset, uh called Max Set. And the goal here is to find assignment that satisfies the maximum number of clauses. And this is from the NPR class. In terms of applications. If we had inefficient sets over or np complete problems over, it was literally, positively influenced. Thousands off problems and applications in industry and and science. I'm not going to read this, but this this, of course, gives a strong motivation toe work on this kind of problems. Now our approach to set solving involves embedding the problem in a continuous space, and you use all the east to do that. So instead of working zeros and ones, we work with minus one across once, and we allow the corresponding variables toe change continuously between the two bounds. We formulate the problem with the help of a close metrics. If if a if a close, uh, does not contain a variable or its negation. The corresponding matrix element is zero. If it contains the variable in positive, for which one contains the variable in a gated for Mitt's negative one, and then we use this to formulate this products caused quote, close violation functions one for every clause, Uh, which really, continuously between zero and one. And they're zero if and only if the clause itself is true. Uh, then we form the define in order to define a dynamic such dynamics in this and dimensional hyper cube where the search happens and if they exist, solutions. They're sitting in some of the corners of this hyper cube. So we define this, uh, energy potential or landscape function shown here in a way that this is zero if and only if all the clauses all the kmc zero or the clauses off satisfied keeping these auxiliary variables a EMS always positive. And therefore, what you do here is a dynamics that is a essentially ingredient descend on this potential energy landscape. If you were to keep all the M's constant that it would get stuck in some local minimum. However, what we do here is we couple it with the dynamics we cooperated the clothes violation functions as shown here. And if he didn't have this am here just just the chaos. For example, you have essentially what case you have positive feedback. You have increasing variable. Uh, but in that case, you still get stuck would still behave will still find. So she is better than the constant version but still would get stuck only when you put here this a m which makes the dynamics in in this variable exponential like uh, only then it keeps searching until he finds a solution on deer is a reason for that. I'm not going toe talk about here, but essentially boils down toe performing a Grady and descend on a globally time barren landscape. And this is what works. Now I'm gonna talk about good or bad and maybe the ugly. Uh, this is, uh, this is What's good is that it's a hyperbolic dynamical system, which means that if you take any domain in the search space that doesn't have a solution in it or any socially than the number of trajectories in it decays exponentially quickly. And the decay rate is a characteristic in variant characteristic off the dynamics itself. Dynamical systems called the escape right the inverse off that is the time scale in which you find solutions by this by this dynamical system, and you can see here some song trajectories that are Kelty because it's it's no linear, but it's transient, chaotic. Give their sources, of course, because eventually knowledge to the solution. Now, in terms of performance here, what you show for a bunch off, um, constraint densities defined by M overran the ratio between closes toe variables for random, said Problems is random. Chris had problems, and they as its function off n And we look at money toward the wartime, the wall clock time and it behaves quite value behaves Azat party nominally until you actually he to reach the set on set transition where the hardest problems are found. But what's more interesting is if you monitor the continuous time t the performance in terms off the A narrow, continuous Time t because that seems to be a polynomial. And the way we show that is, we consider, uh, random case that random three set for a fixed constraint density Onda. We hear what you show here. Is that the right of the trash hold that it's really hard and, uh, the money through the fraction of problems that we have not been able to solve it. We select thousands of problems at that constraint ratio and resolve them without algorithm, and we monitor the fractional problems that have not yet been solved by continuous 90. And this, as you see these decays exponentially different. Educate rates for different system sizes, and in this spot shows that is dedicated behaves polynomial, or actually as a power law. So if you combine these two, you find that the time needed to solve all problems except maybe appear traction off them scales foreign or merely with the problem size. So you have paranormal, continuous time complexity. And this is also true for other types of very hard constraints and sexual problems such as exact cover, because you can always transform them into three set as we discussed before, Ramsey coloring and and on these problems, even algorithms like survey propagation will will fail. But this doesn't mean that P equals NP because what you have first of all, if you were toe implement these equations in a device whose behavior is described by these, uh, the keys. Then, of course, T the continue style variable becomes a physical work off. Time on that will be polynomial is scaling, but you have another other variables. Oxidative variables, which structured in an exponential manner. So if they represent currents or voltages in your realization and it would be an exponential cost Al Qaeda. But this is some kind of trade between time and energy, while I know how toe generate energy or I don't know how to generate time. But I know how to generate energy so it could use for it. But there's other issues as well, especially if you're trying toe do this son and digital machine but also happens. Problems happen appear. Other problems appear on in physical devices as well as we discuss later. So if you implement this in GPU, you can. Then you can get in order off to magnitude. Speed up. And you can also modify this to solve Max sad problems. Uh, quite efficiently. You are competitive with the best heuristic solvers. This is a weather problems. In 2016 Max set competition eso so this this is this is definitely this seems like a good approach, but there's off course interesting limitations, I would say interesting, because it kind of makes you think about what it means and how you can exploit this thes observations in understanding better on a low continues time complexity. If you monitored the discrete number the number of discrete steps. Don't buy the room, Dakota integrator. When you solve this on a digital machine, you're using some kind of integrator. Um and you're using the same approach. But now you measure the number off problems you haven't sold by given number of this kid, uh, steps taken by the integrator. You find out you have exponential, discrete time, complexity and, of course, thistles. A problem. And if you look closely, what happens even though the analog mathematical trajectory, that's the record here. If you monitor what happens in discrete time, uh, the integrator frustrates very little. So this is like, you know, third or for the disposition, but fluctuates like crazy. So it really is like the intervention frees us out. And this is because of the phenomenon of stiffness that are I'll talk a little bit a more about little bit layer eso. >>You know, it might look >>like an integration issue on digital machines that you could improve and could definitely improve. But actually issues bigger than that. It's It's deeper than that, because on a digital machine there is no time energy conversion. So the outside variables are efficiently representing a digital machine. So there's no exponential fluctuating current of wattage in your computer when you do this. Eso If it is not equal NP then the exponential time, complexity or exponential costs complexity has to hit you somewhere. And this is how um, but, you know, one would be tempted to think maybe this wouldn't be an issue in a analog device, and to some extent is true on our devices can be ordered to maintain faster, but they also suffer from their own problems because he not gonna be affect. That classes soldiers as well. So, indeed, if you look at other systems like Mirandizing machine measurement feedback, probably talk on the grass or selected networks. They're all hinge on some kind off our ability to control your variables in arbitrary, high precision and a certain networks you want toe read out across frequencies in case off CM's. You required identical and program because which is hard to keep, and they kind of fluctuate away from one another, shift away from one another. And if you control that, of course that you can control the performance. So actually one can ask if whether or not this is a universal bottleneck and it seems so aside, I will argue next. Um, we can recall a fundamental result by by showing harder in reaction Target from 1978. Who says that it's a purely computer science proof that if you are able toe, compute the addition multiplication division off riel variables with infinite precision, then you could solve any complete problems in polynomial time. It doesn't actually proposals all where he just chose mathematically that this would be the case. Now, of course, in Real warned, you have also precision. So the next question is, how does that affect the competition about problems? This is what you're after. Lots of precision means information also, or entropy production. Eso what you're really looking at the relationship between hardness and cost of computing off a problem. Uh, and according to Sean Hagar, there's this left branch which in principle could be polynomial time. But the question whether or not this is achievable that is not achievable, but something more cheerful. That's on the right hand side. There's always going to be some information loss, so mental degeneration that could keep you away from possibly from point normal time. So this is what we like to understand, and this information laws the source off. This is not just always I will argue, uh, in any physical system, but it's also off algorithm nature, so that is a questionable area or approach. But China gets results. Security theoretical. No, actual solar is proposed. So we can ask, you know, just theoretically get out off. Curiosity would in principle be such soldiers because it is not proposing a soldier with such properties. In principle, if if you want to look mathematically precisely what the solar does would have the right properties on, I argue. Yes, I don't have a mathematical proof, but I have some arguments that that would be the case. And this is the case for actually our city there solver that if you could calculate its trajectory in a loss this way, then it would be, uh, would solve epic complete problems in polynomial continuous time. Now, as a matter of fact, this a bit more difficult question, because time in all these can be re scared however you want. So what? Burns says that you actually have to measure the length of the trajectory, which is a new variant off the dynamical system or property dynamical system, not off its parameters ization. And we did that. So Suba Corral, my student did that first, improving on the stiffness off the problem off the integrations, using implicit solvers and some smart tricks such that you actually are closer to the actual trajectory and using the same approach. You know what fraction off problems you can solve? We did not give the length of the trajectory. You find that it is putting on nearly scaling the problem sites we have putting on your skin complexity. That means that our solar is both Polly length and, as it is, defined it also poorly time analog solver. But if you look at as a discreet algorithm, if you measure the discrete steps on a digital machine, it is an exponential solver. And the reason is because off all these stiffness, every integrator has tow truck it digitizing truncate the equations, and what it has to do is to keep the integration between the so called stability region for for that scheme, and you have to keep this product within a grimace of Jacoby in and the step size read in this region. If you use explicit methods. You want to stay within this region? Uh, but what happens that some off the Eigen values grow fast for Steve problems, and then you're you're forced to reduce that t so the product stays in this bonded domain, which means that now you have to you're forced to take smaller and smaller times, So you're you're freezing out the integration and what I will show you. That's the case. Now you can move to increase its soldiers, which is which is a tree. In this case, you have to make domain is actually on the outside. But what happens in this case is some of the Eigen values of the Jacobean, also, for six systems, start to move to zero. As they're moving to zero, they're going to enter this instability region, so your soul is going to try to keep it out, so it's going to increase the data T. But if you increase that to increase the truncation hours, so you get randomized, uh, in the large search space, so it's it's really not, uh, not going to work out. Now, one can sort off introduce a theory or language to discuss computational and are computational complexity, using the language from dynamical systems theory. But basically I I don't have time to go into this, but you have for heart problems. Security object the chaotic satellite Ouch! In the middle of the search space somewhere, and that dictates how the dynamics happens and variant properties off the dynamics. Of course, off that saddle is what the targets performance and many things, so a new, important measure that we find that it's also helpful in describing thesis. Another complexity is the so called called Makarov, or metric entropy and basically what this does in an intuitive A eyes, uh, to describe the rate at which the uncertainty containing the insignificant digits off a trajectory in the back, the flow towards the significant ones as you lose information because off arrows being, uh grown or are developed in tow. Larger errors in an exponential at an exponential rate because you have positively up north spawning. But this is an in variant property. It's the property of the set of all. This is not how you compute them, and it's really the interesting create off accuracy philosopher dynamical system. A zay said that you have in such a high dimensional that I'm consistent were positive and negatively upon of exponents. Aziz Many The total is the dimension of space and user dimension, the number off unstable manifold dimensions and as Saddam was stable, manifold direction. And there's an interesting and I think, important passion, equality, equality called the passion, equality that connect the information theoretic aspect the rate off information loss with the geometric rate of which trajectory separate minus kappa, which is the escape rate that I already talked about. Now one can actually prove a simple theorems like back off the envelope calculation. The idea here is that you know the rate at which the largest rated, which closely started trajectory separate from one another. So now you can say that, uh, that is fine, as long as my trajectory finds the solution before the projective separate too quickly. In that case, I can have the hope that if I start from some region off the face base, several close early started trajectories, they kind of go into the same solution orphaned and and that's that's That's this upper bound of this limit, and it is really showing that it has to be. It's an exponentially small number. What? It depends on the end dependence off the exponents right here, which combines information loss rate and the social time performance. So these, if this exponents here or that has a large independence or river linear independence, then you then you really have to start, uh, trajectories exponentially closer to one another in orderto end up in the same order. So this is sort off like the direction that you're going in tow, and this formulation is applicable toe all dynamical systems, uh, deterministic dynamical systems. And I think we can We can expand this further because, uh, there is, ah, way off getting the expression for the escaped rate in terms off n the number of variables from cycle expansions that I don't have time to talk about. What? It's kind of like a program that you can try toe pursuit, and this is it. So the conclusions I think of self explanatory I think there is a lot of future in in, uh, in an allo. Continue start computing. Um, they can be efficient by orders of magnitude and digital ones in solving empty heart problems because, first of all, many of the systems you like the phone line and bottleneck. There's parallelism involved, and and you can also have a large spectrum or continues time, time dynamical algorithms than discrete ones. And you know. But we also have to be mindful off. What are the possibility of what are the limits? And 11 open question is very important. Open question is, you know, what are these limits? Is there some kind off no go theory? And that tells you that you can never perform better than this limit or that limit? And I think that's that's the exciting part toe to derive thes thes this levian 10.

>>live from San Diego, California It's the Q covering Koopa and Cloud Native Cot brought to you by Red Cloud. Native Computing Pounding and its ecosystem >>Welcome back. This is the cubes. Fourth year of coverage at Q. Khan Cloud, Native Con. We're here in San Diego. It's 2019. I'm stewed. Minutemen, my host for this afternoon is Justin Warren and happy to welcome to guests from the newly minted platinum member of the CNC F Net Up. Sitting to my right is that Baldwin, who is the director of Cloud Native and Communities Engineering and sitting to his right is Rob Bhaskar, who's the product product strategy for Kubernetes. And it's also a board member on the CME CF, thank you both for joining us. Thank you. All right, s O, you know, maybe start with you. You know, uh, you know, companies that No, I've got plenty of history with net up there. What I've been hearing from that up last few years is you know, the Corvette has always been software, and it is a multi cloud world. I've been hearing this message before. Kind of the cloud native Trinity's piece was going, Of course, there's been some acquisitions and met up continuing to go through its transformations if you will s o help us understand kind of net ops positioning in this ecosystem >>in communities. Yes. Okay, so what we're doing is we're building a product that large manage cloud native workloads on top of community. So we've solved the infrastructure problem. And that's kind of the old problem. We're bored to death. Talking about that problem, but we try to do is try to provide a single painting class to manage on premise. Workloads and off permits were close. So that's what we're trying to do. We're trying to say it's now more about the AP taxonomy in communities. And then what type of tooling do you build to manage that that application and communities and says what we're building right now? That's where we're headed with hybrid. >>There's a piece of it, though, that does draw from the historical strength of map, Of course. So we're building way have, essentially already in marketing capability that allows you to deploy communities an agnostic way, using pure, open unmodified kubernetes on all of the major public clouds, but also on trump. But over time and some of this is already evident. You'll see it married to the storage and data management capabilities that we draw from the historical NetApp and that we're starting to deploy into those public clouds >>with the idea that you should be able to take a project. So project being the name space, new space, having a certain application in it. So you have multiple deployments. I should be able to protect that name space or that project. I feel to move that and the data goes with it. So they were very data where that's what we're trying to do with our. Our software is, you know, make it very data. Where have that aligned with APS inside of communities, >>So maybe step back for a second. What? One of the one of things we've heard a few times at this show before and was talking about the keynote this morning is it is project over company when it comes to the C N C F Project Project over company. So it's about the ecosystem. The C in C F tries not to be opinionated, so it's okay for multiple projects to fitness face not moving up to a platinum a sponsor level. You know, participant here, Ned. It's got lots of history's in participating and driving standards, helping move where the industry's going. Where doesn't it up? See its position in, you know, the participating in the foundation and participating in this ecosystem? >>Yeah, So great question, actually. Love it. It's for my favorite topic. So I think the way we look at it is oftentimes, project to the extent they become ubiquitous, define a standard a de facto standard, so not necessarily ratified by some standards body. And so we're very interested in making sure that in a scenario where you would employ the standard from a technology integration perspective, our capabilities can can operate as an implementation behind the standard. So you get the distinguishing qualities of our capabilities. Our products in our service is Visa VI or in the context of the standard. We're not trying to take you down a walled garden path in a proprietary, uh, journey, if you will weigh, would rather actually compel you to work with us on the basis of the value, not necessarily operating off a proprietary set of interface. Kubernetes broadly perceive it as a defacto standard at this point, there's still some work to be done on running out the edges a lot of underway this week. It's definitely the case that there's a new appeal to making this more off herbal by pardon the expression mere mortals way. Think we can offer Cem, Cem, Cem help in that respect as well? >>Yeah, for us, its usability, right? I mean, that's the reason I started stacking. Cloud was that there was usability problem with kubernetes. I had a usability problem. That's what we're trying. That's how I'm looking at the landscape. And I look at kind of all the projects inside the C N c f. And I look at my role is our role is to How do we tie these together? How do we make these? So they're very, very usable to the users. How were engaging with the community is to try to like a line like this, basically pure upstream projects, and create a usability layer on top of that. But we're not gonna we don't want ever say we're gonna fork into these projects what we're gonna contribute back into these. >>That's one concern that I have heard from. Customers were speaking with some of them yesterday. One of the concerns I had was that when you add that manageability onto the base kubernetes layer, that often very spenders become rather opinionated about which way we think this is a good way to do that. And when you're trying to maintain that compatibility across the ecosystem. So some customers saying, Well, I actually don't want to have to be too closely welded to anyone. Vendor was part of the benefit of Kubernetes. I can move my workloads around. So how do you navigate What? What is the right level of opinion? Tohave and which part should actually just be part of a common sense >>should be along the lines of best practices is how we do it. So like, Let's take a number policy, for example, like applying a sane default network policy to every name space defying a saying default pod security policy. You know, building a cluster in the best practices fashion with security turned on hardening done where you would have done this already as a user. So we're not looking you in any way there, so that's we're not trying. I'm not trying to carry any type of opinion in the product we're trying to do is urbanize your experience across all of this ecosystem so that you don't ever have to think about time now building a cluster on top of Amazon. So I gotta worry about how do I manage this on Amazon? I don't want you to think about those providers anymore, right? And then on top of those on top of that infrastructure, I wanna have a way that you're thinking about managing the applications on those environments in the exact same way. So I'm scaling protecting an application on premise in the identical way I'm doing it in the cloud. >>So if it's the same everywhere, what's the value that you're providing? That means that I should choose your option than something else. >>So wait, do have This is where we have controllers and live inside of the clusters that manage this stuff for the user's so you could rebuild what we're doing, But you would have to roll it all by hands, but you could, you know, we don't stand in the way of your operations either. So, like if we go down, you don't go down that idea, but we do have controllers we have. We're using charities. And so, like our management technology, our controllers are just watching for workload to come into the environment. And then we show that in the interface. But you could just walk away as well if you wanted to. >>There's also a constellation of other service is that we're building around this experience, you know, they do draw again from some of the storage and management capabilities. So staple sets your traditional workloads that want to interact with or transact data against a block or a shared file system. We're providing capabilities for sophisticated qualities of persistence that can be can exist in all of those same public clouds. But moreover, over time, we're gonna be in on premises. Well, we're gonna be able to actually move migrate, place, cash her policy. Your put your persistent data with your workload as you move migrate scale burst would repatriate whatever the model is as you move across in between clouds. >>Okay, How how far down that pathway do you think we are? Because 11 criticism of proven it is is that a lot of the tooling that were used to from more traditional ways of operating this kind of infrastructure isn't really there yet. Hence into the question about we actually need to make this easy to use. How far down that pathway away? >>Why would argue that tooling that I've built has already solved some of those problems. So I think we're pretty far down. The people ride down the path. Now what we haven't done is open sourced. You know all my tools, right? To make it easier on everybody else. >>Get up, Scott. Strong partnerships across the cloud platforms. I had a chance to interview George at the Google Cloud event. New partner of the year. I believe some of the stuff help us understand how you know something about the team building. Interact with the public cloud. You look at anthems and azure Arkin. Of course, Amazon has many different ways. You can do your container and management piece there, you know, to talk a little bit of that relationship and how both with those partners and then across those partners, you know, work. >>Yeah, it's a wow. So how much time we have? So so there's certainly a lot of facets to to that, But drawing from the Google experience. We just announced the general availability of cloud volumes on top. So the ability to stand up and manage your own on top instance and Google's cloud. Likewise, we've announced the general availability of the cloud volume service, which gives you manage put fun as a service experience of shared file system on demand. Google, I believe, is either today or yesterday in London. I guess maybe I'll blame that on the time zone covers, not knowing what what day it was. But the point is that's now generally available. Some of those capabilities are going to be able to be connected to our ability from an ks to deploy, uh on demand kubernetes cluster and deploy applications from a market marketplace experience in a common way, not just with Google, but has your with Amazon. And so, you know, frankly, the story doesn't differ a little bit from one cloud to the next, but the the Endeavour is to provide common capabilities across all of them. It's also the case that we do have people that are very opinionated about I want to live only in the Google or that Microsoft of the Amazon, because we're trying to deliver a rich experience for those folks as well, even if you don't value the agnostic multi cloud expert. >>Yeah and Matt, You know, I'm sure you have a viewpoint on this, but you know, it's that skill set that that's really challenging. And I was at the Microsoft show and you've got people you know. It's not just about dot net, there's all that. They're they're embracing and opened all of these environment. But people tend to have the environment that you used to and for multi cloud to be a reality, it needs to be a little bit easier for me to go between them, but it's still we're still we're making progress. But there's work to do. Yeah, s so I just, you know, you know, I know you're building tools and everything, but what what more do we didn't need to do? What were some of the areas that you know you're hopeful for about a >>year before I need to go for the supreme? It's down. It's coming down to the data side like I need to be able to say that on when I turn on data service is inside of kubernetes. I need be able to have that work would go anywhere, right? And because it is a developer. So I have I'm running a production. I'm running an Amazon. But maybe I'm doing test locally on my bare metal environments. Right? I need I want to be able to maybe sink down some of my data. I'm working with a production down to my test environment. That stuff's missing. There's no one doing that right now, and that's where we're headed. That's the path that's where we're headed. >>Yeah. I'm glad you brought that up, actually, because one of the things that I feel like I heard a little bit last year, but it is violated more this year is we're talking a little bit more to the application to the application developer because, you know, communities is a piece of the infrastructure, But it's about the Colonel. Yeah, yeah, yeah. It's the colonel there. So, you know, how do we make sure you know, we're standing between what the APP developer needs and still making sure that, you know, infrastructure is taken care of because storage and networking they're still hard. >>It is. Yeah. Yeah. I mean, I'm I'm approaching. I'm thinking more along the lines of I'm trying to work about app developers personally than infrastructure This point on for me, you know, like so I have I give you a cluster in three minutes, right? So I don't really have to worry about that problem, you know, way also put Theo on top of the clusters. So it's like we're trying to create this whole narrative that you can manage that environment on day one day, two versions. But and that's for like, an I T manager, right? And society instead of our product. How I'm addressing this is you have personas and so you have this concept. You have an I T manager. They do these things that could set limits for the developer who's building the applications or the service's and pushing those up into the environment. They need to have a sense of freedom, right? And said on that side of the house, you know, I'm trying not to break them out of their tooling. So, like wait part of our product ties in to get s o. We have CD, you know? So you just get push, get commit to a branch and weaken target multiple clusters, Right? But no point to the developer, actually, drafty animal or anything. We make way basically create the container for you. Read the deployment, bring it online. And I feel like there's these lines and that I t guys need to be able to say I need to create the guard rails for the Debs. I don't want to make it seem like I'm creating guardrails for the deaths caused the deaths. Don't like that. That's how I'm balancing it. >>Okay, Because that has always been the tension and that there's a lot of talk about Dev ops, but you don't talkto application developers, and they don't wanna have anything to do with infrastructure. They just want a program to an A p I and get things done. They would like this infrastructure to be seamless. Yeah, >>and what we did, like also what I'm giving them is like service dashboards. Because as a developer, you know, because now you're in charge of your cue, eh? You're writing your tests you're pushing. If your c I is going to ct you on your service in production, right? And so we're delivering dashboards as well for service Is that the developers are running, so they dig in and say, Oh, here's an issue or here's where the issue is probably gonna be at I'm gonna go fix this. Yeah, and we're trying to create that type of like scenario for developer and for an I T manager, >>slightly different angle on it, by understanding that question correctly is part of the complexity of infrastructure is something we're also turned Friday deterministic sort of easy button capability, for perhaps you're familiar with them. That's nice. And a C I product, which we we kind of expand that as hybrid cloud infrastructure. If the intention is to make it a simple private cloud capability and indeed are not, a community service operates directly off of it. It's a big part of actually how we deliver Cloud Service is from it. The point is, is that if you're that application developer, if you want the effective and CASS on prom thing, Endeavor with are not a PhD. I product is to give you that sort of easy button extremes because you didn't really want to be a storage admin network at you didn't want to get into the be mired in the details of infra. So So you know, that's obviously work in progress. But we think we're definitely headed down the right direction >>for him. >>Yeah, it just seemed that a lot of enterprises wanna have the cloud like experience, but they want to be able to bring it home that we're seeing a lot more. Yeah. >>So this is like, this turn cheon from this turnkey cloud on premise and played with think has weaken like the same auto scaling. So take so take the dynamic nature of opportunities. Right. So I have a base cluster size of four worker notes, right? But my work, let's gonna maybe maybe need to have more notes. So my out of scale is gonna increase the size my cluster and decrease the size right Pretty much everybody only do that in the public cloud. I could do that in public and on premise now and so that's That's what we're trying to deliver. And that's nickel stuff. I think >>that there's a lot of advantages thio enterprises operating in that way because I have I people that here I can I can go and buy them, hire them and say way, need you to operate this gear and you, you've already done elsewhere. You can do it in cloud. You can do it on side. I could know run my operations the same across no matter where my applications leave, Which saves me a lot of money on training costs on development costs on generally makes for a much more smooth and seamless experience. So, Rob, if you could just love >>your takeaway on, you know, kind of net up participation here at the event and what you want people to take away off from the show this year. >>So it's certainly the case that we're doing a lot of great work. We, like people toe become aware of it. Not up, of course, is not. I think we talked about this and perhaps other context, not strictly a storage and data management company. Only way do draw from the strength of that as we're providing full stack capabilities in a way that are interconnected with public cloud things like are not a Cuban. Any service is really the foundational glue in many ways how we deliver the application run time, but over time will build a consolation of data centric capabilities around that as well. >>I would just love to get your viewpoint Is someone that you know built a company in this ecosystem. There's so many start ups here. Give us kind of that founder viewpoint of being in. They're so sort of ecosystem of the >>ecosystem. So this is how I came into the ecosystem at the beginning. I would have to say that it does feel different. Att This point, I'm gonna speak as Matt, not as now. And so my my thinking has always been It feels a lot like kind of your really your big fan of that rock bands, right? And you go to a local club way all get to know each other at that local club. There's, like maybe 500 of us or 1000 of us. And then that band gets signed a Warner Brothers and goes to the top it. Now there's 20,000 people or 12,000 people. That's how it feels to me right now, I think. But what I like about it is that just shows the power of the community is now at a point where is drawing in like cities now, not just a small collection of a tribe of people, right? And I think that's a very powerful thing with this community. And like all the where they called the kubernetes summits that they're doing way, didn't have any of those back when we first got going. I mean, it was tough to fill the room, you know, Now, now we can fill the room and it's amazing. And what I like seeing is is people moving past the problem with kubernetes itself and moving into, like, what other problems can I solve on top of kubernetes, you know? So you're starting to see that all these really exciting startups doing really need things, you know, and I really likes it like this vendor hall I really like, you know, because you get to see all the new guys. But there's a lot of stuff going on, and I'm excited to see where the community goes in the next five years. But it's we've gone from 0 to 60 insanely because you guys were at the original coupon. I think, Well, >>it's our fourth year doing the Cube at this show, but absolutely we've watched the early days, You know, I'm not supposed to mention open stack of this show, but we remember talking T o J j. And some of the early people there and wait interviewed Chris McCloskey back into Google days, right? So, yeah, we've been fortunate to be on here, really? Day zero here and definitely great energy. So much. Congrats. So much on the progress. Really appreciate the updates, Everything going. As you said, right, we've reached a certain estate and just adding more value on top of this whole >>environment. We're now like we're in, like, Junior high now. Right on were in grade school for a few years. >>All right, Matt. Rob, Thank you so much for the update. Hopefully not an awkward dance tonight for the junior people. For Justin Warren. I'm stupid and back with more coverage here from Q Khan Cloud native 2019. Diego, Thank you for watching Cute

>> from our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Welcome to this Special Cube conversation here in Palo Alto, California at the Cube Studios at the Cube headquarters. I'm John for the host, like you were a Dustin Kirkland product manager and Google friend of the Cuban. The community with Cooper Netease been on the Cube Cube alumni. Dustin. Welcome to the Cube conversation. >> Thanks. John's a beautiful studio. I've never been in the studio and on the show floor a few times, but this is This is fun. >> Great to have you on a great opportunity to chat about Cooper Netease yet of what you do out some product man's working Google. But really more importantly on this conversation is about the fifth anniversary, the birthday of Cuba Netease. Today we're celebrating the fifth birthday of Cooper Netease. Still, it's still a >> toddler, absolutely still growing. You think about how you know Lennox has been around for a long time. Open stack has been around these other big projects that have been around for, you know, going on decades and Lenox this case and Cooper nineties. It's going so fast, but It's only five years old, you know. >> You know, I remember Adam Open Stack event in Seattle many, many years ago. That was six years ago. Pubes on his 10th year. So many of these look backs moments. This is one of them. I was having a beer with Lou Tucker. J J Kiss Matic was like one of the first comes at the time didn't make it, But we were talking about open stagger like this Cooper Netease thing. This is really hot. This paper, this initiative this could really be the abstraction layer to kind of bring all this cloud Native wasn't part of the time, but it was like more of an open stack. Try and move up to stack. And it turned out it ended up happening. Cooper Netease then went on to change the landscape of what containers did. Dr. Got a lot of credit for pioneering that got the big VC funding became a unicorn, and then containers kind of went into a different direction because of Cooper duties. >> Very much so. I mean, the modernization of software infrastructure has been coming for a long time, and Cooper nutty sort of brings it all brings it all together at this point, but putting software into a container. We've been doing that different forest for for a lot of time, uh, for a long time, but But once you have a lot of containers, what do you do with that? Right? And that was the problem that Cooper Nettie solved so eloquently and has, you know, now for a couple of years, and it just keeps getting better. >> You know, you mentioned modernization. Let's talk about that because I think the modernization the theme is now pretty much prevalent in every vertical. I'll be in D. C. Next week for the Amazon Webster was public sector Summit, where modernization of governments and nations are being discussed. Education, modernization of it. We've seen it here. The media business that were participating in is about not where you store the code. It's how you code. How you build is a mindset shift. This has been the rial revelation around the Dev Ops Movement Infrastructures Code, now called Cloud Native. Share your thoughts on this modernization mindset because it really is how you build. >> Yeah, I think the cross pollination actually across industries and we even we see that even just in the word containers, right and all the imagery around shipping and shipping containers, we've applied these age old concepts that have been I don't have perfected but certainly optimized over decades of, actually centuries or millennia of moving things across water in containers. Right. But we apply that to software and boom. We have the step function difference in the way that we we manage and we orchestrated and administer code. That's one example of that cross pollination, and now you're talking about, like optimizing optimized governments or economies but being able to maybe then apply other concepts that we've come a long way in computer science do de bop set a good example? You know, applying Dev ops principles to non computer feels. Just think about that for a second. >> It's mind blowing. And if you think about also the step function you mentioned because I think this actually changed a lot of the entrepreneurial landscape as well and also has shaped open source and, you know, big news this this quarter is map are going to shut down due one of the biggest do players. Cloudera merge with Horton Works fired their CEO, the founder Michael. So has retired, Some say forced out. I don't think so. I think it's more of his time. I'm Rodel still there. Open source is a business model, you know. Can we be the red hat for her? Duped the red? Not really kind of the viable, but it's evolving. So open source has been impacted by this step function. There's a business impact. Talk about the dynamics with step function both on the business side and on how software's built specifically open source. >> You know, you and I have been around open source for a long, long time. I think it started when I was in college in the late nineties on then through my career at IBM. And it's It's interesting how on the fringe open source was for so long and such so so much of my BM career. And then early time spent onside it at Red Hat. It was it was something that was it was different, was weird. It was. It was very much fringe where the right uh, but now it's in mainstream and it's everywhere, and it's so mainstream that it's almost the defacto standard to just start with open source. But you know, there's some other news that's been happening lately that she didn't bring up. But it's a really touchy aspect of open source right now on that's on some of the licenses and how those licenses get applied by software, especially databases. When offered as a service in the cloud. That's one of the big problems. I think that that's that we're we're working within the open >> source, summarize the news and what it means. What's what's happening? What's the news and what's the really business? Our technical impact to the licensing? What's the issue? What's the core issue? >> Yeah, eso without taking judgment any any way, shape or form on this, the the the TL D are on. This is a number of open source database is most recently cockroach D. B. I have adopted a different licensing model that is nonstandard from an open source perspective. Uh, and from one perspective, they're they're adopting these different licensing models because other vendors can take that software and offered as a service, yes, and in some some cases, like Amazon like Sure, you said, uh, and offered as a as a service, uh, and maybe contribute. Maybe pay money to the smaller startup or the open source community behind it. But not necessarily. Uh, and it's in some ways is quite threatening to open source communities and open source companies on other cases, quite empowering. And it's going to be interesting to see how that plays out. The tension between open sourcing software and eventually making money off of it is something that we've we've seen for, you know, at least 25. >> And it continues to go on today, and this is, to me a real fascinating area that I think is going to be super important to keep an eye on because you want to encourage contribution and openness. Att the same time we look at the scale of just the Lenox foundations numbers. It's pretty massive in terms of now, the open source contribution. When you factor in even China and other nations, it's it's on exponential growth, right? So is it just open source? Is the model not necessarily a business? Yeah. So this is the big question. No one knows. >> I think we crossed that. And open source is the model. Um, and this is where me is a product manager. That's worked around open source. I've spent a lot of time thinking about how to create commercial offerings around open source. I spent 10 years at Economical, the first half of which, as an engineer, the second half of which, as a product manager around, uh, about building services, commercial services around 12 And I learned quite a few things that now apply absolutely to communities as well as to a number of open source startups. That that I've advised on DH kind of given them some perspective on maybe some successful and unsuccessful ways to monetize that that opens. >> Okay, so doesn't talk about Let's get back to Coburg. And so I think this is the next level Talk track is as Cooper Netease has established itself and landed in the industry and has adoption. It's now an expansion votes the land adopted expand. We've seen adoption. Now it's an expansion mode. Where does it go from here? Because you look at the tale signs things like service meshes server. Listen, you get some interesting trends that going to support this expansionary stage of uber netease. What is your view about the next expansion everyway what >> comes next? Yeah, I I think I think the next stage is really about democratizing communities for workloads that you know. It's quite obvious where when communities is the right answer at the scale of a Google or a Twitter or Netflix or, you know, some of these massive services that it is obviously and clearly the best answer to orchestrating containers. Now I think the next question is, how does that same thing that works at that massive scale Also worked for me as a developer at a very small scale helped me develop my software. My small team of five or 10 people. Do I need a coup? Burnett. He's If I'm ah five or 10 person startup. Well, I mean, not the original sort of borde vision of communities. It's probably overkill, but actually the tooling has really advanced, and we now >> have >> communities that makes sense on very small scales. You've got things like a three s from from Rancher. You've got micro Kates from from my colleagues at economical other ways of making shrinking communities down to something that fits, perhaps on devices perhaps at the edge, beyond just the traditional data center and into remote locations that need to deploy manage applications >> on the Cooper Netease clustering the some of the tech side. You know, we've seen some great tech trends as mentioned in Claudia Horton. Works and map Our Let's Take Claudia and Horton work. Remember back in the old days when it was booming? Oh, they were so proud to talk about their clusters. I stood up all these clusters and then I would ask them, Well, what do you doing with it? Well, we're storing data. I think so. That became kind of this use case where standing up the cluster was the use case and they're like, OK, now let's put some data in it. It's a question for you is Coburn. Eddie's a little bit different. I'm not seeing they were seeing real use cases. What are people standing up? Cuban is clusters for what specific Besides the same Besides saying I've done it. Yeah, What's the what's the main use case that you're seeing this that has real value? >> Yeah, actually, there's you just jog t mind of really funny memory. You know, back in those big data days, I was CEO of a startup. We were encrypting data, and we were helping encrypt healthcare data for health care companies and the number of health care companies that I worked with at that time who said they had a big data problem and they had all of I don't know, 33 terabytes worth of worth of data that they needed to encrypt. It was kind of humorous sometimes like, Is that really a big, big data problem? This fits on a single disc, you know, Uh, but yeah, I mean, it's interesting how >> that the hype of of the tech was preceding. The reality needs needs, says Cooper Nettie. So I have a Cuban Eddie's cluster for blank. Fill in the blank. What are people saying? >> Yeah, uh, it's It's largely about the modernization. So I need to modernize my infrastructure. I'm going to adopt the platform. That's probably not, er, the old er job, a Web WebSphere type platform or something like that. I'm investing in hardware investing in Software Middle, where I'm investing in people, and I want all of those things to line up with where industry is going from a software perspective, and that's where Cooper Nighties is sort of the cornerstone piece of that Lennox Of course, that's That's pretty well established >> canoes delivery in an integration piece of is that the pipeline in was, that was the fit on the low hanging fruit use cases of Cooper Netease just development >> process. Or it's the operations it's the operations of now got software that I need to deploy across multiple versions, perhaps multiple sites. Uh, I need to handle that upgrade ideally without downtime in a way that you said service mash in a way that meshes together makes sense. I've got a roll out new certificates I need to address the security, vulnerability, thes air, all the things that Cooper and I used to such a better job at then, what people were doing previously, which was a whole lot of four loops, shell strips and sshh pushing, uh, pushing tar balls around. Maybe Debs or rpm's around. That is what Cooper not he's actually really solves and does an elegant job of solving as just a starting point. And that's just the beginning and, you know, without getting ve injury here, you know, Anthros is the thing that we had at Google have built around Cooper Netease that brings it to enterprise >> here the other day did a tweet. I called Anthem. I just typing too fast. I got a lot of crap on Twitter for that mission. And those multi cloud has been a big part of where Cubans seems to fit. You mentioned some of the licensing changes. Cloud has been a great resource for a lot of the new Web scale applications from all kinds of companies. Now, with several issues seeing a lot more than capabilities, how do you see the next shift with data State coming in? Because God stateless date and you got state full data. Yeah, this has become a conversation point. >> Yeah, I think Kelsey Hightower has said it pretty eloquently, as he usually does around the sort of the serval ist movement and lets lets developers focus on just their code and literally just their code, perhaps even just their function in just their piece of code, without having to be an expert on all of the turtles all the way, all the way down. That's the big difference about service have having written a couple of those functions. I can I can really invest my time on the couple of 100 lines of code that matter and not choosing a destro choosing a cougar Nati is choosing, you know, all the stack underneath. I simply choose the platform where I'm gonna drop that that function, compile it, uploaded and then riff and rub. On that >> fifth anniversary, Cooper Netease were riffing on Cooper Netease. Dustin Circle here inside the Cube Cube Alumni you were recently at the coop con in overseas in Europe, Barcelona, Barcelona, great city. Keeps been there many times. Do was there covering for us. Couldn't make this trip, Unfortunately, had a couple daughter's graduating, so I didn't make the trip. Sorry, guys. Um, what was the summary? What was the takeaway? Was the big walk away from that event? What synthesized? The main stories were the most important stories being >> told. >> Big news, big observations. >> It was a huge event to start with. It was that fear of Barcelona. Um, didn't take over the whole space. But I've been there a number of times from Mobile World Congress. But, you know, this is this is cube con in the same building that hosts all of mobile world Congress. So I think 8,000 attendees was what we saw. It's quite celebratory. You know, I think we were doing some some pre fifth birthday bash celebrations, Key takeaways, hybrid hybrid, Cloud, multi Cloud. I think that's the world that we've evolved into. You know, there was a lot of tension. I think in the early days about must stay on. Prem must go to the cloud. Everything's there's gonna be a winner and a loser and everything's gonna go one direction or another. I think the chips have fallen, and it's pretty obvious now that the world will exist in a very hybrid, multi cloud state. Ultimately, there's gonna be some stuff on Prem that doesn't move. There's going to be some stuff better hosted in one arm or public clouds. That's the multi cloud aspect, Uh, and there will be stubborn stuff at the edge and remote locations and vehicles on oil rigs at restaurants and stores and >> so forth. What's most exciting from a trans statement? What do you what? What's what's getting you excited from what you see on the landscape out there? >> So the tying all of that to Cooper Netease, Cuban aunties, is the thing that basically normalizes all of that. You write your application put it in a container and expect to communities to be there to scale that toe. Operate that top grade that to migrate that over time. From that perspective, Cooper nineties has really ticked, ticked all the boxes, and you've got a lot of choices now about which companies here, you're going to use it and where >> beyond communities, a lot of variety of projects coop flow, you got service messes out there a lot of difference. Project. What's What's a dark horse? What's something that sets out there that people should be paying attention to? That you see emerging? That's notable. That should be paying attention. To >> think is a combination of two things. One is pretty obvious, and that's a ML is coming like a freight train and is sort of the next layer of excitement. I think after Cooper, Netease becomes boring, which hopefully if we've done our jobs well, that communities layer gets settled and we'll evolve. But the sort of the hockey stick hopefully settles down and it becomes something super stable. Uh, the application of machine learning to create artificial intelligence conclusions, trends from things that is sort of the next big trend on then I would say another one If you really want the dark horse. I think it's around communications. And I think it's around the difference in the way that we communicate with one another across all forms of media voice, video chat, writing, how we interact with people, how we interact with our our tools with our software and in fact, how our software in Iraq's with us in our software acts with with other software that communications industry is, it's ripe for some pretty radical disruption. And you know some of the organizations and they're doing that. It's early early days on those >> changes. Final point you mentioned earlier in our conversation here about how Dev Ops is influencing impacting non tech and computer science. Really? What did you mean by that? >> Uh, well, I think you brought up unexpectedly and that that you were looking at the way Uh, some other industries are changing, and I think that cross pollination is actually quite quite powerful when you take and apply a skill and expertise you have outside of your industry. But it adds something new and interesting, too, to your professional environment. That's where you get these provocative operations. He's really creative, innovative things that you know. No one really saw it coming. >> Dave Ops principles apply to other disciplines. Yeah, agility. That's that's pointing down waterfall based processes. That's >> one phenomenal example. Imagine that for governments, right to remove some of the like the pain that you and I know. I've got to go and renew my license. My birthday's coming up. I gotta go to renew my driver's license. You know much. I'm dreading going to the the DMV Root >> Canal driver's license on the same. Exactly >> how waterfall is that experience. And could we could we beam or Mohr Agile More Dev Autopsy and some of our government across >> the U. S. Government's procurement practices airbase upon 1990 standards they still want Request a manual, a physical manual for every product violent? Who does that? >> I know that there are organizations trying to apply some open source principles to government. But I mean, think about, you know, just democracy and how being a little bit more open and transparent in the way that we are in open source code, the ability to accept patches. I have a side project, a passion for brewing beer and I love applying open source practices to the industry of brewing. And that's an example of where use professional work, Tio. Compliment a hobby. >> All right, we got to bring some cubic private label, some Q beer. >> If you like sour beer, I'm in the sour beer. >> That's okay. We like to get the pus for us. Final question for you. Five years from now, Cooper needs to be 10 years old. What's the world gonna look like when we wake up five years from now with two Cuban aunties? >> Yeah, I think, uh, I don't think we're struggling with the Cooper nutties. Uh, the community's layer. At that point, I think that's settled science, inasmuch as Lennox is pretty settled. Science, Yes, there's a release, and it comes out with incremental features and bug fixes. I think Cuban aunties is settled. Science management of of those containers is pretty well settled. Uh, five years from now, I think we end up with software, some software that that's writing software. And I don't quite mean that in the way That sounds scary, uh, and that we're eliminating developers, but I think we're creating Mohr powerful, more robust software that actually creates that that software and that's all built on top of the really strong, robust systems we have underneath >> automation to take the heavy lifting. But the human creation still keeping one of the >> humans Aaron the look it's were We're many decades away from humans being out of the loop on creative processes. >> Dustin Kirkland, he a product manager of Google Uh, Cooper Netease guru also keep alumni here in the studio talking about the coup. Burnett. He's 50 year anniversary. Of course, the kid was president creation during the beginning of the wave of communities. We love the trend we love Cloud would left home a tec. I'm Sean for here in Palo Alto. Thanks for watching.

>> Announcer: Live, from the Mandalay Bay Convention Center in Las Vegas, it's theCUBE covering VMworld 2016. Brought to you by VMware and its ecosystem sponsors. Now, here are your hosts, John Furrier and Stu Miniman. >> Welcome back, everyone. We're live here in Las Vegas for VMworld 2016. This is SiliconANGLE Media's theCUBE. It's our flagship program. We go out to the events and extract the signal from the noise. I'm John Furrier and my co-host this week, Stu Minniman, for three days of wall-to-wall coverage. Our next guest is the chairman and CEO of Dell Technologies, Inc., that's the first time we've actually used that. Congratulations on, I think last Thursday or Wednesday, the name officially became Dell Technology. Michael Dell, welcome back to theCUBE. >> Thank you. Super excited to be with you and obviously super excited about the formation of Dell Technologies as we bring together Dell and EMC and VMware and Pivotal and RSA and Virtustream and SecureWorks and so many other great organizations. >> So Dell Technology, now it's official, but EMC, Dell EMC is not yet official. Quick, give us the update. That's the number one thing people are asking. What's the update with the merger and the China situation. What's the quick update there from your standpoint? >> You know, we announced this back in October of last year and we're very much on track with the original timeline that we said, which was that we'd close between May and October of this year, and on the original terms. So everything is moving along and we're making great progress. >> Chinese government not playing monkey business with you, looking at the big mega-merger and thinking, whoa, slow down. >> We're continuing to work with them, and as I said, we're on track with the original schedule and terms that we said when we announced it back in October of last year. >> Exciting things on the global landscape, we'll get to that in a second. But I want to get your thoughts on VMworld because this is a geek show and this is a technology show and on the keynote they're showing debugging ports, migrating from the cloud, I mean you don't see that. You usually see the pomp and circumstance, all the glamour. Here, I mean you're a geek, you're always getting down and dirty with the technology. Thoughts on this community, because this is, these guys roll their sleeves up. And by the way, they're very vocal on social media so you can always get the Twitter feed, but your thoughts on VMworld, the culture of this ecosystem? >> I thought the demos that Guido showed were incredibly cool, showing sort of the evolution of virtualization to the software-defined data center to the hybrid cloud to now Cross-Cloud and all the things that you can do. And as you saw, live examples with Citibank and Columbia and J & J, these are real live organizations. And of course at VMworld you have the ecosystem of VMware in all of its glory, with the whole industry coming together and, as you said, a passionate group of individuals that are excited about what they're doing and VMware is kind of a big part of how the industry is evolving. And we're thrilled be an even bigger part of it now than we have been in the past. It's not my first time to come to VMworld, of course. >> But again, with now Dell Technologies looming, and the merger is going to be a big part of that. >> Yes. >> Technologies, and I'll ask that specific question later. But I do want to get your thoughts as someone who has been in the industry as a power broker, founder, CEO, now going private, you've seen all the waves of innovation. The ecosystem has become a really important part of it in your world there was the Wintel and the developer communities during those days, for the software business, aka the computer industry per se, but now we're on a new inflection point where the computer industry-like movement is happening with cloud and data center, hyper-converged environments. What does the ecosystem mean? Because we've seen the ecosystem kind of sitting there kind of waiting for this explosion with the cloud. Your thoughts on what the ecosystem means in this new era, vis-a-vis other times in history? >> You know, I don't see them waiting. You think about the kind of armada of companies that are coming along as the ecosystem evolves. Again, you see it out there on the show floor. You take NSX as an example. There's tremendous growth in software-defined networking. And NSX is kind of leading the way. And you see all the leading networking companies in the world here at VMworld using NSX as the platform for the software-defined network. It's just another great example. The original growth in the hypervisor and then into software-defined storage, software-defined networking and you can, if you look further on the show floor, right, you'll see kind of software-defined everything. And all aspects of the network, layers four through seven, eventually being virtualized. From the cutting edge -- >> John: So, virtualized stack. >> New things all the way to the mainstream and of course there's a lot of growth in our industry around converged and hyper-converged because it's making it easy to deploy these solutions in a rapid fashion and we're right in the middle of all this. >> So Michael, you speak pretty passionately about VMware and their role in the ecosystem. There's still a lot of noise out there that people I don't think understand how you're going to finance the debt and there's many people, like still during the keynote this morning, they're like, as soon as the deal's done, VMware is going to be sold off. Really, hardware companies don't want to do software. >> Absolutely incorrect. That's totally wrong. Anybody that says that has no clue what they're talking about. So look, I think first thing is you've got do do some math. If you look at the combined cash flows of Dell and EMC and VMware, what you find is they're many, many times greater than the debt service. And so we have, in fact, an advantage capital structure that allows us to not only do what we're doing and have tremendous scale and investment in innovation, roughly $4.5 billion annually invested in R & D, the largest enterprise systems company in the world, the strongest supply chain, and also have the speed and flexibility with some of these new startup instances. You guys are familiar with what we're doing with Pivotal and Cloud Foundry and all the great things that are going on there. With SecureWorks, with Boomi, so we've got both the speed and agility of a startup plus the scale and breadth with the broadest ecosystem and access to customers, and while we're here at VMworld, we're not just about VMware, right? Dell Technologies is a company that embraces all of the major ecosystems, be it the Microsoft ecosystem, the Linux and OpenStack and container ecosystems. So the hardware platforms that we're creating allow customers the broadest set of solutions to be able to stand up against their requirements. >> So back at Dell World, Michael, you talked about, you had Satya Nadella up on stage, how Microsoft fits and understanding, you know, in many ways Dell Technologies is an arms supplier to a lot of environments. You've got the enterprise data center. You've got the public cloud. Where do you see VMware in this evolving multi-cloud very varied ecosystem? >> I think if you look at VMware's business in the first half of this year, it's done quite well. And when I look at the trends for the forward outlook and kind of growth characteristics, VMware is making a very nice transition into this emerging cloud world. And it's doing that by taking the whole virtualization and software-defined technologies beyond the hypervisor into the whole software-defined data center. And things like the VMware Cloud Foundations make it a lot easier to do that, whether you're doing it on premise in a private cloud or whether you're a service provider, a telco, an IBM, for example. And I think you'll see others as well. And customers that have embranced VMware and of course there are 500,000 plus around the world, are looking for ways to be able to extend out to the public cloud. And the kinds of announcements you saw today with IBM, with the VMware Cross-Cloud initiative, will allow for this to extend deep into the public clouds. >> We're getting some questions from Twitter. I'll read a few of them here. Two questions. Have you met Chairman Chang and what's he like? And two, what of the technologies in the portfolio are you most excited about. And I asked VMware or Dell Technologies and they asked, both. So two questions. Have you met Chairman Chang and what's he like? And what technology are you most excited about? >> I have met a number of the distinguished folks over in China for sure, whether it be in one on one meetings or in group meetings and I'm over there on a pretty regular basis. China is the second largest market in the world for Dell to sell its products. So it's also the second largest economy in the world so that shouldn't be too surprising. But we have roughly $5.5 billion business in China, a big part of our supply chain. On the second question, you know, it's kind of like saying >> John: Your favorite child. >> Which of your children do you love the most, right? So that's not, you can get in a lot of trouble with that. But when I look across the whole -- >> We need to categorize here. I'll just rephrase the question because I think that's, I mean that's a political response, I get that. But let's go into, where do you see the disruption coming from? If you had to point out a disruptive enabler that is a lever for the portfolio, where would you look at and say okay, that's going to be a real enabling technology that's going to one, propel Dell on a domestic and global basis, and two, power the ecosystem? >> I think this digital transformation is real. And I think that we are at the very beginning of this period of time where the cost to make things intelligent is approaching zero and the number of them is going to explode. And so the influence and impact that our industry has on the world will expand geometrically as a result. And so the challenge that every organization is going to have, is how do you take all this information in real time and also in time series, because I think there will be some value to the historical data, and turn it into better insights, to be able to make better decisions, to make better products and services. And we're just at the very beginning of that. So, to me, that is the most exciting thing going on and obviously, we're right in the middle of that from lots of different perspectives. >> I've got to ask you a personal question. And I want to get your thoughts on this as someone who's been in the industry and is a chess master, 3D chess player, also running a big business, global business, billions of dollars. In 1994, Bill Gates wrote The Road Ahead and he talked about the future and he completely missed the internet in his forward-looking book. And I bring that up because now we're living in a time where IOT and autonomous vehicles, looking at digital state, digital transformation is a big part of that, so I ask the question, do you worry about missing something? I don't mean FOMO, fear of missing out, but there are big moves being made like technology in autonomous vehicles, drones, all this AI going on, machine learning, do you look at that and go hmmm. Is that on your mind, like maybe you might miss something and how do you handle that? >> It's a good point. If you look at all the smartest people in the industry, whatever that means, and you say what's their ability to predict what happens in five years, 10 years, 15 years, it's actually not been very good, right? And so that has been humbling, if somebody included me in that category of people that could try to do that. But we've got a lot of smart folks. I think we have, at the core of our company, this concept of having big ears, which means we want to listen and we want to learn. And our job is to take all these things that we're learning from our customers and all of our understanding of the core molecular elements of technology, and make the magic happen in the middle that go solves the problems that customers have. >> Do you see IOT and cars and this kind of consumer experience very real for Dell Technologies to play in? >> I think there's no question that the elemental cost of computing is declining and whenever you see that happening, you see, it's like a gas, right? It expands to fit the space available. And I think you'll absolutely see this explosion, proliferation, you're already seeing it. We have hundreds of IOT projects going already within our company and we know of many, many others, so it's real. >> It's in the early phase of the hype cycle. Michael, we've got to wrap but I want to ask one final question and then kind of wrap it up. Everyone wants to know, what's the future of VMware in your words, talk to the customers that are watching and the people in the ecosystem and employees and partners. What is the future of VMware in the Dell Technologies vision? >> I think VMware has got a very bright future. I've seen this in the past where people said, Oh, you know, the PC is dead so forget about Dell. Everything's going to the cloud, so forget about all these other companies. I don't think that's quite the way it all works. So what I see in VMware is an incredibly vibrant ecosystem that's getting stronger. I see VMware remaining independent and we're obviously the majority shareholder and helping to ensure the ecosystem stays very, very strong. And I see very exciting new things, like NSX. Extending the reach of virtualization technology well beyond the core original business of VMware which was a great business and continues to actually be a great business. >> Michael, thanks for spending the time, with your busy schedule, to join us on theCUBE. I appreciate it. Great to see you. Michael Dell here inside theCUBE. I'm John Furrier with Stu Miniman. You're watching theCUBE from SiliconANGLE Media. We'll be right back with more. I'm John Furrier with Stu Miniman. We'll be right back.