(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)

(mellow music) >> Hello everyone, welcome to theCUBE's coverage of AWS Startup Showcase. This is the closing panel session on AI machine learning, the top startups generating generative AI on AWS. It's a great panel. This is going to be the experts talking about riding the wave in generative AI. We got Ankur Mehrotra, who's the director and general manager of AI and machine learning at AWS, and Clem Delangue, co-founder and CEO of Hugging Face, and Ori Goshen, who's the co-founder and CEO of AI21 Labs. Ori from Tel Aviv dialing in, and rest coming in here on theCUBE. Appreciate you coming on for this closing session for the Startup Showcase. >> Thanks for having us. >> Thank you for having us. >> Thank you. >> I'm super excited to have you all on. Hugging Face was recently in the news with the AWS relationship, so congratulations. Open source, open science, really driving the machine learning. And we got the AI21 Labs access to the LLMs, generating huge scale live applications, commercial applications, coming to the market, all powered by AWS. So everyone, congratulations on all your success, and thank you for headlining this panel. Let's get right into it. AWS is powering this wave here. We're seeing a lot of push here from applications. Ankur, set the table for us on the AI machine learning. It's not new, it's been goin' on for a while. Past three years have been significant advancements, but there's been a lot of work done in AI machine learning. Now it's released to the public. Everybody's super excited and now says, "Oh, the future's here!" It's kind of been going on for a while and baking. Now it's kind of coming out. What's your view here? Let's get it started. >> Yes, thank you. So, yeah, as you may be aware, Amazon has been in investing in machine learning research and development since quite some time now. And we've used machine learning to innovate and improve user experiences across different Amazon products, whether it's Alexa or Amazon.com. But we've also brought in our expertise to extend what we are doing in the space and add more generative AI technology to our AWS products and services, starting with CodeWhisperer, which is an AWS service that we announced a few months ago, which is, you can think of it as a coding companion as a service, which uses generative AI models underneath. And so this is a service that customers who have no machine learning expertise can just use. And we also are talking to customers, and we see a lot of excitement about generative AI, and customers who want to build these models themselves, who have the talent and the expertise and resources. For them, AWS has a number of different options and capabilities they can leverage, such as our custom silicon, such as Trainium and Inferentia, as well as distributed machine learning capabilities that we offer as part of SageMaker, which is an end-to-end machine learning development service. At the same time, many of our customers tell us that they're interested in not training and building these generative AI models from scratch, given they can be expensive and can require specialized talent and skills to build. And so for those customers, we are also making it super easy to bring in existing generative AI models into their machine learning development environment within SageMaker for them to use. So we recently announced our partnership with Hugging Face, where we are making it super easy for customers to bring in those models into their SageMaker development environment for fine tuning and deployment. And then we are also partnering with other proprietary model providers such as AI21 and others, where we making these generative AI models available within SageMaker for our customers to use. So our approach here is to really provide customers options and choices and help them accelerate their generative AI journey. >> Ankur, thank you for setting the table there. Clem and Ori, I want to get your take, because the riding the waves, the theme of this session, and to me being in California, I imagine the big surf, the big waves, the big talent out there. This is like alpha geeks, alpha coders, developers are really leaning into this. You're seeing massive uptake from the smartest people. Whether they're young or around, they're coming in with their kind of surfboards, (chuckles) if you will. These early adopters, they've been on this for a while; Now the waves are hitting. This is a big wave, everyone sees it. What are some of those early adopter devs doing? What are some of the use cases you're seeing right out of the gate? And what does this mean for the folks that are going to come in and get on this wave? Can you guys share your perspective on this? Because you're seeing the best talent now leaning into this. >> Yeah, absolutely. I mean, from Hugging Face vantage points, it's not even a a wave, it's a tidal wave, or maybe even the tide itself. Because actually what we are seeing is that AI and machine learning is not something that you add to your products. It's very much a new paradigm to do all technology. It's this idea that we had in the past 15, 20 years, one way to build software and to build technology, which was writing a million lines of code, very rule-based, and then you get your product. Now what we are seeing is that every single product, every single feature, every single company is starting to adopt AI to build the next generation of technology. And that works both to make the existing use cases better, if you think of search, if you think of social network, if you think of SaaS, but also it's creating completely new capabilities that weren't possible with the previous paradigm. Now AI can generate text, it can generate image, it can describe your image, it can do so many new things that weren't possible before. >> It's going to really make the developers really productive, right? I mean, you're seeing the developer uptake strong, right? >> Yes, we have over 15,000 companies using Hugging Face now, and it keeps accelerating. I really think that maybe in like three, five years, there's not going to be any company not using AI. It's going to be really kind of the default to build all technology. >> Ori, weigh in on this. APIs, the cloud. Now I'm a developer, I want to have live applications, I want the commercial applications on this. What's your take? Weigh in here. >> Yeah, first, I absolutely agree. I mean, we're in the midst of a technology shift here. I think not a lot of people realize how big this is going to be. Just the number of possibilities is endless, and I think hard to imagine. And I don't think it's just the use cases. I think we can think of it as two separate categories. We'll see companies and products enhancing their offerings with these new AI capabilities, but we'll also see new companies that are AI first, that kind of reimagine certain experiences. They build something that wasn't possible before. And that's why I think it's actually extremely exciting times. And maybe more philosophically, I think now these large language models and large transformer based models are helping us people to express our thoughts and kind of making the bridge from our thinking to a creative digital asset in a speed we've never imagined before. I can write something down and get a piece of text, or an image, or a code. So I'll start by saying it's hard to imagine all the possibilities right now, but it's certainly big. And if I had to bet, I would say it's probably at least as big as the mobile revolution we've seen in the last 20 years. >> Yeah, this is the biggest. I mean, it's been compared to the Enlightenment Age. I saw the Wall Street Journal had a recent story on this. We've been saying that this is probably going to be bigger than all inflection points combined in the tech industry, given what transformation is coming. I guess I want to ask you guys, on the early adopters, we've been hearing on these interviews and throughout the industry that there's already a set of big companies, a set of companies out there that have a lot of data and they're already there, they're kind of tinkering. Kind of reminds me of the old hyper scaler days where they were building their own scale, and they're eatin' glass, spittin' nails out, you know, they're hardcore. Then you got everybody else kind of saying board level, "Hey team, how do I leverage this?" How do you see those two things coming together? You got the fast followers coming in behind the early adopters. What's it like for the second wave coming in? What are those conversations for those developers like? >> I mean, I think for me, the important switch for companies is to change their mindset from being kind of like a traditional software company to being an AI or machine learning company. And that means investing, hiring machine learning engineers, machine learning scientists, infrastructure in members who are working on how to put these models in production, team members who are able to optimize models, specialized models, customized models for the company's specific use cases. So it's really changing this mindset of how you build technology and optimize your company building around that. Things are moving so fast that I think now it's kind of like too late for low hanging fruits or small, small adjustments. I think it's important to realize that if you want to be good at that, and if you really want to surf this wave, you need massive investments. If there are like some surfers listening with this analogy of the wave, right, when there are waves, it's not enough just to stand and make a little bit of adjustments. You need to position yourself aggressively, paddle like crazy, and that's how you get into the waves. So that's what companies, in my opinion, need to do right now. >> Ori, what's your take on the generative models out there? We hear a lot about foundation models. What's your experience running end-to-end applications for large foundation models? Any insights you can share with the app developers out there who are looking to get in? >> Yeah, I think first of all, it's start create an economy, where it probably doesn't make sense for every company to create their own foundation models. You can basically start by using an existing foundation model, either open source or a proprietary one, and start deploying it for your needs. And then comes the second round when you are starting the optimization process. You bootstrap, whether it's a demo, or a small feature, or introducing new capability within your product, and then start collecting data. That data, and particularly the human feedback data, helps you to constantly improve the model, so you create this data flywheel. And I think we're now entering an era where customers have a lot of different choice of how they want to start their generative AI endeavor. And it's a good thing that there's a variety of choices. And the really amazing thing here is that every industry, any company you speak with, it could be something very traditional like industrial or financial, medical, really any company. I think peoples now start to imagine what are the possibilities, and seriously think what's their strategy for adopting this generative AI technology. And I think in that sense, the foundation model actually enabled this to become scalable. So the barrier to entry became lower; Now the adoption could actually accelerate. >> There's a lot of integration aspects here in this new wave that's a little bit different. Before it was like very monolithic, hardcore, very brittle. A lot more integration, you see a lot more data coming together. I have to ask you guys, as developers come in and grow, I mean, when I went to college and you were a software engineer, I mean, I got a degree in computer science, and software engineering, that's all you did was code, (chuckles) you coded. Now, isn't it like everyone's a machine learning engineer at this point? Because that will be ultimately the science. So, (chuckles) you got open source, you got open software, you got the communities. Swami called you guys the GitHub of machine learning, Hugging Face is the GitHub of machine learning, mainly because that's where people are going to code. So this is essentially, machine learning is computer science. What's your reaction to that? >> Yes, my co-founder Julien at Hugging Face have been having this thing for quite a while now, for over three years, which was saying that actually software engineering as we know it today is a subset of machine learning, instead of the other way around. People would call us crazy a few years ago when we're seeing that. But now we are realizing that you can actually code with machine learning. So machine learning is generating code. And we are starting to see that every software engineer can leverage machine learning through open models, through APIs, through different technology stack. So yeah, it's not crazy anymore to think that maybe in a few years, there's going to be more people doing AI and machine learning. However you call it, right? Maybe you'll still call them software engineers, maybe you'll call them machine learning engineers. But there might be more of these people in a couple of years than there is software engineers today. >> I bring this up as more tongue in cheek as well, because Ankur, infrastructure's co is what made Cloud great, right? That's kind of the DevOps movement. But here the shift is so massive, there will be a game-changing philosophy around coding. Machine learning as code, you're starting to see CodeWhisperer, you guys have had coding companions for a while on AWS. So this is a paradigm shift. How is the cloud playing into this for you guys? Because to me, I've been riffing on some interviews where it's like, okay, you got the cloud going next level. This is an example of that, where there is a DevOps-like moment happening with machine learning, whether you call it coding or whatever. It's writing code on its own. Can you guys comment on what this means on top of the cloud? What comes out of the scale? What comes out of the benefit here? >> Absolutely, so- >> Well first- >> Oh, go ahead. >> Yeah, so I think as far as scale is concerned, I think customers are really relying on cloud to make sure that the applications that they build can scale along with the needs of their business. But there's another aspect to it, which is that until a few years ago, John, what we saw was that machine learning was a data scientist heavy activity. They were data scientists who were taking the data and training models. And then as machine learning found its way more and more into production and actual usage, we saw the MLOps become a thing, and MLOps engineers become more involved into the process. And then we now are seeing, as machine learning is being used to solve more business critical problems, we're seeing even legal and compliance teams get involved. We are seeing business stakeholders more engaged. So, more and more machine learning is becoming an activity that's not just performed by data scientists, but is performed by a team and a group of people with different skills. And for them, we as AWS are focused on providing the best tools and services for these different personas to be able to do their job and really complete that end-to-end machine learning story. So that's where, whether it's tools related to MLOps or even for folks who cannot code or don't know any machine learning. For example, we launched SageMaker Canvas as a tool last year, which is a UI-based tool which data analysts and business analysts can use to build machine learning models. So overall, the spectrum in terms of persona and who can get involved in the machine learning process is expanding, and the cloud is playing a big role in that process. >> Ori, Clem, can you guys weigh in too? 'Cause this is just another abstraction layer of scale. What's it mean for you guys as you look forward to your customers and the use cases that you're enabling? >> Yes, I think what's important is that the AI companies and providers and the cloud kind of work together. That's how you make a seamless experience and you actually reduce the barrier to entry for this technology. So that's what we've been super happy to do with AWS for the past few years. We actually announced not too long ago that we are doubling down on our partnership with AWS. We're excited to have many, many customers on our shared product, the Hugging Face deep learning container on SageMaker. And we are working really closely with the Inferentia team and the Trainium team to release some more exciting stuff in the coming weeks and coming months. So I think when you have an ecosystem and a system where the AWS and the AI providers, AI startups can work hand in hand, it's to the benefit of the customers and the companies, because it makes it orders of magnitude easier for them to adopt this new paradigm to build technology AI. >> Ori, this is a scale on reasoning too. The data's out there and making sense out of it, making it reason, getting comprehension, having it make decisions is next, isn't it? And you need scale for that. >> Yes. Just a comment about the infrastructure side. So I think really the purpose is to streamline and make these technologies much more accessible. And I think we'll see, I predict that we'll see in the next few years more and more tooling that make this technology much more simple to consume. And I think it plays a very important role. There's so many aspects, like the monitoring the models and their kind of outputs they produce, and kind of containing and running them in a production environment. There's so much there to build on, the infrastructure side will play a very significant role. >> All right, that's awesome stuff. I'd love to change gears a little bit and get a little philosophy here around AI and how it's going to transform, if you guys don't mind. There's been a lot of conversations around, on theCUBE here as well as in some industry areas, where it's like, okay, all the heavy lifting is automated away with machine learning and AI, the complexity, there's some efficiencies, it's horizontal and scalable across all industries. Ankur, good point there. Everyone's going to use it for something. And a lot of stuff gets brought to the table with large language models and other things. But the key ingredient will be proprietary data or human input, or some sort of AI whisperer kind of role, or prompt engineering, people are saying. So with that being said, some are saying it's automating intelligence. And that creativity will be unleashed from this. If the heavy lifting goes away and AI can fill the void, that shifts the value to the intellect or the input. And so that means data's got to come together, interact, fuse, and understand each other. This is kind of new. I mean, old school AI was, okay, got a big model, I provisioned it long time, very expensive. Now it's all free flowing. Can you guys comment on where you see this going with this freeform, data flowing everywhere, heavy lifting, and then specialization? >> Yeah, I think- >> Go ahead. >> Yeah, I think, so what we are seeing with these large language models or generative models is that they're really good at creating stuff. But I think it's also important to recognize their limitations. They're not as good at reasoning and logic. And I think now we're seeing great enthusiasm, I think, which is justified. And the next phase would be how to make these systems more reliable. How to inject more reasoning capabilities into these models, or augment with other mechanisms that actually perform more reasoning so we can achieve more reliable results. And we can count on these models to perform for critical tasks, whether it's medical tasks, legal tasks. We really want to kind of offload a lot of the intelligence to these systems. And then we'll have to get back, we'll have to make sure these are reliable, we'll have to make sure we get some sort of explainability that we can understand the process behind the generated results that we received. So I think this is kind of the next phase of systems that are based on these generated models. >> Clem, what's your view on this? Obviously you're at open community, open source has been around, it's been a great track record, proven model. I'm assuming creativity's going to come out of the woodwork, and if we can automate open source contribution, and relationships, and onboarding more developers, there's going to be unleashing of creativity. >> Yes, it's been so exciting on the open source front. We all know Bert, Bloom, GPT-J, T5, Stable Diffusion, that work up. The previous or the current generation of open source models that are on Hugging Face. It has been accelerating in the past few months. So I'm super excited about ControlNet right now that is really having a lot of impact, which is kind of like a way to control the generation of images. Super excited about Flan UL2, which is like a new model that has been recently released and is open source. So yeah, it's really fun to see the ecosystem coming together. Open source has been the basis for traditional software, with like open source programming languages, of course, but also all the great open source that we've gotten over the years. So we're happy to see that the same thing is happening for machine learning and AI, and hopefully can help a lot of companies reduce a little bit the barrier to entry. So yeah, it's going to be exciting to see how it evolves in the next few years in that respect. >> I think the developer productivity angle that's been talked about a lot in the industry will be accelerated significantly. I think security will be enhanced by this. I think in general, applications are going to transform at a radical rate, accelerated, incredible rate. So I think it's not a big wave, it's the water, right? I mean, (chuckles) it's the new thing. My final question for you guys, if you don't mind, I'd love to get each of you to answer the question I'm going to ask you, which is, a lot of conversations around data. Data infrastructure's obviously involved in this. And the common thread that I'm hearing is that every company that looks at this is asking themselves, if we don't rebuild our company, start thinking about rebuilding our business model around AI, we might be dinosaurs, we might be extinct. And it reminds me that scene in Moneyball when, at the end, it's like, if we're not building the model around your model, every company will be out of business. What's your advice to companies out there that are having those kind of moments where it's like, okay, this is real, this is next gen, this is happening. I better start thinking and putting into motion plans to refactor my business, 'cause it's happening, business transformation is happening on the cloud. This kind of puts an exclamation point on, with the AI, as a next step function. Big increase in value. So it's an opportunity for leaders. Ankur, we'll start with you. What's your advice for folks out there thinking about this? Do they put their toe in the water? Do they jump right into the deep end? What's your advice? >> Yeah, John, so we talk to a lot of customers, and customers are excited about what's happening in the space, but they often ask us like, "Hey, where do we start?" So we always advise our customers to do a lot of proof of concepts, understand where they can drive the biggest ROI. And then also leverage existing tools and services to move fast and scale, and try and not reinvent the wheel where it doesn't need to be. That's basically our advice to customers. >> Get it. Ori, what's your advice to folks who are scratching their head going, "I better jump in here. "How do I get started?" What's your advice? >> So I actually think that need to think about it really economically. Both on the opportunity side and the challenges. So there's a lot of opportunities for many companies to actually gain revenue upside by building these new generative features and capabilities. On the other hand, of course, this would probably affect the cogs, and incorporating these capabilities could probably affect the cogs. So I think we really need to think carefully about both of these sides, and also understand clearly if this is a project or an F word towards cost reduction, then the ROI is pretty clear, or revenue amplifier, where there's, again, a lot of different opportunities. So I think once you think about this in a structured way, I think, and map the different initiatives, then it's probably a good way to start and a good way to start thinking about these endeavors. >> Awesome. Clem, what's your take on this? What's your advice, folks out there? >> Yes, all of these are very good advice already. Something that you said before, John, that I disagreed a little bit, a lot of people are talking about the data mode and proprietary data. Actually, when you look at some of the organizations that have been building the best models, they don't have specialized or unique access to data. So I'm not sure that's so important today. I think what's important for companies, and it's been the same for the previous generation of technology, is their ability to build better technology faster than others. And in this new paradigm, that means being able to build machine learning faster than others, and better. So that's how, in my opinion, you should approach this. And kind of like how can you evolve your company, your teams, your products, so that you are able in the long run to build machine learning better and faster than your competitors. And if you manage to put yourself in that situation, then that's when you'll be able to differentiate yourself to really kind of be impactful and get results. That's really hard to do. It's something really different, because machine learning and AI is a different paradigm than traditional software. So this is going to be challenging, but I think if you manage to nail that, then the future is going to be very interesting for your company. >> That's a great point. Thanks for calling that out. I think this all reminds me of the cloud days early on. If you went to the cloud early, you took advantage of it when the pandemic hit. If you weren't native in the cloud, you got hamstrung by that, you were flatfooted. So just get in there. (laughs) Get in the cloud, get into AI, you're going to be good. Thanks for for calling that. Final parting comments, what's your most exciting thing going on right now for you guys? Ori, Clem, what's the most exciting thing on your plate right now that you'd like to share with folks? >> I mean, for me it's just the diversity of use cases and really creative ways of companies leveraging this technology. Every day I speak with about two, three customers, and I'm continuously being surprised by the creative ideas. And the future is really exciting of what can be achieved here. And also I'm amazed by the pace that things move in this industry. It's just, there's not at dull moment. So, definitely exciting times. >> Clem, what are you most excited about right now? >> For me, it's all the new open source models that have been released in the past few weeks, and that they'll keep being released in the next few weeks. I'm also super excited about more and more companies getting into this capability of chaining different models and different APIs. I think that's a very, very interesting development, because it creates new capabilities, new possibilities, new functionalities that weren't possible before. You can plug an API with an open source embedding model, with like a no-geo transcription model. So that's also very exciting. This capability of having more interoperable machine learning will also, I think, open a lot of interesting things in the future. >> Clem, congratulations on your success at Hugging Face. Please pass that on to your team. Ori, congratulations on your success, and continue to, just day one. I mean, it's just the beginning. It's not even scratching the service. Ankur, I'll give you the last word. What are you excited for at AWS? More cloud goodness coming here with AI. Give you the final word. >> Yeah, so as both Clem and Ori said, I think the research in the space is moving really, really fast, so we are excited about that. But we are also excited to see the speed at which enterprises and other AWS customers are applying machine learning to solve real business problems, and the kind of results they're seeing. So when they come back to us and tell us the kind of improvement in their business metrics and overall customer experience that they're driving and they're seeing real business results, that's what keeps us going and inspires us to continue inventing on their behalf. >> Gentlemen, thank you so much for this awesome high impact panel. Ankur, Clem, Ori, congratulations on all your success. We'll see you around. Thanks for coming on. Generative AI, riding the wave, it's a tidal wave, it's the water, it's all happening. All great stuff. This is season three, episode one of AWS Startup Showcase closing panel. This is the AI ML episode, the top startups building generative AI on AWS. I'm John Furrier, your host. Thanks for watching. (mellow music)

(soft music) >> Hello, everyone. Welcome to theCUBE's presentation of the AWS Startup Showcase. AI and Machine Learning: Top Startups Building Foundational Model Infrastructure. This is season 3, episode 1 of the ongoing series covering the exciting stuff from the AWS ecosystem, talking about machine learning and AI. I'm your host, John Furrier and today we are excited to be joined by Luis Ceze who's the CEO of OctoML and Anna Connolly, VP of customer success and experience OctoML. Great to have you on again, Luis. Anna, thanks for coming on. Appreciate it. >> Thank you, John. It's great to be here. >> Thanks for having us. >> I love the company. We had a CUBE conversation about this. You guys are really addressing how to run foundational models faster for less. And this is like the key theme. But before we get into it, this is a hot trend, but let's explain what you guys do. Can you set the narrative of what the company's about, why it was founded, what's your North Star and your mission? >> Yeah, so John, our mission is to make AI sustainable and accessible for everyone. And what we offer customers is, you know, a way of taking their models into production in the most efficient way possible by automating the process of getting a model and optimizing it for a variety of hardware and making cost-effective. So better, faster, cheaper model deployment. >> You know, the big trend here is AI. Everyone's seeing the ChatGPT, kind of the shot heard around the world. The BingAI and this fiasco and the ongoing experimentation. People are into it, and I think the business impact is clear. I haven't seen this in all of my career in the technology industry of this kind of inflection point. And every senior leader I talk to is rethinking about how to rebuild their business with AI because now the large language models have come in, these foundational models are here, they can see value in their data. This is a 10 year journey in the big data world. Now it's impacting that, and everyone's rebuilding their company around this idea of being AI first 'cause they see ways to eliminate things and make things more efficient. And so now they telling 'em to go do it. And they're like, what do we do? So what do you guys think? Can you explain what is this wave of AI and why is it happening, why now, and what should people pay attention to? What does it mean to them? >> Yeah, I mean, it's pretty clear by now that AI can do amazing things that captures people's imaginations. And also now can show things that are really impactful in businesses, right? So what people have the opportunity to do today is to either train their own model that adds value to their business or find open models out there that can do very valuable things to them. So the next step really is how do you take that model and put it into production in a cost-effective way so that the business can actually get value out of it, right? >> Anna, what's your take? Because customers are there, you're there to make 'em successful, you got the new secret weapon for their business. >> Yeah, I think we just see a lot of companies struggle to get from a trained model into a model that is deployed in a cost-effective way that actually makes sense for the application they're building. I think that's a huge challenge we see today, kind of across the board across all of our customers. >> Well, I see this, everyone asking the same question. I have data, I want to get value out of it. I got to get these big models, I got to train it. What's it going to cost? So I think there's a reality of, okay, I got to do it. Then no one has any visibility on what it costs. When they get into it, this is going to break the bank. So I have to ask you guys, the cost of training these models is on everyone's mind. OctoML, your company's focus on the cost side of it as well as the efficiency side of running these models in production. Why are the production costs such a concern and where specifically are people looking at it and why did it get here? >> Yeah, so training costs get a lot of attention because normally a large number, but we shouldn't forget that it's a large, typically one time upfront cost that customers pay. But, you know, when the model is put into production, the cost grows directly with model usage and you actually want your model to be used because it's adding value, right? So, you know, the question that a customer faces is, you know, they have a model, they have a trained model and now what? So how much would it cost to run in production, right? And now without the big wave in generative AI, which rightfully is getting a lot of attention because of the amazing things that it can do. It's important for us to keep in mind that generative AI models like ChatGPT are huge, expensive energy hogs. They cost a lot to run, right? And given that model usage growth directly, model cost grows directly with usage, what you want to do is make sure that once you put a model into production, you have the best cost structure possible so that you're not surprised when it's gets popular, right? So let me give you an example. So if you have a model that costs, say 1 to $2 million to train, but then it costs about one to two cents per session to use it, right? So if you have a million active users, even if they use just once a day, it's 10 to $20,000 a day to operate that model in production. And that very, very quickly, you know, get beyond what you paid to train it. >> Anna, these aren't small numbers, and it's cost to train and cost to operate, it kind of reminds me of when the cloud came around and the data center versus cloud options. Like, wait a minute, one, it costs a ton of cash to deploy, and then running it. This is kind of a similar dynamic. What are you seeing? >> Yeah, absolutely. I think we are going to see increasingly the cost and production outpacing the costs and training by a lot. I mean, people talk about training costs now because that's what they're confronting now because people are so focused on getting models performant enough to even use in an application. And now that we have them and they're that capable, we're really going to start to see production costs go up a lot. >> Yeah, Luis, if you don't mind, I know this might be a little bit of a tangent, but, you know, training's super important. I get that. That's what people are doing now, but then there's the deployment side of production. Where do people get caught up and miss the boat or misconfigure? What's the gotcha? Where's the trip wire or so to speak? Where do people mess up on the cost side? What do they do? Is it they don't think about it, they tie it to proprietary hardware? What's the issue? >> Yeah, several things, right? So without getting really technical, which, you know, I might get into, you know, you have to understand relationship between performance, you know, both in terms of latency and throughput and cost, right? So reducing latency is important because you improve responsiveness of the model. But it's really important to keep in mind that it often leads diminishing returns. Below a certain latency, making it faster won't make a measurable difference in experience, but it's going to cost a lot more. So understanding that is important. Now, if you care more about throughputs, which is the time it takes for you to, you know, units per period of time, you care about time to solution, we should think about this throughput per dollar. And understand what you want is the highest throughput per dollar, which may come at the cost of higher latency, which you're not going to care about, right? So, and the reality here, John, is that, you know, humans and especially folks in this space want to have the latest and greatest hardware. And often they commit a lot of money to get access to them and have to commit upfront before they understand the needs that their models have, right? So common mistake here, one is not spending time to understand what you really need, and then two, over-committing and using more hardware than you actually need. And not giving yourself enough freedom to get your workload to move around to the more cost-effective choice, right? So this is just a metaphoric choice. And then another thing that's important here too is making a model run faster on the hardware directly translates to lower cost, right? So, but it takes a lot of engineers, you need to think of ways of producing very efficient versions of your model for the target hardware that you're going to use. >> Anna, what's the customer angle here? Because price performance has been around for a long time, people get that, but now latency and throughput, that's key because we're starting to see this in apps. I mean, there's an end user piece. I even seeing it on the infrastructure side where they're taking a heavy lifting away from operational costs. So you got, you know, application specific to the user and/or top of the stack, and then you got actually being used in operations where they want both. >> Yeah, absolutely. Maybe I can illustrate this with a quick story with the customer that we had recently been working with. So this customer is planning to run kind of a transformer based model for tech generation at super high scale on Nvidia T4 GPU, so kind of a commodity GPU. And the scale was so high that they would've been paying hundreds of thousands of dollars in cloud costs per year just to serve this model alone. You know, one of many models in their application stack. So we worked with this team to optimize our model and then benchmark across several possible targets. So that matching the hardware that Luis was just talking about, including the newer kind of Nvidia A10 GPUs. And what they found during this process was pretty interesting. First, the team was able to shave a quarter of their spend just by using better optimization techniques on the T4, the older hardware. But actually moving to a newer GPU would allow them to serve this model in a sub two milliseconds latency, so super fast, which was able to unlock an entirely new kind of user experience. So they were able to kind of change the value they're delivering in their application just because they were able to move to this new hardware easily. So they ultimately decided to plan their deployment on the more expensive A10 because of this, but because of the hardware specific optimizations that we helped them with, they managed to even, you know, bring costs down from what they had originally planned. And so if you extend this kind of example to everything that's happening with generative AI, I think the story we just talked about was super relevant, but the scale can be even higher, you know, it can be tenfold that. We were recently conducting kind of this internal study using GPT-J as a proxy to illustrate the experience of just a company trying to use one of these large language models with an example scenario of creating a chatbot to help job seekers prepare for interviews. So if you imagine kind of a conservative usage scenario where the model generates just 3000 words per user per day, which is, you know, pretty conservative for how people are interacting with these models. It costs 5 cents a session and if you're a company and your app goes viral, so from, you know, beginning of the year there's nobody, at the end of the year there's a million daily active active users in that year alone, going from zero to a million. You'll be spending about $6 million a year, which is pretty unmanageable. That's crazy, right? >> Yeah. >> For a company or a product that's just launching. So I think, you know, for us we see the real way to make these kind of advancements accessible and sustainable, as we said is to bring down cost to serve using these techniques. >> That's a great story and I think that illustrates this idea that deployment cost can vary from situation to situation, from model to model and that the efficiency is so strong with this new wave, it eliminates heavy lifting, creates more efficiency, automates intellect. I mean, this is the trend, this is radical, this is going to increase. So the cost could go from nominal to millions, literally, potentially. So, this is what customers are doing. Yeah, that's a great story. What makes sense on a financial, is there a cost of ownership? Is there a pattern for best practice for training? What do you guys advise cuz this is a lot of time and money involved in all potential, you know, good scenarios of upside. But you can get over your skis as they say, and be successful and be out of business if you don't manage it. I mean, that's what people are talking about, right? >> Yeah, absolutely. I think, you know, we see kind of three main vectors to reduce cost. I think one is make your deployment process easier overall, so that your engineering effort to even get your app running goes down. Two, would be get more from the compute you're already paying for, you're already paying, you know, for your instances in the cloud, but can you do more with that? And then three would be shop around for lower cost hardware to match your use case. So on the first one, I think making the deployment easier overall, there's a lot of manual work that goes into benchmarking, optimizing and packaging models for deployment. And because the performance of machine learning models can be really hardware dependent, you have to go through this process for each target you want to consider running your model on. And this is hard, you know, we see that every day. But for teams who want to incorporate some of these large language models into their applications, it might be desirable because licensing a model from a large vendor like OpenAI can leave you, you know, over provision, kind of paying for capabilities you don't need in your application or can lock you into them and you lose flexibility. So we have a customer whose team actually prepares models for deployment in a SaaS application that many of us use every day. And they told us recently that without kind of an automated benchmarking and experimentation platform, they were spending several days each to benchmark a single model on a single hardware type. So this is really, you know, manually intensive and then getting more from the compute you're already paying for. We do see customers who leave money on the table by running models that haven't been optimized specifically for the hardware target they're using, like Luis was mentioning. And for some teams they just don't have the time to go through an optimization process and for others they might lack kind of specialized expertise and this is something we can bring. And then on shopping around for different hardware types, we really see a huge variation in model performance across hardware, not just CPU vs. GPU, which is, you know, what people normally think of. But across CPU vendors themselves, high memory instances and across cloud providers even. So the best strategy here is for teams to really be able to, we say, look before you leap by running real world benchmarking and not just simulations or predictions to find the best software, hardware combination for their workload. >> Yeah. You guys sound like you have a very impressive customer base deploying large language models. Where would you categorize your current customer base? And as you look out, as you guys are growing, you have new customers coming in, take me through the progression. Take me through the profile of some of your customers you have now, size, are they hyperscalers, are they big app folks, are they kicking the tires? And then as people are out there scratching heads, I got to get in this game, what's their psychology like? Are they coming in with specific problems or do they have specific orientation point of view about what they want to do? Can you share some data around what you're seeing? >> Yeah, I think, you know, we have customers that kind of range across the spectrum of sophistication from teams that basically don't have MLOps expertise in their company at all. And so they're really looking for us to kind of give a full service, how should I do everything from, you know, optimization, find the hardware, prepare for deployment. And then we have teams that, you know, maybe already have their serving and hosting infrastructure up and ready and they already have models in production and they're really just looking to, you know, take the extra juice out of the hardware and just do really specific on that optimization piece. I think one place where we're doing a lot more work now is kind of in the developer tooling, you know, model selection space. And that's kind of an area that we're creating more tools for, particularly within the PyTorch ecosystem to bring kind of this power earlier in the development cycle so that as people are grabbing a model off the shelf, they can, you know, see how it might perform and use that to inform their development process. >> Luis, what's the big, I like this idea of picking the models because isn't that like going to the market and picking the best model for your data? It's like, you know, it's like, isn't there a certain approaches? What's your view on this? 'Cause this is where everyone, I think it's going to be a land rush for this and I want to get your thoughts. >> For sure, yeah. So, you know, I guess I'll start with saying the one main takeaway that we got from the GPT-J study is that, you know, having a different understanding of what your model's compute and memory requirements are, very quickly, early on helps with the much smarter AI model deployments, right? So, and in fact, you know, Anna just touched on this, but I want to, you know, make sure that it's clear that OctoML is putting that power into user's hands right now. So in partnership with AWS, we are launching this new PyTorch native profiler that allows you with a single, you know, one line, you know, code decorator allows you to see how your code runs on a variety of different hardware after accelerations. So it gives you very clear, you know, data on how you should think about your model deployments. And this ties back to choices of models. So like, if you have a set of choices that are equally good of models in terms of functionality and you want to understand after acceleration how are you going to deploy, how much they're going to cost or what are the options using a automated process of making a decision is really, really useful. And in fact, so I think these events can get early access to this by signing up for the Octopods, you know, this is exclusive group for insiders here, so you can go to OctoML.ai/pods to sign up. >> So that Octopod, is that a program? What is that, is that access to code? Is that a beta, what is that? Explain, take a minute and explain Octopod. >> I think the Octopod would be a group of people who is interested in experiencing this functionality. So it is the friends and users of OctoML that would be the Octopod. And then yes, after you sign up, we would provide you essentially the tool in code form for you to try out in your own. I mean, part of the benefit of this is that it happens in your own local environment and you're in control of everything kind of within the workflow that developers are already using to create and begin putting these models into their applications. So it would all be within your control. >> Got it. I think the big question I have for you is when do you, when does that one of your customers know they need to call you? What's their environment look like? What are they struggling with? What are the conversations they might be having on their side of the fence? If anyone's watching this, they're like, "Hey, you know what, I've got my team, we have a lot of data. Do we have our own language model or do I use someone else's?" There's a lot of this, I will say discovery going on around what to do, what path to take, what does that customer look like, if someone's listening, when do they know to call you guys, OctoML? >> Well, I mean the most obvious one is that you have a significant spend on AI/ML, come and talk to us, you know, putting AIML into production. So that's the clear one. In fact, just this morning I was talking to someone who is in life sciences space and is having, you know, 15 to $20 million a year cloud related to AI/ML deployment is a clear, it's a pretty clear match right there, right? So that's on the cost side. But I also want to emphasize something that Anna said earlier that, you know, the hardware and software complexity involved in putting model into production is really high. So we've been able to abstract that away, offering a clean automation flow enables one, to experiment early on, you know, how models would run and get them to production. And then two, once they are into production, gives you an automated flow to continuously updating your model and taking advantage of all this acceleration and ability to run the model on the right hardware. So anyways, let's say one then is cost, you know, you have significant cost and then two, you have an automation needs. And Anna please compliment that. >> Yeah, Anna you can please- >> Yeah, I think that's exactly right. Maybe the other time is when you are expecting a big scale up in serving your application, right? You're launching a new feature, you expect to get a lot of usage or, and you want to kind of anticipate maybe your CTO, your CIO, whoever pays your cloud bills is going to come after you, right? And so they want to know, you know, what's the return on putting this model essentially into my application stack? Am I going to, is the usage going to match what I'm paying for it? And then you can understand that. >> So you guys have a lot of the early adopters, they got big data teams, they're pushed in the production, they want to get a little QA, test the waters, understand, use your technology to figure it out. Is there any cases where people have gone into production, they have to pull it out? It's like the old lemon laws with your car, you buy a car and oh my god, it's not the way I wanted it. I mean, I can imagine the early people through the wall, so to speak, in the wave here are going to be bloody in the sense that they've gone in and tried stuff and get stuck with huge bills. Are you seeing that? Are people pulling stuff out of production and redeploying? Or I can imagine that if I had a bad deployment, I'd want to refactor that or actually replatform that. Do you see that too? >> Definitely after a sticker shock, yes, your customers will come and make sure that, you know, the sticker shock won't happen again. >> Yeah. >> But then there's another more thorough aspect here that I think we likely touched on, be worth elaborating a bit more is just how are you going to scale in a way that's feasible depending on the allocation that you get, right? So as we mentioned several times here, you know, model deployment is so hardware dependent and so complex that you tend to get a model for a hardware choice and then you want to scale that specific type of instance. But what if, when you want to scale because suddenly luckily got popular and, you know, you want to scale it up and then you don't have that instance anymore. So how do you live with whatever you have at that moment is something that we see customers needing as well. You know, so in fact, ideally what we want is customers to not think about what kind of specific instances they want. What they want is to know what their models need. Say, they know the SLA and then find a set of hybrid targets and instances that hit the SLA whenever they're also scaling, they're going to scale with more freedom, right? Instead of having to wait for AWS to give them more specific allocation for a specific instance. What if you could live with other types of hardware and scale up in a more free way, right? So that's another thing that we see customers, you know, like they need more freedom to be able to scale with whatever is available. >> Anna, you touched on this with the business model impact to that 6 million cost, if that goes out of control, there's a business model aspect and there's a technical operation aspect to the cost side too. You want to be mindful of riding the wave in a good way, but not getting over your skis. So that brings up the point around, you know, confidence, right? And teamwork. Because if you're in production, there's probably a team behind it. Talk about the team aspect of your customers. I mean, they're dedicated, they go put stuff into production, they're developers, there're data. What's in it for them? Are they getting better, are they in the beach, you know, reading the book. Are they, you know, are there easy street for them? What's the customer benefit to the teams? >> Yeah, absolutely. With just a few clicks of a button, you're in production, right? That's the dream. So yeah, I mean I think that, you know, we illustrated it before a little bit. I think the automated kind of benchmarking and optimization process, like when you think about the effort it takes to get that data by hand, which is what people are doing today, they just don't do it. So they're making decisions without the best information because it's, you know, there just isn't the bandwidth to get the information that they need to make the best decision and then know exactly how to deploy it. So I think it's actually bringing kind of a new insight and capability to these teams that they didn't have before. And then maybe another aspect on the team side is that it's making the hand-off of the models from the data science teams to the model deployment teams more seamless. So we have, you know, we have seen in the past that this kind of transition point is the place where there are a lot of hiccups, right? The data science team will give a model to the production team and it'll be too slow for the application or it'll be too expensive to run and it has to go back and be changed and kind of this loop. And so, you know, with the PyTorch profiler that Luis was talking about, and then also, you know, the other ways we do optimization that kind of prevents that hand-off problem from happening. >> Luis and Anna, you guys have a great company. Final couple minutes left. Talk about the company, the people there, what's the culture like, you know, if Intel has Moore's law, which is, you know, doubling the performance in few years, what's the culture like there? Is it, you know, more throughput, better pricing? Explain what's going on with the company and put a plug in. Luis, we'll start with you. >> Yeah, absolutely. I'm extremely proud of the team that we built here. You know, we have a people first culture, you know, very, very collaborative and folks, we all have a shared mission here of making AI more accessible and sustainable. We have a very diverse team in terms of backgrounds and life stories, you know, to do what we do here, we need a team that has expertise in software engineering, in machine learning, in computer architecture. Even though we don't build chips, we need to understand how they work, right? So, and then, you know, the fact that we have this, this very really, really varied set of backgrounds makes the environment, you know, it's say very exciting to learn more about, you know, assistance end-to-end. But also makes it for a very interesting, you know, work environment, right? So people have different backgrounds, different stories. Some of them went to grad school, others, you know, were in intelligence agencies and now are working here, you know. So we have a really interesting set of people and, you know, life is too short not to work with interesting humans. You know, that's something that I like to think about, you know. >> I'm sure your off-site meetings are a lot of fun, people talking about computer architectures, silicon advances, the next GPU, the big data models coming in. Anna, what's your take? What's the culture like? What's the company vibe and what are you guys looking to do? What's the customer success pattern? What's up? >> Yeah, absolutely. I mean, I, you know, second all of the great things that Luis just said about the team. I think one that I, an additional one that I'd really like to underscore is kind of this customer obsession, to use a term you all know well. And focus on the end users and really making the experiences that we're bringing to our user who are developers really, you know, useful and valuable for them. And so I think, you know, all of these tools that we're trying to put in the hands of users, the industry and the market is changing so rapidly that our products across the board, you know, all of the companies that, you know, are part of the showcase today, we're all evolving them so quickly and we can only do that kind of really hand in glove with our users. So that would be another thing I'd emphasize. >> I think the change dynamic, the power dynamics of this industry is just the beginning. I'm very bullish that this is going to be probably one of the biggest inflection points in history of the computer industry because of all the dynamics of the confluence of all the forces, which you mentioned some of them, I mean PC, you know, interoperability within internetworking and you got, you know, the web and then mobile. Now we have this, I mean, I wouldn't even put social media even in the close to this. Like, this is like, changes user experience, changes infrastructure. There's going to be massive accelerations in performance on the hardware side from AWS's of the world and cloud and you got the edge and more data. This is really what big data was going to look like. This is the beginning. Final question, what do you guys see going forward in the future? >> Well, it's undeniable that machine learning and AI models are becoming an integral part of an interesting application today, right? So, and the clear trends here are, you know, more and more competitional needs for these models because they're only getting more and more powerful. And then two, you know, seeing the complexity of the infrastructure where they run, you know, just considering the cloud, there's like a wide variety of choices there, right? So being able to live with that and making the most out of it in a way that does not require, you know, an impossible to find team is something that's pretty clear. So the need for automation, abstracting with the complexity is definitely here. And we are seeing this, you know, trends are that you also see models starting to move to the edge as well. So it's clear that we're seeing, we are going to live in a world where there's no large models living in the cloud. And then, you know, edge models that talk to these models in the cloud to form, you know, an end-to-end truly intelligent application. >> Anna? >> Yeah, I think, you know, our, Luis said it at the beginning. Our vision is to make AI sustainable and accessible. And I think as this technology just expands in every company and every team, that's going to happen kind of on its own. And we're here to help support that. And I think you can't do that without tools like those like OctoML. >> I think it's going to be an error of massive invention, creativity, a lot of the format heavy lifting is going to allow the talented people to automate their intellect. I mean, this is really kind of what we see going on. And Luis, thank you so much. Anna, thanks for coming on this segment. Thanks for coming on theCUBE and being part of the AWS Startup Showcase. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>>From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from the cube and E T R. This is breaking analysis with Dave Valante. >>Making technology predictions in 2022 was tricky business, especially if you were projecting the performance of markets or identifying I P O prospects and making binary forecast on data AI and the macro spending climate and other related topics in enterprise tech 2022, of course was characterized by a seesaw economy where central banks were restructuring their balance sheets. The war on Ukraine fueled inflation supply chains were a mess. And the unintended consequences of of forced march to digital and the acceleration still being sorted out. Hello and welcome to this week's weekly on Cube Insights powered by E T R. In this breaking analysis, we continue our annual tradition of transparently grading last year's enterprise tech predictions. And you may or may not agree with our self grading system, but look, we're gonna give you the data and you can draw your own conclusions and tell you what, tell us what you think. >>All right, let's get right to it. So our first prediction was tech spending increases by 8% in 2022. And as we exited 2021 CIOs, they were optimistic about their digital transformation plans. You know, they rushed to make changes to their business and were eager to sharpen their focus and continue to iterate on their digital business models and plug the holes that they, the, in the learnings that they had. And so we predicted that 8% rise in enterprise tech spending, which looked pretty good until Ukraine and the Fed decided that, you know, had to rush and make up for lost time. We kind of nailed the momentum in the energy sector, but we can't give ourselves too much credit for that layup. And as of October, Gartner had it spending growing at just over 5%. I think it was 5.1%. So we're gonna take a C plus on this one and, and move on. >>Our next prediction was basically kind of a slow ground ball. The second base, if I have to be honest, but we felt it was important to highlight that security would remain front and center as the number one priority for organizations in 2022. As is our tradition, you know, we try to up the degree of difficulty by specifically identifying companies that are gonna benefit from these trends. So we highlighted some possible I P O candidates, which of course didn't pan out. S NQ was on our radar. The company had just had to do another raise and they recently took a valuation hit and it was a down round. They raised 196 million. So good chunk of cash, but, but not the i p O that we had predicted Aqua Securities focus on containers and cloud native. That was a trendy call and we thought maybe an M SS P or multiple managed security service providers like Arctic Wolf would I p o, but no way that was happening in the crummy market. >>Nonetheless, we think these types of companies, they're still faring well as the talent shortage in security remains really acute, particularly in the sort of mid-size and small businesses that often don't have a sock Lacework laid off 20% of its workforce in 2022. And CO C e o Dave Hatfield left the company. So that I p o didn't, didn't happen. It was probably too early for Lacework. Anyway, meanwhile you got Netscope, which we've cited as strong in the E T R data as particularly in the emerging technology survey. And then, you know, I lumia holding its own, you know, we never liked that 7 billion price tag that Okta paid for auth zero, but we loved the TAM expansion strategy to target developers beyond sort of Okta's enterprise strength. But we gotta take some points off of the failure thus far of, of Okta to really nail the integration and the go to market model with azero and build, you know, bring that into the, the, the core Okta. >>So the focus on endpoint security that was a winner in 2022 is CrowdStrike led that charge with others holding their own, not the least of which was Palo Alto Networks as it continued to expand beyond its core network security and firewall business, you know, through acquisition. So overall we're gonna give ourselves an A minus for this relatively easy call, but again, we had some specifics associated with it to make it a little tougher. And of course we're watching ve very closely this this coming year in 2023. The vendor consolidation trend. You know, according to a recent Palo Alto network survey with 1300 SecOps pros on average organizations have more than 30 tools to manage security tools. So this is a logical way to optimize cost consolidating vendors and consolidating redundant vendors. The E T R data shows that's clearly a trend that's on the upswing. >>Now moving on, a big theme of 2020 and 2021 of course was remote work and hybrid work and new ways to work and return to work. So we predicted in 2022 that hybrid work models would become the dominant protocol, which clearly is the case. We predicted that about 33% of the workforce would come back to the office in 2022 in September. The E T R data showed that figure was at 29%, but organizations expected that 32% would be in the office, you know, pretty much full-time by year end. That hasn't quite happened, but we were pretty close with the projection, so we're gonna take an A minus on this one. Now, supply chain disruption was another big theme that we felt would carry through 2022. And sure that sounds like another easy one, but as is our tradition, again we try to put some binary metrics around our predictions to put some meat in the bone, so to speak, and and allow us than you to say, okay, did it come true or not? >>So we had some data that we presented last year and supply chain issues impacting hardware spend. We said at the time, you can see this on the left hand side of this chart, the PC laptop demand would remain above pre covid levels, which would reverse a decade of year on year declines, which I think started in around 2011, 2012. Now, while demand is down this year pretty substantially relative to 2021, I D C has worldwide unit shipments for PCs at just over 300 million for 22. If you go back to 2019 and you're looking at around let's say 260 million units shipped globally, you know, roughly, so, you know, pretty good call there. Definitely much higher than pre covid levels. But so what you might be asking why the B, well, we projected that 30% of customers would replace security appliances with cloud-based services and that more than a third would replace their internal data center server and storage hardware with cloud services like 30 and 40% respectively. >>And we don't have explicit survey data on exactly these metrics, but anecdotally we see this happening in earnest. And we do have some data that we're showing here on cloud adoption from ET R'S October survey where the midpoint of workloads running in the cloud is around 34% and forecast, as you can see, to grow steadily over the next three years. So this, well look, this is not, we understand it's not a one-to-one correlation with our prediction, but it's a pretty good bet that we were right, but we gotta take some points off, we think for the lack of unequivocal proof. Cause again, we always strive to make our predictions in ways that can be measured as accurate or not. Is it binary? Did it happen, did it not? Kind of like an O K R and you know, we strive to provide data as proof and in this case it's a bit fuzzy. >>We have to admit that although we're pretty comfortable that the prediction was accurate. And look, when you make an hard forecast, sometimes you gotta pay the price. All right, next, we said in 2022 that the big four cloud players would generate 167 billion in IS and PaaS revenue combining for 38% market growth. And our current forecasts are shown here with a comparison to our January, 2022 figures. So coming into this year now where we are today, so currently we expect 162 billion in total revenue and a 33% growth rate. Still very healthy, but not on our mark. So we think a w s is gonna miss our predictions by about a billion dollars, not, you know, not bad for an 80 billion company. So they're not gonna hit that expectation though of getting really close to a hundred billion run rate. We thought they'd exit the year, you know, closer to, you know, 25 billion a quarter and we don't think they're gonna get there. >>Look, we pretty much nailed Azure even though our prediction W was was correct about g Google Cloud platform surpassing Alibaba, Alibaba, we way overestimated the performance of both of those companies. So we're gonna give ourselves a C plus here and we think, yeah, you might think it's a little bit harsh, we could argue for a B minus to the professor, but the misses on GCP and Alibaba we think warrant a a self penalty on this one. All right, let's move on to our prediction about Supercloud. We said it becomes a thing in 2022 and we think by many accounts it has, despite the naysayers, we're seeing clear evidence that the concept of a layer of value add that sits above and across clouds is taking shape. And on this slide we showed just some of the pickup in the industry. I mean one of the most interesting is CloudFlare, the biggest supercloud antagonist. >>Charles Fitzgerald even predicted that no vendor would ever use the term in their marketing. And that would be proof if that happened that Supercloud was a thing and he said it would never happen. Well CloudFlare has, and they launched their version of Supercloud at their developer week. Chris Miller of the register put out a Supercloud block diagram, something else that Charles Fitzgerald was, it was was pushing us for, which is rightly so, it was a good call on his part. And Chris Miller actually came up with one that's pretty good at David Linthicum also has produced a a a A block diagram, kind of similar, David uses the term metacloud and he uses the term supercloud kind of interchangeably to describe that trend. And so we we're aligned on that front. Brian Gracely has covered the concept on the popular cloud podcast. Berkeley launched the Sky computing initiative. >>You read through that white paper and many of the concepts highlighted in the Supercloud 3.0 community developed definition align with that. Walmart launched a platform with many of the supercloud salient attributes. So did Goldman Sachs, so did Capital One, so did nasdaq. So you know, sorry you can hate the term, but very clearly the evidence is gathering for the super cloud storm. We're gonna take an a plus on this one. Sorry, haters. Alright, let's talk about data mesh in our 21 predictions posts. We said that in the 2020s, 75% of large organizations are gonna re-architect their big data platforms. So kind of a decade long prediction. We don't like to do that always, but sometimes it's warranted. And because it was a longer term prediction, we, at the time in, in coming into 22 when we were evaluating our 21 predictions, we took a grade of incomplete because the sort of decade long or majority of the decade better part of the decade prediction. >>So last year, earlier this year, we said our number seven prediction was data mesh gains momentum in 22. But it's largely confined and narrow data problems with limited scope as you can see here with some of the key bullets. So there's a lot of discussion in the data community about data mesh and while there are an increasing number of examples, JP Morgan Chase, Intuit, H S P C, HelloFresh, and others that are completely rearchitecting parts of their data platform completely rearchitecting entire data platforms is non-trivial. There are organizational challenges, there're data, data ownership, debates, technical considerations, and in particular two of the four fundamental data mesh principles that the, the need for a self-service infrastructure and federated computational governance are challenging. Look, democratizing data and facilitating data sharing creates conflicts with regulatory requirements around data privacy. As such many organizations are being really selective with their data mesh implementations and hence our prediction of narrowing the scope of data mesh initiatives. >>I think that was right on J P M C is a good example of this, where you got a single group within a, within a division narrowly implementing the data mesh architecture. They're using a w s, they're using data lakes, they're using Amazon Glue, creating a catalog and a variety of other techniques to meet their objectives. They kind of automating data quality and it was pretty well thought out and interesting approach and I think it's gonna be made easier by some of the announcements that Amazon made at the recent, you know, reinvent, particularly trying to eliminate ET t l, better connections between Aurora and Redshift and, and, and better data sharing the data clean room. So a lot of that is gonna help. Of course, snowflake has been on this for a while now. Many other companies are facing, you know, limitations as we said here and this slide with their Hadoop data platforms. They need to do new, some new thinking around that to scale. HelloFresh is a really good example of this. Look, the bottom line is that organizations want to get more value from data and having a centralized, highly specialized teams that own the data problem, it's been a barrier and a blocker to success. The data mesh starts with organizational considerations as described in great detail by Ash Nair of Warner Brothers. So take a listen to this clip. >>Yeah, so when people think of Warner Brothers, you always think of like the movie studio, but we're more than that, right? I mean, you think of H B O, you think of t n t, you think of C N N. We have 30 plus brands in our portfolio and each have their own needs. So the, the idea of a data mesh really helps us because what we can do is we can federate access across the company so that, you know, CNN can work at their own pace. You know, when there's election season, they can ingest their own data and they don't have to, you know, bump up against, as an example, HBO if Game of Thrones is going on. >>So it's often the case that data mesh is in the eyes of the implementer. And while a company's implementation may not strictly adhere to Jamma Dani's vision of data mesh, and that's okay, the goal is to use data more effectively. And despite Gartner's attempts to deposition data mesh in favor of the somewhat confusing or frankly far more confusing data fabric concept that they stole from NetApp data mesh is taking hold in organizations globally today. So we're gonna take a B on this one. The prediction is shaping up the way we envision, but as we previously reported, it's gonna take some time. The better part of a decade in our view, new standards have to emerge to make this vision become reality and they'll come in the form of both open and de facto approaches. Okay, our eighth prediction last year focused on the face off between Snowflake and Databricks. >>And we realized this popular topic, and maybe one that's getting a little overplayed, but these are two companies that initially, you know, looked like they were shaping up as partners and they, by the way, they are still partnering in the field. But you go back a couple years ago, the idea of using an AW w s infrastructure, Databricks machine intelligence and applying that on top of Snowflake as a facile data warehouse, still very viable. But both of these companies, they have much larger ambitions. They got big total available markets to chase and large valuations that they have to justify. So what's happening is, as we've previously reported, each of these companies is moving toward the other firm's core domain and they're building out an ecosystem that'll be critical for their future. So as part of that effort, we said each is gonna become aggressive investors and maybe start doing some m and a and they have in various companies. >>And on this chart that we produced last year, we studied some of the companies that were targets and we've added some recent investments of both Snowflake and Databricks. As you can see, they've both, for example, invested in elation snowflake's, put money into Lacework, the Secur security firm, ThoughtSpot, which is trying to democratize data with ai. Collibra is a governance platform and you can see Databricks investments in data transformation with D B T labs, Matillion doing simplified business intelligence hunters. So that's, you know, they're security investment and so forth. So other than our thought that we'd see Databricks I p o last year, this prediction been pretty spot on. So we'll give ourselves an A on that one. Now observability has been a hot topic and we've been covering it for a while with our friends at E T R, particularly Eric Bradley. Our number nine prediction last year was basically that if you're not cloud native and observability, you are gonna be in big trouble. >>So everything guys gotta go cloud native. And that's clearly been the case. Splunk, the big player in the space has been transitioning to the cloud, hasn't always been pretty, as we reported, Datadog real momentum, the elk stack, that's open source model. You got new entrants that we've cited before, like observe, honeycomb, chaos search and others that we've, we've reported on, they're all born in the cloud. So we're gonna take another a on this one, admittedly, yeah, it's a re reasonably easy call, but you gotta have a few of those in the mix. Okay, our last prediction, our number 10 was around events. Something the cube knows a little bit about. We said that a new category of events would emerge as hybrid and that for the most part is happened. So that's gonna be the mainstay is what we said. That pure play virtual events are gonna give way to hi hybrid. >>And the narrative is that virtual only events are, you know, they're good for quick hits, but lousy replacements for in-person events. And you know that said, organizations of all shapes and sizes, they learn how to create better virtual content and support remote audiences during the pandemic. So when we set at pure play is gonna give way to hybrid, we said we, we i we implied or specific or specified that the physical event that v i p experience is going defined. That overall experience and those v i p events would create a little fomo, fear of, of missing out in a virtual component would overlay that serves an audience 10 x the size of the physical. We saw that really two really good examples. Red Hat Summit in Boston, small event, couple thousand people served tens of thousands, you know, online. Second was Google Cloud next v i p event in, in New York City. >>Everything else was, was, was, was virtual. You know, even examples of our prediction of metaverse like immersion have popped up and, and and, and you know, other companies are doing roadshow as we predicted like a lot of companies are doing it. You're seeing that as a major trend where organizations are going with their sales teams out into the regions and doing a little belly to belly action as opposed to the big giant event. That's a definitely a, a trend that we're seeing. So in reviewing this prediction, the grade we gave ourselves is, you know, maybe a bit unfair, it should be, you could argue for a higher grade, but the, but the organization still haven't figured it out. They have hybrid experiences but they generally do a really poor job of leveraging the afterglow and of event of an event. It still tends to be one and done, let's move on to the next event or the next city. >>Let the sales team pick up the pieces if they were paying attention. So because of that, we're only taking a B plus on this one. Okay, so that's the review of last year's predictions. You know, overall if you average out our grade on the 10 predictions that come out to a b plus, I dunno why we can't seem to get that elusive a, but we're gonna keep trying our friends at E T R and we are starting to look at the data for 2023 from the surveys and all the work that we've done on the cube and our, our analysis and we're gonna put together our predictions. We've had literally hundreds of inbounds from PR pros pitching us. We've got this huge thick folder that we've started to review with our yellow highlighter. And our plan is to review it this month, take a look at all the data, get some ideas from the inbounds and then the e t R of January surveys in the field. >>It's probably got a little over a thousand responses right now. You know, they'll get up to, you know, 1400 or so. And once we've digested all that, we're gonna go back and publish our predictions for 2023 sometime in January. So stay tuned for that. All right, we're gonna leave it there for today. You wanna thank Alex Myerson who's on production and he manages the podcast, Ken Schiffman as well out of our, our Boston studio. I gotta really heartfelt thank you to Kristen Martin and Cheryl Knight and their team. They helped get the word out on social and in our newsletters. Rob Ho is our editor in chief over at Silicon Angle who does some great editing for us. Thank you all. Remember all these podcasts are available or all these episodes are available is podcasts. Wherever you listen, just all you do Search Breaking analysis podcast, really getting some great traction there. Appreciate you guys subscribing. I published each week on wikibon.com, silicon angle.com or you can email me directly at david dot valante silicon angle.com or dm me Dante, or you can comment on my LinkedIn post. And please check out ETR AI for the very best survey data in the enterprise tech business. Some awesome stuff in there. This is Dante for the Cube Insights powered by etr. Thanks for watching and we'll see you next time on breaking analysis.

>>Hello everyone. Welcome to the Super Cloud Event preview. I'm John Forry, host of the Cube, and with Dave Valante, host of the popular Super cloud events. This is Super Cloud two preview. I'm joined by industry leader and Cube alumni, Victoria Vigo, vice president of klos Cross Cloud Services at VMware. Vittorio. Great to see you. We're here for the preview of Super Cloud two on January 17th, virtual event, live stage performance, but streamed out to the audience virtually. We're gonna do a preview. Thanks for coming in. >>My pleasure. Always glad to be here. >>It's holiday time. We had the first super cloud on in August prior to VMware, explore North America prior to VMware, explore Europe prior to reinvent. We've been through that, but right now, super Cloud has got momentum. Super Cloud two has got some success. Before we dig into it, let's take a step back and set the table. What is Super Cloud and why is important? Why are people buzzing about it? Why is it a thing? >>Look, we have been in the cloud now for like 10, 15 years and the cloud is going strong and I, I would say that going cloud first was deliberate and strategic in most cases. In some cases the, the developer was going for the path of risk resistance, but in any sizable company, this caused the companies to end up in a multi-cloud world where 85% of the companies out there use two or multiple clouds. And with that comes what we call cloud chaos, because each cloud brings their own management tools, development tools, security. And so that increase the complexity and cost. And so we believe that it's time to usher a new era in cloud computing, which we, you call the super cloud. We call it cross cloud services, which allows our customers to have a single way to build, manage, secure, and access any application across any cloud. Lowering the cost and simplifying the environment. Since >>Dave Ante and I introduced and rift on the concept of Supercloud, as we talked about at reinvent last year, a lot has happened. Supercloud one, it was in August, but prior to that, great momentum in the industry. Great conversation. People are loving it, they're hating it, which means it's got some traction. Berkeley has come on board as with a position paper. They're kind of endorsing it. They call it something different. You call it cross cloud services, whatever it is. It's kind of the same theme we're seeing. And so the industry has recognized something is happening that's different than what Cloud one was or the first generation of cloud. Now we have something different. This Super Cloud two in January. This event has traction with practitioners, customers, big name brands, Sachs, fifth Avenue, Warner, media Financial, mercury Financial, other big names are here. They're leaning in. They're excited. Why the traction in the customer's industry converts over to, to the customer traction. Why is it happening? You, you get a lot of data. >>Well, in, in Super Cloud one, it was a vendor fest, right? But these vendors are smart people that get their vision from where, from the customers. This, this stuff doesn't happen in a vacuum. We all talk to customers and we tend to lean on the early adopters and the early adopters of the cloud are the ones that are telling us, we now are in a place where the complexity is too much. The cost is ballooning. We're going towards slow down potentially in the economy. We need to get better economics out of, of our cloud. And so every single customers I talked to today, or any sizable company as this problem, the developers have gone off, built all these applications, and now the business is coming to the operators and asking, where are my applications? Are they performing? What is the security posture? And how do we do compliance? And so now they're realizing we need to do something about this or it is gonna be unmanageable. >>I wanna go to a clip I pulled out from the, our video data lake and the cube. If we can go to that clip, it's Chuck Whitten Dell at a keynote. He was talking about what he calls multi-cloud by default, not by design. This is a state of the, of the industry. If we're gonna roll that clip, and I wanna get your reaction to that. >>Well, look, customers have woken up with multiple clouds, you know, multiple public clouds. On-premise clouds increasingly as the edge becomes much more a reality for customers clouds at the edge. And so that's what we mean by multi-cloud by default. It's not yet been designed strategically. I think our argument yesterday was it can be, and it should be, it is a very logical place for architecture to land because ultimately customers want the innovation across all of the hyperscale public clouds. They will see workloads and use cases where they wanna maintain an on-premise cloud. On-premise clouds are not going away. I mentioned edge Cloud, so it should be strategic. It's just not today. It doesn't work particularly well today. So when we say multi-cloud, by default we mean that's the state of the world. Today, our goal is to bring multi-cloud by design, as you heard. Yeah, I >>Mean, I, okay, Vittorio, that's, that's the head of Dell Technologies president. He obvious he runs it. Michael Dell's still around, but you know, he's the leader. This is a interesting observation. You know, he's not a customer. We have some customer equips we'll go to as well, but by default it kind of happened not by design. So we're now kind of in a zoom out issue where, okay, I got this environment just landed on me. What, what is the, what's your reaction to that clip of how multi-cloud has become present in, in everyone's on everyone's plate right now to deal with? Yeah, >>I it is, it is multi-cloud by default, I would call it by accident. We, we really got there by accident. I think now it's time to make it a strategic asset because look, we're using multiple cloud for a reason, because all these hyperscaler bring tremendous innovation that we want to leverage. But I strongly believe that in it, especially history repeat itself, right? And so if you look at the history of it, as was always when a new level of obstruction that simplify things, that we got the next level of innovation at the lower cost, you know, from going from c plus plus to Visual basic, going from integrating application at the bits of by layer to SOA and then web services. It's, it's only when we simplify the environment that we can go faster and lower cost. And the multi-cloud is ready for that level of obstruction today. >>You know, you've made some good points. You know, developers went crazy building great apps. Now they got, they gotta roll it out and operationalize it globally. A lot of compliance issues going on. The costs are going up. We got an economic challenge, but also agility with the cloud. So using cloud and or hybrid, you can get better agility. And also moving to the cloud, it's kind of still slow. Okay, so I get that at reinvent this year and at VMware explorer we were observing and we reported that you're seeing a transition to a new kind of ecosystem partner. Ones that aren't just ISVs anymore. You have ISVs, independent software vendors, but you got the emergence of bigger players that just, they got platforms, they have their own ecosystems. So you're seeing ecosystems on top of ecosystems where, you know, MongoDB CEO and the Databricks CEO both told me, we're not an isv, we're a platform built on a cloud. So this new kind of super cloudlike thing is going on. Why should someone pay attention to the super cloud movement? We're on two, we're gonna continue to do these out in the open. Anyone can participate. Why should people pay attention to this? Why should they come to the event? Why is this important? Is this truly an inflection point? And if they do pay attention, what should they pay attention to? >>I would pay attention to two things. If you are customers that are now starting to realize that you have a multi-cloud problem and the costs are getting outta control, look at what the leading vendors are saying, connect the dots with the early adopters and some of the customers that we are gonna have at Super Cloud two, and use those learning to not fall into the same trap. So I, I'll give you an example. I was talking to a Fortune 50 in Europe in my latest trip, and this is an a CIO that is telling me >>We build all these applications and now for compliance reason, the business is coming to me, I don't even know where they are, right? And so what I was telling him, so look, there are other customers that are already there. What did they do? They built a platform engineering team. What is the platform? Engineering team is a, is an operation team that understands how developers build modern applications and lays down the foundation across multiple clouds. So the developers can be developers and do their thing, which is writing code. But now you as a cio, as a, as a, as a governing body, as a security team can have the guardrail. So do you know that these applications are performing at a lower cost and are secure and compliant? >>Patura, you know, it's really encouraging and, and love to get your thoughts on this is one is the general consensus of industry leaders. I talked to like yourself in the round is the old way was soft complexity with more complexity. The cloud demand simplicity, you mentioned abstraction layer. This is our next inflection point. It's gotta be simpler and it's gotta be easy and it's gotta be performant. That's the table stakes of the cloud. What's your thoughts on this next wave of simplicity versus complexity? Because again, abstraction layers take away complexity, they should make it simpler. What's your thoughts? >>Yeah, so I'll give you few examples. One, on the development side and runtime. You, you one would think that Kubernetes will solve all the problems you have Kubernetes everywhere, just look at, but every cloud has a different distribution of Kubernetes, right? So for example, at VMware with tansu, we create a single place that allows you to deploy that any Kubernetes environment. But now you have one place to set your policies. We take care of the differences between this, this system. The second area is management, right? So once you have all everything deployed, how do you get a single object model that tells you where your stuff is and how it's performing, and then apply policies to it as well. So these are two areas and security and so on and so forth. So the idea is that figure out what you can abstract and make common across cloud. Make that simple and put it in one place while always allowing the developers to go underneath and use the differentiated features for innovation. >>Yeah, one of the areas I'm excited, I want to get your thoughts of too is, we haven't talked about this in the past, but it, I'll throw it out there. I think the, the new AI coming out chat, G P T and other things like lens, you see it and see new kinds of AI coming that's gonna be right in the heavy lifting opportunity to make things easier with AI and automation. I think AI will be a big factor in super cloud and, and cross cloud. What's your thoughts? >>Well, the one way to look at AI is, is one of the main, main services that you would want out of a multi-cloud, right? You want eventually, right now Google seems to have an edge, but you know, the competition creates, you know, innovation. So later on you wanna use something from Azure or from or from Oracle or something that, so you want at some point that is gonna be prone every single service in in the cloud is gonna be prone to obstruction and simplification. And I, I'm just excited about to see >>What book, I can't wait for the chat services to write code automatically for us. Well, >>They >>Do, they do. They're doing it now. They do. >>Oh, the other day, somebody, you know that I do this song par this for. So for fun sometimes. And somebody the other day said, ask the AI to write a parody song for multi-cloud. And so I have the lyrics stay >>Tuned. I should do that from my blog post. Hey, write a blog post on this January 17th, Victoria, thanks for coming in, sharing the preview bottom line. Why should people come? Why is it important? What's your final kind of takeaway? Billboard message >>History is repeat itself. It goes to three major inflection points, right? We had the inflection point with the cloud and the people that got left behind, they were not as competitive as the people that got on top o of this wave. The new wave is the super cloud, what we call cross cloud services. So if you are a customer that is experiencing this problem today, tune in to to hear from other customers in, in your same space. If you are behind, tune in to avoid the, the, the, the mistakes and the, the shortfalls of this new wave. And so that you can use multi-cloud to accelerate your business and kick butt in the future. >>All right. Kicking kick your names and kicking butt. Okay, we're here on J January 17th. Super Cloud two. Momentum continues. We'll be super cloud three. There'll be super cloud floor. More and more open conversations. Join the community, join the conversation. It's open. We want more voices. We want more, more industry. We want more customers. It's happening. A lot of momentum. Victoria, thank you for your time. Thank you. Okay. I'm John Farer, host of the Cube. Thanks for watching.

>>The Cube presents Ignite 22, brought to you by Palo Alto Networks. >>Welcome back to Vegas. Guys. We're happy that you're here. Lisa Martin here covering with Dave Valante, Palo Alto Networks Ignite 22. We're at MGM Grand. This is our first day, Dave of two days of cube coverage. We've been having great conversations with the ecosystem with Palo Alto executives, with partners. One of the things that they have is unit 42. We're gonna be talking with them next about cyber intelligence. And the threat data that they get is >>Incredible. Yeah. They have all the data, they know what's going on, and of course things are changing. The state of play changes. Hold on a second. I got a text here. Oh, my Netflix account was frozen. Should I click on this link? Yeah. What do you think? Have you had a, it's, have you had a little bit more of that this holiday season? Yeah, definitely. >>Unbelievable, right? A lot of smishing going on. >>Yeah, they're very clever. >>Yeah, we're very pleased to welcome back one of our alumni to the queue. Wendy Whitmore is here, the SVP of Unit 42. Welcome back, Wendy. Great to have >>You. Thanks Lisa. So >>Unit 42 created back in 2014. One of the things that I saw that you said in your keynote this morning or today was everything old is still around and it's co, it's way more prolific than ever. What are some of the things that Unit 42 is seeing these days with, with respect to cyber threats as the landscape has changed so much the last two years alone? >>You know, it, it has. So it's really interesting. I've been responding to these breaches for over two decades now, and I can tell you that there are a lot of new and novel techniques. I love that you already highlighted Smishing, right? In the opening gate. Right. Because that is something that a year ago, no one knew what that word was. I mean, we, it's probably gonna be invented this year, right? But that said, so many of the tactics that we have previously seen, when it comes to just general espionage techniques, right? Data act filtration, intellectual property theft, those are going on now more than ever. And you're not hearing about them as much in the news because there are so many other things, right? We're under the landscape of a major war going on between Russia and Ukraine of ransomware attacks, you know, occurring on a weekly basis. And so we keep hearing about those, but ultimately these nations aid actors are using that top cover, if you will, as a great distraction. It's almost like a perfect storm for them to continue conducting so much cyber espionage work that like we may not be feeling that today, but years down the road, they're, the work that they're doing today is gonna have really significant impact. >>Ransomware has become a household word in the last couple of years. I think even my mom knows what it is, to some degree. Yeah. But the threat actors are far more sophisticated than they've ever written. They're very motivated. They're very well funded. I think I've read a stat recently in the last year that there's a ransomware attack once every 11 seconds. And of course we only hear about the big ones. But that is a concern that goes all the way up to the board. >>Yeah. You know, we have a stat in our ransomware threat report that talks about how often victims are posted on leak sites. And I think it's once every seven minutes at this point that a new victim is posted. Meaning a victim has had their data, a victim organization had their data stolen and posted on some leak site in the attempt to be extorted. So that has become so common. One of the shifts that we've seen this year in particular and in recent months, you know, a year ago when I was at Ignite, which was virtual, we talked about quadruple extortion, meaning four different ways that these ransomware actors would go out and try to make money from these attacks in what they're doing now is often going to just one, which is, I don't even wanna bother with encrypting your data now, because that means that in order to get paid, I probably have to decrypt it. Right? That's a lot of work. It's time consuming. It's kind of painstaking. And so what they've really looked to do now is do the extortion where they simply steal the data and then threaten to post it on these leak sites, you know, release it other parts of the web and, and go from there. And so that's really a blending of these techniques of traditional cyber espionage with intellectual property theft. Wow. >>How trustworthy are those guys in terms of, I mean, these are hackers, right? In terms of it's really the, the hacker honor system, isn't it? I mean, if you get compromised like that, you really beholden to criminals. And so, you >>Know, so that's one of the key reasons why having the threat intelligence is so important, right? Understanding which group that you're dealing with and what their likelihood of paying is, what's their modus operandi. It's become even more important now because these groups switch teams more frequently than NFL trades, you know, free agents during the regular season, right? Or players become free agents. And that's because their infrastructure. So the, you know, infrastructure, the servers, the systems that they're using to conduct these attacks from is actually largely being disrupted more from law enforcement, international intelligence agencies working together with public private partnerships. So what they're doing is saying, okay, great. All that infrastructure that I just had now is, is burned, right? It's no longer effective. So then they'll disband a team and then they'll recruit a new team and it's constant like mixing and matching in players. >>All that said, even though that's highly dynamic, one of the other areas that they pride themselves on is customer service. So, and I think it's interesting because, you know, when I said they're not wanting to like do all the decryption? Yeah. Cuz that's like painful techni technical slow work. But on the customer service side, they will create these customer service portals immediately stand one up, say, you know, hey it's, it's like an Amazon, you know, if you've ever had to return a package on Amazon for example, and you need to click through and like explain, you know, Hey, I didn't receive this package. A portal window pops up, you start talking to either a bot or a live agent on the backend. In this case they're hu what appeared to be very much humans who are explaining to you exactly what happened, what they're asking for, super pleasant, getting back within minutes of a response. And they know that in order for them to get paid, they need to have good customer service because otherwise they're not going to, you know, have a business. How, >>So what's the state of play look like from between nation states, criminals and how, how difficult or not so difficult is it for you to identify? Do you have clear signatures? My understanding in with Solar Winds it was a little harder, but maybe help us understand and help our audience understand what the state of play is right now. >>One of the interesting things that I think is occurring, and I highlighted this this morning, is this idea of convergence. And so I'll break it down for one example relates to the type of malware or tools that these attackers use. So traditionally, if we looked at a nation state actor like China or Russia, they were very, very specific and very strategic about the types of victims that they were going to go after when they had zero day. So, you know, new, new malware out there, new vulnerabilities that could be exploited only by them because the rest of the world didn't know about it. They might have one organization that they would target that at, at most, a handful and all very strategic for their objective. They wanted to keep that a secret as long as possible. Now what we're seeing actually is those same attackers going towards one, a much larger supply chain. >>So, so lorenzen is a great example of that. The Hafnia attacks towards Microsoft Exchange server last year. All great examples of that. But what they're also doing is instead of using zero days as much, or you know, because those are expensive to build, they take a lot of time, a lot of funding, a lot of patience and research. What they're doing is using commercially available tools. And so there's a tool that our team identified earlier this year called Brute Rael, C4 or BRC four for short. And that's a tool that we now know that nation state actors are using. But just two weeks ago we invested a ransomware attack where the ransomware actor was using that same piece of tooling. So to your point, yak can get difficult for defenders when you're looking through and saying, well wait, they're all using some of the same tools right now and some of the same approaches when it comes to nation states, that's great for them because they can blend into the noise and it makes it harder to identify as >>Quickly. And, and is that an example of living off the land or is that B BRC four sort of a homegrown hacker tool? Is it, is it a, is it a commercial >>Off the shelf? So it's a tool that was actually, so you can purchase it, I believe it's about 2,500 US dollars for a license. It was actually created by a former Red teamer from a couple well-known companies in the industry who then decided, well hey, I built this tool for work, I'm gonna sell this. Well great for Red teamers that are, you know, legitimately doing good work, but not great now because they're, they built a, a strong tool that has the ability to hide amongst a, a lot of protocols. It can actually hide within Slack and teams to where you can't even see the data is being exfiltrated. And so there's a lot of concern. And then now the reality that it gets into the wrong hands of nation state actors in ransomware actors, one of the really interesting things about that piece of malware is it has a setting where you can change wallpaper. And I don't know if you know offhand, you know what that means, but you know, if that comes to mind, what you would do with it. Well certainly a nation state actor is never gonna do something like that, right? But who likes to do that are ransomware actors who can go in and change the background wallpaper on a desktop that says you've been hacked by XYZ organization and let you know what's going on. So pretty interesting, obviously the developer doing some work there for different parts of the, you know, nefarious community. >>Tremendous amount of sophistication that's gone on the last couple of years alone. I was just reading that Unit 42 is now a founding member of the Cyber Threat Alliance includes now more than 35 organizations. So you guys are getting a very broad picture of today's threat landscape. How can customers actually achieve cyber resilience? Is it achievable and how do you help? >>So I, I think it is achievable. So let me kind of parse out the question, right. So the Cyber Threat Alliance, the J C D C, the Cyber Safety Review Board, which I'm a member of, right? I think one of the really cool things about Palo Alto Networks is just our partnerships. So those are just a handful. We've got partnerships with over 200 organizations. We work closely with the Ukrainian cert, for example, sharing information, incredible information about like what's going on in the war, sharing technical details. We do that with Interpol on a daily basis where, you know, we're sharing information. Just last week the Africa cyber surge operation was announced where millions of nodes were taken down that were part of these larger, you know, system of C2 channels that attackers are using to conduct exploits and attacks throughout the world. So super exciting in that regard and it's something that we're really passionate about at Palo Alto Networks in terms of resilience, a few things, you know, one is visibility, so really having a, an understanding of in a real, as much of real time as possible, right? What's happening. And then it goes into how you, how can we decrease operational impact. So that's everything from network segmentation to wanna add the terms and phrases I like to use a lot is the win is really increasing the time it takes for the attackers to get their work done and decreasing the amount of time it takes for the defenders to get their work done, right? >>Yeah. I I call it increasing the denominator, right? And the ROI equation benefit over or value, right? Equals equals or benefit equals value over cost if you can increase the cost to go go elsewhere, right? Absolutely. And that's the, that's the game. Yeah. You mentioned Ukraine before, what have we learned from Ukraine? I, I remember I was talking to Robert Gates years ago, 2016 I think, and I was asking him, yeah, but don't we have the best cyber technology? Can't we attack? He said, we got the most to lose too. Yeah. And so what have we learned from, from Ukraine? >>Well, I, I think that's part of the key point there, right? Is you know, a great offense essentially can also be for us, you know, deterrent. So in that aspect we have as an, as a company and or excuse me, as a country, as a company as well, but then as partners throughout all parts of the world have really focused on increasing the intelligence sharing and specifically, you know, I mentioned Ukrainian cert. There are so many different agencies and other sorts throughout the world that are doing everything they can to share information to help protect human life there. And so what we've really been concerned with, with is, you know, what cyber warfare elements are going to be used there, not only how does that impact Ukraine, but how does it potentially spread out to other parts of the world critical infrastructure. So you've seen that, you know, I mentioned CS rrb, but cisa, right? >>CISA has done a tremendous job of continuously getting out information and doing everything they can to make sure that we are collaborating at a commercial level. You know, we are sharing information and intelligence more than ever before. So partners like Mania and CrowdStrike, our Intel teams are working together on a daily basis to make sure that we're able to protect not only our clients, but certainly if we've got any information relevant that we can share that as well. And I think if there's any silver lining to an otherwise very awful situation, I think the fact that is has accelerated intelligence sharing is really positive. >>I was gonna ask you about this cause I think, you know, 10 or so years ago, there was a lot of talk about that, but the industry, you know, kind of kept things to themselves, you know, a a actually tried to monetize some of that private data. So that's changing is what I'm hearing from you >>More so than ever more, you know, I've, I mentioned I've been in the field for 20 years. You know, it, it's tough when you have a commercial business that relies on, you know, information to, in order to pay people's salaries, right? I think that has changed quite a lot. We see the benefit of just that continuous sharing. There are, you know, so many more walls broken down between these commercial competitors, but also the work on the public private partnership side has really increased some of those relationships. Made it easier. And you know, I have to give a whole lot of credit and mention sisa, like the fact that during log four J, like they had GitHub repositories, they were using Slack, they were using Twitter. So the government has really started pushing forward with a lot of the newer leadership that's in place to say, Hey, we're gonna use tools and technology that works to share and disseminate information as quickly as we can. Right? That's fantastic. That's helping everybody. >>We knew that every industry, no, nobody's spared of this. But did you notice in the last couple of years, any industries in particular that are more vulnerable? Like I think of healthcare with personal health information or financial services, any industries kind of jump out as being more susceptible than others? >>So I think those two are always gonna be at the forefront, right? Financial services and healthcare. But what's been really top of mind is critical infrastructure, just making sure right? That our water, our power, our fuel, so many other parts of right, the ecosystem that go into making sure that, you know, we're keeping, you know, houses heated during the winter, for example, that people have fresh water. Those are extremely critical. And so that is really a massive area of focus for the industry right now. >>Can I come back to public-private partnerships? My question is relates to regulations because the public policy tends to be behind tech, the technology industry as an understatement. So when you take something like GDPR is the obvious example, but there are many, many others, data sovereignty, you can't move the data. Are are, are, is there tension between your desire as our desire as an industry to share data and government's desire to keep data private and restrict that data sharing? How is that playing out? How do you resolve that? >>Well I think there have been great strides right in each of those areas. So in terms of regulation when it comes to breaches there, you know, has been a tendency in the past to do victim shaming, right? And for organizations to not want to come forward because they're concerned about the monetary funds, right? I think there's been tremendous acceleration. You're seeing that everywhere from the fbi, from cisa, to really working very closely with organizations to, to have a true impact. So one example would be a ransomware attack that occurred. This was for a client of ours within the United States and we had a very close relationship with the FBI at that local field office and made a phone call. This was 7:00 AM Eastern time. And this was an organization that had this breach gone public, would've made worldwide news. There would've been a very big impact because it would've taken a lot of their systems offline. >>Within the 30 minutes that local FBI office was on site said, we just saw this piece of malware last week, we have a decryptor for it from another organization who shared it with us. Here you go. And within 60 minutes, every system was back up and running. Our teams were able to respond and get that disseminated quickly. So efforts like that, I think the government has made a tremendous amount of headway into improving relationships. Is there always gonna be some tension between, you know, competing, you know, organizations? Sure. But I think that we're doing a whole lot to progress it, >>But governments will make exceptions in that case. Especially for something as critical as the example that you just gave and be able to, you know, do a reach around, if you will, on, on onerous regulations that, that ne aren't helpful in that situation, but certainly do a lot of good in terms of protecting privacy. >>Well, and I think there used to be exceptions made typically only for national security elements, right? And now you're seeing that expanding much more so, which I think is also positive. Right. >>Last question for you as we are wrapping up time here. What can organizations really do to stay ahead of the curve when it comes to, to threat actors? We've got internal external threats. What can they really do to just be ahead of that curve? Is that possible? >>Well, it is now, it's not an easy task so I'm not gonna, you know, trivialize it. But I think that one, having relationships with right organizations in advance always a good thing. That's a, everything from certainly a commercial relationships, but also your peers, right? There's all kinds of fantastic industry spec specific information sharing organizations. I think the biggest thing that impacts is having education across your executive team and testing regularly, right? Having a plan in place, testing it. And it's not just the security pieces of it, right? As security responders, we live these attacks every day, but it's making sure that your general counsel and your head of operations and your CEO knows what to do. Your board of directors, do they know what to do when they receive a phone call from Bloomberg, for example? Are they supposed supposed to answer? Do your employees know that those kind of communications in advance and training can be really critical and make or break a difference in an attack. >>That's a great point about the testing but also the communication that it really needs to be company wide. Everyone at every level needs to know how to react. Wendy, it's been so great having, >>Wait one last question. Sure. Do you have a favorite superhero growing up? >>Ooh, it's gotta be Wonder Woman. Yeah, >>Yeah, okay. Yeah, so cuz I'm always curious, there's not a lot of women in, in security in cyber. How'd you get into it? And many cyber pros like wanna save the world? >>Yeah, no, that's a great question. So I joined the Air Force, you know, I, I was a special agent doing computer crime investigations and that was a great job. And I learned about that from, we had an alumni day and all these alumni came in from the university and they were in flight suits and combat gear. And there was one woman who had long blonde flowing hair and a black suit and high heels and she was carrying a gun. What did she do? Because that's what I wanted do. >>Awesome. Love it. We >>Blonde >>Wonder Woman. >>Exactly. Wonder Woman. Wendy, it's been so great having you on the program. We, we will definitely be following unit 42 and all the great stuff that you guys are doing. Keep up the good >>Work. Thanks so much Lisa. Thank >>You. Day our pleasure. For our guest and Dave Valante, I'm Lisa Martin, live in Las Vegas at MGM Grand for Palo Alto Ignite, 22. You're watching the Cube, the leader in live enterprise and emerging tech coverage.

>>Hello and welcome back to the Cube's coverage of AWS Reinvent 2022. I'm John Furrier, host of the Cube. We got a great conversation with Patrick Kauflin, vice president of Go to Market Strategy and specialization at Splunk. We're talking about the open cybersecurity scheme of framework, also known as the O C sf, a joint strategic collaboration between Splunk and aws. It's got a lot of traction momentum. Patrick, thanks for coming on the cube for reinvent coverage. >>John, great to be here. I'm excited for this. >>You know, I love this open source movement and open source and continues to add value, almost sets the standards. You know, we were talking at the CNCF Linux Foundation this past fall about how standards are coming outta open source. Not so much the the classic standards groups, but you start to see the developers voting with their code groups deciding what to adopt de facto standards and security is a real key part of that where data becomes key for resilience. And this has been the top conversation at reinvent and all around the industry, is how to make data a key part of building into cyber resilience. So I wanna get your thoughts about the problem that you see that's emerging that you guys are solving with this group kind of collaboration around the ocs f >>Yeah, well look, John, I I think, I think you, you've already, you've already hit the high notes there. Data is proliferating across the enterprise. The attack surface area is rapidly expanding. The threat landscape is ever changing. You know, we, we just had a, a lot of scares around open SSL before that we had vulnerabilities and, and Confluence and Atlassian, and you go back to log four J and SolarWinds before that and, and challenges with the supply chain. In this year in particular, we've had a, a huge acceleration in, in concerns and threat vectors around operational technology. In our customer base alone, we saw a huge uptake, you know, and double digit percentage of customers that we're concerned about the traditional vectors like, like ransomware, like business email compromise, phishing, but also from insider threat and others. So you've got this, this highly complex environment where data continues to proliferate and flow through new applications, new infrastructure, new services, driving different types of outcomes in the digitally transformed enterprise of today. >>And, and what happens there is, is our customers, particularly in security, are, are left with having to stitch all of this together. And they're trying to get visibility across multiple different services, infrastructure applications across a number of different point solutions that they've bought to help them protect, defend, detect, and respond better. And it's a massive challenge. And you know, when our, when our customers come to us, they are often looking for ways to drive more consolidation across a variety of different solutions. They're looking to drive better outcomes in terms of speed to detection. How do I detect faster? How do I bind the thing that when bang in the night faster? How do I then fix it quickly? And then how do I layer in some automation so hopefully I don't have to do it again? Now, the challenge there that really OCF Ocsf helps to, to solve is to do that effectively, to detect and to respond at the speed at which attackers are demanding. >>Today we have to have normalization of data across this entire landscape of tools, infrastructure, services. We have to have integration to have visibility, and these tools have to work together. But the biggest barrier to that is often data is stored in different structures and in different formats across different solution providers, across different tools that are, that are, that our customers are using. And that that lack of data, normalization, chokes the integration problem. And so, you know, several years ago, a number of very smart people, and this was, this was a initiative s started by Splunk and AWS came together and said, look, we as an industry have to solve this for our customers. We have to start to shoulder this burden for our customers. We can't, we can't make our customers have to be systems integrators. That's not their job. Our job is to help make this easier for them. And so OCS was born and over the last couple of years we've built out this, this collaboration to not just be AWS and Splunk, but over 50 different organizations, cloud service providers, solution providers in the cybersecurity space have come together and said, let's decide on a single unified schema for how we're gonna represent event data in this industry. And I'm very proud to be here today to say that we've launched it and, and I can't wait to see where we go next. >>Yeah, I mean, this is really compelling. I mean, it's so much packed in that, in that statement, I mean, data normalization, you mentioned chokes, this the, the solution and integration as you call it. But really also it's like data's not just stored in silos. It may not even be available, right? So if you don't have availability of data, that's an important point. Number two, you mentioned supply chain, there's physical supply chain that's coming up big time at reinvent this time as well as in open source, the software supply chain. So you now have the perimeter's been dead for multiple years. We've been talking with that for years, everybody knows that. But now combined with the supply chain problem, both physical and software, there's so much more to go on. And so, you know, the leaders in the industry, they're not sitting on their hands. They know this, but they're just overloaded. So, so how do leaders deal with this right now before we get into the ocs f I wanna just get your thoughts on what's the psychology of the, of the business leader who's facing this landscape? >>Yeah, well, I mean unfortunately too many leaders feel like they have to face these trade offs between, you know, how and where they are really focusing cyber resilience investments in the business. And, and often there is a siloed approach across security, IT developer operations or engineering rather than the ability to kind of drive visibility integration and, and connection of outcomes across those different functions. I mean, the truth is the telemetry that, that you get from an application for application performance monitoring or infrastructure monitoring is often incredibly valuable when there's a security incident and vice versa. Some of the security data that, that you may see in a security operation center can be incredibly valuable in trying to investigate a, a performance degradation in an application and understanding where that may come from. And so what we're seeing is this data layer is collapsing faster than the org charts are or the budget line items are in the enterprise. And so at Splunk here, you know, we believe security resilience is, is fundamentally a data problem. And one of the things that we do often is, is actually help connect the dots for our customers and bring our customers together across the silos they may have internally so that they can start to see a holistic picture of what resilience means for their enterprise and how they can drive faster detection outcomes and more automation coverage. >>You know, we recently had an event called Super Cloud, we're going into the next gen kind of a cloud, how data and security are all kind of part of this NextGen application. It's not just us. And we had a panel that was titled The Innovators Dilemma, kind of talk about you some of the challenges. And one of the panelists said, it's not the innovator's dilemma, it's the integrator's dilemma. And you mentioned that earlier, and I think this a key point right now into integration is so critical, not having the data and putting pieces together now open source is becoming a composability market. And I think having things snap together and work well, it's a platform system conversation, not a tool conversation. So I really wanna get into where the OCS f kind of intersects with this area people are working on. It's not just solution architects or cloud cloud native SREs, especially where DevSecOps is. So this that's right, this intersection is critical. How does Ocsf integrate into that integration of the data making that available to make machine learning and automation smarter and more relevant? >>Right, right. Well look, I mean, I I think that's a fantastic question because, you know, we talk about, we use Bud buzzwords like machine learning and, and AI all the time. And you know, I know they're all over the place here at Reinvent and, and the, there's so much promise and hope out there around these technologies and these innovations. However, machine learning AI is only as effective as the data is clean and normalized. And, and we will not realize the promise of these technologies for outcomes in resilience unless we have better ways to normalize data upstream and better ways to integrate that data to the downstream tools where detection and response is happening. And so Ocsf was really about the industry coming together and saying, this is no longer the job of our customers. We are going to create a unified schema that represents the, an event that we will all bite down on. >>Even some of us are competitors, you know, this is, this is that, that no longer matters because at the point, the point is how do we take this burden off of our customers and how do we make the industry safer together? And so 15 initial members came together along with AWS and Splunk to, to start to create that, that initial schema and standardize it. And if you've ever, you know, if you've ever worked with a bunch of technical grumpy security people, it's kind of hard to drive consensus about around just about anything. But, but I, I'm really happy to see how quickly this, this organization has come together, has open sourced the schema, and, and, and just as you said, like I think this, this unlocks the potential for real innovation that's gonna be required to keep up with the bad guys. But right now is getting stymied and held back by the lack of normalization and the lack of integration. >>I've always said Splunk was a, it eats data for breakfast, lunch, and dinner and turns it into insights. And I think you bring up the silo thing. What's interesting is the cross company sharing, I think this hits point on, so I see this as a valuable opportunity for the industry. What's the traction on that? Because, you know, to succeed it does take a village, it takes a community of security practitioners and, and, and architects and developers to kind of coalesce around this defacto movement has been, has been the uptake been good? How's traction? Can you share your thoughts on how this is translating across companies? >>Yeah, absolutely. I mean, look, I, I think cybersecurity has a, has a long track record of, of, of standards development. There's been some fantastic standards recently. Things like sticks and taxi for threat intelligence. There's been things like the, you know, the Mir attack framework coming outta mi mir and, and, and the adoption, the traction that we've seen with Attack in particular has been amazing to, to watch how that has kind of roared onto the scene in the last couple of years and has become table stakes for how you do security operations and incident response. And, you know, I think with ocs f we're gonna see something similar here, but, you know, we are in literally the first innings of, of this. So right now, you know, we're architecting this into our, into every part of our sort of backend systems here at Polan. I know our our collaborators at AWS and elsewhere are doing it too. >>And so I think it starts with bringing this standard now that the standard exists on a, you know, in schema format and there, there's, you know, confluence and Jira tickets around it, how do we then sort of build this into the code of, of the, the collaborators that have been leading the way on this? And you know, it's not gonna happen overnight, but I think in the coming quarters you'll start to see this schema be the standard across the leaders in this space. Companies like Splunk and AWS and others who are leading the way. And often that's what helps drive adoption of a standard is if you can get the, the big dogs, so to speak, to, to, to embrace it. And, and, you know, there's no bigger one than aws and I think there's no, no more important one than Splunk in the cybersecurity space. And so as we adopt this, we hope others will follow. And, and like I said, we've got over 50 organizations contributing to it today. And so I think we're off to a running >>Start. You know, it's interesting, choking innovation or having things kind of get, get slowed down has really been a problem. We've seen successes recently over the past few years. Like Kubernetes has really unlocked and accelerated the cloud native worlds of runtime with containers to, to kind of have the consensus of the community to say, Hey, if we just do this, it gets better. I think this is really compelling with the o the ocs F because if people can come together around this and get unified as well as all the other official standards, things can go highly accelerated. So I think, I think it looks really good and I think it's great initiative and I really appreciate your insight on that, on, on your relationship with Amazon. Okay. It's not just a partnership, it's a strategic collaboration. Could you share that relationship dynamic, how to start, how's it going, what's strategic about it? Share to the audience kind of the relationship between Splunk and a on this important OCS ocsf initiative. >>Look, I, I mean I think this, this year marks the, the 10th year anniversary that, that Splunk and AWS have been collaborating in a variety of different ways. I, I think our, our companies have a fantastic and, and long standing relationship and we've, we've partnered on a number of really important projects together that bring value obviously to our individual companies, but also to our shared customers. When I think about some of the most important customers at Splunk that I spend a significant amount of time with, I I I know how many of those are, are AWS customers as well, and I know how important AWS is to them. So I think it's, it's a, it's a collaboration that is rooted in, in a respect for each other's technologies and innovation, but also in a recognition that, that our shared customers want to see us work better together over time. And it's not, it's not two companies that have kind of decided in a back room that they should work together. It's actually our customers that are, that are pushing us. And I think we're, we're both very customer centric organizations and I think that has helped us actually be better collaborators and better partners together because we're, we're working back backwards from our customers >>As security becomes a physical and software approach. We've seen the trend where even Steven Schmidt at Amazon Web Services is, is the cso, he is not the CSO anymore. So, and I asked him why, he says, well, security's also physical stuff too. So, so he's that's right. Whole lens is now expanded. You mentioned supply chain, physical, digital, this is an important inflection point. Can you summarize in your mind why open cybersecurity schema for is important? I know the unification, but beyond that, what, why is this so important? Why should people pay attention to this? >>You know, I, if, if you'll let me be just a little abstract in meta for a second. I think what's, what's really meaningful at the highest level about the O C S F initiative, and that goes beyond, I think, the tactical value it will provide to, to organizations and to customers in terms of making them safer over the coming years and, and decades. I think what's more important than that is it's really the, one of the first times that you've seen the industry come together and say, we got a problem. We need to solve. That, you know, doesn't really have anything to do with, with our own economics. Our customers are, are hurt. And yeah, some of us may be competitors, you know, we got different cloud service providers that are participating in this along with aws. We got different cybersecurity solution providers participating in this along with Splunk. >>But, but folks who've come together and say, we can actually solve this problem if, if we're able to kind of put aside our competitive differences in the markets and approach this from the perspective of what's best for information security as a whole. And, and I think that's what I'm most proud of and, and what I hope we can do more of in other places in this industry, because I think that kind of collaboration from real market leaders can actually change markets. It can change the, the, the trend lines in terms of how we are keeping up with the bad guys. And, and I'd like to see a lot more of >>That. And we're seeing a lot more new kind of things emerging in the cloud next kind of this next generation architecture and outcomes are happening. I think it's interesting, you know, we always talk about sustainability, supply chain sustainability about making the earth a better place. But you're hitting on this, this meta point about businesses are under threat of going under. I mean, we want to keep businesses to businesses to be sustainable, not just, you know, the, the environment. So if a business goes outta business business, which they, their threats here are, can be catastrophic for companies. I mean, there is, there is a community responsibility to protect businesses so they can sustain and and stay Yeah. Stay producing. This is a real key point. >>Yeah. Yeah. I mean, look, I think, I think one of the things that, you know, we, we, we complain a lot of in, in cyber security about the lack of, of talent, the talent shortage in cyber security. And every year we kinda, we kind of whack ourselves over the head about how hard it is to bring people into this industry. And it's true. But one of the things that I think we forget, John, is, is how important mission is to so many people in what they do for a living and how they work. And I think one of the things that cybersecurity is strongest in information Security General and has been for decades is this sense of mission and people work in this industry be not because it's, it's, it's always the, the, the most lucrative, but because it, it really drives a sense of safety and security in the enterprises and the fabric of the economy that we use every day to go through our lives. And when I think about the spun customers and AWS customers, I think about the, the different products and tools that power my life and, and we need to secure them. And, and sometimes that means coming to work every day at that company and, and doing your job. And sometimes that means working with others better, faster, and stronger to help drive that level of, of, of maturity and security that this industry >>Needs. It's a human, is a human opportunity, human problem and, and challenge. That's a whole nother segment. The role of the talent and the human machines and with scale. Patrick, thanks so much for sharing the information and the insight on the Open cybersecurity schema frame and what it means and why it's important. Thanks for sharing on the Cube, really appreciate it. >>Thanks for having me, John. >>Okay, this is AWS Reinvent 2022 coverage here on the Cube. I'm John Furry, you're the host. Thanks for watching.

>>Hey, welcome back everyone. Day three of eight of us Reinvent 2022. I'm John Farmer with Dave Volante, co-host the q Dave. 10 years for us, the leader in high tech coverage is our slogan. Now 10 years of reinvent day. We've been to every single one except with the original, which we would've come to if Amazon actually marketed the event, but they didn't. It's more of a customer event. This is day three. Is the machine learning ai keynote sws up there. A lot of announcements. We're gonna break this down. We got, we got Andy Thra here, vice President, prince Constellation Research. Andy, great to see you've been on the cube before one of our analysts bringing the, bringing the, the analysis, commentary to the keynote. This is your wheelhouse. Ai. What do you think about Swami up there? I mean, he's awesome. We love him. Big fan Oh yeah. Of of the Cuban we're fans of him, but he got 13 announcements. >>A lot. A lot, >>A lot. >>So, well some of them are, first of all, thanks for having me here and I'm glad to have both of you on the same show attacking me. I'm just kidding. But some of the announcement really sort of like a game changer announcements and some of them are like, meh, you know, just to plug in the holes what they have and a lot of golf claps. Yeah. Meeting today. And you could have also noticed that by, when he was making the announcements, you know, the, the, the clapping volume difference, you could say, which is better, right? But some of the announcements are, are really, really good. You know, particularly we talked about, one of that was Microsoft took that out of, you know, having the open AI in there, doing the large language models. And then they were going after that, you know, having the transformer available to them. And Amazon was a little bit weak in the area, so they couldn't, they don't have a large language model. So, you know, they, they are taking a different route saying that, you know what, I'll help you train the large language model by yourself, customized models. So I can provide the necessary instance. I can provide the instant volume, memory, the whole thing. Yeah. So you can train the model by yourself without depending on them kind >>Of thing. So Dave and Andy, I wanna get your thoughts cuz first of all, we've been following Amazon's deep bench on the, on the infrastructure pass. They've been doing a lot of machine learning and ai, a lot of data. It just seems that the sentiment is that there's other competitors doing a good job too. Like Google, Dave. And I've heard folks in the hallway, even here, ex Amazonians saying, Hey, they're train their models on Google than they bring up the SageMaker cuz it's better interface. So you got, Google's making a play for being that data cloud. Microsoft's obviously putting in a, a great kind of package to kind of make it turnkey. How do they really stand versus the competition guys? >>Good question. So they, you know, each have their own uniqueness and the we variation that take it to the field, right? So for example, if you were to look at it, Microsoft is known for as industry or later things that they are been going after, you know, industry verticals and whatnot. So that's one of the things I looked here, you know, they, they had this omic announcement, particularly towards that healthcare genomics space. That's a huge space for hpz related AIML applications. And they have put a lot of things in together in here in the SageMaker and in the, in their models saying that, you know, how do you, how do you use this transmit to do things like that? Like for example, drug discovery, for genomics analysis, for cancer treatment, the whole, right? That's a few volumes of data do. So they're going in that healthcare area. Google has taken a different route. I mean they want to make everything simple. All I have to do is I gotta call an api, give what I need and then get it done. But Amazon wants to go at a much deeper level saying that, you know what? I wanna provide everything you need. You can customize the whole thing for what you need. >>So to me, the big picture here is, and and Swami references, Hey, we are a data company. We started, he talked about books and how that informed them as to, you know, what books to place front and center. Here's the, here's the big picture. In my view, companies need to put data at the core of their business and they haven't, they've generally put humans at the core of their business and data. And now machine learning are at the, at the outside and the periphery. Amazon, Google, Microsoft, Facebook have put data at their core. So the question is how do incumbent companies, and you mentioned some Toyota Capital One, Bristol Myers Squibb, I don't know, are those data companies, you know, we'll see, but the challenge is most companies don't have the resources as you well know, Andy, to actually implement what Google and Facebook and others have. >>So how are they gonna do that? Well, they're gonna buy it, right? So are they gonna build it with tools that's kind of like you said the Amazon approach or are they gonna buy it from Microsoft and Google, I pulled some ETR data to say, okay, who are the top companies that are showing up in terms of spending? Who's spending with whom? AWS number one, Microsoft number two, Google number three, data bricks. Number four, just in terms of, you know, presence. And then it falls down DataRobot, Anaconda data icu, Oracle popped up actually cuz they're embedding a lot of AI into their products and, and of course IBM and then a lot of smaller companies. But do companies generally customers have the resources to do what it takes to implement AI into applications and into workflows? >>So a couple of things on that. One is when it comes to, I mean it's, it's no surprise that the, the top three or the hyperscalers, because they all want to bring their business to them to run the specific workloads on the next biggest workload. As you was saying, his keynote are two things. One is the A AIML workloads and the other one is the, the heavy unstructured workloads that he was talking about. 80%, 90% of the data that's coming off is unstructured. So how do you analyze that? Such as the geospatial data. He was talking about the volumes of data you need to analyze the, the neural deep neural net drug you ought to use, only hyperscale can do it, right? So that's no wonder all of them on top for the data, one of the things they announced, which not many people paid attention, there was a zero eight L that that they talked about. >>What that does is a little bit of a game changing moment in a sense that you don't have to, for example, if you were to train the data, data, if the data is distributed everywhere, if you have to bring them all together to integrate it, to do that, it's a lot of work to doing the dl. So by taking Amazon, Aurora, and then Rich combine them as zero or no ETL and then have Apaches Apaches Spark applications run on top of analytical applications, ML workloads. That's huge. So you don't have to move around the data, use the data where it is, >>I, I think you said it, they're basically filling holes, right? Yeah. They created this, you know, suite of tools, let's call it. You might say it's a mess. It's not a mess because it's, they're really powerful but they're not well integrated and now they're starting to take the seams as I say. >>Well yeah, it's a great point. And I would double down and say, look it, I think that boring is good. You know, we had that phase in Kubernetes hype cycle where it got boring and that was kind of like, boring is good. Boring means we're getting better, we're invisible. That's infrastructure that's in the weeds, that's in between the toes details. It's the stuff that, you know, people we have to get done. So, you know, you look at their 40 new data sources with data Wrangler 50, new app flow connectors, Redshift Auto Cog, this is boring. Good important shit Dave. The governance, you gotta get it and the governance is gonna be key. So, so to me, this may not jump off the page. Adam's keynote also felt a little bit of, we gotta get these gaps done in a good way. So I think that's a very positive sign. >>Now going back to the bigger picture, I think the real question is can there be another independent cloud data cloud? And that's the, to me, what I try to get at my story and you're breaking analysis kind of hit a home run on this, is there's interesting opportunity for an independent data cloud. Meaning something that isn't aws, that isn't, Google isn't one of the big three that could sit in. And so let me give you an example. I had a conversation last night with a bunch of ex Amazonian engineering teams that left the conversation was interesting, Dave. They were like talking, well data bricks and Snowflake are basically batch, okay, not transactional. And you look at Aerospike, I can see their booth here. Transactional data bases are hot right now. Streaming data is different. Confluence different than data bricks. Is data bricks good at hosting? >>No, Amazon's better. So you start to see these kinds of questions come up where, you know, data bricks is great, but maybe not good for this, that and the other thing. So you start to see the formation of swim lanes or visibility into where people might sit in the ecosystem, but what came out was transactional. Yep. And batch the relationship there and streaming real time and versus you know, the transactional data. So you're starting to see these new things emerge. Andy, what do you, what's your take on this? You're following this closely. This seems to be the alpha nerd conversation and it all points to who's gonna have the best data cloud, say data, super clouds, I call it. What's your take? >>Yes, data cloud is important as well. But also the computational that goes on top of it too, right? Because when, when the data is like unstructured data, it's that much of a huge data, it's going to be hard to do that with a low model, you know, compute power. But going back to your data point, the training of the AIML models required the batch data, right? That's when you need all the, the historical data to train your models. And then after that, when you do inference of it, that's where you need the streaming real time data that's available to you too. You can make an inference. One of the things, what, what they also announced, which is somewhat interesting, is you saw that they have like 700 different instances geared towards every single workload. And there are some of them very specifically run on the Amazon's new chip. The, the inference in two and theran tr one chips that basically not only has a specific instances but also is run on a high powered chip. And then if you have that data to support that, both the training as well as towards the inference, the efficiency, again, those numbers have to be proven. They claim that it could be anywhere between 40 to 60% faster. >>Well, so a couple things. You're definitely right. I mean Snowflake started out as a data warehouse that was simpler and it's not architected, you know, in and it's first wave to do real time inference, which is not now how, how could they, the other second point is snowflake's two or three years ahead when it comes to governance, data sharing. I mean, Amazon's doing what always does. It's copying, you know, it's customer driven. Cuz they probably walk into an account and they say, Hey look, what's Snowflake's doing for us? This stuff's kicking ass. And they go, oh, that's a good idea, let's do that too. You saw that with separating compute from storage, which is their tiering. You saw it today with extending data, sharing Redshift, data sharing. So how does Snowflake and data bricks approach this? They deal with ecosystem. They bring in ecosystem partners, they bring in open source tooling and that's how they compete. I think there's unquestionably an opportunity for a data cloud. >>Yeah, I think, I think the super cloud conversation and then, you know, sky Cloud with Berkeley Paper and other folks talking about this kind of pre, multi-cloud era. I mean that's what I would call us right now. We are, we're kind of in the pre era of multi-cloud, which by the way is not even yet defined. I think people use that term, Dave, to say, you know, some sort of magical thing that's happening. Yeah. People have multiple clouds. They got, they, they end up by default, not by design as Dell likes to say. Right? And they gotta deal with it. So it's more of they're inheriting multiple cloud environments. It's not necessarily what they want in the situation. So to me that is a big, big issue. >>Yeah, I mean, again, going back to your snowflake and data breaks announcements, they're a data company. So they, that's how they made their mark in the market saying that, you know, I do all those things, therefore you have, I had to have your data because it's a seamless data. And, and Amazon is catching up with that with a lot of that announcements they made, how far it's gonna get traction, you know, to change when I to say, >>Yeah, I mean to me, to me there's no doubt about Dave. I think, I think what Swamee is doing, if Amazon can get corner the market on out of the box ML and AI capabilities so that people can make it easier, that's gonna be the end of the day tell sign can they fill in the gaps. Again, boring is good competition. I don't know mean, mean I'm not following the competition. Andy, this is a real question mark for me. I don't know where they stand. Are they more comprehensive? Are they more deeper? Are they have deeper services? I mean, obviously shows to all the, the different, you know, capabilities. Where, where, where does Amazon stand? What's the process? >>So what, particularly when it comes to the models. So they're going at, at a different angle that, you know, I will help you create the models we talked about the zero and the whole data. We'll get the data sources in, we'll create the model. We'll move the, the whole model. We are talking about the ML ops teams here, right? And they have the whole functionality that, that they built ind over the year. So essentially they want to become the platform that I, when you come in, I'm the only platform you would use from the model training to deployment to inference, to model versioning to management, the old s and that's angle they're trying to take. So it's, it's a one source platform. >>What about this idea of technical debt? Adrian Carro was on yesterday. John, I know you talked to him as well. He said, look, Amazon's Legos, you wanna buy a toy for Christmas, you can go out and buy a toy or do you wanna build a, to, if you buy a toy in a couple years, you could break and what are you gonna do? You're gonna throw it out. But if you, if you, if part of your Lego needs to be extended, you extend it. So, you know, George Gilbert was saying, well, there's a lot of technical debt. Adrian was countering that. Does Amazon have technical debt or is that Lego blocks analogy the right one? >>Well, I talked to him about the debt and one of the things we talked about was what do you optimize for E two APIs or Kubernetes APIs? It depends on what team you're on. If you're on the runtime gene, you're gonna optimize for Kubernetes, but E two is the resources you want to use. So I think the idea of the 15 years of technical debt, I, I don't believe that. I think the APIs are still hardened. The issue that he brings up that I think is relevant is it's an end situation, not an or. You can have the bag of Legos, which is the primitives and build a durable application platform, monitor it, customize it, work with it, build it. It's harder, but the outcome is durability and sustainability. Building a toy, having a toy with those Legos glued together for you, you can get the play with, but it'll break over time. Then you gotta replace it. So there's gonna be a toy business and there's gonna be a Legos business. Make your own. >>So who, who are the toys in ai? >>Well, out of >>The box and who's outta Legos? >>The, so you asking about what what toys Amazon building >>Or, yeah, I mean Amazon clearly is Lego blocks. >>If people gonna have out the box, >>What about Google? What about Microsoft? Are they basically more, more building toys, more solutions? >>So Google is more of, you know, building solutions angle like, you know, I give you an API kind of thing. But, but if it comes to vertical industry solutions, Microsoft is, is is ahead, right? Because they have, they have had years of indu industry experience. I mean there are other smaller cloud are trying to do that too. IBM being an example, but you know, the, now they are starting to go after the specific industry use cases. They think that through, for example, you know the medical one we talked about, right? So they want to build the, the health lake, security health lake that they're trying to build, which will HIPPA and it'll provide all the, the European regulations, the whole line yard, and it'll help you, you know, personalize things as you need as well. For example, you know, if you go for a certain treatment, it could analyze you based on your genome profile saying that, you know, the treatment for this particular person has to be individualized this way, but doing that requires a anomalous power, right? So if you do applications like that, you could bring in a lot of the, whether healthcare, finance or what have you, and then easy for them to use. >>What's the biggest mistake customers make when it comes to machine intelligence, ai, machine learning, >>So many things, right? I could start out with even the, the model. Basically when you build a model, you, you should be able to figure out how long that model is effective. Because as good as creating a model and, and going to the business and doing things the right way, there are people that they leave the model much longer than it's needed. It's hurting your business more than it is, you know, it could be things like that. Or you are, you are not building a responsibly or later things. You are, you are having a bias and you model and are so many issues. I, I don't know if I can pinpoint one, but there are many, many issues. Responsible ai, ethical ai. All >>Right, well, we'll leave it there. You're watching the cube, the leader in high tech coverage here at J three at reinvent. I'm Jeff, Dave Ante. Andy joining us here for the critical analysis and breaking down the commentary. We'll be right back with more coverage after this short break.

>>We are back in the final stretch at Supercomputing 22 here in Dallas. I'm your host Paul Gillum with my co-host Dave Nicholson, and we've been talking to so many smart people this week. It just, it makes, boggles my mind are next guest. J Poso is the HPC and AI technology strategist at Dell. Jay also has a PhD in astronomy from the University of Texas. And I'm guessing you were up watching the Artemis launch the other night? >>I, I wasn't. I really should have been, but, but I wasn't, I was in full super computing conference mode. So that means discussions at, you know, various venues with people into the wee hours. >>How did you make the transition from a PhD in astronomy to an HPC expert? >>It was actually really straightforward. I did theoretical astrophysics and I was modeling what white dwarfs look like when they create matter and then explode as type one A super Novi, which is a class of stars that blow up. And it's a very important class because they blow up almost exactly the same way. So if you know how bright they are physically, not just how bright they appear in the sky, but if you can determine from first principles how bright they're, then you have a standard ruler for the universe when they go off in a galaxy, you know how far the galaxy is about how faint it is. So to model these though, you had to understand equations of physics, including electron degeneracy pressure, as well as normal fluid dynamics kinds of of things. And so you were solving for an explosive burning front, ripping through something. And that required a supercomputer to have anywhere close to the fat fidelity to get a reasonable answer and, and hopefully some understanding. >>So I've always said electrons are degenerate. I've always said it and I, and I mentioned to Paul earlier, I said, finally we're gonna get a guest to consort through this whole dark energy dark matter thing for us. We'll do that after, after, after the segment. >>That's a whole different, >>So, well I guess super computing being a natural tool that you would use. What is, what do you do in your role as a strategist? >>So I'm in the product management team. I spend a lot of time talking to customers about what they want to do next. HPC customers are always trying to be maximally productive of what they've got, but always wanting to know what's coming next. Because if you think about it, we can't simulate the entire human body cell for cell on any supercomputer day. We can simulate parts of it, cell for cell or the whole body with macroscopic physics, but not at the, you know, atomic level, the entire organism. So we're always trying to build more powerful computers to solve larger problems with more fidelity and less approximations in it. And so I help people try to understand which technologies for their next system might give them the best advance in capabilities for their simulation work, their data analytics work, their AI work, et cetera. Another part of it is talking to our great technology partner ecosystem and learning about which technologies they have. Cause it feeds the first thing, right? So understanding what's coming, and Dell has a, we're very proud of our large partner ecosystem. We embrace many different partners in that with different capabilities. So understanding those helps understand what your future systems might be. That those are two of the major roles in it. Strategic customers and strategic technologies. >>So you've had four days to wander the, this massive floor here and lots of startups, lots of established companies doing interesting things. What have you seen this week that really excites you? >>So I'm gonna tell you a dirty little secret here. If you are working for someone who makes super computers, you don't get as much time to wander the floor as you would think because you get lots of meetings with people who really want to understand in an NDA way, not just in the public way that's on the floor, but what's, what are you not telling us on the floor? What's coming next? And so I've been in a large number of customer meetings as well as being on the floor. And while I can't obviously share the everything, that's a non-disclosure topic in those, some things that we're hearing a lot about, people are really concerned with power because they see the TDP on the roadmaps for all the silicon providers going way up. And so people with power comes heat as waste. And so that means cooling. >>So power and cooling has been a big topic here. Obviously accelerators are, are increasing in importance in hpc not just for AI calculations, but now also for simulation calculations. And we are very proud of the three new accelerator platforms we launched here at the show that are coming out in a quarter or so. Those are two of the big topics we've seen. You know, there's, as you walk the floor here, you see lots of interesting storage vendors. HPC community's been do doing storage the same way for roughly 20 years. But now we see a lot of interesting players in that space. We have some great things in storage now and some great things that, you know, are coming in a year or two as well. So it's, it's interesting to see that diversity of that space. And then there's always the fun, exciting topics like quantum computing. We unveiled our first hybrid classical quantum computing system here with I on Q and I can't say what the future holds in this, in this format, but I can say we believe strongly in the future of quantum computing and that this, that future will be integrated with the kind of classical computing infrastructure that we make and that will help make quantum computing more powerful downstream. >>Well, let's go down that rabbit hole because, oh boy, boy, quantum computing has been talked about for a long time. There was a lot of excitement about it four or five years ago, some of the major vendors were announcing quantum computers in the cloud. Excitement has kind of died down. We don't see a lot of activity around, no, not a lot of talk around commercial quantum computers, yet you're deep into this. How close are we to have having a true quantum computer or is it a, is it a hybrid? More >>Likely? So there are probably more than 20 and I think close to 40 companies trying different approaches to make quantum computers. So, you know, Microsoft's pursuing a topol topological approach, do a photonics based approach. I, on Q and i on trap approach. These are all different ways of trying to leverage the quantum properties of nature. We know the properties exist, we use 'em in other technologies. We know the physics, but trying the engineering is very difficult. It's very difficult. I mean, just like it was difficult at one point to split the atom. It's very difficult to build technologies that leverage quantum properties of nature in a consistent and reliable and durable way, right? So I, you know, I wouldn't wanna make a prediction, but I will tell you I'm an optimist. I believe that when a tremendous capability with, with tremendous monetary gain potential lines up with another incentive, national security engineering seems to evolve faster when those things line up, when there's plenty of investment and plenty of incentive things happen. >>So I think a lot of my, my friends in the office of the CTO at Dell Technologies, when they're really leading this effort for us, you know, they would say a few to several years probably I'm an optimist, so I believe that, you know, I, I believe that we will sell some of the solution we announced here in the next year for people that are trying to get their feet wet with quantum. And I believe we'll be selling multiple quantum hybrid classical Dell quantum computing systems multiple a year in a year or two. And then of course you hope it goes to tens and hundreds of, you know, by the end of the decade >>When people talk about, I'm talking about people writ large, super leaders in supercomputing, I would say Dell's name doesn't come up in conversations I have. What would you like them to know that they don't know? >>You know, I, I hope that's not true, but I, I, I guess I understand it. We are so good at making the products from which people make clusters that we're number one in servers, we're number one in enterprise storage. We're number one in so many areas of enterprise technology that I, I think in some ways being number one in those things detracts a little bit from a subset of the market that is a solution subset as opposed to a product subset. But, you know, depending on which analyst you talk to and how they count, we're number one or number two in the world in supercomputing revenue. We don't always do the biggest splashy systems. We do the, the frontier system at t, the HPC five system at ENI in Europe. That's the largest academic supercomputer in the world and the largest industrial super >>That's based the world on Dell. Dell >>On Dell hardware. Yep. But we, I think our vision is really that we want to help more people use HPC to solve more problems than any vendor in the world. And those problems are various scales. So we are really concerned about the more we're democratizing HPC to make it easier for more people to get in at any scale that their budget and workloads require, we're optimizing it to make sure that it's not just some parts they're getting, that they are validated to work together with maximum scalability and performance. And we have a great HPC and AI innovation lab that does this engineering work. Cuz you know, one of the myths is, oh, I can just go buy a bunch of servers from company X and a network from company Y and a storage system from company Z and then it'll all work as an equivalent cluster. Right? Not true. It'll probably work, but it won't be the highest performance, highest scalability, highest reliability. So we spend a lot of time optimizing and then we are doing things to try to advance the state of HPC as well. What our future systems look like in the second half of this decade might be very different than what they look like right. Now. >>You mentioned a great example of a limitation that we're running up against right now. You mentioned an entire human body as a, as a, as an organism >>Or any large system that you try to model at the atomic level, but it's a huge macro system, >>Right? So will we be able to reach milestones where we can get our arms entirely around something like an entire human organism with simply quantitative advances as opposed to qualitative advances? Right now, as an example, let's just, let's go down to the basics from a Dell perspective. You're in a season where microprocessor vendors are coming out with next gen stuff and those next NextGen microprocessors, GPUs and CPUs are gonna be plugged into NextGen motherboards, PCI e gen five, gen six coming faster memory, bigger memory, faster networking, whether it's NS or InfiniBand storage controllers, all bigger, better, faster, stronger. And I suspect that systems like Frontera, I don't know, but I suspect that a lot of the systems that are out there are not on necessarily what we would think of as current generation technology, but maybe they're n minus one as a practical matter. So, >>But yeah, I mean they have a lifetime, so Exactly. >>The >>Lifetime is longer than the evolution. >>That's the normal technologies. Yeah. So, so what some people miss is this is, this is the reality that when, when we move forward with the latest things that are being talked about here, it's often a two generation move for an individual, for an individual organization. Yep. >>So now some organizations will have multiple systems and they, the system's leapfrog and technology generations, even if one is their real large system, their next one might be newer technology, but smaller, the next one might be a larger one with newer technology and such. Yeah. So the, the biggest super computing sites are, are often running more than one HPC system that have been specifically designed with the latest technologies and, and designed and configured for maybe a different subset of their >>Workloads. Yeah. So, so the, the, to go back to kinda the, the core question, in your opinion, do we need that qualitative leap to something like quantum computing in order to get to the point, or is it simply a question of scale and power at the, at the, at the individual node level to get us to the point where we can in fact gain insight from a digital model of an entire human body, not just looking at a, not, not just looking at an at, at an organ. And to your point, it's not just about human body, any system that we would characterize as being chaotic today, so a weather system, whatever. Do you, are there any milestones that you're thinking of where you're like, wow, you know, I have, I, I understand everything that's going on, and I think we're, we're a year away. We're a, we're, we're a, we're a compute generation away from being able to gain insight out of systems that right now we can't simply because of scale. It's a very, very long question that I just asked you, but I think I, but hopefully, hopefully you're tracking it. What, what are your, what are your thoughts? What are these, what are these inflection points that we, that you've, in your mind? >>So I, I'll I'll start simple. Remember when we used to buy laptops and we worried about what gigahertz the clock speed was Exactly. Everybody knew the gigahertz of it, right? There's some tasks at which we're so good at making the hardware that now the primary issues are how great is the screen? How light is it, what's the battery life like, et cetera. Because for the set of applications on there, we we have enough compute power. We don't, you don't really need your laptop. Most people don't need their laptop to have twice as powerful a processor that actually rather up twice the battery life on it or whatnot, right? We make great laptops. We, we design for all of those, configure those parameters now. And what, you know, we, we see some customers want more of x, somewhat more of y but the, the general point is that the amazing progress in, in microprocessors, it's sufficient for most of the workloads at that level. Now let's go to HPC level or scientific and technical level. And when it needs hpc, if you're trying to model the orbit of the moon around the earth, you don't really need a super computer for that. You can get a highly accurate model on a, on a workstation, on a server, no problem. It won't even really make it break a sweat. >>I had to do it with a slide rule >>That, >>That >>Might make you break a sweat. Yeah. But to do it with a, you know, a single body orbiting with another body, I say orbiting around, but we both know it's really, they're, they're both ordering the center of mass. It's just that if one is much larger, it seems like one's going entirely around the other. So that's, that's not a super computing problem. What about the stars in a galaxy trying to understand how galaxies form spiral arms and how they spur star formation. Right now you're talking a hundred billion stars plus a massive amount of inter stellar medium in there. So can you solve that on that server? Absolutely not. Not even close. Can you solve it on the largest super computer in the world today? Yes and no. You can solve it with approximations on the largest super computer in the world today. But there's a lot of approximations that go into even that. >>The good news is the simulations produce things that we see through our great telescopes. So we know the approximations are sufficient to get good fidelity, but until you really are doing direct numerical simulation of every particle, right? Right. Which is impossible to do. You need a computer as big as the universe to do that. But the approximations and the science in the science as well as the known parts of the science are good enough to give fidelity. So, and answer your question, there's tremendous number of problem scales. There are problems in every field of science and study that exceed the der direct numerical simulation capabilities of systems today. And so we always want more computing power. It's not macho flops, it's real, we need it, we need exo flops and we will need zeta flops beyond that and yada flops beyond that. But an increasing number of problems will be solved as we keep working to solve problems that are farther out there. So in terms of qualitative steps, I do think technologies like quantum computing, to be clear as part of a hybrid classical quantum system, because they're really just accelerators for certain kinds of algorithms, not for general purpose algorithms. I do think advances like that are gonna be necessary to solve some of the very hardest problem. It's easy to actually formulate an optimization problem that is absolutely intractable by the larger systems in the world today, but quantum systems happen to be in theory when they're big and stable enough, great at that kind of problem. >>I, that should be understood. Quantum is not a cure all for absolutely. For the, for the shortage of computing power. It's very good for certain, certain >>Problems. And as you said at this super computing, we see some quantum, but it's a little bit quieter than I probably expected. I think we're in a period now of everybody saying, okay, there's been a lot of buzz. We know it's gonna be real, but let's calm down a little bit and figure out what the right solutions are. And I'm very proud that we offered one of those >>At the show. We, we have barely scratched the surface of what we could talk about as we get into intergalactic space, but unfortunately we only have so many minutes and, and we're out of them. Oh, >>I'm >>J Poso, HPC and AI technology strategist at Dell. Thanks for a fascinating conversation. >>Thanks for having me. Happy to do it anytime. >>We'll be back with our last interview of Supercomputing 22 in Dallas. This is Paul Gillen with Dave Nicholson. Stay with us.