(gentle upbeat music) >> Everyone welcome to the AWS Startup Showcase presented by theCUBE. I'm John Furrier, host of theCUBE. We are here talking about the next big thing in cloud featuring Explorium. For the AI track, we've got AI cybersecurity and life sciences. Obviously AI is hot, machine learning powering that. Today we're joined by Zach Booth, director of global partnerships and channels like Explorium. Zach, thank you for joining me today remotely. Soon we'll be in person, but thanks for coming on. We're going to talk about rethinking external data. Thanks for coming on theCUBE. >> Absolutely, thanks so much for having us, John. >> So you guys are a hot startup. Congratulations, we just wrote about on SiliconANGLE, you're a new $75 million of fresh funding. So you're part of the Amazon partner network and growing like crazy. You guys have a unique value proposition looking at external data and that having a platform for advanced analytics and machine learning. Can you take a minute to explain what you guys do? What is this platform? What's the value proposition and why do you exist? >> Bottom line, we're bringing context to decision-making. The premise of Explorium and kind of this is consistent with the framework of advanced analytics is we're helping customers to reach better, more relevant, external data to feed into their predictive and analytical models. It's quite a challenge to actually integrate and effectively leverage data that's coming from beyond your organization's walls. It's manual, it's tedious, it's extremely time consuming and that's a problem. It's really a problem that Explorium was built to solve. And our philosophy is it shouldn't take so long. It shouldn't be such an arduous process, but it is. So we built a company, a technology that's capable for any given analytical process of connecting a customer to relevant sources that are kind of beyond their organization's walls. And this really impacts decision-making by bringing variety and context into their analytical processes. >> You know, one of the things I see a lot in my interviews with theCUBE and talking to people in the industry is that everyone talks a big game about having some machine learning and AI, they're like, "Okay, I got all this cool stuff". But at the end of the day, people are still using spreadsheets. They're wrangling data. And a lot of it's dominated by these still fenced-off data warehousing and you start to see the emergence of really companies built on the cloud. I saw the snowflake IPO, you're seeing a whole new shift of new brands emerging that are doing things differently, right? And because there's such a need for just move out of the archaic spreadsheet and data presentation layers, it's a slower antiquated, outdated. How do you guys solve that problem? You guys are on the other side of that equation, you're on the new wave of analytics. What are you guys solving? How do you make that work? How do you get on that way? >> So basically the way Explorium sees the world, and I think that most analytical practitioners these days see it in a similar way, but the key to any analytical problem is having the right data. And the challenge that we've talked about and that we're really focused on is helping companies reach that right data. Our focus is on the data part of data science. The science part is the algorithmic side. It's interesting. It was kind of the first frontier of machine learning as practitioners and experts were focused on it and cloud and compute really enabled that. The challenge today isn't so much "What's the right model for my problem?" But it's "What's the right data?" And that's the premise of what we do. Your model's only as strong as the data that it trains on. And going back to that concept of just bringing context to decision-making. Within that framework that we talked about, the key is bringing comprehensive, accurate and highly varied data into my model. But if my model is only being informed with internal data which is wonderful data, but only internal, then it's missing context. And we're helping companies to reach that external variety through a pretty elegant platform that can connect the right data for my analytical process. And this really has implications across several different industries and a multitude of use cases. We're working with companies across consumer packaged goods, insurance, financial services, retail, e-commerce, even software as a service. And the use cases can range between fraud and risk to marketing and lifetime value. Now, why is this such a challenge today with maybe some antiquated or analog means? With a spreadsheet or with a rule-based approach where we're pretty limited, it was an effective means of decision-making to generate and create actions, but it's highly limited in its ability to change, to be dynamic, to be flexible. And with modeling and using data, it's really a huge arsenal that we have at our fingertips. The trick is extracting value from within it. There's obviously latent value from within our org but every day there's more and more data that's being created outside of our org. And that is a challenge to go out and get to effectively filter and navigate and connect to. So we've basically built that tech to help us navigate and query for any given analytical question. Find me the right data rather than starting with what's the problem I'm looking for, now let me think about the right data. Which is kind of akin to going into a library and searching for a specific book. You know which book you're looking for. Instead of saying, there's a world, a universe of data outside there. I want to access it. I want to tap into what's right. Can I use a tool that can effectively query all that data, find what's relevant for me, connect it and match it with my own and distill signals or features from that data to provide more variety into my modeling efforts yielding a robust decision as an output. >> I love that paradigm of just having that searchable kind of paradigm. I got to ask you one of the big things that I've heard people talk about. I want to get your thoughts on this, is that how do I know if I even have the right data? Is the data addressable? Can I find it? Is it even, can I even be queried? How do you solve that problem for customers when they say, "I really want the best analytics but do I even have the data or is it the right data?" How do you guys look at that? >> So the way our technology was built is that it's quite relevant for a few different profile types of customers. Some of these customers, really the genesis of the company started with those cloud-based, model-driven since day one organizations, and they're working with machine learning and they have models in production. They're quite mature in fact. And the problem that they've been facing is, again, our models are only as strong as the data that they're training on. The only data that they're training on is internal data. And we're seeing diminishing returns from those decisions. So now suddenly we're looking for outside data and we're finding that to effectively use outside data, we have to spend a lot of time. 60% of our time spent thinking of data, going out and getting it, cleaning it, validating it, and only then can we actually train a model and assess if there's an ROI. That takes months. And if it doesn't push the needle from an ROI standpoint, then it's an enormous opportunity cost, which is very, very painful, which goes back to their decision-making. Is it even worth it if it doesn't push the needle? That's why there had to be a better way. And what we built is relevant for that audience as well as companies that are in the midst of their digital transformation. We're data rich, but data science poor. We have lots of data. A latent value to extract from within our own data and at the same time tons of valuable data outside of our org. Instead of waiting 18, 36 months to transform ourselves, get our infrastructure in place, our data collection in place, and really start having models in production based on our own data. You can now do this in tandem. And that's what we're seeing with a lot of our enterprise customers. By using their analysts, their data engineers, some of them in their innovation or kind of center of excellences have a data science group as well. And they're using the platform to inform a lot of their different models across lines of businesses. >> I love that expression, "data-rich". A lot of people becoming full of data too. They have a data problem. They have a lot of it. I think I want to get your thoughts but I think that connects to my next question which is as people look at the cloud, for instance, and again, all these old methods were internal, internal to the company, but now that you have this idea of cloud, more integration's happening. More people are connecting with APIs. There's more access to potentially more signals, more data. How does a company go to that next level to connect in and acquire the data and make it faster? Because I can almost imagine that the signals that come from that context of merging external data and that's the topic of this theme, re-imagining external data is extremely valuable signaling capability. And so it sounds like you guys make it go faster. So how does it work? Is it the cloud? Take us through that value proposition. >> Well, it's a real, it's amazing how fast the rate of change organizations have been moving onto the cloud over the past year during COVID and the fact that alternative or external data, depending on how you refer to it, has really, really blown up. And it's really exciting. This is coming in the form of data providers and data marketplaces, and everybody is kind of, more and more organizations are moving from rule-based decision-making to predictive decision making, and that's exciting. Now what's interesting about this company, Explorium, we're working with a lot of different types of customers but our long game has a real high upside. There's more and more companies that are starting to use data and are transformed or already are in the midst of their transformation. So they need outside data. And that challenge that I described is exists for all of them. So how does it really work? Today, if I don't have data outside, I have to think. It's based on hypothesis and it all starts with that hypothesis which is already prone to error from the get-go. You and I might be domain experts for a given use case. Let's say we're focusing on fraud. We might think about a dozen different types of data sources, but going out and getting it like I said, it takes a lot of time harmonizing it, cleaning it, and being able to use it takes even more time. And that's just for each one. So if we have to do that across dozens of data sources it's going to take far too much time and the juice isn't worth the squeeze. And so I'm going to forego using that. And a metaphor that I like to use when I try to describe what Explorium does to my mom. I basically use this connection to buying your first home. It's a very, very important financial decision. You would, when you're buying this home, you're thinking about all the different inputs in your decision-making. It's not just about the blueprint of the house and how many rooms and the criteria you're looking for. You're also thinking external variables. You're thinking about the school zone, the construction, the property value, alternative or similar neighborhoods. That's probably your most important financial decision or one of the largest at least. A machine learning model in production is an extremely important and expensive investment for an organization. Now, the problem is as a consumer buying a home, we have all this data at our fingertips to find out all of those external-based inputs. Organizations don't, which is kind of crazy when I first kind of got into this world. And so, they're making decisions with their first party data only. First party data's wonderful data. It's the best, it's representative, it's high quality, it's high value for their specific decision-making and use cases but it lacks context. And there's so much context in the form of location-based data and business information that can inform decision-making that isn't being used. It translates to sub-optimal decision-making, let's say. >> Yeah, and I think one of the insights around looking at signal data in context is if by merging it with the first party, it creates a huge value window, it gives you observational data, maybe potentially insights into customer behavior. So totally agree, I think that's a huge observation. You guys are definitely on the right side of history here. I want to get into how it plays out for the customer. You mentioned the different industries, obviously data's in every vertical. And vertical specialization with the data it has to be, is very metadata driven. I mean, metadata and oil and gas is different than fintech. I mean, some overlap, but for the most part you got to have that context, acute context, each one. How are you guys working? Take us through an example of someone getting it right, getting that right set up, taking us through the use case of how someone on boards Explorium, how they put it to use, and what are some of the benefits? >> So let's break it down into kind of a three-step phase. And let's use that example of fraud earlier. An organization would have basically past historical data of how many customers were actually fraudulent in the end of the day. So this use case, and it's a core business problem, is with an intention to reduce that fraud. So they would basically provide, going with your description earlier, something similar to an Excel file. This can be pulled from any database out there, we're working with loads of them, and they would provide this what's called training data. This training data is their historical data and would have as an output, the outcome, the conclusion, was this business fraudulent or not? Yes or no. Binary. The platform would understand that data itself to train a model with external context in the form of enrichments. These data enrichments at the end of the day are important, they're relevant, but their purpose is to generate signals. So to your point, signals is the bottom line what everyone's trying to achieve and identify and discover, and even engineer by using data that they have and data that they yet to integrate with. So the platform would connect to your data, infer and understand the meaning of that data. And based on this matching of internal plus external context, the platform automates the process of distilling signals. Or in machine learning this is called, referred to as features. And these features are really the bread and butter of your modeling efforts. If you can leverage features that are coming from data that's outside of your org, and they're quantifiably valuable which the platform measures, then you're putting yourself in a position to generate an edge in your modeling efforts. Meaning now, you might reduce your fraud rate. So your customers get a much better, more compelling offer or service or price point. It impacts your business in a lot of ways. What Explorium is bringing to the table in terms of value is a single access point to a huge universe of external data. It expedites your time to value. So rather than data analysts, data engineers, data scientists, spending a significant amount of time on data preparation, they can now spend most of their time on feature or signal engineering. That's the more fun and interesting part, less so the boring part. But they can scale their modeling efforts. So time to value, access to a huge universe of external context, and scale. >> So I see two things here. Just make sure I get this right 'cause it sounds awesome. So one, the core assets of the engineering side of it, whether it's the platform engineer or data engineering, they're more optimized for getting more signaling which is more impactful for the context acquisition, looking at contexts that might have a business outcome, versus wrangling and doing mundane, heavy lifting. >> Yeah so with it, sorry, go ahead. >> And the second one is you create a democratization for analysts or business people who just are used to dealing with spreadsheets who just want to kind of play and play with data and get a feel for it, or experiment, do querying, try to match planning with policy - >> Yeah, so the way I like to kind of communicate this is Explorium's this one, two punch. It's got this technology layer that provides entity resolution, so matching with external data, which otherwise is a manual endeavor. Explorium's automated that piece. The second is a huge universe of outside data. So this circumvents procurement. You don't have to go out and spend all of these one-off efforts on time finding data, organizing it, cleaning it, etc. You can use Explorium as your single access point to and gateway to external data and match it, so this will accelerate your time to value and ultimately the amount of valuable signals that you can discover and leverage through the platform and feed this into your own pipelines or whatever system or analytical need you have. >> Zach, great stuff. I love talking with you and I love the hot startup action here. Cause you're again, you're on the net new wave here. Like anything new, I was just talking to a colleague here. (indistinct) When you have something new, it's like driving a car for the first time. You need someone to give you some driving lessons or figure out how to operationalize it or take advantage of the one, two, punch as you pointed out. How do you guys get someone up and running? 'Cause let's just say, I'm like, okay, I'm bought into this. So no brainer, you got my attention. I still don't understand. Do you provide a marketplace of data? Do I need to get my own data? Do I bring my own data to the party? Do you guys provide relationships with other data providers? How do I get going? How do I drive this car? How do you answer that? >> So first, explorium.ai is a free trial and we're a product-focused company. So a practitioner, maybe a data analyst, a data engineer, or data scientist would use this platform to enrich their analytical, so BI decision-making or any models that they're working on either in production or being trained. Now oftentimes models that are being trained don't actually make it to production because they don't meet a minimum threshold. Meaning they're not going to have a positive business outcome if they're deployed. With Explorium you can now bring variety into that and increase your chances that your model that's being trained will actually be deployed because it's being fed with the right data. The data that you need that's not just the data that you have. So how a business would start working with us would typically be with a use case that has a high business value. Maybe this could be a fraud use case or a risk use case and B2B, or even B2SMB context. This might be a marketing use case. We're talking about LTV modeling, lookalike modeling, lead acquisition and generation for our CPGs and field sales optimization. Explore and understand your data. It would enrich that data automatically, it would generate and discover new signals from external data plus from your own and feed this into either a model that you have in-house or end to end in the platform itself. We provide customer success to generate, kind of help you build out your first model perhaps, and hold your hands through that process. But typically most of our customers are after a few months time having run in building models, multiple models in production on their own. And that's really exciting because we're helping organizations move from a more kind of rule-based decision making and being their bridge to data science. >> Awesome. I noticed that in your title you handle global partnerships and channels which I'm assuming is you guys have a network and ecosystem you're working with. What are some of the partnerships and channel relationships that you have that you bring to bear in the marketplace? >> So data and analytics, this space is very much an ecosystem. Our customers are working across different clouds, working with all sorts of vendors, technologies. Basically they have a pretty big stack. We're a part of that stack and we want to symbiotically play within our customer stack so that we can contribute value whether they sit here, there, or in another place. Our partners range from consulting and system integration firms, those that perhaps are building out the blueprint for a digital transformation or actually implementing that digital transformation. And we contribute value in both of these cases as a technology innovation layer in our product. And a customer would then consume Explorium afterwards, after that transformation is complete as a part of their stack. We're also working with a lot of the different cloud vendors. Our customers are all cloud-based and data enrichment is becoming more and more relevant with some wonderful machine-learning tools. Be they AutoML, or even some data marketplaces are popping up and very exciting. What we're bringing to the table as an edge is accelerating the connection between the data that I think I want as a company and how to actually extract value from that data. Being part of this ecosystem means that we can be working with and should be working with a lot of different partners to contribute incremental value to our end customers. >> Final question I want to ask you is if I'm in a conference room with my team and someone says, "Hey, we should be rethinking our external data." What would I say? How would I pound my fist on the table or raise my hand in saying, "Hey, I have an idea, we should be thinking this way." What would be my argument to the team, to re-imagine how we deal with external data? >> So it might be a scenario that rather than banging your hands on the table, you might be banging your heads on the table because it's such a challenging endeavor today. Companies have to think about, What's the right data for my specific use cases? I need to validate that data. Is it relevant? Is it real? Is it representative? Does it have good coverage, good depth and good quality? Then I need to procure that data. And this is about getting a license from it. I need to integrate that data with my own. That means I need to have some in-house expertise to do so. And then of course, I need to monitor and maintain that data on an ongoing basis. All of this is a pretty big thing to undertake and undergo and having a partner to facilitate that external data integration and ongoing refresh and monitoring, and being able to trust that this is all harmonized, high quality, and I can find the valuable ones without having to manually pick and choose and try to discover it myself is a huge value add, particularly the larger the organization or partner. Because there's so much data out there. And there's a lot of noise out there too. And so if I can through a single partner or access point, tap into that data and quantify what's relevant for my specific problem, then I'm putting myself in a really good position and optimizing the allocation of my very expensive and valuable data analysts and engineering resources. >> Yeah, I think one of the things you mentioned earlier I thought was a huge point was good call out was it goes beyond the first party data because and even just first party if you just in an internal view, some of the best, most successful innovators that we've been covering with cloud scale is they're extending their first party data to external providers. So they're in the value chains of solutions that share their first party data with other suppliers. And so that's just, again, more of an extension of the first party data. You're kind of taking it to a whole 'nother level of there's another external, external set of data beyond it that's even more important. I think this is a fascinating growth area and I think you guys are onto it. Great stuff. >> Thank you so much, John. >> Well, I really appreciate you coming on Zach. Final word, give a quick plug for the company. What are you up to, and what's going on? >> What's going on with Explorium? We are growing very fast. We're a very exciting company. I've been here since the very early days and I can tell you that we have a stellar working environment, a very, very, strong down to earth, high work ethic culture. We're growing in the sense of our office in San Mateo, New York, and Tel Aviv are growing rapidly. As you mentioned earlier, we raised our series C so that totals Explorium to raising I think 127 million over the past two years and some change. And whether you want to partner with Explorium, work with us as a customer, or join us as an employee, we welcome that. And I encourage everybody to go to explorium.ai. Check us out, read some of the interesting content there around data science, around the processes, around the business outcomes that a lot of our customers are seeing, as well as joining a free trial. So you can check out the platform and everything that has to offer from machine learning engine to a signal studio, as well as what type of information might be relevant for your specific use case. >> All right Zach, thanks for coming on. Zach Booth, director of global partnerships and channels that explorium.ai. The next big thing in cloud featuring Explorium and a part of our AI track, I'm John Furrier, host of theCUBE. Thanks for watching.

>> Announcer: From around the globe, it's theCUBE with digital coverage of VMworld 2020 brought to you by VMware and its ecosystem partners. >> Hello, welcome back to theCUBE's coverage of VMworld 2020. This is theCUBE virtual with VMworld 2020 virtual. I'm John Furrier, your host of theCUBE with Dave Vellante. It's our 11th year covering VMware. We're not in-person, we're virtual but all the content is flowing. Of course, we're here with Pat Gelsinger, the CEO of VMware who's been on theCUBE, all 11 years. This year virtual of theCUBE as we've been covering VMware from his early days in 2010 when theCUBE started, 11 years later, Pat, it's still changing and still exciting. Great to see you, thanks for taking the time. >> Hey, you guys are great. I love the interactions that we have, the energy, the fun, the intellectual sparring and of course the audiences have loved it now for 11 years, and I look forward to the next 11 that we'll be doing together. >> It's always exciting 'cause we have great conversations, Dave, and I like to drill in and really kind of probe and unpack the content that you're delivering at the keynotes, but also throughout the entire program. It is virtual this year which highlights a lot of the cloud native changes. Just want to get your thoughts on the virtual aspect, VMworld's not in-person, which is one of the best events of the year, everyone loves it, the great community. It's virtual this year but there's a slew of content, what should people take away from this virtual VMworld? >> Well, one aspect of it is that I'm actually excited about is that we're going to be well over 100,000 people which allows us to be bigger, right? You don't have the physical constraints, you also are able to reach places like I've gone to customers and maybe they had 20 people attend in prior years. This year they're having 100. They're able to have much larger teams also like some of the more regulated industries where they can't necessarily send people to events like this, The International Audience. So just being able to spread the audience much more. A digital foundation for an unpredictable world, and man, what an unpredictable world it has been this past year. And then key messages, lots of key products announcements, technology announcements, partnership announcements, and of course in all of the VMworld is that hands-on labs, the interactions that will be delivering a virtual. You come to VMware because the content is so robust and it's being delivered by the world's smartest people. >> Yeah, we've had great conversations over the years and we've talked about hybrid cloud, I think, 2012. A lot of the stuff I look back at a lot of the videos was early on we're picking out all these waves, but there was that moment four years ago or so, maybe even four three, I can't even remember it seems like yesterday. You gave the seminal keynote and you said, this is the way the world's going to happen. And since that keynote, I'll never forget, was in Moscone and since then, you guys have been performing extremely well both on the business front as well as making technology bets and it's paying off. So what's next, you got the cloud, cloud scale, is it Space, is it Cyber? All these things are going on what is next wave that you're watching and what's coming out and what can people extract out of VMworld this year about this next wave? >> Yeah, one of the things I really am excited about and I went to my buddy Jensen, I said, boy, we're doing this work in smart mix we really like to work with you and maybe some things to better generalize the GPU. And Jensen challenged me. Now usually, I'm the one challenging other people with bigger visions. This time Jensen said, "hey Pat, I think you're thinking too small. Let's do the entire AI landscape together, and let's make AI a enterprise class works load from the data center to the cloud and to the Edge. And so I'm going to bring all of my AI resources and make VMware and Tanzu the preferred infrastructure to deliver AI at scale. I need you guys to make the GPUs work like first-class citizens in the vSphere environment because I need them to be truly democratized for the enterprise, so that it's not some specialized AI Development Team, it's everybody being able to do that. And then we're going to connect the whole network together in a new and profound way with our Monterey program as well being able to use the Smart NIC, the DPU, as Jensen likes to call it. So now with CPU, GPU and DPU, all being managed through a distributed architecture of VMware. This is exciting, so this is one in particular that I think we are now re-architecting the data center, the cloud and the Edge. And this partnership is really a central point of that. >> Yeah, the NVIDIA thing's huge and I know Dave probably has some questions on that but I asked you a question because a lot of people ask me, is that just a hardware deal? Talking about SmartNICs, you talk about data processing units. It sounds like a motherboard in the cloud, if you will, but it's not just hardware. Can you talk about the aspect of the software piece? Because again, NVIDIA is known for GPUs, we all know that but we're talking about AI here so it's not just hardware. Can you just expand and share what the software aspect of all this is? >> Yeah well, NVIDIA has been investing in their AI stack and it's one of those where I say, this is Edison at work, right? The harder I work, the luckier I get. And NVIDIA was lucky that their architecture worked much better for the AI workload. But it was built on two decades of hard work in building a parallel data center architecture. And they have built a complete software stack for all the major AI workloads running on their platform. All of that is now coming to vSphere and Tanzu, that is a rich software layer across many vertical industries. And we'll talk about a variety of use cases, one of those that we highlight at VMworld is the University, California, San Francisco partnership, UCSF, one of the world's leading research hospitals. Some of the current vaccine use cases as well, the financial use cases for threat detection and trading benefits. It really is about how we bring that rich software stack. This is a decade and a half of work to the VMware platform, so that now every developer and every enterprise can take advantage of this at scale. That's a lot of software. So in many respects, yeah, there's a piece of hardware in here but the software stack is even more important. >> It's so well we're on the sort of NVIDIA, the arm piece. There's really interesting these alternative processing models, and I wonder if you could comment on the implications for AI inferencing at the Edge. It's not just as well processor implications, it's storage, it's networking, it's really a whole new fundamental paradigm, but how are you thinking about that, Pat? >> Yeah, and we've thought about there's three aspects, what we said, three problems that we're solving. One is the developer problem where we said now you develop once, right? And the developer can now say, "hey I want to have this new AI-centric app and I can develop and it can run in the data center on the cloud or at the Edge." Secondly, my Operations Team can be able to operate this just like I do all of my infrastructure, and now it's VMs containers and AI applications. And third, and this is where your question really comes to bear most significantly, is data gravity. Right, these data sets are big. Some of them need to be very low latency as well, they also have regulatory issues. And if I have to move these large regulated data sets to the cloud, boy, maybe I can't do that generally for my Apps or if I have low latency heavy apps at the Edge, huh, I can't pull it back to the cloud or to my data center. And that's where the uniform architecture and aspects of the Monterey Program where I'm able to take advantage of the network and the SmartNICs that are being built, but also being able to fully represent the data gravity issues of AI applications at scale. 'Cause in many cases, I'll need to do the processing, both the learning and the inference at the Edge as well. So that's a key part of our strategy here with NVIDIA and I do think is going to unlock a new class of apps because when you think about AI and containers, what am I using it for? Well, it's the next generation of applications. A lot of those are going to be Edge, 5G-based, so very critical. >> We've got to talk about security now too. I'm going to pivot a little bit here, John, if it's okay. Years ago, you said security is a do-over, you said that on theCUBE, it stuck with us. But there's been a lot of complacency. It's kind of if it ain't broke, don't fix it, but but COVID kind of broke it. And so you see three mega trends, you've got cloud security, you'll see in Z-scaler rocket, you've got Identity Access Management and Octo which I hope there's I think a customer of yours and then you got Endpoint, you're seeing Crowdstrike explode you guys paid 2.7 billion, I think, for Carbon Black, yet Crowdstrike has this huge valuation. That's a mega opportunity for you guys. What are you seeing there? How are you bringing that all together? You've got NSX components, EUC components, you've got sort of security throughout your entire stack. How should we be thinking about that? >> Well, one of the announcements that I am most excited about at VMworld is the release of Carbon Black workload. 'Cause we said we're going to take those carbon black assets and we're going to combine it with workspace one, we're going to build it in NSX, we're going to make it part of Tanzu, and we're going to make it part of vSphere. And Carbon Black workload is literally the vSphere embodiment of Carbon Black in an agent-less way. So now you don't need to insert new agents or anything, it becomes part of the hypervisor itself. Meaning that there's no attack surface available for the bad guys to pursue. But not only is this an exciting new product capability, but we're going to make it free, right? And what I'm announcing at VMworld and everybody who uses vSphere gets Carbon Black workload for free for an unlimited number of VMs for the next six months. And as I said in the keynote, today is a bad day for cyber criminals. This is what intrinsic security is about, making it part of the platform. Don't add anything on, just click the button and start using what's built into vSphere. And we're doing that same thing with what we're doing at the networking layer, this is the last line acquisition. We're going to bring that same workload kind of characteristic into the container, that's why we did the Octarine acquisition, and we're releasing the integration of workspace one with Carbon Black client and that's going to be the differentiator, and by the way, Crowdstrike is doing well, but guess what? So are we, and right both of us are eliminating the rotting dead carcasses of the traditional AV approach. So there's a huge market for both of us to go pursue here. So a lot of great things in security, and as you said, we're just starting to see that shift of the industry occur that I promised last year in theCUBE. >> So it'd be safe to say that you're a cloud native and a security company these days? >> Yeah well, absolutely. And the bigger picture of us is that we're this critical infrastructure layer for the Edge, for the cloud, for the Telco environment and for the data center from every endpoint, every application, every cloud. >> So, Pat, I want to ask you a virtual question we got from the community. I'm going to throw it out to you because a lot of people look at Amazon and the cloud and they say, okay we didn't see it coming, we saw it coming, we saw it scale all the benefits that are coming out of cloud well documented. The question for you is, what's next after cloud? As people start to rethink especially with COVID highlighting and all the scabs out there as people look at their exposed infrastructure and their software, they want to be modern, they want the modern apps. What's next after cloud, what's your vision? >> Well, with respect to cloud, we are taking customers on the multicloud vision, right, where you truly get to say, oh, this workload I want to be able to run it with Azure, with amazon, I need to bring this one on-premise, I want to run that one hosted. I'm not sure where I'm going to run that application, so develop it and then run it at the best place. And that's what we mean by our hybrid multicloud strategy, is being able for customers to really have cloud flexibility and choice. And even as our preferred relationship with Amazon is going super well, we're seeing a real uptick, we're also happy that the Microsoft Azure VMware service is now GA. So there in Marketplace, are Google, Oracle, IBM and Alibaba partnerships, and the much broader set of VMware Cloud partner programs. So the future is multicloud. Furthermore, it's then how do we do that in the Telco network for the 5G build out? The Telco cloud, and how do we do that for the Edge? And I think that might be sort of the granddaddy of all of these because increasingly in a 5G world, we'll be enabling Edge use cases, we'll be pushing AI to the Edge like we talked about earlier in this conversation, we'll be enabling these high bandwidth low latency use cases at the Edge, and we'll see more and more of the smart embodiment smart city, smart street, smart factory, the autonomous driving, all of those need these type of capabilities. >> Okay. >> So there's hybrid and there's multi, you just talked about multi. So hybrid are data, are data partner ETR they do quarterly surveys. We're seeing big uptick in VMware Cloud on AWS, you guys mentioned that in your call. We're also seeing the VMware Cloud, VMware Cloud Foundation and the other elements, clearly a big uptick. So how should we think about hybrid? It looks like that's an extension of on-prem maybe not incremental, maybe a share shift, whereas multi looks like it's incremental but today multi is really running on multiple clouds, but a vision toward incremental value. How are you thinking about that? >> Yeah, so clearly, the idea of multi is truly multiple clouds. Am I taking advantage of multiple clouds being my private clouds, my hosted clouds and of course my public cloud partners? We believe everybody will be running a great private cloud, picking a primary public cloud and then a secondary public cloud. Hybrid then is saying, which of those infrastructures are identical, so that I can run them without modifying any aspect of my infrastructure operations or applications? And in today's world where people are wanting to accelerate their move to the cloud, a hybrid cloud is spot-on with their needs. Because if I have to refactor my applications, it's a couple million dollars per app and I'll see you in a couple of years. If I can simply migrate my existing application to the hybrid cloud, what we're consistently seeing is the time is 1/4 and the cost is 1/8 or less. Those are powerful numbers. And if I need to exit a data center, I want to be able to move to a cloud environment to be able to access more of those native cloud services, wow, that's powerful. And that's why for seven years now, we've been preaching that hybrid is the future, it is not a way station to the future. And I believe that more fervently today than when I declared it seven years ago. So we are firmly on that path that we're enabling a multi and hybrid cloud future for all of our customers. >> Yeah, you addressed that like Cube 2013, I remember that interview vividly was not a weigh station I got hammered answered. Thank you, Pat, for clarifying that going back seven years. I love the vision, you always got the right wave, it's always great to talk to you but I got to ask you about these initiatives that you're seeing clearly. Last year, a year and a half ago, Project Pacific came out, almost like a guiding directional vision. It then put some meat on the bone Tanzu and now you guys have that whole cloud native initiative, it's starting to flower up, thousands of flowers are blooming. This year, Project Monterey has announced. Same kind of situation, you're showing out the vision. What are the plans to take that to the next level? And take a minute to explain how Project Monterey, what it means and how you see that filling out. I'm assuming it's going to take the same trajectory as Pacific. >> Yeah, Monterey is a big deal. This is re-architecting the core of vSphere and it really is ripping apart the IO stack from the intrinsic operation of vSphere and the SX itself because in many ways, the IO, we've been always leveraging the NIC and essentially virtual NICs, but we never leverage the resources of the network adapters themselves in any fundamental way. And as you think about SmartNICs, these are powerful resources now where they may have four, eight, 16 even 32 cores running in the SmartNIC itself. So how do I utilize that resource, but it also sits in the right place? In the sense that it is the network traffic cop, it is the place to do security acceleration, it is the place that enables IO bandwidth optimization across increasingly rich applications where the workloads, the data, the latency get more important both in the data center and across data centers, to the cloud and to the Edge. So this re-architecting is a big deal, we announced the three partners, Intel, NVIDIA Mellanox and Pensando that we're working with, and we'll begin the deliveries of this as part of the core vSphere offerings beginning next year. So it's a big re-architecting, these are our key partners, we're excited about the work that we're doing with them and then of course our system partners like Dell and Lenovo who've already come forward and says, "Yeah we're going to to be bringing these to market together with VMware." >> Pat, personal question for you. I want to get your personal take, your career going back to Intel, you've seen it all but the shift is consumer to enterprise and you look at just recently Snowflake IPO, the biggest ever in the history of Wall Street. It's an enterprise data company, and the enterprise is now relevant. The consumer enterprise feels consumery, we talked about consumerization of IT years and years ago. But now more than ever the hottest financial IPO enterprise, you guys are enterprise. You did enterprise at Intel (laughing), you know the enterprise, you're doing it here at VMware. The enterprise is the consumer now with cloud and all this new landscape. What is your view on this because you've seen the waves, have you seen the historical perspective? It was consumer, was the big thing now it's enterprise, what's your take on all this? How do you make sense of it because it's now mainstream, what's your view on this? >> Well, first I do want to say congratulations to my friend, Frank and the extraordinary Snowflake IPO. And by the way they use VMware, so I not only do I feel a sense of ownership 'cause Frank used to work for me for a period of time, but they're also a customer of ours so go Frank, go Snowflake. We're excited about that. But there is this episodic to the industry where for a period of time, it is consumer-driven and CES used to be the hottest ticket in the industry for technology trends. But as you say, it has now shifted to be more business-centric, and I've said this very firmly, for instance, in the case of 5G where I do not see consumer. A faster video or a better Facebook isn't going to be why I buy 5G. It's going to be driven by more business use cases where the latency, the security and the bandwidth will have radically differentiated views of the new applications that will be the case. So we do think that we're in a period of time and I expect that it's probably at least the next five years where business will be the technology drivers in the industry. And then probably, hey there'll be a wave of consumer innovation, and I'll have to get my black turtlenecks out again and start trying to be cool but I've always been more of an enterprise guy so I like the next five to 10 years better. I'm not cool enough to be a consumer guy and maybe my age is now starting to conspire against me as well. >> Hey, Pat I know you got to go but a quick question. So you guys, you gave guidance, pretty good guidance actually. I wonder, have you and Zane come up with a new algorithm to deal with all this uncertainty or is it kind of back to old school gut feel? >> (laughing) Well, I think as we thought about the year, as we came into the year, and obviously, COVID smacked everybody, we laid out a model, we looked at various industry analysts, what we call the Swoosh Model, right? Q2, Q3 and Q4 recovery, Q1 more so, Q2 more so. And basically, we built our own theories behind that, we tested against many analyst perspectives and we had Vs and we had Ws and we had Ls and so on. We picked what we thought was really sort of grounded in the best data that we could, put our own analysis which we have substantial data of our own customers' usage, et cetera and picked the model. And like any model, you put a touch of conservatism against it, and we've been pretty accurate. And I think there's a lot of things we've been able to sort of with good data, good thoughtfulness, take a view and then just consistently manage against it and everything that we said when we did that back in March has sort of proven out incrementally to be more accurate. And some are saying, "Hey things are coming back more quickly" and then, "Oh, we're starting to see the fall numbers climb up a little bit." Hey, we don't think this goes away quickly, there's still a lot of secondary things to get flushed through, the various economies as stimulus starts tailoring off, small businesses are more impacted, and we still don't have a widely deployed vaccine and I don't expect we will have one until second half of next year. Now there's the silver lining to that, as we said, which means that these changes, these faster to the future shifts in how we learn, how we work, how we educate, how we care for, how we worship, how we live, they will get more and more sedimented into the new normal, relying more and more on the digital foundation. And we think ultimately, that has extremely good upsides for us long-term, even as it's very difficult to navigate in the near term. And that's why we are just raving optimists for the long-term benefits of a more and more digital foundation for the future of every industry, every human, every workforce, every hospital, every educator, they are going to become more digital and that's why I think, going back to the last question this is a business-driven cycle, we're well positioned and we're thrilled for all of those who are participating with Vmworld 2020. This is a seminal moment for us and our industry. >> Pat, thank you so much for taking the time. It's an enabling model, it's what platforms are all about, you get that. My final parting question for you is whether you're a VC investing in startups or a large enterprise who's trying to get through COVID with a growth plan for that future. What does a modern app look like, and what does a modern company look like in your view? >> Well, a modern company would be that instead of having a lot of people looking down at infrastructure, the bulk of my IT resources are looking up at building apps, those apps are using modern CICD data pipeline approaches built for a multicloud embodiment, right, and of course VMware is the best partner that you possibly could have. So if you want to be modern cool on the front end, come and talk to us. >> All right, Pat Gelsinger, the CEO of VMware here on theCUBE for VMworld 2020 virtual, here with theCUBE virtual great to see you virtually, Pat, thanks for coming on, thanks for your time. >> Hey, thank you so much, love to see you in person soon enough but this is pretty good. >> Yeah. >> Thank you Dave. Thank you so much. >> Okay, you're watching theCUBE virtual here for VMworld 2020, I'm John Furrier, Dave Vellante with Pat Gelsinger, thanks for watching. (gentle music)

>> Narrator: From around the globe, it's The Cube, with digital coverage of VMworld 2020, brought to you by VMware and its ecosystem partners. >> Okay, welcome back everyone to The Cube's coverage of VMworld 2020 virtual. I'm John Furrier with Dave Vellante, and Stu Miniman, who's covering VMworld virtually from our Cube virtual studios, where we've been doing The Cube coverage for the past six months virtually. Guys, let's wrap up VMworld virtual this year, different, not in person, still packed with content. Again, they tried to replicate and they did a good job of bringing that site together. They didn't overdrive the platform. They have content, but still a big gap in not having it in person. A lot of action on Twitter. Certainly, we've been commenting on cube.net site, and getting all these videos out. But guys, let's wrap up VMworld this year. Great show. Again, content's virtual. So a lot of asynchronous content. The cloud city, lot of solution demos of obviously, Cube commentary on our side. But Dave, what's your reaction to the past few days? >> Well I thought, you know, as always, VMware has some highlight folks show up to their keynotes. John Donahoe, who knows a little bit about the enterprise 'cause he did a couple of years stinted service now, then he jumped to back to his consumer roots, went to Nike. Interestingly, the service now, the company left is, they're approaching $100 billion evaluation now. They're zoning in on Nike. Of course, and then, you had the Nvidia CEO. Everybody does business with Nvidia. And so, that's kind of a check box, but they actually get the CEO to come to your event. I think it's a big deal. So as always, people want to do business with VMware 'cause they got half a million customers, and I thought that was a pretty impressive gets. >> And the CEO from Nvidia, Jensen Huang. I mean, you couldn't ask for a timely guest because of the news with them buying Arm. >> Huge. >> Nvidia just is a key player in the chip game right now. >> Yeah, and I think too, you know, some of the announcements VMware made around Edge and even Telco, Nvidia is going to be huge there in Arm. You know, we think that that is going to be a really new and interesting AI inferencing at the edge. There were some AI announcements, so very strategic. Again, you know, VMware does a great job of identifying those waves and driving engineering to drive customer value. >> Stu, I want to get your take on the announcements, and Dave, you can chime in too 'cause as we saw the Snowflake IPO, to me, this is, this basically rings the bell for the worldwide global computer industry around cloud native. This, to me, puts the full stake in the ground, cloud native. VMware made some bets, Stu. We go back and look at Gelsinger's moves, and Sanjay's move, and the team's moves. Your thoughts on the announcement there, networking, a lot of multicloud, but it's all about operational cloud native, your thoughts. >> Yeah, well John, cloud's so important, you know? Let me make an analogy here. We all talked about, if this pandemic had happened, enter 15 years ago and we were stuck at home without our Netflix, without our Zoom, without our connectivity, where would we be? John, when we started coming to the VMworld show in 2010, it was a huge amount of gear sitting in Moscone and the amount of trucks that needed to deliver all of that. Of course today, it's all built in the cloud, doing those labs are so much easier, and learning and enabling these technologies can be done so much easier. So I think that that really puts a highlight on where we are with the technology and you know, that was one of the key things that we saw in that announcement. So we're VM, we're fit with the big HyperCloud players, how they're hoping to extend, what they have in a hybrid environment from a management standpoint, starting to push out to Edge Solutions, VMware has strong strength with service providers. So there's a lot of things there to dig into, and that we wouldn't have had if we were talking about this five years ago. >> I just love the glam of the Nvidia 'cause the AI angle there is super important, but I'm, you know, I love the Project Monterey, Stu because it kind of digs out VMware trying to set the agenda on Architecture. This is the end-to-end, you know, whether it's the edge of the network from a work perspective person. Even in space, a purpose-built devices at the edge still need to be updated by software. This is a huge architectural shift. Do you think VMware's got the right moves here? >> Well John, VMware's got some great strength in the service provider environment, and of course, you know, great strength in data center. They've been growing their cloud capabilities. So Edge is still a little bit of a jump ball, as we'd like to say. Absolutely like some of the things that they're doing, strong partnerships. We talked about Nvidia, absolutely one of the companies you want to be closely working to to be successful at the edge. So I like what I'm seeing, but as with anything with VMware, until they have thousands of customer doing it, it's still a little bit early for me to have any final say. >> Stu, 30 seconds left. >> Yeah- >> Tanzu portfolio and partnerships. >> Yeah, so the critique I'd have, John, is VMware have been trying for years to go deeper with developers and they've made some progress, but they haven't done enough. They have moved doing more with open source, they've made a number of acquisitions in the space, but it's all about developers, it's about building those apps. If you talk about a hybrid message, you know, Microsoft, nothing about bit but building new apps. VMware is starting to get there, but they still have work to do. >> Guys, great job, 2020 is in the books. The Cube is via virtually. And again, 10 years ago, John Troyer, Eric Nielsen, Robin Matlock was our partners. Now, we're going with the next generation with VMware the next 10 years. Unpredictable, we'll see how it goes. Thanks for joining us today, appreciate it. Okay, thanks everyone for watching. Cube coverage of VMworld 2020. I'm John Furrier, with Stu Miniman, and Dave Vellante. Thanks for watching.