>> Live from Las Vegas, it's the Cube covering Dell Technologies world 2019. Brought to you by Dell technology and its ecosystem partners. >> Welcome back to Las Vegas, Lisa Martin with John Furrier. You're watching us on the cube live, from our first day of three days of coverage of Dell technology world 2019. We're pleased to welcome Matt Hausmann, senior consultant in Product Marketing from Dell EMC to the cube. Matt, thank you so much for joining us this afternoon. >> Thank you, really appreciate finally getting a chance to get up here with you guys. >> Yeah, so there's only about 15,000 or so people here. Small gathering amongst friends. >> I say, yeah the 15,000 are my closest friends. >> Yes and about 4000 of your closest partners. >> Yes, yeah. >> So this morning kicked off with the keynote, great energy, I think one of my favorite parts was Michael Dell walking out to a queen song that really got me, that got my attention, but a lot of collaboration between the VMware Dell, Dell EMC, Microsoft, great conversations, all talking about digital transformation, which of course, is all made possible in part by data. >> Absolutely. >> There's so much data these days that it's just not possible by humans anymore. Talk to us about the data avalanche, from your perspective in product marketing, what are you seeing? What are you hearing from customers? >> Yeah, so I come from our unstructured data solutions, business, you know, so we really focus and specialized and we're a lot of this data growth is coming from images, audio, video, free text, things like that. We're dealing with that day in and day out. I mean, you hit the nail on the head. I mean, we were basically looking at about 80% of our data is no longer human possible. So to really take advantage of that and move forward of the digital transformation. And that's what we're really seeing new techniques, like artificial intelligence, machine learning, deep learning, are really helping us ask new questions about data and actually get more value from the data. >> Michael Dell said data is the heartbeat of digital transformation on keynote, one of the sound bites I liked how are customers evolved into it, because we saw the big data industry, you know, the Hadoop gone bigger and bigger data warehouses been around for a while, but now data with AI has to be available has to be addressable in new kinds of ways. Where's the customer on that spectrum of the new way to use data? And what is the new way to use data? >> Yeah, I was actually giving a presentation today on data strategy for AI specifically, and the numbers we're looking at about a third of our customers, have dip their toe into AI. So truly, they're realizing there's value in that data, they're realizing these techniques or new techniques are available to them, computes gotten faster, storage, there's been a lot of innovation there. So they can do a lot more with the data than they ever could before. But about a third of our customers have dip their toe and the majority of our customers are talking about doing it today, or in the next couple of years. >> And they want insights, that's a low hanging fruit. Now you got IoT devices with data strategy around IoT, data center on premise activities as data involved. >> Absolutely, I mean, these are all new, you know, basically sources of data for us. So when I look at it from the storage for data perspective, as we just have data coming in from everywhere, and IoT with the low cost sensors and video, this again, is our sweet spot. I mean, data is growing up into the the zettabytes. And now people want to be able to do something with it. >> Unstructured data and structured data is there a different group that you guys aren't wrestling with? Who's got you know, more data? Because object stores great front structured and unstructured data. >> Yeah, I don't think we're wrestling with each other. I think what we're doing is we're figuring out how to deliver an end to end solution for our customers. We've got great products, great innovation on different sides. I mean, I don't want to get too technical interview today. But I mean, as we go through kind of the analytic pipeline for AI, from data preparation, to model development and training and then actually going out and scoring it in real time and doing inference. There's different requirements. So I'd say we're all in the sandbox together and we all have a place and the seat at the table there so. >> Do customers dipping their toe into AI essential to have a partner like Dell EMC and Dell Technologies that understands that's done that as well... >> Sure sure. >> In terms of dipping their toes in have to have a modern IT infrastructure in order to do that. What are some of the things that Delhi MC is doing to infuse AI capabilities into your storage servers, data protection, ec cetera? >> Sure sure. Yeah, I think we're really doing you know, we're bringing a lot of innovation to bring AI to the forefront. So you know, you already mentioned kind of some of the areas there on the storage side, I guess I'd call out one of our products are all flash scale out NAS platform called Isilon. What we're really doing there is trying to simplify this delivery of data for AI. So high, you know, high performance, high bandwidth at massive scale. And then we're partnering with you know, the accelerated compute vendors. The Nvidia, the intel. We're bringing that into our power edge servers delivering 10x delivering 100x performance. And I think the other piece that we're doing is, you know, people have been dipping their toes, that means that we've been doing PLC we've been doing this work for years and years. So we're taking these innovations, we're taking those learnings and we're packaging those up into our ready solutions into our reference architectures really making AI simple, making it accessible. And I think more than anything else is making it faster. So that time to value that we we've thought through all the things you need to think through in that space, and give you a place to start where we can really just start focusing on artificial intelligence on the art of the possible. >> Now I want to get your thoughts on democratization of data. Before that many Cube interviews with other interviews, democratizing data, letting people wrangle it not to being an expert or computer science major. So demography making it easier is one thing. Yeah, you see deep learning trends. In AI, you see machine learning. >> Yeah. >> A machine learning. I mean, it's not democratize yet but it's getting there. How do you see this democratization trend where it's just going to be kind of a natural business practice to deal with either large amounts of streaming data or any data? >> Well, I think that's been going on for a long time actually come from the data warehousing space. We've been talking about that for a decade and maybe 15 years, right as a really a democratizing data, marketizing analytics, be data driven. I think it's really progressed here as Hadoop came on, and the Big Data space, I think that's really paved the way now for artificial intelligence. The analytics software platform that's out there, it's getting easier to use. It's integrated into the full solutions. And it's now optimized. So especially with the advent of GPUs, these tool kits are now optimized for 1, 4, 8, 16 GPUs so you can really, you know, harness the power of the data of the, you know, of the compute to do more with it. So I think that's it's going to continue and I think it's really, it's making easier and simpler. >> Can you give an example of the Cube, I love... I learned so much doing these interviews. But start small get bigger from there always seems to be a best practice. Give a couple examples for people watching on where they can start small with data. I means data can be big too, but I means small but small project scope wise, what's good to get their arms around these data projects? What's a great starting point to get really put the foot in the water and then ultimately full immersion? >> Right, so I guess I wouldn't just start people with a data project. What I would ask them is what's the business problem they're trying to solve? So if they can identify what their need is, so what's my problem? What data do I have available? And then I would go give them a suggestion and maybe what the right solution might be. But I also like what you kind of mentioned the start small and get bigger. So we really focus on the scale outside of it. So, all of the analytic and AI solutions that I work on, you know, we've decoupled compute and storage that was basically mentioned, let you grow either your data or your compute power independently. And so that really lets us right size solution for whatever that business case whatever that use-- >> For flexibility too for the customer, right? >> Absolutely, absolutely cos it's not a one size fits all. That's why I'm hesitant to say hey, this is where you start it's like no tell me what your problem is. Tell me what data you have-- >> It's different for customers, no general purpose answer pretty much really. >> Absolutely, absolutely. And so when we have these flexible platforms, where we've thought through especially in the analytics and AI space, the requirements that you might need to have, we have, you know, partnerships in the space to give you the end to end solution is you can come to us and we can really kind of help your business grow, help direct you where you want to go. >> If you look into your magic crystal ball or ask the magic eight ball, what's the time scale by which customers who are just dipping their toes in really need to get on the AI bandwagon to accelerate their business and not fall behind and actually lose opportunities. Is that 12 months, 24 months, two years, five years? >> Well, I think it's an easy answer. And the answer is now. They should be they should be going out and being creative they should be going out and taking chances. And that's why I look at it right now is, with the innovations we talked to, from you know about compute, about networking, about storage about the analytics software is you have access to more data than you've ever had before. And you have all these tools to do more with it than you ever have before. So now is the right time. And I think we are starting to see some separation of companies already. Those that are going and really embracing AI, and those are kind of putting enough at at arm's length. So I think there's a little bit of a separation, but I don't think it's too late. You might have to rush a little to catch up but now is the right time. Like I said, you've never had more data you've ever had better tools. >> What's the coolest thing you've seen with people using data in a very interesting way? >> So the the coolest thing that I've had first person experience with was actually Sofia the robot. You may have seen her. She's been on Jimmy Fallon. She's done a bunch of keynotes and other things like that. So we had her our magic of AI series two weeks ago in New York City, I got to spend two full days. So Sophia is this humanoid robot, right, she does natural language processing, image detection. She's basically like talking to a real person. So it took me about a half day before, I wasn't just staring at her the whole entire day. (all laugh) This is a little awkward. But I mean, this is really they've taken multiple kind of advanced analytics techniques, AI techniques, and then put them into the kind of the art of the possible. This is where we can get to and I'm not saying every company, every customer, that's a use case that they want to develop. But the fundamental building blocks to build her image detection, natural language processing, right, just about every company out there can take those pieces and then apply it their business. >> So you point the creative opportunities the time is now you can actually solve some of the challenges that could be opportunities, and this cool examples of like the robot, which is great, you know, interesting use case, but there's other you know, business use cases like to drive revenue for instance, or change the business model of a company. >> Right, right. So you know, healthcare is a big space, I think we hear about a lot, but also where we work with customers a lot. So folks that are you know, taking an MRI image, for instance, right? Use image detection to say, hey, what do we have in this image? Hey, maybe you have cancer, use image classification. Well, what what type of cancer do I have? You do that segmentation? Well, how advanced is that cancer? And then you add prediction on top of that, what's the best outcome? What's personalized to me, what's the best treatment. And so we're now able to shrink that down from months or quarters with a lot of guesswork now in the hours and days to give you your personalized, you know, medicine to give you this-- >> That's real value right here. That's a great example of tech for good. >> And when I talk about AI, I always try to bring in the human part of it. >> Yeah. >> I think that, you know, people say AI is magic. Well, at the end of the day, it's some advanced math on a lot of data, right? But the value we're getting, the data that we can actually access there, the questions that we can ask of our data, have a really human impact can help us in our daily lives. And yeah, absolutely, there's going to be a business impact with it as well. >> I think that's a great example also the earlier point about separating compute and storage, because then you can then again, as you said, right size, you can put a data lake out there, if you want, move stuff in and out and deal with it. This is where the value of data, not some static, okay, made an architectural decision. See in 10 years, yeah, it's always fluid. >> So the static is huge, right? If you architect for static, you're not going to be able to deal with the realities of analytics. I'm like 19 years into the data analytic space, the data is always going to grow, the use cases are going to change, they're going to get more complex, and people are going to want it faster. So you really need a flexible architecture that can deal with those nuances and just know that you need to architect for change. >> See, I mentioned when we started, we're here with about 15,000 of our closest friends. What are some of the things that you've heard in your session today that maybe you were surprising to you, insightful, maybe thought thought provoking about how your customers and prospects and partners are looking at AI, especially the human AI collaboration? >> Sure, sure. So in my session, I was kind of surprised people really kind of went into depth with with one of our customer use cases, kind of asking very specifically, you know, what about the outliers and things like that. So what that tells me is that people are really digging into this, and they are trying to figure out how to figure out how to apply it to their business. And so this different than some of the sessions from a couple years ago, where AI was just as, you know, this futuristic thing, as we're having real questions there. And then I was in our emerging technology session this afternoon. And a big part of it was really connected to the trust, the trust between humans and machines, and then also the trust between machines and humans. So, you know, that there's a lot of thought provoking things going on right now. And I think where I've been surprised at is really the acceptance of AI into our lives, right? We all have our cell phones in our pocket. You're no longer lost unless you want to be. You always know where to go out to eat, your car can drive itself. Your house can clean itself, right? We've been slowly accepting these applications. >> For the house part I want to get to that one. I don't have that yet. >> I want that too. >> The zumba, is that the name of the robot? (all laugh) >> Matt, thank you so much for having a lively energetic conversation with John and me about AI. And as you say, the time is now. >> Right. >> We appreciate your time. >> Nice to meet you. Thanks for having me >> Nice to meet you too. >> All right. >> For John Furrier, I'm Lisa Martin. You're watching the Cube live from Dell technology world's 2019 from Vegas. Thanks for watching. (techy music)