 From the Fairmont Hotel, in the heart of Silicon Valley, it's theCUBE, covering when IoT met AI, the intelligence of things. Brought to you by Western Digital. Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Jose at the Fairmont Hotel in an event called, when IoT met AI, the intelligence of things. You've heard about the internet of things and IoT intelligence of things. It's IoT, it's AI, it's AR. All this stuff is really coming to play. It's a very interesting space. Still a lot of startup activities. Still a lot of big companies making plays in the space. So we're excited to be here and really joined by our host. Big thanks to Western Digital for hosting this event with Reed Wright Labs, Dave Tang. He's got a newly promoted since last week's quote, the SVP of Corporate Marketing and Communications for Western Digital. Dave, great to see you as usual. Oh, great to be here. Thanks. So I don't think the need for more storage is going down anytime soon. That's kind of my takeaway. No, no, yeah. This wall of data just keeps growing. Yeah, I think the term we had yesterday at the AG event that we are at, also sponsored by you, is really the flood of data using an agricultural term. But it's pretty fascinating as more and more and more data is not only coming off the sensors, it's coming off the people. It's been used in so many more ways. That's right, yeah. We see it as a virtuous cycle, right? You create more data. You find more uses for that data to harness the power and unleash the promise of that data. And then you create even more data. So we're in that virtuous cycle of creating more and finding more uses of it. Right. And one of the things that we find interesting that's related to this event with IoT and AI is this notion that data is falling into two general categories, right? There's big data and there's fast data. So big data I think everyone is quite familiar with by this time, these large aggregated lakes of data that you can extract information out of, look for insights and connections between data, predict the future and create more prescriptive recommendations, right? Right. And through all of that, you can gain algorithms that help to make predictions or can help machines run based on that data. So we've gone through this phase where we focused a lot on how we harness big data, but now we're taking these algorithms that we've gleaned from that and we're able to put them in real-time applications. Right. And that's sort of in the birth of fast data. It's been really interesting. Right, streaming data, right? We cover Spark Summit, we cover Flink, a new kind of open source project that came out of Berlin that's kind of, some people would say the next generation of Spark and the other thing, you know, good for you guys is it used to be, not only was it old data, but it was a sampling of old data, right? Now on this new data and the data stream, it's all of the data, right? So, and I would actually challenge, I wonder if that separation, as you described, will stay because I got to tell you, the last little drive I bought just last week was an SSD drive, you know, one terabyte. I needed some storage and I had a choice between spinning disk and not, and I went with the flash. I mean, because what's fascinating to me is the second order benefits that we keep hearing time and time and time again once people become a data-driven enterprise are way more than just kind of that top level thing that they thought. Exactly, yeah, and that's sort of that virtuous, like you get a taste and you learn how to use it and then you want more. Right, right. So, yeah, and that's the great thing about the breadth of technologies and products that Western Digital has is from the solid state products, the higher performance flash products that we have to the higher capacity helium-filled drive technologies as well as devices going on up into systems. We cover this whole spectrum of fast data and big data. I'll give you an example. So credit card fraud detection is an interesting area, right? Billions of dollars potentially being lost there. Well, to learn how to predict when transactions are fraudulent, you have to study massive amounts of data, right? Billions of transactions and so that's the big data side of it and then as soon as you do that, you can take those algorithms and run them in real time. So as transactions come in for authorization, those algorithms can determine before they're approved, that one's fraudulent and that one's not. Save a lot of time and processing for fraud claims. So that's a great example of, once you learn something from big data, you apply it to the real-time realm and it's quite valuable, right? And then that spawns you to collect even more data because you want to find new applications and new uses. And then too, kind of this wave of computing back and forth from the shared services computer then the desktop computer now, it's back to the cloud and then now with IoT, right? It's all about the edge and at the end of the day, it's going to be application-specific, what needs to be processed locally, what needs to be processed back at the computer and then all the different platforms. We were again at a navigation for autonomous vehicle show. Who knew there was such a thing that small? And even the attributes of the storage required in the ecosystem of a car, right? In the environmental conditions, that's what I'm looking for. Completely different new opportunity, kind of new class of hardware required to operate in that environment. And again, that still combines cloud and edge, sensors and maps. So just, yeah, absolutely. I don't think the demand's going down, Dave, I think you're in a good spot. You're absolutely right. And even though we try to simplify it into fast data and big data and core and edge, what we're finding is that applications are increasingly specialized and have specialized needs in terms of the type of data. Is it large amounts of data? Is it streaming? What are the performance characteristics? And how is it being transformed? What's the compute aspect of it? And what we're finding is that the days of general purpose compute and storage and memory platforms are fading. And we're getting into environments with increasingly specialized architectures across all those elements, compute, memory and storage. So that's what's really exciting to be in our spot in the industry is that we're looking at creating the future by developing new technologies that continue to fuel that growth even further and fuel the uses of data even further. And fascinating just the ongoing cadence of Moore's Law which I know it's not, you're not making microprocessors but I think it's so powerful. Moore's Law really is a philosophy as opposed to an architectural spec, right? Just this relentless pace of innovation and you guys just continue to push the envelope. So what are your kind of priorities? I can't believe we're halfway through 2017 already but for kind of the balance of the year, kind of what is some of your top of mind things. I know it's exciting times. You go into the merger, the company is in a great space. What are your kind of top priorities for the next several months? Well, so I think as a company that has gone through serial acquisitions and integrations, of course, we're continuing to drive the transformation of the overall business. And fun stuff, right? Not the unique great stuff. Right, that is the hardware, the ERP systems, right? But yeah, the fun stuff includes pushing the limits even further with solid-state technologies with our 3D NAN technologies. We were leading the industry in 64-layer 3D NAN and just yesterday we announced 96-layer 3D NAN. So pushing those limits even further so that we can provide higher capacities in smaller footprints at lower power in mobile devices and out on the edge to drive all these exciting opportunities in IoT and AI. It's crazy. Yeah, it is. It's crazy. It would be terabyte SD cars, terabyte microSD cars. I mean, the amount of power that you guys pack in these smaller and smaller packages, it's magical. I mean, it's absolutely magic. Right, yeah, and the same goes on the other end of the spectrum, right, with high-capacity devices or healing-filled drives or getting higher in capacity, 10, 12, 14 terabyte high-capacity devices for that big data core that all the data has to end up at some point. So we're trying to keep a balance of pushing the limits on both ends. All right, well, Dave, thanks for taking a few minutes out of your busy day and congratulations on all your success. Great to be here. All right, he's Dave Tang from Western Digital. He's changing your world, my world, and everyone else's. We're here at San Jose. You're watching theCUBE. Thanks for watching.