 Live from San Jose in the heart of Silicon Valley. It's theCUBE, covering DataWorks Summit 2018. Brought to you by Hortonworks. Welcome back everyone to theCUBE's live coverage of DataWorks here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We are joined by Mike McNamara. He is the Senior Product and Solutions Marketing at NetApp. Thanks so much for coming on theCUBE. Thanks for having me. First timer, so this is very exciting. Happy to be here. Welcome. So before the cameras were rolling, we were talking about how NetApp has been in this space for a while, but is really just starting to be recognized as a player. So talk a little bit about your company's evolution in terms of data. Sure. So the whole analytics space, something NetApp was in a long time ago and then sort of get out of it. And then over the last several years, we've gotten back in and we recognize it's a huge opportunity for data storage, data management. You look at IDC data massive, massive market, but the opportunity for us is like, you know what, they're mainly using a direct attach storage model where compute and storage is tied together. And now with data just exploding and growing like crazy, it's always been growing, but now it seems like it's just growing like crazy now. That and customers wanting to have data on-prem, but also being able to move it off to the cloud. We're like, hey, this is a great opportunity for us to come in with a solution that's an external storage solution that can come in and show them the benefits of having them more reliable, have an opportunity to move their data off to the cloud. We've got great solutions with that. So it's gone well, but it's been a little bit different. Like at this show, a lot of the people that data scientists, data engineers, some of whom know us, some still don't like, so NetApp, what do you guys do? And so it's a little bit of an edgy education because it's not our traditional buyer, if you will. We look at them as influencers, but certainly want to influence them. We traditionally have sold to say, vice president of infrastructure as an example, or maybe a director of storage admin, but most of those folks are not here. So this is just kind of a new market for us that we've been making inroads. How do data scientists or do they influence the purchase of storage solutions or data management solutions? Sure, so they want to have access to their data and they want to be able to analyze it quickly and effectively want to make sure it's always available, it's always at their fingertips, so to speak. Well, we can help them by giving them very fast, very reliable solutions. And especially with our software, if they want to do, for example, just a virtual clone of that data and just do some testing on that without impacting their production data, we can do that in a staff. We can make their lives a lot easier, so we can show them how, hey, Mr. Data Scientist, we can make your life a little easier. Or miss Data Scientist. Or miss, and we were talking about that. A lot of women in this field, more than we realize, and they're great. So we can help you do your job better. And then that him or her can then influence who's making the purchase decisions. Yeah, training sets, test sets, validation sets of data for the machine learning and analytics development pipeline, yes. You need a solid storage infrastructure to do it right. Absolutely. So when you're getting inside the head of your potential buyer here, the VP of infrastructure or data admin, what is it that you're hearing from those people most? What are their concerns? What keeps them up at night? And when, where do you come in? Yeah, so one of the concerns is oftentimes, hey, how do I, do you have a cloud story? You connect it to the cloud. How, I'm doing things on-prem now, but is there a path? So that's a big one. And we net up pride ourselves on being the most cloud-connected, all-flash storage in the industry. So that's a big, big focus, big push for us. If you saw our marketing, it's data authority for the hybrid cloud. So we really honestly do, whether it's with Google or Azure or AWS, you know, our software runs in those environments. It also runs on-premises, but because it's the same on-tap software, we can move data between those environments. So we get a real good story. So we can, you know, boom, check the box. We got you covered if you want to utilize the cloud. And I think the next piece of that is just from protecting their data. You know, just, again, I said data is just growing so much. You know, I want to make sure it's always available. And it's, we can back it up and all that. And that's been a core, core strength versus like a lot of these traditional solutions they've been using, these direct-attached models. You know, they just don't have anywhere near the enterprise grade data protection that NetApp has always prided itself on over many decades now. And so we can help them do that. And quite honestly, you know, a lot of people think, well, you know, you guys are, you're external storage. How do you compare versus direct-attached storage from a total cost? That's another one. I can tell you definitively, and we've got data to back it up from a total cost of ownership. Because of the fact that all the advantages we bring from an uptime and, you know, from RAID, but, you know, in a Hadoop environment, oftentimes there's three copies of data. Well, with our solution, we can piece of software where there's only one copy of data. So having three versus one is a big savings. But even what we do with the data, compressing it and compacting it, a lot of benefits. So we do have, honest to goodness, upwards to 50% better total cost of ownership versus a DAS model. Do you use machine learning within your portfolio? I'm hearing of more storage planning. Great question, yeah. Incorporating machine learning to automate or facilitate more of the functions in the data protection or data management lifecycle. Yeah, that's a great question. And we do use even that. So we've got a piece of software, what you call ActiveIQ is referred to as AceUpdata. You may have, that may ring a bell. But to answer your question, so we've got thousands and thousands of NetApp systems out there. And those customers that allow us, we have, think of it as kind of a call home feature where we're getting data back from all our installed customers. And then we will go and do predictive analytics and do some machine learning on that data. So then we can go back to those customers and say, hey, you know what, you've got this volume that's unprotected, you should protect this. Or, we can show them, hey, if you were to move that data off into a cloud environment, his maybe performance you would see. So we do do a lot of that predictive- Oh, predictive performance assessment. Or predictive, it sounds like there's anomaly detection in there as well. Anomaly as well, letting them know, hey, you know, it's time for this drive. It may fail on you. We're going to, let's ship you out a new drive now before it happens. So yeah, a lot of, from an analytics predictive analysis going on, and you know, it's a huge benefit to our customers, huge benefit. Oh yeah. I know you're also going, doing a push toward artificial intelligence. So I'd like to hear more about that. And then also, if there's any best practices that have emerged. Sure, sure. Yeah, so yes, that is a big, another big area. So it's kind of a logical progression from where we were, if you will, in the analytics space, data lakes, but now moving into artificial intelligence. It's always been around, but it's really taking more of a more prominent role. I mean, just a quick, fun fact. I read that, you know that the royal wedding that recently happened, you know that Amazon used artificial intelligence to help us, the TV viewer, identify who the guests were. Oh yeah, they were. So you know, it's like, it's everywhere, right? And so for us, we see that trend, ton of data that needs to be managed. And so we kind of look at it, you know, from the edge to the core to the cloud. Those three, if not pillars, but directional ways, taking data in from IoT sensors at the edge, bringing it into the core, doing training, and then the customer so chooses out to the cloud. So yeah, so it is a big, it's a big push for us now. And we're doing a lot with NVIDIA. Is it keep hot now with us? Really, so, you know, this is a bit futuristic, but I can see a role going forward for AI to look into large data volumes, like video objects, to find things like faces and poses and gestures and so forth and scenes, to use that actually, that intelligence to be able to reduce the data sets down, in other words, to reduce the redundant, or to deduplicate so that you can use less storage and then you can reconstruct the original video objects or whatever going forward. I mean, there's a potential use of AI within the storage efficiency. You're right, and that's again, like in the analytics space, how all our inline efficiency capabilities and data protection is very important and then being able to move the data off into the cloud if the customer so chooses or just wants to use the cloud. So yeah, so some of the same benefits from cloud connectivity, performance, and efficiency that analytics apply certainly to AI. You know, another fun fact too about AI, which might help us, you and I living in the Boston area, is that I've read IBM has a patent out to use AI in traffic signaling. So in conjunction with cameras to get AI and, you know, so hopefully that, you know, that works well, it could, you know, alleviate. We get traffic in D.R.A. So I want to check. Well, you got it maybe worse in D.Z., so yeah. I'd like to hear though, if you have any best practices with this moving into AI, how are you experimenting with it and how are you finding it used most efficiently and effectively? Yeah, so I think experience, well one way are eating our own dog food, so to speak, and that we're using it internally. We're using it on, you know, our customer's data as I was explaining to help, you know, look at trends and do analysis. So that's one. And then it's other things, just, you know, partnering with companies like NVIDIA as well and coming out with a joint solution together. So we're doing work with them on different solution areas. Great, great. Well, Mike, thanks so much for coming on theCUBE. Thanks for having me. But having you. You survived. Yes. We'll look forward to many more conversations. All right, here from NetApp. Thank you. You're very much in the game. Indeed, indeed. All right, thank you very much. I'm Rebecca Knight for James Cobilis. We will have more from the CUBE's coverage of data works coming up in just a little bit.