(upbeat electronic music) >> Announcer: Live, from Berlin, Germany it's theCUBE. Covering NetApp Insight 2017. Brought to you by NetApp. Welcome back to theCUBE's live coverage of NetApp Insight here in Berlin, Germany. I'm your host, Rebecca Knight, along with my co-host Peter Burris. We are joined by Deepak Visweswaraiah. He is the senior vice president for data fabric manageability at NetApp. Thanks so much for coming on the show, Deepak. Thank you. So let's talk about the data fabric, and why modern IT needs it to do what it needs to do. For acceleration. I think anyone attending the conference, I thought the keynote that happened yesterday Kenneth Corky from Economist actually talked about how data actually is growing. And then how much of that is becoming more and more important to companies. Not only just from an ability to be able to actually handle data, but how they make their decisions based on the amount of data that they have today. The fact that we have that technology, and we have the mindset to be able to actually handle that data, I think gives that unique power to customers who actually have that data. And within their capacity. So, if you look at it in terms of the amount of data growing and what companies are trying to do with that, the fact is that data is not all in one place, it's not all in one format, it's not all just sitting in some place. Right, in terms of the fact that we call it, you know, data being diverse, data being dynamic and then what have you. So, this data, for any CIO, if you talk to an IT organization and ask them in terms of do you even really know where all your data lives, they probably, you know, 80% of the time they don't know where it is all. And they do not know who is accessing what data. Do they actually really have the access or the right people accessing the right data? And then what have you. So, being able to look at all of this data in different silos that is there, to be able to have visibility across these, to be able to actually handle the diversity of that data, whether it is structured, unstructured, comes from, you know, the edges of the network, whether it is streaming, and different types of, you know, media for that matter, whether it is streaming, video, audios, what have you. With that kind of diversity in the data, and the fact that it lives in multiple places, how do you handle all of that in a seamless fashion? Having a ability to view all of that and making decisions on leveraging the value of that data. So, number one, is really to be able to handle that diversity. What you need is a data fabric that can actually see multiple end points and kind of bring that together in one way and one form with one view for a customer. That's the number one thing, if you will. The second thing is in terms of being able to take this data and do something that's valuable in terms of their decision making. How do I decide to do something with it? I think one of the examples you might have seen today for example, is that, we have 36 billion data points coming from our own customer base, that we bring back to NetApp, and help our customers to understand in the universe of the storage end points with all the data collected, we can actually tell them what may proactively tell them, what maybe going wrong what can actually they do better. And then how can they do this. This is really what that decision making capability is to be able to analyze. It's about being able to provide that data, for analytics to happen. And that analytics may happen whether it happens in the cloud, whether it happens where the data is, it shouldn't really matter, and it's our responsibility to provide or serve that data in the most optimized way to the applications that are analyzing that data. And that analysis actually helps make significant amount of decisions that the customers are actually looking to. The third is, with all of this that is underlying infrastructure that provides the capability to handle this large amount of data, not only, and also that diversity that I talked about. How do you provide that capability for our customers, to be able to go from today's infrastructure in their data center, to be able to have and handle a hybrid way of doing things in terms of their infrastructure that they use within their data center, whether they might actually have infrastructure in the cloud, and leveraging the cloud economics to be able to do what they do best, and, or have service providers and call locators, in terms of having infrastructure that may be. Ability to be able to seamlessly look all of that providing that technology to be able to modernize their data center or in the cloud seamlessly. To be able to handle that with our technology is really the primary purpose of data fabric. And then that's what I believe we provide to our customers. So, people talk about data as an asset. And folks talk about what you need to ensure the data becomes an asset. When we talk about materials we talk about inventory we talk about supply chain, which says there's a linear progression, one of the things that I find fascinating about the term fabric even though there's a technical connotation to it, is it does suggest that in fact what businesses need to do is literally weave a data tapestry that supports what the business is going to do. Because you cannot tell with any certainty it's certainly not a linear progression, but data is going to be connected in a lot of different ways >> Deepak: Yeah To achieve the goals of the business. Tell us a little bit about the processes the underlying technologies and how that informs the way businesses are starting to think about how data does connect? >> Deepak: Can you repeat the last part? How data connects, how businesses are connecting data from multiple sources? And turning it into a real tapestry for the business. Yeah, so as you said, data comes in from various different sources for that matter, in terms of we use mobile devices so much more in the modern era, you actually have data coming in from these kind of sources, or for example in terms of let's say IoT, in terms of sensors, that are all over the place in terms of how that data actually comes along. Now, let's say, in terms of if there is a customer or if there is an organization that is looking at this kind of data that is coming from multiple different sources all coming in to play the one thing is just the sheer magnitude of the data. What typically we have seen is that there is infrastructure at the edge, even if you take the example of internet of things. You try and process the data at the edge as much as you can, and bring back only what is aggregated and what is required back to you know, your data center or a cloud infrastructure or what have you. At the same time, just that data is not good enough because you have to connect that data with the internal data that you have about-- Okay, who is this data coming from and what kind of data, what is that meta-data that connects my customers to the data that is coming in? I can give you a couple of examples in terms of let's say there is an organization that provides weather data to farmers in the corners of a country that is densely populated, but you really can never get into with a data center infrastructure to those kind of remote areas. There are at the edge, where you have these sensors in terms of being able to sample the weather data. And sample also the data of the ground in itself, it terms of being able to, the ultimate goals is to be able to help the farmer in terms of when is the right time to be able to water his field. When is the right time to be able to sow the seeds. When is the right time for him to really cut the crops, when is the most optimized time. So, when this data actually comes back from each of these locations, it's all about being able to understand where this data is coming from, from the location, and being able to connect that to the weather data that is actually coming from the satellites and relating that and collating that to be able to determine and tell a farmer on his mobile device, to be able to say okay, here is the right time, and if you don't actually cut the crops in the next week, you may actually lose the window because of the weather patterns that they see and what have you. That's an example of what I could talk about as far as how do you connect that data that is coming in from various sources. And as a great example, I think, was at the keynote yesterday about a Stanford professor talking about the race track, it's really about that race track and not just about any race track that where the cars are actually making those laps, to be able to understand and predict correctly in terms of when to make that pit stop in a race. You really need the data from that particular race track because it has characteristics that have an impact on the wear and tear of the tires. For example. That's really all about being able to correlate that data. So it's having the understanding of the greater context but the specific context too. >> Deepak: Absolutely, absolutely. Great. You also talked about you talked about the technology that's necessary, but you also mentioned the right mindset. Can you unpack that a little bit for our viewers? The mindset I talked about earlier, was really more in terms of can we actually if you think some time before, we couldn't have attacked some of the problems that we can afford to today. It's really having the mindset of being able to from the data I can do things that I could never do before. We could solve, we can solve things in the nature of being able to being able to impact lives if you will. One of our customers leads a Mercy technology. Has built a out care platform, that provides that has a number of healthcare providers coming together. Where they were actually able to make a significant impact where they could actually determine 40% of the patients coming into their facilities, really were prevented from coming back into with a sepsis kind of diagnosis. Before then, they reduce that sepsis happening in 40% of the time. Which is a significant, significant impact, if you will, for the human. Just having that mindset in terms of you have all the data and you can actually change the world with that data, and you can actually find solutions to problems that you could never have before because you have the technology and you have that data. Which was never there before. So you can actually make those kinds of improvements. It's all about extracting those insights. >> Deepak: Absolutely. Thank you so much for coming on the show, Deepak. It was a pleasure having you Thank you for having me. Thank you very much. I'm Rebecca Knight, for Peter Burris, we will have more from NetApp Insight in just a little bit. (dramatic electronic music)