 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 have two guests for this segment. We have Matt Watts. He is the director and data strategist and director of technology at NetApp. And Kenneth Kukie, a senior editor at The Economist and author of the best-selling book, Big Data, and author of a soon-to-be best-selling book on AI. Welcome. Thank you so much for coming on the show. Pleasure to be here. Thanks. So this is the, we keep hearing NetApps saying this is the day of the data visionary. I'd love to hear both of you talk about what a data visionary is and why companies, why this is a necessary role in today's companies. Okay. So I think if you look at the, the sort of the generations that we've been through, you know, in the sort of the late 90s, early 2000s, it was all about kind of infrastructure with a little bit of application and some data associated to it. And then as we kind of rolled forward to the next decade, the infrastructure discussion became less. It became more about the applications and increasingly more about the data. And if we look at the current decade that we're in right now, the infrastructure discussions have become less and less and less. We're still talking about applications, but the focus is on data. And what we haven't seen so much of during that time is the roles changing. We still have a lot of infrastructure people doing infrastructure roles, a lot of application people doing application roles, but the real value in this explosion of data that we're seeing is in the data. And it's time now that companies really look to put data visionaries, people like that in place to understand how do we exploit it? How do we use it? What should we gather? What could we do with the information that we do gather? So I think the timing is just right now for people to be really considering that. Yeah, I would build on what Matt just said that functionally in the business, in the enterprise, we had the user of data and we had the, maybe the professional who collected the data. Sometimes we had a statistician who would analyze it but pass it along to the user who is an executive, who is an MBA, who is the person who thinks with data and is going to present it to the board or to make a decision based on it. But that person isn't a specialist on data, that person probably, you know, maybe doesn't even know math. And the person is thinking about the broader issues related to the company, the strategic imperatives. Maybe you speak some languages, maybe it's a very good sales person. There's no one in the middle, at least up until now, who can actually play that role of taking the data from the level of the bits and the bytes and in the weeds and the level of the infrastructure and teasing out the value and then translating it into the business strategy that can actually move the company along. Now, sometimes those people are going to actually move up the hierarchy themselves and become the executive, but they need not. Right now, there's so much data that's untapped that you can still have this function of the person who bridges the world of being in the weeds with the infrastructure and with the data itself and the larger, broader executive suite that need to actually use that data. We've never had that function before, but we need to have it now. So let me test you guys, test something in you guys. So what I like to say is we're at the middle of a significant break in the history of computing. The first 50 years or so, it was known process, unknown technology. And so we threw all our time and attention at understanding the technology and new accounting. We knew HR, we even knew supply chain because case law allowed us to decide where a title was when. But today, we're unknown process, known technology. It's going to look like a cloud. Now the details are always got to be worked out, but increasingly we don't know the process. And so we're on a roadmap of discovery that is provided by data. You guys agree with that? So I would agree, but I'd make a nuance, which is I think that's a very nice way of conceptualizing it, I don't disagree. But I would actually say that at the frontier, the technology is still unknown as well. The algorithms are changing, the use cases which you're pointing out, the processes are still are now unknown. And I think that's a really important way to think about it because suddenly a lot of possibility opens up when you admit that the processes are unknown because it's not going to look like the way it looked in the past. But I think for most people, the technology is unknown because the frontier is changing so quickly. What we're doing with image recognition and voice recognition today is so different than it was just three years ago. Deep learning and reinforcement learning. But is it going to require armies of people to understand that? Well, tell me about it. I mean, this is the full, for the moment, yes. I mean, it's a full employment act for data scientists today. I don't see that changing for a generation. So everyone says, oh, what are we going to teach our kids? Teach them math, teach them stats, teach them some coding. There's going to be a huge need. All you have to do is look at the society, look at the world and think about what share of it is actually done well, optimized for outcomes that we all agree with. I would say it's probably between, it's in single percent. It's probably between one and five percent of the world is optimized. One small example, medical science. We collect a lot of data in medicine. Do we use it? No, it's the biggest scandal going on in the world. If patients and citizens really understood a degree to which medical science is still trial and error based on the gumption of the human mind of the doctor and the nurse, rather than the data that they actually already collect but don't reuse, there would be congressional hearings every day. People, there'd be revolutions in the street because here it is, the duty of care of medical practitioners is simply not being upheld. I'd take exception to that and just not to spend too much time on this but at the end of the day, the fundamental role of the doctor is to reduce the uncertainty and the fear and the consequences of the patient. By any means necessary and they are not doing that with data. You're absolutely right that the process of diagnosing and the process of treatment from a technical standpoint would be better but there's still the human aspect of actually taking care of somebody. Yeah, I think that's true when I think there is something of the hand of the healer but I think we're practicing a form of medicine that looks closer to black magic than it does to data science. Bring me the data scientist. And I think an interesting kind of parallel to that is when you jump on a plane how often do you think the pilot actually lands that plane? Doesn't. No, so it still needs somebody there but it still needs somebody as the oversight as that's kind of to make a judgment on it. My father was a cardiologist who was also a flight surgeon in the Air Force in the US and was one of the few people that was empowered by the Airline Pilots Association to determine whether or not someone was fit to fly. And so my dad used to say that he is more worried about the health of a bus driver than he is of an Airline pilot. That's great. So in other words, we've been gizumped by someone whose father was both a doctor and a pilot. This is yet too better than that. So it turns out that we do want Sully on the Hudson when things go awry but in most cases I think we need this blend of the data on one side and the human on the other. The idea that the data just because we're going to go in the world of artificial intelligence and machine learning is going to mean jobs will be eradicated left and right, I think that's a simplification. I think that the nuance that's much more real is that we're going to live in a hybrid world in which we're going to have human beings using data in much more impressive ways than they've ever done it before. So talk about that. I mean, I think you have made this compelling case that we have this huge need for data and this explosion of data plus the human judgment that is needed to either diagnose an illness or whether or not someone's fit to fly or fly a plane. So then where are we going in terms of this data visionary and in terms of say more of a need for AI? Yeah, well if you take a look at medicine what we would have is the diagnosis would probably be done say for a pathology exam by the algorithm but then the healthcare coach, the doctor will intervene and we'll have to both interpret this for first of what it means, translate it to the patient and then discuss with the patient the trade-offs in terms of their lifestyle choices. For some people surgery is the right answer, for others you might not want to do that and it's always different with all of the patients in terms of their age, in terms of whether they have children or not, whether they want the potential of complications it's never so obvious. Just as we do that network, we will do that in medicine we're going to do that in business as well because we're going to take data that we never had about decision, should we go into this market or that market, should we take a risk and gamble with this product a little bit further even though we're not having a lot of sales because the profit margins are so good on it there's no algorithm that can tell you that and in fact you really want the intellectual ambition and the thirst for risk-taking of the human being that defies the data with an instinct that I think it's the right thing to do and even if we're going to have failures with that and we will, we'll have outperformance and that's what we want as well but the society advances by individual passions not by whatever the spreadsheet says. Well there is this issue of agency right? So to the end of the day a human being can get fired a machine can not, a machine, I mean in the US anyway software is covered under the legal strictures of copyright which means it's a speech act so what do you do in circumstances where you need to point a finger at something and making a mistake? You keep coming back to the human being. So there's going to be an interesting interplay over the next few years of how this is going to play out. So how is this working or what's the impact on NetApp as you work with your customers on this stuff? So I think you've got the sort of the AI, ML that's kind of one kind of discussion and that can lead you into all sorts of rat holes or other discussions around well how do we make decisions how do we trust it to make decisions there's a whole kind of sort of aspect that you have to discuss around that. I think if you just bring it back to businesses in general you know all of the businesses that we look at are looking at new ways of creating new opportunities new business models and they're all collecting data I mean we know the story about General Electric used to sell jet engines and now it's much more about what can we do with the data that we collect from the jet engines. So that's finding a new business model and then you've got the human role in that as well is well is there a business model there? We can gather all of this information we can collect it we can refine it we can sort it but is there actually a new business model there? And I think it's those kind of things that are inspiring us as a company to say well we could uncover something incredible here we could unlock that data we can make sure it's where it needs to be when it needs to be there you have the resources to bring to bear to be able to extract value from it you might find a new business model and I think that's the aspect that I think is of real interest to us going forward and kind of inspires a lot of what we're doing. Great, great. Kenneth Matt thank you so much for coming on the show it was a really fun conversation. Thank you. Thank you for having us. We will have more from NetApp Insight just after this.