(energetic music) >> Announcer: From our studios in the heart of Silicon Valley; Palo Alto, California. This is a CUBE Conversation. >> Hi everyone, welcome to this special CUBE Power Panel recorded here in Palo Alto, California. We've got remote guests from around the Internet. We have Evan Anderson, Mark Anderson, Phil Lohaus. Thanks for comin' on. Evan is with INVNT/IP, an organization with companies and individuals that fight nation-sponsored intellectual property theft and also author of the huge report Theft Nation Almost a 100 pages of really comprehensive analysis on it. Mark Anderson with the Future in Review CEO of Pattern, Computer and Strategic New Service Chairman of Future in Review Conference, and author of the book "The Pattern Future: "Find the World's Greatest Secrets "and Predicting the Future Using Discovery Patterns" and Phil Lohaus, American Enterprise Institute. Former intelligent analyst, researcher at the American Enterprise Institute, studying competitive strategy and emerging technologies. Guys, thanks for coming on. This topic is, is industrial IoT the new battleground? Mark, you cover the Future Review. Security is the battleground. It's not just a silo'd space. It's horizontally scalable across every single touch point of the Internet, individuals, national security, companies, global, what's your perspective on this new battleground? >> Well, thank you, I took some time and watched your last presentation on this, which I thought was excellent. And maybe I'll try to pick up from there. There's a lot of discussion there about the technical aspects of IoT, or IIoT, and some of the weaknesses, you know firewalls failing, assuming that someone's in your network. But I think that there's a deeper aspect to this. And the problem I think, John, is that yes, they are in your network already, but the deeper problem here is, who is it? Is it an individual? Is it a state? And whoever it is, I'm going to put something out that I think is going to be worth talking more deeply about, and that is, if people who can do the most damage are already in there, and are ready to do it, the question isn't "Can they?" It's "Why have they not?" And so literally, I think if you ask world leaders today, are they in the electric grid? Yes. Is Russia in ours, are we in theirs? Yes. If you said, is China in our most important areas of enterprise? Absolutely. Is Iran in our banks and so forth? They are. And you actually see states of war going on, that are nuisances, but are not what you might call Cybergeddon. And I really believe that the world leaders are truly afraid. Perhaps more afraid of that than of nuclear war. So the amount of death and destruction that could happen if everybody cut loose at the same time, is so horrifying, my guess is that there's a human restraint involved in this, but that technically, it's already game over. >> Phil, Cybergeddon, I love that term, because that's a part of our theme here, is apocalypse now or later? Industrial IoT, or IIoT, or the Internet, all these touch points are creating a surface area that for penetration's purposes, any packet can get in. Nation-states, malware, you name it. It's all problem. But this is the new war battleground. This is now digital Cybergeddon. Forget the wall on the southern border, physical wall. We're talking about a digital wall. We have major threats going on to our society in the United States, and global. This is new, rules of engagement, or no rules of engagement on how to compete in a digital war. This is something that the government's supposed to protect us for. I mean, if someone drops troops in California, physical people, the government's supposed to stop that. But if it's a digital war, it's packets. And the companies are responsible for all this. This doesn't make any sense to me. Break it down, what's the problem? And how do we solve this? >> Sure, well the problem is is that we're actually facing different kinds of threats than we were typically used to facing in the past. So in the past when we go to war, we may have a problem with a foreign country, or a conflict is coming up. We tend to, and by we I mean the United States, we tend to think of these things as we're going to send troops in, or we're going to actually have a physical fight, or we're going to have some other kind of decisive culmination of events, end of a conflict. What we're dealing with now is very different. And it's actually something that isn't entirely new. But the adversaries that we're facing now, so let's say China, Russia, and Iran, just to kind of throw them into some buckets, they think about war very differently. They think about the information space more broadly, and partially because they've been so used to having to kind of be catching up to America in terms of technology, they found other ways to compete with America, and ways that we really haven't been focusing on. And that really, I would argue, extends most prominently to the information space. And by the information space I'm speaking very broadly. I'm talking about, not just information in terms of social media, and emails, and things like that, but also things like what we're talking about today, like IIoT. And these are new threat landscapes, and ones where our competitors have a integrated way of approaching the conflict, one in which the state and private sectors kind of are molded or fused or at least are compelled to work together and we have a very different space here in the United States. And I'm happy to unpack that as we talk about that today, but what we're now facing, is not just about technical capabilities, it's about differences in governing systems, differences in governing paradigms. And so it's much bigger than just talking about the technical specifics. >> Evan, I want you to weigh in on this because one of the things that I feel strongly about, and this is pretty obvious from the commentary, and experts I talk to is, the United States has always been good at defending itself physically, you know war, in being places. Digitally, we've been really good at offense, but terrible on defense, has been the metaphor. I spoke with former four-star General Keith Alexander, who ran the NSA and was first commander of the cyber command, who is now the CEO of IronNet. He and I were talking on-camera and privately and he's saying, "Look it. "we suck at defense digitally. "We're great at offense, we can take someone out "on the offense." But we're talking about IoT, about monitoring. These are technical challenges. This is network nerds, and software engineers have to solve this problem with the prism of defense. This is a new paradigm. This is what we're kind of getting to. And Mark, you kind of addressed it. But this is the challenge. IoT is going to create more points that we have to defend that we suck now at defending, how are we going to get better. This is the paradox. >> Yeah, I think that's certainly accurate. And one of our problems here is that as a society we've always been open. And that was how the Internet was born. And so we have a real paradigm shift now from a world in which the U.S. was leading an open world, that was using the Internet for, I mean there have been problems with security since day one, but originally the Internet was an information-sharing exercise. And we reached a point in human history now where there are enough malicious hackers that have the capabilities we didn't want them to have, but we need to change that outlook. So, looking at things like Industrial IoT, what you're seeing is not so much that this is the battlefield in specific, it's that everything like it is now the battlefield. So in my work specifically we're focused more on economic problems. Economic conflicts and strategies. And if you look at the doctrines that have come out of our adversaries in the last decade, or really 20 years, they very much did what Phil said, and they looked at our weaknesses, and one of those biggest weaknesses that we've always had is that an open society is also unable necessarily to completely defend itself from those who would seek to exploit that openness. And so we have to figure out as a society, and I believe we are. We're running a fine line, we're negotiating this tightrope right now that involves defending the values and the foundational critical aspects of our society that require openness, while also making sure that all the doors aren't open for adversaries. And so we'll continue to deal with that as a society. Everything is now a battlefield and a much grayer area, and IoT certainly isn't helping. And that's why we have to work so hard on it. >> I want to talk about the economic piece on the next talk track of rounds. Theft, and intellectual property that you cover deeply. But Mark and Phil, this notion of Cybergeddon meets the fact that we have to be more defensive. Again, principles of openness are out there. I mean, we have open source. There is a potential path here. Open source software has been, I think, depending on who you talk to, fourth generation, or fifth, depending on how old you are, but it's now mainstream enough now. Are we ever going to get to a formula where we can actually be strong in defense as well as just offense with respect to protecting digitally? >> Phil, do you want that? >> Well, yeah, I would just say that I'm glad to hear that General Alexander is confident about our offensive capabilities. But one of the... To NSA that is conducting these offensive capabilities. When we talk about Russia, Iran, China, or even a smaller group, like let's say an extremist group or something like that, there's an integration between command and control, that we simply don't have here in the States. For example, the Panasonic and Sony examples always come to mind, as ones where there are attacks that can happen against American companies that then have larger implications that go beyond just those companies. So and this may not be a case where the NSA is even tracking the threat. There's been some legislation that's come out, rather controversial legislation about so-called hacking back initiatives and things like that. But I think everybody knows that this is already kind of happening. The real question is going to be, how does the public sector, and how does the private sector work together to create this environment where they're working in synergy, rather than at cross purposes? >> Yeah, and this brings up, I've heard this before. I've heard people talk about the fact that open source nation states can actually empower by releasing tools in open source via the Dark Web or other vehicles, to not actually have, quote, their finger prints, on any attacks. This seems to be a tactic. >> Or go through criminals, right? Use proxies, things like that. It's getting even more complicated and Alexander's talked about that as well, right? He's talked about the convergence of crime and nation-state actions. So whereas with nation-states it's already hard-attributed enough, if that's being outsourced to either whether it's patriotic hackers or criminal groups, it's even more difficult. >> I think you know, Keith is a good friend of all of ours, obviously, good guy. His point is a good one. I'd like to take it a little more extreme state and say, defense is worth doing and probably hopeless. (everyone laughs) So, as they always say, all it takes is one failure. So, we always talk about defense, but really, he's right. Offense is easy. You want to go after somebody? We can get them. But if you want to play defense against a trillion potential points of failure, there's no chance. One way to say this is, if we ignore individuals for a moment and just look at nation-states, it's pretty clear that any nation-state of size, that wants to get into a certain network, will get in. And then the question will be, Well, once they're in, can they actually do damage? And the answer is probably yeah, they probably can. Well, why don't they? Why don't they do more damage? We're kind of back to the original premise here, that there's some restraint going on. And I suspect that Keith's absolutely right because in general, they don't want to get attacked. They don't want to have to come back at them what they're about to do to your banks or your grid, and we could do that. We all could do that. So my guess is, there's a little bit of failure on our part to have deep discussions about how great our defenses either are, or are not, when frankly the idea of defense is a good idea, worthwhile idea, but not really achievable. >> Yeah, that's a great point. That comes up a lot where it's like, people don't want retaliation, so it's a big, critical event that happens, that's noticeable as a counterstrike or equivalent. But there's been discussion of the, I call it "the slow bleed" where they push the line of where that is, like slowly infiltrate, and just cause disruption and inconvenience, as a tactic. This has become something we're seeing a lot of. Whether it's misinformation campaigns on fake news, to just disrupting operations slowly over time, and just kind of, 1,000 paper cuts, if you will. Your guys' thoughts on that? Is that something you guys see out there that's happening? >> Well, you saw Iran go after our banks. And we were pushing Iran pretty hard on the sanctions. Everybody knows they did that. It wasn't very much fun for anybody. But what they didn't do is take down the entire banking system. Not sure they could, but they didn't. >> Yeah, I would just add there that you see this on multiple fronts. You see this is by design. I'm sure that Mark is talking about this in his report but... they talk about this incremental approach that over time, this is part of the problem, right? Is that we have a very kind of black or white conception of warfare in this country. And a lot of times, even companies are going to think, well you know, we're at peace, so why would I do something that may actually be construed as something that's warlike or offensive or things like that? But in reality, even though we aren't technically at war, all of these other actors view this as a real conflict. And so we have to get creative about how we think about this within the paradigm that we have and the legal strictures that we have here in this country. >> Well there's no doubt at least in my non-expert military opinion, but as someone who is a techie, been on the Internet from day one, all my life, and all those tools, you guys as well, I personally think we're at war. 100%, there's no debate on that. And I think that we have to get better policy around this and understand it better. Because it's happening. And one of the obvious areas that we see in the news everyday, it's Huawei and intellectual property theft. This is an economic impact. I mean just look at what's happening in Brexit in the U.K. If that was essentially manipulated, that's the ultimate smart bomb, is to just destroy their financial system, which ended up happening through that misinformation. So there are economic realizations here, Evan,that not only come from the misinformation campaigns and other attacks, but there's real value with intellectual property. This is the report you put out. Your thoughts? >> There's very much an active conflict going on in the economic sphere, and that's certainly an excellent point. I think one of the most important things that most of the world doesn't quite understand yet, but our adversaries certainly understand, is that wars are fought for usually, just a few reasons. And there's a lot of different justification that goes on. But often it's for economic benefit. And if you look at human history, and you look at modern history, a lot of wars are fought for some form of economic benefit, often in the form of territory, et cetera, but in the modern age, information can directly and very quite obviously translate into economic benefit. And so when you're bleeding information, you're really bleeding money. And when I say information, again, it's a broad word, but intellectual property, which our definition, here at INVNT/IP is quite broad too, is incredibly valuable. And so if you have an adversary that's consistently removing intellectual property from what I would call our information ecosystem, and our business ecosystem, we're losing a lot of economic value there, and that's what wars are fought over. And so to pretend that this conflict is inactive, and to pretend that the underlying economy and economic strength that is bolstered or created by intellectual property isn't critical would be silly. And so I think we need to look at those kinds of dynamics and the kind of Gerasimov Doctrine, and the essential doctrine of unrestricted warfare that came out of the People's Republic of China are focused on avoiding kinetic conflict while succeeding at the kinds of conflict that are more preferable, particularly in an asymmetric environment. So that's what we're dealing with. >> Mark and Phil, people waking up to this reality are certainly. People in the know are that I talk to, but generally speaking across the board, is this a woke moment for tech? This Armageddon now or later? >> Woke moment for politicians not for tech, I think. I'm sure Phil would agree with this, but the old guard, go back to when Keith was running the NSA. But at that time, there was a very clear distinction between military and economic security. And so when you said security, that meant military. And now all the rules have changed. All the ways CFIUS works in the United States have changed. The legislation is changing, and now if you want to talk about security, most major nations equate economic security with national security. And that wasn't true 10 years ago. >> That's a great point. That's really profound, I totally agree. Phil. >> I think you're seeing a change in realization in Washington about this. I mean, if you look at the cybersecurity strategy of 2018, it specifically says that we're going to be moving from a posture of active defense to one of defending forward. And we can get into the discussion about what those words mean, but the way I usually boil down is it means, going from defending, but maybe a little bit forward, to actually going out and making sure that our interests are protected. And the reason why that's important, and we're talking about offense versus defense here, obviously the reason why, from what Mark was saying, if they're already in the networks, and they haven't actually done anything, it's because they're afraid of what that offensive response could be. So it's important that we selectively demonstrate what costs we could impose on different actors for different kinds of actions, especially knowing that they're already operating inside of our network. >> That's a great point. I mean, I think that's again another profound statement because it's almost like the pin in the grenade. Once they pull it, the damage is done. Again, back to our theme, Armageddon, now or later? What's the answer to this, guys? Is it the push to policy conversation and the potential consequences higher? Get that narrative going. Is it more technical protection in the networks? What's some of the things that people are talking about and thinking about around this? >> And it's really all of the above. So the tough part about this for any society and for our society is that it's expensive to live in a world with this much insecurity. And so when these kind of low-level conflicts are going on, it costs money and it costs resources. And companies had to deal with that. They spent a long time trying to dodge security costs, and now particularly with the advent of new law like the GDPR in Europe, it's becoming untenable not to spend that defensive money, even as a company, right? But we also are looking at a deepening to change policy. And I think there's been a lot of progress made. Mark mentioned the CFIUS reforms. There are a lot of different essentially games of Whack-A-Mole being played all around the world right now figuring out how to chase these security problems that we let go too long, but there's many, many, many fronts that we need to-- >> Whack-A-Mole's a great example. The visualization of that is just horrendous. You know, not the ideal scenario. But I got to get your point on this, because one of the things that comes up all the time in our conversations in theCUBE is, the government's job is to protect our securities. So again, if someone came in, and invaded my town in Palo Alto, it's not my responsibility to fight for the town. Maybe defend my own house. But if I'm a company being attacked by Russia, or China or Iran, isn't it the government's responsibility to protect me as a citizen and the company doing business there? So again, this is kind of the confusion that people have. If somebody's going to defend their hack, I certainly got to put security practices in place. This is new ground for the government, digitally speaking. >> When we started this INVNT/IP project, it was about seven years ago. And I was told by a very smart guy in D.C. that our greatest challenge was going to be American corporations, global corporations. And he was absolutely right. Literally in this fight to protect intellectual property, and to protect the welfare even of corporations, our greatest enemies so far have been American corporations. And they lobby hard for China, while China is busy stealing from them, and stealing from their company, and stealing from their country. All that stuff's going on, on a daily basis and they're in D.C. lobbying in favor of China. Don't do anything to make them mad. >> They're getting their pockets picked at the same time. And they're trying to do business in China. They're getting their pockets picked. That's what you're saying. >> They're going for the quarterly earnings report and that's all. >> So the problem is-- >> Yeah so-- >> The companies themselves are kind of self-inflicted wounds here for them. >> Yes. >> Yeah, just to add to that, on this note, there have been some... Business to settle interest. And this is something you're seeing a little bit more of. There's been legislation through CFIUS and things like that. There have been reforms that discourage the flow of Chinese money in the Silicon Valley. And there's actually a measurable difference in that. Because people just don't want to deal with the paperwork. They don't want to deal with the reputational risk, et cetera, et cetera. And this is really going to be the key challenge, is having policy makers not only that are interested in addressing this issue, because not all of them are even convinced it's a problem, if you can believe it or not, but having them interested and then having them understand the issue in a way that the legislation can actually be helpful and not get in the way of things that we value, such as innovation and entrepreneurialism and things like that. So it's going to take sophisticated policy-making and providing incentives so that companies actually want to participate and helping to make America safer. >> You're so right about the politicians. Capitol Hill's really not educated. I mean I tell my kids, and they ask the same questions, just look at Mark Zuckerberg and Sundar Pichai present to the government. They don't even know what an Android phone versus an iPhone is, nevermind what the Internet, and how this global economy works. This has become a makeup problem of the personnel in Capitol Hill. You guys see any movement? I'm seeing some change with a new guard, a new generation of younger people coming in. Certainly from the military, that's an easy when you see people get this. But a new generation of young millennials who are saying, "Hey, why are we doing this the old way?" and actually becoming more informed. Not being the lawyer at law-making. It's actually more technically savvy. Is there any movement, any bright hope there? >> I think there's a little hope in the sense that at a time when Congress has trouble keeping the lights on, they seem to have bipartisan agreement on this set of issues that we're talking about. So, that's hopeful. You know, we've seen a number of strongly bipartisan issues supported in Congress, with the Senate, with the House, all agreeing that this is an issue for us all, that they need to protect the country. They need to protect IP. They need to extend the definition of security. There's no argument there. And that's a very strange thing in today's D.C. to have no argument between the parties. There's no error between the GOP and the Democrats as far as I can tell. They seem to all agree on this, and so it is hopeful. >> Freedom has its costs and I think this is a new era of modern freedom and warfare and protection and all these dynamics are changing, just like Cloud 2.0 is changing application developers. Guys, this is a really important topic. Thank you so much for coming on, appreciate it. Love to do a follow-up on this again with you guys. Thanks for sharing your insight. Some great, profound statements there, appreciate it. Thank you very much. >> Thank you. >> Thanks for having us. >> It's been a CUBE Power Panel here from Palo Alto, California with Evan Anderson, Mark Anderson, and Phil Lohaus. Thank you guys for coming on. Power Panel: The Next Battleground in Industrial IoT. Security is a big part of it. Thanks for watching, this has been theCUBE. (energetic music)

(upbeat intro) >> From our studios in the heart of Silicon Valley, Palo Alto California, this is a CUBE conversation. >> Hello everyone, welcome to the Palo Alto studios of theCUBE, I'm John Furrier host of theCUBE, we're here with a special power panel on industrial IOT, also known as IIOT, industrial IOT, and cybersecurity, with the theme being apocalypse now or later, when will the rug be pulled out from everyone, when will people have to make a move on making sure that the network and security are all teed up and all locked down, as IOT increases the surface area of networks, industrial IOT, where critical equipment or infrastructure is being run for businesses. Got a great panel here, we got Gabe Lowy who's the founder and CEO of Tectonic Advisors, and author of an upcoming research paper on this particular topic. Bryan Skene, vice president of product development at Tempered Networks, and Greg Ness, the CMO, who happened to be available to join us from Tempered Networks as well. Guys, thanks for spending the time to come on this power panel. >> Great to be here. >> So, convergence is a theme we've heard every wave of innovation, the convergence of this, the convergence of networks and apps. Now more than ever, there's a confluence of multiple waves of convergence happening, you're seeing it right now, infrastructure turned into cloud, big data turned into machine learning and AI, you've got future infrastructure like Blockchain around the corner, but in the middle of all this, the security, data, networking, this is kind of the beginning of a cloud 2.0 dynamic, where pure cloud is great for computing network, you native born in the cloud, you scale it up, it's great. Still got challenges but if you're a large company, and you want to actually operate cloud scale anything, and have instrumentation, internet of things, devices, sensors, in factory's, in plants, in cars, your game is changing, if it's connected to the network, it's got power and connectivity, a terrorist, a hacker, a digital terrorist can come in and do all kinds of damage. This is the topic. So Greg, we talked about this panel, what was the motivation for this, what's your thoughts? >> Well, it occurred to us that you know, as you look at all the connectivity that's you know, underway, billions of devices being connected, the level of scale, complexity, and the porosity of what's being connected, is just really incomprehensible, to the people that developed the internet, and it's raising a lot of issues. All around, basically, the number of devices the inability to protect and secure and update those devices, and the sheer amount of money and effort that would have to be applied to protect them is beyond the scope of current IT security stuff. IT's not ready. >> IT, certainly, you and I talk about this all the time, but you know, I love the hype and you know, digital transformation's going to save the world Gabe, talk about the dynamics because the title of this panel, really the subtitle is apocalypse now or later, and this seems to be the modus operandus is that you know, you know what has to hit the fan before any action is taken, you see Capital One, there isn't a day gone by where there's some major breach, major hack, it's a firewall for Capital One, going to an open S3 bucket from some girl whose bragging about it on Twitter, wasn't really a serious hacker, then you've got adversaries that are organized, whether it's state sponsored and or real money making underbelly activities happening, you know there are digital terrorists out there, there are digital thieves, the surface area with IOT is absolutely opened up, we kind of know that, but industrial IOT, just talking about industrial equipment, industrial activities, whether it's critical infrastructure or planting equipment for a company, this is a huge digital problem. What's your take, what's your thesis? >> Yes it is, and building on what Greg said, there's an interesting gap from both sides. The first is that this industrial equipment or critical infrastructure, some of it goes back 20, 25 years. It was not architected to be connected to the internet, but yet with this digital transformation that you eluded to, companies want to find ways of getting that data, putting it into various analytics engines to improve cost efficiencies or decision outcomes. But how do you do that with a lot of equipment out there that runs on different operating systems and really was not built for internet connections. The other side of the gap is that your traditional IT security technologies, firewalls, intrusion protection, VPN's, they in turn were not built or architected to secure this IIOT infrastructure. And that gap creates the vulnerability that opens the door for cyber criminals to come in, or state sponsored cyber attackers to come in and do some serious damage. >> Bryan, I want you to weight in here. You're a network guy, you've been around the block, you've seen the networks evolve, the primitives were clear, the building blocks internet were, the DNS ran, most of what the internet right now, whether you're talking about from the marketing to routing, it's all DNS based, it's IP addresses as well under that. So you've got the IP address, you've got DNS, what else is there? What can be done? Why aren't these problems being solved by traditional firewalls and traditional players out there, is it just the limitation of the infrastructure? Or is there just more cultural DNA, you've got to evolve, what's your take on this? >> Yeah, um the way I think about this is that the internet that we know and we use was mostly built for human beings, I mean, it's been built for humans to use it, humans have discriminating tastes, they decide what to click on, for the most part they are skeptical, they learn through trial and error what's happened with- when people try to fool other people, a machine or you know, you've got a webpage and it's got something misleading, you learn that, you don't click on that any more. And the infrastructure we have today is built to help people avoid these problems, as well as drop packets when they can detect that something is just absolutely wrong. But machines, they don't know any of that, they're not discriminating, they've been built to, well if it's going to be on a network, to trust everything that's talking to them, and to send data and assume that the other side is also trusting them and just acting on the data. So it's just a fundamentally different problem, you know what traditionally the machine networks have had air gaps, they've been air gapped away from any other kinds of data or potential threat. And those air gaps are gone. >> So air gaps were supposed to save us, weren't they? But they're not are they? >> Well, they kept us going as Gabe alluded, for 20 -25 years, machines have been operating, operating critical infrastructure, but you know, with digitalization, with the opportunity to look at that data in the cloud, and do machine learning, and by the way machine learning's being done in the cloud just for scale, so the problem with getting the data from machines, or other things back into the cloud is a huge issue, and if there's an air gap between say the cloud and the thing, we might be somewhere. >> So a lot of incompatible architectures relative to what everyone's doing with cloud, and say hybrid and multi cloud. Gabe, you know the two worlds of information technology or IT people, and operational technology people, that tend to run the IOT world, you know you do sensors to factory floors to whatever, called OT people, operational technologies. I've always said that's a train wreck between those two cultures, they kind of don't like each other. You got IT guys, they're stacking and racking equipment, OT guys, stay out of my world I run propietary stacks, it's lockdown. Pretty locked down from a security standpoint, IT are pretty promiscuous just in the nature of it. As those two worlds collide, is that the thesis of the catastrophe model, as you see that world coming together, what's your thoughts on this? >> Yes, good question. That world has to come together, and I'll give you an analogy to this. About 10, 12 years ago, a lot of people were doubtful that Devops would ever take off, 'cause development guys really didn't like operations guys, they didn't like dealing with them. Here we are 10 years or so later, and everyone's pretty much adopted it, and they're seeing the benefits of it. This OT IT convergence takes it to a much higher level, because the stakes are so much higher, because a cyber attack can cause catastrophic damage. And as a result, these two teams are not only going to have to work together in harmony, but they're going to have to learn each other's stacks in the case of the OT guys, it's their traditional OSI networking stack for IT networks. And for the IT guys, they're going to have to learn the Purdue model, which was the model that's principally used in architecting these OT systems. And unless these two teams do work together, the vulnerabilities and probabilities for a catastrophic event increases significantly. >> That's a great example, Devops was poo-pooed on earlier on, I mean Greg, we were back in 2008 riffing on this, now it's the mainstream. Agilities come from it, the Lean startup, all kinds of cool things, people are talking about, we love cloud, great. Now we bring the OT world together, and IT world together, Gabe, what is the benefit, what is the key ethos around operating technologies and IT guys coming together? Because you know, dev ops would simply abstract away the complexity so developers don't have to do configuration and management, all that provisioning stuff, and still have the reliability. They called it infrastructure as code, so Devops was infrastructure as code, what's the ethos of the two worlds coming together from IT and OT? >> I think the ethos is at a very high level, it's risk management. Because the stakes are so high that the types of losses that could be incurred, you know you mentioned Capital One at the top of the program, yes those are financial losses, but imagine if the losses resulted in thousands or tens of thousands of people getting infected, or perhaps dying. So the need for these two teams to work together is absolutely critical, and so I'd say the key strategic approach to this, both from the IT and the OT side, is to go into it- into strategy or cyber strategy with the premise that the company has already been compromised. And so that starts to get your thinking away from legacy types of technologies that were not architected to prevent these new threats, or defend against them, and now these teams have to start working together from a totally different standpoint, to try and prevent the risks of those catastrophic losses. >> Greg, I want to get your thoughts, you've been in the IT businesses for a long time, you've been a major player in it, historian as well as us in IT, what do you see as contrast between the two cultures of IT and OT, because you got to lock down these networks, you got to have the teamwork between the two, because the surface area with IOT and industrial IOT is so massive, it's so complicated yet it's an opportunity at the same time it's an exposure, I mean just people working at home in IT, I mean the home is a great place to target people because all you got to do is get that light bulb from nest and you're at a fully threaded processor, you could run malware and get all the passwords from the person working at home. So again, from home to industrial, does IT even have the chops to get there? >> Not the way they're architected today around the TCP- IP stack, and that's the challenge, right? So from the 90's to this era, whether it's the mainframes to the networks to the internet to the enterprise web et cetera, compared to this we've had relatively incremental change, as surprising as that sounds. You know, devices being added and every year, every other year, every three years, people are upgrading those endpoints, they're adding more sophisticated security. But this world that you referred to, the world's in collision. It's not evolving at all in parallel. So, you've got devices with no security in mind they're being connected, and you know, calling it the industrial internet of things almost underwhelms what the risk is, it should be the internet of places or spaces, because what these devices can control, control of a factory, a hospital, et cetera, and you think back you know, yes you've got historical perspective, you don't have to go back very far when the Russians were attacking Ukraine, you know, WannaCry, NotPetya, you know they spread all over the place in a matter of weeks, UK hospitals were running on carbon paper, postponing procedures, Maersk shipping had they're shipping- they lost control of their ships at sea, and now you've got VxWorks coming along, saying you know, you're going to have to update that, because there's some serious vulnerabilities here, VxWorks is deployed to cross billions of devices, so I don't think historically there's really a precedent, I mean, if you want to tap into a common interest with military history, you don't even have the semblance of a Maginot Line, and that was a pretty imperfect protection scheme. >> I mean, the opportunity to infect governments, take 'em down within misinformation to actually harming people say through hospital hacks for instance, you know, people could- lives were in danger. And there's also other threats, I mean, you mentioned, it takes one device to be penetrated, at home or at work, I saw an article, came across my desk I saw IBM did some research, this concept of war shipping, where hackers ship their exploits directly on WiFi devices, so people get these devices, hey, free you know, nest light bulb or whatever's going on, they install in their home, oh it's got, I got a free WiFi router, uh-uh, it's got built in malware. It's just got WiFi connectivity. So again, the exploits are getting more complicated, Bryan, the network has to be smart. At the end of the day, this cloud 2.0 theme is beyond compute and storage, networking and security are two underdeveloped areas that need to evolve very quickly to solve these problems, what's your take on this. >> Well, my take on that is that our approach is that if the network has to be so smart that it can watch everything and understand what's good and bad, then we're doomed, so we're going to need to also combine watching packets, the traditional method, deep packet inspection, with divide and conquer. Frankly, it's-as Tom and I said before, the air gaps are gone for OT. I think we need to figure out a way to divide up the networks of things, and give them clean networks if possible, and try to segment them away from the network that the rest of the things are on. So, you know, we don't have enough compute power, we don't have enough memory and resources, but that's not really the fit. We just don't understand what is good traffic versus bad traffic, and we talk about Day Zero attack, and we talk about, try to chase that down with signatures, and you know the- you can watch transactions, people say AI and machine learning, but machine learning means learning good and bad from people. >> How do companies fix this, what's the answer to all this, or is there one? Or it's just going to take catastrophic loss to wake people up? >> Well we can't react to the problem, that's one thing that we all can probably- we all know that if we wait for the catastrophe, and then we try to react to that and solve it, that it's already gone, it's too late. I mean, this is a geometric expansion in complexity of the problem, I don't think there's a silver bullet, I think that there's going to be several things that need to be done, one is to keep inspecting traffic, but another one is again segmenting things that should be talking to each other, away from things that they should not be talking to. And trying to control the peers in the network of things. And you know, Greg something you said reminded me, fundamentally with networking, the TCP-IP, we are using the IP address, to mean the location say if we're talking about places, we're talking about the location of something and the identity of that thing, and most of our security policies, are spelled out in terms of something, an IP address, that is not under our control, and the network has to be kind of so complex as it is growing, with mass proxies, you know, motion, mobility, things are moving. A lot of this wasn't foreseen. >> So, Gabe and Greg, do we have to build new software, a new naming system? Do we have to kind of level up and put an extraction layer on top of the existing systems? What's the answer? >> The answer is a layered approach. Because to try and do a complete rebuild or a retrofit particularly with different operating systems, different versions, incompatible systems, billions of devices, and various types of security solutions that were not built for this, that's not a practical solution. So you've really got to go with an overlay strategy, people are always going to be the vulnerability, they'll fall for fishing attacks, that's why the strategy is that we're already compromised. So if the attacker is already in our network, how do we contain them from doing serious damage? So one strategy for this is micro-segmentation, which is a much more granular approach, to prevent that lateral movement once the attacker is inside the network. And then when you go from there, you can pair that with host identity protocol which has been around for a while, but that was architected specifically to address the networking and security requirements for IIOT environment, because it addresses that gap that we were talking about between traditional security solutions that lack this functionality, and it only allows white-listed communications between hosts or devices that are already approved and only approved to communicate with one another. So you could effectively do a lockdown even if the attacker is already inside your network. >> I want to get back to some of the criteria on this, and I want to also put the plug in for the TechTonic advisors report that's coming out that you are the author of, called securing critical infrastructure against cyber attacks, I read it, great paper. The line that I read, I want to get your thoughts I'm going to read it out loud, I'd love to get your thoughts on this Gabe or anyone else who wants to chime in, it says industrial IOT cybersecurity is beyond the scope of traditional firewall and VPN solutions would struggle to keep up with the scale and variety of modern attacks. What do you mean by that? Give an example, tell me what you mean by that sentence, and what examples can you give? >> Well, I'd say the most important thing is that firewalls were initially built to protect what we call north-south traffic. In other words, traffic that's coming in from the internet into the organization and back out. But now with network expansion, cloud adoption and more and more devices, industrial devices being connected, these firewalls cannot defend against that. They simply were not architected for it, they cannot scale to those proportions, and even if you're using software only versions, those aren't effective either because they do not protect against east-west or in other words lateral traffic. So if you're an organization moving IIOT data from your OT systems across your network into IP analytics systems or software, that's lateral movement. Your firewall- traditional firewall, just not going to be able to handle that and protect against it, so in simple terms, we need a new overlay not to say that firewalls are going away any time soon, they can still protect north-south traffic, but we need a new type of overlay that can protect this type of traffic, micro-segmentation is the strategy to do that and using host identity protocol or HIP protocol is what fills that gap that your traditional security tools were not designed to protect against. >> Greg, I want you to weigh in on this, because you're in this business now, you know the IT world, the criticality of what you just said is super critical to the nature of business, you know the catastrophic example's there, but IT does not move that fast, you know IT, IT'S like molasses, I mean they're slow. What is going to light a fire under IT to get them to be sensitive, I mean it's pretty obvious, can they get there, do they have to re-structure what has to happen in the IT world, because you know, it is a catastrophic end game here if they don't nail down this traffic protection. >> Well a part of the- you know, part of it is education. Because we've been- we've seen wave and wave of incremental innovation in the network, and when it happened it seemed so big and and it produced huge market cap growth with a lot of companies, you know play this guessing game of who is really connecting to the network. And it's evolved kind of gradually, to this big leap we have ahead of us, and IT is going to have to become aware that IIOT is a fundamentally different problem and challenge to solve, and that's going to require new thinking, new purpose built, like Gabe said, approaches, anything like the traditional firewall segmentation is just not going to address what we talked about, the scale issues, the resilience right? So, some of these devices, you don't want them off for one or two percent of the time. And the implications are that it's much more serious. So I think that, you know, more types of attacks are inevitable, and they're going to be even more catastrophic, and we're all aware that NotPetya and WannaCry raised a lot of eyebrows just for how quick it spread and the damage it caused. And we've just seen VxWorks vulnerabilities being announced. We need to prepare now. >> Malware and worms are still popular, it's a problem. Well guys, thanks so much for spending the time on this panel, I'll give you the final word here, share what you think is going to happen over the next 24 months, 12 months, is it going to take catastrophic failure, what's going to happen in your mind, what's going to end up being the trajectory over the next, you know say year. >> Well, unfortunately, sometimes it might take a catastrophic event to get things moving, hopefully not, but I think there's growing recognition as IIOT is growing, that they need new ways to secure this movement of data between OT and IT, and in order to facilitate that securing of data, you're going to have to have that OT and IT convergence occur, because the risk, as you sort of eluded to earlier John, we hear in the headlines about massive data breaches and all this data that's stolen. But the risk in IIOT is not only the exfiltration of the data, the risk is that the attacker has the capacity to take over the infrastructure. And if that happens in a hospital, if it happens with a water treatment facility or government type of defense installation, the outcomes can be disastrous. So the first thing that has to happen is OT IT convergence. Second, they have to start thinking strategically from a standpoint that they have already been breached, and so that changes their viewpoint about the technologies that they have to deploy, and where they have to move to to efficiently get to what I call the iddies, and that's the- you still need the availability, you've got to have visibility into this traffic, you need reliability of this network, obviously it's got to be at scale, it's got to be manageable, and you need security. >> Well, we'd like to have you on again Gabe, because we've talked about this from a national security perspective, not only the hackers potentially risking the business risk there, there's a national security overlay because you know, if the government's attacking our businesses, that's like showing up on the shores of our country, its the government's job to protect the freedom's and safety of the citizens, that includes companies. So why are companies defending themselves with all this capability, what's the role of government in all of this, that's a very important, I think a longer conversation. So, let's pick that one up, a separate one, my favorite topic these days. Critical infrastructure even if it's just business it's the grid, it's the plants that run our country. >> And John, what I'd like to add to that is, I was talking to a friend of mine who's a CIO down here in California yesterday, and we were talking about the ransomware right, that was taking down all these cities. And you know, he goes well the difference between what you guys are talking about and that, is that you can back up your IT systems, right, into the cloud, and that's a growing business to kind of protect and then replicate game over, and he goes, can you back up a hospital? Can you back up a manufacturing plant? Can you back up a fleet of ships? You know, can you back up a control center? Not really, when you lose physical control, it's game over. And people, I think that really needs to sink in. And that was, I think in Gabe's paper when I first read it, that's what really struck me about it, this is a different ballgame. >> Well, I mean, there's many points, there's the technical point there, and there's also the societal point of- you imagine things being taken over by hackers that physically can harm people, and that's again the societal side, technically the incompatible architecture's coming home to roost now, because there's the problem right there, that's the collision that's happened I think, and a lot of education needs to happen fast, Gabe, thanks for writing that paper critical infrastructure against cyber and securing it, Bryan thanks for coming on appreciate it, you want to say, get the final word Bryan, go ahead. Your thoughts, next 12 months. >> I think that if our future, it depends on OT and IT coming together and a lot of education, a lot of change, I don't think we're going to get there, I think that what's going to happen in the next 24 months is that you know, there are lots of innovative schemes and companies and people, working on this and what we need to do is lay down infrastructure that allows OT and IT to keep operating, and not have to do a forklift upgrade and everything that they do, their processes or teach the things how to protect themselves, and again I'm going to go back to air gaps in network, make a logical air gap, if you imagine driverless cars driving around they're not going to, imagine them sharing the same network that we're using to use Snapchat and look at cities and you know, sitting on the internet and looking at Facebook. We're not going to want that. So we need to try and figure out a way to separate the location of the thing from the identity, create policies in terms of the identity, manage that a new layer, and do it in such a way that doesn't change IT. To me that's the key, 'cause I- we've said it here, IT's doesn't move that fast, they can't. It's not a matter of willpower, it's a matter of momentum and intertia. >> Well, I think the forcing function on this is going to be catastrophic event, the subtitle of this panel, apocalypse now or later. And in my opinion, Greg's been, you know, on this JetEye department of defense story. I believe this is one of the most important stories in the technology industry in a long long time, it really highlights the confluence and convergence of two differently designed infrastructure technologies, that have to in a very short time, be re-platformed at high speed, in a very fast short time frame, because the stakes are so high. So guys, thanks so much for spending the time here on this power panel, IIOT, industrial IOT and cyber security apocalypse now or later, something's going to have to happen, it has to happen fast. Gabe, Bryan, Greg thanks for taking the time. This is a cube conversation here in Palo Alto power panel, I'm John Furrier, thanks for watching. (upbeat music)

>> So as folks make their way over from Javits I'm going to give you the least interesting part of the evening and that's my segment in which I welcome you here, introduce myself, lay out what what we're going to do for the next couple of hours. So first off, thank you very much for coming. As all of you know Wikibon is a part of SiliconANGLE which also includes theCUBE, so if you look around, this is what we have been doing for the past couple of days here in the TheCUBE. We've been inviting some significant thought leaders from over on the show and in incredibly expensive limousines driven them up the street to come on to TheCUBE and spend time with us and talk about some of the things that are happening in the industry today that are especially important. We tore it down, and we're having this party tonight. So we want to thank you very much for coming and look forward to having more conversations with all of you. Now what are we going to talk about? Well Wikibon is the research arm of SiliconANGLE. So we take data that comes out of TheCUBE and other places and we incorporated it into our research. And work very closely with large end users and large technology companies regarding how to make better decisions in this incredibly complex, incredibly important transformative world of digital business. What we're going to talk about tonight, and I've got a couple of my analysts assembled, and we're also going to have a panel, is this notion of software is eating the Edge. Now most of you have probably heard Marc Andreessen, the venture capitalist and developer, original developer of Netscape many years ago, talk about how software's eating the world. Well, if software is truly going to eat the world, it's going to eat at, it's going to take the big chunks, big bites at the Edge. That's where the actual action's going to be. And what we want to talk about specifically is the entangling of the internet or the industrial internet of things and IoT with analytics. So that's what we're going to talk about over the course of the next couple of hours. To do that we're going to, I've already blown the schedule, that's on me. But to do that I'm going to spend a couple minutes talking about what we regard as the essential digital business capabilities which includes analytics and Big Data, and includes IIoT and we'll explain at least in our position why those two things come together the way that they do. But I'm going to ask the august and revered Neil Raden, Wikibon analyst to come on up and talk about harvesting value at the Edge. 'Cause there are some, not now Neil, when we're done, when I'm done. So I'm going to ask Neil to come on up and we'll talk, he's going to talk about harvesting value at the Edge. And then Jim Kobielus will follow up with him, another Wikibon analyst, he'll talk specifically about how we're going to take that combination of analytics and Edge and turn it into the new types of systems and software that are going to sustain this significant transformation that's going on. And then after that, I'm going to ask Neil and Jim to come, going to invite some other folks up and we're going to run a panel to talk about some of these issues and do a real question and answer. So the goal here is before we break for drinks is to create a community feeling within the room. That includes smart people here, smart people in the audience having a conversation ultimately about some of these significant changes so please participate and we look forward to talking about the rest of it. All right, let's get going! What is digital business? One of the nice things about being an analyst is that you can reach back on people who were significantly smarter than you and build your points of view on the shoulders of those giants including Peter Drucker. Many years ago Peter Drucker made the observation that the purpose of business is to create and keep a customer. Not better shareholder value, not anything else. It is about creating and keeping your customer. Now you can argue with that, at the end of the day, if you don't have customers, you don't have a business. Now the observation that we've made, what we've added to that is that we've made the observation that the difference between business and digital business essentially is one thing. That's data. A digital business uses data to differentially create and keep customers. That's the only difference. If you think about the difference between taxi cab companies here in New York City, every cab that I've been in in the last three days has bothered me about Uber. The reason, the difference between Uber and a taxi cab company is data. That's the primary difference. Uber uses data as an asset. And we think this is the fundamental feature of digital business that everybody has to pay attention to. How is a business going to use data as an asset? Is the business using data as an asset? Is a business driving its engagement with customers, the role of its product et cetera using data? And if they are, they are becoming a more digital business. Now when you think about that, what we're really talking about is how are they going to put data to work? How are they going to take their customer data and their operational data and their financial data and any other kind of data and ultimately turn that into superior engagement or improved customer experience or more agile operations or increased automation? Those are the kinds of outcomes that we're talking about. But it is about putting data to work. That's fundamentally what we're trying to do within a digital business. Now that leads to an observation about the crucial strategic business capabilities that every business that aspires to be more digital or to be digital has to put in place. And I want to be clear. When I say strategic capabilities I mean something specific. When you talk about, for example technology architecture or information architecture there is this notion of what capabilities does your business need? Your business needs capabilities to pursue and achieve its mission. And in the digital business these are the capabilities that are now additive to this core question, ultimately of whether or not the company is a digital business. What are the three capabilities? One, you have to capture data. Not just do a good job of it, but better than your competition. You have to capture data better than your competition. In a way that is ultimately less intrusive on your markets and on your customers. That's in many respects, one of the first priorities of the internet of things and people. The idea of using sensors and related technologies to capture more data. Once you capture that data you have to turn it into value. You have to do something with it that creates business value so you can do a better job of engaging your markets and serving your customers. And that essentially is what we regard as the basis of Big Data. Including operations, including financial performance and everything else, but ultimately it's taking the data that's being captured and turning it into value within the business. The last point here is that once you have generated a model, or an insight or some other resource that you can act upon, you then have to act upon it in the real world. We call that systems of agency, the ability to enact based on data. Now I want to spend just a second talking about systems of agency 'cause we think it's an interesting concept and it's something Jim Kobielus is going to talk about a little bit later. When we say systems of agency, what we're saying is increasingly machines are acting on behalf of a brand. Or systems, combinations of machines and people are acting on behalf of the brand. And this whole notion of agency is the idea that ultimately these systems are now acting as the business's agent. They are at the front line of engaging customers. It's an extremely rich proposition that has subtle but crucial implications. For example I was talking to a senior decision maker at a business today and they made a quick observation, they talked about they, on their way here to New York City they had followed a woman who was going through security, opened up her suitcase and took out a bird. And then went through security with the bird. And the reason why I bring this up now is as TSA was trying to figure out how exactly to deal with this, the bird started talking and repeating things that the woman had said and many of those things, in fact, might have put her in jail. Now in this case the bird is not an agent of that woman. You can't put the woman in jail because of what the bird said. But increasingly we have to ask ourselves as we ask machines to do more on our behalf, digital instrumentation and elements to do more on our behalf, it's going to have blow back and an impact on our brand if we don't do it well. I want to draw that forward a little bit because I suggest there's going to be a new lifecycle for data. And the way that we think about it is we have the internet or the Edge which is comprised of things and crucially people, using sensors, whether they be smaller processors in control towers or whether they be phones that are tracking where we go, and this crucial element here is something that we call information transducers. Now a transducer in a traditional sense is something that takes energy from one form to another so that it can perform new types of work. By information transducer I essentially mean it takes information from one form to another so it can perform another type of work. This is a crucial feature of data. One of the beauties of data is that it can be used in multiple places at multiple times and not engender significant net new costs. It's one of the few assets that you can say about that. So the concept of an information transducer's really important because it's the basis for a lot of transformations of data as data flies through organizations. So we end up with the transducers storing data in the form of analytics, machine learning, business operations, other types of things, and then it goes back and it's transduced, back into to the real world as we program the real world and turning into these systems of agency. So that's the new lifecycle. And increasingly, that's how we have to think about data flows. Capturing it, turning it into value and having it act on our behalf in front of markets. That could have enormous implications for how ultimately money is spent over the next few years. So Wikibon does a significant amount of market research in addition to advising our large user customers. And that includes doing studies on cloud, public cloud, but also studies on what's happening within the analytics world. And if you take a look at it, what we basically see happening over the course of the next few years is significant investments in software and also services to get the word out. But we also expect there's going to be a lot of hardware. A significant amount of hardware that's ultimately sold within this space. And that's because of something that we call true private cloud. This concept of ultimately a business increasingly being designed and architected around the idea of data assets means that the reality, the physical realities of how data operates, how much it costs to store it or move it, the issues of latency, the issues of intellectual property protection as well as things like the regulatory regimes that are being put in place to govern how data gets used in between locations. All of those factors are going to drive increased utilization of what we call true private cloud. On premise technologies that provide the cloud experience but act where the data naturally needs to be processed. I'll come a little bit more to that in a second. So we think that it's going to be a relatively balanced market, a lot of stuff is going to end up in the cloud, but as Neil and Jim will talk about, there's going to be an enormous amount of analytics that pulls an enormous amount of data out to the Edge 'cause that's where the action's going to be. Now one of the things I want to also reveal to you is we've done a fair amount of data, we've done a fair amount of research around this question of where or how will data guide decisions about infrastructure? And in particular the Edge is driving these conversations. So here is a piece of research that one of our cohorts at Wikibon did, David Floyer. Taking a look at IoT Edge cost comparisons over a three year period. And it showed on the left hand side, an example where the sensor towers and other types of devices were streaming data back into a central location in a wind farm, stylized wind farm example. Very very expensive. Significant amounts of money end up being consumed, significant resources end up being consumed by the cost of moving the data from one place to another. Now this is even assuming that latency does not become a problem. The second example that we looked at is if we kept more of that data at the Edge and processed at the Edge. And literally it is a 85 plus percent cost reduction to keep more of the data at the Edge. Now that has enormous implications, how we think about big data, how we think about next generation architectures, et cetera. But it's these costs that are going to be so crucial to shaping the decisions that we make over the next two years about where we put hardware, where we put resources, what type of automation is possible, and what types of technology management has to be put in place. Ultimately we think it's going to lead to a structure, an architecture in the infrastructure as well as applications that is informed more by moving cloud to the data than moving the data to the cloud. That's kind of our fundamental proposition is that the norm in the industry has been to think about moving all data up to the cloud because who wants to do IT? It's so much cheaper, look what Amazon can do. Or what AWS can do. All true statements. Very very important in many respects. But most businesses today are starting to rethink that simple proposition and asking themselves do we have to move our business to the cloud, or can we move the cloud to the business? And increasingly what we see happening as we talk to our large customers about this, is that the cloud is being extended out to the Edge, we're moving the cloud and cloud services out to the business. Because of economic reasons, intellectual property control reasons, regulatory reasons, security reasons, any number of other reasons. It's just a more natural way to deal with it. And of course, the most important reason is latency. So with that as a quick backdrop, if I may quickly summarize, we believe fundamentally that the difference today is that businesses are trying to understand how to use data as an asset. And that requires an investment in new sets of technology capabilities that are not cheap, not simple and require significant thought, a lot of planning, lot of change within an IT and business organizations. How we capture data, how we turn it into value, and how we translate that into real world action through software. That's going to lead to a rethinking, ultimately, based on cost and other factors about how we deploy infrastructure. How we use the cloud so that the data guides the activity and not the choice of cloud supplier determines or limits what we can do with our data. And that's going to lead to this notion of true private cloud and elevate the role the Edge plays in analytics and all other architectures. So I hope that was perfectly clear. And now what I want to do is I want to bring up Neil Raden. Yes, now's the time Neil! So let me invite Neil up to spend some time talking about harvesting value at the Edge. Can you see his, all right. Got it. >> Oh boy. Hi everybody. Yeah, this is a really, this is a really big and complicated topic so I decided to just concentrate on something fairly simple, but I know that Peter mentioned customers. And he also had a picture of Peter Drucker. I had the pleasure in 1998 of interviewing Peter and photographing him. Peter Drucker, not this Peter. Because I'd started a magazine called Hired Brains. It was for consultants. And Peter said, Peter said a number of really interesting things to me, but one of them was his definition of a customer was someone who wrote you a check that didn't bounce. He was kind of a wag. He was! So anyway, he had to leave to do a video conference with Jack Welch and so I said to him, how do you charge Jack Welch to spend an hour on a video conference? And he said, you know I have this theory that you should always charge your client enough that it hurts a little bit or they don't take you seriously. Well, I had the chance to talk to Jack's wife, Suzie Welch recently and I told her that story and she said, "Oh he's full of it, Jack never paid "a dime for those conferences!" (laughs) So anyway, all right, so let's talk about this. To me, things about, engineered things like the hardware and network and all these other standards and so forth, we haven't fully developed those yet, but they're coming. As far as I'm concerned, they're not the most interesting thing. The most interesting thing to me in Edge Analytics is what you're going to get out of it, what the result is going to be. Making sense of this data that's coming. And while we're on data, something I've been thinking a lot lately because everybody I've talked to for the last three days just keeps talking to me about data. I have this feeling that data isn't actually quite real. That any data that we deal with is the result of some process that's captured it from something else that's actually real. In other words it's proxy. So it's not exactly perfect. And that's why we've always had these problems about customer A, customer A, customer A, what's their definition? What's the definition of this, that and the other thing? And with sensor data, I really have the feeling, when companies get, not you know, not companies, organizations get instrumented and start dealing with this kind of data what they're going to find is that this is the first time, and I've been involved in analytics, I don't want to date myself, 'cause I know I look young, but the first, I've been dealing with analytics since 1975. And everything we've ever done in analytics has involved pulling data from some other system that was not designed for analytics. But if you think about sensor data, this is data that we're actually going to catch the first time. It's going to be ours! We're not going to get it from some other source. It's going to be the real deal, to the extent that it's the real deal. Now you may say, ya know Neil, a sensor that's sending us information about oil pressure or temperature or something like that, how can you quarrel with that? Well, I can quarrel with it because I don't know if the sensor's doing it right. So we still don't know, even with that data, if it's right, but that's what we have to work with. Now, what does that really mean? Is that we have to be really careful with this data. It's ours, we have to take care of it. We don't get to reload it from source some other day. If we munge it up it's gone forever. So that has, that has very serious implications, but let me, let me roll you back a little bit. The way I look at analytics is it's come in three different eras. And we're entering into the third now. The first era was business intelligence. It was basically built and governed by IT, it was system of record kind of reporting. And as far as I can recall, it probably started around 1988 or at least that's the year that Howard Dresner claims to have invented the term. I'm not sure it's true. And things happened before 1988 that was sort of like BI, but 88 was when they really started coming out, that's when we saw BusinessObjects and Cognos and MicroStrategy and those kinds of things. The second generation just popped out on everybody else. We're all looking around at BI and we were saying why isn't this working? Why are only five people in the organization using this? Why are we not getting value out of this massive license we bought? And along comes companies like Tableau doing data discovery, visualization, data prep and Line of Business people are using this now. But it's still the same kind of data sources. It's moved out a little bit, but it still hasn't really hit the Big Data thing. Now we're in third generation, so we not only had Big Data, which has come and hit us like a tsunami, but we're looking at smart discovery, we're looking at machine learning. We're looking at AI induced analytics workflows. And then all the natural language cousins. You know, natural language processing, natural language, what's? Oh Q, natural language query. Natural language generation. Anybody here know what natural language generation is? Yeah, so what you see now is you do some sort of analysis and that tool comes up and says this chart is about the following and it used the following data, and it's blah blah blah blah blah. I think it's kind of wordy and it's going to refined some, but it's an interesting, it's an interesting thing to do. Now, the problem I see with Edge Analytics and IoT in general is that most of the canonical examples we talk about are pretty thin. I know we talk about autonomous cars, I hope to God we never have them, 'cause I'm a car guy. Fleet Management, I think Qualcomm started Fleet Management in 1988, that is not a new application. Industrial controls. I seem to remember, I seem to remember Honeywell doing industrial controls at least in the 70s and before that I wasn't, I don't want to talk about what I was doing, but I definitely wasn't in this industry. So my feeling is we all need to sit down and think about this and get creative. Because the real value in Edge Analytics or IoT, whatever you want to call it, the real value is going to be figuring out something that's new or different. Creating a brand new business. Changing the way an operation happens in a company, right? And I think there's a lot of smart people out there and I think there's a million apps that we haven't even talked about so, if you as a vendor come to me and tell me how great your product is, please don't talk to me about autonomous cars or Fleet Managing, 'cause I've heard about that, okay? Now, hardware and architecture are really not the most interesting thing. We fell into that trap with data warehousing. We've fallen into that trap with Big Data. We talk about speeds and feeds. Somebody said to me the other day, what's the narrative of this company? This is a technology provider. And I said as far as I can tell, they don't have a narrative they have some products and they compete in a space. And when they go to clients and the clients say, what's the value of your product? They don't have an answer for that. So we don't want to fall into this trap, okay? Because IoT is going to inform you in ways you've never even dreamed about. Unfortunately some of them are going to be really stinky, you know, they're going to be really bad. You're going to lose more of your privacy, it's going to get harder to get, I dunno, mortgage for example, I dunno, maybe it'll be easier, but in any case, it's not going to all be good. So let's really think about what you want to do with this technology to do something that's really valuable. Cost takeout is not the place to justify an IoT project. Because number one, it's very expensive, and number two, it's a waste of the technology because you should be looking at, you know the old numerator denominator thing? You should be looking at the numerators and forget about the denominators because that's not what you do with IoT. And the other thing is you don't want to get over confident. Actually this is good advice about anything, right? But in this case, I love this quote by Derek Sivers He's a pretty funny guy. He said, "If more information was the answer, "then we'd all be billionaires with perfect abs." I'm not sure what's on his wishlist, but you know, I would, those aren't necessarily the two things I would think of, okay. Now, what I said about the data, I want to explain some more. Big Data Analytics, if you look at this graphic, it depicts it perfectly. It's a bunch of different stuff falling into the funnel. All right? It comes from other places, it's not original material. And when it comes in, it's always used as second hand data. Now what does that mean? That means that you have to figure out the semantics of this information and you have to find a way to put it together in a way that's useful to you, okay. That's Big Data. That's where we are. How is that different from IoT data? It's like I said, IoT is original. You can put it together any way you want because no one else has ever done that before. It's yours to construct, okay. You don't even have to transform it into a schema because you're creating the new application. But the most important thing is you have to take care of it 'cause if you lose it, it's gone. It's the original data. It's the same way, in operational systems for a long long time we've always been concerned about backup and security and everything else. You better believe this is a problem. I know a lot of people think about streaming data, that we're going to look at it for a minute, and we're going to throw most of it away. Personally I don't think that's going to happen. I think it's all going to be saved, at least for a while. Now, the governance and security, oh, by the way, I don't know where you're going to find a presentation where somebody uses a newspaper clipping about Vladimir Lenin, but here it is, enjoy yourselves. I believe that when people think about governance and security today they're still thinking along the same grids that we thought about it all along. But this is very very different and again, I'm sorry I keep thrashing this around, but this is treasured data that has to be carefully taken care of. Now when I say governance, my experience has been over the years that governance is something that IT does to make everybody's lives miserable. But that's not what I mean by governance today. It means a comprehensive program to really secure the value of the data as an asset. And you need to think about this differently. Now the other thing is you may not get to think about it differently, because some of the stuff may end up being subject to regulation. And if the regulators start regulating some of this, then that'll take some of the degrees of freedom away from you in how you put this together, but you know, that's the way it works. Now, machine learning, I think I told somebody the other day that claims about machine learning in software products are as common as twisters in trail parks. And a lot of it is not really what I'd call machine learning. But there's a lot of it around. And I think all of the open source machine learning and artificial intelligence that's popped up, it's great because all those math PhDs who work at Home Depot now have something to do when they go home at night and they construct this stuff. But if you're going to have machine learning at the Edge, here's the question, what kind of machine learning would you have at the Edge? As opposed to developing your models back at say, the cloud, when you transmit the data there. The devices at the Edge are not very powerful. And they don't have a lot of memory. So you're only going to be able to do things that have been modeled or constructed somewhere else. But that's okay. Because machine learning algorithm development is actually slow and painful. So you really want the people who know how to do this working with gobs of data creating models and testing them offline. And when you have something that works, you can put it there. Now there's one thing I want to talk about before I finish, and I think I'm almost finished. I wrote a book about 10 years ago about automated decision making and the conclusion that I came up with was that little decisions add up, and that's good. But it also means you don't have to get them all right. But you don't want computers or software making decisions unattended if it involves human life, or frankly any life. Or the environment. So when you think about the applications that you can build using this architecture and this technology, think about the fact that you're not going to be doing air traffic control, you're not going to be monitoring crossing guards at the elementary school. You're going to be doing things that may seem fairly mundane. Managing machinery on the factory floor, I mean that may sound great, but really isn't that interesting. Managing well heads, drilling for oil, well I mean, it's great to the extent that it doesn't cause wells to explode, but they don't usually explode. What it's usually used for is to drive the cost out of preventative maintenance. Not very interesting. So use your heads. Come up with really cool stuff. And any of you who are involved in Edge Analytics, the next time I talk to you I don't want to hear about the same five applications that everybody talks about. Let's hear about some new ones. So, in conclusion, I don't really have anything in conclusion except that Peter mentioned something about limousines bringing people up here. On Monday I was slogging up and down Park Avenue and Madison Avenue with my client and we were visiting all the hedge funds there because we were doing a project with them. And in the miserable weather I looked at him and I said, for godsake Paul, where's the black car? And he said, that was the 90s. (laughs) Thank you. So, Jim, up to you. (audience applauding) This is terrible, go that way, this was terrible coming that way. >> Woo, don't want to trip! And let's move to, there we go. Hi everybody, how ya doing? Thanks Neil, thanks Peter, those were great discussions. So I'm the third leg in this relay race here, talking about of course how software is eating the world. And focusing on the value of Edge Analytics in a lot of real world scenarios. Programming the real world for, to make the world a better place. So I will talk, I'll break it out analytically in terms of the research that Wikibon is doing in the area of the IoT, but specifically how AI intelligence is being embedded really to all material reality potentially at the Edge. But mobile applications and industrial IoT and the smart appliances and self driving vehicles. I will break it out in terms of a reference architecture for understanding what functions are being pushed to the Edge to hardware, to our phones and so forth to drive various scenarios in terms of real world results. So I'll move a pace here. So basically AI software or AI microservices are being infused into Edge hardware as we speak. What we see is more vendors of smart phones and other, real world appliances and things like smart driving, self driving vehicles. What they're doing is they're instrumenting their products with computer vision and natural language processing, environmental awareness based on sensing and actuation and those capabilities and inferences that these devices just do to both provide human support for human users of these devices as well as to enable varying degrees of autonomous operation. So what I'll be talking about is how AI is a foundation for data driven systems of agency of the sort that Peter is talking about. Infusing data driven intelligence into everything or potentially so. As more of this capability, all these algorithms for things like, ya know for doing real time predictions and classifications, anomaly detection and so forth, as this functionality gets diffused widely and becomes more commoditized, you'll see it burned into an ever-wider variety of hardware architecture, neuro synaptic chips, GPUs and so forth. So what I've got here in front of you is a sort of a high level reference architecture that we're building up in our research at Wikibon. So AI, artificial intelligence is a big term, a big paradigm, I'm not going to unpack it completely. Of course we don't have oodles of time so I'm going to take you fairly quickly through the high points. It's a driver for systems of agency. Programming the real world. Transducing digital inputs, the data, to analog real world results. Through the embedding of this capability in the IoT, but pushing more and more of it out to the Edge with points of decision and action in real time. And there are four capabilities that we're seeing in terms of AI enabled, enabling capabilities that are absolutely critical to software being pushed to the Edge are sensing, actuation, inference and Learning. Sensing and actuation like Peter was describing, it's about capturing data from the environment within which a device or users is operating or moving. And then actuation is the fancy term for doing stuff, ya know like industrial IoT, it's obviously machine controlled, but clearly, you know self driving vehicles is steering a vehicle and avoiding crashing and so forth. Inference is the meat and potatoes as it were of AI. Analytics does inferences. It infers from the data, the logic of the application. Predictive logic, correlations, classification, abstractions, differentiation, anomaly detection, recognizing faces and voices. We see that now with Apple and the latest version of the iPhone is embedding face recognition as a core, as the core multifactor authentication technique. Clearly that's a harbinger of what's going to be universal fairly soon which is that depends on AI. That depends on convolutional neural networks, that is some heavy hitting processing power that's necessary and it's processing the data that's coming from your face. So that's critically important. So what we're looking at then is the AI software is taking root in hardware to power continuous agency. Getting stuff done. Powered decision support by human beings who have to take varying degrees of action in various environments. We don't necessarily want to let the car steer itself in all scenarios, we want some degree of override, for lots of good reasons. They want to protect life and limb including their own. And just more data driven automation across the internet of things in the broadest sense. So unpacking this reference framework, what's happening is that AI driven intelligence is powering real time decisioning at the Edge. Real time local sensing from the data that it's capturing there, it's ingesting the data. Some, not all of that data, may be persistent at the Edge. Some, perhaps most of it, will be pushed into the cloud for other processing. When you have these highly complex algorithms that are doing AI deep learning, multilayer, to do a variety of anti-fraud and higher level like narrative, auto-narrative roll-ups from various scenes that are unfolding. A lot of this processing is going to begin to happen in the cloud, but a fair amount of the more narrowly scoped inferences that drive real time decision support at the point of action will be done on the device itself. Contextual actuation, so it's the sensor data that's captured by the device along with other data that may be coming down in real time streams through the cloud will provide the broader contextual envelope of data needed to drive actuation, to drive various models and rules and so forth that are making stuff happen at the point of action, at the Edge. Continuous inference. What it all comes down to is that inference is what's going on inside the chips at the Edge device. And what we're seeing is a growing range of hardware architectures, GPUs, CPUs, FPGAs, ASIC, Neuro synaptic chips of all sorts playing in various combinations that are automating more and more very complex inference scenarios at the Edge. And not just individual devices, swarms of devices, like drones and so forth are essentially an Edge unto themselves. You'll see these tiered hierarchies of Edge swarms that are playing and doing inferences of ever more complex dynamic nature. And much of this will be, this capability, the fundamental capabilities that is powering them all will be burned into the hardware that powers them. And then adaptive learning. Now I use the term learning rather than training here, training is at the core of it. Training means everything in terms of the predictive fitness or the fitness of your AI services for whatever task, predictions, classifications, face recognition that you, you've built them for. But I use the term learning in a broader sense. It's what's make your inferences get better and better, more accurate over time is that you're training them with fresh data in a supervised learning environment. But you can have reinforcement learning if you're doing like say robotics and you don't have ground truth against which to train the data set. You know there's maximize a reward function versus minimize a loss function, you know, the standard approach, the latter for supervised learning. There's also, of course, the issue, or not the issue, the approach of unsupervised learning with cluster analysis critically important in a lot of real world scenarios. So Edge AI Algorithms, clearly, deep learning which is multilayered machine learning models that can do abstractions at higher and higher levels. Face recognition is a high level abstraction. Faces in a social environment is an even higher level of abstraction in terms of groups. Faces over time and bodies and gestures, doing various things in various environments is an even higher level abstraction in terms of narratives that can be rolled up, are being rolled up by deep learning capabilities of great sophistication. Convolutional neural networks for processing images, recurrent neural networks for processing time series. Generative adversarial networks for doing essentially what's called generative applications of all sort, composing music, and a lot of it's being used for auto programming. These are all deep learning. There's a variety of other algorithm approaches I'm not going to bore you with here. Deep learning is essentially the enabler of the five senses of the IoT. Your phone's going to have, has a camera, it has a microphone, it has the ability to of course, has geolocation and navigation capabilities. It's environmentally aware, it's got an accelerometer and so forth embedded therein. The reason that your phone and all of the devices are getting scary sentient is that they have the sensory modalities and the AI, the deep learning that enables them to make environmentally correct decisions in the wider range of scenarios. So machine learning is the foundation of all of this, but there are other, I mean of deep learning, artificial neural networks is the foundation of that. But there are other approaches for machine learning I want to make you aware of because support vector machines and these other established approaches for machine learning are not going away but really what's driving the show now is deep learning, because it's scary effective. And so that's where most of the investment in AI is going into these days for deep learning. AI Edge platforms, tools and frameworks are just coming along like gangbusters. Much development of AI, of deep learning happens in the context of your data lake. This is where you're storing your training data. This is the data that you use to build and test to validate in your models. So we're seeing a deepening stack of Hadoop and there's Kafka, and Spark and so forth that are driving the training (coughs) excuse me, of AI models that are power all these Edge Analytic applications so that that lake will continue to broaden in terms, and deepen in terms of a scope and the range of data sets and the range of modeling, AI modeling supports. Data science is critically important in this scenario because the data scientist, the data science teams, the tools and techniques and flows of data science are the fundamental development paradigm or discipline or capability that's being leveraged to build and to train and to deploy and iterate all this AI that's being pushed to the Edge. So clearly data science is at the center, data scientists of an increasingly specialized nature are necessary to the realization to this value at the Edge. AI frameworks are coming along like you know, a mile a minute. TensorFlow has achieved a, is an open source, most of these are open source, has achieved sort of almost like a defacto standard, status, I'm using the word defacto in air quotes. There's Theano and Keras and xNet and CNTK and a variety of other ones. We're seeing range of AI frameworks come to market, most open source. Most are supported by most of the major tool vendors as well. So at Wikibon we're definitely tracking that, we plan to go deeper in our coverage of that space. And then next best action, powers recommendation engines. I mean next best action decision automation of the sort of thing Neil's covered in a variety of contexts in his career is fundamentally important to Edge Analytics to systems of agency 'cause it's driving the process automation, decision automation, sort of the targeted recommendations that are made at the Edge to individual users as well as to process that automation. That's absolutely necessary for self driving vehicles to do their jobs and industrial IoT. So what we're seeing is more and more recommendation engine or recommender capabilities powered by ML and DL are going to the Edge, are already at the Edge for a variety of applications. Edge AI capabilities, like I said, there's sensing. And sensing at the Edge is becoming ever more rich, mixed reality Edge modalities of all sort are for augmented reality and so forth. We're just seeing a growth in certain, the range of sensory modalities that are enabled or filtered and analyzed through AI that are being pushed to the Edge, into the chip sets. Actuation, that's where robotics comes in. Robotics is coming into all aspects of our lives. And you know, it's brainless without AI, without deep learning and these capabilities. Inference, autonomous edge decisioning. Like I said, it's, a growing range of inferences that are being done at the Edge. And that's where it has to happen 'cause that's the point of decision. Learning, training, much training, most training will continue to be done in the cloud because it's very data intensive. It's a grind to train and optimize an AI algorithm to do its job. It's not something that you necessarily want to do or can do at the Edge at Edge devices so, the models that are built and trained in the cloud are pushed down through a dev ops process down to the Edge and that's the way it will work pretty much in most AI environments, Edge analytics environments. You centralize the modeling, you decentralize the execution of the inference models. The training engines will be in the cloud. Edge AI applications. I'll just run you through sort of a core list of the ones that are coming into, already come into the mainstream at the Edge. Multifactor authentication, clearly the Apple announcement of face recognition is just a harbinger of the fact that that's coming to every device. Computer vision speech recognition, NLP, digital assistance and chat bots powered by natural language processing and understanding, it's all AI powered. And it's becoming very mainstream. Emotion detection, face recognition, you know I could go on and on but these are like the core things that everybody has access to or will by 2020 and they're core devices, mass market devices. Developers, designers and hardware engineers are coming together to pool their expertise to build and train not just the AI, but also the entire package of hardware in UX and the orchestration of real world business scenarios or life scenarios that all this intelligence, the submitted intelligence enables and most, much of what they build in terms of AI will be containerized as micro services through Docker and orchestrated through Kubernetes as full cloud services in an increasingly distributed fabric. That's coming along very rapidly. We can see a fair amount of that already on display at Strata in terms of what the vendors are doing or announcing or who they're working with. The hardware itself, the Edge, you know at the Edge, some data will be persistent, needs to be persistent to drive inference. That's, and you know to drive a variety of different application scenarios that need some degree of historical data related to what that device in question happens to be sensing or has sensed in the immediate past or you know, whatever. The hardware itself is geared towards both sensing and increasingly persistence and Edge driven actuation of real world results. The whole notion of drones and robotics being embedded into everything that we do. That's where that comes in. That has to be powered by low cost, low power commodity chip sets of various sorts. What we see right now in terms of chip sets is it's a GPUs, Nvidia has gone real far and GPUs have come along very fast in terms of power inference engines, you know like the Tesla cars and so forth. But GPUs are in many ways the core hardware sub straight for in inference engines in DL so far. But to become a mass market phenomenon, it's got to get cheaper and lower powered and more commoditized, and so we see a fair number of CPUs being used as the hardware for Edge Analytic applications. Some vendors are fairly big on FPGAs, I believe Microsoft has gone fairly far with FPGAs inside DL strategy. ASIC, I mean, there's neuro synaptic chips like IBM's got one. There's at least a few dozen vendors of neuro synaptic chips on the market so at Wikibon we're going to track that market as it develops. And what we're seeing is a fair number of scenarios where it's a mixed environment where you use one chip set architecture at the inference side of the Edge, and other chip set architectures that are driving the DL as processed in the cloud, playing together within a common architecture. And we see some, a fair number of DL environments where the actual training is done in the cloud on Spark using CPUs and parallelized in memory, but pushing Tensorflow models that might be trained through Spark down to the Edge where the inferences are done in FPGAs and GPUs. Those kinds of mixed hardware scenarios are very, very, likely to be standard going forward in lots of areas. So analytics at the Edge power continuous results is what it's all about. The whole point is really not moving the data, it's putting the inference at the Edge and working from the data that's already captured and persistent there for the duration of whatever action or decision or result needs to be powered from the Edge. Like Neil said cost takeout alone is not worth doing. Cost takeout alone is not the rationale for putting AI at the Edge. It's getting new stuff done, new kinds of things done in an automated consistent, intelligent, contextualized way to make our lives better and more productive. Security and governance are becoming more important. Governance of the models, governance of the data, governance in a dev ops context in terms of version controls over all those DL models that are built, that are trained, that are containerized and deployed. Continuous iteration and improvement of those to help them learn to do, make our lives better and easier. With that said, I'm going to hand it over now. It's five minutes after the hour. We're going to get going with the Influencer Panel so what we'd like to do is I call Peter, and Peter's going to call our influencers. >> All right, am I live yet? Can you hear me? All right so, we've got, let me jump back in control here. We've got, again, the objective here is to have community take on some things. And so what we want to do is I want to invite five other people up, Neil why don't you come on up as well. Start with Neil. You can sit here. On the far right hand side, Judith, Judith Hurwitz. >> Neil: I'm glad I'm on the left side. >> From the Hurwitz Group. >> From the Hurwitz Group. Jennifer Shin who's affiliated with UC Berkeley. Jennifer are you here? >> She's here, Jennifer where are you? >> She was here a second ago. >> Neil: I saw her walk out she may have, >> Peter: All right, she'll be back in a second. >> Here's Jennifer! >> Here's Jennifer! >> Neil: With 8 Path Solutions, right? >> Yep. >> Yeah 8 Path Solutions. >> Just get my mic. >> Take your time Jen. >> Peter: All right, Stephanie McReynolds. Far left. And finally Joe Caserta, Joe come on up. >> Stephie's with Elysian >> And to the left. So what I want to do is I want to start by having everybody just go around introduce yourself quickly. Judith, why don't we start there. >> I'm Judith Hurwitz, I'm president of Hurwitz and Associates. We're an analyst research and fault leadership firm. I'm the co-author of eight books. Most recent is Cognitive Computing and Big Data Analytics. I've been in the market for a couple years now. >> Jennifer. >> Hi, my name's Jennifer Shin. I'm the founder and Chief Data Scientist 8 Path Solutions LLC. We do data science analytics and technology. We're actually about to do a big launch next month, with Box actually. >> We're apparent, are we having a, sorry Jennifer, are we having a problem with Jennifer's microphone? >> Man: Just turn it back on? >> Oh you have to turn it back on. >> It was on, oh sorry, can you hear me now? >> Yes! We can hear you now. >> Okay, I don't know how that turned back off, but okay. >> So you got to redo all that Jen. >> Okay, so my name's Jennifer Shin, I'm founder of 8 Path Solutions LLC, it's a data science analytics and technology company. I founded it about six years ago. So we've been developing some really cool technology that we're going to be launching with Box next month. It's really exciting. And I have, I've been developing a lot of patents and some technology as well as teaching at UC Berkeley as a lecturer in data science. >> You know Jim, you know Neil, Joe, you ready to go? >> Joe: Just broke my microphone. >> Joe's microphone is broken. >> Joe: Now it should be all right. >> Jim: Speak into Neil's. >> Joe: Hello, hello? >> I just feel not worthy in the presence of Joe Caserta. (several laughing) >> That's right, master of mics. If you can hear me, Joe Caserta, so yeah, I've been doing data technology solutions since 1986, almost as old as Neil here, but been doing specifically like BI, data warehousing, business intelligence type of work since 1996. And been doing, wholly dedicated to Big Data solutions and modern data engineering since 2009. Where should I be looking? >> Yeah I don't know where is the camera? >> Yeah, and that's basically it. So my company was formed in 2001, it's called Caserta Concepts. We recently rebranded to only Caserta 'cause what we do is way more than just concepts. So we conceptualize the stuff, we envision what the future brings and we actually build it. And we help clients large and small who are just, want to be leaders in innovation using data specifically to advance their business. >> Peter: And finally Stephanie McReynolds. >> I'm Stephanie McReynolds, I had product marketing as well as corporate marketing for a company called Elysian. And we are a data catalog so we help bring together not only a technical understanding of your data, but we curate that data with human knowledge and use automated intelligence internally within the system to make recommendations about what data to use for decision making. And some of our customers like City of San Diego, a large automotive manufacturer working on self driving cars and General Electric use Elysian to help power their solutions for IoT at the Edge. >> All right so let's jump right into it. And again if you have a question, raise your hand, and we'll do our best to get it to the floor. But what I want to do is I want to get seven questions in front of this group and have you guys discuss, slog, disagree, agree. Let's start here. What is the relationship between Big Data AI and IoT? Now Wikibon's put forward its observation that data's being generated at the Edge, that action is being taken at the Edge and then increasingly the software and other infrastructure architectures need to accommodate the realities of how data is going to work in these very complex systems. That's our perspective. Anybody, Judith, you want to start? >> Yeah, so I think that if you look at AI machine learning, all these different areas, you have to be able to have the data learned. Now when it comes to IoT, I think one of the issues we have to be careful about is not all data will be at the Edge. Not all data needs to be analyzed at the Edge. For example if the light is green and that's good and it's supposed to be green, do you really have to constantly analyze the fact that the light is green? You actually only really want to be able to analyze and take action when there's an anomaly. Well if it goes purple, that's actually a sign that something might explode, so that's where you want to make sure that you have the analytics at the edge. Not for everything, but for the things where there is an anomaly and a change. >> Joe, how about from your perspective? >> For me I think the evolution of data is really becoming, eventually oxygen is just, I mean data's going to be the oxygen we breathe. It used to be very very reactive and there used to be like a latency. You do something, there's a behavior, there's an event, there's a transaction, and then you go record it and then you collect it, and then you can analyze it. And it was very very waterfallish, right? And then eventually we figured out to put it back into the system. Or at least human beings interpret it to try to make the system better and that is really completely turned on it's head, we don't do that anymore. Right now it's very very, it's synchronous, where as we're actually making these transactions, the machines, we don't really need, I mean human beings are involved a bit, but less and less and less. And it's just a reality, it may not be politically correct to say but it's a reality that my phone in my pocket is following my behavior, and it knows without telling a human being what I'm doing. And it can actually help me do things like get to where I want to go faster depending on my preference if I want to save money or save time or visit things along the way. And I think that's all integration of big data, streaming data, artificial intelligence and I think the next thing that we're going to start seeing is the culmination of all of that. I actually, hopefully it'll be published soon, I just wrote an article for Forbes with the term of ARBI and ARBI is the integration of Augmented Reality and Business Intelligence. Where I think essentially we're going to see, you know, hold your phone up to Jim's face and it's going to recognize-- >> Peter: It's going to break. >> And it's going to say exactly you know, what are the key metrics that we want to know about Jim. If he works on my sales force, what's his attainment of goal, what is-- >> Jim: Can it read my mind? >> Potentially based on behavior patterns. >> Now I'm scared. >> I don't think Jim's buying it. >> It will, without a doubt be able to predict what you've done in the past, you may, with some certain level of confidence you may do again in the future, right? And is that mind reading? It's pretty close, right? >> Well, sometimes, I mean, mind reading is in the eye of the individual who wants to know. And if the machine appears to approximate what's going on in the person's head, sometimes you can't tell. So I guess, I guess we could call that the Turing machine test of the paranormal. >> Well, face recognition, micro gesture recognition, I mean facial gestures, people can do it. Maybe not better than a coin toss, but if it can be seen visually and captured and analyzed, conceivably some degree of mind reading can be built in. I can see when somebody's angry looking at me so, that's a possibility. That's kind of a scary possibility in a surveillance society, potentially. >> Neil: Right, absolutely. >> Peter: Stephanie, what do you think? >> Well, I hear a world of it's the bots versus the humans being painted here and I think that, you know at Elysian we have a very strong perspective on this and that is that the greatest impact, or the greatest results is going to be when humans figure out how to collaborate with the machines. And so yes, you want to get to the location more quickly, but the machine as in the bot isn't able to tell you exactly what to do and you're just going to blindly follow it. You need to train that machine, you need to have a partnership with that machine. So, a lot of the power, and I think this goes back to Judith's story is then what is the human decision making that can be augmented with data from the machine, but then the humans are actually training the training side and driving machines in the right direction. I think that's when we get true power out of some of these solutions so it's not just all about the technology. It's not all about the data or the AI, or the IoT, it's about how that empowers human systems to become smarter and more effective and more efficient. And I think we're playing that out in our technology in a certain way and I think organizations that are thinking along those lines with IoT are seeing more benefits immediately from those projects. >> So I think we have a general agreement of what kind of some of the things you talked about, IoT, crucial capturing information, and then having action being taken, AI being crucial to defining and refining the nature of the actions that are being taken Big Data ultimately powering how a lot of that changes. Let's go to the next one. >> So actually I have something to add to that. So I think it makes sense, right, with IoT, why we have Big Data associated with it. If you think about what data is collected by IoT. We're talking about a serial information, right? It's over time, it's going to grow exponentially just by definition, right, so every minute you collect a piece of information that means over time, it's going to keep growing, growing, growing as it accumulates. So that's one of the reasons why the IoT is so strongly associated with Big Data. And also why you need AI to be able to differentiate between one minute versus next minute, right? Trying to find a better way rather than looking at all that information and manually picking out patterns. To have some automated process for being able to filter through that much data that's being collected. >> I want to point out though based on what you just said Jennifer, I want to bring Neil in at this point, that this question of IoT now generating unprecedented levels of data does introduce this idea of the primary source. Historically what we've done within technology, or within IT certainly is we've taken stylized data. There is no such thing as a real world accounting thing. It is a human contrivance. And we stylize data and therefore it's relatively easy to be very precise on it. But when we start, as you noted, when we start measuring things with a tolerance down to thousandths of a millimeter, whatever that is, metric system, now we're still sometimes dealing with errors that we have to attend to. So, the reality is we're not just dealing with stylized data, we're dealing with real data, and it's more, more frequent, but it also has special cases that we have to attend to as in terms of how we use it. What do you think Neil? >> Well, I mean, I agree with that, I think I already said that, right. >> Yes you did, okay let's move on to the next one. >> Well it's a doppelganger, the digital twin doppelganger that's automatically created by your very fact that you're living and interacting and so forth and so on. It's going to accumulate regardless. Now that doppelganger may not be your agent, or might not be the foundation for your agent unless there's some other piece of logic like an interest graph that you build, a human being saying this is my broad set of interests, and so all of my agents out there in the IoT, you all need to be aware that when you make a decision on my behalf as my agent, this is what Jim would do. You know I mean there needs to be that kind of logic somewhere in this fabric to enable true agency. >> All right, so I'm going to start with you. Oh go ahead. >> I have a real short answer to this though. I think that Big Data provides the data and compute platform to make AI possible. For those of us who dipped our toes in the water in the 80s, we got clobbered because we didn't have the, we didn't have the facilities, we didn't have the resources to really do AI, we just kind of played around with it. And I think that the other thing about it is if you combine Big Data and AI and IoT, what you're going to see is people, a lot of the applications we develop now are very inward looking, we look at our organization, we look at our customers. We try to figure out how to sell more shoes to fashionable ladies, right? But with this technology, I think people can really expand what they're thinking about and what they model and come up with applications that are much more external. >> Actually what I would add to that is also it actually introduces being able to use engineering, right? Having engineers interested in the data. Because it's actually technical data that's collected not just say preferences or information about people, but actual measurements that are being collected with IoT. So it's really interesting in the engineering space because it opens up a whole new world for the engineers to actually look at data and to actually combine both that hardware side as well as the data that's being collected from it. >> Well, Neil, you and I have talked about something, 'cause it's not just engineers. We have in the healthcare industry for example, which you know a fair amount about, there's this notion of empirical based management. And the idea that increasingly we have to be driven by data as a way of improving the way that managers do things, the way the managers collect or collaborate and ultimately collectively how they take action. So it's not just engineers, it's supposed to also inform business, what's actually happening in the healthcare world when we start thinking about some of this empirical based management, is it working? What are some of the barriers? >> It's not a function of technology. What happens in medicine and healthcare research is, I guess you can say it borders on fraud. (people chuckling) No, I'm not kidding. I know the New England Journal of Medicine a couple of years ago released a study and said that at least half their articles that they published turned out to be written, ghost written by pharmaceutical companies. (man chuckling) Right, so I think the problem is that when you do a clinical study, the one that really killed me about 10 years ago was the women's health initiative. They spent $700 million gathering this data over 20 years. And when they released it they looked at all the wrong things deliberately, right? So I think that's a systemic-- >> I think you're bringing up a really important point that we haven't brought up yet, and that is is can you use Big Data and machine learning to begin to take the biases out? So if you let the, if you divorce your preconceived notions and your biases from the data and let the data lead you to the logic, you start to, I think get better over time, but it's going to take a while to get there because we do tend to gravitate towards our biases. >> I will share an anecdote. So I had some arm pain, and I had numbness in my thumb and pointer finger and I went to, excruciating pain, went to the hospital. So the doctor examined me, and he said you probably have a pinched nerve, he said, but I'm not exactly sure which nerve it would be, I'll be right back. And I kid you not, he went to a computer and he Googled it. (Neil laughs) And he came back because this little bit of information was something that could easily be looked up, right? Every nerve in your spine is connected to your different fingers so the pointer and the thumb just happens to be your C6, so he came back and said, it's your C6. (Neil mumbles) >> You know an interesting, I mean that's a good example. One of the issues with healthcare data is that the data set is not always shared across the entire research community, so by making Big Data accessible to everyone, you actually start a more rational conversation or debate on well what are the true insights-- >> If that conversation includes what Judith talked about, the actual model that you use to set priorities and make decisions about what's actually important. So it's not just about improving, this is the test. It's not just about improving your understanding of the wrong thing, it's also testing whether it's the right or wrong thing as well. >> That's right, to be able to test that you need to have humans in dialog with one another bringing different biases to the table to work through okay is there truth in this data? >> It's context and it's correlation and you can have a great correlation that's garbage. You know if you don't have the right context. >> Peter: So I want to, hold on Jim, I want to, >> It's exploratory. >> Hold on Jim, I want to take it to the next question 'cause I want to build off of what you talked about Stephanie and that is that this says something about what is the Edge. And our perspective is that the Edge is not just devices. That when we talk about the Edge, we're talking about human beings and the role that human beings are going to play both as sensors or carrying things with them, but also as actuators, actually taking action which is not a simple thing. So what do you guys think? What does the Edge mean to you? Joe, why don't you start? >> Well, I think it could be a combination of the two. And specifically when we talk about healthcare. So I believe in 2017 when we eat we don't know why we're eating, like I think we should absolutely by now be able to know exactly what is my protein level, what is my calcium level, what is my potassium level? And then find the foods to meet that. What have I depleted versus what I should have, and eat very very purposely and not by taste-- >> And it's amazing that red wine is always the answer. >> It is. (people laughing) And tequila, that helps too. >> Jim: You're a precision foodie is what you are. (several chuckle) >> There's no reason why we should not be able to know that right now, right? And when it comes to healthcare is, the biggest problem or challenge with healthcare is no matter how great of a technology you have, you can't, you can't, you can't manage what you can't measure. And you're really not allowed to use a lot of this data so you can't measure it, right? You can't do things very very scientifically right, in the healthcare world and I think regulation in the healthcare world is really burdening advancement in science. >> Peter: Any thoughts Jennifer? >> Yes, I teach statistics for data scientists, right, so you know we talk about a lot of these concepts. I think what makes these questions so difficult is you have to find a balance, right, a middle ground. For instance, in the case of are you being too biased through data, well you could say like we want to look at data only objectively, but then there are certain relationships that your data models might show that aren't actually a causal relationship. For instance, if there's an alien that came from space and saw earth, saw the people, everyone's carrying umbrellas right, and then it started to rain. That alien might think well, it's because they're carrying umbrellas that it's raining. Now we know from real world that that's actually not the way these things work. So if you look only at the data, that's the potential risk. That you'll start making associations or saying something's causal when it's actually not, right? So that's one of the, one of the I think big challenges. I think when it comes to looking also at things like healthcare data, right? Do you collect data about anything and everything? Does it mean that A, we need to collect all that data for the question we're looking at? Or that it's actually the best, more optimal way to be able to get to the answer? Meaning sometimes you can take some shortcuts in terms of what data you collect and still get the right answer and not have maybe that level of specificity that's going to cost you millions extra to be able to get. >> So Jennifer as a data scientist, I want to build upon what you just said. And that is, are we going to start to see methods and models emerge for how we actually solve some of these problems? So for example, we know how to build a system for stylized process like accounting or some elements of accounting. We have methods and models that lead to technology and actions and whatnot all the way down to that that system can be generated. We don't have the same notion to the same degree when we start talking about AI and some of these Big Datas. We have algorithms, we have technology. But are we going to start seeing, as a data scientist, repeatability and learning and how to think the problems through that's going to lead us to a more likely best or at least good result? >> So I think that's a bit of a tough question, right? Because part of it is, it's going to depend on how many of these researchers actually get exposed to real world scenarios, right? Research looks into all these papers, and you come up with all these models, but if it's never tested in a real world scenario, well, I mean we really can't validate that it works, right? So I think it is dependent on how much of this integration there's going to be between the research community and industry and how much investment there is. Funding is going to matter in this case. If there's no funding in the research side, then you'll see a lot of industry folk who feel very confident about their models that, but again on the other side of course, if researchers don't validate those models then you really can't say for sure that it's actually more accurate, or it's more efficient. >> It's the issue of real world testing and experimentation, A B testing, that's standard practice in many operationalized ML and AI implementations in the business world, but real world experimentation in the Edge analytics, what you're actually transducing are touching people's actual lives. Problem there is, like in healthcare and so forth, when you're experimenting with people's lives, somebody's going to die. I mean, in other words, that's a critical, in terms of causal analysis, you've got to tread lightly on doing operationalizing that kind of testing in the IoT when people's lives and health are at stake. >> We still give 'em placebos. So we still test 'em. All right so let's go to the next question. What are the hottest innovations in AI? Stephanie I want to start with you as a company, someone at a company that's got kind of an interesting little thing happening. We start thinking about how do we better catalog data and represent it to a large number of people. What are some of the hottest innovations in AI as you see it? >> I think it's a little counter intuitive about what the hottest innovations are in AI, because we're at a spot in the industry where the most successful companies that are working with AI are actually incorporating them into solutions. So the best AI solutions are actually the products that you don't know there's AI operating underneath. But they're having a significant impact on business decision making or bringing a different type of application to the market and you know, I think there's a lot of investment that's going into AI tooling and tool sets for data scientists or researchers, but the more innovative companies are thinking through how do we really take AI and make it have an impact on business decision making and that means kind of hiding the AI to the business user. Because if you think a bot is making a decision instead of you, you're not going to partner with that bot very easily or very readily. I worked at, way at the start of my career, I worked in CRM when recommendation engines were all the rage online and also in call centers. And the hardest thing was to get a call center agent to actually read the script that the algorithm was presenting to them, that algorithm was 99% correct most of the time, but there was this human resistance to letting a computer tell you what to tell that customer on the other side even if it was more successful in the end. And so I think that the innovation in AI that's really going to push us forward is when humans feel like they can partner with these bots and they don't think of it as a bot, but they think about as assisting their work and getting to a better result-- >> Hence the augmentation point you made earlier. >> Absolutely, absolutely. >> Joe how 'about you? What do you look at? What are you excited about? >> I think the coolest thing at the moment right now is chat bots. Like to be able, like to have voice be able to speak with you in natural language, to do that, I think that's pretty innovative, right? And I do think that eventually, for the average user, not for techies like me, but for the average user, I think keyboards are going to be a thing of the past. I think we're going to communicate with computers through voice and I think this is the very very beginning of that and it's an incredible innovation. >> Neil? >> Well, I think we all have myopia here. We're all thinking about commercial applications. Big, big things are happening with AI in the intelligence community, in military, the defense industry, in all sorts of things. Meteorology. And that's where, well, hopefully not on an every day basis with military, you really see the effect of this. But I was involved in a project a couple of years ago where we were developing AI software to detect artillery pieces in terrain from satellite imagery. I don't have to tell you what country that was. I think you can probably figure that one out right? But there are legions of people in many many companies that are involved in that industry. So if you're talking about the dollars spent on AI, I think the stuff that we do in our industries is probably fairly small. >> Well it reminds me of an application I actually thought was interesting about AI related to that, AI being applied to removing mines from war zones. >> Why not? >> Which is not a bad thing for a whole lot of people. Judith what do you look at? >> So I'm looking at things like being able to have pre-trained data sets in specific solution areas. I think that that's something that's coming. Also the ability to, to really be able to have a machine assist you in selecting the right algorithms based on what your data looks like and the problems you're trying to solve. Some of the things that data scientists still spend a lot of their time on, but can be augmented with some, basically we have to move to levels of abstraction before this becomes truly ubiquitous across many different areas. >> Peter: Jennifer? >> So I'm going to say computer vision. >> Computer vision? >> Computer vision. So computer vision ranges from image recognition to be able to say what content is in the image. Is it a dog, is it a cat, is it a blueberry muffin? Like a sort of popular post out there where it's like a blueberry muffin versus like I think a chihuahua and then it compares the two. And can the AI really actually detect difference, right? So I think that's really where a lot of people who are in this space of being in both the AI space as well as data science are looking to for the new innovations. I think, for instance, cloud vision I think that's what Google still calls it. The vision API we've they've released on beta allows you to actually use an API to send your image and then have it be recognized right, by their API. There's another startup in New York called Clarify that also does a similar thing as well as you know Amazon has their recognition platform as well. So I think in a, from images being able to detect what's in the content as well as from videos, being able to say things like how many people are entering a frame? How many people enter the store? Not having to actually go look at it and count it, but having a computer actually tally that information for you, right? >> There's actually an extra piece to that. So if I have a picture of a stop sign, and I'm an automated car, and is it a picture on the back of a bus of a stop sign, or is it a real stop sign? So that's going to be one of the complications. >> Doesn't matter to a New York City cab driver. How 'about you Jim? >> Probably not. (laughs) >> Hottest thing in AI is General Adversarial Networks, GANT, what's hot about that, well, I'll be very quick, most AI, most deep learning, machine learning is analytical, it's distilling or inferring insights from the data. Generative takes that same algorithmic basis but to build stuff. In other words, to create realistic looking photographs, to compose music, to build CAD CAM models essentially that can be constructed on 3D printers. So GANT, it's a huge research focus all around the world are used for, often increasingly used for natural language generation. In other words it's institutionalizing or having a foundation for nailing the Turing test every single time, building something with machines that looks like it was constructed by a human and doing it over and over again to fool humans. I mean you can imagine the fraud potential. But you can also imagine just the sheer, like it's going to shape the world, GANT. >> All right so I'm going to say one thing, and then we're going to ask if anybody in the audience has an idea. So the thing that I find interesting is traditional programs, or when you tell a machine to do something you don't need incentives. When you tell a human being something, you have to provide incentives. Like how do you get someone to actually read the text. And this whole question of elements within AI that incorporate incentives as a way of trying to guide human behavior is absolutely fascinating to me. Whether it's gamification, or even some things we're thinking about with block chain and bitcoins and related types of stuff. To my mind that's going to have an enormous impact, some good, some bad. Anybody in the audience? I don't want to lose everybody here. What do you think sir? And I'll try to do my best to repeat it. Oh we have a mic. >> So my question's about, Okay, so the question's pretty much about what Stephanie's talking about which is human and loop training right? I come from a computer vision background. That's the problem, we need millions of images trained, we need humans to do that. And that's like you know, the workforce is essentially people that aren't necessarily part of the AI community, they're people that are just able to use that data and analyze the data and label that data. That's something that I think is a big problem everyone in the computer vision industry at least faces. I was wondering-- >> So again, but the problem is that is the difficulty of methodologically bringing together people who understand it and people who, people who have domain expertise people who have algorithm expertise and working together? >> I think the expertise issue comes in healthcare, right? In healthcare you need experts to be labeling your images. With contextual information where essentially augmented reality applications coming in, you have the AR kit and everything coming out, but there is a lack of context based intelligence. And all of that comes through training images, and all of that requires people to do it. And that's kind of like the foundational basis of AI coming forward is not necessarily an algorithm, right? It's how well are datas labeled? Who's doing the labeling and how do we ensure that it happens? >> Great question. So for the panel. So if you think about it, a consultant talks about being on the bench. How much time are they going to have to spend on trying to develop additional business? How much time should we set aside for executives to help train some of the assistants? >> I think that the key is not, to think of the problem a different way is that you would have people manually label data and that's one way to solve the problem. But you can also look at what is the natural workflow of that executive, or that individual? And is there a way to gather that context automatically using AI, right? And if you can do that, it's similar to what we do in our product, we observe how someone is analyzing the data and from those observations we can actually create the metadata that then trains the system in a particular direction. But you have to think about solving the problem differently of finding the workflow that then you can feed into to make this labeling easy without the human really realizing that they're labeling the data. >> Peter: Anybody else? >> I'll just add to what Stephanie said, so in the IoT applications, all those sensory modalities, the computer vision, the speech recognition, all that, that's all potential training data. So it cross checks against all the other models that are processing all the other data coming from that device. So that the natural language process of understanding can be reality checked against the images that the person happens to be commenting upon, or the scene in which they're embedded, so yeah, the data's embedded-- >> I don't think we're, we're not at the stage yet where this is easy. It's going to take time before we do start doing the pre-training of some of these details so that it goes faster, but right now, there're not that many shortcuts. >> Go ahead Joe. >> Sorry so a couple things. So one is like, I was just caught up on your incentivizing programs to be more efficient like humans. You know in Ethereum that has this notion, which is bot chain, has this theory, this concept of gas. Where like as the process becomes more efficient it costs less to actually run, right? It costs less ether, right? So it actually is kind of, the machine is actually incentivized and you don't really know what it's going to cost until the machine processes it, right? So there is like some notion of that there. But as far as like vision, like training the machine for computer vision, I think it's through adoption and crowdsourcing, so as people start using it more they're going to be adding more pictures. Very very organically. And then the machines will be trained and right now is a very small handful doing it, and it's very proactive by the Googles and the Facebooks and all of that. But as we start using it, as they start looking at my images and Jim's and Jen's images, it's going to keep getting smarter and smarter through adoption and through very organic process. >> So Neil, let me ask you a question. Who owns the value that's generated as a consequence of all these people ultimately contributing their insight and intelligence into these systems? >> Well, to a certain extent the people who are contributing the insight own nothing because the systems collect their actions and the things they do and then that data doesn't belong to them, it belongs to whoever collected it or whoever's going to do something with it. But the other thing, getting back to the medical stuff. It's not enough to say that the systems, people will do the right thing, because a lot of them are not motivated to do the right thing. The whole grant thing, the whole oh my god I'm not going to go against the senior professor. A lot of these, I knew a guy who was a doctor at University of Pittsburgh and they were doing a clinical study on the tubes that they put in little kids' ears who have ear infections, right? And-- >> Google it! Who helps out? >> Anyway, I forget the exact thing, but he came out and said that the principle investigator lied when he made the presentation, that it should be this, I forget which way it went. He was fired from his position at Pittsburgh and he has never worked as a doctor again. 'Cause he went against the senior line of authority. He was-- >> Another question back here? >> Man: Yes, Mark Turner has a question. >> Not a question, just want to piggyback what you're saying about the transfixation of maybe in healthcare of black and white images and color images in the case of sonograms and ultrasound and mammograms, you see that happening using AI? You see that being, I mean it's already happening, do you see it moving forward in that kind of way? I mean, talk more about that, about you know, AI and black and white images being used and they can be transfixed, they can be made to color images so you can see things better, doctors can perform better operations. >> So I'm sorry, but could you summarize down? What's the question? Summarize it just, >> I had a lot of students, they're interested in the cross pollenization between AI and say the medical community as far as things like ultrasound and sonograms and mammograms and how you can literally take a black and white image and it can, using algorithms and stuff be made to color images that can help doctors better do the work that they've already been doing, just do it better. You touched on it like 30 seconds. >> So how AI can be used to actually add information in a way that's not necessarily invasive but is ultimately improves how someone might respond to it or use it, yes? Related? I've also got something say about medical images in a second, any of you guys want to, go ahead Jennifer. >> Yeah, so for one thing, you know and it kind of goes back to what we were talking about before. When we look at for instance scans, like at some point I was looking at CT scans, right, for lung cancer nodules. In order for me, who I don't have a medical background, to identify where the nodule is, of course, a doctor actually had to go in and specify which slice of the scan had the nodule and where exactly it is, so it's on both the slice level as well as, within that 2D image, where it's located and the size of it. So the beauty of things like AI is that ultimately right now a radiologist has to look at every slice and actually identify this manually, right? The goal of course would be that one day we wouldn't have to have someone look at every slice to like 300 usually slices and be able to identify it much more automated. And I think the reality is we're not going to get something where it's going to be 100%. And with anything we do in the real world it's always like a 95% chance of it being accurate. So I think it's finding that in between of where, what's the threshold that we want to use to be able to say that this is, definitively say a lung cancer nodule or not. I think the other thing to think about is in terms of how their using other information, what they might use is a for instance, to say like you know, based on other characteristics of the person's health, they might use that as sort of a grading right? So you know, how dark or how light something is, identify maybe in that region, the prevalence of that specific variable. So that's usually how they integrate that information into something that's already existing in the computer vision sense. I think that's, the difficulty with this of course, is being able to identify which variables were introduced into data that does exist. >> So I'll make two quick observations on this then I'll go to the next question. One is radiologists have historically been some of the highest paid physicians within the medical community partly because they don't have to be particularly clinical. They don't have to spend a lot of time with patients. They tend to spend time with doctors which means they can do a lot of work in a little bit of time, and charge a fair amount of money. As we start to introduce some of these technologies that allow us to from a machine standpoint actually make diagnoses based on those images, I find it fascinating that you now see television ads promoting the role that the radiologist plays in clinical medicine. It's kind of an interesting response. >> It's also disruptive as I'm seeing more and more studies showing that deep learning models processing images, ultrasounds and so forth are getting as accurate as many of the best radiologists. >> That's the point! >> Detecting cancer >> Now radiologists are saying oh look, we do this great thing in terms of interacting with the patients, never have because they're being dis-intermediated. The second thing that I'll note is one of my favorite examples of that if I got it right, is looking at the images, the deep space images that come out of Hubble. Where they're taking data from thousands, maybe even millions of images and combining it together in interesting ways you can actually see depth. You can actually move through to a very very small scale a system that's 150, well maybe that, can't be that much, maybe six billion light years away. Fascinating stuff. All right so let me go to the last question here, and then I'm going to close it down, then we can have something to drink. What are the hottest, oh I'm sorry, question? >> Yes, hi, my name's George, I'm with Blue Talon. You asked earlier there the question what's the hottest thing in the Edge and AI, I would say that it's security. It seems to me that before you can empower agency you need to be able to authorize what they can act on, how they can act on, who they can act on. So it seems if you're going to move from very distributed data at the Edge and analytics at the Edge, there has to be security similarly done at the Edge. And I saw (speaking faintly) slides that called out security as a key prerequisite and maybe Judith can comment, but I'm curious how security's going to evolve to meet this analytics at the Edge. >> Well, let me do that and I'll ask Jen to comment. The notion of agency is crucially important, slightly different from security, just so we're clear. And the basic idea here is historically folks have thought about moving data or they thought about moving application function, now we are thinking about moving authority. So as you said. That's not necessarily, that's not really a security question, but this has been a problem that's been in, of concern in a number of different domains. How do we move authority with the resources? And that's really what informs the whole agency process. But with that said, Jim. >> Yeah actually I'll, yeah, thank you for bringing up security so identity is the foundation of security. Strong identity, multifactor, face recognition, biometrics and so forth. Clearly AI, machine learning, deep learning are powering a new era of biometrics and you know it's behavioral metrics and so forth that's organic to people's use of devices and so forth. You know getting to the point that Peter was raising is important, agency! Systems of agency. Your agent, you have to, you as a human being should be vouching in a secure, tamper proof way, your identity should be vouching for the identity of some agent, physical or virtual that does stuff on your behalf. How can that, how should that be managed within this increasingly distributed IoT fabric? Well a lot of that's been worked. It all ran through webs of trust, public key infrastructure, formats and you know SAML for single sign and so forth. It's all about assertion, strong assertions and vouching. I mean there's the whole workflows of things. Back in the ancient days when I was actually a PKI analyst three analyst firms ago, I got deep into all the guts of all those federation agreements, something like that has to be IoT scalable to enable systems agency to be truly fluid. So we can vouch for our agents wherever they happen to be. We're going to keep on having as human beings agents all over creation, we're not even going to be aware of everywhere that our agents are, but our identity-- >> It's not just-- >> Our identity has to follow. >> But it's not just identity, it's also authorization and context. >> Permissioning, of course. >> So I may be the right person to do something yesterday, but I'm not authorized to do it in another context in another application. >> Role based permissioning, yeah. Or persona based. >> That's right. >> I agree. >> And obviously it's going to be interesting to see the role that block chain or its follow on to the technology is going to play here. Okay so let me throw one more questions out. What are the hottest applications of AI at the Edge? We've talked about a number of them, does anybody want to add something that hasn't been talked about? Or do you want to get a beer? (people laughing) Stephanie, you raised your hand first. >> I was going to go, I bring something mundane to the table actually because I think one of the most exciting innovations with IoT and AI are actually simple things like City of San Diego is rolling out 3200 automated street lights that will actually help you find a parking space, reduce the amount of emissions into the atmosphere, so has some environmental change, positive environmental change impact. I mean, it's street lights, it's not like a, it's not medical industry, it doesn't look like a life changing innovation, and yet if we automate streetlights and we manage our energy better, and maybe they can flicker on and off if there's a parking space there for you, that's a significant impact on everyone's life. >> And dramatically suppress the impact of backseat driving! >> (laughs) Exactly. >> Joe what were you saying? >> I was just going to say you know there's already the technology out there where you can put a camera on a drone with machine learning within an artificial intelligence within it, and it can look at buildings and determine whether there's rusty pipes and cracks in cement and leaky roofs and all of those things. And that's all based on artificial intelligence. And I think if you can do that, to be able to look at an x-ray and determine if there's a tumor there is not out of the realm of possibility, right? >> Neil? >> I agree with both of them, that's what I meant about external kind of applications. Instead of figuring out what to sell our customers. Which is most what we hear. I just, I think all of those things are imminently doable. And boy street lights that help you find a parking place, that's brilliant, right? >> Simple! >> It improves your life more than, I dunno. Something I use on the internet recently, but I think it's great! That's, I'd like to see a thousand things like that. >> Peter: Jim? >> Yeah, building on what Stephanie and Neil were saying, it's ambient intelligence built into everything to enable fine grain microclimate awareness of all of us as human beings moving through the world. And enable reading of every microclimate in buildings. In other words, you know you have sensors on your body that are always detecting the heat, the humidity, the level of pollution or whatever in every environment that you're in or that you might be likely to move into fairly soon and either A can help give you guidance in real time about where to avoid, or give that environment guidance about how to adjust itself to your, like the lighting or whatever it might be to your specific requirements. And you know when you have a room like this, full of other human beings, there has to be some negotiated settlement. Some will find it too hot, some will find it too cold or whatever but I think that is fundamental in terms of reshaping the sheer quality of experience of most of our lived habitats on the planet potentially. That's really the Edge analytics application that depends on everybody having, being fully equipped with a personal area network of sensors that's communicating into the cloud. >> Jennifer? >> So I think, what's really interesting about it is being able to utilize the technology we do have, it's a lot cheaper now to have a lot of these ways of measuring that we didn't have before. And whether or not engineers can then leverage what we have as ways to measure things and then of course then you need people like data scientists to build the right model. So you can collect all this data, if you don't build the right model that identifies these patterns then all that data's just collected and it's just made a repository. So without having the models that supports patterns that are actually in the data, you're not going to find a better way of being able to find insights in the data itself. So I think what will be really interesting is to see how existing technology is leveraged, to collect data and then how that's actually modeled as well as to be able to see how technology's going to now develop from where it is now, to being able to either collect things more sensitively or in the case of say for instance if you're dealing with like how people move, whether we can build things that we can then use to measure how we move, right? Like how we move every day and then being able to model that in a way that is actually going to give us better insights in things like healthcare and just maybe even just our behaviors. >> Peter: Judith? >> So, I think we also have to look at it from a peer to peer perspective. So I may be able to get some data from one thing at the Edge, but then all those Edge devices, sensors or whatever, they all have to interact with each other because we don't live, we may, in our business lives, act in silos, but in the real world when you look at things like sensors and devices it's how they react with each other on a peer to peer basis. >> All right, before I invite John up, I want to say, I'll say what my thing is, and it's not the hottest. It's the one I hate the most. I hate AI generated music. (people laughing) Hate it. All right, I want to thank all the panelists, every single person, some great commentary, great observations. I want to thank you very much. I want to thank everybody that joined. John in a second you'll kind of announce who's the big winner. But the one thing I want to do is, is I was listening, I learned a lot from everybody, but I want to call out the one comment that I think we all need to remember, and I'm going to give you the award Stephanie. And that is increasing we have to remember that the best AI is probably AI that we don't even know is working on our behalf. The same flip side of that is all of us have to be very cognizant of the idea that AI is acting on our behalf and we may not know it. So, John why don't you come on up. Who won the, whatever it's called, the raffle? >> You won. >> Thank you! >> How 'about a round of applause for the great panel. (audience applauding) Okay we have a put the business cards in the basket, we're going to have that brought up. We're going to have two raffle gifts, some nice Bose headsets and speaker, Bluetooth speaker. Got to wait for that. I just want to say thank you for coming and for the folks watching, this is our fifth year doing our own event called Big Data NYC which is really an extension of the landscape beyond the Big Data world that's Cloud and AI and IoT and other great things happen and great experts and influencers and analysts here. Thanks for sharing your opinion. Really appreciate you taking the time to come out and share your data and your knowledge, appreciate it. Thank you. Where's the? >> Sam's right in front of you. >> There's the thing, okay. Got to be present to win. We saw some people sneaking out the back door to go to a dinner. >> First prize first. >> Okay first prize is the Bose headset. >> Bluetooth and noise canceling. >> I won't look, Sam you got to hold it down, I can see the cards. >> All right. >> Stephanie you won! (Stephanie laughing) Okay, Sawny Cox, Sawny Allie Cox? (audience applauding) Yay look at that! He's here! The bar's open so help yourself, but we got one more. >> Congratulations. Picture right here. >> Hold that I saw you. Wake up a little bit. Okay, all right. Next one is, my kids love this. This is great, great for the beach, great for everything portable speaker, great gift. >> What is it? >> Portable speaker. >> It is a portable speaker, it's pretty awesome. >> Oh you grabbed mine. >> Oh that's one of our guys. >> (lauging) But who was it? >> Can't be related! Ava, Ava, Ava. Okay Gene Penesko (audience applauding) Hey! He came in! All right look at that, the timing's great. >> Another one? (people laughing) >> Hey thanks everybody, enjoy the night, thank Peter Burris, head of research for SiliconANGLE, Wikibon and he great guests and influencers and friends. And you guys for coming in the community. Thanks for watching and thanks for coming. Enjoy the party and some drinks and that's out, that's it for the influencer panel and analyst discussion. Thank you. (logo music)

(upbeat music) >> Welcome back to the program, and we're going to dig into the number one topic on the minds of every technology organization, that's cybersecurity. You know, survey data from ETR, our data partner, shows that among CIOs and IT decision makers, cybersecurity continues to rank as the number one technology priority to be addressed in the coming year. That's ahead of even cloud migration and analytics. And with me to discuss this critical topic area, are Jim Shook, who's the Global Director of Cybersecurity and Compliance Practice at Dell Technologies, and he's joined by Andrew Gonzalez, who focuses on cloud and infrastructure consulting at DXC Technology. Gents, welcome, good to have you. >> Thanks Dave, great to be here. >> Thank you. >> Jim, let's start with you. What are you seeing from the front lines in terms of the attack surface and how are customers responding these days? >> It's always up and down and back and forth. The bad actors are smart, they adapt to everything that we do. So we're seeing more and more, kind of living off the land. They're not necessarily deploying malware, makes it harder to find what they're doing. And I think though, Dave, we've adapted and this whole notion of cyber resilience really helps our customers figure this out. And the idea there goes beyond cybersecurity, it's, let's protect as much as possible so we keep the bad actors out as much as we can, but then let's have the ability to adapt to, and recover to the extent that the bad actors are successful. So we're recognizing that we can't be perfect a hundred percent of the time against a hundred percent of the bad actors. Let's keep out what we can, but then recognize and have that ability to recover when necessary. >> Yeah, thank you. So Andrew, you know, I like what Jim was saying, about living off the land, of course, meaning using your own tooling against you, kind of hiding in plain sight, if you will. But, and as Jim was saying, you can't be perfect. But, so given that, what's your perspective on what good cybersecurity hygiene looks like? >> Yeah, so you have to understand what your crown jewel data looks like, what a good copy of a recoverable asset looks like when you look at an attack if it were to occur, right? How you get that copy of data back into production, and not only that but what that golden image actually entails. So, whether it's networking, storage, some copy of a source code, intellectual property, maybe seem to be data or an active directory or DNS dump, right? Understanding what your data actually entails that you can protect it, and that you can build out your recovery plan for it. >> So, and, where's that live? Where's that gold copy? You put in a yellow sticky? No, it's got to be, you got to be somewhere safe, right? So you have to think about that chain as well, right? >> Absolutely, yeah. So, a lot of folks have not gone through the exercise of identifying what that golden copy looks like. Everyone has a DR scenario, everyone has a DR strategy but actually identifying what that golden crown jewel data, let's call it, actually entails as one aspect of it and then where to put it, how to protect it, how to make it immutable and isolated? That's the other portion of it. >> You know, if I go back to sort of earlier part of last decade, you know, cybersecurity was kind of a checkoff item. And then as you got toward the middle part of the decade and I'd say clearly by 2016 it, security became a boardroom issue. It was on the agenda, you know, every quarter at the board meetings. So, compliance is no longer the driver, is my point. The driver is business risk, real loss of reputation or data, you know, or money, et cetera. What are the business implications of not having your cyber house in order today? >> They're extreme, Dave. I mean the, you know, bad actors are good at what they do. These losses by organizations, tens, hundreds of millions into the billions sometimes, plus the reputational damage that's difficult to really measure. There haven't been a lot of organizations that have actually been put out of business by an attack, at least not directly, if they're larger organizations, but that's also on the table too. So you can't just rely on, oh, we need to do, you know, A, B, and C because our regulators require it. You need to look at what the actual risk is to the business and then come up with the strategy from there. >> You know, Jim, staying with you, one of the most common targets we hear of attackers is to go after the backup corpus. So how should customers think about protecting themselves from that tactic? >> Well, Dave, you hit on it before, right? Everybody's had the backup and DR strategies for a long time going back to requirements that we had in place for physical disaster or human error. And that's a great starting point for a resilience capability, but that's all it is, is a starting point. Because the bad actors will, they also understand that you have those capabilities and they've adapted to that. In every sophisticated attack that we see the backup is a target, the bad actors want to take it out or corrupt it or do something else to that backup so that it's not available to you. That's not to say they're always successful and it's still a good control to have in place because maybe it will survive. But you have to plan beyond that. So, the capabilities that we talk about with resilience, let's harden that backup infrastructure. You've already got it in place, let's use the capabilities that are there like immutability and other controls to make it more difficult for the bad actors to get to. But then, as Andrew said, that gold copy, that critical systems, you need to protect that in something that's more secure which commonly we might say a cyber vault, although there's a lot of different capabilities for cyber vaulting, some far better than others, and that's some of the things that we focus on. >> You know, it's interesting, but I've talked to a lot of CIOs about this is, prior to the pandemic, they, you know, had their, as you're pointing out, Jim, they had their DR strategy in place but they felt like they weren't business resilient and they realized that when we had the forced march to digital. So, Andrew, are there solutions out there to help with this problem? Do you guys have an answer to this? >> Yeah, absolutely. So, I'm glad you brought up resiliency. We take a position that to be cyber resilient it includes operational resiliency, it includes understanding at the C-level what the implication of an attack means, as we stated, and then how to recover back into production. When you look at protecting that data, not only do you want to put it into what we call a vault, which is a Dell technology that is an offline immutable copy of your crown jewel data but also how to recover it in real-time. So DXC offers a, I don't want to call it a turnkey solution, since we architect these specific each client needs, right? When we look at what client data entails, their recovery point objectives, recovery time objectives, what we call quality of the restoration. But when we architect these out we look at not only how to protect the data but how to alert and monitor for attacks in real-time. How to understand what we should do when a breaches in progress. Putting together with our security operations centers a forensic and recovery plan and a runbook for the client. And then being able to cleanse and remediate so that we can get that data back into production. These are all services that DXC offers in conjunction with the Dell solution to protect and recover, and keep bad actors out. And if we can't keep 'em out, to ensure that we are back into production in short order. >> You know, this discussion we've been having about DR kind of versus resilience, and you were just talking about RPO and RTO, I mean, it used to be that a lot of firms wouldn't even test their recovery 'cause it was too risky or, you know, maybe they tested it on, you know, July 4th or something like that. But I'm inferring that's changed. I wonder if we could, you know, double click on recovery, how hard is it to test that recovery and how quickly are you seeing organizations recover from attacks? >> So it depends, right, on the industry vertical, what kind of data, again. Financial services client compared to a manufacturing client are going to be two separate conversations. We've seen it as quickly as being able to recover in six hours, in 12 hours. In some instances we have the grace period of a day to a couple days. We do offer the ability to run scenarios once a quarter where we can stand up in our systems the production data that we are protecting to ensure that we have a good recoverable copy, but it depends on the client. >> I really like the emphasis here, Dave, that you're raising and that Andrew's talking about. It's not on the technology of how the data gets protected it's focused on the recovery, that's all that we want to do. And so the solution with DXC really focuses on generating that recovery for customers. I think where people get a little bit twisted up on their testing capability is you have to think about different scenarios. So, there are scenarios where the attack might be small, it might be limited to a database or an application. It might be really broadly-based, like the NotPetya attacks from a few years ago. The regulatory environment, we call those attacks severe but plausible. So you can't necessarily test everything with the infrastructure but you can test some things with the infrastructure. Others, you might sit around on a tabletop exercise or walk through what that looks like to really get that recovery kind of muscle memory so that people know what to do when those things occur. But the key to it, as Andrew said before, have to focus down what are those critical applications? What do we need? What's most important? What has to come back first? And that really will go a long way towards having the right recovery points and recovery times from a cyber disaster. >> Yeah, makes sense, understanding the value of that data is going to inform you how to respond and how to prioritize. Andrew, one of the things that we hear a lot on theCUBE, especially lately is around, you know, IOT, IIOT, Industry 4.0, the whole OT security piece of it. And the problem being that, you know, traditionally operations technologies have been air-gapped often by design. But as businesses, increasingly they're driving initiatives like Industry 4.0 and they're connecting these OT systems to IT systems. They're, you know, driving efficiency, preventative maintenance, et cetera. So, a lot of data flowing through the pipes, if you will. What are you seeing in terms of the threats to critical infrastructure and how should customers think about addressing these issues? >> Yeah, so bad actors can come in many forms. We've seen instances of social engineering, we've seen, USB stick dropped in a warehouse. That data that is flowing through the IOT devices is as sensitive now as your core mainframe infrastructure data. So, when you look at it from a protection standpoint, conceptually it's not dissimilar from what we've been talking about, where you want to understand, again, what the most critical data is. Looking at IOT data and applications is no different than your core systems now, right? Depending on what your business is, right? So when we're looking at protecting these, yes, we want firewalls, yes, we want air gap solutions, yes, we want front-end protection but we're looking at it from a resiliency perspective. Putting that data, understanding what data entails to put in the vault from an IOT perspective is just as critical as it is for your core systems. >> Jim, anything you can add to this topic? >> Yeah, I think you hit on the key points there, everything is interconnected. So even in the days where maybe people thought the OT systems weren't online, oftentimes the IT systems are talking to them or controlling theM, SCADA systems, or perhaps supporting them. Think back to the pipeline attack of last year. All the public testimony was that the OT systems didn't get attacked directly but there was uncertainty around that and the IT systems hadn't been secured so that caused the OT systems to have to shut down. It certainly is a different recovery when you're shutting them down on your own versus being attacked but the outcome was the same that the business couldn't operate. So, you really have to take all of those into account. And I think that does go back to exactly what Andrew's saying, understanding your critical business services and then the applications and data, and other components that support those and drive those and making sure those are protected, you understand them, you have the ability to recover them if necessary. >> So guys, I mean you made the point, I mean, you're right, the adversary is highly capable, they're motivated 'cause the ROI is so, it's so lucrative. It's like this never ending battle that cybersecurity pros, you know, go through. It really is kind of frontline, sort of technical heroes, if you will. And so, but sometimes it just feels daunting. Why are you optimistic about the future of cyber from the good guys' perspective? >> I think we're coming at the problem the right way, Dave, so that focus. I'm so pleased with the idea that we are planning that the systems aren't going to be a hundred percent capable every single time and let's figure that out, right? That's real world stuff. So, just as the bad actors continue to adapt and expand, so do we. And I think the differences there, the common criminals, it's getting harder and harder for them. The more sophisticated ones, they're tough to beat all the time. And of course, you've raised the question of some nation states and other activities but there's a lot more information sharing, there's a lot more focus from the business side of the house and not just the IT side of the house that we need to figure these things out. >> Yeah, to add to that, I think furthering education for the client base is important. You brought up a point earlier, it used to be a boardroom conversation due to compliance reasons. Now as we have been in the market for a while we continue to mature the offerings, it's further education for not only the business itself but for the IT systems and how they interconnect, and working together so that these systems can be protected and continue to be evolved and continue to be protected through multiple frameworks as opposed to seeing it as another check the box item that the board has to adhere to. >> All right guys, we got to go. Thank you so much. Great conversation on a really important topic. Keep up the good work, appreciate it. >> Thanks Dan. >> Thank you. >> All right, thank you for watching. Stay tuned for more excellent discussions around the partnership between Dell Technologies and DXC Technology. We're talking about solving real world problems, how this partnership has evolved over time. Really meeting the changing enterprise landscape challenges. Keep it right there. (upbeat music)

>> Welcome back to the program. My name is Dave Valante and in this session, we're going to explore one of the more interesting topics of the day. IoT for Smart Factories. And with me are, Todd Edmunds,the Global CTO of Smart Manufacturing Edge and Digital Twins at Dell Technologies. That is such a cool title. (chuckles) I want to be you. And Dr. Aditi Banerjee, who's the Vice President, General Manager for Aerospace Defense and Manufacturing at DXC Technology. Another really cool title. Folks, welcome to the program. Thanks for coming on. >> Thanks Dave. >> Thank you. Great to be here. >> Nice to be here. >> Todd, let's start with you. We hear a lot about Industry 4.0, Smart Factories, IIoT. Can you briefly explain, what is Industry 4.0 all about and why is it important for the manufacturing industry? >> Yeah. Sure, Dave. You know, it's been around for quite a while and it's gone by multiple different names, as you said. Industry 4.0, Smart Manufacturing, Industrial IoT, Smart Factory. But it all really means the same thing, its really applying technology to get more out of the factories and the facilities that you have to do your manufacturing. So, being much more efficient, implementing really good sustainability initiatives. And so, we really look at that by saying, okay, what are we going to do with technology to really accelerate what we've been doing for a long, long time? So it's really not- it's not new. It's been around for a long time. What's new is that manufacturers are looking at this, not as a one-of, two-of individual Use Case point of view but instead they're saying, we really need to look at this holistically, thinking about a strategic investment in how we do this. Not to just enable one or two Use Cases, but enable many many Use Cases across the spectrum. I mean, there's tons of them out there. There's Predictive maintenance and there's OEE, Overall Equipment Effectiveness and there's Computer Vision and all of these things are starting to percolate down to the factory floor, but it needs to be done in a little bit different way and really to really get those outcomes that they're looking for in Smart Factory or Industry 4.0 or however you want to call it. And truly transform, not just throw an Industry 4.0 Use Case out there but to do the digital transformation that's really necessary and to be able to stay relevant for the future. I heard it once said that you have three options. Either you digitally transform and stay relevant for the future or you don't and fade into history. Like, 52% of the companies that used to be on the Fortune 500 since 2000. Right? And so, really that's a key thing and we're seeing that really, really being adopted by manufacturers all across the globe. >> Yeah. So, Aditi, it's like digital transformation is almost synonymous with business transformation. So, is there anything you'd add to what Todd just said? >> Absolutely. Though, I would really add that what really drives Industry 4.0 is the business transformation. What we are able to deliver in terms of improving the manufacturing KPIs and the KPIs for customer satisfaction, right? For example, improving the downtime or decreasing the maintenance cycle of the equipments or improving the quality of products, right? So, I think these are lot of business outcomes that our customers are looking at while using Industry 4.0 and the technologies of Industry 4.0 to deliver these outcomes. >> So, Aditi, I wonder if I could stay with you and maybe this is a bit esoteric but when I first first started researching IoT and Industrial IoT 4.0, et cetera, I felt, well, there could be some disruptions in the ecosystem. I kind of came to the conclusion that large manufacturing firms, Aerospace Defense companies the firms building out critical infrastructure actually had kind of an incumbent advantage and a great opportunity. Of course, then I saw on TV somebody now they're building homes with 3D printers. It like blows your mind. So that's pretty disruptive. But, so- But they got to continue, the incumbents have to continue to invest in the future. They're well-capitalized. They're pretty good businesses, very good businesses but there's a lot of complexities involved in kind of connecting the old house to the new addition that's being built, if you will, or this transformation that we're talking about. So, my question is, how are your customers preparing for this new era? What are the key challenges that they're facing in the the blockers, if you will? >> Yeah, I mean the customers are looking at Industry 4.0 for Greenfield Factories, right? That is where the investments are going directly into building the factories with the new technologies, with the new connectivities, right? For the machines, for example, Industrial IoT having the right type of data platforms to drive computational analytics and outcomes, as well as looking at Edge versus Cloud type of technologies, right? Those are all getting built in the Greenfield Factories. However, for the Install-Based Factories, right? That is where our customers are looking at how do I modernize these factories? How do I connect the existing machine? And that is where some of the challenges come in on the legacy system connectivity that they need to think about. Also, they need to start thinking about cybersecurity and operation technology security because now you are connecting the factories to each other. So, cybersecurity becomes top of mind, right? So, there is definitely investment that is involved. Clients are creating roadmaps for digitizing and modernizing these factories and investments in a very strategic way. So, perhaps they start with the innovation program and then they look at the business case and they scale it up, right? >> Todd, I'm glad you did brought up security, because if you think about the operations technology folks, historically they air-gaped the systems, that's how they created security. That's changed. The business came in and said, 'Hey, we got to connect. We got to make it intelligence.' So, that's got to be a big challenge as well. >> It absolutely is, Dave. And, you know, you can no longer just segment that because really to get all of those efficiencies that we talk about, that IoT and Industrial IoT and Industry 4.0 promise, you have to get data out of the factory but then you got to put data back in the factory. So, no longer is it just firewalling everything is really the answer. So, you really have to have a comprehensive approach to security, but you also have to have a comprehensive approach to the Cloud and what that means. And does it mean a continuum of Cloud all the way down to the Edge, right down to the factory? It absolutely does. Because no one approach has the answer to everything. The more you go to the Cloud the broader the attack surface is. So, what we're seeing is a lot of our customers approaching this from kind of that hybrid right ones run anywhere on the factory floor down to the Edge. And one of the things we're seeing too, is to help distinguish between what is the Edge and bridge that gap between, like, Dave, you talked about IT and OT and also help what Aditi talked about is the Greenfield Plants versus the Brownfield Plants that they call it, that are the legacy ones and modernizing those. It's great to kind of start to delineate what does that mean? Where's the Edge? Where's the IT and the OT? We see that from a couple of different ways. We start to think about really two Edges in a manufacturing floor. We talk about an Industrial Edge that sits... or some people call it a Far Edge or a Thin Edge, sits way down on that plant, consists of industrial hardened devices that do that connectivity. The hard stuff about how do I connect to this obsolete legacy protocol and what do I do with it? And create that next generation of data that has context. And then we see another Edge evolving above that, which is much more of a data and analytics and enterprise grade application layer that sits down in the factory itself; that helps figure out where we're going to run this? Does it connect to the Cloud? Do we run Applications On-Prem? Because a lot of times that On-Prem Application it needs to be done. 'Cause that's the only way that it's going to work because of security requirements, because of latency requirements performance and a lot of times, cost. It's really helpful to build that Multiple-Edge strategy because then you kind of, you consolidate all of those resources, applications, infrastructure, hardware into a centralized location. Makes it much, much easier to really deploy and manage that security. But it also makes it easier to deploy new Applications, new Use Cases and become the foundation for DXC'S expertise and Applications that they deliver to our customers as well. >> Todd, how complex are these projects? I mean, I feel like it's kind of the the digital equivalent of building the Hoover Dam. I mean, its.. so yeah. How long does a typical project take? I know it varies, but what are the critical success factors in terms of delivering business value quickly? >> Yeah, that's a great question in that we're- you know, like I said at the beginning, this is not new. Smart Factory and Industry 4.0 is not new. It's been, it's people have been trying to implement the Holy Grail of Smart Factory for a long time. And what we're seeing is a switch, a little bit of a switch or quite a bit of a switch to where the enterprises and the IT folks are having a much bigger say and they have a lot to offer to be able to help that complexity. So, instead of deploying a computer here and a Gateway there and a Server there, I mean, you go walk into any manufacturing plant and you can see Servers sitting underneath someone's desk or a PC in a closet somewhere running a critical production application. So, we're seeing the enterprise have a much bigger say at the table, much louder voice at the table to say, we've been doing this enterprise all the time. We know how to really consolidate, bring Hyper-Converged Applications, Hyper-Converged Infrastructure to really accelerate these kind of applications. Really accelerate the outcomes that are needed to really drive that Smart Factory and start to bring that same capabilities down into the Mac on the factory floor. That way, if you do it once to make it easier to implement, you can repeat that. You can scale that. You can manage it much easily and you can then bring that all together because you have the security in one centralized location. So, we're seeing manufacturers that first Use Case may be fairly difficult to implement and we got to go down in and see exactly what their problems are. But when the infrastructure is done the correct way when that- Think about how you're going to run that and how are you going to optimize the engineering. Well, let's take that what you've done in that one factory and then set. Let's make that across all the factories including the factory that we're in, then across the globe. That makes it much, much easier. You really do the hard work once and then repeat. Almost like cookie cutter. >> Got it. Thank you. >> Aditi, what about the skillsets available to apply these to these projects? You got to have knowledge of digital, AI, Data, Integration. Is there a talent shortage to get all this stuff done? >> Yeah, I mean, definitely. Lot different types of skillsets are needed from a traditional manufacturing skillset, right? Of course, the basic knowledge of manufacturing is important. But the digital skillsets like IoT, having a skillset in in different Protocols for connecting the machines, right? That experience that comes with it. Data and Analytics, Security, Augmented Virtual Reality Programming. Again, looking at Robotics and the Digital Twin. So, the... It's a lot more connectivity software, data-driven skillsets that are needed to Smart Factory to life at scale. And, you know, lots of firms are recruiting these types of resources with these skill sets to accelerate their Smart Factory implementation, as well as consulting firms like DXC Technology and others. We recruit, we train our talent to provide these services. >> Got it. Aditi, I wonder if we could stay on you. Let's talk about the partnership between DXC and Dell. What are you doing specifically to simplify the move to Industry 4.0 for customers? What solutions are you offering? How are you working together, Dell and DXC to bring these to market? >> Yeah, Dell and DXC have a very strong partnership and we work very closely together to create solutions, to create strategies and how we are going to jointly help our clients, right? So, areas that we have worked closely together is Edge Compute, right? How that impacts the Smart Factory. So, we have worked pretty closely in that area. We're also looked at Vision Technologies. How do we use that at the Edge to improve the quality of products, right? So, we have several areas that we collaborate in and our approaches that we want to bring solutions to our client and as well as help them scale those solutions with the right infrastructure, the right talent and the right level of security. So, we bring a comprehensive solution to our clients. >> So, Todd, last question. Kind of similar but different, you know. Why Dell, DXC, pitch me? What's different about this partnership? Where are you confident that you're going to be to deliver the best value to customers? >> Absolutely. Great question. You know, there's no shortage of Bespoke Solutions that are out there. There's hundreds of people that can come in and do individual Use Cases and do these things and just, and that's where it ends. What Dell and DXC Technology together bring to the table is we do the optimization of the engineering of those previously Bespoke Solutions upfront, together. The power of our scalable enterprise grade structured industry standard infrastructure, as well as our expertise in delivering package solutions that really accelerate with DXC's expertise and reputation as a global trusted advisor. Be able to really scale and repeat those solutions that DXC is so really, really good at. And Dell's infrastructure and our, 30,000 people across the globe that are really, really good at that scalable infrastructure to be able to repeat. And then it really lessens the risk that our customers have and really accelerates those solutions. So it's again, not just one individual solutions it's all of the solutions that not just drive Use Cases but drive outcomes with those solutions. >> Yeah, you're right. The partnership has gone, I mean I first encountered it back in, I think it was 2010. May of 2010. We had guys both on the, I think you were talking about converged infrastructure and I had a customer on, and it was actually the manufacturing customer. It was quite interesting. And back then it was how do we kind of replicate what's coming in the Cloud? And you guys have obviously taken it into the digital world. Really want to thank you for your time today. Great conversation and love to have you back. >> Thank you so much. It was a pleasure speaking with you. I agree. >> All right, keep it right there for more discussions that educate and inspire on "The Cube."

>> Live from Las Vegas, it's theCUBE, covering Informatica World 2019. Brought to you by Informatica. >> Welcome back, everyone, to theCUBE's live coverage of Informatica World 2019. I'm your host, Rebecca Knight, along with my cohost, John Furrier. We have two guests for this segment. We have Charlie Emer. He is the senior director data management and governance strategy at Honeywell. Thanks for joining us. >> Thank you. >> And Randy Mickey, senior vice president global professional services at Informatica. Thanks for coming on theCUBE. >> Thank you. >> Charlie, I want to start with you. Honeywell is a household name, but tell us a little bit about the business now and about your role at Honeywell. >> Think about it this way. When I joined Honeywell, even before I knew Honeywell, all I thought was thermostats. That's what people would think about Honeywell. >> That's what I thought. >> But Honeywell's much bigger than that. Look, if you go back to the Industrial Revolution, back in, I think, '20s, we talked about new things. Honeywell was involved from the beginning making things. But we think this year and moving forward in this age, Honeywell is looking at it as the new Industrial Revolution. What is that? Because Honeywell makes things. We make aircraft engines, we make aircraft parts. We make everything, household goods, sensors, all types of sensors. We make things. So when we say the new Industrial Revolution is about the Internet of Things, who best to participate because we make those things. So what we are doing now is what we call IIOT, Industrial Internet of Things. Now, that is what Honeywell is about, and that's the direction we are heading, connecting those things that we make and making them more advancing, sort of making life easier for people, including people's quality of life by making those things that we make more usable for them and durable. >> Now, you're a broad platform customer of Informatica. I'd love to hear a little bit from both of you about the relationship and how it's evolved over the years. >> Look, we look at Informatica as supporting our fundamentals, our data fundamentals. For us to be successful in what we do, we need to have good quality data, well governed, well managed, and secure. Not only that, and also accessible. And we using Informatica almost end to end. We are using Informatica for our data movement ETL platform. We're using Informatica for our data quality. We're using Informatica for our master data management. And we have Informatica beginning now to explore and to use Informatica big data management capabilities. And more to that, we also utilize Informatica professional services to help us realize those values from the platforms that we are deploying. IIoT, Industrial IoT has really been a hot trend. Industrial implies factories building big things, planes, wind farms, we've heard that before. But what's interesting is these are pre-existing physical things, these plants and all this manufacturing. When you add digital connectivity to it and power, it's going to change what they were used to be doing to new things. So how do you see Industrial IoT changing or creating a builder culture of new things? Because this connect first, got to have power and connectivity. 5G's coming around, Wi-Fi 6 is around the corner. This is going to light up all these devices that might have had battery power or older databases. What's the modernization of these industrial environments going to look like in your view? First of all, let me give you an example of the value that is coming with this connectivity. Think of it, if you are an aircraft engineer. Back in the day, a plane landed in Las Vegas. You went and inspected it, physically, and checked in your manual when to replace a part. But now Honeywell is telling you, we're connecting directly to the mechanic who is going to inspect the plane, and there will be sort of in their palms they can see and say wait a minute. This part, one more flight and I should replace this part. Now, we are advising you now, doing some predictive analytics, and telling you when this part could even fail. We're telling you when to replace it. So we're saying okay, the plane is going to fly from here to California. Prepare the mechanics in California when it lands with the part so they can replace it. That's already safety 101. So guaranteeing safety, sort of improving the equity or the viability of the products that we produce. When we're moving away from continue to build things because people still need those things built, safety products, but we're just making them more. We've heard supply chain's a real low-hanging fruit on this, managing the efficiency so there's no waste. Having someone ready at the plane is efficient. That's kind of low-hanging fruit. Any ideas on some of the creativity of new applications that's going to come from the data? Because now you start getting historical data from the connections, that's where I think the thing can get interesting here. Maybe new jobs, new types of planes, new passenger types. >> We are not only using the data to improve on the products and help us improve customer needs, design new products, create new products, but we also monitorizing that data, allowing our partners to also get some insights from that data to develop their own products. So creating sort of an environment where there is a partnership between those who use our products. And guess what, most of the people who use our products, our products actually input into their products. So we are a lot more business-to-business company than a B2C. So I see a lot of value in us being able to share that intelligence, that insight, in our data at a level of scientific discovery for our partners. >> Randy, I want to bring you into the conversation a little bit here (laughs). >> Thanks. >> So you lead Informatica's professional services. I'm interested to hear your work with Honeywell, and then how it translates to the other companies that you engage with. Honeywell is such a unique company, 130 years of innovation, inventor of so many important things that we use in our everyday lives. That's not your average company, but talk a little bit about their journey and how it translates to other clients. >> Sure, well, you could tell, listening to Charlie, how strategic data is, as well as our relationship. And it's not just about evolution from their perspective, but also you mentioned the historicals and taking advantage of where you've been and where you need to go. So Charlie's made it very clear that we need to be more than just a partner with products. We need to be a partner with outcomes for their business. So hence, a professional services relationship with Honeywell and Charlie and the organization started off more straightforward. You mentioned ETL, and we started off 2000, I believe, so 19 years ago. So it's been a journey already, and a lot more to go. But over the years you can kind of tell, using data in different ways within the organization, delivering business outcomes has been at the forefront, and we're viewed strategically, not just with the products, but professional services as well, to make sure that we can continue to be there, both in an advisory capacity, but also in driving the right outcomes. And something that Charlie even said this morning was that we were kind of in the fabric. We have a couple of team members that are just like Honeywell team members. We're in the fabric of the organization. I think that's really critically important for us to really derive the outcomes that Charlie and the business need. >> And data is so critical to their business. You have to be, not only from professional services, but as a platform. Yes. This is kind of where the value comes from. Now, I can't help but just conjure up images of space because I watch my kids that watch, space is now hot. People love space. You see SpaceX landing their rocket boosters to the finest precision. You got Blue Origin out there with Amazon. And they are Honeywell sensors either. Honeywell's in every manned NASA mission. You have a renaissance of activity going on in a modern way. This is exciting, this is critical. Without data, you can't do it. >> Absolutely, I mean, also sometimes we take a break. I'm a fundamentalist. I tell everybody that excitement is great, but let's take a break. Let's make sure the fundamentals are in place. And we actually know what is it, what are those critical data that we need to be tracking and managing? Because you don't just have to manage a whole world of data. There's so much of it, and believe me, there's not all value in everything. You have to be critical about it and strategic about it. What are the critical data that we need to manage, govern, and actually, because it's expensive to manage the critical data. So we look at a value tree as well, and say, okay, if we, as Honeywell, want to be able to be also an efficient business enabler, we have to be efficient inside. So there's looking out, and there's also looking inside to make sure that we are in the right place, we are understanding our data, our people understand data. Talking about our relationship with IPS, Informatica Professional Services, one of the things that we're looking at is getting the right people, the engineers, the people to actually realize that okay, we have the platform, we've heard of Clare, We heard of all those stuff. But where are the people to actually go and do the real stuff, like actually programming, writing the code, connecting things and making it work? It's not easy because the technology's going faster than the capabilities in terms of people, skills. So the partnership we're building with Informatica professional services, and we're beginning to nurture, inside that, we want to be in a position were Honeywell doesn't have to worry so much about the churn in terms of getting people and retraining and retraining and retraining. We want to have a reliable partner who is also moving with the certain development and the progress around the products that we bought so we can have that success. So the partnership with IPS is for the-- >> The skill gaps we've been talking about, I know she's going to ask next, but I'll just jump in because I know there's two threads here. One is there's a new generation coming into the workforce, okay, and they're all data-full. They've been experiencing the digital lifestyle, the engineering programs. To data, it's all changing. What are some of the new expertise that really stand out when evaluating candidates, both from the Informatica side and also Honeywell? What's the ideal candidate look like, because there's no real four-year degree anymore? Well, Berkeley just had their first class of data analytics. That's new two-generation. But what are some of those skills? There's no degree out there. You can't really get a degree in data yet. >> Do you want to talk about that? >> Sure, I can just kick off with what we're looking at and how we're evolving. First of all, the new graduates are extremely innovative and exciting to bring on. We've been in business for 26 years, so we have a lot of folks that have done some great work. Our retention is through the roof, so it's fun to meld the folks that have been doing things for over 10, 15 years, to see what the folks have new ideas about how to leverage data. The thing I can underscore is it's business and technology, and I think the new grads get that really, really well in terms of data. To them, data's not something that's stored somewhere in the cloud or in a box. It's something that's practically applied for business outcomes, and I think they get that right out of school, and I think they're getting that message loud and clear. Lot of hybrid programs. We do hire direct from college, but we also hire experienced hires. And we look for people that have had degrees that are balanced. So the traditional just CS-only degrees, still very relevant, but we're seeing a lot of people do hybrids because they know they want to understand supply chain along with CS and data. And there are programs around just data, how organizations can really capitalize on that. >> And also we're hearing, too, that having domain expertise is actually just as important as having the coding skills because you got to know what an outcome looks like before you collect the data. You got to know what checkmate is if you're going to play chess. That's the old expression, right? >> I think people with the domain, both the hybrid experience or expertise, are more valuable to the company because maybe from the product perspective, from building products, you could be just a scientist, code the code. But when you come to Honeywell, for example, we want you to be able to understand, think about materials. Want you to be able to understand what are the products, what are the materials that we use. What are the inputs that we have to put into these products? Now a simple thing like a data scientist deciding what the right correct value of what an attribute should be, that's not something that because you know code you can determine. You have to understand the domain, the domain you're dealing with. You have to understand the context. So that comes, the question of context management, understanding the context and bringing it together. That is a big challenge, and I can tell you that's a big gap there. >> Big gap indeed, and understand the business and the data too. >> Yes. >> Charles, Randy, thank you both so much for coming on theCUBE. It's been a great conversation. >> Thank you. >> Thank you. >> I'm Rebecca Knight for John Furrier. You are watching theCUBE. (funky techno music)

>> Narrator: Live from San Francisco, it's theCUBE, covering Spark Summit 2017. Brought to you by Databricks. >> Welcome back we're here at theCube at Spark Summit 2017. I'm David Goad here with George Gilbert, George. >> Good to be here. >> Thanks for hanging with us. Well here's the other man of the hour here. We just talked with Ali, the CEO at Databricks and now we have the Chief Architect and co-founder at Databricks, Reynold Xin. Reynold, how are you? >> I'm good. How are you doing? >> David: Awesome. Enjoying yourself here at the show? >> Absolutely, it's fantastic. It's the largest Summit. It's a lot interesting things, a lot of interesting people with who I meet. >> Well I know you're a really humble guy but I had to ask Ali what should I ask Reynold when he gets up here. Reynold is one of the biggest contributors to Spark. And you've been with us for a long time right? >> Yes, I've been contributing for Spark for about five or six years and that's probably the most number of commits to the project and lately more I'm working with other people to help design the roadmap for both Spark and Databricks with them. >> Well let's get started talking about some of the new developments that you want maybe our audience at theCUBE hasn't heard here in the keynote this morning. What are some of the most exciting new developments? >> So, I think in general if we look at Spark, there are three directions I would say we doubling down. One the first direction is the deep learning. Deep learning is extremely hot and it's very capable but as we alluded to earlier in a blog post, deep learning has reached sort of a mass produced point in which it shows tremendous potential but the tools are very difficult to use. And we are hoping to democratize deep learning and do what Spark did to big data, to deep learning with this new library called deep learning pipelines. What it does, it integrates different deep learning libraries directly in Spark and can actually expose models in sequel. So, even the business analysts are capable of leveraging that. So, that one area, deep learning. The second area is streaming. Streaming, again, I think that a lot of customers have aspirations to actually shorten the latency and increase the throughput in streaming. So, the structured streaming effort is going to be generally available and last month alone on Databricks platform, I think out customers processed three trillion records, last month alone using structured streaming. And we also have a new effort to actually push down the latency all the way to some millisecond range. So, you can really do blazingly fast streaming analytics. And last but not least is the SEQUEL Data Warehousing area, Data warehousing I think that it's a very mature area from the outset of big data point of view, but from a big data one it's still pretty new and there's a lot of use cases that's popping up there. And Spark with approaches like the CBO and also impact here in the database runtime with DBIO, we're actually substantially improving the performance and the capabilities of data warehousing futures. >> We're going to dig in to some of those technologies here in just a second with George. But have you heard anything here so far from anyone that's changed your mind maybe about what to focus on next? So, one thing I've heard from a few customers is actually visibility and debugability of the big data jobs. So many of them are fairly technical engineers and some of them are less sophisticated engineers and they have written jobs and sometimes the job runs slow. And so the performance engineer in me would think so how do I make the job run fast? The different way to actually solve that problem is how can we expose the right information so the customer can actually understand and figure it out themselves. This is why my job is slow and this how I can tweak it to make it faster. Rather than giving people the fish, you actually give them the tools to fish. >> If you can call that bugability. >> Reynold: Yeah, Debugability. >> Debugability. >> Reynold: And visibility, yeah. >> Alright, awesome, George. >> So, let's go back and unpack some of those kind of juicy areas that you identified, on deep learning you were able to distribute, if I understand things right, the predictions. You could put models out on a cluster but the really hard part, the compute intensive stuff, was training across a cluster. And so Deep Learning, 4J and I think Intel's BigDL, they were written for Spark to do that. But with all the excitement over some of the new frameworks, are they now at the point where they are as good citizens on Spark as they are on their native environments? >> Yeah so, this is a very interesting question, obviously a lot of other frameworks are becoming more and more popular, such as TensorFlow, MXNet, Theano, Keras and Office. What the Deep Learning Pipeline library does, is actually exposes all these single note Deep Learning tools as highly optimized for say even GPUs or CPUs, to be available as a estimator or like a module in a pipeline of the machine learning pipeline library in spark. So, now users can actually leverage Spark's capability to, for example, do hyper parameter churning. So, when you're building a machine learning model, it's fairly rare that you just run something once and you're good with it. Usually have to fiddle with a lot of the parameters. For example, you might run over a hundred experiments to actually figure out what is the best model I can get. This is where actually Spark really shines. When you combine Spark with some deep learning library be it BigDL or be it MXNet, be it TensorFlow, you could be using Spark to distribute that training and then do cross validation on it. So you can actually find the best model very quickly. And Spark takes care of all the job scheduling, all the tolerance properties and how do you read data in from different data sources. >> And without my dropping too much in the weeds, there was a version of that where Spark wouldn't take care of all the communications. It would maybe distribute the models and then do some of the averaging of what was done out on the cluster. Are you saying that all that now can be managed by Spark? >> In that library, Spark will be able to actually take care of picking the best model out of it. And there are different ways you an design how do you define the best. The best could be some average of some different models. The best could be just pick one out of this. The best could be maybe there's a tree of models that you classify it on. >> George: And that's a hyper parameter configuration choice? >> So that is actually building functionality in Sparks machine learning pipeline. And now what we're doing is now you can actually plug all those deep learning libraries directly into that as part of the pipeline to be used. Another maybe just to add, >> Yeah, yeah, >> Another really cool functionality of the deep learning pipeline is transfer learning. So as you said, deep learning takes a very long time, it's very computationally demanding. And it takes a lot of resources, expertise to train. But with transfer learning what we allow the customers to do is they can take an existing deep learning model as well train in a different domain and they we'd retrain it on a very small amount of data very quickly and they can adapt it to a different domain. That's how sort of the demo on the James Bond car. So there is a general image classifier that we train it on probably just a few thousand images. And now we can actually detect whether a car is James Bond's car or not. >> Oh, and the implications there are huge, which is you don't have to have huge training data sets for modifying a model of a similar situation. I want to, in the time we have, there's always been this debate about whether Sparks should manage state, whether it's database, key value store. Tell us how the thinking about that has evolved and then how the integration interfaces for achieving that have evolved. >> One of the, I would say, advantages of Spark is that it's unbiased and works with a variety of storage systems, be it Cassandra, be it Edgebase, be it HDFS, be is S3. There is a metadata management functionality in Spark which is the catalog of tables that customers can define. But the actual storage sits somewhere else. And I don't think that will change in the near future because we do see that the storage systems have matured significantly in the last few years and I just wrote blog post last week about the advantage of S3 over HDFS for example. The storage price is being driven down by almost a factor of 10X when you go to the cloud. I just don't think it makes sense at this point to be building storage systems for analytics. That said, I think there's a lot of building on top of existing storage system. There's actually a lot of opportunities for optimization on how you can leverage the specific properties of the underlying storage system to get to maximum performance. For example, how are you doing intelligent caching, how do you start thinking about building indexes actually against the data that's stored for scanned workloads. >> With Tungsten's, you take advantage of the latest hardware and where we get more memory intensive systems and now that the Catalyst Optimizer has a cost based optimizer or will be, and large memory. Can you change how you go about knowing what data you're managing in the underlying system and therefore, achieve a tremendous acceleration in performance? >> This is actually one area we invested in the DBIO module as part of Databricks Runtime, and what DBIO does, a lot of this are still in progress, but for example, we're adding some form of indexing capability to add to the system so we can quickly skip and prune out all the irrelevant data when the user is doing simple point look-ups. Or if the user is doing a scan heavy workload with some predicates. That actually has to do with how we think about the underlying data structure. The storage system is still the same storage system, like S3, but were adding actually indexing functionalities on top of it as part of DBIO. >> And so what would be the application profiles? Is it just for the analytic queries or can you do the point look-ups and updates in that sort of scenario too? >> So it's interesting you're talking about updates. Updates is another thing that we've got a lot of future requests on. We're actively thinking about how we will support update workload. Now, that said, I just want to emphasize for both use case of doing point look-ups and updates, we're still talking about in the context of analytic environment. So we would be talking about for example maybe bulk updates or low throughput updates rather than doing transactional updates in which every time you swipe a credit card, some record gets updated. That's probably more belongs on the transactional databases like Oracle or my SEQUEL even. >> What about when you think about people who are going to run, they started out with Spark on prem, they realize they're going to put much more of their resources in the cloud, but with IIOT, industrial IOT type applications they're going to have Spark maybe in a gateway server on the edge? What do you think that configuration looks like? >> Really interesting, it's kind of two questions maybe. The first is the hybrid on prem, cloud solution. Again, so one of the nice advantage of Spark is the couple of storage and compute. So when you want to move for example, workloads from one prem to the cloud, the one you care the most about is probably actually the data 'cause the compute, it doesn't really matter that much where you run it but data's the one that's hard to move. We do have customers that's leveraging Databricks in the cloud but actually reading data directly from on prem the reliance of the caching solution we have that minimize the data transfer over time. And is one route I would say it's pretty popular. Another on is, with Amazon you can literally give them just a show ball of functionality. You give them hard drive with trucks, the trucks will ship your data directly put in a three. With IOT, a common pattern we see is a lot of the edge devices, would be actually pushing the data directly into some some fire hose like Kinesis or Kafka or, I'm sure Google and Microsoft both have their own variance of that. And then you use Spark to directly subscribe to those topics and process them in real time with structured streaming. >> And so would Spark be down, let's say at the site level. if it's not on the device itself? >> It's a interesting thought and maybe one thing we should actually consider more in the future is how do we push Spark to the edges. Right now it's more of a centralized model in which the devices push data into Spark which is centralized somewhere. I've seen for example, I don't remember exact the use case but it has to do with some scientific experiment in the North Pole. And of course there you don't have a great uplink of all the data connecting transferring back to some national lab and rather they would do a smart parsing there and then ship the aggregated result back. There's another one but it's less common. >> Alright well just one minute now before the break so I'm going to give you a chance to address the Spark community. What's the next big technical challenge you hope people will work on for the benefit of everybody? >> In general Spark came along with two focuses. One is performance, the other one's ease of use. And I still think big data tools are too difficult to use. Deep learning tools, even harder. The barrier to entry is very high for office tools. I would say, we might have already addressed performance to a degree that I think it's actually pretty usable. The systems are fast enough. Now, we should work on actually make (mumbles) even easier to use. It's what also we focus a lot on at Databricks here. >> David: Democratizing access right? >> Absolutely. >> Alright well Reynold, I wish we could talk to you all day. This is great. We are out of time now. Want to appreciate you coming by theCUBE and sharing your insights and good luck with the rest of the show. >> Thank you very much David and George. >> Thank you all for watching here were at theCUBE at Sparks Summit 2017. Stay tuned, lots of other great guests coming up today. We'll see you in a few minutes.