(upbeat music) >> Welcome friends to HPE Ezmeral's analytics unleashed. I couldn't be more excited to have you here today. We have a packed and informative agenda. It's going to give you not just a perspective on what HPE Ezmeral is and what it can do for your organization, but you should leave here with some insights and perspectives that will help you on your edge to cloud data journey in general. The lineup we have today is awesome. We have industry experts like Kirk Borne, who's going to talk about the shape this space will take to key customers and partners who are using Ezmeral technology as a fundamental part of their stack to solve really big, hairy, complex real data problems. We will hear from the execs who are leading this effort to understand the strategy and roadmap forward as well as give you a sneak peek into the new ISV ecosystem that is hosted in the Ezmeral marketplace. And finally, we have some live music being played in the form of three different demos. There's going to be a fun time so do jump in and chat with us at any time or engage with us on Twitter in real time. So grab some coffee, buckle up and let's get going. (upbeat music) Getting data right is one of the top priorities for organizations to affect digital strategy. So right now we're going to dig into the challenges customers face when trying to deploy enterprise wide data strategies and with me to unpack this topic is Kirk Borne, principal data scientist, and executive advisor, Booz Allen Hamilton. Kirk, great to see you. Thank you sir, for coming into the program. >> Great to be here, Dave. >> So hey, enterprise scale data science and engineering initiatives, they're non-trivial. What do you see as some of the challenges in scaling data science and data engineering ops? >> The first challenge is just getting it out of the sandbox because so many organizations, they, they say let's do cool things with data, but how do you take it out of that sort of play phase into an operational phase? And so being able to do that is one of the biggest challenges, and then being able to enable that for many different use cases then creates an enormous challenge because do you replicate the technology and the team for each individual use case or can you unify teams and technologies to satisfy all possible use cases. So those are really big challenges for companies organizations everywhere to about. >> What about the idea of, you know, industrializing those those data operations? I mean, what does that, what does that mean to you? Is that a security connotation, a compliance? How do you think about it? >> It's actually, all of those I'm industrialized to me is sort of like, how do you not make it a one-off but you make it a sort of a reproducible, solid risk compliant and so forth system that can be reproduced many different times. And again, using the same infrastructure and the same analytic tools and techniques but for many different use cases. So we don't have to rebuild the wheel, reinvent the wheel re reinvent the car. So to speak every time you need a different type of vehicle you need to build a car or a truck or a race car. There's some fundamental principles that are common to all of those. And that's what that industrialization is. And it includes security compliance with regulations and all those things but it also means just being able to scale it out to to new opportunities beyond the ones that you dreamed of when you first invented the thing. >> Yeah. Data by its very nature as you well know, it's distributed, but for a you've been at this awhile for years we've been trying to sort of shove everything into a monolithic architecture and in in hardening infrastructures or around that. And in many organizations it's become a block to actually getting stuff done. But so how, how are you seeing things like the edge emerge How do you, how do you think about the edge? How do you see that evolving and how do you think customers should be dealing with with edge and edge data? >> Well, that's really kind of interesting. I had many years at NASA working on data systems, and back in those days, the idea was you would just put all the data in a big data center and then individual scientists would retrieve that data and do analytics on it do their analysis on their local computer. And you might say that's sort of like edge analytics so to speak because they're doing analytics at their home computer, but that's not what edge means. It means actually doing the analytics the insights discovery at the point of data collection. And so that's that's really real time business decision-making you don't bring the data back and then try to figure out some time in the future what to do. And I think in autonomous vehicles a good example of why you don't want to do that because if you collect data from all the cameras and radars and lidars that are on a self-driving car and you move that data back to a data cloud while the car is driving down the street and let's say a child walks in front of the car you send all the data back at computes and does some object recognition and pattern detection. And 10 minutes later, it sends a message to the car. Hey, you need to put your brakes off. Well, it's a little kind of late at that point. And so you need to make those discoveries those insight discoveries, those pattern discoveries and hence the proper decisions from the patterns in the data at the point of data collection. And so that's data analytics at the edge. And so, yes, you can ring the data back to a central cloud or distributed cloud. It almost doesn't even matter if, if if your data is distributed sort of any use case in any data scientist or any analytic team and the business can access it then what you really have is a data mesh or a data fabric that makes it accessible at the point that you need it, whether it's at the edge or on some static post event processing, for example typical business quarter reporting takes a long look at your last three months of business. Well, that's fine in that use case, but you can't do that for a lot of other real time analytic decision making. >> Well, that's interesting. I mean, it sounds like you think of the edge not as a place, but as you know where it makes sense to actually, you know the first opportunity, if you will, to process the data at at low latency where it needs to be low latency is that a good way to think about it? >> Yeah, absolutely. It's the low latency that really matters. Sometimes we think we're going to solve that with things like 5G networks. We're going to be able to send data really fast across the wire. But again, that self-driving car has yet another example because what if you, all of a sudden the network drops out you still need to make the right decision with the network not even being . >> That darn speed of light problem. And so you use this term data mesh or data fabric double-click on that. What do you mean by that? >> Well, for me, it's, it's, it's, it's sort of a unified way of thinking about all your data. And when I think of mesh, I think of like a weaving on a loom, or you're creating a blanket or a cloth and you do weaving and you do that all that cross layering of the different threads. And so different use cases in different applications in different techniques can make use of this one fabric no matter what, where it is in the, in the business or again, if it's at the edge or, or back at the office one unified fabric, which has a global namespace. So anyone can access the data they need and sort of uniformly no matter where they're using it. And so it's, it's a way of unifying all of the data and use cases and sort of a virtual environment that it could have that no log you need to worry about. So what's what's the actual file name or what's the actual server this thing is on you can just do that for whatever use case you have. Let's I think it helps you enterprises now to reach a stage which I like to call the self-driving enterprise. Okay. So it's modeled after the self-driving car. So the self-driving enterprise needs the business leaders in the business itself, you would say needs to make decisions oftentimes in real time. All right. And so you need to do sort of predictive modeling and cognitive awareness of the context of what's going on. So all of these different data sources enable you to do all those things with data. And so, for example, any kind of a decision in a business any kind of decision in life, I would say is a prediction. It's you say to yourself if I do this such and such will happen if I do that, this other thing will happen. So a decision is always based upon a prediction about outcomes, and you want to optimize that outcome. So both predictive and prescriptive analytics need to happen in this in this same stream of data and not statically afterwards. And so that's, self-driving enterprises enabled by having access to data wherever you and whenever you need it. And that's what that fabric, that data fabric and data mesh provides for you, at least in my opinion. >> Well, so like carrying that analogy like the self-driving vehicle you're abstracting that complexity away in in this metadata layer that understands whether it's on prem or in the public cloud or across clouds or at the edge where the best places to process that data. What makes sense, does it make sense to move it or not? Ideally, I don't have to. Is that how you're thinking about it is that why we need this notion of a data fabric >> Right. It really abstracts away all the sort of the complexity that the it aspects of the job would require, but not every person in the business is going to have that familiarity with with the servers and the access protocols and all kinds of it related things. And so abstracting that away. And that's in some sense, what containers do basically the containers abstract away all the information about servers and connectivity and protocols and all this kind of thing. You just want to deliver some data to an analytic module that delivers me an insight or a prediction. I don't need to think about all those other things. And so that abstraction really makes it empowering for the entire organization. We like to talk a lot about data democratization and analytics democratization. This really gives power to every person in the organization to do things without becoming an it expert. >> So the last, last question we have time for here. So it sounds like. Kirk, the next 10 years of data are not going to be like the last 10 years, it'd be quite different. >> I think so. I think we're moving to this. Well, first of all, we're going to be focused way more on the why question, like, why are we doing this stuff? The more data we collect, we need to know why we're doing it. And what are the phrases I've seen a lot in the past year which I think is going to grow in importance in the next 10 years is observability. So observability to me is not the same as monitoring. Some people say monitoring is what we do. But what I like to say is, yeah, that's what you do but why you do it is observability. You have to have a strategy. Why, what, why am I collecting this data? Why am I collecting it here? Why am I collecting it at this time resolution? And so, so getting focused on those, why questions create be able to create targeted analytics solutions for all kinds of diff different business problems. And so it really focuses it on small data. So I think the latest Gartner data and analytics trending reports, so we're going to see a lot more focus on small data in the near future >> Kirk borne. You're a dot connector. Thanks so much for coming on the cube and being a part of the program. >> My pleasure (upbeat music) (relaxing upbeat music)

(upbeat music) >> Hey, everyone. Welcome to theCUBE's presentation of the AWS Startup Showcase, season two, episode four. I'm your host, Lisa Martin. This topic is cybersecurity detect and protect against threats. Very excited to welcome a CUBE alumni back to the program. Snehal Antani, the co-founder and CEO of Horizon3 joins me. Snehal, it's great to have you back in the studio. >> Likewise, thanks for the invite. >> Tell us a little bit about Horizon3, what is it that you guys do? You were founded in 2019, got a really interesting group of folks with interesting backgrounds, but talk to the audience about what it is that you guys are aiming to do. >> Sure, so maybe back to the problem we were trying to solve. So my background, I was a engineer by trade, I was a CIO at G Capital, CTO at Splunk and helped grow scale that company. And then took a break from industry to serve within the Department of Defense. And in every one of my jobs where I had cyber security in my responsibility, I suffered from the same problem. I had no idea I was secure or that we were fixing the right vulnerabilities or logging the right data in Splunk or that our tools and processes and people worked together well until the bad guys had showed up. And by then it was too late. And what I wanted to do was proactively verify my security posture, make sure that my security tools were actually effective, that my people knew how to respond to a breach before the bad guys were there. And so this whole idea of continuously verifying my security posture through security testing and pen testing became a passion project of mine for over a decade. And through my time in the DOD found the right group of an early people that had offensive cyber experience, that had defensive cyber experience, that knew how to build and ship and deliver software at scale. And we came together at the end of 2019 to start Horizon3. >> Talk to me about the current threat landscape. We've seen so much change in flux in the last couple of years. Globally, we've seen the threat actors are just getting more and more sophisticated as is the different types of attacks. What are you seeing kind of horizontally across the threat landscape? >> Yeah, the biggest thing is attackers don't have to hack in using Zero-days like you see in the movies. Often they're able to just log in with valid credentials that they've collected through some mechanism. As an example, if I wanted to compromise a large organization, say United Airlines, one of the things that an attacker's going to go off and do is go to LinkedIn and find all of the employees that work at United Airlines. Now you've got say, 7,000 pilots. Of those pilots, you're going to figure out quickly that their user IDs and passwords or their user IDs at least are first name, last initial @united.com. Cool, now I have 7,000 potential logins and all it takes is one of them to reuse a compromised password for their corporate email, and now you've got an initial user in the system. And most likely, that initial user has local admin on their laptops. And from there, an attacker can dump credentials and find a path to becoming a domain administrator. And what happens oftentimes is, security tools don't detect this because it looks like valid behavior in the organization. And this is pretty common, this idea of collecting information on an organization or a target using open source intelligence, using a mix of credential spraying and kind of low priority or low severity exploitations or misconfigurations to get in. And then from there, systematically dumping credentials, reusing those credentials, and finding a path towards compromise. And less than 2% of CVEs are actually used in exploits. Most of the time, attackers chain together misconfigurations, bad product defaults. And so really the threat landscape is, attackers don't hack in, they log in. And organizations have to focus on getting the basics right and fundamentals right first before they layer on some magic easy button that is some security AI tools hoping that that's going to save their day. And that's what we found systemically across the board. >> So you're finding that across the board, probably pan-industry that a lot of companies need to go back to basics. We talk about that a lot when we're talking about security, why do you think that is? >> I think it's because, one, most organizations are barely treading water. When you look at the early rapid adopters of Horizon3's pen testing product, autonomous pen testing, the early adopters tended to be teams where the IT team and the security team were the same person, and they were barely treading water. And the hardest part of my job as a CIO was deciding what not to fix. Because the bottleneck in the security process is the actual capacity to fix problems. And so, fiercely prioritizing issues becomes really important. But the tools and the processes don't focus on prioritizing what's exploitable, they prioritize by some arbitrary score from some arbitrary vulnerability scanner. And so we have as a fundamental breakdown of the small group of folks with the expertise to fix problems tend to be the most overworked and tend to have the most noise to need to sift through. So they don't even have time to get to the basics. They're just barely treading water doing their day jobs and they're often sacrificing their nights and weekends. All of us at Horizon3 were practitioners at one point in our career, we've all been called in on the weekend. So that's why what we did was fiercely focus on helping customers and users fix problems that truly matter, and allowing them to quickly reattack and verify that the problems were truly fixed. >> So when it comes to today's threat landscape, what is it that organizations across the board should really be focused on? >> I think, systemically, what we see are bad password or credential policies, least access privileged management type processes not being well implemented. The domain user tends to be the local admin on the box, no ability to understand what is a valid login versus a malicious login. Those are some of the basics that we see systemically. And if you layer that with it's very easy to say, misconfigure vCenter, or misconfigure a piece of Cisco gear, or you're not going to be installing, monitoring security observability tools on that HPE Integrated Lights Out server and so on. What you'll find is that you've got people overworked that don't have the capacity to fix. You have the fundamentals or the basics not well implemented. And you have a whole bunch of blind spots in your security posture. And defenders have to be right every time, attackers only have to be right once. And so what we have is this asymmetric fight where attackers are very likely to get in, and we see this on the news all the time. >> So, and nobody, of course, wants to be the next headline, right? Talk to me a little bit about autonomous pen testing as a service, what you guys are delivering, and what makes it unique and different than other tools that have been out, as you're saying, that clearly have gaps. >> Yeah. So first and foremost was the approach we took in building our product. What we set upfront was, our primary users should be IT administrators, network engineers, and that IT intern who, in three clicks, should have the power of a 20-year pen testing expert. So the whole idea was empower and enable all of the fixers to find, fix, and verify their security weaknesses continuously. That was the design goal. Most other security products are designed for security people, but we already know they're task saturated, they've got way too many tools under the belt. So first and foremost, we wanted to empower the fixers to fix problems that truly matter. The second part was, we wanted to do that without having to install credentialed agents all over the place or writing your own custom attack scripts, or having to do a bunch of configurations and make sure that it's safe to run against production systems so that you could test your entire attack surface. Your on-prem, your cloud, your external perimeter. And this is where AWS comes in to be very important, especially hybrid customers where you've got a portion of your infrastructure on AWS, a portion on-prem, and you use Horizon3 to be able to attack your complete attack surface. So we can start on-prem and we will find say, the AWS credentials file that was mistakenly saved on a shared drive, and then reuse that to become admin in the cloud. AWS didn't do anything wrong, the cloud team didn't do anything wrong, a developer happened to share a password or save a password file locally. That's how attackers get in. So we can start from on-prem and show how we can compromise the cloud, start from the cloud and show how we can compromise on-prem. Start from the outside and break in. And we're able to show that complete attack surface at scale for hybrid customers. >> So showing that complete attack surface sort of from the eyes of the attacker? >> That's exactly right, because while blue teams or the defenders have a very specific view of their environment, you have to look at yourself through the eyes of the attacker to understand what are your blind spots, what do they see that you don't see. And it's actually a discipline that is well entrenched within military culture. And that's also important for us as the company. We're about a third of Horizon3 served in US special operations or the intelligence community with the United States, and then DOD writ large. And a lot of that red team mindset, view yourself through the eyes of the attacker, and this idea of training like you fight and building muscle memory so you know how to react to the real incident when it occurs is just ingrained in how we operate, and we disseminate that culture through all of our customers as well. >> And at this point in time, every business needs to assume an attacker's going to get in. >> That's right. There are way too many doors and windows in the organization. Attackers are going to get in, whether it's a single customer that reused their Netflix password for their corporate email, a patch that didn't get applied properly, or a new Zero-day that just gets published. A piece of Cisco software that was misconfigured, not buy anything more than it's easy to misconfigure these complex pieces of technology. Attackers are going to get in. And what we want to understand as customers is, once they're in, what could they do? Could they get to my crown jewel's data and systems? Could they borrow and prepare for a much more complicated attack down the road? If you assume breach, now you want to understand what can they get to, how quickly can you detect that breach, and what are your ways to stifle their ability to achieve their objectives. And culturally, we would need a shift from talking about how secure I am to how defensible are we. Security is kind of a point in time state of your organization. Defensibility is how quickly you can adapt to the attacker to stifle their ability to achieve their objective. >> As things are changing constantly. >> That's exactly right. >> Yeah. Talk to me about a typical customer engagement. If there's, you mentioned folks treading water, obviously, there's the huge cybersecurity skills gap that we've been talking about for a long time now, that's another factor there. But when you're in customer conversations, who are you talking to? Typically, what are they coming to you for help? >> Yeah. One big thing is, you're not going to win and win a customer by taking 'em out to steak dinners. Not anymore. The way we focus on our go to market and our sales motion is cultivating champions. At the end of the proof of concept, our internal measure of successes is, is that person willing to get a Horizon3 tattoo? And you do that, not through steak dinners, not through cool swag, not through marketing, but by letting your results do the talking. Now, part of those results should not require professional services or consulting. The whole experience should be self-service, frictionless, and insightful. And that really is how we've designed the product and designed the entire sales motion. So a prospect will learn or discover about us, whether it's through LinkedIn, through social, through the website, but often because one of their friends or colleagues heard about us, saw our result, and is advocating on our behalf when we're not in the room. From there, they're going to be able to self-service, just log in to our product through their LinkedIn ID, their Google ID. They can engage with a salesperson if they want to. They can run a pen test right there on the spot against their home without any interaction with a sales rep. Let those results do the talking, use that as a starting point to engage in a more complicated proof of value. And the whole idea is we don't charge for these, we let our results do the talking. And at the end, after they've run us to find problems, they've gone off and fixed those issues, and they've rerun us to verify that what they've fixed was properly fixed, then they're hooked. And we have a hundred percent technical win rate with our prospects when they hit that find-fix-verify cycle, which is awesome. And then we get the tattoo for them, at least give them the template. And then we're off to the races. >> Sounds like you're making the process more simple. There's so much complexity behind it, but allowing users to be able to actually test it out themselves in a simplified way is huge. Allowing them to really focus on becoming defensible. >> That's exactly right. And the value is, especially now in security, there's so much hype and so much noise. There's a lot more time being spent self-discovering and researching technologies before you engage in a commercial discussion. And so what we try to do is optimize that entire buying experience around enabling people to discover and research and learn. The other part, remember is, offensive cyber and ethical hacking and so on is very mysterious and magical to most defenders. It's such a complicated topic with many nuance tools that they don't have the time to understand or learn. And so if you surface the complexity of all those attacker tools, you're going to overwhelm a person that is already overwhelmed. So we needed the experience to be incredibly simple and optimize that find-fix-verify aha moment. And once again, be frictionless and be insightful. >> Frictionless and insightful. Excellent. Talk to me about results, you mentioned results. We love talking about outcomes. When a customer goes through the PoC, PoV that you talked about, what are some of the results that they see that hook them? >> Yeah, the biggest thing is, what attackers do today is they will find a low from machine one plus a low from machine two equals compromised domain. What they're doing is they're chaining together issues across multiple parts of your system or your organization to opone your environment. What attackers don't do is find a critical vulnerability and exploit that single machine. It's always a chain, always multiple steps in the attack. And so the entire product and experience in, actually, our underlying tech is around attack paths. Here is the path, the attack path an attacker could have taken. That node zero our product took. Here is the proof of exploitation for every step along the way. So you know this isn't a false positive. In fact, you can copy and paste the attacker command from the product and rerun it yourself and see it for yourself. And then here is exactly what you have to go fix and why it's important to fix. So that path, proof, impact, and fix action is what the entire experience is focused on. And that is the results doing the talking, because remember, these folks are already overwhelmed, they're dealing with a lot of false positives. And if you tell them you've got another critical to fix, their immediate reaction is "Nope, I don't believe you. This is a false positive. I've seen this plenty of times, that's not important." So you have to, in your product experience and sales process and adoption process, immediately cut through that defensive or that reflex. And it's path, proof, impact. Here's exactly what you fix, here are the exact steps to fix it, and then you're off to the races. What I learned at Splunk was, you win hearts and minds of your users through amazing experience, product experience, amazing documentation. >> Yes. >> And a vibrant community of champions. Those are the three ingredients of success, and we've really made that the core of the product. So we win on our documentation, we win on the product experience, and we've cultivated pretty awesome community. >> Talk to me about some of those champions. Is there a customer story that you think really articulates the value of node zero and what it is that you are doing? >> Yeah, I'll tell you a couple. Actually, I just gave this talk at Black Hat on war stories from running 10,000 pen tests. And I'll try to be gentle on the vendors that were involved here, but the reality is, you got to be honest and authentic. So a customer, a healthcare organization ran a pen test and they were using a very well-known managed security services provider as their security operations team. And so they initiate the pen test and they wanted to audit their response time of their MSSP. So they run the pen test and we're in and out. The whole pen test runs two hours or less. And in those two hours, the pen test compromises the domain, gets access to a bunch of sensitive data, laterally maneuvers, rips the entire environment apart. It took seven hours for the MSSP to send an email notification to the IT director that said, "Hey, we think something suspicious is going on." >> Wow. >> Seven hours! >> That's a long time. >> We were in and out in two, seven hours for notification. And the issue with that healthcare company was, they thought they had hired the right MSSP, but they had no way to audit their performance. And so we gave them the details and the ammunition to get services credits to hold them accountable and also have a conversation of switching to somebody else. >> Accountability is key, especially when we're talking about the threat landscape and how it's evolving day to day. >> That's exactly right. Accountability of your suppliers or your security vendors, accountability of your people and your processes, and not having to wait for the bad guys to show up to test your posture. That's what's really important. Another story that's interesting. This customer did everything right. It was a banking customer, large environment, and they had Fortinet installed as their EDR type platform. And they initiate us as a pen test and we're able to get code execution on one of their machines. And from there, laterally maneuver to become a domain administrator, which in security is a really big deal. So they came back and said, "This is absolutely not possible. Fortinet should have stopped that from occurring." And it turned out, because we showed the path and the proof and the impact, Fortinet was misconfigured on three machines out of 5,000. And they had no idea. >> Wow. >> So it's one of those, you want to don't trust that your tools are working, don't trust your processes, verify them. Show me we're secure today. Show me we're secure tomorrow. And then show me again we're secure next week. Because my environment's constantly changing and the adversary always has a vote. >> Right, the constant change in flux is huge challenge for organizations, but those results clearly speak for themselves. You talked about speed in terms of time, how quickly can a customer deploy your technology, identify and remedy problems in their environment? >> Yeah, this find-fix-verify aha moment, if you will. So traditionally, a customer would have to maybe run one or two pen tests a year. And then they'd go off and fix things. They have no capacity to test them 'cause they don't have the internal attack expertise. So they'd wait for the next pen test and figure out that they were still exploitable. Usually, this year's pen test results look identical than last year's. That isn't sustainable. So our customers shift from running one or two pen tests a year to 40 pen tests a month. And they're in this constant loop of finding, fixing, and verifying all of the weaknesses in their infrastructure. Remember, there's infrastructure pen testing, which is what we are really good at, and then there's application level pen testing that humans are much better at solving. >> Okay. >> So we focus on the infrastructure side, especially at scale. But can you imagine, 40 pen tests a month, they run from the perimeter, the inside from a specific subnet, from work from home machines, from the cloud. And they're running these pen tests from many different perspectives to understand what does the attacker see from each of these locations in their organization and how do they systemically fix those issues? And what they look at is, how many critical problems were found, how quickly were they fixed, how often do they reoccur. And that third metric is important because you might fix something, but if it shows up again next week because you've got bad automation, you're in a rat race. So you want to look at that reoccurrence rate also. >> The reoccurrence rate. What are you most excited about as, obviously, the threat landscape continues to evolve, but what are you most excited about for the company and what it is that you're able to help organizations across industries achieve in such tumultuous times? >> Yeah. One of the coolest things is, because I was a customer for many of these products, I despised threat intelligence products. I despised them. Because there were basically generic blog posts. Maybe delivered as a data feed to my Splunk environment or something. But they're always really generic. Like, "You may have a problem here." And as a result, they weren't very actionable. So one of the really cool things that we do, it's just part of the product is this concept of flares, flares that we shoot up. And the idea is not to cause angst or anxiety or panic, but rather we look at threat intelligence and then because all of the insights we have from your pen test results, we connect those two together and say, "Your VMware Horizon instance at this IP is exploitable. You need to fix it as fast as possible, or is very likely to be exploited. And here is the threat intelligence and in the news from CSAI and elsewhere that shows why it's important." So I think what is really cool is we're able to take together threat intelligence out in the wild combined with very precise understanding of your environment to give you very accurate and actionable starting points for what you need to go fix or test or verify. And when we do that, what we see is almost like, imagine this ball bouncing, that is the first drop of the ball, and then that drives the first major pen test. And then they'll run all these subsequent pen tests to continue to find and fix and verify. And so what we see is this tremendous amount of excitement from customers that we're actually giving them accurate, detailed information to take advantage of, and we're not causing panic and we're not causing alert and fatigue as a result. >> That's incredibly important in this type of environment. Last question for you. If autonomous pen testing is obviously critical and has tremendous amount of potential for organizations, but it's only part of the equation. What's the larger vision? >> Yeah, we are not a pen testing company and that's something we decided upfront. Pen testing is a sensor. It collects and understands a tremendous amount of data for your attack surface. So the natural next thing is to analyze the pen test results over time to start to give you a more accurate understanding of your governance, risk, and compliance posture. So now what happens is, we are able to allow customers to go run 40 pen tests a month. And that kind of becomes the initial land or flagship product. But then from there, we're able to upsell or increase value to our customers and start to compete and take out companies like Security Scorecard or RiskIQ and other companies like that, where there tended to be, I was a user of all those tools, a lot of garbage in, garbage out. Where you can't fill out a spreadsheet and get an accurate understanding of your risk posture. You need to look at your detailed pen test results over time and use that to accurately understand what are your hotspots, what's your recurrence rate and so on. And being able to tell that story to your auditors, to your regulators, to the board. And actually, it gives you a much more accurate way to show return on investment of your security spend also. >> Which is huge. So where can customers and those that are interested go to learn more? >> So horizonthree.ai is the website. That's a great starting point. We tend to very much rely on social channels, so LinkedIn in particular, to really get our stories out there. So finding us on LinkedIn is probably the next best thing to go do. And we're always at the major trade shows and events also. >> Excellent. Snehal, it's been a pleasure talking to you about Horizon3, what it is that you guys are doing, why, and the greater vision. We appreciate your insights and your time. >> Thank you, likewise. >> All right. For my guest, I'm Lisa Martin. We want to thank you for watching the AWS Startup Showcase. We'll see you next time. (gentle music)

>>Hey everyone. Welcome to the Cube's presentation of the AWS startup showcase. Season two, episode four, I'm your host. Lisa Martin. This topic is cybersecurity detect and protect against threats. Very excited to welcome a Cub alumni back to the program. SNA hall, autonomy, the co-founder and CEO of horizon three joins me SNA hall. It's great to have you back in the studio. >>Likewise, thanks for the invite. >>Tell us a little bit about horizon three. What is it that you guys do you we're founded in 2019? Got a really interesting group of folks with interesting backgrounds, but talk to the audience about what it is that you guys are aiming to do. >>Sure. So maybe back to the problem we were trying to solve. So my background, I was a engineer by trade. I was a CIO at G capital CTO at Splunk and helped, helped grows scale that company and then took a break from industry to serve within the department of defense. And in every one of my jobs where I had cyber security in my responsibility, I suffered from the same problem. I had no idea I was secure or that we were fixing the right vulnerabilities or logging the right data in Splunk or that our tools and processes and people worked together well until the bad guys had showed up. And by then it was too late. And what I wanted to do was proactively verify my security posture, make sure that my security tools were actually effective, that my people knew how to respond to a breach before the bad guys were there. And so this whole idea of continuously verifying my security posture through security testing and pen testing became a, a passion project of mine for over a decade. And I, through my time in the DOD found the right group of an early people that had offensive cyber experience that had defensive cyber experience that knew how to build and ship and, and deliver software at scale. And we came together at the end of 2019 to start horizon three. >>Talk to me about the current threat landscape. We've seen so much change in flux in the last couple of years globally. We've seen, you know, the threat actors are just getting more and more sophisticated as is the different types of attacks. What are you seeing kind of horizontally across the threat landscape? >>Yeah. The biggest thing is attackers don't have to hack in using zero days. Like you see in the movies. Often they're able to just log in with valid credentials that they've collected through some mechanism. As an example, if I wanted to compromise a large organization, say United airlines, one of the things that an attacker's gonna go off and do is go to LinkedIn and find all of the employees that work at United airlines. Now you've got, say 7,000 pilots of those pilots. You're gonna figure out quickly that their use varie and passwords or their use varie@leastarefirstnamelastinitialatunited.com. Cool. Now I have 7,000 potential logins and all it takes is one of them to reuse a compromise password for their corporate email. And now you've got an initial user in the system and most likely that initial user has local admin on their laptops. And from there, an attacker can dump credentials and find a path to becoming a domain administrator. >>And what happens oftentimes is security tools. Don't detect this because it looks like valid behavior in the organization. And this is pretty common. This idea of collecting information on an organization or a topic or target using open source intelligence, using a mix of credentialed spraying and kinda low priority or low severity exploitations or misconfigurations to get in. And then from there systematically dumping credentials, reusing those credentials and finding a path towards compromise and almost less than 2% of, of CVEs are actually used in exploits. Most of the time attackers chain together misconfigurations bad product defaults. And so really the threat landscape is attackers don't hack in. They log in and organizations have to focus on getting the basics right and fundamentals right first, before they layer on some magic, easy button that is some security AI tools hoping that that's gonna save their day. And that's what we found systemically across the board. >>So you're finding that across the board, probably pan industry, that, that a lot of companies need to go back to basics. We talk about that a lot when we're talking about security, why do you think that >>Is? I think it's because one, most organizations are barely treading water. When you look at the early rapid adopters of horizon threes, pen testing, product, autonomous pen testing, the early adopters tended to be teams where the it team and the security team were the same person and they were barely treading water. And the hardest part of my job as a CIO was deciding what not to fix because the bottleneck in the security processes, the actual capacity to fix problems. And so fiercely prioritizing issues becomes really important, but the, the tools and the processes don't focus on prioritizing what's exploitable, they prioritize, you know, by some arbitrary score from some arbitrary vulnerability scanner. And so we have as a fundamental breakdown of the small group of folks with the expertise to fix problems, tend to be the most overworked and tend to have the most noise to need to sift through. So they don't even have time to get to the basics. They're just barely treading water doing their day jobs. And they're often sacrificing their nights and weekends. All of us at horizon three were practitioners at one point in our career, we've all been called in on the weekend. So that's why, what we did was fiercely focus on helping customers and users fix problems that truly matter, and allowing them to quickly retack and verify that the problems were truly fixed. >>So when it comes to today's threat landscape, what is it that organizations across the board should really be focused on? >>I think systemically what we see are bad password or credential policies, least access, privileged management type processes, not being well implemented. The domain user tends to be the local admin on the box, no ability to understand what is a valid login versus a, a malicious login. Those are some of the basics that we see systemically. And if you layer that with, it's very easy to say misconfigure vCenter, or misconfigure a piece of Cisco gear, or you're not gonna be installing monitoring and OB observa security observability tools on that. HP integrated lights out server. And so on. What you'll find is that you've got people overworked that don't have the capacity to fix. You have the fundamentals or the basics, not, not well implemented. And you have a whole bunch of blind spots in your security posture, and defenders have to be right. Every time attackers only have to be right once. And so what we have is this asymmetric fight where attackers are very likely to get in. And we see this on the news all the time. >>So, and, and nobody of course wants to be the next headline. Right? Talk to me a little bit about autonomous pen testing as a service, what you guys are delivering and what makes it unique and different than other tools that have been out there as, as you're saying that clearly have >>Gaps. Yeah. So first and foremost was the approach we took in building our product. What we set up front was our primary users should be it administrators, network, engineers, and P. And that, that it intern who in three clicks should have the power of a 20 year pen testing expert. So the whole idea was empower and enable all of the fixers to find, fix in verify their security weaknesses continuously. That was the design goal. Most other security products are designed for security people, but we already know they're they're task saturated. They've got way too many tools under the belt. So first and foremost, we wanted to empower the fixers to fix problems. That truly matter, the second part was we wanted to do that without having to install credentialed agents all over the place or writing your own custom attack scripts, or having to do a bunch of configurations and make sure that it's safe to run against production systems so that you could, you could test your entire attack surface your on-prem, your cloud, your external perimeter. >>And this is where AWS comes in to be very important, especially hybrid customers where you've got a portion of your infrastructure on AWS, a portion on-prem and you use horizon three to be able to attack your complete attack surface. So we can start on Preem and we will find, say the AWS credentials file that was mistakenly saved on a, a share drive, and then reuse that to become admin in the cloud. AWS didn't do anything wrong. The cloud team didn't do anything wrong. A developer happened to share a password or save a password file locally. That's how attackers get in. So we can start from on-prem and show how we can compromise the cloud, start from the cloud and, and, and show how we can compromise. On-prem start from the outside and break in. And we're able to show that complete attack surface at scale for hybrid customers. >>So showing that complete attack surface sort of from the eyes of the attacker, >>That's exactly right, because while blue teams or the defenders have a very specific view of their environment, you have to look at yourself through the eyes of the attacker to understand what are your blind spots? What do do they see that you don't see? And it's actually a discipline that is well entrenched within military culture. And that's also important for us as the company. We're about a third of horizon, three served in us special operations or the intelligence community with the United States, and then do OD writ large. And a lot of that red team mindset view yourself through the eyes of the attacker and this idea of training. Like you fight in building muscle memories. So you know how to react to the real incident when it occurs is just ingrained in how we operate. And we disseminate that culture through all of our customers as well. >>And, and at this point in time, it's, every business needs to assume an attacker's gonna get in >>That's right. There are way too many doors and windows in the organization. Attackers are going to get in, whether it's a single customer that reused their Netflix password for their corporate email, a patch that didn't get applied properly, or a new zero day that just gets published a piece of Cisco software that was misconfigured, you know, not by anything more than it's easy to misconfigure. These complex pieces of technology attackers are going to get in. And what we want to understand as customers is once they're in, what could they do? Could they get to my crown Jewel's data and systems? Could they borrow and prepare for a much more complicated attack down the road? If you assume breach, now you wanna understand what can they get to, how quickly can you detect that breach and what are your ways to stifle their ability to achieve their objectives. And culturally, we would need a shift from talking about how secure I am to how defensible are we. Security is kind of a state, a point in time, state of your organization, defense ability is how quickly you can adapt to the attacker to stifle their ability to achieve their objective >>As things are changing >>Constantly. That's exactly right. >>Yeah. Talk to me about a typical customer engagement. If there's, you mentioned folks treading water, obviously there's the huge cybersecurity skills gap that we've been talking about for a long time. Now that's another factor there, but when you're in customer conversations, who were you talking to? What typically are, what are they coming to you for help? >>Yeah. One big thing is you're not gonna win and, and win a customer by taking 'em out to steak dinners. Not anymore. The way we focus on, on our go to market and our sales motion is cultivating champions. At the end of the proof of concept, our internal measure of successes is that person willing to get a horizon three tattoo. And you do that, not through state dinners, not through cool swag, not through marketing, but by letting your results do the talking. Now, part of those results should not require professional services or consulting it. The whole experience should be self-service frictionless and insightful. And that really is how we've designed the product and designed the entire sales motion. So a prospect will learn or discover about us, whether it's through LinkedIn, through social, through the website, but often because one of their friends or colleagues heard about us saw our result and is advocating on our behalf. >>When we're not in the room from there, they're gonna be able to self-service just log to our product through their LinkedIn ID, their Google ID. They can engage with a salesperson if they want to, they can run a pen test right there on the spot against their home, without any interaction with a sales rep, let those results do the talking, use that as a starting point to engage in a, in a more complicated proof of value. And the whole idea is we don't charge for these. We let our results do the talking. And at the end, after they've run us to find problems they've gone off and fixed those issues. And they've rerun us to verify that what they've fixed was properly fixed, then they're hooked. And we have a hundred percent technical win rate with our prospects when they hit that fine fix verify cycle, which is awesome. And then we get the tattoo for them, at least give them the template. And then we're off to the races >>That it sounds like you're making the process more simple. There's so much complexity behind it, but allowing users to be able to actually test it out themselves in a, in a simplified way is huge. Allowing them to really focus on becoming defensible. >>That's exactly right. And you know, the value is we're all, especially now in security, there's so much hype and so much noise. There's a lot more time being spent, self discovering and researching technologies before you engage in a commercial discussion. And so what we try to do is optimize that entire buying experience around enabling people to discover and research and learn the other part, right. Remember is offensive cyber and ethical hacking. And so on is very mysterious and magical to most defenders. It's such a complicated topic with many nuance tools that they don't have the time to understand or learn. And so if you surface the complexity of all those attacker tools, you're gonna overwhelm a person that is already overwhelmed. So we needed the, the experience to be incredibly simple and, and optimize that fine fix verify aha moment. And once again, be frictionless and be insightful, >>Frictionless and insightful. Excellent. Talk to me about results. You mentioned results. We, we love talking about outcomes. When a customer goes through the, the POC POB that you talked about, what are some of the results that they see that hook them? >>Yeah. The biggest thing is what attackers do today is they will find a low from machine one, plus a low from machine two equals compromised domain. What they're doing is they're chaining together issues across multiple parts of your system or your organization to hone your environment. What attackers don't do is find a critical vulnerability and exploit that single machine it's always a chain is always, always multiple steps in the attack. And so the entire product and experience in actually our underlying tech is around attack pads. Here is the path, the attack path an attacker could have taken. You know, that node zero, our product took here is the proof of exploitation for every step along the way. So, you know, this isn't a false positive, in fact, you can copy and paste the attacker command from the product and rerun it yourself and see it for yourself. >>And then here is exactly what you have to go fix and why it's important to fix. So that path proof impact and fix action is what the entire experience is focused on. And that is the results doing the talking, because remember, these folks are already overwhelmed. They're dealing with a lot of false positives. And if you tell them you've got another critical to fix their immediate reaction is Nope. I don't believe you. This is a false positive. I've seen this plenty of times. That's not important. So you have to in your product experience in sales process and adoption process immediately cut through that defensive or that reflex and its path proof impact. Here's exactly what you fix here are the exact steps to fix it. And then you're off to the races. What I learned at Splunk was you win hearts and minds of your users through amazing experience, product experience, amazing documentation, yes, and a vibrant community of champions. Those are the three ingredients of success, and we've really made that the core of the product. So we win on our documentation. We win on the product experience and we've cultivated pretty awesome community. >>Talk to me about some of those champions. Is there a customer story that you think really articulates the value of no zero and what it is that, that you are doing? Yeah. >>I'll tell you a couple. Actually, I just gave this talk at black hat on war stories from running 10,000 pen tests. And I'll try to be gentle on the vendors that were involved here, but the reality is you gotta be honest and authentic. So a customer, a healthcare organization ran a pen test and they were using a very well known, managed security services provider as their, as their security operations team. And so they initiate the pen test and they were, they wanted to audit their response time of their MSSP. So they run the pen test and we're in and out. The whole pen test runs two hours or less. And in those two hours, the pen test compromises, the domain gets access to a bunch of sensitive data. Laterally, maneuvers rips the entire entire environment apart. It took seven hours for the MSSP to send an email notification to the it director that said, Hey, we think something's suspicious is wow. Seven hours. That's >>A long time >>We were in and out in two, seven hours for notification. And the issue with that healthcare company was they thought they had hired the right MSSP, but they had no way to audit their performance. And so we gave them the, the details and the ammunition to get services credits to hold them accountable and also have a conversation of switching to somebody else. >>That accountability is key, especially when we're talking about the, the threat landscape and how it's evolving day to day. That's >>Exactly right. Accountability of your suppliers or, or your security vendors, accountability of your people and your processes, and not having to wait for the bad guys to show up, to test your posture. That's, what's really important. Another story is interesting. This customer did everything right. It was a banking customer, large environment, and they had Ford net installed as their, as their EDR type platform. And they, they initiate us as a pen test and we're able to get code execution on one of their machines. And from there laterally maneuver to become a domain administrator, which insecurity is a really big deal. So they came back and said, this is absolutely not possible. Ford net should have stopped that from occurring. And it turned out because we showed the path and the proof and the impact Forder net was misconfigured on three machines out of 5,000. And they had no idea. Wow. So it's one of those you wanna don't trust that your tools are working. Don't trust your processes. Verify them, show me we're secure today. Show me we're secured tomorrow. And then show me again, we're secure next week, because my environment's constantly changing. And the, and the adversary always has a vote, >>Right? The, the constant change in flux is, is huge challenge for organizations, but those results clearly speak for themselves. You, you talked about the speed in terms of time, how quickly can a customer deploy your technology, identify and remedy problems in their environment. >>Yeah. You know, this fine fix verify aha moment. If you will. So traditionally a customer would have to maybe run one or two pen tests a year and then they'd go off and fix things. They have no capacity to test them cuz they don't have the internal attack expertise. So they'd wait for the next pen test and figure out that they were still exploitable. Usually this year's pen test results look identical the last years that isn't sustainable. So our customers shift from running one or two pen tests a year to 40 pen tests a month. And they're in this constant loop of finding, fixing and verifying all of the weaknesses in their infrastructure. Remember there's infrastructure, pen testing, which is what we are really good at. And then there's application level pen testing that humans are much better at solving. Okay. So we focus on the infrastructure side, especially at scale, but can you imagine so 40 pen tests a month, they run from the perimeter, the inside from a specific subnet from work from home machines, from the cloud. And they're running these pen tests from many different perspectives to understand what does the attacker see from each of these locations in their organization and how do they systemically fix those issues? And what they look at is how many critical problems were found, how quickly were they fixed? How often do they reoccur? And that third metric is important because you might fix something. But if it shows up again next week, because you've got bad automation, you're not gonna you're in a rat race. So you wanna look at that reoccurrence rate also >>The recurrence rate. What are you most excited about as obviously the threat landscape continues to evolve, but what are you most excited about for the company and what it is that you're able to help organizations across industries achieve in such tumultuous times? Yeah. You >>Know, one of the coolest things is back because I was a customer for many of these products, I, I despised threat intelligence products. I despised them because they were basically generic blog posts maybe delivered as a, as a, as a data feed to my Splunk environment or something. But they're always really generic. Like you may have a problem here. And as a result, they weren't very actionable. So one of the really cool things that we do, it's just part of the product is this concept of, of flares flares that we shoot up. And the idea is not to be, to cause angst or anxiety or panic, but rather we look at threat intelligence and then because all, all the insights we have from your pen test results, we connect those two together and say your VMware horizon instance at this IP is exploitable. You need to fix it as fast as possible or as very likely to be exploited. >>And here is the threat intelligence and in the news from CSUN elsewhere, that shows why it's important. So I think what is really cool is we're able to take together threat intelligence out in the wild combined with very precise understanding of your environment, to give you very accurate and actionable starting points for what you need to go fix or test or verify. And when we do that, what we see is almost like, imagine this ball bouncing, that is the first drop of the ball. And then that drives the first major pen test. And then they'll run all these subsequent pen tests to continue to find and fix and verify. And so what we see is this tremendous amount of AC excitement from customers that we're actually giving them accurate, detailed information to take advantage of, and we're not causing panic and we're not causing alert, fatigue as a result. >>That's incredibly important in this type of environment. Last question for you. If, if autonomous pen testing is obviously critical and has tremendous amount of potential for organizations, but it's not, it's only part of the equation. What's the larger vision. >>Yeah. You know, we are not a pen testing company and that's something we decided upfront. Pen testing is a sensor. It collects and understands a tremendous amount of data for your attack surface. So the natural next thing is to analyze the pen test results over time, to start to give you a more accurate understanding of your governance risk and compliance posture. So now what happens is we are able to allow customers to go run 40 pen tests a month. And that kind of becomes the, the initial land or flagship product. But then from there we're able to upsell or increase value to our customers and start to compete and take out companies like security scorecard or risk IQ and other companies like that, where there tended to be. I was a user of all those tools, a lot of garbage in garbage out, okay, where you can't fill out a spreadsheet and get an accurate understanding of your risk posture. You need to look at your detailed pen, test results over time and use that to accurately understand what are your hotspots, what's your recurrence rate and so on. And being able to tell that story to your auditors, to your regulators, to the board. And actually it gives you a much more accurate way to show return on investment of your security spend also, which >>Is huge. So where can customers and, and those that are interested go to learn more. >>So horizon three.ai is the website. That's a great starting point. We tend to very much rely on social channels. So LinkedIn in particular to really get our stories out there. So finding us on LinkedIn is probably the next best thing to go do. And we're always at the major trade shows and events also. >>Excellent SNA. It's been a pleasure talking to you about horizon three. What it is that you guys are doing, why and the greater vision we appreciate your insights and your time. >>Thank you, likewise. >>All right. For my guest. I'm Lisa Martin. We wanna thank you for watching the AWS startup showcase. We'll see you next time.

>>live from Stanford University. It's the queue covering Stanford women in data science 2020. Brought to you by Silicon Angle Media. >>Yeah, yeah, and welcome to the Cube. I'm your host Sonia Category and we're live at Stanford University, covering the fifth annual Woods Women in Data Science Conference. Joining us today is new Sha Ajami, who's the director of urban water policy for Stanford. You should welcome to the Cube. Thank you for having me. Absolutely. So tell us a little bit about your role. So >>I directed around water policy program at Stanford. We focused on building solutions for resilient cities to try to use data science and also the mathematical models to better understand how water use is changing and how we can build a future cities and infrastructure to address the needs of the people in the US, in California and across the world. >>That's great. And you're gonna give a talk today about how to build water security using big data. So give us a preview of your talk. >>Sure. So the 20th century water infrastructure model was very much of a >>top down model, >>so we built solutions or infrastructure to bring water to people, but people were not part of the loop. They were not the way that they behaved their decision making process. What they used, how they use it wasn't necessarily part of the process and the assume. There's enough water out there to bring water to people, and they can do whatever they want with it. So what we're trying to do is you want to change this paradigm and try to make it more bottom up at to engage people's decision making process and the uncertainty associated with that as part of the infrastructure planning process. Until I'll be talking, I'll talk a little bit about that. >>And where is the most water usage coming from? So, >>interestingly enough, in developed world, especially in the in the western United States, 50% of our water is used outdoors for grass and outdoor spacing, which we don't necessarily are dependent on. Our lives depend on it. I'll talk about the statistics and my talk, but grass is the biggest club you're going in the US while you're not really needing it for food consumption and also uses four times more water >>than than >>corn, which is which is a lot of water. And in California alone, if you just think about some of the spaces that we have grass or green spaces, we have our doors in the in. The in the malls are institutional buildings or different outdoor spaces. We have some of that water. If we can save, it can provide water for about a 1,000,000 or two million people a year. So that's a lot of water that we can be able to we can save and use, or you are actually a repurpose for needs that you really half. >>So does that also boil down to like people of watering their own lawns? Or is the problem for a much bigger grass message? >>Actually, interestingly enough, that's only 10% of that water out the water use. The rest of it is actually the residential water use, which is what you and I, the grass you and I have in our backyard and watering it so that water is even more than that amount that I mentioned. So we use a lot of water outdoors and again. Some of these green spaces are important for community building for making sure everybody has access to green spaces and people. Kids can play soccer or play outdoors, but really our individual lawns and outdoor spaces. If there are not really a native you know landscaping, it's not something that views enough to justify the amount of water you use for that purpose. >>So taking longer showers and all the stuff is very minimal compared to no, not >>at all. Sure, those are also very, very important. That's another 50% of our water. They're using that urban areas. It is important to be mindful the baby wash dishes. Maybe take shower the baby brush rt. They're not wasting water while you're doing that. And a lot of other individual decisions that we make that can impact water use on a daily basis. >>Right, So So tell us a little bit more about right now in California, We just had a dry February was the 1st 150 years, and you know, this is a huge issue for cities, agriculture and for potential wildfires. So tell us about your opinion about that. So, >>um, the 20th century's infrastructure model I mentioned at the beginning One of the flaws in that system is that it assumes that we will have enough snow in the mountains that would melt during the spring and summer time and would provide us water. The problem is, climate change has really, really impacted that assumption, and now you're not getting as much snow, which is comes back to the fact that this February we have not received any snow. We're still in the winter and we have spring weather and we don't really have much snow on the mountain. Which means that's going to impact the amount of water we have for summer and spring time this year. We had a great last year. We got enough water in our reservoirs, which means that you can potentially make it through. But then you have consecutive years that are dry and they don't receive a lot of water precipitation in form of snow or rain. That will become a very problematic issue to meet future water demands in California. >>And do you think this issue is along with not having enough rainfall, but also about how we store water, or do you think there should be a change in that policy? >>Sure, I think that it definitely has something also in the way we store water and be definitely you're in the 21st century. We have different problems and challenges. It's good to think about alternative ways off a storing water, including using groundwater sources. Groundwater as a way off, storing excess water or moving water around faster and making sure we use every drop of water that falls on the ground and also protecting our water supplies from contamination or pollution. >>And you see it's ever going to desalination or to get clean water. So, interestingly >>enough, I think desalination definitely has worth in other parts of the world, and then they have. Then you have smaller population or you have already tapped out of all the other options that are available to you. Desalination is expensive. Solution costs a lot of money to build this infrastructure and also again depends on you know, this centralized approach that we will build something and provide resources to people from from that location. So it's very costly to build this kind of solutions. I think for for California we still have plenty of water that we can save and repurpose, I would say, and also we still can do recycling and reuse. We can capture our stone water and reuse it, so there's so many other, cheaper, more accessible options available before you go ahead and build a desalination plants >>and you're gonna be talking about sustainable water resource management. So tell us a little bit more about that, too. So the thing with >>water mismanagement and occasionally I use also the word like building resilient water. Future is all about diversifying our water supply and being mindful of how they use our water, every drop of water that use its degraded on. It needs to be cleaned up and put back in the environment, so it always starts from the bottom. The more you save, the less impact you have on the environment. The second thing is you want to make sure every trouble wanted have used. We can use it as many times possible and not make it not not. Take it, use it, lose its right away, but actually be able to use it multiple times for different purposes. Another point that's very important, as actually majority of the water they've used on a daily basis is it doesn't need to be extremely clean drinking water quality. For example, if you tell someone that you're flushing down our toilets. Drinkable water would surprise you that we would spend this much time and resources and money and energy to clean that water to flush it down the toilet video using it. So So basically rethinking the way we built this infrastructure model is very important, being able to tailor water to the needs that we have and also being mindful of Have you use that resource? >>So is your research focus mainly on California or the local community? We actually >>are solutions that we built on our California focus. Actually, we try to build solutions that can be easily applied to different places. Having said that, because you're working from the bottom up, wavy approach water from the bottom up, you need to have a local collaboration and local perspective to bring to their to this picture on. A lot of our collaborators have been so far in California, we have had data from them. We were able to sort of demonstrate some of the assumptions we had in California. But we work actually all over the world. We have collaborators in Europe in Asia and they're all trying to do the same thing that we dio on. You're trying to sort of collaborate with them on some of the projects in other parts of the world. >>That's awesome. So going forward, what do you hope to see with sustainable water management? So, to >>be honest with you, I would often we think about technology as a way that would solve all our problems and move us out of the challenges we have. I would say technology is great, but we need to really rethink the way we manager resource is on the institutions that we have on there. We manage our data and information that we have. And I really hope that became revolutionized that part of the water sector and disrupt that part because as we disrupt this institutional part >>on the >>system, provide more system level thinking to the water sector, I'm hoping that that would change the way we manage our water and then actually opens up space for some of these technologies to come into play as >>we go forward. That's awesome. So before we leave here, you're originally from Tehran. Um and and now you're in this data science industry. What would you say to a kid who's abroad, who wants to maybe move here and have a career in data science? >>I would say Study hard, Don't let anything to disk or do you know we're all equal? Our brains are all made the same way. Doesn't matter what's on the surface. So, um so I and encourage all the girls study hard and not get discouraged and fail as many times as you can, because failing is an opportunity to become more resilient and learn how to grow. And, um and I have, and I really hope to see more girls and women in this in these engineering and stem fields, to be more active on, become more prominent. >>Have you seen a large growth within the past few years? Definitely, >>the conversation is definitely there, and there are a lot more women, and I love how Margot and her team are sort of trying to highlight the number of people who are out there. And working on these issues because that demonstrates that the field wasn't necessarily empty was just not not highlighted as much. So for sure, it's very encouraging to see how much growth you have seen over the years for sure >>you shed. Thank you so much. It's really inspiring all the work you do. Thank you for having me. So no, Absolutely nice to meet you. I'm Senator Gary. Thanks for watching the Cube and stay tuned for more. Yeah, yeah, yeah.

(electronic music) >> From New York City, it's theCUBE. Covering, New Relic Futurestack 2019. Brought to you by New Relic. >> Hi, I'm Stu Minamin and we're here at New Relic's Futurestack 2019 in the middle of Manhattan. Right next door to Grand Central Station at the Grand Hyatt. Right next door to Grand Central Station at the Grand Hyatt. Happy to welcome to the program, first time guest, Nadya Duke Boone, who's the vice president and general manager of application monitoring here at New Relic. Thanks so much for joining us. >> You're welcome, it's great to be here. >> All right, so, a lot of announcements this morning. Of course, observability front and center Lou talking about how that fits into this space. You have handled really kind of the APM product inside New Relic, so I'm hoping you can help us understand kind of the journey that New Relic's going on. And I've heard in the marketplace, you know, there's AI ops, and there's observability in all of these things. And, you know, APM was the old world for the monolith. So, you know, how does New Relic help live across all of these environments that customers are living in today, and you know, undergoing so much change and new things? >> So as Lou talked about this morning, we think to be an observability platform like New Relic 1, you've got to be open, connected and programmable. That is, we think about that within the application monitoring space, um, we really think it comes down to the matter and issue of like, what are the questions you need to ask. And that really depends on like what stacks you need to see and what are the questions you need to ask. And so, I think it's a false dichotomy to say you need to like, pick a side in observability or monitoring. I think it's really a yes/and. You don't have to pick a side. And with New Relic, what we're able to do whether using our agents and all the rich data they give you or they're using our open platform, the important thing is that we're able to bring it all together in one place. So you can get all your questions answered. >> Yeah, I spent lots of time in my career trying to help break down silos. You know, the traditional infrastructure world, the networking and storage and compute teams. >> Sure >> You know, virtualization helped pull some things together. Software tends to be a unifying factor, but when I look at, you know, the people that own application and the developers. I mean, you've got monoliths, you've got this containerization in microservices coming. You've got the new serverless environments here. You've got a lot of fragmentation inside the customers. How does that impact your business today and are we going to see those, you know, pulled together over time? >> Yeah, what we hear from customers is that, you know, they're going to be running heterogenius environments for a long time. If you're over a year old company, you're not running a single tech stack. You've made choices for your business needs and you need to be able to see across your whole estate. And where New Relic's adding value for our customers, is by bringing this all together and connecting it. So, you can actually see, let's say from a lambda function and our lambda agents, all the way back through your Java monolith and down to the server whether it's running containers or on bare metal, you can see all the way down. And then you can connect it out to you front end as well. And I think it's that ability to see across, is where we're playing. >> All right, uh, can you bring us inside your customers? What are some of the challenges they're facing? And how do you help them along those transformations that they're undergoing? Cause, as you've said, they're going to have this heterogenius environment for quite a long time. >> Yeah, well I think one of the thing they're saying is that they're trying to move faster. And one of the ways they're moving faster is by changing the process by which they build software. So, you know, we've been talking about DevOps for years. We've been talking about Agile for much longer than years. Um, but those changes bring about new needs also, for observability. Cause now, you've got a team that maybe wants to see very deeply with, um, the things they're on call for. But software refuses to break neatly at team boundaries. It just won't, it's going to break wherever it wants to break. So you need to be able to quickly assess, across your whole enterprise what's going on and help those teams talk to you. So, that's definitely a problem we're solving for our customers now. And if I were to pick one more, that I'm hearing, um, well, I'll pick one from this morning and that's cost management, right. As people move to the Cloud, um, its so powerful and easy to be able to start up new services in the Cloud but then, do you know what you have, do you know what is costs, do you know how to optimize? Um, we announced 12 new applications this morning. One of them is addressing exactly that point. >> Yeah, um, okay, what are some of the challenges customers have really monitoring across these different environments? I think cost, it's, well, the promise of Cloud is to help me understand and control my cost quite a bit. But, you know, I understand my data center cost and, in general, much more than I do what I have in the Cloud. >> So, you mean, trying to understand in their software? >> So, I guess, just, if they have these different environments that need to span from a monitoring standpoint what are some of the challenges that customers have and the differences and how does New Relic pull those together for them? >> Well, I think some of it is bringing their teams together. If you've got folks that have a Dev accent and an Ops accent, they may have different points of view about monitoring right? And so, a Dev team might be saying lets go all in on this method or this tool. But an Ops team might be saying something else. And then as you introduce new technologies and maybe now people don't always want to run an agent. They want to have complete visibility over their software. And so, with New Relic, we're giving them those choices. We're giving them, like, hey, you can run an agent, you can, if you've already got stuff at Zipkin, cause maybe, internally, you've got like a great Zipkin champion. Like, great, we're going to be there with you on that too. So, we want to be able to help these teams come together. Um, rather than forcing them to sort of live in silos. >> All right, uh, Lou put a real emphasis talking about platform. And he said platform with a capital 'P'. >> Yeah >> Help us understand a little bit about that and the impact that's going to have for your customers. >> Yeah, absolutely, I think, you know, anyone can say I've got more than one product, therefore I have a platform I think. When we talk about a Platform, we think of software engineers, a Platform is something I can build on. So, I think a capital 'p' Platform is the ability to build apps, to be able to extend it, to be able to add data because you're open. Um, and then the power that we bring, you know, I got to put in my plug, is by connecting it all together. Um, but I think the power of the Platform, um, has been really showing off in the work that we've been doing with our customers to build these new applications. >> All right, um, you mentioned open, which was one of the three features of the Platform itself. Uh, there's open and with API'S and then there's open source can you help us tease through a little bit because there's the openness and then there's some open source pieces. How do those go together and um, I guess, more importantly, what does it mean for the customers? >> Mhmm, thanks for asking, cause I do think those words kind of got tumbled up. So, let's first, let me like tease it apart a little bit. So, first part of open, you sort of already mentioned this, is like, we're open to all data. So, metrics, vents, logs, traces, you can send that data. That's, that's the first thing. You don't have to be running a New Relic agent to use New Relic. The second part though, uh, is that we are actually building and contributing to the open source community software development kits and exporters to make it easy for our customers. And so, we've shipped, we're shipping Open Census and Drop Wizard and Micrometer and exporters and Prometheus scrapers so that these are open source tools that our customers can get, can extend if they need to, to get that data in. So, we're making it easy to get the open data in by providing these open source tools. Um, and we're in there with the communities contributing to the communities as well. And then, finally, you know, the last one is with our new programmable Platform, we are also all in on open source on that. So, we're contributing to open source for folks building on New Relic and our customers are telling us that they're excited to also be able to do that and to share and exchange with each other. >> There's value to the customer and I guess the question is, your relationship with your customer is going to change though. As they're building applications not just, you know, more than just a tool. And I've heard from many of the customers that use New Relic, is, they talk about the partnership. And it really is taking that partnership to the next level. What I say is, New Relic is not coming out and saying oh, we're an open source company and we're building our company around open source. So, you know, it seems that somewhat a maturation of the model but not open source being the be all and end all of New Relic's mission. >> Our mission is to help customers build more perfect software. I mean, that's why we come to work. Is to help them do that and we think this is the right step. Um, to be able to do that and our community around New Relic, as you said, is excited and dynamic. It's great to be here at Futurestack and hear them talking to each other and hear the buzz. I was at our customer advisory board meeting yesterday which is 11 execs from some of our biggest customers and they were talking about how excited they are to see how this is going to help them with their business cause they can connect, now their telemetry data to sort of higher order business problems. Um, and they're also excited to share. So, I think it's the right step for New Relic and our customers. >> There's a lot of startups out there that attack pieces of what New Relic's trying to deliver. Um, you know, how does New Relic look at the landscape out there and the challenge when you're trying to be a platform is, are you providing good enough solutions? Or, you know, are you providing, you know, best solutions across all of these environments? >> Yeah, I think any of our point solutions could go head to head with anything on the market. Um, you know, and the fact that the market is so dynamic is because it's a real problem space for people who are building software. So, folks are going to keep innovating and coming up with new ideas and my mission is to make sure that everyone writing software, is instrumenting it and able to observe it. So I think, I love that more and more folks are joining this conversation. I think it's a great time to be working on monitoring observability. >> Okay, uh, let's start at the top talking a little bit about observability, what should customers be looking at, should they be thinking about that? What feedback are you getting from some of your key customers? Uh, in the space in general and how New Relic's looking to address it? >> Yep, well I think comes down to, a little bit of what we talked about earlier, visibility and answerability and if I were talking to an exec or if I was talking to an engineer, and I was looking at their tools, you know, whatever level you're at and saying, what do you need to monitor how can you get that data in and can you answer the questions? Do you have the tools, the ability to query, to connect the data. Um, to see, hey there's an event that happened and how did my systems change? So I think a lot of it comes down to, is it visible, can I ask the questions? And then for every stack, and no matter what job I'm doing. >> All right, um, when we look at this broad term which gets overused some, but, digital transformation Um, the comment I've made is the long pole in the tent of going through that transformation, really is the application portfolio. You know, I can modernize my platform, I can go to Cloud, but, you know, changing my applications, especially the ones that run my business, is really tough you know. If I'm a company that's been around 15-20 years, you know, I probably have applications that are as old as the company, if not longer. >> Yep. >> Uh, just broadly, how are your customers doing, uh, are they being able to kind of, you know, move along that modernization journey of the application uh, better today than they might have a couple of years ago, or just kind of macro level? >> I think so, I think, you know, between what the Cloud vendors are doing and what we're doing, folks are getting both tools and they're also getting support. I think, you know, the community, the software engineering community is really leaning into this moment. And talking about how to do these types of trasnformations. So I think there's a lot of just, knowledge sharing going on, there's a lot of advice and consulting that you can get. And then I think the tools are lending themselves to being able to do, you know, some people move to the Cloud or lift and shift. Some people use it as an excuse to re-architect. A lot of folks pick and choose. Because not every apps work the same and some apps are, you know, are, um. For some given app, it might be a more relevant time to change it, a more relevant time to let it stay put and you can make those choices. And I think people are approaching it with a certain rational sense. >> Yeah, uh, one last question for you, New Relic's a leader in, according to, the analyst firms that look at the APM market. New Relic's doing a lot of the things that I hear from, you know, the startups getting lots of money thrown at them, so, how should customers think of New Relic today? >> I think, we're the best leading APM product on the market for a reason. And we can never rest our laurel. So I think customers should at us as a trusted partner. Who's going to continue to grow and meet them wherever they are. Our customers are going to Cloud, we want to be there first to meet them there and welcome them in the door. And that comes back to how do we help customers through digital transformation? We're a big software company. We get it, like, we are going through the same, we go through these same questions ourselves. Um, and we talk to our customers all the time. So I think for our customers, it's like, we're the platform and the right partner. Because we're never going to stop. >> Nadya, thank you so much for sharing the updates. Congratulations on the launch today and, uh, best of luck going forward. >> Thanks a bunch. >> All right, lots more here at New Relic Futurestack 2019, I'm Stu Minamin, thanks for watching theCUBE. (electronic music)

>> from the Cosmopolitan Hotel in Las Vegas, Nevada. It's the Cube covering Cooper inspired. 2019. Brought to you by Cooper. >> Welcome to the Cube. Lisa Martin on the ground at Cooper Inspire 19 at the Cosmopolitan, the chic Cosmopolitan in Las Vegas. Very pleased to be joined by my friend David Chang, the VP of business from Hello. Sign a drop box company. David, Welcome to the Cube. >> Thank you for having me on. >> Great to have you here. It is a lot of fun. You could really geek out talking technology all day >> too much. So, >> yeah, there's that >> play that you gotta gamble. It'll keep it real. >> You know, I have no skills in that whatsoever, but maybe I'll try it. I'll take your advice. Give her audience an overview of Hello. Sign. Sure. Drop box Company. What? You guys are what you do. All that good stuff. >> Great. Great. So hello. Sign is today one of the fastest growing, if not the fastest growing electronic signature company in market place today and today we host, I think, over 100,000 paying businesses that use one of our products and over 150 different countries. today we actually were acquired by Dropbox. Sure, everybody's familiar Dropbox or one of the biggest brands in the Internet industry today by the leader in consumer and business files Thinking chair. So John Box actually purchased this, you know, for a number of reasons. First of all, even amazing product and cultural fit with them. But also, Electronic Signature Day is an enormous market. It is one piece of the overall digital transformation, but Elektronik, six year alone, analysts view, is probably a $25,000,000,000 industry, which we've only barely scratched the surface. So it's a huge opportunity, absolutely, and it's that big. That's exactly the you know. That's actually what's shocking about how big it is, because if you think about almost in every business, there are not just one, but probably dozens of different use cases where you need to sign documents. So electronic signature honestly is relevant for everything from all your sales agreements to all of your HR and offer letter and on boarding agreement. It's relevant specifically for all of your procurement and buying agreements, all your vendors contracts that need to be signed, your supply agreements that needs to be signed and D A s o purchase orders. All these documents need to be signed. And today you know, only a few of these use cases have been brought into the digital arena. So there's a whole huge area to grow. And with Dropbox being a leader and content management, where you normally store your documents, >> right, it's >> a natural workflow extension two haven't signed by. Hello, son. >> Excellent. Well, one of the things that we've been talking a lot about we talk about this in every show is the effects of consumer is Asian. And we talked about this yesterday with Rob Bernstein, Cooper's CEO in a number of gas yesterday and today is that we're consumers every day, even when we're at work. Oh, I forgot. I gotta buy this when we go on Amazon, we know we could get it in a day, but now we have the same expectations whether we're buying business, you know, software or what not? And we also want to be able to do things from our mobile phone, including sign. Hey, I got this new job offer or whatever happens to be without having out. Oh my God, there's a pdf. I have to go home, get to my desktop, talk to me about PDS because I can imagine when people either fill them out manually, then they scanning back in and somebody's gotta print it out or fax it. That date is stuck in Pdf. How does hello sign work to free dot data in a Pierre? >> Sure, our design philosophy really is about, you know, make making a superior user experience both for the person who needs to get a document, a document side, but also somebody who's actually gonna be signing it. So when we designed our products, you might as easy as possible for user's to sign that and recognizing some of the difficulties with P D EFS and signing on your mobile phone. We've made our products specifically Mobley responsive, so they don't have to pension, screen, pension, pension scan and all that kind of stuff and typing data. We make it very easy walking through the data entry process to streamline the whole process. We just want to make user customer satisfaction first and foremost >> moving the friction, probably getting documents signed much faster. >> Absolutely. I mean the base, you know, benefits associated the signature. Overall you know, our honestly getting your documents signed significantly faster and more efficiently. We have customers that used to take up to two weeks to get a contract signed. And, you know, as a salesperson, that gets your real nervous, right? So we've seen those contracts now get signed in less than a day. Also, Elektronik senator provides a tonic transparency. So throughout the process, we can actually provide notifications that let the sales people know that somebody's opened up the the >> end. Lt >> looked at the document, reviewed it, signed it, completed it. And even if the document has been signed, the consent of reminders to make sure to sign it. And the third thing is, you know you can't can't emphasize this enough. The value associate with productivity increases. Come on. Everyone's gone out. Printed out the document, walked it over to the scanning machine, you know, then uploading it back in your computer, you know that that whole step, you know, should be completely digital and automated as >> much as >> possible. So we see productivity increases to some of our customers between two x three x for X right in the number in reducing the number of man hours people have to spend to get >> documents only. Is that a cost savings? But all of the you can think of all the other benefits like we're talking about, even for the procurement officers were talking about it at Kuba inspires. It's not just saving money. It's all of the other ripple effects that cost savings, resource, reallocations, speed. All enable this digital transformation, which then enables the business Thio capture new customers. Increased customer, lifetime value, shareholder value. There's a lot of upside to this, >> especially for a company like Cooper. First of all, it's an incredible fit for what we do. Procurement documents. That whole host, um, they need to be signed but by, you know, utilizing Hello, son. We really facilitate that whole experience, and we're very excited to expand our partnership today. We're Cooper Advantage partner. >> Tell me about the Cooper Advantage program benefits. Who wins your >> coop? Advantage is this very unique marketplace that Cooper's brought together. They're pulling together both their customers, some of their lead customers and their matching them with some of the suppliers selected suppliers that provide their customers. Ah, whole host of service is that they need so it could be everything from goods and office supplies. All the way to service is like travel service is, and staffing service is all the way to software key software that their customers would utilize in conjunction with their procurement business. Spend management So companies like close on. So by matchmaking it for the suppliers, they get some pre negotiated discounts that offer them immediate savings off of buying direct from retail and then from ah, supplier side. We get huge benefits because we get to meet some of the most targeted companies that we want. So Cooper effectively is one of our favorite matchmakers. >> Nice. So, yeah, there's a tremendous amount of suppliers in their program. I forget the number and I don't want to misquote it. But I can imagine Cooper customer that's using them for procurement and expenses and invoices and payments. I talked a lot about Cooper pains of new things today. Well, then have the opportunity through the Cooper Advantage program to do prick human contract Scorpios with Hello sign as the e signature. >> Exactly, really, exactly. And that that is, like I said, a great match for what their customers need and by being virtue of a coupe advantage part. Sorry. Keep advantage Supplier. We've been pre vetted by Cooper have also worked out some special pre negotiated discounts with Cooper to make sure we passed that value on to their customers. >> So some of the things that came out today regarding yesterday as well with the Amazon extension you and I talked about the consumer ization affect a few minutes ago. What opportunities is that? Open up to Hello, sign for Cooper paid to be able to enable I t folks to have this visibility for the entire software from search to management. With this consume arised approach, open up doors for Hello Sign. >> Well, I think you know, if you look at the total life cycle of any purchase right from from beginning to end from everything from identifying the products that you want to being able to, you know, negotiate and secure a price that is good for you, you know that whole process. There's always tradition, but a lot of friction there. So the same way that there's friction on the e commerce side, we'll check out and purchase right and getting lining up your payment and Internet payment information Cooper. Streamlining that whole thing for the customer so long without sod is if there's documents they're associated with that with that workflow than by using companies like Hello Sign and our products were able to continue that process of digital izing the end and purchase cycle. >> And I imagine, from an information security perspective, everything >> Come on the old >> days usedto signed >> a contract and I thought, Oh, my boss's desk, Anybody could come by and pick that up So nowadays we you know nowadays we keep it stored securely in the cloud. We have some of the highest security requirements of any signature company out there, and that really matches Cupid's philosophy as well. They go overboard on security, which we really appreciate. That mission is completely lard with each other. >> Awesome. So last few seconds here. I know that you guys are early in the acquisition with Dropbox. What's exciting You for the rest of the calendar. 19. Since all these fiscal years are different. And what's next with you guys in Cuba? Yeah, >> So first of all, with Dropbox, we're just excited to be part of an enormous community of over 500,000,000 users globally So it's It's It's the reach is insane. >> I know >> my mom. Yeah, I think everybody has a DROPBOX account on >> eso getting introduced to their segments, whether it's a consumer segment, SMB and increasingly, the business segment offers huge brand recognition and the potential for new customers with Dropbox. So there's a great synergy from a go to market perspective, and with Cooper, we're very excited about the next stage of our partnership is entering the Cooper Link program. So, uh, you know said Now Cooper customers will be able to sign and send for signature from within the Cooper clr module. Eso any of their contracts vendor agreements that are stored within Cooper without ever having to leave Cooper. You consent for signature and seek the document back. And for a company like Cooper, this is a great strategic value. A because of the benefit it brings its customers, but also with all the great features that Cooper's coming out with leading edge. They want to keep a cz much of that procurement experience from within Cooper. They want Cooper to be that system of record per se and system of transaction for all your business. Ben Management So now you don't have to leave Cooper to perform to get your contract signed. You can do it from all within one place within Cooper, and we enable that. >> That's awesome. That's that's what we want. Keep him. In the experience of that, they actually adopted. They get it done. They're more efficient and and and well, David, it's been such a pleasure to >> have you on >> the Cube. Thank you for joining me today. >> Thanks, Lisa. >> All right, we'll see you next. Time for David Chang. I'm Lisa Martin. You're watching the Cube from Cooper Inspired 19. Thanks for watching.

>> fly from San Francisco. It's the Cube covering Google Club next nineteen Tio by Google Cloud and its ecosystem partners. >> Hey, welcome back. Everyone live here in San Francisco, California is the cubes coverage of Google Cloud Next twenty nineteen star Third day of three days of wall to wall coverage. John for a maiko stupid demon devil on things out around the floor. Getting stories, getting scoops. Of course, we're here with Sadeer has Bay. Who's the director of product management? Google Cloud. So great to see you again. Go on Back on last year, I'LL see Big Query was a big product that we love. We thought the fifty many times about database with geek out on the databases. But it's not just about the databases. We talked about this yesterday, all morning on our kickoff. There is going to be database explosion everywhere. Okay, it's not. There's no one database anymore. It's a lot of databases, so that means data in whatever database format document relational, Unstructured. What you want to call it is gonna be coming into analytical tools. Yes, this's really important. It's also complex. Yeah, these be made easier. You guys have made their seers announcements Let's get to the hard news. What's the big news from your group around Big Queria Mail Auto ml Some of the news share >> the news. Perfect, I think not. Just databases are growing, but also applications. There's an explosion off different applications. Every organization is using hundreds of them, right from sales force to work today. So many of them, and so having a centralized place where you can bring all the data together, analyze it and make decisions. It's critical. So in that realm to break the data silos, we have announced a few important things that they went. One is clouded effusion, making it easy for customers to bring in data from different sources on Prum Ices in Cloud so that you can go out and as you bring the data and transform and visually just go out and move the data into Big query for for analysis, the whole idea is the board and have Dragon drop called free environment for customers to easily bring daytime. So we have, like, you know, a lot of customers, just bringing in all the data from their compromise. The system's oracle, my sequel whatever and then moving that into into big Query as they analyze. So that's one big thing. Super excited about it. A lot of attraction, lot of good feedback from our customers that they went. The second thing is Big Query, which is our Cloud Skill Data warehouse. We have customers from few terabytes to hundreds of terabytes with it. Way also have an inline experience for customers, like a data analyst who want to analyze data, Let's say from sales force work, they are from some other tools like that if you want to do that. Three. I have made hundred less connectors to all these different sense applications available to our partners. Like five Grand Super Metrics in Macquarie five four Barrel Box out of the box for two five clicks, >> you'LL be able to cloud but not above, but I guess that's afraid. But it's important. Connectors. Integration points are critical table stakes. Now you guys are making that a table stakes, not an ad on service the paid. You >> just basically go in and do five clicks. You can get the data, and you can use one of the partners connectors for making all the decisions. And also that's there. and we also announced Migration Service to migrate from candidate that shift those things. So just making it easy to get data into recipe so that you can unlock the value of the data is the first thing >> this has become the big story here. From the Cube standpoint on DH student, I've been talking about day all week. Data migration has been a pain in the butt, and it's critical linchpin that some say it could be the tell sign of how well Google Cloud will do in the Enterprise because it's not an easy solution. It's not just, oh, just move stuff over And the prizes have unique requirements. There's all kinds of governance, all kinds of weird deal things going on. So how are you guys making it easy? I guess that's the question. How you gonna make migrating in good for the enterprise? >> I think the one thing I'll tell you just before I had a customer tell me one pain. You have the best highways, but you're on grams to the highway. Is that a challenge? Can you pick that on? I'm like here are afraid. Analogy. Yeah, it's great. And so last year or so we have been focused on making the migration really easy for customers. We know a lot of customers want to move to cloud. And as they moved to cloud, we want to make sure that it's easy drag, drop, click and go for migration. So we're making that >> holding the on ramps basically get to get the data in the big challenge. What's the big learnings? What's the big accomplishment? >> I think the biggest thing has Bean in past. People have to write a lot ofthe court to go ahead and do these kind of activities. Now it is becoming Click and go, make it really cold free environment for customers. Make it highly reliable. And so that's one area. But that's just the first part of the process, right? What customers want is not just to get data into cloud into the query. They want to go out and get a lot of value out off it. And within that context, what we have done is way made some announcements and, uh, in the in that area. One big thing is the B I engine, because he'd be a engine. It's basically an acceleration on top of the query you get, like subsequently, agency response times for interactive dash boarding, interactive now reporting. So that's their butt in with that. What we're also announced is connected sheets, so connected sheets is basically going to give you spreadsheet experience on top ofthe big credit data sets. You can analyze two hundred ten billion rose off data and macquarie directly with drag drop weakened upriver tables again. Do visualizations customers love spreadsheets in general? >> Yeah, City area. I'm glad you brought it out. We run a lot of our business on sheep's way of so many of the pieces there and write if those the highways, we're using our data. You know what's the first step out of the starts? What are some of the big use cases that you see with that? >> So I think Andy, she is a good example of so air. Isha has a lot of their users operational users. You needed to have access to data on DH, so they basically first challenge was they really have ah subsequently agency so that they can actually do interact with access to the data and also be an engine is helping with that. They used their story on top. Off half now Big Quit it, Gordon. Make it accessible. Be engine will vote with all the other partner tooling too. But on the other side, they also needed to have spread sheet like really complex analysis of the business that they can improve operation. Last year we announced they have saved almost five to ten percent on operational costs, and in the airline, that's pretty massive. So basically they were able to go out and use our connective sheets experience. They have bean early Alfa customer to go out and use it to go in and analyse the business, optimize it and also so that's what customers are able to do with connected sheets. Take massive amounts of data off the business and analyze it and make better. How >> do we use that? So, for a cost, pretend way want to be a customer? We have so many tweets and data points from our media. I think fifty million people are in our kind of Twitter network that we've thought indexed over the years I tried to download on the C S V. It's horrible. So we use sheets, but also this They've had limitations on the han that client. So do we just go to Big Query? How would we work >> that you can use data fusion with you? Clicks move later into Big Query wants you now have it in big query in sheets. You will have an option from data connectors Macquarie. And once you go there, if you're in extended al far, you should get infection. Alfa. And then when you click on that, it will allow you to pick any table in bickering. And once you link the sheets to be query table, it's literally the spreadsheet is a >> run in >> front and got through the whole big query. So when you're doing a favour tables when you're saying Hey, aggregate, by this and all, it actually is internally calling big credit to do those activities. So you remove the barrier off doing something in the in the presentation layer and move that to the engine that actually can do the lot skill. >> Is this shipping? Now you mention it. Extended beta. What's the product? >> It's an extended out far for connected sheets. Okay, so it's like we're working with few customers early on board and >> make sure guys doing lighthouse accounts classic classic Early. >> If customers are already G sweet customer, we would love to get get >> more criteria on the connected sheets of Alfa sending bait after Now What's what's the criteria? >> I think nothing. If customers are ready to go ahead and give us feedback, that's what we care of. Okay, so you want to start with, like, twenty twenty five customers and then expanded over this year and expand it, >> maybe making available to people watching. Let us let us know what the hell what do they go? >> Throw it to me and then I can go with that. Folks, >> sit here. One of the other announcements saw this week I'm curious. How it connects into your pieces is a lot of the open source databases and Google offering those service maybe even expand as because we know, as John said in the open there, the proliferation of databases is only gonna increase. >> I think open source way announced lot of partnerships on the databases. Customers need different types of operational databases on. This is a great, great opportunity for us to partner with some of our partners and providing that, and it's not just data basis. We also announced announced Partnership with Confident. I've been working with the confident team for last one place here, working on the relationship, making sure our customers haven't. I believe customers should always have choice. And we have our native service with Cloud pops up. A lot of customers liked after they're familiar with CAFTA. So with our relationship with Khan fluent and what we announced now, customers will get native experience with CAFTA on Jessie P. I'm looking forward to that, making sure our customers are happy and especially in the streaming analytic space where you can get real time streams of data you want to be, Oh, directly analytics on top of it. That is a really high value add for us, So that's great. And so so that's the That's what I'm looking forward to his customers being able to go out and use all of these open source databases as well as messaging systems to go ahead and and do newer scenarios for with us. >> Okay, so you got big Big query. ML was announced in G. A big query also has auto support Auto ml tables. What does that mean? What's going what's going on today? >> So we announced aquarium L at Kew Blast next invader. So we're going Ta be that because PML is basically a sequel interface to creating machine learning models at scale. So if you have all your data and query, you can write two lines ofthe sequel and go ahead and create a model tow with, Let's say, clustering. We announced plastering. Now we announced Matrix factory ization. One great example I will give you is booking dot com booking dot com, one of the largest travel portals in the in the world. They have a challenge where all the hotel rooms have different kinds off criteria which says they have a TV. I have a ll the different things available and their problem was data quality. There was a lot of challenges with the quality of data they were getting. They were able to use clustering algorithm in sequel in Macquarie so that they could say, Hey, what are the anomalies in this data? Sets and identify their hotel rooms. That would say I'm a satellite TV, but no TV available. So those claims direct Lansing stuff. They were easily able to do with a data analyst sequel experience so that's that. >> That's a great example of automation. Yeah, humans would have to come in, clean the data that manually and or write scripts, >> so that's there. But on the other side, we also have, Ah, amazing technology in Auto Emma. So we had our primal table are normal vision off thermal available for customers to use on different technologies. But we realized a lot of problems in enterprise. Customers are structured data problems, So I have attained equerry. I want to be able to go in and use the same technology like neural networks. It will create models on top of that data. So with auto Emel tables, what we're enabling is customers can literally go in auto Emel Table Portal say, Here is a big query table. I want to be able to go out and create a model on. Here is the column that I want to predict from. Based on that data, and just three click a button will create an automated the best model possible. You'LL get really high accuracy with it, and then you will be able to go out and do predictions through an FBI or U can do bulk predictions out and started back into Aquarian also. So that's the whole thing when making machine learning accessible to everyone in the organization. That's our goal on with that, with a better product to exactly it should be in built into the product. >> So we know you've got a lot of great tech. But you also talk to a lot of customers. Wonder if you might have any good, you know, one example toe to really highlight. Thie updates that you >> think booking dot com is a good example. Our scent. Twentieth Century Fox last year shared their experience off how they could do segmentation of customers and target customers based on their past movies, that they're watched and now they could go out and protect. We have customers like News UK. They're doing subscription prediction like which customers are more likely to subscribe to their newspapers. Which ones are trying may turn out s o those He examples off how machine learning is helping customers like basically to go out and target better customers and make better decisions. >> So, do you talk about the ecosystem? Because one of things we were riffing on yesterday and I was giving a monologue, Dave, about we had a little argument, but I was saying that the old way was a lot of people are seeing an opportunity to make more margin as a system integrated or global less I, for instance. So if you're in the ecosystem dealing with Google, there's a margin opportunity because you guys lower the cost and increase the capability on the analytic side. Mention streaming analytics. So there's a business model moneymaking opportunity for partners that have to be kind of figured out. >> I was the >> equation there. Can you share that? Because there's actually an opportunity, because if you don't spend a lot of time analyzing the content from the data, talk aboutthe >> money means that there's a huge opportunity that, like global system integrators, to come in and help our customers. I think the big challenges more than the margin, there is lot of value in data that customers can get out off. There's a lot of interesting insights, not a good decision making they can do, and a lot of customers do need help in ramping up and making sure they can get value out of that. And it's a great opportunity for our global Asai partners and I've been meeting a lot of them at the show to come in and help organizations accelerate the whole process off, getting insights from from their data, making better decisions, do no more machine learning, leverage all of that. And I think there is a huge opportunity for them to come in. Help accelerate. What's the >> play about what some other low hanging fruit opportunities I'LL see that on ramping or the data ingestion is one >> one loving fruit? Yes, I think no hanging is just moving migration. Earlier, he said. Break the data silos. Get the data into DCP. There's a huge opportunity for customers to be like, you know, get a lot of value. By that migration is a huge opportunity. A lot of customers want to move to cloud, then they don't want to invest more and more and infrastructure on them so that they can begin level Is the benefits off loud? And I think helping customers my great migrations is going to be a huge Obviously, we actually announced the migration program also like a weak back also way. We will give training credits to our customers. We will fund some of the initial input, initial investment and migration activities without a side partners and all, so that that should help there. So I think that's one area. And the second area, I would say, is once the data is in the platform getting value out ofit with aquarium in auto ml, how do you help us? It must be done. I think that would be a huge opportunity. >> So you feel good too, dear. But, you know, build an ecosystem. Yeah. You feel good about that? >> Yeah, way feel very strongly about our technology partners, which are like folks like looker like tableau like, uh, talent confluence, tri factor for data prep All of those that partner ecosystem is there great and also the side partner ecosystem but for delivery so that we can provide great service to our customers >> will be given good logos on that slide. I got to say, Try facts and all the other ones were pretty good etcetera. Okay, so what's the top story for you in the show here, besides your crew out on the date aside for your area was a top story. And then generally, in your opinion, what's the most important story here in Google Cloud next. >> I think two things in general. The biggest news, I think, is open source partnership that we have announced. I'm looking forward to that. It's a great thing. It's a good thing both for the organizations as well as us on DH. Then generally, you'LL see lot off examples of enterprise customers betting on us from HSBC ends at bank that was there with mean in the session. They talked about how they're getting value out ofthe outof our data platform in general, it's amazing to see a lot more enterprises adopting and coming here telling their stories, sharing it with force. >> Okay, thanks so much for joining us. Look, you appreciate it. Good to see you again. Congratulations. Perfect fusion ingesting on ramps into the into the superhighway of Big Query Big engine. They're they're large scale data. Whereas I'm Jeffers dipping them in. We'LL stay with you for more coverage after this short break

>> Hello, and welcome to the special. Keep conversation here in Palo Alto, California John Furrier, Co-Host of the Cube. We're here with Jonathan Rosenberg, CTO chief technology officer and head of AI for Five9. Jonathan. Great. Great to see you. Thanks for coming in. >> Thanks. My pleasure to be here. >> So you've had a stellar career? Certainly. Technical career going way back to Lucent Technologies. Now here at Five9, Cisco along the way. You've been a really technical guru. You've seen the movie before. This's happening. Every wave of innovation, multiple ways you've been on. Now you're on the next wave, which is cloud AI, CTO Five9. Rapidly growing company. Yes, it is. What attracted you to five? >> Yeah, Great question. There's actually a lot of things that brought me to Five9. I think probably the most important thing is that I've got this belief, and I'm very motivated for myself. A least to do technology and innovate and create new things. And this belief that were on the cusp of the next generation of technology in the collaboration industry. And that next generation is going to be powered by artificial intelligence, and one of the ways I sort of talked about this is that if you look at the entire history of collaboration, up til now meetings, telephony, messaging was to figure out, a way to get the bits of data from one person to another person fast enough to have a conversation. That's it. You know, once we got the audio connected, we just moved the audio packets in the video packets and messaging from one place to another. And we didn't actually analyze any of that because we couldn't. We didn't have the technology to do that. But now, with the arrival of artificial intelligence and particular speech recognition, natural language processing, we can apply those technologies to that content and take all this dark data that's been basically thrown away the instant it was received, to process it and do things. And that is going to completely transform every field of collaboration, from meetings to messaging, to telephony. And I believe that so strongly, that is, That's great. That's going to be my next job. I wanna work on that. And it's going to start in the Contact Center because a contact center is the ideal place to do that. It's the tip of the spear for AI in collaboration, >> and it's in a really great area. Disruptive innovation are absolutely so Take us through the impact was one of things I have observed in this industry is you have You know, I don't want to say mainframe clients served to go back to date myself, but there was that wave of client server computer >> mainframes. Cool again. We just called clout. Now, hey, is >> exactly. So you have these structural industry waves take us through the waves of how we got here and what's different now? And why can't the old guard or the older incumbents surviving if you're not out in front that next wave your driftwood. So what? What's What's his ways mean? Why is this important? What has to change to be successful? >> Exactly. So there's been this this whole like you said these waves. So the first wave of telecommunications was like hardware: circuit switching, big iron switches, sitting in telco data centers, you know, And then that era transitioned to software and that was with the arrival voiceover IP and technologies like SIP, and that made it more less expensive. And anyone could do it, and it transformed the industry. The next wave, the third wave were still like halfway through and in some areas, actually, just beginning contact, center was early here, the third wave is cloud, right is now we're moving that software to a totally new delivery vehicle that allows us to deliver innovation and speed. And that wave has now enabled us to start the next wave, which is on ly in its infancy, which is AI right, and the application of machine learning techniques to automate all kinds of aspects of how people communicate in collaborate. >> I think cloud is a great example of Seen a. I, which had been a concept around when I was in computer science. Back in the eighties, there was a guy you know theory, and it's the science of it is not so much change, but computing's available. The data to be analysed for the first time is available. Yeah, you mentioned analyzing the bits writings. There's now a key part. What does it actually mean? Teo. Someone who's has a contact center has a large enterprise. Says, you know what? I got to modernize. How does A I fit them? What is actually going on, >> right? Great question. So a I actually consult lots different problem at the end of the day again, Hey, eyes like this, Let's. It's the biggest buzz word right on. It's in my title. So, like I'm a little guilty, right? >> We'll get a pay raise for, But >> what? It comes down to this, really this Korean machine learning, which is really like a fancy new algorithmic technique for taking a bunch of data and sort of making a decision based on it. So And it turns out, as we've learned that if you have enough data and you can have enough computing and we optimize the algorithms, you could do some amazing things, right? And it's been applied to areas like speech recognition and image recognition and all these kind of things. Self driving cars that are all about decision process is, Do I go left? I go right? Is this Bob? Is this Alice? Did the users say and or did they say or write those air all decision process? Is that these tools economy? What does it mean? The Contact Center? It means everything in the context. And if you look at the conduct center. It's all about decision. Process is, you know, where should this call get routed? What's the right agent to handle the call right now? When the agent gets the call, what kind of things should they be saying? What I do with the call after the call is done, How should the agent use their time? All those things are decision processes and their key to the contact center. So so, aye, aye. And Emily going to transform every aspect of it and, most importantly, analyzing what the person is saying connecting with the customer, allowing the age to >> be more. You know, I think this is really one of the most cutting edge areas of the business. And the technology and throw in CEO was talking about an emotional cognitive recognition around. Yeah, connecting with customers and data certainly is going to be a part of that. But as machine learning continues to get it, Sea legs. Yeah, you seeing kind of two schools of thought? I call it the Berklee School. Hard core mathematics. Throw math at it. And then you've got this other side of a machine learning which is much more learning. Yeah, it's less math. More about adaptive and self learning. One's deterministic one's non deterministic is starting to see these use cases where Yeah, there's a deterministic outcome, right throw machine learning at a great exactly helped humans come curate, create knowledge, create value that you've got a new emerging use case of non deterministic, like machine learning environments where I could be driving my test Look down the road or my company's run the Contact Center. I gotto understand what's gonna happen before it happens. Right? Talkabout this. What's your thoughts on this is This isn't really new, pioneering area. What's your view on >> this? Yeah, so I think it actually straight sort of a key point. I wantto narrow enough from what she said, which is that a lot of these problems still, it's about the combination of man and machine, right? It's that there's things that you know are going to be hard for the machine to predict. So the human in their usage of the product, teaches the machine, and the machine, as it observes, helped the human achieved mastery. And that human part, by the way, is even more important in the conduct centre than anywhere else. At the end of the day, your customer and you call up, you're reaching for human connection. You're calling this. You want to talk, you've got a problem. You need someone to not just give you the answers, but empathize with youto understand you. Right? And if you go back to anything about the best experience you've ever had when you called up for support or get a question answered. He was like it was someone who understood you who's friendly, polite, empathetic, funny. And they knew exactly what they were doing, right? And they solve it for you. So the way I think about that, is that actually the future of the context. Dinner is a combination of human and machine, and the human delivers the heart, and the machine delivers the master. >> And I just noticed your I'm looking at Twitter, right? And you just tweeted this forty minutes to go the future of Contact Center. Nice. A combination of human and machine human delivers heart. The machines lose mastery. I think this is so important because unpacking that words like trust come out True relationship. So you asked about my experiences is when I've gotten what I needed, You know, all ledger, the outcome I wanted. Plus I felt good about right. I trusted it. I trusted the truth. It was. And he's seeing that in media today with fake news. You're seeing it with Digital has kind of almost created, anonymous, non trustworthy its data. There's been no real human. Yeah, packaging. So I think you're I'm hearing you You're on the side of humans and machines, not just machines being the silver bullet. >> Absolutely, absolutely. And again, it goes back to sort of the history of the contact centre has been this desire to, like, just make it cheaper, right? But as the world is changing, and as customer experience is more important than ever before and is now, technology is enabling us to allow agents and human beings to be more effective through this. The symbiotic relationship that we're going to form with each other, like we can actually deliver amazing customer experiences. And that's what really matters. And that idea of trust I want to come back to that word that's like super Central to this entire thing. You know, you have that as a user, you have to trust the brand you have to trust the information you're getting from the agent. You have to trust the product that you're calling them talking about, and that's central to everything that we need to do. In fact, it's a It's a fundamental aspect of our entire business. In fact, if you again think about it for a moment here, we're going to customers who are looking to buy a context, and we're saying, Trust us, we're going to put it in the cloud, We're going to run it, We're going to operate it for you and we're going to deliver a great, highly reliable experience that takes trust to sew one of things that back to your early early question. Why did come two, five, nine? One of the things it has done is build this amazing trust with its customers to its huge, amazing reliability. Up time, a great human process of how we go in work with our customers. It's about building trust in every single >> way. So I want to put in the spot because I know you've seen many ways of innovation. You've seen a lot of different times, but now it's more accelerated. Got cloud computing at a much more accelerated innovation cycle. So as users expect interact with certain kind of environment. Roman talked about this in his interview. CEO Control. So you just want to be served on the channels that they want to be served in. So having a system that they have to go to to get support, They wanted where they are. And so how is the future of the customer interaction? Whether it's support our engagement is going to take place in context to nonlinear discovery, progression, meaning or digging a service themselves in the organic digital space. I honestly want to go to a site per se. How do you see the future evolving around this notion of organic discovery? Talking to their friends, finding things out? Does that impact how Five9 sees the future? >> Yeah, absolutely. And I think it gets back to sort of an old idea of Omni channel. I mean, this is something that the context people been talking about for, like forever, like the last ten years, right? And and its original meeting was just this idea. Oh, you know, you can talk to us via chat, or you can send us an e mail or you can send us a text or you could call us right and we'll work with you on any of those, like you said. Actually, what's more interesting is as customers and users moved between those things, and it actually switches from reactive to proactive right where we actually treat those channels as well. Depending on what the situation is, we're going to gather information from all these different data sources, and then we're going toe, find the right way to reach out to you and allow you to reach out to us in the most official. >> So you see a real change in user expectation experience with relative rule contact? >> Yeah, I mean, I mean, the one thing that technology is delivered is a change in user expectations on how things work. And if you look at the way we as human beings communicate with each other, it's dramatically different today than it was really just just a few years ago. >> So, Johnny, let's look under the hood now in terms of the customer environment, because certainly I've seen Legacy after Legacy sisters being deployed. It's almost like cyber security kind of matches the same kind of trend that in your world, which is throw money at something and build it out. So there's a lot of sprawl of solutions out there and trying to solve these problems. How does the customer deal with that? And they're going forward there on this new wave. They want to be modernized, but they got legacy. They had legacy process, legacy, culture. What's the key technical architecture, How you see them deploying this? What's the steps of the patient and her opinion? >> It will surprise you not one drop when I say it's go to the cloud, all right, and there are real reasons for it and by the way, this is going to be going to be talking about this at Enterprise Connect. So, So tune in Enterprise Connect. I'm going to be talking about this. Um, there's a ton of reasons, five huge ones, actually, about why people need to get to the cloud. And one of them is actually one of the ones we've been talking about here, which is a lot of this. Modernization is rooted in artificial intelligence. It turns out you just cannot do artificial intelligence on promise you cannot. So the traditional gear, which used to be installed and operated by legacy vendors like a VIA, you know, they go in, and Genesis, they go in the install a thing and it works just for one customer at a time. The oly way artificial intelligence works is when it gathers data across multiple customers. So multi tendency and artificial intelligence go hand in hand. And so if you want to take any benefit from the stuff that we've been talking about this conversation, the first step is you gotta take your context int the cloud just to begin building and adding your data on the set and then leverage the technologies and they come out >> So data is the central equation And in all this because good data feed's good machine learning good machine learning feeds Great a. I So data is the heart of this, yes. So data making data in the cloud addressable seems to be a key. Thought Your reaction and what are you guys doing with? >> Absolutely, absolutely. And this is, by the way, another reason why I joined five nine, that I've been speculating here. I said, All right, if Date if ya if the future is about a I miss, I said, That's what I want to do in collaboration. You need data to do that. You actually have to work for a company that has a lot of data. So market leadership matters. And if you go look at the contact center and you go look at all the industry and analyst reports like it made it pretty obvious, like who to go to there is like the leader in cloud Conduct. Sonar with with tons of agents and tons of data is Five9 and ah, and so that's That's why you're so building the data aggregating data. That's one of the first things I'm working on here is how do we increase and utilize the data that we've been gathering for years. >> And and a lot of that we've had this conscious with many customs before about Silas Silas. Kill innovation When it comes to data address ability, your thoughts on that and what customs Khun due to start thinking about breaking down those silent >> exactly so In fact, Silas have been a big part of the history of especially on premise systems. Once in fact, Afghan one silo for inbound contacts and are different for outbound. Different departments, by the way, also had their own different comic centers. And then you had other tools that on the other data, if you don't like a separate tool over there for serum and a different tool over there for WFOR debut Fam and something else for Q M. And all these things were like barely integrated together in the cloud that becomes much more natural. Spring these technologies together and the data can begin to flow from the systems in and out of each other. And that means that we have a much greater access to data and correlated data across these different things that allows us to automate all over the place. So it's this positive reinforcement sile cycle that you only get one year when you've gone to the club. >> The question I want to ask you, it's more customers on pretend I'm a customer for second. I won't ask you, Jonathan, what's the core innovation for me to think about and bring to my organization? If I want to go down the modern monitors you. How do you answer that question? What is the core innovation? Stretch it. I should have Marcy moving through the cloud is one beyond that is itjust cloud. Then what else? What, Juanito? Be preaching internally and organizing my culture >> around. Yeah, great questions. So, I mean, I think the cloud is sort of the enabler of many of these different pieces of innovation. Right? So velocity and speed is one of them. And then setting up and adjusting these things used to be super super hard. Ah, you wanted to add agents seats? Oh, my gosh, enough to go binding hardware and racket stack boxes and whatever. So even simple things like reactive nous, right? That's something that's important to talk about is that many of our customers and our businesses are highly seasonal. Right? We've seen like someone showed me a graph. This was like, Oh, my gosh, it was It was a company that was doing ah, telethon. And they said, Here's how many agents they have over this year. It was like two agents, and then it shut up. It's like five hundred agents of phones. Two days exactly. Drop back down. And I'm like, if you think about a business like that, you could never even do that. And so the so cloud is nice, but the way you talk about it, and as an I t buyer of these technologies, you talk your business owners about reacted nous speed, velocity, right? That's what matters to a business and then customer experience. >> You're one of the things that just to kind of end of second, I want to get your thoughts on. I'm gonna bring kind of industry trend. That's I think, might be a way to kind of talk about some of these core problems on data. Most mainstream people look at Facebook and saying, Well, what a debacle. They used my data. These men against me. I'm not in control of my data. You're seeing that weaponization people saying elections were rigged. So weaponizing data for bad is this content, and this context ends right? An infrastructure that's right, >> that's right. >> But there's also the other side, which is, you actually make it for good. So you started thinking about this people starting to realize Wow, I should be thinking about my data and the infrastructure that I have to create a better outcome. That's right, Your thoughts on that as people start to think about II in terms of the business context, right? How did they get to that moment where they can saying, I don't want anyone weaponizing did against me. I want to use it for good. How did the head of the company comes back to >> trust, by the way, right? Is that you know, on and to some degree that's an uphill battle due to some of these debacles that you just talked about. But Contact Center is a different beast of the whole thing. And interestingly, it's an area where there's already been an assumption by users that when they interact with the contact center, that data is sort of used to improve the experience. I mean, every contacts and the first thing I say, by the way, this call may be recorded for training. Um, honoring purses, Captain, that they are right. It's it's already opt in. There's an assumption that that's exactly how that is being used. So it's This is another reason. By the way, what's a contact center is? It was the tip of the spear because it was a place where there was already permission, where the data is exactly the kind of stuff that had already been subject to analysis and Attock customer expectation that that's actually what was happening. The expectation was there they building action, that data what was missing. So now we're filling in the ability to action on that All that data with artificial intelligence >> and final question. What's your vision going forward? A CTO and aye, aye. What's the vision of Five9? What do what do you see? The twenty miles stair for Five9 within consciousness. We just talked about >> it. So? So it's It's about revolution. I'll be honest. Right on. I tell people like, I'm not like an incremental, steady Eddie CTo like I do things because I want to make big changes. And I believe that the context and R is on the cusp of a massive change. And my boss, Rohan said this and this has been actually central to how I'm thinking about this. The Contact Center in the next five years will be totally different than the twenty five years before that. It's a technologist. I say. Wow, five years like that's not very long in terms of softer development. That's what we were going pretty much rewrite our entire stack over the next five years. And show. What should that start to look like? So for me, it's about how do we completely reimagine every single aspect of the context center to revolutionize the experience by merging together, human and machine and totally new >> and the innovation strategies cloud in a cloud and and and data great job and great to have you on pleasure. Great, great conversation. Quick plug for you guys. Going to be a enterprise, connect to Cuba. Lbi. They're covering the event as well. What you going to talk about that? What? Some of the interactions? What will be the hallway conversations? What's your objective? What's your focus >> exactly? So so I'm going to be having my own session. We're going to be talking about the five reasons that you may not think about to goto context on the cloud. I've hinted already. A James of them. I think we're too well. That's you can you know, A. I is clearly central and I'm going to start to talk about the other four. >> Great, great conversation. A lot of change. Massive change happening. Great innovation Stretch. Great mission here at Five9. Great, great mission around. Changing and reimagine. More change the next five years in the past twenty five years. Again cloud computing eyes doing it will be winners. Will be losers will be following it here on the Cube. Jonathan Rosenberg, CTO ahead of AI at Five9. I'm John Furrier with the Cube. Thanks for watching.

>> Coming back when we were a DOS spreadsheet company. I did a short stint at Microsoft and then joined Frank Quattrone when he spun out of Morgan Stanley to create what would become the number three tech investment (upbeat music) >> Host: Live from mid-town Manhattan, it's theCUBE covering the BigData New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. (upbeat electronic music) >> Welcome back, everyone. We're here, live, on day two of our three days of coverage of BigData NYC. This is our event that we put on every year. It's our fifth year doing BigData NYC in conjunction with Hadoop World which evolved into Strata Conference, which evolved into Strata Hadoop, now called Strata Data. Probably next year will be called Strata AI, but we're still theCUBE, we'll always be theCUBE and this our BigData NYC, our eighth year covering the BigData world since Hadoop World. And then as Hortonworks came on we started covering Hortonworks' data summit. >> Arun: DataWorks Summit. >> DataWorks Summit. Arun Murthy, my next guest, Co-Founder and Chief Product Officer of Hortonworks. Great to see you, looking good. >> Likewise, thank you. Thanks for having me. >> Boy, what a journey. Hadoop, years ago, >> 12 years now. >> I still remember, you guys came out of Yahoo, you guys put Hortonworks together and then since, gone public, first to go public, then Cloudera just went public. So, the Hadoop World is pretty much out there, everyone knows where it's at, it's got to nice use case, but the whole world's moved around it. You guys have been, really the first of the Hadoop players, before ever Cloudera, on this notion of data in flight, or, I call, real-time data but I think, you guys call it data-in-motion. Batch, we all know what Batch does, a lot of things to do with Batch, you can optimize it, it's not going anywhere, it's going to grow. Real-time data-in-motion's a huge deal. Give us the update. >> Absolutely, you know, we've obviously been in this space, personally, I've been in this for about 12 years now. So, we've had a lot of time to think about it. >> Host: Since you were 12? >> Yeah. (laughs) Almost. Probably look like it. So, back in 2014 and '15 when we, sort of, went public and we're started looking around, the thesis always was, yes, Hadoop is important, we're going to love you to manage lots and lots of data, but a lot of the stuff we've done since the beginning, starting with YARN and so on, was really enable the use cases beyond the whole traditional transactions and analytics. And Drop, our CO calls it, his vision's always been we've got to get into a pre-transactional world, if you will, rather than the post-transactional analytics and BIN and so on. So that's where it started. And increasingly, the obvious next step was to say, look enterprises want to be able to get insights from data, but they also want, increasingly, they want to get insights and they want to deal with it in real-time. You know while you're in you shopping cart. They want to make sure you don't abandon your shopping cart. If you were sitting at at retailer and you're on an island and you're about to walk away from a dress, you want to be able to do something about it. So, this notion of real-time is really important because it helps the enterprise connect with the customer at the point of action, if you will, and provide value right away rather than having to try to do this post-transaction. So, it's been a really important journey. We went and bought this company called Onyara, which is a bunch of geeks like us who started off with the government, built this batching NiFi thing, huge community. Its just, like, taking off at this point. It's been a fantastic thing to join hands and join the team and keep pushing in the whole streaming data style. >> There's a real, I don't mean to tangent but I do since you brought up community I wanted to bring this up. It's been the theme here this week. It's more and more obvious that the community role is becoming central, beyond open-source. We all know open-source, standing on the shoulders before us, you know. And Linux Foundation showing code numbers hitting up from $64 million to billions in the next five, ten years, exponential growth of new code coming in. So open-source certainly blew me. But now community is translating to things you start to see blockchain, very community based. That's a whole new currency market that's changing the financial landscape, ICOs and what-not, that's just one data point. Businesses, marketing communities, you're starting to see data as a fundamental thing around communities. And certainly it's going to change the vendor landscape. So you guys compare to, Cloudera and others have always been community driven. >> Yeah our philosophy has been simple. You know, more eyes and more hands are better than fewer. And it's been one of the cornerstones of our founding thesis, if you will. And you saw how that's gone on over course of six years we've been around. Super-excited to have someone like IBM join hands, it happened at DataWorks Summit in San Jose. That announcement, again, is a reflection of the fact that we've been very, very community driven and very, very ecosystem driven. >> Communities are fundamentally built on trust and partnering. >> Arun: Exactly >> Coding is pretty obvious, you code with your friends. You code with people who are good, they become your friends. There's an honor system among you. You're starting to see that in the corporate deals. So explain the dynamic there and some of the successes that you guys have had on the product side where one plus one equals more than two. One plus one equals five or three. >> You know IBM has been a great example. They've decided to focus on their strengths which is around Watson and machine learning and for us to focus on our strengths around data management, infrastructure, cloud and so on. So this combination of DSX, which is their data science work experience, along with Hortonworks is really powerful. We are seeing that over and over again. Just yesterday we announced the whole Dataplane thing, we were super excited about it. And now to get IBM to say, we'll get in our technologies and our IP, big data, whether it's big Quality or big Insights or big SEQUEL, and the word has been phenomenal. >> Well the Dataplane announcement, finally people who know me know that I hate the term data lake. I always said it's always been a data ocean. So I get redemption because now the data lakes, now it's admitting it's a horrible name but just saying stitching together the data lakes, Which is essentially a data ocean. Data lakes are out there and you can form these data lakes, or data sets, batch, whatever, but connecting them and integrating them is a huge issue, especially with security. >> And a lot of it is, it's also just pragmatism. We start off with this notion of data lake and say, hey, you got too many silos inside the enterprise in one data center, you want to put them together. But then increasingly, as Hadoop has become more and more mainstream, I can't remember the last time I had to explain what Hadoop is to somebody. As it has become mainstream, couple things have happened. One is, we talked about streaming data. We see all the time, especially with HTF. We have customers streaming data from autonomous cars. You have customers streaming from security cameras. You can put a small minify agent in a security camera or smart phone and can stream it all the way back. Then you get into physics. You're up against the laws of physics. If you have a security camera in Japan, why would you want to move it all the way to California and process it. You'd rather do it right there, right? So with this notion of a regional data center becomes really important. >> And that talks to the Edge as well. >> Exactly, right. So you want to have something in Japan that collects all of the security cameras in Tokyo, and you do analysis and push what you want back here, right. So that's physics. The other thing we are increasingly seeing is with data sovereignty rules especially things like GDPR, there's now regulation reasons where data has to naturally stay in different regions. Customer data from Germany cannot move to France or visa versa, right. >> Data governance is a huge issue and this is the problem I have with data governance. I am really looking for a solution so if you can illuminate this it would be great. So there is going to be an Equifax out there again. >> Arun: Oh, for sure. >> And the problem is, is that going to force some regulation change? So what we see is, certainly on the mugi bond side, I see it personally is that, you can almost see that something else will happen that'll force some policy regulation or governance. You don't want to screw up your data. You also don't want to rewrite your applications or rewrite you machine learning algorithms. So there's a lot of waste potential by not structuring the data properly. Can you comment on what's the preferred path? >> Absolutely, and that's why we've been working on things like Dataplane for almost a couple of years now. We is to say, you have to have data and policies which make sense, given a context. And the context is going to change by application, by usage, by compliance, by law. So, now to manage 20, 30, 50 a 100 data lakes, would it be better, not saying lakes, data ponds, >> [Host} Any Data. >> Any data >> Any data pool, stream, river, ocean, whatever. (laughs) >> Jacuzzis. Data jacuzzis, right. So what you want to do is want a holistic fabric, I like the term, you know Forrester uses, they call it the fabric. >> Host: Data fabric. >> Data fabric, right? You want a fabric over these so you can actually control and maintain governance and security centrally, but apply it with context. Last not least, is you want to do this whether it's on frame or on the cloud, or multi-cloud. So we've been working with a bank. They were probably based in Germany but for GDPR they had to stand up something in France now. They had French customers, but for a bunch of new reasons, regulation reasons, they had to sign up something in France. So they bring their own data center, then they had only the cloud provider, right, who I won't name. And they were great, things are working well. Now they want to expand the similar offering to customers in Asia. It turns out their favorite cloud vendor was not available in Asia or they were not available in time frame which made sense for the offering. So they had to go with cloud vendor two. So now although each of the vendors will do their job in terms of giving you all the security and governance and so on, the fact that you are to manage it three ways, one for OnFrame, one for cloud vendor A and B, was really hard, too hard for them. So this notion of a fabric across these things, which is Dataplane. And that, by the way, is based by all the open source technologies we love like Atlas and Ranger. By the way, that is also what IBM is betting on and what the entire ecosystem, but it seems like a no-brainer at this point. That was the kind of reason why we foresaw the need for something like a Dataplane and obviously couldn't be more excited to have something like that in the market today as a net new service that people can use. >> You get the catalogs, security controls, data integration. >> Arun: Exactly. >> Then you get the cloud, whatever, pick your cloud scenario, you can do that. Killer architecture, I liked it a lot. I guess the question I have for you personally is what's driving the product decisions at Hortonworks? And the second part of that question is, how does that change your ecosystem engagement? Because you guys have been very friendly in a partnering sense and also very good with the ecosystem. How are you guys deciding the product strategies? Does it bubble up from the community? Is there an ivory tower, let's go take that hill? >> It's both, because what typically happens is obviously we've been in the community now for a long time. Working publicly now with well over 1,000 customers not only puts a lot of responsibility on our shoulders but it's also very nice because it gives us a vantage point which is unique. That's number one. The second one we see is being in the community, also we see the fact that people are starting to solve the problems. So it's another elementary for us. So you have one as the enterprise side, we see what the enterprises are facing which is kind of where Dataplane came in, but we also saw in the community where people are starting to ask us about hey, can you do multi-cluster Atlas? Or multi-cluster Ranger? Put two and two together and say there is a real need. >> So you get some consensus. >> You get some consensus, and you also see that on the enterprise side. Last not least is when went to friends like IBM and say hey we're doing this. This is where we can position this, right. So we can actually bring in IGSC, you can bring big Quality and bring all these type, >> [Host} So things had clicked with IBM? >> Exactly. >> Rob Thomas was thinking the same thing. Bring in the power system and the horsepower. >> Exactly, yep. We announced something, for example, we have been working with the power guys and NVIDIA, for deep learning, right. That sort of stuff is what clicks if you're in the community long enough, if you have the vantage point of the enterprise long enough, it feels like the two of them click. And that's frankly, my job. >> Great, and you've got obviously the landscape. The waves are coming in. So I've got to ask you, the big waves are coming in and you're seeing people starting to get hip with the couple of key things that they got to get their hands on. They need to have the big surfboards, metaphorically speaking. They got to have some good products, big emphasis on real value. Don't give me any hype, don't give me a head fake. You know, I buy, okay, AI Wash, and people can see right through that. Alright, that's clear. But AI's great. We all cheer for AI but the reality is, everyone knows that's pretty much b.s. except for core machine learning is on the front edge of innovation. So that's cool, but value. [Laughs] Hey I've got the integrate and operationalize my data so that's the big wave that's coming. Comment on the community piece because enterprises now are realizing as open source becomes the dominant source of value for them, they are now really going to the next level. It used to be like the emerging enterprises that knew open source. The guys will volunteer and they may not go deeper in the community. But now more people in the enterprises are in open source communities, they are recruiting from open source communities, and that's impacting their business. What's your advice for someone who's been in the community of open source? Lessons you've learned, what is the best practice, from your standpoint on philosophy, how to build into the community, how to build a community model. >> Yeah, I mean, the end of the day, my best advice is to say look, the community is defined by the people who contribute. So, you get advice if you contribute. Which means, if that's the fundamental truth. Which means you have to get your legal policies and so on to a point that you can actually start to let your employees contribute. That kicks off a flywheel, where you can actually go then recruit the best talent, because the best talent wants to stand out. Github is a resume now. It is not a word doc. If you don't allow them to build that resume they're not going to come by and it's just a fundamental truth. >> It's self governing, it's reality. >> It's reality, exactly. Right and we see that over and over again. It's taken time but it as with things, the flywheel has changed enough. >> A whole new generation's coming online. If you look at the young kids coming in now, it is an amazing environment. You've got TensorFlow, all this cool stuff happening. It's just amazing. >> You, know 20 years ago that wouldn't happen because the Googles of the world won't open source it. Now increasingly, >> The secret's out, open source works. >> Yeah, (laughs) shh. >> Tell everybody. You know they know already but, This is changing some of the how H.R. works and how people collaborate, >> And the policies around it. The legal policies around contribution so, >> Arun, great to see you. Congratulations. It's been fun to watch the Hortonworks journey. I want to appreciate you and Rob Bearden for supporting theCUBE here in BigData NYC. If is wasn't for Hortonworks and Rob Bearden and your support, theCUBE would not be part of the Strata Data, which we are not allowed to broadcast into, for the record. O'Reilly Media does not allow TheCube or our analysts inside their venue. They've excluded us and that's a bummer for them. They're a closed organization. But I want to thank Hortonworks and you guys for supporting us. >> Arun: Likewise. >> We really appreciate it. >> Arun: Thanks for having me back. >> Thanks and shout out to Rob Bearden. Good luck and CPO, it's a fun job, you know, not the pressure. I got a lot of pressure. A whole lot. >> Arun: Alright, thanks. >> More Cube coverage after this short break. (upbeat electronic music)

(clicking) >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're at PagerDuty Summit in San Francisco at Pier 27, I got to look at it. I've never been here before. It's a cool facility right on the water, between Pier 39 and the Bay Bridge. We're really excited to have back, I can't believe it's been like three years. To have Sheila Jordan, she's a CIO of Symantec and last we saw you was, I looked it up it was Service Now Knowledge 2014. >> Yes that's correct. >> Sheila, great to see you. >> Sheila: Nice to see you. Thanks for being here. >> Absolutely. So I think when we first talked you were just starting in your role in Symantec and now you're three years into it, you just got off a panel about leading digital transformation, so just give us kind of a general view of what you've been up to and how has that journey been progressing? >> Right, well it's been quite a journey and I would say that it's been really a transformational journey. So the vision for Symantec really is to become the largest cyber security company in the world. And that vision really started two, two and half years ago and I'd say that today it's a reality. When I was hired, I was actually hired to in source IT, so we completed that and then when we went through the Veritas separation, so we separated the company with Veritas which was a pretty significant separation. And then subsequently we've acquired four or five companies, we've recently acquired the Blue Coat company, which with that acquisition, we get our CEO Greg Clark. And then we've also acquired some other companies on the consumer side so the LifeLock business is really tied to our consumer digital safety. So we've been very busy and now we've just announced a small divestiture on our website security business. So lots of acquisitions, lots of change, lots of transformation, that really would been bringing into the organization. >> Jeff: Right and you talked on the panel your job is you got to keep the lights on and keep things moving. Then you've got this acquisition and in your case big, the split the divestiture. But then you still want to innovate and you've talked about looking at new applications, and I thought a really interesting comment you made was about shadow IT. >> Right >> And shadow IT is not all bad. There's a reason that somebody decided to take that action. And really they're trying to understand why? And what was the application requirement? And not just throw it out as unauthorized use. Pretty interesting lesson. >> Sheila: Well a couple things on that. Working in an engineering organization you can't ignore when there's apps being used and come up, because there's a need. Obviously there's a need that the IT organization isn't providing and so what it that need? And what is that capability that the organization is looking for? Now the cool thing is we have technology called CASB which is the Cloud Access Security Broker. That allows us to look at the entire environment of what both cloud applications of who's using what. So for example, we are sanctioned and our standard is box, but I can look across the organization and see what cloud applications we're using and if Drop Box appears, that's a question to say no that doesn't make sense, our standard's box. But the reality is is that all other applications that might be coming out of the engineering organization's using, we should be asking ourselves why? What capability are we not delivering? And how do we bring that into the IT arsenal? >> Jeff: Right, right. And essentially you bring up the box example because another thing you talked about on the stage was your cloud adoption. So kind of you threw out a number, 62%. So I'm not exactly sure what 62% is. But where was it when you got there? What is 62%? What are you measuring? And there's conversations about direct ROI but it's a much more complicated formula than just a simple ROI. >> Yes it really is, and I would say that first of all, from an IT perspective, I think any CIO has the obligation to help the organization run, change, and grow. And forward thinking CEOs really understand that technology can be used to not only run the company, that's kind of old school legacy total cost of ownership costs. Really super important, but it's not only run, but how do you use the technology to change and grow? So when you have opportunities like Saas, that allow the CIOs to have, reduce our total cost of ownership, be more agile, have the Saas providers update their products and solutions and all of that, that's kind of on the Saas providers. It makes our job a little easier or different I'd say. What I mean by that is the role of the CIO hasn't changed. Our job is to protect the company's assets. All of our company's assets and our data whether that's customer data, employee data, partner data. And yet five or seven years ago, it was these monolithic applications it was a private data center. on-prem physical data center. It was massive or monolithic geopcs. All of that has changed. So the role hasn't changed but now we've got to think about Saas applications. Cloud, infrastructure as a service. Public cloud on the infrastructure side. We think about all the applications that are coming in on our mobile devices. We think about IOT, we think about structured and unstructured data. Our role is the same, but how we have to manage that complexity to help our companies and enable our companies run, change, and grow; it's just very different. >> Jeff: And then you get involved in kind of investigating how the second order impacts? Kind of the law of unintended positive consequences by going to a Saas application, for instance. Or going to some of these platforms that doesn't show up in the simple ROI analysis. >> No, I agree with that. But I also think it's total cost of ownership but it's also as important today, as a agility. Everyone wants to get to market faster. Everyone wants to feel to be more productive. So it's really the combination of both total cost of ownership and agility. >> Yeah you said an interesting thing too. "Speed is a habit." Which is a really interesting quote. Because everybody wants speed. >> Absolutely >> And we just had another guest who talked about speed actually does correlate to better software. Because it forces you to do that. But everybody wants speed. You got to have it. So the other, you were all over, I got notes. We could go on all day. I won't go on all day, but somebody talked about what are the limits? What are the limits of applications? As you made a really interesting comment that at the end of the day, it's just about the data flow, and having a horizontal view from your seat. You may find that there's other ways to skin that cat based on what other people are doing. >> Sheila: Right, so I would say one of the reasons I love being in IT, is we see horizontally. There's many functions in the company that see in those silos, but we get to see horizontally which means we see the redundancies in an organization and some of the gaps. And so and as the world changes, that it's less about these monolithic, huge applications, but more about cloud and Saas. It really becomes important about the data flow. Where is the data? Not only is it in that say sales force application, but how does that sales force application move to a box? And how does that content move from box to say some of the collaboration tools in technology and how does that move and flow? Our role has to be about, one: Understanding the data flow and really where that exists. And how do we enable the entire business? Every function to be even more productive. But also how we protect and secure that. So, I think it's so exciting that not only are we doing, our view in IT is to deliver that unified, end to end experience. And it all comes down to the reference architecture approach. But the other part why I'm so excited about Symantec is because we're moving into the notion and the vision of having an integrated cyber defense platform. And I'll explain that for one second. Because historically, the security business has been really fragmented. Point solutions to protect every layer of your architecture. So whether you had a point solution in infrastructure, or end points, or data, or at the web gateway layer. Whatever that was, and what happened is, over time, our recent report would suggest that a large enterprise has anywhere between 65 and 85 security products in there enterprise. Large, large enterprise. >> 65 to 85. >> Security products >> Point solutions. >> In their enterprise. (Jeff chuckles) Yeah and so >> Tough to manage. >> It becomes, yeah it really does. One of the visions that Greg Clark and Mike Fey have for our company, is why can't we be, and deliver this integrated, cyber defense platform? Because it's really connected. We then have products that will live at each layer of the architecture but connected. And so the really super cool thing about that, is that the white spaces between those fragmented products, really are breeding grounds for the bad guys to come in and stay awhile and sit and watch and observe. If you have all that legacy technology and legacy applications, it just becomes a breeding ground. And when you have an integrated cyber trends platform that actually allows it to be much more integrated and really reduce some of the risks and all for our CEOs and customers, a better opportunity to effectively manage their environment. >> Right and you guys are a security company, but also you're a CIO of trying to protect stuff. So you're in a really good spot. Cause the other thing that's happening is this radical increase in the tax services. Especially as we go beyond cloud and APIs to edge economy and IOT devices. As you kind of look at the future of both for protecting your own stuff but also helping to deliver the products for your customers, if the security space is really really rapidly evolving. >> Rapidly evolving and becoming even more important. Because again, the flow of data from your sales force application to your mobile device to IOT back to a content solution. Back to some of the collaboration. The flow of data, is now app to app, or Saas to Saas. Saas to device, device to infrastructure as a service, so it really is the flow of data is so dynamic, and so security becomes just super critical to make sure we're securing that data in motion. >> Right, Right. Yeah it's crazy. And even if you have the most secure systems, you might have lapses in protocol which we hear like some of the CAWS breaches, where somebody didn't configure something right. Alright so, I could keep you here all day (Sheila chuckles) But I won't. But I want to give you that last word. What's next? And there was a little bit of conversation on the panel, so I want to open that up again. As you kind of look forward or, the cloud thing's kind of done, the API thing is kind of done as you look forward, what's kind of the next ... Never say five years in this business. Next couple years, you're excited about the move in the industry forward. >> Sheila: Well I actually think, and I know it might be an overused term, but I really think that we're just scratching the surface on AI artificial intelligence and machine learning. We're using a lot of that in our products today and how we're building our security products. But when I think about corporate IT, and I think about how we deliver statistics and information about our business. So transactional reporting on bookings and revenue and forecast and expenses, there needs to be a better, more predictive way of analyzing that data and understanding it in a much more sophisticated AI. Machine learning that we get our customer insights. And we really start to use those insights into building out that kind of knowledge as we move forward. I look forward to really beginning to really really have some strategies on AI and machine learning in corporate IT. >> Alright, well Sheila Jordan it was great to see you. Hopefully it won't be >> Nice to see you! >> Three years >> Three years till we see you again! CIO of Symantec. I'm Jeff Frick. You're watching theCUBE from PagerDuty Summit San Francisco. Thanks for watching. >> Sheila: Thank you so much. (upbeat electronic music)