>>live from Atlanta, Georgia. It's the Q covering Answerable Fest 2019. Brought to you by Red Hat. >>Okay, welcome back. Everyone is the live Cube coverage for two days here in Atlanta, Georgia for instable fest. I'm John Furrier, My Coast stupid in with the Cube. Ted Julian, vice president, product management, formerly CEO. Resilient now part of an IBM company. Back to doing V P of product management. Again, you don't really ask. Welcome to welcome back to the Cube. Good to see you. It's a >>pleasure to be here. Thanks. >>So I see product management. Holistic thinking is the big discussion here. The thing that's coming out of this event is configuration management, a siloed point activity now, more of a platform. You're seeing more of a systems architecture thinking going into some of these platform discussion. Security certainly has been there. They're here now. A lot of pressure, the out of things built in with security but maintaining the onslaught of threats and landscape changes going on. That's what you do. >>It's rough out there. >>What what's going on? What are the key trends that customers should be aware of when thinking about configurations? Because automation can help. Yeah, maybe all use cases, but >>way need to do something and because customers definitely need help. The alerts that they're dealing with them both in the volume and the severity is like nothing we've ever seen before. At the same time we're talking about earlier, right, the regulatory impact also really big difference just in the last two or three years. Huge skills, gap shortage also a critical problem. People can't find enough people to do this work. That's very difficult to keep so clearly we need to do something different. And there's no doubt that orchestration and automation and configuration management, as a component of that is we've barely scratched the surface of the potential there. To help solve some of >>the open source is, is helping a lot of people now. Seeing the light first was cloud, the skeptics said. There's no security and cloud now. There is open source securities there, but still, proprietary systems have security. But the mayor may not be talented. Your point, so automation is an opportunity. How are companies dealing with the mishmash or the multi platform solutions that are out there >>at your right to ask the question it is driving, um, the problem in a big way. Years ago we tried this security automation within security, like in the early days of firewalls and the Web and stuff like that, and it didn't go well. Unintended consequences. But think two things have changed. The environments changed, which has raised the stakes for the need to be able to do this stuff to a whole different level. But at the same time, the technology matured enormously. There's been multiple platforms shifts since then, and so security teams. They're both kind of desperate for a better solution, but also better options now than they had before. And so it's for this reason that we're starting to see people adopt orchestration and automation now in a way that we didn't see in the last time around. >>But the thing is that we were hearing here is that people are trying to automate the same things and some of these holes in the infrastructure, whether it's an S three bucket, this is basic stuff. This is not rocket science. Yeah, so on these known use cases, this makes total sense that a playbook or automation could help kind of feel those holes. >>We talk about it as a journey, you know? And I don't think any two organizations journey is the same, nor does it really even need to be the same. So we've seen some customers, for example, take the approach of what's a high volume type of incident that we deal with. And if we could apply orchestration and automation, they were gonna get great our eye right? We see 4000 phishing attacks every month or what have you. And that's certainly one way to do it. Yeah, but those other times with one, >>though, I have to go >>into that point. There's other people that are like, you know, gathering forensics on an end point right now. Incredibly manual process. We need to be able to do that globally. Do we do it every day? No, we don't. But if we could automate that and get those results back in more like a couple hours, as opposed to two days, because the guy we need in Sweden is out of the office or whatever, that could mean the difference between ah, low level incident were able to contain and something that goes global. And so that's the use case we wanna chase, so I don't think there's a right or wrong answer. >>Depends on the environment. Ah, whole host of the whole thing about security is no general purpose software anymore. You have to really make it custom because every environments different. >>I mean, gosh, you guys Aaron Arcee, right? It's nuts. There's thousands of vendors. I mean, there's hundreds of vendors that are really products. They're not the features masquerading as products that are masquerading as companies. But there's a reason why that's been the case, and it's because the risk is so high. >>The desperation to >>yes, exactly good word choice. Yeah. >>So what? One of the things that reminded me of security is this morning hearing about, you know, J P. Morgan going through the transformation from the ticketing system. Tau wait to make a great case study two. I need to be able to automate things. So, you know, we know that response time is so critically important in the security area. So tell us how that meshes together from security and automation toe be able to response, and you know, whether it be patching or, you know, responding to an attack, >>there's huge opportunity gains there on. We've seen customers do some really remarkable things that start with what you're discussing, which is if we could automate that fishing process to a degree and we have 4000 of those a month and we're able to maybe shrink a response time by 80 some or more percent, which is what we've seen. That's a lot of savings right there. And you know, the meat and potatoes there is. You already have a fishing Neil Alias. Probably that that employees report those phishing attacks, too. But what if we just monitored that? We stripped those emails, stripped out the attachments, and we could automate all the manual grunt work that an analyst would otherwise do right? Is that and is there in execute a ble? Is that execute herbal? Unknown bad? What command and control servers is it talk to? Are those known bads those air 10 tabs That analyst could have opening their browser if we could automate all of that. So when they go into the case, it's all just sitting there for them. Huge time saver. >>It's the great proof point of the people plus machines. How do you make make sure that the people that when they get the information, they're not having to do too much grunt work. They get really focused on the things where their expertise in skill sets are needed, as opposed to just buried. You >>nailed it. I mean, automation is a great role to play, but it really is a subset of orchestration. It's when you can bring those two things together and really fuse the people process and technology via orchestration. That's when you get really game changing improvements. >>Talk about the relationship between you guys or silly, unanswerable. Where's the fit? What you guys doing together? Why year give a quick plug for what you working on? >>Yeah, absolutely. So just by working with customers, we kind of discovered that there was this growing groundswell of answerable use within our customer base. It was largely an I T, whereas that IBM resilient. We're selling mainly in a security. Um, and once we uncovered that were like, Oh my gosh, there's all these integrations that already exists. They're already using them for I t use cases on that side of the house, but a lot of the same work needs to be done as part of a security workflow. And so we built our integration where, literally you install that integration into resilient. And we have a visual workflow editor where you can define a sophisticated workflow. And what's that? Integration is in place. All of your instable integrations air there for you. You drag and drop them on near workflow. You can string them all together. I mean, it's really, really powerful. >>It's interesting. Stew and I and David Lattin Ovary Brother Q. Post. We got hundreds of events we see every conference. Everyone's going for the control plane layer. Don't control the data. I mean, it's aspiration, but it's You can't just say it. You gotta earn it. What's happening here is interesting in this country. Configuration management. Little sector is growing up because they control the plumbing, the control of the hardware, the piece parts right to the operating system. So the abstraction lee. It provides great value as it moves up the stack, no doubt, and this is where the impact is, and you guys are seeing it. So this dependency between or the interdependence between software glue that ties the core underpinnings together, whether it's observe ability data. It's not a silo, just context, which they're integrating together. This the collision course? Yeah. What's the impact gonna be here? What's your thesis on this? >>That's why there is such great synergy is because they are really were sort of the domain expertise Doreen experts on the security point of view and our ability to leverage that automation set of functions that answerable provides into this framework where you can define that workflow and all the rest that specific to some security use cases eyes just very, very complimentary to one another. >>This is a new kind of a 2.0 Kana infrastructure dynamic, where this enables program ability. Because if these are the control switch is on the gear and the equipment and the network routes, >>yeah, and where things get really interesting is when you do that in the context of ah, workflow and a case management system, which is part of what we provide, then you get a lot of really valuable metrics that are otherwise lost. If you're purely just at a point to point tool to to automation realm, and that allows you to look at organizational improvements because you're able to marry. Well, first of all, you can do things like better understand what kind of value those I t controls. Air providing you and the automation that you're able to deliver. But you can relate that to your people in your process as well. And so you can see, for example, that while we have two teams, they're doing that the ones in the day shift ones in the night shift. They have access to the same tool sets, but ones more effective than the other. First of all, you know that. But then, having known that you can now drill into that and figure out OK, why is the day shift better than the night shift? And you can say, Oh, well, they're doing things a little bit differently, maybe with how they're orchestrating this other team is, Or maybe they're not orchestrating it. All right? And you're having that. And then now you are able to knowledge share and, um improve that process to drive that continuous improvement. >>So this operational efficiency comes from breaking down these siloed exactly mentality data sets or staff? >>Yeah, and pairing. That was not just as I said, the IittIe automation aspect of weaken now do that 80% faster. But what about the people in the process aspect? We even bring that into the mix as well. You get that next limit layer of insight which kind of allows you to tap into another layer of productivity. >>So this is an alignment issue. This brings that back. The core cultural shift of Dev ups. This is the beginning of what operationalize ng Dev ops looks like. >>Yes. Yeah, >>people are working together. >>It's really, really well put. I mean, it gets back to how this question got started, which is what is this energy? And to me, this energy really is that you have these siloed all too often siloed functions of I t operations and security operations. And this integration between resilient and answerable is the glue that starts to pull those two things together to unlock everything we just talked about. >>Awesome. That's great. >>Yeah, well, you know, research has shown that you know, Dev Ops embracing, delivering and shipping code more frequently actually can improve security. Not You know what? We have to go through this separate process and slow everything down. So are you seeing what? What is that kind of end state organization look like? Oh, >>I mean, that's a huge transformation. And it's something that on the security field we've been struggling with for the longest time, because when we were in kind of a waterfall mode of sort of doing things I mean your timeframe of uncovering a security issue, addressing it in code code, getting deployed to a meaningful enough fashion and over a long enough time to get a benefit that could be years, right? But now that we're in this model, I mean, that could be so much, much more quickly obtained and obviously not only other great just General Roo I improvements that come from that, but your ability to shrink the threat window as a result of this as well as huge and that is crucial because all the same things that us, the good guys they're doing to be able to automate our defenses, the bad guys, they're doing the same thing in terms of how they're automating their attacks. And so we really have to. We have no choice. >>So, Ted, you were acquired by IBM. IBM made quite sizeable acquisition with Red Hat. Tell us what your IBM with danceable. How that should play out >>there is just enormous potential. And answerable is a big, big piece of it, without a doubt. And I think we're just scratching the tip of the iceberg for the benefits. They're just in the from resilience point of view. And, you know, we're not to stay in touch because we have some really interesting things coming down the pike in terms of next gen platforms and the role that that answer will complain those two and how those stretch across the security portfolio with an IBM more broadly and then even beyond that. >>Well, we want to keep in touch. We certainly have initiated Cube coverage this year on security. Cyber little bit going for a broader than the enterprise. Looking at the edge edges. You know about the perimeter. Being just disabled by this new service area takes one penetration lightbulb I p address. So again, organizing and configuring these policy based systems sounds like a configuration problem. Yeah, it is. This is where the software's gonna do it. Ted, Thanks for coming on. Sharing the insights. Any other updates on your front. What do you are most interested in what? Give us a quick update on what you're working on. >>Um, well, we're just getting started with the answerable stuff, so that's particularly notable here, but also kind of modern, modernizing our portfolio, and that really gets to the whole open shift side of the equation and the Red Hat acquisition as well, So not ready to announce anything yet. But some interesting things going on there that that kind of pull this all together and that serve as just one part of the foundation for the marriage between red at 9 p.m. and wanna sneak a value can bring the >>customers any sneak peek at all on the new direct. Sorry time. At least lips sink ships Don't do it. Love to no. >>Blame me for asking. >>Hey, I got a feeling hasn't automation. And somewhere in there Ted, thanks for sharing your insights. It was great to see Cuba coverage here. Danceable fest. I'm jumpers to minimum, breaking out all the action as this new automation feeds A I's gonna change the stack game as data is moving up to stack. This isn't Cube. Bring all the data will be back up to the short break. >>Um

(upbeat techno music) >> From San Francisco, it's theCUBE. Covering Sumo Logic Illuminate 2018. Now, here's Jeff Frick. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're at Sumo Logic Illuminate, at the Hyatt, at the airport in Burlingame. We're excited to have, from Virgin Money Digital Bank, two great guests, we love to get customers on, Andy Makings, he is the head of Cloud operations, Andy, good to see you and Julian Howe, head of Cloud Business Office. >> Hi >> So welcome gentlemen. >> Thank you. >> Great to be here. >> How're you liking the weather? >> It's great. >> Improvement already. (laughing) >> Improvement already, alright. So let's jump into it Virgin Money Digital Bank, what is that exactly? >> Yep. >> Do you want to take that? >> Oh okay, I've been there longer, yeah. 2017, start of 2017, they decided to build a completely new digital bank, for Virgin Money, as an offshoot to the core bank, Virgin Money Brand, using the same banking license with our partner, TenX Future Technologies in London. So building a bank from scratch, whole new business model, data driven, data analytics, Big Data DevOps Agile, whole new business model completely. >> So just starting in 2017 so, you're still pretty early on the journey? >> Yeah, so still in the build phase, pilot phase, and then go live next year. >> So A, what are the key drivers to that decision? That's a pretty innovative decision, which doesn't surprise us, right? Virgin always seems to be kind of out on the leading edge but, when the conversation happened, what is a hundred percent digital bank? How is that different than a traditional bank, besides obvious things, like branches, but, what are some of the motivations, some of the attributes? >> I think they wanted to leverage the brand, of course, Virgin, 'cause there's a lot of new digital bank start ups, which they're competing against. So they thought , let's do it from scratch, let's do it how we want it, make it truly focus on data, driving customer value through the data. And they thought, we can compete because we've got this big Virgin Digital Brand, that we can really use to get customer base. >> Right. >> Yeah, so I think that was the big driver, compared to what they're currently doing, with the bank, the core bank, and what they want to do with the brand new bank. >> Wow. But it's not co-mingled so you're not leveraging existing data, existing clients, or all those things, or are you seeing kind of a transfer over? >> Eventually we may, but that's the future. Yeah, the first thing is to launch the digital bank and then we'll see where the Big Data platform, that we're putting in, drives. Yeah, it makes sense to economies of scale to obviously migrate the rest of the customers. >> So when does it launch, what's the timeframe? >> 2019. >> 2019? >> Yeah, absolutely. >> Okay, so you're here at Sumo Logic, what role is Sumo playing in this big project? >> Well, so from my perspective, so I'm looking at, so Andy's been involved in, as he said, for the last 12 months, in terms of building the new platform, really making sure that we're bringing on the bleeding edge technologies, and tech partners and, certainly from my perspective, it's around making sure that I understand who we're going with, what technologies we're using, and how we can utilize those technologies, going forward, to really make sure that our customers are getting the best service from the new digital proposition. >> Right. >> And Sumo Logic is absolutely part of that. >> And are you building your own cloud eco system, in the back, or are you using one of the public clouds? >> Yeah, I'm using Amazon, Amazon public cloud. >> Using Amazon public cloud. >> Yeah, so my team's responsible for building the Big Data platform, TenX Future Technologies are responsible for building the API based banking platform, and then we take streams of data into the data and analytics platform that we're building. So Sumo, obviously, is our logging platform, and we'll then use more and more features of Sumo as they release, so, logging initially, everything goes into Sumo, for the whole of the Amazon platform that we're building, and the data lake, and then what we'll do later on is we just started beta work to do the SIM implementation for security and then we're revolutionizing the SOCs, security operation center, as well to be cloud based, sort of driven because, obviously traditionally, we've been hosted in data centers. >> Right, so you're using it now as part of your build-out process, but then you'll be using it again obviously in your operations as well. >> Absolutely. And yeah and some of the messaging out from this morning with the keynote around just the business intelligence and customer metrics and data that Sumo Logic can almost sort of draw in and present back. >> Right. >> I think that's really powerful. >> Right, are there certain kind of customer features that you look forward to offering that you just can't do in the traditional bank or is it more a lot of kind of marginal improvements because you've got the data? >> It's more the agility, I think. >> Yeah. >> Agility of build. Agility of delivering new business features so it's business driven. As I say we're doing proper DevOps, proper agile across the business in the new digital bank. >> Right. >> Whereas before it's more traditional in the core bank, as we call it. >> Right. >> So it's silos of teams, sand storage, yeah, systems administrators, legacy, so. >> And it is, yeah, that transition into a digital business, as well so how we're set up and how we're aligned, not just the technologies that we're looking to use and the companies we're looking to partner with. >> Right, so on the data driven, you know, being a data driven company in this new bank, I'm fascinated by some of the financing options that are there now, I mean these are some of the pure digital plays that you've been talking about where they're making loan decisions based on some really strange factors that you would think, no way could you make this loan based on a traditional kind of analysis, you would never do it. >> Yep. >> And yet they're pulling some data somewhere that's telling them that this is actually a good loan, so I assume those are the types of things you're looking forward to? >> Yeah, of course. So when we take the feeds from obviously TenX, the platform that TenX is providing with the new customers but you also take feeds from the existing data warehouses, yeah and then we build business models on top of that in the data lake with the data science team and they then get pushed back in to feed scoring models and things like that across the digital platform. And that will just grow and grow. There'll be more and more models as the business gets more mature. >> Right, any super big hurdles that you didn't anticipate that you got to get over to make this happen? >> Technically no, I think more about business transformation. Yeah, we're still part of a bigger bank that holds a bank licensing so a lot of it's around education of cloud, public cloud so that's been key, we've done quite a lot of presentations to the core bank. Especially around the security teams and managing expectations and what they need to look at and how dynamic. We're using LAMDA a lot, so they've got to get their head around how all that works and yeah, what they're doing with that and how dynamic it is. We can spin out thousands of servers in minutes. That's been a bit of a hurdle. >> Right. >> But I think we're getting there and I think the next few months as we build more of the platform we'll definitely get there better. >> Yeah. >> And I think you hit the nail on the head around agility. It's being agile enough and being able to keep pace with, this, the innovation you see with companies like Sumo Logic. >> Right so it's like the parent Virgin Bank kind of looking over the shoulder, going, hey, hey, hey, what're you got? How do I get some of that? >> No, they're fully involved, obviously. They're excited, same as we are, by the prospect of what we're doing because it should drive more customers. >> Well I was going to say, is there going to be some spillover, I would imagine, in terms of innovation and features and those types of things as well? >> Yeah. >> I know already some of the tools we're putting in, we've gone through the pain of going past the security validation and put in, they're now looking and go, well actually that's really useful for hybrid cloud if you want to move some of the existing workloads into public cloud. If we want to, say, leverage marketing or leverage log platforms or leverage monitoring platforms? >> Right. >> As well as the automation we're putting in, we can easily, all the designs have been built to bring in other business units and business areas within the current business. >> Yeah, I'm curious was there push back on using a public cloud for this all 100% digital bank? How did that decision finally get sorted out? I mean, I think generally we're past it for a lot of people obviously in our business but I would imagine, there's still some stodgy guys that are, you know, wearing very expensive suits in mahogany row that are probably like, are you kidding me, you know? >> Yeah, there's still a lot of compliance to sort out. Obviously we've done some, there's more to do as we go nearer to production. >> Yeah. >> There's been some hurdles, we'd be lying if we said there wasn't. >> Right. >> But there's definitely been some hurdles but I think we're getting there and of course, other additional banks have done it in Amazon as well. >> Exactly. >> You're following that model and you need to get through the regulatory compliance. >> And it's about having, making decisions based on facts and there's increasing numbers of facts around how secure and how successful and the benefits that cloud platforms give you. >> Right, it took a while for the facts to kind of out weigh the hype, right? Not so much the hype but the scare. >> The scare is thing, yeah, once you can show, you know we did a BAC late last year to show that we could do it and it was secure and it went through more pen testing than most of the current products would go through, purely because of that scare. >> Right, right. >> They were scared of going to public cloud. >> Interesting. >> Yeah. >> So when again is the anticipated launch date? I won't hold you to it, I'm not-- >> Yeah, 2019. >> Yeah so next year. >> 2019, yeah. >> Yeah, 2019. >> Sometime between January 1 and December 30th? >> Yeah, yeah. (laughing) >> I think it's Q1, I think officially it's Q1. >> Alright. >> Early rather than late. >> Early rather than later, yes. >> It's a great story, I mean an old bank coming out with 100% digital bank. >> Yeah. >> It'd be an interesting story to watch unfold, we'll look forward to it. >> Absolutely. >> Yeah, thank you. >> Alright Andy, Julian, thanks for taking a few minutes of your day and I hope you enjoy the rest of your time almost in San Francisco, you got to get up there, at least one, right? >> Yeah, we're going to try to go there, yeah. >> Alright he's Andy, he's Julian, I'm Jeff. You're watching theCUBE. We're at Sumo Logic Illuminate 2018. Thanks for watching. (upbeat techno music)

(upbeat electronic music) >> Voiceover: Live from New York City, it's theCUBE, covering Lenovo Transform 2017. Brought to you by Lenovo. >> Welcome back to theCUBE's coverage of Lenovo Transform. I'm your host, Rebecca Knight, along with my cohost, Stu Miniman, who is a senior analyst at Wikibon. We are joined today by Julian Box. He is the founder and CEO of Calligo, and Shekhar Mishra, who is the director of product management here at Lenovo. Thanks so much for coming on the show. >> Thank you. >> So Julian, I want to start with you. Tell us a little bit about Calligo and your business challenges. >> Calligo is six years old now. We're a cloud service provider, but we do things slightly differently. We were set up with data privacy at its core, which is a little bit of a paradox for cloud, of course, because you shouldn't really care where the data is, but I believed people would care where the data was, and what laws were applicable, and who could look at the data, and so forth. Fast forward to today, and we've had Edward Snowden, and now we've got the EU GDPR, which, some people would say, is a lot tougher now because of Edward Snowden's stuff that he actually showed was going on. Interestingly, a lot of that stuff, was really focused very much on the U.S. and not really about outside the U.S. We focus very much around any organization that touches EU citizens. We have a privacy play around that. We do it just slightly differently than a standard cloud service provider. >> I do want to get into that new EU regulation you were talking about, but can you tell us a little bit about why you chose Lenovo? >> There's a lot of history there. Right back in the day, I was true blue in the '80s, coding away in the midrange, and I've always had that link with IBM. Then, through the acquisition that Lenovo did, we flowed into Lenovo, and it's been actually very, very good. Some people questioned whether that was a good move, but I saw what they'd done with the ThinkPad, and the Think Range, and the PC, and I was pretty confident it was going to carry on. We've been very happy with what we've had so far. >> Shekhar, want to bring you into the discussion. You've been talking a lot about infrastructure, things like server, storage, and networking. Bring us into how cloud fits into the Lenovo portfolio with the announcements that we've been talking about today. >> Definitely. If you really look at, not the how, but why people are moving towards having cloud structure, people like as he was talking earlier, that service provider, they're looking really for the agility and simplicity that a lot of the public cloud brings, but then, as he was talking, that a lot of the regulatory issues, SLA, security concerns really prohibit them to actually put everything on a public cloud, right? They want those benefits, but they want that at their own terms, right? The best people who can provide that is one who are able to embrace openness, play with the ecosystem, like partners, like Microsoft, Nutanix, and VMware, and also provide a very solid infrastructure, to run those things, right? We, as a company, Lenovo DCG, can offer that. Those are the key values, but also going beyond that, if you think about, cloud is really simple, but once you get it deployed and working, that is a big "if" there, right? What we have done as a strategy is to simplify this, to increase the kind of value for our customers. We promote this as a pre-integrated solution, which is really a turnkey with the simple support so customers are not running around for support or having to deploy it on their own terms, things like that. >> I would actually say, the idea of cloud is simple. Once you really get into it, it's not so simple. I've been at the Amazon re:Invent show for many years. They're adding 1,000 new features every year. That's not simple. Julian, six years? I mean, that's like multiple lifetimes since you started your company. The whole service provider marketplace has changed a lot. Can you talk about what's been changing in your business? You're involved with the Microsoft Azure Stack. How do you look at the public cloud, and that hybrid layer, and envision your role going forward? >> Yeah, it has changed a lot. If someone had asked me that we would be doing a Microsoft stack cloud-based system a few years ago, I wouldn't have thought we would be, but because of the way people perceive data now, and where it is, and where it's held, there's more and more of a demand that, "I want my data, and I want it executed "in the location, the jurisdiction that I live in." Microsoft, and Amazon, and all the other places, they can't be in every single country in the world, clearly. The scale is not there. Even for them, it's not. The Azure Stack is a way, I think, that Microsoft's going to attempt to deal with some of those challenges around actually where data is processed. That gives us an opportunity because we have a lot of clients that won't put their data into the Azure cloud because of where the Azure cloud actually is right now, but when we put it into the jurisdictions we're in, we've got a lot of people wanting to use it. The sooner we get it, the better, really. >> You look at it more from a actual, physical location more than kind of control or governance? >> No, that all goes part and parcel, but the starting point is jurisdictional position in the data. With the EU, you're either in the EU, or you're not in the EU, clearly. With the GDPR law, it's switching. It's switching to become who that person actually is. At the moment, it's all around where the data is. With the GDPR, it's more focused on the individual. The individual doesn't have to live in the EU anymore, but it's still protected by these same laws. People do care, very much so, where the data is actually going to be. Businesses don't want to be caught out either, and they have the challenges of actually processing the data, or controlling the data, as it's known. As a service provider, one of the biggest changes for us, is that we're now liable for some of the processes of what actually happens to that data. Before, it was just the client that was using it. Now, it's proportionally between the two of us. We have a role as a processor, and they have a role as a control of that data. Therefore, again, it comes down to, how do we minimize the risks? How do we ensure that we are meeting the obligations that we have under these new laws? It becomes easier if you're actually doing it in a jurisdiction that has the appropriate laws, or is physically in the EU. There's a thing called a adequacy rating that the EU give to a certain set of countries. You can apply for it. Anybody can apply for it, but only about a dozen or so countries around the world actually have it. What this gives them is the ability to be seen as being in the EU, even though you're not in the EU, from a data protection perspective. >> Companies are really fundamentally rethinking how they approach data privacy. Shekhar, how are you partnering with other companies and helping them work through this? I mean, your example with Calligo, and other companies, too, that are affected? >> That is one of the biggest challenge, if you would think about this. Not only have the companies have to think about, yes, I have to go to a cloud and have a cloud strategy, but the whole deployment model, the mindset of the companies themselves are also shifting, and they need to shift. A very simple example I'll give you, for instance. We have a very prominent educational institute. They're budget right now was allocated to build three more buildings, for instance, to accommodate the influx of new students coming in. They're now talking to us, respect to Azure Stack, that, "Should I move some of that budget "to build up an Azure Stack versus building a new building?" No one thought two years back that IT will be actually competing with the construction. It's very weird to think of that way. One of the key reasons, when you ask them, is, look, Amazon is there, but I cannot just go there. I need that flexibility, but I need it on my own terms, and that this makes sense for me. We are partnering with people like Microsoft to create those. We are doing innovations on a platform itself from the compute all the way to the networking, so as you asked earlier, we own, enter, and stack, whether it is compute, storage, or networking, we have our own IP around it, so we can really create that security across the platform. We are not trying to create an island for customers where you have to work towards the propriety solution because that's totally against the whole cloud model then. That's why we partner with Microsoft. We are partners with VMware, we are partners with Nutanix, and then other networking players also, but that helps our clients to get the best of the breed solution, the software, on a best of breed infrastructure. >> Where do you see data privacy right now? I mean, famously, Europeans and Americans look at data privacy very differently, just individually, consumers, also businesses. Edward Snowden, is he a hero, is he a villain? I mean, there's so many questions, and we're still really a society wrestling with all of this. How does Lenovo approach this? You talked about the mindset. >> From a piracy perspective, you see that, we have a very strict policy around the security and, what do you call, the real vicinity of the infrastructure itself. We do unique things inside our infrastructure itself. We control our infrastructure lineup, the manufacturing and everything. We have certain features enabled which are default, like IPv6 for instance, right? It won't let us ever go in a mode where it can be compromised in any way. We bring that into our software stack all the way from the comware. Those kind of things are helping us drive and maintain that piracy issue. >> Julian, Lenovo, of course, has a long history partnering with both Intel and with Microsoft. When I look at the first generation of Azure Stack, there's not a lot of feature differentiation. Microsoft says, "This is the configuration "you're going to offer, lock it in." So why Lenovo, in your mind? Because there's another three companies, two of which have more market share and other positions. What led you down the path of Lenovo? >> For me, it was very much the history that Lenovo and the Lenovo team that they inherited from IBM have got. They led the way when virtualization first came out. I remember when the 440 was released back in 2001, 2002, something like that, people didn't understand why it was being built. It was because they were ahead of the game. They could see that virtualization was coming. I think Lenovo has the edge from a capabilities perspective. The XClarity tool, I think it's the best management tool that's out there right now. And reliability. I've been using their technology for a very long time now, in all it's forms, and you can see why they're number one, because they genuinely hardly ever ... Literally, I can hardly think, in the last six years, we've probably replaced a couple of spinning disks. That's about it. It really is that reliable, actually. >> Julian, want to get your input. You've been looking at the Azure Stack here. Azure Pack's existed for a while. We've been talking about Azure Stack for a couple of years. This'll be a 1.0 release. What does it mean for your business and your customers? Are there things that you're looking at beyond the 1.0 that will expand it even further? >> Yeah, clearly, on the first version, it's not going to have every single feature that you want it to have, but it will have a lot of the things that our clients are calling for right now. I'm speaking to them right now, and they're prepared to wait for the extra features to come along. Right now, they can't get any of it, so we're giving them a big chunk of it, and they will take the extra features as they come along. As to the point you mentioned a little bit earlier about, it is what we're given, that's true, but people want it to be exactly the same as the big one. We don't care that it's not exactly the same. That said, it will be deployed alongside our standard infrastructure and server offering, which we call CloudCore, and again, it's all Lenovo equipment, not just the Azure Stack. We're 100% Flash. We guarantee any workload. We do things very, very slightly differently in a lot of cases, and you combine these two technologies because clearly, the Azure Stack does stuff that CloudCore doesn't, and CloudCore can do stuff that Azure doesn't do, so we actually think we can give a combination there that you wouldn't typically be able to get. Of course, they're right next to each other running at super high speeds, and not different clouds going across much slower high latency links. Lots and lots of positive stuff. >> Shekhar, from your standpoint in product development, what excites you the most about Azure Stack, and what your customers expect today, and what you see in the future from Lenovo? >> You asked a question that, that it's fixed, and is that a constraint? Actually, my view, I feel that, other than minor tweaks, customers actually don't want a lot of variations because that actually simplifies an environment, right? Today, there's a lot of overhead and management. What my group is really focused on is not about so much on what infrastructure layer. It's more about what the end to end solution is, and not just from a point product, but how the customer is consuming in the entire life cycle of it. All the way from when they start thinking about Azure Stack, for instance, how do you make sure that what kind of data is right on Azure and what is not? How do you make sure that, how much of Azure do they really need? How do they make sure that it's going to audit and ship promptly? And then they can deploy it. By the way, once you deploy it, how am I going to maintain it, right? Our onsite professional go and train them. Then, once you have it deployed, how do I do ongoing management? I'm going to have issues. Who is going to help me? Because this is now built with multiple things. We think of all those entrance consumption, and that's what the whole motivation around ThinkAjile is, to make all of that simplified for our clients, all the way from deployment, to support, to management, and things like that. >> Great point on the consistency because, if you ask any customer, "What version of Azure are you running?" they'll laugh at you 'cause Microsoft takes care of that, and you would want the customer environment to be similar. >> For us, the fact that they're actually going to come and commission it for us is one less thing I have to organize, I have to resource. Literally, the rack turns up, they do the commission, and give us two cables to plug into our core switches, and away we go. The time to delivery is far quicker for us. As we want to roll these out quite quickly around the globe, with everything else that we are up to at the moment, that's another massive plus for us. We actually like the fact that it's coming in this set form, and these guys are going to look after it for us at that lower level, and we're operating, run it with our clients, and that, again, is huge benefit for us. >> Julian, Shekhar, thank you so much for joining us. It's been a pleasure. >> [Julian And Shekhar] Thank you. >> I'm Rebecca Knight for Stu Miniman. We will have more from Lenovo Transform after this. (upbeat electronic music)

(upbeat electronic music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We are live in downtown San Francisco, Moscone Center at the RSA conference. It's one of the biggest conferences, I think after like Salesforce and Oracle that they have in Moscone on the tech scene. Over 40,000 professionals here talking about security, I think it was 34,000 last year. It's so busy they can't find a space for theCUBE, so we just have to make our way in. We're really excited by our next guest, Ted Julian from IBM Resistance, Resilience, excuse me. >> Thank you, it's alright. >> And you are the co-founder of VP Product Management. >> That's right. >> Welcome. >> Thanks, good to be here Jeff, thanks. >> And you said IBM actually purchased a company, >> Ted: A year ago. >> A year ago. So happy anniversary. >> Ted: Yeah, thanks. >> So how is that going? >> It's great. Business is really going well, it's been thrilling to get our product in place and a lot more customers and really see it help make a difference for them. >> Yeah we, Jesse Proudman is a many time CUBE alumni, his company is Blue Box, also bought by IBM. >> Ted: Yes. >> A little while ago, also had a really good experience of, kind of bringing all that horse power. >> They know what they are doing. >> To what his situation was. So let's jump into it. >> Sure. >> Security, it's kind of a dark and ominous keynote this morning. The attack's surface is growing with our homes and IOT. The bad guys are getting smarter, the governments are getting involved, there's just not necessarily bad guys. What's kind of your perspective as you see it year after year acquisition? 40,000 professionals here focused on this problem. >> We are not winning. >> We are not winning? >> Unfortunately, I mean, I guess as a species. Again, what is it? We saw a survey recently from the Ponemon Institute. 70% of organizations acknowledge they didn't have an incident response plan. So you talk about that stuff in the keynote where sort of a breach was inevitable. What are you going to do? Well the thing you'd need to have is a response plan to deal with it, and 70% don't. Cost of a breach also, according to Ponemon Institute is up to $4 million on average, obviously they can be a lot larger than that. >> Right. >> So there's a lot of work to be done to do better. >> And then you hook up a new device, and they are on that new device as soon as it plugs into the internet. They say within an hour, they ran a test today. So is the, I mean where are we winning, Where are we getting better? I mean, I've heard crazy stats that people don't even know they've been breached for like 245 days. >> Ted: Yeah. >> Is that coming down? Are we getting better? >> Certainly the best in the business are, and really the challenge I think as an industry is to percolate that down through the rest of the marketplace. Everybody is going to be breached, so it's not whether or not you are breached, it's how you deal with it come the day, that's really going to differentiate the good organizations from the bad ones. And that's where we've been able to help our customers quite a bit by using our platform to help them get a consistence and repeatable process for how they deal with that inevitable breach when it happens. >> That's interesting. So how much if it is you know kind of building a process for when these things happen versus just the cool, sexy technology that people like to talk about? >> Oh, it's everything. I mean one of the hottest trends that you're going to be seeing all over the show is automation and orchestration. Which is critically important as part of the sort of you get an alert and how do you enrich that to understand that, once you understand that how can you quickly come to sort of a course of action that you want to take. How can you implement that course of action very efficiently? Those things are all important. Computers can help a lot with that but at the end of the day it's smart people making good decisions that are going to be the success factor that determines how well you do. >> Right, right. Another kind of theme that we are hearing over and over is really collaboration amongst the companies amongst the competitors, sharing information about the threat profiles, about the threats that are coming in to kind of enable everybody to actually kind of be on the same team. That didn't always used to be the case, was it? >> Well, people have been working on this for a while but I think what's been a challenge is getting people to feel comfortable contributing their data into that data set. Naturally they are very sensitive about that, right? >> Right. >> This is some of our most confidential information that we've had a security issue and we're really not you know, dying to give that out to the general public. And so I think it's been, the industry's been trying to figure out how can we show enough value back when that information's contributed to some kind of a forum to make people feel more comfortable about doing that? So I think we've seen a little bit of progress over this last year and they'll be more going forward, but this is a, It's marathon not a sprint, I think to solve that problem. But, it is crucial because if we can get to that point that's what ultimately allows us to turn the tables on the bad guys. Because they cooperate, big time, they are sharing vulnerabilities, they are sharing tactics, they are sharing information about targets, and it's only when the good guys similarly share what they're experiencing that we'll have that opportunity to turn the table on them. >> It's funny we had a Verizon thing the other night and the guy said if you are from the investigator point of view, it's probably like a police investigator. They see the same pattern over and over and over and over and over it's only when it's the first time it's happen to you that's it's unique and different. So really the way to kind of short-circuit the whole response. >> How do you find out you've been breached? There is short list. One, Brian Crebs, very famous reporter happens to find out, he tells you. Number two, FBI. >> They tell you. >> Unfortunately, that's usually, it's usually external sources like that as oppose to organization internal systems that tip them off to a breach. Another example of how we are doing better but we need to do a lot better. >> And then there's this whole thing coming up called IOT, right. And 5G and all these connected device in the home, our cars, our nest, So the attacks surface gets giant. Like I said, they said in the keynote, you plug something in the internet they are on it within an hour. How does that really change the way that you kind of think about the problem? >> It makes it a lot harder. The attack surface gets harder, gets bigger, the potential risks go up quite a bit, right. I mean you are talking about heart implants, or things like that which may have connectivity to some degree, then obviously the stakes are severe. But the thing that makes those devices even trickier is so often they're embedded systems, and so unlike your Windows PC's or your Mac where, I mean it's updating itself all the time. >> Right, right. >> And you barely even think about it, you turn it on one morning and there is a new update. A little harder to make those update happen on IOT kinds of devices, either because they're harder to get to or the system's aren't as open or people aren't use to allowing those updates to occur. So even though we may know about the vulnerabilities patching them up is even harder in an IOT environment typically than in a traditional. >> It's crazy. Alright, so give us a little update on Resilient. What exactly is do you guys do inside this crazy eco-system of protecting us all? >> Sure. So five or six years ago, myself and my co-founder John started the company and it was really was acknowledging that we've gone through the era of prevention, to detection and now it's all about response. And at the end of the day when organizations were trying to deal with that we saw them using ticketing systems, spreadsheet, email, chat I mean a mess. And so we built our platform, the Resilient IRP from the ground up specifically to help them tie together the people processing in technology around incident response. And that's gone amazing. I mean the growth that we've seen even before the IBM acquisition but afterwards has been breath taking. And more recently we been adding more and more intelligence in automation and orchestration into the platform, to help not only advise people what to do, which we've done forever, but help them do it, click a bottom and we'll deploy that patch or we'll revoke that user's privileges or what have you. >> Right. Yeah a lot of conversation about kind of evolution of big data, evolution of things like Sparks so that you know can react in real time as opposed to kind of looking back after the fact and then trying to go and sell something. >> For sure. And for us it's really empowering that human. It's either the enrichment activity where they'd normally go to 10 different screens, to look up different data about a malware thread or about vulnerabilities, we just spoon feed that to them right within the platforms so they don't have to have those 10 tabs opened in the browser. And after they'd had a chance to evaluate that, and they want to know what to do, again they don't have to go to another tool and make that action happen, they can as click a button within Resilient and we'll do that for them. >> Alright. Ted Julian, we are rooting for you. >> Ted: Thanks, yeah. >> IBM, give him some more recourses. He's Ted Julian and I'm Jeff Frick. You're watching theCUBE at RSA Conference 2017, at Moscone Center, San Francisco. Thanks for watching.

>>Hi everyone. And welcome to data citizens. Thank you for making the time to join me and the over 5,000 data citizens like you that are looking to become United by data. My name is Jim Cushman. I serve as the chief product officer at Collibra. I have the benefit of sharing with you, the product, vision, and strategy of Culebra. There's several sections to this presentation, and I can't wait to share them with you. The first is a story of how we're taking a business user and making it possible for him or her data, use data and gain. And if it and insight from that data, without relying on anyone in the organization to write code or do the work for them next I'll share with you how Collibra will make it possible to manage metadata at scales, into the billions of assets. And again, load this into our software without writing any code third, I will demonstrate to you the integration we have already achieved with our newest product release it's data quality that's powered by machine learning. >>Right? Finally, you're going to hear about how Colibra has become the most universally available solution in the market. Now, we all know that data is a critical asset that can make or break an organization. Yet organizations struggle to capture the power of their data and many remain afraid of how their data could be misused and or abused. We also observe that the understanding of and access to data remains in the hands of just a small few, three out of every four companies continue to struggle to use data, to drive meaningful insights, all forward looking companies, looking for an advantage, a differentiator that will set them apart from their peers and competitors. What if you could improve your organization's productivity by just 5%, even a modest 5% productivity improvement compounded over a five-year period will make your organization 28% more productive. This will leave you with an overwhelming advantage over your competition and uniting your data. >>Litter employees with data is the key to your success. And dare I say, sorry to unlock this potential for increased productivity, huge competitive advantage organizations need to enable self-service access to data for everyday to literate knowledge worker. Our ultimate goal at Cleaver has always been to enable this self-service for our customers to empower every knowledge worker to access the data they need when they need it. But with the peace of mind that your data is governed insecure. Just to imagine if you had a single integrated solution that could deliver a seamless governed, no code user experience of delivering the right data to the right person at the right time, just as simply as ordering a pair of shoes online would be quite a magic trick and one that would place you and your organization on the fast track for success. Let me introduce you to our character here. >>Cliff cliff is that business analyst. He doesn't write code. He doesn't know Julian or R or sequel, but is data literate. When cliff has presented with data of high quality and can actually help find that data of high-quality cliff knows what to do with it. Well, we're going to expose cliff to our software and see how he can find the best data to solve his problem of the day, which is customer churn. Cliff is going to go out and find this information is going to bring it back to him. And he's going to analyze it in his favorite BI reporting tool. Tableau, of course, that could be Looker, could be power BI or any other of your favorites, but let's go ahead and get started and see how cliff can do this without any help from anyone in the organization. So cliff is going to log into Cleaver and being a business user. >>The first thing he's going to do is look for a business term. He looks for customer churn rate. Now, when he brings back a churn rate, it shows him the definition of churn rate and various other things that have been attributed to it such as data domains like product and customer in order. Now, cliff says, okay, customer is really important. So let me click on that and see what makes up customer definition. Cliff will scroll through a customer and find out the various data concepts attributes that make up the definition of customer and cliff knows that customer identifier is a really important aspect to this. It helps link all the data together. And so cliff is going to want to make sure that whatever source he brings actually has customer identifier in it. And that it's of high quality cliff is also interested in things such as email address and credit activity and credit card. >>But he's now going to say, okay, what data sets actually have customer as a data domain in, and by the way, why I'm doing it, what else has product and order information? That's again, relevant to the concept of customer churn. Now, as he goes on, he can actually filter down because there's a lot of different results that could potentially come back. And again, customer identifier was very important to cliff. So cliff, further filters on customer identifier any further does it on customer churn rate as well. This results in two different datasets that are available to cliff for selection, which one to use? Well, he's first presented with some data quality information you can see for customer analytics. It has a data quality score of 76. You can see for sales data enrichment dataset. It has a data quality score of 68. Something that he can see right at the front of the box of things that he's looking for, but let's dig in deeper because the contents really matter. >>So we see again the score of 76, but we actually have the chance to find out that this is something that's actually certified. And this is something that has a check mark. And so he knows someone he trusts is actually certified. This is a dataset. You'll see that there's 91 columns that make up this data set. And rather than sifting through all of that information, cliff is going to go ahead and say, well, okay, customer identifier is very important to me. Let me search through and see if I can find what it's data quality scores very quickly. He finds that using a fuzzy search and brings back and sees, wow, that's a really high data quality score of 98. Well, what's the alternative? Well, the data set is only has 68, but how about, uh, the customer identifier and quickly, he discovers that the data quality for that is only 70. >>So all things being equal, customer analytics is the better data set for what cliff needs to achieve. But now he wants to look and say, other people have used this, what have they had to say about it? And you can see there are various reviews for different reviews from peers of his, in the organization that have given it five stars. So this is encourages cliffs, a confidence that this is great data set to use. Now cliff wants to look a little bit more detailed before he finally commits to using this dataset. Cliff has the opportunity to look at it in the broader set. What are the things can I learn about customer analytics, such as what else is it related to? Who else uses it? Where did it come from? Where does it go and what actually happens to it? And so within our graph of information, we're able to show you a diagram. >>You can see the customer analytics actually comes from the CRM cloud system. And from there you can inherit some wonderful information. We know exactly what CRM cloud is about as an overall system. It's related to other logical models. And here you're actually seeing that it's related to a policy policy about PII or personally identifiable information. This gets cliff almost the immediate knowledge that there's going to be some customer information in this PII information that he's not going to be able to see given his user role in the organization. But cliff says, Hey, that's okay. I actually don't need to see somebody's name and social security number to do my work. I can actually work with other information in the data file. That'll actually help me understand why our customers churning in, what can I actually do about it. If we dig in deeper, we can see what is personally identifiable information that actually could cause issues. >>And as we scroll down and take a little bit of a focus on what we call or what you'll see here is customer phone, because we'll show that to you a little bit later, but these show the various information that once cliff actually has it fulfilled and delivered to him, he will see that it's actually massed and or redacted from his use. Now cliff might drive in deeper and see more information. And he says, you know what? Another piece that's important to me in my analysis is something called is churned. This is basically suggesting that has a customer actually churned. It's an important flag, of course, because that's the analysis that he's performing cliff sees that the score is a mere 65. That's not exactly a great data quality score, but cliff has, is kind of in a hurry. His bosses is, has come back and said, we need to have this information so we can take action. >>So he's not going to wait around to see if they can go through some long day to quality project before he pursues, but he is going to come up and use it. The speed of thinking. He's going to create a suggestion, an issue. He's going to submit this as a work queue item that actually informs others that are responsible for the quality of data. That there's an opportunity for improvement to this dataset that is highly reviewed, but it may be, it has room for improvement as cliff is actually typing in his explanation that he'll pass along. We can also see that the data quality is made up of multiple components, such as integrity, duplication, accuracy, consistency, and conformity. Um, we see that we can submit this, uh, issue and pass it through. And this will go to somebody else who can actually work on this. >>And we'll show that to you a little bit later, but back to cliff, cliff says, okay, I'd like to, I'd like to work with this dataset. So he adds it to his data basket. And just like if he's shopping online, cliff wants that kind of ability to just say, I want to just click once and be done with it. Now it is data and there's some sensitivity about it. And again, there's an owner of this data who you need to get permission from. So cliff is going to provide information to the owner to say, here's why I need this data. And how long do I need this data for starting on a certain date and ending on a certain date and ultimately, what purpose am I going to have with this data? Now, there are other things that cliff can choose to run. This one is how do you want this day to deliver to you? >>Now, you'll see down below, there are three options. One is borrow the other's lease and others by what does that mean? Well, borrow is this idea of, I don't want to have the data that's currently in this CRM, uh, cloud database moved somewhere. I don't want it to be persistent anywhere else. I just want to borrow it very short term to use in my Tablo report and then poof be gone. Cause I don't want to create any problems in my organization. Now you also see lease. Lease is a situation where you actually do need to take possession of the data, but only for a time box period of time, you don't need it for an indefinite amount of time. And ultimately buy is your ability to take possession of the data and have it in perpetuity. So we're going to go forward with our bar use case and cliff is going to submit this and all the fun starts there. >>So cliff has actually submitted the order and the owner, Joanna is actually going to receive the request for the order. Joanna, uh, opens up her task, UCS there's work to perform. It says, oh, okay, here's this there's work for me to perform. Now, Joanna has the ability to automate this using incorporated workflow that we have in Colibra. But for this situation, she's going to manually review that. Cliff wants to borrow a specific data set for a certain period of time. And he actually wants to be using in a Tablo context. So she reviews. It makes an approval and submits it this in turn, flips it back to cliff who says, okay, what obligations did I just take on in order to work for this data? And he reviews each of these data sharing agreements that you, as an organization would set up and say, what am I, uh, what are my restrictions for using this data site? >>As cliff accepts his notices, he now has triggered the process of what we would call fulfillment or a service broker. And in this situation we're doing a virtualization, uh, access, uh, for the borrow use case. Cliff suggests Tablo is his preferred BI and reporting tool. And you can see the various options that are available from power BI Looker size on ThoughtSpot. There are others that can be added over time. And from there, cliff now will be alerted the minute this data is available to them. So now we're running out and doing a distributed query to get the information and you see it returns back for raw view. Now what's really interesting is you'll see, the customer phone has a bunch of X's in it. If you remember that's PII. So it's actually being massed. So cliff can't actually see the raw data. Now cliff also wants to look at it in a Tablo report and can see the visualization layer, but you also see an incorporation of something we call Collibra on the go. >>Not only do we bring the data to the report, but then we tell you the reader, how to interpret the report. It could be that there's someone else who wants to use the very same report that cliff helped create, but they don't understand exactly all the things that cliff went through. So now they have the ability to get a full interpretation of what was this data that was used, where did it come from? And how do I actually interpret some of the fields that I see on this report? Really a clever combination of bringing the data to you and showing you how to use it. Cliff can also see this as a registered asset within a Colibra. So the next shopper comes through might actually, instead of shopping for the dataset might actually shop for the report itself. And the report is connected with the data set he used. >>So now they have a full bill of materials to run a customer Shern report and schedule it anytime they want. So now we've turned cliff actually into a creator of data assets, and this is where intelligent, it gets more intelligence and that's really what we call data intelligence. So let's go back through that magic trick that we just did with cliff. So cliff went into the software, not knowing if the source of data that he was looking for for customer product sales was even available to him. He went in very quickly and searched and found his dataset, use facts and facets to filter down to exactly what was available. Compare to contrast the options that were there actually made an observation that there actually wasn't enough data quality around a certain thing was important to him, created an idea, or basically a suggestion for somebody to follow up on was able to put that into his shopping basket checkout and have it delivered to his front door. >>I mean, that's a bit of a magic trick, right? So, uh, cliff was successful in finding data that he wanted and having it, deliver it to him. And then in his preferred model, he was able to look at it into Tableau. All right. So let's talk about how we're going to make this vision a reality. So our first section here is about performance and scale, but it's also about codeless database registration. How did we get all that stuff into the data catalog and available for, uh, cliff to find? So allow us to introduce you to what we call the asset life cycle and some of the largest organizations in the world. They might have upwards of a billion data assets. These are columns and tables, reports, API, APIs, algorithms, et cetera. These are very high volume and quite technical and far more information than a business user like cliff might want to be engaged with those very same really large organizations may have upwards of say, 20 to 25 million that are critical data sources and data assets, things that they do need to highly curate and make available. >>But through that as a bit of a distillation, a lifecycle of different things you might want to do along that. And so we're going to share with you how you can actually automatically register these sources, deal with these very large volumes at speed and at scale, and actually make it available with just a level of information you need to govern and protect, but also make it available for opportunistic use cases, such as the one we presented with cliff. So as you recall, when cliff was actually trying to look for his dataset, he identified that the is churned, uh, data at your was of low quality. So he passed this over to Eliza, who's a data steward and she actually receives this work queue in a collaborative fashion. And she has to review, what is the request? If you recall, this was the request to improve the data quality for his churn. >>Now she needs to familiarize herself with what cliff was observing when he was doing his shopping experience. So she digs in and wants to look at the quality that he was observing and sure enough, as she goes down and it looks at his churn, she sees that it was a low 65% and now understands exactly what cliff was referring to. She says, aha, okay. I need to get help. I need to decide whether I have a data quality project to fix the data, or should I see if there's another data set in the organization that has better, uh, data for this. And so she creates a queue that can go over to one of her colleagues who really focuses on data quality. She submits this request and it goes over to, uh, her colleague, John who's really familiar with data quality. So John actually receives the request from Eliza and you'll see a task showing up in his queue. >>He opens up the request and finds out that Eliza's asking if there's another source out there that actually has good is churned, uh, data available. Now he actually knows quite a bit about the quality of information sturdiness. So he goes into the data quality console and does a quick look for a dataset that he's familiar with called customer product sales. He quickly scrolls down and finds out the one that's actually been published. That's the one he was looking for and he opens it up to find out more information. What data sets are, what columns are actually in there. And he goes down to find his churned is in fact, one of the attributes in there. It actually does have active rules that are associated with it to manage the quality. And so he says, well, let's look in more detail and find out what is the quality of this dataset? >>Oh, it's 86. This is a dramatic improvement over what we've seen before. So we can see again, it's trended quite nicely over time each day, it hasn't actually degraded in performance. So we actually responds back to realize and say, this data set, uh, is actually the data set that you want to bring in. It really will improve. And you'll see that he refers to the refined database within the CRM cloud solution. Once he actually submits this, it goes back to Eliza and she's able to continue her work. Now when Eliza actually brings this back open, she's able to very quickly go into the database registration process for her. She very quickly goes into the CRM cloud, selects the community, to which she wants to register this, uh, data set into the schemas community. And the CRM cloud is the system that she wants to load it in. >>And the refined is the database that John told her that she should bring in. After a quick description, she's able to click register. And this triggers that automatic codeless process of going out to the dataset and bringing back its metadata. Now metadata is great, but it's not the end all be all. There's a lot of other values that she really cares about as she's actually registering this dataset and synchronizing the metadata she's also then asked, would you like to bring in quality information? And so she'll go out and say, yes, of course, I want to enable the quality information from CRM refined. I also want to bring back lineage information to associate with this metadata. And I also want to select profiling and classification information. Now when she actually selects it, she can also say, how often do you want to synchronize this? This is a daily, weekly, monthly kind of update. >>That's part of the change data capture process. Again, all automated without the require of actually writing code. So she's actually run this process. Now, after this loads in, she can then open up this new registered, uh, dataset and actually look and see if it actually has achieved the problem that cliff set her out on, which was improved data quality. So looking into the data quality for the is churn capability shows her that she has fantastic quality. It's at a hundred, it's exactly what she was looking for. So she can with confidence actually, uh, suggest that it's done, but she did notice something and something that she wants to tell John, which is there's a couple of data quality checks that seem to be missing from this dataset. So again, in a collaborative fashion, she can pass that information, uh, for validity and completeness to say, you know what, check for NOLs and MPS and send that back. >>So she submits this onto John to work on. And John now has a work queue in his task force, but remember she's been working in this task forklift and because she actually has actually added a much better source for his churn information, she's going to update that test that was sent to her to notify cliff that the work has actually been done and that she actually has a really good data set in there. In fact, if you recall, it was 100% in terms of its data quality. So this will really make life a lot easier for cliff. Once he receives that data and processes, the churn report analysis next time. So let's talk about these audacious performance goals that we have in mind. Now today, we actually have really strong performance and amazing usability. Our customers continue to tell us how great our usability is, but they keep asking for more well, we've decided to present to you. >>Something you can start to bank on. This is the performance you can expect from us on the highly curated assets that are available for the business users, as well as the technical and lineage assets that are more available for the developer uses and for things that are more warehoused based, you'll see in Q1, uh, our Q2 of this year, we're making available 5 million curated assets. Now you might be out there saying, Hey, I'm already using the software and I've got over 20 million already. That's fair. We do. We have customers that are actually well over 20 million in terms of assets they're managing, but we wanted to present this to you with zero conditions, no limitations we wouldn't talk about, well, it depends, et cetera. This is without any conditions. That's what we can offer you without fail. And yes, it can go higher and higher. We're also talking about the speed with which you can ingest the data right now, we're ingesting somewhere around 50,000 to a hundred thousand records per and of course, yes, you've probably seen it go quite a bit faster, but we are assuring you that that's the case, but what's really impressive is right now, we can also, uh, help you manage 250 million technical assets and we can load it at a speed of 25 million for our, and you can see how over the next 18 months about every two quarters, we show you dramatic improvements, more than doubling of these. >>For most of them leading up to the end of 2022, we're actually handling over a billion technical lineage assets and we're loading at a hundred million per hour. That sets the mark for the industry. Earlier this year, we announced a recent acquisition Al DQ. LDQ brought to us machine learning based data quality. We're now able to introduce to you Collibra data quality, the first integrated approach to Al DQ and Culebra. We've got a demo to follow. I'm really excited to share it with you. Let's get started. So Eliza submitted a task for John to work on, remember to add checks for no and for empty. So John picks up this task very quickly and looks and sees what's what's the request. And from there says, ah, yes, we do have a quality check issue when we look at these churns. So he jumps over to the data quality console and says, I need to create a new data quality test. >>So cliff is able to go in, uh, to the solution and, uh, set up quick rules, automated rules. Uh, he could inherit rules from other things, but it starts with first identifying what is the data source that he needs to connect to, to perform this. And so he chooses the CRM refined data set that was most recently, uh, registered by Lysa. You'll see the same score of 86 was the quality score for the dataset. And you'll also see, there are four rules that are associated underneath this. Now there are various checks that, uh, that John can establish on this, but remember, this is a fairly easy request that he receives from Eliza. So he's going to go in and choose the actual field, uh, is churned. Uh, and from there identify quick rules of, uh, an empty check and that quickly sets up the rules for him. >>And also the null check equally fast. This one's established and analyzes all the data in there. And this sets up the baseline of data quality, uh, for this. Now this data, once it's captured then is periodically brought back to the catalog. So it's available to not only Eliza, but also to cliff next time he, uh, where to shop in the environment. As we look through the rules that were created through that very simple user experience, you can see the one for is empty and is no that we're set up. Now, these are various, uh, styles that can be set up either manually, or you can set them up through machine learning again, or you can inherit them. But the key is to track these, uh, rule creation in the metrics that are generated from these rules so that it can be brought back to the catalog and then used in meaningful context, by someone who's shopping and the confidence that this has neither empty nor no fields, at least most of them don't well now give a confidence as you go forward. >>And as you can see, those checks have now been entered in and you can see that it's a hundred percent quality score for the Knoll check. So with confidence now, John can actually respond back to Eliza and say, I've actually inserted them they're up and running. And, uh, you're in good status. So that was pretty amazing integration, right? And four months after our acquisition, we've already brought that level of integration between, uh, Colibra, uh, data intelligence, cloud, and data quality. Now it doesn't stop there. We have really impressive and high site set early next year. We're getting introduced a fully immersive experience where customers can work within Culebra and actually bring the data quality information all the way in as well as start to manipulate the rules and generate the machine learning rules. On top of it, all of that will be a deeply immersive experience. >>We also have something really clever coming, which we call continuous data profiling, where we bring the power of data quality all the way into the database. So it's continuously running and always making that data available for you. Now, I'd also like to share with you one of the reasons why we are the most universally available software solutions in data intelligence. We've already announced that we're available on AWS and Google cloud prior, but today we can announce to you in Q3, we're going to be, um, available on Microsoft Azure as well. Now it's not just these three cloud providers that were available on we've also become available on each of their marketplaces. So if you are buying our software, you can actually go out and achieve that same purchase from their marketplace and achieve your financial objectives as well. We're very excited about this. These are very important partners for, uh, for our, for us. >>Now, I'd also like to introduce you our system integrators, without them. There's no way we could actually achieve our objectives of growing so rapidly and dealing with the demand that you customers have had Accenture, Deloitte emphasis, and even others have been instrumental in making sure that we can serve your needs when you need them. Uh, and so it's been a big part of our growth and will be a continued part of our growth as well. And finally, I'd like to actually introduce you to our product showcases where we can go into absolute detail on many of the topics I talked about today, such as data governance with Arco or data privacy with Sergio or data quality with Brian and finally catalog with Peter. Again, I'd like to thank you all for joining us. Uh, and we really look forward to hearing your feedback. Thank you..

>>From around the globe. It's the cube with digital coverage of AWS reinvent 2020 sponsored by Intel, AWS, and our community partners. >>Hi everyone. Welcome back to the cubes. Virtual coverage of AWS reinvent 2020 virtual. We are the cube virtual I'm John ferry, your host with my coach, Dave Alante for keynote analysis from Swami's machine learning, all things, data huge. Instead of announcements, the first ever machine learning keynote at a re-invent Dave. Great to see you. Thanks Johnny. And from Boston, I'm here in Palo Alto. We're doing the cube remote cube virtual. Great to see you. >>Yeah, good to be here, John, as always. Wall-to-wall love it. So, so, John, um, how about I give you my, my key highlights from the, uh, from the keynote today, I had, I had four kind of curated takeaways. So the first is that AWS is, is really trying to simplify machine learning and use machine intelligence into all applications. And if you think about it, it's good news for organizations because they're not the become machine learning experts have invent machine learning. They can buy it from Amazon. I think the second is they're trying to simplify the data pipeline. The data pipeline today is characterized by a series of hyper specialized individuals. It engineers, data scientists, quality engineers, analysts, developers. These are folks that are largely live in their own swim lane. Uh, and while they collaborate, uh, there's still a fairly linear and complicated data pipeline, uh, that, that a business person or a data product builder has to go through Amazon making some moves to the front of simplify that they're expanding data access to the line of business. I think that's a key point. Is there, there increasingly as people build data products and data services that can monetize, you know, for their business, either cut costs or generate revenue, they can expand that into line of business where there's there's domain context. And I think the last thing is this theme that we talked about the other day, John of extending Amazon, AWS to the edge that we saw that as well in a number of machine learning tools that, uh, Swami talked about. >>Yeah, it was great by the way, we're live here, uh, in Palo Alto in Boston covering the analysis, tons of content on the cube, check out the cube.net and also check out at reinvent. There's a cube section as there's some links to so on demand videos with all the content we've had. Dave, I got to say one of the things that's apparent to me, and this came out of my one-on-one with Andy Jassy and Andy Jassy talked about in his keynote is he kind of teased out this idea of training versus a more value add machine learning. And you saw that today in today's announcement. To me, the big revelation was that the training aspect of machine learning, um, is what can be automated away. And it's under a lot of controversy around it. Recently, a Google paper came out and the person was essentially kind of, kind of let go for this. >>But the idea of doing these training algorithms, some are saying is causes more harm to the environment than it does good because of all the compute power it takes. So you start to see the positioning of training, which can be automated away and served up with, you know, high powered ships and that's, they consider that undifferentiated heavy lifting. In my opinion, they didn't say that, but that's clearly what I see coming out of this announcement. The other thing that I saw Dave that's notable is you saw them clearly taking a three lane approach to this machine, learning the advanced builders, the advanced coders and the developers, and then database and data analysts, three swim lanes of personas of target audience. Clearly that is in line with SageMaker and the embedded stuff. So two big revelations, more horsepower required to process training and modeling. Okay. And to the expansion of the personas that are going to be using machine learning. So clearly this is a, to me, a big trend wave that we're seeing that validates some of the startups and I'll see their SageMaker and some of their products. >>Well, as I was saying at the top, I think Amazon's really trying, working hard on simplifying the whole process. And you mentioned training and, and a lot of times people are starting from scratch when they have to train models and retrain models. And so what they're doing is they're trying to create reusable components, uh, and allow people to, as you pointed out to automate and streamline some of that heavy lifting, uh, and as well, they talked a lot about, uh, doing, doing AI inferencing at the edge. And you're seeing, you know, they, they, uh, Swami talked about several foundational premises and the first being a foundation of frameworks. And you think about that at the, at the lowest level of their S their ML stack. They've got, you know, GPU's different processors, inferential, all these alternative processes, processors, not just the, the Xav six. And so these are very expensive resources and Swami talked a lot about, uh, and his colleagues talked a lot about, well, a lot of times the alternative processor is sitting there, you know, waiting, waiting, waiting. And so they're really trying to drive efficiency and speed. They talked a lot about compressing the time that it takes to, to run these, these models, uh, from, from sometimes weeks down to days, sometimes days down to hours and minutes. >>Yeah. Let's, let's unpack these four areas. Let's stay on the firm foundation because that's their core competency infrastructure as a service. Clearly they're laying that down. You put the processors, but what's interesting is the TensorFlow 92% of tensor flows on Amazon. The other thing is that pie torch surprisingly is back up there, um, with massive adoption and the numbers on pie torch literally is on fire. I was coming in and joke on Twitter. Um, we, a PI torch is telling because that means that TensorFlow is originally part of Google is getting, is getting a little bit diluted with other frameworks, and then you've got MX net, some other things out there. So the fact that you've got PI torch 91% and then TensorFlow 92% on 80 bucks is a huge validation. That means that the majority of most machine learning development and deep learning is happening on AWS. Um, >>Yeah, cloud-based, by the way, just to clarify, that's the 90% of cloud-based cloud, uh, TensorFlow runs on and 91% of cloud-based PI torch runs on ADM is amazingly massive numbers. >>Yeah. And I think that the, the processor has to show that it's not trivial to do the machine learning, but, you know, that's where the infrared internship came in. That's kind of where they want to go lay down that foundation. And they had Tanium, they had trainee, um, they had, um, infrared chow was the chip. And then, you know, just true, you know, distributed training training on SageMaker. So you got the chip and then you've got Sage makers, the middleware games, almost like a machine learning stack. That's what they're putting out there >>And how bad a Gowdy, which was, which is, which is a patrol also for training, which is an Intel based chip. Uh, so that was kind of interesting. So a lot of new chips and, and specialized just, we've been talking about this for awhile, particularly as you get to the edge and do AI inferencing, you need, uh, you know, a different approach than we're used to with the general purpose microbes. >>So what gets your take on tenant? Number two? So tenant number one, clearly infrastructure, a lot of announcements we'll go through those, review them at the end, but tenant number two, that Swami put out there was creating the shortest path to success for builders or machine learning builders. And I think here you lays out the complexity, Dave butts, mostly around methodology, and, you know, the value activities required to execute. And again, this points to the complexity problem that they have. What's your take on this? >>Yeah. Well you think about, again, I'm talking about the pipeline, you collect data, you just data, you prepare that data, you analyze that data. You, you, you make sure that it's it's high quality and then you start the training and then you're iterating. And so they really trying to automate as much as possible and simplify as much as possible. What I really liked about that segment of foundation, number two, if you will, is the example, the customer example of the speaker from the NFL, you know, talked about, uh, you know, the AWS stats that we see in the commercials, uh, next gen stats. Uh, and, and she talked about the ways in which they've, well, we all know they've, they've rearchitected helmets. Uh, they've been, it's really a very much database. It was interesting to see they had the spectrum of the helmets that were, you know, the safest, most safe to the least safe and how they've migrated everybody in the NFL to those that they, she started a 24%. >>It was interesting how she wanted a 24% reduction in reported concussions. You know, you got to give the benefit of the doubt and assume some of that's through, through the data. But you know, some of that could be like, you know, Julian Edelman popping up off the ground. When, you know, we had a concussion, he doesn't want to come out of the game with the new protocol, but no doubt, they're collecting more data on this stuff, and it's not just head injuries. And she talked about ankle injuries, knee injuries. So all this comes from training models and reducing the time it takes to actually go from raw data to insights. >>Yeah. I mean, I think the NFL is a great example. You and I both know how hard it is to get the NFL to come on and do an interview. They're very coy. They don't really put their name on anything much because of the value of the NFL, this a meaningful partnership. You had the, the person onstage virtually really going into some real detail around the depth of the partnership. So to me, it's real, first of all, I love stat cast 11, anything to do with what they do with the stats is phenomenal at this point. So the real world example, Dave, that you starting to see sports as one metaphor, healthcare, and others are going to see those coming in to me, totally a tale sign that Amazon's continued to lead. The thing that got my attention was is that it is an IOT problem, and there's no reason why they shouldn't get to it. I mean, some say that, Oh, concussion, NFL is just covering their butt. They don't have to, this is actually really working. So you got the tech, why not use it? And they are. So that, to me, that's impressive. And I think that's, again, a digital transformation sign that, that, you know, in the NFL is doing it. It's real. Um, because it's just easier. >>I think, look, I think, I think it's easy to criticize the NFL, but the re the reality is, is there anything old days? It was like, Hey, you get your bell rung and get back out there. That's just the way it was a football players, you know, but Ted Johnson was one of the first and, you know, bill Bellacheck was, was, you know, the guy who sent him back out there with a concussion, but, but he was very much outspoken. You've got to give the NFL credit. Uh, it didn't just ignore the problem. Yeah. Maybe it, it took a little while, but you know, these things take some time because, you know, it's generally was generally accepted, you know, back in the day that, okay, Hey, you'd get right back out there, but, but the NFL has made big investments there. And you can say, you got to give him, give him props for that. And especially given that they're collecting all this data. That to me is the most interesting angle here is letting the data inform the actions. >>And next step, after the NFL, they had this data prep data Wrangler news, that they're now integrating snowflakes, Databricks, Mongo DB, into SageMaker, which is a theme there of Redshift S3 and Lake formation into not the other way around. So again, you've been following this pretty closely, uh, specifically the snowflake recent IPO and their success. Um, this is an ecosystem play for Amazon. What does it mean? >>Well, a couple of things, as we, as you well know, John, when you first called me up, I was in Dallas and I flew into New York and an ice storm to get to the one of the early Duke worlds. You know, and back then it was all batch. The big data was this big batch job. And today you want to combine that batch. There's still a lot of need for batch, but when people want real time inferencing and AWS is bringing that together and they're bringing in multiple data sources, you mentioned Databricks and snowflake Mongo. These are three platforms that are doing very well in the market and holding a lot of data in AWS and saying, okay, Hey, we want to be the brain in the middle. You can import data from any of those sources. And I'm sure they're going to add more over time. Uh, and so they talked about 300 pre-configured data transformations, uh, that now come with stage maker of SageMaker studio with essentially, I've talked about this a lot. It's essentially abstracting away the, it complexity, the whole it operations piece. I mean, it's the same old theme that AWS is just pointing. It's its platform and its cloud at non undifferentiated, heavy lifting. And it's moving it up the stack now into the data life cycle and data pipeline, which is one of the biggest blockers to monetizing data. >>Expand on that more. What does that actually mean? I'm an it person translate that into it. Speak. Yeah. >>So today, if you're, if you're a business person and you want, you want the answers, right, and you want say to adjust a new data source, so let's say you want to build a new, new product. Um, let me give an example. Let's say you're like a Spotify, make it up. And, and you do music today, but let's say you want to add, you know, movies, or you want to add podcasts and you want to start monetizing that you want to, you want to identify, who's watching what you want to create new metadata. Well, you need new data sources. So what you do as a business person that wants to create that new data product, let's say for podcasts, you have to knock on the door, get to the front of the data pipeline line and say, okay, Hey, can you please add this data source? >>And then everybody else down the line has to get in line and Hey, this becomes a new data source. And it's this linear process where very specialized individuals have to do their part. And then at the other end, you know, it comes to self-serve capability that somebody can use to either build dashboards or build a data product. In a lot of that middle part is our operational details around deploying infrastructure, deploying, you know, training machine learning models that a lot of Python coding. Yeah. There's SQL queries that have to be done. So a lot of very highly specialized activities, what Amazon is doing, my takeaway is they're really streamlining a lot of those activities, removing what they always call the non undifferentiated, heavy lifting abstracting away that it complexity to me, this is a real positive sign, because it's all about the technology serving the business, as opposed to historically, it's the business begging the technology department to please help me. The technology department obviously evolving from, you know, the, the glass house, if you will, to this new data, data pipeline data, life cycle. >>Yeah. I mean, it's classic agility to take down those. I mean, it's undifferentiated, I guess, but if it actually works, just create a differentiated product. So, but it's just log it's that it's, you can debate that kind of aspect of it, but I hear what you're saying, just get rid of it and make it simpler. Um, the impact of machine learning is Dave is one came out clear on this, uh, SageMaker clarify announcement, which is a bias decision algorithm. They had an expert, uh, nationally CFUs presented essentially how they're dealing with the, the, the bias piece of it. I thought that was very interesting. What'd you think? >>Well, so humans are biased and so humans build models or models are inherently biased. And so I thought it was, you know, this is a huge problem to big problems in artificial intelligence. One is the inherent bias in the models. And the second is the lack of transparency that, you know, they call it the black box problem, like, okay, I know there was an answer there, but how did it get to that answer and how do I trace it back? Uh, and so Amazon is really trying to attack those, uh, with, with, with clarify. I wasn't sure if it was clarity or clarified, I think it's clarity clarify, um, a lot of entirely certain how it works. So we really have to dig more into that, but it's essentially identifying situations where there is bias flagging those, and then, you know, I believe making recommendations as to how it can be stamped. >>Nope. Yeah. And also some other news deep profiling for debugger. So you could make a debugger, which is a deep profile on neural network training, um, which is very cool again on that same theme of profiling. The other thing that I found >>That remind me, John, if I may interrupt there reminded me of like grammar corrections and, you know, when you're typing, it's like, you know, bug code corrections and automated debugging, try this. >>It wasn't like a better debugger come on. We, first of all, it should be bug free code, but, um, you know, there's always biases of the data is critical. Um, the other news I thought was interesting and then Amazon's claiming this is the first SageMaker pipelines for purpose-built CIC D uh, for machine learning, bringing machine learning into a developer construct. And I think this started bringing in this idea of the edge manager where you have, you know, and they call it the about machine, uh, uh, SageMaker store storing your functions of this idea of managing and monitoring machine learning modules effectively is on the edge. And, and through the development process is interesting and really targeting that developer, Dave, >>Yeah, applying CIC D to the machine learning and machine intelligence has always been very challenging because again, there's so many piece parts. And so, you know, I said it the other day, it's like a lot of the innovations that Amazon comes out with are things that have problems that have come up given the pace of innovation that they're putting forth. And, and it's like the customers drinking from a fire hose. We've talked about this at previous reinvents and the, and the customers keep up with the pace of Amazon. So I see this as Amazon trying to reduce friction, you know, across its entire stack. Most, for example, >>Let me lay it out. A slide ahead, build machine learning, gurus developers, and then database and data analysts, clearly database developers and data analysts are on their radar. This is not the first time we've heard that. But we, as the kind of it is the first time we're starting to see products materialized where you have machine learning for databases, data warehouse, and data lakes, and then BI tools. So again, three different segments, the databases, the data warehouse and data lakes, and then the BI tools, three areas of machine learning, innovation, where you're seeing some product news, your, your take on this natural evolution. >>Well, well, it's what I'm saying up front is that the good news for, for, for our customers is you don't have to be a Google or Amazon or Facebook to be a super expert at AI. Uh, companies like Amazon are going to be providing products that you can then apply to your business. And, and it's allowed you to infuse AI across your entire application portfolio. Amazon Redshift ML was another, um, example of them, abstracting complexity. They're taking, they're taking S3 Redshift and SageMaker complexity and abstracting that and presenting it to the data analysts. So that, that, that individual can worry about, you know, again, getting to the insights, it's injecting ML into the database much in the same way, frankly, the big query has done that. And so that's a huge, huge positive. When you talk to customers, they, they love the fact that when, when ML can be embedded into the, into the database and it simplifies, uh, that, that all that, uh, uh, uh, complexity, they absolutely love it because they can focus on more important things. >>Clearly I'm this tenant, and this is part of the keynote. They were laying out all their announcements, quick excitement and ML insights out of the box, quick, quick site cue available in preview all the announcements. And then they moved on to the next, the fourth tenant day solving real problems end to end, kind of reminds me of the theme we heard at Dell technology worlds last year end to end it. So we are starting to see the, the, the land grab my opinion, Amazon really going after, beyond I, as in pass, they talked about contact content, contact centers, Kendra, uh, lookout for metrics, and that'll maintain men. Then Matt would came on, talk about all the massive disruption on the, in the industries. And he said, literally machine learning will disrupt every industry. They spent a lot of time on that and they went into the computer vision at the edge, which I'm a big fan of. I just loved that product. Clearly, every innovation, I mean, every vertical Dave is up for grabs. That's the key. Dr. Matt would message. >>Yeah. I mean, I totally agree. I mean, I see that machine intelligence as a top layer of, you know, the S the stack. And as I said, it's going to be infused into all areas. It's not some kind of separate thing, you know, like, Coobernetti's, we think it's some separate thing. It's not, it's going to be embedded everywhere. And I really like Amazon's edge strategy. It's this, you, you are the first to sort of write about it and your keynote preview, Andy Jassy said, we see, we see, we want to bring AWS to the edge. And we see data center as just another edge node. And so what they're doing is they're bringing SDKs. They've got a package of sensors. They're bringing appliances. I've said many, many times the developers are going to be, you know, the linchpin to the edge. And so Amazon is bringing its entire, you know, data plane is control plane, it's API APIs to the edge and giving builders or slash developers, the ability to innovate. And I really liked the strategy versus, Hey, here's a box it's, it's got an x86 processor inside on a, throw it over the edge, give it a cool name that has edge in it. And here you go, >>That sounds call it hyper edge. You know, I mean, the thing that's true is the data aspect at the edge. I mean, everything's got a database data warehouse and data lakes are involved in everything. And then, and some sort of BI or tools to get the data and work with the data or the data analyst, data feeds, machine learning, critical piece to all this, Dave, I mean, this is like databases used to be boring, like boring field. Like, you know, if you were a database, I have a degree in a database design, one of my degrees who do science degrees back then no one really cared. If you were a database person. Now it's like, man data, everything. This is a whole new field. This is an opportunity. But also, I mean, are there enough people out there to do all this? >>Well, it's a great point. And I think this is why Amazon is trying to extract some of the abstract. Some of the complexity I sat in on a private session around databases today and listened to a number of customers. And I will say this, you know, some of it I think was NDA. So I can't, I can't say too much, but I will say this Amazon's philosophy of the database. And you address this in your conversation with Andy Jassy across its entire portfolio is to have really, really fine grain access to the deep level API APIs across all their services. And he said, he said this to you. We don't necessarily want to be the abstraction layer per se, because when the market changes, that's harder for us to change. We want to have that fine-grained access. And so you're seeing that with database, whether it's, you know, no sequel, sequel, you know, the, the Aurora the different flavors of Aurora dynamo, DV, uh, red shift, uh, you know, already S on and on and on. There's just a number of data stores. And you're seeing, for instance, Oracle take a completely different approach. Yes, they have my SQL cause they know got that with the sun acquisition. But, but this is they're really about put, is putting as much capability into a single database as possible. Oh, you only need one database only different philosophy. >>Yeah. And then obviously a health Lake. And then that was pretty much the end of the, the announcements big impact to health care. Again, the theme of horizontal data, vertical specialization with data science and software playing out in real time. >>Yeah. Well, so I have asked this question many times in the cube, when is it that machines will be able to make better diagnoses than doctors and you know, that day is coming. If it's not here, uh, you know, I think helped like is really interesting. I've got an interview later on with one of the practitioners in that space. And so, you know, healthcare is something that is an industry that's ripe for disruption. It really hasn't been disruption disrupted. It's a very high, high risk obviously industry. Uh, but look at healthcare as we all know, it's too expensive. It's too slow. It's too cumbersome. It's too long sometimes to get to a diagnosis or be seen, Amazon's trying to attack with its partners, all of those problems. >>Well, Dave, let's, let's summarize our take on Amazon keynote with machine learning, I'll say pretty historic in the sense that there was so much content in first keynote last year with Andy Jassy, he spent like 75 minutes. He told me on machine learning, they had to kind of create their own category Swami, who we interviewed many times on the cube was awesome. But a lot of still a lot more stuff, more, 215 announcements this year, machine learning more capabilities than ever before. Um, moving faster, solving real problems, targeting the builders, um, fraud platform set of things is the Amazon cadence. What's your analysis of the keynote? >>Well, so I think a couple of things, one is, you know, we've said for a while now that the new innovation cocktail is cloud plus data, plus AI, it's really data machine intelligence or AI applied to that data. And the scale at cloud Amazon Naylor obviously has nailed the cloud infrastructure. It's got the data. That's why database is so important and it's gotta be a leader in machine intelligence. And you're seeing this in the, in the spending data, you know, with our partner ETR, you see that, uh, that AI and ML in terms of spending momentum is, is at the highest or, or at the highest, along with automation, uh, and containers. And so in. Why is that? It's because everybody is trying to infuse AI into their application portfolios. They're trying to automate as much as possible. They're trying to get insights that, that the systems can take action on. >>And, and, and actually it's really augmented intelligence in a big way, but, but really driving insights, speeding that time to insight and Amazon, they have to be a leader there that it's Amazon it's, it's, it's Google, it's the Facebook's, it's obviously Microsoft, you know, IBM's Tron trying to get in there. They were kind of first with, with Watson, but with they're far behind, I think, uh, the, the hyper hyper scale guys. Uh, but, but I guess like the key point is you're going to be buying this. Most companies are going to be buying this, not building it. And that's good news for organizations. >>Yeah. I mean, you get 80% there with the product. Why not go that way? The alternative is try to find some machine learning people to build it. They're hard to find. Um, so the seeing the scale of kind of replicating machine learning expertise with SageMaker, then ultimately into databases and tools, and then ultimately built into applications. I think, you know, this is the thing that I think they, my opinion is that Amazon continues to move up the stack, uh, with their capabilities. And I think machine learning is interesting because it's a whole new set of it's kind of its own little monster building block. That's just not one thing it's going to be super important. I think it's going to have an impact on the startup scene and innovation is going, gonna have an impact on incumbent companies that are currently leaders that are under threat from new entrance entering the business. >>So I think it's going to be a very entrepreneurial opportunity. And I think it's going to be interesting to see is how machine learning plays that role. Is it a defining feature that's core to the intellectual property, or is it enabling new intellectual property? So to me, I just don't see how that's going to fall yet. I would bet that today intellectual property will be built on top of Amazon's machine learning, where the new algorithms and the new things will be built separately. If you compete head to head with that scale, you could be on the wrong side of history. Again, this is a bet that the startups and the venture capitals will have to make is who's going to end up being on the right wave here. Because if you make the wrong design choice, you can have a very complex environment with IOT or whatever your app serving. If you can narrow it down and get a wedge in the marketplace, if you're a company, um, I think that's going to be an advantage. This could be great just to see how the impact of the ecosystem this will be. >>Well, I think something you said just now it gives a clue. You talked about, you know, the, the difficulty of finding the skills. And I think that's a big part of what Amazon and others who were innovating in machine learning are trying to do is the gap between those that are qualified to actually do this stuff. The data scientists, the quality engineers, the data engineers, et cetera. And so companies, you know, the last 10 years went out and tried to hire these people. They couldn't find them, they tried to train them. So it's taking too long. And now that I think they're looking toward machine intelligence to really solve that problem, because that scales, as we, as we know, outsourcing to services companies and just, you know, hardcore heavy lifting, does it doesn't scale that well, >>Well, you know what, give me some machine learning, give it to me faster. I want to take the 80% there and allow us to build certainly on the media cloud and the cube virtual that we're doing. Again, every vertical is going to impact a Dave. Great to see you, uh, great stuff. So far week two. So, you know, we're cube live, we're live covering the keynotes tomorrow. We'll be covering the keynotes for the public sector day. That should be chock-full action. That environment is going to impact the most by COVID a lot of innovation, a lot of coverage. I'm John Ferrari. And with Dave Alante, thanks for watching.

>>from around the globe. It's the Cube with digital coverage of AWS Public Sector online brought to you by Amazon Web services. Oven Welcome back to the cubes. Virtual coverage of Amazon Web services. Eight. Of his public sector summit online. We couldn't be there in person, but we're doing remote interviews. I'm John Curry. Your host of the Cube got a great segment from Asia Pacific on the other side of the world from California about social impact, transforming, teaching and learning with cloud technology. Got three great guests. You go. Richard is the CEO and co founder of Guys Tech and Jean Te'o, CEO and founder of Solve Education Founders and CEOs of startups is great. This is squad was the AIPAC regional head. Education, health care, not for profit and research. Ray Ws, he head start big program Vincent. Thanks for coming on, Janine. And you go Thank you for joining. >>Thanks for having us, John. >>We're not there in person. We're doing remote interviews. I'm really glad to have this topic because now more than ever, social change is happening. Um, this next generation eyes building software and applications to solve big problems. And it's not like yesterday's problems there. Today's problems and learning and mentoring and starting companies are all happening virtually digitally and also in person. So the world's changing. So, um, I gotta ask you, Vincent, we'll start with you and Amazon. Honestly, big started builder culture. You got two great founders here. CEO is doing some great stuff. Tell us a little bit what's going on. A pack, >>A lot of >>activity. I mean, reinvent and some it's out. There are really popular. Give us an update on what's happening. >>Thank you. Thank you for the question, John. I think it's extremely exciting, especially in today's context, that we are seeing so much activities, especially in the education technology sector. One of the challenges that we saw from our education technology customers is that they are always looking for help and support in many off the innovation that they're trying to develop the second area off observation that we had waas, that they are always alone with very limited resources, and they usually do not know where to look for in terms, off support and in terms off who they can reach out to. From a community standpoint, that is actually how we started and developed this program called A W s. At START. It is a program specifically for education technology companies that are targeting delivering innovative education solutions for the education sector. And we bring specific benefits to these education technology companies when they join the program. Aws ed start. Yeah, three specific areas. First one is that we support them with technical support, which is really, really key trying to help them navigate in the various ranges off A W S services that allows them to develop innovative services. The second area is leaking them and building a community off like minded education technology founders and linking them also to investors and VCs and lastly, off course, in supporting innovation. We support them with a bit off AWS cop credits promotional credits for them so that they can go on experiment and develop innovations for their customers. >>That's great stuff. And I want to get into that program a little further because I think that's a great example of kind of benefits AWS provides actually free credits or no one is gonna turn away free credits. We'll take the free credits all the time all day long, but really it's about the innovation. Um, Jean, I want to get your thoughts. How would solve education? Born? What problems were you solving? What made you start this company and tell us your story? >>Thank you so much for the question. So, actually, my co founder was invited to speak at an African innovation forum a couple of years back on the topic that he was sharing with. How can Africa skip over the industrialization face and go direct to the knowledge economy? Onda, the discussion went towards in orderto have access to the knowledge economy, unique knowledge. And how do you get knowledge Well through education. So that's when everybody in the conference was a bit stuck right on the advice waas. In order to scale first, we need to figure out a way to not well, you know, engaging the government and schools and teachers, but not depend on them for the successful education initiated. So and that's was what pain walk away from the conference. And when we met in in Jakarta, we started talking about that also. So while I'm Singaporean, I worked in many developing countries on the problem that we're trying to solve this. It might be shocking to you, but UNESCO recently published over 600 million Children and you are not learning on. That is a big number globally right on out of all the SDG per se from U N. Education. And perhaps I'm biased because I'm a computer engineer. But I see that education is the only one that can be solved by transforming bites. But since the other stg is like, you know, poverty or hunger, right, actually require big amount of logistic coordination and so on. So we saw a very, um, interesting trend with mobile phones, particularly smartphones, becoming more and more ubiquitous. And with that, we saw a very, uh, interesting. Fortunately for us to disseminate education through about technology. So we in self education elevate people out of poverty, true, providing education and employment opportunities live urging on tech. And we our vision is to enable people to empower themselves. And what we do is that we do an open platform that provides everyone effected education. >>You could How about your company? What problem you're you saw And how did it all get started? Tell us your vision. >>Thanks, John. Well, look, it all started. We have a joke. One of the co founder, Matthew, had a has a child with severe learning disorder and dyslexia, and he made a joke one day about having another one of them that would support those those kids on Duh. I took the joke seriously, So we're starting sitting down and, you know, trying to figure out how we could make this happen. Um, so it turns out that the dyslexia is the most common learning disorder in the world, with an estimated 10 to 20% off the worldwide population with the disorder between context between 750 million, up to 1.5 billion individual. With that learning disorder on DSO, where we where we sort of try and tackle. The problem is that we've identified that there's two key things for Children with dyslexia. The first one is that knowing that it is dislikes. Yeah, many being assessed. And the second is so what? What do we do about it? And so given or expertise in data science and and I, we clearly saw, unfortunately off, sort of building something that could assess individual Children and adults with dyslexia. The big problem with the assessment is that it's very expensive. We've met parents in the U. S. Specifically who paid up to 6000 U. S. Dollars for for diagnosis within educational psychologist. On the other side, we have parents who wait 12 months before having a spot. Eso What we so clearly is that the observable symptom of dyslexia are reading and everyone has a smartphone and you're smart. Smartphone is actually really good to record your voice. Eso We started collecting order recording from Children and adults who have been diagnosed with dyslexia, and we then trying a model to recognize the likelihood of this lecture by analyzing audio recording. So in theory, it's like diagnosed dyslexic, helping other undiagnosed, dyslexic being being diagnosed. So we have now an algorithm that can take about 10 minutes, which require no priors. Training cost $20. Andi, anyone can use it. Thio assess someone's likelihood off dyslexia. >>You know, this is the kind of thing that really changes the game because you also have learning progressions that air nonlinear and different. You've got YouTube. You got videos, you have knowledge bases, you've got community. Vincent mentioned that Johnny and you mentioned, you know making the bits driver and changing technology. So Jeannine and Hugo, please take a minute to explain, Okay? You got the idea. You're kicking the tires. You're putting it together. Now you gotta actually start writing code >>for us. We know education technology is not you. Right? Um, education games about you. But before we even started, we look at what's available, and we quickly realize that the digital divide is very real. Most technology out there first are not designed for really low and devices and also not designed for people who do not have Internet at hope so way. So with just that assessment, we quickly realized we need toe do something about on board, but something that that that problem is one eyes just one part of the whole puzzle. There's two other very important things. One is advocacy. Can we prove that we can teach through mobile devices, And then the second thing is motivation it again. It's also really obvious, but and people might think that, you know, uh, marginalized communities are super motivated to learn. Well, I wouldn't say that they are not motivated, but just like all of us behavioral changes really hard right. I would love to work out every day, but, you know, I don't really get identity do that. So how do we, um, use technology to and, um, you know, to induce that behavioral change so that date, so that we can help support the motivation to learn. So those are the different things that we >>welcome? >>Yeah. And then the motivated community even more impactful because then once the flywheel gets going and it's powerful, Hugo, your reaction to you know, you got the idea you got, You got the vision you're starting to put. Take one step in front of the other. You got a W s. Take us through the progression, understand the startup. >>Yeah, sure. I mean, what Jane said is very likely Thio what we're trying to do. But for us, there's there's free key things that in order for us to be successful and help as much people as we can, that is free things. The first one is reliability. The second one is accessibility, and the other one is affordability. Eso the reliability means that we have been doing a lot of work in the scientific approach as to how we're going to make this work. And so we have. We have a couple of scientific publications on Do we have to collect data and, you know, sort of published this into I conferences and things like that. So make sure that we have scientific evidence behind us that that support us. And so what that means that we had Thio have a large amount of data >>on and >>put this to work right on the other side. The accessibility and affordability means that, Julian said. You know it needs to be on the cloud because if it's on the cloud, it's accessible for anyone with any device with an Internet connection, which is, you know, covering most of the globe, it's it's a good start on DSO the clock. The cloud obviously allow us to deliver the same experience in the same value to clients and and parent and teacher and allied health professionals around the world. Andi. That's why you know, it's it's been amazing to to be able to use the technology on the AI side as well. Obviously there is ah lot of benefit off being able to leverage the computational power off off the cloud to to make better, argue with them and better training. >>We're gonna come back to both of you on the I question. I think that's super important. Benson. I want to come back to you, though, because in Asia Pacific and that side of the world, um, you still have the old guard, the incumbents around education and learning. But there is great penetration with mobile and broadband. You have great trends as a tailwind for Amazon and these kinds of opportunity with Head Start. What trends are you seeing that are now favoring you? Because with co vid, you know the world is almost kind of like been a line in the sand is before covert and after co vid. There's more demand for learning and education and community now than ever before, not just for education, the geopolitical landscape, everything around the younger generation. There's, um, or channels more data, the more engagement. How >>are you >>looking at this? What's your vision of these trends? Can you share your thoughts on how that's impacting learning and teaching? >>So there are three things that I want to quickly touch on number one. I think government are beginning to recognize that they really need to change the way they approach solving social and economic problems. The pandemic has certainly calls into question that if you do not have a digital strategy, you can't You can find a better time, uh, to now develop and not just developed a digital strategy, but actually to put it in place. And so government are shifting very, very quickly into the cloud and adopting digital strategy and use digital strategy to address some of the key problems that they are facing. And they have to solve them in a very short period of time. Right? We will talk about speed, three agility off the cloud. That's why the cloud is so powerful for government to adult. The second thing is that we saw a lot of schools closed down across the world. UNESCO reported what 1.5 billion students out of schools. So how then do you continue teaching and learning when you don't have physical classroom open? And that's where education, technology companies and, you know, heroes like Janine's Company and others there's so many of them around our ableto come forward and offer their services and help schools go online run classrooms online continue to allow teaching and learning, you know, online and and this has really benefited the overall education system. The third thing that is happening is that I think tertiary education and maybe even catch off education model will have to change. And they recognize that, you know, again, it goes back to the digital strategy that they got to have a clear digital strategy. And the education technology companies like, what? Who we have here today, just the great partners that the education system need to look at to help them solve some of these problems and get toe addressing giving a solution very, very quickly. >>Well, I know you're being kind of polite to the old guard, but I'm not that polite. I'll just say it. There's some old technology out there and Jenny and you go, You're young enough not to know what I t means because you're born in the cloud. So that's good for you. I remember what I t is like. In fact, there's a There's a joke here in the United States that with everyone at home, the teachers have turned into the I T department, meaning they're helping the parents and the kids figure out how to go on mute and how toe configure a network adds just translation. If they're routers, don't work real problems. I mean, this was technology. Schools were operating with low tech zooms out there. You've got video conferencing, you've got all kinds of things. But now there's all that support that's involved. And so what's happening is it's highlighting the real problems of the institutional technology. So, Vincent, I'll start with you. Um, this is a big problem. So cloud solves that one. You guys have pretty much helped. I t do things that they don't want to do any more by automation. This >>is an >>opportunity not necessary. There's a problem today, but it's an opportunity tomorrow. You just quickly talk about how you see the cloud helping all this manual training and learning new tools. >>We are all now living in a cloud empowered economy. Whether we like it or not, we are touching and using services. There are powered by the cloud, and a lot of them are powered by the AWS cloud. But we don't know about it. A lot of people just don't know, right Whether you are watching Netflix, um Well, in the old days you're buying tickets and and booking hotels on Expedia or now you're actually playing games on epic entertainment, you know, playing fortnight and all those kind of games you're already using and a consumer off the cloud. And so one of the big ideas that we have is we really want to educate and create awareness off club computing for every single person. If it can be used for innovation and to bring about benefits to society, that is a common knowledge that everyone needs to happen. So the first big idea is want to make sure that everyone actually is educated on club literacy? The second thing is, for those who have not embarked on a clear cloud strategy, this is the time. Don't wait for for another pandemic toe happen because you wanna be ready. You want to be prepared for the unknown, which is what a lot of people are faced with, and you want to get ahead of the curve and so education training yourself, getting some learning done, and that's really very, very important as the next step to prepare yourself toe face the uncertainty and having programs like AWS EC start actually helps toe empower and catalyzed innovation in the education industry that our two founders have actually demonstrated. So back to you Join. >>Congratulations on the head. Start. We'll get into that real quickly. Uh, head start. But let's first get the born in the cloud generation, Janine. And you go, You guys were competing. You gotta get your APS out there. You gotta get your solutions. You're born in the cloud. You have to go compete with the existing solutions. How >>do you >>view that? What's your strategy? What's your mindset? Janine will start with you. >>So for us, way are very aware that we're solving a problem that has never been solved, right? If not, we wouldn't have so many people who are not learning. So So? So this is a very big problem. And being able to liberate on cloud technology means that we're able to just focus on what we do best. Right? How do we make sure that learning is sufficient and learning is, um, effective? And how do we keep people motivated and all those sorts of great things, um, leveraging on game mechanics, social network and incentives. And then while we do that on the outside way, can just put almost out solved everything to AWS cloud technology to help us not worry about that. And you were absolutely right. The pandemic actually woke up a lot of people and hands organizations like myself. We start to get queries from governments on brother, even big NGOs on, you know, because before cove it, we had to really do our best to convince them until our troops are dry and way, appreciate this opportunity and and also we want to help people realized that in order to buy, adopting either blended approach are a adopting technology means that you can do mass customization off learning as well. And that's what could what we could do to really push learning to the next level. So and there are a few other creative things that we've done with governments, for example, with the government off East Java on top of just using the education platform as it is andare education platform, which is education game Donald Civilization. Um, they have added in a module that teaches Cove it because, you know, there's health care system is really under a lot of strain there, right and adding this component in and the most popular um mitigate in that component is this This'll game called hopes or not? And it teaches people to identify what's fake news and what's real news. And that really went very popular and very well in that region off 25 million people. So tech became not only just boring school subjects, but it can be used to teach many different things. And following that project, we are working with the federal government off Indonesia to talk about anti something and even a very difficult topic, like sex education as well. >>Yeah, and the learning is nonlinear, horizontally scalable, its network graft so you can learn share about news. And this is contextual data is not just learning. It's everything is not like, you know, linear learning. It's a whole nother ballgame, Hugo. Um, your competitive strategy. You're out there now. You got the covert world. How are you competing? How is Amazon helping you? >>Absolutely. John, look, this is an interesting one, because the current competitors that we have, uh, educational psychologist, they're not a tech, So I wouldn't say that we're competing against a competitive per se. I would say that we're competing against the old way of doing things. The challenge for us is to, um, empower people to be comfortable. We've having a machine, you know, analyzing your kids or your recording and telling you if it's likely to be dislikes. Yeah, and in this concept, obviously, is very new. You know, we can see this in other industry with, you know, you have the app that stand Ford created to diagnose skin cancer by taking a photo of your skin. It's being done in different industry. Eso The biggest challenge for us is really about the old way of doing things. What's been really interesting for us is that, you know, education is lifelong, you know, you have a big part in school, but when you're an adult, you learn on Did you know we've been doing some very interesting work with the Justice Department where, you know, we look at inmate and you know, often when people go to jail, they have, you know, some literacy difficulty, and so we've been doing some very interesting working in this field. We're also doing some very interesting work with HR and company who want to understand their staff and put management in place so that every single person in the company are empowered to do their job and and and, you know, achieve success. So, you know, we're not competing against attack. And often when we talk to other ethnic company, we come before you know, we don't provide a learning solution. We provide a assessment solution on e assessment solution. So, really, John, what we're competing against is an old way of doing things. >>And that's exactly why clouds so successful. You change the economics, you're actually a net new benefit. And I think the cloud gives you speed and you're only challenges getting the word out because the economics air just game changing. Right, So that's how Amazon does so well, um, by the way, you could take all our recordings from the Cube, interviews all my interviews and let me know how ideo Okay, so, um, got all the got all the voice recordings from my interview. I'm sure the test will come back challenging. So take a look at that e. I wanna come back to you. But I wanna ask the two founders real quick for the folks watching. Okay on Dhere about Amazon. They know the history. They know the startups that started on Amazon that became unicorns that went public. I mean, just a long list of successes born in the cloud You get big pay when you're successful. Love that business model. But for the folks watching that were in the virtual garages, air in their houses, innovating and building out new ideas. What does Ed start mean for them? How does it work? Would you would recommend it on what are some of the learnings that you have from work with Head Start? >>But our relationship X s start is almost not like client supplier relationship. It's almost like business partners. So they not only help us with protect their providing the technology, but on top of that, they have their system architect to work with my tech team. And they have, you know, open technical hours for us to interact. And on top of that, they do many other things, like building a community where, you know, people like me and Google can meet and also other opportunities, like getting out the word out there. Right. As you know, all of their, uh, startups run on a very thin budget. So how do we not pour millions of dollars into getting out without there is another big benefit as well. So, um definitely very much recommend that start. And I think another big thing is this, right? Uh, what we know now that we have covert and we have demand coming from all over the place, including, like, even a lot of interest, Ally from the government off Gambia, you know? So how do we quickly deploy our technology right there? Or how do we deploy our technology from the the people who are demanding our solution in Nigeria? Right. With technology that is almost frameless. >>Yeah. The great enabling technology ecosystem to support you. And they got the region's too. So the region's do help. I love we call them Cube Region because we're on Amazon. We have our cloud, Hugo, um, and start your observations, experience and learnings from working with aws. >>Absolutely. Look, this is a lot to say, so I'll try and making sure for anyone, but but also for us on me personally, also as an individual and as a founder, it's really been a 365 sort of support. So like Johnny mentioned, there's the community where you can connect with existing entrepreneur you can connect with expert in different industry. You can ask technical expert and and have ah, you know office our every week. Like you said Jenny, with your tech team talking to cloud architect just to unlock any problem that you may have on day and you know, on the business side I would add something which for us has been really useful is the fact that when we when we've approached government being able to say that we have the support off AWS and that we work with them to establish data integrity, making sure everything is properly secured and all that sort of thing has been really helpful in terms off, moving forward with discussion with potential plant and and government as well. So there's also the business aspect side of things where when people see you, there's a perceived value that you know, your your entourage is smart people and and people who are capable of doing great things. So that's been also really >>helpful, you know, that's a great point. The APP SEC review process, as you do deals is a lot easier. When here on AWS. Vincent were a little bit over time with a great, great great panel here. Close us out. Share with us. What's next for you guys? You got a great startup ecosystem. You're doing some great work out there and education as well. Healthcare. Um, how's your world going on? Take a minute, Thio. Explain what's going on in your world, >>John, I'm part of the public sector Team Worldwide in AWS. We have very clear mission statements on by the first is you know, we want to bring about destructive innovation and the AWS Cloud is really the platform where so many off our techs, whether it's a text, healthtech golf text, all those who are developing solutions to help our governments and our education institutions or health care institutions to really be better at what they do, we want to bring about those disruptive innovations to the market as fast as possible. It's just an honor on a privilege for us to be working. And why is that important? It's because it's linked to our second mission, which is to really make the world a better place to really deliver. Heck, the kind of work that Hugo and Janina doing. You know, we cannot do it by ourselves. We need specialists and really people with brilliant ideas and think big vision to be able to carry out what they are doing. And so we're just honored and privileged to be part off their work And in delivering this impact to society, >>the expansion of AWS out in your area has been phenomenal growth. I've been saying to Teresa Carlson, Andy Jassy in the folks that aws for many, many years, that when you move fast with innovation, the public sector and the private partnerships come together. You're starting to see that blending. And you've got some great founders here, uh, making a social impact, transforming, teaching and learning. So congratulations, Janine and Hugo. Thank you for sharing your story on the Cube. Thanks for joining. >>Thank you. Thank >>you, John. >>I'm John Furry with the Cube. Virtual were remote. We're not in person this year because of the pandemic. You're watching a divest Public sector online summit. Thank you for watching

>>Hello. My name is Isaac twang and I am on the faculty at MIT in electrical engineering and computer science and in physics. And it is a pleasure for me to be presenting at today's NTT research symposium of 2020 to share a little bit with you about programmable quantum simulators theory and practice the simulation of physical systems as described by their Hamiltonian. It's a fundamental problem which Richard Fineman identified early on as one of the most promising applications of a hypothetical quantum computer. The real world around us, especially at the molecular level is described by Hamiltonians, which captured the interaction of electrons and nuclei. What we desire to understand from Hamiltonian simulation is properties of complex molecules, such as this iron molded to them. Cofactor an important catalyst. We desire there are ground States, reaction rates, reaction dynamics, and other chemical properties, among many things for a molecule of N Adams, a classical simulation must scale exponentially within, but for a quantum simulation, there is a potential for this simulation to scale polynomials instead. >>And this would be a significant advantage if realizable. So where are we today in realizing such a quantum advantage today? I would like to share with you a story about two things in this quest first, a theoretical optimal quantum simulation, awkward them, which achieves the best possible runtime for generic Hamiltonian. Second, let me share with you experimental results from a quantum simulation implemented using available quantum computing hardware today with a hardware efficient model that goes beyond what is utilized by today's algorithms. I will begin with the theoretically optimal quantum simulation uncle rhythm in principle. The goal of quantum simulation is to take a time independent Hamiltonian age and solve Schrodinger's equation has given here. This problem is as hard as the hardest quantum computation. It is known as being BQ P complete a simplification, which is physically reasonable and important in practice is to assume that the Hamiltonian is a sum over terms which are local. >>For example, due to allow to structure these local terms, typically do not commute, but their locality means that each term is reasonably small, therefore, as was first shown by Seth Lloyd in 1996, one way to compute the time evolution that is the exponentiation of H with time is to use the lead product formula, which involves a successive approximation by repetitive small time steps. The cost of this charterization procedure is a number of elementary steps, which scales quadratically with the time desired and inverse with the error desired for the simulation output here then is the number of local terms in the Hamiltonian. And T is the desired simulation time where Epsilon is the desired simulation error. Today. We know that for special systems and higher or expansions of this formula, a better result can be obtained such as scaling as N squared, but as synthetically linear in time, this however is for a special case, the latest Hamiltonians and it would be desirable to scale generally with time T for a order T time simulation. >>So how could such an optimal quantum simulation be constructed? An important ingredient is to transform the quantum simulation into a quantum walk. This was done over 12 years ago, Andrew trials showing that for sparse Hamiltonians with around de non-zero entries per row, such as shown in this graphic here, one can do a quantum walk very much like a classical walk, but in a superposition of right and left shown here in this quantum circuit, where the H stands for a hazard market in this particular circuit, the head Mar turns the zero into a superposition of zero and one, which then activate the left. And the right walk in superposition to graph of the walk is defined by the Hamiltonian age. And in doing so Childs and collaborators were able to show the walk, produces a unitary transform, which goes as E to the minus arc co-sign of H times time. >>So this comes close, but it still has this transcendental function of age, instead of just simply age. This can be fixed with some effort, which results in an algorithm, which scales approximately as towel log one over Epsilon with how is proportional to the sparsity of the Hamiltonian and the simulation time. But again, the scaling here is a multiplicative product rather than an additive one, an interesting insight into the dynamics of a cubit. The simplest component of a quantum computer provides a way to improve upon this single cubits evolve as rotations in a sphere. For example, here is shown a rotation operator, which rotates around the axis fi in the X, Y plane by angle theta. If one, the result of this rotation as a projection along the Z axis, the result is a co-sign squared function. That is well-known as a Ravi oscillation. On the other hand, if a cubit is rotated around multiple angles in the X Y plane, say around the fee equals zero fee equals 1.5 and fee equals zero access again, then the resulting response function looks like a flat top. >>And in fact, generalizing this to five or more pulses gives not just flattered hops, but in fact, arbitrary functions such as the Chevy chef polynomial shown here, which gets transplants like bullying or, and majority functions remarkably. If one does rotations by angle theta about D different angles in the X Y plane, the result is a response function, which is a polynomial of order T in co-sign furthermore, as captured by this theorem, given a nearly arbitrary degree polynomial there exists angles fi such that one can achieve the desired polynomial. This is the result that derives from the Remez exchange algorithm used in classical discreet time signal processing. So how does this relate to quantum simulation? Well recall that a quantum walk essentially embeds a Hamiltonian insight, the unitary transform of a quantum circuit, this embedding generalize might be called and it involves the use of a cubit acting as a projector to control the application of H if we generalize the quantum walk to include a rotation about access fee in the X Y plane, it turns out that one obtains a polynomial transform of H itself. >>And this it's the same as the polynomial in the quantum signal processing theorem. This is a remarkable result known as the quantum synchrony value transformed theorem from contrast Julian and Nathan weep published last year. This provides a quantum simulation auger them using quantum signal processing. For example, can start with the quantum walk result and then apply quantum signal processing to undo the arc co-sign transformation and therefore obtain the ideal expected Hamiltonian evolution E to the minus I H T the resulting algorithm costs a number of elementary steps, which scales as just the sum of the evolution time and the log of one over the error desired this saturates, the known lower bound, and thus is the optimal quantum simulation algorithm. This table from a recent review article summarizes a comparison of the query complexities of the known major quantum simulation algorithms showing that the cubitus station and quantum sequel processing algorithm is indeed optimal. >>Of course, this optimality is a theoretical result. What does one do in practice? Let me now share with you the story of a hardware efficient realization of a quantum simulation on actual hardware. The promise of quantum computation traditionally rests on a circuit model, such as the one we just used with quantum circuits, acting on cubits in contrast, consider a real physical problem from quantum chemistry, finding the structure of a molecule. The starting point is the point Oppenheimer separation of the electronic and vibrational States. For example, to connect it, nuclei, share a vibrational mode, the potential energy of this nonlinear spring, maybe model as a harmonic oscillator since the spring's energy is determined by the electronic structure. When the molecule becomes electronically excited, this vibrational mode changes one obtains, a different frequency and different equilibrium positions for the nuclei. This corresponds to a change in the spring, constant as well as a displacement of the nuclear positions. >>And we may write down a full Hamiltonian for this system. The interesting quantum chemistry question is known as the Frank Condon problem. What is the probability of transition between the original ground state and a given vibrational state in the excited state spectrum of the molecule, the Frank content factor, which gives this transition probability is foundational to quantum chemistry and a very hard and generic question to answer, which may be amiable to solution on a quantum computer in particular and natural quantum computer to use might be one which already has harmonic oscillators rather than one, which has just cubits. This has provided any Sonic quantum processors, such as the superconducting cubits system shown here. This processor has both cubits as embodied by the Joseph's injunctions shown here, and a harmonic oscillator as embodied by the resonant mode of the transmission cavity. Given here more over the output of this planar superconducting circuit can be connected to three dimensional cavities instead of using cubit Gates. >>One may perform direct transformations on the bull's Arctic state using for example, beam splitters, phase shifters, displacement, and squeezing operators, and the harmonic oscillator, and may be initialized and manipulated directly. The availability of the cubit allows photon number resolve counting for simulating a tri atomic two mode, Frank Condon factor problem. This superconducting cubits system with 3d cavities was to resonators cavity a and cavity B represent the breathing and wiggling modes of a Triumeq molecule. As depicted here. The coupling of these moles was mediated by a superconducting cubit and read out was accomplished by two additional superconducting cubits, coupled to each one of the cavities due to the superconducting resonators used each one of the cavities had a, a long coherence time while resonator States could be prepared and measured using these strong coupling of cubits to the cavity. And Posana quantum operations could be realized by modulating the coupling cubit in between the two cavities, the cavities are holes drilled into pure aluminum, kept superconducting by millikelvin scale. >>Temperatures microfiber, KT chips with superconducting cubits are inserted into ports to couple via a antenna to the microwave cavities. Each of the cavities has a quality factor so high that the coherence times can reach milliseconds. A coupling cubit chip is inserted into the port in between the cavities and the readout and preparation cubit chips are inserted into ports on the sides. For sake of brevity, I will skip the experimental details and present just the results shown here is the fibrotic spectrum obtained for a water molecule using the Pulsonix superconducting processor. This is a typical Frank content spectrum giving the intensity of lions versus frequency in wave number where the solid line depicts the theoretically expected result and the purple and red dots show two sets of experimental data. One taken quickly and another taken with exhaustive statistics. In both cases, the experimental results have good agreement with the theoretical expectations. >>The programmability of this system is demonstrated by showing how it can easily calculate the Frank Condon spectrum for a wide variety of molecules. Here's another one, the ozone and ion. Again, we see that the experimental data shown in points agrees well with the theoretical expectation shown as a solid line. Let me emphasize that this quantum simulation result was obtained not by using a quantum computer with cubits, but rather one with resonators, one resonator representing each one of the modes of vibration in this trial, atomic molecule. This approach represents a far more efficient utilization of hardware resources compared with the standard cubit model because of the natural match of the resonators with the physical system being simulated in comparison, if cubit Gates had been utilized to perform the same simulation on the order of a thousand cubit Gates would have been required compared with the order of 10 operations, which were performed for this post Sonic realization. >>As in topically, the Cupid motto would have required significantly more operations because of the need to retire each one of the harmonic oscillators into some max Hilbert space size compared with the optimal quantum simulation auger rhythms shown in the first half of this talk, we see that there is a significant gap between available quantum computing hardware can perform and what optimal quantum simulations demand in terms of the number of Gates required for a simulation. Nevertheless, many of the techniques that are used for optimal quantum simulation algorithms may become useful, especially if they are adapted to available hardware, moving for the future, holds some interesting challenges for this field. Real physical systems are not cubits, rather they are composed from bolt-ons and from yawns and from yawns need global anti-Semitism nation. This is a huge challenge for electronic structure calculation in molecules, real physical systems also have symmetries, but current quantum simulation algorithms are largely governed by a theorem, which says that the number of times steps required is proportional to the simulation time. Desired. Finally, real physical systems are not purely quantum or purely classical, but rather have many messy quantum classical boundaries. In fact, perhaps the most important systems to simulate are really open quantum systems. And these dynamics are described by a mixture of quantum and classical evolution and the desired results are often thermal and statistical properties. >>I hope this presentation of the theory and practice of quantum simulation has been interesting and worthwhile. Thank you.

>> live from Anaheim, California. It's the queue covering nutanix dot next twenty nineteen. Brought to you by Nutanix. >> Welcome back, everyone to the cubes. Live coverage of new tannic dot Next. I'm your host. Rebecca Night, along with my co host, John Furrier, were joined by two guests for the segment. We have Julie O'Brien. She is the senior vice president of corporate marketing. Welcome, Julie. Thank you. And we have Michelle Taylor Smith, the senior director of corporate social responsibility, here in Nutanix. Thank you so much for coming on the Cube. >> Thanks for having us >> sown over sixty five hundred attendees. There were twenty thousand people who were live streaming. The key note. You have a huge audience. Congratulations on the show. What are you hoping? Attendees cut. Come away with an customers and partners who are here. What are you What is sort of the big message that you want people to come away with? >> Yeah, so I mean, this year for us, it's our tenth anniversary as a company, and we are so humbled and honored to have all of these customers and partners on the journey with us. So a big part of the show is just to say thank you for being an early builder, believer and dreamer with us, and the best is yet to come. So lots of innovation happen ng and H. C I. And really trying to show people how we convey the right partner for them as they're moving to the hybrid cloud >> D. Rogers on earlier talking about this his journey as well. And it's interesting. Just a few years ago, you were still raising money. You won't even public now your public ten years old, but there's still the entrepreneurial energy s you know, he calls it the billion dollar start up, and there's now competition. So game is on scene successes out. There's not, like, hidden in plain sight like it was just just a few years ago. You guys have doing great. Congratulations. >> Thank you. And >> now you have competition. You had loyal customers. What's next? What's the What's the big strategy and how you guys build on that momentum? What do you guys thinking about? >> Oh boy, I would say, you know, as we look at the customer journey, right state, Step one is really about modernizing your data center, and that is our sweet spot. That's where Nutanix started as a company. H c I Ray. Step two is really about How do we help customers take all that goodness what they see with the public cloud and bring that into their own private cloud. We call that an enterprise cloud and then really the next step of the journey. But a customer may already be there. Today is how to Weybridge. Multiple clouds, right and multiple clouds to customers. Could be it could be the edge, which might be an eye ot application. It could be a remote office brand shop is. So what that cloud strategy looks like for people could be very different, depending what vertical there in what industry there in. So I would say what to watch for us. And what's next is we're all headed with this next generation of many clouds, not just one. >> And you guys have a monster net promoter score, which is a score that measures loyalty. And if your customs would promote it to their peers, it's like ninety. It's like a monster's. >> It's been over ninety on average for the last five years now, which is no easy feat, and you know, we tell customers all the time. Keep us hungry. Keep us honest, right? Tell us how we're doing. And we want to keep that score high too. Because that's a great reflection of you know, how they're valuing the relationship. Not just the product, but what happens after you buy the product. So, yeah, we know, as we evolve the portfolio going from just HC ay, tio multiple products that will get harder. So we've got to start to figure out How do we bring in Sameh I Some, uh, maybe machine learning so that when you call in and you might be a flow customer and Rebecca might be in a static customer And we know how to row you to the right person the right time, which is really nice. As you know, when you call support, you want to get somebody right there who's not saying Hold on. They passed you too, Michelle. Michelle saying Hold on. Let me pass. You too, John. Right? You want an expert? I'm gonna carry you all the way through. And hopefully you heard some great stories this morning. Some of our early customers who have shared that what it's meant for them. >> So delighting customers is obviously your top priority. But but Nutanix is doing a lot of other kind of good, good in the world. I want to bring you into the conversation a little. Michelle, tell us about the heart initiative. >> Absolutely. So I've been with Nutanix for a little over six and a half years now, and this spirit of giving and caring has been with the company, actually still run channel marketing. Um, but it's been with that, though the whole time that I've been there. But about three years ago, Julie actually asked if I wanted to start dot heart or sexually start RCS o R program, which became dot heart. And it's an amazing way of giving back. In fact, last year it got incorporated officially into our values of hungry, humble and honest, done with heart. And so it absolutely is part just intrinsic in the company s. So what we do is, uh we're very conscious and aware of diversity. And so we put a lot of effort towards helping women and underrepresented groups for sue their love of technology. >> And this is also sort of ah, maybe a sub theme of the show is is that inclusion and that element to it. So talk about some of theseventies that you're having particularly to help bring up women in tech and also under upper underrepresented minorities. >> Absolutely doing it well, what he talking about, what we're doing in the booth and I could talk about the women's lunch. Yeah, absolutely. Eso one of things we are doing. So women, Onda, underrepresented groups and actually people just starting their careers don't have the same network that people with established careers have. And so what we were doing in our booth this time is for collecting career advice. And so, in effect, what we're doing is we're bringing the advice to people because they don't necessarily have the same networks to go out and ask for every piece of advice that we get. We're going to donate five dollars to an organization called Ignite, which helps high school girls become aware of and pursue careers in stem. So it's it's been great so far. I love when people come up there and there, you know, what are you doing? And all of sudden you start telling them they're like a well, they should do this and write it down. And so we're actually we have a wall. People write down their advice and we put it up on the wall. And then after the event, we're going to collect it and start putting it into a blogged. And then we also have, Ah, Twitter program that we're doing or Twitter initiative that we're doing right now that once a week, we send out some of the advice and get people tio chime in and add more advice. So it's It's been a lot of fun, >> yes, and then every dot Next for the past few, we've been doing a women in tech lunch. And so I know one of your guest speakers later today is going to be Doctor Ayana Harward, uh, from Georgia Tech on Robotics. So she's actually going to be sharing some of her thoughts on mentorship at the women's lunch. We also have a longtime Nutanix friend and adviser, Harvard Business School professor Deepak Mk Ultra, who, uh is very much focused on the art of negotiation to solve conflicts, and he's going to be talking about how to do things like how do you negotiate a salary increase some of those sweaty palm conversations that you need to have a CZ. You're moving through your career, so those are two of our speakers, and then we also have two sponsors that are also gonna be spending some time, too, from Veritas >> gas and W W t. So >> So I want to I want to put you two both on the spot. You're both women in technology, and we know about from the unfortunate headlines about just the bro culture that exists in technology. And we also know about the dearth of women leaders in this industry in this industry that is shaping our social, political, economic lives in such important ways today. So what? What is some career advice that you're going to put up there on the high? Would you what would What would you say to a young woman who is entering this field? I have got so much to say. How much time >> do we have? I think one thing that I've learned along the way sometimes, you know, women tend to be very heads down. If I do a great job, someone will notice, and I will move forward and and sometimes we're not comfortable with popping our heads up on DH, helping to market a little bit about what we have done and making sure that people see the goodness right and that might not feel right. Or it might feel like you're overly marketing yourself. But I think being able to articulate what you want and why you deserve it, er is so important. And don't view it is shooting your own horn. View it as an opportunity to share how you're contributing and where you want to see that path forward. And just don't be afraid to ask which what you want, what your ultimate >> goals are. Um, Mind falls into a principle of nutanix, which is get comfortable being uncomfortable and basically, if if you get an opportunity, go for it on day. I'll be very candid when Julie offered me this role and she said, Do you want to do CSR? I thought it meant customer service rep, and I'm like, I don't want to do it at all And, uh and then she said, Oh, no, it's it's social responsibility and I still thought I had no idea what it wass and the fact that you know Julian team. We're willing to take a chance on me doing it. But the fact of just going absolutely out of my comfort zone learning something new, trying something new on DH, just just going for it was great. And I would tell people to do that all the time and it'LL just it'LL teach you so much more even about the roles that you know about just going and doing something different will teach you so much more about yourself and about other roles so great of us way >> also hear about mentoring and paying it forward. Yes. What do you guys do there? Because a lot of younger generations coming into the workforce who don't have the scar, tissue or experience the networks are now starting to establish. This is an opportunity. >> It is a big opportunity. So Wendy Pfeiffer, who's our CIA, sits on the board of Girls in Tech, so we're very involved there. She is so warm and so uh, open about helping to keep pass on what she's learned a lot on the way to. I think anyone that you run into Nutanix is very honored and humbled to be approached as a mentor. Their number women that I mentor inside of Nutanix as well as outside of nutanix lining. It's so important to help people understand what you've learned, whether good or bad along the way, Right, because just like we're learning here dot Next with your conversations, what have you done? What have you tried? Um, you need that in in your progression and your career to know if there's anything that >> you know, I would Two things I would add is one is nobody got to where they are in their career without somebody helping them along the way. And so there's a big discussion now, which is actually what Dr Howard is going to talk about that goes beyond mentorship to sponsorship. And so how do you how do you actually help push people forward, um, and and help them in their careers? And then the other thing, too, is I was listening to something the other day. It was a really interesting conversation that before, um, there were ways that people could oppress other people in in society. And what they're saying now today, people are, is helping to oppress different groups is the fact of who you help and So when you think about who you can help think about outside of your friend's kids or you know someone, who else can you help there that wouldn't normally have access to somebody like you or somebody like, you know, in your circle or whatever, and And that's hugely helpful and without just helping the same group continue to progress generation after generation, >> paying it forward to different on >> expanding the next athletics. Exactly. So this is a hugely competitive industry, and I know that Nutanix cannot hire sales and marketing people fast enough to What are you doing? I was going to ask you, though, how do you market nutanix to prospective applicants? What is? I mean? You just talked about the ability to reinvent yourself as an employee, which is something that so many people are looking for in a long career, doing different things, being in different fields and really getting to experience other things. But what are the other? What sort of the unique selling points for for nutanix that you try to take on new people >> s o. The culture, I think, is so differentiating overall. So Michelle mentioned, you know, hungry, humble, honest with heart on. So it's our job in marketing. Teo also help our recruiting teams get that message out and not just show people. These are the words, but actually give them great stories. Michelle just put together a Superfund campaign. I don't know if it's in the >> wild yet. It's it's hitting, probably next week. This one is sitting. It >> was actually it's featuring real NUTANIX employees sharing their feelings about being at nutanix thie initial passes, all still shots. But you can actually see the fun that people are having from all ages. You know, genders. It's a really diverse fund set of actual employees. So it's really you know, in this day and age, you could get a job anywhere, right? But where is that job going to make you feel excited to get out of bed every morning? Right? And I firmly believe that's the culture that we haven't nutanix and >> way gotta. Yeah, another, I would add to that is, um, it's it's dubbed internally Is the You campaign, and it's about you matter. So how you can get, go get a job anywhere, but are you oftentimes gonna go get stuck in a corner and you're going to sit there in code, you're gonna go sit there and do that or you're working on one piece of one feature of this at Nutanix. You actually have opportunities to work on big, bold projects experience, uh, contributing and honestly mattering as as an individual, which I think is huge. And you're not just a number. >> Well, Julian Michelle, thank you both. So much for coming on the Cube. That was really, really fun. Time talking, Teo. >> Yeah. Thanks for having us. >> Thank you very much. >> I'm Rebecca Knight. For John. For her. We will have so much more from nutanix dot Next coming up in just a little bit.

>> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're having a Cube conversation in our Palo Alto studio, the conference season hasn't got to full swing yet, so we can have a little bit more relaxed atmosphere here in the studio and we're really excited, as part of our continuing coverage for the Data Makes Possible sponsored by Western Digital, looking at cool applications, really the impact of data and analytics, ultimately it gets stored usually on a Western Digital hard drive some place, and this is a great segment. Who doesn't like talking about sports, and football, and advanced analytics? And we're really excited, I have John Pollard here, he is the VP of Business Development for Zebra Sports, John, great to see you. >> Jeff, thanks for having me. >> Absolutely, so before we jump into the fun stuff, just a little bit of background on Zebra Sports and Zebra Technologies. >> Okay well, first, Zebra Technologies is a publicly traded company, we started in the late 1960s, and really what we do is we track enterprise assets in industries typically like healthcare, retail, travel and logistics, and transportation. And what we've done is take that heritage and bring that over into the world of sports, starting four years ago with our relationship with the NFL as the official player tracking technology. >> It's such a great story of an old-line company, right? based in Illinois-- >> Yeah, Lincolnshire. >> Outside of Chicago, right? RFID tags, and inventory management, and all this kind of old-school stuff. But then to take that into this really dynamic world, A, of sports, but even more, advanced analytics, which is relatively new. And we've been at it for a few years, but what a great move by the company to go into this space. How did they choose to do that? >> Well it was an opportunity that just came to them through an RFP, the NFL had investigated different technologies to track players including optical and a GPS-based technologies, and now of course with Zebra, our location and technologies are based on RFID. And so we just took the heritage and our capabilities of really working at the edge of enterprises in those traditional industries from transactional moments, to inventory control moments, to analytics at the end, and took that model and ported it over to football, and it's turned out to be a very good relationship for us in a couple of ways. We've matured as a sports business over the four years, we've developed more opportunities to take our solutions, not just in-game but moving them into the practice facilities for NFL teams, but it's also opened up the aperture for other industries to now appreciate how we can track minute types of information, like players moving around on the football field, and translating it into usable information. >> So, for the people that aren't familiar, they can do a little homework. But basically you have a little tag, a little sensor, that goes onto the shoulder pads, right? >> There's two chips. >> Two chips, and from that you can tell where that player is all the time and how they move, how they fast they move, acceleration and all the type of stuff, right? >> Correct, we put two chips inside of the shoulder pads for down linemen, or people who play with their hands on the ground, we put a third chip between the shoulder blades. Those chips communicate with receiver boxes that have been installed across the perimeter or around the perimeter of a stadium, and they blink 12 times per second. And that does tell you who's on the field, where they are on the field, and in proximity to other players on the field. And once the play starts itself, we can see how fast they're going, we can calculate change of direction, acceleration and deceleration metrics, we can also see, as you know with football, interesting information like separation from a wide receiver in defensive back, which is critical when you're evaluating players' capabilities. >> So, this started about four years ago, right? >> Yes, we started our relationship with the league in-game, four years ago. >> Okay, so I'd just love to kind of hear your take on how the evolution of the introduction of this data was received by the league, received by the teams, something they'd never had before, right? Kind of a look and feel and you can look at film, but not to the degree and the tightness of tolerances that you guys are able to deliver. >> Well, like any new technology and information resource, it takes time to first of all determine what you want to do with that information, you have an idea when you start, and then it evolves over time. And so what we started with was tagging the players themselves and during the time, what we've really enjoyed in working with the NFL is that the league has to be very pragmatic and thoughtful when introducing new technologies and information. So they studied and researched the information to determine how much of this information do they share with the clubs, how much do they share with the fans and the media, and then what type of information sharing, what does that mean in terms of impact of the integrity of the game and fair competition. So, for the first two years it was more of a research and testing type of process, and starting in 2016 you started to see more of an acceleration of that data being shared with the clubs. Each club would receive their own data for in-game, and then we would start to see some of that trickle out through the NFL's Next Gen Stats brand banner on their NFL.com site. And so then we start to see more of that and then what I think we've really seen pick up pace certainly in 2017 is more utilization of this information from a media perspective. We're seeing it more integrated into the broadcasts themselves, so you have like kind of a live tracking set of information that keeps you contextually involved in the game. >> Right. And you were involved in advanced analytics before you joined Zebra, so you've been kind of in this advanced stats world for a while. So how did it change when you actually had a real-time sensor on people's bodies? >> Yeah it does feel a bit like Groundhog Day, right? I started more in the stats and advanced analytics when I worked for STATS LLC. In 2007, I developed a piece of software for the New Orleans Saints that they used to track observational statistics to game video. And it was a similar type of experience in starting in 2009 and introducing that to teams where it took about three or four years where teams started to feel like that new information resource was not a nice to have but a need to have, a premium ingredient that they could use for game planning, and then player evaluation, and also the technology could provide them some efficiencies. We're seeing that now with the tracking data. We just returned from the NFL Combine a couple weeks ago, and what I felt in all the conversations that we had with clubs was that there was a high level of appreciation and a lot of interest in how tracking data can help facilitate their traditional scouting and player evaluation processes, the technology itself how can it make the teams more efficient in evaluating players and developing game plans, so there's a lot of excitement. We've kind of hit that tipping point, if I may, where there's general acceptance and excitement about the data and then it's incumbent upon us as a partner with the league and with the teams for our practice clients to teach them how to use the analytics and statistics effectively. >> So I'm just curious, some of the specific data points that you've seen evolve over time and also the uses. I think you were talking about a little bit off camera that originally it was really more the training staff and it was really more kind of the health of the player. Then I would imagine it evolved to now you can actually see what's going on in terms of better analysis, but I would imagine it's going to evolve where coaches are getting that feedback in real-time on a per-play basis and are making in-game adjustments based on this real-time data. >> Well technically that's feasible today but then there's the rules of engagement with the league itself, and so the teams themselves, and the coaches, and the sideline aren't seeing this tracking data live, whether it be in the booth or on the sidelines. Now in a practice environment, that's what teams are using our system for. With inside of three seconds they're seeing real-time information show up about players during practice. Let's take an example, a player during practice who's coming back from injury. You might want to monitor their output during the week as they come back and they make sure that they're ready for the game on a week to week basis. Trainers are now able to see that information and take that over to a position coach or a head coach and make them aware of the performance of the player during practice. And I think sometimes people think with tracking data it's all about managing in the health of the player and making sure they don't overwork. Where really, the antithesis of that is you can actually also identify players who aren't necessarily reaching their maximum output that will help them build throughout the week from peak performance during a game. And so a lot of teams like to say okay, I have a wide receiver, I know their max miles per hour, is, let's use an example, 20.5 miles an hour. He hasn't hit his max yet during the entire week, so let's get him into some drills and some sessions, where he can start hitting that max so that we reduce the potential for injury on game day. >> Right, another area that probably a lot of people would never think is you also put sensors on the refs. So you know not only where the refs are, but are they in the right positions technically and kind of from a best practices to make the calls for the areas that they're trying to cover. >> Right. >> There's got to be, was their a union pushback on this type of stuff? I mean there's got to be some interesting kind of dynamics going on. >> Yeah as far as the referees, I know that referees are tagged and the NFL uses that information and correlates that with the play calls themselves. We're not involved in that process but I know they're utilizing the information. In addition to the referees I should add, we also have a tag in the ball itself. >> [Jeff] That's right. >> 2017 season was the first year that we had every single game had a tagged ball. Now that tagged information in the ball was not shared with the clubs yet, the league is still researching the information, like they did with the players' stuff. A couple years of research, then they decide to distribute that to the teams and the media. So we are tracking a lot of assets, we also have tags in the first down markers and the pylons and I'll just cut to the chase, there are people who will say okay, does that mean you can use these chips and this technology to identify first down marks or when a ball might break the plane for a potential touchdown? Technically you can do that, and that's something the league may be researching, but right now that's not part of our charter with them. >> Right, so I'm just curious about the conversations about the data and the use of the data. 'Cause as you said there's a lot of raw data, and there's kind of governance issues and rules of engagement, and then there's also what types of analytics get applied on top of that data, and then of course also it's about context, what's the context of the analytics? So I wonder if you could speak to the kind of the evolution of that process, what were people looking at when you first introduced this four years ago, and how has it moved over time in terms of adding new analytics on top of that data set? >> That's one of my favorite topics to talk about, when we first started with the league and engaging teams for the practice solution or providing them analytics, they in essence got a large raw data file of XY coordinates, you can imagine (laughs) it was a gigantic hard drive-- >> Even better, XY coordinates. >> And put it into a spreadsheet and go. There was some of that early on and really what we had to do through the power of software, is develop and application platform that would help teams manage and organize this data appropriately, develop the appropriate reports, or interesting reports and analysis. And over the last two or three years I think we've really found our stride at Zebra in providing solutions to go along with the capabilities of the technology itself. So at first it was strength and conditioning coaches, plowing through this information in great detail or analytics staffs, and what we've seen over the last 24 months is director of analytics now, personnel staff, coaches as well, a broadening group of people inside of a football organization start to use this data because the software itself allows them to do so. I'll give an example, instead of just tabular information, and charts and graphs, we now take the data and we can plot them into a play field schematic, which as you know as we talked off camera you're very familiar with football, that just automates the process of what teams do today manually, is develop play cards so they can do self-study and advanced scouting techniques. That's all automated today, and not only that, it's animated because we have the tracking information and we can merge that to game video. So we're just trying to make the tools with the software more functional so everybody in the organization can utilize it beyond strength and conditioning, which is important, but now we're broadening the aperture and appealing to everybody in the organization. >> Do you do, I can just see you can do play development too, if you plug in everybody's speeds and feeds, you have a certain duration of time, you can probably AB test all types of routes, and timing on drops and now you know how hard the guy throws the ball to come up with a pretty wide array of options, I would imagine within the time window. >> Exactly, a couple of examples I could give, when we meet with teams we have every player, let's say on a team and we know all the routes they ran during an entire season. So you can imagine on a visualization tool, you can imagine, it's like a spaghetti chart of different routes and then you start breaking down the scenarios of context like we talked about earlier, it's third down, it's in the red zone, it's receptions. And so that becomes a smaller set of lines that you see on the chart. I'll tell you Jeff, when we start meeting with teams at the Combine and we start showing them their X or a primary receiver, or their slot receiver tendencies visually, they start leaning forward a bit, oh my goodness, we spend way too much time on the same route when we're targeting for touch down passes. Or we're right-handed too much, we have to change that up. That's the most gratifying thing, is that you're taking a picture and you're really illuminating and those coaches who intrinsically know that, but once they see a visual cue, it validates something in their head that either they have to change or evolve something in their game plan or their practice regimen. >> Well, that's what I was going to ask, and you lead right into it is, what are some of the things that get the old-school person or the people that just don't get that, they don't get it, they don't have the time, they don't believe it, or maybe believe it but they don't have the time, they're afraid to understand. What are some of those kind of light bulb moments when they go okay, I get it, as you said, most of the time if they're smart, it's going to be kind of a validation of something they've already felt, but they've never actually had the data in front of them. >> Right, that's exactly right. So that, the first thing is just quantifying, providing a quantifiable empirical set of evidence to support what they intrinsically know as professional evaluators or coaches. So we always say that they data itself and the technology isn't meant to be a silver bullet. It's now a new premium ingredient that can help support the processes that existed in the past and hopefully provide some efficiency. And so that's the first thing, I think the visual, the example I showed about the wide receiver tendencies when they're thrown to in the red zone, that always gets people leaning forward a little bit. Also with running backs, third down in three plus yards, or third down in short situations, and my right-hander to left-hander when I'm on a certain hash. Again the visualization just allows them to really mark something in their head-- >> Just in the phase. >> Where it makes them really understand. Another example that's interesting is players who play on special teams who are also wide receivers, so as we know, linebackers and tight ends tend to be, and quarterbacks tend to be involved in special teams. Well is there an effect when they've covered kick offs and punts, a large amount of those in a game, did that affect them on side a ball play, for instance? Think about Julian Edelman two Superbowls ago, he played 93 snaps against the Atlanta Falcons. and when you look at the route-- >> [Jeff] He played 93 snaps? >> Yeah, between special, because it went into overtime, right? It was an offensive game-- >> And he's on all the-- >> He played a lot of snaps, he played 93 snaps. how does that affect his route integrity? Not only the types and quality of the route, but the depth and speed he gets to those points, those change over time. So this type of information can give the experts just a little bit more information to find that edge. And I have a great mentor of mine, I have to bring him up, Gill Brant, former VP of Personnel to Dallas Cowboys, with Tex Schramm and Tom Landry, he looks at this type of information and he says, what would a team pay for one more victory? >> So as we know, all coaches and professional organizations and college are looking for an edge, and if we can provide that with our technology through efficiencies and some type of support information resource then we're doing our job. >> I just wanted to, before I let you go, just the human factors on that. I mean, football coaches are notoriously crazy workers and, right, you can always watch more films. So now you're adding a whole new category of data and information. How's that being received on their side? Is it, are they going to have to put new staff and resources against this? I mean, there's only so many hours in a day and I can't help but think of the second tier or third tier coaches who are going to be on the hook for going through this. Or can you automate so much of it so it's not necessarily this additional burden that they have to take on? 'Cause I would imagine if the Cowboys are doing it, the Eagles got to do it, the Giants got to do it, and the Washington Redskins got to do it, right? >> Right, right, well each team as you might expect, their cultures are different. And I would say two or three years ago you started to see more teams hire literally by title, director of analytics, or director of football information, instead of sharing that responsibility between two or three people that already existed in the organization. So that staffing I think occurred a couple, two or three years ago or over the last two or three years. This becomes another element for those staffs to work with. But also along that process over the last two or three years is, really, I always try to say in talking to teams and I'll be on the road again here soon talking to clubs after pro days conclude, is forget about staffs and analytics and that idea. Do you want to be information driven, and do you want to be efficient? And that's something everybody can grasp onto, whether you're the strength and conditioning coach, personnel staff or scout, or a position coach, or a head coach, or a coordinator. So we try to be information driven, and then that seems to ease the process of people thinking I have to hire more people. What I really need to do is ask my people that are already in place to maybe be more curious about this information, and if we're going to invest in a resource that can help support them and make them more efficient, make sure we leverage it. And so that's our process that we work with, it varies by team, some teams have large, large expansive staffs. That doesn't necessarily mean, in my opinion the most effective staff is using information. Sometimes it's the organizations that run very lean with a few set of people, but very focused and moving in one direction. >> I love it, data for efficiency, right? In God we trust, everybody else bring data. One of my favorite lines that we hear over and over and over at these shows. >> In fact, I might borrow that next week. >> You could take that one, alright. >> Thank you, Jeff. >> Well John, thanks for taking a few minutes and stopping by and participating in this Western Digital program, because it is all about the data and it is about efficiency, so it's not necessarily trying to kill people with more tools, but help them be better. >> That's what we're trying to do, I appreciate the opportunity and love to talk to you more. >> Absolutely, well hopefully we'll see you again. He's John Pollard, I'm Jeff Frick, you're watching theCUBE from Palo Alto studios, thanks for watching, we'll see you next time. (Upbeat music)