(upbeat music) >> Hi everybody, this is Dave Vellante. And welcome to this CUBE conversation where we welcome an ESG senior analyst, Bob Laliberte Bob, good to see you. >> Great to see you too. Thanks for having me >> Love it, I love to have the analyst sessions. Set it up. What's your scope, what's your area of expertise? >> So my coverage area right now is networking in its entirety. So that spans everything from enterprise networking, wired, wireless, campus, data center, et cetera. All the way up through telco and, in cloud networking. >> So how do you look at the landscape? One of the big things I think about a lot is how does the shift to cloud migration? How does that affect the existing, network layers? I mean, you got Cisco as the big whale and it's just, it's amazing to me. They still have whatever percent market share they have 60, 65% of the market. Are things, what's happening in the competitive landscape. How is cloud affecting that? >> That's a great question. I think the interesting piece is so many times organizations think about the network as plumbing. But the reality is the it's really important plumbing because as you talk about cloud and things get more distributed, well, guess what connects those distributed locations? It's the network. And so organizations as they've moved to the cloud you've seen a big shift with things like SD-WAN and so forth. How do I get more efficient connectivity up to that cloud? How do I not only enable able better connectivity between my data centers in the cloud, but now all my remote workers in the cloud. And so there's been a lot of big shifts going on that have driven the importance of having not only network, but secure networks. So like I said, cloud is one thing, and you're moving your applications there. But with the pandemic you saw the remote work. Think about the network administrators who we're managing, hey, I've got to control network connections between my data centers, a couple clouds and maybe dozens maybe a hundred remote branches. And now I'm connecting to 10,000 micro branches that I need to ensure that they can connect up to these applications and so forth. Hell of a lot more complex environment today than it used to be for these network teams. When we look at the, what we're seeing, how the networking providers are responding it's by driving comprehensive end-to-end solutions. So unifying, wired, wireless, and WAN. Driving efficiencies there. You're seeing even ThousandEyes for Cisco and things like that. Because they know the Internet's becoming more integral part of the corporate network. So being able to drive those types of things being able to, I think look at how to drive those operational efficiencies through AI and ML. So one of the big shifts we've seen in networking is the transition to cloud-based network management. And obviously that couple of things that helps with, first of all, the operations teams who are working remotely can more easily access it. But once all that data is up in the cloud, it creates a platform to be able to invest in AI/ML, and be able to drive intelligent alerting and even automation. And that's really what's needed because as the environments get more distributed and complex, you need to have that those operational efficiencies that automation, that intelligence to help them. >> How has remote work and hybrid work affected sort of network, spending priorities. Obviously when the pandemic hit you had to accommodate end points. And I always have this theory okay, when people come back to the office and I know it's going to be a different world but, the HQ probably needs some love as well. So has that been a tailwind for the industry? >> Absolutely, that's what we're seeing now. I think when the pandemic first hit, everyone said I've got to ramp up my VPNs. I've got to scale out my concentrators. I've got to add more firewalls in my data center. And then after a while, when they realized this was here to stay, they said, okay we just created that hub-and-spoke network that we just got rid of with SD-WAN. So what are the better solutions we can implement? So now you're seeing them not only implement better networking solutions for the remote workers. But reimagining what the campus looks like. Because it's not going to be ever 100% full or maybe it will, but how, for how many times a year will it be 100% full? So you've got to go from 80% cubes and 20% conference and collaboration areas, to 80% collaboration areas and 20% cubes. So we're seeing a lot of transition taking place in the campus environment as organizations are deploying newer technologies like Wi-Fi 6E. That have greater bandwidth to allow for those collaboration apps to run in those collaboration areas. Instead of just having the single wired conference room for video. Everyone's got to be able to run their video, voice and video collaboration apps. >> So how do you look at the landscape now? Again, you can't talk about networking without talking about Cisco. I think they, up there, I saw you and Zeus as talking about out, Cisco's quarter and other networking topics. Their long term guidance is for 60% growth for a company that size that's really outstanding. I mean, Cisco's, really has always been an execution machine of course. And it's a new era now under Chuck. There are more than ankle biters. If you look at Arista's doing pretty well there's guys like Extreme, there's others that are out there but nobody seemed to be able to unseat Cisco. What's happening in the landscape? >> I mean, that's a great question. Cisco's just been around for so long and been so big for so long. And you have to also keep in mind that with Cisco it's not just about the technology, but the fact from a if you think about it from a cultural standpoint these are workers who have been trained on Cisco since, some of them since high school. The educational component that Cisco has done has groomed generations of network technologists. So when they come into the market, they're fully familiar and used to Cisco. Plus they make a really good product and they've got products that cover everything. They cover the whole gambit. So they're still able to maintain their share. They're able to grow. They're able to move. They've made a shift last year. They announced in last spring that they were going to focus more on end-to-end. So instead of just having, hey, here's a point product, here's a point product. Here's a point product. Let's think about it in its entirety. Let's deliver a complete end-to-end solution solve bigger problems for customers, which obviously makes it much harder to remove when you're just trying to remove a piece of that single problem. But the other competitors are also having good years. And I think also the rising tide floats all boats. And so because of this distributed nature, the importance of the network, everyone is doing that. Plus obviously this has to be said, the supply chain issues where people are ordering ahead as well. But organizations, you look at Arista, they've gone from just being a data center company to expanding all the way down to the campus edge, wireless, right there creating an end-to-end environment Extreme did the same thing. They went out and made a lot of acquisitions. They pulled them all together, integrated. They're all moving to this cloud based end-to-end network management. Arista has been on a tear, bringing in a lot of, not only innovative technology, but innovative technologists. So if you look at some of the organizations they bought. I keep calling it Route 128, it's 128 Technologies. So sorry folks I live in Massachusetts. It's always been Route 128. >> You Remember when don't we. 128 Technology's Mist was their big. Mist was their, Mist was kind of like their VMware. VMware to EMC was Mist was to Juniper. And so we call it the Mistification of Juniper where every organization, every company they bring in they're rolling under that and this the AI engine. So they're bringing in 128 Technologies into that. They've got their own, their own stuff under that, their wired switches. So they've got this unified wired and wireless and WAN assurance now that they have. They've been gaining a lot of traction with that. And again, for the things we were talking about because it's far more distributed and complex. You need to have, It's not like people are getting replaced. It's not like, hey, we're leveraging this automation so that we can get rid of network teams. It's because it's getting so much more complex just to have the same number of people manage that more complex environment. We need those intelligence solutions. >> So I want to ask you about network and multi-cloud. And so it's kind of tongue in cheek because we coined this term super cloud. And so what we meant by that, so here's the premise. And I wonder you could give us your perspective. Multi-cloud, I've said many times is I think largely a symptom of multi-vendor I run in this, I run in AWS or, Azure, I've done the work to understand their primitives and or Google, whatever it is. But it's not like an abstraction layer that's floating above all those but now you're starting to see that. In fact, it re:Invent in November. The ecosystem it seemed like was everybody was focused on developing what we call these super clouds. And again, it's tongue in cheek, this abstraction layer it hides the underlying complexity of the primitives and the APIs adds incremental value on top of that. So there's a company Prosimo, which Steve Herrod, is invested in and others Praveen Akkiraju, whom I'm sure you know from Viptela. Aviatrix is another company that's sort of, Steve Malaney has come on theCUBE and talked about what they're doing. Like yeah, that's super cloud. It seems like it's something new and different than just multi-cloud which is kind of connecting in to different clouds. It's that value on top. What do you think about that? And what does that mean for networking? >> That's a really good point because we are starting to see the inception of organizations going beyond having multiple cloud providers and looking at starting to deploy applications across multiple clouds. It's still really early. The vast majority of organizations are still, I use this application for this cloud and this application for that cloud. But that's the next frontier. That's what they're trying to solve is how do I create this basically cloud fabric and make it as simple as possible. And again, all the things we've been talking about how do I, instead of you having to learn Amazon, Google, Azure networking technology, learn mine, I'll take care of it, but I'll abstract all that complexity from you and make it so much simpler to be able to connect to these interconnect, and connect to them in a seamless fashion. And so that's what they're really trying to do is they're. And the hard part is it takes really sophisticated solutions to remove that high level of complexity and make it simple for an organization to do that. So yeah, absolutely. >> If I had more time I'd make it shorter as somebody who writes a lot. And I think you're right. I think it is future. It's not definitely not here today, but the other thing is it ties into digital transformation. We used this again, throw that buzzword around but, companies not just tech company, I mean everybody's becoming like a tech company, but organizations, financial services companies, healthcare they're building their own clouds on top of the hyperscalers who spend $100 billion a year on CapEx. And that seems to be a trend that I think is going to take legs over this next decade. Just like in the previous decade everybody was thinking, okay, we're going to SaaSify our business softwares (indistinct) the world. And now it's software and cloud services are the way in which I'm going to create customer experiences. >> Correct, yeah. It's why should I go out and make an investment in technology when the technology's already there? And I can rent it for when I need it scale it as I need it and, and do all of that. I agree with that. I think that's something that we're seeing. The interesting part though is that when we look at our data points, probably let than 40% of the applications and workloads are in the cloud today. So there's still a role that the corporate data center plays. We are seeing over time. They expect that to progress and transition but I think there's still always going to be maybe a quarter of the workloads and applications may never leave. Depending on how they're built, et cetera. So there's always going to be that distributed environment where you've got workloads in the private data centers, workloads in multiple public clouds. And also, the big thing too is don't forget about the edge. We're seeing a lot more edge activity take place as organizations recognize, as they deploy more IOT devices, and want to get realtime business insights they've got to deploy the compute there. >> Well, and that's something that I wanted to ask you about, but going back to what you just said, which is, I agree with you. So that suggests to me, Bob that we're just kind of, with cloud just entering the steep part of the S curve. Amazon's headed toward $100 billion, run rate business. Maybe they probably won't get there this year but they will next year. We're entering that steep growth phase, really could be. It's incredible. But I wanted to ask you about the edge. Because you're right is we got to move compute to the edge, ARM is going to dominate. I would think, the edge. They already are with our smartphones. How do you see the cloud guys participating in the edge? Whether it was Andy Jassy, or now Adam Selipsky or anybody at Amazon. They have the dogma of in the fullness of time all workloads are going to be in the cloud. So they either have to change their definition of cloud. Or they're wrong. So what's your thought on that? >> I think it really starts coming down to what's your definition of edge. And so, much like when the cloud technologies first came about and you had all the shadow IT. Everyone running off, and everyone thought oh this is all great, until you realized you had to operationalize it and you had to pull the brakes. Stop doing that. We're going to make sure IT operations. >> Call the CIO up. Exactly, finding out where stuff was by going through accounting and seeing credit card charges. For the edge what we've seen I think is maybe organizations really saying I've got to deploy my servers in my own site. Right at that edge in order to get the lowest possible latency. And so what I think we're starting to see is organizations looking at that and saying, okay well I'm in a metro and I've got 25 locations in a metro. And I've deployed technology to every single one of those sites. Do I need it there? Or can I put it in an Equinix facility that's less than five milliseconds from all 25 sites? So I think there's starting to be this pragmatic approach of looking at let's look at the edge, let's take a look at what type of latencies. What is our definition of real time. When do we actually need the data and so forth? What kind of connectivity do we have? And then from there figure out how we go about connecting it. And so for companies like AWS and Google and Azure a lot of them there's local zones and things like that. They're deploying them in those colos because they don't have data centers in every metro but they can leverage an Equinix. They can leverage someone else's hardware that's there to deploy their software stack within that location. So I think that's something that we're starting to see more and more of as the edge. And obviously the association with the telcos as well. They've got a great footprint. If you want to get close to the edge with their colos Their home offices and things like that and whatnot. Their ability to move the compute closer to the edge, the base stations of the antennas and things like that, are certainly significant. And that's why you're seeing the wavelengths and things like that, programs like that. >> So I was going to close, but there some really interesting topics you just brought up. Call it whatever you going to call it near edge, far edge or deep edge. And you mentioned real time. Yeah. So for those Equinix data centers, I don't need, true real time. But for Tesla, I need real time. I need real time inference at the edge probably using a bunch of ARM cores and I can't go back to any cloud. How do you look at that? Both, I would think big markets. Do you have a sense as to, is one bigger than the other? Are they both just enormous or we don't even know yet. >> I'm not sure that we know yet. I think certainly, it's riding the tail of the IOTs. So the more sensors, the more things that are deployed the more that, that data businesses realize they can leverage that data to make real time business insights to drive either better experiences. And if you're in retail. So location based services and real time offer management it doesn't do any good to offer a coupon for something that you've, that's 40 yards behind you. That that's past, like you said with the cars there's, I've seen some studies recently. They say, well, based on the latency, if the command is to stop and you're at one millisecond, it stops within four inches. If you are at 50 milliseconds, it stops 10 feet later. That's a big difference. And I don't know if those numbers are right but you get the idea about the impact, what the real time impact is of. >> Margin is not huge. >> Exactly, so that's where organizations, I think first and foremost need to take a pragmatic approach to determine what is real time for us. What's our definition of it. And then that can lead them to where do I need to place this compute technology? And then that goes to how do I then connect to it? So for the Teslas and so forth, obviously you're going to want 5G connections if possible. Ultra low latency and not just any 5G. The good stuff, the millimeter bandwidth stuff that that's the ultra low latency. >> So let's wrap. So, what's going on in your research world obviously the big, big acquisition tech target they seem to be investing in ESG. You guys are really growing and hiring. That's awesome. Any research that you're working on? >> Yeah, there's a couple of couple of projects we have going on right now. We're wrapping up a four part distributed cloud research series. So we did it on distributed cloud infrastructure. Applications, observability. And now this last one is on the edge. Coincidentally. So we're working on that. We've got some new network modernization research that we've published. And we're going to be looking, from a networking perspective looking at end-to-end network modernization which will be coming out soon. >> Awesome, Bob, thanks so much for coming on theCUBE. I really would love to have you back and chat about some of those things. Observability hot space. God, I wish we had more time. >> Absolutely, appreciate it, thanks. >> And thank you for watching this CUBE conversation. This is Dave Vellante and we'll see you next time. (upbeat music)

(upbeat music) >> Edge computing is projected to be a multi-trillion dollar business. It's hard to really pinpoint the size of this market let alone fathom the potential of bringing software, compute, storage, AI and automation to the edge and connecting all that to clouds and on-prem systems. But what is the edge? Is it factories? Is it oil rigs, airplanes, windmills, shipping containers, buildings, homes, race cars. Well, yes and so much more. And what about the data? For decades we've talked about the data explosion. I mean, it's a mind-boggling but guess what we're going to look back in 10 years and laugh what we thought was a lot of data in 2020. Perhaps the best way to think about Edge is not as a place but when is the most logical opportunity to process the data and maybe it's the first opportunity to do so where it can be decrypted and analyzed at very low latencies. That defines the edge. And so by locating compute as close as possible to the sources of data to reduce latency and maximize your ability to get insights and return them to users quickly, maybe that's where the value lies. Hello everyone and welcome to this CUBE conversation. My name is Dave Vellante and with me to noodle on these topics is Omer Asad, VP and GM of Primary Storage and Data Management Services at HPE. Hello Omer, welcome to the program. >> Thanks Dave. Thank you so much. Pleasure to be here. >> Yeah. Great to see you again. So how do you see the edge in the broader market shaping up? >> Dave, I think that's a super important question. I think your ideas are quite aligned with how we think about it. I personally think enterprises are accelerating their sort of digitization and asset collection and data collection, they're typically especially in a distributed enterprise, they're trying to get to their customers. They're trying to minimize the latency to their customers. So especially if you look across industries manufacturing which has distributed factories all over the place they are going through a lot of factory transformations where they're digitizing their factories. That means a lot more data is now being generated within their factories. A lot of robot automation is going on, that requires a lot of compute power to go out to those particular factories which is going to generate their data out there. We've got insurance companies, banks, that are creating and interviewing and gathering more customers out at the edge for that. They need a lot more distributed processing out at the edge. What this is requiring is what we've seen is across analysts. A common consensus is this that more than 50% of an enterprises data especially if they operate globally around the world is going to be generated out at the edge. What does that mean? New data is generated at the edge what needs to be stored. It needs to be processed data. Data which is not required needs to be thrown away or classified as not important. And then it needs to be moved for DR purposes either to a central data center or just to another site. So overall in order to give the best possible experience for manufacturing, retail, especially in distributed enterprises, people are generating more and more data centric assets out at the edge. And that's what we see in the industry. >> Yeah. We're definitely aligned on that. There's some great points and so now, okay. You think about all this diversity what's the right architecture for these multi-site deployments, ROBO, edge? How do you look at that? >> Oh, excellent question, Dave. Every customer that we talked to wants SimpliVity and no pun intended because SimpliVity is reasoned with a simplistic edge centric architecture, right? Let's take a few examples. You've got large global retailers, they have hundreds of global retail stores around the world that is generating data that is producing data. Then you've got insurance companies, then you've got banks. So when you look at a distributed enterprise how do you deploy in a very simple and easy to deploy manner, easy to lifecycle, easy to mobilize and easy to lifecycle equipment out at the edge. What are some of the challenges that these customers deal with? These customers, you don't want to send a lot of IT staff out there because that adds cost. You don't want to have islands of data and islands of storage and promote sites because that adds a lot of states outside of the data center that needs to be protected. And then last but not the least how do you push lifecycle based applications, new applications out at the edge in a very simple to deploy manner. And how do you protect all this data at the edge? So the right architecture in my opinion needs to be extremely simple to deploy so storage compute and networking out towards the edge in a hyper converged environment. So that's we agree upon that. It's a very simple to deploy model but then comes how do you deploy applications on top of that? How do you manage these applications on top of that? How do you back up these applications back towards the data center, all of this keeping in mind that it has to be as zero touch as possible. We at HPE believe that it needs to be extremely simple, just give me two cables, a network cable, a power cable, fire it up, connect it to the network, push it state from the data center and back up it state from the edge back into the data center, extremely simple. >> It's got to be simple 'cause you've got so many challenges. You've got physics that you have to deal, you have latency to deal with. You got RPO and RTO. What happens if something goes wrong you've got to be able to recover quickly. So that's great. Thank you for that. Now you guys have heard news. What is new from HPE in this space? >> Excellent question, great. So from a deployment perspective, HPE SimpliVity is just gaining like it's exploding like crazy especially as distributed enterprises adopted as it's standardized edge architecture, right? It's an HCI box has got storage computer networking all in one. But now what we have done is not only you can deploy applications all from your standard V-Center interface from a data center, what have you have now added is the ability to backup to the cloud right from the edge. You can also back up all the way back to your core data center. All of the backup policies are fully automated and implemented in the distributed file system that is the heart and soul of the SimpliVity installation. In addition to that, the customers now do not have to buy any third-party software. Backup is fully integrated in the architecture and it's then efficient. In addition to that now you can backup straight to the client. You can back up to a central high-end backup repository which is in your data center. And last but not least, we have a lot of customers that are pushing the limit in their application transformation. So not only, we previously were one-on-one leaving VMware deployments out at the edge site now evolved also added both stateful and stateless container orchestration as well as data protection capabilities for containerized applications out at the edge. So we have a lot of customers that are now deploying containers, rapid manufacture containers to process data out at remote sites. And that allows us to not only protect those stateful applications but back them up back into the central data center. >> I saw in that chart, it was a line no egress fees. That's a pain point for a lot of CIOs that I talked to. They grit their teeth at those cities. So you can't comment on that or? >> Excellent question. I'm so glad you brought that up and sort of at the point that pick that up. So along with SimpliVity, we have the whole Green Lake as a service offering as well, right? So what that means Dave is, that we can literally provide our customers edge as a service. And when you compliment that with with Aruba Wired Wireless Infrastructure that goes at the edge, the hyperconverged infrastructure as part of SimpliVity that goes at the edge. One of the things that was missing with cloud backups is that every time you back up to the cloud, which is a great thing by the way, anytime you restore from the cloud there is that egress fee, right? So as a result of that, as part of the GreenLake offering we have cloud backup service natively now offered as part of HPE, which is included in your HPE SimpliVity edge as a service offering. So now not only can you backup into the cloud from your edge sites, but you can also restore back without any egress fees from HPE's data protection service. Either you can restore it back onto your data center, you can restore it back towards the edge site and because the infrastructure is so easy to deploy centrally lifecycle manage, it's very mobile. So if you want to deploy and recover to a different site, you could also do that. >> Nice. Hey, can you, Omer, can you double click a little bit on some of the use cases that customers are choosing SimpliVity for particularly at the edge and maybe talk about why they're choosing HPE? >> Excellent question. So one of the major use cases that we see Dave is obviously easy to deploy and easy to manage in a standardized form factor, right? A lot of these customers, like for example, we have large retailer across the US with hundreds of stores across US, right? Now you cannot send service staff to each of these stores. Their data center is essentially just a closet for these guys, right? So now how do you have a standardized deployment? So standardized deployment from the data center which you can literally push out and you can connect a network cable and a power cable and you're up and running and then automated backup, elimination of backup and state and DR from the edge sites and into the data center. So that's one of the big use cases to rapidly deploy new stores, bring them up in a standardized configuration both from a hardware and a software perspective and the ability to backup and recover that instantly. That's one large use case. The second use case that we see actually refers to a comment that you made in your opener, Dave, was when a lot of these customers are generating a lot of the data at the edge. This is robotics automation that is going up in manufacturing sites. These is racing teams that are out at the edge of doing post-processing of their cars data. At the same time there is disaster recovery use cases where you have campsites and local agencies that go out there for humanity's benefit. And they move from one site to the other. It's a very, very mobile architecture that they need. So those are just a few cases where we were deployed. There was a lot of data collection and there was a lot of mobility involved in these environments, so you need to be quick to set up, quick to backup, quick to recover. And essentially you're up to your next move. >> You seem pretty pumped up about this new innovation and why not. >> It is, especially because it has been taught through with edge in mind and edge has to be mobile. It has to be simple. And especially as we have lived through this pandemic which I hope we see the tail end of it in at least 2021 or at least 2022. One of the most common use cases that we saw and this was an accidental discovery. A lot of the retail sites could not go out to service their stores because mobility is limited in these strange times that we live in. So from a central recenter you're able to deploy applications. You're able to recover applications. And a lot of our customers said, hey I don't have enough space in my data center to back up. Do you have another option? So then we rolled out this update release to SimpliVity verse from the edge site. You can now directly back up to our backup service which is offered on a consumption basis to the customers and they can recover that anywhere they want. >> Fantastic Omer, thanks so much for coming on the program today. >> It's a pleasure, Dave. Thank you. >> All right. Awesome to see you, now, let's hear from Red Bull Racing an HPE customer that's actually using SimpliVity at the edge. (engine revving) >> Narrator: Formula one is a constant race against time Chasing in tens of seconds. (upbeat music) >> Okay. We're back with Matt Cadieux who is the CIO Red Bull Racing. Matt, it's good to see you again. >> Great to see you Dave. >> Hey, we're going to dig in to a real world example of using data at the edge in near real time to gain insights that really lead to competitive advantage. But first Matt tell us a little bit about Red Bull Racing and your role there. >> Sure. So I'm the CIO at Red Bull Racing and at Red Bull Racing we're based in Milton Keynes in the UK. And the main job for us is to design a race car, to manufacture the race car and then to race it around the world. So as CIO, we need to develop, the IT group needs to develop the applications use the design, manufacturing racing. We also need to supply all the underlying infrastructure and also manage security. So it's really interesting environment that's all about speed. So this season we have 23 races and we need to tear the car apart and rebuild it to a unique configuration for every individual race. And we're also designing and making components targeted for races. So 23 and movable deadlines this big evolving prototype to manage with our car but we're also improving all of our tools and methods and software that we use to design make and race the car. So we have a big can-do attitude of the company around continuous improvement. And the expectations are that we continue to say, make the car faster. That we're winning races, that we improve our methods in the factory and our tools. And so for IT it's really unique and that we can be part of that journey and provide a better service. It's also a big challenge to provide that service and to give the business the agility of needs. So my job is really to make sure we have the right staff, the right partners, the right technical platforms. So we can live up to expectations. >> And Matt that tear down and rebuild for 23 races, is that because each track has its own unique signature that you have to tune to or are there other factors involved? >> Yeah, exactly. Every track has a different shape. Some have lots of straight, some have lots of curves and lots are in between. The track surface is very different and the impact that has on tires, the temperature and the climate is very different. Some are hilly, some have big curbs that affect the dynamics of the car. So all that in order to win you need to micromanage everything and optimize it for any given race track. >> COVID has of course been brutal for sports. What's the status of your season? >> So this season we knew that COVID was here and we're doing 23 races knowing we have COVID to manage. And as a premium sporting team with Pharma Bubbles we've put health and safety and social distancing into our environment. And we're able to able to operate by doing things in a safe manner. We have some special exceptions in the UK. So for example, when people returned from overseas that they did not have to quarantine for two weeks, but they get tested multiple times a week. And we know they're safe. So we're racing, we're dealing with all the hassle that COVID gives us. And we are really hoping for a return to normality sooner instead of later where we can get fans back at the track and really go racing and have the spectacle where everyone enjoys it. >> Yeah. That's awesome. So important for the fans but also all the employees around that ecosystem. Talk about some of the key drivers in your business and some of the key apps that give you competitive advantage to help you win races. >> Yeah. So in our business, everything is all about speed. So the car obviously needs to be fast but also all of our business operations need to be fast. We need to be able to design a car and it's all done in the virtual world, but the virtual simulations and designs needed to correlate to what happens in the real world. So all of that requires a lot of expertise to develop the simulations, the algorithms and have all the underlying infrastructure that runs it quickly and reliably. In manufacturing we have cost caps and financial controls by regulation. We need to be super efficient and control material and resources. So ERP and MES systems are running and helping us do that. And at the race track itself. And in speed, we have hundreds of decisions to make on a Friday and Saturday as we're fine tuning the final configuration of the car. And here again, we rely on simulations and analytics to help do that. And then during the race we have split seconds literally seconds to alter our race strategy if an event happens. So if there's an accident and the safety car comes out or the weather changes, we revise our tactics and we're running Monte-Carlo for example. And use an experienced engineers with simulations to make a data-driven decision and hopefully a better one and faster than our competitors. All of that needs IT to work at a very high level. >> Yeah, it's interesting. I mean, as a lay person, historically when I think about technology in car racing, of course I think about the mechanical aspects of a self-propelled vehicle, the electronics and the light but not necessarily the data but the data's always been there. Hasn't it? I mean, maybe in the form of like tribal knowledge if you are somebody who knows the track and where the hills are and experience and gut feel but today you're digitizing it and you're processing it and close to real time. Its amazing. >> I think exactly right. Yeah. The car's instrumented with sensors, we post process and we are doing video image analysis and we're looking at our car, competitor's car. So there's a huge amount of very complicated models that we're using to optimize our performance and to continuously improve our car. Yeah. The data and the applications that leverage it are really key and that's a critical success factor for us. >> So let's talk about your data center at the track, if you will. I mean, if I can call it that. Paint a picture for us what does that look like? >> So we have to send a lot of equipment to the track at the edge. And even though we have really a great wide area network link back to the factory and there's cloud resources a lot of the tracks are very old. You don't have hardened infrastructure, don't have ducks that protect cabling, for example and you can lose connectivity to remote locations. So the applications we need to operate the car and to make really critical decisions all that needs to be at the edge where the car operates. So historically we had three racks of equipment like I said infrastructure and it was really hard to manage, to make changes, it was too flexible. There were multiple panes of glass and it was too slow. It didn't run our applications quickly. It was also too heavy and took up too much space when you're cramped into a garage with lots of environmental constraints. So we'd introduced hyper convergence into the factory and seen a lot of great benefits. And when we came time to refresh our infrastructure at the track, we stepped back and said, there's a lot smarter way of operating. We can get rid of all the slow and flexible expensive legacy and introduce hyper convergence. And we saw really excellent benefits for doing that. We saw up three X speed up for a lot of our applications. So I'm here where we're post-processing data. And we have to make decisions about race strategy. Time is of the essence. The three X reduction in processing time really matters. We also were able to go from three racks of equipment down to two racks of equipment and the storage efficiency of the HPE SimpliVity platform with 20 to one ratios allowed us to eliminate a rack. And that actually saved a $100,000 a year in freight costs by shipping less equipment. Things like backup mistakes happen. Sometimes the user makes a mistake. So for example a race engineer could load the wrong data map into one of our simulations. And we could restore that DDI through SimpliVity backup at 90 seconds. And this enables engineers to focus on the car to make better decisions without having downtime. And we sent two IT guys to every race, they're managing 60 users a really diverse environment, juggling a lot of balls and having a simple management platform like HPE SimpliVity gives us, allows them to be very effective and to work quickly. So all of those benefits were a huge step forward relative to the legacy infrastructure that we used to run at the edge. >> Yeah. So you had the nice Petri dish in the factory so it sounds like your goals are obviously number one KPIs speed to help shave seconds, awesome time, but also cost just the simplicity of setting up the infrastructure is-- >> That's exactly right. It's speed, speed, speed. So we want applications absolutely fly, get to actionable results quicker, get answers from our simulations quicker. The other area that speed's really critical is our applications are also evolving prototypes and we're always, the models are getting bigger. The simulations are getting bigger and they need more and more resource and being able to spin up resource and provision things without being a bottleneck is a big challenge in SimpliVity. It gives us the means of doing that. >> So did you consider any other options or was it because you had the factory knowledge? It was HCI was very clearly the option. What did you look at? >> Yeah, so we have over five years of experience in the factory and we eliminated all of our legacy infrastructure five years ago. And the benefits I've described at the track we saw that in the factory. At the track we have a three-year operational life cycle for our equipment. When in 2017 was the last year we had legacy as we were building for 2018, it was obvious that hyper-converged was the right technology to introduce. And we'd had years of experience in the factory already. And the benefits that we see with hyper-converged actually mattered even more at the edge because our operations are so much more pressurized. Time is even more of the essence. And so speeding everything up at the really pointy end of our business was really critical. It was an obvious choice. >> Why SimpliVity, why'd you choose HPE SimpliVity? >> Yeah. So when we first heard about hyper-converged way back in the factory, we had a legacy infrastructure overly complicated, too slow, too inflexible, too expensive. And we stepped back and said there has to be a smarter way of operating. We went out and challenged our technology partners, we learned about hyperconvergence, would enough the hype was real or not. So we underwent some PLCs and benchmarking and the PLCs were really impressive. And all these speed and agility benefits we saw and HPE for our use cases was the clear winner in the benchmarks. So based on that we made an initial investment in the factory. We moved about 150 VMs and 150 VDIs into it. And then as we've seen all the benefits we've successfully invested and we now have an estate in the factory of about 800 VMs and about 400 VDIs. So it's been a great platform and it's allowed us to really push boundaries and give the business the service it expects. >> Awesome fun stories, just coming back to the metrics for a minute. So you're running Monte Carlo simulations in real time and sort of near real-time. And so essentially that's if I understand it, that's what ifs and it's the probability of the outcome. And then somebody got to make, then the human's got to say, okay, do this, right? Was the time in which you were able to go from data to insight to recommendation or edict was that compressed and you kind of indicated that. >> Yeah, that was accelerated. And so in that use case, what we're trying to do is predict the future and you're saying, well and before any event happens, you're doing what ifs and if it were to happen, what would you probabilistic do? So that simulation, we've been running for awhile but it gets better and better as we get more knowledge. And so that we were able to accelerate that with SimpliVity but there's other use cases too. So we also have telemetry from the car and we post-process it. And that reprocessing time really, is it's very time consuming. And we went from nine, eight minutes for some of the simulations down to just two minutes. So we saw big, big reductions in time. And ultimately that meant an engineer could understand what the car was doing in a practice session, recommend a tweak to the configuration or setup of it and just get more actionable insight quicker. And it ultimately helps get a better car quicker. >> Such a great example. How are you guys feeling about the season, Matt? What's the team's sentiment? >> I think we're optimistic. Thinking our simulations that we have a great car we have a new driver lineup. We have the Max Verstapenn who carries on with the team and Sergio Cross joins the team. So we're really excited about this year and we want to go and win races. And I think with COVID people are just itching also to get back to a little degree of normality and going racing again even though there's no fans, it gets us into a degree of normality. >> That's great, Matt, good luck this season and going forward and thanks so much for coming back in theCUBE. Really appreciate it. >> It's my pleasure. Great talking to you again. >> Okay. Now we're going to bring back Omer for quick summary. So keep it right there. >> Narrator: That's where the data comes face to face with the real world. >> Narrator: Working with Hewlett Packard Enterprise is a hugely beneficial partnership for us. We're able to be at the cutting edge of technology in a highly technical, highly stressed environment. There is no bigger challenge than Formula One. (upbeat music) >> Being in the car and driving in on the limit that is the best thing out there. >> Narrator: It's that innovation and creativity to ultimately achieves winning of this. >> Okay. We're back with Omer. Hey, what did you think about that interview with Matt? >> Great. I have to tell you, I'm a big formula One fan and they are one of my favorite customers. So obviously one of the biggest use cases as you saw for Red Bull Racing is track side deployments. There are now 22 races in a season. These guys are jumping from one city to the next they got to pack up, move to the next city, set up the infrastructure very very quickly. An average Formula One car is running the thousand plus sensors on, that is generating a ton of data on track side that needs to be collected very quickly. It needs to be processed very quickly and then sometimes believe it or not snapshots of this data needs to be sent to the Red Bull back factory back at the data center. What does this all need? It needs reliability. It needs compute power in a very short form factor. And it needs agility quick to set up, quick to go, quick to recover. And then in post processing they need to have CPU density so they can pack more VMs out at the edge to be able to do that processing. And we accomplished that for the Red Bull Racing guys in basically two of you have two SimpliVity nodes that are running track side and moving with them from one race to the next race to the next race. And every time those SimpliVity nodes connect up to the data center, collect up to a satellite they're backing up back to their data center. They're sending snapshots of data back to the data center essentially making their job a whole lot easier where they can focus on racing and not on troubleshooting virtual machines. >> Red bull Racing and HPE SimpliVity. Great example. It's agile, it's it's cost efficient and it shows a real impact. Thank you very much Omer. I really appreciate those summary comments. >> Thank you, Dave. Really appreciate it. >> All right. And thank you for watching. This is Dave Volante for theCUBE. (upbeat music)

>> live from Boston, Massachusetts. It's the Cube covering A W s reinforce 2019 brought to you by Amazon Web service is and its ecosystem partners come >> back, Everyone live Coverage of AWS reinforced their first conference, The Cube here in Boston. Messages some jumper. MacOS David Lattin escapes Jesse rusting >> CT on co >> founder of Extra Cube alumni. Great to see you again. VM World Reinvent >> Now the new conference reinforce not a team. A >> summit reinforced a branded event around Cloud security. This is in your wheelhouse. >> Thank you for having me. Yeah, it's a spectacular event. Unbelievable turnout. I think there's 8000 people here. Maybe more. I know that's what they were expecting for an event that was conceived of, or at least announced barely six months ago. The turnout's just >> wait. Many conversation in the past on the Cube and others cloud security now having its own conference. It's not like a like a security conference like Black at Def Con, which is like a broader security. This is really focused on cloud security and the nuances involved for on premises and cloud as it's evolving. It's certainly a lot more change coming on this kind of spins into your direction you would talking this year in the front end. >> It absolutely does. First, it speaks to market demand. Clearly, there was demand for a cloud security focused conference, and that's why this exists. Every survey that I've seen lists security extremely high on the list of anxieties or even causes for delay for shifting workloads to the cloud. So Amazon takes security extremely seriously. >> And then my own personal >> view is that cloud security has been somewhat nascent and immature. And we're seeing, you know, hopefully kind of Ah, somewhere rapid, a >> lot of motivation in that market. Certainly a lot of motivated people want to see it go faster and there spitting in building that out. So I gotta ask >> you before you get off the show, I actually say something if I may. I mean, it's been a long time coming. Yeah, this to your point, Jesse. There was a real need for it, and I think Amazon deserves a lot of credit for that. But at the same time, I think Amazon. There's a little criticism there. I mean, I think that the message that reinvent that's always been we got the best security. We got the most features as I come on in, and the whole theme here of the shared responsibility model, which I'd love to get into, I think was somewhat misunderstood by some of those high high level messaging. So I didn't want to put that out there as a topic that we might touch on. Great. Let's talk about it. Okay, so I do think it was misunderstood. The shared responsibility model. I think the messaging was Hey, the cloud is more secure than your existing data centers. Come on in. And I think a lot of people naively entered waters and then realized, Oh, wait a minute. There's a lot that we still have toe secure. We can't just set it and forget it. I mean, you agree with that? >> I I think that's a controversial topic. I do agree with it. I think it continues to be misunderstood. Shared responsibility model in some ways is Amazon saying We're going the security infrastructure and we're going to give you the tools. But organizations air still expected to follow best practices, certainly, and implement their own, hopefully best in class security operations. >> It's highly nuanced. You can say sharing data see increases visibility into into threats and also of making quality alerts. But I think it's a little bit biased, Dave for Amazon to satiate responsibility because they're essentially want to share in the security posture because they're saying we'll do this. You do that as inherently shared. So why wouldn't they say that? >> Well, I guess we're gonna say way want to own everything? Well, I guess my weight So this show is that I really like their focus on that. I think they shone a light on it and for the goodness of the the industry in the community they have. But it is a bit >> nuanced, and they've said some controversial, perhaps even trajectory statements. In the keynote yesterday, I was I was amused to hear that security is everybody everyone's job, which is something I wholeheartedly believe in. But at the same time, you know, David said that he didn't believe Stephen Step Rather said that he didn't believe in depth set cops, and that seemed a little bit of odds because I but I think they're probably really Steven Schmidt. Steven >> so eight of us. But at the same time, there was a narrative around. Security is code. So, yes, there were some contradictions in messaging, so this smaller remains small ones. They were nuanced but remains some confusion. And that's why people look to the ecosystem to help acorns. And this goes back to >> my earlier point. I I believe that cloud security is really quite nascent. When we look at the way we look at the landscape of vendors, we see a number of vendors that really are kind of on Prem security solutions. They're trying to shoehorn into the cloud way, see a lot of essentially vulnerability scanning and static image scanning. But wait, don't see, in my opinion, that much really best in class security so solutions. And I think until relatively recently it was very hard to enable some of them. And that's why I'd love to talk about the VPC traffic marrying announcement, because I think that was actually the most impactful announcement >> that I want to get to it. So So this is ah, a new on the way. By the way, the other feedback up ahead on the Cube is the sessions here have been so good because you can dig deeper than what you can get it re invent given tries. This is a good example. Explained that the that story because this has been one of the most important stories, the traffic mirroring >> well, unlike >> reinvent. I think this show is Is Maura about education than it is about announcements? No, Amazon announced. A few new service is going into G ET, but these were service is, for the most part, that we already knew you were coming here like God Watchtower in security hub. But the BBC traffic mirroring was really the announcement of this show. And, gosh, it's been a long time in coming 11 closely held belief I've had for a long time is that in the fullness of time, there's really nothing of value that that you can do on Prem that you wouldn't eventually be able to do in the cloud. And it's just been a head scratcher for me. WIFE. For so many years, we've been unable to get any sort of view, mirror or tap of the traffic for diagnostic or analytic purpose is something you could do on prim so easily, with a span porter and network tap and in the cloud we've been having to do kind of back flips and workarounds and software taps and things like that. But with this announcement, it's finally here. It's native >> explain VPC Chapman. What is it for? The folks watching might not know it. Why it's wife. What is it and why is it important? >> So BBC traffic marrying is a network tap that is built into E. C. To networking. What it means is that you can configure a V p c traffic mirror four individual E C two instances actually down to the e n I. Level. You can configure filters and you can send that to a target for analysis purposes. And this analysis could be for diagnostics. But I think much more important is for security. Extra hop is is really began as a network analytics platform way do network detection and response. So this type of this ability to analyze the traffic in real time to run predictive models against it to detect in real time suspicious behaviors and potential threats, I think is absolutely game changing for someone security posture. >> And you guys have been on the doorstep of this day in day out. So this is like a great benefit to you guys. As a company, I can see that. I see That's a great thing for you guys. What's the impact of the customers? Because what is the good news that comes out of the traffic nearing for them? What's the impact of their environment? >> Well, it's all about >> friction. First, I wantto clarify that we've been running in a WS for over six years, six or seven years, so we've had that solution. But it's required some friction in the deployment process because our customers had to install some sort of software tap, which was usually an agent, that was analyzing that there was really gathering the packets in some sort of promiscuous mood and then sending them to us in a tunnel. Where is now? This is This is built into the service into the infrastructure. There's no performance penalty at all. You can configure it. You have I am rolls and policies to secure it. All of the friction goes away. I think, for the kind of the first time in in cloud history, you can now get extremely high quality network security analytics with practically the flip of a switch. >> So It's not another thing do manage. It's like you say, inherit to the network. John and I have heard this this week at this event from practitioners that they want to see less just incremental security products and Maur step function and what they mean by that is way want products that actually take action or give us a script that we can implement, or or actually fix the problem for us. Will this announcement on others that you guys were involved in take that next step more proactive security that these guys so a couple of thoughts >> on that first, the answer is yes, it can, and you're absolutely right. Remediation is extremely important, especially for attacks that they're fast and destructive. When you think about kind of the when you think about attack patterns, their attacks are low and slow. Their attacks their advanced in persistent but the taxes, air fast and destructive movie the speed that is really beyond the ability for humans to respond. And for those sorts of attacks, I think you absolutely need some sort of automated remediation. The most common solutions are some form of blocking the traffic, quarantining the traffic or maybe locking the accounts, and you're kind of blocking. Quarantining and locking are my top three, and then various forms of auditing and forensics go along the way. Amazon actually has a very good tool box for that already. And there are security orchestration, products that can help. And for products like extra hop, the ability to feed a detection into an action is actually a trivial form of integration that we offer out of the box. So the answer is yes. >> But let me go >> back to kind of the incrementalist approach as well that you mentioned. I kind of think about the space and really, really broad strokes and organizations for the last 10 years or so have really highly invested in prevention and protection. So a lot of this is your perimeter defense and in point protection, and the technologies have gotten better. Firewalls have turned into next generation firewalls and antivirus agents have turned into next generation anti virus or in point detection and response. But I strongly believe that network security has and in some ways just kind of lagged behind, and it's really ripe for innovation. And that's why that's what we've really spent the last decade >> building. And that's why you're excited about the traffic BPC traffic nearing because it allows for parallel analytics and so more real time, >> more real >> time. But the network has great properties that nothing else has. When you think about network security with the network itself is close to ground Truth as you can get, it's very hard to tamper with, and it's impossible to turn off those air great properties for cyber security. And you can't say that about something like that. Logs, which are from time to time disabled and scrubbed on. You certainly can't say that about en Pointe agents, which are often worked around and in some cases even used as a better for attack. >> I'm gonna ask you Okay, on that point, I get that. So the next question would come to my mind is okay with the surface here. With coyote expanding and with cloud, you have a sprawling surface area. So the surface area is growing just by default by natural evolution, connecting to the cloud people of back hauling their data into the cloud. All this is good stuff. >> Absolutely. Call it the attack surface, and it is absolutely glowing perhaps in an exponential >> about that dynamic, one sprawling attack air. Because that's just the environment now. And what's the best practice to kind of figure out security posture? Great, great >> question. People talk a lot about the dissolution of the perimeter, and I think I think that's a bit of the debate. And regardless of your views on that, we can all believe that the perimeter is changing and that workloads are moving around and that users are becoming more mobile. But I think an extremely important point is that every enterprise just about is hybrid. So we actually need protection for a hybrid attack surface. And that's an area where I believe extra hop offers a great solution because we have a solution that runs on premises in physical data centers are on campuses, which, no matter how much work, would you move to the cloud. You still have some sort of user on some sort of laptop or some sort of work station in some sort of campus environment, way workin in private cloud environments that are virtualized. And then, of course, we work in public cloud environments, and another announcement that we just made it this show, which I also think is game changing, is our revealed ex cloud offering. So this is an SAS. This is a sass based, network detection and response solution, which means that I talked about removing friction by marrying the traffic. But in this case, all >> you have to >> do is mirror the traffic, pointed to our sass, and we'll do all of the management mean that So is that in the streets for you that is in the marketplace. We launched it yesterday, >> So it's great integration point for you guys. Get it, get on board more customers. >> And I think I think solutions like ours are absolutely best practices and required to secure this hybrid attacks in the >> marketplace. What was that experience like, you know, Amazon >> was actually great to work with. I don't mean to say that with disbelief. You work with you work with such a large company. You kind of have certain expectations, and they exceeded all of my expectations in terms of their responsiveness. They worked with us extremely closely to get into the marketplace. They made recommendations with partners who could help accelerate our efforts. But >> in addition to the >> marketplace, we actually worked with them closely on the VPC traffic marrying feature. There was something we began talking with them about a SW far back, as I think last December, even before reinvent, they were extremely responsive to our feedback. They move very, very quickly. They've actually just >> been a delight to work. There's a question about you talking about the nana mutability of logs, and they go off line sometimes. And yet the same time there's been tens of $1,000,000,000 of value creation from that industry. Are there things that our magic there or things that you can learn from the analytics of analyzing logs that you could bring over to sort of what you're positioning is a more modern and cloud like approach? Or is there some kind of barrier to entry doing that? Can you shed some light on Jesse? That's >> a great question, and this is where I'll say it's a genius of the end situation, not a tyranny of the or so I'm not telling people. Don't collect your logs or analyze them. Of course you should do that, you know that's the best practice. But chances are that that space, you know, the log analysis and the, you know, the SIM market has become so mature. Chances are you're already doing that. And I'm not gonna tell organizations that they shouldn't have some sort of point protection. Of course you should. But what I am saying is that the network itself is a very fundamental data source that has all of those properties that are really good for cyber security and the ability that analyze what's going on in your environment in real time. Understand which users air involved? Which resource is air accessed? And are these behavioral patterns of suspicious and do they represent potential threats? I think that's very powerful. I have a I have a whole threat research team that we've built that just runs attacks, simulations and they run attack tools so that we can take behavioral profiles and understand what these look like in the environment. We build predictive models around how we expect you re sources and users and end points to behave. And when they deviate from those models, that's how we know something suspicious is going on. So this is definitely a a genius of the end situation. John >> reminds me of your you like you're very fond of saying, Hey, what got you here is not likely to move you forward. And that's kind of the takeaway for practitioners is >> yeah. I mean, you gotta build on your success. I mean, having economies of scale is about not having Disick onyx of scale, meaning you always constantly reinventing your product, not building on the success. And then you're gonna have more success if you can't trajectory if you it's just basic competitive strategy product strategy. But the thing that's interesting here is is that as you get more successful and you continue to raise the bar, which is an Amazon term, they work with you better. So if you're raising the bar and you did your own network security probably like OK, now we get parallel traffic mirroring so that >> that's true. But I think we've also heard the Amazon is I think they caught maniacally customer focused, right? And so I think that this traffic marrying capability really is due to customer demand. In fact, when you when you were if you were at the Kino when they made the announcement, that was the announcement where I feel like every phone in the in the whole auditorium went up. That's the announcement where I think there's a lot of excitement and for security practitioners in particular, and SEC ops teams I think this. I think this really reduces some anxiety they have, because cloud workloads really tend to be quite opaque. You have logs, you have audit logs, but it's very difficult to know what actually going on there and who is actually accessing that environment. And, even more important, where is my data going? This is where we can have all sorts of everything from a supply chain attack to a data exfiltration on. It's extremely important to to be able to have that visibility into these clouds >> way agree. We've been saying on the cue many, many years now that the network is the last bottleneck, really, where that script gets flipped upside down where Workloads air dictating Dev ops. Now the network piece is here, so I think this is going to create a lot of innovation. That's our belief. Love to follow up Mawr in Palo Alto. When we get back on this hybrid cloud, I think that's a huge opportunity. I think there's a create a blind spot for companies because that's where the the attackers will go, because they'll know that the hybrids rolling out and that'll be a vulnerability area >> one that's, you know, it's an arms race. Network security is not new. It's been around for decades. But the attack the attackers in the attacks have become more sophisticated, and as a result, you know the defenders need to raise their game as well. This is why, on the one hand, there's there's so much hype and I think machine learning in some ways is oversold. But in other ways, it is a great tool in our arsenal. You know, the machine learning the predictive models, the behavioral models, they really do work. And it really is the next evolution for defensive >> capabilities. Thanks for coming on. Great insight. >> One last question. The beer. Extra guys have been here way did in the past. It's been a while since >> we've done that, but it comes from early days when when I founded the company, people would ask you in the name extra hoppy. Oh, are you guys an online brewery? And we were joking. We said no, that that was extra hops way embraced it and We actually worked with a local brewer that has since been acquired by a major beverage brands. I >> don't know that. I just heard way built our own >> label, and it was the ex Rob Wired P. A. It was it was extremely well received. Every time we visit a customer they'd ask us to bring here. >> That's pretty. You gotta go back to proven formula. Thanks for the insights. Let's follow up when we get back in Palo Alto in our studio on his high breathing's a compelling conversation network Security Network analytics innovation areas where all the action's happening here in Boston, 80 best reinforced. Keep coverage. We'll be right back.

>> Live from New York, it's theCUBE. Covering theCUBE New York City 2018. Brought to you by Silicon Angle Media and its ecosystem partners. >> Hi, welcome back. I'm your host Sonia Tagare with my cohost Dave Vellante, and we're here at theCUBE NYC covering everything big data, AI, and the cloud. And this week is also New York Fashion Week, and with us today we have a guest who intersects both of those technologies, so Billie Whitehouse, CEO of Wearable X, thank you so much for being on. >> It's a pleasure, thank you for having me. >> Great to see you. >> Thank you. >> So your company Wearable X, which intersects fashion and technology, tell us more about that. >> So Wearable X started five years ago. And we started by building clothes that had integrated haptic feedback, which is just vibrational feedback on the body. And we really believe that we can empower clothing with technology to do far more than it ever has for you before, and to really give you control back of your life. >> That's amazing. So can you tell us more about the haptic, how it works and what the technology is about? >> Absolutely. So the haptics are integrated with accelerometers and they're paired through conductive pathways around the body, and specifically this is built for yoga in a line called NadiX. And Nadi is spelled N-A-D-I. I know that I have a funny accent so sometimes it helps to spell things out. They connect and understand your body orientation and then from understanding your body orientation we pair that back with your smartphone and then the app guides you with audio, how to move into each yoga pose, step by step. And at the end we ask you to address whether you made it into the pose or not by reading the accelerometer values, and then we give you vibrational feedback where to focus. >> And the accelerometer is what exactly? It's just a tiny device... Does it protrude or is it just...? >> I mean it's as invisibly integrated as we can get it so that we can make it washable and tumble-dryable. >> So I know I rented a car recently, big SUV with the family and when I started backing up or when I get close to another car, it started vibrating. So is it that kind of sensation? It was sort of a weird warning but then after a while I got used to it. It was kind of training me. Is that-- >> Precisely. >> Sort of the same thing? And it's just the pants or the leggings, or is it the top as well? >> So it's built in through the ankles, behind the knees and in the hip of the yoga pants, and then we will release upper body work as well. >> Alright, so let's double click on this. So if I'm in a crescent pose and I'm leaning too far forward, will it sort of correct me or hit me in the calf and say, "Put your heel down," or how would that work? >> Exactly. So the audio instructions will give you exactly the kind of instructions you would get if you were in a class. And then similarly to what you would get if you had a personal instructor, the vibrations will show you where to isolate and where to ground down, or where to lift up, or where to rotate, and then at the end of the pose, the accelerometer values are read and we understand whether you made it into the pose or whether you didn't quite get there, and whether you're overextended or not. And then we ask you to either go back and work on the pose again or move forward and move on to the next pose. >> That is amazing. I usually have to ask my daughters or my wife, "Is this right?" And then they'll just shake their heads. Now what do you do with the data? Do you collect the data and can I review and improve, feed it back? How does that all work? >> So the base level membership, which is free, is you don't see your progress tracking as yet. But we're about to release our membership, where you pay $10 a month, and with that you get progress tracking as a customer. Us on the back end, we can see how often people make it into particular poses. We can also see which ones they don't make it into very well, but we don't necessarily share that. >> And so presumably it tracks other things besides, like frequency, duration of the yoga? >> Exactly. Minutes of yoga, precisely right. >> Different body parts, or not necessarily? >> So the accelerometers are just giving us an individual value, and then we determine what pose you're in, so I don't know what you mean by different body parts? >> In other words, which parts of my body I'm working out or maybe need to work on? >> Oh precisely. Yeah if you're overextending a particular knee or an ankle, we can eventually tell you that very detailed. >> And how long have you been doing this? >> It's five years. >> Okay. And so what have you learned so far from all this data that you've collected? >> Well I mean, I'm going to start from a human learning first, and then I'll give you the data learnings. The human learning for me is equally as interesting. The language on the body and how people respond to vibration was learning number one. And we even did tests many years ago with a particular product, an upper body product, with kids, so aged between eight and 13, and I played a game of memory with them to see if they could learn and understand different vibrational sequences and what they meant. And it was astounding. They would get it every single time without fail. They would understand what the vibrations meant and they would remember it. For us, we are then trying to replicate that for yoga. And that has been a really interesting learning, to see how people need and understand and want to have audio cues with their vibrational feedback. From a data perspective, the biggest learning for us is that people are actually spending between 13 to 18 minutes inside the app. So they don't necessarily want an hour and a half class, which is what we originally thought. They want short, quick, easy-to-digest kind of flows. And that for me was very much a learning. They're also using it at really interesting times of the day. So it's before seven AM, in the middle of the day between 11 and three, and then after nine PM. And that just so happens to be when studios are shut. So it makes sense that they want to use something that's quick and easy for them, whether it's early morning when they have a big, full day, or late night 'cause they need to relax. >> Sounds like such a great social impact. Can you tell us more about why you decided to make this? >> Yeah, for me there was a personal problem. I was paying an extraordinary amount to go to classes, I was often in a class with another 50 people and not really getting any of the attention that I guess I thought I deserved, so I was frustrated. I was frustrated that I was paying so much money to go into class and not getting the attention, had been working with haptic feedback for quite some time at that point, realized that there was this language on the body that was being really underutilized, and then had this opportunity to start looking at how we could do it for yoga. Don't get me wrong, I had several engineers tell me this wasn't possible about three and a half years ago, and look at us now, we're shipping product and we're in retail and it's all working, but it took some time. >> So you're not an engineer, I take it? >> I am not an engineer. >> You certainly don't dress like an engineer, but you never know. What's your background? >> My background is in design. And I truly think that design, for us, has always come first. And I hope that it continues to be that way. I believe that designers have an ability to solve problems in, dare I say, in a horizontal way. We can understand pockets of things that are going on, whether it's the problem, whether it's ways to solve the solutions, and we can combine the two. It's not just about individual problem solving on a minute level; it's very much a macro view. And I hope that more and more designers go into this space because I truly believe that they have an ability to solve really interesting problems by asking empathetic questions. >> And how does the tech work? I mean, what do you need besides the clothing and the accelerometers to make this work? >> So we have a little device called the pulse. And the pulse has our Bluetooth module and our battery and our PCB, and that clips just behind the left knee. Now that's also the one spot on the body that during yoga doesn't get in the way, and we have tested that on every body shape you can imagine across five different continents, because we wanted to make sure that the algorithms that we built to understand the poses were going to be fair for everybody. So in doing that, that little pulse, you un-clip when you want to wash and dry. >> And is that connected to the app as well? >> Exactly, that's connected via Bluetooth to your app. >> That's great. So you have all your data in your hand and you know exactly what kind of yoga poses you're doing, where you need to strengthen up. >> Exactly. >> That's great. >> And is it a full program? In other words, are there different yoga programs I can do, or am I on my own for that? How does that work? >> So with the base level membership, you can choose different yoga instructors around New York that you'd like to follow, and then you can get progress tracking, you can get recommendations, and they are timed between that 10 to 20 minutes. If you want to pay the slightly more premium membership, you can actually build your own playlists, and that's something that our customers have said they're really interested in. It means that you can build a sequence of poses that is really defined by you, that is good for your body. So that means instead of going to a class where you end up getting a terrible teacher, or music that you don't like, you can actually build your own class and then share that with your friends as well. >> Is it a Spotify-like model, where the teachers get compensation at the back end, or how does that all work? >> Exactly. Yes, precisely. >> And what do you charge for this? >> So the pants are $250, and then the base level membership is $10 a month, and then the slightly more premium is $30 a month. >> If you think about how much you would spend for a yoga class, that actually seems like a pretty good deal. >> And trust me, when you start calculating, when you go to yoga at least once a week, and it's $20 a week and then you're like, "Oh, and I went every week this year," you realize that it racks up very quickly. >> Well plus the convenience of doing it... I love having... To be able to do it at six a.m. without having to go to a class, especially where I live in Boston, when it's cold in the winter, you don't even want to go out. (all laughing) >> So what do you think the future of the wearable industry is? >> This is a space that I get really excited about. I believe in a version of the future, which has been titled "enchanted objects." And the reason I sort of put it in inverted commas is I think that often has sometimes a magical element to it that people think is a little too far forward. But for me, I really believe that this is possible. So not only do I believe that we will have our own body area network, which I like to call an app store for the body, but I believe every object will have this. And there was a beautiful Wired article last month that actually described why the Japanese culture are adopting robotics and automation in a way that western culture often isn't. And that is because the Shinto religion is the predominant religion in Japan, and they believe that every object has a soul. And if in believing that, you're designing for that object to have a soul and a personality and an ecosystem, and dare we call it, a body area network for each object, then that area network can interface with yours or mine or whoever's, and you can create this really interesting communication that is enchanted and delightful, and not about domination. It's not about screens taking over the world and being in charge of you, and us being dominated by them, as often we see in culture now. It's about having this really beautiful interface between technology and objects. And I really believe that's going to be the version of the future. >> And looking good while you do it. >> Precisely. >> You've got visions to take this beyond yoga, is that right? Other sports, perhaps cycling and swimming and skiing, I can think of so many examples. >> Exactly. Well for us, we're focused on yoga to start with. And certainly areas that I would say are in the gaps. I like to think of our products as being very touch-focused and staying in areas of athleisure or sports that are around touch. So where you would get a natural adjustment from a coach or a teacher, our products can naturally fit into that space. So whether it is squats or whether it is Pilates, they're certainly in our pipeline. But in the immediate future, we're certainly looking at the upper body and in meditation, and how we can remind you to roll your shoulders back and down, and everyone sits up straight. And then longer term, we're looking at how we can move this into physiotherapy, and so as you mentioned, you can enter in that you have a left knee injury, and we'll be able to adjust what you should be working on because of that. >> Is there a possibility of a breathing component, or is that perhaps there today? Such an important part of yoga is breathing. >> 100%. That is very much part of what we're working on. I would say more silently, but very much will launch soon. >> Well it sounds like it's going to have such a positive impact on so many people and that it's going to be in so many different industries. >> I hope so. Yeah that's the plan. >> Well Billie Whitehouse, thank you so much for being on theCUBE, and Dave, thank you. We're here at theCUBE NYC, and stay tuned, don't go anywhere, we'll be back. (inquisitive electronic music)

(techno music) >> (Narrator) Live from Washington, DC. It's Cube conversations with John Furrier. (techno music) >> Hello and welcome to the special exclusive Cube Conversations here in Washington, DC. I'm John Furrier host of the Cube. Here at Amazon Web Services Headquarter World Headquarters for Public Sector Summit in Arlington, Virginia. Our special guest is Tricia Davis-Muffett, who is the Director of Marketing for Worldwide Amazon Web Services. Thanks for joining me. >> Yep. >> So we see each other and reinvent Public Sector Summit, but you're always running around. You got so many things going on. >> I am. >> Big responsibility here. (Tricia laughs) >> You guys are running hard and you have great culture, Teresa's team. Competitive, like to have fun. Don't like to lose. (Tricia laughs) >> What's it like being a marketer for the fastest growing hottest product in Washington, DC and around the world? >> Yeah. I mean it's really been amazing. When I came here, I kind of took a leap of faith on the company because it's four and a half years ago that I came. I literally accepted the job before we had even gotten our first fed ramp approval. So it wasn't entirely sure that this was going be the place to go to for technology for the government, but I really loved the way that we were helping the government innovate and save money of course. I think most of us who are in Public Sector have a passion for citizens, and for making government better and so that's really what I saw in Teresa and her team that they had such a passion to do that and that the technology was going to help the government really improve the lives of citizens. It's been great. One of the things that's been amazing is the passion that our customers have for our technology. I think they get a little taste of it and they go "Wow, I can't believe what I can do "that I thought was impossible before." And so I love seeing what our customers do with the technology. >> It's something people would think might be easy to be a marketer for Amazon, but if you think about it, you have so much speed in your business. You have a cult of personality in the Cloud addiction, or Cloud value. In addition to the outcomes that are happening. >> Uh huh. >> We're a customer and one kind of knows that's pretty biased on it. We've seen the success ourselves, but you guys have a community. Everywhere you go, you're seeing Amazon as they take more territory down. Public Cloud originally, and now Enterprise, and Public Cloud, Public Sector Enterprise, Public Cloud. Each kind of wave of territory that Amazon goes in to Amazon Web Services, is a huge community. >> Yeah. >> And so that's another element. I mean Public Sector Summit last year it felt like Reinvent. So this years going to be bigger. >> Yeah. We had 65 hundred plus people attend last year, just in the Washington DC area and we've also expanded that program now and we are taking our Public Sector Summit specifically for government education non-profit around the world. So this year we will be in Brussels, and Camber, Australia. We have great adoption in Australia as well with the government there. In Singapore, Ottawa. So we're really expanding quite a bit and helping governments around the world to adopt. >> So if that's a challenge, how are you going to handle that because you guys have always been kind of with Summits. Do you coattail Summits? Do you go separate? >> No. We go separate. We actually have the Public Sector Summits we take the experience of our technology to government towns that wouldn't typically get a Summit. So for instance here in the United States of course, San Francisco and New York there's a lot of commercial businesses. We have our big Summits there, but there's not as much commercial business here in Washington DC, so really Public Sector takes the lead here. And then we focus on some of the things that really are most important to our Public Sector customers. Things like, procurement and acquisition. Things like the security and compliance that's so critical in the government sector. And then also, we do a really careful job of curating our customers, because we know that our government customers want to hear from each other. They want to hear from people who are blazing a trail within the Public Sector. They don't necessarily want to hear about what we want to say. They want to hear what their peers are doing with the technology. So last year, we had over a hundred of our Public Sector customers speaking to each other about what they were doing with the Cloud. >> And I find that's impressive. I actually commented on the Cube that week that it's interesting you let the customers do the talking. I mean, that's the best ultimate sign of success and traction. >> Yeah. And the great thing is, you know I've worked in other places in the Public Sector and government customers can be kind of shy about talking about what they're doing. You know, there are very motivated to just keep things going calmly, quietly, you know get their jobs done. But I think... >> Well, it doesn't hurt when you have the top guy at the CIA say, "Best decision we've ever made." "It's the most innovative thing we've ever done." I mean talk about being shy. >> Yeah. >> That's the CIA, by the way. That's the CIA. And we've also had, people like NASA JPL who've been very outspoken. Tom Soderstrom said that it was conservatively 1/100th of the cost of what it would have been if he had built out the infrastructure himself to build the infrastructure for his Mars landing. I mean that kind of... >> It just keeps giving. You lower prices. Okay I got to change gears, because a couple things that I've observed to every Reinvent, as being a customer and I think I've used Amazon I first came out as an entrepreneur. (inaudible) had no URL support, but that's showing my age. (Tricia laughs) But, here's the thing, you guys have enabled customers to solve problems that they couldn't solve in the past. >> (Tricia) Right. >> You mentioned NASA and then a variety of other (inaudible). But you guys are also in Public Sectors specifically are doing new things. New problems that no ones ever seen before. And society, entrepreneurship, diversity inclusion, education, non-profits. You don't think of Gov Cloud and Public Sector; you think non-profits, education. So it's kind of these sectors that are coming together. This is a new phenomenon. Can you talk and explain the dynamic behind that and the opportunity? >> Sure. I love to hear the stories of what our customers are doing when they really are tackling a problem that no one had thought of before. So for instance, at Reinvent this year, one of our Public Sector customers who spoke was Thorne. And they are using AI to crawl the dark web and help find people who are trafficking children in human trafficking, and that's a great use of AI and that's the kind of thing. It also helps our public servants because it helps to make police officers' jobs more effective. So of course we know that police officers, there are never enough police officers to go around. There's never enough detectives to look into everything that they need to and this makes them so much more effective to make the world a safer, better place. I also love some of the things about educational outcomes. Ivy Tech Community College is one of our great community college customers. And their using big data analysis to put together all of the different data sets that they have about their students and identify who might be at risk of failing a class 10 days into the semester so that they can help intervene with those students. >> Where was that class when I needed it? >> I know. >> Popup and say, "Hey homework time." >> I mean it really is looking at what kind of issues that they're having very early on with attendance, with different behavioral things. >> A great example at Reinvent with the California Community College system. That was a very interesting way. He was up there bragging like it was nobody's business. >> Yeah, and I think the community colleges that really goes into this idea of we're trying to expand opportunity for a wide-range of people. You might think of computer scientists as that's going to be all the Carnegie Mellon and Stanford and MIT people. And of course those are great contributors to computer science, but the fact is that computer science is so critical in so many aspects of life and in so many different kinds of careers. We know that one of the limiters to our own growth is going to be the talent that we have available to take advantage of the technology. We've been really working hard to expand opportunity for a wide-range of people, so that any smart person with an idea, can be using our technology, that's part of what's behind building the AWS Educate Program, which is a program to offer free computer science training to any university student or college student anywhere in the world. >> So it's a program you guys are doing? >> (Tricia) This is a program we are doing, >> What's it called again? >> AWS Educate. And it's a program that offers free credits to use AWS to any student who is enrolled in any kind of university or college anywhere around the world. >> That's a gateway drug to Cloud computing. >> Absolutely. >> Free resources. >> Yeah, and we're giving them a training path so that they can... >> So they want to write some code, or whatever they want to do. >> Yeah, and they can take different paths and learn. Okay, I want to learn a data science pathway, so I'm going to go that way. I want to learn a websites pathway. And they can go through things and build a portfolio of projects that they've actually built. >> So can they tap into some of the AWS AI tools too? >> They can tap into a wide range of tools and they have different levels of tiers of credits that they get, so it's a really great program to really open up Cloud computing. >> Now is there any limitations on that? What grade levels, is it college and above? >> Actually at Reinvent we just opened it up to students 14 and above. >> (John) Beautiful. That's awesome. >> And we also have a program called... >> How do they prove they're a student? >> Having a school, an EDU email address, or their school being registered through the program. >> (John) Okay, that's awesome. >> And then we also have another program called We Power Tech, and that really is a program to help open up the talent pool again to women to underserved communities, to people of different ethnic backgrounds who might not see themselves in technology because they don't see themselves as computer programmers on TV or whatever. >> Or they don't see their peer group in there, or some sort of might be an inclusion issue. >> Right and we're looking at if you take educate and We Power Tech, we're looking at that full pipeline of talent all the way from kids who are deciding should I pursue computer science or not, all the way through to professionals and getting them to try to stay in technology. >> So you guys are legit on this. You're not going to just check the box and focus on narrow things. A lot of companies do that, where they go oh we're targeting young girls or women. You guys are looking at the spectrum broader. >> Yep. And we're really looking at different communities and helping people to find their community in technology so that they can find supportive networks and also find people to mentor them or find people to mentor who are elsewhere. >> How big of a problem is it right now in today's culture and in the online culture to find peers and friends to do work like this? Because it just doesn't seem to me like there's been any innovation in online message groups. Seems like so 30 years ago. (Tricia laughs) >> Yeah. I think it is tough and I think there are somethings that we're trying to break through. For instance, a lot of the role models out there are the same people over and over again. We're trying to find new role models. And we find that through our customers. We find customers who are doing interesting work and we're trying to cultivate their voice and help put them on stage. >> New voices because it's new things. Machine learning, these are new disciplines. Data science across the board. >> Yeah, and one of the things that I love about the technology is it really is has democratizing affect. If you have an idea, you can make that idea happen for very little money, with just your ingenuity and your ability to stick to it. >> I got to ask you the hard question. Shouldn't be hard for you, but Amazon is gritty. It's been called gritty by me, hustling, but they're very good with their money. They don't really waste a lot in marketing. >> Yeah we're frugal. >> Very frugal, but you're very efficient, so I got to ask your favorite gorilla marketing technique. Cause you guys do more with less. >> (Tricia) We do. >> Once been criticized in Wired magazine. I remember reading years ago about they were comparing the Schwag bag to Reinvent. (Tricia laughs) Google almost gave out phones. It's kind of like typical reporter, but my point is you guys spend your money on education to engineers. You don't skip on that, but you might not put the flair onto an event, but now you guys are doing it. >> I think there are two things. So one of them is the aesthetic of our events. We typically do have a very stripped down aesthetic and we've made frugal look cool. I think that's one of the things I learned when I came here was go ahead and have the concrete floor and put quotes from customers there instead of paying to carpet it. So don't waste money on things that don't add value that's one of the core tenants of what we do in marketing. >> Get a better band instead of the rug. You guys have always had great music. >> We do always have great music. >> Tricia, tell me about your favorite program or project you've done a lot over the years. Pick your favorite child. What's your favorite? You have a lot of great stuff going on. Do you have a favorite? >> I think that my favorite is probably the City on a Cloud Innovation Challenge which is something we've done every year for the last four years. And we really went and asked cities, "Tell us what you're doing with our technology." Because we weren't sure what they were doing cause it's not very expensive for cities to run on us. We found that they were doing incredible things. They were doing water monitoring in their cities to help improve the quality of life of their citizens. They were delivering education more effectively. They were helping their transportation run in a more effective way. New York City Department of Transportation was doing really cool citizen facing apps to help them manage their transportation challenges and also cities all around the world. We've had people put in things about garbage management in Jerusalem and about lighting management in a Japanese city. We've had all kinds of really interesting stories come out and I just love hearing what the customers are doing and this year we added a Dream Big category where we said, "If you had the money, what would "you do with technology in your city?" and we've been really thrilled to be able to offer grants and fund some of those things to help cities get started. >> That's awesome. Not only is it engaging for them to engage with you through the program, it's inspirational. The use cases are everything from IOT to every computer. >> Yeah and we've also had partners submit as well, and we've learned about things like parking applications that cities are putting in place to help their citizens find better parking or all kinds of really interesting. How to keep track of the tree and do a tree census in their cities. Things like that. >> Maybe I'll borrow that and give you credit for it as a Cube question. What would you do if you had unlimited money? >> Exactly. (John laughs) Well the great part is that most of the cities find out that they can do what they want to do with very little money. They think it's going to be millions of dollars and then they realize, "Oh my gosh, it's going to be hard "for me to spend this 50 thousand dollar grant "because it doesn't cost that much." >> That's awesome and you got a big event coming up in June. Public Sector Summit again. Any preview on that? Any thing you can share? I'm sure it's a lot of things up in the air. >> A lot of really cool things. We are very excited to have some of our great customers on stage again. We're also this year going to have a pre day where we're going to feature Air and Space workloads on AWS. So that's going to be really interesting. I think we're going to have Blue Origin there and we're going to talk about what it's going to take to get to the next planet. >> And certainly that's beautiful for Cloud and also a huge robotics trend. People love to geek out on space related stuff. >> Yep. >> Awesome. Well the Cube will be there. Any numbers? Is it going to be the same location? >> It's going to be the same location at the Convention Center June 20th and 21st. We're going to have boot camps and certification labs and all that kind of stuff. I expect we'll grow again, so definitely more than seven thousand people. >> How big was the first one? >> Oh my gosh, the first one was in a little hotel conference room. I think there were a hundred and 50 people there. (Tricia laughs) >> Sounds like Reinvent happening all over again. We've seen this movie before. >> (Tricia) Yep. >> Tricia, thanks so much for coming on the Cube here. In the headquarters of Amazon Web Services Public Sector Summit in Washington DC. We're in Arlington, Virginia, right next to the nation's capital. I'm John Furrier. Thanks for watching. (techno music)

>> Announcer: Live from New York City it's The Cube, covering IBM data science for all. Brought to you by IBM. (light digital music) >> Welcome back to data science for all. It's a whole new game. And it is a whole new game. >> Dave Vellante, John Walls here. We've got quite a distinguished panel. So it is a new game-- >> Well we're in the game, I'm just happy to be-- (both laugh) Have a swing at the pitch. >> Well let's what we have here. Five distinguished members of our panel. It'll take me a minute to get through the introductions, but believe me they're worth it. Jennifer Shin joins us. Jennifer's the founder of 8 Path Solutions, the director of the data science of Comcast and part of the faculty at UC Berkeley and NYU. Jennifer, nice to have you with us, we appreciate the time. Joe McKendrick an analyst and contributor of Forbes and ZDNet, Joe, thank you for being here at well. Another ZDNetter next to him, Dion Hinchcliffe, who is a vice president and principal analyst of Constellation Research and also contributes to ZDNet. Good to see you, sir. To the back row, but that doesn't mean anything about the quality of the participation here. Bob Hayes with a killer Batman shirt on by the way, which we'll get to explain in just a little bit. He runs the Business over Broadway. And Joe Caserta, who the founder of Caserta Concepts. Welcome to all of you. Thanks for taking the time to be with us. Jennifer, let me just begin with you. Obviously as a practitioner you're very involved in the industry, you're on the academic side as well. We mentioned Berkeley, NYU, steep experience. So I want you to kind of take your foot in both worlds and tell me about data science. I mean where do we stand now from those two perspectives? How have we evolved to where we are? And how would you describe, I guess the state of data science? >> Yeah so I think that's a really interesting question. There's a lot of changes happening. In part because data science has now become much more established, both in the academic side as well as in industry. So now you see some of the bigger problems coming out. People have managed to have data pipelines set up. But now there are these questions about models and accuracy and data integration. So the really cool stuff from the data science standpoint. We get to get really into the details of the data. And I think on the academic side you now see undergraduate programs, not just graduate programs, but undergraduate programs being involved. UC Berkeley just did a big initiative that they're going to offer data science to undergrads. So that's a huge news for the university. So I think there's a lot of interest from the academic side to continue data science as a major, as a field. But I think in industry one of the difficulties you're now having is businesses are now asking that question of ROI, right? What do I actually get in return in the initial years? So I think there's a lot of work to be done and just a lot of opportunity. It's great because people now understand better with data sciences, but I think data sciences have to really think about that seriously and take it seriously and really think about how am I actually getting a return, or adding a value to the business? >> And there's lot to be said is there not, just in terms of increasing the workforce, the acumen, the training that's required now. It's a still relatively new discipline. So is there a shortage issue? Or is there just a great need? Is the opportunity there? I mean how would you look at that? >> Well I always think there's opportunity to be smart. If you can be smarter, you know it's always better. It gives you advantages in the workplace, it gets you an advantage in academia. The question is, can you actually do the work? The work's really hard, right? You have to learn all these different disciplines, you have to be able to technically understand data. Then you have to understand it conceptually. You have to be able to model with it, you have to be able to explain it. There's a lot of aspects that you're not going to pick up overnight. So I think part of it is endurance. Like are people going to feel motivated enough and dedicate enough time to it to get very good at that skill set. And also of course, you know in terms of industry, will there be enough interest in the long term that there will be a financial motivation. For people to keep staying in the field, right? So I think it's definitely a lot of opportunity. But that's always been there. Like I tell people I think of myself as a scientist and data science happens to be my day job. That's just the job title. But if you are a scientist and you work with data you'll always want to work with data. I think that's just an inherent need. It's kind of a compulsion, you just kind of can't help yourself, but dig a little bit deeper, ask the questions, you can't not think about it. So I think that will always exist. Whether or not it's an industry job in the way that we see it today, and like five years from now, or 10 years from now. I think that's something that's up for debate. >> So all of you have watched the evolution of data and how it effects organizations for a number of years now. If you go back to the days when data warehouse was king, we had a lot of promises about 360 degree views of the customer and how we were going to be more anticipatory in terms and more responsive. In many ways the decision support systems and the data warehousing world didn't live up to those promises. They solved other problems for sure. And so everybody was looking for big data to solve those problems. And they've begun to attack many of them. We talked earlier in The Cube today about fraud detection, it's gotten much, much better. Certainly retargeting of advertising has gotten better. But I wonder if you could comment, you know maybe start with Joe. As to the effect that data and data sciences had on organizations in terms of fulfilling that vision of a 360 degree view of customers and anticipating customer needs. >> So. Data warehousing, I wouldn't say failed. But I think it was unfinished in order to achieve what we need done today. At the time I think it did a pretty good job. I think it was the only place where we were able to collect data from all these different systems, have it in a single place for analytics. The big difference between what I think, between data warehousing and data science is data warehouses were primarily made for the consumer to human beings. To be able to have people look through some tool and be able to analyze data manually. That really doesn't work anymore, there's just too much data to do that. So that's why we need to build a science around it so that we can actually have machines actually doing the analytics for us. And I think that's the biggest stride in the evolution over the past couple of years, that now we're actually able to do that, right? It used to be very, you know you go back to when data warehouses started, you had to be a deep technologist in order to be able to collect the data, write the programs to clean the data. But now you're average causal IT person can do that. Right now I think we're back in data science where you have to be a fairly sophisticated programmer, analyst, scientist, statistician, engineer, in order to do what we need to do, in order to make machines actually understand the data. But I think part of the evolution, we're just in the forefront. We're going to see over the next, not even years, within the next year I think a lot of new innovation where the average person within business and definitely the average person within IT will be able to do as easily say, "What are my sales going to be next year?" As easy as it is to say, "What were my sales last year." Where now it's a big deal. Right now in order to do that you have to build some algorithms, you have to be a specialist on predictive analytics. And I think, you know as the tools mature, as people using data matures, and as the technology ecosystem for data matures, it's going to be easier and more accessible. >> So it's still too hard. (laughs) That's something-- >> Joe C.: Today it is yes. >> You've written about and talked about. >> Yeah no question about it. We see this citizen data scientist. You know we talked about the democratization of data science but the way we talk about analytics and warehousing and all the tools we had before, they generated a lot of insights and views on the information, but they didn't really give us the science part. And that's, I think that what's missing is the forming of the hypothesis, the closing of the loop of. We now have use of this data, but are are changing, are we thinking about it strategically? Are we learning from it and then feeding that back into the process. I think that's the big difference between data science and the analytics side. But, you know just like Google made search available to everyone, not just people who had highly specialized indexers or crawlers. Now we can have tools that make these capabilities available to anyone. You know going back to what Joe said I think the key thing is we now have tools that can look at all the data and ask all the questions. 'Cause we can't possibly do it all ourselves. Our organizations are increasingly awash in data. Which is the life blood of our organizations, but we're not using it, you know this a whole concept of dark data. And so I think the concept, or the promise of opening these tools up for everyone to be able to access those insights and activate them, I think that, you know, that's where it's headed. >> This is kind of where the T shirt comes in right? So Bob if you would, so you've got this Batman shirt on. We talked a little bit about it earlier, but it plays right into what Dion's talking about. About tools and, I don't want to spoil it, but you go ahead (laughs) and tell me about it. >> Right, so. Batman is a super hero, but he doesn't have any supernatural powers, right? He can't fly on his own, he can't become invisible on his own. But the thing is he has the utility belt and he has these tools he can use to help him solve problems. For example he as the bat ring when he's confronted with a building that he wants to get over, right? So he pulls it out and uses that. So as data professionals we have all these tools now that these vendors are making. We have IBM SPSS, we have data science experience. IMB Watson that these data pros can now use it as part of their utility belt and solve problems that they're confronted with. So if you''re ever confronted with like a Churn problem and you have somebody who has access to that data they can put that into IBM Watson, ask a question and it'll tell you what's the key driver of Churn. So it's not that you have to be a superhuman to be a data scientist, but these tools will help you solve certain problems and help your business go forward. >> Joe McKendrick, do you have a comment? >> Does that make the Batmobile the Watson? (everyone laughs) Analogy? >> I was just going to add that, you know all of the billionaires in the world today and none of them decided to become Batman yet. It's very disappointing. >> Yeah. (Joe laughs) >> Go ahead Joe. >> And I just want to add some thoughts to our discussion about what happened with data warehousing. I think it's important to point out as well that data warehousing, as it existed, was fairly successful but for larger companies. Data warehousing is a very expensive proposition it remains a expensive proposition. Something that's in the domain of the Fortune 500. But today's economy is based on a very entrepreneurial model. The Fortune 500s are out there of course it's ever shifting. But you have a lot of smaller companies a lot of people with start ups. You have people within divisions of larger companies that want to innovate and not be tied to the corporate balance sheet. They want to be able to go through, they want to innovate and experiment without having to go through finance and the finance department. So there's all these open source tools available. There's cloud resources as well as open source tools. Hadoop of course being a prime example where you can work with the data and experiment with the data and practice data science at a very low cost. >> Dion mentioned the C word, citizen data scientist last year at the panel. We had a conversation about that. And the data scientists on the panel generally were like, "Stop." Okay, we're not all of a sudden going to turn everybody into data scientists however, what we want to do is get people thinking about data, more focused on data, becoming a data driven organization. I mean as a data scientist I wonder if you could comment on that. >> Well I think so the other side of that is, you know there are also many people who maybe didn't, you know follow through with science, 'cause it's also expensive. A PhD takes a lot of time. And you know if you don't get funding it's a lot of money. And for very little security if you think about how hard it is to get a teaching job that's going to give you enough of a pay off to pay that back. Right, the time that you took off, the investment that you made. So I think the other side of that is by making data more accessible, you allow people who could have been great in science, have an opportunity to be great data scientists. And so I think for me the idea of citizen data scientist, that's where the opportunity is. I think in terms of democratizing data and making it available for everyone, I feel as though it's something similar to the way we didn't really know what KPIs were, maybe 20 years ago. People didn't use it as readily, didn't teach it in schools. I think maybe 10, 20 years from now, some of the things that we're building today from data science, hopefully more people will understand how to use these tools. They'll have a better understanding of working with data and what that means, and just data literacy right? Just being able to use these tools and be able to understand what data's saying and actually what it's not saying. Which is the thing that most people don't think about. But you can also say that data doesn't say anything. There's a lot of noise in it. There's too much noise to be able to say that there is a result. So I think that's the other side of it. So yeah I guess in terms for me, in terms of data a serious data scientist, I think it's a great idea to have that, right? But at the same time of course everyone kind of emphasized you don't want everyone out there going, "I can be a data scientist without education, "without statistics, without math," without understanding of how to implement the process. I've seen a lot of companies implement the same sort of process from 10, 20 years ago just on Hadoop instead of SQL. Right and it's very inefficient. And the only difference is that you can build more tables wrong than they could before. (everyone laughs) Which is I guess >> For less. it's an accomplishment and for less, it's cheaper, yeah. >> It is cheaper. >> Otherwise we're like I'm not a data scientist but I did stay at a Holiday Inn Express last night, right? >> Yeah. (panelists laugh) And there's like a little bit of pride that like they used 2,000, you know they used 2,000 computers to do it. Like a little bit of pride about that, but you know of course maybe not a great way to go. I think 20 years we couldn't do that, right? One computer was already an accomplishment to have that resource. So I think you have to think about the fact that if you're doing it wrong, you're going to just make that mistake bigger, which his also the other side of working with data. >> Sure, Bob. >> Yeah I have a comment about that. I've never liked the term citizen data scientist or citizen scientist. I get the point of it and I think employees within companies can help in the data analytics problem by maybe being a data collector or something. I mean I would never have just somebody become a scientist based on a few classes here she takes. It's like saying like, "Oh I'm going to be a citizen lawyer." And so you come to me with your legal problems, or a citizen surgeon. Like you need training to be good at something. You can't just be good at something just 'cause you want to be. >> John: Joe you wanted to say something too on that. >> Since we're in New York City I'd like to use the analogy of a real scientist versus a data scientist. So real scientist requires tools, right? And the tools are not new, like microscopes and a laboratory and a clean room. And these tools have evolved over years and years, and since we're in New York we could walk within a 10 block radius and buy any of those tools. It doesn't make us a scientist because we use those tools. I think with data, you know making, making the tools evolve and become easier to use, you know like Bob was saying, it doesn't make you a better data scientist, it just makes the data more accessible. You know we can go buy a microscope, we can go buy Hadoop, we can buy any kind of tool in a data ecosystem, but it doesn't really make you a scientist. I'm very involved in the NYU data science program and the Columbia data science program, like these kids are brilliant. You know these kids are not someone who is, you know just trying to run a day to day job, you know in corporate America. I think the people who are running the day to day job in corporate America are going to be the recipients of data science. Just like people who take drugs, right? As a result of a smart data scientist coming up with a formula that can help people, I think we're going to make it easier to distribute the data that can help people with all the new tools. But it doesn't really make it, you know the access to the data and tools available doesn't really make you a better data scientist. Without, like Bob was saying, without better training and education. >> So how-- I'm sorry, how do you then, if it's not for everybody, but yet I'm the user at the end of the day at my company and I've got these reams of data before me, how do you make it make better sense to me then? So that's where machine learning comes in or artificial intelligence and all this stuff. So how at the end of the day, Dion? How do you make it relevant and usable, actionable to somebody who might not be as practiced as you would like? >> I agree with Joe that many of us will be the recipients of data science. Just like you had to be a computer science at one point to develop programs for a computer, now we can get the programs. You don't need to be a computer scientist to get a lot of value out of our IT systems. The same thing's going to happen with data science. There's far more demand for data science than there ever could be produced by, you know having an ivory tower filled with data scientists. Which we need those guys, too, don't get me wrong. But we need to have, productize it and make it available in packages such that it can be consumed. The outputs and even some of the inputs can be provided by mere mortals, whether that's machine learning or artificial intelligence or bots that go off and run the hypotheses and select the algorithms maybe with some human help. We have to productize it. This is a constant of data scientist of service, which is becoming a thing now. It's, "I need this, I need this capability at scale. "I need it fast and I need it cheap." The commoditization of data science is going to happen. >> That goes back to what I was saying about, the recipient also of data science is also machines, right? Because I think the other thing that's happening now in the evolution of data is that, you know the data is, it's so tightly coupled. Back when you were talking about data warehousing you have all the business transactions then you take the data out of those systems, you put them in a warehouse for analysis, right? Maybe they'll make a decision to change that system at some point. Now the analytics platform and the business application is very tightly coupled. They become dependent upon one another. So you know people who are using the applications are now be able to take advantage of the insights of data analytics and data science, just through the app. Which never really existed before. >> I have one comment on that. You were talking about how do you get the end user more involved, well like we said earlier data science is not easy, right? As an end user, I encourage you to take a stats course, just a basic stats course, understanding what a mean is, variability, regression analysis, just basic stuff. So you as an end user can get more, or glean more insight from the reports that you're given, right? If you go to France and don't know French, then people can speak really slowly to you in French, you're not going to get it. You need to understand the language of data to get value from the technology we have available to us. >> Incidentally French is one of the languages that you have the option of learning if you're a mathematicians. So math PhDs are required to learn a second language. France being the country of algebra, that's one of the languages you could actually learn. Anyway tangent. But going back to the point. So statistics courses, definitely encourage it. I teach statistics. And one of the things that I'm finding as I go through the process of teaching it I'm actually bringing in my experience. And by bringing in my experience I'm actually kind of making the students think about the data differently. So the other thing people don't think about is the fact that like statisticians typically were expected to do, you know, just basic sort of tasks. In a sense that they're knowledge is specialized, right? But the day to day operations was they ran some data, you know they ran a test on some data, looked at the results, interpret the results based on what they were taught in school. They didn't develop that model a lot of times they just understand what the tests were saying, especially in the medical field. So when you when think about things like, we have words like population, census. Which is when you take data from every single, you have every single data point versus a sample, which is a subset. It's a very different story now that we're collecting faster than it used to be. It used to be the idea that you could collect information from everyone. Like it happens once every 10 years, we built that in. But nowadays you know, you know here about Facebook, for instance, I think they claimed earlier this year that their data was more accurate than the census data. So now there are these claims being made about which data source is more accurate. And I think the other side of this is now statisticians are expected to know data in a different way than they were before. So it's not just changing as a field in data science, but I think the sciences that are using data are also changing their fields as well. >> Dave: So is sampling dead? >> Well no, because-- >> Should it be? (laughs) >> Well if you're sampling wrong, yes. That's really the question. >> Okay. You know it's been said that the data doesn't lie, people do. Organizations are very political. Oftentimes you know, lies, damned lies and statistics, Benjamin Israeli. Are you seeing a change in the way in which organizations are using data in the context of the politics. So, some strong P&L manager say gets data and crafts it in a way that he or she can advance their agenda. Or they'll maybe attack a data set that is, probably should drive them in a different direction, but might be antithetical to their agenda. Are you seeing data, you know we talked about democratizing data, are you seeing that reduce the politics inside of organizations? >> So you know we've always used data to tell stories at the top level of an organization that's what it's all about. And I still see very much that no matter how much data science or, the access to the truth through looking at the numbers that story telling is still the political filter through which all that data still passes, right? But it's the advent of things like Block Chain, more and more corporate records and corporate information is going to end up in these open and shared repositories where there is not alternate truth. It'll come back to whoever tells the best stories at the end of the day. So I still see the organizations are very political. We are seeing now more open data though. Open data initiatives are a big thing, both in government and in the private sector. It is having an effect, but it's slow and steady. So that's what I see. >> Um, um, go ahead. >> I was just going to say as well. Ultimately I think data driven decision making is a great thing. And it's especially useful at the lower tiers of the organization where you have the routine day to day's decisions that could be automated through machine learning and deep learning. The algorithms can be improved on a constant basis. On the upper levels, you know that's why you pay executives the big bucks in the upper levels to make the strategic decisions. And data can help them, but ultimately, data, IT, technology alone will not create new markets, it will not drive new businesses, it's up to human beings to do that. The technology is the tool to help them make those decisions. But creating businesses, growing businesses, is very much a human activity. And that's something I don't see ever getting replaced. Technology might replace many other parts of the organization, but not that part. >> I tend to be a foolish optimist when it comes to this stuff. >> You do. (laughs) >> I do believe that data will make the world better. I do believe that data doesn't lie people lie. You know I think as we start, I'm already seeing trends in industries, all different industries where, you know conventional wisdom is starting to get trumped by analytics. You know I think it's still up to the human being today to ignore the facts and go with what they think in their gut and sometimes they win, sometimes they lose. But generally if they lose the data will tell them that they should have gone the other way. I think as we start relying more on data and trusting data through artificial intelligence, as we start making our lives a little bit easier, as we start using smart cars for safety, before replacement of humans. AS we start, you know, using data really and analytics and data science really as the bumpers, instead of the vehicle, eventually we're going to start to trust it as the vehicle itself. And then it's going to make lying a little bit harder. >> Okay, so great, excellent. Optimism, I love it. (John laughs) So I'm going to play devil's advocate here a little bit. There's a couple elephant in the room topics that I want to, to explore a little bit. >> Here it comes. >> There was an article today in Wired. And it was called, Why AI is Still Waiting for It's Ethics Transplant. And, I will just read a little segment from there. It says, new ethical frameworks for AI need to move beyond individual responsibility to hold powerful industrial, government and military interests accountable as they design and employ AI. When tech giants build AI products, too often user consent, privacy and transparency are overlooked in favor of frictionless functionality that supports profit driven business models based on aggregate data profiles. This is from Kate Crawford and Meredith Whittaker who founded AI Now. And they're calling for sort of, almost clinical trials on AI, if I could use that analogy. Before you go to market you've got to test the human impact, the social impact. Thoughts. >> And also have the ability for a human to intervene at some point in the process. This goes way back. Is everybody familiar with the name Stanislav Petrov? He's the Soviet officer who back in 1983, it was in the control room, I guess somewhere outside of Moscow in the control room, which detected a nuclear missile attack against the Soviet Union coming out of the United States. Ordinarily I think if this was an entirely AI driven process we wouldn't be sitting here right now talking about it. But this gentlemen looked at what was going on on the screen and, I'm sure he's accountable to his authorities in the Soviet Union. He probably got in a lot of trouble for this, but he decided to ignore the signals, ignore the data coming out of, from the Soviet satellites. And as it turned out, of course he was right. The Soviet satellites were seeing glints of the sun and they were interpreting those glints as missile launches. And I think that's a great example why, you know every situation of course doesn't mean the end of the world, (laughs) it was in this case. But it's a great example why there needs to be a human component, a human ability for human intervention at some point in the process. >> So other thoughts. I mean organizations are driving AI hard for profit. Best minds of our generation are trying to figure out how to get people to click on ads. Jeff Hammerbacher is famous for saying it. >> You can use data for a lot of things, data analytics, you can solve, you can cure cancer. You can make customers click on more ads. It depends on what you're goal is. But, there are ethical considerations we need to think about. When we have data that will have a racial bias against blacks and have them have higher prison sentences or so forth or worse credit scores, so forth. That has an impact on a broad group of people. And as a society we need to address that. And as scientists we need to consider how are we going to fix that problem? Cathy O'Neil in her book, Weapons of Math Destruction, excellent book, I highly recommend that your listeners read that book. And she talks about these issues about if AI, if algorithms have a widespread impact, if they adversely impact protected group. And I forget the last criteria, but like we need to really think about these things as a people, as a country. >> So always think the idea of ethics is interesting. So I had this conversation come up a lot of times when I talk to data scientists. I think as a concept, right as an idea, yes you want things to be ethical. The question I always pose to them is, "Well in the business setting "how are you actually going to do this?" 'Cause I find the most difficult thing working as a data scientist, is to be able to make the day to day decision of when someone says, "I don't like that number," how do you actually get around that. If that's the right data to be showing someone or if that's accurate. And say the business decides, "Well we don't like that number." Many people feel pressured to then change the data, change, or change what the data shows. So I think being able to educate people to be able to find ways to say what the data is saying, but not going past some line where it's a lie, where it's unethical. 'Cause you can also say what data doesn't say. You don't always have to say what the data does say. You can leave it as, "Here's what we do know, "but here's what we don't know." There's a don't know part that many people will omit when they talk about data. So I think, you know especially when it comes to things like AI it's tricky, right? Because I always tell people I don't know everyone thinks AI's going to be so amazing. I started an industry by fixing problems with computers that people didn't realize computers had. For instance when you have a system, a lot of bugs, we all have bug reports that we've probably submitted. I mean really it's no where near the point where it's going to start dominating our lives and taking over all the jobs. Because frankly it's not that advanced. It's still run by people, still fixed by people, still managed by people. I think with ethics, you know a lot of it has to do with the regulations, what the laws say. That's really going to be what's involved in terms of what people are willing to do. A lot of businesses, they want to make money. If there's no rules that says they can't do certain things to make money, then there's no restriction. I think the other thing to think about is we as consumers, like everyday in our lives, we shouldn't separate the idea of data as a business. We think of it as a business person, from our day to day consumer lives. Meaning, yes I work with data. Incidentally I also always opt out of my credit card, you know when they send you that information, they make you actually mail them, like old school mail, snail mail like a document that says, okay I don't want to be part of this data collection process. Which I always do. It's a little bit more work, but I go through that step of doing that. Now if more people did that, perhaps companies would feel more incentivized to pay you for your data, or give you more control of your data. Or at least you know, if a company's going to collect information, I'd want you to be certain processes in place to ensure that it doesn't just get sold, right? For instance if a start up gets acquired what happens with that data they have on you? You agree to give it to start up. But I mean what are the rules on that? So I think we have to really think about the ethics from not just, you know, someone who's going to implement something but as consumers what control we have for our own data. 'Cause that's going to directly impact what businesses can do with our data. >> You know you mentioned data collection. So slightly on that subject. All these great new capabilities we have coming. We talked about what's going to happen with media in the future and what 5G technology's going to do to mobile and these great bandwidth opportunities. The internet of things and the internet of everywhere. And all these great inputs, right? Do we have an arms race like are we keeping up with the capabilities to make sense of all the new data that's going to be coming in? And how do those things square up in this? Because the potential is fantastic, right? But are we keeping up with the ability to make it make sense and to put it to use, Joe? >> So I think data ingestion and data integration is probably one of the biggest challenges. I think, especially as the world is starting to become more dependent on data. I think you know, just because we're dependent on numbers we've come up with GAAP, which is generally accepted accounting principles that can be audited and proven whether it's true or false. I think in our lifetime we will see something similar to that we will we have formal checks and balances of data that we use that can be audited. Getting back to you know what Dave was saying earlier about, I personally would trust a machine that was programmed to do the right thing, than to trust a politician or some leader that may have their own agenda. And I think the other thing about machines is that they are auditable. You know you can look at the code and see exactly what it's doing and how it's doing it. Human beings not so much. So I think getting to the truth, even if the truth isn't the answer that we want, I think is a positive thing. It's something that we can't do today that once we start relying on machines to do we'll be able to get there. >> Yeah I was just going to add that we live in exponential times. And the challenge is that the way that we're structured traditionally as organizations is not allowing us to absorb advances exponentially, it's linear at best. Everyone talks about change management and how are we going to do digital transformation. Evidence shows that technology's forcing the leaders and the laggards apart. There's a few leading organizations that are eating the world and they seem to be somehow rolling out new things. I don't know how Amazon rolls out all this stuff. There's all this artificial intelligence and the IOT devices, Alexa, natural language processing and that's just a fraction, it's just a tip of what they're releasing. So it just shows that there are some organizations that have path found the way. Most of the Fortune 500 from the year 2000 are gone already, right? The disruption is happening. And so we are trying, have to find someway to adopt these new capabilities and deploy them effectively or the writing is on the wall. I spent a lot of time exploring this topic, how are we going to get there and all of us have a lot of hard work is the short answer. >> I read that there's going to be more data, or it was predicted, more data created in this year than in the past, I think it was five, 5,000 years. >> Forever. (laughs) >> And that to mix the statistics that we're analyzing currently less than 1% of the data. To taking those numbers and hear what you're all saying it's like, we're not keeping up, it seems like we're, it's not even linear. I mean that gap is just going to grow and grow and grow. How do we close that? >> There's a guy out there named Chris Dancy, he's known as the human cyborg. He has 700 hundred sensors all over his body. And his theory is that data's not new, having access to the data is new. You know we've always had a blood pressure, we've always had a sugar level. But we were never able to actually capture it in real time before. So now that we can capture and harness it, now we can be smarter about it. So I think that being able to use this information is really incredible like, this is something that over our lifetime we've never had and now we can do it. Which hence the big explosion in data. But I think how we use it and have it governed I think is the challenge right now. It's kind of cowboys and indians out there right now. And without proper governance and without rigorous regulation I think we are going to have some bumps in the road along the way. >> The data's in the oil is the question how are we actually going to operationalize around it? >> Or find it. Go ahead. >> I will say the other side of it is, so if you think about information, we always have the same amount of information right? What we choose to record however, is a different story. Now if you want wanted to know things about the Olympics, but you decide to collect information every day for years instead of just the Olympic year, yes you have a lot of data, but did you need all of that data? For that question about the Olympics, you don't need to collect data during years there are no Olympics, right? Unless of course you're comparing it relative. But I think that's another thing to think about. Just 'cause you collect more data does not mean that data will produce more statistically significant results, it does not mean it'll improve your model. You can be collecting data about your shoe size trying to get information about your hair. I mean it really does depend on what you're trying to measure, what your goals are, and what the data's going to be used for. If you don't factor the real world context into it, then yeah you can collect data, you know an infinite amount of data, but you'll never process it. Because you have no question to ask you're not looking to model anything. There is no universal truth about everything, that just doesn't exist out there. >> I think she's spot on. It comes down to what kind of questions are you trying to ask of your data? You can have one given database that has 100 variables in it, right? And you can ask it five different questions, all valid questions and that data may have those variables that'll tell you what's the best predictor of Churn, what's the best predictor of cancer treatment outcome. And if you can ask the right question of the data you have then that'll give you some insight. Just data for data's sake, that's just hype. We have a lot of data but it may not lead to anything if we don't ask it the right questions. >> Joe. >> I agree but I just want to add one thing. This is where the science in data science comes in. Scientists often will look at data that's already been in existence for years, weather forecasts, weather data, climate change data for example that go back to data charts and so forth going back centuries if that data is available. And they reformat, they reconfigure it, they get new uses out of it. And the potential I see with the data we're collecting is it may not be of use to us today, because we haven't thought of ways to use it, but maybe 10, 20, even 100 years from now someone's going to think of a way to leverage the data, to look at it in new ways and to come up with new ideas. That's just my thought on the science aspect. >> Knowing what you know about data science, why did Facebook miss Russia and the fake news trend? They came out and admitted it. You know, we miss it, why? Could they have, is it because they were focused elsewhere? Could they have solved that problem? (crosstalk) >> It's what you said which is are you asking the right questions and if you're not looking for that problem in exactly the way that it occurred you might not be able to find it. >> I thought the ads were paid in rubles. Shouldn't that be your first clue (panelists laugh) that something's amiss? >> You know red flag, so to speak. >> Yes. >> I mean Bitcoin maybe it could have hidden it. >> Bob: Right, exactly. >> I would think too that what happened last year is actually was the end of an age of optimism. I'll bring up the Soviet Union again, (chuckles). It collapsed back in 1991, 1990, 1991, Russia was reborn in. And think there was a general feeling of optimism in the '90s through the 2000s that Russia is now being well integrated into the world economy as other nations all over the globe, all continents are being integrated into the global economy thanks to technology. And technology is lifting entire continents out of poverty and ensuring more connectedness for people. Across Africa, India, Asia, we're seeing those economies that very different countries than 20 years ago and that extended into Russia as well. Russia is part of the global economy. We're able to communicate as a global, a global network. I think as a result we kind of overlook the dark side that occurred. >> John: Joe? >> Again, the foolish optimist here. But I think that... It shouldn't be the question like how did we miss it? It's do we have the ability now to catch it? And I think without data science without machine learning, without being able to train machines to look for patterns that involve corruption or result in corruption, I think we'd be out of luck. But now we have those tools. And now hopefully, optimistically, by the next election we'll be able to detect these things before they become public. >> It's a loaded question because my premise was Facebook had the ability and the tools and the knowledge and the data science expertise if in fact they wanted to solve that problem, but they were focused on other problems, which is how do I get people to click on ads? >> Right they had the ability to train the machines, but they were giving the machines the wrong training. >> Looking under the wrong rock. >> (laughs) That's right. >> It is easy to play armchair quarterback. Another topic I wanted to ask the panel about is, IBM Watson. You guys spend time in the Valley, I spend time in the Valley. People in the Valley poo-poo Watson. Ah, Google, Facebook, Amazon they've got the best AI. Watson, and some of that's fair criticism. Watson's a heavy lift, very services oriented, you just got to apply it in a very focused. At the same time Google's trying to get you to click on Ads, as is Facebook, Amazon's trying to get you to buy stuff. IBM's trying to solve cancer. Your thoughts on that sort of juxtaposition of the different AI suppliers and there may be others. Oh, nobody wants to touch this one, come on. I told you elephant in the room questions. >> Well I mean you're looking at two different, very different types of organizations. One which is really spent decades in applying technology to business and these other companies are ones that are primarily into the consumer, right? When we talk about things like IBM Watson you're looking at a very different type of solution. You used to be able to buy IT and once you installed it you pretty much could get it to work and store your records or you know, do whatever it is you needed it to do. But these types of tools, like Watson actually tries to learn your business. And it needs to spend time doing that watching the data and having its models tuned. And so you don't get the results right away. And I think that's been kind of the challenge that organizations like IBM has had. Like this is a different type of technology solution, one that has to actually learn first before it can provide value. And so I think you know you have organizations like IBM that are much better at applying technology to business, and then they have the further hurdle of having to try to apply these tools that work in very different ways. There's education too on the side of the buyer. >> I'd have to say that you know I think there's plenty of businesses out there also trying to solve very significant, meaningful problems. You know with Microsoft AI and Google AI and IBM Watson, I think it's not really the tool that matters, like we were saying earlier. A fool with a tool is still a fool. And regardless of who the manufacturer of that tool is. And I think you know having, a thoughtful, intelligent, trained, educated data scientist using any of these tools can be equally effective. >> So do you not see core AI competence and I left out Microsoft, as a strategic advantage for these companies? Is it going to be so ubiquitous and available that virtually anybody can apply it? Or is all the investment in R&D and AI going to pay off for these guys? >> Yeah, so I think there's different levels of AI, right? So there's AI where you can actually improve the model. I remember when I was invited when Watson was kind of first out by IBM to a private, sort of presentation. And my question was, "Okay, so when do I get "to access the corpus?" The corpus being sort of the foundation of NLP, which is natural language processing. So it's what you use as almost like a dictionary. Like how you're actually going to measure things, or things up. And they said, "Oh you can't." "What do you mean I can't?" It's like, "We do that." "So you're telling me as a data scientist "you're expecting me to rely on the fact "that you did it better than me and I should rely on that." I think over the years after that IBM started opening it up and offering different ways of being able to access the corpus and work with that data. But I remember at the first Watson hackathon there was only two corpus available. It was either the travel or medicine. There was no other foundational data available. So I think one of the difficulties was, you know IBM being a little bit more on the forefront of it they kind of had that burden of having to develop these systems and learning kind of the hard way that if you don't have the right models and you don't have the right data and you don't have the right access, that's going to be a huge limiter. I think with things like medical, medical information that's an extremely difficult data to start with. Partly because you know anything that you do find or don't find, the impact is significant. If I'm looking at things like what people clicked on the impact of using that data wrong, it's minimal. You might lose some money. If you do that with healthcare data, if you do that with medical data, people may die, like this is a much more difficult data set to start with. So I think from a scientific standpoint it's great to have any information about a new technology, new process. That's the nice that is that IBM's obviously invested in it and collected information. I think the difficulty there though is just 'cause you have it you can't solve everything. And if feel like from someone who works in technology, I think in general when you appeal to developers you try not to market. And with Watson it's very heavily marketed, which tends to turn off people who are more from the technical side. Because I think they don't like it when it's gimmicky in part because they do the opposite of that. They're always trying to build up the technical components of it. They don't like it when you're trying to convince them that you're selling them something when you could just give them the specs and look at it. So it could be something as simple as communication. But I do think it is valuable to have had a company who leads on the forefront of that and try to do so we can actually learn from what IBM has learned from this process. >> But you're an optimist. (John laughs) All right, good. >> Just one more thought. >> Joe go ahead first. >> Joe: I want to see how Alexa or Siri do on Jeopardy. (panelists laugh) >> All right. Going to go around a final thought, give you a second. Let's just think about like your 12 month crystal ball. In terms of either challenges that need to be met in the near term or opportunities you think will be realized. 12, 18 month horizon. Bob you've got the microphone headed up, so I'll let you lead off and let's just go around. >> I think a big challenge for business, for society is getting people educated on data and analytics. There's a study that was just released I think last month by Service Now, I think, or some vendor, or Click. They found that only 17% of the employees in Europe have the ability to use data in their job. Think about that. >> 17. >> 17. Less than 20%. So these people don't have the ability to understand or use data intelligently to improve their work performance. That says a lot about the state we're in today. And that's Europe. It's probably a lot worse in the United States. So that's a big challenge I think. To educate the masses. >> John: Joe. >> I think we probably have a better chance of improving technology over training people. I think using data needs to be iPhone easy. And I think, you know which means that a lot of innovation is in the years to come. I do think that a keyboard is going to be a thing of the past for the average user. We are going to start using voice a lot more. I think augmented reality is going to be things that becomes a real reality. Where we can hold our phone in front of an object and it will have an overlay of prices where it's available, if it's a person. I think that we will see within an organization holding a camera up to someone and being able to see what is their salary, what sales did they do last year, some key performance indicators. I hope that we are beyond the days of everyone around the world walking around like this and we start actually becoming more social as human beings through augmented reality. I think, it has to happen. I think we're going through kind of foolish times at the moment in order to get to the greater good. And I think the greater good is using technology in a very, very smart way. Which means that you shouldn't have to be, sorry to contradict, but maybe it's good to counterpoint. I don't think you need to have a PhD in SQL to use data. Like I think that's 1990. I think as we evolve it's going to become easier for the average person. Which means people like the brain trust here needs to get smarter and start innovating. I think the innovation around data is really at the tip of the iceberg, we're going to see a lot more of it in the years to come. >> Dion why don't you go ahead, then we'll come down the line here. >> Yeah so I think over that time frame two things are likely to happen. One is somebody's going to crack the consumerization of machine learning and AI, such that it really is available to the masses and we can do much more advanced things than we could. We see the industries tend to reach an inflection point and then there's an explosion. No one's quite cracked the code on how to really bring this to everyone, but somebody will. And that could happen in that time frame. And then the other thing that I think that almost has to happen is that the forces for openness, open data, data sharing, open data initiatives things like Block Chain are going to run headlong into data protection, data privacy, customer privacy laws and regulations that have to come down and protect us. Because the industry's not doing it, the government is stepping in and it's going to re-silo a lot of our data. It's going to make it recede and make it less accessible, making data science harder for a lot of the most meaningful types of activities. Patient data for example is already all locked down. We could do so much more with it, but health start ups are really constrained about what they can do. 'Cause they can't access the data. We can't even access our own health care records, right? So I think that's the challenge is we have to have that battle next to be able to go and take the next step. >> Well I see, with the growth of data a lot of it's coming through IOT, internet of things. I think that's a big source. And we're going to see a lot of innovation. A new types of Ubers or Air BnBs. Uber's so 2013 though, right? We're going to see new companies with new ideas, new innovations, they're going to be looking at the ways this data can be leveraged all this big data. Or data coming in from the IOT can be leveraged. You know there's some examples out there. There's a company for example that is outfitting tools, putting sensors in the tools. Industrial sites can therefore track where the tools are at any given time. This is an expensive, time consuming process, constantly loosing tools, trying to locate tools. Assessing whether the tool's being applied to the production line or the right tool is at the right torque and so forth. With the sensors implanted in these tools, it's now possible to be more efficient. And there's going to be innovations like that. Maybe small start up type things or smaller innovations. We're going to see a lot of new ideas and new types of approaches to handling all this data. There's going to be new business ideas. The next Uber, we may be hearing about it a year from now whatever that may be. And that Uber is going to be applying data, probably IOT type data in some, new innovative way. >> Jennifer, final word. >> Yeah so I think with data, you know it's interesting, right, for one thing I think on of the things that's made data more available and just people we open to the idea, has been start ups. But what's interesting about this is a lot of start ups have been acquired. And a lot of people at start ups that got acquired now these people work at bigger corporations. Which was the way it was maybe 10 years ago, data wasn't available and open, companies kept it very proprietary, you had to sign NDAs. It was like within the last 10 years that open source all of that initiatives became much more popular, much more open, a acceptable sort of way to look at data. I think that what I'm kind of interested in seeing is what people do within the corporate environment. Right, 'cause they have resources. They have funding that start ups don't have. And they have backing, right? Presumably if you're acquired you went in at a higher title in the corporate structure whereas if you had started there you probably wouldn't be at that title at that point. So I think you have an opportunity where people who have done innovative things and have proven that they can build really cool stuff, can now be in that corporate environment. I think part of it's going to be whether or not they can really adjust to sort of the corporate, you know the corporate landscape, the politics of it or the bureaucracy. I think every organization has that. Being able to navigate that is a difficult thing in part 'cause it's a human skill set, it's a people skill, it's a soft skill. It's not the same thing as just being able to code something and sell it. So you know it's going to really come down to people. I think if people can figure out for instance, what people want to buy, what people think, in general that's where the money comes from. You know you make money 'cause someone gave you money. So if you can find a way to look at a data or even look at technology and understand what people are doing, aren't doing, what they're happy about, unhappy about, there's always opportunity in collecting the data in that way and being able to leverage that. So you build cooler things, and offer things that haven't been thought of yet. So it's a very interesting time I think with the corporate resources available if you can do that. You know who knows what we'll have in like a year. >> I'll add one. >> Please. >> The majority of companies in the S&P 500 have a market cap that's greater than their revenue. The reason is 'cause they have IP related to data that's of value. But most of those companies, most companies, the vast majority of companies don't have any way to measure the value of that data. There's no GAAP accounting standard. So they don't understand the value contribution of their data in terms of how it helps them monetize. Not the data itself necessarily, but how it contributes to the monetization of the company. And I think that's a big gap. If you don't understand the value of the data that means you don't understand how to refine it, if data is the new oil and how to protect it and so forth and secure it. So that to me is a big gap that needs to get closed before we can actually say we live in a data driven world. >> So you're saying I've got an asset, I don't know if it's worth this or this. And they're missing that great opportunity. >> So devolve to what I know best. >> Great discussion. Really, really enjoyed the, the time as flown by. Joe if you get that augmented reality thing to work on the salary, point it toward that guy not this guy, okay? (everyone laughs) It's much more impressive if you point it over there. But Joe thank you, Dion, Joe and Jennifer and Batman. We appreciate and Bob Hayes, thanks for being with us. >> Thanks you guys. >> Really enjoyed >> Great stuff. >> the conversation. >> And a reminder coming up a the top of the hour, six o'clock Eastern time, IBMgo.com featuring the live keynote which is being set up just about 50 feet from us right now. Nick Silver is one of the headliners there, John Thomas is well, or rather Rob Thomas. John Thomas we had on earlier on The Cube. But a panel discussion as well coming up at six o'clock on IBMgo.com, six to 7:15. Be sure to join that live stream. That's it from The Cube. We certainly appreciate the time. Glad to have you along here in New York. And until the next time, take care. (bright digital music)