(light music) (airy white noise rumbling) >> Hey everyone, welcome back to Las Vegas. It's theCUBE. We're here, day four of our coverage of AWS re:Invent 22. There's been about, we've heard, north of 55,000 folks here in person. We're seeing only a fraction of that but it's packed in the expo center. We're at the Venetian Expo, Lisa Martin, Dave Vellante. Dave, we've had such great conversations as we always do on theCUBE. With the AWS ecosystem, we're going to be talking with another partner on that ecosystem and what they're doing to innovate together next. >> Well, we know security is the number one topic on IT practitioners, mine, CIOs, CISOs. We also know that they don't have the bench strength, that's why they look to manage service providers, manage service security providers. It's a growing topic, we've talked about it. We talked about it at re:Inforce earlier this year. I think it was July, actually, and August, believe it or not, not everybody was at the Cape. It was pretty well attended conference and that's their security focus conference, exclusive on security. But there's a lot of security here too. >> Lot of security, we're going to be talking about that next. We have two guests from Capgemini joining us. Mike Wasielewski, the head of cloud security, and NextGen secure architectures, welcome Mike. Anne Saunders also joins us, the Director of Cybersecurity Technology Partnerships at Capgemini, welcome Anne. >> Thank you. >> Dave: Hey guys. >> So, day four of the show, how you feeling? >> Anne: Pretty good. >> Mike: It's a long show. >> It is a long, and it's still jamming in here. Normally on the last day, it dwindles down. Not here. >> No, the foot traffic around the booth and around the totality of this expo floor has been amazing, I think. >> It really has. Anne, I want to start with you. Capgemini making some moves in the waves in the cloud and cloud security spaces. Talk to us about what Cap's got going on there. >> Well, we actually have a variety of things going on. Very much partner driven. The SOC Essentials offering that Mike's going to talk about shortly is the kind of the starter offer where we're going to build from and build out from. SOC Essentials is definitely critical for establishing that foundation. A lot of good stuff coming along with partners. Since I manage the partners, I'm kind of keen on who we get involved with and how we work with them to build out value and focus on our overall cloud security strategy. Mike, you want to talk about SOC Essentials? >> Yeah, well, no, I mean, I think at Capgemini, we really say cybersecurity is part of our DNA and so as we look at what we do in the cloud, you'll find that security has always been an underpinning to a lot of what we deliver, whether it's on the DevSecOps services, migration services, stuff like that. But what we're really trying to do is be intentional about how we approach the security piece of the cloud in different ways, right? Traditional infrastructure, you mentioned the totality of security vendors here and at re:Inforce. We're really seeing that you have to approach it differently. So we're bringing together the right partners. We're using what's part of our DNA to really be able to drive the next generation of security inside those clouds for our clients and customers. So as Anne was talking about, we have a new service called the Capgemini Cloud SOC Essentials, and we've really brought our partners to bear, in this case Trend Micro, really bringing a lot of their intelligence and building off of what they do so that we can help customers. Services can be pretty expensive, right, when you go for the high end, or if you have to try to run one yourself, there's a lot of time, I think you mentioned earlier, right, the people's benches. It's really hard to have a really good cybersecurity people in those smaller businesses. So what we're trying to do is we're really trying to help companies, whether you're the really big buyers of the world or some of the smaller ones, right? We want to be able to give you the visibility and ability to deliver to your customers securely. So that's how we're approaching security now and we're cloud SOC Essentials, the new thing that we're announcing while we were here is really driving out of. >> When I came out of re:Invent, when you do these events, you get this Kool-Aid injection and after a while you're like hm, what did I learn? And one of the things that struck me in talking to people is you've got the shared responsibility model that the cloud has sort of created and I know there's complexities across cloud but let's just keep it at cloud generically for a moment. And then you've got the CISO, the AppDev, AppSecDev group is being asked to do a lot. They're kind of being dragged into security that's really not their wheelhouse and then you've got audit which is like the last line of defense. And so one of the things that struck me at re:Inforce is like, okay, Amazon, great job for their portion of the shared responsibility model but I didn't hear a lot in terms of making the CISO's life easier and I'm guessing that's where you guys come in. I wonder if you could talk about that trend, that conceptual layers that I just laid out and where you guys fit. >> Mike: Sure, so I think first and foremost, I always go back to a quote from, I think it's attributed to Peter Drucker, whether that's right or wrong, who knows? But culture eats strategy for breakfast, right? And I think what we've seen in our conversations with whether you're talking to the CISO, the application team, the AppDev team, wherever throughout the organization, we really see that culture is what's going to drive success or failure of security in the org, and so what we do is we really do bring that totality of perspective. We're not just cloud, not just security, not just AppDev. We can really bring across the totality of the Capgemini estate. So that when we go, and you're right, a CISO says, I'm having a hard time getting the app people to deliver what I need. If you just come from a security perspective, you're right, that's what's going to happen. So what we try to do is so, we've got a great DevSecOps service, for example in the cloud where we do that. We bring all the perspectives together, how do we align KPIs? That's a big problem, I think, for what you're seeing, making CISO's lives easier, is about making sure that the app team KPIs are aligned with the CISO's but also the CISO's KPIs are aligned with the app teams. And by doing that, we have had really great success in a number of organizations by giving them the tools then and the people on our side to be able to make those alignments at the business level, to drive the right business outcome, to drive the right security outcome, the right application outcome. That's where I think we've really come to play. >> Absolutely, and I will say from a partnering perspective, what's key in supporting that strategy is we will learn from our partners, we lean on our partners to understand what the trends they're seeing and where they're having an impact with regards to supporting the CISO and supporting the overall security strategy within a company. I mean, they're on the cutting edge. We do a lot to track their technology roadmaps. We do a lot to track how they build their buyer personas and what issues they're dealing with and what issues they're prepared to deal with regards to where they're investing and who's investing in them. A lot of strategy around which partner to bring in and support, how we're going to address the challenges, the CISO and the IT teams are having to kind of support that overall. Security is a part of everything, DNA kind of strategy. >> Yeah, do you have a favorite example, Anne, of a partner that came in with Capgemini, helped a customer really be able to do what Capgemini is doing and that is, have cybersecurity be actually part of their DNA when there's so many challenges, the skills gap. Any favorite example that really you think articulates how you're able to enable organizations to achieve just that? >> Anne: Well, actually the SOC Essentials offering that we're rolling out is a prime example of that. I mean, we work very, very closely with Trend on all fronts with regards to developing it. It's one of those completely collaborative from day one to going to the customer and that it's almost that seamless connectivity and just partnering at such a strategic level is a great example of how it's done right, and when it's done right, how successful it can be. >> Dave: Why Trend Micro? Because I mean, I'm sure you've seen, I think that's Optiv, has the eye test with all the tools and you talk to CISOs, they're like really trying to consolidate those tools. So I presume there's a portfolio play there, but tell us, tell the audience a little bit more about why Trend Micro and I mean your branding with them, why those guys? >> Well, it goes towards the technology, of course, and all the development they've done and their position within AWS and how they address assuring security for our clients who are moving onto and running their estates on AWS. There's such a long heritage with regards to their technology platform and what they've developed, that deep experience, that kind of the strength of the technology because of the longevity they've had and where they sit within their domain. I try to call partners out by their domain and their area of expertise is part of the reason, I mean. >> Yeah, I think another big part of it is Gartner is expecting, I think they published this out in the next three years, we expect to see another consolidation both inside of the enterprises as well as, I look back a couple years, when Palo Alto went on a very nice spending spree, right? And put together a lot of really great companies that built their Prisma platform. So what I think one of the reasons we picked Trend in this particular case is as we look forward for our customers and our clients, not just having point solutions, right? This isn't just about endpoint protection, this isn't just about security posture management. This is really who can take the totality of the customer's problems and deliver on the right outcomes from a single platform, and so when we look at companies like Trend, like Palo, some of the bigger partners for us, that's where we try to focus. They're definitely best in breed and we bring those to our customers too for certain things. But as we look to the future, I think really finding those partners that are going to be able to solve a swath of problems at the right price point for their customers, that is where I think we see the industry moving. >> Dave: And maybe be around as an independent company. Was that a factor as well? I mean, you see Thoma Bravo buying up all his hiring companies and right, so, and maybe they're trying to create something that could be competitive, but you're saying Trend Micros there, so. >> Well I think as Anne mentioned, the 30 year heritage, I think, of Trend Micro really driving this and I've done work with them in various past things. There's also a big part of just the people you like, the people that are good to work with, that are really trying to be customer obsessed, going back right, at an AWS event, the ones that get the cloud tend to be able to follow those Amazon LPs as well, right, just kind of naturally, and so I think when you look at the Trend Micros of the world, that's where that kind of cloud native piece comes out and I like working with that. >> In this environment, the macro environment, lets talk a bit, earning season, it's really mixed. I mean you're seeing some really good earnings, some mixed earnings, some good earnings with cautious guidance. So nobody really (indistinct), and it was for a period time there was a thinking that security was non-discretionary and it's clearly non-discretionary, but the CISO, she or he, doesn't have unlimited budgets, right? So what are you seeing in terms of how are customers dealing with this challenging macro environment? Is it through tools consolidation? Is that a play that's going on? What are you seeing in the customer base? >> Anne: I see ways, and we're working through this right now where we're actually weaving cybersecurity in at the very beginning of how we're designing offers across our entire offer portfolio, not just the cybersecurity business. So taking that approach in the long run will help contain costs and our hope, and we're already seeing it, is it's actually helping change the perception that security's that cost center and that final obstacle you have to get over and it's going to throw your margins off and all that sort of stuff. >> Dave: I like that, its at least is like a security cover charge. You're not getting in unless we do the security thing. >> Exactly, a security cover charge, that's what you should call it. >> Yeah. >> Like it. >> Another piece though, you mentioned earlier about making CISO's life easier, right? And I think, as Anne did a really absolutely true about building it in, not to the security stack but application developers, they want visibility they want observability, they want to do it right. They want CI/CD pipeline that can give them confidence in their security. So should the CISO have a budget issue, right? And they can't necessarily afford, but the application team as they're looking at what products they want to purchase, can I get a SaaS or a DaaS, right? The static or dynamic application security testing in my product up front and if the app team buys into that methodology, the CISO convinces them, yes, this is important. Now I've got two budgets to pull from, and in the end I end up with a cheaper, a lower cost of a service. So I think that's another way that we see with like DevSecOps and a few other services, that building in on day one that you mentioned. >> Lisa: Yeah. >> Getting both teams involved. >> Dave: That's interesting, Mike, because that's the alignment that you were talking about earlier in the KPIs and you're not a tech vendor saying, buy my product, you guys have deep consultancy backgrounds. >> Anne: And the customer appreciates that. >> Yeah. >> Anne: They see us as looking out for their best interest when we're trying to support them and help them and bringing it to the table at the very beginning as something that is there and we're conscientious of, just helps them in the long run and I think, they're seeing that, they appreciate that. >> Dave: Yeah, you can bring best practice around measurements, alignment, business process, stuff like that. Maybe even some industry expertise which you're not typically going to get from a product company. >> Well, one thing you just mentioned that I love talking about with Capgemini is the industry expertise, right? So when you look at systems integrators, there are a lot of really, really good ones. To say otherwise would be foolish. But Capgemini with our acquisition of Altran, a couple years ago, I think think it was, right? How many other GSIs or SIs are actually building silicon for IoT chips? So IoT's huge right now, the intelligent industry moving forward is going to drive a lot of those business outcomes that people are looking for. Who else can say we've built an autonomous vehicle, Capgemini can. Who can say that we've built the IoT devices from the ground up? We know not just how to integrate them into AWS, into the IoT services in the cloud, but to build and have that secure development for the firmware and all and that's where I think our customers really look to us as being those industry experts and being able to bring that totality of our business to bear for what they need to do to achieve their objectives to deliver to their customer. >> Dave: That's interesting. I mean, using silicon as a differentiator to drive a lot of business outcomes and security. >> Mike: Absolutely. >> I mean you see what Amazon's doing in silicon, Look at Apple. Look at what Tesla's doing with silicon. >> Dave: That's where you're seeing a lot of people start focusing 'cause not everybody can do it. >> Yeah. >> It's hard. >> Right. >> It's hard. >> And you'll see some interesting announcements from us and some interesting information and trends that we'll be driving because of where we're placed and what we have going around security and intelligent industry overall. We have a lot of investment going on there right now and again, from the partner perspective, it's an ecosystem of key partners that collectively work together to kind of create a seamless security posture for an intelligent industry initiative with these companies that we're working with. >> So last question, probably toughest question, and that's to give us a 30 second like elevator pitch or a billboard and I'm going to ask you, Anne, specifically about the SOC Essentials program powered by Trend Micro. Why should organizations look to that? >> Organizations should move to it or work with us on it because we have the expertise, we have the width and breadth to help them fill the gaps, be those eyes, be that team, the police behind it all, so to speak, and be the team behind them to make sure we're giving them the right information they need to actually act effectively on maintaining their security posture. >> Nice and then last question for you, Mike is that billboard, why should organizations in any industry work with Capgemini to help become an intelligent industrial player. >> Mike: Sure, so if you look at our board up top, right, we've got our tagline that says, "get the future you want." And that's what you're going to get with Capgemini. It's not just about selling a service, it's not just about what partners' right in reselling. We don't want that to be why you come to us. You, as a company have a vision and we will help you achieve that vision in a way that nobody else can because of our depth, because of the breadth that we have that's very hard to replicate. >> Awesome guys, that was great answers. Mike, Anne, thank you for spending some time with Dave and me on the program today talking about what's new with Capgemini. We'll be following this space. >> All right, thank you very much. >> For our guests and for Dave Vellante, I'm Lisa Martin, you're watching theCUBE, the leader in live enterprise and emerging tech coverage. (gentle light music)

foreign to the AWS re invent Cube coverage I'm John Furrier here with thecube got a great guest line up here talking about computer vision at the edge and saramba product lead AWS events mobile app and Steven White solution architect for Edge ml thanks for joining me today computer vision at the edge with adios Panorama thanks for coming on happy to be here so what is Ada's Panorama let's get that out there right away what's the focus of that let's define what that is and we'll get into this computer vision at the Edge Story yeah so thanks Sean uh AWS Panorama is our managed uh computer vision at the Ed service and so to put that perspective you know imagine with me the last time that you've been into a restaurant or maybe your favorite retail store or even office building and didn't notice a camera and so we were talking to customers and trying to understand you know what is it that they do with all of this uh video content that they're collecting and surprisingly we found out that large part of this data just sits on a hard drive somewhere and never gets used and so as we dug in a little deeper to better understand you know why this data is just sitting there I think there were three main themes that continue to come up across the board uh one is you know around privacy right privacy security a lot of the data that's being captured with these cameras tend to be either intellectual property that is you know focused on kind of the Manifest factoring process or maybe about their products that they don't want to get out there you know or and or it could just be a private pii data privacy data related to their employee Workforce and and maybe even customers so you know privacy is is a big concern second was just the amount of bandwidth that cameras create and produce tend to be uh prohibitive from for you know sending back to a centralized location for processing uh each camera stream tends to generate about a couple of megabytes of data so it could get very voluminous as you've got tons of cameras at your location and the other issue was around just the latency required to take action on the data so a lot of times especially in the manufacturing space um you know as as you've got a manufacturing line of products that are coming through and you need to take action in milliseconds and so latency is extremely important from process processing time to taking action so those three uh main drivers you know we ended up developing this AWS service called Panorama that addressed these three main challenges with uh you know with analyzing video content and database Panorama in particular there's there's two main components right we've got the compute platform that is about the size of a sheet of paper your standard you know eight and a half by eleven size sheet of paper so the platform itself is extremely compact it's a it's a video and and deep learning algorithms it sits at the customer premise and directly interfaces with video cameras using the standard IP protocols collects that data uh processes it and then immediately deletes the data so there isn't any any information that's actually stored at the location and you know basically the only thing that's left over is just metadata that describes that data and then the other key component here is the cloud um you know service component which helps manage the fleet of devices that are existing so all of these Panorama appliances that are sitting at your premise there's a cloud component that helps you configure you know operationalize check the health as well as deploy applications and configure cameras so that's uh basically you know the the service is really hopefully optimal or you know is focused on um helping customers really make use of all of their video data at the edge you know the theme here at re invent this year is applications we've seen things like connect add value to customers this is one of those situations where everyone's got cameras it's easy to connect to an IP address and Cloud kind of gives you all those Services there are a lot of real world applications that people can can Implement with this because with the cloud you kind of have this ability to kind of stand it up and get value out of that data what are some of the real world applications that it was because they're implementing with the camera because I mean I can see a lot of use cases here where I can you don't have to build the clouds there for me I can stand it up and start getting value what kind of use cases do you see implementing from your customers yeah so our customers are really amazing with the different types of problems um and opportunities that they bring to us for uh using computer vision at the edge in their data um you know we've got everything from animal Warfare use cases to being able to use you know video to uh to to make sure that you know food processing and just you know the health of animals is uh is uh sufficient we've got cases in manufacturing doing visual inspection and anomaly detection so looking at products that are on the conveyor belt as they're being manufactured and put together to make sure that obviously they're they're put together in the right in the right way um and then we've got different port authority and airports that use uh for you know security and cargo tracking to make sure that the products get to where they're supposed to go in a timely and efficient manner manager manage and then finally one of the use cases that really show facing a re invent this year is a part of our retail analytics portfolio which is line counting and so in particular we see a lot of customers in the retail space such as quick service restaurants even you know Peril retail and convenience stores where they want to better understand um you know whether their product is being made to the customer specification we've got like french fry use cases to see how the quality of that french fry is um you know over time and if they need to make a new batch when they've got a influx of customers coming in and to understanding employee to customer ratio maybe they need to put somebody on the cash register you know at busy time so there's really just a big number of customers you know opportunities that we've really solving with the computer vision service looks like a great service Panorama looking good and I want to get your thoughts you have the events happy the product lead take us through with your app I know you have decided to use it was Panorama I was a fit for you this year at re invent 2022 but you know you've been doing this event app for a while now take us through the app when it started how it's evolved and kind of what's the focus this year of course Sean app started in 26 4 re invent and since we've really expanded this year we've actually supported up to 34 events for AWS and continue to expand that for future years for this year though specifically we wanted to contribute to the overall event experience at re invent by helping people go through the process of checking in and picking up their badge in a more formed and efficient way so we decided that the AWS Panorama team and their computer vision and Edge capabilities were the best fit to analyze the lines and the registration kiosks that we have on site at both the Venetian and MGM at the airport we'll have digital signage showcasing our bad pickup wait times that will help attendees select which badge pickup location that they want to go to and see the current wait times live on those signs as well as through the mobile app so I can basically um get the feel for the line size when to come in does it give me a little recognition of who I am and kind of when I get there there's a TIA pull up my records as I do a little intelligence behind the scenes give us a little peek under the covers what's the solution look like so you do have to sign into the mobile app with your registration and so with that we will have your QR code specific for your check-in experience available to you you'll see that at the top of the screen and we'll know once you've checked in that will disappear but if you haven't checked in that Banner is at the top of the event screen and when you tap that that's when you can see all the different options where you can go and pick up your badge we do have five locations this year for badge pickup and the app will help you kind of navigate which one of those options will be best for you given you know maybe you want to pick it up right away at the airport or you may want to go even to one of your other Hotel options that we'll have um to pick it up at foreign okay now I gotta get I got to ask you on the app what's the coolest thing you got going on this year what's new every year there seems to be a new feature what's the focus this year so can you share a a peek on some of the key features yeah so our biggest and most popular features are always around the session catalog and calendar as you can utilize both to of course organize your event schedule and really stay on top of what you want to do on site and get the most out of your reinvent experience this year we have a few new exciting features of course badge pickup line counting is is one of our biggest but we also will have a one-way calendar sync so you can sync all of your calendar activities to your native device calendar as well as pure talk which is our newest feature that we launched at the start of November where you can interact with other attendees who have opted in and even set up time on site to meet one-on-one with them we've also filled that experience with peer talk experts that include AWS experts that are ready to meet and interact with attendees who have interest on site you know I love this topic it's a very cool video we love video we're doing this remote video I'm getting ready for you know all the action and and analyzing it video's cool and so to me if we could look at the video and say hey we haven't soon that might have body cams in the future um video is great people love videos very engaging but always people that say what about my privacy so how do you guys put in place uh mechanisms to preserve attendee privacy yeah I think so I'm not I think you know you and our customers share the same concern and so we have built uh foundationally that AWS Panorama to address you know both privacy and security concerns with uh associated with all this video content and so in particular the AWS Panorama Appliance is something that sits at the customer premise it interface directly with video cameras uh the data all the video that's processed is immediately deleted nothing stored um and you know the outcome of the processing is just simple metadata so it's Text data that you know as an example in the case of the AWS uh line counting solution that we're demoing this year at Panorama along with you know the events team uh it's simply a count of the number of people in the video at any given time so so you know we we do take privacy uh at heart and have made every effort to address them and what are some of the things that you're doing at the event app I mean I'm imagining you're probably looking at space I mean there's a fire marshal issues around you know people do you take it to that level I mean what's how far are you pushing the envelope on on Panorama what are some of the things that you guys are doing besides check-ins or anything you can share on what's Happening the area where we're utilizing you know Anonymous attendee data otherwise other things in the app are very Anonymous just in nature I mean you do sign in but besides that everything we collect is anonymous and we don't collect unless you consent with the cookie consent that appears right when you first launch the app experience besides that we do have as I mentioned peer talk and and that's just where you're sharing information that you want to share with other attendees on site and then we do have session surveys where you can provide information that you wish about how this survey or how the sessions rather went that you attended on-site yeah Stephen you're you're uh your title has you the solution architect for Edge ml this is the Ultimate Edge use case you're seeing here I mean it's a big part of the future of how companies are going to use video and data just what's your reaction to all this I mean we're at a time it's very kind of an interesting time in the history of the industry as you look at this this is a really big part of of the future with video and Edge like I mentioned users are involved people are involved spaces are involved kind of a fun area what's your reaction to where this is right now so personally I'm very passionate about this uh particular solution and service I've been doing computer vision now for 12 years I started doing in the cloud but when I heard about you know customers really looking for an edge component solution and this you know AWS was still in the early stages I knew I had to be a part of it and so I I you know work with some amazing talented engineers and scientists putting this solution together and of course you know our customers continue to bring us these amazing use cases that you know that just I wouldn't get an opportunity to um you know witness without without you know the support of our customers and so we've got some amazing opportunity amazing projects and you know I just love the love to uh experience that with our customers and partners yeah and and Stephen this is like one of those times where the industry has always had this everyone's scratching the niche somewhere but then you get cloud and scale and data come in and just it accelerates some of these areas that were you know I won't say not growing fast but very interesting like computer vision video events technology in the cloud is changing in a good way some of these areas uh and we're seeing that like computer vision as you mentioned Stephen so Ann event same thing I can imagine this event app will blow up to probably be all things Amazon events and and be the touch Touchstone for all customers and attendees I'm probably thinking the road map there's looking pretty interesting with all the vision you have there what's your what's your reaction to the cloud scale meets events absolutely yeah I know we we have a lot of events that happen at AWS and our goal is to have as many of them in the app as possible where it makes sense right we have a lot of partial Day events to multi-day events and the multi-day events are definitely the area where it's harder for an attendee to organize all that they have to do going on on site as well as everything surrounding the event pre-event uh topics and sessions looking up what they want to do to make sure that they're getting the most of their time on site so we really want to make sure that that's something that an attendee can do with our app as well as it showcase as many of the AWS Services as we have like we are doing here with Panorama we have a few other services in the app as well Amazon location service and Amazon connect to name a couple and we hope to just include more and more with each year as well as more events as the time goes on I'm sure your roadmaps looking great the computer vision is awesome I mean this is a mashup integration apis are going to come around the corner so much excitement after re invent love to follow up with you guys and find out more I think this is a super interesting area the convergence of what you guys are working on to kind of wrap up where do you guys see um AWS Panorama going and where can people learn more about how to get involved how to use the service how to test it out where's this going and how do people learn more but first off you can get customers can get more information about panorama from our website aws.amazon.com Panorama and you know I think where we're going is super exciting you know we continue to improve the product to add support for as an example containers we've added support for Hardware acceleration to improve the number of cameras that we can support so we've you know we've got um you know we can support now with a single device up to 30 40 cameras we've got the ability now to support many different uh we continue to expand the interface types that we support um you know and the different types of even adding sensors and you know expanding to Sensor Fusion so not just computer vision but we've learned from customers that they actually want to incorporate other uh other sensor types and other interfaces so we're bringing in the ability to handle you know computer vision and video but also many other data types as well all right and and Stephen thank you for sharing great stuff computer vision at the edge with Panorama thanks for coming on thecube appreciate it thanks for coming on thank you okay AWS coverage here in the cube I'm John for your host thanks for watching

>> Narrator: Live from San Francisco. It's the theCUBE covering RSA Conference 2020 San Francisco, brought to you by SiliconAngle Media. >> Alright, welcome to theCUBE coverage here at RSA Conference in San Francisco and Moscone Halls, theCUBE. I'm John Furrier, the host of theCUBE, in a cyber security is all about encryption data and also security. We have a very hot startup here, that amazing guest, Dr. Ellison Anne Williams, CEO and Founder of Enveil just recently secured a $10 million Series A Funding really attacking a real problem around encryption and use. Again, data ,security, analytics, making it all secure is great. Allison, and thanks for coming on. Appreciate your time. >> Thanks for having me. >> So congratulations on the funding before we get started into the interview talking about the hard news, you guys that are around the funding. How long have you guys been around? What's the funding going to do? What are you guys doing? >> Yeah, so we're about three and a half years old as a company. We just announced our Series A close last week. So that was led by C5. And their new US Funds The Impact Fund and participating. Other partners included folks like MasterCard, Capital One Ventures, Bloomberg, Beta 1843, etc. >> So some names jumped in C5 led the round. >> For sure. >> How did this get started? What was the idea behind this three years you've been actually doing some work? Are you going to production? Is it R&D? Is it in market? Give us a quick update on the status of product and solution? >> Yeah, so full production. For production of the product. We're in fact in 2.0 of the release. And so we got our start inside of the National Security Agency, where I spent the majority of my career. And we developed some breakthroughs in an area of technology called homomorphic encryption, that allows you to perform computations into the encrypted domain as if they were in the unencrypted world. So the tech had never existed in a practical capacity. So we knew that bringing seeds of that technology out of the intelligence community and using it to seed really and start the company, we would be creating a new commercial market. >> So look at this, right? So you're at the NSA, >> Correct >> Your practitioner, they're doing a lot of work in this area, pioneering a new capability. And did the NSA spin it out did they fund it was the seed capital there or did you guys bootstrap it >> No. So our seed round was done by an entity called Data Tribe. So designed to take teams in technologies that were coming out of the IC that wanted to commercialize to do so. So we took seed funding from them. And then we were actually one of the youngest company ever to be in the RSA Innovation Sandbox here in 2017, to be one of the winners and that's where the conversation really started to change around this technology called homomorphic encryption, the market category space called securing data in use and what that meant. And so from there, we started running the initial version of a product out in the commercial world and we encountered two universal reaction. One that we were expecting and one that we weren't. And the one that we were expecting is that people said, "holy cow, this actually works". Because what we say we do keeping everything encrypted during processing. Sounds pretty impossible. It's not just the math. And then the second reaction that we encountered that we weren't expecting is those initial early adopters turned around and said to us, "can we strategically invest in you?" So our second round of funding was actually a Strategic Round where folks like Bloomberg beta,Thomson Reuters, USA and Incue Towel came into the company. >> That's Pre Series A >> Pre Series A >> So you still moving along, if a sandbox, you get some visibility >> Correct. >> Then were the products working on my god is you know, working. That's great. So I want to get into before I get into some of the overhead involved in traditionally its encryption there always has been that overhead tax. And you guys seem to solve that. But can you describe first data-at-rest versus data-in-motion and data-in-user. data at rest, as means not doing anything but >> Yeah, >> In flight or in you so they the same, is there a difference? Can you just tell us the difference of someone this can be kind of confusing. >> So it's helpful to think of data security in three parts that we call the triad. So securing data at rest on the file system and the database, etc. This would be your more traditional in database encryption, or file based encryption also includes things like access control. The second area, the data security triad is securing data- in- transit when it's moving around through the network. So securing data at rest and in transit. Very well solution. A lot of big name companies do that today, folks like Talus and we partner with them, Talus, Gemalto, etc. Now, the third portion of the data security triad is what happens to that data when you go use or process it in some way when it becomes most valuable. And that's where we focus. So as a company, we secure data-in-use when it's being used or processed. So what does that mean? It means we can do things like take searches or analytics encrypt them, and then go run them without ever decrypting them at any point during processing. So like I said, this represents a new commercial market, where we're seeing it manifest most often right now are in things like enabling secure data sharing, and collaboration, or enabling secure data monetization, because its privacy preserving and privacy enabling as a capability. >> And so that I get this right, the problem that you solved is that during the end use parts of the triad, it had to be decrypted first and then encrypted again, and that was the vulnerability area. Look, can you describe kind of like, the main problem that you guys saw was that-- >> So think more about, if you've got data and you want to give me access to it, I'm a completely different entity. And the way that you're going to give me access to it is allowing me to run a search over your data holdings. We see this quite a bit in between two banks in the areas of anti-money laundering or financial crime. So if I'm going to go run a search in your environment, say I'm going to look for someone that's an EU resident. Well, their personal information is covered under GDPR. Right? So if I go run that search in your environment, just because I'm coming to look for a certain individual doesn't mean you actually know anything about that. And so if you don't, and you have no data on them whatsoever, I've just introduced a new variable into your environment that you now have to account for, From a risk and liability perspective under something like GDPR. Whereas if you use us, we could take that search encrypt it within our walls, send it out to you and you could process it in its encrypted state. And because it's never decrypted during processing, there's no risk to you of any increased liability because that PII or that EU resident identifier is never introduced into your space. >> So the operating side of the business where there's compliance and risk management are going to love this, >> For sure. >> Is that really where the action is? >> Yes, compliance risk privacy. >> Alright, so get a little nerdy action on this one. So encryption has always been an awesome thing depending on who you talk to you, obviously, but he's always been a tax associate with the overhead processing power. He said, there's math involved. How does homeomorphic work? Does it have problems with performance? Is that a problem? Or if not, how do you address that? Where does it? I might say, well, I get it. But what's the tax for me? Or is your tax? >> Encryption is never free. I always tell people that. So there always is a little bit of latency associated with being able to do anything in an encrypted capacity, whether that's at rest at in transit or in use. Now, specifically with homomorphic encryption. It's not a new area of encryption. It's been around 30 or so years, and it had often been considered to be the holy grail of encryption for exactly the reasons we've already talked about. Doing things like taking searches or analytics and encrypting them, running them without ever decrypting anything opens up a world of different types of use cases across verticals and-- >> Give those use case examples. What would be some that would be low hanging fruit. And it would be much more higher level. >> Some of the things that we're seeing today under that umbrella of secure data sharing and collaboration, specifically inside of financial services, for use cases around anti-money laundering and financial crimes so, allowing two banks to be able to securely collaborate with with each other, along the lines of the example that I gave you just a second ago, and then also for large multinational banks to do so across jurisdictions in which they operate that have different privacy and secrecy regulations associated with them. >> Awesome. Well, Ellison, and I want to ask you about your experience at the NSA. And now as an entrepreneur, obviously, you have some, you know, pedigree at the NSA, really, you know, congratulations. It's going to be smart to work there, I guess. Secrets, you know, >> You absolutely do. >> Brains brain surgeon rocket scientist, so you get a lot of good stuff. But now that you're on the commercial space, it's been a conversation around how public and commercial are really trying to work together a lot as innovations are happening on both sides of the fence there. >> Yeah. >> Then the ICC and the Intelligence Community as well as commercial. Yeah, you're an entrepreneur, you got to go make money, you got shareholders down, you got investors? What's the collaboration look like? How does the world does it change for you? Is it the same? What's the vibe in DC these days around the balance between collaboration or is there? >> Well, we've seen a great example of this recently in that anti-money laundering financial crime use case. So the FCA and the Financial Conduct Authority out of the UK, so public entity sponsored a whole event called a tech spread in which they brought the banks together the private entities together with the startup companies, so your early emerging innovative capabilities, along with the public entities, like your privacy regulators, etc, and had us all work together to develop really innovative solutions to real problems within the banks. In the in the context of this text spread. We ended up winning the know your customer customer due diligence side of the text brand and then at the same time that us held an equivalent event in DC, where FinCEN took the lead, bringing in again, the banks, the private companies, etc, to all collaborate around this one problem. So I think that's a great example of when your public and your private and your private small and your private big is in the financial services institutions start to work together, we can really make breakthroughs-- >> So you see a lot happening >> We see a lot happening. >> The encryption solution actually helped that because it makes sense. Now you have the sharing the encryption. >> Yeah. >> Does that help with some of the privacy and interactions? >> It breaks through those barriers? Because if we were two banks, we can't necessarily openly, freely share all the information. But if I can ask you a question and do so in a secure and private capacity, still respecting all the access controls that you've put in place over your own data, then it allows that collaboration to occur, whereas otherwise I really couldn't in an efficient capacity. >> Okay, so here's the curveball question for you. So anybody Startup Series today, but you really got advanced Series A, you got a lot of funding multiple years of operation. If I asked you what's the impact that you're going to have on the world? What would you say to that, >> Over creating a whole new market, completely changing the paradigm about where and how you can use data for business purposes. And in terms of how much funding we have, we have, we've had a few rounds, but we only have 15 million into the company. So to be three and a half years old to see this new market emerging and being created with with only $15 million. It's really pretty impressive. >> Yeah, it's got a lot of growth and keep the ownership with the employees and the founders. >> It's always good, but being bootstrap is harder than it looks, isn't it? >> Yeah. >> Or how about society at large impact. You know, we're living global society these days and get all kinds of challenges. You see anything else in the future for your vision of impact. >> So securing data and your supplies horizontally across verticals. So far we've been focused mainly on financial services. But I think healthcare is a great vertical to move out in. And I think there are a lot of global challenges with healthcare and the more collaborative that we could be from a healthcare standpoint with our data. And I think our capabilities enable that to be possible. And still respecting all the privacy regulations and restrictions. I think that's a whole new world of possibility as well. >> And your secret sauce is what math? What's that? What's the secret sauce, >> Math, Math and grit. >> Alright, so thanks for sharing the insights. Give a quick plug for the company. What are you guys looking to do? Honestly, $10 million in funding priorities for you and the team? What do you guys live in to do? >> So priorities for us? privacy is a global issue now. So we are expanding globally. And you'll be hearing more about that very shortly. We also have new product lines that are going to be coming out enabling people to do more advanced decisioning in a completely secure and private capacity. >> And hiring office locations DC. >> Yes. So our headquarters is in DC, but we're based on over the world, so we're hiring, check out our web page. We're hiring for all kinds of roles from engineering to business functionality >> And virtual is okay virtual hires school >> Virtual hires is great. We're looking for awesome people no matter where they are. >> You know, DC but primary. Okay, so great to have you gone. Congratulations for one, the financing and then three years of bootstrapping and making it happen. Awesome. >> Thank you. >> Thank you for coming ,appreciate it. So keep coming to your RSA conference in Moscone. I'm John Furrier. Thanks for watching more after this short break (pop music playing)

>> Live from Mountain View, California, it's theCUBE! Covering DevNet Create 2019, brought to you by Cisco. >> Hi, welcome to theCUBE's coverage of Cisco DevNet Create 2019, Lisa Martin with John Furrier, we've been here all day, talking about lots of very inspiring, educational, collaborative folks, and we're pleased to welcome to theCUBE Anne Gentle, developer experience manager for Cisco DevNet, Anne, thank you so much for joining us on theCUBE today. >> Thank you so much for having me. >> So this event, everything's like, rockstar start this morning with Susie, Mandy, and the team with the keynotes, standing room only, I know when I was walking out. >> I loved it, yes. >> Yes, there's a lot of bodies in here, it's pretty toasty. >> Yeah. >> The momentum that you guys have created, pun intended. >> Oh, yes. >> No, I can't take credit for that, is really, you can feel it, there's a tremendous amount of collaboration, this is your second create? >> Second create, yeah, so I've been with DevNet itself for about year and a half, and started at Cisco about three years ago this month, but I feel like developer experience is one of my first loves, when I really started to understand how to advocate for the developer experience. So DevNet just does a great job of not only advocating within Cisco, but outside of Cisco as well, so we make sure that their voice is heard, if there's some oddity with an API, which, you know, I'm really into API design, API style, we can kind of look at that first, and kind of look at it sideways and then talk to the teams, okay is there a better way to think about this from a developer standpoint. >> It's great, I love the API love there, it's going around a lot here. DevNet create a cloud native vibe that's kind of integrating and cross-pollinating into DevNet, Cisco proper. You're no stranger to cloud computing early days, and ecosystems that have formed naturally and grown, some morph, some go different directions, so you're involved in OpenStack, we know that, we've talked before about OpenStack, just some great successes as restarts, restarts with OpenStack ultimately settled in what it did, the CNCF, the Cloud Native Computing Foundation, is kind of the cloud native OpenStack model. >> Yeah, yeah. >> You've seen the communities grow, and the market's maturing. >> Definitely, definitely. >> So what's your take on this, because it creates kind of a, the creator builder side of it, we hear builder from Amazon. >> Yeah, I feel like we're able to bring together the standards, one of the interesting things about OpenStack was okay, can we do open standards, that's an interesting idea, right? And so, I think that's partially what we're able to do here, which is share, open up about our experiences, you know, I just went to a talk recently where the SendGrid former advocate is now working more on the SDK side, and he's like, yeah the travel is brutal, and so I just kind of graduated into maintaining seven SDKs. So, that's kind of wandering from where you were originally talking, but it's like, we can share with each other not only our hardships, but also our wins as well, so. >> API marketplaces is not a new concept, Apache was acquired-- >> Yes. >> By a big company, we know that name, Google. But now it's not just application programming interface marketplaces, with containers and server space, and microservices. >> Right. >> The role of APIs growing up on a whole other level is happening. >> Exactly. >> This is where you hear Cisco, and frankly I'm blown away by this, at the Cisco Live, that all the portfolio (mumbles) has APIs. >> True, yes, exactly. >> This is just a whole changeover, so, APIs, I just feel a whole other 2.0 or 3.0 level is coming. >> Absolutely. >> What's your take on this, because-- >> So, yeah, in OpenStack we documented like, two APIs to start, and then suddenly we had 15 APIs to document, right, so, learn quick, get in there and do the work, and I think that that's what's magical about APIs, is, we're learning from our designs in the beginning, we're bringing our users along with us, and then, okay, what's next? So, James Higginbotham, I saw one of his talks today, he's really big in the API education community, and really looking towards what's next, so he's talking about different architectures, and event-driven things that are going to happen, and so even talking about, well what's after APIs, and I think that's where we're going to start to be enabled, even as end users, so, sure, I can consume APIs, I'm pretty good at that now, but what are companies building on top of it, right? So like GitHub is going even further where you can have GitHub actions, and this is what James is talking about, where it's like, well the API enabled it, but then there's these event-driven things that go past that. So I think that's what we're starting to get into, is like, APIs blew up, right? And we're beyond just the create read. >> So, user experience, developer experience, back to what you do, and what Mandy was talking about. You can always make it easier, right? And so, as tools change, there's more tools, there's more workloads, there's more tools, there's more this, more APIs, so there's more of everything coming. >> Yeah. >> It's a tsunami to the developer, what are some of the trends that you see to either abstract away complexities, and, or, standardize or reduce the toolchains? >> Love where you're going with this, so, the thing is, I really feel like in the last, even, since 2010 or so, people are starting to understand that REST APIs are really just HTTP protocol, we can all understand it, there's very simple verbs to memorize. So I'm actually starting to see that the documentation is a huge part of this, like a huge part of the developer experience, because if, for one, there are APIs that are designed enough that you can memorize the entire API, that blows me away when people have memorized an API, but at the same time, if you look at it from like, they come to your documentation every day, they're reading the exact information they can give, they're looking at your examples, of course they're going to start to just have it at their fingertips with muscle memory, so I think that's, you know, we're starting to see more with OpenAPI, which is originally called Swagger, so now the tools are Swagger, and OpenAPI is the specification, and there's just, we can get more done with our documentation if we're able to use tools like that, that start to become industry-wide, with really good tools around them, and so one of the things that I'm really excited about, what we do at DevNet, is that we can, so, we have a documentation tool system, that lets us not only publish the reference information from the OpenAPI, like very boring, JSON, blah blah blah, machines can read it, but then you can publish it in these beautiful ways that are easy to read, easy to follow, and we can also give people tutorials, code examples, like everything's integrated into the docs and the site, and we do it all from GitHub, so I don't know if you guys know that's how we do our site from the back side, it's about 1000 or 2000 GitHub repos, is how we build that documentation. >> Everything's going to GitHub, the network configurations are going to GitHub, it's programmable, it's got to be in GitHub. >> Yes, it's true, and everything's Git-based right? >> So, back to the API question, because I think I'm connecting some dots from some of the conversations we had, we heard from some of the community members, there's a lot of integration touchpoints. Oh, a call center app on their collaboration talks to another database, which talks to another database, so these disparate systems can be connected through APIs, which has been around for a while, whether it's an old school SOAP interface, to, you know, HTTP and REST APIs, to full form, cooler stuff now. But it's also more of a business model opportunity, because the point is, if your API is your connection point-- >> Yes. >> There's potential business deals that could go on, but if you don't have good documentation, it's like not having a good business model. >> Right, and the best documentation really understands a user's task, and so that's why API design is so important, because if you need to make sure that your API looks like someone's daily work, get the wording right, get the actual task right, make sure that whatever workflow you've built into your API can be shown through in any tutorial I can write, right? So yeah, it's really important. >> What's the best practice, where should I go? I want to learn about APIs, so then I'm going to have a couple beers, hockey's over, it's coming back, Sharks are going to the next round, Bruins are going to the next round, I want to dig into APIs tonight. Where do I go, what are some best practices, what should I do? >> Yeah, alright, so we have DevNet learning labs, and I'm telling you because I see the web stats, like, the most popular ones are GitHub, REST API and Python, so you're in good company. Lots of people sitting on their couches, and a lot of them are like 20 minutes at a time, and if you want to do like an entire set that we've kind of curated for you all together, you should go to developer.cisco.com/startnow, and that's basically everything from your one-on-ones, all the way up to, like, really deep dive into products, what they're meant to do, the best use cases. >> Okay, I got to ask you, and I'll put you on the spot, pick your favorite child. Gold standard, what's the best APIs that you like, do you think are the cleanest, tightest? >> Oh, best APIs I like, >> Best documented? >> So in the technical writing world, the gold standard that everyone talks about is the Stripe documentation, so that's in financial tech, and it's very clean, we actually can do something like it with a three column layout-- >> Stripe (mumbles) payment gateway-- >> Stripe is, yeah, the API, and so apparently, from a documentation standpoint, they're just, people just go gaga for their docs, and really try to emulate them, so yeah. And as far as an API I use, so I have a son with type one diabetes, I don't know if I've shared this before, but he has a continuous glucose monitor that's on his arm, and the neat thing is, we can use a REST API to get the data every five minutes on how his blood sugar is doing. So when you're monitoring this, to me that's my favorite right now, because I have it on my watch, I have it on my phone, I know he's safe at school, I know he's safe if he goes anywhere. So it's like, there's so many use cases of APIs, you know? >> He's got the policy-based program, yeah. >> He does, yes, yes. >> Based upon where's he's at, okay, drink some orange juice now, or, you know-- >> Yes, get some juice. >> Get some juice, so, really convenient real-time. >> Yes, definitely, and he, you know, he can see it at school too, and just kind of, not let his friends know too much, but kind of keep an eye on it, you know? >> Automation. >> Yeah, exactly, exactly. >> Sounds like great cloud native, cool. You have a Meraki hub in your house? >> I don't have one at home. >> Okay. >> Yeah, I need to set one up, so yeah, we're terrible net nannies and we monitor everything, so I think I need Meraki at home. (laughing) >> It's a status symbol now-- >> It is now! >> We're hearing in the community. Here in the community of DevNet, you got to have a Meraki hub in your, switch in your house. >> It's true, it's true. >> So if you look back at last year's Create versus, I know we're just through almost day one, what are some of the things that really excite you about where this community of now, what did they say this morning, 585,000 strong? Where this is going, the potential that's just waiting to be unlocked? >> So I'm super excited for our Creator awards, we actually just started that last year, and so it's really neat to see, someone who won a Creator award last year, then give a talk about the kind of things he did in the coming year. And so I think that's what's most exciting about looking a year ahead for the next Create, is like, not only what do the people on stage do, but what do the people sitting next to me in the talks do? Where are they being inspired? What kind of things are they going to invent based on seeing Susie's talk about Wi-Fi 6? I was like, someone invent the thing so that when I go to a hotel, and my kids' devices take all the Wi-Fi first, and then I don't have any, someone do that, you know what I mean, yeah? >> Parental rights. >> So like, because you're on vacation and like, everybody has two devices, well, with a family of four-- [John] - They're streaming Netflix, Amazon Prime-- >> Yeah, yeah! >> Hey, where's my video? >> Like, somebody fix this, right? >> Maybe we'll hear that next year. >> That's what I'm saying, someone invent it, please. >> And thank you so much for joining John and me on theCUBE this afternoon, and bringing your wisdom and your energy and enthusiasm, we appreciate your time. >> Thank you. >> Thank you. >> For John Furrier, I am Lisa Martin, you're watching theCUBE live from Cisco DevNet Create 2019. Thanks for watching. (upbeat music)

(upbeat electronic music) >> Live from Washington D.C. It's theCUBE. Covering Inforum D.C. 2018. Brought to you by Infor. >> And welcome back to Washington D.C. We're in the Washington Convention Center here for Inforum 2018, continuing the coverage here on theCUBE, I'm John Walls with Dave Vellante, we're joined now by Anne Benedict, who is the S.V.P. of human resources at Infor. Anne good afternoon to you. >> Thank you, thanks for having me. >> You bet, thanks for being here. Now, 17,000 employees, so obviously you've got a lot of responsibility there. You're not only an Infor Executive, but you wear the hat of being an Infor client, (laughs) as well. Tell us about that, and how that works out, and, I guess, how you can test drive a lot of different services on your own before it goes out to the market. >> I like to joke that I feel like I have the best HR leadership role in the business, or in the world perhaps, because I get to not only lead a great company full of great people, 17,000 employees around the world, I'm so proud of them, but then I also get to be a customer of one of the greatest products in the HCM world that there is, and I have a direct line to the product managers, to the developers, to the consultants who can really help us to use our product to it's fullest advantage internally for our selves. So, it's like a toy box that every H.R. executive dreams of, and it's right there at my door step to test, to use, to innovate with them. They're always open to our ideas, our feedback internally. We're often a beta customer for the features, and functionality that are coming out to our customers, so it's a great position to be in. >> So what about the relationship, because there is a great give and take. Obviously, because you are a tremendous resource on the development side. What is that exchange like, and how does that work in terms of what's working, what's not, what you think others would want instead, or what they'd like to tweak a little bit. How does that work? >> So, you know, we're trying to sort of straddle a balance between using the product as it's intended to be designed for the breadth of our customers, no matter what industry they're in. We're obviously in a technology industry, but we have a lot of health care customers, government customers, services customers who have their own particular needs. So, we like to experiment with the technology the way it's designed for other industries, but then also I can make adjustments for use for our own company as a services company, as a technology company, and a good example of that for example is I'm working very closely with product management right now to help them design the next iteration of what our talent management suite will look like. So, we have a design concept for how we want to give performance feedback, for example, internally at Infor, and we're sharing that design the product management team to help them create the next version of the product that will meet the design requirements that we've set out for ourselves, and that I think a lot of other companies are moving towards. It's a modern approach to talent management, and we're working very closely hand in hand with product management to make sure they're designing something that we, we're co-designing it with them really. So, what I'm expecting is for us to have a really great next iteration of that product that is very modern, and up to date on what science is telling us about performance feedback. >> So, you're a pioneer, in a way, but you probably don't want to mess with with core H.R., that's table stakes. Talent management is something that, frankly, not a lot of companies do well. So, you may be more receptive to experimentation there. Is that a fair assertion? >> Yeah, I would say that's true, and also my background is, I grew up in H.R. with quite a breadth of experiences, but my depth of expertise has always been on the talent management and leadership development side. So, that's been sort of where I've been wanting to play with the product, and give my point of view on where I think it should evolve. It's just my particular strength that I bring, I think, to this role and to the product as well. >> How do you see the role of the Senior H.R. Executive evolving? How has it changed in the last several years? How is, maybe, digital transformation, this whole big data, the data movement? How does that factor into that role, and your vision of where that goes? >> Yeah, I think companies are looking for a different type of H.R. Executive than they have in the past. And I was fortunate that this wasn't by design. It was very serendipitous, but my career path led me, I think, in the exact right direction. So, I started my first 10 years of my career as a consultant at Mercer doing H.R. consulting. So, I was consulting the companies how to make, how to create the best H.R. department possible, how to create H.R. strategy, how to operationalize that. And, it was that consulting mindset that I've taken with me throughout my career. After consulting I moved internally to various companies, and that skill set of just being able to identify a problem, come up with a solution, and measure an implementation, I've taken with me in my role. So, I think companies are looking for H.R. executives who bring that sort of mind set to the role. And, I think that's what I've been able to do at Infor. And then, I think also when I was a consultant I was also advising customers and clients on technology, and how to use technology for H.R., so that's why I'm so thrilled to have this role, because it's the best of both worlds where I get to play with the technology, and also be a cutting edge H.R. leader. >> Alright so-- >> Hopefully. >> How do you asses the Infor HCM capabilities? Come on, give us the good, the bad, what's on the to do list. You know, give us the rundown. >> Yeah, I think it's a phenomenal product, and I'm not just saying that. >> Okay, what makes it phenomenal? >> When I walked in the door a year and nine months ago we were just about to go live with the multi-tenant cloud product. We were one of the first to do that, and we did it in over 65 countries with 17,000 employees, and since then we have subsequently rolled out more functionality, benefits enrollment, absence management, compensation planning, LMS, and each time we learn a little bit more. I can't underestimate the importance of getting the process right before you get the technology in, and the change management that goes around it. If I would say, I would give us a B it might have been around those areas, but the product itself is really it has the perfect balance of coming out of the box with some functionality that you can use right away that's best practice process. >> So you get value right off the bat. >> Yeah, and not a lot of configuration required, easy to get in. We implemented it with that broad scope in a very, very short amount of time, which is almost impossible with our competitors, so. So, I think for that it's fantastic, and then for the specific needs that we've had it's been very easy to build that in as well, so it has best of both worlds I would say. >> So, we saw some pretty cool demos yesterday around talent science, and it struck me as an audience member. There were all kinds of different kinds of attributes of, you know, ambition and et cetera, et cetera, et cetera, but you know the one that wasn't on there was like performer, but it struck me that these attributes lead to performance. I guess that's the basic philosophy, but I wanted to test that with you. Just give me the bottom line. >> Yeah. >> But it really is more complicated than that, isn't it? >> It is, yeah, and that's one of the most exciting things about H.R. right now too, I think. And this comes back to H.R. Executive of the future is, I come from an IO Psychology background where data, we used to have to do experiments on subjects with, and collecting data was always the hardest part to studying work, and studying personalities, studying behavior, and now we have all this data available to us that we've never had before. And, talent science is a perfect example of how data is really empowering our decisions. And, to answer your question about how it is predicting performance; A particular attribute doesn't necessarily lead to performance in any role. So, in one role, ambition, really high ambition is actually not a factor for success. In another role, it is. So, it really is, there is no right personality profile that can predict success in any role. It's very role specific. And what talent science is able to do is really find the science behind what is the specific role that will lead to success, and what are the attributes that will lead to non-success, also in a role. And, that's such a powerful thing. What we've found with talent science is that depending on the role we can reduce turnover by 20 up to 70% by choosing people who fit a role profile versus those who don't. >> It's interesting it's like, you know, those books, like the seven attributes or-- >> Or Covey-- >> Of highly successful people, but essentially you're codifying that by role. And, that's true. It doesn't just work for any role. Sales person may be different than an engineer, may be different than a an operations person et cetera. >> So, this is really fascinating, because you have the human science, right, we're all imperfect, we make crazy decisions, sometimes irrational, we act wild, or predictably, whatever it is. And, now you're taking data science, and overlaying with that, so you're trying to come up with some kind of predictable markers, or whatever, for imperfect beings in a way. How's all that merging, I mean, how is technology being the glue in that process? >> Yeah, well I think there's no such thing as right and wrong, or perfect and imperfect. You know, I could get into a leadership speil, but any strength that either of you might have, if you use that to an extreme it becomes a weakness, actually. And, like I used in the example of ambition, high ambition in certain roles, may not be a factor toward success. Where as other roles it might be. Whatever particular DNA, behavioral DNA, that you bring to a role as an individual, it's incumbent upon us as a company to figure out what is the right role for the personality that you bring, and the behavior, and the strengths that you have. And, that's what we're really able to do with talent science, which is, so, if you apply for a role where you don't match the profile I may be able to propose to you, hey, you have really high ambition that's not right for this role, but it may be right for this other role. Have you ever considered that? And, that way we can really, you know, we talk about human potential here, at Inforum. That's the real tool, real tangible way that we can really find the human potential in every single person, no matter what their profile looks like, or strengths, or weaknesses, or faults, as you say. Whatever-- >> Whatever it is, right? >> Whatever they come with we can find the right fit. >> Does technology, generally, and say artificial intelligence or machine intelligence, specifically, can it moderate or adjudicate human bias? Or, does it actually reinforce it? >> Yeah, that's a very good question, and obviously very pertinent to today. I think, a couple of things. So, the assessment I'm speaking of, we would never rely on the machine to make a decision. So, it's telling you, as a manager, here are some of the gaps that a particular individual has towards the role that you are planning to hire them for, but we suggest that you ask these interview questions, and make a decision for yourself. So, you really can't replace that human intervention in the process, that human judgment, their sense from an interview, but it really helps them hone the interview in on the things that they really should focus on. Figuring out, are we comfortable with those gaps? Does the person realize they have those gaps? And, really, for both the candidate and the manager to make the right decision. So, in the assessment it's always, we never rely on the machine to make a decision. But, it is incumbent on us to make sure that as we're designing these tools, as we're designing the technology behind them that we have as much diversity in the people who are designing them as possible. To make sure they're being designed in a way that doesn't have bias built into them. And, that's why it's so important for us to have diversity in technology. Why we're doing SB code. Why we believe in bringing up people from all backgrounds to participate in technology, 'cause it's so important to have that diversity, as we're building this stuff. >> Can't take the humans out of the equation, yet. >> There's still some gut check, right, there's still some intuition that has to come into play here. >> Yeah, absolutely, and that's one of the attributes of humans that we, machines can't replace yet. So, that ability to empathize, the ability to show all the emotional skills, we know machines can't do that today, maybe someday they will. But, today they can't, so humans will bring that. But, I really think that the power comes in the combination of AI, and machines, and humans. And, that's what we're talking about here around human potential. It's the power of the combination of the two. And, I think we will see that that combination will be required for a very long time, before machines take over the world (laughs) >> I always tell the story, John and I interviewed Garry Kasparov. >> That was great. >> The great chess champion. >> Chess master. >> When he lost to the IBM supercomputer, instead of giving up he said, "I'm going to beat the supercomputer", so he took machines plus humans to beat the supercomputer, so to this day the greatest chess player in the world is a machine and a supercomputer. So, that is a great example of augmentation. Now, it probably doesn't work so well for autonomous vehicles, but-- (all laughing) >> Well now, thanks for being with us. Thanks for sharing the story. We appreciate that, the time. And, if you see our application come down the pike-- >> Okay (laughs) >> Flag us where we're deficient, if you would, please. >> You'll be welcome, you're welcome. >> Excellent (laughs) >> Thanks for having me. >> Thank you, Anne Benedict, thanks for being with us. We'll be back with more here on theCUBE. We're live in the nation's capitol, Washington D.C. >> That was awesome. >> Thank you. (upbeat electronic music)

>> Announcer: Live from Vancouver, Canada it's theCUBE covering OpenStack Summit North America 2018. Brought to you by Red Hat, the OpenStack Foundation and its ecosystem partners. >> Welcome back to theCUBE here at OpenStack Summit 2018 in Vancouver. I'm Stu Miniman with co-host this week is John Troyer. I'm happy to welcome to the program, first time guest. It's Anne Bertucio, who is the Kata Containers Community Manager with the OpenStack Foundation. Thanks so much for joining us. >> Thank you for having me. >> All right, it's our pleasure and the containers has been a discussion we've been having for a few years now. I remember when we were last year in Vancouver, three years ago that the joke was it was Docker, Docker, Docker year. Tell us a little bit first your role, how long you've been with the foundation, and what you're covering there. >> Absolutely, I've been with the foundation for going on three years at this point. The Kata Containers Project we announced in December. It's come up and come in there as a community manager helping them figure out since December to the launch now, in less than six months we had to figure out how are we going to work together. How are we going to merge two code bases and we have to create a new open source project and new community. So leading that has been a big part of my work. >> So there's a whole track on Containers now. Give us a little bit of flavor for our audience that couldn't be sitting in the keynote and attend all the sessions. What were they missing? >> I think the major theme was security. Mia, she's the PM of security at Google. She opened it up saying containers don't contain. And I almost wished we'd been on a game show. Like containers don't contain. That was the theme of the day and we talked about where did Kata come from? Kata came from how do we answer that question. I think people got so excited about performance and portability about containers. We forgot about security a little bit and now we're seeing some of the ramifications and it's time to make this the year of security. >> So you talk about bringing two code basis together. Can you talk a little bit about what some of the ingredients are here to get to our dish that we finally call Kata Containers Projects? >> Yeah, absolutely, so we have ren-V from Hyper and we had Clear Containers from Intel. And they both looked at things a little differently like Hyper has a fracty implementation that was really critical to their customers. Clear Containers are becoming a little bit from runC Vert containers. And what we arrive at for 1.0 is the OCI compatible runtime is going to put a lightweight VM around your container, and we're thrilled to look beyond 1.0 and to things like supporting hardware accelerators. >> So it may be just to raise it up one level before we go on. How do containers in some sense, let's repeat maybe what you said, see if I get it right. >> Anne: Yeah. >> It's wrapping a container and a lightweight VM. And that gives us the isolation and security that's traditionally associated with a virtual machine with all the APIs and flexibility and performance, and all the other goodness of a container. One container in one VM is the first implementation. >> Yeah, I think the easy way to think about, you're talking about Docker Docker Docker. So in Kata, really instead of using runC as your runtime, we would just say Kata runtime, and now we have our Docker containers but they're wrapped in these light weight VMs each with their own kernel. >> I think back to the early days when we were trying to figure out what these whole containers were and was that the death of virtualization? It was like VMs, gosh they take minutes to spin up, and container is super fast. Security, oh VMs yeah, there's security there but we need to move fast, fast, fast. So explain how this helps bring together the peanut butter and chocolate, if we will? >> Absolutely, oh I love peanut butter and chocolate but that's really what it is. Like you were saying virtualization, yes. Super secure, slow. I think I have a clip art chart with a sad turtle on it. A little bit slower. The container is super fast, we're getting a little nervous about security. I think we maybe see groups and name spaces are good, but people who are enterprise environments. They've been putting full blown VMs around their containers 'cause they were saying well it's not enough. And I need two isolation boundaries, not just one. >> Right, in terms of some of the use cases then. I imagine multitenancy would be one and then perhaps even, I think some of the newest trend defense in depth of even an individual app putting different zones in different components or different risk zones in their own containers, their own VMs. Even inside an individual app just making sure that the different components can only talk to each other in ways that they're suppose to. >> Absolutely, I think it's anytime where you're running untrusted code, or you have questions about what's going on there or you just want a heightened security. Kata is an easy used case then. >> Sure, I guess my VMware call it microsegmentation would be their buzz word on it. >> Oh I got to think about what mine is going to be. >> Or we can all use the same words, it's good. >> So Anne, Intel Clear Containers was a piece of this. Of course Intel partners with everyone there. Give us a little bit also the ecosystem and the team that makes this up. Is this, people out there will be like, oh, well but Docker has their solution and VMware has their solution. How does this fit into the broader ecosystem? >> Our team is incredibly diverse. I've just been thrilled with 1.0. We had 40 contributors from a good diversity of companies. Our architecture committee, it's Google, it's Huawei, Hyper, Intel and Microsoft and I think we've, I was saying in the other note the other day. I was on a call for a architecture committee and we had AMD, ARM and Intel all talking about the same solution. So it's the beauty of open source that we've brought all of these groups together. >> One of the things that also struck us especially if we've been here. The diversity of the show is always really good. The main keynote, it's not oh, did they brought up some people of diversities. Oh no, these are the project leads and therefore they're doing this. Can you touch on some of the diversity and activities at the show itself? >> In terms of technologies, we're looking at or? >> No, I just, so there is, I'm just saying you talked about the community, the diversity of companies as well, the diversity of people. So we've got lots of the women inclusion. >> Oh sure. >> Things like that. >> Yeah, I know we had the executive producer of Chasing Grace was here and I know she's been, Jennifer Clower, is that correct? >> Stu: Yes, Jennifer Clower. We actually interviewed her last week at a different show. >> Oh fantastic. Yeah her document has been incredibly well received. I know she's making the rounds to get the word out there about what's going on with Women in Tech. And we were more than thrilled to host her and have her here and be apart of conversation. >> Clear Community is a big part of OpenStack, the OpenStack Summit and care of the OpenStack Foundation. In terms of Kata Containers, you work for the OpenStack Foundation. Is Kata officially then part of the OpenStack or does that have a different governance model? >> That's a great question. This is an area of confusion because it's the first time the foundation is broken out and there's the OpenStack Project, and there's Kata Containers the Project, but we both live at the OpenStack Foundation. >> John: Okay. >> I think the guiding principles though, and it's really helped us over the last four months is that the OSF, OpenStack Foundation, we believe in open source, open design, open development and open community. And Kata, we were like that's a great home. We believe in that as well. >> Any customers that are yet talking about their early usage of Kata that you can share? >> I think we have a lot of customers from runV and Clear Containers and Kata is going to be their next path forward. So with 1.0 out yesterday, I'm excited to see. We should see some upgrades real soon here. >> What's the path for them to get from where they are to the 1.0? Is that pretty straightforward? >> It should be, yeah, we think so. And they have their support from Intel and from Hyper to help them out with that as well. >> Stu: Okay. >> I was going to ask is Kata Containers, is it integrated in an API or is OpenStack necessary for it or is it independent of, from an infrastructure perspective, OpenStack, the stack? >> Yeah, it's completely independent, but it's also compatible. >> John: Okay. >> You can run on Azure, Google, OpenStack, agnostic of the infrastructure underneath it. >> John: Great. >> Anne, want to give you a final word. Takeaways from the show that you'd want people to have. >> Absolutely, I think the final word is containers are fantastic, it's probably time to take a look at your container architecture. Think about it from a security perspective, and I would encourage everyone to go check out Kata Containers and see if that's the solution for them. >> Anne Bertucio, really appreciate you joining and sharing with us everything happening. It can work with or without the OpenStack Containers. Absolutely a big trend, but security absolutely top of mind from everyone we've talked to. If it's not top of mind of a company, I'm always a little bit worried about them. For John Troyer, I'm Stu Miniman. We'll be back with lots more coverage here from OpenStack Summit 2018 in Vancouver. Thanks for watching theCUBE. (uptempo techno music)

>> Narrator: Live from Boston, Massachusetts. It's the Cube. Covering OpenStack Summit 2017. Brought to you by the OpenStack Foundation, Red Hat an additional ecosystem is support. >> Welcome back to the Cube. I'm Stu Miniman joined by my cohost John Troyer. Happy to welcome to the program two first-time guests. We have Anne McCormick who is a technical leader with Cisco. And we also have Sandhya Dasu who is a OpenStack engineer with Cisco. Thank you both for joining us. >> Sandhya: Thank you. >> Anne: Thank you. >> So Anne let's start with you, tell us just a little bit about your role at Cisco and what you're involved with when it comes to OpenStack. >> Absolutely, I've been at Cisco for 11 years. I have been working on OpenStack for about two-and-a-half now. It's been a blast, I've been to six different summits. I'm having a great time. My role at Cisco is I work under the Metacloud acquisition which is basically a managed, on-prem Cloud solution. And what my role is, is to bring Cisco technology into those deployments, so basically bringing the power of Cisco networking into OpenStack. >> Great so just to clarify, you weren't part of the Metacloud, you were part of Cisco. >> Anne: Yes. >> And you're working with that team who we know. We actually interviewed them back before the acquisition. Great to see you. Sandhya, tell us a little about your role, what you do at Cisco and with OpenStack. >> Sure, I have been with OpenStack the last three years. And Cisco about the same time as Anne, about 11 years. Worked in different routing technologies. But in OpenStack I'm responsible for the Neutron ML2 mechanism driver for Cisco UCS managers. So I've been having a great time in the OpenStack community. Developing in Neutron, giving upstreaming code and stuff like that, yeah. >> We wanted to talk about women of OpenStack but also the Women of OpenStack organization. Can you talk a little bit about what that group is here in the OpenStack community and how you got involved? >> Absolutely, Women of OpenStack is fantastic. It's something I discovered at my very first summit in Paris. I was a little leery going in 'cause I wasn't sure what the attitude would be, if it's us versus them kind of thing, that's definitely not what I'm looking for. But what I found was an extremely inclusive and encouraging community of women and men. It basically addresses the need for more women in technology and tries to make the community a more welcoming place and I think it takes both men and women to do that. And I think their charter is fantastic. They have really great events. >> Yeah, so I have been involved with Women in OpenStack also. Like Anne said, very inclusive community. I have been able to be at different levels of involvement, at different times based on the other work that I'm doing. But I also believe that just showing up and doing your work everyday is also setting a good example for everybody else to feel welcome. >> Great could you share a little bit, maybe start with Anne. The activities going on at the show. We know, like, just down the road from us here there's the Women in OpenStack lounge. I believe there's a lunch you had. What does it encompass at one of the summits? >> Yes, that's fairly typical that they have a lounge area. Today they had a working session during lunch. To kind of go over different things and discussion points. Also yesterday there was a speed mentoring session that I was a part of, it was fantastic. It was my first time doing that but I really enjoyed it. And they have ongoing mentoring for six month sessions which I'm also starting to get involved with. And I know I'm missing one, but there's just so many activities that they do, it's great. >> Sandhya? >> So I help out mostly with people trying to put their first code out for review. And I think that seems a bit daunting in the beginning because this is a very big community. You get a lot of code reviews. From lots of different people, how do you handle all the feedback? So I help out with people with their first upstreaming goal. Once they enter OpenStack. >> So I mean, tech has some diversity challenges, right. We, it's well-known, many communities in the technical realm, right? So the OpenStack community being an open source community. Comes out of a particular set of codes of conduct and expectations and participation. What have your experiences been working in the OpenStack community over the years? Does it feel, is it a, is it a welcoming egalitarian community? I mean, the Code of Conduct, last week we just had, there were some issues in the Kubernetes community which were swiftly addressed. I think the people's awareness actually is much higher than it was even say five years ago, let alone 10 or 20. But how have your experiences been working in OpenStack as a diverse and supportive community? >> I've found that my experience in the OpenStack community has been extremely positive. So I find that, I mean, before the open source, before I got into open source I did work with smart engineers but a comparatively smaller number. But now you get to interact with a whole, large number of really smart people and I think you should tap into that portion of your experience more than anything else. So the first time, I mean, I always found that I was happy with the code that I put out for review. But after making all the changes that I got as review comments, I was really proud of the output. So I think there are lots of positives in this environment. You need to make use of that, focus on that. And in terms of the Code of Conduct. I have only had very positive experiences here. >> And I find the community to be equally welcoming. When I walk into one of these big rooms with a predominantly male population I don't go in thinking I'm a female minority, I go in thinking I'm an engineer and this is my tribe, you know? So I think it's great. >> Alright, anything in particular that Sandhya was talking about. You know, setting an example as an engineer and as a female engineer. Anne what has your experience been? >> It's interesting, when I first started out in engineering. I got a scholarship to an engineering school, that was my first, when I started off on the road. And I remember being so proud and going up to receive this scholarship. And I heard somebody next to me say, "Oh what a waste, they're giving it to a girl." And it's funny because it had never until that point occurred to me that there might be any kind of perception like that. So my first knee-jerk reaction was, "Well I guess all the dinosaurs didn't go extinct." But after that-- >> John: Good for you. >> (laughs) But I mean I could easily have been bitter about it but instead I kind of saw it as an opportunity to set an example and to lead with my work and with my confidence. And to help to change the perception that gender matters when it comes to what you do for a living 'cause I don't believe it does. >> I studied in engineering. I know when I had group projects and had women on the project it helped, you need diversity of ideas. You need diversity of background and skillset. Sandhya, any comments about just diversity in general that you'd comment from the engineering standpoint? >> I think like Anne mentioned, once in a while you do get, you are conscious of the fact that there are very few other women in the room. But that's really, that should not be hindering your progress in any way. Just focus on being an engineer. And I think after a point everybody starts looking past the gender thing and just look at your work. >> Once you're around the table or you know, working on a shared whiteboard or Google doc, right, the gender falls away, you're working on the project. >> Sandhya: Exactly. >> Anne: Absolutely. >> And the same thing applies to IRC too. It's a very democratic channel. Everyone has an equal voice. And then in the end it turns out to be a meritocracy there. And if you have a good idea people will take it. Otherwise like everybody else, you just have to work on tweaking it. >> The concept of mentoring has come up a couple of times in this conversation already. As people look at the diverse workforce, and diverse workforce in tech. People talk about things like the pipeline problem. But from what I understand and have read, you know, a lot of it is supporting underrepresented groups within their careers and in their career growth right? And so that, a lot of that comes down to setting examples and mentoring. Can you talk a little bit about Women of OpenStack and how you talked about speed mentoring maybe and how, one let's talk about Women of OpenStack and mentoring. And then maybe even how you're doing mentoring in your own personal career at Cisco. >> Absolutely, mentoring is something that I'm kind of new to but it's becoming a passion of mine. As a way to both give back and to help encourage other people but also I get something out of it. I get inspired by the energy that people bring to things and by the enthusiasm. Yesterday at my speed mentoring session, one of the women that I talked to was very, very qualified and very excited about OpenStack. She has a full time job that doesn't involve OpenStack so she was involved in OpenStack on the side, you know, 'cause that's fun (laughs) to do on the side. But basically she was telling me that it was hard for her to break into the community. And she was a little bit shy about handing off her resume and stuff. And I think, I kind of said to her, "You know you're selling yourself short. "You've got a lot of enthusiasm. "And I think companies would be inspired by that. "And want to include you." So it was just kind of a nice way to help inspire people and encourage them. >> Have you done any mentoring yourself? >> Yes so I find that while I'm mentoring someone there's something that I get out of it too, because whenever you talk to a new grad you get this enthusiasm, this burst of enthusiasm. That helps you fuel your own work again. But I have heard lot of people discouraging each other from entering this field because they say it's not set up for their success. But then I think that's a self-possessing processing. So the more of them that there are in this field the better it is for everyone else. So that should not be a reason for not getting into this field. >> Sandhya could you talk to us a little about your upstream contributions, what things you've been proud of and excited about when it comes to OpenStack in general? >> Yeah so I have been active in the Neutron community. Mostly in the ML2 area of Neutron plugins. What I'm working on is the Cisco UCS mechanism driver for the Neutron ML2. What it helps you do is to use the Cisco UCS Manager to set up virtual networks, Neutron virtual networks and configure SR-IOV ports. And basically use the entire UCS ecosystem in context of OpenStack. >> Great, Anne you've been to six of these summits. Anything as you reflect back, just the maturity of the project, the maturity of the community. Or one of the themes this week has been kind of resetting expectations about what OpenStack is and isn't. What's your take on the community? >> That's interesting. I feel that there was a bit of a bubble perhaps. Maybe a year or so ago with OpenStack. But I don't think, I don't think we have to reset expectations too far. I do think that it's necessary, I don't think it's going anywhere. I think it's evolving and I really do see it as the second wave of the Internet. So we need it and I think it's great. >> Anne, Sandhya, really appreciate you joining, sharing your perspectives. We always love to have the diverse experience and OpenStack actually one of the better shows in making sure we have, you know, smart, energetic, contributing, you know, women participants in the community. We've had a number on, have a few more. So thanks so much for joining us and thanks for all of your contributions in the community. >> Anne: Thank you very much. >> Sandhya: Thank you for having us. >> John and myself, we'll be back with lots more coverage here from the Cube at OpenStack Summit Boston, Massachusetts. Thanks for watching the Cube. (upbeat techno music)

>>Hi, everyone. Welcome to this program. Sponsored by HPE. I'm your host, Lisa Martin. We're here talking about being confident and trusting your server security with HPE. I have two guests here with me to talk about this important topic. Cole Humphreys joins us global server security product manager at HPE and Anne Potton trusted supply chain program lead at HPE guys. It's great to have you on the program. Welcome. >>Hi, thanks. Thank you. It's nice to be here, Anne. >>Let's talk about really what's going on there. Some of the trends, some of the threats there's so much change going on. What is HPE seeing? >>Yes. Good question. Thank you. Yeah. You know, cyber security threats are increasing everywhere and it's causing disruption to businesses and governments alike worldwide. You know, the global pandemic has caused limited employee availability. Originally this has led to material shortages and these things opens the door perhaps even wider for more counterfeit parts and products to enter the market. And these are challenges for consumers everywhere. In addition to this, we're seeing the geopolitical environment has changed. We're seeing, you know, rogue nation states using cybersecurity warfare tactics to immobilize an entity's ability to operate and perhaps even use their tactics for revenue generation, the Russian invasion of Ukraine as one example, but businesses are also under attack. You know, for example, we saw solar winds, software supply chain was attacked two years ago, which unfortunately went a notice for several months and then this was followed by the colonial pipeline attack and numerous others. >>You know, it just seems like it's almost a daily occurrence that we hear of a cyber attack on the evening news. And in fact, it's estimated that the cyber crime cost will reach over 10 and a half trillion dollars by 2025 and will be even more profitable than the global transfer of all major illegal drugs combined. This is crazy, you know, the macro environment in which companies operate in has changed over the years. And you know, all of these things together and coming from multiple directions presents a cybersecurity challenge for an organization and in particular it's supply chain. And this is why HPE is taking proactive steps to mitigate supply chain risk so that we can provide our customers with the most secure products and services. >>So Cole, let's bring you into the conversation and did a great job of summarizing the major threats that are going on the tumultuous landscape. Talk to us Cole about the security gap. What is it? What is HPE seeing and why are organizations in this situation? >>Hi, thanks Lisa. You know, what we're seeing is as this threat landscape increases to, you know, disrupt or attempt to disrupt our customers and our partners and ourselves, I, it's a kind of a double edge if you will, because you're seeing the increase in attacks, but what you're not seeing is that equal to growth of the skills and the experiences required to address the scale. So it really puts the pressure on companies because you have a skill gap, a talent gap, if you will. There's, you know, for example, there are projected to be three and a half million cyber roles open in the next few years, right? So all this scale is growing and people are just trying to keep up, but the gap is growing just literally the people to stop the bad actors from attacking the data and, and to complicate matters. You're also seeing a dynamic change of the who and the, how the attacks are happening, right? >>The classic attacks that you've seen, you know, and the SDK and all the, you know, the history books, those are not the standard plays anymore. You'll have, you know, nation states going after commercial entities and, you know, criminal syndicates and alluded to that. There's more money in it than the international drug trade. So you can imagine the amount of criminal interest in getting this money. So you put all that together. And the increasing of attacks, it just is really pressing down is, is literally, I mean, the reports we're reading over half of everyone, obviously the most critical infrastructure cares, but even just mainstream computing requirements need to have their data protected, help me protect my workloads and they don't have the people in house, right? So that's where partnership is needed, right? And that's where we believe, you know, our approach with our partner ecosystem is it's not HPE delivering everything ourself, but all of us in this together is really what we believe. The only way we're gonna be able to get this done. >>So collets double click on that HPE and its partner ecosystem can provide expertise that companies and every industry are lacking. You're delivering HPE as a 360 degree approach to security. Talk about what that 360 degree approach encompasses. >>Thank you. It is, it is an approach, right? Because I feel that security is a, it is a, it is a thread that will go through the entire construct of a technical solution, right there. Isn't a, oh, if you just buy this one server with this one feature, you don't have to worry about anything else. It's really it's everywhere. And at least the way we believe it, it's everywhere. And it in a 360 degree approach, the way we like to frame it is it's, it's this beginning with our supply chain, right? We take a lot of pride in the designs, you know, the really smart engineering teams, the design, our technology, our awesome world class global operations team, working in concert to deliver some of these technologies into the market. That is a huge, you know, great capability, but also a huge risk to customers, cuz that is the most vulnerable place that if you inject some sort of malware or, or tampering at that point, you know, the rest of the story really becomes mute because you've already defeated, right? >>And then you move in to you physically deployed that through our global operations. Now you're in an operating environment. That's where automation becomes key, right? We have software innovations in, you know, our ILO product of management inside those single servers. And we have really cool new grain lake for compute operations management services out there that give customers more control back and more information to deal with this scaling problem. And then lastly, as you begin to wrap up, you know, the natural life cycle and you need to move to new platforms and new technologies, right? We think about the exit of that life cycle and how do we make sure we dispose of the data and, and move those products into a secondary life cycle so that we can move back into this kind of circular 360 degree approach. We don't wanna leave our customers hanging anywhere in this entire journey. >>That 360 degree approach is so critical, especially given as we've talked about already in this segment, the changes, the dynamics in the environment. And as Cole said, this is this 360 degree approach that HPE is delivering is beginning in the manufacturing supply chain seems like the first line of defense against cyber attackers talked to us about why that's important. And where did the impetus come from? Was that COVID was that customer demand? >>Yep. Yep. Yeah. The supply chain is critical. Thank you. So in 2018, we, we could see all of these cybersecurity issues starting to emerge and predicted that this would be a significant challenge for our industry. So we formed a strategic initiative called the trusted supply chain program designed to mitigate cybersecurity risk in the supply chain and really starting at the product with the product life cycle, starting at the product design phase and moving through sourcing and manufacturing, how we deliver products to our customers and ultimately a product's end of life that Cole mentioned. So in doing this, we're able to provide our customers with the most secure products and services, whether they're buying their servers from, for their data center or using our own GreenLake services. So just to give you some examples, something that is foundational to our trusted supply chain program, we've built a very robust cybersecurity supply chain risk management program that includes assessing our risk at our all factories and our suppliers. >>Okay. We're also looking at strengthening our software supply chain by developing mechanisms to identify software vulnerabilities and hardening our own software build environments to protect against counterfeit parts that I mentioned in the beginning from entering our supply chain, we've recently started a blockchain program so that we can identify component provenance and trace part parts back to their original manufacturers. So our security efforts, you know, continue even after product manufacturing, we offer three different levels of secure delivery services for our customers, including, you know, a dedicated truck and driver or perhaps even an exclusive use vehicle. We can tailor our delivery services to whatever the customer needs. And then when a product is at its end of life, products are either recycled or disposed using our approved vendors. So our servers are also equipped with the one button secure erase that erases every bite of data, including firmware data and talking about products, we've taken additional steps to provide additional security features for our products. >>Number one, we can provide platform certificates that allow the user to cryptographically verify that their server hasn't been tampered with from the time it left the manufacturing facility to the time that it arrives at the customer's factory facility. In addition to that, we've launched a dedicated line of trusted supply chain servers with additional security features, including secure configuration lock chassis intrusion detection. And these are assembled at our us factory by us vetted employees. So lots of exciting things happening within the supply chain, not just to shore up our own supply chain risk, but also to provide our customer the most. So that announcement. >>All right, thank you. You know, they've got great setup though, because I think you gotta really appreciate the whole effort that we're putting into, you know, bringing these online. But one of the just transparently the gaps we had as we proved this out was as you heard, this initial proof was delivered with assembly in the us factory employees, you know, fantastic program really successful in all our target industries and, and even expanding to places we didn't really expect it to, but it's kind of going to the point of security. Isn't just for one industry or one set of customers, right? We're seeing it in our partners. We're seeing it in different industries than we have in the past. And, but the challenge was we couldn't get this global right out the gate, right? This has been a really heavy transparently, a us federal activated focus, right? >>If, if you've been tracked in what's going on since may of last year, there's been a call to action to improve a nation cybersecurity. So we've been all in on that and we have an opinion and we're working hard on that, but we're a global company, right? How can we get this out to the rest of the world? Well guess what, this month we figured it out and well, let's take a lot more than those month. We did a lot of work that we figured it out and we have launched a comparable service globally called server security optimization service, right? HPE server security optimization service for proli. I like to call it, you know, S S O S sauce, right? Do you wanna be clever HPE sauce that we can now deploy globally? We get that product hardened in the supply chain, right? Because if you take the best of your supply chain and you take your technical innovations, that you've innovated into the server, you can deliver a better experience for your customers, right? >>So the supply chain equals server technology and our awesome, you know, services teams deliver supply chain security at that last mile. And we can deliver it in the European markets. And now in the Asia Pacific markets right now, we could always just, we could ship it from the us to other markets. So we could always fulfill this promise, but I think it's just having that local access into your partner ecosystem and stuff just makes more sense, but it is big deal for us because now we have activated a meaningful supply chain security benefit for our entire global network of partners and customers, and we're excited about it. And we hope our customers are too. >>That's huge Cole. And, and in terms of this significance of the impact that HPE is delivering through its partner ecosystem globally as the supply chain continues to be one of the terms on everyone's lips here, I'm curious Cole, we just couple months ago, we're at discover. Can you talk about what HPE is doing here from a, a security perspective, this global approach that it's taking as it relates to what HPE was talking about at discover, in terms of we wanna secure the enterprise to deliver these experiences from edge to cloud. >>You know, I feel like for, for me, and, and I think you look at the shared responsibility models and you know, other frameworks out there, the way we're the way I believe it to be is this is it's, it's a solution, right? There's not one thing, you know, if you use HPE supply chain, the end, or if you buy an HPE pro line the end, right. It is an integrated connectedness with our, as a service platform, our service and support commitments, you know, our extensive partner ecosystem, our alliances, all of that comes together to ultimately offer that assurance to a customer. And I think these are specific, meaningful proof points in that chain of custody, right? That chain of trust, if you will, because as the world becomes more, zero trust, we are gonna have to prove ourselves more, right. And these are those kind of technical I credentials and identities and, you know, capabilities that a modern approach to security need. >>Excellent, great work there. And let's go ahead and, and take us home, take the audience through what you think ultimately, what HPE is doing, really infusing security at that 360 degree approach level that we talked about. What are some of the key takeaways that you want the audience that's watching here today to walk away with? >>Right. Right. Thank you. Yeah. You know, with the increase in cyber security threats, everywhere affecting all businesses globally, it's gonna require everyone in our industry to continue to evolve in our supply chain security in our product security in order to protect our customers in our business, continuity protecting our supply chain is something that HPE is very committed to and takes very seriously. So, you know, I think regardless of whether our customers are looking for an on-prem solution or a GreenLake service, you know, HPE is proactively looking for in mitigating any security risk in this supply chain so that we can provide our customers with the most secure products and services. >>Awesome. Ann and Cole. Thank you so much for joining me today, talking about what HPE is doing here and why it's important as our program is called to be confident and trust your server security with HPE and how HPE is doing that. Appreciate your insights on your time. >>Thank you so much for having thank >>You, Lisa, >>For Cole Humphreys and Anne Potton I'm Lisa Martin. We wanna thank you for watching this segment in our series. Be confident and trust your server security with HPE. We'll see you soon.

(upbeat music) >> Hi, everyone, welcome to this program sponsored by HPE. I'm your host, Lisa Martin. We're here talking about being confident and trusting your server security with HPE. I have two guests here with me to talk about this important topic. Cole Humphreys joins us, global server security product manager at HPE, and Ann Potten, trusted supply chain program lead at HPE. Guys, it's great to have you on the program, welcome. >> Hi, thanks. >> Thank you. It's nice to be here. >> Ann let's talk about really what's going on there. Some of the trends, some of the threats, there's so much change going on. What is HPE seeing? >> Yes, good question, thank you. Yeah, you know, cybersecurity threats are increasing everywhere and it's causing disruption to businesses and governments alike worldwide. You know, the global pandemic has caused limited employee availability originally, this has led to material shortages, and these things opens the door perhaps even wider for more counterfeit parts and products to enter the market, and these are challenges for consumers everywhere. In addition to this, we're seeing the geopolitical environment has changed. We're seeing rogue nation states using cybersecurity warfare tactics to immobilize an entity's ability to operate, and perhaps even use their tactics for revenue generation. The Russian invasion of Ukraine is one example. But businesses are also under attack, you know, for example, we saw SolarWinds' software supply chain was attacked two years ago, which unfortunately went unnoticed for several months. And then, this was followed by the Colonial Pipeline attack and numerous others. You know, it just seems like it's almost a daily occurrence that we hear of a cyberattack on the evening news. And, in fact, it's estimated that the cyber crime cost will reach over $10.5 trillion by 2025, and will be even more profitable than the global transfer of all major illegal drugs combined. This is crazy. You know, the macro environment in which companies operate in has changed over the years. And, you know, all of these things together and coming from multiple directions presents a cybersecurity challenge for an organization and, in particular, its supply chain. And this is why HPE is taking proactive steps to mitigate supply chain risk, so that we can provide our customers with the most secure products and services. >> So, Cole, let's bring you into the conversation. Ann did a great job of summarizing the major threats that are going on, the tumultuous landscape. Talk to us, Cole, about the security gap. What is it, what is HPE seeing, and why are organizations in this situation? >> Hi, thanks, Lisa. You know, what we're seeing is as this threat landscape increases to, you know, disrupt or attempt to disrupt our customers, and our partners, and ourselves, it's a kind of a double edge, if you will, because you're seeing the increase in attacks, but what you're not seeing is an equal to growth of the skills and the experiences required to address the scale. So it really puts the pressure on companies, because you have a skill gap, a talent gap, if you will, you know, for example, there are projected to be 3 1/2 million cyber roles open in the next few years, right? So all this scale is growing, and people are just trying to keep up, but the gap is growing, just literally the people to stop the bad actors from attacking the data. And to complicate matters, you're also seeing a dynamic change of the who and the how the attacks are happening, right? The classic attacks that you've seen, you know, in the espionage in all the, you know, the history books, those are not the standard plays anymore. You'll have, you know, nation states going after commercial entities and, you know, criminal syndicates, as Ann alluded to, that there's more money in it than the international drug trade, so you can imagine the amount of criminal interest in getting this money. So you put all that together and the increasing of attacks it just is really pressing down as literally, I mean, the reports we're reading over half of everyone. Obviously, the most critical infrastructure cares, but even just mainstream computing requirements need to have their data protected, "Help me protect my workloads," and they don't have the people in-house, right? So that's where partnership is needed, right? And that's where we believe, you know, our approach with our partner ecosystem this is not HPE delivering everything ourself, but all of us in this together is really what we believe the only way we're going to be able to get this done. >> So, Cole, let's double-click on that, HPE and its partner ecosystem can provide expertise that companies in every industry are lacking. You're delivering HPE as a 360-degree approach to security. Talk about what that 360-degree approach encompasses. >> Thank you, it is an approach, right? Because I feel that security it is a thread that will go through the entire construct of a technical solution, right? There isn't a, "Oh, if you just buy this one server with this one feature, you don't have to worry about anything else." It's really it's everywhere, at least the way we believe it, it's everywhere. And in a 360-degree approach, the way we like to frame it, is it's this beginning with our supply chain, right? We take a lot of pride in the designs, you know, the really smart engineering teams, the designer, technology, our awesome, world-class global operations team working in concert to deliver some of these technologies into the market, that is, you know, a great capability, but also a huge risk to customers. 'Cause that is the most vulnerable place that if you inject some sort of malware or tampering at that point, you know, the rest of the story really becomes mute, because you've already defeated, right? And then, you move in to you physically deployed that through our global operations, now you're in an operating environment. That's where automation becomes key, right? We have software innovations in, you know, our iLO product of management inside those single servers, and we have really cool new GreenLake for compute operations management services out there that give customers more control back and more information to deal with this scaling problem. And then, lastly, as you begin to wrap up, you know, the natural life cycle, and you need to move to new platforms and new technologies, we think about the exit of that life cycle, and how do we make sure we dispose of the data and move those products into a secondary life cycle, so that we can move back into this kind of circular 360-degree approach. We don't want to leave our customers hanging anywhere in this entire journey. >> That 360-degree approach is so critical, especially given, as we've talked about already in this segment, the changes, the dynamics in the environment. Ann, as Cole said, this 360-degree approach that HPE is delivering is beginning in the manufacturing supply chain, seems like the first line of defense against cyberattackers. Talk to us about why that's important and where did the impetus come from? Was that COVID, was that customer demand? >> Yep, yep. Yeah, the supply chain is critical, thank you. So in 2018, we could see all of these cybersecurity issues starting to emerge and predicted that this would be a significant challenge for our industry. So we formed a strategic initiative called the Trusted Supply Chain Program designed to mitigate cybersecurity risk in the supply chain, and really starting with the product life cycle, starting at the product design phase and moving through sourcing and manufacturing, how we deliver products to our customers and, ultimately, a product's end of life that Cole mentioned. So in doing this, we're able to provide our customers with the most secure products and services, whether they're buying their servers for their data center or using our own GreenLake services. So just to give you some examples, something that is foundational to our Trusted Supply Chain Program we've built a very robust cybersecurity supply chain risk management program that includes assessing our risk at all factories and our suppliers, okay? We're also looking at strengthening our software supply chain by developing mechanisms to identify software vulnerabilities and hardening our own software build environments. To protect against counterfeit parts, that I mentioned in the beginning, from entering our supply chain, we've recently started a blockchain program so that we can identify component provenance and trace parts back to their original manufacturers. So our security efforts, you know, continue even after product manufacturing. We offer three different levels of secured delivery services for our customers, including, you know, a dedicated truck and driver, or perhaps even an exclusive use vehicle. We can tailor our delivery services to whatever the customer needs. And then, when a product is at its end of life, products are either recycled or disposed using our approved vendors. So our servers are also equipped with the One-Button Secure Erase that erases every byte of data, including firmware data. And talking about products, we've taken additional steps to provide additional security features for our products. Number one, we can provide platform certificates that allow the user to cryptographically verify that their server hasn't been tampered with from the time it left the manufacturing facility to the time that it arrives at the customer's facility. In addition to that, we've launched a dedicated line of trusted supply chain servers with additional security features, including Secure Configuration Lock, Chassis Intrusion Detection, and these are assembled at our U.S. factory by U.S. vetted employees. So lots of exciting things happening within the supply chain not just to shore up our own supply chain risk, but also to provide our customers with the most secure product. And so with that, Cole, do you want to make our big announcement? >> All right, thank you. You know, what a great setup though, because I think you got to really appreciate the whole effort that we're putting into, you know, bringing these online. But one of the, just transparently, the gaps we had as we proved this out was, as you heard, this initial proof was delivered with assembly in the U.S. factory employees. You know, fantastic program, really successful in all our target industries and even expanding to places we didn't really expect it to. But it's kind of going to the point of security isn't just for one industry or one set of customers, right? We're seeing it in our partners, we're seeing it in different industries than we have in the past. But the challenge was we couldn't get this global right out the gate, right? This has been a really heavy, transparently, a U.S. federal activated focus, right? If you've been tracking what's going on since May of last year, there's been a call to action to improve the nation's cybersecurity. So we've been all in on that, and we have an opinion and we're working hard on that, but we're a global company, right? How can we get this out to the rest of the world? Well, guess what? This month we figured it out and, well, it's take a lot more than this month, we did a lot of work, but we figured it out. And we have launched a comparable service globally called Server Security Optimization Service, right? HPE Server Security Optimization Service for ProLiant. I like to call it, you know, SSOS Sauce, right? Do you want to be clever? HPE Sauce that we can now deploy globally. We get that product hardened in the supply chain, right? Because if you take the best of your supply chain and you take your technical innovations that you've innovated into the server, you can deliver a better experience for your customers, right? So the supply chain equals server technology and our awesome, you know, services teams deliver supply chain security at that last mile, and we can deliver it in the European markets and now in the Asia Pacific markets, right? We could ship it from the U.S. to other markets, so we could always fulfill this promise, but I think it's just having that local access into your partner ecosystem and stuff just makes more sense. But it is a big deal for us because now we have activated a meaningful supply chain security benefit for our entire global network of partners and customers and we're excited about it, and we hope our customers are too. >> That's huge, Cole and Ann, in terms of the significance of the impact that HPE is delivering through its partner ecosystem globally as the supply chain continues to be one of the terms on everyone's lips here. I'm curious, Cole, we just couple months ago, we're at Discover, can you talk about what HPE is doing here from a security perspective, this global approach that it's taking as it relates to what HPE was talking about at Discover in terms of we want to secure the enterprise to deliver these experiences from edge to cloud. >> You know, I feel like for me, and I think you look at the shared-responsibility models and, you know, other frameworks out there, the way I believe it to be is it's a solution, right? There's not one thing, you know, if you use HPE supply chain, the end, or if you buy an HPE ProLiant, the end, right? It is an integrated connectedness with our as-a-service platform, our service and support commitments, you know, our extensive partner ecosystem, our alliances, all of that comes together to ultimately offer that assurance to a customer, and I think these are specific meaningful proof points in that chain of custody, right? That chain of trust, if you will. Because as the world becomes more zero trust, we are going to have to prove ourselves more, right? And these are those kind of technical credentials, and identities and, you know, capabilities that a modern approach to security need. >> Excellent, great work there. Ann, let's go ahead and take us home. Take the audience through what you think, ultimately, what HPE is doing really infusing security at that 360-degree approach level that we talked about. What are some of the key takeaways that you want the audience that's watching here today to walk away with? >> Right, right, thank you. Yeah, you know, with the increase in cybersecurity threats everywhere affecting all businesses globally, it's going to require everyone in our industry to continue to evolve in our supply chain security and our product security in order to protect our customers and our business continuity. Protecting our supply chain is something that HPE is very committed to and takes very seriously. So, you know, I think regardless of whether our customers are looking for an on-prem solution or a GreenLake service, you know, HPE is proactively looking for and mitigating any security risk in the supply chain so that we can provide our customers with the most secure products and services. >> Awesome, Anne and Cole, thank you so much for joining me today talking about what HPE is doing here and why it's important, as our program is called, to be confident and trust your server security with HPE, and how HPE is doing that. Appreciate your insights and your time. >> Thank you so much for having us. >> Thank you, Lisa. >> For Cole Humphreys and Anne Potten, I'm Lisa Martin, we want to thank you for watching this segment in our series, Be Confident and Trust Your Server Security with HPE. We'll see you soon. (gentle upbeat music)

>>Hey everyone. Lisa Martin here with John farrier. Welcome to the women in tech global event, featuring international women's day. John, this is an exciting day, March 8th, 2022. How did this all get started? >>Well, we started it out when we realized there was more stories to be told with virtual, with COVID. The virtualization of virtual events allowed us to do more stories. So we've been on this new format where we're creating seasons and episodic events, meaning you can still do an event and do 30 interviews like we're doing here for international women's day from around the world. We could have done a hundred there's enough stories out there. There's thousands of stories out there that need to be told, need to be scaled. And so we're just scratching the surface. So we are just starting to do is celebrate international women's day with as many videos we could do in a week, which is 30 and be part of widths and Stanford here in California, as part of their events with Stanford. And we're going to continue with international women's day. >>It's the big celebration, it's the big day, but then when it's over, we're going to continue with more episodes. So this is technically, I guess, season one episode, one of the international women's community site portal is going to be open and open to everyone. Who's going to be a community vibe and, uh, we'll get sponsors, but overall it's about bringing people together, creating tribes, letting people form their own communities and hopefully, uh, making the world a better place and supporting the mission, which is a great mission. Diversity inclusion and equity is a big mission. Uh, it's good for everyone. Everyone wins. >>Everyone does win. What are some of the interesting conversations that you've had with our international women's day guests that really were poignant to you? >>Well, the, one of the things was interesting by region. They had different kind of, um, feelings. The Asia Pacific was heavily skewed on a lot of international diversity around culture. Latin America was just all cloud computing. For instance, I felt that to be very technical, uh, more than agents in the interviews. Um, um, more diversity I study in Asia Pacific and Amy. It was really interesting because you have a lot going on there right now in Europe. So, um, and I'll see from a technical standpoint, data sovereignty and sustainability are two big themes. So from a tech trend standpoint, it was really amazing leaders. We interviewed, um, from technical, uh, folks to analysts, to senior executives in the C-suite. So it really good mix of people in the program. Uh, for today, >>We also had a young girl that I had the chance to speak with her and her father. And it was such a lovely conversation cause it reminded me of my dad's relationship with me. But she was told in high school age, no, you can't do physics. No, you can't do computer science. So the parents pulled her out of school. And so the, and she's brand new in her career path. And it was so nice to hear, to see that, that family, the role models within the family saying she wants to do physics and computer science. Let's find a place for her to be able to do that and have her start being able to, to build her own personal board of directors. At the age of like 22, 23, >>We hit an entrepreneur down in New Zealand. I interviewed she was from indigenous area and she had no milk or food on the table. They were so poor. They could barely get food. She worked her way through it and went to school. Education was number when it goes, she was so persistent, she got her education. And now she's the CEO of an AI company, amazing person. And she's like, Hey, there's no wall I can't run through. So that attitude was just so refreshing. And that was a consistent this year and it wasn't an in your face. It was just more of we're here, we're kicking butt. So let's keep it going. So on the entrepreneurial side, I found that really awesome on the senior leadership side, it was very much, um, community oriented, very open about sharing their experiences and also being a sponsor. So you're going to hear a lot about breaking the bias, but it's also about sponsoring opportunities and then helping people get involved so that they can get understand biases because everyone brings biases to the table. So I personally learned a lot this, this, this, uh, event. >>Yeah. I think the, the light that was shined on the bias was incredibly important. You know, the break, the bias, as you said, is the theme of this year's international women's day. And I, and I asked everybody that I spoke with, what does that mean to you? And where do you think we are on that journey? A world free of bias and stereotypes and discrimination. Obviously we're not there yet, but a lot of the women talked about the fact that that light is shining brightly, that the awareness is there, that for diversity equity and inclusion and having that awareness, there is a great launching launching pad, if you will, for being able to make more progress on actually breaking the bias. >>Yeah. That was a great point. I would also say to add to that by saying a lot of comments were on the same theme of check your bias when you fall, you speak in meetings. And it was just a lot of like protocol tactical, uh, ways to do things like, think about other people in the room versus just barreling ahead. Most guys do that actually. Um, and so that was another instructful thing. I think the other thing too was is that there was, again, more and more sharing. I mean, we had one person that you interviewed, her name was Anne green. Yeah. She's doing her own series. Uh, we're content. She's interviewing people, she's being a mentor and sharing it through content, Manny theory of AWS in Singapore, she's in space and Aero science talking about how the satellites are helping in the Ukraine, give information to everyone on the ground, not just governments and that's helping democracy. And that she's really excited that that contributes to some good there. Um, and she fled from a town where it was bombed. She was in a war zone and she escaped and got educated. So education's a theme. Um, don't let anyone tell you, you can't do it. Uh, and don't think there's only one pathway, right? This is tons of opportunities for participating in the tech economy for good, uh, in, in, in tech. So those are the keys. >>That's always been one of my favorite themes when we do women in tech events, John is that there is no direct pathway necessarily. I always love understanding those stories, but this year, one of the things that also was really clear was that women feeling what can't I do. And that sentiment was really echoed throughout. I think everybody that I spoke with that there was no, can I do this? Why can't I Not confidence? Which is palpable. Even when you're doing an interview by zoom, you can feel it. You can be inspired by, >>Well, at least a year, you do all the, a lot of the interviews. You're the face I had, you know, step aside for you because you're amazing. But one of the things you, you get appreciate this and love to get your reaction. One of the things I observed this year was because it was international focus, there was huge cube demand to be come to their region. We had one of the guests that won from Bahrain. She's like, I'll do the cube here. I'll be the host. So I think there was a real appetite for this kind of open dialogue conversations where they want the cube to come to their area. And so I know anyone watching wants to be a cube host in those areas, let us know, um, we're open. And to me that was more refreshing. Cause you know, me, I always wanna see the cube global go everywhere. But this year people are actually turning on their own cameras. They're doing their own interviews. They're sharing content and content creates community and bonding. And that was the big experience I saw this year was a lot more user generated activity engagement with each other in the group. >>I think that may have even been a product of the last two years of the pandemic of people really understanding the importance of community and collaboration and that it can be done via if you're only limited to video, you can do that. You can build a community and grow it and foster it in that way and create the content that really helps support it. >>That's a great point. That's actually one of the guests said COVID polled the future forward and digital. We see the value and other on the cyber side, um, Sally, as I mentioned there, um, earlier who we interviewed before, she's a cyber policy analyst and she's so smart. She's like, yeah, this is putting fold forward. And people understand cyber now, cyber misinformation, cyber war, the role of working at home, being isolated versus community. These are core societal issues that need to be solved and it's not just code that solves them. So it's going to be solved by the community. And that's really, that was the key. One of the key messages. It was very refreshing. >>It was very refreshing. I always love hearing the stories. I, the more personal the story, the more real it is and the more opportunities I think that it unlocks for the audience watching. Yeah, >>I mean, we had one person said she did a project on the side. It's going to be your big initiative within Amazon. You know, Amazon, one of our sponsors has a slogan think big, but deep dive deep. And she took a project on about educating, um, young girls and young women. And it turned out to be basically a build lab inside schools. And it took off. It is so successful side project, side hustle gone, gone big. So again, sparks of creativity, innovation can come from anywhere. It's just great stories. >>Another thing that came up in several of the conversations that I had was the data, the data that support that organizations that have at least 30% females at the executive level are better performing organizations. They are more profitable as well. So it was fun to kind of call out if we're talking about data science, what not the data that supports why international women's day is what it is, why it's becoming even bigger than that and the importance of showcasing those voices so that she can be what she can see. >>Yeah. Amazing stories. I got to say it again. I think the virtual studios where we have now with the pandemic is going to give us much more opportunities to get those stories out. And Lisa, you've done an amazing job. Your interviews were awesome. Thank you. And we can do a hundred. We'll give you a hundred interviews a week. >>We can, are you setting me up? No, it was fun. The international influence this year was fun. I mean, I think I started one of my interview days at 6:00 AM and it was just exciting to be able to connect to different parts of the world and to hear these stories and for the cube to be able to be the platform that is sharing all of that >>And the diversity of the interviews itself and the diversity of the environments that for instance, in Asia Pacific and your are diverse areas and they see it it's much further along. They live it every day. They know the benefits. So that again, that was another aha moment for us, I think this year. >>So how many, how many segments do we have for international women's day John >>30 segments, uh, 32 counting our little segments here. So 32 interviews. Um, we're going to probably add a section on the site for people to submit stories like a directory, uh, this, a zillion things going on, women of web three, Sandy, Carter's putting on an event. I know there's a security called. She S she scarcity events, she security, uh, going on women in security. Um, there's tons of activities it's vibrant tomorrow. Today. It'll be very much bumping up. So we'll try to curate as much links as possible. >>Awesome. John has been great doing this program with you. I look forward to seeing the interviews and being inspired by the many, many stories. You're going to be watching the cubes coverage of women in tech global event, featuring international women's day for John furrier. I'm Lisa Martin. We'll see you soon.

>> Live from Las Vegas It's theCUBE! Covering the AWS Accenture Executive Summit. Brought to you by Accenture. >> Welcome back everyone to theCUBE's live coverage of the AWS Executive Summit here at the Venetian. I'm your host, Rebecca Knight. We have two guests for this segment. We have Akhtar Saeed, VP Solution Delivery, Southern Glazers Wine and Spirits, and Michael Noel, Managing Director Applied Intelligence at Accenture. Thank you so much for coming on the show. >> Thank you. >> Thank you for having us. >> I think this is going to be a fun one. We're talking about wine and spirits. >> Absolutely. (laughs) >> Akhtar, tell our viewers a little bit about Southern Glazer. >> Yeah, so Southern Glazer Wine and Spirits is a privately held company. We are in about 44 states, and we are the largest distributor of wine and spirits. >> Okay, in 44 states. What was the business problem you were trying to solve in terms of the partnership that you formed with Accenture? >> Yeah, so we started this initiative before Southern and Glazer merged. >> And that was in? >> It was 2016. So southern was already looking at how to enhance our technology, how to provide better data analytics, and how to create one source of truth. So that's what drove this and we were looking to partner with appropriate system integrator and right technology to be able to help deliver well if the company to be able to do analytics and data analysis. >> So you had two separate companies merging together and I like this idea, one source of truth. What does that mean, what did that mean for you? >> Well what it means to us is that since you have quite a few data marts out there and everybody is looking at the numbers a little differently, we spend a lot of time trying to say, hey is this right or is this right? So we want to bring all the data together saying this is what the data is and this is how we're going to standardize it, that's what we're trying to do. >> Okay, so this one source, now, Michael, in terms of that, is that a common, common issue particularly among companies that are merging would you say? >> No absolutely you have businesses that might be in the same industry but they might have different processes to try to get to the same answer, right, and the answer's never really the same. So having this concept of a clean room that allows you to take your various aspects of a business and combine that from a data point of view, a business metrics point of view and a business process point of view, this one source, helps you consolidate and streamline that so you can see that integrated view across your new business model really. >> So where do you begin? So you bring in Accenture and AWS and where do you start? >> So like you've mentioned, in 2016, Glazer and Southern Wine Spirits came together and merged, it actually accelerated process because we needed what Mike mentioned as a clean room where we could put this data and won't have to merge at data centers on day one and have the reporting, common reporting platform being available for the new SGWS and that's what we started so we said, okay what is the key performance indicators, the key metrics that we need going into day one? and that's what we want to populate the data with to begin with to make sure that information is available when the day one for merger comes through. >> Okay and so what were those indicators? >> There were several indicators, there were several business reports, people needed the supply chain, they needed to understand the data, what the inventory looks like they needed to know how we were doing across the markets. So all those indicators, that's what we put together. >> Okay, okay, and so how do you work with the client in this respect, how do you and AWS sort of help the client look at what the core business challenges are and then say okay, this is how we're going to attack this problem? >> Right, no that's a good question. I think the main thing is understanding, what does the business need? and how is the technology going to support what the business needs, right? that's first and foremost, right, and then getting alignment and understanding that is really what drives a roadmap to say here's what we're going to do, here's the order we're going to do it in and here's the value that we expect to get out of following these steps one by one and I think one thing we learned is you have to be directionally correct, you may not be exact but as long as we're making progress in the right direction, you course correct as you need to, right, based upon as the business learns new things and as the market changes and what not and that's really how we accomplish this. >> And is it a co-creative process or, how closely are you working with Accenture and AWS? >> Oh, very closely with Accenture and AWS, it's very co-creative, I mean we are really working hand-in-hand. I mean, as Mike alluded, you start certain ways a journey and you realize, gee, this may work but I have to change a little bit here and there's several time we had to change team's direction how to get there and how to approach it and to deliver value. >> Well let's talk, let's get into the nitty gritty with the architecture and components. So what did this entail, coming to this clean room, this one source of truth? >> Yeah, AWR architecture is based on AWS' platform or Accenture's AIP, Accenture Insights platform which runs on AWS and we have, what we did right from the beginning we said we're going to have a data link, we're going to have a hadoop environment where we're going to all our data there And then for analytics research we're going to use Redshift, on top of that for reporting we use Tableau, and we have a homegrown tool called Compass for reporting also that we use. So that's how we initially started, initially we were feeding data directly into it, because we needed to stand the system up relatively quickly. The advantage to us, we didn't have to deal with infrastructure, that was all set up at AWS, we just to need to make sure we load our data and make sure we make the reports available. >> Were you going to add something to that? >> Yeah I know that the concept around, because the merger is expediting this clean room which allows you to stand up an analytics as a service model, to start bringing your data, to start building out your reporting analytics quickly right, which should really speak to market to understanding their position, as an integrated company was so important. So building the Accenture Insights platform on the AWS platform, was a huge success in order to allow them to start going down that path.. >> Yeah I want to hear about some of the innovative stuff you're doing around data analytics and really let's bring it back down to earth too and say actually so this is what we could learn and see, in terms of what was selling what was not selling, what were you finding out? >> So at this point we have about 6000 users on the platform approximately. Initially we had some challenges, I'll be very frank upfront, that everything does not go smooth. That's where we then say "Okay what do I do differently?" We started with dense storage, nodes and we soon found it's not meeting our needs. Then we enhanced Tougaloo dense cluster, and they helped us by about by 70%, that it drove the speed, but the queue length was still long, with Redshift we were still not getting the performance we needed. Then we went to second generation of dense computers and clusters and we got some more leverage, but really the breakthrough came when we said "we need to really reevaluate "how we've been doing our workload management." Some of our queries were very short term report queries real quick, others were loading data that took a while. And that's the challenge we had to overcome, with the workload management we were able to create, where we were able to bump queries and send them to different directions and create that capacity. And that's what really had a breakthrough in terms of technology for us, till that time we were struggling, I'll be honest, but once we got that breakthrough, we were able to comfortably deliver what business needed from data perspective and from businesses perspective. Mike would you like to add... >> Yeah, in addition to AWS, using Redshift has really been a really important, I guess decision and solution in place here, because not only are we using it for loading massive amounts of data, but it's also being used for power users, to generate very adhoc and large queries, to be able to support other analytic type needs right? And I think Redshift has allowed us to scale quickly as we needed to based upon certain times of year, certain market conditions or whatever, Redshift has really allowed us to do that. In order to support where the business demands have really grown exponentially since we've been putting this in place. And it all starts with architecting, and we said, and delivering all around the data. And then how do you enable the capabilities, not just data as a foundation but you know real time analytics, and looking at what looking at what could be, you know, forecasting and predicting what's happening in the future, using artificial intelligence, machine learning and that's really where the platform is taking us next. >> I want to talk about that, but I want to ask you quickly about the skills challenge, because introducing a new technology, there's going to be maybe some resistance and maybe simply your workers aren't quite up to speed. So can you talk a little bit about what you experienced, and then also how you overcame it? >> Yeah, I mean we had several challenges, I mean I'll put it in two big buckets, one is just change management. Anytime you're changing technology on this many users, they're comfortable with something they know, a known commodity, here's something new, that's a challenge. And one should not ignore, we need to pay a lot of attention on how to manage change. That's one, second challenge was within the technical group itself, because we were changing technology on them also right, and we had to overcome the skill sets, we were not the company, who were using open source a lot. So we had to overcome that and say how do we train our folks, how do we get knowledge? And in that case Accenture was great partner with us, they helped us tremendously and AWS professional services, they were able to help us and we had a couple of folks from professional services, they had really helped us with our technology to help drive that change. So you have to tackle from both sides, but we're doing pretty well at this point, we have found our own place, where we can drive through this together. >> In terms of what you were talking about earlier, in terms of what is next with predictive analytics and machine learning, can you talk a little bit about the most exciting things that are coming down the pipeline in terms of Southern Glazer? >> I think that's a great question, I think there's multiple way to look at it. From a business point of view right it's, how do they gain further insights by looking at as much different data sets as possible, right, whether it be internal data, external data, how do we combine that to really understand the customers better? And looking at how they approach things from a future point of view, we've been able to predict what's going to happen in the marketplace so I think it's about looking at all the different possible datasets out there and combining that to really understand what they can do from an art of the possible point of view. >> Can you give us some examples of terms of combining data sets so you're looking at, I mean, drinking patterns or what do we have here? >> I mean you have third party data, right, and TD links and those kind of things, you pull that data in and then you have our own data, then we have data from suppliers right, so that where we combine it and say okay what is this telling me, what story is this putting together telling me? I don't think we are there all the way, we have started on the journey, right now we are at what I call the, this one source of truth and we still have some more sub-editors loading to it, but that's the vision that, how do we pull in all that information and create predictive analysis down the road and be able to see what that means and how we'll be driving? >> And so you're really in the infancy of this? >> Yes, I mean it's a journey right, some may say that you're not in infancy, you're in the middle somewhere, somebody said, if they were ahead of us, it's all depending where you want to put this on that chart but we at least have taken first steps and we have one place where the data's available to us now, we're just going to keep adding to it and now it's a matter of how should we start to use it? >> In terms of lessons that you've learned along the way and you've been very candid in talking about some of the challenges that you've had to overcome but what would you say are some of the biggest takeaways that you have from this process? >> Yeah the biggest takeaway for me would be, as I've already mentioned, change management, don't ignore that, pay attention to that because that's what really drives it, second one that I'll say is probably, have a broader vision but when you execute make sure you look at the smaller things that you can measure, you can deliver against because you would have to take some steps to adjust to that so those are the two things, the third have the right partners with you because you can't go alone on this, you need to make sure you understand who you're going to work with and create a relation with them and saying "hey it's okay to have tough conversations", we have plenty of challenging conversations when we were having issues but it's as a team how you overcome those and deliver value, that's what matters. >> High praise for you Michael (laughs) at Accenture here, but what would you say in terms of being a partner with Southern Glazer and having helped and observed this company, what would you say are some of the biggest learnings from your perspective? >> Oddly enough I think the technology's the easier part of all this, right, I think that's fair to say without a doubt but really I think, really focusing on making the business successful, right, if everything you do is tied around making the business successful, then the rest will just kind of, you know, go along the way right because that's really the guiding principles right and then you saw that with technology right and that's really I think what we've learned most and foremost is, bring the business along, right, educating them and understanding what they really need and focusing on listening, alright, and trying to answer those specific questions, right, I think that's really the biggest factor we've learned over the past journey, yeah. >> And finally so we're here at AWS re:Invent, 60,000 people descending here on Sin City, what most excites you about, why do you come first of all and most excites you about the many announcements and innovations that we're seeing here this week? >> Yeah, so I'll be honest, this is the first time I've come to this conference but it's been really exciting, what excites me about these things is the new innovation, you learn new things, you say "hey, how can I go back "and apply this and do something different "and add more value back?" That's what excites me. >> Now, no I think you're absolutely right, I think, AWS is obviously a massive disruptor across any industry and their commitment to new technology, new innovation and the practicality of how we can start using some of that quickly I think is really exciting, right, because we've been working on this journey for a while and now there's some things that they've announced today, I think that we can go back and apply it pretty quickly, right, to really even further accelerate Southern Glazer's, you know, pivot to being a fully digital company. >> So a fully digital company, this is my last question (laughs) sorry, your advice for a company that is like yours, about to embark on this huge transformation, as you said, don't ignore the change management, the technology can sometimes be the easy part but do you have any other words of wisdom for a company that's in your shoes? >> All the words of wisdom I'll have is just I think I've already mentioned, three things they'll probably need to focus on, just take the first step, right, that's the hardest part, I think Anne even said this morning that some companies just never take the first step, take that first step and you have to, this is where the industry is going and data is going to be very important so you have to take the first step saying how do I get better, handle on the data. >> Excellent, great. Well Michael, Akhtar, thank you so much for coming on theCUBE this has been a real pleasure, thinking about Southern Glazer, next time bring some alchohol. >> Absolutely. (laughs) It's Vegas! >> Thank you, appreciate it. >> Great. I'm Rebecca Knight, we'll have more of theCUBE's live coverage of the AWS executive summit coming up in just a few moments, stay with us. (light music)

>> Live from Washington, D.C., it's theCUBE! Covering Inforum D.C. 2018. Brought to you by Infor. >> Good afternoon, and welcome back to the Walter Washington Convention Center, we're at Inforum 2018, here live on theCUBE, John Walls with Dave Vellante, and it's a pleasure now to welcome the CEO of Infor, Charles Phillips with us. Charles, good to see ya! >> Good to see you guys again, another year. It's great, it's great. >> Yeah, I tell ya, you are a man of demand aren't you? I mean, tell me about the week so far for you, how it's gone, and just your overall thoughts about the show? >> Yeah, it's been a fun Inforum for 2018 here. Great attendance, and a lot of energy level, and the common feedback we get is you guys just keep innovating and bringing new things, this is great, and that's why they come, they want to see what we're working on and kind of dream the art of the possible. We know what you, what we think you get a couple years ago, but if we don't have someone pushing us and painting a picture of what we could be doing, and we just think we might be missing it, so we want to hear it first hand. So that's what the conference is about, and hopefully they got that. >> Well, certainly thematically, human potential, you talk about that, you see that on the keynote stage, that's been a very consistent theme with our guests here, we've heard that a lot, you hear it down on the show floor. Talk about the theme if you would, a little bit, in terms of it's development, where that came from, and in how you think that's being expressed here this week. >> Well, we're one of the few companies that build mission critical operational systems, be it manufacturing or hospital operations, but we're also in HCM in a big way. And so we were talking to kind of both sides of the house, for some applications you're talking to the line of business manager, but for HCM you're talking to the CHRO, and rarely were those two people talking, and we saw obvious synergies. Don't you want to know how your people are doing, how to allocate people, and how they're performing, how they're changing the outcomes on a manufacturing floor or in a hospital, and a lot of HR directors weren't thinking like that because they think of HR, and they have their own world, they go to HR conferences and that's it. And the manufacturing guys are the same thing, and so we're trying to bring these two worlds together and say "Actually, you're in the same business, it's the same goals, and you actually could help each other a lot." And so by focusing on putting the employee at the center of all these applications and mapping all these operational processes to HR data, it's a different way of thinking about the role of HR. They can actually help drive the business, not just be an administrative function, and so it's resonating with a lot of the CHROs we met with, 'cause they want a seat at the table, they want to be more strategic, and this is a way for them to do that and at the same time the operational people want to know how their people are doing, want to develop talent, and want to know what are the tools out there I could be doing differently, and how am I doing, and which employees are working the best So, I think we can bring both sides together. >> So I first met Infor through AWS, at re:Invent, Pam Murphy came on, and we were like Infor? Back then it was like 2012, 2013 was kind of Infor who? And then we were invited to New Orleans, and then started to learn more about your micro-vertical strategy and a little bit about the platform, it was somewhat opaque to me. And now, fast forward last year and this year it's really starting to come in to view. The OS, the platform vision, the Birst acquisition, and of course Coleman, and I'm a sucker for platform plays especially when there's real R&D behind it that's actually having a business impact. So I wonder if you could talk about that piece of the strategy, I love the stack, was that sort of always your vision and now you're getting aggressive in it, did it sort of come together serendipitously, how'd we get here? >> Having our own stack and a platform was always the vision, but it's a lot harder to do than it sounds like, and it takes time. And so, when we arrived almost eight years ago, there were different applications, all had their own separate stacks and would say "This is not going to work." So, we need, just to be able to scale, to be able to serve multiple industries with different products, we can't have every development organization building their stack as well. So we set about taking that away from the development groups we're going to do this as a shared service, but it takes time, and as we build it you will adopt components of it. So what's changed is we've built out the entire stack, so, starting with ION, with integration, then we added document management, workflow, analytics, now AI and a lot of other services, Mongoose, platform as a service, on and on and on, in collaboration, those things took time, they're all on a single platform, federated security, single siloed across it all, and now it makes the developers job who's developing apps so much simpler. So they have Infor OS for the immediate platform, for cloud services they have AWS, I don't have to worry about any of those things anymore, just go and develop industry functionality. So, it's come together nicely, but the fact that we had the time to do it and the money to do it, and we weren't public, and we told our investors "This is the only way this is going to scale, this is the future, and it'll pay out later, you just got to trust us." And now that we've gotten there, they're seeing the synergy and go "Okay, now we see why you did that." >> So, Michael Dell's been on theCUBE many times, he used to talk about the 90 day shot clock, we obviously see what he's done in terms of transforming; but I want to talk about your business a little bit, because you've had that patient capital, I mean you're a quasi-public company in the sense that you do report so we can see the numbers on the income statement, but the income statement doesn't really tell the whole story It's about three billion in revenue, several hundred billion dollars on the balance sheet, but if you look at the SaaS component of it it looks rather small, maybe about 25% of the business, but from a booking standpoint I'm sure it's much, much larger than that. So how should we interpret the income statement in terms of the momentum in your business, where is all the action? >> So as a percentage of our sales, it's the highest of any of our competitors, so, about 70% of our new sales are on SaaS, we have about a $700 million SaaS business, so it's growing. There's nothing we can do about the maintenance piece of it, if it's related to perpetual, so if you take that out, it's a big percentage of our business. And over time the maintenance will turn into SaaS, so that's one of our big opportunities to look at that maintenance space and say "Move those over to cloud customers." and that's usually a financially lucrative thing for us to do, because we do even more for them, because they usually add on four or five other products when they move, they replace these third party products and so we get a bigger suite of products if they decide to move to the cloud. So that's part of the strategy, that's what UpgradeX is, let's move you from on-premise, so that maintenance revenue will turn into SaaS revenue, but bigger SaaS revenue over time. >> So let me make sure I understand, so it's not the classic case where you see a lot of software companies that are going from a perpetual model to a ratable model, you're goin' from a maintenance model which is ratable to a ratable model which is SaaS, but there's cohorts sales which increase the top line, is that correct? >> Exactly. So usually, because of what we do, we're doing something mission critical. So if you're going to take that, then you should do ACM financials, all the other things around it. So why would I move to core and leave the edge on-premise? So, almost by definition we have to do the whole suite. So when we do that it expands the deal, 'cause on-premise we may have been one vendor with 30 other ones existing, but the whole reason they want to get out of all of that is to move to the cloud and simplify. So we can't take all that with us, so we have to have the full suites, we've built that now. So now we can move them, but, it expands the size of the deal because we're replacing all these other products. >> Okay, and then some of the stats, just correct me if I don't get this right. Your SaaS business grown 50% faster than Oracle's, growing at a rate, I'd say 2X SAP's and a rate comparable to Workday, are those correct figures? >> Those are correct, and profitable. >> Oh, and profitable. >> Throw that in. (all laugh) >> Right, so okay. And then last year Koch Industries invested, so you kind of recap the company, you've made a big deal about that. One of the things that we've noted is you're seeing a tailwind there in terms of guys like Accenture and Capgemini, we've asked them "Do you guys service Koch Industries?" they said "Yep!" they helped us see the opportunity, and they said "Look, look for something substantive, we're not going to try to force you to do something, but we want you to take a look." So that's been helpful. Talk about that and maybe other things Koch has brought to the table? >> It's a, the relationship with the integrators is evolving, it probably was not a plus for us in the first four, five years. More recent years we've won enough deals where they had to say "Okay, we can't keep losin' these deals." And where they wanted to get engaged. Koch helped, because they had relationships and they wanted to run that business, that's why they're implementing our products globally, and so, they're a large customer for all of these guys, and one of the largest for Deloitte for instance, but what's really more-- that helped, but it was more the, what was happening in the market, the fact that we're in a Liberty Steel and replace SAP, or that we're in a Travis Perkins interview with SAP and Microsoft, so, if you're on the wrong side of those deals enough times your manager starts to ask you what's goin' on, and you got all these people on the bench here, okay, we train them for Infor if they're winning in that region, or in that industry. So, we just had to earn our way into it, our initial strategy was not one that, at least on the surface, looked like it was integrator-friendly because we were trying to take all those mods they like to do and put 'em in the product, and that's the whole thesis, let's the take the vertical industry features and let's put it in there once, I don't want everybody customizing my apps, we do that. And so now they've had to move up, okay we can do other things, configuration, changed management, there's AI, there's other things you can do, but you're not going to do that. So now that they've accepted that, there's a basis for us to work together, and, it just had to take time to get there. >> What can you tell us about where you want to go with this? I mean you've presided over public companies before, you know that business well, you were a rockstar analyst, is there an advantage to being a public company, is that something that you eventually want to do? >> I would say there are pluses and minuses, our board is evaluating that, that's going to be their call. The upside is, it would solve probably our biggest challenge which is brand recognition, almost instantly, because would be a top 10 tech IPO. It makes it a little easier to hire people because they can see public currency, they can value more quickly, and it gives you some acquisition currency; so those are the positives. But then you're on the 90 day cycle, and we're kind of on that anyway, 'cause we report publicly and we have publicly traded bonds. So for us it's, in some sense we have the worst of all worlds, right? We have the discipline of being a public company, and the scrutiny, without the capital, (laughs) and the branding, so. I think that's what everybody's evaluating. Every bank on Wall Street's visiting us telling us to go now, the window's great, you have the numbers. >> Oh, of course. (Dave and John laugh) >> And so, so we could do it, I just don't know what their decision's going to be. The advantages to being private as well, you have a little more flexibility obviously, and, we don't need the capital, we have plenty of capital coming from Koch and others who want to invest. >> Well, the flip side of that too, is you get to write your own narrative, right? >> Yeah. >> I mean, we're talkin' about the nuances of the income statement, the Street is obviously right now hooked on growth heroin, and if you got the transition in the base it doesn't become a tailwind, so, no rush from that standpoint. I want to pivot to the theme of this event, which is the human potential. My understanding is you sort of were instrumental in coming up with that. HCM this year got a big play on stage, where's that come from? >> Yeah, just as I talk to CEOs who are struggling to find talent, like I mentioned on stage 6.7 million jobs that are unfulfilled. It's not like we don't have people here, we have people here with their own skills, so, you're not going to fill those jobs any other way, we're not doing immigration to any degree and scaling more, that's been shut down. We have an aging population with the baby boomers, so the most logical thing that you would do is train people who are already here who want to work. And, let's take people who have jobs that they probably aren't thrilled about, and give them different skills so they can fill these 6.7 million jobs. So to do that, you have to make these applications easier to use, and I felt like we're probably in the best position to do it because we actually know what they do for a living, 'cause we wrote all those last features in those industries, we understand what they do. And if you're just doin' HR replication or financials, you actually have no idea what they do. So, we had to learn those jobs to automate those jobs, so we can find ways to use our HCM applications to better train people, professional development, coaching, take all these HR skills, and put them as part of the applications in the context of while you're working. >> We had Anne Benedict on just a little bit ago talking about really a test case that you can be for yourself. So how are you putting these things to practice yourself, and how are you working out maybe some kinks before you take them out to somebody else? And so, you can leverage your own success for your own success, and also learn from mistakes too I would think. >> We do. So we have this program called Infor at Infor, where everything we do, we want it to be on an Infor product, which was not the case when we arrived. Like a lot of companies, a mish mash of different things, and so we've implemented not only HR Financials of course, Birst, but the big innovation has really been talent science, that every employee we hire has to take that test, and all the executives have taken it as well. And what we've discovered is, is that, when people hire and go against the talent science recommendation, 68% of the time they end up being wrong. So it's better at judging people than people are sometimes, and you can't use it exclusively, but it'll tell you these are the things you should look into, some questions you might want to ask, here's how they rate on certain skillsets, they're very well meshed for this job, they look like they'd see their best performance in this area, but ask these questions. And so people don't know how to interview and how to think about this, and so, having a guide to go into an interview is actually pretty helpful. We hire much better people now by using that. >> So it's like StrengthsFinder in a way? >> No, it's different from that, this is AI, it's kind of Moneyball for business people. >> Well you're talking about that today, almost there. >> Yeah so it's 39 personality attributes, behavioral attributes we call them, so, empathy, resistance to authority, do you have the ambition or not, and depending on the job, you think all those things are good, depends on the job, so. For some jobs, it's actually better to have low ambition because, a lot of our customers who have low wage, fast food service jobs, people who have ambition are going to leave in four months, right? They're not going to stay, so, okay we're not going to be here long, at least know that going in, and know who wants to get promoted, and other people are fine with it. And so it depends on the mix of skills, just like I said, 39 attributes, and for that job role, you tune it to the people who like that job, they look like this. And, we've also found that it's 60% more diverse when you hire using science, because you don't know that when you're looking at the data, what they look like. >> It must've been super interesting getting those reports. You took it, obviously right? >> Yeah I took it. >> How'd you do? (laughs) >> Uhhh, nobody really likes their profile. (all laugh) >> I was going to say, I imagine I would be really defensive about this, oh I don't know. >> This can't be right! >> That is not me! I am not like that! (all laughing) >> Every person on our executive team said the same thing so. That's what it's for is to, you have certain perceptions even about yourself, and it calls it out, right? And there's no gaming the system because the questions have no right or wrong answer, it just puts you in scenarios that you answer what would you do, how do you feel about this? You're not clear what they're trying to get at, and you only have 27 minutes or 22 minutes to do the test. >> So you can't game it? >> You can't game it. >> Data doesn't lie! >> And we built the science, we know when someones trying to game it, they're taking to long on multiples, and changing their answers too much, so it's-- And we've now, I think we've tested some 200 million people over time, over years, so we have 20 years of data about people. >> That's, I mean, sounds unique, certainly unique of being infused into enterprise software, I've not seen anything like this from another enterprise software company. Can you confirm that, or? >> Yeah, so, we're the only ones that do this at scale, there's a few startups trying to do it, but they're trying to do it all facial recognition which is, we think pretty ridiculous, we're trying to get away from physical attributes not use that. So there's a company out there doing that, depending on your facial movements, but this is, we're eliciting responses about your personality in response to situations that we give you, and have a bunch of scientists that crunch the data and they basically shape it to the job role. And they test your best performance, and you get a DNA profile for your best performance for that job role, and then, that's what you're matching, and it's highly accurate. So we had a company on the Las Vegas Strip use it, because they have to hire in volume a lot, and essentially what they wanted to do was get better blackjack dealers. You need somebody that's good at math, good under pressure, not too emotive, don't give away anything; and so we did that, fine tuned the test, they call us back nine months later and said "We need you to change the test." We said "We did exactly what you wanted, what happened?" He said well, the winnings went up 30%, but everybody's leaving the hotel in 24 hours 'cause they lost all their money, so we don't need them to be that good. (all laugh) >> Dial it down a little bit. >> Which we did. And so that's part of the service is we fine tune it, you tell us what your goals are, and we'll tune to that. >> That's a great story. The other surprise for me this week has been the emphasis on robotic process automation, it's a space that we've kina looked at. And a lot of people are scared about software robots replacing humans, but if you talk to people who are using RPA, they love it. It's taking away these mundane tasks, I didn't realize that you guys had such capabilities there? >> Yeah, so we built that as part of a Coleman RPA platform, and not only can we automate and use RPA for ourselves, but we've built a whole development environment for our customers to build their own, 'cause we can't think of every process that they might want to automate, and we gave that platform to our partners as well, so. We don't want them doing database schema work anymore, and they used to get paid for that, there's other things you can do up the stack in AI, here's what we want you to focus on. So we had that meeting on Monday with the partners, and they all agreed that's what we're going to do. But there's tons of mundane things that people shouldn't be spending time on, and they can be much more productive, it makes them more loyal to the company, they're enjoying their job more, and they're thinking and innovating more. So I don't see it as replacing people, as making people better. And giving that engagement that I talked about during the keynote, they're engaged now, because they can do things that are more value adding now. >> So, back to New Orleans next year? That's the first Inforum that theCUBE was ever at was in N'Orleans, and, jazz, you like jazz, obviously, right? >> I like jazz, I met with the mayor when I was down there, Mitch Landrieu at the time, and he became a customer after that meeting, so the city of New Orleans runs on Infor software, it's another reason to go there; so thank you. >> You've get--nice. >> Yeah, thank you Mitch, so that worked well. And so as a thank you we're going back down there, they're a big customer now, and it's always fun, you know what I mean, you know. >> That's great. >> Just, before you go, you mention, I watched in the keynote this morning, Brooks Koepka. >> Yes. So you're working with him. I do a little bit of work on the golf side as well, so I was just intrigued because, he's not the, well he's not Tiger, right? >> Yeah. >> U.S. Open Champion, twice over. What was the attraction to him, and then can you play in the golf world a little bit, and with those brands, and is that an entry into that world? >> Well, we always like to bet on the scrappy guy, the next up and coming generation guy, and that's kind of our brand that's what we are, the Brooklyn Nets, someone who's not quite there yet, but they're moving up, that's kind of our scrappiness, that's why we like the whole Brooklyn image as well. And we started talkin' to him, like I said, before he won the U.S. Open, because he was ranking pretty high, moving up, but wasn't well known. A quite guy, very personable when you meet him, we thought he'd be good in front of clients, let's bet on his career, and we're going to work with him; and literally three weeks later he wins the U.S. Open, we go "Okay." (all laugh) >> Good grab! >> We'll take it! (laughs) So, we didn't even think it'd happen that quickly, and now he's a rockstar so. We were planning on hosting a CX event with him, and, we're not sure how many people are going to come, but when that happened, now, everybody RSVP'd right away of course. So now it's doing exactly what we wanted. >> Do you play golf? >> I don't play golf, I just started playing, 'cause we were doing these golf tournaments with customers over the last year, but I haven't had enough time to get out there yet. >> I'll bet Brooks would give you a lesson or two. (laughs) >> Yeah, he, a lot of people want to lesson from him. >> Charles thank you >> Alright, thank you guys, >> for the time, great show. >> Good to see ya again. See ya in New Orleans. >> Thank you, yeah. >> Congratulations. >> Alright guys, see ya. >> Wonderful week here in Washington, D.C. Back with more live on theCUBE here from D.C. right after this. (bubbly music)

(vibrant orchestral music) >> Hi, I'm Peter Burns and welcome to another CUBE Conversation from our beautiful studios in Palo Alto, California. I'm actually very excited about today's conversation because we'll be talking about the potential of human beings, of people within organizations, given this tumultuous change in this digital transformation. And to help talk about some of these crucial issues we've got, Emily He from, who is senior vice president of HCM Cloud marketing from Oracle. Emily, welcome to theCUBE. >> Thank you for having me. >> So let's just jump right into it. Let's start by, I mean Oracle's got to interesting approach. Cloud a customer, the idea of bringing the Cloud or forming applications into Cloud services. So why don't we start, what is going on with HCM Cloud at Oracle? >> You said exactly the right thing: which is, we have a very unique approach to the cloud. So we spent the last few years completely rewriting our HCM application for the Cloud. And when I think about 11 years ago when iPhone first came into being, a lot of the HR, HCM vendors rushed to embrace the mobile interface because they think that's the panacea for user adoption. As long as HR software as existed, we've always had issues with user adoptions. The early Cloud vendors really just moved their applications to the Cloud and their focus is to simplify the user interfaces by delivering this modern user experience. The problem is, that didn't really solve the fundamental user adoption problem. There data quality issues, data security issues. The work flow was cumbersome and the user interface wasn't friendly enough, right? So when Oracle started rewriting the Cloud a few years back, we took a very different approach because we already had hundreds of thousands of customers. And they had real business problems. They had complex business problems. So we're asking fundamentally very different questions. The questions we're asking is: How can we use the Cloud, and move our customers data to the Cloud by allowing them to manage the data autonomously? So we can insure data quality, data security. And how can we make the work flow so flexible that they can adjust their business processes to meet the ever changing market conditions. And lastly, how can we push our user experience to the next frontier by embracing Chatbot, voice UI, AI and deliver that really human experience. And that's exactly what we have in Oracle HCM Cloud. We have the Auntie Anne solution, and we're doing really interesting things to push the user experience to the new frontier. >> Well, that's one of the reasons why I'm so fascinated by this topic is 'cause in many respects, as you said, HCM used to be just a set of HR processes: pay roll, hiring, separating. There was just a set of processes you had to do to comply with local employment laws. >> Exactly. >> But now we're talking about using technology to do much more, to actually mediate the activities of human beings in more complex ways, incorporating a different ways of thinking about incentives so that human facing systems, supported by AI, augmented by AI allow this incredible resource, that exist with most organizations to be more productive, more fulfilled, happier and ultimately a better resource to customers. Have I got that right? >> That's such a great point. And that's why I'm so excited about the possibility AI brings to the world of business applications. If you think back on the way we approach applications in the past, we architected business processes and we used technology to deliver to those business processes. So it's an input based system and a predictable output will come out. With AI, now you have all these data from different sources and you can get insight from the data, but more importantly, the system is now suggesting actions, it's suggesting decisions, and human beings can use those insight to create more solutions. And we're also in a situation where potentially robots are working alongside humans. So what is the definition of workforce anymore? Do we include machines in our workforce management solutions and how do we think about that? And I'm personally fascinated by the possibility of having machines augment human tasks and look at the world in a completely different way. >> Well, I think you brought up this interesting point earlier, this essential point earlier that there's been an adoption problem associated with some of these complex people-oriented applications. It might very well be that as we rethink these applications and we focus more on how AI and other types of things can augment the way people work. Because a lot of employees are saying, wait a minute, I'm not process driven. I have a set of responsibilities. I have some agency within this business to serve customers. So how can we bring together those things so that the people can do what they're suppose to do. It might actually increase the likelihood that these HCM applications get adopted. Whaddya think? >> Yah, exactly. If you think about the way we're using enterprise software now, it's actually not very natural, fun or human. Every time you go through the same process, you fill out the form and some outcome will come out. Now I don't think anyone is thrilled to come to work and use enterprise software application. It's almost like you have a coworker and every time you see him, you're having the same conversation. What's your name, what's your address, what's your phone number, right? And in contrast, the way people are engaging with consumer technology is totally different. I use Siri, and I use Google Maps to navigate my traffic. And my kids have hour long conversations with Alexa. Telling jokes and ask science questions. My son is getting Siri to do his homework, math homework, which is very distressing for me. But that's a different conversation all together. And I think that's the way humans want you engaged with enterprise technology. It's already happening, so it's really our collective work, organizations responsibility to bring that type of technology to work, but like you said, there are many open questions we have to answer. >> And not the least of which, it's just not mediate, having an interaction with a machine. But also having conversations and having machinery be able to pick that up. Be able to turn that into subsequent tasks and actions so that human beings are spending more time on the creative side. And I know you have some great examples of this. Companies that are rethinking, so how they go from a human being attended to a customer problem and how that person, perhaps far away from a normal IT process, can actually quickly translate that into something that can scale within the business. >> Yeah, exactly. Yesterday, I think I mentioned this to you before, I was listening to a podcast about how Airbnb is architecting their customer experience and the way they do it is when they think about their ideal customer experience, they have one customer in mind and they really focus on re-imagining how they can deliver this wow experience, but once they nail the experience, then they got good feedback from the customer. They use machines to scale that to millions of customers. And I think that's going to be the way people want to work in the future. Human beings are uniquely good at being creative, problem solving and that's what they enjoy doing. So if we can have them focus on those tasks and have the machines help us scale things that we know will work and use them to get insight to further fine tune the experience, that'll be such a better way to work. >> I totally agree and I think that one of the important derivatives of that is the idea that increasingly we're talking about more collaboration, recognizing and amplifying the strengths of individuals and bringing them into a work force so that everybody is more confident, more comfortable and capable of working together. Certainly that's something HCM wants to do. But it also creates a new question and we spent a lot of time on theCUBE working with executives, like yourself, talking about this. How are we going to incorporate additional diversity into the workforce with an attacking with other worlds, how do you see this whole process coming together? So technology can make it easier, can liberate the potential of a lot of diverse people within a workforce. Yah, I am a huge believer in diversity. I think diversity is good for the workforce and I personally spend a lot of time promoting diversity in the leadership rank. And there are a couple of things: One is, we definitely can use software to foster more diversity in the work place. For example, if we use software to screen resumes, we can eliminate some of the demographic data to reduce bias and the software also has the ability to, for example, help us identify the ideal candidate from looking at our existing employees and come up with the right criteria, so we can get the right candidates on board. But I also think, in this new world we still have more work to do to psychologically set ourselves up for leadership positions. And I talk to a lot of women and this is the advice I usually give them: The first thing is, this applies to both men and women. You need to, really be conscious of the kind of the personal brand you're building and when I talk about personal brand, I don't mean that you go on Twitter and tweet about your personal life and tweet sheer content. It's really about being conscious about the value you are trying to exhibit at work and use your day to day actions to demonstrate those values, and that will help you create a reputation that will have a stronger impact on your career than anything else. The other thing I notice about women is, the strength for women is, women are naturally empathetic so we're very collaborative, we want to help each other, but at the same time, sometimes that can hold us back because you don't want to hurt other people's feelings by stepping forward and taking on leadership position. And men are usually much better at raising their hands and saying, "I'm ready for this position." So I think women can learn from men, and the way to do it is something I call Micro Bravery. And that is, I believe courage is a muscle you can exercise. The more you use it, the better you'll be at it and if everyday you can push yourself to do something that you're uncomfortable with, maybe it's giving someone performance feedback or maybe it's standing up and presenting, maybe it's coming here and having a conversation with you on tv. The more you do that, the more you are going to take risks and the more comfortable you will be in stepping into those leadership positions. The other thing that I noticed about a lot of women is when they have a family, they hesitate to take leadership positions. Because they think they're part is now the family and they can't do both. I firmly believe we can do both. As a matter of fact, I think being a parent makes you a really good leader because there's so many lessons you can learn from being a parent. One of the things I find helpful is, now that I have children, every time I make a tough decision I always ask myself: If I make this decision and I tell my kids, would they proud of me? If they told me, they make this decision would I be proud of them. So it kind of help you bring humanity to work and really strengthen your moral compass. So those are things I usually tell women to be more effective at work place and hopefully they, more women will assume the leadership roles. >> I love hearing that in theCUBE. So just to quickly summarize. We've talked about how women in particular, but overall, we're going to get an increasingly diverse workforce that's going to be applied to increasingly complex problems and the powerful role that software can play if it's set up right to facilitate collaboration, facilitate interaction, augment the human experience, so we can do more, do more productively, make everyone more happy. >> Exactly, I couldn't have said it better. >> Emily He, the senior vice president of marketing at Oracle HCM. Thank you very much for being on theCube. >> Thank you so much for having me. (dramatic music)

>> Narrator: Live from Vancouver, Canada it's theCUBE covering OpenStack Summit North America 2018. Brought to you by Red Hat, the OpenStack Foundation, and its ecosystem partners. >> Welcome back to theCUBE, I'm Stu Miniman, with my cohost John Troyer, and happy to welcome to the program, fresh off the container keynote, Chris Hodge, who's the senior strategic program manager with the OpenStack Foundation. Thanks so much for joining us. >> Oh yeah, thanks so much for having me. >> Alright, so short trip for you, then John's coming from the Bay Area, I'm coming from the east coast. You're coming up from Portland, which is where it was one of the attendees at the Portland OpenStack Summit, they said, "OpenStack has arrived, theCUBE's there." So, shout out to John Furrier and the team who were there early. I've been to all the North America ones since. You've been coming here for quite a while and it's now your job. >> I've been to every OpenStack Summit since then. And to the San Francisco Summit prior to that, so it was, yeah, I've been a regular. >> Okay so for those people that might not know, what's a Foundation member do these days? Other than, you know, you're working on some of the tech, you're giving keynotes, you know, what's a day in the life? >> Yeah, I mean, I mean for me, I feel like I'm really lucky because the OpenStack Foundation, you know, has you know, kind of given me a lot of freedom to go interact with other communities and that's been one of my primary tasks, to go out and work with adjacent communities and really work with them to build integrations between OpenStack and right now, particularly, Kubernetes and the other applications that are being hosted by the CNCF. >> Yeah, so I remember, and I've mentioned it a few times this week, three years ago we were sitting in the other side of the convention center, with theCUBE and it was Docker, Docker, Docker. The container sessions were overflowing and then a year later it was, you know, oh my gosh, Kubernetes. >> Chris: Yeah. (chuckles) >> This wave of, does one overtake the other, how do they fit together, and you know, in the keynotes yesterday and I'm sure your keynote today, talked a lot a bit about you know, the various ways that things fit together, because with open source communities in general and tech overall, it's never binary, it's always, it depends, and there's five different ways you could put things together depending on your needs. So, what are you seeing? >> I mean it's almost, yeah, I mean saying that it's one or the other and that one has to win and the other has to lose is actually kind of, it's kind of silly, because when we talk about Kubernetes and we talk about Docker, we're generally talking about applications. And, you know, and, with Kubernetes, when you're very focused on the applications you want to have existing infrastructure in place. I mean, this is what it's all about. People talk about, "I'm going to run my Kubernetes application "on the cloud, and the cloud has infrastructure." Well, OpenStack is infrastructure. And in fact, it is open source, it's an open source cloud. And so, so for me it feels like it's a very natural match, because you have your open application delivery system and then it integrates incredibly well with an open source cloud and so whether you're looking for a public cloud running on OpenStack or you're hosting a private cloud, you know, to me it's a very natural pairing to say that you have an OpenStack cloud, you have a bunch of integrations into Kubernetes and that the two work together. >> I think this year that that became a lot clearer, both in the keynotes and some of the sessions. The general conversation we've had with folks about the role of Kubernetes or an orchestration or the cloud layer, the application layer, the application deployment layer say, and the infrastructure somebody's got to manage the compute the network storage down here. At least, in this architectural diagram with my hands but, you can also, a couple of demos here showed deploying Kubernetes on bare metal alongside OpenStack, with that as the provider. Can you talk a little bit about that architectural pattern? It makes sense, I think, but then, you know, it's a apparent contradiction, wait a minute so now the Kubernetes is on the bare metal? So talk about that a little bit. >> So, I think, I think one of the ways you can think about resolving the contradiction is OpenStack is a bunch of applications. When you go and you install OpenStack we have all of these microsurfaces that are, some are user facing and some are controlling the architecture underneath. But they're applications and Kubernetes is well-suited for application delivery. So, say that you're starting with bare metal. You're starting with a bare metal cloud. Maybe managed by OpenStack, so you have OpenStack there at the bottom with Ironic, and you're managing your bare metal. You could easily install Kubernetes on that and that would be at your infrastructure layer, so this isn't Kubernetes that you're giving to your users, it's not Kubernetes that you're, you know, making world facing, this is internally for your organization for managing your infrastructure. But, you want OpenStack to provide that cloud infrastructure to all of your users. And since OpenStack is a big application with a lot of moving parts, Kubernetes actually becomes a very powerful tool, or any other container orchestration scheme becomes a very powerful tool for saying that you drop OpenStack on top of that and then all of a sudden you have a public cloud that's available for, you know, for the users within your organization, or you could be running a public cloud and providing those services for other people. And then suddenly that becomes a great platform for hosting Kubernetes applications on, and so the layers kind of interleave with one another. But even if you're not interested in that. Let's say you're running Kubernetes as bare metal and you're just, you want to have Kubernetes here providing some things. There's still things that OpenStack provides that you may already have existing in your infrastructure. >> Kubernetes kind of wants, it wants to access some storage. >> It wants to consume storage for example, and so we have OpenStack Cinder, which right now it supports you know, somewhere between, you know over 70 storage drivers, like these drivers exist and the nice thing about it is... You have one API to access this and we have two drivers within that, two Cinder drivers, you can either choose the, the flex volume storage or the container storage interface, the CSI storage interface. And Cinder just provides that for you. And that means if you have mixed storage within your data center, you put it all behind a Cinder API and you have one interface to your Kubernetes. >> So Chris, I believe that's one of the pieces of I believe it's called the Cloud Provider OpenStack. You talked about in the keynote. Maybe walk us through with that. >> Cloud Provider OpenStack is a project that is hosted within the, within the Kubernetes community. And it's... The owner of that code is the SIG OpenStack community inside of Kubernetes. I'm one of the three leads, one of the three SIG leads of that group and, that code does a number of things. The first is there's a cloud manager interface that is a consistent interface for Kubernetes to access infrastructure information in clouds. So information about a node, when a node joins a system, Kubernetes will know about it. Ways to attach storage, ways to provision load balancers. The cloud manager interface allows Kubernetes to do this on any cloud, whether it be Azure or GCE or Amazon. Also OpenStack. Cloud Provider OpenStack is the specific code that allows us to do that, and in fact we were, OpenStack was one of the first providers that existed in upstream Kubernetes you know, so it's kind of, we've been there since the very beginning, like this has been a, you know, an effort that's happened from the beginning. >> Somewhat non-ironically, right? A lot of that you've talked about, the OpenStack Foundation and this OpenStack Summit, a lot of the things talked about here are not OpenStack per se, the components, they are containers, there's the OpenDev Conference here, colocated. Is there confusion, there doesn't, I'm getting it straight in my head, Is there, was there, did you sense any confusion of folks here or is that, if you're in it you understand what's going on and why all these different threads are flowing together in kind of an open infrastructure conversation. It seems like the community gets it and understand it and is broadened because of it. >> Yeah, I mean, to me I've seen a tremendous shift over the last year in the general understanding of the community of the role all of these different applications play. And I think it's really, it's actually a testament to the success of all of these projects, in particular, we're building open APIs, we're building predictable behavior, and once you have that, and you have many people, many different organizations that are able to provide that, they're all able to communicate with one another and leverage the strengths of the other projects. >> All of a sudden, a standard interface, low and behold, right? A thousand flowers bloom on top. >> You know, it essentially allows you to build new things on top of that, new more interesting things. >> Alright, Chris, any interesting customer stories out of the keynote that we should share with the audience? >> I mean, there are so many fantastic stories that you can talk about, I mean, of course we saw the CERN keynote, where they're running managed Kubernetes on top of OpenStack. They have over 250 Kubernetes clusters doing research that are managed by OpenStack Magnum. I mean that's just, to me that's just tremendous. That this is being used in production, it's being used in science, and it's not just across one cloud, it's across many clouds and, You know, we also have AT&T, which has been working very hard on combining OpenStack and Kubernetes to manage their next generation of, of teleco infrastructure. And so, they've been big drivers along with SK Telecom on using Kubernetes as an infrastructure layer and then putting OpenStack on top of that, and then delivering applications with that. And so those are, you know we, the OpenStack Foundation just published on Monday a new white paper about OpenStack, how OpenStack works with containers and these are just a couple of the case studies that we actually have listed in that white paper. >> Chris, you're at the interface between OpenStack, which has become more mature and more stable, and containers, which, although it is maturing is still a little bit, is moving fast, right? Containers and Kubernetes both, a lot of development. Every summit, a lot of new projects, lot of new ways of installing, lot of new components, lot of new snaps. All sorts of things. What are you looking forward to now over the next year in terms of container maturity and how that's going to help us? >> So... People are talking so much now about security with containers and this is another really exciting thing that's coming out of our work because, you know, during one of the container keynotes, one of the things that was kind of driven home was containers don't contain. But, we're actually, at the OpenStack Foundation, we're kind of taking that on, and we, and my colleague Anne Bertucio has been leading a project, you know, has been community manager for a product called Kata Containers, which is, you know, you could almost call it containers that do contain. So I think that this is going to be really exciting in the next year as we talk more and more about we're building more generic interfaces and allowing all sorts of new approaches to solving complex problems, be it in security, be it in performance, be it in logging and monitoring. And so, I think, so the tools that are coming out of this and you know, creating these abstractions and how people are creatively innovating on top of those is pretty exciting. >> The last thing I'm hoping you can help connect the dots for us on is, when we talk Kubernetes, we're talking about multi-cloud. One of the big problems about Kubernetes, you know, came out of Google from you know, if you just say, "Why would Google do this?" It's like, well, there's that one really big cloud out there and if I don't have some portability and be able to move things, that one cloud might just continue to dominate. So, help connect OpenStack to how it lives in this multi-cloud world. Kubernetes is a piece of that, but you know, maybe, would love your viewpoint. >> Yeah, so. This is happening on so many levels. We see lots of large organizations who want to take back control of the cost of cloud and the cost of their cloud infrastructure and so they're starting to pull away from the big public clouds and invest more in private infrastructure. We see this with companies like eBay, we see it with companies like AT&T and Walmart, where they're investing heavily in OpenStack clouds. So that they have more control over the cost and how their applications are delivered. But you're also seeing this in a lot of... Like especially municipalities outside of the United States, you know, different governments that have data restrictions, restrictions on where data lives and how it's accessed, and we're seeing more governments and more businesses overseas that are turning to OpenStack as a way to have cloud infrastructure that is on their home soil, that you know, kind of meets the requirements that are necessary, you know that are necessary for them. And then kind of the third aspect of all of this is sometimes you just, sometimes you need to have lots of availability across, you know, many clouds. And you can have a private cloud, but possibly, in order to serve your customers, you might need public cloud resources, and federation across, across this, both in OpenStack and Kubernetes is improving at such an incredible pace that it becomes very easy to say that I have two, three, four, five clouds, but we're able to, we're able to combine them all and make them all look like one. >> Alright, well Chris Hodge, we really appreciate the updates on OpenStack and Kubernetes in all the various permutations. >> Yeah, it was great talking about it. This is, I mean this is the work that I love and I'm excited about, and this is, you know, I'm looking forward to it, I have fun with it and I keep looking forward to everything that's coming. >> Awesome, well we love to be able to share these stories, the technologists, the customers and everything going on in the industry. For John Troyer, I'm Stu Miniman, back with more coverage here from OpenStack Summit 2018 in beautiful Vancouver, British Columbia. Thanks for watching theCUBE. (tech music)

>> There's a movement that's sweeping across businesses everywhere here in this country and around the world. And it's all about data. Today businesses are being inundated with data. To the tune of over two and a half million gigabytes that'll be generated in the next 60 seconds alone. What do you do with all that data? To extract insights you typically turn to a data scientist. But not necessarily anymore. At least not exclusively. Today the ability to extract value from data is becoming a shared mission. A team effort that spans the organization extending far more widely than ever before. Today, data science is being democratized. >> Data Sciences for All: It's a Whole New Game. >> Welcome everyone, I'm Katie Linendoll. I'm a technology expert writer and I love reporting on all things tech. My fascination with tech started very young. I began coding when I was 12. Received my networking certs by 18 and a degree in IT and new media from Rochester Institute of Technology. So as you can tell, technology has always been a sure passion of mine. Having grown up in the digital age, I love having a career that keeps me at the forefront of science and technology innovations. I spend equal time in the field being hands on as I do on my laptop conducting in depth research. Whether I'm diving underwater with NASA astronauts, witnessing the new ways which mobile technology can help rebuild the Philippine's economy in the wake of super typhoons, or sharing a first look at the newest iPhones on The Today Show, yesterday, I'm always on the hunt for the latest and greatest tech stories. And that's what brought me here. I'll be your host for the next hour and as we explore the new phenomenon that is taking businesses around the world by storm. And data science continues to become democratized and extends beyond the domain of the data scientist. And why there's also a mandate for all of us to become data literate. Now that data science for all drives our AI culture. And we're going to be able to take to the streets and go behind the scenes as we uncover the factors that are fueling this phenomenon and giving rise to a movement that is reshaping how businesses leverage data. And putting organizations on the road to AI. So coming up, I'll be doing interviews with data scientists. We'll see real world demos and take a look at how IBM is changing the game with an open data science platform. We'll also be joined by legendary statistician Nate Silver, founder and editor-in-chief of FiveThirtyEight. Who will shed light on how a data driven mindset is changing everything from business to our culture. We also have a few people who are joining us in our studio, so thank you guys for joining us. Come on, I can do better than that, right? Live studio audience, the fun stuff. And for all of you during the program, I want to remind you to join that conversation on social media using the hashtag DSforAll, it's data science for all. Share your thoughts on what data science and AI means to you and your business. And, let's dive into a whole new game of data science. Now I'd like to welcome my co-host General Manager IBM Analytics, Rob Thomas. >> Hello, Katie. >> Come on guys. >> Yeah, seriously. >> No one's allowed to be quiet during this show, okay? >> Right. >> Or, I'll start calling people out. So Rob, thank you so much. I think you know this conversation, we're calling it a data explosion happening right now. And it's nothing new. And when you and I chatted about it. You've been talking about this for years. You have to ask, is this old news at this point? >> Yeah, I mean, well first of all, the data explosion is not coming, it's here. And everybody's in the middle of it right now. What is different is the economics have changed. And the scale and complexity of the data that organizations are having to deal with has changed. And to this day, 80% of the data in the world still sits behind corporate firewalls. So, that's becoming a problem. It's becoming unmanageable. IT struggles to manage it. The business can't get everything they need. Consumers can't consume it when they want. So we have a challenge here. >> It's challenging in the world of unmanageable. Crazy complexity. If I'm sitting here as an IT manager of my business, I'm probably thinking to myself, this is incredibly frustrating. How in the world am I going to get control of all this data? And probably not just me thinking it. Many individuals here as well. >> Yeah, indeed. Everybody's thinking about how am I going to put data to work in my organization in a way I haven't done before. Look, you've got to have the right expertise, the right tools. The other thing that's happening in the market right now is clients are dealing with multi cloud environments. So data behind the firewall in private cloud, multiple public clouds. And they have to find a way. How am I going to pull meaning out of this data? And that brings us to data science and AI. That's how you get there. >> I understand the data science part but I think we're all starting to hear more about AI. And it's incredible that this buzz word is happening. How do businesses adopt to this AI growth and boom and trend that's happening in this world right now? >> Well, let me define it this way. Data science is a discipline. And machine learning is one technique. And then AI puts both machine learning into practice and applies it to the business. So this is really about how getting your business where it needs to go. And to get to an AI future, you have to lay a data foundation today. I love the phrase, "there's no AI without IA." That means you're not going to get to AI unless you have the right information architecture to start with. >> Can you elaborate though in terms of how businesses can really adopt AI and get started. >> Look, I think there's four things you have to do if you're serious about AI. One is you need a strategy for data acquisition. Two is you need a modern data architecture. Three is you need pervasive automation. And four is you got to expand job roles in the organization. >> Data acquisition. First pillar in this you just discussed. Can we start there and explain why it's so critical in this process? >> Yeah, so let's think about how data acquisition has evolved through the years. 15 years ago, data acquisition was about how do I get data in and out of my ERP system? And that was pretty much solved. Then the mobile revolution happens. And suddenly you've got structured and non-structured data. More than you've ever dealt with. And now you get to where we are today. You're talking terabytes, petabytes of data. >> [Katie] Yottabytes, I heard that word the other day. >> I heard that too. >> Didn't even know what it meant. >> You know how many zeros that is? >> I thought we were in Star Wars. >> Yeah, I think it's a lot of zeroes. >> Yodabytes, it's new. >> So, it's becoming more and more complex in terms of how you acquire data. So that's the new data landscape that every client is dealing with. And if you don't have a strategy for how you acquire that and manage it, you're not going to get to that AI future. >> So a natural segue, if you are one of these businesses, how do you build for the data landscape? >> Yeah, so the question I always hear from customers is we need to evolve our data architecture to be ready for AI. And the way I think about that is it's really about moving from static data repositories to more of a fluid data layer. >> And we continue with the architecture. New data architecture is an interesting buzz word to hear. But it's also one of the four pillars. So if you could dive in there. >> Yeah, I mean it's a new twist on what I would call some core data science concepts. For example, you have to leverage tools with a modern, centralized data warehouse. But your data warehouse can't be stagnant to just what's right there. So you need a way to federate data across different environments. You need to be able to bring your analytics to the data because it's most efficient that way. And ultimately, it's about building an optimized data platform that is designed for data science and AI. Which means it has to be a lot more flexible than what clients have had in the past. >> All right. So we've laid out what you need for driving automation. But where does the machine learning kick in? >> Machine learning is what gives you the ability to automate tasks. And I think about machine learning. It's about predicting and automating. And this will really change the roles of data professionals and IT professionals. For example, a data scientist cannot possibly know every algorithm or every model that they could use. So we can automate the process of algorithm selection. Another example is things like automated data matching. Or metadata creation. Some of these things may not be exciting but they're hugely practical. And so when you think about the real use cases that are driving return on investment today, it's things like that. It's automating the mundane tasks. >> Let's go ahead and come back to something that you mentioned earlier because it's fascinating to be talking about this AI journey, but also significant is the new job roles. And what are those other participants in the analytics pipeline? >> Yeah I think we're just at the start of this idea of new job roles. We have data scientists. We have data engineers. Now you see machine learning engineers. Application developers. What's really happening is that data scientists are no longer allowed to work in their own silo. And so the new job roles is about how does everybody have data first in their mind? And then they're using tools to automate data science, to automate building machine learning into applications. So roles are going to change dramatically in organizations. >> I think that's confusing though because we have several organizations who saying is that highly specialized roles, just for data science? Or is it applicable to everybody across the board? >> Yeah, and that's the big question, right? Cause everybody's thinking how will this apply? Do I want this to be just a small set of people in the organization that will do this? But, our view is data science has to for everybody. It's about bring data science to everybody as a shared mission across the organization. Everybody in the company has to be data literate. And participate in this journey. >> So overall, group effort, has to be a common goal, and we all need to be data literate across the board. >> Absolutely. >> Done deal. But at the end of the day, it's kind of not an easy task. >> It's not. It's not easy but it's maybe not as big of a shift as you would think. Because you have to put data in the hands of people that can do something with it. So, it's very basic. Give access to data. Data's often locked up in a lot of organizations today. Give people the right tools. Embrace the idea of choice or diversity in terms of those tools. That gets you started on this path. >> It's interesting to hear you say essentially you need to train everyone though across the board when it comes to data literacy. And I think people that are coming into the work force don't necessarily have a background or a degree in data science. So how do you manage? >> Yeah, so in many cases that's true. I will tell you some universities are doing amazing work here. One example, University of California Berkeley. They offer a course for all majors. So no matter what you're majoring in, you have a course on foundations of data science. How do you bring data science to every role? So it's starting to happen. We at IBM provide data science courses through CognitiveClass.ai. It's for everybody. It's free. And look, if you want to get your hands on code and just dive right in, you go to datascience.ibm.com. The key point is this though. It's more about attitude than it is aptitude. I think anybody can figure this out. But it's about the attitude to say we're putting data first and we're going to figure out how to make this real in our organization. >> I also have to give a shout out to my alma mater because I have heard that there is an offering in MS in data analytics. And they are always on the forefront of new technologies and new majors and on trend. And I've heard that the placement behind those jobs, people graduating with the MS is high. >> I'm sure it's very high. >> So go Tigers. All right, tangential. Let me get back to something else you touched on earlier because you mentioned that a number of customers ask you how in the world do I get started with AI? It's an overwhelming question. Where do you even begin? What do you tell them? >> Yeah, well things are moving really fast. But the good thing is most organizations I see, they're already on the path, even if they don't know it. They might have a BI practice in place. They've got data warehouses. They've got data lakes. Let me give you an example. AMC Networks. They produce a lot of the shows that I'm sure you watch Katie. >> [Katie] Yes, Breaking Bad, Walking Dead, any fans? >> [Rob] Yeah, we've got a few. >> [Katie] Well you taught me something I didn't even know. Because it's amazing how we have all these different industries, but yet media in itself is impacted too. And this is a good example. >> Absolutely. So, AMC Networks, think about it. They've got ads to place. They want to track viewer behavior. What do people like? What do they dislike? So they have to optimize every aspect of their business from marketing campaigns to promotions to scheduling to ads. And their goal was transform data into business insights and really take the burden off of their IT team that was heavily burdened by obviously a huge increase in data. So their VP of BI took the approach of using machine learning to process large volumes of data. They used a platform that was designed for AI and data processing. It's the IBM analytics system where it's a data warehouse, data science tools are built in. It has in memory data processing. And just like that, they were ready for AI. And they're already seeing that impact in their business. >> Do you think a movement of that nature kind of presses other media conglomerates and organizations to say we need to be doing this too? >> I think it's inevitable that everybody, you're either going to be playing, you're either going to be leading, or you'll be playing catch up. And so, as we talk to clients we think about how do you start down this path now, even if you have to iterate over time? Because otherwise you're going to wake up and you're going to be behind. >> One thing worth noting is we've talked about analytics to the data. It's analytics first to the data, not the other way around. >> Right. So, look. We as a practice, we say you want to bring data to where the data sits. Because it's a lot more efficient that way. It gets you better outcomes in terms of how you train models and it's more efficient. And we think that leads to better outcomes. Other organization will say, "Hey move the data around." And everything becomes a big data movement exercise. But once an organization has started down this path, they're starting to get predictions, they want to do it where it's really easy. And that means analytics applied right where the data sits. >> And worth talking about the role of the data scientist in all of this. It's been called the hot job of the decade. And a Harvard Business Review even dubbed it the sexiest job of the 21st century. >> Yes. >> I want to see this on the cover of Vogue. Like I want to see the first data scientist. Female preferred, on the cover of Vogue. That would be amazing. >> Perhaps you can. >> People agree. So what changes for them? Is this challenging in terms of we talk data science for all. Where do all the data science, is it data science for everyone? And how does it change everything? >> Well, I think of it this way. AI gives software super powers. It really does. It changes the nature of software. And at the center of that is data scientists. So, a data scientist has a set of powers that they've never had before in any organization. And that's why it's a hot profession. Now, on one hand, this has been around for a while. We've had actuaries. We've had statisticians that have really transformed industries. But there are a few things that are new now. We have new tools. New languages. Broader recognition of this need. And while it's important to recognize this critical skill set, you can't just limit it to a few people. This is about scaling it across the organization. And truly making it accessible to all. >> So then do we need more data scientists? Or is this something you train like you said, across the board? >> Well, I think you want to do a little bit of both. We want more. But, we can also train more and make the ones we have more productive. The way I think about it is there's kind of two markets here. And we call it clickers and coders. >> [Katie] I like that. That's good. >> So, let's talk about what that means. So clickers are basically somebody that wants to use tools. Create models visually. It's drag and drop. Something that's very intuitive. Those are the clickers. Nothing wrong with that. It's been valuable for years. There's a new crop of data scientists. They want to code. They want to build with the latest open source tools. They want to write in Python or R. These are the coders. And both approaches are viable. Both approaches are critical. Organizations have to have a way to meet the needs of both of those types. And there's not a lot of things available today that do that. >> Well let's keep going on that. Because I hear you talking about the data scientists role and how it's critical to success, but with the new tools, data science and analytics skills can extend beyond the domain of just the data scientist. >> That's right. So look, we're unifying coders and clickers into a single platform, which we call IBM Data Science Experience. And as the demand for data science expertise grows, so does the need for these kind of tools. To bring them into the same environment. And my view is if you have the right platform, it enables the organization to collaborate. And suddenly you've changed the nature of data science from an individual sport to a team sport. >> So as somebody that, my background is in IT, the question is really is this an additional piece of what IT needs to do in 2017 and beyond? Or is it just another line item to the budget? >> So I'm afraid that some people might view it that way. As just another line item. But, I would challenge that and say data science is going to reinvent IT. It's going to change the nature of IT. And every organization needs to think about what are the skills that are critical? How do we engage a broader team to do this? Because once they get there, this is the chance to reinvent how they're performing IT. >> [Katie] Challenging or not? >> Look it's all a big challenge. Think about everything IT organizations have been through. Some of them were late to things like mobile, but then they caught up. Some were late to cloud, but then they caught up. I would just urge people, don't be late to data science. Use this as your chance to reinvent IT. Start with this notion of clickers and coders. This is a seminal moment. Much like mobile and cloud was. So don't be late. >> And I think it's critical because it could be so costly to wait. And Rob and I were even chatting earlier how data analytics is just moving into all different kinds of industries. And I can tell you even personally being effected by how important the analysis is in working in pediatric cancer for the last seven years. I personally implement virtual reality headsets to pediatric cancer hospitals across the country. And it's great. And it's working phenomenally. And the kids are amazed. And the staff is amazed. But the phase two of this project is putting in little metrics in the hardware that gather the breathing, the heart rate to show that we have data. Proof that we can hand over to the hospitals to continue making this program a success. So just in-- >> That's a great example. >> An interesting example. >> Saving lives? >> Yes. >> That's also applying a lot of what we talked about. >> Exciting stuff in the world of data science. >> Yes. Look, I just add this is an existential moment for every organization. Because what you do in this area is probably going to define how competitive you are going forward. And think about if you don't do something. What if one of your competitors goes and creates an application that's more engaging with clients? So my recommendation is start small. Experiment. Learn. Iterate on projects. Define the business outcomes. Then scale up. It's very doable. But you've got to take the first step. >> First step always critical. And now we're going to get to the fun hands on part of our story. Because in just a moment we're going to take a closer look at what data science can deliver. And where organizations are trying to get to. All right. Thank you Rob and now we've been joined by Siva Anne who is going to help us navigate this demo. First, welcome Siva. Give him a big round of applause. Yeah. All right, Rob break down what we're going to be looking at. You take over this demo. >> All right. So this is going to be pretty interesting. So Siva is going to take us through. So he's going to play the role of a financial adviser. Who wants to help better serve clients through recommendations. And I'm going to really illustrate three things. One is how do you federate data from multiple data sources? Inside the firewall, outside the firewall. How do you apply machine learning to predict and to automate? And then how do you move analytics closer to your data? So, what you're seeing here is a custom application for an investment firm. So, Siva, our financial adviser, welcome. So you can see at the top, we've got market data. We pulled that from an external source. And then we've got Siva's calendar in the middle. He's got clients on the right side. So page down, what else do you see down there Siva? >> [Siva] I can see the recent market news. And in here I can see that JP Morgan is calling for a US dollar rebound in the second half of the year. And, I have upcoming meeting with Leo Rakes. I can get-- >> [Rob] So let's go in there. Why don't you click on Leo Rakes. So, you're sitting at your desk, you're deciding how you're going to spend the day. You know you have a meeting with Leo. So you click on it. You immediately see, all right, so what do we know about him? We've got data governance implemented. So we know his age, we know his degree. We can see he's not that aggressive of a trader. Only six trades in the last few years. But then where it gets interesting is you go to the bottom. You start to see predicted industry affinity. Where did that come from? How do we have that? >> [Siva] So these green lines and red arrows here indicate the trending affinity of Leo Rakes for particular industry stocks. What we've done here is we've built machine learning models using customer's demographic data, his stock portfolios, and browsing behavior to build a model which can predict his affinity for a particular industry. >> [Rob] Interesting. So, I like to think of this, we call it celebrity experiences. So how do you treat every customer like they're a celebrity? So to some extent, we're reading his mind. Because without asking him, we know that he's going to have an affinity for auto stocks. So we go down. Now we look at his portfolio. You can see okay, he's got some different holdings. He's got Amazon, Google, Apple, and then he's got RACE, which is the ticker for Ferrari. You can see that's done incredibly well. And so, as a financial adviser, you look at this and you say, all right, we know he loves auto stocks. Ferrari's done very well. Let's create a hedge. Like what kind of security would interest him as a hedge against his position for Ferrari? Could we go figure that out? >> [Siva] Yes. Given I know that he's gotten an affinity for auto stocks, and I also see that Ferrari has got some terminus gains, I want to lock in these gains by hedging. And I want to do that by picking a auto stock which has got negative correlation with Ferrari. >> [Rob] So this is where we get to the idea of in database analytics. Cause you start clicking that and immediately we're getting instant answers of what's happening. So what did we find here? We're going to compare Ferrari and Honda. >> [Siva] I'm going to compare Ferrari with Honda. And what I see here instantly is that Honda has got a negative correlation with Ferrari, which makes it a perfect mix for his stock portfolio. Given he has an affinity for auto stocks and it correlates negatively with Ferrari. >> [Rob] These are very powerful tools at the hand of a financial adviser. You think about it. As a financial adviser, you wouldn't think about federating data, machine learning, pretty powerful. >> [Siva] Yes. So what we have seen here is that using the common SQL engine, we've been able to federate queries across multiple data sources. Db2 Warehouse in the cloud, IBM's Integrated Analytic System, and Hortonworks powered Hadoop platform for the new speeds. We've been able to use machine learning to derive innovative insights about his stock affinities. And drive the machine learning into the appliance. Closer to where the data resides to deliver high performance analytics. >> [Rob] At scale? >> [Siva] We're able to run millions of these correlations across stocks, currency, other factors. And even score hundreds of customers for their affinities on a daily basis. >> That's great. Siva, thank you for playing the role of financial adviser. So I just want to recap briefly. Cause this really powerful technology that's really simple. So we federated, we aggregated multiple data sources from all over the web and internal systems. And public cloud systems. Machine learning models were built that predicted Leo's affinity for a certain industry. In this case, automotive. And then you see when you deploy analytics next to your data, even a financial adviser, just with the click of a button is getting instant answers so they can go be more productive in their next meeting. This whole idea of celebrity experiences for your customer, that's available for everybody, if you take advantage of these types of capabilities. Katie, I'll hand it back to you. >> Good stuff. Thank you Rob. Thank you Siva. Powerful demonstration on what we've been talking about all afternoon. And thank you again to Siva for helping us navigate. Should be give him one more round of applause? We're going to be back in just a moment to look at how we operationalize all of this data. But in first, here's a message from me. If you're a part of a line of business, your main fear is disruption. You know data is the new goal that can create huge amounts of value. So does your competition. And they may be beating you to it. You're convinced there are new business models and revenue sources hidden in all the data. You just need to figure out how to leverage it. But with the scarcity of data scientists, you really can't rely solely on them. You may need more people throughout the organization that have the ability to extract value from data. And as a data science leader or data scientist, you have a lot of the same concerns. You spend way too much time looking for, prepping, and interpreting data and waiting for models to train. You know you need to operationalize the work you do to provide business value faster. What you want is an easier way to do data prep. And rapidly build models that can be easily deployed, monitored and automatically updated. So whether you're a data scientist, data science leader, or in a line of business, what's the solution? What'll it take to transform the way you work? That's what we're going to explore next. All right, now it's time to delve deeper into the nuts and bolts. The nitty gritty of operationalizing data science and creating a data driven culture. How do you actually do that? Well that's what these experts are here to share with us. I'm joined by Nir Kaldero, who's head of data science at Galvanize, which is an education and training organization. Tricia Wang, who is co-founder of Sudden Compass, a consultancy that helps companies understand people with data. And last, but certainly not least, Michael Li, founder and CEO of Data Incubator, which is a data science train company. All right guys. Shall we get right to it? >> All right. >> So data explosion happening right now. And we are seeing it across the board. I just shared an example of how it's impacting my philanthropic work in pediatric cancer. But you guys each have so many unique roles in your business life. How are you seeing it just blow up in your fields? Nir, your thing? >> Yeah, for example like in Galvanize we train many Fortune 500 companies. And just by looking at the demand of companies that wants us to help them go through this digital transformation is mind-blowing. Data point by itself. >> Okay. Well what we're seeing what's going on is that data science like as a theme, is that it's actually for everyone now. But what's happening is that it's actually meeting non technical people. But what we're seeing is that when non technical people are implementing these tools or coming at these tools without a base line of data literacy, they're often times using it in ways that distance themselves from the customer. Because they're implementing data science tools without a clear purpose, without a clear problem. And so what we do at Sudden Compass is that we work with companies to help them embrace and understand the complexity of their customers. Because often times they are misusing data science to try and flatten their understanding of the customer. As if you can just do more traditional marketing. Where you're putting people into boxes. And I think the whole ROI of data is that you can now understand people's relationships at a much more complex level at a greater scale before. But we have to do this with basic data literacy. And this has to involve technical and non technical people. >> Well you can have all the data in the world, and I think it speaks to, if you're not doing the proper movement with it, forget it. It means nothing at the same time. >> No absolutely. I mean, I think that when you look at the huge explosion in data, that comes with it a huge explosion in data experts. Right, we call them data scientists, data analysts. And sometimes they're people who are very, very talented, like the people here. But sometimes you have people who are maybe re-branding themselves, right? Trying to move up their title one notch to try to attract that higher salary. And I think that that's one of the things that customers are coming to us for, right? They're saying, hey look, there are a lot of people that call themselves data scientists, but we can't really distinguish. So, we have sort of run a fellowship where you help companies hire from a really talented group of folks, who are also truly data scientists and who know all those kind of really important data science tools. And we also help companies internally. Fortune 500 companies who are looking to grow that data science practice that they have. And we help clients like McKinsey, BCG, Bain, train up their customers, also their clients, also their workers to be more data talented. And to build up that data science capabilities. >> And Nir, this is something you work with a lot. A lot of Fortune 500 companies. And when we were speaking earlier, you were saying many of these companies can be in a panic. >> Yeah. >> Explain that. >> Yeah, so you know, not all Fortune 500 companies are fully data driven. And we know that the winners in this fourth industrial revolution, which I like to call the machine intelligence revolution, will be companies who navigate and transform their organization to unlock the power of data science and machine learning. And the companies that are not like that. Or not utilize data science and predictive power well, will pretty much get shredded. So they are in a panic. >> Tricia, companies have to deal with data behind the firewall and in the new multi cloud world. How do organizations start to become driven right to the core? >> I think the most urgent question to become data driven that companies should be asking is how do I bring the complex reality that our customers are experiencing on the ground in to a corporate office? Into the data models. So that question is critical because that's how you actually prevent any big data disasters. And that's how you leverage big data. Because when your data models are really far from your human models, that's when you're going to do things that are really far off from how, it's going to not feel right. That's when Tesco had their terrible big data disaster that they're still recovering from. And so that's why I think it's really important to understand that when you implement big data, you have to further embrace thick data. The qualitative, the emotional stuff, that is difficult to quantify. But then comes the difficult art and science that I think is the next level of data science. Which is that getting non technical and technical people together to ask how do we find those unknown nuggets of insights that are difficult to quantify? Then, how do we do the next step of figuring out how do you mathematically scale those insights into a data model? So that actually is reflective of human understanding? And then we can start making decisions at scale. But you have to have that first. >> That's absolutely right. And I think that when we think about what it means to be a data scientist, right? I always think about it in these sort of three pillars. You have the math side. You have to have that kind of stats, hardcore machine learning background. You have the programming side. You don't work with small amounts of data. You work with large amounts of data. You've got to be able to type the code to make those computers run. But then the last part is that human element. You have to understand the domain expertise. You have to understand what it is that I'm actually analyzing. What's the business proposition? And how are the clients, how are the users actually interacting with the system? That human element that you were talking about. And I think having somebody who understands all of those and not just in isolation, but is able to marry that understanding across those different topics, that's what makes a data scientist. >> But I find that we don't have people with those skill sets. And right now the way I see teams being set up inside companies is that they're creating these isolated data unicorns. These data scientists that have graduated from your programs, which are great. But, they don't involve the people who are the domain experts. They don't involve the designers, the consumer insight people, the people, the salespeople. The people who spend time with the customers day in and day out. Somehow they're left out of the room. They're consulted, but they're not a stakeholder. >> Can I actually >> Yeah, yeah please. >> Can I actually give a quick example? So for example, we at Galvanize train the executives and the managers. And then the technical people, the data scientists and the analysts. But in order to actually see all of the RY behind the data, you also have to have a creative fluid conversation between non technical and technical people. And this is a major trend now. And there's a major gap. And we need to increase awareness and kind of like create a new, kind of like environment where technical people also talks seamlessly with non technical ones. >> [Tricia] We call-- >> That's one of the things that we see a lot. Is one of the trends in-- >> A major trend. >> data science training is it's not just for the data science technical experts. It's not just for one type of person. So a lot of the training we do is sort of data engineers. People who are more on the software engineering side learning more about the stats of math. And then people who are sort of traditionally on the stat side learning more about the engineering. And then managers and people who are data analysts learning about both. >> Michael, I think you said something that was of interest too because I think we can look at IBM Watson as an example. And working in healthcare. The human component. Because often times we talk about machine learning and AI, and data and you get worried that you still need that human component. Especially in the world of healthcare. And I think that's a very strong point when it comes to the data analysis side. Is there any particular example you can speak to of that? >> So I think that there was this really excellent paper a while ago talking about all the neuro net stuff and trained on textual data. So looking at sort of different corpuses. And they found that these models were highly, highly sexist. They would read these corpuses and it's not because neuro nets themselves are sexist. It's because they're reading the things that we write. And it turns out that we write kind of sexist things. And they would sort of find all these patterns in there that were sort of latent, that had a lot of sort of things that maybe we would cringe at if we sort of saw. And I think that's one of the really important aspects of the human element, right? It's being able to come in and sort of say like, okay, I know what the biases of the system are, I know what the biases of the tools are. I need to figure out how to use that to make the tools, make the world a better place. And like another area where this comes up all the time is lending, right? So the federal government has said, and we have a lot of clients in the financial services space, so they're constantly under these kind of rules that they can't make discriminatory lending practices based on a whole set of protected categories. Race, sex, gender, things like that. But, it's very easy when you train a model on credit scores to pick that up. And then to have a model that's inadvertently sexist or racist. And that's where you need the human element to come back in and say okay, look, you're using the classic example would be zip code, you're using zip code as a variable. But when you look at it, zip codes actually highly correlated with race. And you can't do that. So you may inadvertently by sort of following the math and being a little naive about the problem, inadvertently introduce something really horrible into a model and that's where you need a human element to sort of step in and say, okay hold on. Slow things down. This isn't the right way to go. >> And the people who have -- >> I feel like, I can feel her ready to respond. >> Yes, I'm ready. >> She's like let me have at it. >> And the people here it is. And the people who are really great at providing that human intelligence are social scientists. We are trained to look for bias and to understand bias in data. Whether it's quantitative or qualitative. And I really think that we're going to have less of these kind of problems if we had more integrated teams. If it was a mandate from leadership to say no data science team should be without a social scientist, ethnographer, or qualitative researcher of some kind, to be able to help see these biases. >> The talent piece is actually the most crucial-- >> Yeah. >> one here. If you look about how to enable machine intelligence in organization there are the pillars that I have in my head which is the culture, the talent and the technology infrastructure. And I believe and I saw in working very closely with the Fortune 100 and 200 companies that the talent piece is actually the most important crucial hard to get. >> [Tricia] I totally agree. >> It's absolutely true. Yeah, no I mean I think that's sort of like how we came up with our business model. Companies were basically saying hey, I can't hire data scientists. And so we have a fellowship where we get 2,000 applicants each quarter. We take the top 2% and then we sort of train them up. And we work with hiring companies who then want to hire from that population. And so we're sort of helping them solve that problem. And the other half of it is really around training. Cause with a lot of industries, especially if you're sort of in a more regulated industry, there's a lot of nuances to what you're doing. And the fastest way to develop that data science or AI talent may not necessarily be to hire folks who are coming out of a PhD program. It may be to take folks internally who have a lot of that domain knowledge that you have and get them trained up on those data science techniques. So we've had large insurance companies come to us and say hey look, we hire three or four folks from you a quarter. That doesn't move the needle for us. What we really need is take the thousand actuaries and statisticians that we have and get all of them trained up to become a data scientist and become data literate in this new open source world. >> [Katie] Go ahead. >> All right, ladies first. >> Go ahead. >> Are you sure? >> No please, fight first. >> Go ahead. >> Go ahead Nir. >> So this is actually a trend that we have been seeing in the past year or so that companies kind of like start to look how to upscale and look for talent within the organization. So they can actually move them to become more literate and navigate 'em from analyst to data scientist. And from data scientist to machine learner. So this is actually a trend that is happening already for a year or so. >> Yeah, but I also find that after they've gone through that training in getting people skilled up in data science, the next problem that I get is executives coming to say we've invested in all of this. We're still not moving the needle. We've already invested in the right tools. We've gotten the right skills. We have enough scale of people who have these skills. Why are we not moving the needle? And what I explain to them is look, you're still making decisions in the same way. And you're still not involving enough of the non technical people. Especially from marketing, which is now, the CMO's are much more responsible for driving growth in their companies now. But often times it's so hard to change the old way of marketing, which is still like very segmentation. You know, demographic variable based, and we're trying to move people to say no, you have to understand the complexity of customers and not put them in boxes. >> And I think underlying a lot of this discussion is this question of culture, right? >> Yes. >> Absolutely. >> How do you build a data driven culture? And I think that that culture question, one of the ways that comes up quite often in especially in large, Fortune 500 enterprises, is that they are very, they're not very comfortable with sort of example, open source architecture. Open source tools. And there is some sort of residual bias that that's somehow dangerous. So security vulnerability. And I think that that's part of the cultural challenge that they often have in terms of how do I build a more data driven organization? Well a lot of the talent really wants to use these kind of tools. And I mean, just to give you an example, we are partnering with one of the major cloud providers to sort of help make open source tools more user friendly on their platform. So trying to help them attract the best technologists to use their platform because they want and they understand the value of having that kind of open source technology work seamlessly on their platforms. So I think that just sort of goes to show you how important open source is in this movement. And how much large companies and Fortune 500 companies and a lot of the ones we work with have to embrace that. >> Yeah, and I'm seeing it in our work. Even when we're working with Fortune 500 companies, is that they've already gone through the first phase of data science work. Where I explain it was all about the tools and getting the right tools and architecture in place. And then companies started moving into getting the right skill set in place. Getting the right talent. And what you're talking about with culture is really where I think we're talking about the third phase of data science, which is looking at communication of these technical frameworks so that we can get non technical people really comfortable in the same room with data scientists. That is going to be the phase, that's really where I see the pain point. And that's why at Sudden Compass, we're really dedicated to working with each other to figure out how do we solve this problem now? >> And I think that communication between the technical stakeholders and management and leadership. That's a very critical piece of this. You can't have a successful data science organization without that. >> Absolutely. >> And I think that actually some of the most popular trainings we've had recently are from managers and executives who are looking to say, how do I become more data savvy? How do I figure out what is this data science thing and how do I communicate with my data scientists? >> You guys made this way too easy. I was just going to get some popcorn and watch it play out. >> Nir, last 30 seconds. I want to leave you with an opportunity to, anything you want to add to this conversation? >> I think one thing to conclude is to say that companies that are not data driven is about time to hit refresh and figure how they transition the organization to become data driven. To become agile and nimble so they can actually see what opportunities from this important industrial revolution. Otherwise, unfortunately they will have hard time to survive. >> [Katie] All agreed? >> [Tricia] Absolutely, you're right. >> Michael, Trish, Nir, thank you so much. Fascinating discussion. And thank you guys again for joining us. We will be right back with another great demo. Right after this. >> Thank you Katie. >> Once again, thank you for an excellent discussion. Weren't they great guys? And thank you for everyone who's tuning in on the live webcast. As you can hear, we have an amazing studio audience here. And we're going to keep things moving. I'm now joined by Daniel Hernandez and Siva Anne. And we're going to turn our attention to how you can deliver on what they're talking about using data science experience to do data science faster. >> Thank you Katie. Siva and I are going to spend the next 10 minutes showing you how you can deliver on what they were saying using the IBM Data Science Experience to do data science faster. We'll demonstrate through new features we introduced this week how teams can work together more effectively across the entire analytics life cycle. How you can take advantage of any and all data no matter where it is and what it is. How you could use your favorite tools from open source. And finally how you could build models anywhere and employ them close to where your data is. Remember the financial adviser app Rob showed you? To build an app like that, we needed a team of data scientists, developers, data engineers, and IT staff to collaborate. We do this in the Data Science Experience through a concept we call projects. When I create a new project, I can now use the new Github integration feature. We're doing for data science what we've been doing for developers for years. Distributed teams can work together on analytics projects. And take advantage of Github's version management and change management features. This is a huge deal. Let's explore the project we created for the financial adviser app. As you can see, our data engineer Joane, our developer Rob, and others are collaborating this project. Joane got things started by bringing together the trusted data sources we need to build the app. Taking a closer look at the data, we see that our customer and profile data is stored on our recently announced IBM Integrated Analytics System, which runs safely behind our firewall. We also needed macro economic data, which she was able to find in the Federal Reserve. And she stored it in our Db2 Warehouse on Cloud. And finally, she selected stock news data from NASDAQ.com and landed that in a Hadoop cluster, which happens to be powered by Hortonworks. We added a new feature to the Data Science Experience so that when it's installed with Hortonworks, it automatically uses a need of security and governance controls within the cluster so your data is always secure and safe. Now we want to show you the news data we stored in the Hortonworks cluster. This is the mean administrative console. It's powered by an open source project called Ambari. And here's the news data. It's in parquet files stored in HDFS, which happens to be a distributive file system. To get the data from NASDAQ into our cluster, we used IBM's BigIntegrate and BigQuality to create automatic data pipelines that acquire, cleanse, and ingest that news data. Once the data's available, we use IBM's Big SQL to query that data using SQL statements that are much like the ones we would use for any relation of data, including the data that we have in the Integrated Analytics System and Db2 Warehouse on Cloud. This and the federation capabilities that Big SQL offers dramatically simplifies data acquisition. Now we want to show you how we support a brand new tool that we're excited about. Since we launched last summer, the Data Science Experience has supported Jupyter and R for data analysis and visualization. In this week's update, we deeply integrated another great open source project called Apache Zeppelin. It's known for having great visualization support, advanced collaboration features, and is growing in popularity amongst the data science community. This is an example of Apache Zeppelin and the notebook we created through it to explore some of our data. Notice how wonderful and easy the data visualizations are. Now we want to walk you through the Jupyter notebook we created to explore our customer preference for stocks. We use notebooks to understand and explore data. To identify the features that have some predictive power. Ultimately, we're trying to assess what ultimately is driving customer stock preference. Here we did the analysis to identify the attributes of customers that are likely to purchase auto stocks. We used this understanding to build our machine learning model. For building machine learning models, we've always had tools integrated into the Data Science Experience. But sometimes you need to use tools you already invested in. Like our very own SPSS as well as SAS. Through new import feature, you can easily import those models created with those tools. This helps you avoid vendor lock-in, and simplify the development, training, deployment, and management of all your models. To build the models we used in app, we could have coded, but we prefer a visual experience. We used our customer profile data in the Integrated Analytic System. Used the Auto Data Preparation to cleanse our data. Choose the binary classification algorithms. Let the Data Science Experience evaluate between logistic regression and gradient boosted tree. It's doing the heavy work for us. As you can see here, the Data Science Experience generated performance metrics that show us that the gradient boosted tree is the best performing algorithm for the data we gave it. Once we save this model, it's automatically deployed and available for developers to use. Any application developer can take this endpoint and consume it like they would any other API inside of the apps they built. We've made training and creating machine learning models super simple. But what about the operations? A lot of companies are struggling to ensure their model performance remains high over time. In our financial adviser app, we know that customer data changes constantly, so we need to always monitor model performance and ensure that our models are retrained as is necessary. This is a dashboard that shows the performance of our models and lets our teams monitor and retrain those models so that they're always performing to our standards. So far we've been showing you the Data Science Experience available behind the firewall that we're using to build and train models. Through a new publish feature, you can build models and deploy them anywhere. In another environment, private, public, or anywhere else with just a few clicks. So here we're publishing our model to the Watson machine learning service. It happens to be in the IBM cloud. And also deeply integrated with our Data Science Experience. After publishing and switching to the Watson machine learning service, you can see that our stock affinity and model that we just published is there and ready for use. So this is incredibly important. I just want to say it again. The Data Science Experience allows you to train models behind your own firewall, take advantage of your proprietary and sensitive data, and then deploy those models wherever you want with ease. So summarize what we just showed you. First, IBM's Data Science Experience supports all teams. You saw how our data engineer populated our project with trusted data sets. Our data scientists developed, trained, and tested a machine learning model. Our developers used APIs to integrate machine learning into their apps. And how IT can use our Integrated Model Management dashboard to monitor and manage model performance. Second, we support all data. On premises, in the cloud, structured, unstructured, inside of your firewall, and outside of it. We help you bring analytics and governance to where your data is. Third, we support all tools. The data science tools that you depend on are readily available and deeply integrated. This includes capabilities from great partners like Hortonworks. And powerful tools like our very own IBM SPSS. And fourth, and finally, we support all deployments. You can build your models anywhere, and deploy them right next to where your data is. Whether that's in the public cloud, private cloud, or even on the world's most reliable transaction platform, IBM z. So see for yourself. Go to the Data Science Experience website, take us for a spin. And if you happen to be ready right now, our recently created Data Science Elite Team can help you get started and run experiments alongside you with no charge. Thank you very much. >> Thank you very much Daniel. It seems like a great time to get started. And thanks to Siva for taking us through it. Rob and I will be back in just a moment to add some perspective right after this. All right, once again joined by Rob Thomas. And Rob obviously we got a lot of information here. >> Yes, we've covered a lot of ground. >> This is intense. You got to break it down for me cause I think we zoom out and see the big picture. What better data science can deliver to a business? Why is this so important? I mean we've heard it through and through. >> Yeah, well, I heard it a couple times. But it starts with businesses have to embrace a data driven culture. And it is a change. And we need to make data accessible with the right tools in a collaborative culture because we've got diverse skill sets in every organization. But data driven companies succeed when data science tools are in the hands of everyone. And I think that's a new thought. I think most companies think just get your data scientist some tools, you'll be fine. This is about tools in the hands of everyone. I think the panel did a great job of describing about how we get to data science for all. Building a data culture, making it a part of your everyday operations, and the highlights of what Daniel just showed us, that's some pretty cool features for how organizations can get to this, which is you can see IBM's Data Science Experience, how that supports all teams. You saw data analysts, data scientists, application developer, IT staff, all working together. Second, you saw how we support all tools. And your choice of tools. So the most popular data science libraries integrated into one platform. And we saw some new capabilities that help companies avoid lock-in, where you can import existing models created from specialist tools like SPSS or others. And then deploy them and manage them inside of Data Science Experience. That's pretty interesting. And lastly, you see we continue to build on this best of open tools. Partnering with companies like H2O, Hortonworks, and others. Third, you can see how you use all data no matter where it lives. That's a key challenge every organization's going to face. Private, public, federating all data sources. We announced new integration with the Hortonworks data platform where we deploy machine learning models where your data resides. That's been a key theme. Analytics where the data is. And lastly, supporting all types of deployments. Deploy them in your Hadoop cluster. Deploy them in your Integrated Analytic System. Or deploy them in z, just to name a few. A lot of different options here. But look, don't believe anything I say. Go try it for yourself. Data Science Experience, anybody can use it. Go to datascience.ibm.com and look, if you want to start right now, we just created a team that we call Data Science Elite. These are the best data scientists in the world that will come sit down with you and co-create solutions, models, and prove out a proof of concept. >> Good stuff. Thank you Rob. So you might be asking what does an organization look like that embraces data science for all? And how could it transform your role? I'm going to head back to the office and check it out. Let's start with the perspective of the line of business. What's changed? Well, now you're starting to explore new business models. You've uncovered opportunities for new revenue sources and all that hidden data. And being disrupted is no longer keeping you up at night. As a data science leader, you're beginning to collaborate with a line of business to better understand and translate the objectives into the models that are being built. Your data scientists are also starting to collaborate with the less technical team members and analysts who are working closest to the business problem. And as a data scientist, you stop feeling like you're falling behind. Open source tools are keeping you current. You're also starting to operationalize the work that you do. And you get to do more of what you love. Explore data, build models, put your models into production, and create business impact. All in all, it's not a bad scenario. Thanks. All right. We are back and coming up next, oh this is a special time right now. Cause we got a great guest speaker. New York Magazine called him the spreadsheet psychic and number crunching prodigy who went from correctly forecasting baseball games to correctly forecasting presidential elections. He even invented a proprietary algorithm called PECOTA for predicting future performance by baseball players and teams. And his New York Times bestselling book, The Signal and the Noise was named by Amazon.com as the number one best non-fiction book of 2012. He's currently the Editor in Chief of the award winning website, FiveThirtyEight and appears on ESPN as an on air commentator. Big round of applause. My pleasure to welcome Nate Silver. >> Thank you. We met backstage. >> Yes. >> It feels weird to re-shake your hand, but you know, for the audience. >> I had to give the intense firm grip. >> Definitely. >> The ninja grip. So you and I have crossed paths kind of digitally in the past, which it really interesting, is I started my career at ESPN. And I started as a production assistant, then later back on air for sports technology. And I go to you to talk about sports because-- >> Yeah. >> Wow, has ESPN upped their game in terms of understanding the importance of data and analytics. And what it brings. Not just to MLB, but across the board. >> No, it's really infused into the way they present the broadcast. You'll have win probability on the bottom line. And they'll incorporate FiveThirtyEight metrics into how they cover college football for example. So, ESPN ... Sports is maybe the perfect, if you're a data scientist, like the perfect kind of test case. And the reason being that sports consists of problems that have rules. And have structure. And when problems have rules and structure, then it's a lot easier to work with. So it's a great way to kind of improve your skills as a data scientist. Of course, there are also important real world problems that are more open ended, and those present different types of challenges. But it's such a natural fit. The teams. Think about the teams playing the World Series tonight. The Dodgers and the Astros are both like very data driven, especially Houston. Golden State Warriors, the NBA Champions, extremely data driven. New England Patriots, relative to an NFL team, it's shifted a little bit, the NFL bar is lower. But the Patriots are certainly very analytical in how they make decisions. So, you can't talk about sports without talking about analytics. >> And I was going to save the baseball question for later. Cause we are moments away from game seven. >> Yeah. >> Is everyone else watching game seven? It's been an incredible series. Probably one of the best of all time. >> Yeah, I mean-- >> You have a prediction here? >> You can mention that too. So I don't have a prediction. FiveThirtyEight has the Dodgers with a 60% chance of winning. >> [Katie] LA Fans. >> So you have two teams that are about equal. But the Dodgers pitching staff is in better shape at the moment. The end of a seven game series. And they're at home. >> But the statistics behind the two teams is pretty incredible. >> Yeah. It's like the first World Series in I think 56 years or something where you have two 100 win teams facing one another. There have been a lot of parity in baseball for a lot of years. Not that many offensive overall juggernauts. But this year, and last year with the Cubs and the Indians too really. But this year, you have really spectacular teams in the World Series. It kind of is a showcase of modern baseball. Lots of home runs. Lots of strikeouts. >> [Katie] Lots of extra innings. >> Lots of extra innings. Good defense. Lots of pitching changes. So if you love the modern baseball game, it's been about the best example that you've had. If you like a little bit more contact, and fewer strikeouts, maybe not so much. But it's been a spectacular and very exciting World Series. It's amazing to talk. MLB is huge with analysis. I mean, hands down. But across the board, if you can provide a few examples. Because there's so many teams in front offices putting such an, just a heavy intensity on the analysis side. And where the teams are going. And if you could provide any specific examples of teams that have really blown your mind. Especially over the last year or two. Because every year it gets more exciting if you will. I mean, so a big thing in baseball is defensive shifts. So if you watch tonight, you'll probably see a couple of plays where if you're used to watching baseball, a guy makes really solid contact. And there's a fielder there that you don't think should be there. But that's really very data driven where you analyze where's this guy hit the ball. That part's not so hard. But also there's game theory involved. Because you have to adjust for the fact that he knows where you're positioning the defenders. He's trying therefore to make adjustments to his own swing and so that's been a major innovation in how baseball is played. You know, how bullpens are used too. Where teams have realized that actually having a guy, across all sports pretty much, realizing the importance of rest. And of fatigue. And that you can be the best pitcher in the world, but guess what? After four or five innings, you're probably not as good as a guy who has a fresh arm necessarily. So I mean, it really is like, these are not subtle things anymore. It's not just oh, on base percentage is valuable. It really effects kind of every strategic decision in baseball. The NBA, if you watch an NBA game tonight, see how many three point shots are taken. That's in part because of data. And teams realizing hey, three points is worth more than two, once you're more than about five feet from the basket, the shooting percentage gets really flat. And so it's revolutionary, right? Like teams that will shoot almost half their shots from the three point range nowadays. Larry Bird, who wound up being one of the greatest three point shooters of all time, took only eight three pointers his first year in the NBA. It's quite noticeable if you watch baseball or basketball in particular. >> Not to focus too much on sports. One final question. In terms of Major League Soccer, and now in NFL, we're having the analysis and having wearables where it can now showcase if they wanted to on screen, heart rate and breathing and how much exertion. How much data is too much data? And when does it ruin the sport? >> So, I don't think, I mean, again, it goes sport by sport a little bit. I think in basketball you actually have a more exciting game. I think the game is more open now. You have more three pointers. You have guys getting higher assist totals. But you know, I don't know. I'm not one of those people who thinks look, if you love baseball or basketball, and you go in to work for the Astros, the Yankees or the Knicks, they probably need some help, right? You really have to be passionate about that sport. Because it's all based on what questions am I asking? As I'm a fan or I guess an employee of the team. Or a player watching the game. And there isn't really any substitute I don't think for the insight and intuition that a curious human has to kind of ask the right questions. So we can talk at great length about what tools do you then apply when you have those questions, but that still comes from people. I don't think machine learning could help with what questions do I want to ask of the data. It might help you get the answers. >> If you have a mid-fielder in a soccer game though, not exerting, only 80%, and you're seeing that on a screen as a fan, and you're saying could that person get fired at the end of the day? One day, with the data? >> So we found that actually some in soccer in particular, some of the better players are actually more still. So Leo Messi, maybe the best player in the world, doesn't move as much as other soccer players do. And the reason being that A) he kind of knows how to position himself in the first place. B) he realizes that you make a run, and you're out of position. That's quite fatiguing. And particularly soccer, like basketball, is a sport where it's incredibly fatiguing. And so, sometimes the guys who conserve their energy, that kind of old school mentality, you have to hustle at every moment. That is not helpful to the team if you're hustling on an irrelevant play. And therefore, on a critical play, can't get back on defense, for example. >> Sports, but also data is moving exponentially as we're just speaking about today. Tech, healthcare, every different industry. Is there any particular that's a favorite of yours to cover? And I imagine they're all different as well. >> I mean, I do like sports. We cover a lot of politics too. Which is different. I mean in politics I think people aren't intuitively as data driven as they might be in sports for example. It's impressive to follow the breakthroughs in artificial intelligence. It started out just as kind of playing games and playing chess and poker and Go and things like that. But you really have seen a lot of breakthroughs in the last couple of years. But yeah, it's kind of infused into everything really. >> You're known for your work in politics though. Especially presidential campaigns. >> Yeah. >> This year, in particular. Was it insanely challenging? What was the most notable thing that came out of any of your predictions? >> I mean, in some ways, looking at the polling was the easiest lens to look at it. So I think there's kind of a myth that last year's result was a big shock and it wasn't really. If you did the modeling in the right way, then you realized that number one, polls have a margin of error. And so when a candidate has a three point lead, that's not particularly safe. Number two, the outcome between different states is correlated. Meaning that it's not that much of a surprise that Clinton lost Wisconsin and Michigan and Pennsylvania and Ohio. You know I'm from Michigan. Have friends from all those states. Kind of the same types of people in those states. Those outcomes are all correlated. So what people thought was a big upset for the polls I think was an example of how data science done carefully and correctly where you understand probabilities, understand correlations. Our model gave Trump a 30% chance of winning. Others models gave him a 1% chance. And so that was interesting in that it showed that number one, that modeling strategies and skill do matter quite a lot. When you have someone saying 30% versus 1%. I mean, that's a very very big spread. And number two, that these aren't like solved problems necessarily. Although again, the problem with elections is that you only have one election every four years. So I can be very confident that I have a better model. Even one year of data doesn't really prove very much. Even five or 10 years doesn't really prove very much. And so, being aware of the limitations to some extent intrinsically in elections when you only get one kind of new training example every four years, there's not really any way around that. There are ways to be more robust to sparce data environments. But if you're identifying different types of business problems to solve, figuring out what's a solvable problem where I can add value with data science is a really key part of what you're doing. >> You're such a leader in this space. In data and analysis. It would be interesting to kind of peek back the curtain, understand how you operate but also how large is your team? How you're putting together information. How quickly you're putting it out. Cause I think in this right now world where everybody wants things instantly-- >> Yeah. >> There's also, you want to be first too in the world of journalism. But you don't want to be inaccurate because that's your credibility. >> We talked about this before, right? I think on average, speed is a little bit overrated in journalism. >> [Katie] I think it's a big problem in journalism. >> Yeah. >> Especially in the tech world. You have to be first. You have to be first. And it's just pumping out, pumping out. And there's got to be more time spent on stories if I can speak subjectively. >> Yeah, for sure. But at the same time, we are reacting to the news. And so we have people that come in, we hire most of our people actually from journalism. >> [Katie] How many people do you have on your team? >> About 35. But, if you get someone who comes in from an academic track for example, they might be surprised at how fast journalism is. That even though we might be slower than the average website, the fact that there's a tragic event in New York, are there things we have to say about that? A candidate drops out of the presidential race, are things we have to say about that. In periods ranging from minutes to days as opposed to kind of weeks to months to years in the academic world. The corporate world moves faster. What is a little different about journalism is that you are expected to have more precision where people notice when you make a mistake. In corporations, you have maybe less transparency. If you make 10 investments and seven of them turn out well, then you'll get a lot of profit from that, right? In journalism, it's a little different. If you make kind of seven predictions or say seven things, and seven of them are very accurate and three of them aren't, you'll still get criticized a lot for the three. Just because that's kind of the way that journalism is. And so the kind of combination of needing, not having that much tolerance for mistakes, but also needing to be fast. That is tricky. And I criticize other journalists sometimes including for not being data driven enough, but the best excuse any journalist has, this is happening really fast and it's my job to kind of figure out in real time what's going on and provide useful information to the readers. And that's really difficult. Especially in a world where literally, I'll probably get off the stage and check my phone and who knows what President Trump will have tweeted or what things will have happened. But it really is a kind of 24/7. >> Well because it's 24/7 with FiveThirtyEight, one of the most well known sites for data, are you feeling micromanagey on your people? Because you do have to hit this balance. You can't have something come out four or five days later. >> Yeah, I'm not -- >> Are you overseeing everything? >> I'm not by nature a micromanager. And so you try to hire well. You try and let people make mistakes. And the flip side of this is that if a news organization that never had any mistakes, never had any corrections, that's raw, right? You have to have some tolerance for error because you are trying to decide things in real time. And figure things out. I think transparency's a big part of that. Say here's what we think, and here's why we think it. If we have a model to say it's not just the final number, here's a lot of detail about how that's calculated. In some case we release the code and the raw data. Sometimes we don't because there's a proprietary advantage. But quite often we're saying we want you to trust us and it's so important that you trust us, here's the model. Go play around with it yourself. Here's the data. And that's also I think an important value. >> That speaks to open source. And your perspective on that in general. >> Yeah, I mean, look, I'm a big fan of open source. I worry that I think sometimes the trends are a little bit away from open source. But by the way, one thing that happens when you share your data or you share your thinking at least in lieu of the data, and you can definitely do both is that readers will catch embarrassing mistakes that you made. By the way, even having open sourceness within your team, I mean we have editors and copy editors who often save you from really embarrassing mistakes. And by the way, it's not necessarily people who have a training in data science. I would guess that of our 35 people, maybe only five to 10 have a kind of formal background in what you would call data science. >> [Katie] I think that speaks to the theme here. >> Yeah. >> [Katie] That everybody's kind of got to be data literate. >> But yeah, it is like you have a good intuition. You have a good BS detector basically. And you have a good intuition for hey, this looks a little bit out of line to me. And sometimes that can be based on domain knowledge, right? We have one of our copy editors, she's a big college football fan. And we had an algorithm we released that tries to predict what the human being selection committee will do, and she was like, why is LSU rated so high? Cause I know that LSU sucks this year. And we looked at it, and she was right. There was a bug where it had forgotten to account for their last game where they lost to Troy or something and so -- >> That also speaks to the human element as well. >> It does. In general as a rule, if you're designing a kind of regression based model, it's different in machine learning where you have more, when you kind of build in the tolerance for error. But if you're trying to do something more precise, then so much of it is just debugging. It's saying that looks wrong to me. And I'm going to investigate that. And sometimes it's not wrong. Sometimes your model actually has an insight that you didn't have yourself. But fairly often, it is. And I think kind of what you learn is like, hey if there's something that bothers me, I want to go investigate that now and debug that now. Because the last thing you want is where all of a sudden, the answer you're putting out there in the world hinges on a mistake that you made. Cause you never know if you have so to speak, 1,000 lines of code and they all perform something differently. You never know when you get in a weird edge case where this one decision you made winds up being the difference between your having a good forecast and a bad one. In a defensible position and a indefensible one. So we definitely are quite diligent and careful. But it's also kind of knowing like, hey, where is an approximation good enough and where do I need more precision? Cause you could also drive yourself crazy in the other direction where you know, it doesn't matter if the answer is 91.2 versus 90. And so you can kind of go 91.2, three, four and it's like kind of A) false precision and B) not a good use of your time. So that's where I do still spend a lot of time is thinking about which problems are "solvable" or approachable with data and which ones aren't. And when they're not by the way, you're still allowed to report on them. We are a news organization so we do traditional reporting as well. And then kind of figuring out when do you need precision versus when is being pointed in the right direction good enough? >> I would love to get inside your brain and see how you operate on just like an everyday walking to Walgreens movement. It's like oh, if I cross the street in .2-- >> It's not, I mean-- >> Is it like maddening in there? >> No, not really. I mean, I'm like-- >> This is an honest question. >> If I'm looking for airfares, I'm a little more careful. But no, part of it's like you don't want to waste time on unimportant decisions, right? I will sometimes, if I can't decide what to eat at a restaurant, I'll flip a coin. If the chicken and the pasta both sound really good-- >> That's not high tech Nate. We want better. >> But that's the point, right? It's like both the chicken and the pasta are going to be really darn good, right? So I'm not going to waste my time trying to figure it out. I'm just going to have an arbitrary way to decide. >> Serious and business, how organizations in the last three to five years have just evolved with this data boom. How are you seeing it as from a consultant point of view? Do you think it's an exciting time? Do you think it's a you must act now time? >> I mean, we do know that you definitely see a lot of talent among the younger generation now. That so FiveThirtyEight has been at ESPN for four years now. And man, the quality of the interns we get has improved so much in four years. The quality of the kind of young hires that we make straight out of college has improved so much in four years. So you definitely do see a younger generation for which this is just part of their bloodstream and part of their DNA. And also, particular fields that we're interested in. So we're interested in people who have both a data and a journalism background. We're interested in people who have a visualization and a coding background. A lot of what we do is very much interactive graphics and so forth. And so we do see those skill sets coming into play a lot more. And so the kind of shortage of talent that had I think frankly been a problem for a long time, I'm optimistic based on the young people in our office, it's a little anecdotal but you can tell that there are so many more programs that are kind of teaching students the right set of skills that maybe weren't taught as much a few years ago. >> But when you're seeing these big organizations, ESPN as perfect example, moving more towards data and analytics than ever before. >> Yeah. >> You would say that's obviously true. >> Oh for sure. >> If you're not moving that direction, you're going to fall behind quickly. >> Yeah and the thing is, if you read my book or I guess people have a copy of the book. In some ways it's saying hey, there are lot of ways to screw up when you're using data. And we've built bad models. We've had models that were bad and got good results. Good models that got bad results and everything else. But the point is that the reason to be out in front of the problem is so you give yourself more runway to make errors and mistakes. And to learn kind of what works and what doesn't and which people to put on the problem. I sometimes do worry that a company says oh we need data. And everyone kind of agrees on that now. We need data science. Then they have some big test case. And they have a failure. And they maybe have a failure because they didn't know really how to use it well enough. But learning from that and iterating on that. And so by the time that you're on the third generation of kind of a problem that you're trying to solve, and you're watching everyone else make the mistake that you made five years ago, I mean, that's really powerful. But that doesn't mean that getting invested in it now, getting invested both in technology and the human capital side is important. >> Final question for you as we run out of time. 2018 beyond, what is your biggest project in terms of data gathering that you're working on? >> There's a midterm election coming up. That's a big thing for us. We're also doing a lot of work with NBA data. So for four years now, the NBA has been collecting player tracking data. So they have 3D cameras in every arena. So they can actually kind of quantify for example how fast a fast break is, for example. Or literally where a player is and where the ball is. For every NBA game now for the past four or five years. And there hasn't really been an overall metric of player value that's taken advantage of that. The teams do it. But in the NBA, the teams are a little bit ahead of journalists and analysts. So we're trying to have a really truly next generation stat. It's a lot of data. Sometimes I now more oversee things than I once did myself. And so you're parsing through many, many, many lines of code. But yeah, so we hope to have that out at some point in the next few months. >> Anything you've personally been passionate about that you've wanted to work on and kind of solve? >> I mean, the NBA thing, I am a pretty big basketball fan. >> You can do better than that. Come on, I want something real personal that you're like I got to crunch the numbers. >> You know, we tried to figure out where the best burrito in America was a few years ago. >> I'm going to end it there. >> Okay. >> Nate, thank you so much for joining us. It's been an absolute pleasure. Thank you. >> Cool, thank you. >> I thought we were going to chat World Series, you know. Burritos, important. I want to thank everybody here in our audience. Let's give him a big round of applause. >> [Nate] Thank you everyone. >> Perfect way to end the day. And for a replay of today's program, just head on over to ibm.com/dsforall. I'm Katie Linendoll. And this has been Data Science for All: It's a Whole New Game. Test one, two. One, two, three. Hi guys, I just want to quickly let you know as you're exiting. A few heads up. Downstairs right now there's going to be a meet and greet with Nate. And we're going to be doing that with clients and customers who are interested. So I would recommend before the game starts, and you lose Nate, head on downstairs. And also the gallery is open until eight p.m. with demos and activations. And tomorrow, make sure to come back too. Because we have exciting stuff. I'll be joining you as your host. And we're kicking off at nine a.m. So bye everybody, thank you so much. >> [Announcer] Ladies and gentlemen, thank you for attending this evening's webcast. If you are not attending all cloud and cognitive summit tomorrow, we ask that you recycle your name badge at the registration desk. Thank you. Also, please note there are two exits on the back of the room on either side of the room. Have a good evening. Ladies and gentlemen, the meet and greet will be on stage. Thank you.