>>Hey everyone. Welcome back to the queue of Lisa Martin with Dave Valante and we're live in Vegas. This is snowflake summit, 22, their fourth annual event. A lot of people here, a lot of news, a lot to unpack so far, and this is only day, day one. We've got two guests here with us to talk about, uh, cyber security, a very important topic, please welcome Omar singer the head of cyber security strategy at snowflake and Julie Chilo VP of security at Guild education. Welcome. Thank >>You. Thank you >>For having all of >>Our favorite topics. Yeah. Oh >>One. It's not boring. >>You know this much and you have so much more to learn now. So here >>We go. Cybersecurity is, is not to say it's boring. Not boring is an understatement. Yeah. Omar, I wanna start with you so much news coming out today. Talk to us about what's new with cybersecurity. Workload is snowflakes. Flywheel of innovation just seems to be getting bigger and faster. >>Yeah. Yeah. Well, well, I'll tell you it's been a long road to get to where we are today. Um, my initial role at snowflake was to lead security engineering. So I've actually been using snowflake as the home for security data, basically from day one. And we saw that it worked, it worked really well. And we started hearing from customers that they were dealing with some of the same challenges that we faced as an internal security team. And we decided as snowflake that we wanna bring the benefits of the data cloud to cyber security teams at all of our customers. And that's what the workload is all about. >>Talk to us about the, the voice of the customer. Obviously we saw a lot of customer stories heard your customer. We're gonna be talking about Guild education in a minute, but in the voice of the customer, in terms of being influential, obviously you were an internal customer drinking that champagne like this tastes really good. This is better of the Flaco <laugh>, but how is the voice of the customer influential in terms of the, the cybersecurity workload, as we've seen the threat landscape change so much in the last two years alone? >>Sure, sure. And you know, security, it's a really hard problem. We like to think of it as a data problem. And when you start thinking about it, that way snowflake is re very relevant for it. But many security teams don't yet think about their challenge as a data challenge. And so they're struggling with a very fragmented data landscape. The facts are all over the place and they're not able to ask the kind of questions that they need to understand. Where are my risks? How are the bad guys gonna try to get into my network? And they can't reflect that to leadership to everybody that really cares about cyber security. This is a board level concern today without the unified data and without the analytics. Um, they really can't do any of that. And, and yeah, representing the customer is, is a big part of what I do. And we have great customers like, like Julie, who's been kind of with us on this journey. She's, she's a part of the movement. I mean, Julie, what, what has it been like, uh, for, for you? >>Oh, it's been, uh, it's been game changer for, for Guild for sure. When we first, uh, started, I didn't one, I didn't know this was a concept <laugh> so when I first started talking O me and, um, snowflake, uh, I had just heard through the grapevine that, that you could do, like, this was a thing you could use the data, you could get everything you needed in one place. And, um, it's been game changing for my team. Uh, we, we were in many different security tools. They were all isolated, siloed, and we're now able to move everything into one, uh, one area, uh, and get we're getting close to the one pane of glass, which I, um, I just heard was a mythical concept for >>Security for >>A long time. Yeah. For a long time. Um, so it's, uh, it's just been amazing and it's, uh, brought us closer to our data ops team. So I'm here this week, uh, with somebody from data ops, actually, that's awesome to help us out. >>So can you describe that further? I'm I'm, I'm, I'm amazed and skeptical the, the, the I'm imagining, you know, the Optiv chart that says eight, 8 million security tools on there, are you actually able, uh, describe how you're able to consolidate your tooling? >>So, one of, one of the biggest problem, one of the biggest problems we were facing initially was our SIM, um, the security incident and event management tool could not take anything from our DevSecOps tools. And so any security that we had in a developer pipeline was really isolated to that tool, and we could never get it into a SIM Sims just aren't meant they're not built to handle that they're built to handle, um, not, not really old school networks and, and data center traffic and everything I have is in the cloud. And so we were really, I, everything was isolated. So with snowflake, what we do is we, um, worked with our data ops team. We can move things from, um, like our, our scanning tools for, for the developer pipelines into snowflake. We can use then correlate different things such as, from like eight year ADP. Like if a, do you have somebody pushing code to production who's out on vacation, you can actually do that correlation with snowflake that was never available before. These are things we could never do before. And we're able to, um, just do correlations. You could not get in that you cannot get in a SIM. >>Why couldn't I just throw those into any old, you know, run of the mill cloud data warehouse? >>Well, you know, it's not just the scale, it's the complexity of the data. I think snowflake how we have the, the sche on read and then all of the kind of things that make snowflake really good for other departments turns out, works really well for security. And it's the ecosystem too. Nobody else has this ecosystem approach. You know, you heard on the keynote today that snowflake is the, this disrupting, um, the, the software application development, right? All, all that kind of focus. The tool consolidation doesn't need to mean that you only have one tool you can actually have best of breed, choose the tool you want. As long as the data's consolidated, you're not building more silos. And that's what our partners are doing. They're separating the application from the data. They're bringing the work to the data, and that's what you hear here. So Julie's team can still choose to use a variety of tools that get the job done, but all those tools are working off of the single source of truth. And that, that is unique to what snowflake >>Can enable. So we, we are Reiss. Uh, we should have asked you about Guild education, explain your, your, your organization. >>Oh, what does Guild do? Uh, so we're a late stage startup. Uh, we manage education as a benefit for, for large companies. So we, we house data from very large organizations with like their workforce and, and help students help, help their workforce go back to school. >>Okay. So unpacking some of the things you said, schema on Reed, but not necessarily no schema on, right. It's a little different, right. Because you're ingesting. Yeah. And then you're determining the scheme on read that's right. Right. Okay. So that makes it simple and fast for zoom, but you get data in and then you figure it out, bringing work to data. Can we just double click on that a little bit? Cuz I think when I think about that, we've heard terms like over the years bring compute to the data. That's what Hadoop was supposed to do. And it didn't, you know, it was like, everything was mm-hmm <affirmative> shoved. So what do you mean by that? How, how, what, what actually does that >>Mean? Yeah. So if you think about the traditional SAS solution, the vendor needed to invest in a data center and to have a data platform that would be scalable and robust because their service dependent on it and they couldn't trust that the customer would have that kind of data platform on the customer's side. What Snowflake's data cloud has done has democratized the data platform. So now you have startups to fortune 500 S the vendors, the customers, they're all uneven footing when it comes to the data platform. So now the vendors can say, bring your own snowflake. Why not? You know, and they can focus on building the best application to solve the real challenges that security teams have. But by the way, not only cybersecurity, we see this and for example, the, um, customer data space as well. So we're seeing more and more kind of SaaS industries seeing this approach and the applications are gonna come yeah. To the data platform of choice, uh, for the practitioner. >>Julie, can we talk about some of the outcomes that Guild education has achieved so far by working with this solution in terms of, we look at the threat landscape and how it's changed so much the last couple of years and how it's a matter of if, or sorry, when not, if I get hit with an attack, how, what are some of the key outcomes that a snowflake partnership and technology has enabled you to achieve? >>So the, the biggest one, again, it's around the Def sec ops program, um, where you see so many attacks these days happening in the code base. So you really have to be careful with your, your pipeline where the code's getting moved through, who has access, who can move code into production. Um, and these are so the, like if you're using GitHub or, um, like using a scanning tool called snake, they're, they're separate, like they're completely separate the only way that we can see who's moving code into production, or if there was a vulnerability or somebody turned off, the security tool is to move these logs, this data into snowflake, uh, and our engineering teams were already using snowflake. Uh, so that made it, that was an easy transition for us. I didn't have to go out and convince another team to support us somewhere else, but a great example where we were, we're seeing great, um, savings, not only in people time, but, but for security, um, we were having problems or the security or the <laugh>, the engineers were turning off our secure codes scanner. >>And we didn't find out until a little bit later. Uh, oh yeah. Yeah. So found out we, my team, we had a team, we spent about 160 hours going through a thousand pole requests manually. And I said, no, no more go find the go figure out where this data exists. We put it in a snowflake and we can create an automatic, uh, ping to the security team saying, Hey, they turned off the, the scanner, go check and see what, why did the scanner get turned off? So it's an immediate response from my team instead of finding out two months later. And this is just, isn't something you can do right now. That's you can't set it up. So, um, makes it so easy. Ping goes to slack. We can go to the, immediately to the engineering team and say, why did you >>Using using automation? >>Yeah. Did you, did you turn this off? Why did you turn it off? Get an exception in so one, it like helps with compliance, so we're not messing up our SOC two audit. Uh, and then two, from a security perspective, we are able to, to trust, but verify, um, which is a big part of the DevSecOps landscape, where they need code to move into production. They need a scan to run in under five minutes. My team can't be there to scan, you know, 10, like 10 times a day or a hundred times a day. So we have to automate all of that and then just get information as it comes in. >>Is it accurate to say that, um, you're not like shutting off your tools, you're just taking advantage of them and compressing the time to get value out of them or are you actually reducing the tool sets? >>No, we don't. Well, no, we, our goal wasn't to reduce the tool set. I mean, we did actually get rid of the SIM we were using. Uh, so we were partnering with one of, um, uh, snowflakes partners, um, >>Because yeah, but you still have a SIM, >>We still have it. It's just minimized what goes to the SIM, because most of what I care about, isn't actually going to a SIM. Yeah. It's all the other pieces that are in a cloud because we use all like, we're, we're a hundred percent in the cloud. I don't have servers, I don't have firewalls. We don't have routes routers or switches. So all the things I care about live in a cloud somewhere. And, and I want that information. And so a lot of times, um, especially when it comes to the engineering tools, they were already sending the information to snowflake or they're also interested. And so we're partnering like it's, we're doubling up on the use of the >>Data. Okay. And you couldn't get that outta your SIM. Maybe you're asking your SIM to do too much, or it just didn't deliver. >>No systems are built on search engines. You know, they don't, >>They, they can't do it. >>You kind of knew what you were looking for and you say, Hey, where did I see this? Where did I see that? Very different from data analytics and the kinds of question that security teams really want to ask. These are emergent properties. You need context, you need sequel, you need Python. That's how you ask the questions that security teams really want to ask the legacy Sims. They don't let you ask that kind of question. They weren't built with that in mind. And they're so expensive that by moving off of them, to this approach, you kind of pay for all these other solutions that, that then you can bring on. >>That seems to make the, what you just said. There was brilliant. It seems to make the customer conversation quite easy if they're saying, well, why should I replace my SIM? It's doing just fine. You just nailed it with, with what you said there. >>So, yeah. And we're, and we're seeing that happen extensively. And I'm excited that we have customers here at summit talking about their experience, moving off of a legacy SIM where the security team was off to the side, away from the rest of the company to a unified approach, the SIM and the other security solutions working on top of the snowflake and a collaboration between security and the data >>Team. So what does your security ecosystem look like? You've got SIM partners. Do you have identity access partners, endpoint partner. Absolutely. >>Describe that compliance automation ass. Yeah. We hear about companies really struggling to meet all the compliance requirements. Well, if all the data's already centralized, then I can kind of prove to my auditors and not just once a quarter, but once a day, I can make sure that all the environment is in compliance with whatever standard I have. So we see a lot of that cloud security is another big one because there's just 10 times more things happening in the cloud environment than in the data center. Everything is so heavily instrumented. And so we see cloud security solutions as significant as well. And the identity space, the list goes on and on. We do see the future being the entire security program uses connected applications with a single source of truth in the company's snowflake. And >>Would you say centralized, you, you it's logically centralized, right? I mean, it's virtually centralized, right? It's not, >>Well, that's >>Not shoved into one container, right? >>I mean, it's right. Well, that's the beauty of the data cloud, right? We, everybody that's on the data cloud is able to collaborate. And so whether it's in the same account or table or database, you know, that's really besides the point because all of the platform investments that snowflake is making on cross region, cross cloud collaboration means that once it's in snowflake, then it is unified and can be used together. But >>I think people misunderstand that sometimes. And BEWA made this point, uh, as the Christian about the global nature of, of snowflake and it's globally distributed, but it's logically a data cloud. >>Yeah. I like to call it one big database in the sky. You know, that's how I explain to security teams that are kind of new to the concept, but >>It's not, it's could be a lot of little databases, but it, but having the same framework, the same governance structure, the same security >>You're right. I think that's how it's achieved is what you're describing. You know, I think from the outcome, what the security team needs to know is that when there's some breach hitting the headline and they need to go to their leadership and say, I can assure you, we were not affected. They can be confident in that answer because they have access to the data, wherever it is in the world, they have access to ask you the questions they need to ask. >>And that confidence is critical. These days as that threat landscape just continues to change. Thank you both so much for joining us. Thank you. Talking about from a cyber security perspective, some of the things that are new, new at snowflake, what you guys are doing at Guild education and how you're really transforming the organization with the data cloud, we appreciate your insights. Thank you for having us. Thank you. Thanks you guys for our guests and Dave ante. I'm Lisa Martin. You're watching the queue live from Las Vegas on the show floor of snowflake summit 22. We'll be right back with our next guest.

>> Speaker: From around the Globe, it's theCUBE with digital coverage of DockerCon Live 2020, brought to you by Docker and its ecosystem partners. >> Hello, how you doing? Welcome to DockerCon. We're kind of halfway through now, I guess. Thank you for joining us on this session. So my name is Elton, I'm a Docker Captain. And I'm joined by Julie who was also a Docker Captain. This is actually this session was Julie's idea. We were talking about this learning of Docker and how it's a light bulb moment for lots of people. But Julie, she came up with this great idea for DevOps. So I'll let Julie introduce herself, and tell you a bit about what we're going to talk about. >> Thanks, Elton. So I'm Julie Lerman. I'm a Software Coach. I'm a developer. I've been a developer for over 30 years. I work independently and I'm a Docker captain. Also a Microsoft Regional Director. I wouldn't let them put it on there, because it makes people think I work for Microsoft but I don't. (he laughs) >> Yeah, so it's a weird title. So the Microsoft ID the Regional Director, it's like a kind of Uber, MVP. So I'm an MVP. And that's fine. That's just like a community recognition, just like you get with a Docker captain. So MVP is kind of like the micro version, Julie's MVP too. But then you get the Regional Director which is something that MVP get. >> Doesn't matter. >> I'm not surprised Julie. >> Stop, a humble man. (he laughs) >> We've been using Docker for years 10 years between. >> You probably, how long ago was your Docker aha moment? >> So 2014 I first started using Docker, so I was working on a project, where I was assaulting for a team who were building an Android tablet, and they were building the whole thing, so they Spec out the tablet, they got a bill over in the far East. They were building their own OS their own app to run on and of course all that stacks within it. But they was all talking to the services that were running in the power they wanted to use as your for that and .NET that was on-prem, though that technology historically . So I came in to do the .NET stuff is running in as your, but I got really friendly with the Linux guys. It was very DevOps, it was one team who did the whole thing. And they were using Docker for that their build tools, and for have the and the CI tools, and they were running their own get server and it was all in. >> Already until 2014. That's pretty cool. >> Yeah, pretty early introduction to it. And it was super cool. So I'd always been interested in Linux, but never really dug into it. Because the entry bar was so high runs nothing in it. So you read about this great open source project, and then you go and look at the documentation and you have to download the source code and build it and it's like, well, I'm not going to be doing that stuff. And then Docker came along. I do Docker run. (he laughs) >> Well, I would say it was a little definitely delayed from that. I'm still thinking Wait, when you first started saying that this company was building their own android system, you start thinking, they're building software, but no, they weren't building everything, which is pretty amazing. So, I have to say it took me quite a while, but I was also behind on understanding virtual machines. (both laughs) So, Docker comes along, and I have lots of friends who are using it, I spent a lot of time with Michelle Noorali this Monday, and she's big container person. And most of the people I hear talking about Docker are really doing DevOps, which is not my thing. As a developer, I always just said, let somebody else do that stuff. I want to code an architect and do things like that. And I also do a lot of data work. I'm not like a big data person doing analytics. Or I'm not a DBA. I'm more very involved in getting data in and out of applications. So my aha moment, I would say was like, four years ago, after Microsoft moved SQL Server over to Linux, and then put it inside a Docker image. So that was my very first experience, just saying, oh, what does this do and I downloaded the image. And Docker run. And then like literally I was like, holy smokes. SQL Servers already installed. The containers up like that, and then it's got to run a couple of Bashan SQL scripts to get all the system tables, and databases and things like that. So that's another 15 seconds. But that was literally for me. The not really aha, it was more like OMG, and I'll keep the EFF out just to keep it clean here. It was my OMG moment with Docker. So getting that start, then I worked with the SQL Server image and container and did some different things, with that in applications. And then eventually, expanded my knowledge out bit by bit, and got a deeper understanding of it and tried more things. So I get to a comfort level and then add to it and add to it. >> Yeah. And I think that the great thing about that is that as you're going on that journey that aha moments keep coming, along we had another aha moment this week, with the new announcement that you can use your Docker compose files, and use your Docker commands to spin stuff up running in as your container instances. So like that you've kept up that learning journey is there if you want to go down, How do I take my monolithic application, and break up into pieces and run those in containers? Like suddenly the fact that you can just glue all these things together in run it on one platform, and manage everything in the same way? And these light bulbs keep on coming. So, you've seen the modernization things that people are doing that's a lot of the work that I do now, and taking these big applications, you just write a Docker file, and you've got your 15 year old .NET application running in the container. And you can run that in the cloud with no changes to code, and not see them. But that's super powerful for people. >> And I think one of the really important things, especially for people like you and I, who are also teachers, and is to try to really remember that moment, because I know a lot of times, when people are deeply expert in something it they forget how hard it was, or what it felt like not to understand it that context. So I still have held on to that. So when I talk, I like to do introduction, I like to help people get that aha moment. And then I say, Okay, now go on to the, they're really expert people. You're ready to learn more, but it's really important to especially, maybe we're teachers, conference speakers, book authors, pluralsight, etc. But lots of other people, who are working on teams they might already be somebody who's gotten there with Docker, and they want to help their teammates understand Docker. So I think it's really important to, for everybody who wants to share that to kind of have a little empathy, and remember what that was like, and understand that sometimes it just takes explaining it a different way explaining maybe, just tweaking your expression, or some of the words or your analogies. >> Yeah, that's definitely true. And you often find this it's a technology, that people really become affectionate for, they have a real deep feeling for documents, once they start using it, and you get these internal champions in companies who say, "This is the stuff I've been using, I've been using this at home or whatever." And they want to bring it into their project, and it's pretty cool to be able to say to them this is, take me on the same journey that you've been on, or you've been on a journey, which was probably slightly more investment for you, because you had to learn from scratch. But now you can relay that back into your own project. So you can take, you don't have to take everyone from scratch like you did. You can say, here's the Docker file for our own application. This is how it works. And bringing things into the terms that people are using everyday , I think is something that's super powerful. Why because you're completely strange. (he laughs) >> Oh, I was being really cool about your video. (both laughs) Maybe it's just how it streaming back to me. I think the teacher thing again, like we'll work a little harder and, bump our knees and stub our toes, or tear our hair out or whatever pain we have to go through, with that learning because, it's also kind of obsessive. And you can steer people away from those things, although it's also helpful to let them be aware like this might happen, and if it does, it's because of this. But that's not the happy path. >> Yeah, absolutely. And I think, it's really interesting talking to people about the time you're trying to get to what problem are they trying to solve? It's interesting, you talk about DevOps there, and how that sort of not an area, that you've done a lot of stuff in. Writing a couple of organizations, whether they're really trying hard to move that model, and trying to break down the barriers, between the team who build the software, and the team who run the software, but they have those barriers, but 20 years, it's really hard to write that stuff down. And it's a big cultural shift, it needs a lot of investment. But if you can make a technological change as well, if you can get people using the same tools, the same languages, the same processes to do things, that makes it so much easier. Like now my operators are using Docker files, on there and the security team are going into the Docker file and cozening it, or DevOps team or building up my compose file, and everyone's using the same thing, it really helps a lot, to bind people together to work on the same area. >> I also do a lot of work in domain Dave Vellante design, and that whole idea of collaboration, and bringing together teams, that don't normally work together, and bringing them together, and enabling them to find a way to collaborate giving them tools for collaboration, just like what you're saying with, having the same terms and using the same tools. So that's really powerful. You gave me a great example of one of your clients, aha moments with Docker. Do you remember which that was? The money yes, it's a very powerful Aha. >> Yes. >> She cherish that. >> The company that I've worked for before, when I was doing still get thought that I can sort a thing, and they knew I'd go into containers. I was working for Docker at the time. And I went in just as if I wasn't a sales pitch or anything, I was just as a favor to talk to them about what containers would look like if payments, their operation, big heavy Windows users, huge number of environment, lots of VMs that are all running stuff, to get the isolation, and give them what they needed. And I did this presentation of IT. So it wasn't a technical thing. It was very high level, it was about how containers kind of work. And I'm fundamentally a technical person, so I probably have more detail in there. And then you would get from a sales pitch, but it was very much about, you can take your applications, you can wrap them up the running these things for containers, you still get the isolation, you can run loads more of them on the same hardware that you've got, and you don't pay a Windows license each of those containers, you pay a license for the server that the right one. >> That's it, that's the moment. >> And the head of IT said that's going to save us millions of dollars. (he laughs) And that was his aha moment. >> I'm going to wrap that into my conference session, about getting to the Docker, for sure getting that aha moment. My experience is less that but wow, I mean, that's so powerful. When you're talking to come C level people about making those kinds of changes, because you need to have their buy in. So as a developer and somebody who works with developers, and that's kind of my audience, my experience more has been, when I'm giving conference presentations, and I'll start out in a room of people, and I have to say, when I'm at .NET focus conference, I find that the not there yet with Docker. Part of the audience is a big one. So I kind of do a poll at the beginning of the talk. Who's heard of Docker, obviously, they're in the room, but curious because you still don't really understand it. And that's usually a bulk of the room. And what I like to ask at the end is, of all of you that, that first group, like, do you feel like you get it now, like you just get what it is and what it does, as opposed to I don't know what this thing is. It's for rocket scientists. Is that's how I felt about it. I was like, I'm just a developer. It wasn't my thing. But now, I'm still not doing DevOps, I use Docker as a really important tool, during development and test and that's actually one of it I'm going to be talking about that. But it's my session a little later. Oh, like the next hour. It's about using Docker, that my aha Docker, SQL Server, in an image and but using that in Dave Vellante, it's not about the DevOps and the CI/CD and Kubernetes, I can spell it. (he laughs) Especially when I get to say k eight s, Like I even know the cool Lingo (mumbles) on Twitter. (he laughs) >> I think that's one of the cool things about this technology stack in particular, I think to get the most out of it, you need to dig in really light if you want to, if you're looking at doing this stuff in production, if you're attracted by the fact that I can have a managed container platform in anytime. And I can deploy my app, everywhere using the same set of things that compose files or humidity files or whatever. And if you really want to take advantage of that, you kind of have to get down to the principles understand all go on a proper kind of learning journey. But if you don't want to do that, you can kind of stop wherever it makes sense for you. So like even when I'm talking to different audiences, is a lot strangely enough, I did a pool size large bin this morning. It was quite a specific topic. It was about building applications in containers. So is about using containers, to compile your app and then package it, so you can build anywhere. But even a session like that, the first maybe two minutes, I give a lightning quick overview, of what containers are and how you use them. Here's exactly like you say, people will come to a session, if it's got Docker or humanities in the title. But if they don't have the entry requirements. They've never really used this stuff. And we were up here and it's a big dump for them. So I try and always have that introductory slide. >> I had to do that on the fly. >> Sorry. >> I've done that on the fly in conference, because yes, doing like, ASP.NET Core with Entity Framework and containers. And, 80% of the room, really didn't know anything about Docker. So, instead of talking like five minutes about Docker and then demoing the rest, I ended up spending more time talking about Docker, to make sure everybody was really you could tell that difference when they're like oh, like that they understood enough, in order to be follow along and understand the value of what it was that I was there to show, about it in that core, I'm also this is making me remember that first time I actually use Docker compose, because it was a while, I was just using the SQL Server, Docker image, in on my development machine for quite a while. And because I wasn't deploying, I was learning and exploring and so I was on my development machine, so I didn't need to do anything else. So the first time I really started orchestrating, that was yet another aha moment. But I was ready for it then. I think you know if you start with Docker compose and you don't haven't done the other, maybe I would write but I was ready, because I'd already gotten used to using the tooling and, really understanding what was going on with containers. Then that Docker compose was like yeah. (he laughs) >> It's just the next one, in the line is a great comment actually in the chat about someone in the chat. >> From chat? >> Yeah, from Steve saying, that he could see there would be an aha moment for his about security. And actually that's absolutely, it's so when security people, first want to get their head around containers, they get worried that if someone can compromise the app in the container, they might get a break out, and get to all the other containers. And suddenly, instead of having one VM compromised, you have 100 containers compromised. But actually, when you dig into it so much easier to get this kind of defense in depth, when you're building in containers, because you have your tape on an image that's owned by your team who produced the path, whether or not they will have their own images, that are built with best practices. You can sign your images, through your platform doesn't run anything that isn't signed, you have a full history of exactly what's in the source code is what's in production, there's all sorts of, ways you can layer on security that, attract that side of the audience. >> I've been looking at you this whole time, and like I forgot about the live chat. There's the live chat. (he laughs) There's Scott Johnston in live chat. >> Yes. >> People talking about Kubernetes and swarm. I'm scrolling through quickly to see if anybody's saying, well, my aha moment was. >> There was a good one. What was this one from Fatima earlier on, Maya was pointing out with almost no configuration onto a VM, and couldn't believe it never looked back on us. >> Yeah. >> That's exactly, on one command, if your image is mostly built, SaaS has some sensible defaults, it just all works. And everyone's (mumbles). >> Yeah, and the thing that I'm doing in my session is, what I love. the fact that for development team, Development Testing everybody on the team, and then again on up the pipeline to CI/CD. It's just a matter of, not only do you have your SaaS code, but in your SaaS code, you've got your Docker compose, and your Docker compose just makes sure, that you have the development environment that you need, all the frame, everything that you need is just there, without having to go out and find it and install it. >> There were no gap in a development environment with CI build the production. So I'm hearing, you don't hear but I can hear that we need to wrap up. >> Oh, yeah. >> Get yourself prepared for your next session, which everyone should definitely, I'll be watching everyone else do. So thanks everyone for joining. Thanks, Julie for a great idea for a conversation, was about 4050 we'll have a beer with and I would, I would Yeah. >> Yeah, we live many thousands of miles away from one another. >> Well, hopefully next year, there will be a different topic on how we can all meet some of you guys. >> And I do need to point out, the last time we were together, Elton, I got a copy of Alan's book and he signed it. (both laughs) And we took a picture of it. >> There are still more books on the stand >> Yeah, I know that's an old book, but it's the one that you signed. Thank you so much. >> Thanks everyone for joining and we'll enjoy the rest of the topic home. >> Bye. (soft music)

>>from the Cube Studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a cube conversation. >>Hi, everybody. This is Dave Volante with Cuban. Welcome to the special digital presentation. We're really digging into how IBM is operational izing and automating the AI and data pipeline not only for its clients, but also for itself. And with me is Julie Lockner, who looks after offering management and IBM Data and AI portfolio really great to see you again. >>Great, great to be here. Thank you. Talk a >>little bit about the role you have here at IBM. >>Sure, so my responsibility in offering >>management and the data and AI organization is >>really twofold. One is I lead a team that implements all of the back end processes, really the operations behind any time we deliver a product from the Data and AI team to the market. So think about all of the release cycle management are seeing product management discipline, etcetera. The other role that I play is really making sure that I'm We are working with our customers and making sure they have the best customer experience and a big part of that is developing the data ops methodology. It's something that I needed internally >>from my own line of business execution. But it's now something that our customers are looking for to implement in their shops as well. >>Well, good. I really want to get into that. So let's let's start with data ops. I mean, I think you know, a lot of people are familiar with Dev Ops. Not maybe not everybody's familiar with data ops. What do we need to know about data? >>Well, I mean, you bring up the point that everyone knows Dev ops. And in fact, I think you know what data ops really >>does is bring a lot of the benefits that Dev Ops did for application >>development to the data management organizations. So when we look at what is data ops, it's a data management. Uh, it is a data management set of principles that helps organizations bring business ready data to their consumers. Quickly. It takes it borrows from Dev ops. Similarly, where you have a data pipeline that associates a business value requirement. I have this business initiative. It's >>going to drive this much revenue or this must cost >>savings. This is the data that I need to be able to deliver it. How do I develop that pipeline and map to the data sources Know what data it is? Know that I can trust it. So ensuring >>that it has the right quality that I'm actually using, the data that it was meant >>for and then put it to use. So in in history, most data management practices deployed a waterfall like methodology. Our implementation methodology and what that meant is all the data pipeline >>projects were implemented serially, and it was done based on potentially a first in first out program management office >>with a Dev Ops mental model and the idea of being able to slice through all of the different silos that's required to collect the data, to organize it, to integrate it, the validate its quality to create those data integration >>pipelines and then present it to the dashboard like if it's a Cognos dashboard >>or a operational process or even a data science team, that whole end to end process >>gets streamlined through what we're pulling data ops methodology. >>So I mean, as you well know, we've been following this market since the early days of Hadoop people struggle with their data pipelines. It's complicated for them, there's a a raft of tools and and and they spend most of their time wrangling data preparing data moving data quality, different roles within the organization. So it sounds like, you know, to borrow from from Dev Ops Data offices is all about streamlining that data pipeline, helping people really understand and communicate across. End the end, as you're saying, But but what's the ultimate business outcome that you're trying to drive? >>So when you think about projects that require data to again cut costs Teoh Artemia >>business process or drive new revenue initiatives, >>how long does it take to get from having access to the data to making it available? That duration for every time delay that is spent wasted trying to connect to data sources, trying to find subject matter experts that understand what the data means and can verify? It's quality, like all of those steps along those different teams and different disciplines introduces delay in delivering high quality data fat, though the business value of data ops is always associated with something that the business is trying to achieve but with a time element so if it's for every day, we don't have this data to make a decision where either making money or losing money, that's the value proposition of data ops. So it's about taking things that people are already doing today and figuring out the quickest way to do it through automation or work flows and just cutting through all the political barriers >>that often happens when these data's cross different organizational boundaries. >>Yes, sir, speed, Time to insights is critical. But in, you know, with Dev Ops, you really bringing together of the skill sets into, sort of, you know, one Super Dev or one Super ops. It sounds with data ops. It's really more about everybody understanding their role and having communication and line of sight across the entire organization. It's not trying to make everybody else, Ah, superhuman data person. It's the whole It's the group. It's the team effort, Really. It's really a team game here, isn't it? >>Well, that's a big part of it. So just like any type of practice, there's people, aspects, process, aspects and technology, right? So people process technology, and while you're you're describing it, like having that super team that knows everything about the data. The only way that's possible is if you have a common foundation of metadata. So we've seen a surgeons in the data catalog market in the last, you know, 67 years. And what what the what? That the innovation in the data catalog market has actually enabled us to be able >>to drive more data ops pipelines. >>Meaning as you identify data assets you captured the metadata capture its meaning. You capture information that can be shared, whether they're stakeholders, it really then becomes more of a essential repository for people don't really quickly know what data they have really quickly understand what it means in its quality and very quickly with the right proper authority, like privacy rules included. Put it to use >>for models, um, dashboards, operational processes. >>Okay. And we're gonna talk about some examples. And one of them, of course, is IBM's own internal example. But help us understand where you advise clients to start. I want to get into it. Where do I get started? >>Yeah, I mean, so traditionally, what we've seen with these large data management data governance programs is that sometimes our customers feel like this is a big pill to swallow. And what we've said is, Look, there's an operator. There's an opportunity here to quickly define a small project, align into high value business initiative, target something that you can quickly gain access to the data, map out these pipelines and create a squad of skills. So it includes a person with Dev ops type programming skills to automate an instrument. A lot of the technology. A subject matter expert who understands the data sources in it's meeting the line of business executive who translate bringing that information to the business project and associating with business value. So when we say How do you get started? We've developed A I would call it a pretty basic maturity model to help organizations figure out. Where are they in terms of the technology, where are they in terms of organizationally knowing who the right people should be involved in these projects? And then, from a process perspective, we've developed some pretty prescriptive project plans. They help you nail down. What are the data elements that are critical for this business business initiative? And then we have for each role what their jobs are to consolidate the data sets map them together and present them to the consumer. We find that six week projects, typically three sprints, are perfect times to be able to a timeline to create one of these very short, quick win projects. Take that as an opportunity to figure out where your bottlenecks are in your own organization, where your skill shortages are, and then use the outcome of that six week sprint to then focus on billing and gaps. Kick off the next project and iterating celebrate the success and promote the success because >>it's typically tied to a business value to help them create momentum for the next one. >>That's awesome. I want to get into some examples, I mean, or we're both Massachusetts based. Normally you'd be in our studio and we'd be sitting here for face to face of obviously with Kobe. 19. In this crisis world sheltering in place, you're up somewhere in New England. I happened to be in my studio, but I'm the only one here, so relate this to cove it. How would data ops, or maybe you have a, ah, a concrete example in terms of how it's helped, inform or actually anticipate and keep up to date with what's happening with both. >>Yeah, well, I mean, we're all experiencing it. I don't think there's a person >>on the planet who hasn't been impacted by what's been going on with this Cupid pandemic prices. >>So we started. We started down this data obscurity a year ago. I mean, this isn't something that we just decided to implement a few weeks ago. We've been working on developing the methodology, getting our own organization in place so that we could respond the next time we needed to be able todo act upon a data driven decision. So part of the step one of our journey has really been working with our global chief data officer, Interpol, who I believe you have had an opportunity to meet with an interview. So part of this year Journey has been working with with our corporate organization. I'm in a line of business organization where we've established the roles and responsibilities we've established the technology >>stack based on our cloud pack for data and Watson knowledge padlock. >>So I use that as the context. For now, we're faced with a pandemic prices, and I'm being asked in my business unit to respond very quickly. How can we prioritize the offerings that are going to help those in critical need so that we can get those products out to market? We can offer a 90 day free use for governments and hospital agencies. So in order for me to do that as a operations lead or our team, I needed to be able to have access to our financial data. I needed to have access to our product portfolio information. I needed to understand our cloud capacity. So in order for me to be able to respond with the offers that we recently announced and you'll you can take a look at some of the examples with our Watson Citizen Assistant program, where I was able to provide the financial information required for >>us to make those products available from governments, hospitals, state agencies, etcetera, >>that's a That's a perfect example. Now, to set the stage back to the corporate global, uh, the chief data office organization, they implemented some technology that allowed us to, in just data, automatically classify it, automatically assign metadata, automatically associate data quality so that when my team started using that data, we knew what the status of that information >>was when we started to build our own predictive models. >>And so that's a great example of how we've been partnered with a corporate central organization and took advantage of the automated, uh, set of capabilities without having to invest in any additional resources or head count and be able to release >>products within a matter of a couple of weeks. >>And in that automation is a function of machine intelligence. Is that right? And obviously, some experience. But you couldn't you and I when we were consultants doing this by hand, we couldn't have done this. We could have done it at scale anyway. It is it is it Machine intelligence and AI that allows us to do this. >>That's exactly right. And you know, our organization is data and AI, so we happen to have the research and innovation teams that are building a lot of this technology, so we have somewhat of an advantage there, but you're right. The alternative to what I've described is manual spreadsheets. It's querying databases. It's sending emails to subject matter experts asking them what this data means if they're out sick or on vacation. You have to wait for them to come back, and all of this was a manual process. And in the last five years, we've seen this data catalog market really become this augmented data catalog, and the augmentation means it's automation through AI. So with years of experience and natural language understanding, we can home through a lot of the metadata that's available electronically. We can calm for unstructured data, but we can categorize it. And if you have a set of business terms that have industry standard definitions through machine learning, we can automate what you and I did as a consultant manually in a matter of seconds. That's the impact that AI is have in our organization, and now we're bringing this to the market, and >>it's a It's a big >>part of where I'm investing. My time, both internally and externally, is bringing these types >>of concepts and ideas to the market. >>So I'm hearing. First of all, one of the things that strikes me is you've got multiple data, sources and data that lives everywhere. You might have your supply chain data in your er p. Maybe that sits on Prem. You might have some sales data that's sitting in a sas in a cloud somewhere. Um, you might have, you know, weather data that you want to bring in in theory. Anyway, the more data that you have, the better insights that you could gather assuming you've got the right data quality. But so let me start with, like, where the data is, right? So So it's it's anywhere you don't know where it's going to be, but you know you need it. So that's part of this right? Is being able >>to get >>to the data quickly. >>Yeah, it's funny. You bring it up that way. I actually look a little differently. It's when you start these projects. The data was in one place, and then by the time you get through the end of a project, you >>find out that it's moved to the cloud, >>so the data location actually changes. While we're in the middle of projects, we have many or even during this this pandemic crisis. We have many organizations that are using this is an opportunity to move to SAS. So what was on Prem is now cloud. But that shouldn't change the definition of the data. It shouldn't change. It's meaning it might change how you connect to it. It might also change your security policies or privacy laws. Now, all of a sudden, you have to worry about where is that data physically located? And am I allowed to share it across national boundaries right before we knew physically where it waas. So when you think about data ops, data ops is a process that sits on top of where the data physically resides. And because we're mapping metadata and we're looking at these data pipelines and automated work flows, part of the design principles are to set it up so that it's independent of where it resides. However, you have to have placeholders in your metadata and in your tool chain, where we're automating these work flows so that you can accommodate when the data decides to move. Because the corporate policy change >>from on prem to cloud. >>And that's a big part of what Data ops offers is the same thing. By the way, for Dev ops, they've had to accommodate building in, you know, platforms as a service versus on from the development environments. It's the same for data ops, >>and you know, the other part that strikes me and listening to you is scale, and it's not just about, you know, scale with the cloud operating model. It's also about what you were talking about is you know, the auto classification, the automated metadata. You can't do that manually. You've got to be able to do that. Um, in order to scale with automation, That's another key part of data office, is it not? >>It's a well, it's a big part of >>the value proposition and a lot of the part of the business case. >>Right then you and I started in this business, you know, and big data became the thing. People just move all sorts of data sets to these Hadoop clusters without capturing the metadata. And so as a result, you know, in the last 10 years, this information is out there. But nobody knows what it means anymore. So you can't go back with the army of people and have them were these data sets because a lot of the contact was lost. But you can use automated technology. You can use automated machine learning with natural, understand natural language, understanding to do a lot of the heavy lifting for you and a big part of data ops, work flows and building these pipelines is to do what we call management by exception. So if your algorithms say 80% confident that this is a phone number and your organization has a low risk tolerance, that probably will go to an exception. But if you have a you know, a match algorithm that comes back and says it's 99% sure this is an email address, right, and you have a threshold that's 98%. It will automate much of the work that we used to have to do manually. So that's an example of how you can automate, eliminate manual work and have some human interaction based on your risk threshold. >>That's awesome. I mean, you're right, the no schema on write said. I throw it into a data lake. Data Lake becomes a data swamp. We all know that joke. Okay, I want to understand a little bit, and maybe you have some other examples of some of the use cases here, but there's some of the maturity of where customers are. It seems like you've got to start by just understanding what data you have, cataloging it. You're getting your metadata act in order. But then you've got you've got a data quality component before you can actually implement and get yet to insight. So, you know, where are customers on the maturity model? Do you have any other examples that you can share? >>Yeah. So when we look at our data ops maturity model, we tried to simplify, and I mentioned this earlier that we try to simplify it so that really anybody can get started. They don't have to have a full governance framework implemented to to take advantage of the benefits data ops delivers. So what we did is we said if you can categorize your data ops programs into really three things one is how well do you know your data? Do you even know what data you have? The 2nd 1 is, and you trust it like, can you trust it's quality? Can you trust it's meeting? And the 3rd 1 is Can you put it to use? So if you really think about it when you begin with what data do you know, write? The first step is you know, how are you determining what data? You know? The first step is if you are using spreadsheets. Replace it with a data catalog. If you have a department line of business catalog and you need to start sharing information with the department's, then start expanding to an enterprise level data catalog. Now you mentioned data quality. So the first step is do you even have a data quality program, right. Have you even established what your criteria are for high quality data? Have you considered what your data quality score is comprised of? Have you mapped out what your critical data elements are to run your business? Most companies have done that for there. They're governed processes. But for these new initiatives And when you identify, I'm in my example with the covert prices, what products are we gonna help bring to market quickly? I need to be able to >>find out what the critical data elements are. And can I trust it? >>Have I even done a quality scan and have teams commented on it's trustworthiness to be used in this case, If you haven't done anything like that in your organization, that might be the first place to start. Pick the critical data elements for this initiative, assess its quality, and then start to implement the work flows to re mediate. And then when you get to putting it to use, there's several methods for making data available. One is simply making a gate, um, are available to a small set of users. That's what most people do Well, first, they make us spreadsheet of the data available, But then, if they need to have multiple people access it, that's when, like a Data Mart might make sense. Technology like data virtualization eliminates the need for you to move data as you're in this prototyping phase, and that's a great way to get started. It doesn't cost a lot of money to get a virtual query set up to see if this is the right join or the right combination of fields that are required for this use case. Eventually, you'll get to the need to use a high performance CTL tool for data integration. But Nirvana is when you really get to that self service data prep, where users can weary a catalog and say these are the data sets I need. It presents you a list of data assets that are available. I can point and click at these columns I want as part of my data pipeline and I hit go and automatically generates that output or data science use cases for it. Bad news, Dashboard. Right? That's the most mature model and being able to iterate on that so quickly that as soon as you get feedback that that data elements are wrong or you need to add something, you can do it. Push button. And that's where data obscurity should should bring organizations too. >>Well, Julie, I think there's no question that this covert crisis is accentuated the importance of digital. You know, we talk about digital transformation a lot, and it's it's certainly riel, although I would say a lot of people that we talk to we'll say, Well, you know, not on my watch. Er, I'll be retired before that all happens. Well, this crisis is accelerating. That transformation and data is at the heart of it. You know, digital means data. And if you don't have data, you know, story together and your act together, then you're gonna you're not gonna be able to compete. And data ops really is a key aspect of that. So give us a parting word. >>Yeah, I think This is a great opportunity for us to really assess how well we're leveraging data to make strategic decisions. And if there hasn't been a more pressing time to do it, it's when our entire engagement becomes virtual like. This interview is virtual right. Everything now creates a digital footprint that we can leverage to understand where our customers are having problems where they're having successes. You know, let's use the data that's available and use data ops to make sure that we can generate access. That data? No, it trust it, Put it to use so that we can respond to >>those in need when they need it. >>Julie Lockner, your incredible practitioner. Really? Hands on really appreciate you coming on the Cube and sharing your knowledge with us. Thank you. >>Thank you very much. It was a pleasure to be here. >>Alright? And thank you for watching everybody. This is Dave Volante for the Cube. And we will see you next time. >>Yeah, yeah, yeah, yeah, yeah

>>from around the globe. It's the Cube with coverage of Susic on digital brought to you by Susan. Right? >>Welcome back. I'm Stew Minuteman. And this is the Cube's coverage of Silicon Digital 20. Really excited to be digging into some of the cloud discussions. I've got two guests joining me now, one from across the pond and one from across the country. So joining me is Billy Baldwin. She is the senior director of Global CSP Alliance sales with Lisa. Coming from across the pond and coming from California is Mikhail Fradkov, who is a principal business development at Amazon Web services. Thank you both for joining us. >>Thank you to really, really exciting. >>All right, So, Julie, obviously, you know, we know we're limit Been proliferating the cloud something that almost I think really understand. You know, cloud, you know, big piece of the overall soussa discussion. Um, bring us inside a little bit, You know your role. And of course, the long partnership that business ad with AWS. >>Yeah. So? So my role is working with, you know, the major hyper scale is in the public cloud providers in offering solutions that's driving digital transformation. And this modernization even more so in today's current climate. We're seeing, you know, modernization transformation is being driven out of necessity. The necessity now due to the yeah, the code 19 impact. So I really want to draw on. You know, we've been working with AWS for the last 10 years. We've serviced, you know, thousands of customers between us who are looking at how they innovate on D drive, you know, flexibility and agility into into their, you know, the right and then there accounts. So it's really important that, you know, we look at how we support our customers from a, uh, and integrated support perspective and how we can we can move them forward in the in the digital transformation journey. >>Awesome. So Mikhail and I want to hear what Julie talked about. I think about when I when I look at AWS, you talk about builders when you go to the conference that Amazon told, you know, innovation is absolutely something there. So talk to us a little bit about how you know the Linux community in general, and to save costs more specifically are engaged. And you know a piece of what AWS is doing. >>Sure, So in general, it's a bless. I'm responsible for making sure that our customers are successful as they go through there. Well, its information Jordan you or business transformation. Jordan is so those those are all involved transformations. And, um, it's It's actually an interesting position to be in because you see it first hand on the ground for all the challenges and all the all the interesting problems that customers get the soul and then it's a sexual incredibly exciting that we're having this conversation in the framework of silicon because open source is incredibly important for a glass and stable beta last week understand how important open source is for customer success, and therefore we've been involved in contributing to the projects from from very early on, that justly mix and a VM and Java and kubernetes. So we see we see a lot of a lot of proliferation in the space and then another. Another interest in I guess below that, I would say, is if you think about the open source notion, which is largely around community, so there is this sort of like a juxtaposition off the cathedral and the bazaar, right, and then so the bazaar is the vibrant community off people, commentaries with ideas, and they're they're pursuing them and they innovate. And so something similar we see in the um yes, it the blast community with several 1,000,000 customers day today. They're sold in challenges and bring in lots of lots of requests for innovation, and I >>want to call it puts >>pressure on Amazon to innovate. But it's a lot of inspiration, right? And then, therefore it's It's interesting to see right that because of all the innovation and all those requests, customers get access on Amazon toe. All the features such as same was open source, Right? So you capitalize on this on this innovation, and you're there is well, customers can request, let's say, in financial service industry and then so you get a lot of security features. All the only controls would say, like I saw saw some combines and then some of the most stringent compliance already, like product guys that say the blessed, that stuff. That's one of the examples. >>Yeah, I e starts are Julie. That customer flywheel that you talked about is what we really want. They're so Julie, you want wanted to comment on what he was saying. >>I was just kind of just to kind of reinforce, you know, that whole community in that whole innovation based as well because from an open source perspective, you know that that sense of community is really driving those changes on with the AWS platform. It's got a very rich functionality behind it. You know, it's one of the, you know, the first time platform. So it does have that degree of innovation, you know, from from Day One. And that's just being driven by the by, the by, our customers who are pushing the envelope family in everyone more on. That's where you know the relationship between, you know, Souza on AWS is really, really started to excel. Looking at how we we move into that container space now as well, and help the customers, you know, modernize not only their, you know, the the cloud native that's going straight to cloud. But how do they modernize modernize their legacy applications as well? Um, and how today, you know, take, you know, take their on premise environments on, make them more effective and more efficient, and by using public cloud to be able to do that. >>Yeah. Julia, I'm glad you brought that up, because absolutely, there's opportunity. But there's challenges there. Customers really have. You know, it's either hybrid or multi cloud deployment. You know, container ization. Kubernetes are absolutely enable is there? But I wonder if you could bring us inside. What Susie doing? You have any customer examples of you know how they're really making this change? We know that it's still the majority of applications have not been modernized. They've not going cloud native. They're not ready for these environments. So how are customers working through this ultimate journey? >>Yeah, I mean, it's really, really complex. And I did a presentation on our sales summit talking about, you know, Gardner's five. Ours about you know, what applications can move to the cloud, how easy it is to do that. And I think there is some research done last year with for like, one, um, where the previous year there was a lot of customers said, Yeah, we're moving to fired and it's easy. And then this year, when they rerun the survey, it was No, it's really hard. We need partners. We need to look at how we how we do this. And so you know, every application is going to be able to be moved hours, and it's really Orton that they know the customers have a strategy and look at what they're doing on prem it and then start to identify what is you know, what is cloud friendly? What do they need to do to kind of go forward? You know, Do they need to be, you know, rewrite an application? Do they need to re factor it kind of just be a lift and shift. And so what we're doing with with AWS is, you know, we've been working with partners like both, for example, who built out to retail application platform to be able to migrate those customers quickly into a more cost effective and efficient way of delivering businesses because they will say, you know, even more so in the current scenario there, you know margins are being squeezed. They need to be looked at being ableto deliver higher, you know, return on investment and to share with any of their, you know, in with their spend. So, you know, that's that's one area that we are kind of like, Look, you know, looking at as well. We've had great success with it. Um, we've also got a quick start programs with with AWS that allows, you know, customers you need to migrate quickly and easily. To be able todo to take those applications on their environments on DNA, move them on to the public cloud. So that so that those are two key areas that we're really looking at, you know, driving. Yeah, they're driving forward because it's critical because it is complex. Um, you need to have a Roma. You need to have a strategy about how you do, and you need to identify and include the stakeholders when they move. You know, when you're changing your environment to make sure that you haven't missed anything, >>something that would love to hear your viewpoint on this to you know, when you look at the Amazon ecosystem, you've got a huge AWS marketplace. Obviously, the integrators help customers work through their various environments and how to modernize them. How to move there, you know, what are you seeing in the customer base, for example, you can help share as to how they're moving along. >>Sure. One of the way we have to understand right, a little bit off the context. So all this all this talk about, let's say, cloud migration and innovation, it's not. It's not an abstract sort of exercise, an absolute discipline. It happens for its right if we look. If you look at the innovative companies at a fast moving companies, effectively, they they see on average time to value metrics about 420 times faster. Then let's say what people slow companies, right? And then So that's That was a lot of pressure on companies to actually embrace, embrace this innovation and the digital transformation and engage with customers in the way that they have never done before, such as just technology enable so many things, so many protect right and then this. With any opportunity it comes, Here comes a challenge on then, as Julie pointed out, it's a it's a difficult exercise. Let's let's not mince words here. So and therefore we have to make sure that everybody is a line. Let's say customer goes through this exercise right that that they're trying to change their processes. The leadership sets new goals. The leadership says new objectives. They have to change the culture they have to train people. So that is that it's not just the challenge of the patient right there within the hour, then outside of the company, you want to make sure that effectively, everybody, everybody comes to the table is there's a lot of value and very much alive, and that just that this is where we see, I guess a lot of, um, a lot of opportunity because as as you go through this process, um, you have to, right, you have to have the right stakeholders who have you have to have trained people, right? And then if you look at another statistics that just 86 companies or so they have a first step and the other 86 infrastructure spend this to on premise And the reason for that is companies cannot not hire and train and train faster, right? So therefore, on AWS side, we we invest a lot in training programs and certification programs as well as we have the vibrant community off partners who can step in and help us with challenges such as we have a system off JIA size and the size, so we have with both hands off the size M s P s, whatever we have providers. And then effectively back to what results here is that you have the synergy. Not not only the change going from from the inside company. >>They >>also have the support structure. As Joe talked about Big Start, we have training and then we have programmatic support, right cattle, how to navigate passengers for that. And then as the switch swollen, you mentioned their new processes. This is this is where the power of the cloud comes in and part of the community. So all those challenges they have been sold. So you can take some of the blueprints and apply them as is. And >>you can you can >>pick and choose what? What? Your bias. So, for example, you can go with cloud native tools with Amazon Web services at the very same time you can. You can also pick products. For example, SuSE Cloud application platform, which provides you with this. I wouldn't call it, um it's slightly more opinionated approach how to how to implement your develops practices and agile practices. And then it's still making Iran's on top of Amazon elastic container service. So yeah, and then, as as Jules mentioned. We program, for example, in the work of success with it >>and just touching on that point because, you know, we talk about we're not islands, you know, we have to engage with the partners. You know, we want to make sure the customer success is at the heart of everything that we do. Um, and we have to bring in the right skill sets at the right time, you know, to to make make that journey as easy as possible and as quickly as possible on. So that's the you know that that's the beauty of community. That's the beauty of partners on benders coming together with the customer at the heart off of everything that they do on. You know, I know that's a very strong message that you're going to get from, you know, from Susie Con. But it's from message that we showed with Aws as well, about how do we do the right thing for the customer and how do we, you know, and how do we enable that success? But then to be successful, which will drive Ultimate six, you know, successes as partners as well? >>Excellent, Julie. One of the big things we're themes we heard in the keynote was talking about the developer community's obviously to say in AWS A lot of developers, anything specific for the developers out there That that either. The highlight >>s so obviously we've got the cloud application platform on. We've got the quick starts as well. So for May is you know, you've got a proven a proven platform with real aws that, you know, the infrastructure available there, the ease of which, you know, cloud application platform can sit on top of that of the eks elastic. The services is really, really critical. And, you know, for me, it would be just, you know, just try it, um, on and give us your feedback as well. I think that's really important, because the way that you know, we drive innovation is through that, you know, the cut, the feedback from our customers and people actually using that, you know, the services. I think McHale pointed to the earlier as well. You know, the innovation that they've seen has been driven by, you know, customers actually saying we want this feature. We want this put pressure on from a from a dev ops community is you know there are alternatives out there and you know, you should, you know, to try. You should try. Look at you know, if that suit your needs better. I look at how you can use a trusted partner like AWS and and Susie Teoh to actually meet some of those new needs they're coming aboard. >>And it's also to Julius Point right? Way cannot overemphasize the importance off builders off people who own on this innovation within the company. And be because the biggest thing that companies can do for their success is to enable builders and as as we mentioned before, right? So the process is this challenging the other multiple parties involved, but the very same time to empower people to drive this change, it's almost like instead of directing them like, Oh, um, the space is pretty pretty interesting analogy. So instead of if you want people to know how to build the ship so you do not you do not tell them. Oh, go gather wood and then, like, this is how you hammer things together. You just you just make sure that they yearn for the C. And then ultimately this is This is what drives the innovation. And here we have essentially with with, for example, Susak Capital radical enable people and they they practice the develops, they can practice, schedule and essentially align. This was this fast time to value practices, right? So that that is the tooling. And then you take weeks starts and then you put literally innovation into into those people's hands, for example, it So that's one big start allows you to bring up the whole environment and pretty much like minutes. Well, let's say if you want to go to innovate on the sisters again, you take big start and then well, is that the takes takes a little bit more involved. So maybe, Like like in an hour and 1/2 you have a safe environment, and then you have essentially start innovating there and >>excellent. Well, Mikhail and Julie thank you so much for the updates. You know, love hearing about innovation companies. Absolutely. Building is what differentiates us is the companies that are ready for today's modern era. So thank you so much for joining us. >>Thank you. Thank you. Thank you. Thank you. >>Thank you, Julia. >>Alright. We'll be back with lots more coverage from SuSE icon Digital 20. I'm stew minimum. And thank you for watching the Cube. >>Yeah, yeah, yeah, yeah, yeah, yeah.

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi everybody this is David on tape with the cube and welcome to the special digital presentation we're really digging into how IBM is operationalizing and automating the AI and data pipeline not only for its clients but also for itself and with me is Julie Lochner who looks after offering management and IBM's data and AI portfolio Julie great to see you again okay great to be here thank you talk a little bit about the role you have here at IBM sure so my responsibility in offering management in the data and AI organization is really twofold one is I lead a team that implements all of the back-end processes really the operations behind anytime we deliver a product from the data AI team to the market so think about all of the release cycle management pricing product management discipline etc the other roles that I play is really making sure that um we are working with our customers and making sure they have the best customer experience and a big part of that is developing the data ops methodology it's something that I needed internally from my own line of business execution but it's now something that our customers are looking for to implement in their shops as well well good I really want to get into that and so let's let's start with data ops I mean I think you know a lot of people are familiar with DevOps not maybe not everybody's familiar with the data Ops what do we need to know about data well I mean you bring up the point that everyone knows DevOps and and then in fact I think you know what data Ops really does is bring a lot of the benefits that DevOps did for application development to the data management organizations so when we look at what is data ops it's a data management it's a it's a data management set of principles that helps organizations bring business ready data to their consumers quickly it takes it borrows from DevOps similarly where you have a data pipeline that associates a business value requirement I have this business initiative it's gonna drive this much revenue or this much cost savings this is the data that I need to be able to deliver it how do I develop that pipeline and map to the data sources know what data it is know that I can trust it so ensuring that it has the right quality that I'm actually using the data that it was meant for and then put it to use so in in history most dated management practices deployed a waterfall like methodology or implementation methodology and what that meant is all the data pipeline projects were implemented serially and it was dawn based on potentially a first-in first-out program management office with a DevOps mental model and the idea of being able to slice through all of the different silos that's required to collect the data to organize it to integrate it to validate its quality to create those data integration pipelines and then present it to the dashboard like if it's a Cognos dashboard for a operational process or even a data science team that whole end-to-end process gets streamlined through what we're calling data ops methodology so I mean as you well know we've been following this market since the early days of a dupe and people struggle with their data pipelines it's complicated for them there's a raft of tools and and and they spend most of their time wrangling data preparing data improving data quality different roles within the organization so it sounds like you know to borrow from from DevOps data OPS's is all about REME lining that data pipeline helping people really understand and communicate across end to end as you're saying but but what's the ultimate business outcome that you're trying to drive so when you think about projects that require data to again cut cost to automate a business process or drive new revenue initiatives how long does it take to get from having access to the data to making it available that duration for every time delay that is spent wasted trying to connect to data sources trying to find subject matter experts that understand what the data means and can verify its quality like all of those steps along those different teams and different disciplines introduces delay in delivering high quality data fast so the business value of data Ops is always associated with something that the business is trying to achieve but with a time element so if it's for every day we don't have this data to make a decision we're either making money or losing money that's the value proposition of data ops so it's about taking things that people are already doing today and figuring out the quickest way to do it through automation through workflows and just cutting through all of the political barriers that often happens when these data's cross different organizational boundaries yeah so speed time to insights is critical but to in and then you know with DevOps you're really bringing together the skill sets into sort of you know one super dev or one super ops it sounds with data ops it's really more about everybody understanding their role and having communication and line-of-sight across the entire organization it's not trying to make everybody a superhuman data person it's the whole it's the group it's the team effort really it's really a team game here isn't it well that's a big part of it so just like any type of practice there's people aspects process aspects and technology right so people process technology and while you're you're describing it like having that super team that knows everything about the data the only way that's possible is if you have a common foundation of metadata so we've seen a surgeons in the data catalog market and last you know six seven years and what what the what that the innovation in the data catalog market has actually enabled us to be able to drive more data ops pipelines meaning as you identify data assets you've captured the metadata you capture its meaning you capture information that can be shared whether they're stakeholders it really then becomes more of a essential repository for people to really quickly know what data they have really quickly understand what it means in its quality and very quickly with the right proper authority like privacy rules included put it to use for models you know dashboards operational processes okay and and we're gonna talk about some examples and one of them of course is ibm's own internal example but but help us understand where you advise clients to start I want to get into it where do I get started yeah I mean so traditionally what we've seen with these large data management data governance programs is that sometimes our customers feel like this is a big pill to swallow and what we've said is look there's an opera there's an opportunity here to quickly define a small project align it to a high-value business initiative target something that you can quickly gain access to the data map out these pipelines and create a squad of skills so it includes a person with DevOps type programming skills to automate an instrument a lot of the technology a subject matter expert who understands the data sources and its meaning a line of business executive who can translate bringing that information to the business project and associating with business value so when we say how do you get started we've developed a I would call it a pretty basic maturity model to help organizations figure out where are they in terms of the technology where are they in terms of organizationally knowing who the right people should be involved in these projects and then from a process perspective we've developed some pretty prescriptive project plans that help you nail down what are the data elements that are critical for this business business initiative and then we have for each role what their jobs are to consolidate the datasets map them together and present them to the consumer we find that six-week projects typically three sprints are perfect times to be able to in a timeline to create one of these very short quick win projects take that as an opportunity to figure out where your bottlenecks are in your own organization where your skill shortages are and then use the outcome of that six-week sprint to then focus on filling in gaps kick off the next project and iterate celebrate the success and promote the success because it's typically tied to a business value to help them create momentum for the next one all right that's awesome I want to now get into some examples I mean or you're we're both massachusetts-based normally you'd be in our studio and we'd be sitting here face-to-face obviously with kovat 19 in this crisis we're all sheltering in place you're up in somewhere in New England I happen to be in my studio believe it but I'm the only one here so relate this to kovat how would data ops or maybe you have a concrete example in in terms of how it's helped inform or actually anticipate and keep up-to-date with what's happening with building yeah well I mean we're all experiencing it I don't think there's a person on the planet who hasn't been impacted by what's been going on with this coded pandemic crisis so we started we started down this data obscurity a year ago I mean this isn't something that we just decided to implement a few weeks ago we've been working on developing the methodology getting our own organization in place so that we could respond the next time we needed to be able to you know act upon a data-driven decision so part of step one of our journey has really been working with our global chief data officer Interpol who I believe you have had an opportunity to meet with an interview so part of this year journey has been working with with our corporate organization I'm in the line of business organization where we've established the roles and responsibilities we've established the technology stack based on our cloud pack for data and Watson knowledge catalog so I use that as the context for now we're faced with a pandemic crisis and I'm being asked in my business unit to respond very quickly how can we prioritize the offerings that are gonna help those in critical need so that we can get those products out to market we can offer a you know 90-day free use for governments and Hospital agencies so in order for me to do that as a operations lead for our team I needed to be able to have access to our financial data I needed to have access to our product portfolio information I needed to understand our cloud capacity so in order for me to be able to respond with the offers that we recently announced you know you can take a look at some of the examples with our Watson citizen assistant program where I was able to provide the financial information required for us to make those products available for governments hospitals state agencies etc that's a that's a perfect example now to to set the stage back to the corporate global chief data office organization they implemented some technology that allowed us to ingest data automatically classify it automatically assign metadata automatically associate data quality so that when my team started using that data we knew what the status of that information was when we started to build our own predictive models and so that's a great example of how we've partnered with a corporate central organization and took advantage of the automated set of capabilities without having to invest in any additional resources or headcount and be able to release products within a matter of a couple of weeks and in that automation is a function of machine intelligence is that right and obviously some experience but but you couldn't you and I when we were consultants doing this by hand we couldn't have done this we could have done it at scale anyways it is it machine intelligence an AI that allows us to do this that's exactly right and as you know our organization is data and AI so we happen to have the a research and innovation teams that are building a lot of this technology so we have somewhat of an advantage there but you're right the alternative to what I've described is manual spreadsheets it's querying databases it's sending emails to subject matter experts asking them what this data means if they're out sick or on vacation you have to wait for them to come back and all of this was a manual process and in the last five years we've seen this data catalog market really become this augmented data catalog and that augmentation means it's automation through AI so with years of experience and natural language understanding we can comb through a lot of the metadata that's available electronically we can comb through unstructured data we can categorize it and if you have a set of business terms that have industry standard definitions through machine learning we can automate what you and I did as a consultant manually in a matter of seconds that's the impact the AI is had in our organization and now we're bringing this to the market and it's a it's a big part of where I'm investing my time both internally and externally is bringing these types of concepts and ideas to the market so I'm hearing first of all one of the things that strikes me is you've got multiple data sources and data lives everywhere you might have your supply chain data and your ERP maybe that sits on Prem you might have some sales data that's sitting in the SAS store in a cloud somewhere you might have you know a weather data that you want to bring in in theory anyway the more data that you have the better insights that you can gather assuming you've got the right data quality but so let me start with like where the data is right so so it sits anywhere you don't know where it's gonna be but you know you need it so that that's part of this right is being able to read it quickly yeah it's funny you bring it up that way I actually look a little differently it's when you start these projects the data was in one place and then by the time you get through the end of a project you find out that it's a cloud so the data location actually changes while we're in the middle of projects we have many or coming even during this this pandemic crisis we have many organizations that are using this as an opportunity to move to SAS so what was on Prem is now cloud but that shouldn't change the definition of the data it shouldn't change its meaning it might change how you connect to it um it might also change your security policies or privacy laws now all of a sudden you have to worry about where is that data physically located and am I allowed to share it across national boundaries right before we knew physically where it was so when you think about data ops data ops is a process that sits on top of where the data physically resides and because we're mapping metadata and we're looking at these data pipelines and automated workflows part of the design principles are to set it up so that it's independent of where it resides however you have to have placeholders in your metadata and in your tool chain where we oughta mating these workflows so that you can accommodate when the data decides to move because of corporate policy change from on-prem to cloud then that's a big part of what data Ops offers it's the same thing by the way for DevOps they've had to accommodate you know building in you know platforms as a service versus on from the development environments it's the same for data ops and you know the other part that strikes me and listening to you is scale and it's not just about you know scale with the cloud operating model it's also about what you're talking about is you know the auto classification the automated metadata you can't do that manually you've got to be able to do that in order to scale with automation that's another key part of data Ops is it not it's well it's a big part of the value proposition and a lot of a part of the business base right then you and I started in this business you know and Big Data became the thing people just move all sorts of data sets to these Hadoop clusters without capturing the metadata and so as a result you know in the last 10 years this information is out there but nobody knows what it means anymore so you can't go back with the army of people and have them query these data sets because a lot of the contact was lost but you can use automated technology you can use automated machine learning with natural under Snatcher Alang guaa Jing to do a lot of the heavy lifting for you and a big part of data ops workflows and building these pipelines is to do what we call management-by-exception so if your algorithms say you know 80% confident that this is a phone number and your organization has a you know low risk tolerance that probably will go to an exception but if you have a you know a match algorithm that comes back and says it's 99 percent sure this is an email address right and you I have a threshold that's 98% it will automate much of the work that we used to have to do manually so that's an example of how you can automate eliminate manual work and have some human interaction based on your risk threshold now that's awesome I mean you're right the no schema on right said I throw it into a data leg the data link becomes the data swap we all know that joke okay I want to understand a little bit and maybe you have some other examples of some of the use cases here but there's some of the maturity of where customers are I mean it seems like you got to start by just understanding what data you have cataloging it you're getting your metadata act in order but then you've got a you've got a data quality component before you can actually implement and get yet to insight so you know where our customers on the on the maturity model do you have any other examples that you can share yeah so when we look at our data ops maturity model we tried to simplify it I mentioned this earlier that we try to simplify it so that really anybody can get started they don't have to have a full governance framework implemented to take advantage of the benefits data ops delivers so what we did we said if you can categorize your data ops programs into really three things one is how well do you know your data do you even know what data you have the second one is and you trust it like can you trust its quality can you trust its meeting and the third one is can you put it to use so if you really think about it when you begin with what data do you know right the first step is you know how are you determining what data you know the first step is if you are using spreadsheets replace it with a data catalog if you have a department line of business catalog and you need to start sharing information with the departments then start expanding to an enterprise level data catalog now you mentioned data quality so the first step is do you even have a data quality program right have you even established what your criteria are for high quality data have you considered what your data quality score is comprised of have you mapped out what your critical data elements are to run your business most companies have done that for they're they're governed processes but for these new initiatives and when you identify I'm in my example with the Kovach crisis what products are we gonna help bring to market quickly I need to be able to find out what the critical data elements are and can I trust it have I even done a quality scan and have teams commented on its trustworthiness to be used in this case if you haven't done anything like that in your organization that might be the first place to start pick the critical data elements for this initiative assess its quality and then start to implement the workflows to remediate and then when you get to putting it to use there's several methods for making data available you know one is simply making a data Mart available to a small set of users that's what most people do well first they make a spreadsheet of the data available but then if they need to have multiple people access it that's when like a data Mart might make sense technology like data virtualization eliminates the need for you to move data as you're in this prototyping phase and that's a great way to get started it doesn't cost a lot of money to get a virtual query set up to see if this is the right join or the right combination of fields that are required for this use case eventually you'll get to the need to use a high performance ETL tool for data integration but Nirvana is when you really get to that self-service data prep where users can query a catalog and say these are the data sets I need it presents you a list of data assets that are available I can point and click at these columns I want as part of my you know data pipeline and I hit go and it automatically generates that output for data science use cases for a Cognos dashboard right that's the most mature model and being able to iterate on that so quickly that as soon as you get feedback that that data elements are wrong or you need to add something you can do it push button and that's where data observation to bring organizations to well Julie I think there's no question that this kovat crisis is accentuated the importance of digital you know we talk about digital transformation a lot and it's it's certainly real although I would say a lot of people that we talk to will say well you know not on my watch or I'll be retired before that all happens will this crisis is accelerating that transformation and data is at the heart of it you know digital means data and if you don't have your data you know story together and your act together then you're gonna you're not going to be able to compete and data ops really is a key aspect of that so you know give us a parting word all right I think this is a great opportunity for us to really assess how well we're leveraging data to make strategic decisions and if there hasn't been a more pressing time to do it it's when our entire engagement becomes virtual like this interview is virtual write everything now creates a digital footprint that we can leverage to understand where our customers are having problems where they're having successes you know let's use the data that's available and use data ops to make sure that we can iterate access that data know it trust it put it to use so that we can respond to those in need when they need it Julie Locker your incredible practitioner really hands-on really appreciate you coming on the Kuban and sharing your knowledge with us thank you okay thank you very much it was a pleasure to be here all right and thank you for watching everybody this is Dave Volante for the cube and we will see you next time [Music]

>> From Cambridge Massachusetts, it's The Cube covering MIT Chief Data Officer, and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. (electronic music) >> Welcome back to MIT in Cambridge, Massachusets everybody. You're watching The Cube, the leader in live tech coverage. My name is Dave Vellante I'm here with Paul Gillin. Day two of the of the MIT Chief Data Officer Information Quality Conference. One of the things we like to do, at these shows, we love to profile Boston area start-ups that are focused on data, and in particular we love to focus on start-ups that are founded by women. Julie Johnson is here, She's the Co-founder and CEO of Armored Things. Julie, great to see you again. Thanks for coming on. >> Great to see you. >> So why did you start Armored Things? >> You know, Armored Things was created around a mission to keep people safe. Early in the time where were looking at starting this company, incidents like Las Vegas happened, Parkland happened, and we realized that the world of security and operations was really stuck in the past right? It's a manual solutions generally driven by a human instinct, anecdotal evidence, and tools like Walkie-Talkies and video cameras. We knew there had to be a better way right? In the world of Data that we live in today, I would ask if either of you got in your car this morning without turning on Google Maps to see where you were going, and the best route with traffic. We want to help universities, ball parks, corporate campuses do that for people. How do we keep our people safe? By understanding how they live. >> Yeah, and stay away from Lambert Street in Cambridge by the way. >> (laughing) >> Okay so, you know in people, when they think about security they think about cyber, they think about virtual security, et cetera et cetera, but there's also the physical security aspect. Can you talk about the balance of those two? >> Yeah, and I think both are very important. We actually tend to mimic some of the revolutions that have happened on the cyber security side over the last 10 years with what we're trying to do in the world of physical security. So, folks watching this who are familiar with cyber security might understand concepts like anomaly detection, SIEM and SOAR for orchestrated response. We very much believe that similar concepts can be applied to the physical world, but the unique thing about the physical world, is that it has defined boundaries, right? People behave in accordance with their environment. So, how do we take the lessons learned in cyber security over 10 to 15 years, and apply them to that physical world? I also believe that physical and cyber security are converging. So, are there things that we know in the physical world because of how we approach the problem? That can be a leading indicator of a threat in either the physical world or the digital world. What many people don't understand is that for some of these cyber security hacks, the first weak link is physical access to your network, to your data, to your systems. How do we actually help you get an eye on that, so you already have some context when you notice it in the digital realm. >> So, go back to the two examples you sited earlier, the two shooting examples. Could those have been prevented or mitigated in some way using the type of technology you're building? >> Yeah, I hate to say that you could ever prevent an incident like that. Everyone wants us to do better. Our goal is to get a better sense predicatively of the leading indicators that tell you you have a problem. So, because we're fundamentally looking at patterns of people and flow, I want to know when a normal random environment starts to disperse in a certain way, or if I have a bottle neck in my environment. Because if then I have that type of incident occur, I already know where my hotspots are, where my pockets of risk are. So, I can address it that much more efficiently from a response perspective. >> So if people are moving quickly away from a venue, it might be and indication that there's something wrong- >> It could be, Yeah. That demands attention. >> Yeah, when you go to a baseball game, or when you go to work I would imagine that you generally have a certain pattern of behavior. People know conceptually what those patterns are. But, we're the first effort to bring them data to prove what those patterns are so that they can actually use that data to consistently re-examine their operations, re-examine their security from a staffing perspective, from a management perspective, to make sure that they're using all the data that's at their disposal. >> Seems like there would be many other applications beyond security of this type of analysis. Are you committed to the security space, or do you have broader ambitions? >> Are we committed to the security space is a hundred percent. I would say the number one reason why people join our team, and the number one reason why people call us to be customers is for security. There's a better way to do things. We fundamentally believe that every ball park, every university, every corporate campus, needs a better way. I think what we've seen though is exactly what you're saying. As we built our software, for security in these venues, and started with an understanding of people and flow, there's a lot that falls out of that right? How do I open gates that are more effective based on patterns of entry and exit. How do I make sure that my staffing's appropriate for the number of people I have in my environment. There's lots of other contextual information that can ultimately drive a bottom line or top line revenue. So, you take a pro sports venue for example. If we know that on a 10 degree colder day people tend to eagres more early in the game, how do we adjust our food and beverage strategy to save money on hourly workers, so that we're not over staffing in a period of time that doesn't need those resources. >> She's talking about the physical and the logical security worlds coming together, and security of course has always been about data, but 10 years ago it was staring at logs increasing the machines are helping us do that, and software is helping us do that. So can you add some color to at least the trends in the market generally, and then maybe specifically what you're doing bringing machine intelligence to the data to make us more secure. >> Sure, and I hate to break it to you, but logs are still a pretty big part of what people are watching on a daily basis, as are video cameras. We've seen a lot of great technology evolve in the video management system realm. Very advanced technology great at object recognition and detecting certain behaviors with a video only solution, right? How do we help pinpoint certain behaviors on a specific frame or specific camera. The only problem with that is, if you have people watching those cameras, you're still relying on humans in the loop to catch a malicious behavior, to respond in the event that they're notified about something unusual. That still becomes a manual process. What we do, is we use data to watch not only cameras, but we are watching your cameras, your Wi-Fi, access control. Contextual data from public transit, or weather. How do we get this greater understanding of your environment that helps us watch everything so that we can surface the things that you want the humans in the loop to pay attention to, right? So, we're not trying to remove the human, we're trying to help them focus their time and make decisions that are backed by data in the most efficient way possible. >> How about the concerns about The Surveillance Society? In some countries, it's just taken for granted now that you're on camera all the time. In the US that's a little bit more controversial. Is what your doing, do you have to be sensitive to that in designing the tools you're building? >> Yeah, and I think to Dave's question, there are solutions like facial recognition which are very much working on identifying the individual. We have a philosophy as a company, that security doesn't necessarily start with the individual, it starts with the aggregate. How do we understand at an aggregate macro level, the patterns in an environment. Which means I don't have to identify Paul, or I don't have to identify Dave. I want to look for what's usual and unusual, and use that as the basis of my response. There's certain instances where you want to know who people are. Do I want to know who my security personnel are so I can dispatch them more efficiently? Absolutely. Let's opt those people in and allow them to share the information they need to share to be better resources for our environment. But, that's the exception not the norm. If we make the norm privacy first, I think we'll be really successful in this emerging GDPR data centric world. >> But I could see somebody down the road saying hey can you help us find this bad guy? And my kids at camp this week, This is his 7th year of camp, and this year was the first year my wife, she was able to sign up for a facial recognition thing. So, we used to have to scroll through hundreds and hundreds of pictures to see oh, there he is! And so Deb signs up for this thing, and then it pings you when your son has a picture taken. >> Yeah. And I was like, That's awesome. Oh. (laughing) >> That's great until you think about it. >> But there aren't really any clear privacy laws today. And so you guys are saying, look it, we're looking at the big picture. >> That's right. >> But that day is coming isn't it? >> There's certain environments that care more than others. If you think about universities, which is where we first started building our technology, they cared greatly about the privacy of their students. Health care is a great example. We want to make sure that we're protecting peoples personal data at a different level. Not only because that's the right thing to do, but also from a regulatory perspective. So, how do we give them the same security without compromising the privacy. >> Talk about Bottom line. You mentioned to us earlier that you just signed a contract with a sports franchise, you're actually going to help them, help save them money by deploying their resources more efficiently. How does your technology help the bottom line? >> Sure, you're average sporting venue, is getting great information at the point a ticket is scanned or a ticket is purchased, they have very little visibility beyond that into the customer journey during an event at their venue. So, if you think about again, patterns of people and flow from a security perspective, at our core we're helping them staff the right gates, or figure out where people need to be based on hot spots in their environment. But, what that also results in is an ability to drive other operational benefits. Do we have a zone that's very low utilization that we could use as maybe even a benefit to our avid fans. Send them to that area, get traffic in that area, and now give them a better concession experience because of it, right? Where they're going to end up spending more money because they're not waiting in line in the different zone. So, how do we give them a dashboard in real time, but also alerts or reports that they can use on an ongoing basis to change their decision making going forward. >> So, give us the company overview. Where are you guys at with funding, head count, all that good stuff. >> So, we raised a seed round with some great Boston and Silicon Valley investors a year ago. So, that was Glasswing is a Boston AI focused fund, has been a great partner for us, and Inovia which is Canada's largest VC fund recently opened a Silicon Valley office. We just started raising a series A about a week ago. I'm excited to say those conversation have been going really well so far. We have some potential strategic partners who we're excited about who know data better then anyone else that we think would help us accelerate our business. We also have a few folks who are very familiar with the large venue space. You know, the distributed campuses, the sporting and entertainment venues. So, we're out looking for the right partner to lead our series A round, and take our business to the next level, but where we are today with five really great branded customers, I think we'll have 20 by the end of next year, and we won't stop fighting 'till we're at every ball park, every football stadium, every convention center, school. >> The big question, at some point will you be able to eliminate security lines? (laughing) >> I don't think that's my core mission. (laughing) But, optimistically I'd love to help you. Right, I think there's some very talented people working on that challenge, so I'll defer that one to them. >> And rough head count today? >> We have 23 people. >> You're 23 people so- >> Yeah, I headquartered in Boston Post Office Square. >> Awesome, great location. So, and you say you've got five customers, so you're generating revenue? >> Yes >> Okay, good. Well, thank you for coming in The Cube >> Yeah, thank you. >> And best of luck with the series A- >> I appreciate it and going forward >> Yeah, great. >> All right, and thank you for watching. Paul Gillin and I will be back right after this short break. This is The Cube from MIT Chief Data Officer Information Quality Conference in Cambridge. We'll be right back. (electronic music)

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

>> Live from San Francisco, it's theCUBE! Covering IBM Think 2019. Brought to you by IBM. >> Welcome back, live here in San Francisco, it's theCUBE's coverage of IBM Think 2019. I'm John Furrier--Stu Miniman. Stu, four days, we're on our fourth day, the sun's shining, they've shut down Howard Street here at IBM. Big event for IBM, in San Francisco, not Las Vegas. Lot of great cloud action, lot of great AI data developers. Great story, good to see you again. Our next two guests, Julie Lockner, Director, Offering Management, Portfolio Operations at IBM, Data+AI, great to see you. >> Thank you, it's great to see you too, thank you. >> And Jay Limburn, Director of Offering Management, IBM Data+AI, thanks for coming on. >> Hey guys, great to be here. >> So, we've chatted many times at events, the role of data. So, we're religious about data, data flows through our blood, but IBM has put it all together now. All the reorgs are over, everyone's kind of, the table is set for IBM. The data path is clear, it's part of applications. It's feeding the apps. AI's the key workload inside the application. This is now a fully set-up group, give us the update, what's the focus? >> Yeah, it's really exciting because, if you think about it, before, we were called IBM Analytics, and that really is only a part of what we do. Now that we're Data+AI, that means that not only are we responsible for delivering data assets, and technology that supports those data assets to our customers, but infusing AI, not only in the technologies that we have, but also helping them build applications so they can fuse AI into their business processes. >> It's pretty broad, I mean, data's very much a broad swath of things. Analytics, you know, wrangling data, setting things up, cataloging them. Take me through how you guys set this up. How do you present it to the marketplace? How are clients engaged with it? Because it's pretty broad. But it could be, it needs to be specific. Take us through the methodology. >> So, you probably heard a lot of people today talk about the ladder to AI, right? This is IBM's view of how we explain our client's journey towards AI. It really starts at the bottom rung of the ladder, where we've got the collection of information. Collect your data. Once you've collected your data, you move up to the next rung, which is the Organize. And this is really where all the governance stuff comes in. This is how we can provide a view across that data, understand that data, provide trust to that data, and then serve that up to the consumers of that information, so they can actually use that in AI. That's where all the data science capabilities come in, allowing people to actually be able to consume that information. >> So, the bottom set is just really all the hard and heavy lifting that data scientists actually don't want to do. >> And writing algorithms, the collecting, the ingesting of data from any source, that's the bottom? And then, tell me about that next layer up, from the collection-- >> So, Collect is the physical assets or the collection of the data that you're going to be using for AI. If you don't get that foundation right, it doesn't really make sense. You have to have the data first. The piece in the middle that Jay was referring to, that's called Organize, our whole divisions are actually organized around these ladders to AI, so, Collect, Organize, Analyze, Infuse. On the Organize side, as Jay was mentioning, it's all about inventorying the data assets, knowing what data you have, then providing data quality rules, governance, compliance-type offerings, that allow organizations to not just know your data, trust your data, but then make it available so you can use your data, and the users are those data scientists, they're the analytics teams, they're the operation organizations that need to be able to build their solutions on top of trusted data. >> So, where does the Catalog fit in? Which level does that come into? >> Yeah, so, think of the Data Catalog as the DNS for data, all right? It's the way in which you can provide a full view of all of your information. Whether it's structured information, unstructured information, data you've got on PRAM and data you've got in a cloud somewhere. >> That's in the Organize layer, right? >> That's all in the Organize layer. So, if you can collect that information, you can then provide capabilities that allow you to understand the quality of that data, know where that data's come from, and then, finally, if you serve that up inside a compelling, business-friendly experience, so that a data scientist can go to one place, quickly make a decision on if that's the right data for them, and allow them to go and be productive by building a data science model, then we're really able to move the needle on making those data science organizations efficient, allowing us to build better models to transform their business. >> Yeah, and a big part of that is, if you think about what makes Amazon successful, it's because they know where all their products are, from the vendor, to when it shows up on the doorstep. What the Catalog provides is really the similar capability of, I would call it inventory management of your data assets, where we know where the data came from, its source--in that Collect layer-- who's transformed it, who's accessed it, if they're even allowed to see it, so, data privacy policies are part of that, and then being able to just serve up that data to those users. Being able to see that whole end-to-end lineage is a key point, critical point of the ladder to AI. Especially when you start to think about things like bias detection, which is a big part of the Analyze layer. >> But one of the things we've been digging into on theCUBE is, is data the next flywheel of innovation? You know, it used to be I just had my information, many years ago we started talking about, "Okay, I need to be able to access all that other information." We hear things like 80% of the data out there isn't really searchable today. So, how do you see data, data gravity, all those pieces, as the next flywheel of innovation? >> Yeah, I think it's key. I mean, we've talked a lot about how, you can't do AI without information architecture. And it's absolutely true. And getting that view of that data in a single location, so it is like the DNS of the internet. So you know exactly where to search, you can get hold of that data, and then you've got tools that give you self-service access to actually get hold of the data without any need of support from IT to get access to it. It's really a key-- >> Yeah, but to the point you were just asking about, data gravity? I mean, being able to do this where the data resides. So, for example, we have a lot of our customers that are mergers and acquisitions. Some teams have a lot of data assets that are on-premises, others have large data lakes in AWS or Azure. How do you inventory those assets and really have a view of what you have available across that landscape? Part of what we've been focusing on this year is making our technology work across all of those clouds. And having a single view of your assets but knowing where it resides. >> So, Julie, this environment is a bit more complicated than the old data warehousing, or even what we were looking at with big data and Hadoop and all those pieces. >> Isn't that the truth? >> Help explain why we're actually going to be able to get the information, leverage and drive new business value out of data today, when we've struggled so many times in the past. >> Well, I think the biggest thing that's changed is the adoption of DevOps, and when I say adoption of DevOps and things like containerization and Docker containers, Kubernetes, the ability to provision data assets very quickly, no matter where they are, build these very quick value-producing applications based on AI, Artificial Intelligence APIs, is what's allowing us to take advantage of this multi-cloud landscape. If you didn't have that DevOps foundation, you'd still be building ETL jobs in data warehouses, and that was 20 years ago. Today, it's much more about these microservices-based architecture, building up these AI-- >> Well, that's the key point, and the "Fuse" part of the stack, I think, or ladder. Stack? Ladder? >> Ladder. (laughs) >> Ladder to success! Is key, because you're seeing the applications that have data native into the app, where it has to have certain characteristics, whether it's a realtime healthcare app, or retail app, and we had the retail folks on earlier, it's like, oh my god, this now has to be addressable very fast, so, the old fenced-off data warehouse-- "Hey, give me that data!"--pull it over. You need a sub-second latency, or milliseconds. So, this is now a requirement. >> That's right. >> So, how are people getting there? What are some use cases? >> Sure. I'll start with the healthcare 'cause you brought that up. One of the big use cases for technology that we provide is really around taking information that might be realtime, or batch data, and providing the ability to analyze that data very quickly in realtime to the point where you can predict when someone might potentially have a cardiac arrest. And yesterday's keynote that Rob Thomas presented, a demonstration that showed the ability to take data from a wearable device, combine it with data that's sitting in an Amazon... MySQL database, be able to predict who is the most at-risk of having a potential cardiac arrest! >> That's me! >> And then present that to a call center of cardiologists. So, this company that we work with, iCure, really took that entire stack, Organize, Collect, Organize, Analyze, Infuse, and built an application in a matter of six weeks. Now, that's the most compelling part. We were able to build the solution, inventory their data assets, tie it to the industry model, healthcare industry model, and predict when someone might potentially-- >> Do you have that demo on you? The device? >> Of course I do. I know, I know. So, here is, this is called a BraveHeart Life Sensor. And essentially, it's a Bluetooth device. I know! If you put it on! (laughs) >> If I put it on, it'll track... Biometric? It'll start capturing information about your heart, ECG, and on Valentine's Day, right? My heart to yours, happy Valentine's Day to my husband, of course. The ability to be able to capture all this data here on the device, stream it to an AI engine that can then immediately classify whether or not someone has an anomaly in their ECG signal. You couldn't do that without having a complete ladder to AI capability. >> So, realtime telemetry from the heart. So, I see timing's important if you're about to have a heart attack. >> Yeah. >> Pretty important. >> And that's a great example of, you mentioned the speed. It's all about being able to capture that data in whatever form it's coming in, understand what that data is, know if you can trust that data, and then put it in the hands of the individuals that can do something valuable with the analysis from that data. >> Yeah, you have to able to trust it. Especially-- >> So, you brought up earlier bias in data. So, I want to bring that up in context of this. This is just one example of wearables, Fitbits, all kinds of things happening. >> New sources of tech, yeah. >> In healthcare, retail, all kinds of edge, realtime, is bias of data. And the other one's privacy because now you have a new kind of data source going into the cloud. And then, so, this fits into what part of the ladder? So, the ladder needs a secure piece. >> Tell me about that. >> Yeah, it does. So, that really falls into that Organize piece of that ladder, the governance aspects around it. If you're going to make data available for self-service, you've got to still make sure that that data's protected, and that you're not going to go and break any kind of regulatory law around that data. So, we actually can use technology now to understand what that data is, whether it contains sensitive information, credit card numbers, and expose that information out to those consumers, yet still masking the key elements that should be protected. And that's really important, because data science is a hugely inefficient business. Data scientists are spending too much time looking for information. And worse than that, they actually don't have all the information available that they need, because certain information needs to be protected. But what we can do now is expose information that wasn't previously available, but protect just the key parts of that information, so we're still ensuring it's safe. >> That's a really key point. It's the classic iceberg, right? What you see: "Oh, data science is going to "change the game of our business!" And then when they realize what's underneath the water, it's like, all this set-up, incompatible data, dirty data, data cleaning, and then all of a sudden it just doesn't work, right? This is the reality. Are you guys seeing this? Do you see that? >> Yeah, absolutely. I think we're only just really at the beginning of a crest of a wave, here. I think organizations know they want to get to AI, the ladder to AI really helps explain and it helps to understand how they can get there. And we're able then to solve that through our technology, and help them get there and drive those efficiencies that they need. >> And just to add to that, I mean, now that there's more data assets available, you can't manually classify, tag and inventory all that data, determine whether or not it contains sensitive data. And that's where infusing machine learning into our products has really allowed our customers to automate the process. I mentioned, the only way that we were able to deploy this application in six weeks, is because we used a lot of the embedded machine learning to identify the patient data that was considered sensitive, tag it as patient data, and then, when the data scientists were actually building the models in that same environment, it was masked. So, they knew that they had access to the data, but they weren't allowed to see it. It's perfectly--especially with HIMSS' conference this week as well! You were talking about this there. >> Great use case with healthcare. >> Love to hear you speak about the ecosystem being built around this. Everything, open APIs, I'm guessing? >> Oh, yeah. What kind of partners are-- >> Jay, talk a little bit-- >> Yeah, so, one of the key things we're doing is ensuring that we're able to keep this stuff open. We don't want to curate a proprietary system. We're already big supporters of open source, as you know, in IBM. One of the things that we're heavily-invested in is our open metadata strategy. Open metadata is part of the open source ODPi Foundation. Project Egeria defines a standard for common metadata interchange. And what that means is that, any of these metadata systems that adopt this standard can freely share and exchange metadata across that landscape, so that wherever your data is, whichever systems it's stored in, wherever that metadata is harvested, it can play part of that network and share that metadata across those systems. >> I'd like to get your thoughts on something, Julie. You've been on the analyst side, you're now at IBM. Jay, if you can weigh in on this too, that'd be great. We, here, we see all the trends and go to all the events and one of the things that's popping up that's clear within the IBM ecosystem because you guys have a lot of business customers, is that a new kind of business app developer's coming in. And we've seen data science highlight the citizen data scientist, so if data is code, part of the application, and all the ladder stuff kind of falls into place, that means we're going to see new kinds of applications. So, how are you guys looking at, this is kind of a, not like the cloud-native, hardcore DevOps developer. It's the person that says, "Hey, I can innovate "a business model." I see a business model innovation that's not so much about building technology, it's about using insight and a unique... Formula or algorithm, to tweak something. That's not a lot of programming involved. 'Cause with Cloud and Cloud Private, all these back end systems, that's an ecosystem partner opportunity for you guys, but it's not your classic ISV. So, there's a new breed of business apps that we see coming, your thoughts on this? >> Yeah, it's almost like taking business process optimization as a discipline, and turning it into micro-applications. You want to be able to leverage data that's available and accessible, be able to insert that particular Artificial Intelligence machine learning algorithm to optimize that business process, and then get out of the way. Because if you try to reinvent your entire business process, culture typically gets in the way of some of these things. >> I thought, as an application value, 'cause there's value creation here, right? >> Absolutely. >> You were talking about, so, is this a new kind of genre of developer, or-- >> It really is, I mean... If you take the citizen data scientist, an example that you mentioned earlier. It's really about lowering the entry point to that technology. How can you allow individuals with lower levels of skills to actually get in and be productive and create something valuable? It shouldn't be just a practice that's held away for the hardcore developer anymore. It's about lowering the entry point with the set of tools. One of the things we have in Watson Studio, for example, our data science platform, is just that. It's about providing wizards and walkthroughs to allow people to develop productive use models very easily, without needing hardcore coding skills. >> Yeah, I also think, though, that, in order for these value-added applications to be built, the data has to be business-ready. That's how you accelerate these application development life cycles. That's how you get the new class of application developers productive, is making sure that they start with a business-ready foundation. >> So, how are you guys going to go after this new market? What's the marketing strategy? Again, this is like, forward-pioneering kind of things happening. What's the strategy, how are you going to enable this, what's the plan? >> Well, there's two parts of it. One is, when Jay was mentioning the Open Metadata Repository Services, our key strategy is embedding Catalog everywhere and anywhere we can. We believe that having that open metadata exchange allows us to open up access to metadata across these applications. So, really, that's first and foremost, is making sure that we can catalog and inventory data assets that might not necessarily be in the IBM Cloud, or in IBM products. That's really the first step. >> Absolutely. The second step, I would say, is really taking all of our capabilities, making them, from the ground up, microservices-enabled, delivering them through Docker containers and making sure that they can port across whatever cloud deployment model our customers want to be able to execute on. And being able to optimize the runtime engines, whether it's data integration, data movement, data virtualization, based on data gravity, that you had mentioned-- >> So, something like a whole new developer program opportunity to bring to the market. >> Absolutely. I mean, there is, I think there is a huge opportunity for, from an education perspective, to help our customers build these applications. But it starts with understanding the data assets, understanding what they can do with it, and using self-service-type tools that Jay was referring to. >> And all of that underpinned with the trust. If you don't trust your data, the data scientist is not going to know whether or not they're using the right thing. >> So, the ladder's great. Great way for people to figure out where they are, it's like looking in the mirror, on the organization. How early is this? What inning are we in? How do you guys see the progression? How far along are we? Obviously, you have some data, examples, some people are doing it end-to-end. What's the maturity look like? What's the uptake? >> Go ahead, Jay. >> So, I think we're at the beginning of a crest of a wave. As I say, there's been a lot of discussion so far, even if you compare this year's conference to last year's. A lot of the discussion last year was, "What's possible with AI?" This year's conference is much more about, "What are we doing with AI?" And I think we're now getting to the point where people can actually start to be productive and really start to change their business through that. >> Yeah and, just to add to that, I mean, the ladder to AI was introduced last year, and it has gained so much adoption in the marketplace and our customers, they're actually organizing their business that way. So, the Collect divisions are the database teams, are now expanding to Hadoop and Cloudera, and Hortonworks and Mongo. They're organizing their data governance teams around the Organize pillar, where they're doing things like data integration, data replication. So, I feel like the maturity of this ladder to AI is really enabling our customers to achieve it much faster than-- >> I was talking to Dave Vellante about this, and we're seeing that, you know, we've been covering IBM since, it's the 10th year of theCUBE, all ten years. It's been, watching the progression. The past couple of years has been setting the table, everyone seems to be pumping, it makes sense, everything's hanging together, it's in one group. Data's not one, "This group, that group," it's all, Data, AI, all Analytics, all Watson. Smart, and the ladder just allows you to understand where a customer is, and then-- >> Well, and also, we mentioned the emphasis on open source. It allows our customers to take an inventory of, what do they have, internally, with IBM assets, externally, open source, so that they can actually start to architect their information architecture, using the same kind of analogy. >> And an opportunity for developers too, great. Julie, thanks for coming on. Jay, appreciate it. >> Thank you so much for the opportunity, happy Valentine's Day! Happy Valentine's Day, we're theCUBE. I'm John Furrier, Stu Miniman here, live in San Francisco at the Moscone Center, and the whole street's shut down, Howard Street. Huge event, 30,000 people, we'll be back with more Day Four coverage after this short break.

(upbeat music) >> Live from Toronto, Canada, it's theCUBE, covering Blockchain Futurist Conference 2018. Brought to you by theCUBE. >> Hey, welcome back everyone, here's theCUBE live here in Toronto, Canada in Ontario for Untraceable presents Blockchain Futurist Conference. I'm John Furrier here with Al Burgio, Julie Lyle for the wrap up of the show. Special guests, industry legend Al, serial entrepreneur, Julie, CMO, Barnes and Noble. >> (laughs) >> Great career you've had and you're here new to, first time, we're going to have these big events. At the wrap up we try to get a handle on it and I think the big story here, for me at least, was, during this week, you got a futurist conference, while the price of crypto was plummeting to an all-time low for the year. Yet everyone's upbeat, 'cause they're talking about the future, not about prices. This has been a big part of what we see, build out durable companies, real entrepreneurial activity. Sure, they want to make profit. People scrounging a little bit here and there but most of the time upbeat. >> It's hard to judge things or understand things from afar, John, and people tend to look at prices all day long but that doesn't necessarily give you an indication of what's going on with blockchain technology with some of the organizations out there. The team at Untraceable by far a leader, not just in Canada but internationally with people that are able to try out the entrepreneurs and what have you and it's events like this with just a couple days you get yourself brought up to speed and keep your finger on the pulse. >> Big names. >> Yeah huge names. >> And a futurist event, you got to have some players, some whales on the money side, check, got whose actually inventing the future, entrepreneurial hustle, pitch competitions happening, so all this is blending together. Julie, your perspective, first time seeing a crypto culture community, what's your observation? >> Well I would echo what Al has said about the event itself, it was really well organized and what I was impressed with, surprised actually, but impressed with was the combination of both the technologists as well as the investors and those that are trying to understand how to build these commercial communities and commercial applications out. For a marketer like myself, it's difficult enough to see around corners, but to understand this technology and to have people here who are really trying to target it at solving a specific real-world business problem, it seems like a natural extension of the march on towards bigger and greater, more powerful communities. >> And the technology is interesting, because in previous jobs you've had, you've innovated with data, real-time user data, user experience. Now the shift of token economics potentially could have a huge slingshot advantage to create new opportunities, instrumentation, targeted experiences. Seeing that big time here but the plumbing's not yet in place. It's like the roads aren't paved out. When is blockchain going to be good? >> Yeah, so everyone, there's a clear sentiment: blockchain's the future, the visions are amazing. Ironically, the name of the conference is the Blockchain Futurist Conference and so you have some visions of this that are maybe five to 10 years out, but many of what others are working on, it's the here and now, right? >> Yeah. >> You have opportunities that can demonstrate product market fit today. Others maybe within the next 24 months and they're working hard to do that, fostering their communities of early adopters, businesses perhaps, consumers. In the market in general there's this concern, when's the use going to happen. Quite frankly, we're seeing early stage projects, companies going to market extremely quick. Normally this is the stuff that private companies do. You don't hear the successes and failures; most fail. >> Irrational exuberance certainly happening, going on, but that's ending, you're starting to see that with some of the bubble popping a little bit. It's not so much a mega pop, it's more of a big air coming out of it. But I want to ask both you guys, as senior industry players, because I see couple things happening that are eye level: Token economics is driving a new business model innovation. Blockchain is infrastructure, making things go immutable, having advantages of decentralized infrastructure. And the middle between the two is interoperability. These are the core themes. How do we get all those working together and what would be the benefits of all those working together? Interoperability is a big theme of this event. >> Yeah, it starts with obviously having a forum where you can collaborate with like-minded individuals and you're hearing a lot of these conversations happening and getting a sense of what people are working on as well. It's a new emerging technology. In terms of interoperability, I tend to look at integration as perhaps more important than a focus around interoperability, looking at pre-existing systems in the market and really identifying ways where they can slowly, gradually use aspects of or features of blockchain to really start this shift and this movement and this evolution towards web 3.0. >> Julie, your observations about business model innovation, opportunities that marketers and senior people should be thinking about, mindset-wise? >> Loyalty, obviously, would be a great application, but I think there's far more sophisticated business models around actually, again, the communities, the power of networks, right, and artificial intelligence, blockchain and just what the internet and technology is doing to drive those communities and to empower those consumers. That's where this is headed. It seems to me like a very natural extension. I would also say though, that there's a lot of work to be done in corporate America, private or public businesses. There's a lot of infrastructure to build that interoperability and to make it a seamless experience that will either drive value and adoption or won't, and we've seen that with other technologies fail as well. >> We've seen the same classic adopts, cloud computing, same thing >> Absolutely. >> Amazon, no one's ever going to use it. Oh my God, let's make it consumable and easy. Boom, usage goes up. >> Absolutely. >> Same kind of thing going on here. >> Yeah, user interface is evolving for all things blockchain. >> Alright, guys, thanks so much for coming on. Final predictions, you want to dare make a prediction, Al? >> Before a prediction, one of the things I'd really like to highlight for this event really was having the opportunity to share the stage with someone like Larry King. >> Take a minute to explain what happened. Larry King, the legend-- >> Legend. >> Was here, explain what happened. >> The CNN Larry King. We had fellow legends on the stage and I was humbled to be in their presence. Larry King really was here. He had the opportunity to interview some of the brightest minds in blockchain and in a lot of ways help bring legitimacy to this event, let along the space. Conversations that we'd hear in the hallways of people having conversations with people that they know and sharing with them that they were attending this event and oh, is it blockchain, is it bitcoin, you're going to one of those conferences and then mentioning that one of the headliners was Larry King, is all of a sudden-- >> What was he like, what was your impression of him? Certainly getting up there but-- >> I would say it's exactly the Larry King we know. His questions were phenomenal, really engaging and he knew how to direct those questions. Each question he had for the right fellow attendee on stage. It was awesome. >> Awesome. Well, congratulations, a great job. That's a wrap here, live in Toronto, Canada in Ontario with the Futurist Conference CUBE coverage. Special guests, Al Burgio, Julie here at theCUBE. Thanks for watching, see you next time. (upbeat music)

>> Welcome back everybody, Jeff Frick here with theCUBE. It's International Women's Day 2018. There's a ton of events happening all over the world. Check the social media stream, you'll be amazed. But we're excited to be here, downtown San Francisco, at the Accenture event. It's called Getting to Equal, 400 people, it's a packed house here at the Hotel Nikko, and we're really excited to have the authors of some really important research here as our next guests. This is Julie Sweet, the CEO of North America for Accenture. Good to see you, Julie. >> Great, thanks for having me today. >> And Ellyn Shook, the Chief Leadership and HR Officer at Accenture. Great to see you. >> Thank you, Jeff. >> All right. So Ellen, I want to start with you just cause I noticed your title, and I wrote it down, I've never seen, we do hundreds of events, thousands of interviews, I've never seen Chief Leadership and HR. Where did that title come from, and why is "Leadership" ahead of "HR"? That's a pretty significant statement. >> It is, it is, and Accenture's a talent-led business, and part of being a talent-led business is growing our people to grow our business, so leadership and leadership development is essential to our business. It's a core competency of ours, and that's why my title is Chief Leadership & Human Resources Officer. >> And Leadership before HR, meaning you really need people to get out in front. >> Yes. >> It's not about compliance, >> Yes, leaders at all levels. >> and this and that, leaders of all levels. >> Correct, correct. >> Okay, so let's talk about the research. >> Sure. >> It says, "When she rises, we all rise." I think it's pretty common, and everybody knows hopefully by this point, that diversity of opinion, diversity of teams, leads to better business outcomes. So what specifically is this piece of research, and give us a little background. >> Sure, the research, I think, is groundbreaking because never have I seen a piece of research that looks at the cultural aspects of an organization and really helps to articulate very transparently, what are the biggest accelerators in a culture for equality? And that's what the research is about. >> And you've identified, and is this an ongoing research, is this the first time it's been published, is it kind of an annual thing? >> Every year we publish a piece of research about gender equality, and this year we put a different lens on it to really look at equality for all. >> So you've identified 40 kind of key areas, but of those 40, really 14 are the big hitters. Is that accurate? >> That's correct. >> So what are some of those 14? >> Well, I would put them, we've put them in three categories. The first is bold leadership, so think about companies like Accenture who set targets and have CEOs who are very clear about their priorities. The second is comprehensive action, so think about policies and practices that are really effective. And then finally third, which I think is often under focused on, which is an empowering environment. What does it feel like to be at work every day? Do they ask you to dress a certain way? Is there flexible time for all? And it's the combination of these 14 factors that really makes a difference about creating a culture of equality where men and women advance. And what was really impressive is we saw that, in companies with these factors, women were five times more likely to advance to director or senior manager, and men were two times more likely. And so it really is about, when she rises, all rise, and that is probably one of the most exciting things about the research. >> It's really interesting, we just had Lisa on from The Modist, and you know, I would never have thought of clothing and dress as such a significant factor, but you've got that identified in that third bucket that you mentioned. And in fact, it's the number one attribute. So what are some of the other surprises that kind of came out of the research? >> Well, I think one of the surprises was that companies that, as part of comprehensive action, that implemented maternity leave only, it actually had a negative effect on women's advancement. But where companies implemented parental leave, so it was for men and women, it eliminated that negative bias. And it really goes to the importance that these policies, and actions, and the focus need to be about women and men. And when you start putting women too much in a category, like flex time is a mommy track, as opposed to flex time being something that men and women commonly do, it really changes how it feels to, does it feel inclusive every day at work? >> Right. >> Yeah, so companies really need to, I think what the research showed very strongly is that companies need to look at programs, policies, practices, and an environment that levels the playing field rather than isolating any particular gender or other form of diversity. >> But it's interesting, kind of law of unintended consequences, I think that panel that you were on earlier, one of the gentlemen said, since the not me, there's been reports of, >> Me too. >> for me too, excuse me, a lot of hashtags today. That there's been people doing, men scared of mentoring maybe that they weren't before. I don't know how true that is, but no it is kind of interesting to think, are there some kind of counter balances, as you said, if there's just maternity and not parental leave that need to be thought about? That probably people aren't thinking it through that far. >> Well and I think, one of the things as we saw in the research is that it's not about also one action, and so the way that companies really create a culture of equality is it's a combination of these factors. And you said something when we first started that I think is really important, and that was, you said, well it's really commonly known that diversity is important. And I think that people do need to understand that, we are optimistic about where we are today because, as a company, we're constantly in the c-suite. We serve in the U.S., 3/4 of the fortune 500, and as much as we're talking as a leader in digital disruption and artificial intelligence, the conversation quickly turns to people, to talent, to diversity, and so there's a real business lens that's on this, and that's the context in which we're operating. >> Right, and we can go to Grace Hooper, we do a ton of women's events as well as large conventions. And most people, I think, hopefully have figured it out, that it's not just about doing the right thing, it's about actually having better business outcomes. You get better outcomes with diversity of opinions, diversity of teams, you think about things that you just wouldn't think about. You don't have that same experience, everybody has a bias from where they come from, so you want to get some other people and have different points of view, different lenses to look at things. So it is really important. But why do you think things feel like they're changing now? What's important about, March 8th, 2018, versus say a year ago when you started doing some of this research? Is it the tipping point that it feels like, or? >> I think there's a couple of factors that are coming together right now. First of all, we're living in the digital age, and the digital age is all about innovation and innovation fast. And as you just said, you cannot innovate without diversity. Diversity is a form of, you're able to tap into creativity, and it's a source of competitive advantages for organizations in this age. But also what's happening in culture around the world, the me too movement as well as other things that are occurring for women around the world, and it's a moment in time where a movement can really start to happen. And I think, companies who look at culture as an accelerator of change are going to be the winners. >> Right, so what impacted bold leadership? We had from the Golden State Warriors on earlier and I think there's, what's great about sports teams is we all get to see them do their business. And we get to see the scoresheet at the end of the day, we don't necessarily get to see that in other companies. But really a fantastic example of new leadership coming in, made bold sweeping changes, probably a little bit of luck, which most success stories have, but you know significant top-down culture change. So how do you see cultures changing with bold leadership and old-line companies? Can the old guard flip? Do they need to bring in new blood? How are people executing bold leadership? >> Well first of all, I do think that it's not about old-line, it's not about young, it's really about leadership. And so it is very dependent on who is the CEO and what kind of a board we have, and so, we don't, both of us don't subscribe to the idea that you have to be born digital to be have a great culture >> To be digital. >> Yeah to be digital. And I would say that, one of the key things we saw in the study was around transparency of goals. And we talk a lot at Accenture about transparency creates trust. And so when you think about, how do you change a culture? Bold leadership is in part to find in the research by the willingness to set public goals, and to be transparent and that creates the trust. The trust of your employees, and the trust of the people you want to attract. And what I often will say that is, when we put out our statistics in the U.S, we're the first professional services firm, it wasn't that we had phenomenal statistics, but the fact that we were willing to put them out created trust that we were trying to change. And it helped people want to be a part of that change. >> Right. I mean you know that, you guys are in this business, if you can't measure it, you can't improve it. It's interesting, the Anita Borg organization puts out a self-assessment, we do their show, and Grace Hopper, to have companies. Again, not necessarily that they're going to score high but at least they recognize the problem, they're trying to measure it, they're trying to set a base line and make moves. We've heard that from Brian at Intel, Intel's making moves. And you guys have made a very definitive statement, write a line in the sand, at 2025, you're going to hit 50%. I believe that's the goal. >> Correct. And not only do we say that we're going to do it but we're doing something about it. And a lot of companies will say they want to achieve gender equality, but it's actually the actions that you take every single day. And then, of course, reporting on your progress, whether it's what you wanted to see or not, just the full transparency around the scorecard is important. >> Yeah, it's so critically important cause again, if you can't measure it, you can't change it. So great event here, as you look forward into 2018, I still can't believe we're a quarter of the way in to the year, it shocks me. (laughs) What are some of the priorities for 2018, if we sit down here again a year from now, where will you have moved on that measure, what are some of the things that are your top priorities around this initiative this year? >> Well I know for me, we certainly are trying to make sure that we continue to make progress, but I also think there's a growing conversation about the intersectionality of diversity, and so, it's women in color, it's race and the workforce, and so. We're a global company, but certainly in the U.S, which is part of the business I lead, we are not only focusing on gender, but the intersectionality of diversity and on race. >> Yeah and I think just broadening the conversation from gender diversity to true equality for all is really the big push for us here at Accenture now. And I think it's essential that no part of our organization or no individual gets left behind. And that's what we're really focused on. >> Well that's great, and so I want to thank you for having us, and wish you well in 2018, and really a fantastic event and super, super initiative. >> Come back in 2019 and we'll show you our progress. >> Alright. >> Exactly. >> She's Julie, she's Ellyn, and I'm Jeff, you're watching theCUBE from International Women's Day at the Accenture event in downtown San Francisco. Thanks for watching.

>> Narrator: Live from Las Vegas, it's theCUBE, covering AWS re:Invent 2017, presented by AWS, Intel, and our ecosystem of partners. >> Hello everyone, welcome to a special CUBE presentation here, live in Las Vegas for Amazon Web Service's AWS re:Invent 2017. This is theCUBE's fifth year here. We've been watching the progression. I'm John Furrier with Justin here as my co-host. Our two next guests are Bob Rogers, the chief data scientist at Intel, and Julie Cardoa, who's the CEO of Thorn. Great guests, showing some AI for good. Intel, obviously, good citizen and great technology partner. Welcome to theCUBE. >> Thank you, thanks for having us! >> So, I saw your talk you gave at the Public Sector Breakfast this morning here at re:Invent. Packed house, fire marshal was kicking people out. Really inspirational story. Intel, we've talked at South by Southwest. You guys are really doing a lot of AI for good. That's the theme here. You guys are doing incredible work. >> Julie: Thank you. >> Tell your story real quick. >> Yeah, so Thorn is a nonprofit, we started about five years ago, and we are just specifically dedicated to build new technologies to defend children form sexual abuse. We were seeing that, as, you know, new technologies emerge, there's new innovation out there, how child sexual abuse was presenting itself was changing dramatically. So, everything from child sex trafficking online, to the spread of child sexual abuse material, livestreaming abuse, and there wasn't a concentrated effort to put the best and brightest minds and technology together to be a part of the solution, and so that's what we do. We build products to stop child abuse. >> John: So you're a nonprofit? >> Julie: Yep! >> And you're in that public sector, but you guys have made a great progress. What's the story behind it? How did you get to do so effective work in such a short period of time as a nonprofit? >> Well, I think there's a couple things to that. One is, well, we learned a lot really quickly, so what we're doing today is not what we thought we would do five years ago. We thought we were gonna talk to big companies, and push them to do more, and then we realized that we actually needed to be a hub. We needed to build our own engineering teams, we needed to build product, and then bring in these companies to help us, and to add to that, but there had to be some there there, and so we actually have evolved. We're a nonprofit, but we are a product company. We have two products used in 23 countries around the world, stopping abuse every day. And I think the other thing we learned is that we really have to break down silos. So, we didn't, in a lot of our development, we didn't go the normal route of saying, okay, well this is a law enforcement job, so we're gonna go bid for a big government RFE. We just went and built a tool and gave it to a bunch of police officers and they said, "Wow, this works really well, "we're gonna keep using it." And it kinda spread like wildfire. >> And it's making a difference. It's really been a great inspirational story. Check out Thorn, amazing work, real use case, in my mind, a testimonial for how fast you can accelerate. Congratulations. Bob, I wanna get your take on this because it's a data problem that, actually, the technology's applying to a problem that people have been trying to crack the code on for a long time. >> Yeah, well, it's interesting, 'cause the context is that we're really in this era of AI explosion, and AI is really computer systems that can do things that only humans could do 10 years ago. That's kind of my basic way of thinking about it, so the problem of being able to recognize when you're looking at two images of the same child, which is the piece that we solved for Thorn, actually, you know, is a great example of using the current AI capabilities. You start with the problem of, if I show an algorithm two different images of the same child, can it recognize that they're the same? And you basically customize your training to create a very specific capability. Not a basic image recognition or facial recognition, but a very specific capability that's been trained with specific examples. I was gonna say something about what Julie was describing about their model. Their model to create that there there has been incredible because it allows them to really focus our energy into the right problems. We have lots of technology, we have lots of different ways of doing AI and machine learning, but when we get a focus on this is the data, this is the exact problem we need to solve, and this is the way it needs to work for law enforcement, for National Center for Missing and Exploited Children. It has really just turned the knob up to 11, so to speak. >> I mean, this is an example where, I mean, we always talk about how tech transformation can make things go faster. It's such an obvious problem. I mean, it's almost everyone kinda looks away because it's too hard. So, I wanna ask you, how do people make this happen for other areas for good? So, for instance, you know, what was the bottlenecks before? What solved the problem, because, I mean, you could really make a difference here. You guys are. >> Well, I think there's a couple things. I think you hit on one, which is this is a problem people turn away from. It's really hard to look at. And the other thing is is there's not a lot of money to be made in using advanced technology to find missing and exploited children, right? So, it did require the development of a nonprofit that said, "We're gonna do this, "and we're gonna fundraise to get it done." But it also required us to look at it from a technology angle, right? I think a lot of times people look at social issues from the impact angle, which we do, but we said, "What if we looked at it "from a different perspective? "How can technology disrupt in this area?" And then we made that the core of what we do, and we partnered with all the other amazing organizations that are doing the other work. And I think, then, what Bob said was that we created a hub where other experts could plug into, and I think, in any other issue area that you're working on, you can't just talk about it and convene people. You actually have to build, and when you build, you create a platform that others can add to, and I think that is one of the core reasons why we have seen so much progress, is we started out convening and really realized that wasn't gonna last very long, and then we built, and once we started building, we scaled. >> So, you got in the market quickly with something. >> Yeah. >> So, one of the issues with any sort of criminal enterprise is it tends to end up in a bit of an arms race, so you've built this great technology but then you've gotta keep one step ahead of the bad guys. So, how are you actually doing that? How are you continuing to invest in this and develop it to make sure that you're always one step ahead? >> So, I can address that on a couple of levels. One is, you know, working with Thorn, and I lead a program at Intel called the Safer Children Program, where we work with Thorn and also the National Center for Missing and Exploited Children. Those conversations bring in all of the tech giants, and there's a little bit of sibling rivalry. We're all trying to throw in our best tech. So, I think we all wanna do as well as we can for these partnerships. The other thing is, just in very tactical terms, working with Thorn, we've actually, Thorn and with Microsoft, we've created a capability to crowdsource more data to help improve the accuracy of these deep learning algorithms. So, by getting critical mass around this problem, we've actually now created enough visibility that we're getting more and more data. And as you said earlier, it's a data problem, so if you have enough data, you can actually create the models with the accuracy and the capability that you need. So, it starts to feed on itself. >> Julie talked about the business logic, how she attacked that. That's really, 'cause I think one thing notable, good use case, but from a tech perspective, how does the cloud fit in with Intel specifically? Because it really, the cloud is an enabler too. >> Bob: Yeah, absolutely. >> How's that all working with Intel? And you go on about whole new territory you guys are forging in here, it's awesome, but the cloud. >> Right, so, for us, the cloud is an incredible way for us to make our compute capability available to anyone who needs to do computing, especially in this data-driven algorithm era where more and more machine learning, more and more AI, more and more data-driven problems are coming to the fore, doing that work on the cloud and being able to scale your work according to how much data is coming in at any time, it makes the cloud a really natural place for us. And of course, Intel's hardware is a core component of pretty much all the cloud that you could connect to. >> And the compute that you guys provide, and Amazon adds to it, their cloud is impressive. Now, I'd like to know what you guys are gonna be talking about in your session. You have a session here at re:Invent. What's the title of the session, what's the agenda, is it the same stuff here, what's gonna be talked about? >> So, we're talking about life-changing AI applications, and in specific we're gonna talk about, at the end Julie will talk about what Thorn has done with the child-finder and the AI that we and Microsoft built for them. We'll also, I'll start out by talking about Intel's role broadly in the computing and AI space. Intel really looks to take all of its different hardware, and networking, and memory assets, and make it possible for anybody to do the kinds of artificial intelligence or machine learning they need to do. And then in the middle, there's a really cool deployment on AWS sandwich that (something) will talk about how they've taken the models and really dialed them up in terms of how fast you can go through this data, so that we can go through millions and millions of images in our searches, and come back with results really, really fast. So, it's a great sort of three piece story about the conception of AI, the deployment at scale and with high performance, and then how Thorn is really taking that and creating a human impact around it. >> So, Bob, I asked you the Intel question because no one calls up Intel and says, "Hey, give me some AI for good." I mean, I wish that would be the case. >> Well, they do now. >> If they do, well, share your strategy, because cloud makes sense. I could see how you could provision easily, get in there, really empowering people to do stuff that's passionable and relevant. But how do you guys play in all of this? 'Cause I know you supply stuff to the cloud guys. Is this a formal program you're doing at Intel? Is this a one-off? >> Yeah, so Safer Children is a formal program. It started with two other folks, Lisa Davis and Lisa Theinai, going to the VP of the entire data center group and saying, "There is an opportunity to make a big impact "with Intel technology, and we'd like to do this." And it started literally because Intel does actually want to do good work for humankind, and frankly, the fact that these people are using our technology and other technology to hurt children, it steams our dumplings, frankly. So, it started with that. >> You've been a team player with Amazon and everyone else. >> Exactly, so then, once we've been able to show that we can actually create technology and provide infrastructure to solve these problems, it starts to become a self-fulfilling prophecy where people are saying, "Hey, we've got this "interesting adjacent problem that "this kind of technology could solve. "Is there an opportunity to work together and solve that?" And that fits into our bigger, you know, people ask me all the time, "Why does Intel have a chief data scientist?" We're a hardware company, right? The answer is-- >> That processes a lot of data! >> Yes, that processes a lot of data. Literally, we need to help people know how to get value from their data. So, if people are successful with their analytics and their AI, guess what, they're gonna invest in their infrastructure, and it sort of lifts Intel's boat across the board. >> You guys have always been a great citizen, and great technology provider, and hats off to Intel. Julie, tell a story about an example people can get a feel for some of the impact, because I saw you on stage this morning with Theresa Carlson, and we've been tracking her efforts in the public sector have been amazing, and Intel's been part of that too, congratulations. But you were kind of emotional, and you got a lot of applause. What's some of the impact? Tell a story of how important this really is, and your work at Thorn. >> Yeah, well, I mean, one of the areas we work in is trying to identify children who are being sold online in the US. A lot of people, first of all, think that's happening somewhere else. No, that's here in this country. A lot of these kids are coming out of foster care, or are runaways, and they get convinced by a pimp or a trafficker to be sold into prostitution, basically. So, we have 150,000 escort ads posted every single day in this country, and somewhere in there are children, and it's really difficult to look through that with your eye, and determine what's a child. So, we built a tool called Spotlight that basically reads and analyzes every ad as it comes in, and we layer on smart algorithms to say to an officer, "Hey, this is an ad you need to pay attention to. "It looks like this could be a child." And we've had over 6,000 children identified over the last year. >> John: That's amazing. >> You know, it happens in a situation where, you know, you have online it says, you know, this girl's 18, and it's actually a 15-year-old girl who met a man who said he was 17, he was actually 30, had already been convicted of sex trafficking, and within 48 hours of meeting this girl, he had her up online for sale. So, that sounds like a unique incident. It is not unique, it happens every single day in almost every city and town across this country. And the work we're doing is to find those kids faster, and stop that trauma. >> Well, I just wanna say congratulations. That's great work. We had a CUBE alumni, founder of CloudAir, Jeff Hammerbacher, good friend of theCUBE. He had a famous quote that he said on theCUBE, then said on the Charlie Rose Show, "The best minds of our generations "are thinking about how to make people click ads. "That sucks." This is an example where you can really put the best minds on some of the real important things. >> Yeah, we love Jeff. I read that quote all the time. >> It's really a most important quote. Well, thanks so much. Congratulations, great inspiration, great story. Bob, thanks for coming on, appreciate it. CUBE live coverage here at AWS re:Invent 2017, kicking off day one of three days of wall-to-wall coverage here, live in Las Vegas. We'll be right back with more after this short break.

>> Announcer: Live, from Stanford University, it's theCUBE, covering the Women in Data Science Conference 2017. >> Hi, I'm Lisa Martin, welcome back to theCUBE. We are live at Stanford University at the second annual Women in Data Science Conference, the one-day tech conference and we are joined by Julie Yoo, who is the founder and chief data scientist of Pymetrics. Julie, you were on the customer panel today. So welcome to theCUBE. >> Thank you. >> It's great to have you, it's such an interesting background. >> Julie: Thank you. >> Neuroscience meets engineering, or engineering meets neuroscience. I'd love for us to understand a little bit more about those two, how they're combined, and also, about Pymetrics. But give us a little bit of a background, as a woman in the sciences, how you got to where you are now. >> As you mentioned, my background's in computer engineering and I went into PhD program in electrical and computer engineering 'cause I wanted to study artificial intelligence. I was fascinated by the notion of artificial intelligence. So my research topic started in automatic speech recognition systems, building computers to decode and decipher human speech. After a couple of years, I got frustrated with just the engineering approach or statistical methods-based approach to improving the existing speech recognition systems that are out there, 'cause I thought to myself, We're trying to make computers understand human speech and mimic human function when we don't really understand how our brain works and I don't really know exactly what happens when you listen to you speak, when I listen to you speak and when you listen to I speak, what is going on? We didn't really have a good sense, so I wanted to study neuroscience. So I quit engineering and I went into PhD program in neuroscience and there, I started doing a lot of neuroimaging study, just looking at human cognition and just figuring out what is going on when people perceive and process these signals that are out there. >> And was your idea to eventually marry the two? >> I didn't really think about it that way, but it just sort of happened, as in like, my background in engineering sort of homed me into doing some of the projects that I did when I was doing my PhD and my post-doc. And while I was doing all that, I just evolved to be a data scientist without, really, me realizing I was doing everything that a typical data scientist would do. And this was even before 2008. The job title of data scientist wasn't even around then, so it sort of happened because of where I came from and because what I was interested in and as I was doing that, it just ended up being a good marriage. >> And there it was. Talk to us, tell people what Pymetrics is and what the genesis of this company was. >> Pymetrics is a platform that uses neuroscience-based games and data science to promote predictive and bias-free hiring. How we became a product was because I was going through post-doc and my co-founder was also going through business school and we were both going through the phase of, Okay, we don't want to stay in academia. What do we want to do with our lives? And at the time, we realized a lot of the career-advising tools that are out there were not scientific and they were not data-driven and we felt that there is a clear need for a tool that can actually use all these data that are out there to help people figure out what they should be doing with their lives. So we thought we were uniquely positioned to use our background in engineering and neuroscience and build a product that could actually solve these challenging problem and that's how we started Pymetrics. >> That's fantastic. You started about three years ago in 2013. So, really getting rid of some of the biases, share with us what some of the biases are. Is it test scores, SATs, MCATs, GPAs? >> There are many, many different kinds of biases in hiring process right now, I think. There is a preconception of what an engineer should look like and I think that plays a lot. And when you do going to an interview, how you look and how you dress, it adds to the bias. There is ethnic bias, there's gender bias, and there is bias based on test scores and what school you went to. So we want to remove ourselves from that and really get down to what kind of person you are and are you really... I guess, have the right set of skills to succeed in certain job functions. We do that by measuring, instead of taking your subjective answers from questionnaires, we do that by objectively measuring your behavior and these games are based on neuroscience research so we know that they actually measure things that we want them to measure, for instance, your ability to pay attention, your risk appetite, and all those things that we think matters as to what makes you good at certain things and not so good at some other things. So we use these objective data and data science and predictive modeling to come up with predictions as to how good you will be in certain career versus some other career. >> Really, an incredible need for that. It's game-based, so it's an actual game that people will play that will help understand more of who they are as a person, their behaviors, those patterns. Tell us a little bit about the invention of the game, what was it like, who was it for? >> The games were actually sourced from neuroscience research community. We did not create these games. What we did was we actually just took them from research and medical settings and applied it through hiring. We know that these are relevant to measuring your attributes and your personality, so why not use it for hiring and career advising, because it makes sense. We're trying to measure your qualities, your soft skills and what-not, why not just use it for something that could really benefit from these sort of data. What we did do is we actually made these games, they're not really called games in research community, but we made it shorter and we made it more applicable to the things that we are trying to use if for. >> You took feedback from some of your earlier adopters who were saying maybe it's taking me too long, maybe some of the recruiters might say, they gave you some very viable feedback that have helped you optimize the products. >> Right, as a data scientist, I always think the more data, the better, but that also means that people would have to sit in front of their computers and play an hour-long battery of games and a lot of people were thinking that it might be just a tad too long and companies felt that spending 45 minutes to an hour could be a discouraging thing and people felt fatigue effect and we could see that in the results, so we ended up making it shorter. We went from 20 games to 12 games and we cut it down to 25 minutes long and I think, now, we're in the sweet spot where we do get enough data but, at the same time, we're not making it an hour long. >> Right, so this is really targeted for people coming out of university programs, whether it's bachelor's, master's, doctorate, et cetera, and also, what type of companies who are looking to hire, what's kind of your target market for that? >> I think mostly Fortune 500 companies 'cause a lot of these companies do hire in large volume, so it helps to have us go to these companies and build their models based off of their employees. And if a smaller company comes along and they only have 10 employees in the job function, then it's extremely difficult for us to build the model base off of their 10 employees, whereas if it's a larger corporation, then we can have 200 employees play and we can build the model based on their data. So generally, large corporations is our target clients. >> I'm curious, in terms of some of the data that you are seeing, that you're analyzing, are you seeing, we look at data science as a great example of the event that we're at, in report from Forbes recently that said it's the best job to apply for in 2017. We're looking at now what's going to be happening, predicted over the course of the next year, and that's a shortage in talent. Are you seeing, with some of the data that you're taking in, are you seeing things that are mapping to that, like people that are really geared towards that? Or are you seeing more companies that are looking for computer-industry, data-science type roles? Is that increasing, as well? >> I think companies are definitely looking for more data scientists and I think, also, people are figuring out that there are data science programs like graduate school programs and I think that supply of data scientists is definitely increasing, but at the same time, or more so, the demand for data scientists is increasing. And not to mention, the available data that's out there is increasing at a faster rate than anything else. Yeah, it is, I think, the best time to be a data scientist right now. >> Let me ask you one more question about looking at skills. We have such a great cross-section at this event of leaders in retail, in obviously, what you're doing in neuroscience-gaming-merging world. We've got professors here. Data science is such an interesting topic, it's obviously very horizontal. From a skill set perspective, kind of the traditional skills of being a statistician, mathematics, being a hacker, a lot of the things that we've been hearing around the show today, and really aligns with what you're doing is more on the behavioral insight side of, you have to be able to communicate what you're seeing and be able to apply it. I'd love to understand a profile of an ideal data scientist that you guys are seeing from your data. What are some of the other behavioral attributes that maybe are some of the non-teachable things that you're seeing that really come up that this would be a great career path for someone? >> Personally, I think intellectual curiosity is number one, and they would have to have strong self-motivation and discipline because you could love analyzing data and you could just be doing that for how many days, I don't know, and that's it. You could actually come up with a good story. You've got to be a good storyteller and if you have artistic flair to make the data beautiful, then even better. But it is important to go from the beginning of the project where you have a bunch of data set and actually come up with actionable results that people can use. And you're not only always going to be communicating with a data scientist, so you need to be able to present your data in a more succinct and easily-digestible way. >> That sounds like, as the chief data scientist for Pymetrics, that's what you're looking for to hire on your team. Give us a little bit, last question here, just a little bit of an overview of what your data science team looks like at Pymetrics, as you're helping to leverage this data to give people opportunities with careers. What does your team look like? >> Our team has a very diverse background. We have a few PhD's in Physics and you know, well, I have a PhD in Neuroscience and there's other data scientists with PhD's in Physics. We actually have one guy who majored in Data Science and we have another guy who majored in Bio Engineering. So it's definitely a diverse background. But the general theme is that you do need a good, quantitative foundation. So, whether it's engineering or physics, it is still helpful to have that statistical or analytical mind and if you can actually apply that, and actually love solving problems then I think data scientist is a right goal. >> So you're on the career panel at WiDS2017, is that the advice that you would give to kind of, the next generation of kids that are interested in this but aren't quite sure what industry they would want to go into? >> What industry? I think, I mean if they're even remotely interested in going into data science, I would encourage them to pursue it. I think it is one of the most fascinating fields right now and there's never going to be a shortage of needs for data scientists. So if you like it, if you think you are going to be pretty good at it, I say go for it. >> Fantastic. And you've got a great audience here. This is being live streamed in 20 cities, I think across the globe, or 75 cities, I have to get those stats right. But, there's a big opportunity here to be an influencer and we thank you for spending some time with us. Best of luck on the panel. >> Thank you. >> Thank you for watching. I'm Lisa Martin, we are live with theCUBE, at Women and Data Science 2017, #WiDS2017. Stick around, we'll be right back. (upbeat mellow music)

>>Hey everyone. Hello from Las Vegas. Lisa Martin here with you, and I'm on the show floor at Reinvent. But we have a very special program series that the Cube has been doing called Women of the Cloud. It's brought to you by aws and I'm so pleased to have an excellent panel of women leaders in technology and in cloud to talk about their tactical recommendations for you, what they see as found, where they've helped organizations be successful with cloud. Please welcome my three guests, Tara Kapor, president and Chief Revenue Officer, consulting and Digital Transformations, NTT Data. We have Rachel Mu, aws, head of North America, partner sales from aws, and Jimmy Barnes joins us as well, managing director, investment banking engineering at Goldman Sachs. It is so great to have you guys on this power panel. I love it. Thank you for joining me. >>Thank >>You. Let's start with you. Give us a little bit of, of your background at NTT Data and I, and I understand NTT has a big focus on women in technology and in stem. Talk to us a little bit about that and then we'll go around the table. >>Perfect, thank you. Thank you. So brand new role for me at Entity Data. I started three months back and it's a fascinating company. We are about 22 billion in size. We work across industries on multiple innovative use cases. So we are doing a ton of work on edge analytics in the cloud, and that's where we are here with aws. We are also doing a ton of work on the private 5G that we are rolling out and essentially building out industry-wide use cases across financial services, manufacturing, tech, et cetera. Lots of women identity. We essentially have women run cloud program today. We have a gal called Nore Hanson who is our practice leader for cloud. We have Matine who's Latifa, who's our AWS cloud leader. We have Molly Ward who leads up a solutions on the cloud. We have an amazing lady in Mona who leads up our marketing programs. So a fantastic plethora of diverse women driving amazing work identity on cloud. >>That's outstanding to hear because it's one of those things that you can't be what you can't see. Right. We all talk about that. Rachel, talk a little bit about your role and some of the focus that AWS has. I know they're big customer obsession, I'm sure obsessed with other things as well. >>Sure. So Rachel Muir, pleased to be here again. I think this will be my third time. So a big fan of the Cube. I'm fortunate enough to lead our North America partner and channel business, and I'll tell you, I've been at AWS for a little under two years, and honestly, it's been probably the best two years of my career. Just in terms of where the cloud is, where it's headed, the business outcomes that we can deliver with our customers and with our partners is absolutely remarkable. We get to, you know, make the impossible possible every day. So I'm, I'm thrilled to be here and I'm thrilled to, to be part of this inaugural Women of the Cloud panel. >>Oh, I'm prepared to have all three of you. One of the things that feedback, kind of pivoting off what, Rachel, one of the things that you said that one of our guests, some of several of our guests have said is that coming out of Adams keynote this morning, it just seems limitless what AWS can do and I love that it gives me kind of chills what they can do with cloud computing and technology, with its ecosystem of partners with its customers like Goldman Sachs. Jimmy, talk to us a little bit about you, your role at Goldman Sachs. You know, we think of Goldman Sachs is a, is a huge financial institution, but it's also a technology company. >>Yeah. I mean, since the age of 15 I've been super passionate about how we can use technology to transform business and simplify modernized business processes. And it's, I'm so thrilled that I have the opportunity to do that at Goldman Sachs as an engineer. I recently moved about two years ago into the investment banking business and it's, you know, it's best in class, one of the top companies in terms of mergers and acquisitions, IPOs, et cetera. But what surprised me is how technology enables all the businesses across the board. Right? And I get to be leading the digital platform for building out the digital platform for in the investment banking business where we're modernizing and transforming existing businesses. These are not new businesses. It's like sometimes I liken it to trying to change the train while it's moving, right? These are existing businesses, but now we get to modernize and transform on the cloud. Right. Not just efficiency for the business by efficiency for technologists as >>Well. Right, right. Sticking with you, Jimmy. I wanna understand, so you've been, you've been interested in tech since you were young. I only got into tech and accidentally as an adult. I'm curious about your career path, but talk to us about that. What are some of the recommendations that you would have for other women who might be looking at, I wanna be in technology, but I wanna work for some of the big companies and they don't think about the Goldman Sachs or some of the other companies like Walmart that are absolutely technology driven. What's your advice for those women who want to grow their career? >>I also, growing up, I was, I was interested in various things. I, I loved doing hair. I used to do my own hair and I used to do hair for other students at school and I was also interested in running an entertainment company. And I used actually go around performing and singing and dancing with a group of friends, especially at church. But what amazed me is when I landed my first job at a real estate agent and everything was being done manually on paper, I was like, wow, technology can bring transformation anywhere and everywhere. And so whilst I have a myriad of interest, there's so many ways that technology can be applied. There's so many different types of disciplines within technology. It's not, there's hands on, like I'm colder, I like to code, but they're product managers, there are business analysts, there are infrastructure specialist. They're a security specialist. And I think it's about pursuing your passion, right? Pursuing your passion and identifying which aspects of technology peak your interest. And then diving in. >>Love that. Diving in. Rachel, you're shaking your head. You definitely are in alignment with a lot of what >>Duties I am. So, you know, interesting enough, I actually started my career as a civil engineer and eventually made it into, into technology. So very similar. I saw in, you know, heavy highway construction how manual some of these processes were. And mind you, this was before the cloud. And I sat down and wrote a little computer program to automate a lot of these manual tasks. And for me it was about simplification of the customer journey and really figuring out how do you deliver value. You know, on fast forward, say 20 plus years, here I am with AWS who has got this amazing cloud platform with over 200 services. And when I think about what we do in tech, from business transformation to modernizing to helping customers think about how do they create new business models, I've really found, I've really found my sweet spot, and I'll say for anyone who wants to get into tech or even switch careers, there's just a couple words of advice that I have. And it's really two words, just start. >>Yes, >>That's it. Just start. Because sometimes later becomes never. And you know, fuel your passion, be curious, think about new things. Yes. And just >>Start, I love that. Just start, you should get t-shirts made with that. Tell me a little bit about some of your recommendations. Obviously just start is great when follow your passion. What would you say to those out there looking to plan the letter? >>So, you know, my, my story's a little bit like jus because I did not want to be in tech. You know, I wanted an easy life. I did well in school and I wanted to actually be an air hostess. And when I broke that to my father, you know, the standard Indian person, now he did, he, you know, he wanted me to go in and be an engineer. Okay? So I was actually push into computer engineering, graduated. But then really two things today, right? When I look back, really two pieces, two areas I believe, which are really important for success. One is, you know, we need to be competent. And the second is we need to be confident, right? Yes, yes. It's so much easier to be competent because a lot of us diverse women, diverse people tend to over rotate on knowing their technical skills, right? Knowing technical skills important, but you need to know how to potentially apply those to business, right? Be able to define a business roi. And I see Julie nodding because she wants people to come in and give her a business ROI for programs that you're executing at Goldman Sachs. I presume the more difficult part though is confidence. >>Absolutely. It's so hard, especially when, when we're younger, we don't know. Raise your hand because I guarantee you either half the people in the, in the room or on the zoom these days weren't listening or have the same question and are too afraid to ask because they don't have the confidence. That's right. Give me, let's pivot on confidence for a minute, Jim, and let's go back to how would you advise your younger self to find your confidence? >>That's, that's a tough one because I feel like even this older self is still finding exercise to, to be real. But I think it's about, I would say it's not praise. I think it's about praising yourself, like recognizing your accomplishments. When I think about my younger self, I think I, I like to focus more on what I didn't do or what I didn't accomplish, instead of majoring and focusing on all the accomplishments and the achievements and reminding myself of those day after day after day. And I think it's about celebrating your wins. >>I love that. Celebrating your wins. Do you agree, Rachel? >>I do. Here's the hard part, and I look around this table of amazing business leaders and I can guarantee that every single one of us sometime this year woke up and said, oh my gosh, I don't know how to do that. Oh >>Yeah. But >>What we haven't followed that by is, I don't know how to do that yet. Right. And here's the other thing I would tell my younger self is there will be days where every single one of us falls apart. There will be days when we feel like we failed at work. There will be days when you feel like you failed as a parent or you failed as a spouse. There'll be days where you have a kid in the middle of target screaming and crying while you're trying to close a big business deal and you just like, oh my gosh, is this really my life? But what I would tell my younger self is, look, the crying, the chaos, the second guessing yourself, the successes, every single one of those are milestones. And it's triumphant, it's tragic, but every single thing that we have been through is fiercely worthwhile. And it's what got us >>Here. Absolutely. Absolutely. Think of all the trials and tribulations and six and Zacks that got you to this table right now. Yep. So Terry, you brought up confidence. How would you advise the women out there won't say you're gonna know stuff. The women out there now that are watching those that are watching right there. Hi. How would you advise them to really find their, their ability to praise themselves, recognize all of the trials and the tribulations as milestones as Rachel said, and really give themselves a seat at the table, raise their hand regardless of who else is in the room? >>You know, it's a, it's a more complex question just because confidence stems from courage, right? Confidence also stems from the belief that you're going to be treated fairly right now in an organization for you to be treated fairly. You need to have, be surrounded by supporters that are going to promote your voice. And very often women don't invest enough in building that support system around them. Yeah. Right. We have mentors, and mentors are great because they come in and they advise us and they'll tell us what we need to go out and do. We really need a team of sponsors Yes. Who come in and support us in the moment in the business. Give us the informal channel because very often we are not plugged into the informal channel, right. So we don't get those special projects or assignments or even opportunities to prove that we can do the tough task. Yeah. So, you know, my, my advice would be to go out and build a network of sponsors. Yes. And if you don't have one, be a sponsor for someone else. That's right. I love that. Great way to win sponsorship is by extending it todos. >>And sometimes too, it's about, honestly, I didn't even know the difference between a mentor and a sponsor until a few years ago. And I started thinking, who are I? And then I started realizing who they were. That's right. And some of the conversations that we've had on the cube about women in technology, women of the cloud with some of the women leaders have said, build, and this is kind of like, sort of what you were saying, build your own personal board of directors. Yeah. And that, oh, it gives me chills. It's just, it's so important for, for not just women, but anybody, for everybody. But it's so important to do that. And if you, you think about LinkedIn as an example, you have a network, it's there, utilize it, figure out who your mentors are, who your sponsors are, who are gonna help you land the next thing, start building that reputation. But having that board of directors that you can kind of answer to or have some accountability towards, I think is hugely very >>Important. Yeah. >>Very important. I think, you know, just for, just for those that are listening, a really important distinction for me was mentors are people that you have that help you with, Hey, here's the situation that you were just in. They advise you on the situation. Sponsors are the people that stick up for you when you're not in the room to them. Right. Sponsors are the ones that say, Hey, I think so and so not only needs to have a seat at the table, but they need to build the table. And that's a really important delineation. Yeah. Between mentors and sponsors. And everybody's gotta have a sponsor both within their company and outside of their company. Someone that's advocating for them on their behalf when they don't even know it. Yeah. Yeah. >>I love that you said that. Build the table. It reminds me of a quote that I heard from Will I am, I know, very random. It was a podcast he did with Oprah Winfrey on ai. He's very into ai and I was doing a panel on ai, so I was doing a lot of research and he said, similar for Rachel to build the table, don't wait for a door to open. You go build a door. And I just thought, God, that is such brilliant advice. It is. It's hard to do. It is. Especially when, you know, the four of us in this room, there's a lot of women around here, but we are in an environment where we are the minority women of color are also the minority. What do you guys think where tech is in terms of de and I and really focusing on De and I as as really a very focused strategic initiative. Turner, what do you think? >>So, you know, I just, I, I spoke earlier about the women that we have at Entity Data, right? We have a fabulous team of women. And joining this team has been a moment of revelation for me coming in. I think to promote dni, we all need to start giving back, right? Yes. So today, I would love to announce that we at Entity would like to welcome all of you out there. You know, folks that have diverse ideas, you know, ISV, partners with diverse solutions, thought leaders out there who want to contribute into the ecosystem, right? Customers out there who want to work with companies that are socially responsible, right? We want to work with all of you, come back, reach out to us and be a part of the ecosystem because we can build this together, right? AWS has an amazing platform that gives us an opportunity to do things differently. Yes. Right. Entity data is building a women powered cloud team. And I want to really extend that out to everyone else to be a part this ecosystem, >>But a fantastic opportunity. You know, when we talk about diversity and inclusion and equity, it needs to be intentional for organization. It sounds very intentional at ntt. I know that that intention is definitely there at AWS as well. What are your thoughts on where tech is with respect to diversity? Even thought diversity? Because a lot of times we tend to go to our comfort zones. We do. And so we tend to start creating these circles of kind of like, you know, think tanks and they think alike people to go outside of that comfort zone. It's part of building the table, of building the, is the table and getting people from outside your comfort zone to come in and bring in diverse thought. Because can you imagine the potential of technology if we have true thought diversity in an organization? >>Right? It's, it's incredible. So one of the things that I always share with my team is we've got the opportunity to really change the outcome, right? As you know, you talked about Will I am I'm gonna talk about Bono from you too, right? One of, one of his favorite quotes is, we are the people we've been waiting for. Oh, I love that. And when you think about that, that is us. There is no one else that's gonna change the outcome and continue to deliver some of the business outcomes and the innovation that we are if we don't continue to raise our hand and we don't continue to, to inspire the next generation of leaders to do the same thing. And what I've found is when you start openly sharing what your innovation ideas are or how you're leveraging your engineering background, your stories and your successes, and, and frankly, some of your failures become the inspiration for someone you might not even know. Absolutely. And that's the, you know, that's the key. You're right. Inclusion, diversity, equity and accessibility, yes. Have to be at the forefront of every business decision. And I think too often companies think that, you know, inclusion, diversity, equity and accessibility is one thing, and business outcomes are another. And they're not. No, they are one in the same. You can't build business outcomes without also focusing on inclusion, diversity, equity, accessibility. That's the deliberate piece. >>And, and it has to be deliberate. Jimmy, I wanna ask you, we only have a couple of minutes left, but you're a woman in tech, you're a woman of color. What was that like for you? You, you were very intentional knowing when you were quite young. Yeah. What you wanted to do, but how have you navigated that? Because I can't imagine that was easy. >>It wasn't. I remember, I always tell the story and the, the two things that I really wanted to emphasize today when I thought about this panel is rep representation matters and showing up matters, right? And there's a statement, there's a flow, I don't know who it's attributed to, but be the change you want to see. And I remember walking through the doors of Goldman Sachs 15 years ago and not seeing a black female engineer leader, right? And at that point in time, I had a choice. I could be like, oh, there's no one look like, there's no one that looks like me. I don't belong here. Or I could do what I actually did and say, well, I'm gonna be that person. >>Good, >>Right? I'm going to be the chain. I'm going to show up and I am going to have a seat at the table so that other people behind me can also have a seat at the table. And I think that I've had the privilege to work for a company who has been inclusive, who has had the right support system, the right structures in place, so that I can be that person who is the first black woman tech fellow at Goldman Sachs, who is one of the first black females to be promoted up the rank as a, from analysts to managing director at the company. You know, that was not just because I determined that I belong here, but because the company ensure that I felt that I belong. >>Right. >>That's a great point. They ensure that you felt that. Yeah. You need to be able to feel that. Last question, we've only got about a minute left. 2023 is just around the corner. What comes to your mind, Jimmy will stick with you as you head into the new year. >>Sorry, can you repeat >>What comes to mind priorities for 2023 that you're excited about? >>I'm excited about the democratization of data. Yeah. I'm excited about a lot of the announcements today and I, I think there is a, a huge shift going on with this whole concept of marketplaces and data exchanges and data sharing. And I think both internally and externally, people are coming together more. Companies are coming together more to really de democratize and make data available. And data is power. But a lot of our businesses are running, running on insights, right? And we need to bring that data together and I'm really excited about the trends that's going on in cloud, in technology to actually bring the data sets together. >>Touro, what are you most excited about as we head to 2023? >>I think I'm really excited about the possibilities that entity data has right here, right now, city of Las Vegas, we've actually rolled out a smart city project. So saving citizens life, using data edge analytics, machine learning, being able to predict adverse incidents before they happen, and then being able to take remediation action, right? So that's technology actually working in real time to give us tangible results. We also sponsor the Incar races. Lots of work happening there in delivering amazing customer experience across the platform to millions of users real time. So I think I'm just excited about technology coming together, but while that's happening, I think we really need to be mindful at this time that we don't push our planet into per right. We need to be sustainable, we need to be responsible. >>Absolutely. Rachel, take us out. What are you most excited about going into 2023? >>So, you know, there are so many trends that are, that we could talk about, but I'll tell you at aws, you know, we're big. We, we impact the world. So we've gotta be really thoughtful and humble about what it is that we do. So for me, what I'm most excited about is, you know, one of our leadership principles is about, you know, with what broad responsibility brings, you know, you've got to impact sustainability and many of those other things. And for me, I think it's about waking up every day for our customers, for our partners, and for the younger generations. And being better, doing better, and making better for this planet and for, you know, the future generations to come. So >>I think your tag line just start applies to all of that. It does. It has been an absolute pleasure. And then really an honor to talk to you on the program. Thank you all for joining me, sharing your experiences, sharing what you've accomplished, your recommendations for those others who might be our same generation or older or younger. All really beautiful advice. Thank you so much for your time and your insights. We appreciate it. >>Thank you. Thank you. >>For my guests, I'm Lisa Martin. You're watching The Cube, the leader in live enterprise and emerging tech coverage. Thanks for watching.

>>The cube is back at Vemo 2022. I was happy to be live. Dave ante, Dave Nicholson and Dave Russell three Daves. Dave is the vice president of enterprise strategy at Veeam. Great to see you again, my friend. Thanks for coming >>On. Uh, it's always a pleasure. And Dave, I can remember your name. I can't remember >>Your name as well. <laugh> so wow. How many years has it been now? I mean, add on COVID is four years now. >>Yeah, well, three, three solid three. Yeah, Fallon blue. Uh, last year, Miami little secret. We're gonna go there again next year. >>Okay, so you joined Veeam >>Three. Oh, me four. Yeah, >>Yeah, yeah. Four is four, right? Okay. Wow. >>Um, time flies, man. >>Interesting. What your background, former analyst analyze your time at Veeam and the market and the changes in the customer base. What, what have you seen? What are the big takeaways? Learnings? >>Yeah. You know, what's amazing to me is we've done a lot more research now, ourselves, right? So things that we intuitively thought, things that we experienced by talking to customers, and of course our partners, we can now actually prove. So what I love is that we take the exact same product and we go down market up market. We go across geographies, we go different verticals and we can sell that same exact product to all constituencies because the differences between them are not that great. If it was the three Dave company or the 3m company, what you're looking for is reliable recovery, ease of use those things just transcend. And I think there used to be a time when we thought enterprise means something very different than mid-market than does SMB. And certainly your go to market plans are that way, but not the product plans. >>So the ransomware study, we had Jay buff on earlier, we were talking about it and we just barely scratched the surface. But how were you able to get people to converse with you in such detail? Was it, are you using phone surveys? Are you, are, are you doing web surveys? Are you doing a combination? Deep >>Dives? Yeah. So it was web based and it was anonymous on both ends, meaning no one knew VE was asking the questions. And also we made the promise that none of your data is ever gonna get out, not even to say a large petroleum company, right. Everything is completely anonymized. And we were able to screen people out very effectively, a lot of screener questions to make sure we're dealing with the right person. And then we do some data integrity checking on the back end. But it's amazing if you give people an opportunity, they're actually very willing to tell you about their experience as long as there's no sort of ramification about putting the company or themselves at risk. >>So when I was at IDC, we did a lot of surveys, tons of surveys. I'm sure you did a lot of surveys at Gartner. And we would look at vendor surveys like, eh, well, this kind of the questions are rigged or it's really self-serving. I don't sense that in your surveys, you you've, you've always, you've still got that independent analyst gene. Is that, I mean, it's gotta be, is it by design? Is it just happen that ransomware is a topic that just sort of lends itself to that. Maybe you could talk about your philosophy there. >>Yeah. Well, two part answer really, because it's definitely by design. We, we really want the information. I mean, we're using this to fuel or inform our understanding of the market, what we should build next, what we should message next. So we really want the right data. So we gotta ask the right questions. So Jason, our colleague, Julie, myself, we work really hard on trying to make sure we're not leading the witness down a certain path. We're not trying to prove our own thesis. We're trying to understand what the market really is thinking. And when it comes to ransomware, we wanna know what we don't know, meaning we found a few surprises along the way. A lot of it was confirmational, but that's okay too. As long as you can back that up, cuz then it's not just Avenger's opinion. Of course, a vendor that says that they can help you do something has data that says, they think you uni have a problem with this, but now we can actually point to it and have a more interesting kind of partnership conversation about if you are like 1000 other enterprises globally, this may be what you're seeing. >>And there are no wrong answers there. Meaning even if they say that is absolutely not what we're seeing. Great. Let's have that conversation that's specific to you. But if you're not sure where to start, we've got a whole pool of data to help guide that conversation. >>Yeah. Shout out to Julie Webb does a great job. She's a real pro and yes. And, and really makes sure that, like you say, you want the real, real answers. So what were some of the things that you were excited about or to learn about? Um, in the survey again, we, we touched just barely touched on it in 15 minutes with Jason, but what, what's your take? Well, >>Two that I'd love to point out. I mean, unfortunately Jason probably mentioned this one, you know, only 19% answered when we said, did you pay the ransom? And only 19% said, no, I didn't pay the ransom. And I was a hundred percent successful in my recovery. You know, we're in Vegas, one out of five odds. That's not good. Right? That's a go out of business spot. That's not the kind of 80 20 you want to hear. That's not exactly exactly. Now more concerning to me is 5% said no ransom was asked for. And you know, my phrase on that is that's, that's an arson event. It's not an extortion event. Right. I just came to do harm. That's really troubling. Now there's a huge percentage there that said we paid the ransom about 24% said we paid the ransom and we still couldn't restore the data. So if you add up that 24 in that five, that 29%, that was really scary to me. >>Yeah. So you had the 19%. Okay. That's scary enough. But then you had the wrecking ball, right? Ah, we're just gonna, it's like the mayhem commercial. Yes. Yeah. See ya. Right. Okay. So <laugh>, that's, that's wild. So we've heard a lot about, um, ransomware. The thing that interests me is, and we've had a big dose of ransomware as analysts in these last, you know, 12, 18 months and more. But, but, but it's really escalated. Yeah. Seems like, and by the way, you're sharing this data, which is amazing. Right. So I actually want to dig in and steal some of the, the data. I think that's cool. Right? Definitely. You gave us a URL this morning. Um, so, but you, your philosophy is to share the data. So everybody sees it, your customers, your prospects, your competitors, but your philosophy is to why, why are you sharing that data? Why don't you just keep it to yourself and do it quietly with customers? >>Yeah. You know, I think this is such a significant event. No one vendor's gonna solve it all. Realistically, we may be tied for number one in market share statistically speaking, but we have 12.5%. Right. So we're not gonna be able to do greater good if we're keeping that to ourselves. And it's really a notion of this awareness level, just having the conversation and having that more open, even if it's not us, I think is gonna be beneficial. It speaks to the value of backup and why backup is still relevant this day and age. >>I dunno if you're comfortable answering this, but I'll ask anyway, when you were a Gartner analyst, did you get asked about ransomware a lot? >>No. >>Very rarely or never. >>Almost never. Yeah. And that was four years ago. Literally. Like it >>Was a thing back then, right? I mean it wasn't of course prominent, but it was, it was, I guess it wasn't that >>20 16, 20 17, you know, it's, it's interesting because at a couple of levels you have the, um, the willingness of participants to share their stories, which is a classic example of people coming together to fight a common fo. Yeah, yeah. Right. In the best of times, that's what happens. And now you're sharing that information out. One of the reasons why some would argue we've gotten to this place is because day zero exploits have been stockpiled and they haven't been shared. So you go to, you know, you go, you go through the lineage that gets you to not pet cat as an example. Yes. And where did it come from? Hey, it was something that we knew about. Uh, but we didn't share it. Right. We waited until it happened because maybe we thought we could use it in, in some way. It's, it's an, it's an interesting philosophical question. I, I don't know. I don't know. I don't know where, if that's, uh, the third, it's the one, the third rail you don't want to touch, but basically we're, we are, I guess we're just left to sort through whatever, whatever we have to sort through in that regard. But it is interesting left to industry's own devices. It's sharing an openness. >>Yeah. You know, it's, I almost think it's like open source code. Right? I mean, the promise there is together, we can all do something better. And I think that's true with this ransomware research and the rest of the research we do too. We we've freely put it out there. I mean, you can download the link, no problem. Right. And go see the report. We're fine with that. You know, we think it actually is very beneficial. I remember a long time ago, it was actually Sam Adams that said, uh, you know, Hey, there's a lot of craft brewers out there now, you know, is, are you as a craft brewery now? Successful? Are you worried about that? No. We want every craft brewery to be successful because it creates a better awareness. Well, an availability market, it's still Boston reference. >>What did another Boston reference? Yes. Thank you, >>Boston. And what <laugh>. >>Yeah. So, you know, I, I, I feel like we've seen these milestone, you know, watershed events in, in security. I mean, stucks net sort of yeah. Informed us what's possible with nation states, even though it's highly likely that us and Israel were, were behind that, uh, the, the solar winds hack people are still worried about. Yes. Okay. What's next. Even, even something now. And so everybody's now on high alert even, I don't know how close you guys followed it, but the, the, uh, the Okta, uh, uh, breach, which was a fairly benign incident. And technically it was, was very, very limited and very narrow in scope. But CISOs that I talked to were like, we are really paranoid that there's another shoe to drop. What do we do? So the, the awareness is way, way off the charts. It begs the question. What's next. Can you, can you envision, can you stay ahead? It's so hard to stay ahead of the bad guys, but, but how are you thinking about that? What this isn't the end of it from your standpoint? >>No, it's not. And unfortunately it's because there's money to be made, right? And the barrier to entry is relatively low. It's like hiring a Hitman. You know, you don't actually have to even carry out the bad act yourself and get your own hands dirty. And so it's not gonna end, but it it's really security is everyone's responsibility. Veeam is not really a full time security company, but we play a role in that whole ecosystem. And even if you're not in the data center as an employee of a company, you have a role to play in security. You know, don't click that link, lock the door behind you, that type of thing. So how do you stay ahead of it? I think you just continually keep putting a focus on it. It's like performance. You're never gonna be done. There's always something to tune and to work on, but that can be overwhelming. So the positive I try to tell someone is to your point, Dave, look, a lot of these vulnerabilities were known for quite some time. If you were just current on your patch levels, this could have been prevented, right? You could have closed that window. So the thing that I often say is if you can't do everything and probably none of us can do something and then repeat, do it again, try to get a little bit better every period of time. Whether that's every day, every quarter, what case may be, do what you can. >>Yeah. So ransomware obviously very lucrative. So your job is to increase the denominator. So the ROI is lower, right? And that's a, that's a constant game, right? >>Absolutely. It is a crime of opportunity. It's indiscriminate. And oftentimes non-targeted now there are state sponsored events to your point, but largely it's like the fishermen casting the net out into the ocean. No idea with certainty, what's gonna come back. So I'm just gonna keep trying and trying and trying our goal is to basically you wanna be the house on the neighborhood that looks the least inviting. >>We've talked about this. I mean, any, anyone can be a, a, a ransomware as to go in the dark web, ransomware's a service. Oh, I gotta, I can put a stick into a server and a way I go and I get some Bitcoin right. For it. So, so that's, so, so organizations really have to take this seriously. I think they are. Um, well you tell me, I mean, in your discussions with, with, with customers, >>It's changed. Yeah. You know, I would say 18 months ago, there was a subset of customers out there saying vendors, crying Wolf, you know, you're trying to scare us into making a purchase decision or move off of something that we're working with. Now. I think that's almost inverted. Now what we see is people are saying, look, my boss or my boss's boss's boss, and the security team are knocking on my door asking, what are we gonna do? What's our response? You know, how prepared are we? What kind of things do we have in place? What does our backup practice do to support ransomware? The good news though, going back to the awareness side is I feel like we're evangelizing this a little less as an industry. Meaning the security team is well aware of the role that proper backup and availability can play. That was not true. A handful of years ago. >>Well, that's the other thing too, is that your study showed the closer the practitioner was to the problem. Yes. The more problems there were, that's an awareness thing. Yes. That's not a, that's not, oh, just those guys had visibility. I wanna ask you cuz you've You understand from an application view, right. There's only so much Veeam can do. Um, and then the customer has to have processes in place that go beyond just the, the backup and recovery technology. So, so from an application perspective, what are you advising customers where you leave off and they really have to take over this notion of shared responsibility is really extending beyond cloud security. >>Yeah. Uh, the model that I like is interestingly enough, what we see with Caston in the Kubernetes space. Mm-hmm <affirmative> is there, we're selling into two different constituencies, potentially. It's the infrastructure team that they're worried about disaster recovery. They're worried about backup, but it's the app dev DevOps team. Hey, we're worried about creating the application. So we're spending a lot of focus with the casting group to say, great, go after that shift, left crowd, talk to them about a data availability, disaster recovery, by the way you get data movement or migration for free with that. So migration, maybe what you're first interested in on day one. But by doing that, by having this kind of capability, you're actually protecting yourself from day two issues as well. >>Yeah. So Let's see. Um, what haven't we hit on in this study? There was so much data in there. Uh, is that URL, is that some, a private thing that you guys shared >>Or is it no. Absolutely. >>Can, can you share the >>URL? Yeah, absolutely. It's V E E so V two E period am so V with the period between the E and the a forward slash RW 22. So ransomware 22 is the research project. >>So go there, you download the zip file, you get all the graphics. Um, I I'm gonna dig into it, uh, maybe as early as this, this Friday or this weekend, like to sort of expose that, uh it's you guys obviously want this, I think you're right. It's it's it's awareness needs to go up to solve this problem. You know, I don't know if it's ever solvable, but the only approach is to collaborate. Right. So I, I dunno if you're gonna collaborate with your head-to-head competitors, but you're certainly happy to share the data I've seen Dave, some competitors have pivoted from data protection or even data management to security. Yes. I see. I wonder if I could run a premise by, I see that as an adjacency to your business, but not sort of throwing you into the security bucket. What are your thoughts on that? >>Yeah. You know, certainly respect everything other competitors are doing, you know, and some are getting very, you know, making some good noise and getting picked up on that. However, we're unapologetically a backup company. Mm-hmm, <affirmative>, we're a backup company. First. We're worried about security. We're worried about, you know, data reuse and supporting shift, left types of things, but we're not gonna apologize for being in the backup availability business, not, not at all. However, there's a role that we can play. Having said that that we're a role. We're a component. If you're in the secondary storage market, like backup or archiving. And you're trying to imply that you're going to help prevent or even head off issues on the primary storage side. That might be a little bit of a stretch. Now, hopefully that can happen that we can go get better as an industry on that. >>But fundamentally we're about ensuring that you're recoverable with reliability and speed when you need it. Whether we're no matter what the issue is, because we like to say ransomware is a disaster. Unfortunately there's other kind of disasters that happen as well. Power failures still happen. Natural issues still occur, et cetera. So all these things have to be accounted for. You know, one of our survey, um, data points basically said all the things that take down a server that you didn't plan on. It's basically humans at the top human error, someone accidentally deleted something and then malicious humans, someone actually came after you, but there's a dozen other things that happened too. So you've gotta prepare for all of that. So I guess what I would end up with saying is you remember back in the centralized data centers, especially the mainframe days, people would say, we're worried about the smoking hole or the smoking crater event. Yeah. Yeah. The probability of a plane crashing into your data bunker was relatively low. That was when it got all the discussion though, what was happening every single day is somebody accidentally deleted a file. And so you need to account on both ends of the spectrum. So we don't wanna over rotate. And we also, we don't want to signal to 450,000 beam customers around the world that we're abandoning you that were not about backup. That's still our core >>Effort. No, it's pretty straightforward. You're just telling people to back up in a way that gives them a certain amount of mitigation yes. Or protection in the event that something happens. And no, I don't remember anything about mainframe. He does though though, much older than me >>EF SMS. So I even know what it stands for. Count key data don't even get me started. So, and, and it wasn't thank you for that answer. I didn't mean to sort of a set up question, but it was more of a strategy question and I wish wish I could put on your analyst hat because I, I feel, I'll just say it. I feel as though it's a move to try to get a tailwind. Maybe it's a valuation play. I don't know. But I, I, it resonated with me three years ago when everybody was talking data management and nobody knew what that meant. Data management. I'm like Oracle. >>Right. >>And now it's starting to become a little bit more clear. Um, but Danny Allen stuff and said, it's all about the backup. I think that was one of his keynote messages. So that really resonated with me cuz he said, yeah, it starts with backup and recovery. And that's what, what matters most to these customers. So really was a strategy question. Now maybe it does have valuation impact. Maybe there's a big market there that can be consolidated. You know, uh, we, this morning in the analyst session, we heard about your new CEO's objectives of, you know, grabbing more market share. So, and that's, that's an adjacency. So it's gonna be interesting to see how that plays out far too many security vendors. As, as we know, the backup and recovery space is getting more crowded and that is maybe causing people to sort of shift. I don't know, whatever right. Or left, I guess, shift. Right. I'm not sure, but um, it's gonna be really interesting to watch because this has now become a really hot space after, you know, it's been some really interesting momentum in certain pockets, but now it's everywhere it's coming ubiquitous. So I'll give you the last word Dave on, uh, day one, VEON 20, 22. >>Yeah. Well boy, so many things I could say to kind of land the plane on, but we're just glad to be back in person. It's been three years since we've had a live event in those three years, we've gone from 300,000 customers to 450,000 customers. The release cadence, even in the pandemic has been the greatest in the company's history in 2020, 2021, there's only about three dozen software only companies that have hit a billion dollars and we're one of them. And that, you know, that mission is why hasn't changed and that's why we wanna stay consistent. One of the things Danny always likes to say is, you know, we keep telling the same story because we're not wanting to deviate off of that story and there's more work to be done. And to honors point, you know, Hey, if you have ambitious goals, you're gonna have to look at spreading your wings out a little bit wider, but we're never gonna abandon being a backup. Well, >>It's, it's clear to me, Dave on was not brought in to keep you steady at a billion. I think he's got a site set on five and then who knows what's next? Dave Russell, thanks so much for coming back in the cube. Great to >>See always a pleasure. Thank you. >>All right. That's a wrap for Dave one. Dave ante and Dave Nicholson will be backed tomorrow with a full day of coverage. Check out Silicon angle.com for all the news, uh, youtube.com/silicon angle. You can get these videos. They're all, you know, flying up Wiki bond.com for some of the research in this space. We'll see you tomorrow.

(upbeat music) >> Hey everyone. Welcome back to theCUBE's coverage of Coupa Inspire 2022. We are in Las Vegas at the beautiful Cosmopolitan hotel. I'm your host, Lisa Martin. Brian McKillips joins me next, a managing director at Accenture. Brian, it's great to have you on the program. >> Thanks for having me, I'm glad to be here. >> So you have an interesting, you lead a lot of stuff at Accenture and I want to read this off, so I get it right. You lead the intelligent platform services strategy and the industry and functions platform group. Talk to me about those responsibilities. >> Yeah, so the intelligent platform services is the place in the business where we have kind of our large software partners, SAP, Oracle, Microsoft, Workday, Salesforce and Adobe. And we kind of think of ourselves as kind of the engine that powers industry and functional solutions, right? And the way Accenture's gone to market over the last couple of years has been kind of bringing together our breadth of experience all the way from strategy, all the way through operations and these big technology transformations are at the core of that. So that's what we do in intelligent platform services. And we recently launched this what we call the industry and functions platforms group because we realized there's a lot of strategic partners that are critical for us to be have a strong practice around, COUPA being one of them, you know in the supply chain and sourcing and procurement space so that we could create a home to be able to deliver these solutions globally and at scale. So I lead both kind of the strategy across all of IPS and then the new industry and functions platform group. >> Got it. All right. So you're here to talk to me about composable technology. First of all, define that for the audience so they understand what you're talking about. >> Yeah, you bet. So, you know, at Accenture, we're talking a lot about this is the age of compressed transformation, meaning, you know, change is only going to speed up and the need to change and so our clients are really struggling with not only kind of moving fast but that pressure around having to change as dynamics around the world change. So in the age of compressed transformation, we were really talking about how our clients should be kind of reorienting the way they think about their tech stack. And because, you know, historically a lot of us grew up in kind of monolithic implementations with, you know one software provider. But today it's really about composing technology to create new industry, new ways to solve industry problems, functional processes, customer experiences, right? And so composable technology we think about it in three parts. One is a cloud foundation that is, you know, the hyperscalers are a critical part of that. Secondly, our digital core and these are the kind of the historic software packages at the center of a lot of the industry and functional business processes. So you think about SAP and Oracle and Salesforce and things like that. But then around that digital core you have composable elements to be able to plug in. And that could be things like other software packages but it's also kind of industry IP or you know, edge devices, you know think IOT, think smart appliances, think and when you put, pull all those things together you need to be able to not only configure it once but configure and reconfigure as the dynamics of the marketplace change. >> So composable technology isn't necessarily new but has the pandemic been an accelerator of some of the things that you're seeing now in terms of why it's important, what's different about it now as being a foundation for competitive differentiation? >> Yeah, for sure. And it's, you know, I, anybody who's in technology say, you know, you tell them about this idea, they're like, well this isn't new, we've had service oriented architectures for 20 years. >> Right. >> You know, we've been talking about integrating things forever, but the you know, much like we all five to seven years ago we knew that we'd be using our phones to pay for pretty much everything but the tech hadn't caught up, right. Not every restaurant or store that you went to had the point of sale set up, right. So we all kind of knew that was coming. And the same thing has kind of happened around this idea of about composable technology and the three things that are new are one is that the cloud foundation is here, right. >> Yes. >> Where, you know, you now have not only kind of hyperscale high speed compute in at the core you actually have at the edge as well. And the same thing with high speed network, you know you have Starlink, you have 5G rolling out. So you have that cloud foundation that really wasn't there before. The second thing that's happening is the posture of a lot of the ecosystem, major ecosystem players has changed, right. And this started, you know when Satya Nadella took over Microsoft where Microsoft was very much a kind of a closed environment. >> Right. >> Where Satya under his leadership has really kind of changed the posture of being able to integrate into that. And we've seen that really pretty much across the entire landscape. And then lastly, it's become, you know, cheaper and, you know, quicker to be able to integrate with platforms like MuleSoft and others where there's kind of full scale integration platforms. So those are, those are the kind of the things that are new that allows for composable technology to be here in the real world. >> So it's something that's tangible, it's real organizations need to be on this bandwagon I imagine or they're going to be left behind. Gartner had some interesting stats that your team sent over and they were talking about these stats that were very compelling in terms of a seismic shift which always, you hear seismic living in California I think earthquakes, but something substantial. And they said, this seismic shift is going to happen by 2023. And I thought, hang on, that's less than a year away. >> Yeah. >> And they talked about by 2023, organizations that have adopted an intelligent composable approach will outpace competition 80% in the speed of new feature implementation. So if an organization hasn't started on that now is it too late? >> I would say not necessarily too late but they need to look for ways to change their disposition, right. And one of the ways that we've been helping clients do this is through pre-integrated solutions, right. So you know, in the past, the motion would be we would work with a client, they would work with our kind of strategists and consultants and say, what does the the future of supply chain look like for example. And if the client liked it, they would say, okay, I love it, what do I do next? Right. Then there would be another consulting engagement, another consulting engagement and then there would be a blueprint and architecture and at some point there was an implementation and a run. We've actually said we're investing heavily with our ecosystem partners to be able to pre-integrate solutions. So when that supply chain strategist says this is what the post COVID supply chain should look like and the client says, I love it what do I do next, that strategist can turn around and say, well, we've got a pre-integrated solution with SAP at the core sitting on a Microsoft Azure stack integrated with Coupa, wrapped with AI and machine learning and we can drop that and configure it for an environment. So that's how we're working with clients who are in that position that really need to kind of change their disposition is to bring these pre-integrated solutions and drop them in. >> Where are your conversations at the C- Suite level? Because this is, I hear many things in what you just said. Part of it is change management, which is very challenging. There's, people are very resistant to that. >> Brian: Yeah. >> One of the things that we've learned in the last two years is if it's going to come it's going to come but where are your conversations within that executive suite in terms of getting buy-in and going this is the direction we have to go in. >> Brian: Yeah. >> Because our business needs to be not just survive but thrive. >> Yeah. Yeah. These are, I mean, there are certainly of course in kind of traditional channels of tech whether it's, you know, the CIO or the CTO, but increasingly we're seeing this is a CEO discussion and, you know, our CEO Julie Sweet, is very, very market pacing and is having top to top conversations talking about compressed transformation, talking about composable technology because it's no longer just a, you know, a back office function as you know, right. I mean, this is really core to how companies you know, are, change their business models, make money, right. And it's a constant evolution. And that's why we talk about that kind of configuring and reconfiguring, it's not just coming in, implementing once, run it for five years and then when it's time to upgrade, we come back. >> No. >> We really want to be the partner with our clients to basically move in and, you know, across the patch whether it's specific industry processes, specific functional processes, specific customer experiences, we want to be the partner that is constantly tuning and configuring and reconfiguring and composing these solutions from across the ecosystem. >> And helping those businesses in any industry evolve as you talked about this compressed timeline, compressed transformation, such an interesting way of describing it but it's really true, it's what we've been living the last couple of years. >> Brian: Yeah. And so I want to get into Accenture's technology vision. You touched on this a little bit but there was some stats that your team provided that I thought were really, really interesting, a survey that Accenture did, 77% of executives, and we were just talking about the C-suite, state that their tech architecture is becoming critical to the overall success of the organization. So that awareness is there for sure en masse. Another thing that, stat that was interesting was 90% of business and IT execs agree that to be agile we always talk about agility, right, be resilient, organizations need to fast forward this digital transformation at the core. There's that compressed transformation. >> Brian: Yeah. >> Those are very high numbers. >> Brian: Yeah. >> In terms of where organizations say we see where we need to be. What's the vision at Accenture to help organizations get there fast? >> Yeah. Well, I think it's, you know, the thing that came to mind as you were talking is that we have, you know, major clients that have had this had in the, you know consumer packaged goods and apparel space that have had one way that they've done business is directly through retailers, you know, for pretty much their whole existence. Suddenly they need to shift to a direct to consumer model both in terms of marketing, in terms of commerce and that's not, you know, you don't just flip a switch in the back office and, you know, call IT and say hey, hey, can you change around a few things? It's actually shifting the entire core, it touches everything, it touches point of sale, it touches the customer experience, it touches supply chain, it touches employee experience even, right. >> Yeah. >> And so that's why I think it's so important for, you know technology leaders and business leaders to continue to kind of integrate themselves more tightly. >> Yes. >> To be able to make these business model transformations not just, you know, the tech that supports things. >> It's essential. >> Yeah. >> You know, we often in so many shows, Brian, we talk about alignment of business and technology, but it's not trivial. >> Yeah, yeah. >> It's absolutely fundamental to the success of every organization. And they've got to do so and as you said, I'm going to use your, your word, the compressed transformation. >> Yeah. >> A compressed timeframe. So talk to me about some customer examples where you really feel that Accenture and Coupa have helped this organization transform its supply chain to be able to be, use composable technology. >> Brian: Yeah. >> To be a leader in its industry. >> Yeah. Well, one example of that is a major industrial client that we have that has global operations across the world. And they're on a journey to kind of upgrade their digital core ERP that they've been on for a long time. And that's a multi-year journey. But at, you know, today they have needs for sourcing and procurement solutions in specific geographies around the world like Japan, for example. So what we've been able to do and it's a relatively simple example but quickly work with the client and Coupa to identify the right Coupa solution that's born in the cloud that has a great kind of user experience and implement that quickly as well as integrated it into the digital core, right. So they're not separate things. And it becomes part of that architecture, right. It just starts to kind of show the flexibility of when you have, when you come with a kind of composable technology point of view, the way we can help our clients do that. And in some other cases it's even more, you know, more cutting edge. So think about a utilities client, for example that has IOT sensors on their wires and when the, when that wire swings too far they say something's wrong. Automatically it goes back to the digital core cuts a ticket and finds the closest worker. >> Lisa: Okay. >> To then dispatch. The worker then can put on their hollow lens, for example and climb the pole and get directions on how to solve the problem right then and there, right? That's another example of you know, multiple systems, edge devices things coming together in order to create that. And it's only going to get faster, you know, with the metaverse. >> Lisa: Right. >> You know, with web 3.0 coming, with blockchain becoming more and more mainstream, companies need to be thinking about in this age of compressed transformation how to do that composable technology that you can figure and reconfigure. >> Do you think that we're in an age of compressed transformation or is that how it's going to be going forward given the global climate the last two years? >> Yeah. It's definitely going to be that way going forward over the next, you know, probably for the large part of the, the remainder of our career. I mean, we're, our CTO, Paul Daugherty, talks about us being an mega cycle, right? There's so many things changing. And even without these externalities of, you know, political issues and pandemics, you know, the introduction of AI and machine learning, a lot of these technologies I just mentioned, it's, the change is happening in every industry, in every, you know kind of area of the marketplace and in a way that's, you know, that's really exciting, right. And we get to help our clients be able to kind of solve those things not just once, but continually >> There's a tremendous amount of opportunity that's come from compressed transformation, right. A lot of opportunity, a lot of potential. What are some of the things that you're looking forward to say in the next year, as we talked about some of those business and lines of business and IT folks understand we've got to move in this direction. What excites you about the potential that you have to help these organizations really transform? >> Yeah, well, I think, I mean, the, we just came out with our new tech vision which is about the metaverse. And I think that the things that excite me are there's brand new ways like we've lived in a world where transactions take place in a very predictable way with local currencies through a single channel. And that was, that's been sort of fixed for a long time. The fundamentals of the economy or actually in the marketplace are starting to change in terms of how do we transact with things like cryptocurrencies, things like non fungible tokens, you know, all these things that we didn't, you know, they weren't, even the metaverse these were not main line words, even six you know, months ago, 12 months ago. >> Lisa: Right, right. >> Now these things, you know, every it seems like every month there's something new that is, you know, seismic to use your word that is shifting the fundamentals of the marketplace. And I think that's what's really exciting. I mean, that's where, I mean, it's probably one of the most exciting times to be in business, be in the marketplace. It certainly has a lot of challenges. >> Lisa: Yes. >> But, you know, I think we're really about using, you know, the promise of technology to unlock human ingenuity and this is a great time to be able to unlock that human ingenuity. >> And that's such a great alignment with Coupa. I was just in the keynote and there was an Accenture video, Julie Sweet was talking to some other folks about that. Great alignment in the partnership. Brian, thank you for joining me talking about composable technology, what's new, why and the potential that organizations and every business have to use it to unlock competitive advantages. >> Brian: Yeah. >> We appreciate your insights and your time. >> You bet. Pleasure to be here. >> All right. With Brian McKillips, I'm Lisa Martin. You're watching theCUBEe from Coupa Inspire 2022. (upbeat music)