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Meagen Eisenberg, Lacework | International Women's Day 2023


 

>> Hello and welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE. Got a variety of interviews across the gamut from topics, women in tech, mentoring, pipelining, developers, open source, executives. Stanford's having International Women's Day celebration with the women in data science, which we're streaming that live as well. Variety of programs. In this segment, Meagen Eisenberg, friend of theCUBE, she's the CMO of Laceworks, is an amazing executive, got a great journey story as a CMO but she's also actively advising startups, companies and really pays it forward. I want to say Meagen, thank you for coming on the program and thanks for sharing. >> Yeah, thank you for having me. I'm happy to be here. >> Well, we're going to get into some of the journey celebrations that you've gone through and best practice what you've learned is pay that forward. But I got to say, one of the things that really impresses me about you as an executive is you get stuff done. You're a great CMO but also you're advised a lot of companies, you have a lot of irons in the fires and you're advising companies and sometimes they're really small startups to bigger companies, and you're paying it forward, which I love. That's kind of the spirit of this day. >> Yeah, I mean, I agree with you. When I think about my career, a lot of it was looking to mentors women out in the field. This morning I was at a breakfast by Eileen and we had the CEO of General Motors on, and she was talking about her journey nine years as a CEO. And you know, and she's paying it forward with us. But I think about, you know, when you're advising startups, you know, I've gathered knowledge and pattern recognition and to be able to share that is, you know, I enjoy it. >> Yeah. And the startups are also fun too, but it's not always easy and it can get kind of messy as you know. Some startups don't make it some succeed and it's always like the origination story is kind of rewritten and then that's that messy middle. And then it's like that arrows that don't look like a straight line but everyone thinks it's great and you know, it's not for the faint of heart. And Teresa Carlson, who I've interviewed many times, former Amazon, now she's the president of Flexport, she always says, sometimes startups on certain industries aren't for the faint of heart so you got to have a little bit of metal, right? You got to be tough. And some cases that you don't need that, but startups, it's not always easy. What have you learned? >> Yeah, I mean, certainly in the startup world, grit, creativity. You know, when I was at TripActions travel company, pandemic hits, nobody's traveling. You cut budget, you cut heads, but you focus on the core, right? You focus on what you need to survive. And creativity, I think, wins. And, you know, as a CMO when you're marketing, how do you get through that noise? Even the security space, Lacework, it's a fragmented market. You've got to be differentiated and position yourself and you know, be talking to the right target audience and customers. >> Talk about your journey over the years. What have you learned? What's some observations? Can you share any stories and best practices that someone watching could learn from? I know there's a lot of people coming into the tech space with the generative AI things going on in Cloud computing, scaling to the edge, there's a lot more aperture for technical jobs as well as just new roles and new roles that haven't, you really don't go to college for anymore. You got cybersecurity you're in. What are some of the things that you've done over your career if you can share and some best practices? >> Yeah, I think number one, continual learning. When I look through my career, I was constantly reading, networking. Part of the journey is who you're meeting along the way. As you become more senior, your ability to hire and bring in talent matters a lot. I'm always trying to meet with new people. Yeah, if I look at my Amazon feed of books I've bought, right, it kind of chronicle of my history of things I was learning about. Right now I'm reading a lot about cybersecurity, how the, you know, how how they tell me the world ends is the one I'm reading most recently. But you've got to come up to speed and then know the product, get in there and talk to customers. Certainly on the marketing front, anytime I can talk with the customer and find out how they're using us, why they love us, that, you know, helps me better position and differentiate our company. >> By the way, that book is amazing. I saw Nicole speak on Tuesday night with John Markoff and Palo Alto here. What a great story she told there. I recommend that book to everyone. It goes in and she did eight years of research into that book around zero day marketplaces to all the actors involved in security. And it was very interesting. >> Yeah, I mean, it definitely wakes you up, makes you think about what's going on in the world. Very relevant. >> It's like, yeah, it was happening all the time, wasn't it. All the hacking. But this brings me, this brings up an interesting point though, because you're in a cybersecurity area, which by the way, it's changing very fast. It's becoming a bigger industry. It's not just male dominated, although it is now, it's still male dominated, but it's becoming much more and then just tech. >> Yeah, I mean it's a constantly evolving threat landscape and we're learning, and I think more than ever you need to be able to use the data that companies have and, you know, learn from it. That's one of the ways we position ourselves. We're not just about writing rules that won't help you with those zero day attacks. You've got to be able to understand your particular environment and at any moment if it changes. And that's how we help you detect a threat. >> How is, how are things going with you? Is there any new things you guys got going on? Initiatives or programs for women in tech and increasing the range of diversity inclusion in the industry? Because again, this industry's getting much wider too. It's not just specialized, it's also growing. >> Yes, actually I'm excited. We're launching secured by women, securedbywomen.com and it's very much focused on women in the industry, which some studies are showing it's about 25% of security professionals are women. And we're going to be taking nominations and sponsoring women to go to upcoming security events. And so excited to launch that this month and really celebrate women in security and help them, you know, part of that continual learning that I talked about, making sure they're there learning, having the conversations at the conferences, being able to network. >> I have to ask you, what inspired you to pursue the career in tech? What was the motivation? >> You know, if I think way back, originally I wanted to be on the art side and my dad said, "You can do anything as long as it's in the sciences." And so in undergrad I did computer science and MIS. Graduated with MIS and computer science minor. And when I came out I was a IT engineer at Cisco and you know, that kind of started my journey and decided to go back and get my MBA. And during that process I fell in love with marketing and I thought, okay, I understand the buyer, I can come out and market technology to the IT world and developers. And then from there went to several tech companies. >> I mean my father was an engineer. He had the same kind of thing. You got to be an engineer, it's a steady, stable job. But that time, computer science, I mean we've seen the evolution of computer science now it's the most popular degree at Berkeley we've heard and around the world and the education formats are changing. You're seeing a lot of people's self-training on YouTube. The field has really changed. What are some of the challenges you see for folks trying to get into the industry and how would you advise today if you were talking to your young self, what would you, what would be the narrative? >> Yeah, I mean my drawback then was HTML pages were coming out and I thought it would be fun to design, you know, webpages. So you find something you're passionate about in the space today, whether it's gaming or it's cybersecurity. Go and be excited about it and apply and don't give up, right? Do whatever you can to read and learn. And you're right, there are a ton of online self-help. I always try to hire women and people who are continual learners and are teaching themselves something. And I try to find that in an interview to know that they, because when you come to a business, you're there to solve problems and challenges. And the folks that can do that and be innovative and learn, those are the ones I want on my team. >> It's interesting, you know, technology is now impacting society and we need everyone involved to participate and give requirements. And that kind of leads my next question for you is, like, in your opinion, or let me just step back, let me rephrase. What are some of the things that you see technology being used for, for society right now that will impact people's lives? Because this is not a gender thing. We need everybody involved 'cause society is now digital. Technology's pervasive. The AI trends now we're seeing is clearly unmasking to the mainstream that there's some cool stuff happening. >> Yeah, I mean, I think ChatGPT, think about that. All the different ways we're using it we're writing content and marketing with it. We're, you know, I just read an article yesterday, folks are using it to write children's stories and then selling those stories on Amazon, right? And the amount that they can produce with it. But if you think about it, there's unlimited uses with that technology and you've got all the major players getting involved on it. That one major launch and piece of technology is going to transform us in the next six months to a year. And it's the ability to process so much data and then turn that into just assets that we use and the creativity that's building on top of it. Even TripActions has incorporated ChatGPT into your ability to figure out where you want when you're traveling, what's happening in that city. So it's just, you're going to see that incorporated everywhere. >> I mean we've done an interview before TripAction, your other company you were at. Interesting point you don't have to type in a box to say, I'm traveling, I want a hotel. You can just say, I'm going to Barcelona for Mobile World Congress, I want to have a good time. I want some tapas and a nice dinner out. >> Yes. Yeah. That easy. We're making it easy. >> It's efficiency. >> And actually I was going to say for women specifically, I think the reason why we can do so much today is all the technology and apps that we have. I think about DoorDash, I think about Waze you know, when I was younger you had to print out instructions. Now I get in the car real quick, I need to go to soccer practice, I enter it, I need to pick them up at someone's house. I enter it. It's everything's real time. And so it takes away all the things that I don't add value to and allows me to focus on what I want in business. And so there's a bunch of, you know, apps out there that have allowed me to be so much more efficient and productive that my mother didn't have for sure when I was growing up. >> That is an amazing, I think that actually illustrates, in my opinion, the best example of ChatGPT because the maps and GPS integration were two techs, technologies merged together that replace driving and looking at the map. You know, like how do you do that? Like now it's automatically. This is what's going to happen to creative, to writing, to ideation. I even heard Nicole from her book read said that they're using ChatGPT to write zero day exploits. So you seeing it... >> That's scary stuff. You're right. >> You're seeing it everywhere. Super exciting. Well, I got to ask you before you get into some of the Lacework things that you're involved with, cause I think you're doing great work over there is, what was the most exciting projects you've worked on in your career? You came in Cisco, very technical company, so got the technical chops, CSMIS which stands for Management of Information Science for all the young people out there, that was the state of the art back then. What are some of the exciting things you've done? >> Yeah, I mean, I think about, I think about MongoDB and learning to market to developers. Taking the company public in 2017. Launching Atlas database as a service. Now there's so much more of that, you know, the PLG motion, going to TripActions, you know, surviving a pandemic, still being able to come out of that and all the learnings that went with it. You know, they recently, I guess rebranded, so they're Navan now. And then now back in the security space, you know, 14 years ago I was at ArcSite and we were bought by HP. And so getting back into the security world is exciting and it's transformed a ton as you know, it's way more complicated than it was. And so just understanding the pain of our customers and how we protect them as is fun. And I like, you know, being there from a marketing standpoint. >> Well we really appreciate you coming on and sharing that. I got to ask you, for folks watching they might be interested in some advice that you might have for them and their career in tech. I know a lot of young people love the tech. It's becoming pervasive in our lives, as we mentioned. What advice would you give for folks watching that want to start a career in tech? >> Yeah, so work hard, right? Study, network, your first job, be the best at it because every job after that you get pulled into a network. And every time I move, I'm hiring people from the last job, two jobs before, three jobs before. And I'm looking for people that are working hard, care, you know, are continual learners and you know, add value. What can you do to solve problems at your work and add value? >> What's your secret networking hack or growth hack or tip that you can share? Because you're a great networker by the way. You're amazing and you do add a lot of value. I've seen you in action. >> Well, I try never to eat alone. I've got breakfast, I've got lunch, I've got coffee breaks and dinner. And so when I'm at work, I try and always sit and eat with a team member, new group. If I'm out on the road, I'm, you know, meeting people for lunch, going for dinner, just, you know, don't sit at your desk by yourself and don't sit in the hotel room. Get out and meet with people. >> What do you think about now that we're out of the pandemic or somewhat out of the pandemic so to speak, events are back. >> Yes. >> RSA is coming up. It's a big event. The bigger events are getting bigger and then the other events are kind of smaller being distributed. What's your vision of how events are evolving? >> Yeah, I mean, you've got to be in person. Those are the relationships. Right now more than ever people care about renewals and you are building that rapport. And if you're not meeting with your customers, your competitors are. So what I would say is get out there Lacework, we're going to be at RSA, we're going to be at re:Inforce, we're going to be at all of these events, building relationships, you know, coffee, lunch, and yeah, I think the future of events are here to stay and those that don't embrace in person are going to give up business. They're going to lose market share to us. >> And networking is obviously very key on events as well. >> Yes. >> A good opportunity as always get out to the events. What's the event networking trick or advice do you give folks that are going to get out to the networking world? >> Yeah, schedule ahead of time. Don't go to an event and expect people just to come by for great swag. You should be partnering with your sales team and scheduling ahead of time, getting on people's calendars. Don't go there without having 100 or 200 meetings already booked. >> Got it. All right. Let's talk about you, your career. You're currently at Lacework. It's a very hot company in a hot field, security, very male dominated, you're a leader there. What's it like? What's the strategies? How does a woman get in there and be successful? What are some tricks, observations, any data you can share? What's the best practice? What's the secret sauce from Meagen Eisenberg? >> Yes. Yeah, for Meagen Eisenberg. For Lacework, you know, we're focused on our customers. There's nothing better than getting, being close to them, solving their pain, showcasing them. So if you want to go into security, focus on their, the issues and their problems and make sure they're aware of what you're delivering. I mean, we're focused on cloud security and we go from build time to run time. And that's the draw for me here is we had a lot of, you know, happy, excited customers by what we were doing. And what we're doing is very different from legacy security providers. And it is tapping into the trend of really understanding how much data you have and what's happening in the data to detect the anomalies and the threats that are there. >> You know, one of the conversations that I was just having with a senior leader, she was amazing and I asked her what she thought of the current landscape, the job market, the how to get promoted through the careers, all those things. And the response was interesting. I want to get your reaction. She said interdisciplinary skills are critical. And now more than ever, the having that, having a set of skills, technical and social and emotional are super valuable. Do you agree? What's your reaction to that and what would, how would you reframe that? >> Yeah, I mean, I completely agree. You can't be a leader without balance. You've got to know your craft because you're developing and training your team, but you also need to know the, you know, how to build relationships. You're not going to be successful as a C-level exec if you're not partnering across the functions. As a CMO I need to partner with product, I need to partner with the head of sales, I need to partner with finance. So those relationships matter a ton. I also need to attract the right talent. I want to have solid people on the team. And what I will say in the security, cybersecurity space, there's a talent shortage and you cannot hire enough people to protect your company in that space. And that's kind of our part of it is we reduce the number of alerts that you're getting. So you don't need hundreds of people to detect an issue. You're using technology to show, you know, to highlight the issue and then your team can focus on those alerts that matter. >> Yeah, there's a lot of emerging markets where leveling up and you don't need pedigree. You can just level up skill-wise pretty quickly. Which brings me to the next question for you is how do you keep up with all the tech day-to-day and how should someone watching stay on top of it? Because I mean, you got to be on top of this stuff and you got to ride the wave. It's pretty turbulent, but it's still growing and changing. >> Yeah, it's true. I mean, there's a lot of reading. I'm watching the news. Anytime something comes out, you know, ChatGPT I'm playing with it. I've got a great network and sharing. I'm on, you know, LinkedIn reading articles all the time. I have a team, right? Every time I hire someone, they bring new information and knowledge in and I'm you know, Cal Poly had this learn by doing that was the philosophy at San Luis Obispo. So do it. Try it, don't be afraid of it. I think that's the advice. >> Well, I love some of the points you mentioned community and network. You mentioned networking. That brings up the community question, how could people get involved? What communities are out there? How should they approach communities? 'Cause communities are also networks, but also they're welcoming people in that form networks. So it's a network of networks. So what's your take on how to engage and work with communities? How do you find your tribe? If someone's getting into the business, they want support, they might want technology learnings, what's your approach? >> Yeah, so a few, a few different places. One, I'm part of the operator collective, which is a strong female investment group that's open and works a lot with operators and they're in on the newest technologies 'cause they're investing in it. Chief I think is a great organization as well. You've got a lot of, if you're in marketing, there's a ton of CMO networking events that you can go to. I would say any field, even for us at Lacework, we've got some strong CISO networks and we do dinners around you know, we have one coming up in the Bay area, in Boston, New York, and you can come and meet other CISOs and security leaders. So when I get an invite and you know we all do, I will go to it. I'll carve out the time and meet with others. So I think, you know, part of the community is get out there and, you know, join some of these different groups. >> Meagen, thank you so much for spending the time. Final question for you. How do you see the future of tech evolving and how do you see your role in it? >> Yeah, I mean, marketing's changing wildly. There's so many different channels. You think about all the social media channels that have changed over the last five years. So when I think about the future of tech, I'm looking at apps on my phone. I have three daughters, 13, 11, and 8. I'm telling you, they come to me with new apps and new technology all the time, and I'm paying attention what they're, you know, what they're participating in and what they want to be a part of. And certainly it's going to be a lot more around the data and AI. I think we're only at the beginning of that. So we will continue to, you know, learn from it and wield it and deal with the mass amount of data that's out there. >> Well, you saw TikTok just got banned by the European Commission today around their staff. Interesting times. >> It is. >> Meagen, thank you so much as always. You're a great tech athlete. Been following your career for a while, a long time. You're an amazing leader. Thank you for sharing your story here on theCUBE, celebration of International Women's Day. Every day is IWD and thanks for coming on. >> Thank you for having me. >> Okay. I'm John Furrier here in theCUBE Studios in Palo Alto. Thank you for watching, more to come stay with us. (bright music)

Published Date : Feb 23 2023

SUMMARY :

you for coming on the program Yeah, thank you for having me. That's kind of the spirit of this day. But I think about, you know, and it can get kind of messy as you know. and you know, be talking to the right What are some of the how the, you know, I recommend that book to everyone. makes you think about what's happening all the time, wasn't it. rules that won't help you you guys got going on? and help them, you know, and you know, that kind and around the world and the to design, you know, webpages. It's interesting, you know, to figure out where you Interesting point you That easy. I think about Waze you know, and looking at the map. You're right. Well, I got to ask you before you get into And I like, you know, some advice that you might have and you know, add value. You're amazing and you If I'm out on the road, I'm, you know, What do you think about now and then the other events and you are building that rapport. And networking is obviously do you give folks that just to come by for great swag. any data you can share? and the threats that are there. the how to get promoted You're using technology to show, you know, and you got to ride the wave. and I'm you know, the points you mentioned and you can come and meet other and how do you see your role in it? and new technology all the time, Well, you saw TikTok just got banned Thank you for sharing your Thank you for watching,

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Nir Zuk, Palo Alto Networks | An Architecture for Securing the Supercloud


 

(bright upbeat music) >> Welcome back, everybody, to the Supercloud 2. My name is Dave Vellante. And I'm pleased to welcome Nir Zuk. He's the founder and CTO of Palo Alto Networks. Nir, good to see you again. Welcome. >> Same here. Good to see you. >> So let's start with the right security architecture in the context of today's fragmented market. You've got a lot of different tools, you've got different locations, on-prem, you've got hardware and software. Tell us about the right security architecture from your standpoint. What's that look like? >> You know, the funny thing is using the word security in architecture rarely works together. (Dave chuckles) If you ask a typical information security person to step up to a whiteboard and draw their security architecture, they will look at you as if you fell from the moon. I mean, haven't you been here in the last 25 years? There's no security architecture. The architecture today is just buying a bunch of products and dropping them into the infrastructure at some relatively random way without really any guiding architecture. And that's a huge challenge in cybersecurity. It's always been, we've always tried to find ways to put an architecture into writing blueprints, whatever you want to call it, and it's always been difficult. Luckily, two things. First, there's something called zero trust, which we can talk a little bit about more, if you want, and zero trust among other things is really a way to create a security architecture, and second, because in the cloud, in the supercloud, we're starting from scratch, we can do things differently. We don't have to follow the way we've always done cybersecurity, again, buying random products, okay, maybe not random, maybe there is some thinking going into it by buying products, one of the other, dropping them in, and doing it over 20 years and ending up with a mess in the cloud, we have an opportunity to do it differently and really have an architecture. >> You know, I love talking to founders and particularly technical founders from StartupNation. I think I saw an article, I think it was Erie Levine, one of the founders or co-founders of Waze, and he had a t-shirt on, it said, "Fall in love with the problem, not the solution." Is that how you approached architecture? You talk about zero trust, it's a relatively new term, but was that in your head when you thought about forming the company? >> Yeah, so when I started Palo Alto Networks, exactly, by the way, 17 years ago, we got funded January, 2006, January 18th, 2006. The idea behind Palo Alto Networks was to create a security platform and over time take more and more cybersecurity functions and deliver them on top of that platform, by the way, as a service, SaaS. Everybody thought we were crazy trying to combine many functions into one platform, best of breed and defense in death and putting all your eggs in the same basket and a bunch of other slogans were flying around, and also everybody thought we were crazy asking customers to send information to the cloud in order to secure themselves. Of course, step forward 17 years, everything is now different. We changed the market. Almost all of cybersecurity today is delivered as SaaS and platforms are ruling more and more the world. And so again, the idea behind the platform was to over time take more and more cybersecurity functions and deliver them together, one brain, one decision being made for each and every packet or system call or file or whatever it is that you're making the decision about and it works really, really well. As a side effect, when you combine that with zero trust and you end up with, let's not call it an architecture yet. You end up with with something where any user, any location, both geographically as well as any location in terms of branch office, headquarters, home, coffee shop, hotel, whatever, so any user, any geographical location, any location, any connectivity method, whether it is SD1 or IPsec or Client VPN or Client SVPN or proxy or browser isolation or whatever and any application deployed anywhere, public cloud, private cloud, traditional data center, SaaS, you secure the same way. That's really zero trust, right? You secure everything, no matter who the user is, no matter where they are, no matter where they go, you secure them exactly the same way. You don't make any assumptions about the user or the application or the location or whatever, just because you trust nothing. And as a side effect, when you do that, you end up with a security architecture, the security architecture I just described. The same thing is true for securing applications. If you try to really think and not just act instinctively the way we usually do in cybersecurity and you say, I'm going to secure my traditional data center applications or private cloud applications and public cloud applications and my SaaS applications the same way, I'm not going to trust something just because it's deployed in the private data center. I'm not going to trust two components of an application or two applications talking to each other just because they're deployed in the same place versus if one component is deployed in one public cloud and the other component is deployed in another public cloud or private cloud or whatever. I'm going to secure all of them the same way without making any trust assumptions. You end up with an architecture for securing your applications, which is applicable for the supercloud. >> It was very interesting. There's a debate I want to pick up on what you said because you said don't call it an architecture yet. So Bob Muglia, I dunno if you know Bob, but he sort of started the debate, said, "Supercloud, think of it as a platform, not an architecture." And there are others that are saying, "No, no, if we do that, then we're going to have a bunch of more stove pipes. So there needs to be standard, almost a purist view. There needs to be a supercloud architecture." So how do you think about it? And it's a bit academic, I know, but do you think of this idea of a supercloud, this layer of value on top of the hyperscalers, do you think of that as a platform approach that each of the individual vendors are responsible for the architecture? Or is there some kind of overriding architecture of standards that needs to emerge to enable the supercloud? >> So we can talk academically or we can talk practically. >> Yeah, let's talk practically. That's who you are. (Dave laughs) >> Practically, this world is ruled by financial interests and none of the public cloud providers, especially the bigger they are has any interest of making it easy for anyone to go multi-cloud, okay? Also, on top of that, if we want to be even more practical, each of those large cloud providers, cloud scale providers have engineers and all these engineers think they're the best in the world, which they are and they all like to do things differently. So you can't expect things in AWS and in Azure and GCP and in the other clouds like Oracle and Ali and so on to be the same. They're not going to be the same. And some things can be abstracted. Maybe cloud storage or bucket storage can be abstracted with the layer that makes them look the same no matter where you're running. And some things cannot be abstracted and unfortunately will not be abstracted because the economical interest and the way engineers work won't let it happen. We as a third party provider, cybersecurity provider, and I'm sure other providers in other areas as well are trying or we're doing our best. We're not trying, we are doing our best, and it's pretty close to being the way you describe the top of your supercloud. We're building something that abstracts the underlying cloud such that securing each of these clouds, and by the way, I would add private cloud to it as well, looks exactly the same. So we use, almost always, whenever possible, the same terminology, no matter which cloud we're securing and the same policy and the same alerts and the same information and so on. And that's also very important because when you look at the people that actually end up using the product, security engineers and more importantly, SOC, security operations center analysts, they're not going to study the details of each and every cloud. It's just going to be too much. So we need to abstract it for them. >> Yeah, we agree by the way that the supercloud definition is inclusive of on-prem, you know, what you call private cloud. And I want to pick up on something else you said. I think you're right that abstracting and making consistent across clouds something like object storage, get put, you know, whether it's an S3 bucket or an Azure Blob, relatively speaking trivial. When you now bring that supercloud concept to something more complex like security, first of all, as a technically feasible and inferring the answer there is yes, and if so, what do you see as the main technical challenges of doing so? >> So it is feasible to the extent that the different cloud provide the same functionality. Then you step into a territory where different cloud providers have different paths services and different cloud providers do things a little bit differently and they have different sets of permissions and different logging that sometimes provides all the information and sometimes it doesn't. So you end up with some differences. And then the question is, do you abstract the lowest common dominator and that's all you support? Or do you find a way to be smarter than that? And yeah, whatever can be abstracted is abstracted and whatever cannot be abstracted, you find an easy way to represent that to your users, security engineers, security analysts, and so on, which is what I believe we do. >> And you do that by what? Inventing or developing technology that presents that experience to users? Could you be more specific there? >> Yeah, so different cloud providers call their storage in different names and you use different ways to configure them and the logs come out the same. So we normalize it. I mean, the keyword is probably normalization. Normalize it. And we try to, you know, then you have to pick a winner here and to use someone's terminology or you need to invent new terminology. So we try to use the terminology of the largest cloud provider so that we have a better chance of doing that but we can't always do that because they don't support everything that other cloud providers provide, but the important thing is, with or thanks to that normalization, our customers both on the engineering side and on the user side, operations side end up having to learn one terminology in order to set policies and understand attacks and investigate incidents. >> I wonder if I could pick your brain on what you see as the ideal deployment model to achieve this supercloud experience. For example, do you think instantiating your stack in multiple regions and multiple clouds is the right way to do it? Or is building a single global instance on top of the clouds a more preferable way? Are maybe other models we should consider? What do you see as the trade off of these different deployment models and which one is ideal in your view? >> Yeah, so first, when you deploy cloud security, you have to decide whether you're going to use agents or not. By agents, I mean something working, something running inside the workload. Inside a virtual machine on the container host attached to function, serverless function and so on and I, of course, recommend using agents because that enables prevention, it enables functionality you cannot get without agents but you have to choose that. Now, of course, if you choose agent, you need to deploy AWS agents in AWS and GCP agents in GCP and Azure agents in Azure and so on. Of course, you don't do it manually. You do it through the CICD pipeline. And then the second thing that you need to do is you need to connect with the consoles. Of course, that can be done over the internet no matter where your security instances is running. You can run it on premise, you can run it in one of the other different clouds. Of course, we don't run it on premise. We prefer not to run it on premise because if you're secured in cloud, you might as well run in the cloud. And then the question is, for example, do you run a separate instance for AWS for GCP or for Azure, or you want to run one instance for all of them in one of these clouds? And there are advantages and disadvantages. I think that from a security perspective, it's always better to run in one place because then when you collect the information, you get information from all the clouds and you can start looking for cross-cloud issues, incidents, attacks, and so on. The downside of that is that you need to send all the information to one of the clouds and you probably know that sending data out of the cloud costs a lot of money versus keeping it in the cloud. So theoretically, you can build an architecture where you keep the data for AWS in AWS, Azure in Azure, GCP in GCP, and then you try to run distributed queries. When you do that, you find out you'd end up paying more for the compute to do that than you would've paid for sending all the data to a central location. So we prefer the approach of running in one place, bringing all the data there, and running all the security, the machine learning or whatever, the rules or whatever it is that you're running in one place versus trying to create a distributed deployment in order to try to save some money on the data, the network data transfers. >> Yeah, thank you for that. That makes a lot of sense. And so basically, should we think about the next layer building security data lake, if you will, and then running machine learning on top of that if I can use that term of a data lake or a lake house? Is that sort of where you're headed? >> Yeah, look, the world is headed in that direction, not just the cybersecurity world. The world is headed from being rule-based to being data-based. So cybersecurity is not different and what we used to do with rules in the past, we're now doing with machine learning. So in the past, you would define rules saying, if you see this, this, and this, it's an attack. Now you just throw the data at the machine, I mean, I'm simplifying it, but you throw data at a machine. You'll tell the machine, find the attack in the data. It's not that simple. You need to build the right machine learning models. It needs to be done by people that are both cybersecurity experts and machine learning experts. We do it mostly with ex-military offensive people that take their offensive knowledge and translate it into machine learning models. But look, the world is moving in that direction and cybersecurity is moving in that direction as well. You need to collect a lot of data. Like I said, I prefer to see all the data in one place so that the machine learning can be much more efficient, pay for transferring the data, save money on the compute. >> I think the drop the mic quote it ignite that you had was within five years, your security operation is going to be AI-powered. And so you could probably apply that to virtually any job over the next five years. >> I don't know if any job. Certainly writing essays for school is automated already as we've seen with ChatGPT and potentially other things. By the way, we need to talk at some point about ChatGPT security. I don't want to think what happens when someone spends a lot of money on creating a lot of fake content and teaches ChatGPT the wrong answer to a question. We start seeing ChatGPT as the oracle of everything. We need to figure out what to do with the security of that. But yeah, things have to be automated in cybersecurity. They have to be automated. They're just too much data to deal with and it's just not even close to being good enough to wait for an incident to happen and then going investigate the incident based on the data that we have. It's better to look at all the data all the time, millions of events per second, and find those incidents before they happen. There's no way to do that without machine learning. >> I'd love to have you back and talk about ChatGPT. I know they're trying to put in some guardrails but there are a lot of unintended consequences, aren't there? >> Look, if they're not going to have a person filtering the data, then with enough money, you can create thousands or tens of thousands of pieces of articles or whatever that look real and teach the machine something that is totally wrong. >> We were talking about the hyper skills before and I agree with you. It's very unlikely they're going to get together, band together, and create these standards. But it's not a static market. It's a moving train, if you will. So assuming you're building this cross cloud experience which you are, what do you want from the hyperscalers? What do you want them to bring to the table? What is a technology supplier like Palo Alto Networks bring? In other words, where do you see ongoing as your unique value add and that moat that you're building and how will that evolve over time vis-a-vis the hyperscaler evolution? >> Yeah, look, we need APIs. The more data we have, the more access we have to more data, the less restricted the access is and the cheaper the access is to the data because someone has to pay today for some reason for accessing that data, the more secure their customers are going to be. So we need help and are helping by the way a lot, all of them in finding easy ways for customers to deploy things in the cloud, access data, and again, a lot of data, very diversified data and do it in a cost-effective way. >> And when we talk about the edge, I presume you look at the edge as just another data center or maybe it's the reverse. Maybe the data center is just another edge location, but you're seeing specific edge security solutions come out. I'm guessing that you would say, that's not what we want. Edge should be part of that architecture that we talked about earlier. Do you agree? >> Correct, it should be part of the architecture. I would also say that the edge provides an opportunity specifically for network security, whereas traditional network security would be deployed on premise. I'm talking about internet security but half network security market, and not just network security but also the other network intelligent functions like routing and QS. We're seeing a trend of pushing those to the edge of the cloud. So what you deploy on premise is technology for bringing packets to the edge of the cloud and then you run your security at the edge, whatever that edge is, whether it's a private edge or public edge, you run it in the edge. It's called SASE, Secure Access Services Edge, pronounced SASE. >> Nir, I got to thank you so much. You're such a clear thinker. I really appreciate you participating in Supercloud 2. >> Thank you. >> All right, keep it right there for more content covering the future of cloud and data. This is Dave Vellante for John Furrier. I'll be right back. (bright upbeat music)

Published Date : Feb 17 2023

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Nir, good to see you again. Good to see you. in the context of today's and second, because in the cloud, Is that how you approached architecture? and my SaaS applications the same way, that each of the individual So we can talk academically That's who you are. and none of the public cloud providers, and if so, what do you see and that's all you support? and on the user side, operations side is the right way to do it? and then you try to run about the next layer So in the past, you would that you had was within five years, and teaches ChatGPT the I'd love to have you that look real and teach the machine and that moat that you're building and the cheaper the access is to the data I'm guessing that you would and then you run your Nir, I got to thank you so much. the future of cloud and data.

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Breaking Analysis: ChatGPT Won't Give OpenAI First Mover Advantage


 

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> OpenAI The company, and ChatGPT have taken the world by storm. Microsoft reportedly is investing an additional 10 billion dollars into the company. But in our view, while the hype around ChatGPT is justified, we don't believe OpenAI will lock up the market with its first mover advantage. Rather, we believe that success in this market will be directly proportional to the quality and quantity of data that a technology company has at its disposal, and the compute power that it could deploy to run its system. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we unpack the excitement around ChatGPT, and debate the premise that the company's early entry into the space may not confer winner take all advantage to OpenAI. And to do so, we welcome CUBE collaborator, alum, Sarbjeet Johal, (chuckles) and John Furrier, co-host of the Cube. Great to see you Sarbjeet, John. Really appreciate you guys coming to the program. >> Great to be on. >> Okay, so what is ChatGPT? Well, actually we asked ChatGPT, what is ChatGPT? So here's what it said. ChatGPT is a state-of-the-art language model developed by OpenAI that can generate human-like text. It could be fine tuned for a variety of language tasks, such as conversation, summarization, and language translation. So I asked it, give it to me in 50 words or less. How did it do? Anything to add? >> Yeah, think it did good. It's large language model, like previous models, but it started applying the transformers sort of mechanism to focus on what prompt you have given it to itself. And then also the what answer it gave you in the first, sort of, one sentence or two sentences, and then introspect on itself, like what I have already said to you. And so just work on that. So it it's self sort of focus if you will. It does, the transformers help the large language models to do that. >> So to your point, it's a large language model, and GPT stands for generative pre-trained transformer. >> And if you put the definition back up there again, if you put it back up on the screen, let's see it back up. Okay, it actually missed the large, word large. So one of the problems with ChatGPT, it's not always accurate. It's actually a large language model, and it says state of the art language model. And if you look at Google, Google has dominated AI for many times and they're well known as being the best at this. And apparently Google has their own large language model, LLM, in play and have been holding it back to release because of backlash on the accuracy. Like just in that example you showed is a great point. They got almost right, but they missed the key word. >> You know what's funny about that John, is I had previously asked it in my prompt to give me it in less than a hundred words, and it was too long, I said I was too long for Breaking Analysis, and there it went into the fact that it's a large language model. So it largely, it gave me a really different answer the, for both times. So, but it's still pretty amazing for those of you who haven't played with it yet. And one of the best examples that I saw was Ben Charrington from This Week In ML AI podcast. And I stumbled on this thanks to Brian Gracely, who was listening to one of his Cloudcasts. Basically what Ben did is he took, he prompted ChatGPT to interview ChatGPT, and he simply gave the system the prompts, and then he ran the questions and answers into this avatar builder and sped it up 2X so it didn't sound like a machine. And voila, it was amazing. So John is ChatGPT going to take over as a cube host? >> Well, I was thinking, we get the questions in advance sometimes from PR people. We should actually just plug it in ChatGPT, add it to our notes, and saying, "Is this good enough for you? Let's ask the real question." So I think, you know, I think there's a lot of heavy lifting that gets done. I think the ChatGPT is a phenomenal revolution. I think it highlights the use case. Like that example we showed earlier. It gets most of it right. So it's directionally correct and it feels like it's an answer, but it's not a hundred percent accurate. And I think that's where people are seeing value in it. Writing marketing, copy, brainstorming, guest list, gift list for somebody. Write me some lyrics to a song. Give me a thesis about healthcare policy in the United States. It'll do a bang up job, and then you got to go in and you can massage it. So we're going to do three quarters of the work. That's why plagiarism and schools are kind of freaking out. And that's why Microsoft put 10 billion in, because why wouldn't this be a feature of Word, or the OS to help it do stuff on behalf of the user. So linguistically it's a beautiful thing. You can input a string and get a good answer. It's not a search result. >> And we're going to get your take on on Microsoft and, but it kind of levels the playing- but ChatGPT writes better than I do, Sarbjeet, and I know you have some good examples too. You mentioned the Reed Hastings example. >> Yeah, I was listening to Reed Hastings fireside chat with ChatGPT, and the answers were coming as sort of voice, in the voice format. And it was amazing what, he was having very sort of philosophy kind of talk with the ChatGPT, the longer sentences, like he was going on, like, just like we are talking, he was talking for like almost two minutes and then ChatGPT was answering. It was not one sentence question, and then a lot of answers from ChatGPT and yeah, you're right. I, this is our ability. I've been thinking deep about this since yesterday, we talked about, like, we want to do this segment. The data is fed into the data model. It can be the current data as well, but I think that, like, models like ChatGPT, other companies will have those too. They can, they're democratizing the intelligence, but they're not creating intelligence yet, definitely yet I can say that. They will give you all the finite answers. Like, okay, how do you do this for loop in Java, versus, you know, C sharp, and as a programmer you can do that, in, but they can't tell you that, how to write a new algorithm or write a new search algorithm for you. They cannot create a secretive code for you to- >> Not yet. >> Have competitive advantage. >> Not yet, not yet. >> but you- >> Can Google do that today? >> No one really can. The reasoning side of the data is, we talked about at our Supercloud event, with Zhamak Dehghani who's was CEO of, now of Nextdata. This next wave of data intelligence is going to come from entrepreneurs that are probably cross discipline, computer science and some other discipline. But they're going to be new things, for example, data, metadata, and data. It's hard to do reasoning like a human being, so that needs more data to train itself. So I think the first gen of this training module for the large language model they have is a corpus of text. Lot of that's why blog posts are, but the facts are wrong and sometimes out of context, because that contextual reasoning takes time, it takes intelligence. So machines need to become intelligent, and so therefore they need to be trained. So you're going to start to see, I think, a lot of acceleration on training the data sets. And again, it's only as good as the data you can get. And again, proprietary data sets will be a huge winner. Anyone who's got a large corpus of content, proprietary content like theCUBE or SiliconANGLE as a publisher will benefit from this. Large FinTech companies, anyone with large proprietary data will probably be a big winner on this generative AI wave, because it just, it will eat that up, and turn that back into something better. So I think there's going to be a lot of interesting things to look at here. And certainly productivity's going to be off the charts for vanilla and the internet is going to get swarmed with vanilla content. So if you're in the content business, and you're an original content producer of any kind, you're going to be not vanilla, so you're going to be better. So I think there's so much at play Dave (indistinct). >> I think the playing field has been risen, so we- >> Risen and leveled? >> Yeah, and leveled to certain extent. So it's now like that few people as consumers, as consumers of AI, we will have a advantage and others cannot have that advantage. So it will be democratized. That's, I'm sure about that. But if you take the example of calculator, when the calculator came in, and a lot of people are, "Oh, people can't do math anymore because calculator is there." right? So it's a similar sort of moment, just like a calculator for the next level. But, again- >> I see it more like open source, Sarbjeet, because like if you think about what ChatGPT's doing, you do a query and it comes from somewhere the value of a post from ChatGPT is just a reuse of AI. The original content accent will be come from a human. So if I lay out a paragraph from ChatGPT, did some heavy lifting on some facts, I check the facts, save me about maybe- >> Yeah, it's productive. >> An hour writing, and then I write a killer two, three sentences of, like, sharp original thinking or critical analysis. I then took that body of work, open source content, and then laid something on top of it. >> And Sarbjeet's example is a good one, because like if the calculator kids don't do math as well anymore, the slide rule, remember we had slide rules as kids, remember we first started using Waze, you know, we were this minority and you had an advantage over other drivers. Now Waze is like, you know, social traffic, you know, navigation, everybody had, you know- >> All the back roads are crowded. >> They're car crowded. (group laughs) Exactly. All right, let's, let's move on. What about this notion that futurist Ray Amara put forth and really Amara's Law that we're showing here, it's, the law is we, you know, "We tend to overestimate the effect of technology in the short run and underestimate it in the long run." Is that the case, do you think, with ChatGPT? What do you think Sarbjeet? >> I think that's true actually. There's a lot of, >> We don't debate this. >> There's a lot of awe, like when people see the results from ChatGPT, they say what, what the heck? Like, it can do this? But then if you use it more and more and more, and I ask the set of similar question, not the same question, and it gives you like same answer. It's like reading from the same bucket of text in, the interior read (indistinct) where the ChatGPT, you will see that in some couple of segments. It's very, it sounds so boring that the ChatGPT is coming out the same two sentences every time. So it is kind of good, but it's not as good as people think it is right now. But we will have, go through this, you know, hype sort of cycle and get realistic with it. And then in the long term, I think it's a great thing in the short term, it's not something which will (indistinct) >> What's your counter point? You're saying it's not. >> I, no I think the question was, it's hyped up in the short term and not it's underestimated long term. That's what I think what he said, quote. >> Yes, yeah. That's what he said. >> Okay, I think that's wrong with this, because this is a unique, ChatGPT is a unique kind of impact and it's very generational. People have been comparing it, I have been comparing to the internet, like the web, web browser Mosaic and Netscape, right, Navigator. I mean, I clearly still remember the days seeing Navigator for the first time, wow. And there weren't not many sites you could go to, everyone typed in, you know, cars.com, you know. >> That (indistinct) wasn't that overestimated, the overhyped at the beginning and underestimated. >> No, it was, it was underestimated long run, people thought. >> But that Amara's law. >> That's what is. >> No, they said overestimated? >> Overestimated near term underestimated- overhyped near term, underestimated long term. I got, right I mean? >> Well, I, yeah okay, so I would then agree, okay then- >> We were off the charts about the internet in the early days, and it actually exceeded our expectations. >> Well there were people who were, like, poo-pooing it early on. So when the browser came out, people were like, "Oh, the web's a toy for kids." I mean, in 1995 the web was a joke, right? So '96, you had online populations growing, so you had structural changes going on around the browser, internet population. And then that replaced other things, direct mail, other business activities that were once analog then went to the web, kind of read only as you, as we always talk about. So I think that's a moment where the hype long term, the smart money, and the smart industry experts all get the long term. And in this case, there's more poo-pooing in the short term. "Ah, it's not a big deal, it's just AI." I've heard many people poo-pooing ChatGPT, and a lot of smart people saying, "No this is next gen, this is different and it's only going to get better." So I think people are estimating a big long game on this one. >> So you're saying it's bifurcated. There's those who say- >> Yes. >> Okay, all right, let's get to the heart of the premise, and possibly the debate for today's episode. Will OpenAI's early entry into the market confer sustainable competitive advantage for the company. And if you look at the history of tech, the technology industry, it's kind of littered with first mover failures. Altair, IBM, Tandy, Commodore, they and Apple even, they were really early in the PC game. They took a backseat to Dell who came in the scene years later with a better business model. Netscape, you were just talking about, was all the rage in Silicon Valley, with the first browser, drove up all the housing prices out here. AltaVista was the first search engine to really, you know, index full text. >> Owned by Dell, I mean DEC. >> Owned by Digital. >> Yeah, Digital Equipment >> Compaq bought it. And of course as an aside, Digital, they wanted to showcase their hardware, right? Their super computer stuff. And then so Friendster and MySpace, they came before Facebook. The iPhone certainly wasn't the first mobile device. So lots of failed examples, but there are some recent successes like AWS and cloud. >> You could say smartphone. So I mean. >> Well I know, and you can, we can parse this so we'll debate it. Now Twitter, you could argue, had first mover advantage. You kind of gave me that one John. Bitcoin and crypto clearly had first mover advantage, and sustaining that. Guys, will OpenAI make it to the list on the right with ChatGPT, what do you think? >> I think categorically as a company, it probably won't, but as a category, I think what they're doing will, so OpenAI as a company, they get funding, there's power dynamics involved. Microsoft put a billion dollars in early on, then they just pony it up. Now they're reporting 10 billion more. So, like, if the browsers, Microsoft had competitive advantage over Netscape, and used monopoly power, and convicted by the Department of Justice for killing Netscape with their monopoly, Netscape should have had won that battle, but Microsoft killed it. In this case, Microsoft's not killing it, they're buying into it. So I think the embrace extend Microsoft power here makes OpenAI vulnerable for that one vendor solution. So the AI as a company might not make the list, but the category of what this is, large language model AI, is probably will be on the right hand side. >> Okay, we're going to come back to the government intervention and maybe do some comparisons, but what are your thoughts on this premise here? That, it will basically set- put forth the premise that it, that ChatGPT, its early entry into the market will not confer competitive advantage to >> For OpenAI. >> To Open- Yeah, do you agree with that? >> I agree with that actually. It, because Google has been at it, and they have been holding back, as John said because of the scrutiny from the Fed, right, so- >> And privacy too. >> And the privacy and the accuracy as well. But I think Sam Altman and the company on those guys, right? They have put this in a hasty way out there, you know, because it makes mistakes, and there are a lot of questions around the, sort of, where the content is coming from. You saw that as your example, it just stole the content, and without your permission, you know? >> Yeah. So as quick this aside- >> And it codes on people's behalf and the, those codes are wrong. So there's a lot of, sort of, false information it's putting out there. So it's a very vulnerable thing to do what Sam Altman- >> So even though it'll get better, others will compete. >> So look, just side note, a term which Reid Hoffman used a little bit. Like he said, it's experimental launch, like, you know, it's- >> It's pretty damn good. >> It is clever because according to Sam- >> It's more than clever. It's good. >> It's awesome, if you haven't used it. I mean you write- you read what it writes and you go, "This thing writes so well, it writes so much better than you." >> The human emotion drives that too. I think that's a big thing. But- >> I Want to add one more- >> Make your last point. >> Last one. Okay. So, but he's still holding back. He's conducting quite a few interviews. If you want to get the gist of it, there's an interview with StrictlyVC interview from yesterday with Sam Altman. Listen to that one it's an eye opening what they want- where they want to take it. But my last one I want to make it on this point is that Satya Nadella yesterday did an interview with Wall Street Journal. I think he was doing- >> You were not impressed. >> I was not impressed because he was pushing it too much. So Sam Altman's holding back so there's less backlash. >> Got 10 billion reasons to push. >> I think he's almost- >> Microsoft just laid off 10000 people. Hey ChatGPT, find me a job. You know like. (group laughs) >> He's overselling it to an extent that I think it will backfire on Microsoft. And he's over promising a lot of stuff right now, I think. I don't know why he's very jittery about all these things. And he did the same thing during Ignite as well. So he said, "Oh, this AI will write code for you and this and that." Like you called him out- >> The hyperbole- >> During your- >> from Satya Nadella, he's got a lot of hyperbole. (group talks over each other) >> All right, Let's, go ahead. >> Well, can I weigh in on the whole- >> Yeah, sure. >> Microsoft thing on whether OpenAI, here's the take on this. I think it's more like the browser moment to me, because I could relate to that experience with ChatG, personally, emotionally, when I saw that, and I remember vividly- >> You mean that aha moment (indistinct). >> Like this is obviously the future. Anything else in the old world is dead, website's going to be everywhere. It was just instant dot connection for me. And a lot of other smart people who saw this. Lot of people by the way, didn't see it. Someone said the web's a toy. At the company I was worked for at the time, Hewlett Packard, they like, they could have been in, they had invented HTML, and so like all this stuff was, like, they just passed, the web was just being passed over. But at that time, the browser got better, more websites came on board. So the structural advantage there was online web usage was growing, online user population. So that was growing exponentially with the rise of the Netscape browser. So OpenAI could stay on the right side of your list as durable, if they leverage the category that they're creating, can get the scale. And if they can get the scale, just like Twitter, that failed so many times that they still hung around. So it was a product that was always successful, right? So I mean, it should have- >> You're right, it was terrible, we kept coming back. >> The fail whale, but it still grew. So OpenAI has that moment. They could do it if Microsoft doesn't meddle too much with too much power as a vendor. They could be the Netscape Navigator, without the anti-competitive behavior of somebody else. So to me, they have the pole position. So they have an opportunity. So if not, if they don't execute, then there's opportunity. There's not a lot of barriers to entry, vis-a-vis say the CapEx of say a cloud company like AWS. You can't replicate that, Many have tried, but I think you can replicate OpenAI. >> And we're going to talk about that. Okay, so real quick, I want to bring in some ETR data. This isn't an ETR heavy segment, only because this so new, you know, they haven't coverage yet, but they do cover AI. So basically what we're seeing here is a slide on the vertical axis's net score, which is a measure of spending momentum, and in the horizontal axis's is presence in the dataset. Think of it as, like, market presence. And in the insert right there, you can see how the dots are plotted, the two columns. And so, but the key point here that we want to make, there's a bunch of companies on the left, is he like, you know, DataRobot and C3 AI and some others, but the big whales, Google, AWS, Microsoft, are really dominant in this market. So that's really the key takeaway that, can we- >> I notice IBM is way low. >> Yeah, IBM's low, and actually bring that back up and you, but then you see Oracle who actually is injecting. So I guess that's the other point is, you're not necessarily going to go buy AI, and you know, build your own AI, you're going to, it's going to be there and, it, Salesforce is going to embed it into its platform, the SaaS companies, and you're going to purchase AI. You're not necessarily going to build it. But some companies obviously are. >> I mean to quote IBM's general manager Rob Thomas, "You can't have AI with IA." information architecture and David Flynn- >> You can't Have AI without IA >> without, you can't have AI without IA. You can't have, if you have an Information Architecture, you then can power AI. Yesterday David Flynn, with Hammersmith, was on our Supercloud. He was pointing out that the relationship of storage, where you store things, also impacts the data and stressablity, and Zhamak from Nextdata, she was pointing out that same thing. So the data problem factors into all this too, Dave. >> So you got the big cloud and internet giants, they're all poised to go after this opportunity. Microsoft is investing up to 10 billion. Google's code red, which was, you know, the headline in the New York Times. Of course Apple is there and several alternatives in the market today. Guys like Chinchilla, Bloom, and there's a company Jasper and several others, and then Lena Khan looms large and the government's around the world, EU, US, China, all taking notice before the market really is coalesced around a single player. You know, John, you mentioned Netscape, they kind of really, the US government was way late to that game. It was kind of game over. And Netscape, I remember Barksdale was like, "Eh, we're going to be selling software in the enterprise anyway." and then, pshew, the company just dissipated. So, but it looks like the US government, especially with Lena Khan, they're changing the definition of antitrust and what the cause is to go after people, and they're really much more aggressive. It's only what, two years ago that (indistinct). >> Yeah, the problem I have with the federal oversight is this, they're always like late to the game, and they're slow to catch up. So in other words, they're working on stuff that should have been solved a year and a half, two years ago around some of the social networks hiding behind some of the rules around open web back in the days, and I think- >> But they're like 15 years late to that. >> Yeah, and now they got this new thing on top of it. So like, I just worry about them getting their fingers. >> But there's only two years, you know, OpenAI. >> No, but the thing (indistinct). >> No, they're still fighting other battles. But the problem with government is that they're going to label Big Tech as like a evil thing like Pharma, it's like smoke- >> You know Lena Khan wants to kill Big Tech, there's no question. >> So I think Big Tech is getting a very seriously bad rap. And I think anything that the government does that shades darkness on tech, is politically motivated in most cases. You can almost look at everything, and my 80 20 rule is in play here. 80% of the government activity around tech is bullshit, it's politically motivated, and the 20% is probably relevant, but off the mark and not organized. >> Well market forces have always been the determining factor of success. The governments, you know, have been pretty much failed. I mean you look at IBM's antitrust, that, what did that do? The market ultimately beat them. You look at Microsoft back in the day, right? Windows 95 was peaking, the government came in. But you know, like you said, they missed the web, right, and >> so they were hanging on- >> There's nobody in government >> to Windows. >> that actually knows- >> And so, you, I think you're right. It's market forces that are going to determine this. But Sarbjeet, what do you make of Microsoft's big bet here, you weren't impressed with with Nadella. How do you think, where are they going to apply it? Is this going to be a Hail Mary for Bing, or is it going to be applied elsewhere? What do you think. >> They are saying that they will, sort of, weave this into their products, office products, productivity and also to write code as well, developer productivity as well. That's a big play for them. But coming back to your antitrust sort of comments, right? I believe the, your comment was like, oh, fed was late 10 years or 15 years earlier, but now they're two years. But things are moving very fast now as compared to they used to move. >> So two years is like 10 Years. >> Yeah, two years is like 10 years. Just want to make that point. (Dave laughs) This thing is going like wildfire. Any new tech which comes in that I think they're going against distribution channels. Lina Khan has commented time and again that the marketplace model is that she wants to have some grip on. Cloud marketplaces are a kind of monopolistic kind of way. >> I don't, I don't see this, I don't see a Chat AI. >> You told me it's not Bing, you had an interesting comment. >> No, no. First of all, this is great from Microsoft. If you're Microsoft- >> Why? >> Because Microsoft doesn't have the AI chops that Google has, right? Google is got so much core competency on how they run their search, how they run their backends, their cloud, even though they don't get a lot of cloud market share in the enterprise, they got a kick ass cloud cause they needed one. >> Totally. >> They've invented SRE. I mean Google's development and engineering chops are off the scales, right? Amazon's got some good chops, but Google's got like 10 times more chops than AWS in my opinion. Cloud's a whole different story. Microsoft gets AI, they get a playbook, they get a product they can render into, the not only Bing, productivity software, helping people write papers, PowerPoint, also don't forget the cloud AI can super help. We had this conversation on our Supercloud event, where AI's going to do a lot of the heavy lifting around understanding observability and managing service meshes, to managing microservices, to turning on and off applications, and or maybe writing code in real time. So there's a plethora of use cases for Microsoft to deploy this. combined with their R and D budgets, they can then turbocharge more research, build on it. So I think this gives them a car in the game, Google may have pole position with AI, but this puts Microsoft right in the game, and they already have a lot of stuff going on. But this just, I mean everything gets lifted up. Security, cloud, productivity suite, everything. >> What's under the hood at Google, and why aren't they talking about it? I mean they got to be freaked out about this. No? Or do they have kind of a magic bullet? >> I think they have the, they have the chops definitely. Magic bullet, I don't know where they are, as compared to the ChatGPT 3 or 4 models. Like they, but if you look at the online sort of activity and the videos put out there from Google folks, Google technology folks, that's account you should look at if you are looking there, they have put all these distinctions what ChatGPT 3 has used, they have been talking about for a while as well. So it's not like it's a secret thing that you cannot replicate. As you said earlier, like in the beginning of this segment, that anybody who has more data and the capacity to process that data, which Google has both, I think they will win this. >> Obviously living in Palo Alto where the Google founders are, and Google's headquarters next town over we have- >> We're so close to them. We have inside information on some of the thinking and that hasn't been reported by any outlet yet. And that is, is that, from what I'm hearing from my sources, is Google has it, they don't want to release it for many reasons. One is it might screw up their search monopoly, one, two, they're worried about the accuracy, 'cause Google will get sued. 'Cause a lot of people are jamming on this ChatGPT as, "Oh it does everything for me." when it's clearly not a hundred percent accurate all the time. >> So Lina Kahn is looming, and so Google's like be careful. >> Yeah so Google's just like, this is the third, could be a third rail. >> But the first thing you said is a concern. >> Well no. >> The disruptive (indistinct) >> What they will do is do a Waymo kind of thing, where they spin out a separate company. >> They're doing that. >> The discussions happening, they're going to spin out the separate company and put it over there, and saying, "This is AI, got search over there, don't touch that search, 'cause that's where all the revenue is." (chuckles) >> So, okay, so that's how they deal with the Clay Christensen dilemma. What's the business model here? I mean it's not advertising, right? Is it to charge you for a query? What, how do you make money at this? >> It's a good question, I mean my thinking is, first of all, it's cool to type stuff in and see a paper get written, or write a blog post, or gimme a marketing slogan for this or that or write some code. I think the API side of the business will be critical. And I think Howie Xu, I know you're going to reference some of his comments yesterday on Supercloud, I think this brings a whole 'nother user interface into technology consumption. I think the business model, not yet clear, but it will probably be some sort of either API and developer environment or just a straight up free consumer product, with some sort of freemium backend thing for business. >> And he was saying too, it's natural language is the way in which you're going to interact with these systems. >> I think it's APIs, it's APIs, APIs, APIs, because these people who are cooking up these models, and it takes a lot of compute power to train these and to, for inference as well. Somebody did the analysis on the how many cents a Google search costs to Google, and how many cents the ChatGPT query costs. It's, you know, 100x or something on that. You can take a look at that. >> A 100x on which side? >> You're saying two orders of magnitude more expensive for ChatGPT >> Much more, yeah. >> Than for Google. >> It's very expensive. >> So Google's got the data, they got the infrastructure and they got, you're saying they got the cost (indistinct) >> No actually it's a simple query as well, but they are trying to put together the answers, and they're going through a lot more data versus index data already, you know. >> Let me clarify, you're saying that Google's version of ChatGPT is more efficient? >> No, I'm, I'm saying Google search results. >> Ah, search results. >> What are used to today, but cheaper. >> But that, does that, is that going to confer advantage to Google's large language (indistinct)? >> It will, because there were deep science (indistinct). >> Google, I don't think Google search is doing a large language model on their search, it's keyword search. You know, what's the weather in Santa Cruz? Or how, what's the weather going to be? Or you know, how do I find this? Now they have done a smart job of doing some things with those queries, auto complete, re direct navigation. But it's, it's not entity. It's not like, "Hey, what's Dave Vellante thinking this week in Breaking Analysis?" ChatGPT might get that, because it'll get your Breaking Analysis, it'll synthesize it. There'll be some, maybe some clips. It'll be like, you know, I mean. >> Well I got to tell you, I asked ChatGPT to, like, I said, I'm going to enter a transcript of a discussion I had with Nir Zuk, the CTO of Palo Alto Networks, And I want you to write a 750 word blog. I never input the transcript. It wrote a 750 word blog. It attributed quotes to him, and it just pulled a bunch of stuff that, and said, okay, here it is. It talked about Supercloud, it defined Supercloud. >> It's made, it makes you- >> Wow, But it was a big lie. It was fraudulent, but still, blew me away. >> Again, vanilla content and non accurate content. So we are going to see a surge of misinformation on steroids, but I call it the vanilla content. Wow, that's just so boring, (indistinct). >> There's so many dangers. >> Make your point, cause we got to, almost out of time. >> Okay, so the consumption, like how do you consume this thing. As humans, we are consuming it and we are, like, getting a nicely, like, surprisingly shocked, you know, wow, that's cool. It's going to increase productivity and all that stuff, right? And on the danger side as well, the bad actors can take hold of it and create fake content and we have the fake sort of intelligence, if you go out there. So that's one thing. The second thing is, we are as humans are consuming this as language. Like we read that, we listen to it, whatever format we consume that is, but the ultimate usage of that will be when the machines can take that output from likes of ChatGPT, and do actions based on that. The robots can work, the robot can paint your house, we were talking about, right? Right now we can't do that. >> Data apps. >> So the data has to be ingested by the machines. It has to be digestible by the machines. And the machines cannot digest unorganized data right now, we will get better on the ingestion side as well. So we are getting better. >> Data, reasoning, insights, and action. >> I like that mall, paint my house. >> So, okay- >> By the way, that means drones that'll come in. Spray painting your house. >> Hey, it wasn't too long ago that robots couldn't climb stairs, as I like to point out. Okay, and of course it's no surprise the venture capitalists are lining up to eat at the trough, as I'd like to say. Let's hear, you'd referenced this earlier, John, let's hear what AI expert Howie Xu said at the Supercloud event, about what it takes to clone ChatGPT. Please, play the clip. >> So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest to get a, you know, another shot to the openAI sort of the level." You know, I did a (indistinct) >> Line up. >> A hundred million dollar is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So a hundred- >> Guys a hundred million dollars, that's an astoundingly low figure. What do you make of it? >> I was in an interview with, I was interviewing, I think he said hundred million or so, but in the hundreds of millions, not a billion right? >> You were trying to get him up, you were like "Hundreds of millions." >> Well I think, I- >> He's like, eh, not 10, not a billion. >> Well first of all, Howie Xu's an expert machine learning. He's at Zscaler, he's a machine learning AI guy. But he comes from VMware, he's got his technology pedigrees really off the chart. Great friend of theCUBE and kind of like a CUBE analyst for us. And he's smart. He's right. I think the barriers to entry from a dollar standpoint are lower than say the CapEx required to compete with AWS. Clearly, the CapEx spending to build all the tech for the run a cloud. >> And you don't need a huge sales force. >> And in some case apps too, it's the same thing. But I think it's not that hard. >> But am I right about that? You don't need a huge sales force either. It's, what, you know >> If the product's good, it will sell, this is a new era. The better mouse trap will win. This is the new economics in software, right? So- >> Because you look at the amount of money Lacework, and Snyk, Snowflake, Databrooks. Look at the amount of money they've raised. I mean it's like a billion dollars before they get to IPO or more. 'Cause they need promotion, they need go to market. You don't need (indistinct) >> OpenAI's been working on this for multiple five years plus it's, hasn't, wasn't born yesterday. Took a lot of years to get going. And Sam is depositioning all the success, because he's trying to manage expectations, To your point Sarbjeet, earlier. It's like, yeah, he's trying to "Whoa, whoa, settle down everybody, (Dave laughs) it's not that great." because he doesn't want to fall into that, you know, hero and then get taken down, so. >> It may take a 100 million or 150 or 200 million to train the model. But to, for the inference to, yeah to for the inference machine, It will take a lot more, I believe. >> Give it, so imagine, >> Because- >> Go ahead, sorry. >> Go ahead. But because it consumes a lot more compute cycles and it's certain level of storage and everything, right, which they already have. So I think to compute is different. To frame the model is a different cost. But to run the business is different, because I think 100 million can go into just fighting the Fed. >> Well there's a flywheel too. >> Oh that's (indistinct) >> (indistinct) >> We are running the business, right? >> It's an interesting number, but it's also kind of, like, context to it. So here, a hundred million spend it, you get there, but you got to factor in the fact that the ways companies win these days is critical mass scale, hitting a flywheel. If they can keep that flywheel of the value that they got going on and get better, you can almost imagine a marketplace where, hey, we have proprietary data, we're SiliconANGLE in theCUBE. We have proprietary content, CUBE videos, transcripts. Well wouldn't it be great if someone in a marketplace could sell a module for us, right? We buy that, Amazon's thing and things like that. So if they can get a marketplace going where you can apply to data sets that may be proprietary, you can start to see this become bigger. And so I think the key barriers to entry is going to be success. I'll give you an example, Reddit. Reddit is successful and it's hard to copy, not because of the software. >> They built the moat. >> Because you can, buy Reddit open source software and try To compete. >> They built the moat with their community. >> Their community, their scale, their user expectation. Twitter, we referenced earlier, that thing should have gone under the first two years, but there was such a great emotional product. People would tolerate the fail whale. And then, you know, well that was a whole 'nother thing. >> Then a plane landed in (John laughs) the Hudson and it was over. >> I think verticals, a lot of verticals will build applications using these models like for lawyers, for doctors, for scientists, for content creators, for- >> So you'll have many hundreds of millions of dollars investments that are going to be seeping out. If, all right, we got to wrap, if you had to put odds on it that that OpenAI is going to be the leader, maybe not a winner take all leader, but like you look at like Amazon and cloud, they're not winner take all, these aren't necessarily winner take all markets. It's not necessarily a zero sum game, but let's call it winner take most. What odds would you give that open AI 10 years from now will be in that position. >> If I'm 0 to 10 kind of thing? >> Yeah, it's like horse race, 3 to 1, 2 to 1, even money, 10 to 1, 50 to 1. >> Maybe 2 to 1, >> 2 to 1, that's pretty low odds. That's basically saying they're the favorite, they're the front runner. Would you agree with that? >> I'd say 4 to 1. >> Yeah, I was going to say I'm like a 5 to 1, 7 to 1 type of person, 'cause I'm a skeptic with, you know, there's so much competition, but- >> I think they're definitely the leader. I mean you got to say, I mean. >> Oh there's no question. There's no question about it. >> The question is can they execute? >> They're not Friendster, is what you're saying. >> They're not Friendster and they're more like Twitter and Reddit where they have momentum. If they can execute on the product side, and if they don't stumble on that, they will continue to have the lead. >> If they say stay neutral, as Sam is, has been saying, that, hey, Microsoft is one of our partners, if you look at their company model, how they have structured the company, then they're going to pay back to the investors, like Microsoft is the biggest one, up to certain, like by certain number of years, they're going to pay back from all the money they make, and after that, they're going to give the money back to the public, to the, I don't know who they give it to, like non-profit or something. (indistinct) >> Okay, the odds are dropping. (group talks over each other) That's a good point though >> Actually they might have done that to fend off the criticism of this. But it's really interesting to see the model they have adopted. >> The wildcard in all this, My last word on this is that, if there's a developer shift in how developers and data can come together again, we have conferences around the future of data, Supercloud and meshs versus, you know, how the data world, coding with data, how that evolves will also dictate, 'cause a wild card could be a shift in the landscape around how developers are using either machine learning or AI like techniques to code into their apps, so. >> That's fantastic insight. I can't thank you enough for your time, on the heels of Supercloud 2, really appreciate it. All right, thanks to John and Sarbjeet for the outstanding conversation today. Special thanks to the Palo Alto studio team. My goodness, Anderson, this great backdrop. You guys got it all out here, I'm jealous. And Noah, really appreciate it, Chuck, Andrew Frick and Cameron, Andrew Frick switching, Cameron on the video lake, great job. And Alex Myerson, he's on production, manages the podcast for us, Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters. Rob Hof is our editor-in-chief over at SiliconANGLE, does some great editing, thanks to all. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast, wherever you listen. Publish each week on wikibon.com and siliconangle.com. Want to get in touch, email me directly, david.vellante@siliconangle.com or DM me at dvellante, or comment on our LinkedIn post. And by all means, check out etr.ai. They got really great survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, We'll see you next time on Breaking Analysis. (electronic music)

Published Date : Jan 20 2023

SUMMARY :

bringing you data-driven and ChatGPT have taken the world by storm. So I asked it, give it to the large language models to do that. So to your point, it's So one of the problems with ChatGPT, and he simply gave the system the prompts, or the OS to help it do but it kind of levels the playing- and the answers were coming as the data you can get. Yeah, and leveled to certain extent. I check the facts, save me about maybe- and then I write a killer because like if the it's, the law is we, you know, I think that's true and I ask the set of similar question, What's your counter point? and not it's underestimated long term. That's what he said. for the first time, wow. the overhyped at the No, it was, it was I got, right I mean? the internet in the early days, and it's only going to get better." So you're saying it's bifurcated. and possibly the debate the first mobile device. So I mean. on the right with ChatGPT, and convicted by the Department of Justice the scrutiny from the Fed, right, so- And the privacy and thing to do what Sam Altman- So even though it'll get like, you know, it's- It's more than clever. I mean you write- I think that's a big thing. I think he was doing- I was not impressed because You know like. And he did the same thing he's got a lot of hyperbole. the browser moment to me, So OpenAI could stay on the right side You're right, it was terrible, They could be the Netscape Navigator, and in the horizontal axis's So I guess that's the other point is, I mean to quote IBM's So the data problem factors and the government's around the world, and they're slow to catch up. Yeah, and now they got years, you know, OpenAI. But the problem with government to kill Big Tech, and the 20% is probably relevant, back in the day, right? are they going to apply it? and also to write code as well, that the marketplace I don't, I don't see you had an interesting comment. No, no. First of all, the AI chops that Google has, right? are off the scales, right? I mean they got to be and the capacity to process that data, on some of the thinking So Lina Kahn is looming, and this is the third, could be a third rail. But the first thing What they will do out the separate company Is it to charge you for a query? it's cool to type stuff in natural language is the way and how many cents the and they're going through Google search results. It will, because there were It'll be like, you know, I mean. I never input the transcript. Wow, But it was a big lie. but I call it the vanilla content. Make your point, cause we And on the danger side as well, So the data By the way, that means at the Supercloud event, So one of the VCs actually What do you make of it? you were like "Hundreds of millions." not 10, not a billion. Clearly, the CapEx spending to build all But I think it's not that hard. It's, what, you know This is the new economics Look at the amount of And Sam is depositioning all the success, or 150 or 200 million to train the model. So I think to compute is different. not because of the software. Because you can, buy They built the moat And then, you know, well that the Hudson and it was over. that are going to be seeping out. Yeah, it's like horse race, 3 to 1, 2 to 1, that's pretty low odds. I mean you got to say, I mean. Oh there's no question. is what you're saying. and if they don't stumble on that, the money back to the public, to the, Okay, the odds are dropping. the model they have adopted. Supercloud and meshs versus, you know, on the heels of Supercloud

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Amit Eyal Govrin, Kubiya.ai | Cube Conversation


 

(upbeat music) >> Hello everyone, welcome to this special Cube conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE in theCUBE Studios. We've got a special video here. We love when we have startups that are launching. It's an exclusive video of a hot startup that's launching. Got great reviews so far. You know, word on the street is, they got something different and unique. We're going to' dig into it. Amit Govrin who's the CEO and co-founder of Kubiya, which stands for Cube in Hebrew, and they're headquartered in Bay Area and in Tel Aviv. Amit, congratulations on the startup launch and thanks for coming in and talk to us in theCUBE >> Thank you, John, very nice to be here. >> So, first of all, a little, 'cause we love the Cube, 'cause theCUBE's kind of an open brand. We've never seen the Cube in Hebrew, so is that true? Kubiya is? >> Kubiya literally means cube. You know, clearly there's some additional meanings that we can discuss. Obviously we're also launching a KubCon, so there's a dual meaning to this event. >> KubCon, not to be confused with CubeCon. Which is an event we might have someday and compete. No, I'm only kidding, good stuff. I want to get into the startup because I'm intrigued by your story. One, you know, conversational AI's been around, been a category. We've seen chat bots be all the rage and you know, I kind of don't mind chat bots on some sites. I can interact with some, you know, form based knowledge graph, whatever, knowledge database and get basic stuff self served. So I can see that, but it never really scaled or took off. And now with Cloud Native kind of going to the next level, we're starting to see a lot more open source and a lot more automation, in what I call AI as code or you know, AI as a service, machine learning, developer focused action. I think you guys might have an answer there. So if you don't mind, could you take a minute to explain what you guys are doing, what's different about Kubiya, what's happening? >> Certainly. So thank you for that. Kubiya is what we would consider the first, or one of the first, advanced virtual assitants with a domain specific expertise in DevOps. So, we respect all of the DevOps concepts, GitOps, workflow automation, of those categories you've mentioned, but also the added value of the conversational AI. That's really one of the few elements that we can really bring to the table to extract what we call intent based operations. And we can get into what that means in a little bit. I'll save that maybe for the next question. >> So the market you're going after is kind of, it's, I love to hear starters when they, they don't have a Gartner Magic quadrant, they can fit nicely, it means they're onto something. What is the market you're going after? Because you're seeing a lot of developers driving a lot of the key successes in DevOps. DevOps has evolved to the point where, and DevSecOps, where developers are driving the change. And so having something that's developer focused is key. Are you guys targeting the developers, IT buyers, cloud architects? Who are you looking to serve with this new opportunity? >> So essentially self-service in the world of DevOps, the end user typically would be a developer, but not only, and obviously the operators, those are the folks that we're actually looking to help augment a lot of their efforts, a lot of the toil that they're experiencing in a day to day. So there's subcategories within that. We can talk about the different internal developer tools, or platforms, shared services platforms, service catalogs are tangential categories that this kind of comes on. But on top of that, we're adding the element of conversational AI. Which, as I mentioned, that's really the "got you". >> I think you're starting to see a lot of autonomous stuff going on, autonomous pen testing. There's a company out there doing I've seen autonomous AI. Automation is a big theme of it. And I got to ask, are you guys on the business side purely in the cloud? Are you born in the cloud, is it a cloud service? What's the product choice there? It's a service, right? >> Software is a service. We have the classic, Multi-Tenancy SAAS, but we also have a hybrid SAAS solution, which allows our customers to run workflows using remote runners, essentially hosted at their own location. >> So primary cloud, but you're agnostic on where they could consume, how they want to' consume the product. >> Technology agnostic. >> Okay, so that's cool. So let's get into the problem you're solving. So take me through, this will drive a lot of value here, when you guys did the company, what problems did you hone in on and what are you guys seeing as the core problem that you solve? >> So we, this is a unique, I don't know how unique, but this is a interesting proposition because I come from the business side, so call it the top down. I've been in enterprise sales, I've been in a CRO, VP sales hat. My co-founder comes from the bottom up, right? He ran DevOps teams and SRE teams in his previous company. That's actually what he did. So, we met each other halfway, essentially with me seeing a lot of these problems of self-service not being so self-service after all, platforms hitting walls with adoption. And he actually created his own self-service platform, within his last company, to address his own personal pains. So we essentially kind of met with both perspectives. >> So you're absolutely hardcore on self-service. >> We're enabling self-service. >> And that basically is what everybody wants. I mean, the developers want self-service. I mean, that's kind of like, you know, that's the nirvana. So take us through what you guys are offering, give us an example of use cases and who's buying your product, why, and take us through that whole piece. >> Do you mind if I take a step back and say why we believe self-service has somewhat failed or not gotten off. >> Yeah, absolutely. >> So look, this is essentially how we're looking at it. All the analysts and the industry insiders are talking about self-service platforms as being what's going to' remove the dependency of the operator in the loop the entire time, right? Because the operator, that scarce resource, it's hard to hire, hard to train, hard to retain those folks, Developers are obviously dependent on them for productivity. So the operators in this case could be a DevOps, could be a SecOps, it could be a platform engineer. It comes in different flavors. But the common denominator, somebody needs an access request, provisioning a new environment, you name it, right? They go to somebody, that person is operator. The operator typically has a few things on their plate. It's not just attending and babysitting platforms, but it's also innovating, spinning up, and scaling services. So they see this typically as kind of, we don't really want to be here, we're going to' go and do this because we're on call. We have to take it on a chin, if you may, for this. >> It's their child, they got to' do it. >> Right, but it's KTLOs, right, keep the lights on, this is maintenance of a platform. It's not what they're born and bred to do, which is innovate. That's essentially what we're seeing, we're seeing that a lot of these platforms, once they finally hit the point of maturity, they're rolled out to the team. People come to serve themselves in platform, and low and behold, it's not as self-service as it may seem. >> We've seen that certainly with Kubernetes adoption being, I won't say slow, it's been fast, but it's been good. But I think this is kind of the promise of what SRE was supposed to be. You know, do it once and then babysit in the sense of it's working and automated. Nothing's broken yet. Don't call me unless you need something, I see that. So the question, you're trying to make it easier then, you're trying to free up the talent. >> Talent to operate and have essentially a human, like in the loop, essentially augment that person and give the end users all of the answers they require, as if they're talking to a person. >> I mean it's basically, you're taking the virtual assistant concept, or chat bot, to a level of expertise where there's intelligence, jargon, experience into the workflows that's known. Not just talking to chat bot, get a support number to rebook a hotel room. >> We're converting operational workflows into conversations. >> Give me an example, take me through an example. >> Sure, let's take a simple example. I mean, not everyone provisions EC2's with two days (indistinct). But let's say you want to go and provision new EC2 instances, okay? If you wanted to do it, you could go and talk to the assistant and say, "I want to spin up a new server". If it was a human in the loop, they would ask you the following questions: what type of environment? what are we attributing this to? what type of instance? security groups, machine images, you name it. So, these are the questions that typically somebody needs to be armed with before they can go and provision themselves, serve themselves. Now the problem is users don't always have these questions. So imagine the following scenario. Somebody comes in, they're in Jira ticket queue, they finally, their turn is up and the next question they don't have the answer to. So now they have to go and tap on a friend, or they have to go essentially and get that answer. By the time they get back, they lost their turn in queue. And then that happens again. So, they lose a context, they lose essentially the momentum. And a simple access request, or a simple provision request, can easily become a couple days of ping pong back and forth. This won't happen with the virtual assistant. >> You know, I think, you know, and you mentioned chat bots, but also RPA is out there, you've seen a lot of that growth. One of the hard things, and you brought this up, I want to get your reaction to, is contextualizing the workflow. It might not be apparent, but the answer might be there, it disrupts the entire experience at that point. RPA and chat bots don't have that contextualization. Is that what you guys do differently? Is that the unique flavor here? Is that difference between current chat bots and RPA? >> The way we see it, I alluded to the intent based operations. Let me give a tangible experience. Even not from our own world, this will be easy. It's a bidirectional feedback loop 'cause that's actually what feeds the context and the intent. We all know Waze, right, in the world of navigation. They didn't bring navigation systems to the world. What they did is they took the concept of navigation systems that are typically satellite guided and said it's not just enough to drive down the 280, which typically have no traffic, right, and to come across traffic and say, oh, why didn't my satellite pick that up? So they said, have the end users, the end nodes, feed that direction back, that feedback, right. There has to be a bidirectional feedback loop that the end nodes help educate the system, make the system be better, more customized. And that's essentially what we're allowing the end users. So the maintenance of the system isn't entirely in the hands of the operators, right? 'Cause that's the part that they dread. And the maintenance of the system is democratized across all the users that they can teach the system, give input to the system, hone in the system in order to make it more of the DNA of the organization. >> You and I were talking before you came on this camera interview, you said playfully that the Siri for DevOps, which kind of implies, hey infrastructure, do something for me. You know, we all know Siri, so we get that. So that kind of illustrates kind of where the direction is. Explain why you say that, what does that mean? Is that like a NorthStar vision that you guys are approaching? You want to' have a state where everything's automated in it's conversational deployments, that kind of thing. And take us through why that Siri for DevOps is. >> I think it helps anchor people to what a virtual assistant is. Because when you hear virtual assistant, that can mean any one of various connotations. So the Siri is actually a conversational assistant, but it's not necessarily a virtual assistant. So what we're saying is we're anchoring people to that thought and saying, we're actually allowing it to be operational, turning complex operations into simple conversations. >> I mean basically they take the automate with voice Google search or a query, what's the score of the game? And, it also, and talking to the guy who invented Siri, I actually interviewed on theCUBE, it's a learning system. It actually learns as it gets more usage, it learns. How do you guys see that evolving in DevOps? There's a lot of jargon in DevOps, a lot of configurations, a lot of different use cases, a lot of new technologies. What's the secret sauce behind what you guys do? Is it the conversational AI, is it the machine learning, is it the data, is it the model? Take us through the secret sauce. >> In fact, it's all the above. And I don't think we're bringing any one element to the table that hasn't been explored before, hasn't been done. It's a recipe, right? You give two people the same ingredients, they can have complete different results in terms of what they come out with. We, because of our domain expertise in DevOps, because of our familiarity with developer workflows with operators, we know how to give a very well suited recipe. Five course meal, hopefully with Michelin stars as part of that. So a few things, maybe a few of the secret sauce element, conversational AI, the ability to essentially go and extract the intent of the user, so that if we're missing context, the system is smart enough to go and to get that feedback and to essentially feed itself into that model. >> Someone might say, hey, you know, conversational AI, that was yesterday's trend, it never happened. It was kind of weak, chat bots were lame. What's different now and with you guys, and the market, that makes a redo or a second shot at this, a second bite at the apple, as they say. What do you guys see? 'Cause you know, I would argue that it's, you know, it's still early, real early. >> Certainly. >> How do you guys view that? How would you handle that objection? >> It's a fair question. I wasn't around the first time around to tell you what didn't work. I'm not afraid to share that the feedback that we're getting is phenomenal. People understand that we're actually customizing the workflows, the intent based operations to really help hone in on the dark spots. We call it last mile, you know, bottlenecks. And that's really where we're helping. We're helping in a way tribalize internal knowledge that typically hasn't been documented because it's painful enough to where people care about it but not painful enough to where you're going to' go and sit down an entire day and document it. And that's essentially what the virtual assistant can do. It can go and get into those crevices and help document, and operationalize all of those toils. And into workflows. >> Yeah, I mean some will call it grunt work, or low level work. And I think the automation is interesting. I think we're seeing this in a lot of these high scale situations where the talented hard to hire person is hired to do, say, things that were hard to do, but now harder things are coming around the corner. So, you know, serverless is great and all this is good, but it doesn't make the complexity go away. As these inflection points continue to drive more scale, the complexity kind of grows, but at the same time so is the ability to abstract away the complexity. So you're starting to see the smart, hired guns move to higher, bigger problems. And the automation seems to take the low level kind of like capabilities or the toil, or the grunt work, or the low level tasks that, you know, you don't want a high salaried person doing. Or I mean it's not so much that they don't want to' do it, they'll take one for the team, as you said, or take it on the chin, but there's other things to work on. >> I want to add one more thing, 'cause this goes into essentially what you just said. Think about it's not the virtual system, what it gives you is not just the intent and that's one element of it, is the ability to carry your operations with you to the place where you're not breaking your workflows, you're actually comfortable operating. So the virtual assistant lives inside of a command line interface, it lives inside of chat like Slack, and Teams, and Mattermost, and so forth. It also lives within a low-code editor. So we're not forcing anyone to use uncomfortable language or operations if they're not comfortable with. It's almost like Siri, it travels in your mobile phone, it's on your laptop, it's with you everywhere. >> It makes total sense. And the reason why I like this, and I want to' get your reaction on this because we've done a lot of interviews with DevOps, we've met at every CubeCon since it started, and Kubernetes kind of highlights the value of the containers at the orchestration level. But what's really going on is the DevOps developers, and the CICD pipeline, with infrastructure's code, they're basically have a infrastructure configuration at their disposal all the time. And all the ops challenges have been around that, the repetitive mundane tasks that most people do. There's like six or seven main use cases in DevOps. So the guardrails just need to be set. So it sounds like you guys are going down the road of saying, hey here's the use cases you can bounce around these use cases all day long. And just keep doing your jobs cause they're bolting on infrastructure to every application. >> There's one more element to this that we haven't really touched on. It's not just workflows and use cases, but it's also knowledge, right? Tribal knowledge, like you asked me for an example. You can type or talk to the assistant and ask, "How much am I spending on AWS, on US East 1, on so and so customer environment last week?", and it will know how to give you that information. >> Can I ask, should I buy a reserve instances or not? Can I ask that question? 'Cause there's always good trade offs between buying the reserve instances. I mean that's kind of the thing that. >> This is where our ecosystem actually comes in handy because we're not necessarily going to' go down every single domain and try to be the experts in here. We can tap into the partnerships, API, we have full extensibility in API and the software development kit that goes into. >> It's interesting, opinionated and declarative are buzzwords in developer language. So you started to get into this editorial thing. So I can bring up an example. Hey cube, implement the best service mesh. What answer does it give you? 'Cause there's different choices. >> Well this is actually where the operator, there's clearly guard rails. Like you can go and say, I want to' spin up a machine, and it will give you all of the machines on AWS. Doesn't mean you have to get the X one, that's good for a SAP environment. You could go and have guardrails in place where only the ones that are relevant to your team, ones that have resources and budgetary, you know, guidelines can be. So, the operator still has all the control. >> It was kind of tongue in cheek around the editorialized, but actually the answer seems to be as you're saying, whatever the customer decided their service mesh is. So I think this is where it gets into as an assistant to architecting and operating, that seems to be the real value. >> Now code snippets is a different story because that goes on to the web, that goes onto stock overflow, and that's actually one of the things. So inside the CLI, you could actually go and ask for code snippets and we could actually go and populate that, it's a smart CLI. So that's actually one of the things that are an added value of that. >> I was saying to a friend and we were talking about open source and how when I grew up, there was no open source. If you're a developer now, I mean there's so much code, it's not so much coding anymore as it is connecting and integrating. >> Certainly. >> And writing glue layers, if you will. I mean there's still code, but it's not, you don't have to build it from scratch. There's so much code out there. This low-code notion of a smart system is interesting 'cause it's very matrix like. It can build its own code. >> Yes, but I'm also a little wary with low-code and no code. I think part of the problem is we're so constantly focused on categories and categorizing ourselves, and different categories take on a life of their own. So low-code no code is not necessarily, even though we have the low-code editor, we're not necessarily considering ourselves low-code. >> Serverless, no code, low-code. I was so thrown on a term the other day, architecture-less. As a joke, no we don't need architecture. >> There's a use case around that by the way, yeah, we do. Show me my AWS architecture and it will build the architect diagram for you. >> Again, serverless architect, this is all part of infrastructure's code. At the end of the day, the developer has infrastructure with code. Again, how they deploy it is the neuron. That's what we've been striving for. >> But infrastructure is code. You can destroy, you know, terraform, you can go and create one. It's not necessarily going to' operate it for you. That's kind of where this comes in on top of that. So it's really complimentary to infrastructure. >> So final question, before we get into the origination story, data and security are two hot areas we're seeing fill the IT gap, that has moved into the developer role. IT is essentially provisioned by developers now, but the OP side shifted to large scale SRE like environments, security and data are critical. What's your opinion on those two things? >> I agree. Do you want me to give you the normal data as gravity? >> So you agree that IT is now, is kind of moved into the developer realm, but the new IT is data ops and security ops basically. >> A hundred percent, and the lines are so blurred. Like who's what in today's world. I mean, I can tell you, I have customers who call themselves five different roles in the same day. So it's, you know, at the end of the day I call 'em operators 'cause I don't want to offend anybody because that's just the way it is. >> Architectural-less, we're going to' come back to that. Well, I know we're going to' see you at CubeCon. >> Yes. >> We should catch up there and talk more. I'm looking forward to seeing how you guys get the feedback from the marketplace. It should be interesting to hear, the curious question I have for you is, what was the origination story? Why did you guys come together, was it a shared problem? Was it a big market opportunity? Was it an itch you guys were scratching? Did you feel like you needed to come together and start this company? What was the real vision behind the origination? Take a take a minute to explain the story. >> No, absolutely. So I've been living in Palo Alto for the last couple years. Previous, also a founder. So, you know, from my perspective, I always saw myself getting back in the game. Spent a few years in AWS essentially managing partnerships for tier one DevOps partners, you know, all of the known players. Some in public, some of them not. And really the itch was there, right. I saw what everyone's doing. I started seeing consistency in the pains that I was hearing back, in terms of what hasn't been solved. So I already had an opinion where I wanted to go. And when I was visiting actually Israel with the family, I was introduced by a mutual friend to Shaked, Shaked Askayo, my co-founder and CTO. Amazing guy, unbelievable technologists, probably one the most, you know, impressive folks I've had a chance to work with. And he actually solved a very similar problem, you know, in his own way in a previous company, BlueVine, a FinTech company where he was head of SRE, having to, essentially, oversee 200 developers in a very small team. The ratio was incongruent to what the SRE guideline would tell. >> That's more than 10 x rate developer. >> Oh, absolutely. Sure enough. And just imagine it's four different time zones. He finishes day shift and you already had the US team coming, asking for a question. He said, this is kind of a, >> Got to' clone himself, basically. >> Well, yes. He essentially said to me, I had no day, I had no life, but I had Corona, I had COVID, which meant I could work from home. And I essentially programed myself in the form of a bot. Essentially, when people came to him, he said, "Don't talk to me, talk to the bot". Now that was a different generation. >> Just a trivial example, but the idea was to automate the same queries all the time. There's an answer for that, go here. And that's the benefit of it. >> Yes, so he was able to see how easy it was to solve, I mean, how effective it was solving 70% of the toil in his organization. Scaling his team, froze the headcount and the developer team kept on going. So that meant that he was doing some right. >> When you have a problem, and you need to solve it, the creativity comes out of the woodwork, you know, invention is the mother of necessity. So final question for you, what's next? Got the launch, what are you guys hope to do over the next six months to a year, hiring? Put a plug in for the company. What are you guys looking to do? Take a minute to share the future vision and get a plug in. >> A hundred percent. So, Kubiya, as you can imagine, announcing ourselves at CubeCon, so in a couple weeks. Opening the gates towards the public beta and NGA in the next couple months. Essentially working with dozens of customers, Aston Martin, and business earn in. We have quite a few, our website's full of quotes. You can go ahead. But effectively we're looking to go and to bring the next operator, generation of operators, who value their time, who value the, essentially, the value of tribal knowledge that travels between organizations that could be essentially shared. >> How many customers do you guys have in your pre-launch? >> It's above a dozen. Without saying, because we're actually looking to onboard 10 more next week. So that's just an understatement. It changes from day to day. >> What's the number one thing people are saying about you? >> You got that right. I know it's, I'm trying to be a little bit more, you know. >> It's okay, you can be cocky, startups are good. But I mean they're obviously, they're using the product and you're getting good feedback. Saving time, are they saying this is a dream product? Got it right, what are some of the things? >> I think anybody who doesn't feel the pain won't know, but the folks who are in the trenches, or feeling the pain, or experiencing this toil, who know what this means, they said, "You're doing this different, you're doing this right. You architected it right. You know exactly what the developer workflows," you know, where all the areas, you know, where all the skeletons are hidden within that. And you're attending to that. So we're happy about that. >> Everybody wants to clone themselves, again, the tribal knowledge. I think this is a great example of where we see the world going. Make things autonomous, operationally automated for the use cases you know are lock solid. Why wouldn't you just deploy? >> Exactly, and we have a very generous free tier. People can, you know, there's a plugin, you can sign up for free until the end of the year. We have a generous free tier. Yeah, free forever tier, as well. So we're looking for people to try us out and to give us feedback. >> I think the self-service, I think the point is, we've talked about it on the Cube at our events, everyone says the same thing. Every developer wants self-service, period. Full stop, done. >> What they don't say is they need somebody to help them babysit to make sure they're doing it right. >> The old dashboard, green, yellow, red. >> I know it's an analogy that's not related, but have you been to Whole Foods? Have you gone through their self-service line? That's the beauty of it, right? Having someone in a loop helping you out throughout the time. You don't get confused, if something's not working, someone's helping you out, that's what people want. They want a human in the loop, or a human like in the loop. We're giving that next best thing. >> It's really the ratio, it's scale. It's a scaling. It's force multiplier, for sure. Amit, thanks for coming on, congratulations. >> Thank you so much. >> See you at KubeCon. Thanks for coming in, sharing the story. >> KubiyaCon. >> CubeCon. Cube in Hebrew, Kubiya. Founder, co-founder and CEO here, sharing the story in the launch. Conversational AI for DevOps, the theory of DevOps, really kind of changing the game, bringing efficiency, solving a lot of the pain points of large scale infrastructure. This is theCUBE, CUBE conversation, I'm John Furrier, thanks for watching. (upbeat electronic music)

Published Date : Oct 18 2022

SUMMARY :

on the startup launch We've never seen the Cube so there's a dual meaning to this event. I can interact with some, you know, but also the added value of the conversational AI. a lot of the key successes in DevOps. a lot of the toil that they're What's the product choice there? We have the classic, Multi-Tenancy SAAS, So primary cloud, So let's get into the call it the top down. So you're absolutely I mean, the developers want self-service. Do you mind if I take a step back So the operators in this keep the lights on, this is of the promise of what SRE all of the answers they require, experience into the We're converting operational take me through an example. So imagine the following scenario. Is that the unique flavor here? that the end nodes help the Siri for DevOps, So the Siri is actually a is it the data, is it the model? the system is smart enough to a second bite at the apple, as they say. on the dark spots. And the automation seems to it, is the ability to carry So the guardrails just need to be set. the assistant and ask, I mean that's kind of the thing that. and the software development implement the best service mesh. of the machines on AWS. but actually the answer So inside the CLI, you could actually go I was saying to a And writing glue layers, if you will. So low-code no code is not necessarily, I was so thrown on a term the around that by the way, At the end of the day, You can destroy, you know, terraform, that has moved into the developer role. the normal data as gravity? is kind of moved into the developer realm, in the same day. to' see you at CubeCon. the curious question I have for you is, And really the itch was there, right. the US team coming, asking for a question. myself in the form of a bot. And that's the benefit of it. and the developer team kept on going. of the woodwork, you know, and NGA in the next couple months. It changes from day to day. bit more, you know. It's okay, you can be but the folks who are in the for the use cases you know are lock solid. and to give us feedback. everyone says the same thing. need somebody to help them That's the beauty of it, right? It's really the ratio, it's scale. Thanks for coming in, sharing the story. sharing the story in the launch.

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Maribel Lopez & Zeus Kerravala | theCUBE on Cloud 2021


 

>>from around the globe. It's the Cube presenting Cuban cloud brought >>to you by silicon angle. Okay, we're back. Here. Live Cuban Cloud. And this is Dave. Want with my co host, John Ferrier Were all remote. We're getting into the analyst power half hour. Really pleased to have Maribel Lopez here. She's the principal and founder of Lopez Research and Zias Caraballo, who is the principal and founder of ZK research. Guys, great to see you. Let's get into it. How you doing? >>Great. How you been? Good, >>thanks. Really good. John's hanging in there quarantining and, uh, all healthy, So I hope you guys are too. Hey, Mary, But let's start with you. You know, here we are on 2021 you know, just exited one of the strangest years, if not the strangest year of our lives. But looking back in the past decade of cloud and we're looking forward. How do you see that? Where do we come from? Where we at and where we going >>When we obviously started with the whole let's build a public cloud and everything was about public cloud. Uh, then we went thio the notion of private cloud than we had hybrid cloud and multi cloud. So we've done a lot of different clouds right now. And I think where we are today is that there's a healthy recognition on the cloud computing providers that you need to give it to the customers the way they want it, not the way you've decided to build it. So how do you meet them where they are so that they can have a cloud like experience wherever they want their data to be? >>Yes and yes, you've, you know, observed, This is well, in the early days of cloud, you heard a lot of rhetoric. It was private cloud And and then now we're, you know, hearing a lot of multi cloud and so forth. But initially, a lot of the traditional vendors kind of pooh poohed it. They called us analysts. We said we were all cloud crazy, but they seem to have got their religion. >>Well, everything. Everyone's got a definition of cloud, but I actually think we are right in the midst of another transformation of clouds Miracle talked about. We went from, you know, private clouds, which is really hosting the public cloud to multi cloud hybrid cloud. And if you look at the last post that put on Silicon Angle, which was talking about five acquisition of Volterra, I actually think we're in the midst of the transition to what's called distributed Club, where if you look at modernized cloud apps today, they're actually made up of services from different clouds on also distributed edge locations. And that's gonna have a pretty profound impact on the way we build out, because those distributed edges be a telco edge, cellular vagina. Th whatever the services that lived there are much more ephemeral in nature, right? So the way we secure the way we connect changes quite a bit. But I think that the great thing about Cloud is we've seen several several evolutionary changes. So what the definition is and we're going through that now, which is which is pretty cool to think about, right? It's not a static thing. Um, it's, uh, you know, it's a it's an ongoing transition. But I think, uh, you know, we're moving into this distributed Cloudera, which to me is a lot more complex than what we're dealing with in the Palace. >>I'm actually pretty excited about that because I think that this move toe edge and the distribution that you've talked about, it's like we now have processing everywhere. We've got it on devices, we've got it in, cars were moving, the data centers closer and closer to where the action's happening. And I think that's gonna be a huge trend for 2021. Is that distributed that you were talking about a lot of edge discussion? You >>know what? The >>reason we're doing This, too, is we want. It's not just we're moving the data closer to the user, right? And some. If you think you brought up the autonomous vehicle right in the car being an edge, you think of the data that generates right? There's some things such as the decision to stop or not right that should be done in car. I don't wanna transport that data all the way back to Google him back to decide whether I want to stop. You could also use the same data determine whether drivers driving safely for insurance purposes, right? So the same data give me located at the edge or in a centralized cloud for different purposes, and I think that's what you know, kind of cool about this is we're being able to use our data and much different ways. Now. >>You know, it's interesting is it's so complex. It's mind blowing because this is distributed computing. Everyone kind of agrees this is where it is. But if you think about the complexity and I want to get your guys reaction to this because you know some of the like side fringe trend discussions are data sovereignty, misinformation as a vulnerability. Okay, you get the chips now you got gravitas on with Amazon in front. Apple's got their own chips. Intel is gonna do a whole new direction. So you've got tons of computer. And then you mentioned the ephemeral nature. How do you manage those? What's the observe ability look like? They're what's the trust equation? So all these things kind of play into it. It sounds almost mind blowing, just even thinking about it. But how do you guys, this analyst tryto understand where someone's either blowing bullshit or kind of like has the real deal? Because all those things come into play? I mean, you could have a misinformation campaign targeting the car. Let's say Hey, you know that that data is needs to be. This is this is misinformation who's a >>in a lot of ways, this creates almost unprecedented opportunity now for for starts and for companies to transform right. The fundamental tenet of my research has always been share shifts happen when markets transition and we're in the middle of the big one. If the computer resource is we're using, John and the application resource will be using or ephemeral nature than all the things that surrounded the way we secured the way we connect. Those also have to be equal, equally agile, right, So you can't have, you know, you think of a micro services based application being secured with traditional firewalls, right? Just the amount of, or even virtual the way that the length of time it takes to spend those things up is way too long. So in many ways, this distributed cloud change changes everything in I T. And that that includes all of the services in the the infrastructure that we used to secure and connect. And that's a that is a profound change, and you mentioned the observe ability. You're right. That's another thing that the traditional observe ability tools are based on static maps and things and, you know, traditional up, down and we don't. Things go up and down so quickly now that that that those don't make any sense. So I think we are going to see quite a rise in different types of management tools and the way they look at things to be much more. I suppose you know Angela also So we can measure things that currently aren't measurable. >>So you're talking about the entire stack. Really? Changing is really what you're inferring anyway from your commentary. And that would include the programming model as well, wouldn't it? >>Absolutely. Yeah. You know, the thing that is really interesting about where we have been versus where we're going is we spent a lot of time talking about virtual izing hardware and moving that around. And what does that look like? And that, and creating that is more of a software paradigm. And the thing we're talking about now is what is cloud is an operating model look like? What is the manageability of that? What is the security of that? What? You know, we've talked a lot about containers and moving into a different you know, Dev suck ups and all those different trends that we've been talking about, like now we're doing them. So we've only got into the first crank of that. And I think every technology vendor we talked to now has to address how are they going to do a highly distributed management and security landscape? Like, what are they gonna layer on top of that? Because it's not just about Oh, I've taken Iraq of something server storage, compute and virtualized it. I now have to create a new operating model around it. In a way, we're almost redoing what the OS I stack looks like and what the software and solutions are for that. >>So >>it was really Hold on, hold on, hold on their lengthened. Because that side stack that came up earlier today, Mayor. But we're talking about Yeah, we were riffing on the OSC model, but back in the day and we were comparing the S n a definite the, you know, the proprietary protocol stacks that they were out there and someone >>said Amazon's S N a. Is that recall? E think that's what you said? >>No, no. Someone in the chest. That's a comment like Amazon's proprietary meaning, their scale. And I said, Oh, that means there s n a But if you think about it, that's kind of almost that can hang. Hang together. If the kubernetes is like a new connective tissue, is that the TCP pipe moment? Because I think Os I kind of was standardizing at the lower end of the stack Ethernet token ring. You know, the data link layer physical layer and that when you got to the TCP layer and really magic happened right to me, that's when Cisco's happened and everything started happening then and then. It kind of stopped because the application is kinda maintain their peace there. A little history there, but like that's kind of happening now. If you think about it and then you put me a factor in the edge, it just kind of really explodes it. So who's gonna write that software? E >>think you know, Dave, your your dad doesn't change what you build ups. It's already changed in the consumer world, you look atyou, no uber and Waze and things like that. Those absolute already highly decomposed applications that make a P I calls and DNS calls from dozens of different resource is already right. We just haven't really brought that into the enterprise space. There's a number, you know, what kind of you know knew were born in the cloud companies that have that have done that. But they're they're very few and far between today. And John, your point about the connectivity. We do need to think about connectivity at the network layer. Still, obviously, But now we're creating that standardization that standardized connectivity all the way a player seven. So you look at a lot of the, you know, one of the big things that was a PDP. I calls right, you know, from different cloud services. And so we do need to standardize in every layer and then stitch that together. So that does make It does make things a lot more complicated. Now I'm not saying Don't do it because you can do a whole lot more with absolute than you could ever do before. It's just that we kind of cranked up the level of complexity here, and flowered isn't just a single thing anymore, right? That's that. That's what we're talking about here It's a collection of edges and private clouds and public clouds. They all have to be stitched together at every layer in orderto work. >>So I was I was talking a few CEOs earlier in the day. We had we had them on, I was asking them. Okay, So how do you How do you approach this complexity? Do you build that abstraction layer? Do you rely on someone like Microsoft to build that abstraction layer? Doesn't appear that Amazon's gonna do it, you know? Where does that come from? Or is it or is it dozens of abstraction layers? And one of the CEO said, Look, it's on us. We have to figure out, you know, we get this a p I economy, but But you guys were talking about a mawr complicated environment, uh, moving so so fast. Eso if if my enterprise looks like my my iPhone APs. Yes, maybe it's simpler on an individual at basis, but its app creep and my application portfolio grows. Maybe they talk to each other a little bit better. But that level of complexity is something that that that users are gonna have to deal >>with what you thought. So I think quite what Zs was trying to get it and correct me if I'm wrong. Zia's right. We've got to the part where we've broken down what was a traditional application, right? And now we've gotten into a P. I calls, and we have to think about different things. Like we have to think about how we secure those a p I s right. That becomes a new criteria that we're looking at. How do we manage them? How do they have a life cycle? So what was the life cycle of, say, an application is now the life cycle of components and so that's a That's a pretty complex thing. So it's not so much that you're getting app creep, but you're definitely rethinking how you want to design your applications and services and some of those you're gonna do yourself and a lot of them are going to say it's too complicated. I'm just going to go to some kind of SAS cloud offering for that and let it go. But I think that many of the larger companies I speak to are looking for a larger company to help them build some kind of framework to migrate from what they've used with them to what they need tohave going forward. >>Yeah, I think. Where the complexities. John, You asked who who creates the normalization layer? You know, obviously, if you look to the cloud providers A W s does a great job of stitching together all things AWS and Microsoft does a great job of stitching together all things Microsoft right in saying with Google. >>But >>then they don't. But if if I want to do some Microsoft to Amazon or Google Toe Microsoft, you know, connectivity, they don't help so much of that. And that's where the third party vendors that you know aviatrix on the network side will tear of the security side of companies like that. Even Cisco's been doing a lot of work with those companies, and so what we what we don't really have And we probably won't for a while if somebody is gonna stitch everything together at every >>you >>know, at every layer. So Andi and I do think we do get after it. Maribel, I think if you look at the world of consumer APS, we moved to a lot more kind of purpose built almost throwaway apps. They serve a purpose or to use them for a while. Then you stop using them. And in the enterprise space, we really haven't kind of converted to them modeling on the mobile side. But I think that's coming. Well, >>I think with micro APS, right, that that was kind of the issue with micro APS. It's like, Oh, I'm not gonna build a full scale out that's gonna take too long. I'm just gonna create this little workflow, and we're gonna have, like, 200 work flows on someone's phone. And I think we did that. And not everybody did it, though, to your point. So I do think that some people that are a little late to the game might end up in in that app creep. But, hey, listen, this is a fabulous opportunity that just, you know, throw a lot of stuff out and do it differently. What What? I think what I hear people struggling with ah lot is be to get it to work. It typically is something that is more vertically integrated. So are you buying all into a Microsoft all you're buying all into an Amazon and people are starting to get a little fear about doing the full scale buy into any specific platform yet. In absence of that, they can't get anything to work. >>Yeah, So I think again what? What I'm hearing from from practitioners, I'm gonna put a micro serve. And I think I think, uh, Mirabelle, this is what you're implying. I'm gonna put a micro services layer. Oh, my, my. If I can't get rid of them, If I can't get rid of my oracle, you know, workloads. I'm gonna connect them to my modernize them with a layer, and I'm gonna impart build that. I'm gonna, you know, partner to get that done. But that seems to be a a critical path forward. If I don't take that step, gonna be stuck in the path in the past and not be able to move forward. >>Yeah, absolutely. I mean, you do have to bridge to the past. You you aren't gonna throw everything out right away. That's just you can't. You can't drive the bus and take the wheels off that the same time. Maybe one wheel, but not all four of them at the same time. So I think that this this concept of what are the technologies and services that you use to make sure you can keep operational, but that you're not just putting on Lee new workloads into the cloud or new workloads as decomposed APS that you're really starting to think about. What do I want to keep in whatever I want to get rid of many of the companies you speak Thio. They have thousands of applications. So are they going to do this for thousands of applications? Are they gonna take this as an opportunity to streamline? Yeah, >>well, a lot of legacy never goes away, right? And I was how companies make this transition is gonna be interesting because there's no there's no really the fact away I was I was talking to this one company. This is New York Bank, and they've broken their I t division down into modern I t and legacy I t. And so modern. Everything is cloud first. And so imagine me, the CEO of Legacy i e 02 miracles. But what they're doing, if they're driving the old bus >>and >>then they're building a new bus and parallel and eventually, you know, slowly they take seats out of the old bus and they take, you know, the seat and and they eventually start stripping away things. That old bus, >>But >>that old bus is going to keep running for a long time. And so stitching the those different worlds together is where a lot of especially big organizations that really can't commit to everything in the cloud are gonna struggle. But it is a It is a whole new world. And like I said, I think it creates so much opportunity for people. You know, e >>whole bus thing reminds me that movie speed when they drive around 55 miles an hour, just put it out to the airport and just blew up E >>got But you know, we all we all say that things were going to go away. But to Zia's point, you know, nothing goes away. We're still in 2021 talking about mainframes just as an aside, right? So I think we're going to continue tohave some legacy in the network. But the But the issue is ah, lot will change around that, and they're gonna be some people. They're gonna make a lot of money selling little startups that Just do one specific piece of that. You know, we just automation of X. Oh, >>yeah, that's a great vertical thing. This is the This is the distributed network argument, right? If you have a note in the network and you could put a containerized environment around it with some micro services um, connective tissue glue layer, if you will software abstract away some integration points, it's a note on the network. So if in mainframe or whatever, it's just I mean makes the argument right, it's not core. You're not building a platform around the mainframe, but if it's punching out, I bank jobs from IBM kicks or something, you know, whatever, Right? So >>And if those were those workloads probably aren't gonna move anywhere, right, they're not. Is there a point in putting those in the cloud? You could say Just leave them where they are. Put a connection to the past Bridge. >>Remember that bank when you talk about bank guy we interviewed in the off the record after the Cube interviews like, Yeah, I'm still running the mainframe, so I never get rid of. I love it. Run our kicks job. I would never think about moving that thing. >>There was a large, large non US bank who said I buy. I buy the next IBM mainframe sight unseen. Andi, he's got no choice. They just write the check. >>But milliseconds is like millions of dollars of millisecond for him on his back, >>so those aren't going anywhere. But then, but then, but they're not growing right. It's just static. >>No, no, that markets not growing its's, in fact. But you could make a lot of money and monetizing the legacy, right? So there are vendors that will do that. But I do think if you look at the well, we've already seen a pretty big transition here. If you look at the growth in a company like twilio, right, that it obviates the need for a company to rack and stack your own phone system to be able to do, um, you know, calling from mobile lapse or even messaging. Now you just do a P. I calls. Um, you know, it allows in a lot of ways that this new world we live in democratizes development, and so any you know, two people in the garage can start up a company and have a service up and running another time at all, and that creates competitiveness. You know much more competitiveness than we've ever had before, which is good for the entire industry. And, you know, because that keeps the bigger companies on their toes and they're always looking over their shoulder. You know what, the banks you're looking at? The venues and companies like that Brian figure out a way to monetize. So I think what we're, you know well, that old stuff never going away. The new stuff is where the competitive screen competitiveness screen. >>It's interesting. Um IDs Avery. Earlier today, I was talking about no code in loco development, how it's different from the old four g l days where we didn't actually expand the base of developers. Now we are to your point is really is democratizing and, >>well, everybody's a developer. It could be a developer, right? A lot of these tools were written in a way that line of business people create their own APs to point and click interface is, and so the barrier. It reminds me of when, when I started my career, I was a I. I used to code and HTML build websites and then went to five years. People using drag and drop interface is right, so that that kind of job went away because it became so easy to dio. >>Yeah, >>sorry. A >>data e was going to say, I think we're getting to the part. We're just starting to talk about data, right? So, you know, when you think of twilio, that's like a service. It's connecting you to specific data. When you think of Snowflake, you know, there's been all these kinds of companies that have crept up into the landscape to feel like a very specific void. And so now the Now the question is, if it's really all about the data, they're going to be new companies that get built that are just focusing on different aspects of how that data secured, how that data is transferred, how that data. You know what happens to that data, because and and does that shift the balance of power about it being out of like, Oh, I've created these data centers with large recommend stack ums that are virtualized thio. A whole other set of you know this is a big software play. It's all about software. >>Well, we just heard from Jim Octagon e You guys talking earlier about just distributed system. She basically laid down that look. Our data architectures air flawed there monolithic. And data by its very nature is distributed so that she's putting forth the whole new paradigm around distributed decentralized data models, >>which Howie shoe is just talking about. Who's gonna build the visual studio for data, right? So programmatic. Kind of thinking around data >>I didn't >>gathering. We didn't touch on because >>I do think there's >>an opportunity for that for, you know, data governance and data ownership and data transport. But it's also the analytics of it. Most companies don't have the in house, um, you know, data scientists to build on a I algorithms. Right. So you're gonna start seeing, you know, cos pop up to do very specific types of data. I don't know if you saw this morning, um, you know, uniforms bought this company that does, you know, video emotion detection so they could tell on the video whether somebody's paying attention, Not right. And so that's something that it would be eso hard for a company to build that in house. But I think what you're going to see is a rise in these, you know, these types of companies that help with specific types of analytics. And then you drop you pull those in his resource is into your application. And so it's not only the storage and the governance of the data, but also the analytics and the analytics. Frankly, there were a lot of the, uh, differentiation for companies is gonna come from. I know Maribel has written a lot on a I, as have I, and I think that's one of the more exciting areas to look at this year. >>I actually want to rip off your point because I think it's really important because where we left off in 2020 was yes, there was hybrid cloud, but we just started to see the era of the vertical eyes cloud the cloud for something you know, the cloud for finance, the cloud for health care, the telco and edge cloud, right? So when you start doing that, it becomes much more about what is the specialized stream that we're looking at. So what's a specialized analytic stream? What's a specialized security stack stream? Right? So until now, like everything was just trying to get to what I would call horizontal parody where you took the things you had before you replicated them in a new world with, like, some different software, but it was still kind of the same. And now we're saying, OK, let's try Thio. Let's try to move out of everything, just being a generic sort of cloud set of services and being more total cloud services. >>That is the evolution of everything technology, the first movement. Everything doing technology is we try and make the old thing the new thing look like the old thing, right? First PCs was a mainframe emulator. We took our virtual servers and we made them look like physical service, then eventually figure out, Oh, there's a whole bunch of other stuff that I could do then I couldn't do before. And that's the part we're trying to hop into now. Right? Is like, Oh, now that I've gone cloud native, what can I do that I couldn't do before? Right? So we're just we're sort of hitting that inflection point. That's when you're really going to see the growth takeoff. But for whatever reason, and i t. All we ever do is we're trying to replicate the old until we figure out the old didn't really work, and we should do something new. >>Well, let me throw something old and controversial. Controversial old but old old trope out there. Consumerism ation of I t. I mean, if you think about what year was first year you heard that term, was it 15 years ago? 20 years ago. When did that first >>podcast? Yeah, so that was a long time ago >>way. So if you think about it like, it kind of is happening. And what does it mean, right? Come. What does What does that actually mean in today's world Doesn't exist. >>Well, you heard you heard. Like Fred Luddy, whose founder of service now saying that was his dream to bring consumer like experiences to the enterprise will. Well, it didn't really happen. I mean, service not pretty. Pretty complicated compared toa what? We know what we do here, but so it's It's evolving. >>Yeah, I think there's also the enterprise ation of consumer technology that John the companies, you know, you look a zoom. They came to market with a highly consumer facing product, realized it didn't have the security tools, you know, to really be corporate great. And then they had to go invest a bunch of money in that. So, you know, I think that waken swing the pendulum all the way over to the consumer side, but that that kind of failed us, right? So now we're trying to bring it back to center a little bit where we blend the two together. >>Cloud kind of brings that I never looked at that way. That's interesting and surprising of consumer. Yeah, that's >>alright, guys. Hey, we gotta wrap Zs, Maribel. Always a pleasure having you guys on great great insights from the half hour flies by. Thanks so much. We appreciate it. >>Thank >>you guys. >>Alright, keep it right there. Mortgage rate content coming from the Cuban Cloud Day Volonte with John Ferrier and a whole lineup still to come Keep right there.

Published Date : Jan 22 2021

SUMMARY :

It's the Cube presenting Cuban to you by silicon angle. You know, here we are on 2021 you know, just exited one of the strangest years, recognition on the cloud computing providers that you need to give it to the customers the way they want it, It was private cloud And and then now we're, you know, hearing a lot of multi cloud And if you look at the last post that put on Silicon Angle, which was talking about five acquisition of Volterra, Is that distributed that you were talking about and I think that's what you know, kind of cool about this is we're being able to use our data and much different ways. And then you mentioned the ephemeral nature. And that's a that is a profound change, and you mentioned the observe ability. And that would include the programming model as well, And the thing we're talking about now is what is cloud is an operating model look like? and we were comparing the S n a definite the, you know, the proprietary protocol E think that's what you said? And I said, Oh, that means there s n a But if you think about it, that's kind of almost that can hang. think you know, Dave, your your dad doesn't change what you build ups. We have to figure out, you know, we get this a p But I think that many of the larger companies I speak to are looking for You know, obviously, if you look to the cloud providers A W s does a great job of stitching together that you know aviatrix on the network side will tear of the security side of companies like that. Maribel, I think if you look at the world of consumer APS, we moved to a lot more kind of purpose built So are you buying all into a Microsoft all you're buying all into an Amazon and If I don't take that step, gonna be stuck in the path in the past and not be able to move forward. So I think that this this concept of what are the technologies and services that you use And I was how companies make this transition is gonna out of the old bus and they take, you know, the seat and and they eventually start stripping away things. And so stitching the those different worlds together is where a lot got But you know, we all we all say that things were going to go away. I bank jobs from IBM kicks or something, you know, And if those were those workloads probably aren't gonna move anywhere, right, they're not. Remember that bank when you talk about bank guy we interviewed in the off the record after the Cube interviews like, I buy the next IBM mainframe sight unseen. But then, but then, but they're not growing right. But I do think if you look at the well, how it's different from the old four g l days where we didn't actually expand the base of developers. because it became so easy to dio. A So, you know, when you think of twilio, that's like a service. And data by its very nature is distributed so that she's putting forth the whole new paradigm Who's gonna build the visual studio for data, We didn't touch on because an opportunity for that for, you know, data governance and data ownership and data transport. the things you had before you replicated them in a new world with, like, some different software, And that's the part we're trying to hop into now. Consumerism ation of I t. I mean, if you think about what year was first year you heard that So if you think about it like, it kind of is happening. Well, you heard you heard. realized it didn't have the security tools, you know, to really be corporate great. Cloud kind of brings that I never looked at that way. Always a pleasure having you guys Mortgage rate content coming from the Cuban Cloud Day Volonte with John Ferrier and

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Mohit Lad, ThousandEyes | CUBEConversations, November 2019


 

our Studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hey welcome back they're ready Jeff Rick here with the cube we're in our Palo Alto studios today to have a conversation with a really exciting company they've actually been around for a while but they've raised a ton of money and they're doing some really important work in the world in which we live today which is a lot different than the world was when they started in 2010 so we're excited to welcome to the studio he's been here on before Mohit ladee is the CEO and co-founder of Thousand Eyes mode great to see you great to see you as well as pretty to be here yeah welcome back but for people that didn't see the last video or not that familiar with Thousand Eyes tell them a little bit kind of would a thousand eyes all about absolutely so in today's world the cloud is your new data center the Internet is your new network and SAS is your new application stack and thousand eyes is built to be the the only thing that can really help you see across all three of these like it's your own private environment I love that I love that kind of setup and framing because those are the big three things and as you said all those things have moved from inside your control to outside of your control so in 2010 is that was that division I mean when you guys started the company UCLA I guess a while ago now what was that the trend what did you see what yes what kind of started it so it's really interesting right so our background as a founding company with two founders we did our PhD at UCLA in computer science and focused on internet and we were fascinated by the internet because it was just this complex system that nobody understood but we knew even then that it would meaningfully change our lives not just as consumers but even as enterprise companies so we had this belief that it's going to be the backbone of the modern enterprise and nobody quite understood how it worked because everyone was focused on your own data center your own network and so our entire vision at that point was we want people to feel the power of seeing the internet like your network that's sort of where we started and then as we started to expand on that vision it was clear to us that the Internet is what brings companies together what brings the cloud closer to the enterprise what brings the SAS applications closer to the enterprise right so we expanded into into cloud and SAS as well so when you had that vision you know people had remote offices and they would set up they would you know set up tunnels and peer-to-peer and all kinds of stuff why did you think that it was gonna go to that next step in terms of the internet you know just kind of the public Internet being that core infrastructure yes we were at the at the very early stages of this journey to cloud right and at the same time you had companies like Salesforce you had office 365 they were starting to just make it so much easier for companies to deploy a CRM you don't have to stand up these massive servers anymore its cloud-based so it was clear to us that that was gonna be the new stack and we knew that you had to build a fundamentally different technology to be able to operate in that stack and it's not just about visibility it's about making use of collective information as well because you're going from a private environment with your own data center your own private network your own application stack to something that's sitting in the cloud which is a shared environment going over the Internet which is the same network that carries cat videos that your kids watch it's carrying production traffic now for your core applications and so you need a different technology stack and you need to really sort of benefit from this notion of collective intelligence of knowing what everybody sees together as one view so I'm here I think I think Salesforce was such an important company in terms of getting enterprises to trust a SAS application for really core function which just sales right I think that was a significant moment in moving the dial was there a killer app for you guys that was you know for your customers the one where they finally said wait you know we need a different level of his ability to something that we rely on that's coming to us through an outside service so it's interesting right when we started the company we had a lot of advisors that said hey your position should be you're gonna help enterprises enforce SLA with Salesforce and we actually took a different position because what we realized was Salesforce did all the right stuff on their data centers but the internet could mess things up or enterprise companies that were not ready to move to cloud didn't have the right architectures would have some bottlenecks in their own environment because they are backhauling traffic from their London office to New York and then exiting from New York they're going back to London so all this stuff right so we took the position of really presenting thousand eyes as a way to get transparency into this ecosystem and we we believe that if we take this position if we want to help both sides not just the enterprise companies we want to help sales force we want to have enterprise companies and just really present it as a means of finding a common truth of what is actually going on it works so much better right so there wasn't really sort of one killer application but we found that anything that was real-time so if you think about video based applications or any sort of real-time communications based so the web access of the world they were just very sensitive to network conditions and internet conditions same with things that are moving a lot of data back and forth so these applications like Salesforce office 365 WebEx they just are demanding applications on the infrastructure and even if they're done great if the infrastructure doesn't it doesn't give you a great experience right and and and you guys made a really interesting insight too it's an it's an all your literature it's it's a really a core piece of what you're about and you know when you owned it you could diagnose it and hopefully you could fix it or call somebody else to fix it but when you don't own it it's a very different game and as you guys talked about it's really about finding the evidence or everyone's not pointing fingers back in and forth a to validate where the actual problem is and then to also help those people fix the problem that you don't have direct control of so it's a very different you know kind of requirement to get things fixed when they have to get fixed yeah and the first aspect of that is visibility so as an example right you generally don't have a problem going from one part of your house to another part of your house because you own the whole place you know exactly what sits between the two rooms that you're trying to get to you don't you don't have run into surprises but when you're going from let's say Palo Alto to San Francisco and you have two options you can take the 101 or 280 you need to know what you expect to see before you get on one of those options right and so the Internet is very similar you have these environments that you have no idea what to expect and if you don't see that with the right level of granularity that you would in your own environments you would make decisions that you have you know you have no control over right the visibility is really important but it's giving that lens like making it feel like a google maps of the internet that gives you the power to look at these environments like it's your private network that's the hard part right and then so what you guys have done as I understand is you've deployed sensors basically all over the Internet all at an important pops yeah an important public clouds and important enterprises etc so that you now have a view of what's going on it I can have that view inside my enterprise by leveraging your infrastructure is that accurate correct and so this is where the notion of being able to set up this sort of data collection environment is really difficult and so we have created all of this over years so enterprise companies consumer companies they can leverage this infrastructure to get instant results so there's zero implementation what right but the key to that is also understanding the internet itself and so this is where a research background comes in play because we studied we did years of research on actually modeling the internet so we know what strategic locations to put these probes that to give good coverage we know how to fill the gaps and so it's not just a numbers game it's how you deploy them where you deploy them and knowing that connectivity we've created this massive infrastructure now that can give you eyes on the internet and we leverage all of their data together so if let's say hypothetically you know AT&T has an issue that same issue is impacting multiple customers through all our different measurements so it's like ways if you're using ways to get from point A to point B if Waze was just used by your family members and nobody else it would give you completely useless information values in that collective insight right and then now you also will start to be able to until every jamel and AI and you know having all that data and apply just more machine learning to it to even better get out in front of problems I imagine as much as as is to be able to identify it so that's a really interesting point right so the first thing we have to tackle is making a complex data set really accessible and so we have a lot of focus into essentially getting insights out of it using techniques that are smarter than the brute-force techniques to get insights out and then present it in manners that it's accessible and digestible and then as we look into the next stages we're going to bring more and more things like learning and so on to take it even further right it's funny the accessible and digestible piece I've just had a presentation the other day and there was a woman from a CSO at a big bank and she talked about you know the problem of false positives and in in early days I mean their biggest issues was just too much data coming in from too many sensors and and too many false positives to basically bury people so I didn't have time to actually service the things that are a priority so you know a nice presentation of a whole lot of data that's a big difference to make it actual it is absolutely true and now that the example I'll give you is oftentimes when you think about companies that operate with a strong network core like we do they are in the weeds right which is important but what is really important is tying that intelligence to business impact and so the entire product portfolio we've built it's all about business impact user experience and then going into connecting the dots or the network side so we've seen some really interesting events and as much as we know the internet every day I wake up and I see something that surprises me right we've had customers that have done migrations to cloud that have gone horribly wrong right so we the latest when I was troubleshooting with the customer was where we saw they migrated from there on from data center to Amazon and the user experience was 10x worse than what it was on their own data the app once they moved to Amazon okay and what had happened there was the whole migration to Amazon included the smart sort of CDN where they were fronting your traffic at local sites but the traffic was going all over the place so from if a user was in London instead of going to the London instance of Amazon they were going to Atlanta they were going to Los Angeles and so the whole migration created a worse user experience and you don't have that lens because you don't see that in a net portion of that right that's what we like we caught it instantly and we were able to showcase that hey this is actually a really bad migration and it's not that Amazon is bad it's just it's been implemented incorrectly right so ya fix these things and those are all configurations all Connecticut which is so very easy all the issues you hear about with with Amazon often go back to miss configuration miss settings suboptimal leaving something open so to have that visibility makes a huge impact and it's more challenging because you're trying to configure different components of this environment right so you have a cloud component you have the internet component your own network you have your own firewalls and you used to have this closed environment now it's hybrid it involves multiple parties multiple skill sets so a lot of things can really go wrong yeah I think I think you guys you guys crystallize very cleanly is kind of the inside out and outside in approach both you know a as as a service consumer yep right I'm using Salesforce I'm using maybe s3 I'm using these things that I need and I want to focus on that and I want to have a good experience I want my people to be able to get on their Salesforce account and book business but but don't forget the other way right because as people are experiencing my service that might be connecting through and aggregating many other services along the way you know I got to make sure my customer experience is big and you guys kind of separate those two things out and really make sure people are focusing on both of them correct and it's the same technology but you can use that for your production services which are revenue generating or you can use that for your employee productivity the the visibility that you provide is is across a common stack but on the production side for example because of the way the internet works right your job is not just to ensure a great performance in user experience your job is also to make sure that people are actually reaching your site and so we've seen several instances where because of the way internet works somebody else could announce that their google.com and they could suck a bunch of traffic from the Internet and this happens quite routinely in the notion of what is now known as DP hijacks or sometimes DNS hijacks and the the one that I remember very well is when there was the small ISP in Nigeria that announced the identity of the address block for Google and that was picked up by China Telecom which was picked up by a Russian telco and now you have Russia China and Nigeria in the path for traffic to Google which is actually not even going to Google's right those kinds of things are very possible because of the way the internet how fast those things kind of rise up and then get identified and then get shut off is this hours days weeks in this kind of example so it really depends because if you are let's say you were Google in this situation right you're not seeing a denial of service attack T or data centers in fact you're just not seeing traffic running it because somebody else is taking it away right it's like identity theft right like I somebody takes your identity you wouldn't get a mail in your inbox saying hey your identity has been taken back so I see you have to find it some other way and usually it's the signal by the time you realize that your identity has been stolen you have a nightmare ahead of you all right so you've got some specific news a great great conversation you know it's super insightful to talk people that are in the weeds of how all the stuff works but today you have a new a new announcement some new and new offering so tell us about what's going on so we have a couple of announcements today and coming back to this notion of the cloud being a new data center the internet your new network right two things were announcing today is one we're announcing our second version of the cloud then benchmark performance comparison and what this is about is really helping people understand the nuances the performance difference is the architecture differences between Amazon Google ad your IBM cloud and Alibaba cloud so as you make decisions you actually understand what is the right solution for me from a performance architecture standpoint so that's one it's a fascinating report we found some really interesting findings that surprised us as well and so we're releasing that we're also touching on the internet component by releasing a new product which we call as Internet insights and that is giving you the power to actually look at the internet more holistically like you own the entire internet so that is really something we're all excited about because it's the first time that somebody can actually see the Internet see all these connections see what is going on between major service providers and feel like you completely owned the environment so are people using information like that to dynamically you know kind of reroute the way that they handle their traffic or is it more just kind of a general health you know kind of health overview you know how much of it do I have control over how much should I have control over and how much of I just need to know what's going on so yeah so in just me great question so the the best way I can answer that is what I heard CIO say in a CIO forum we were presenting it where they were a customer it's a large financial services customer and somebody asked the CIO what was the value of thousand I wasn't the way he explained it which was really fascinating was phase one of thousand eyes when we started using it was getting rid of technical debt because we would keep identifying issues which we could fix but we could fix the underlying root cause so it doesn't happen again and that just cleared the technical debt that we had made our environment much better and then we started to optimize the environments to just get better get more proactive so that's a good way to think about it when you think about our customers most of the times they're trying to just not have their hair on fire right that's the first step right once we can help them with that then they go on to tuning optimising and so on but knowing what is going on is really important for example if you're providing a.com service like cube the cube comm right it's its life and you're providing it from your data center here you have two up streams like AT&T and Verizon and Verizon is having issues you can turn off that connection and read all your customers back live having a full experience if you know that's the issues right right the remediation is actually quite quite a few times it's very straight forward if you know what you are trying to solve right so do you think on the internet insights this is going to be used just more for better remediation or do you think it's it's kind of a step forward and getting a little bit more proactive and a little bit more prescriptive and getting out ahead of the issues or or can you because these things are kind of ephemeral and come and go so I think it's all of the about right so one the things that the internet insights will help you is with planning because as you expand into new geo so if you're a company that's launching a service in a new market right that immediately gives you a landscape of who do you connect with where do you host right now you can actually visualize the entire network how do you reach your customer base the best right so that's the planning aspect and if you plan right you would actually reduce a lot of the trouble that you see so we had this customer of ours that was deploying Estevan software-defined man in there a she offices and they used thousand eyes to evaluate two different ISPs that they were looking at one of them had this massive time-of-day congestion so every time every day at nine o'clock the latency would get doubled because of congestion it's common in Asia the other did not have time of day congestion and with that view they could implement the entire Estevan on the ice pea that actually worked well for them so planning is important part of this and then the other aspect of this is the thing that folks often don't realize is internet is not static it's constantly changing so you know AT&T may connect to where I is in this way it connects it differently it connects to somebody else and so having that live map as you're troubleshooting customer experience issues so let's say you have customers from China that are having a ton of issues all of a sudden or you see a drop of traffic from China now you can relate that information of where these customers are coming from with our view of the health of the Chinese internet and which specific ISPs are having issues so that's the kind of information merger that simply doesn't happen today right promote is a fascinating discussion and we could go on and on and on but unfortunately do not have all day but I really like what you guys are doing the other thing I just want to close on which which I thought was really interesting is you know a lot of talked about digital transformation we always talk about digital transformation everybody wants a digital transfer eyes it but you really boiled it down into really three create three critical places that you guys play the digital experience in terms of what what the customers experience you know getting to cloud everybody wants to get to cloud so one can argue how much and what percentage but everybody's going to cloud and then as you said in this last example the modern when as you connect all these remote sites and you guys have a play in all of those places so whatever you thought about in 2010 that worked out pretty well thank you and we had a really strong vision but kudos to the team that we have in place that has stretched it and really made the most out of that so excited good job and thanks for for stopping by sharing the story thank you for hosting always fun to be here absolutely all right well he's mo and I'm Jeff you're watching the cube when our Palo Alto studio is having a cube conversation thanks for watching we'll see you next time [Music]

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Sanjay Poonen, VMware | CUBEconversations, March 2020


 

>> Announcer: From theCUBE studios in Palo Alto and Boston connecting with thought leaders all around the world. This is a CUBE conversation. >> Hello everybody, welcome to this special CUBE conversation. My name is Dave Vellante and you're watching theCUBE. We're here with Sanjay Poonen who's the COO of VMware and a good friend of theCUBE. Sanjay great to see you. Thanks for coming on. >> Dave it's a pleasure. In these new circumstances, shelter at home and remote working. I hope you and your family are doing well. >> Yeah, and back at you Sanjay. Of course I saw you on Kramer Mad Money the other night. I was jealous. I said, "I need Sanjay on to get an optimism injection." You're a great leader And I think, a role model for all of us. And of course the "Go Niners" in the background really incented me to get-- I got my Red Sox cap and we have a lack of sports, but, and we miss it, But hey, we're making the best. >> Okay Red Sox is better than the Patriots. Although I love the Patriots. If i was in the east coast, especially now that Brady's gone. I guess you guys are probably ruing a little bit that Jimmy G came to us. >> I am a huge Tampa Bay fan all of a sudden. I be honest with you. Tom Brady can become a Yankee and I would root for them. I tell you that's how much I love the guy. But anyway, I'm really excited to have you on. It's obviously as you mentioned, these times are tough, but we're making the best do and it's great to see you. You are a huge optimist, but I want to ask you, I want to start with Narendra Modi just announced, basically a lockdown for 21 days. 1.3 billion people in your native country. I wonder if you could give us some, some thoughts on that. >> I'm, my parents live half their time in Bangalore and half here. They happen to be right now in the US, and they're doing well. My dad's 80 and my mom's 77. I go to India a lot. I spent about 18 years of my life there, and the last 32 odd years here and I still go there a lot. Have a lots friends and my family there. And , it's I'm glad that the situation is kind of , as best as they can serve it. It's weird, I was watching some of the social media photos of Bangalore. I tweeted this out last night. The roads look so clean and beautiful. I mean, it looks like 40 years ago when I was growing up. When I would take a bicycle to school. I mean Bangalore's one of the most beautiful cities in India, very green and you can kind of see it all again. And I think, as I've been watching some of the satellite photos of the various big cities to just watch sort of Mother Nature. Obviously, we're in a tough time and, I open my empathy and thoughts and prayers go to every family that's affected by this. And certainly ones who have lost loved ones, but it's sort of, I think it's neat, that we're starting to see some of the beautiful aspects of nature. Even as we deal with the tough aspects of sheltered home. And the incredible tough impacts of this pandemic across the world. >> Yeah, I think you're right. There is a silver lining as much as, our hearts go out to those that are that are suffering. You're seeing the canals in Venice run clear. As you mentioned, the nitrous oxide levels over China. what's going on in Bangalore. So, there is a little bit of light in the end of the tunnel for the environment, I hope. and at least there's an indication that we maybe, need to be more sensitized to this. Okay, let's get into it. I want to ask you, so last week in our breaking analysis. We worked with a data company called ETR down in New York City. They do constant surveys of CIO's. I want to read you something that they came out with just on Monday and get your reaction. Basically, their annual growth and IT spend they're saying, is showing a slight decline for 2020. As a significant number of organizations plan to cut and/or delay IT expenditures due to the coronavirus. Though the current climate may suggest worse many organizations are accelerating spending for 2020 as they ramp up their work-from-home infrastructure. These organizations are offsetting what would otherwise be a notable decline in global IT spend versus last year. Now we've gone from the 4% consensus at the beginning of the year. ETR brought it down to zero percent and then just on Monday, they went to slight negative. But, what's not been reported widely is the somewhat offsetting factor of work-from-home infrastructure. VMware obviously plays there. So I wonder if you could comment on what you're seeing. >> Yeah, Dave, I think , we'll have to see . I'm not an economic pundit. So we're going to have to see what the, IT landscape looks like in the overall sense and we'll probably play off GDP. Certain industries: travel, hospitality, I mean, it's brutal for them. I mean, and I hope that, what I really hope, that's going to happen to that industry, especially there's an infusion through recovery type of bill. Is that no real big company goes under, and goes bankrupt. I mean kind of the situation in 2008. I mean, people wondering what will happen to the Airlines. Boeing, hospital-- these are ic-- some of them like Boeing are iconic brands of the United States and of the world. There's only two real companies that make planes. So we've got to make sure that those industries stay afloat and stay good for the health of the world. Health of the US economy, jobs, and so on. That's always one end. Listen, health and safety of our employees always comes first. Before we even think about that. I always tell people the profits of VMware will wait if you are not well, if your loved ones not well, if your going to take care of people, take care of that first. We will be fine. This too shall pass. But if you're healthy, let's turn our attention because we're not going to just sit at home and play games. We're going to serve our customers. How do we do that? A lot of our customers are adjusting to this new normal. As a result, they have to either order devices with a laptop, screens, things of those kinds, to allow a work-from-home environment to be as close to productive as they work environment. So I expect that there will be a surge in the, sort of, end points that people need. I will have to see how Dell and HP and Lenovo, but I expect that they will probably see some surge in their laptops. As people, kind of, want those in the home and hopefully their supply chains are able to respond. But then with every one of those endpoints and screens that we need now for these types of organizations. You need to manage them, end point management. Often, you need virtual desktops on them. You need to end point security and then in some cases you will probably need, if it's a remote office, branch office, and into the home office, network security and app acceleration. So those Solutions, end point management, Workspace ONE, inclusive of a full-fledged virtual desktop capability That's our product Workspace ONE. Endpoint Securities, Carbon Black and the Network Platform NSX being software-defined was relegated for things like, load balancers and SDWAN capabilities and it's kind of almost feels like good, that we got those solutions, the last three, four years through acquisitions, in many cases. I mean, of course, Airwatch and Nicira were six, seven, eight years ago. But even SD-WAN, we acquired Velocloud three and a half years ago, Carbon Black just four months ago, and Avi in the last year. Those are all parts of that kind of portfolio now, and I feel we were able to, as customers come to us we're not going in ambulance-chasing. But as customers come to us and say, "What do you have as a work-at-home "for business continuity?" We're able to offer them a solution. So we did a webcast earlier this week. Where we talked about, we're calling it work in home with business continuity. It's led with our EUC offerings Workspace ONE. Accompanied by Carbon Black to secure that, and then underneath it, will obviously be the cloud foundation and our Network capabilities of NSX. >> Yeah, so I want to double down on that because it was not, the survey results, showed it was not just collaboration tools. Like Zoom and WebEx and gotomeeting Etc. It was, as you're pointing out, it was other infrastructure that was of VPN's. It was Network bandwidth. It was virtualization, security because they need to secure that work-from-home infrastructure. So a lot of sort of, ancillary activity. It was surprising to me, when I saw the data, that 21% of the CIO's that we surveyed, said that they actually plan on spending more in 2020 because of these factors. And so now we're tracking that daily. And the sentiment changes daily. I showed some other data that showed the CIO sentiment through March. Every day of the survey it dropped. Okay, so it's prudent to be cautious. But nonetheless, people to your point aren't just sitting on their hands. They're not standing still. They're moving to support this new work-from-home normal. >> Yeah, I mean listen, I forgot to say that, Yeah, we are using the video collaboration tools. Zoom a lot. We use Slack. We'll use Teams. So we are, those are accompanied. We were actually one of the first customers to use Zoom. I'm a big fan of my friend Eric Yuan and what they're doing there in modernizing, making it available on a mobile device. Just really fast. They've been very responsive and they reciprocated by using Workspace ONE there. We've been doing ads joined to VMware and zoom in the market for the last several years. So we're a big fan of their technology. So far be it from me to proclaim that the only thing you need here's VMware. There's a lot of other things on the stack. I think the best way, Dave, for us that we've sought to do this is again, I'm very sensitive to not ambulance-chase, which is, kind of go after this. To do it authentically, and the way that authentically is to be, I think Satya Nadella put this pretty well in an interview he did yesterday. Be a first responder to the first responder. A digital first responder, if I could. So when the, our biggest customers are hospital and school and universities and retailers and pharmacies. These are some of our biggest customers. They are looking, in some cases, actually hire more people to serve their communities and customers. And every one of them, as they , hire new people and so and so on, will I just naturally coming to us and when they come to us, serve them. And it's been really gratifying Dave. If I could read you the emails I've been getting the last few days. I got one from a very prominent City, the United States, the mayor's office, the CTO, just thanking us and our people. For being available who are being careful not to, we're being very sensitive to the pricing. To making sure customers don't feel like, in any way, that we're looking at the economics of it will always come just serve your customer. I got an email yesterday from a very large pharmacy. Routinely we were talking to folks in the, in the healthcare industry. University, a president of a school. In fact, Southern New Hampshire University, who I mentioned Jim Cramer. Sent me a note saying, "hey, we're really grateful you even mentioned our name." and I'm not doing this because, Southern New Hampshire University is doing an incredible job of moving a lot of their platform to online to help tens of thousands. And they were one of the early customers to adopt virtual desktops, and the cloud desktops, and the services. So, as we call. So in any of these use cases, I just tell our employees, "Be authentic. "First off take care of your families. "It's really important to take care of your own health and safety. But once you've done that, be authentic in serving our customers." That's what VR has always done. From the days of dying green, to bombers, to Pat, and all of us here now. Take care of our customers and we'll be fine. >> Yeah, and I perfectly understand your sensitivity to that notion of ambulance-chasing and I'm by no means trying to bait you into doing that. But I would stress, the industry needs you and the tech it-- many in the tech industry, like VMware, have very strong balance sheets. They're extremely viable companies and we as a community, as an industry, need companies like VMware to step up, be flexible on pricing, and terms, and payment, and things like that nature. Which it sounds like you're doing. Because the heroes that are on the front lines, they're fighting a battle every day, every hour, every minute and they need infrastructure to be able to work remotely with the stay-at-home mandates. >> I think that's right. And listen, let me talk a little bit of one of the things you talked about. Which is financing and we moved a lot of our business to increasingly, to the cloud. And SaaS and subscription services are a lot more radical than offer license and maintenance. We make that choice available to customers, in many cases we lead with cloud-first solutions. And then we also have financing services from our partners like Dell financial services that really allow a more gradual, radibal payment. Do people want financing? And , I think if there are other scenarios. Jim asked me on his show, "What will you do if one of your companies go bankrupt?" I don't know, that's an unprecedented, we didn't have, we had obviously, the financial crisis. I wasn't here at VMware during the dot-com blow up where companies just went bankrupt in 2000. I was at Informatica at the time. So, I'm sure we will see some unprecedented-- but I will tell you, we have a very fortunate to be profitable, have a good balance sheet. Whatever scenario, if we take care of our customers, I mean, we have been very fortunate to be one of the highest NPS, Net promoter scorer, companies in the industry. And , I've been reaching out to many of our top customers. Just a courtesy, without any agenda other than, we're just checking in. A friend in need is a friend indeed. It's a line that I remembered. And just reach out your customers. Hey listen. Checking in. No, other than can we help you, if there's anything and thank you, especially for ones who are retailers, pharmacies, hospitals, first responders. Thank them for what they're doing to serve many of their people. Especially people in retail. Think about the people who have to go into warehouses to service us, to deliver the stuff that comes to our home. I mean, these people are potentially at risk, but they do it. Put on masks. Braving health situations. That often need the paycheck. We're very grateful for that, and our hope is that this world situation, listen, I mentioned it on on TV as a kind of a little bit of a traffic jam. I love to ski and when I go off and to Tahoe, I tell my family, "I don't know how long it's going to take." with check up on Waze or Google Maps and usually takes four hours, no traffic. Every now and then it'll take five, six, seven. Worst case eight. I had some situation, never happen to me but some of my friends would just got stuck there and had to sleep in their car. But it's pretty much the case, you will eventually get there. I was talking to my dad, who is 80, and he's doing well. And he said, this feels a little bit like World War Two because you're kind of, in many places there. They had a bunker, shelter. Not just shelter in place, but bunker shelter in that time. But that lasted, whatever five, six years. I don't think this is going to last five, six years. It may be five, six months. It might be a whole year. I don't know. I can guarantee it's not going to be six years. So it won't be as bad as World War two. It certainly won't be as bad as the Spanish Flu. Which took 39 people and two percent of the world. Including five percent of my country, India in the 1918 to 1920 period, a hundred years ago. So we will get through this. I like, we shall overcome. I'm not going to sing it for you. It's one of my favorite Louis Armstrong songs, but find ways by which you encourage, uplift people. Making sure, it is tough, it is very tough times and we have to make sure that we get through this. That jobs are preserved as best as we can because that's the part I'm really, really concerned about. The loss of jobs and how we're going to recover as US economy, but we will make it through this. >> Yeah, and I want to sort of second what you're saying. That look, I know there are a lot of people at home that going a little bit stir crazy and this, the maybe a little bit of depression setting in. But to your point, we have to be empathic for those that are suffering. The elderly, who are in intensive care and also those frontline workers. And then I love your optimism. We will get through this. This is not the Spanish Flu. We have, it's a different world, a different technology world. Our focus, like many other small businesses is, we obviously want to survive. We want to maintain our full employment. We want to serve our customers and we, as you, believe that that is the recipe for getting through this. And so, I love the optimism. >> And listen, and we can help be a part of my the moment you texted me and said, "Hey, can I be in your show?" If it helps you drive, whatever you need, sponsorship revenue, advertising. I'm here and the same thing for all of our friends who have to adjust the way in which the wo-- we want to be there to help them. And I've chosen as best as I can, in terms of how I can support my family, the sort of five, five of us at home now. All fighting over bandwidth, the three kids, and my wife, and I. To be positive with them, to be in my social media presence, as best as possible. Every day to be positive in what I tweet out to the world And point people to a hope of what's going to come. I don't know how long this is going to last. But I can tell you. I mean, just the fact that you and I are talking over video interview. High fidelity, reasonably high fidelity, high bandwidth. The ability to connect. I mean it is a whole lot better than a lot of what happened in World War 2 or the Spanish flu. And I hope at the end of it, some of us, some of this will forever change our life. I hope for for example in a lot of our profession. We have to travel to visit customers. And now that I'm building some of these relationships virtually. I hope that maybe my travel percentage will drop. It's actually good for the environment, good for my family life. But if we can lower that percentage, still get things done through Zoom calls, and Workspace ONE, and things of those kinds, that would be awesome. So that's how I think about the way in which I'm adapting my life. And then I set certain personal goals. This year, for example, we're expanding a lot of our focus in security. We have a billion dollar security business and we're looking to grow that NSX, Common Black, Workspace ONE, and accompanying tools and I made it a goal to try and meet at all my sales teams. A thousand C-ISOs. I mean off I know a lot of CIO's in the 25 years, I've had, maybe five, six thousand of them in the world. And blessed to build that relationship over the years of my SAP and VMware experience, but I don't know. I mean, I knew probably 50 or 100. Maybe a few hundred CISO's. And now that we have a portfolio it's relevant to grant them and I think very compelling across network security and End Point security. We own the companies with such a strong portfolio in both those areas. I'm reaching out to them and I'm happy to tell you, I connected, I've got the names of 1,000 of the top CISO's in the Fortune 1000, Global 2000, and connecting with many of them through LinkedIn and other mixers. I hope I talked to many of them through the course of the year. And many of them will be virtual conversations. Again, just to talk to them about being a trusted advisor to us. Seeing if we can help them. And then of course, there will be a product pitch for NSX and Carbon Black and how we're different from whoever it is, Palo Alto and F5 and Netscaler and the SD line players or semantic McAfee Crowdstrike. We're differentiated so I want to certainly earn some of the business. But these are ways in which you adjust to a virtual kind of economy. Where I'm not having to physically go and meet them. >> Yeah, and we share your optimism and those CISO's are, they're heroes, superheroes on the front line. I'll tell ya a quick aside. So John Furrier and I, we're in Barcelona. When really, the coronavirus came to our heightened awareness and John looked at me and said, "Dave we've been doing digital for 10 years. "We have to take all of the software that we've developed, "all these assets and help our customers pivot." So we share that optimism and we're actually lucky to be able to have the studios and be able to have these conversations with you guys. So again, we share that, that optimism. I want to ask you, just on guidance. A lot of companies have come out and said we're not giving guidance anymore. I didn't see anything relative to VMware. Have you guys announced anything on guidance in terms of how you're going to communicate? Where are you at with that? >> No, I think we're just, I mean listen, we take this very carefully because of reg FD and the regulations of public company. So we just allow the normal quarterly ins. And of outside of that, if our CFO decides they may. But right now we're just continuing business as usual. We're in the middle of our, kind of, whatever, middle of our quarter. Quarter ends April. So work hard do the best we can in all the regions, be available for all of our teams. Pat, myself, and others we're, to the extent that we're healthy and we're doing well, but thank God, is reach out to CISO's and CIO's and CTO's and CEOs and help them. And I believe people will spend money. The questions we have to go over. And I think the stronger will survive. The companies with better balance sheet and unfortunately, some of the weaker companies won't. And I think quite frankly, if you do your job well. I don't mean this in any negative sense. The stronger companies will take share in these environments. I was watching a segment for John Chambers. He has been through a number of different, when I know him, so an I have, I've talked to him about some of the stuff. He will tell you that he, advises is a lot of his companies now. From the experiences he saw in 2008, 2001, in many of the crisis and supply chain issues. This is a time where leadership counts. The strong get stronger. Never waste a good crisis, as Winston Churchill said. And as you do that, the strong will come strong because you figure out ways by which, if you're going to make changes that were planned for one or two years from now. Maybe a good time to make them is now. And as you do that you communicate a vision for where you're going. Very clearly to your employees. Again incessantly over and over again. They, hopefully, are able to repeat it in their own words in a simple fashion, and then you get all of your employees in our case 30,000 plus employees of VMware lined up. So one of the things that we've been doing a lot of these days is communicate, communicate, communicate, internally. I've talked a lot about our communication with customer. But inside, our employees, we do calls with our top leaders over Zoom. Calls, intimate calls, and many, often we're adjusting to where I'll say a few words. I have a mandatory every two week goal with all of my senior most leaders. I'll speak for about five minutes and then for the next 25 minutes, the top 12, 15 of them I listen. To things, I want all of them to speak up. There's nobody who should stay silent, because I want to hear what's going on in that corner of the world. >> But fantastic Sanjay. Well, I mean, Boeing, I heard this morning's going to get some support from the government. And strategically that's very important for our country. Congress finally passed, looks like they're passing that bill, and support which is awesome. It's been, especially for all these small businesses that are struggling and want to maintain full employment. I heard Steve Mnuchin the other day saying, "Look, we're talking about two months of payroll "for people if they agree to keep people employed. "or hire them back." I mean the Fed. people say, oh the FED is out of arrows. The Feds, not out of arrows. I mean, I'm not an economist either. But the Fed. has a lot of bullets in their gun, as they say. So Sanjay, thanks so much. You're an awesome leader and really an inspirational executive and a good friend so thank you so much for coming on theCUBE. >> Dave, always a pleasure. Please say hi to all of my friends, your co-anchors, and the staff at CUBE. Thank them for all their hard work. It's a pleasure to talk to you this morning. I wish you, your family, and your friends and all of our community, stay safe and be well. >> Thank you Sanjay and thank you for watching everybody. This is Dave Vellante for the cube and we'll see you next time. (soft music)

Published Date : Mar 25 2020

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in Palo Alto and Boston and a good friend of theCUBE. I hope you and your family are doing well. in the background really incented me to get-- Although I love the Patriots. and it's great to see you. I mean Bangalore's one of the most beautiful cities I want to read you something I mean kind of the situation in 2008. that 21% of the CIO's that we surveyed, From the days of dying green, to bombers, to Pat, and the tech it-- in the 1918 to 1920 period, a hundred years ago. But to your point, I mean, just the fact that you and I and be able to have these conversations with you guys. And I think quite frankly, if you do your job well. I mean the Fed. It's a pleasure to talk to you this morning. and we'll see you next time.

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Mohit Lad, ThousandEyes | CUBEConversations, October 2019


 

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hey welcome back here ready Jeff Rick here with the cube we're in our Palo Alto studios today to have a cube conversation with a really exciting company they've actually been around for a while but they've raised a ton of money and they're doing some really important work in the world in which we live today which is a lot different than the world was when they started in 2010 so we're excited to welcome to the studio he's been around before Mohit lad he is the CEO and co-founder of thousand ice mode great to see you great to see you as well thrilled to be here yeah welcome back but for people that didn't see the last video or not that familiar with thousand ice tell them a little bit kind of would a thousand eyes all about absolutely so in today's world the cloud is your new data center the Internet is your new network and SAS is your new application stack and thousand eyes is built to be the the only thing that can really help you see across all three of these like it's your own private environment I love that I love that kind of setup and framing because those are the big three things and as you said all those things have moved from inside your control to outside of your control so in 2010 is that was that division I mean when you guys started the company UCLA I guess a while ago now what was that the trend what did you see what yes what kind of started it so it's really interesting right so our background is a founding company with two founders we did our PhD at UCLA in computer science and focused on Internet and we were fascinated by the internet because it was just this complex system that nobody understood but we knew even then that it would meaningfully change our lives not just as consumers but even as enterprise companies so we had this belief that it's gonna be the backbone of the modern enterprise and nobody quite understood how it worked because everyone was focused on your own data center your own network and so our entire vision at that point was we want people to feel the power of seeing the internet like your network that's sort of where we started and then as we started to expand on that vision it was clear to us that the internet is what brings companies together what brings the cloud closer to the enterprise what brings the SAS applications closer to the enterprise right so we expanded into into cloud and SAS as well so when you had that vision you know people had remote offices and they would set up they would you know set up tunnels and peer-to-peer and all kinds of stuff why did you think that it was going to go to that next step in terms of the Internet you know just kind of the public Internet being that core infrastructure yes so we were at the at the very early stages of this journey to cloud right and at the same time you had companies like Salesforce you had office 365 they were starting to just make it so much easier for companies to deploy a CRM you don't have to stand up these massive servers anymore its cloud-based so it was clear to us that that was gonna be the new stack and we knew that you had to build a fundamentally different technology to be able to operate in that stack and it's not just about visibility it's about making use of collective information as well because you're going from a private environment with your own data center your own private network your own application stack to something that's sitting in the cloud which is a shared environment going over the Internet which is the same network that carries cat videos that your kids watch it's carrying production traffic now for your core applications and so you need a different technology stack and you need to really sort of benefit from this notion of collective intelligence of knowing what everybody sees together as one view so I'm curious force was such an important company in terms of getting enterprises to trust a SAS application for really core function with just sales right I think that was a significant moment in moving the dial was there a killer app for you guys that was you know for your customers the one where they finally said wait you know we need a different level of visibility to something that we rely on that's coming to us through an outside service so it's interesting right when we started the company we had a lot of advisors that said hey your position should be you're gonna help enterprises enforce SLA with Salesforce and we actually took a different position because what we realized was Salesforce did all the right stuff on their data centers but the internet could mess things up or enterprise companies that were not ready to move the cloud didn't have the right architectures would have some bottlenecks in their own environment because they are backhauling traffic from their London office to New York and then exiting from New York they're going back to London so all this stuff right so we took the position of really presenting thousand eyes as a way to get transparency into this ecosystem and we we believe that if we take this position if we want to help both sides not just the enterprise companies we want to help sales force we want to have enterprise companies and just really present it as a means of finding a common truth of what is actually going on it works so much better right so there wasn't really sort of one killer application but we found that anything that was real-time so if you think about video based applications or any sort of real-time communications based so the web access of the world they were just very sensitive to network conditions and internet conditions same with things that are moving a lot of data back and forth so these applications like Salesforce office 365 WebEx they just are demanding applications on the infrastructure and even if they're run great if the infrastructure doesn't it doesn't give you a great experience right and and and you guys made a really interesting insight to its and it's an all your literature it's it's a really a core piece of what you're about and you know when you owned it you could diagnose it and hopefully you could fix it or call somebody else to fix it but when you don't own it it's a very different game and as you guys talked about it's really about finding the evidence or everyone's not pointing fingers back in and forth a to validate where the actual problem is and then to also help those people fix the problem that you don't have direct control of so it's a very different you know kind of requirement to get things fixed when they have to get fixed yeah and the first aspect of that is visibility so as an example right you generally don't have a problem going from one part of your house to another part of your house because you own the whole place you know exactly what sits between the two rooms that you're trying to get to you don't you don't have run into surprises but when you're going from let's say Palo Alto to San Francisco and you have two options you can take 101 or 280 you need to know what you expect to see before you get on one of those options right and so the Internet is very similar you have these environments that you have no idea what to expect and if you don't see that with the right level of granularity that you would in your own environments you would make decisions that you have you know you have no control over right the visibility is really important but it's giving that lens like making it feel like a google maps of the internet that gives you the power to look at these environments like it's your private network that's the hard part right and then so what you guys have done as I understand is you've deployed sensors basically all over the Internet all at an important pops yeah and a point in public clouds and important enterprises etc so that you now have a view of what's going on it I can have that view inside my enterprise by leveraging your infrastructures that accurate correct and so this is where the notion of being able to set up this sort of data collection environment is really difficult and so we have created all of this over years so enterprise companies consumer companies they can leverage this infrastructure to get instant results so there's zero implementation in what right but the key to that is also understanding the internet itself and so this is where a research background comes in play because we studied we did years of research on actually modeling the Internet so we know what strategic locations to put these probes that to give good coverage we know how to fill the gaps and so it's not just a numbers game it's how you deploy them where you deploy them and knowing that connectivity we've created this massive infrastructure now that can give you eyes on the internet and we leverage all of their data together so if let's say hypothetically you know AT&T has an issue that same issue is impacting multiple customers through all our different measurements so it's like ways if you're using ways to get from point A to point B if Waze was just used by your family members and nobody else it would give you completely useless information values in that collective insight right and then now you also will start to be able to leverage ml and AI and you know having all that data and apply just more machine learning to it to even better get in get out in front of problems I imagine as much as as is to be able to identify so that's a really interesting point right so the first thing we have to tackle is making a complex data set really accessible and so we have a lot of focus into essentially getting insights out of it using techniques that are smarter than the brute-force techniques you get insights out and then present it in manners that it's accessible and digestible and then as we look into the next stages we're going to bring more and more things like learning and so on to take it even further right it's funny the accessible and digestible piece I was just had a presentation the other day and there was a woman from a CSO at a big bank and she talked about you know the problem of false positives and in in early days I mean their biggest issues was just too much data coming in from too many sensors and and too many false positives to basically bury people so they didn't have time to actually service the things that are a priority so you know a nice presentation of a whole lot of data makes a big difference to make it action it is absolutely true and now that the example I'll give you is oftentimes when you think about companies that operate with a strong network core like we do they're in the weeds right which is important but what is really important is tying that intelligence to business impact and so the entire product portfolio we've built it's all about business impact user experience and then going into connecting the dots or the network side so we've seen some really interesting events and as much as we know the internet every day I wake up and I see something that surprises me right we've had customers that have done migrations to cloud that have gone horribly wrong right so we the latest when I was troubleshooting with the customer was where we saw they migrated from there on from data center to Amazon and the user experience was 10x worse than what it was on their own data of the app once they moved to Amazon okay and what had happened there was the whole migration to Amazon included the smart sort of CDN where they were fronting your traffic at local sites but the traffic was going all over the place so from if a user was in London instead of going to the London instance of Amazon they were going to Atlanta or they were going to Los Angeles and so the whole migration created a worse user experience and you don't have that lens because you don't see that in a net portion of that right that's why we like we caught it instantly and we were able to showcase that hey this is actually a really bad migration and it's not that Amazon is bad it's just it's been implemented incorrectly right so yeah fix these things and those are all configurations all Connecticut which is so very easy all the issues you hear about with with Amazon often go back to miss configuration miss settings suboptimal leaving something open so to have that visibility makes a huge impact and it's more challenging because you're trying to configure different components of this environment right so you have a cloud component you have the Internet component your own network you have your own firewalls and you used to have this closed environment now it's hybrid it involves multiple parties multiple skill sets so a lot of things can really go wrong I think I think you guys you guys crystallized very cleanly is kind of the inside out and outside in approach both you know a as as a service consumer yeah right I'm using Salesforce I'm using maybe s3 I'm using these things that I need and I want to focus on that and I want to have a good experience I want my people to be able to get on their Salesforce account and book business but but don't forget the other way right because as people are experiencing my service that might be connecting through and aggregating many other services along the way you know I got to make sure my customer experience is big and you guys kind of separate those two things out and really make sure people are focusing on both of them correct and it's the same technology but you can use that for your production services which are revenue generating or you can use that for your employee productivity the visibility that you provide is is across a common stack but on the production side for example because of the way the internet works right your job is not just to ensure a great performance in user experience your job is also to make sure that people are actually reaching your site and so we've seen several instances where because of the way internet works somebody else could announce that their google.com and they could suck a bunch of traffic from the internet and this happens quite routinely in the notion of what is now known as DP hijacks or sometimes DNS hijacks and the the one that I remember very well is when there was the small ISP in Nigeria that announced the identity of the address block for Google and that was picked up by China Telecom which was picked up by a Russian telco and now you have Russia China and Nigeria in the path for traffic to Google which is actually not even going to Google's right those kinds of things are very possible because of the way the internet how fast those things kind of rise up and then get identified and then get shut off is this hours days weeks in this kind of example so it really depends because if you are let's say you were Google in this situation right you're not seeing a denial of service attack to your data centers in fact you're just not seeing traffic running in because somebody else is taking it away right it's like identity theft right like I somebody takes your identity you wouldn't get a mail in your inbox saying hey your identity has been taken back so easy you have to find it some other way and usually it's the signal by the time you realize that your identity has been stolen you have a nightmare ahead of you alright so you got some specific news a great great conversation you know it's super insightful to talk to people that are in the weeds of how all the stuff works but today you have a new a new announcement some new and new offering so tell us about what's going on so we have a couple of announcements today and coming back to this notion of the cloud being a new data center the internet your new network right two things were announcing today is one we're announcing our second version of the cloud then benchmark performance comparison and what this is about is really helping people understand the nuances the performance difference is the architecture differences between Amazon Google as your IBM cloud and Alibaba cloud so as you make decisions you actually understand what is the right solution for me from a performance architecture standpoint so that's one it's a fascinating report we found some really interesting findings that surprised us as well and so we're releasing that we're also touching on the internet component by releasing a new product which we call as internet insights and that is giving you the power to actually look at the internet more holistically like you own the entire internet so that is really something we're all excited about because it's the first time that somebody can actually see the Internet see all these connections see what is going on between major service providers and feel like you completely owned the environment so are people using information like that to dynamically you know kind of reroute the way that they handle their traffic or is it more just kind of a general health you know kind of health overview you know how much of it do I have control over how much should I have control over and how much of I just need to know what's going on so yeah so it just me great question so the the best way I can answer that is what I heard CIO say in a CIO forum we were presenting at where they were a customer it's a large financial services customer and somebody asked the CIO what was the value of thousand I wasn't the way he explained it which was really fascinating was phase one of thousand eyes when we started using it was getting rid of technical debt because we would keep identifying issues which we could fix but we could fix the underlying root cause so it doesn't happen again and that just cleared the technical debt that we had made our environment much better and then we started to optimize the environments to just get better get more proactive so that's a good way to think about it when you think about our customers most of the times they're trying to just not have their hair on fire right that's the first step right once we can help them with that then they go on to tuning optimizing and so on but knowing what is going on is really important for example if you're providing a.com sir is like cube the cube comm right it's its life and you're providing it from your data center here you have two up streams like AT&T and Verizon and Verizon is having issues you can turn off that connection and let all your customers back live having a full experience if you know that's the issues right right the remediation is actually quite quite a few times it's very straightforward if you know what you're trying to solve right so do you think on the internet insights this is going to be used just more for better remediation or do you think it's it's kind of a step forward and getting a little bit more proactive and a little bit more prescriptive and getting out ahead of the issues or or can you because these things are kind of ephemeral and come and go so I think it's all of the about right so one the things that the internet insights will help you is with planning because as you expand into new geo so if you're a company that's launching a service in a new market right that immediately gives you a landscape of who do you connect with where do you host right as now you can actually visualize the entire network how do you reach your customer base the best right so that's the planning aspect and if you plan right you would actually reduce a lot of the trouble that you see so we had this customer of ours that was deploying Estevan Software Defined one in there a she offices and they used thousand eyes to evaluate two different ISPs that they were looking at one of them had this massive time-of-day congestion so every time every day at nine o'clock the latency would get doubled because of congestion it's common in Asia the other did not have time of day congestion and with that view they could implement the entire Estevan on the ice pea that actually worked well for them so planning is important part of this and then the other aspect of this is the thing that folks often don't realize is Internet is not static it's constantly changing so you know AT&T might connect to Verizon this way it connects it differently it connects to somebody else and so having that live map as you're troubleshooting customer experience issues so let's say you have customers from China that are having a ton of issues all of a sudden or you see a drop of traffic from China now you can relate that information of where these customers are coming from with our view of the health of the Chinese Internet and which specific ISPs are having issues so that's the kind of information merger that simply doesn't happen today right promote is a fascinating discussion and we could go on and on and on but unfortunately do not have all day but I really like what you guys are doing the other thing I just want to close on which which I thought was really interesting is you know a lot of talk about digital transformation we always talk about digital transformation everybody wants the digital transfer eyes it but you really boiled it down into really three create three critical places that you guys play the digital experience in terms of what what the customers experience you know getting to cloud everybody wants to get to cloud someone can argue how much and what percentage but everybody's going to cloud and then as you said in this last example the MA when as you connect all these remote sites and you guys have a play in all of those places so whatever you thought about in 2010 that worked out pretty well thank you and we had a really strong vision but kudos to the team that we have in place that has stretched it and really made the most out of that so excited good job and thanks for for stopping by sharing the story thank you for hosting always a fun to be here absolutely all right well he's mo and I'm Jeff you're watching the cube when our power out the studio's having a cute conversation thanks for watching we'll see you next time [Music]

Published Date : Nov 1 2019

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Chandar Pattabhiram, Coupa | Coupa Insp!re19


 

>> Announcer: From the Cosmopolitan Hotel in Las Vegas, Nevada, it's theCUBE. Covering Coupa Inspire 2019. Brought to you by Coupa. >> Welcome to theCUBE. Lisa Martin on the ground at Coupa Inspire '19 from the Vegas. I'm very pleased to welcome not Bono, not Sting, it's Chandar, the CMO of Coupa. Chandar, welcome to theCUBE. >> Lisa, thank you, it's great to be here today. >> This is a really cool event. Procurement is sexy. >> It is sexy. >> It can be so incredibly transformative to any organization. I loved how the last two days, what you guys have done is a great job of articulating Coupa's value in procurement, invoicing, payments, expense, through the voices of your customers and I think there's no better brand value that you can get. >> Sure, absolutely. >> Tell us a little bit about your role as the CMO of Coupa and marketing in a fast-growing company with a product that people might go, "I haven't heard of that, what is that again?" >> Yeah, it's a good question. I think if I look at it, my role is at Coupa, especially, for Coupa, what's interesting about it, as you said, is that every company makes money, every company spends money. So, invariably, Coupa can be used across a set of different companies. One from the Golden State Warriors to Procter & Gamble to the Lukemia & Lymphoma Society. Across the board. And then, from our perspective, holistically, we're looking at business, but managed from different aspects of spend. You said procurement was in expenses. So, my role is to build a marketing engine to get the flywheel effect of first you drive awareness. All marketing starts with awareness and you said people haven't heard of it. And so, to first to drive awareness in a very thoughtful way to the right contextual community we want to go after. And, two, drive acquisition, we'll drive close synergies between sales and marketing to ultimately drive pipeline and win rates and ultimately deals. And then, very importantly in today's world, is to drive the advocacy and get your most passionate customers to evangelize about the brand, so that you create the flywheel effect of awareness, acquisition, and advocacy. And, that's really what my role today is. >> And, I love how I read an article where you call that the stairway to marketing heaven. So, I thought, I wonder if you're a guitar guy, but you're right. It's how to drive awareness, but in a meaningful, thoughtful way. Especially today, with all all the technology, we wake up with it, right? Our phone is our alarm clock. We are bombarded by ads. If we're on Instagram, following our favorite celebrities or whatnot and it's scary when they have the right context, but it has to be thoughtful. We need to know our audience. So, you describe this stairway to marketing heaven, as you just mentioned, it's awareness, it's acquisition, which is key. But, I feel like a lot of companies don't forget the advocacy part, but they don't invest enough in it because that's the best salesperson for your technology, is the people that are using it successfully, right? >> Totally. Yeah, so, in fact, there was a study about a couple of years which looked at how balanced the boat is in terms of spending in presale versus post-sale. And, it's interesting that 87% of B2B marketing spend was presale. In other words, only 13% of people were investing in retention marketing, adoption mastery, customer marketing, and this is what advocacy marketing. And, in today's world, that doesn't work because you got to balance the boat because, to your point, you're getting in a peer-bond world where your existing customers are your best sellers. And, prospects who have all the buying power today are looking to your existing customers to guide them in their purchasing decisions. So, as an organization, if you balance the boat, then you're going to get the flywheel effect going for you in terms of driving the right advocacy across all channels. Just not your own channel if you earn channels to ultimately drive that acquisition going. >> Do you think that's actually more valuable? 'Cause it's one thing to have on your .com site, your social media sites, all these great things about your technologies, etc., coming from customers or from product experts, from influencers. Talk about the value. As technology advances so much and we are influenced by so many other channels, the value of the earned channel and that peer-to-peer relationship. >> Yeah, I think, as I say, that every mom says her baby is good-looking. But, in software, not every baby is really good-looking. Which means, if you take that analogy and extend it, if you're coming to your own channel, invariably, you're going to see some great customer videos about your product, you're going to see some great endorsements and testimonials, you're going to see some great quotes about your product. The reality, there's no bad news about your product on your own website, on your own channel. But, the reality is there are some, some people who might have different opinions. If you go to Glassdoor, no company gets a five on Glassdoor. And, if you take the same thing and extend it to earned channels for advocacy, folks like G2 Crowd, TrustRadius, and B2B, for example, are becoming more relevant today than before because two things. One is 85% of our customers' journey is self-directed. >> Lisa: That much? >> That much and Forrester has anywhere from 60 to 80, but reality is whether you're buying a car or you're buying Coupa. Today, a customer is discovering more journeys. And, in that process, they are looking to more of these earned channels as validation of which ones to go after than just your own channels. So, that's why we got to balance the boat and distribute our advocacy spend dollars across both your own channels and your earned channels. And, that's really important for you and the flywheel will pay off for you over time from that perspective. >> It will and that seems like a lot of the things that Suzy Irwin was talking about to the audience earlier. That's common sense. Why is it that you see these marketing budgets that are so heavily weighted towards just getting awareness, getting customers acquired, and then not thinking about retention marketing account based marketing. >> I'll tell you why. I think any smart CMO will conceptually agree with you. Nobody's going to say, of course, this is not important for me to get advocacy. The challenge comes in in terms of how that marketing department is measured. What gets measured gets funding at the end of the day. >> Lisa: That's a good point. >> And, reality is a lot of these B2B companies are still measuring marketing based on, what's the pipeline you're driving and what's at the top of the funnel metrics that you're driving? In reality, that's a little bit of a skewed thing because then if that's what you're being measured at the board level, at the executive level, then guess what? All your funding is going to go towards that. But, really, the true measurement of marketing, one, is about, yes, you have to get pipeline. You have to influence win rates at the bottom of the funnel and that's where product marketing comes in. But, as importantly, you have to look at the number of brand advocates you create and lifetime value of a customer. >> Yes, CLV, yes. >> And, that's really, really, customer lifetime value is so important because in a SaaS business, ultimately, the Mufasa metric, I'm a Lion King fan. The Mufasa metric is really lifetime value because if a customer stays longer with you, pays you more, and is shouting from the rooftop, then, invariably, that SaaS business is doing well. And, that's why you have to balance the boat in terms of post-advocacies, post-acquisition spend into advocacy, as much as you've done in pre-acquisition. >> When you came into Coupa a couple of years ago, have you been able to shift those budgets because you're able to demonstrate the value that that advocacy piece generates with the flywheel? >> Absolutely and I have a very progressive-thinking CEO who's partners with me on this too. So, we've been absolutely able to do that. In fact, what we're trying to do at the end of the day and most software companies, the real goal should be creating a tribe. In technology, you have to create a tribe to be a titan. And, it's just not about the capability, it's about the community. And, that's really what we're trying to do at Coupa is to create the tribal community feeling. So, if the community is bigger than the brand, it is about the community itself and learning, sharing, and growing with each other and being successful. And, we're just fostering that. So, from that perspective, if you look at this conference and the investment we're making here, some of the programs we're doing in terms of advocacy, what we call spend sellers, etc., is all about that community tribal feeling and go establish that. To use some inspiration from our consumer brands, if you really think about it, people don't buy what they want. People buy what they want to be. So, let me give you what I mean by that. What I want could be a bike. It could be any motorbike, but what I want to be could be part of a very special community and that's why Harley Davidson is successful. What I want could be any stationary bike today, but what I want to be is part of some cool community like Peloton. That's why Peloton is successful. So, similarly for us, what I want could be some spend management software, but what I want to be is part of this community, this cool club, and that's the feeling we're trying to create in the post-acquisition cycle. >> I love that you said that because you talked about that this morning and I loved how you had the word community on the slide and then broke that out into communication unity. And, one of the senses that I got yesterday when-- >> Chandar: Rob was talking about it. >> Yeah, when Rob kicked off everything is this is a very collaborative community. We think about that in terms in terms even like a developer community or something like that. But, Coupa is now managing $1.2 trillion of spend through the platform that every other business that's using Coupa gets to benefit from. It's customer-centric, it's supplier-centric, but it's about applying the right technologies, AI, machine learning, to all this data, so everybody benefits. >> That's right and one of the interesting aspects of community building is one aspect of community building is that Marc Benioff had a great, evangelistic marketing was a way of community building. He would come in and really evangelize and this is where we're going and you all need to come with us. When I was at Marketo, it was interesting. Community building was through more educational marketing and doing it through this, I'm going to educate you through though leadership. Another good way of community building is through product intelligence, which is community intelligence. So, collectively, the sum of all parts are smarter than the parts themselves. And, Rob has a great line, which says, "None of us is as smart as all of us." And, the fundamental community intelligence offering is based on this first principle. So, example, if I'm the community of Coupa customers, the next customer is smarter than the previous customer because the collective intelligence grew, which means I can then go benchmark it myself. I gave an example this morning of USO, the company that provides services to the United States troops. And, when Rick Quaintance at USO benchmarked himself using community intelligence, versus the rest of the community, he realizes that his invoice cycle times are seven times lower. So, that kind of intelligence is extremely beneficial and invaluable to companies. So, that's the value of the community, is providing the collective intelligence. Waze is a great consumer example. Those of us who use Waze for traffic know that it's all community driven and each one of us is smarter because we're collectively using it. It's the same concept in applying that to B2B software. >> So, as we see, you mentioned the over 80% of the buying decision is self-directed whether we're buying a car or Coupa software. Did Coupa foresee that in the last decade to see we're going to have to go to a more community-driven collaboration because the consumer of any thing, any product or service, is going to be so empowered 'cause that's a part of the Coupa foundation. >> It is. >> Lisa: Which, we don't see a lot in companies that are 10 plus years old. >> Yeah, and credit to Rob for his vision for this. It's because I think early part of the company, he wrote into the contracts that the company can benefit. Collectively, every company can benefit by being part of this community. And, the fact is data's aggregated, abstracted, there's no information that is sensitive, etc. But, the fact is we all can collectively benefit through it. That was a great vision of Rob and early people and that's benefited us because the benefit is really over scale and time. Now, your $1.2 trillion, it is really statistically significant in each different industry to get that intelligence. And, that is one of the other reasons we launched our business spend index. It's called spendindex.com. Where we can use the billions of dollars spent in the community to provide a leading indicator of economic growth based on current business spend sentiment. You think of ADP as this payroll, it's called ADP payroll thing that comes out and the gross domestic product report comes out. Those tend to be rear-view mirror lagging indicators. But, as we're using community-based intelligence to provide a windshield, a leading indicator of where the economy is going. So, there's so many different use cases. Benefiting based on spend you're doing as well as where the economy is going and all this is based on the intelligence. >> It's so powerful because, to your point, you're not looking behind. >> Chandar: It's the windshield. >> Exactly, able to be looking forward. So, with all the announcements and the great things that have come out with the AWS expansion, what you guys are doing with Coupa Pay. I was shocked to learn the percentages of businesses that are still writing paper checks. Or, the fact that a lot of companies have 10 plus banks that they're working with. There's still so much manual processes. You must just be, the future is so bright, you got to wear shades with Coupa. But, what excites you about what you guys have announced the last coupe of days and the feedback that you're hearing from your tribe? >> I think there's two kinds of things. One is continue to set the innovation agenda for the industry. And, really, you have to look at every customer on their unique journey of maturity and maturation, so we have a very thoughtful, what we call, maturity index, The business spend management index. Whereas, you are seeing some of these customers, for example, you mentioned, may be in the first stage of this maturity, where, for them, it's just getting automation and going from paper to paperless could be the first step. But, some other customers might say, "I've gotten there, "but I want to get the next level of sophistication "to orchestrate these business spend processes." So, what's exciting for us in the feedback is we're creating product capability across this maturation journey for our customers to make them successful at each of those places. And, Coupa Pay is one example of that. Whereas, some of the other pieces we talked about, we announced about some of the community offerings that we did also is on that. So, that's one exciting piece. The other exciting piece that customers tell us at this conference is, "Foster platforms for us "to engage with each other, learn from each other, "share from each other, and grow with each other." So, even stuff that Rob talked about, which is sourced together. This concept of customers coming together to drive a sourcing process and, again, the collective intelligence in the community, that, we're getting very, very positive feedback from that perspective. And, ultimately, Rob has a really good saying that, "It is not about customer satisfaction. "It is about customer success." That's a delineation there. A customer could be very satisfied with you, but they may not be necessarily successful. And, we say, it's not about satisfaction. It's about success. And, by creating this innovation cycle and then having a post-implementation process that's getting true value, that's truly how we drive customer success. >> And, something that I've heard over and over as I've talked to a number of your customers yesterday and today is how much they're feeling Coupa is listening. Their feedback is being incorporated. They're actually influencing the development of the technology and that was loud and clear the last two days. >> Yeah, I think there is, Rob talked about the number of features that are being influenced by the community and we have these-- >> 300 plus in the last 12 months. >> Yes, 300 plus in the last 12 months. And, there's this concept of two ears, one mouth. And, listen, learn, and innovate and that's the philosophy here. But, it's a right mix of listening to customers, learning from them, and getting the right input from them for driving innovation, as well as having strategic vision on where this market is going and having the right mix of those to provide the capability to customers. >> Wow, you're on a rocket ship. Chandar, it was great to have you on theCUBE. You'll have to come back. >> Yes, Lisa, absolutely, I'll come back and it was a pleasure being here. Awesome. >> Awesome, thank you so much. For Chandar, I'm Lisa Martin and you're watching theCUBE from Coupa Inspire '19. Thanks for watching. (techno music)

Published Date : Jun 26 2019

SUMMARY :

Brought to you by Coupa. it's Chandar, the CMO of Coupa. This is a really cool event. I loved how the last two days, what you guys to get the flywheel effect of first you drive awareness. that the stairway to marketing heaven. in terms of driving the right advocacy across all channels. 'Cause it's one thing to have on your And, if you take the same thing and extend it and the flywheel will pay off for you over time Why is it that you see these marketing budgets What gets measured gets funding at the end of the day. of the funnel and that's where product marketing comes in. And, that's why you have to balance the boat And, it's just not about the capability, And, one of the senses that I got yesterday when-- but it's about applying the right technologies, and doing it through this, I'm going to educate you Did Coupa foresee that in the last decade that are 10 plus years old. in the community to provide a leading indicator It's so powerful because, to your point, and the feedback that you're hearing from your tribe? And, really, you have to look at every customer of the technology and that was loud and that's the philosophy here. Chandar, it was great to have you on theCUBE. and it was a pleasure being here. and you're watching theCUBE from Coupa Inspire '19.

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Amit Walia, Informatica | CUBEConversations, May 2019


 

(funky guitar music) >> From our studios, in the heart of Silicon Valley, Palo Alto, California, This is theCUBE conversation. >> Everyone welcome to this CUBE conversation here in Palo Alto, California CUBE studios, I'm John Furrier, the host of theCUBE. Were with CUBE alumni, special guest Amit Walia, President of Products & Marketing at Informatica. Amit, it's great to see you. It's been a while. It's been a couple of months, how's things? >> Good to be back as always. >> Welcome back. Okay, Informatica worlds is coming up, we have a whole segment on that but we have been covering you guys for a long long time, data is at the center of the value proposition again and again, it's more amplified now, the fog is lifting. >> Sure. >> And the world is now seeing what we were talking about four years ago. (giggles) >> Yeah. >> With data, what's new? What's the big trends that going on that you guys are doubling down on? What's new, what's changed? Give us the update. >> Sure. I think we have been talking the last couple of years, I think your right, data has becoming more and more important. I think, three things we see a lot. One is obviously, you saw this whole world of digital transformation. I think that has de faintly has picked up so much steam now. I mean, every company is going digital and obviously that creates a whole new paradigm shift for companies to carry out almost recreate themselves, rebuild them, so data becomes the new definition. And that's what we call those things you saw at Infomatica even before data3.org, but data is the center of everything, right? And you see the volume of data growth, you know, the utilization of data to make decisions, whether it's, you know, decisions on the shop floor, decisions basically related to cyber security or whatever it is. And the key to what you see different now is the whole AI assisted data management. I mean the scale of complexity, the scale of growth, you know, multi-cloud, multi-platform, all the stuff that is in front of us, it's really difficult to run the old way of doing things, so that's why we see one thing that we see a whole lot is AI is becoming a lot more mainstream, still early days but it's assisting the whole ability for companies, what I call, exploit data to really become a lot more transformative. >> You have been on this for a while, again we can go back to theCUBE archives, we can almost pull out clips from two years ago, be relevant today, you know, the data control, understanding >> Yeah. >> Understanding where the data governance is-- >> Sure. >> That's always a foundational thing but you guys nailed the chat bots, you have been doing AI was previous announcements, this is putting a lot of pressure on you, the president of the products, you got to get this out there. >> What's new? What's happening inside Informatica? pedaling as fast as you can? What is some of the updates? >> No. >> Gives us the-- >> The best example always is like a duck, right? Your really swimming and feel things are calm at the top and then you are really paddling. No, I think it's great for us. I think, I look at AI's, AI is like, there is so much FUD [fear, uncertainty and doubt] around it and machine learning AI. We look at it as two different ways. One is how we leverage machine learning within our products to help our customers. Making it easy for them, like I said, so many different data types, think of IOT data, unstructured data, streaming data, how do you bring all that stuff together and marry it with your existing transactional data to make sense. So, we're leveraging a lot of machine learning to make the internal products a lot more easier to consume, a lot more smarter, a lot more richer. The second thing is that, we're what we call it our AI, CLAIRE, which we unveiled, if you remember, a couple of years ago at the Informatica World. How that then helps our customers make smarter decisions, you know, in data science and all of these data workbenches, you know, the old statistical models is only as good as they can ever be. So, we leveraging helping our customers see the value proposition of our AI, CLAIRE, then to what I make things that, you know, find patterns, you know, statistical models cannot. So, to me I look at both of those really, leveraging ML to shape our products, which is where we do a lot of innovation and then creating our AI, CLAIRE, to help customers to make smarter decisions, easier decisions, complex decisions, which I called the humans or statistical models, really cannot. >> Well this is the balance with machines and humans. >> Right. >> working together, you guys have nailed this before and I'm, I think this was two years ago. I started to hear the words, land, adopt, expand, form you guys, right? Which is, you got to get adoption. >> Right. >> And so, as you're iterating on this product focus, you got to getting working, making secure your products-- >> Big, big maniacal focus on that one. >> So, tell me what you have learned there because that's a hard thing. >> Right. >> You guy are doing well at it. You got to get adoption, which means you got to listen customers, you got to do the course correction. >> Yeah. >> what's the learnings coming out of that piece of that. >> That's actually such a good point. We've made such, we've always been a customer centric company but as you said, like, as whole world shifted towards a new subscription cloud model, we've really focused on helping our customers adopt our products and you know, in this new world, customers are struggling with new architectures and everything, so we doubled down on what we called customer success. Making sure we can help our customers adopt the products and by the way it's to our benefit. Our customers get value really quickly and of course we believe in what we call a customer for life. Our ability to then grow with our customers and help them deliver value becomes a lot better. So, we really focused, so, we have globally across the board customers, success managers, we really invest in our customers, the moment a customer buys a product from us, we directly engage with them to help them understand for this use case, how you implement the product. >> It's not just self service, that's one thing that I appreciate 'cause I know how hard it is to build products these days, especially with the velocity of change but it's also when you have a large scale data. >> Yeah. >> You need automation, you got to have machine learning, you got to have these disciplines. >> Sure. >> And this is both on your end and but also on the customer. >> Yes. >> Any on the updates on the CLAIRE and some customer learnings you're seeing that are turning into use cases or best practices, what are some of them? >> So many of them. So take a simple example, right? I mean, we think of, we take these things for granted, right? I mean, take note, we don't talk about IOB these days right? All these cell cells, we were streaming data, right? Or even robots on the shop floor. So much of that data has no schema, no structure, no definition, it's coming, right? Netflix data and for customers there is a lot of volume in it, a lot of it could be junk, right? So, how do you first take that volume of data? Create some structure to it for you to do analytics. You can only do analytics if you put some structure to it, right? So, first thing is I've leverage CLAIRE, we help our customers to create, what I call, schema and you can create some structure to it. Then what we do allow is basically CLAIRE through CLAIRE, it can naturally bring what we have the data quality on top of it, like how much of it is irrelevant, how much of it is noise, how much of it really makes sense, so, then, as you said it, signal from the noise We are helping our customers get signal from the noise of data. That's where it AI comes very handy because it's very manual, cumbersome, time consuming and sometimes very difficult to do. So, that's a area we have leveraged creating structure and data quality on top and finding rules that didn't naturally probably didn't exist, that you and me wouldn't be able to see. Machines are able to do it and to your point, our belief is, this is my 100% belief, we believe AI assisting the humans. We have given the value of CLAIRE to our users, so it complements you and that's where we are trying to help our users get more productive and deliver more value to you faster. >> Productivity is multifold, it's like, also, efficiency, people wasting time on project that can be automated, so you can focus that valuable resource somewhere else. >> Yeah. >> Okay, let's shift gears onto Informatica World coming up. Let's spend some time on that. What's the focus this year, the show, it's coming up, right around the corner, what's going to be the focus? What's going to be the agenda? What's on the plate? >> Give you a quick sense on how it's shape up, it's probably going to be our Informatica World. So, it's 20th year, again back in Waze, you know, we love Waze of course. We have obviously, a couple of days lined up over there, I know you guys will be there too. A great set of speakers. Obviously, we will have me on stage, speakers like, we'll have some, the CEO of Google Cloud, Thomas Kurian is going to be there, we'll have on the main stage with Anil, we'll have the CEO of Databricks, Ali, with me, we'll also have CMO of AWS, Ariel, there, then we have a couple of customers lined up, Simon from Credit Suisse, Daniel is the CDO of Nissan, we also have the Head of AI, Simon Guggenheimer from Microsoft as well as the Chief Product Officer of Tableau, Francois Ajenstat, so, we have a great line up of speakers, customers and some of our very very strategic partners with us. If you remember last year, We also had Scott Guthrie there main stage. 80 plus sessions, pretty much 90% lead by customers. We have 70 to 80 customers presenting. >> Technical sessions or going to be a Ctrack? >> Technical, business, we have all kinds of tracks, we have hands on labs, we have learnings, customers really want to learn our products, talk with the experts, some want to the product managers, some want to talk to the engineers, literally so many hands on labs, so, it's going to be a full blown couple of days for us. >> What's the pitch for someone watching that never been Informatica World? Why should they come for the show? >> I'll always tell them three things. Number one is that, it's a user conference for our customers to learn all things about data management and of course in that context they learn a lot about. So, they learn a lot about the industry. So, day one we kick it off by market perspectives. We are giving a sense on how the market is going, how everybody is stepping back from the day to and understanding, where are these digital transformation, AI, where is all the world of data going. We've got some great annalists coming, talkings, some customers talking, we are talking about futures over there. Then it is all about hands on learning, right?, learning about the product. Hearing from some of these experts, right?, from the industry experts as well as our customers, teaching what to do and what not to do and networking, it's always go to network, right, it's a great place for people to learn from each other. So, it's a great forum for all those three things but the theme this year is all about AI. I talked about CLAIRE, I'll in fact our tagline this year is, Clarity Unleashed. We really want, basically, AI has been developing over the last couple of years, it's becoming a lot more mainstream, for us in our offerings and this year we're really taking it mainstream, so, it's kind of like, unleashing it for everybody can genuinely use it, truly use it, for the day to day data management activities. >> Clarity is a great theme, I mean, it plays on CLAIRE but this is what we're starting to see some visiblility into some clear >> Yeah. >> Economic benefits, business benefits. >> Yep. >> Technical benefits, >> Yep. >> Kind of all starting to come in. How would you categorize those three areas because you know, generally that's the consensus these days that what was once a couple years ago was, like, foggy when you see, now you're starting to see that lift, you're seeing economic, business and technical benefits. >> To me it's all about economic and business. So, technology plays a role in driving value for the business, right, I'm a full believer in that, right, and if you think about some of the trends today, right, a billion users are coming into play that will be assisted by AI. Data is doubling every year, you know the volume of data, >> Yep. >> The amount of, and I always say business users today, I mean, I run a business, I want, I always say, tomorrow data, yesterday to make a decision today. It's just in time and that's where AI comes into play. So our goal is to help organizations transform themselves, truly be more productive, reduce operation cost, by the way governance and compliance, that's becoming such a mainstream topic. It's not just basically making analytical decisions. How do you make sure your data is safe and secure, you don't want to get basically get hit by all of these cyber attacks, they're all are coming after data. So, governance, compliance of data that's becoming very, so, those-- >> Again you guys are right on the data thing. >> Yeah. >> I want to get your reaction, you mentioned some stats. >> Sure. >> I've got some stats here. Data explosion, 15.3 zettabytes per year >> Yeah, in global traffic. >> Yeah. >> 500 million business data users and growing 20 billion in connected devices, one billion workers will be assisted by machine learning, so, thanks for plugging those stats but I want to get your reaction to some of these other points here. 80% of enterprises are looking at multicloud, their really evaluating where the data sits in that equation >> Sure. And the other thing is the responsibility and role of the Chief Data Officer >> Yes. >> These are new dynamics, I think you guys will be addressing that into the event. >> Absolutely, absolutely. >> Because organizational dynamics, skill gaps are issues but also you have multicloud. So your thoughts on those to. >> That's a big thing, look at, in the old world, John, Hidrantes is always still in large enterprises, right, and it's going to stay here. In fact I think it's not just cloud, think of it this way, on-premise is still here, it's not going a way. It's reducing in scope but then you have this multicloud world, SAS apps, PAS apps, infrastructure, if I'm a customer, I want to do all of it but the biggest problem is that my data is everywhere, how do I make sense of it and then how do I govern it, like my customer data is sitting somewhere in this SAS app, in that platform, on this on-prem application transaction app I'm running, how do I connect the three and how do I make sense it doesn't get, I can have a governance control around it. That's when data management becomes more important but more complex but that's why AI comes in to making it easier. What are the things we've seen a lot, as you touched upon, is the rise of CDO. In fact we have Daniel from Nissan, she is the CDO of Nissan North America, on main stage, talking about her role and how they have leveraged data to transform themselves. That is something we're seeing a lot more because you know, the role of the CDO is making sure that is not only a sense of governance and compliance, a sense of how do we even understand the value of data across an enterprise. Again, I see, one of the things we going to talk about is system thinking around data. We call it System Thinking 3.0, data is becoming a platform. See, there was OSA-D hardware layer whether it is server, or compute, we believe that data is becoming a platform in itself. Whether you think about it in terms of scale, in terms of governance, in terms of AI, in terms of privacy, you have to think of data as a platform. That's the other big thing. >> I think that is a very powerful statement and I like to get your thoughts, we had many conversations on camera, off camera, around product, Silicon Valley, Venture Capital, how can startups create value. On of the old antigens use to be, build a platform, that's your competitive strategy, you were a platform company and that was a strategic competitive advantage. >> Yes. >> That was unique to the company, they created enablement, Facebook is a great example. >> Yeah. >> They monetized all the data from the users, look where they are. >> Sure. >> If you think about platforms today. >> Sure. >> It seems to be table steaks, not as a competitive advantage but more of a foundational. >> Sure. >> Element of all businesses. >> Yeah. >> Not just startups and enterprises. This seems to be a common thread, do you agree with that, that platforms becoming table steaks, 'cause of if we have to think like systems people >> Mm-hmm. >> Whether it's an enterprise. >> Sure. >> Or a supplier, then holistically the platform becomes table steaks on premer or cloud. Your reaction to that. Do you agree? >> No, I think I agree. I'll say it slightly differently, yes. I think platform is a critical component for any enterprise when they think of their end to end technology strategy because you can't do piece meals otherwise you become a system integrator of your own, right? But it's no easy to be a platform player itself, right, because as a platform player, the responsibility of what you have to offer your customer becomes a lot bigger. So, we obviously has this intelligent data platform but the other thing is that the rule of the platform is different too. It has to be very modular and API driven. Nobody wants to buy a monolithic platform. I don't want to, as a enterprise, I don't buy all now, I'm going to implement five years of platform. You want it, it's going to be like a Lego block, okay you, it builds by itself. Not monolithic, very API driven, maybe microservices based and that's our belief that in the new world, yes, platform is very critical for to accelerate your transformational journeys or data driven transformational journeys but the platform better be API driven, microservices based, very nimble that is not a percussor to value creation but creates value as you go along. >> It's all, kind of up to, depends on the customer it could have a thin foundational data platform, from you guys for instance, then what you're saying, compose. >> Of different components. >> On whatever you need. >> For example you have data integration platform, you can do data quality on top, you can do master data management on top, you can provide governance, you can provide privacy, you can do cataloging, it all builds. >> Yeah. >> It's not like, oh my gosh, I have go do all these things over the course of five years, then I get value. You got to create value all along. >> Yeah. >> Today's customers want value like, in two months, three months, you don't want to wait for a year or two. >> This is the excatly the, I think, the operating system, systems mindset. >> Yes. >> You were referring too, this is kind of how enterprises are behaving now. There is the way you see on-premise, >> Yep. >> Thinking around data, cloud, multicloud emerging, it's a systems view distributed computing, with the right Lego blocks. >> That's what our belief is. That's what we heard from customers. See our, I spend most of my time talking to customers and are we trying to understand what customers want today and you know, some of this latent demands that they have, sometimes can't articulate, my job, I always end up on the road most of the time, just hearing customers, that's what they want. They want exactly to your point, a platform that builds, not monolithic, but they do want a platform. They do want to make it easy for them not to do everything piece meal. Every project is a data project. Whether it's a customer experience project, whether it's a governance project, whether it's nothing else but a analytical project, it's a data project. You don't repeat it every time. That's what they want. >> I know you got a hard stop but I want to get your thoughts on this because I have heard the word, workload, mentioned so many more times in the past year, if there was a tag cloud of all theCUBE conversations where the word workload was mentioned, it would be the biggest font. (laughs) >> Yes. >> Workload has been around for a while but now you are seeing more workloads coming on. >> Yeah. >> That's more important for data. >> Yes. >> Workloads being tied into data. >> Absolutely. >> And then sharing data across multiple workloads, that's a big focus, do you see that same thing? >> We absolutely see that and the unique thing we see also is that newer workloads are being created and the old workloads are not going away, which is where the hybrid becomes very important. See, we serve large enterprises and their goal is to have a hybrid. So, you know, I'm running a old transaction workload order here, I want to have a experimental workload, I want to start a new workload, I want all of them to talk to each other, I don't want them to become silos and that's when they look to us to say connect the dots for me, you can be in the cloud, as an example, our cloud platform, you know last time, we talked about a 5 trillion transactions a month, today is double that, eight to ten trillion transactions a month. Growing like crazy but our traditional workload is also still there so we connect the dots for our customers. >> Amit, thank you for coming on sharing your insights, obviously you guys are doing well. You've got 300,000 developers, billions in revenue, thanks for coming on, appreciate the insight and looking forward to your Informatica World. >> Thank you very much. >> Amit Walia here inside theCUBE, with theCUBE conversation, in Palo Alto, thanks for watching.

Published Date : May 10 2019

SUMMARY :

in the heart of Silicon Valley, I'm John Furrier, the host of theCUBE. but we have been covering you guys And the world is now seeing what we were talking about that you guys are doubling down on? And the key to what you see different now but you guys nailed the chat bots, then to what I make things that, you know, working together, you guys have nailed this before So, tell me what you have learned there which means you got to listen customers, and you know, in this new world, but it's also when you have a large scale data. You need automation, you got to have machine learning, and but also on the customer. and you can create some structure to it. so you can focus that valuable resource somewhere else. What's the focus this year, I know you guys will be there too. so, it's going to be a full blown couple of days for us. how everybody is stepping back from the day to because you know, generally that's the consensus and if you think about some of the trends today, right, How do you make sure your data is safe and secure, I've got some stats here. but I want to get your reaction and role of the Chief Data Officer I think you guys will be addressing that into the event. are issues but also you have multicloud. Again, I see, one of the things we going to talk about and I like to get your thoughts, they created enablement, Facebook is a great example. They monetized all the data from the users, It seems to be table steaks, do you agree with that, Do you agree? the responsibility of what you have to offer from you guys for instance, you can do master data management on top, over the course of five years, then I get value. three months, you don't want to wait for a year or two. This is the excatly the, I think, the operating system, There is the way you see on-premise, it's a systems view distributed computing, and you know, some of this latent demands that they have, I know you got a hard stop but now you are seeing more workloads coming on. and the unique thing we see also is that Amit, thank you for coming on sharing your insights, with theCUBE conversation, in Palo Alto,

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Darryl Sladden, Cisco | DevNet Create 2019


 

>> Live from Mountain View, California, it's theCUBE covering DevNet Create 2019 brought to you by Cisco. >> Hello everyone, welcome back to theCUBE's live coverage here in Mountain View, California for the theCUBE's coverage of Cisco DevNet Create. It's a small, intimate event where we're bringing the cloud native creation world with the DevNet community within Cisco and of course building applications, programming networks, that's the theme. I'm John Furrier, your host, our next guest is Darryl Sladden, senior technical product manager at Cisco, 20 year veteran, built voice over IP systems. He's a coder, he's a builder, he's a creator. Great to see you, thanks for coming on. >> Thank you so much, I'm glad to be here. >> And you're a fan? >> I love being on theCUBE. Because-- >> And the trivia behind that? Share the context, you had a product, you built one? >> Yes, the first product management job at Cisco was building the Cisco Unified Border Element and of course, that became the Cube, so any time you mention Cube inside of Cisco, that's going to be my product. >> The renaissance within Cisco theCUBE is back and we're embedded in there. Of course we're breaking all the borders down, getting the data. Tell us what's going on in your world? Obviously you've seen a lot of waves. I mean voice over IP that you were involved in? >> Yeah. >> That took, that old PBX telephone-- >> Right. >> Got digital, created massive innovation. That's an inflection point moment. We're seeing a few of those big waves happening now. One of them's an architectural changes around IoT, Wi-fi 6, 5G, cloud computing all coming together. This is an interesting opportunity. What's your focus? Where do you fit into all that? >> Yeah, where I fit in is this is a massive change and one of the problem sets that hasn't been solved yet is how do I understand where I am indoors? There's been great solutions that have unlocked huge amount of value with the GPS system outdoors. You always know where you are, a lot of way to find out exactly the right, it always amazes me at how accurate they are at how long it's going to take me to get to the Computer Museum. But how do I know once I've got into the museum that theCUBE is in the upstairs, in the back corner? That's where we need to solve that problem and I think we're at the crux of that. >> Waze is a great example because one of the things I'm amazed by with Waze is how fast they report the incidents that are going on. People are so actively rapid of adding, inputting the data. You got data junkies adding it and there's been some side effects. The side streets are always clogged. (laughing) >> Police always know-- >> So in physical locations where Wi-fi 6 for instance comes out? >> Yeah. >> You're going to have new capabilities in bandwidth and throughput and coverage areas, these dense areas. It's going to create a navigation opportunity for either machines to machines, machines to humans, humans to machines, humans to humans, within a physical construct. >> Yeah. >> How do you see that evolving? Use cases? What's the pattern? >> Right. What I really see evolving is taking advantage of some of the capabilities that have already existed in wi-fi, meaning ranging from individual IPs but some of the new things that are coming with Wi-fi 6 is Wi-fi 6 creates a great baseline but there are new things where, 802.11mc for example, which is an extension of Wi-fi 6, has what's called fine timing measurement. I can now, with these super accurate chip sets, know the speed of light is about one nanosecond to go about three feet. If I have an accurate clock, now I can know how far I am from the APs. >> Yeah. >> And I can solve that in indoor locations. >> So a lot of physics involved? >> A lot of rates of physics involved. >> Alright, so what products are you working on now to make all this happen. Take us through some of the things that are out there that you've got your fingers on. >> Yeah, so what I'm working on is Cisco's new location platform, it's called Cisco DNA Spaces and so what we're focusing on is digitizing that indoor space. So people spend of their economic activity are indoors. Whether it's in a hotel, where they're selling the rooms, or a restaurant where they're selling food inside the spaces, but what goes on in that physical space? People don't have that same level of knowledge that you do on the web, right? When I go to a webpage and I shop for outdoor furniture? The next two weeks I'm followed by ads about outdoor furniture. But if I go to Home Depot and I spend an hour in the outdoor furniture aisle, they don't know about that. Now, it allows you to digitize that indoor space and provide that context for other types of applications. >> So the value, I mean I'm not saying, now they're going to know you actually shopped at Home Depot, now your ad go to Home Depot. (laughing) But the value is not so much in the advertising. It's really in the efficiencies around work, play, office. These are the things that are going to be impacted because, you know, take healthcare for instance? Manufacturing? How people do work? How services are delivered? Just like in the consumer side, we all relate to the iPhone days when oh my god, I can have GPS on a phone. Now I do a mash up on a Google Map. >> Right. >> Are you saying the same thing for buildings? You're going to import like architectural drawings? How do you get all of this built out? What's the playbook? >> Yeah. The playbook really will be starting at the larger buildings that will be put into Google Maps or put into other places where it can start to get really accurate indoor locations and then never losing things, right? Be able to know where you are indoors. Being able to always find your stuff, not only where you are but maybe I put a tag on some of my assets and I always know where they are? The idea of nurses becoming more efficient because they're going to know where that wheelchair is if I need to find a wheelchair to move a patient out of an office. All of these things just become a little bit more efficient but that just builds on a huge scale when that happens at scale. >> Darryl, talk about the impact of this because you built and deployed disruptive technology in the past. For the folks watching, whether it's an enterprise architect or CIO or CEO or facilities manager, whoever, what is the impact of these new location based services to their business? How should they be thinking about it, holistically? >> Yeah. >> What's your view? >> My real view is that you want to look at it from a platform, so you're not going to have one company. Even at Cisco, we're not going to solve every application but what you do want to do is build a platform that's extensible, right? We'll take in data from multiple sources, whether it APs or video cameras, other things, create a platform that normalizes that location, and then opens that up. So that's what happened as the mainframes transitioned to client server computing. Once you start breaking things up? That's really the value and so I think the CIOS and architects out there, shouldn't be looking at point products as much as understanding that a location platform will help them unlock the value moving forward. >> Talk about the data. How is the data traversing through this? Because obviously you mentioned connecting things like cameras and other things? It could be medical equipment, it could be anything. IoT's going to be a tsunami of opportunity, applications that are going to create a lot of opportunity. How should I think about the data flow? And the role of machine learning and data in all of this? Is that going to be a key part of this? >> Absolutely, the way that we're looking at it is there's kind of two groups. There's the ones that are all in on the cloud, and we are offering this as a software as a subscription service so you buy it on a subscription basis and you let Cisco deal with the problems. Of course with a regulated environment of access to the data and backing it up and restoring it and making sure it's well curated. Or you can decide, yeah I want to run it on premises. If you want it on prem you have to understand you're going to have to deal with those same problems of back up, the data will get really large as you start to collect more and more location and how are you going to best extract value from that data? So I think you really want to look at that this is something that's going to continue to expand and do I want to make that a core competence by running it myself? Or maybe turn that over to cloud service? >> So in terms of what's real and not real or what's coming and what's real today? So you mentioned there's some location services as a SAS. Talk about what's available now from your customer standpoint. >> Yeah. >> What can they get going on and what's coming around the corner? >> Yeah, so what they can get going on today is that location services, Cisco DNA Spaces. So if you go to ciscodnaspaces.com there's free trials available, it's a great sort of application. But more importantly, it provides you that initial start, right? What's coming is more and more applications will take advantage of that, right? We got a great one for things like student success, so that you know a student is inside of a classroom and then if he doesn't come to class for a couple days in a row? Oh maybe he needs counseling? Maybe his car broke down? You can start to do these really interesting student success applications as an example of a vertical. So the vertical applications are starting to really proliferate, but what's available today is the platform. >> So you see verticals really booming on this? >> Yeah. >> They're going to take advantage of it? Alright, so just kind of zoom out and put your industry hat on, not your Cisco hat. When you look at wi-fi and 5G or other technologies that are out there, what's the big movement? What moves the ball down the field the most? Is it going to be wi-fi and 5G? Because it seems like, you know, inch by inch, unified communication seemed stalled, now it's got an uplift with cloud, with data, more great user experiences. SD-WAN's been around for a long time and getting a resurgence. I mean campus networking had been around for a long, long time. >> I know. (chuckling) >> People go to stadiums, want to do Instagram and do videos. What's the big technology lever here? What's the big tailwind for location based in-building stuff? >> What I start to see for this is improving standards and improving accuracy, right? Until you get to that point where it's reliable and replaceable and I can really depend on it? It's all a niche product. I think that's been happening for literally the last eight years in this industry. Lots of niche examples of things that have been successful but it hasn't exploded, until you build that platform where I can absolutely, with reliability say, this device is at this point at this time? >> Yeah. >> Then you can start to really expand but that's really-- >> The timing and the through put, to your point earlier? >> Yeah. >> Okay, thoughts on DevNet, just to wrap up. What's here? Going on in the show here? DevNet Create, Susie did a good job of bringing communities together. A lot of co-creation, they're creating new things. This is a new application environment, programmable. What's your thoughts on DevNet? >> Yeah, I love being around some of the smartest people in the world here. (laughing) It's great. Humbling just to be able to talk to some of these guys. But I do think that really creates the community that teaches everything from little things, like I learned a quick, great new little API trick that I hadn't learned and maybe I taught some people some of the stuff that we're doing about streaming APIs. What I really like about this is all these small little interactions build something really good. >> Yeah. And you build API into all the products that's only going to create more enablement. >> Yeah. >> More creativity. The creativity's flowing big time. >> Right. >> Darryl, thanks for coming on. >> Well thank you so much. >> Great to see you. Thanks, a CUBE fan. >> Right. (laughing) >> Author of the product called The Cube at Cisco back in the day. I'm John Furrier, back with more live coverage after this short break. (light digital music)

Published Date : Apr 25 2019

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brought to you by Cisco. for the theCUBE's coverage of Cisco DevNet Create. I love being on theCUBE. and of course, that became the Cube, getting the data. Where do you fit into all that? and one of the problem sets that hasn't been solved yet Waze is a great example because one of the things It's going to create a navigation opportunity of some of the capabilities that have already existed Alright, so what products are you working on now that you do on the web, right? These are the things that are going to be impacted Be able to know where you are indoors. in the past. That's really the value and so I think the CIOS Is that going to be a key part of this? and how are you going to best extract value So you mentioned there's some location services as a SAS. so that you know a student is inside of a classroom Is it going to be wi-fi and 5G? I know. What's the big technology lever here? What I start to see for this Going on in the show here? and maybe I taught some people some of the stuff that's only going to create more enablement. The creativity's flowing big time. Great to see you. Right. Author of the product called The Cube at Cisco

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Meagen Eisenberg, TripActions | CUBEConversation, March 2019


 

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hello and welcome to this special cube conversation here in Palo Alto California cube headquarters I'm Jennifer echoes the cube our guest here is Megan Eisenberg CMO of a new hot company called trip actions formerly the CMO at MongoDB before that taki sign we've known each other some advisory boards great to see you yes great to see you as well so exciting new opportunity for you at trip actions just transitioned from MongoDB which by the way had great earnings they did what was the big secret to Mongo DB z earnings tell us well it's fresh and I think they're executing and their growth is amazing they're bringing their costs down I mean they're they've got product market fit their developers love them and so I'm proud and not surprised you're there for four years yeah transformed their go-to market so that fruits coming off the tree yes yeah it's exciting to see the you know process people technology all coming together and seeing them scale and do so well in the markets yes you know being here in 20 years living in California Palo Alto you see the rocket ships the ones that flame out the ones that make it and there's a pattern right when you start to see companies that are attracting talent ones that have pedigree VCS involved yeah raising the kind of rounds in a smart way where there's traction product market fit you kind of take special notice and one of the companies that you're now working for trip actions yes seems to have the parameters so it's off the pad it's going up its orbit or taking off you guys have really growing you got a new round of funding one hundred fifty million dollars yes unique application in a market that is waiting to be disrupted yes travel about company you work for transactions trip actions is a fast growing business travel platform we service customers like we work slack zoom box and we're growing we're adding 200 customers a month and it's amazing just to see these fast-growing companies right when they hit product market fit I think the keys are they've gotten a massive addressable market which we have 800 billion online travel they're solving a pain and they're disrupting a legacy the legacy providers that are out there we're three and a half years old and we are you know really focused on the customer experience giving you the choice that you want when you book making it easy down to six minutes not an hour to book something and we've got 24/7 support which not many can compete with you know it's interesting you know I look at these different ways of innovation especially SAS and mobile apps you know chapter one of this wave great economics yeah and once you get that unit economics visibility say great SAS efficacious happened but now we're kind of in a chapter two I think you guys kind of fit into this chapter to where it's not just SAS cuz you know we've seen travel sites get out there you book travel it's chapter two of SAS is about personalization you see machine learning you got cloud economics new ventures are coming out of the woodwork where you could take a unique idea innovate on it and disrupt a category that seems to be what you guys are doing talk about this new dynamic because this is not just another travel app when you guys are doing gets a unique angle on this applying some tech with the Corpse talked about that this chapter to kind of assess business I think when I think about chapter 2 I think about all the data that's out there I think about the machine learning I think about how we understand the user and personalize everything to them to make it frictionless and these apps that I love on my phone are because they they know what I want before I want it and I just took a trip to Dallas this week and the app knew I needed to check in it was one click told me my flight was delayed gave me options checked me in for my hotel I mean it was just amazing experience that I haven't seen before and it's really if you think about that that business travel trip there's 40 steps you have to do along the way there's got to be a way to make it easier because all we want to do is get to the business meeting and get back we don't want to deal with weather we don't want to deal with Hotel issues or flight changes and our app is specific to when you look at it you've got a chat 24/7 and someone's taking care of you that concierge service and we can do that because the amount of data we're looking at we're learning from it and we make it easier for travel manager half the people go rogue and don't even book through their travel solution it's because it's not tailored to them so this is the thing I want to get it so you guys aren't like a consumer app per se you have a specific unique target audience on this opportunity its travel management I'm I'm gonna date myself but back when I broke into the business they would have comes like Thomas Cook would handle all the travel for youlet Packard when I worked there in the 80s and you had these companies I had these contracts and they would do all the travel for the employees yes today it's hard to find that those solutions out there yes I would say it's hard to find one that you love and trip Actions has designed something that our travelers love and it is it's for business travel it's for your business trips it's taking care of your air your hotel your car your rail whatever you need and making sure that you can focus on the trip focus on getting there and not just the horrible experience we've all had it you travel a lot I traveled certainly back and forth to the East Coast and to take those problems away so I can focus on my business is what it's so just just look at this right so you guys are off to unicorn the funding great valuation growing like crazy got employees so people looking for jobs because they're hiring probably yeah but you're targeting not consumers to download the app it's for businesses that want to have company policies and take all that pressure off yes of the low so as a user can't buy myself can't just use the app or get I know you can Nano that's the the the whole thing is that as a user there's three things we're providing to one inventory and choice so you go and you know all the options you get the flight you want it's very clear and art we have a new storefront where it shows you what's in policy what's not so we've got that its ease of use it's booking quickly nobody wants to waste time dealing with this stuff right you want to go in booked quickly and then when you're on the trip you need 24/7 support because things go wrong airline travel gets cancelled weather happens you need to change something in your trip and so yes the user has the app on their phone can book it can you do it fast and can get support if they need it so stand alone usually can just use it as a consumer app but when you combine with business that's the magic that you guys see is that the opportunity yes I should say as a consumer as a business traveler so you're doing it through your company so I'm getting reimbursed for the companies the company is your customer yes the company's our customer is the traveler yes okay got it so if we want to have a travel desk in our company which we don't have yet yes it would we would sign up as a company and then all your employees would have the ease of use to book travel so what happens what's the sum of the numbers in terms of customers you have said 200 month-over-month yes we're over 1500 customers we're adding 200 a month we've got some significant growth it's amazing to see product market and the cost of the solution tell people $25 a booking and there's no add-on costs after that if you need to make as many changes as you need because of the trip calls on it you do it so basically per transaction yes well Little Feat one of our dollars yes okay so how do you guys see this growing for the company what's the some of the initiatives you guys are doing a new app yes mo what's what's the plan it's a massive market 800 billion right and we've only just started we've got a lot of customers but we've got many more to go after we are international so we have offices around the world we have an Amsterdam office we've got customers travelling all over so we're you know continuing to deliver on that experience and bringing on more customers we just on-boarded we were ten thousand travelers and will continue to onboard more and more so as head of marketing what's the current staff you have openings you mentioned yet some some some open recs yes yes hi are you gonna build out I've got 20 open Rex on the website so I'm hiring in all functions we're growing that fast and what's the marketing strategy what's your plan can you give it a little teaser on yes thinking core positioning go to market what are some of the things you're thinking about building out marketing CloudStack kind of thing what's what's going on all of these things my three top focuses are one marketing sales systems making sure we have that mark tech stack and that partnership with the sales tech stack second thing is marketing sales alignment that closed-loop we're building we're building pipeline making sure when people come in there's a perfect partnership to service what they need and then our our brand and messaging and it's the phase I love in these companies it's really building and it's the people process and technology to do that in the core positioning is what customer service being the most user-friendly what's the core position we're definitely focused on the traveler I would say we're we're balancing customer experience in making sure we get that adoption but also for the travel managers making sure that they can administer the solution and they get the adoption and we align the ascent in the incentives between the traveler and the travel manager and customer profile what small munis I business to large enterprise we have SMB and we're going all the way up to enterprise yes has it been much of a challenge out there in the business travel side I'm just don't know that's why I'm asking is like because we don't have one I can see our r-cube team having travel challenge we always do no centralizing that making that available but it'd have to be easier is it hard to get is there a lot of business travel firms out there is what are some of the challenges that you guys are going after there well I I think what matters is one picking the solution and being able to implement it quickly we have customers implementing in a week right it's understanding how we load your policies get you on board get your cut you're you're really your employees traveling and so it's pretty fast onboarding and we're able to tailor solutions to what people need what are some of the policies that are typical that might be out there that people like yeah so maybe for hotels you may have New York and your your policy is $500 a night what the I would say a normal typical behavior would someone would book it at $4.99 they go all the way up to the limit we've actually aligned our incentives with the travel managers and the employees and that if you save your company money you save and get rewards back so let's say you book it for 400 that $100 savings $30 goes back to the employee and rewards they can get an Amazon card donate to Cherry charity whatever they'd like to kind of act like an owner cuz they get a kickback yes that's the dot so that's how you an interest adoption yes what other adoption concerns you guys building around with the software and or programs to make it easy to use and we're constantly thinking about the experience we want to make sure just I mean I think about what I used to drive somewhere I'd pull out a map and map it out and then I got lucky and you could do MapQuest and now you have ways we are that ways experience when you're traveling we're thinking about everything you need to do that customer when they leave their front door all the way to the trip all the things that can hang them up along the way we're trying to remove that friction that's a very example I mean Waze is a great service yes these Google Maps or even Apple Maps ways everyone goes to backed away yes yeah I don't I mean ways did cause a lot of Street congestion the back streets of Palo Alto we're gonna expedite our travelers well it's a great utility new company what what attracted you to the opportunity when was some of the because you had a kid going over there MongoDB what it was the yeah motivation to come over to the hot startup yeah you know I love disruptive companies I love massive addressable markets good investors and a awesome mission that I can get behind you know I'm a mom of three kids and I did a lot of travel I'm your typical road warrior and I wanted to get rid of the pain of travel and the booking systems that existed before trip actions and so I was drawn to the team the market and the product that's awesome well you've been a great CMO your career has been phenomenal of great success as a CPM mother of three you know the challenges of juggling all this life is short you got to be using these apps to make sure you get on the right plane I mean I know I'm always getting back for my son's lacrosse game or yes event at school this is these are like it's like ways it's not necessary in the travel portfolio but it's a dynamic that the users care about this is the kind of thing that you guys are thinking about is that right yeah definitely I mean I always think about my mom when she worked in having three daughters and I work and have three daughters I feel like I can do so much more I've got door - I've got urban sitter I've got ways I've got Google Calendar I've got trip actions right I've got all these technologies that allow me to do more and not focus on things that are not that productive and I have no value add on it just makes me more efficient and productive how about some of the tech before we get in some of the industry questions I want to talk about some of the advantages on the tech side is there any machine learning involved what's some what's not what's some of the secret sauce and the app yeah definitely we're constantly learning our users preferences so when you go in we start to learn what you what hotels you're gonna select what where do you like to be near the office do you like to be near downtown we're looking at your flights do aisle window nobody wants middle yes but we're we're learning about your behaviors and we can predict pretty closely one if you're gonna book and two what you're gonna book and as we continue learning you that's why we make you more efficient that's why we can do it in six minutes instead of an hour that's awesome so Megan a lot of things going on you've been a progressive marker you love Terry's tech savvy you've done a lot of implementations but we're in a sea change now where you know people that think differently they gonna think okay I need to be on an app for your case with with business travel it's real policies there so you want to also make it good for the user experience again people centric this personalization has been kind of a cutting edge concept now in this chapter to a lot of CMOS are either they're they're not are trying to get there what are you finding in the industry these days that's a best practice to help people cross that bridge as they think they cracked the code on one side then realize wow it's a whole another chapter to go you know I think traditionally a lot of times we think we need we're aligning very much with sales and that matters that go to market marketing sales aligned but when it comes to products and a customer experience it's that alignment with marketing and the product and engineering team and really understanding the customer and what they want and listening and hearing and testing and and making sure we're partnering in those functions in terms of distribution getting the earned concept what's your thoughts on her and media yeah I mean I definitely think it's the direction right there's a ton of noise out there so you've got to be on topic you've got to understand what people care about you've got to hit them in the channel that they care about and very quick right is you don't have time nobody's gonna watch something that's 30 minutes long you get seconds and so part of the earned is making sure you're relevant you what they care about and they can find you and content big part of that for you guys huge part of it yes and understanding the influencers in the market who's talking about travel who's who is out there leading ahead you know leading in these areas that travel managers go and look to you know making sure we're in front of them and they get to see what we're delivering I like how you got the incentives of the employees to get kind of a line with the business I mean having that kind of the perks yes if you align with the company policies the reward could be a Starbucks card or vacation one more time oh whatever they the company want this is kind of the idea right yeah they kind of align the incentives and make the user experience both during travel and post travel successful that's right yes making sure that they are incented to go but they have a great experience okay if you explain the culture of the company to someone watching then maybe interested in using the app or buying you guys as a team what's the trip actions culture like if you had to describe it yeah I would say one we love travel too we are fast growing scaling and we're always raising the bar and so it's learning and it's moving fast but learning from it and continually to improve it's certainly about the user all of the users so not just the travel manager but our travelers themselves we love dogs if you ever come to the Palo Alto office we've got a lot of dogs we love our pups and just you know building something amazing and it's hard to be the employees gonna know that's a rocket ship so it's great get a hold on you got a run hard yes that's the right personality to handle the pace because you're hiring a lot of people and I think that's a part of the learning we need continual learning because we are scaling so fast you have to reinvent what we need to do next and not a lot of people have seen that type of scale and in order to do it you have to learn and help others learn and move fast well great to see you thanks for coming in and sharing the opportunity to give you the final plug for the company share what who you what positions you're hiring for what's your key hires what are you guys trying to do give a quick plug to the company yeah so I mean we've grown 5x and employees so we're hiring across the board from a marketing standpoint I'm hiring in content and product marketing I'm hiring designers I'm hiring technical I you know I love my marketing technology so we're building out our tech stack our website pretty much any function all right you heard it here trip actions so when you get the product visibility those unit economics as they say in the VC world they've got a rocket ship so congratulations keep it up yeah now you're in palo alto you can come visit us here anytime yes love to Meagen Eisenberg CMO trip access here inside the cube I'm John Ferrier thanks for watching you [Music]

Published Date : Mar 15 2019

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David McCurdy, State of Colorado | Commvault GO 2018


 

>> Announcer: Live from Nashville, Tennessee, it's theCUBE. Covering Commvault GO 2018. Brought to you by Commvault. >> Welcome back to Nashville, Tennessee. This is Commvault GO, and you are watching theCUBE. I'm Stu Miniman, with my co-host Keith Townsend. Happy to welcome to the program, this is a user conference, so we love digging in with the users. I've got David Mccurdy, who's the CTO from the great state of Colorado. Thanks so much for joining us. >> Great to be here. It's a great event, I'm happy to be here. We're here to evangelize the great work Colorado's been doing, with Commvault, and just in general. >> Alright great, so we're from Chicago, Boston, and Colorado, Denver. So we're not going to talk football, but tell us a little bit about, you know, you're CTO, love talking to the CTOs. What's your technology charter? Give us a little bit of the thumbnail, as to kind of, you know, what divisions you support, how many people you have, that sort of thing. >> Yeah, so the way the state's set up is I work underneath the Governor. We're an office of the Governor, so it's actually the Governor's Office of Information Technology. We support all the traditional branches of government, that people think of, in terms of agencies, like the Department of Health and Human Services, Medicaid, Department of Corrections, DMV, Department of Revenue. So all the big agencies all fall under our department. And then about 800 of the 900 staff inside of OIT report to me directly. And that's all the infrastructure and application stacks, all the strategy. Chief Data office, Chief Transformation office. A lot of responsibility, lots of fun, lots of long weekends, but it's been a good row for the last four years. >> David before we dig into some of the data protection stuff, I love, you talk about innovation. You talk about technology transformation. First of all, IT in general, and government specifically, often get, you know, labeled with the, oh well they do things the old way, and they've got no budgets, and they never make any changes. I've had some great case studies. I've talked with people in roles like yours, so give us a little bit of, what's it like to be working under state government this day and age, with 2018, with technology. >> It's very exciting. It's very exciting to work for Colorado specifically. I don't know if it translates to all other states. I've talked to other CIOs and CTOs around the country, but we have a very supportive governor. He just announced his campaign to run for president, maybe, we'll see how that goes. But outside of that, he's very innovative. He took a business trip to Israel, came back, and set up a cyber security lab in the state, because he thinks there's a major need for more cyber security and those disciplines. In Colorado, today, we're running negative 14% unemployment for security jobs, so it's just, huge opportunity. Outside of that, my boss, Suma Nallapati, is state CIO. Right underneath him, is all about innovation. How can we make Colorado number one in everything we do. And that's really the goal. What the governor said, the way he talks about technology, he wants technology to be elegant. That's not word you hear a lot. But when you think about that and apply it to technology, there's a very specific outcome you're trying to get out of that. >> Alright, well David, at this show, we're all talking about data. And everybody's, you know, it's what can I do with my data? And how do I make sure that things don't get wrong? Well, anybody that's been in the IT for a while is, Murphy's Law sometimes does play out. So you've actually had a couple of experiences. Some good things you've learned, but some challenges that you had, maybe share with us what happened. >> Yeah, I mean one of the things I'm here to talk about is we kicked off an initiative called Backup Colorado. And what it was is, it was consolidating all the backup and recovery services for all those agencies I just named, plus some more, right. Monster project, monster task. It was all born out of a major data failure the state had. We were a fairly new organization. We were immature. We were still running things in a siloed environment. Most of the country, most large organizations have gone down the IT consolidation path. We were a few years down the road, and we got hit with a major data loss event. And it was specific to marijuana data, which makes some people smile, some people frown, but it's a very interesting topic. It wasn't interesting to lose customer data though. I don't care if you're a private organization, or a public organization, this was real data loss. And it highlighted the need for a focused approach to solving those problems. So we went about just kind of transforming the whole space. First, put a proposal on the table. Going to the general assembly. Going to the Governor saying, this is what we need to do. They signed off on it, and then we implemented it, right. We got tens if not hundreds of people together around the state. We coordinated agencies. We got people on board that didn't want to be on board. They liked the silo approach. They liked their agencies doing their own thing. But you can't do anything right 16 different ways. You don't have to do it one way, but it can't be 16. But we took a standardized approach, and we worked with Commvault as our partner to deploy a complete backup and recovery system for the state. Highly successful project. Rolled out, standardized. Everything you could want. While we're doing that, we are completely changing our application and infrastructure stacks. We are consolidating all of our servers into three data centers in the state. We're bursting into the cloud. We're replatforming on software, the service. You know, all those. I'm responsible for each one of those stacks. My guidance was just go and change the world, right. In a very non-senile way, we went out there, and we were like, how can we do this thoughtfully. How can we do it, but push, blaze new trails, that type of thing. And the story that I've been sharing is, we got to see the end results of that. What kicked it off, was a public disaster, but the state was hit with a ransomware attack. Very targeted, very coordinated. They hit one of our larger agencies. We had good security in place, but there's always stuff that can happen, as you've kind of eluded to. And because of this project, because of the team coordinated effort, because of the technology, because of the stuff we were leveraging, we were able to bring that agency back whole. Which a lot of organizations cannot say. A lot of the technologies cannot say, with as many systems that were impacted for the time period they were, to bring that agency back whole, and actually have the executive director of that agency, doing very similar conversations as we're doing now. How can dots around the country, roll out a plan very similar to this? >> Well David, people process technology, you guys are changing processes, you're changing technology, extremely disruptive. Talk about the impact on your people. What mindset, or what changes did you have to make organization wide. 800 people was a lot of people to get in line. What did you start, what did you do? What was successful, well not so much. >> Well first I had to get my customers on board, right. And compelling events helped bring customers on board. I don't think that's the best way of doing it, but always leverage a compelling event. In this case, we had a compelling event. We had the onus from our executive branch and a legislative branch. So we had the hammer if we needed it to get it done. The team actually came together. We ran a very successful RFP. We baked off competitors in the space. And it was a beautiful thing to see all my server engineers, all my desktop guys, all my database guys and gals comin' in and working together to make this project happen. I didn't have to sell them on it. They came to me and said, we think this is the best technology stack for the state. When I recognized, when I heard them, they all got on board and we were able to roll it out. And so I think it was that team approach, not top down, but you know, let's all come together and find the right thing for the state. I think that was why it was so successful. It was a team approach, and we had executive buy in, we were able to get it done. >> You talked about how Commvault helped with that transition, 16 different backup products, if the state was like any other organization, there's at least 15, 16 different backup products, people like what they use. And transitioning to something new requires training, support. How did Commvault help you guys in that transition? >> You know, they were a great partner, all the way through the RFP process, to bringing it in and doing training. We have a big thing at the state, the technology stack, we do luncheon learns, so there's lots of training. Commvault brought a lot of resources. We had engineers specifically assigned from Commvault to help with the project, the roll out, and then the transition. So a very effective partner, in terms of helping us along the way. It never helps to have that kind of hammer, as I said before, to push it forward. I really couldn't have asked for anything more. I spoke a little bit about this the other day. When we had this compelling event with the ransomware this year, I picked up the phone, and I got an answer right away. And I said we're going to need you once again. And they showed up. Commvault showed up. The great thing was, we didn't need them, right. My engineers had an effective turnover and training. They got the initial alerts before anybody did, before any of our security groups, anybody, Commvault detected this ransomware really before any of my tool suites because of the way it came into our organization. Which was kind of cool. But just in general, a great partnership. They were there all the way through the recovery of CDOT as support for our team. Really weren't needed just because of the effective transition. >> That's an interesting point. You talk about, you would think it would be the security tool that would be alerting you. Commvault and companies like it, sit in an interesting position. You've got data, you've got metadata. That surprise you that that was the tool that helped alert you in the first? >> Shocked me, shocked me, right. I mean we spent a lot of money building stacks of tools to protect the state, and very effective tools. There's nothing against those tool suites specifically. We were actually rolling out another tool that week that ultimately would've prevented it. That being said, stuff happens and the way this ransomware came in, bypassed that visibility. But Commvault, looking at our backups every night, taking differentials of 'em, saw encrypted files on disk, sent out an alert. The teams knew exactly what to do. Got executives on the phone. Got security ops on the phone. And it kicked off from there, so yeah, shocked, you know, happy that we caught it. Not the way I would have wanted, but that's why you've got layers of security. That's why you've got layers of teams to support each other. >> So specifies, outside of the support capability that Commvault provided and one, helping you guys get alerted to the event, and then the support reacting to the event, talk to us. What did they take to recover from the event? Was this a multi-month thing? Multi-week, multi-hour? How did you guys recover and how much did you recover? >> It took us a little over a month to recover. It's actually a great conversation maybe for another time. But building a structure in an open attack. Like when you have a coordinated resources from other countries, trying to do the United States, or the state of Colorado harm, the first thing you're going to do is make sure they're outside of your environment. So for about the first two weeks, we had everybody from the National Guard to the Defense Department there, helping us evaluate the situation. Getting it to a place where we felt comfortable bringing the department back up. Once we reached that point, and there is never a clear line in the sand. There's a role for the CIO and the CTO in that place to say, hey, now's time we've done everything we can and then we've very methodically started bringing desktops online and servers online. And Commvault played a huge role in that as well as some other vendors. But, in all, we restored about 192 servers. Some were infected, some weren't, but just from a sensibility stake, we wanted to go back to clean backups, clean restores, a place where the customer felt comfortable. We were able to do it in a way that there was no data loss to the customer or at least manage data loss. Meaning, in some cases, their systems, they wanted to go really back on, because their data didn't change very much in there. My biggest pinpoint in this whole process is, I want to bring that department up much faster, right. There's two sides that you're looking at: How do you protect the department in the short term? And how do you protect them in the long term? So I had to look at both sides of it. Very interesting experience. Don't wish it on anybody. >> David, last thing I want to ask is, the role of data, how do you, inside the state of Colorado, look at the role of data and the changing role of data? And if you look at Commvault, they are really expanding where they play. They're playing in multi-cloud. They've got artificial intelligence helping them. They're helping with governance and compliance. How do you see them lined up? Where do you see your relationship going with them in the future? >> Well, obviously, I like to stay with partners that take care of me, so there's obviously an affinity there, in terms of how they've helped the state in the last year. The data is really two parts, the agencies data, and then the resident, and the customers of the state of Colorado's data, right. So you first got to look at who owns and who is the steward of the data. And as IT for the state, our role is protecting that data, both in the short and long term. But as it becomes more and more of an asset, and we all know data is an asset today, it's almost the most critical asset. So protecting it is just as important as how you're going to innovate with it. So we are very excited about how we're going to be leveraging data in the future. Some of the issues we're talking about, the Department of Transportation wants to take their data for Road X and change how people drive. You know, very similar as to how you may use Waze and stuff like that. The DOTs around the country want to take that data and leverage it all over the place. So you're not only taking an asset that was leveraged for a very different purpose 10 years ago and completely transforming industries, you're doing that all across state government, right. The impetus, the need for protecting it, using it, I'm very excited with where they're going and how they look at data, Commvault specifically. I had a great conversation with our CTO last year about, I'm storing all this data on FASTDISK anyway. Why can't I use this as a data lake? How can I get metadata for your customers? How can I take this in places where maybe the founders of this company didn't even envision 20 years ago. It's very exciting how they're looking at the technology and where they can take it. AI is one of my focus areas for the year. I'm going to listen to everybody's pitch and I'm going to choose the right ones, because I do think it's transformative. If they can do it correctly and ultimately lessen the burden on IT, that's what we're looking for, right. That's what AI should bring to the table, is the ability for IT to do more with less. So that's what we're looking for and I'm excited what they're going to do with it. >> Alright, well David Mccarthy, we really appreciate you joining us, sharing your story. For Keith Townsend, I'm Stu Miniman. We'll be back with more coverage here from Commvault GO in Nashville, Tennessee. Thanks for watching theCUBE. >> David: Thank you. (lively tech music)

Published Date : Oct 10 2018

SUMMARY :

Brought to you by Commvault. so we love digging in with the users. the great work Colorado's been doing, as to kind of, you know, what divisions you support, And that's all the infrastructure and application stacks, of the data protection stuff, And that's really the goal. Well, anybody that's been in the IT for a while is, because of the stuff we were leveraging, Talk about the impact on your people. and find the right thing for the state. if the state was like any other organization, because of the way it came into our organization. the security tool that would be alerting you. and the way this ransomware came in, So specifies, outside of the support capability from the National Guard to the Defense Department there, look at the role of data and the changing role of data? is the ability for IT to do more with less. you joining us, sharing your story. David: Thank you.

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theCUBE Insights | Splunk .conf18


 

>> Announcer: Live from Orlando, Florida It's theCUBE covering .conf18. Brought to you by Splunk. >> Welcome back to theCUBE's coverage of Splunk .conf18. It's Florida week. I'm Stu Miniman, and my co-host for this week is Dave Vellante. Dave, I'm really excited. You've done this show a handful of times. It's our seventh year doing theCUBE here. It is my first time here. Thought I understood a few of the pieces and what's going on, but it's really been crystallizing to me. When we talk about on theCUBE, for the last couple of years, data is at the center of everything, and in the keynote this morning they talked about Splunkers are at the crossroads of data. I've talked to a bunch of practitioners here. People come to them to try to get access to data, and the vision that they've laid out this week for Splunk Next is how they can do a massive TAM expansion, try to get from the 16,000 users that they have today to 10x more. So, what's your take been on where we are today and what Splunk of the future looks like? >> Well so Stu, as you know, the keynotes are offsite, about a half hour away from the hotel where we're broadcasting, and there's like 8,000 buses that they're jamming customers in. It's a bit of a pain to get there, so logistically it's not ideal. So I thought the keynotes today, just remotely, we didn't hop in the bus because we had to miss a lot of the keynotes yesterday, to get back here. So we watched remotely today. It just felt like there wasn't as much energy in the room. And I think that's for a couple of reasons, and I'll get into that. But before I do, you're right. This is my fourth .conf, and I was struck by in the audience at how few people actually, it was probably less than a third of the audience, when they asked people to stand up, had been to four or more .confs. A ton of people, first year or second year. So, why is that relevant? It's relevant because these are new people. The core of Splunk's audience are security people and IT operations management people. And so with that many newbies, newbies, they're trying to learn about how they can get more value out of the tool. Today's announcements were all about line of business and industrial IOT. And frankly, a lot of people in the audience didn't directly care. Now, I'll explain why it's important, and why they actually do care and will care going forward. But the most important thing here is that we are witnessing a massive TAM expansion, total available market expansion, for Splunk. Splunk's a one point six, one point seven billion dollar company. They're going to blow through two billion. This is a playbook that we've seen before, out of the likes of particularly ServiceNow. I'm struck by the way in which Splunk is providing innovation for non-IT people. It's exactly the playbook that ServiceNow has used, and it works beautifully, and we'll get into some of that. >> So Dave, one of the things that really struck me, we had seven customers on the program yesterday, and the relationship between Splunk and the customers is a little different. You always hear, oh well, I love this technology. Lots of companies. You've been telling me how passionate you were. But really partnerships that you talk about, when you talked about, we had an insurance company from Toronto, and how they're thinking about how the security and risks that they look at, how that passes on to their customers. So many, it's not just people are using Splunk, but it's how it affects their business, how it affects their ultimate end users, and that value of data is something that we come back to again and again. >> So the classic Splunk user is somebody in IT, IT operations management, or the security knock. And they're hardcore data people, they're looking at screens all day and they love taking a bath in data. And Splunk has completely changed their lives, because rather than having to manually go through log files, Splunk has helped them organize that sort of messy data, as Doug Merritt said yesterday. Today, the whole conversation was about expanding into line of business and industrial IOT. These are process engineers, there weren't a lot of process engineers in the audience today. That's why I think not a lot of people were excited about it. I'm super excited about it because this is going to power, I've always been a bull on Splunk. This is going to power the next wave of growth at Splunk. Splunk is a company that got to the public markets without having to raise a ton of capital, unlike what you're seeing today. You're seeing hundreds of millions of dollars raised before these companies IPO. So, Splunk today in the keynotes, first of all, they had a lot of fun. I was laughing my you-know-what off at the auditions. I mean, I don't really, some of that stuff is kind of snarky, but I thought it was hilarious. What they did is, they said, well Doug Merritt wasn't a shoo-in to keynote at this, so we auditioned a bunch of people. So they came in, and people were singing, they were goofing, you know, hello, Las Vegas! We're not in Las Vegas, we're in Orlando this year. I thought it was really, really funny and well done. You know Stu, we see a lot of this stuff. >> Yeah, absolutely. Fun is definitely part of the culture here at Splunk, love that we talked about yesterday, the geeky t-shirts with all the jokes on that and everything. Absolutely so much going on. But, Dave there's something I knew coming in, and we've definitely heard it today in the keynotes, developers are such an audience that everybody is trying to go after, and you talk about kind of the traditional IT and security might not really be the developer audience, but absolutely, that's where Splunk is pushing towards. They announced the beta of the Splunk Developer Cloud, a number of other products that they've put in beta or are announcing. What's your take as to how they go beyond kind of the traditional Splunk user? >> Yeah so that's what I was saying. This is to me a classic case of, we saw this with ServiceNow, who's powering their way through five billion land and expand, something that Christian Chabot, former CEO of Tableau used to talk about. Where you come in and you get a foot in the door, and then it just spreads. You get in like a tick, and then it spreads to other parts of the business. So let's go through some of the announcements. Splunk Next, they built on top of that today. Splunk Business Flow, they showed, what I thought was an awesome demo. They had a business person, it was an artificial example of the game company. What was the name of the game company? >> Stu: Buttercup Sames. >> Buttercup Games. So they took a bunch of data, they ingested a bunch of data on the business workflow. And it was just that, it was just a big, giant flow of data. It looked like a huge search. So the business user was like, well what am I supposed to do with this? He then ingested that into Splunk Business Flow, and all of a sudden, you saw a flow chart of what all that data actually said in terms of where buyers were exiting the system, calling the call center, et cetera. And then they were able to make changes through this beautiful graphical user interface. So we'll come back to that, because one would be skeptical naturally as to, is it really that easy? They also announced Splunk for industrial IOT. So the thing I like about this, Stu, and we've seen a lot of IOT announcements in the past year from IT companies. What's happening is that IT companies are coming in with a top-down message to industrial IOT and OT, Operations Technology, professionals. We think that is not the right approach. It's going to be a bottoms-up approach, driven by the operations technology professionals, these process engineers. What Splunk is doing, and the brilliance of what Splunk is doing is they're starting with the data. We heard today, OEE. What's OEE? I haven't heard that term. It's called Overall Equipment Effectiveness. These aren't words that you hear from IT people. So, they're speaking a language of OT people, they're starting with the data, so what we have seen thus far is, frankly a lot of box companies saying, hey we're going to put a box at the edge. Or a lot of wireless companies saying, hey, we're going to connect the windmill. Or analytics companies saying, we're going to instrument the windmill. The engineers are going to decide how it gets instrumented, when it get instrumented, what standards are going to be used. Those are headwinds for a lot of the IT companies coming in over the top. What Splunk is doing is saying, we're going to start with the data coming off the machines. And we're going to speak your language, and we're going to bring you tooling you can use to analyze that operations data with a very specific use case, which is predictive maintenance. So instead of having to do a truck roll to see if the windmill is working properly, we're going to send you data, and you're going to have to roll the truck until the data says there's going to be a problem. So I really like that. Your thoughts on Splunk's IOT initiative versus some of the others we've seen? >> Yeah, Dave. That dynamic of IT versus OT, Splunk definitely came across as very credible. The customers we've talked to, the language that they use. You talk about increasing plan for performance and up time. How can they take that machine learning and apply it to the IOT space, it all makes a lot of sense. Once again, it's not Splunk pushing their product, it's, you're going to have more data from more different sources, and therefore it makes sense to be able to leverage the platform and take that value that you've been seeing with Splunk in more spaces. >> So the other thing that they announced was machine learning and natural language processing four dot oh. They had BMW up on the stage, talking about, that was really a good IOT example, but also predicting traffic patterns. If you think about Waze, you and I, well I especially, use Waze, I know that Waze is wrong. It's telling me I'm going to get there at four thirty, and I know traffic is building up in Boston, I'm not going to get there until ten to five, and Waze somehow doesn't know that. BMW had an example of using predictive analytics to predict what traffic flow is going to look like in the future so I thought that was pretty strong. >> And I loved in the BMW example, they've got it married with Alexa so the business person, sitting at their desk can say, hey Alexa, go ask Splunk something about my data, and get that result back. So pretty powerful example, really obvious to see how we get the value of data to the business user, even faster. >> Now the problem is, I'm going to mention some of the challenges I see in some of these initiatives. The problem with NLP is NLP sucks. Okay, it's not that good today, but it's going to get better. They used an example on stage with Alexa, it obviously worked, they had it rehearsed. It doesn't always work that way, so we know that. They also announced the Splunk Developer Cloud. They said it was three Fs: familiar, flexible, and fast. What I love about this is, this is big data, actually in action. Splunk, as I've been saying all week, they never use the term big data when big data was all on the hype cycle, they now use the term big data. Back when everybody was hyping big data, the big vacuum was applications. Pivotal came out, Paul Maritz had the vision, We're going to be the big data application development platform. Pivotal's done okay there, but it's not taking the world by storm. It's a public company, it had a decent IPO, but it's not like killing it. Splunk is now, maybe a little late to the game, a little later than Pivotal, or maybe even on IBM, but they key is, Splunk has the data. I keep coming back to the data. The data is the linchpin of all of this. Splunk also announced SplunkTV, that's nice, you're in the knock, and you got smart TV. Woo hoo! That's kind of cool. >> Yeah but Dave, on the Developer Cloud, this is a cloud native application, so it's fitting with that model for next generation apps, and where they're going to live, definitely makes a lot of sense. >> They talked about integrating Spark and TensorFlow, which is important obviously in that world. Stu, you in particular, John Ferrier as well, spent a lot of time, Jim Kabilis in the developer community. What's your take on what they announced? I know it was sort of high level, but you saw some demos, you heard their language. There were definitely some developers in the room. I would say, as a constituency, they sounded pretty excited. They were a relatively small number, maybe hundreds, not thousands. >> One of the feedback I heard from the community is being able to work with containers and dockers, something that people were looking for. They're delivering on that. We talked to one of the customers that is excited about using Kubernetes in this environment. So, absolutely, Splunk is reaching out to those communities, working with them. When we talked to the field executive yesterday, she talked about- >> Dave: Susan St. Ledger >> How Splunk is working with a lot of these open source communities. And so yeah, good progress. Good to see where Splunk's moving. Absolutely they listen to their customers. >> So, land and expand, Splunk does not use that term. It's my term that I stole from Christian Chabot and Tableau. Certainly we saw that with ServiceNow. We're seeing a very similar playbook. Workday, in many ways, is trying it as well, but Workday's going from HR into financials and ERP, which is a way more entrenched business. The thing I love about Splunk, is they're doing stuff that's new. Splunk was solving a problem that nobody else could solve before, whereas Workday and ServiceNow, as examples, were essentially replacing legacy systems. Workday was going after PeopleSoft. ServiceNow was going after BMC. Tableau, I guess was going after old, tired OBI. So they were sort of disruptive in that sense. Splunk was like, we can do stuff that nobody's been able to do before. >> Yeah Dave, the last thing that I want to cover in this analysis segment is, we talk about the data. It's the people interacting with it. We've been talking for years, there's not enough skills in data scientists. There's so many companies that we're going to be your platform for everything. Splunk is a platform company, but with a big ecosystem at the center of everything they do. It's the data, it's the data that's most important. They're not trying to say, this is the rigid structure. We talked about a lot yesterday, how Splunk is going to let you use the data where you want it, when you want it. How do you look at what Splunk does, the Splunkers out there, all the people coming to them? Compare and contrast against the data scientists. >> Well this is definitely one of the big challenges. To me, the role of a Splunker, they're IT operations people, they're people in the security knock, and Splunk is a tool for them, to make them more productive, and they've fallen in love with it. You've seen the guys running around with the fez, and that's pretty cool. They've created a whole new class of skill sets in the organization. I see the data scientists as, again, becoming a Splunker and using the tools. Splunk are giving the data scientists tools, that they perhaps didn't have before, and giving them a way to collaborate. I'll come back to that a little bit. If I go through the announcements, I see some challenges here, Stu. Splunk next for the LLB. Is it really as easy as Splunk has shown? As time will tell, we're going to have to just talk to people and see how quickly it gets adopted. Can Splunk democratize data for the line of business? Well on the IOT side, it's all about the operations technology professionals. How does Splunk reach those people? It's got to reach them through partnerships and the ecosystem. It's not going to do a belly to belly direct sales, or it's not going to be able to scale. We heard that from Susan St. Ledger yesterday. She didn't get into IOT because it hadn't been announced yet, but she hinted at that. So that's going to be a big thing. The OT standards, how is Splunk going to adopt those. The other thing is, a lot of the operations technology data is analog. There's a headwind there, which is the pace at which the engineers are going to digitize. Splunk really can't control that in a big way. But, there's a lot of machine data and that's where they're focusing. I think that's really smart of Splunk. The other thing, generally, and I don't know the answer to this Stu, is how does Splunk get transaction data into the system? They may very well may do it, but we heard yesterday, data is messy. There is no such thing as unstructured data. We've heard that before. Well there's certainly a thing as structured data, and it's in databases, and it's in transaction systems. I've always felt like this is one of IBM's advantages, as they got the mainframe data. Bringing transaction data and analytic data together, in real time, is very important, whether it's to put an offer in front of the customer before you lose that customer, to provide better customer service. Those transaction systems and that data are critical. I just don't know the answer to how much of that is getting into the Splunk system. And again, as I said before, is it really that easy as Spark and TensorFlow integration enough? It sounds like the developers will be able to handle it. NLP will evolve, we talked about that as a headwind. Those are some of the challenges I see, but I don't think they're insurmountable at all. I think Splunk is in a really good position, if not the best position to take advantage of this. Why? Because digital transformation is all about data, and Splunk is data. They're all about data. They don't have to go find the data, obviously they have to ingest the data, but the data's there. If you're a Splunker, you have access to that data. All the data? Not necessarily, but you can bring that through their API platforms, but a lot of the data that you need is already there. That's a huge, huge advantage for Splunk. >> Well, Dave, this is one of the best conferences I've been at, with data at the core. It's been so great to talk to the customers. We really appreciate the partnership of Splunk. Splunk events team, grown this from seven years ago, when we started a 600 person show, to almost 10,000 now. So for those of you that don't know, there's so much that goes on behind the scenes to make something like this go off. Really appreciate the partnership and the sponsorship that allows us to help us document this, bring it out to our communities. The analysis segments that we do, we actually bring in podcast form. Go to iTunes or Spotify, your favorite podcast player, look for theCUBE insights. Of course go to theCUBE.net for the video. SiliconANGLE.com for all of the news. Wikibon.com for the research, and always feel free to reach out with us, if you've got questions, or want to know what shows we're going to be in next. For my cohost, Dave Vellante who is Dvellante on Twitter. I'm Stu Miniman, at stu on Twitter, and thanks so much for watching theCUBE. (techno music)

Published Date : Oct 3 2018

SUMMARY :

Brought to you by Splunk. and in the keynote this morning they talked about a lot of the keynotes yesterday, to get back here. and the relationship between Splunk Splunk is a company that got to the public markets Fun is definitely part of the culture here at Splunk, This is to me a classic case of, we saw this What Splunk is doing, and the brilliance of what Splunk and therefore it makes sense to be able to leverage So the other thing that they announced was And I loved in the BMW example, they've got it married Now the problem is, I'm going to mention some Yeah but Dave, on the Developer Cloud, in the developer community. One of the feedback I heard from the community Absolutely they listen to their customers. that nobody's been able to do before. the Splunkers out there, all the people coming to them? if not the best position to take advantage of this. SiliconANGLE.com for all of the news.

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Aaron Kalb, Alation | CUBEconversations June 2018


 

(stirring music) >> Hi, I'm Peter Burris, and welcome to another CUBE Conversation from theCUBE Studios in beautiful Palo Alto, California. Got a great conversation today. We're going to be talking about some of the new advances that are associated with big data analytics and improving the rate at which human beings, people who actually work with data, can get more out of their data, be more certain about their data, and improve the social system that actually is dependent upon data. To do that, we've got Aaron Kalb of Alation here with us. Aaron is the co-founder and is VP of design and strategic initiatives. Aaron, welcome back to theCUBE. >> Thanks so much for having me, Peter. >> So, then, let's start this off. The concern that a lot of folks have when they think about analytics, big data, and the promise of some of these new advanced technologies is they see how they could be generating significant business value, but they observe that it often falls short. It falls short for technological reasons, you know, setting up the infrastructure is very, very, difficult. But we've started solving that by moving a lot of these workloads to the cloud. They also are discovering that the toolchains can be very complex, but they're starting to solve that by working with companies with vision, like Alation, about how you can bring these things together more easily. There are some good things happening within the analytics space, but one of the biggest challenges is, even if you set up your pipelines and your analytics systems and applications right, you still encounter resistance inside the business, because human beings don't necessarily have a natural affinity for data. Data is not something that's easy to consume, it's not something easy to recognize. People just haven't been trained in it. We need more that makes it easy to identify data quality, data issues, et cetera. Tell us a little bit about what Alation's doing to solve that human side, the adoption side of the challenge. >> That's a great point and a great question, Peter. Fundamentally, what we see is it used to be a problem of quantity. There wasn't enough ability to generate data assets, and to distribute them, and to get to them. Now, there's just an overwhelming amount of places to gather data. The problem becomes finding development data for your need, understanding and putting it into context, and most fundamentally, trusting that it's actually telling you a true story about the world. You know, what we find now is, as there's been more self-service analytics, there's more and more dashboards and queries and content being generated, and often an executive will look at two different answers to the same question that are trending in totally different directions. They'll say, "I can't trust any of this. "On paper, I want to be data-driven, "but in actuality, I'm just going to go back to my gut, "'cause the data is not always trustworthy, "and it's hard to tell what's trustworthy and what's not." >> This is, even after they've found the data and enough people have been working on it to say, to put it in context to say, "Yes, this data is being used in marketing," or, "This data has been used in operations production." there's another layer of branding or whatnot that we can put on data that says, "This data is appropriate for use in this way." Is that what we're talking about here? >> Absolutely right. To help with finding and understanding data, you can group it and make it browsable by topic. You can enable keyword search over it in that natural language. That's stuff that Alation has done in the past. What we're excited to unveil now is this idea of trust check, which is all about saying, wherever you're at in that data value chain of taking raw data and schematizing it and eventually producing pretty dashboards and visualizations, that at every step, we can ensure that only the most trustworthy data sets are being used, because any problem upstream flows downstream. >> So, trust check. >> Trust check. >> Trust check, it's something that comes out of Alation. Is it also being used with other visualization tools or other sources or other applications? >> That's a great question. It's all of the above. Trust check starts with saying, if I'm an analyst who wants to create a dashboard or a visualization, I'm going to have to write some SQL query to do that. What we've done in that context with Alation Compose, is our home-grown SQL tool, is provided a tool, and trust check kind of gets its name from spell check. It used to be there was a dictionary, and you could look it up by hand, and you could look it up online, but that's a lot of work for every single word to check it. And then, you know, Microsoft, I think, was the first innovative saying, "Oh, let's put a little red squiggle that you can't miss "right in your workflow as you're writing, "so you don't have to go to it, it comes to you." We do the exact same thing. I'm about to query a table that is deprecated or has a data quality issue. I immediately see bright red on my screen, can't miss it, and I can fix my behavior. That's as I'm creating a data asset. We also, through our partnerships with Salesforce and with Tableau, each of whom have very popular visualization tools, to, say. if people are consuming a dashboard, not a SQL query, but looking at a Tableau dashboard or a visualization in Salesforce Einstein Analytics, what would it mean to badge right there and then, put a stamp of approval on the most trustworthy sources and a warning or caveat on things that might have an upstream data quality problem? >> So, when you say warning or caveat, you're saying literally that there are exceptions or there are other concerns associated with the data, and reviewing that as part of the analytic process. >> That's exactly right. Much like, again, spell check underlines, or looking at, if you think about if I'm driving in my car with Waze, and it says, "Oh, traffic up ahead, view route this way." What does it mean to get in the user interface where people live, whether they're a business user in Salesforce or Tableau, or a data analyst in a query tool, right there in their flow having onscreen indications of everything happening below the tip of the iceberg that affects their work and the trustworthiness of the data sets they're using. >> So that's what it is. I'll tell you a quick story about spell check. >> Please. >> Many years ago, I'm old enough that I was one of the first users of some of these tools. When you typed in IBM, Microsoft Word would often change it to DUM, which was kind of interesting, given the things that were going on between them. But it leads you to ask questions. How does this work? I mean, how does spell check work? Well, how does trust check work, because that's going to have an enormous implication. People have to trust how trust check works. Tell us a little bit about how trust check works. >> Absolutely. How do you trust trust check? The little red or yellow or bright, salient indicators we've designed are just to get your attention. Then, as a user, you can click into those indicators and see why is this appearing. The biggest reason that an indicator will appear in a trust check context is that a person, a data curator or data steward, has put a warning or a deprecation on the data set. It's not, you know, oh, IBM doesn't like Microsoft, or vice versa. You know, you can see the sourcing. It isn't just, oh, because Merriam-Webster says so. It emerges from the logic of your own organization. But now Alation has this entire catalog backing trust check where it gives a bunch of signals that can help those curators and stewards to decide what indicators to put on what objects. For example, we might observe, this table used to be refreshed frequently. It hasn't in a while. Does that mean it's ripe for getting a bit of a warning on it? Or, people aren't really using this data set. Is there a reason for that? Or, something upstream was just flagged having a data quality issue. That data quality issue might flow downstream like pollution in a creek, and that can be an indication of another reason why you might want to label data as not trustworthy. >> In Alation context with Salesforce and Tableau partners, and perhaps some others, this trust check ends up being a social moniker for what constitutes good data that is branded as a consequence of both technological as well as social activities around that data captured by Alation. I got that right? >> That's exactly right. We're taking technical signals and social signals, because what happens in our customers today before we launched trust check, what they would do is, if you had the time, you would phone a friend. You'd say, "Hey, you seem to be data-savvy. "Does this number look weird to you? "Do you know what's going on? "Is something wrong with the table that it's sourced from?" The problem is, that person's on vacation, and you're out of luck. This is saying, let's push everything we know across that entire chain, from the rawest data to the most polished asset and have all that information pushed up to where you live in the moment you're making a decision, should I trust this data, how should I use it? >> In the whole, going back to this whole world of big data and analytics, we're moving more of the workloads to the cloud to get rid of the infrastructure problems. We're utilizing more integrated toolchains to get rid of the complexity associated with a lot of the analytic pipelines. How does trust check then applied, go back to this notion of human beings not being willing to accept somebody else's data. Give us that use case of how someone's going to sit down in a boardroom or at a strategic meeting or whatever else it is, see trust check, and go, "I get it." >> Absolutely, that's a fantastic question. There's two reasons why, even though all organizations, or 80% according to Gartner, claim they're committed to being data-driven. You still have these moments, people say, "Yeah, I see the numbers, "but I'm going to ignore them, or discount them, "or be very skeptical of them." One issue is just how much of the data that gets to you in the boardroom or the exec team meeting is wrong. We had an incredibly successful data-driven customer who did an internal audit and found that 1/3 of the numbers that appeared in the PowerPoint presentations on which major business decisions were being made, a full 1/3 of them were off by an extraordinary amount, an amount so big that it would, the decision would've cut the other way had the number been accurate. The sheer volume of bad data coming in to undermine trust. The second is, even if only 5% of the data were untrustworthy, if you don't know which is which, the 95% that's trustworthy and the 5% that's not, you still might not be able to use it with confidence. We believe that having trust check be at every stage in this data value chain will solve, actually, both problems by having that spell-check-like experience in the query tool, which is where most analytics projects start. We can reduce the amount of garbage going into the meeting rooms where business choices are being made. And by putting that badge saying "This is certified," or, "Take this with a grain of salt," or, "No, this is totally wrong," that putting that badge on the visualizations that business leaders are looking at in Salesforce and Tableau, and over time, in ideally every tool that anybody would use in an enterprise, we can also help distinguish the wheat from the chaff in that context as well. We think we're attacking both parts of this problem, and that will really drive a data-driven culture truly being adoptable in an organization. >> I want to tie a couple things that you said here. You mentioned the word design a couple times. You're the VP of design at Alation. It also sounds like when you're talking about design, you're not just talking about design of the interface or the software. You're talking about design of how people are going to use the software. What is the extent to which design, what's the scope of design as you see it in this context of advanced analytics, and is trust check just a first step that you're taking? Tell us a little bit about that. >> Yeah, that's a great set of questions, Peter. Design for us means really looking at humans, and starting by listening and watching. You know, a lot of people in the cataloging space and the governance space, they list a lot of should statements. "People should adopt this process, "because otherwise, mistakes will be made." >> Because Gartner said 80% of you have! >> Right, exactly. We think the shoulds only get you so far. We want to really understand the human psychology. How do people actually behave when they're under pressure to move quickly in a rapidly changing environment, when they're afraid of being caught having made a mistake? There's all these pressures people are under. And so, it's not realistic to say, again, you could imagine saying, "Oh, every time before you go out the door, "go to MapQuest or some sort of traffic website "and look up the route and print it out, "so you make sure you plot correctly." No one has time for that, just like no one has time to look up every single word in their essay or their memo or their email and look it up in the dictionary to see if it's right. But when you have an intervention that comes into somebody's flow and is impossible to miss, and is an angel on your shoulder keeping you from making a mistake, or, you know, in-car navigation that tells you in real time, "Here's how you should route." Those sort of things fit into somebody's lifestyle and actually move impact. Our idea is, let's meet people where they are. Acknowledge the challenges that humans face and make technology that really helps them and comes to them instead of scolding them and saying, "Oh, you should change your flow in this uncomfortable way "and come to us, "and that's the only way "you'll achieve the outcome you want." >> Invest the tool into the process and into the activity, as opposed to force people to alter the activity around the limitations or capabilities of the tool. >> Exactly right. And so, while design is optimizing the exact color and size and UI/UX both in our own tools and working with our partners to optimize that, it's starting at an even bigger level of saying, "How do we design the entire workflow "so humans can do what they do best "and the computer just gives them "what they need in real time?" >> And as something as important, and this kind of takes it full circle, something as important and potentially strategic as advanced analytics, having that holistic view is really going to determine success or failure in a lot of businesses. >> That is absolutely right, Peter, and you asked earlier, "Is this just the beginning?" That's absolutely true. Our goal is to say, whatever part of the analytics process you are in, that you get these realtime interventions to help you get the information that's relevant to you, understand what it means in the context you're in, and make sure that it's trustworthy and reliable so people can be truly data-driven. >> Well, there's a lot of invention going on, but what we're really seeking here is changes in social behavior that lead to consequential improvements in business. Aaron Kalb, VP of design and strategic initiatives at Alation, thanks very much for talking about this important advance in how we think about analytics. >> Thank you so much for having me, Peter. >> This is, again, Peter Burris. This has been a CUBE Conversation. Until next time. (stirring music)

Published Date : Jul 12 2018

SUMMARY :

and improving the rate at which human beings, and the promise of some of these new advanced technologies and to distribute them, and to get to them. Is that what we're talking about here? That's stuff that Alation has done in the past. Trust check, it's something that comes out of Alation. "Oh, let's put a little red squiggle that you can't miss and reviewing that as part of the analytic process. and the trustworthiness of the data sets they're using. I'll tell you a quick story about spell check. But it leads you to ask questions. and that can be an indication of another reason I got that right? and have all that information pushed up to where you live to get rid of the infrastructure problems. that gets to you in the boardroom What is the extent to which design, and the governance space, and make technology that really helps them and comes to them around the limitations or capabilities of the tool. and UI/UX both in our own tools and this kind of takes it full circle, to help you get the information that's relevant to you, that lead to consequential improvements in business. This is, again, Peter Burris.

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Hardik Bhatt, Amazon Web Services | AWS Public Sector Summit 2018


 

(techno music) >> Live, from Washington DC, it's theCUBE. Covering AWS Public Sector Summit, 2018. Brought to you by Amazon Web Services and its ecosystem partners. >> Okay, welcome back, everyone, this is the live CUBE coverage here in Washington DC for AWS Public Sector Summit 2018. This is the, kind of like the reinvent for Public Sector. I'm John Furrier, f my co-host Stu Miniman, our next guest is Hardik Bhatt, Smart Cities Vertical Lead for Amazon Web Services, been a former CIO, knows the state and local governments cold. This is a very key area around Internet of Things and technology with cloud, because smart cities have to do not only technology roll outs for some of the new capabilities, but all manage some of the societal changes, like self-driving cars and a variety of other things, from instrumenting sensors and traffic lights and video cam ... I mean, this is a little, just a little ... Welcome to theCUBE. >> Thank you very much, John. Good to see you, Stu, good morning. Looking forward to having a great conversation. >> So, smart cities obviously is really hot, but we love it, because it brings life, and work, life, and play together, because we all live in towns, and we live in cities, and the cities provide services to the residents, transportation, sidewalks, and things that we take for granted in the analog world. Now there's a whole digital set of services coming big time. So, are they prepared? (laughs) It used to be buy a mainframe, then move it to a minicomputer, get a Local Area Network, buy some PCs, buy some network tablets, now the cloud's here. What's your assessment of the smart cities landscape for state and local governments? Because it really is something that's on the front burner, in terms of figuring it out. What's the architecture? Lot of questions. What's your, what's the state of the union, if you will, for-- >> You know it has been, like, how the governments have been for many years, right? Governments exist so that they can provide better services, they can provide better quality of life, they can create an environment where businesses thrive, jobs can be created, education can be given, and you can build a workforce and talent, et cetera. And smart cities is just, I'd say, a trend where, you know, you're using multitudes of technology to kind of help the government get its mission accomplished in a smoother, faster, better, cheaper manner. And a lot of times, I've seen, because how smart cities movement started a decade ago, we kind of compare smart cities with the Internet of Things or the sensors, but smart cities is much more than just the IoT, or the Internet of Things, I mean if you're talking about creating a new stream of data that is real-time, whether coming in from sensors, coming from video, you already as a government, I used to be a CIO for the City of Chicago, we used petabytes of data that was already sitting in my data center, and then there's also this whole third-party data. So smart cities is a lot about how do you as a city are aggregating this different sources of data and then making some action from it, so that ultimately, going back to the city's priorities, you are giving better public safety, or you're providing better public health, or you're providing better education or you're providing, better providing government services. So that's what we are seeing. Our customers are, when we say smart cities, they jump right into, "What problems are you solving?" And that, to me, is the core for Amazon, core for Amazon Web Services. We want to know our customers' problems and then work backwards to solve them. >> What are some of the problems right now that are low-hanging fruit? Because obviously it's an evolution. You set the architecture up, but ultimately governments would love to have some revenue coming in from businesses. You mention that. Education is certainly there. What are some of the challenges there? Is it pre-existing stuff, or is it new opportunities? What are some of the trends you're seeing for use cases? It is actually both pre-existing stuff that they are trying to solve, as well the new stuff, the new opportunities that are getting created, because the technology is much different than what it used to be 10 years ago. The cloud, especially, is creating a lot more new opportunities, because of the nimbleness it brings, the agility it brings. So, in transportation side, we are seeing on one hand, multiple departments, multi-jurisdictional, so state transportation department, as well as a local transportation department, working together to create kind of a virtual information sharing environment or a virtual command center, so that they can detect an accident, a traffic incident, much quicker and respond to that, because now they can aggregate this data. And they're also now adding to that some public safety information. So whether it is a police department, fire department, EMS, so that they can address that incident quickly and then not only clear the traffic and clear the congestion, or reduce the congestion time, but they can also address the, any public safety issue that may have arisen out of that incident that has happened. So, the Department of Transportation, the USDOT, through the Federal Highway Administration, has been giving out $60 million worth of grants to six to ten recipients. The grant, this year's grant period, just closed on Monday, and we worked with multiple customers who are looking to kind of respond to that. So on one hand, it is that. So this is an age-old problem, but new technology can help you solve that. On the other hand, another customer that we worked with is looking for on-demand micro-transit solutions. As you can see, all the ride-sharing applications are making easier to jump in a car and move to one place to the other. It is causing a dip in transit ridership. So the public transit agents, they are looking for solutions to that. So they are looking at, "Can we build an on-demand microtransit "so you can pool your friends and jump into a transit van, as opposed to a private car?" And then you can go from point A to point B in a much more affordable manner. So they are looking at that. On the public health side, you know, we have the DC Benefits Exchange, Health Benefits Exchange, is on AWS, and they have seen significant savings. They have seen $1.8 million of annual savings because they are using cloud and cloud services. On the other hand, you have State of Georgia, which is using Alexa. So they have built Alexa Skills where you can ask, as a resident of State of Georgia getting SNAP benefit, the Supplemental Nutritional Assistance, the food-stamp program, you can say, "Alexa, what's my SNAP balance?" So based on the answer then, based on the balance you know, you can plan your, you know, where you're going to use that money. So we are seeing large volume of data now coming on the cloud where the governments are looking to move kind of the needle. We are also seeing this nimble, quick solutions that can start going out. And we are seeing a lot of driver behind the innovation is our City on a Cloud challenge. So we have seen the City on a Cloud winners, since last so many years, are kind of the ones who are driving innovation and they're also driving a lot of collaboration. So I can, there are three trends that I can jump into as we kind of talk more. >> Yeah, it's interesting. I think back a decade ago, when you talk smarter cities, you'd see this video, and it would look like something out of a science fiction. It's like, you know, "Oh, the flying taxi'll come, "and it will get you and everything." But what I, the stories I have when I talk to CIOs in cities and the like, it's usually more about, it's about data. It's about the underlying data, and maybe it's a mobile app, maybe it's a thing like Alexa Skills. So help us understand a little bit, what does the average citizen, what do they see? How does their, you know, greater transparency and sharing of information and collaboration between what the agencies are doing and, you know, the citizenship. >> I think that's a great question. I mean that is what, as a former CIO, I always had to balance between, what I do creates internal government efficiency, but the citizens don't feel it, don't see it, they don't, it doesn't get in the news media. And on the other hand, I also have to, to my governor, to my mayor, to the agency directors, have to give them visible wins. So, I'll give you an example, so City of Chicago, back in the day, in 2010 when I was the CIO. We did a contract with our AWS, currently AWS Partner Socrata, to open up the data. So that was kind of the beginning of the Open Data Movement, and eventually, I left the city, I went work for Cisco, and the city government continued to kind of build on top of Socrata. And they build what they called the Windy Grid, which is basically bringing all of their various sets of data, so 311, code violations, inspections, crime, traffic, and they built an internal data analytics engine. So now, agencies can use that data. And now, what they did, two years ago, they were one of the City on a Cloud Challenge winners, and they, Uturn Data Solutions is our partner that was the winner of that, and they built Chicago Open Grid. So they basically opened that up on a map-based platform. So now as a citizen of Chicago, I can go on Chicago Open Grid, and I can see which restaurants in, surrounding my area, have failed inspections. Have they failed inspection because of a mice infestation, or was it something very minor, so I can decide whether I want to go to that restaurant or not. I can also look at the crime patterns in my area, I can look at the property values, I can look at the education kind of quality in the schools in my neighborhood. So, we have seen kind of now, and it's all on AWS cloud. >> This open data is interesting to me. Let's take that to another level. That's just the user side of it, there's also a delivery value. I saw use cases in Chicago around Health and Human Services, around being more efficient with either vaccines, or delivery of services based on demographics and other profile, all because of open data. So this brings up a question that comes up a lot, and we're seeing here is a trend, is Amazon Web Services public sector has been really good. Teresa Carlson has done an amazing job leaning on partners to be successful. Meaning it's a collaboration. What's that like in the state and local government? What's the partner landscape look like? What are the benefits for partners to work with AWS? Because it seems obvious to me, it might not be obvious to them. But if they have an innovative idea, whether it's to innovate something on the edge of the network in their business, they can do it, and they can scale with Amazon. What is the real benefits of partnering with AWS? >> You hit a key point on there. Teresa has done a fantastic job in customer management as well as building our partners. Similarly, we have a great leader within the state and local government, Kim Majerus. She leads all of our state and local government business. And her focus is exactly like Teresa: How can we help the customers, and also how can we enable partners to help customers? So I'll give you and example. The City of Louisville in Kentucky. They were a City on a Cloud winner, and they, basically what they're building with a partner of ours, Slingshot, they (laughs) get, I was, I used to be in Traffic Management Authority, back in my days, and we used to do traffic studies. So, basically, they send an intern out with clicker or have those black strips to count the number of cars, and based on that, we can plan whether we want to increase the signal timing on this approach, or we can plan the detours if we close the street, what's the, and it's all manual. It used to take, cost us anywhere from 10 to 50 thousand dollars, every traffic study. So what Louisville did with Slingshot is they got the free Waze data that they get gives all of the raw traffic information. Slingshot brought that on to a AWS platform, and now they are building a traffic analysis tool, which now you can do like a snap of a finger, get the analysis and you can manage the signal-approach timing. The cool thing about this is, they're building it in open source code. And the code's available on GitHub, and I was talking to the Chief Data Officer of Louisville, who's actually going to be speaking at this event later today. 12 other cities have already looked into this. They've started to download the code, and they are starting to use it. So, collaboration through partners also enables collaboration amongst all of our customers. >> And also, I'd just point out, that's a great example, love that, and that's new for me to hear that. But also, to me the observation is, it's new data. So being able to be responsive, to look at that opportunity. Now, it used to be in the old world, and I'm sure you can attest to this, being a CIO back in the day, is okay, just say there's new data available, you have to provision IT. >> Oh my God, yeah. >> I mean, what, old way, new way. I mean, compare and contrast the time it would take to do that with what you can do today. >> It's a big, huge difference. I'll tell you as the CIO for the State of Illinois, when I started in early 2015, in my first performance management session, I asked my Infrastructure Management Team to give me the average days it takes to build a server, 49 days. I mean, you're talking seven weeks or maybe, if you talk, 10 business weeks. It's not acceptable. I mean the way the pace of innovation is going, with AWS on cloud, you are talking about minutes you can spin up that server. And that's what we are seeing, a significant change, and that's why Louisville-- >> And I think you got to think it's even worse when you think about integration, personnel requirements, the meetings that have to get involved. It's a nightmare. Okay, so obviously cloud, we know cloud, we love cloud, we use cloud ourselves. So I got to ask you this could, City in a Cloud program, which we've covered in the past, so last year had some really powerful winners. This has been a very successful program. You're involved in it, you have unique insights, you've been on both sides of the table. How is that going? How is it inspiring other cities? What's the camaraderie like? What's the peer review? Is there a peer, is there a network building? How is that spreading? >> That is actually enabling collaboration in a significant manner. Because, you know, you are openly telling what you want to do, and then you are doing that. Everybody is watching you. Like Louisville is a perfect example where they built this, they're building this, and they're going to share it through open source code to all the cities. 12 is just the beginning. I'd not be surprised if there are 120 cities that are going to do this. Because who doesn't want to save two hundred, three hundred thousand dollars a year? And also lots of time to do the traffic studies. Same thing we have seen with, as Virginia Beach is building their Early Flood Warning System. There are other cities who are looking into, like how do we, New Orleans? And others are looking at, "How do we take what Virginia Beach has built? "And how can we use it for us?" And yesterday, they announced this year of the winners that includes Las Vegas, that includes LA Information Technology Department, that includes the City of Philadelphia, and I've been in conversations with all of the CIOs, CDOs, and the leaders of these agencies. The other thing, John, I have seen is, there's a phenomenal leadership that's out there right now in the cities and states that they want to innovate, they want to collaborate, and they want to kind of make a big difference. >> Hold on, hold on, so one more question, this is a really good question, want to get, follow-up on that. But this, what you're talking about to me signifies really the big trend going on right now in this modern era. You've got large cloud scale. You have open source, open sharing, and collaboration happening. This is the new network effect. This is the flywheel. This is uniquely different. This kind of categorizes cloud. And this wasn't available when IT systems and processes were built, 20, 30 years ago. I mean, this is the big shift, you, I mean do you agree? >> Absolutely, this is the big shift, the availability of the cloud, the ubiquitous nature of mobile platform that people have. The newer way of, like, the natural language processing, use of Alexa is becoming so prevalent in government. I mean, in City of Chicago, 50% of the 311 calls that we used to get in 2010, 3 1/2 million of those were informational in nature. If I could offload that on to my Alexa Skills, I can free up my workforce, the 311 call-takers, to do much better, higher-level, you know, call-taking, as opposed to this. So you're absolutely right. I've seen the trends we are seeing is, there is lots of collaboration going on between the governments and partners. I'm also seeing the governments are going at modernization from different points based on their pain points. And I'm also seeing a definite acceleration in modernization. Government, because the technology, AWS, the cloud, our services that we are seeing. And the pace of innovation that AWS brings is also enabling the acceleration in governments. >> Yeah, to help put a point on the, on the conversation here, there's been for years discussion about, "Well, what is the changing role of the CIO?" You've sat on that side of the table, you know, worked with lots of COs, what do you see is the role of the future for the CIO when, specifically when you talk state and local governments? >> I would say CIO is the kind of has to be an enabler of government services. Because if I go back to my city days and working with a mayor, or my state days, working with a governor, at the end of the day, the governor or the mayor is looking at creating better quality of life, providing better health, better education, better safety, et cetera. And CIO is kind of the key partner in that metrics to enable what the governor, what the mayor, the agency directors want to do. And because now data enables the CIO to kind of quickly give solutions, or AI services, Alexa and Polly and Rekog ... All of these things give you, give me as a CIO, ability to provide quick wins to the mayor, to the governor, and also very visible wins. We are seeing that, you know, CIO is becoming a uniquely positioned individual and leader to kind of enable the government. >> All right, thanks so much for comin' on theCUBE. Love the insight, love to follow up. You bring a great perspective and great insight and Amazon's lucky to have you on the team. Lot of great stuff goin' on in the cities and local governments. It's a good opportunity for you guys. Thanks for coming on, appreciate it. >> Thank you very much. >> It's theCUBE live here in Washington DC for AWS, Amazon Web Services Public Sector Summit, I'm John Furrier, Stu Miniman, again second year of live coverage. It's a packed house, a lot of great cloud action. Again, the game has changed. It's a whole new world, cloud scale, open source, collaboration, mobile, all this new data's here. This is the opportunity, this is what theCUBE's doing. We're doin' our part, sharing the data with you. Stay with us, more coverage from day two, here in Washington, after this short break. (techno music)

Published Date : Jun 21 2018

SUMMARY :

Brought to you by Amazon Web Services for some of the new capabilities, Good to see you, Stu, good morning. and the cities provide services to the residents, and you can build a workforce and talent, et cetera. So based on the answer then, based on the balance you know, It's about the underlying data, and eventually, I left the city, I went work for Cisco, What are the benefits for partners to work with AWS? get the analysis and you can manage and that's new for me to hear that. the time it would take to do that I mean the way the pace of innovation is going, the meetings that have to get involved. in the cities and states that they want to innovate, This is the new network effect. I mean, in City of Chicago, 50% of the 311 calls And CIO is kind of the key partner in that metrics and Amazon's lucky to have you on the team. This is the opportunity, this is what theCUBE's doing.

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Kashif Mahbub, Automation Anywhere | Automation Anywhere Imagine 2018


 

>> From Times Square, in the heart of New York City, it's theCUBE. Covering, Imagine 2018. Brought to you by, Automation Anywhere. >> Hey welcome back everybody, Jeff Frick here with theCUBE. We're in downtown Manhattan at Automation Anywhere, Imagine 2018. Eleven hundred people buzzing all around us here. The eco system is hot, everybody's looking at all the various solutions, all the various bots, all the various activities going on. And we're excited to have relatively newcomer to the company. He's Kashif Mahbub, the VP, Product Marketing for Automation Anywhere. Kashif, welcome. >> Thanks for having me. >> So you said you've been here, we've had all the founders on I think, so you've been here about a year. So, first impressions, I imagine this is your first show, what do you think? >> It's actually my third show. >> Oh it is your show. >> Things are moving. >> Oh, were you a customer before? >> Company standard, yeah by company standards I would say I'm a veteran. (Jeff laughing) So we are doing these shows, all of these marketing activities at a very rapid pace. >> Very good, so one of the topics we haven't talked about so much today is this kind of digital workforce concept. And you guys have a really specific idea of what makes, kind of taking these things to actually be considered a digital workforce. So what are those three things that you guys combine, to have something that's unique in the market place? >> So we pioneered the concept of digital workforce. And in our parlance, in our definition, a digital workforce, especially at enterprise scale, comprises of three key components. RPA, which is robotic process automation. Cognitive automation, which is the ability of using AI and machine learning capabilities with the RPA. And last but not least, smart analytics. So, the combination of these three make up what we call a digital workforce. If any of one of these elements is missing, we feel that's not really a true digital workforce. So it is the workforce platform that we call enterprise, combines all of these capabilities together to really deliver a true enterprise class, digital workforce platform. >> Now how long have you guys been baking in the AI component of it, in the cognition piece. 'Cause there's a lot of talk about cognitive computing, and it's a big theme that IBM has had for a long, long time, and we're seeing AI work itself in to all kinds of interesting applications. Now kind of, where was your guys' AI journey, how long have you been at it, and where are you seeing kind of the break through to get to this digital workforce concept? >> So automation anywhere has been around for about 15 years now. So we have a very mature product. I look after the enterprise platform, and we just released version 11. So it makes it the most mature platform in the industry at the moment. Now to answer your question about AI, and bringing AI into it, that's fairly recent. But we are based in the heart of Silicon Valley, Google is one of our customers, so is Tesla, so is LinkedIn. These are three big AI companies, with their own AI Technology, yet they use Automation Anywhere platform as well. So, there is AI, and then there is AI with RPA. So think of it as purpose built AI capabilities that are infused through our digital workforce platform, to enhance our RPA capabilities. And you bring in analytics, then we talk about predictive analytics. So overall again, it's building a digital workforce that is enhanced by AI, that is enhanced by cognitive capabilities, so that RPA is not just RPA. It's RPA to the next level. >> Right, and really RPA that's gonna evolve. RPA that's eventually gonna write itself right, or write new versions of itself based on new things. And process improvement, new discoveries in terms of better ways to get things done. Using those other two legs of the spool. >> Yes, so you will see a lot of publications out there that talk about RPA evolving into AI, or AI taking over RPA. The fact is, there is again AI, and then there is AI combined with RPA. So if you take Google's example. Google uses us in the back end, yet it is one of the largest AI companies in the world. So AI, think of it as a big hammer. It has to be used very carefully, and we have purpose built AI into our product to make sure that we extract all the unstructured data. And then we, as Mihir mentioned, our CEO mentioned earlier in the key note, it is feeding this RPA monster that needs more and more data. And all of that data comes through our AI and cognitive capabilities. >> Right, and we know right, and for the machines to learn, they need more, and more, and more data so they get better and better. It's just the way computers do learn. It's very different from the way humans learn, it's a slightly different model. >> It's about building a digital map. You know, we use Waze and Google Maps and all of these different GPS driven capabilities to find our way around, Manhattan for example. Or Bay area for that matter. (Jeff laughing) Think of our digital workforce platform with AI capabilities and with analytics capabilities, as a digital map of an enterprise. We touch so many different infrastructure components. From CRM systems to ERP systems to HRIS systems, that the amount of data that we capture that passes through our system, gives us perhaps the best look that anyone can have into how data flows through an enterprise. And what's the best way to use it. >> Right, so I'm curious in terms of those vertical applications that you described, where have you seen the biggest impact now that you've started to bring the AI in? Are there certain verticals that are just ripe for significant positive change, and some that are less so? >> Yeah absolutely. So, there is a lot of data locked in documents still. So banking, finance and insurance. Those are the three verticals, three industries where our first step with our IQ Bot, which is our cognitive product. We have seen a lot of traction there. The reason for that is again, when we decipher these documents, when we decipher and capture all the data, we then use it very intelligently in automating the processes. So the first step to answer your question would be, organizations, industries that use unstructured data that is locked into their documents, all this dark data of methodology. We unlock that data, and then we use RPA and we feed this RPA monster to really automate the various processes. >> Every time you guys talk about all the data locked in these documents, I can't help but think of the old OCR days, when I got my first $1000 flatbed scanner to try to read a couple documents. It never worked back then, the era of a different place. >> Funny that you mention that because the OCR technology that got built into a lot of scanners later on, a lot of that technology we use under the covers, but at a much more enhanced level. So we partner with some of the best OCR technologies out there, but then we put AI on top of that to really take it to the next level. So when the data comes out of a simple OCR process, it's no longer just some data that you can, like we used to see. Now it's data that is structured, that can be automated in a few clicks. >> It has context right. And most importantly it has context, which makes all the difference in the world. Okay, so what are some of your priorities for next year, before I let you go. What are some of the things you're working on? If we sit down a year from now, what are we gonna be talking about that's new? Don't tell me any secrets, no NDA's have been signed here. (Jeff laughing) >> At Imagine, we come with an approach of an open book. Open kimono if you will, and we share all that we are working on. And all that we are working today, but also going forward. So AI is a big element of that. Automation, combined with any sort of automation, especially RPA combined with AI and machine learning capabilities, that's already, we have a product, as opposed to just an idea. It's a working product with dozens of organizations using it. But then we are infusing that AI into RPA, and making it intelligent RPA. Making it an intelligent digital workforce platform. That's the ultimate goal, and we are already well on our way. >> Alright well Kashif, thanks for a taking a few minutes of your time and congrats on a great show. >> Thank you, thanks for having me Jeff. >> Alright he's Kashif, I'm Jeff, you're watching theCUBE from Automation Anywhere Imagine 2018 in New York City, thanks for watching. (electronic music)

Published Date : Jun 1 2018

SUMMARY :

in the heart of New all the various bots, So you said you've been here, So we are doing these shows, all of these Very good, so one of the topics So it is the workforce platform guys been baking in the So it makes it the most mature platform to get things done. And all of that data comes through and for the machines to learn, that the amount of data that we capture So the first step to answer the era of a different place. So we partner with some of the best difference in the world. And all that we are working a few minutes of your time in New York City, thanks for watching.

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Sheri Bachstein & Mary Glackin | IBM Think 2018


 

>> Narrator: From Las Vegas, it's the Cube, covering IBM Think 2018, brought to you by IBM. >> Welcome back to Las Vegas, everybody. You're watching the Cube, the leader in live tech coverage. My name is Dave Vellante, and this is day three of our wall-to-wall coverage of IBM's inaugural Think conference. Mary Glackin's here, she's the vice president of weather business solutions, public, private partnerships, IBM Watson, and she's joined by Sheri Bachstein as the global head of consumer business at the Weather Company, an IBM company. Ladies, welcome to the Cube, thanks so much for coming on. >> Thank you, you're welcome. >> Thanks. >> Alright, Mary, going to start with the Weather Company. When IBM acquired the Weather Company, a lot of people were like, "What?", and they said, "Okay, data science, I get that.", and then, there was an IoT spin on that. Obviously, you have a lot of data, but, I got to ask you, what business are you in? >> So, what we like to say is we're in, not in the weather business, we're in the decision business. We're really dedicated, everyday, to help businesses, make the best decisions possible, and Sheri works on the consumer end of the business to do exactly the same thing. >> So, talk about your respective roles. Sheri, you're on the consumer side, as Mary just said, what does that entail? >> So, the consumer side is any touchpoint where we're bringing weather and weather insights to our consumers, whether it's on our weather channel app, whether it's on our web platform, mobile web, on wearables, so, it's anywhere where we're connecting with consumers, and, as Mary said, it's really about helping consumers make decisions. In our field, the forecast and some of the weather data has become a commodity almost, and we've actually shared our weather data with a lot of partners, and, so, now, we're using machine learning and data science to really come up with weather insights to help consumers make decisions, and it could be something just as simple as what to wear today, what's going to happen for a big event, or it can be around how do I keep people safe during severe weather. >> Yeah, I mean, we all look at the weather. I mean, I look at it everyday. >> Yeah. >> Of course, when you travel, like, what do I bring, what do I wear? Living in the East Coast these days, a lot of storms that we've >> That's right. >> encountered in the East Coast. I wonder if you could talk about life at IBM. I mean, again, it was a curious acquisition to a lot of people. Have you guys assimilated, how has it changed your business? >> I would say pretty dramatically. So, coming back to IBM acquiring us, they acquired us, really, for two reasons. One is we had some underlying technology that was really of interest to them that they're leveraging today, but the other part was because weather impacts so many businesses. So, as we've come into IBM, we've had alliances with IBM research. We're working on a pretty exciting project in bringing the next generation weather model to market, using high performance computing there. We've had alliances, definitely, through Watson in bringing AI into our products, and then, our product lines marry up with a lot of IBM product lines. So, we've rolled out a really exciting offering in closed captioning, and it really works well with some of the classical media business, weather media business that we have been providing. >> So, how do you guys make money? Maybe we could talk about the consumer side and the business side. A lot of people must ask that question. >> Yeah. >> They're advertising, okay, fine, >> Yeah. >> but that's not the core of what you guys do. >> Yeah, so, on the consumer side, a big majority of our revenue is drive by advertising, but we had to look at that business as well, 'cause as programmatic advertising has kind of taken up the landscape, how did we pivot to really generate more revenue, and, so, we've done that by creating Watson advertising, and that was one of the first implementations of Watson after the acquisition on the consumer side, and what we've done is we've created an open, scalable environment that, now, we can not only sell meaningful insights on our platform, but we can now give that to our partners, that they can go off our property and use the weather insights, we can use different data around location and media to help our partners really have a better experience, not only on our platform, but on any publisher's platform. >> So, that's your customers using Watson for advertising to drive their business. >> That's right. >> It's not like IBM is getting into the advertising business, per se, directly, is that right? >> Right, well, we're leveraging the power of Watson to create these insights. One of the products we created is called Weather FX, and, really, what it's doing, it's taking predictive analytics on the retail side, which is really an underused technology for retailers, but taking our historical weather data, mixing it with their retail data' to come up with insights so we can come up with interesting things that, say, in the northeast, like right now, during the winter, soda sells tremendously during very snowy or rainy winters. We can look at, you know, strawberry Pop-Tarts sell fairly well right before a hurricane, and, so, these are insights that we can bring to retailers, but it helps them with their supply chain, it helps them with their inventory, it can actually even help them with pricing, and, so, this is one of the ways we're taking our weather technology and marrying it with the advertising world to help provide those insights. >> For real, with the strawberry Pop-Tarts? >> For real, yeah, I guess, you know, you don't have to cook 'em or something. I don't know, so, yeah. >> Right, yeah, it's simple if the lights go out, okay. I mean, we want to ask you about your title, public and private partnerships. It's interesting, what is that all about? >> So, it's really about the fact that weather has really been something that's been shared globally around the world for hundreds of years at this point, and, so, the Weather Company and IBM take it very seriously that we be good partners in that community of weather providers. So, one of the things that we feel passionately about is we have a shared safety mission with national meteorological services globally. So, here in the US, we transmit, Sheri's team does, the warnings that come from the National Weather Service unaltered with attribution to the National Weather Service. We feel that it's really important that there's a sole authoritative voice when there's really danger. So, we share that safety mission, and then, we're trying to help in other parts of the world. We've had some partnerships to try to increase the observing in Africa which is really a part of the world that's under-observed. So, some of IBM's philanthropic efforts have been helping to fill in there and work with those national met services. So, it's really one of the really fun parts of my job. >> You know, we talk a lot about digital transformation, and Ginni Rometty was talking about the incumbent disruptors, and we've been riffing on that all week. We've made the observation that companies that are digital have data at their core, and they've organized, sort of, human expertise around that data. Most companies, Fortune 1000, are built around human expertise and built around other assets, the bottling plant or the factory, et cetera. I look at the Weather Company as a data company, that's probably fair. Did you evolve into that data is clearly at your core? Has it always been, and it's very interesting that IBM has acquired this company as it changes its DNA. I wonder if you could address that. >> Go ahead (laughs). >> So, I think there's a couple aspects around our data. There's obviously the weather data which is really powerful, but then, there's also location data. We're one of the largest location data providers besides Google and some of the others, because our weather accuracy starts with location which is really important. We have 250 million users that use our application, and we want to give them the most accurate forecast, and that starts with location. Because we add value, users will opt in to give us that data which is really important to us that we do keep their data private and opt in to that to get that location data. So, that's really powerful, because, now we can deliver products based on time and location and weather, and it just makes for better weather insights for, not only our consumers, but for our businesses. >> Yeah, yeah. >> Do you use, I mean, how do you use social? I mean, you know how Waze tells you where the traffic is and you report back. Do you guys rely heavily on that, or do you more rely on machines to help you with your forecast? Is it a combination? >> So, I could talk a little bit. One of our new market areas we've been going into is ground transportation. So, we do have a partner that's providing us some transportation, traffic information, but what we bring to it is being able to do, the predictive thing, is to take the weather piece and how that's going to influence that traffic. So, as the storm comes through, we know by looking at past events what that will mean and we bring that piece to the table. So, it's an example of how we go, not just giving you a weather forecast, but really forecasting the impacts and giving you insights, so that if you're running a large trucking operation, you can reroute fleets around it and avoid weather like that and keep people safe. >> Talk about, oh, go ahead, please. >> One of the brands within our portfolio is Weather Underground, and what they brought to the table for us is a personal weather station that works. So, we have about 270,000 around the world, and these are people that just really love the weather. They have a personal weather station in their backyard and they provide that data that then goes into Mary's team in helping looking at the forecast. So, that's one of the ways that we're using kind of a social network in sensoring to influence some of the work that we're doing. >> I mean, the weather forecast, for years, have been the butt of many jokes. You guys are data science oriented, data scientists, the data doesn't lie. We just keep iterating >> Yeah. >> and make it better and better and better. What could you tell us about the improvements of the forecast over the last decade? Maybe Bill Belichick makes jokes about the weather and you hear it, you say, "You know, actually "the weather's predictions have gotten much better." You guys measure it, what can you share with us? >> Oh, it's gotten so much better over the course of my career, it's pretty dramatic and it's getting better still. You're going to see some real breakthroughs coming up. So, one of the things that we've really put a lot of bets on in IBM is the internet of things, >> Dave: Right. >> and, so, we are, today, pulling off of cellphones atmospheric pressure data and that's going into our next generation model. So, this'll be more data than anybody has powering that model. So, you're able to augment traditional data sources like, you may or may not know, we still launch weather balloons twice a day to measure through the atmosphere, but, in our technology, we take data off of airplanes, we take data off of cellphones, we'll soon be taking data off of cars which will tell us when the windshield wipers are moving, is it raining or not, when the anti-lock brakes things lock, that roads are icy, all of that. So, all of that will come in to improve forecasting. >> So, this requires partnerships with all that and amazing supply chain. >> Absolutely. >> I presume IBM helps there as well, but did you have a lot of that in motion prior to the acquisition, how does that all work? >> I think we've really been empowered by IBM. >> Yep, absolutely. >> Yeah. >> There's no question about that, and it's about finding the win-win. When we work with car manufacturers they're looking to have safe experiences for their drivers and we can help in that regard, and, as we move into autonomous vehicles, there's just going to be even more demand for very high resolution, accurate weather information. >> Am I correct at all, the weather data from all these devices actually goes back to the IBM cloud, is that right, and that's where the models are iterated and developed, is that correct, or does some of it stay out in the network? >> It's all a cloud-based operation that's here. We do do some, I mentioned before that we're working with IBM research on next generation high-performance computing which is actually, it can be cloud-based, but it's also on Prim-based, because of the very large cores we need for computing these models. We're going to run a very high-resolution model globally at a very high frequency. >> So, thinking about some of the industries that you're helping, I mean, you mentioned retail before. Obviously, government's very interested in this. I would imagine investors are interested in the weather in a big way. >> Yeah. >> Maybe you could talk about some of the more interesting industries, use cases, business models. >> Yeah, there's a lot out there, there's traditional ones we've served for years like energy traders that are very interested in, you know, because they're trying to make decisions about that. The financial services sector is also very interested. When they can get some additional insights through footfall traffic, if they know certain stores are seeing more footfall traffic, that will give them some indication, a little edge up in the marketplace for that. So, we see those kind of things, and other traditional areas as well, agriculture, what you would expect there. >> So people, you know, you hear a lot of talk in the press about artificial intelligence and Elon Musk predictions and the like, but here's an example where machine intelligence, everybody welcomes, keeps getting better and better and better. How far could we take AI and weather? Where do you see this going in the next 10 years? >> So, on the consumer side, I think it's really about transforming the way that we're delivering weather on the digital platform, the new age of the weather app will say, and, really, users want a personalized experience. They want to know how the weather's going to impact me, but they don't want to personalize, right? So, that's where machine learning is coming in, that we can be able to provide those insights. We'll know that, maybe, you're an allergy sufferer or migraine sufferer, and we're going to tell you that the conditions are right for that you might have symptoms related to that around health. So, there's a lot of ways, on the consumer side, more personalized experience, giving you more assurance that you don't have to, necessarily, go to the app to find information. We're going to send it to you more proactively, and, so, machine learning is helping us do that cognitive science as well. So, it's a pretty exciting time to be part of the weather. >> Yeah, that bum knee I have, you know, you might want to get ahead of the pain. >> That's right, with the arthritis, yes, yes, so, definitely. >> Alright, Mary, we'll give you last word on IBM Think and, you know, the whole trend of AI and weather. >> So, I think it's really exciting. I think Ginni says it really well. It's about AI and the person as well. You know, AI doesn't take over. It's really finding the way to AI to really assist decision makers and that's we're going on the business end of things is really sorting through tons and tons of data to really provide the insights that people can make, businesses can make really great decisions. >> Well, it's always been a really fascinating acquisition to me, and, now, just to see how it's evolving is really amazing. So, Sheri and Mary, thanks very much for coming on the Cube >> Thank you. >> and sharing your experiences. >> Thanks so much. >> Great, thank you. >> You're welcome, alright, keep it right there, everybody, you're watching the Cube. We're live from Think 2018 and we'll be right back. (techno beat)

Published Date : Mar 21 2018

SUMMARY :

Narrator: From Las Vegas, it's the Cube, as the global head of consumer business When IBM acquired the Weather Company, of the business to do exactly the same thing. So, talk about your respective roles. In our field, the forecast and some of the weather data Yeah, I mean, we all look at the weather. encountered in the East Coast. in bringing the next generation weather model to market, So, how do you guys make money? of Watson after the acquisition on the consumer side, So, that's your customers using Watson One of the products we created is called Weather FX, For real, yeah, I guess, you know, I mean, we want to ask you about your title, So, here in the US, we transmit, I look at the Weather Company as There's obviously the weather data which is really powerful, to help you with your forecast? So, as the storm comes through, go ahead, please. So, that's one of the ways that we're using I mean, the weather forecast, for years, of the forecast over the last decade? So, one of the things that we've really So, all of that will come in to improve forecasting. So, this requires partnerships with all that and it's about finding the win-win. on Prim-based, because of the very large cores that you're helping, I mean, you mentioned retail before. the more interesting industries, use cases, that are very interested in, you know, and the like, but here's an example of the weather app will say, and, really, of the pain. with the arthritis, yes, yes, so, definitely. and, you know, the whole trend of AI and weather. It's about AI and the person as well. So, Sheri and Mary, thanks very much We're live from Think 2018 and we'll be right back.

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Wikibon Action Item | De-risking Digital Business | March 2018


 

>> Hi I'm Peter Burris. Welcome to another Wikibon Action Item. (upbeat music) We're once again broadcasting from theCube's beautiful Palo Alto, California studio. I'm joined here in the studio by George Gilbert and David Floyer. And then remotely, we have Jim Kobielus, David Vellante, Neil Raden and Ralph Finos. Hi guys. >> Hey. >> Hi >> How you all doing? >> This is a great, great group of people to talk about the topic we're going to talk about, guys. We're going to talk about the notion of de-risking digital business. Now, the reason why this becomes interesting is, the Wikibon perspective for quite some time has been that the difference between business and digital business is the role that data assets play in a digital business. Now, if you think about what that means. Every business institutionalizes its work around what it regards as its most important assets. A bottling company, for example, organizes around the bottling plant. A financial services company organizes around the regulatory impacts or limitations on how they share information and what is regarded as fair use of data and other resources, and assets. The same thing exists in a digital business. There's a difference between, say, Sears and Walmart. Walmart mades use of data differently than Sears. And that specific assets that are employed and had a significant impact on how the retail business was structured. Along comes Amazon, which is even deeper in the use of data as a basis for how it conducts its business and Amazon is institutionalizing work in quite different ways and has been incredibly successful. We could go on and on and on with a number of different examples of this, and we'll get into that. But what it means ultimately is that the tie between data and what is regarded as valuable in the business is becoming increasingly clear, even if it's not perfect. And so traditional approaches to de-risking data, through backup and restore, now needs to be re-thought so that it's not just de-risking the data, it's de-risking the data assets. And, since those data assets are so central to the business operations of many of these digital businesses, what it means to de-risk the whole business. So, David Vellante, give us a starting point. How should folks think about this different approach to envisioning business? And digital business, and the notion of risk? >> Okay thanks Peter, I mean I agree with a lot of what you just said and I want to pick up on that. I see the future of digital business as really built around data sort of agreeing with you, building on what you just said. Really where organizations are putting data at the core and increasingly I believe that organizations that have traditionally relied on human expertise as the primary differentiator, will be disrupted by companies where data is the fundamental value driver and I think there are some examples of that and I'm sure we'll talk about it. And in this new world humans have expertise that leverage the organization's data model and create value from that data with augmented machine intelligence. I'm not crazy about the term artificial intelligence. And you hear a lot about data-driven companies and I think such companies are going to have a technology foundation that is increasingly described as autonomous, aware, anticipatory, and importantly in the context of today's discussion, self-healing. So able to withstand failures and recover very quickly. So de-risking a digital business is going to require new ways of thinking about data protection and security and privacy. Specifically as it relates to data protection, I think it's going to be a fundamental component of the so-called data-driven company's technology fabric. This can be designed into applications, into data stores, into file systems, into middleware, and into infrastructure, as code. And many technology companies are going to try to attack this problem from a lot of different angles. Trying to infuse machine intelligence into the hardware, software and automated processes. And the premise is that meaty companies will architect their technology foundations, not as a set of remote cloud services that they're calling, but rather as a ubiquitous set of functional capabilities that largely mimic a range of human activities. Including storing, backing up, and virtually instantaneous recovery from failure. >> So let me build on that. So what you're kind of saying if I can summarize, and we'll get into whether or not it's human expertise or some other approach or notion of business. But you're saying that increasingly patterns in the data are going to have absolute consequential impacts on how a business ultimately behaves. We got that right? >> Yeah absolutely. And how you construct that data model, and provide access to the data model, is going to be a fundamental determinant of success. >> Neil Raden, does that mean that people are no longer important? >> Well no, no I wouldn't say that at all. I'm talking with the head of a medical school a couple of weeks ago, and he said something that really resonated. He said that there're as many doctors who graduated at the bottom of their class as the top of their class. And I think that's true of organizations too. You know what, 20 years ago I had the privilege of interviewing Peter Drucker for an hour and he foresaw this, 20 years ago, he said that people who run companies have traditionally had IT departments that provided operational data but they needed to start to figure out how to get value from that data and not only get value from that data but get value from data outside the company, not just internal data. So he kind of saw this big data thing happening 20 years ago. Unfortunately, he had a prejudice for senior executives. You know, he never really thought about any other people in an organization except the highest people. And I think what we're talking about here is really the whole organization. I think that, I have some concerns about the ability of organizations to really implement this without a lot of fumbles. I mean it's fine to talk about the five digital giants but there's a lot of companies out there that, you know the bar isn't really that high for them to stay in business. And they just seem to get along. And I think if we're going to de-risk we really need to help companies understand the whole process of transformation, not just the technology. >> Well, take us through it. What is this process of transformation? That includes the role of technology but is bigger than the role of technology. >> Well, it's like anything else, right. There has to be communication, there has to be some element of control, there has to be a lot of flexibility and most importantly I think there has to be acceptability by the people who are going to be affected by it, that is the right thing to do. And I would say you start with assumptions, I call it assumption analysis, in other words let's all get together and figure out what our assumptions are, and see if we can't line em up. Typically IT is not good at this. So I think it's going to require the help of a lot of practitioners who can guide them. >> So Dave Vellante, reconcile one point that you made I want to come back to this notion of how we're moving from businesses built on expertise and people to businesses built on expertise resident as patterns in the data, or data models. Why is it that the most valuable companies in the world seem to be the ones that have the most real hardcore data scientists. Isn't that expertise and people? >> Yeah it is, and I think it's worth pointing out. Look, the stock market is volatile, but right now the top-five companies: Apple, Amazon, Google, Facebook and Microsoft, in terms of market cap, account for about $3.5 trillion and there's a big distance between them, and they've clearly surpassed the big banks and the oil companies. Now again, that could change, but I believe that it's because they are data-driven. So called data-driven. Does that mean they don't need humans? No, but human expertise surrounds the data as opposed to most companies, human expertise is at the center and the data lives in silos and I think it's very hard to protect data, and leverage data, that lives in silos. >> Yes, so here's where I'll take exception to that, Dave. And I want to get everybody to build on top of this just very quickly. I think that human expertise has surrounded, in other businesses, the buildings. Or, the bottling plant. Or, the wealth management. Or, the platoon. So I think that the organization of assets has always been the determining factor of how a business behaves and we institutionalized work, in other words where we put people, based on the business' understanding of assets. Do you disagree with that? Is that, are we wrong in that regard? I think data scientists are an example of reinstitutionalizing work around a very core asset in this case, data. >> Yeah, you're saying that the most valuable asset is shifting from some of those physical assets, the bottling plant et cetera, to data. >> Yeah we are, we are. Absolutely. Alright, David Foyer. >> Neil: I'd like to come in. >> Panelist: I agree with that too. >> Okay, go ahead Neil. >> I'd like to give an example from the news. Cigna's acquisition of Express Scripts for $67 billion. Who the hell is Cigna, right? Connecticut General is just a sleepy life insurance company and INA was a second-tier property and casualty company. They merged a long time ago, they got into health insurance and suddenly, who's Express Scripts? I mean that's a company that nobody ever even heard of. They're a pharmacy benefit manager, what is that? They're an information management company, period. That's all they do. >> David Foyer, what does this mean from a technology standpoint? >> So I wanted to to emphasize one thing that evolution has always taught us. That you have to be able to come from where you are. You have to be able to evolve from where you are and take the assets that you have. And the assets that people have are their current systems of records, other things like that. They must be able to evolve into the future to better utilize what those systems are. And the other thing I would like to say-- >> Let me give you an example just to interrupt you, because this is a very important point. One of the primary reasons why the telecommunications companies, whom so many people believed, analysts believed, had this fundamental advantage, because so much information's flowing through them is when you're writing assets off for 30 years, that kind of locks you into an operational mode, doesn't it? >> Exactly. And the other thing I want to emphasize is that the most important thing is sources of data not the data itself. So for example, real-time data is very very important. So what is your source of your real-time data? If you've given that away to Google or your IOT vendor you have made a fundamental strategic mistake. So understanding the sources of data, making sure that you have access to that data, is going to enable you to be able to build the sort of processes and data digitalization. >> So let's turn that concept into kind of a Geoffrey Moore kind of strategy bromide. At the end of the day you look at your value proposition and then what activities are central to that value proposition and what data is thrown off by those activities and what data's required by those activities. >> Right, both internal-- >> We got that right? >> Yeah. Both internal and external data. What are those sources that you require? Yes, that's exactly right. And then you need to put together a plan which takes you from where you are, as the sources of data and then focuses on how you can use that data to either improve revenue or to reduce costs, or a combination of those two things, as a series of specific exercises. And in particular, using that data to automate in real-time as much as possible. That to me is the fundamental requirement to actually be able to do this and make money from it. If you look at every example, it's all real-time. It's real-time bidding at Google, it's real-time allocation of resources by Uber. That is where people need to focus on. So it's those steps, practical steps, that organizations need to take that I think we should be giving a lot of focus on. >> You mention Uber. David Vellante, we're just not talking about the, once again, talking about the Uberization of things, are we? Or is that what we mean here? So, what we'll do is we'll turn the conversation very quickly over to you George. And there are existing today a number of different domains where we're starting to see a new emphasis on how we start pricing some of this risk. Because when we think about de-risking as it relates to data give us an example of one. >> Well we were talking earlier, in financial services risk itself is priced just the way time is priced in terms of what premium you'll pay in terms of interest rates. But there's also something that's softer that's come into much more widely-held consciousness recently which is reputational risk. Which is different from operational risk. Reputational risk is about, are you a trusted steward for data? Some of that could be personal information and a use case that's very prominent now with the European GDPR regulation is, you know, if I ask you as a consumer or an individual to erase my data, can you say with extreme confidence that you have? That's just one example. >> Well I'll give you a specific number on that. We've mentioned it here on Action Item before. I had a conversation with a Chief Privacy Officer a few months ago who told me that they had priced out what the fines to Equifax would have been had the problem occurred after GDPR fines were enacted. It was $160 billion, was the estimate. There's not a lot of companies on the planet that could deal with $160 billion liability. Like that. >> Okay, so we have a price now that might have been kind of, sort of mushy before. And the notion of trust hasn't really changed over time what's changed is the technical implementations that support it. And in the old world with systems of record we basically collected from our operational applications as much data as we could put it in the data warehouse and it's data marked satellites. And we try to govern it within that perimeter. But now we know that data basically originates and goes just about anywhere. There's no well-defined perimeter. It's much more porous, far more distributed. You might think of it as a distributed data fabric and the only way you can be a trusted steward of that is if you now, across the silos, without trying to centralize all the data that's in silos or across them, you can enforce, who's allowed to access it, what they're allowed to do, audit who's done what to what type of data, when and where? And then there's a variety of approaches. Just to pick two, one is where it's discovery-oriented to figure out what's going on with the data estate. Using machine learning this is, Alation is an example. And then there's another example, which is where you try and get everyone to plug into what's essentially a new system catalog. That's not in a in a deviant mesh but that acts like the fabric for your data fabric, deviant mesh. >> That's an example of another, one of the properties of looking at coming at this. But when we think, Dave Vellante coming back to you for a second. When we think about the conversation there's been a lot of presumption or a lot of bromide. Analysts like to talk about, don't get Uberized. We're not just talking about getting Uberized. We're talking about something a little bit different aren't we? >> Well yeah, absolutely. I think Uber's going to get Uberized, personally. But I think there's a lot of evidence, I mentioned the big five, but if you look at Spotify, Waze, AirbnB, yes Uber, yes Twitter, Netflix, Bitcoin is an example, 23andme. These are all examples of companies that, I'll go back to what I said before, are putting data at the core and building humans expertise around that core to leverage that expertise. And I think it's easy to sit back, for some companies to sit back and say, "Well I'm going to wait and see what happens." But to me anyway, there's a big gap between kind of the haves and the have-nots. And I think that, that gap is around applying machine intelligence to data and applying cloud economics. Zero marginal economics and API economy. An always-on sort of mentality, et cetera et cetera. And that's what the economy, in my view anyway, is going to look like in the future. >> So let me put out a challenge, Jim I'm going to come to you in a second, very quickly on some of the things that start looking like data assets. But today, when we talk about data protection we're talking about simply a whole bunch of applications and a whole bunch of devices. Just spinning that data off, so we have it at a third site. And then we can, and it takes to someone in real-time, and then if there's a catastrophe or we have, you know, large or small, being able to restore it often in hours or days. So we're talking about an improvement on RPO and RTO but when we talk about data assets, and I'm going to come to you in a second with that David Floyer, but when we talk about data assets, we're talking about, not only the data, the bits. We're talking about the relationships and the organization, and the metadata, as being a key element of that. So David, I'm sorry Jim Kobielus, just really quickly, thirty seconds. Models, what do they look like? What are the new nature of some of these assets look like? >> Well the new nature of these assets are the machine learning models that are driving so many business processes right now. And so really the core assets there are the data obviously from which they are developed, and also from which they are trained. But also very much the knowledge of the data scientists and engineers who build and tune this stuff. And so really, what you need to do is, you need to protect that knowledge and grow that knowledge base of data science professionals in your organization, in a way that builds on it. And hopefully you keep the smartest people in house. And they can encode more of their knowledge in automated programs to manage the entire pipeline of development. >> We're not talking about files. We're not even talking about databases, are we David Floyer? We're talking about something different. Algorithms and models are today's technology's really really set up to do a good job of protecting the full organization of those data assets. >> I would say that they're not even being thought about yet. And going back on what Jim was saying, Those data scientists are the only people who understand that in the same way as in the year 2000, the COBOL programmers were the only people who understood what was going on inside those applications. And we as an industry have to allow organizations to be able to protect the assets inside their applications and use AI if you like to actually understand what is in those applications and how are they working? And I think that's an incredibly important de-risking is ensuring that you're not dependent on a few experts who could leave at any moment, in the same way as COBOL programmers could have left. >> But it's not just the data, and it's not just the metadata, it really is the data structure. >> It is the model. Just the whole way that this has been put together and the reason why. And the ability to continue to upgrade that and change that over time. So those assets are incredibly important but at the moment there is no way that you can, there isn't technology available for you to actually protect those assets. >> So if I combine what you just said with what Neil Raden was talking about, David Vallante's put forward a good vision of what's required. Neil Raden's made the observation that this is going to be much more than technology. There's a lot of change, not change management at a low level inside the IT, but business change and the technology companies also have to step up and be able to support this. We're seeing this, we're seeing a number of different vendor types start to enter into this space. Certainly storage guys, Dylon Sears talking about doing a better job of data protection we're seeing middleware companies, TIBCO and DISCO, talk about doing this differently. We're seeing file systems, Scality, WekaIO talk about doing this differently. Backup and restore companies, Veeam, Veritas. I mean, everybody's looking at this and they're all coming at it. Just really quickly David, where's the inside track at this point? >> For me it is so much whitespace as to be unbelievable. >> So nobody has an inside track yet. >> Nobody has an inside track. Just to start with a few things. It's clear that you should keep data where it is. The cost of moving data around an organization from inside to out, is crazy. >> So companies that keep data in place, or technologies to keep data in place, are going to have an advantage. >> Much, much, much greater advantage. Sure, there must be backups somewhere. But you need to keep the working copies of data where they are because it's the real-time access, usually that's important. So if it originates in the cloud, keep it in the cloud. If it originates in a data-provider, on another cloud, that's where you should keep it. If it originates on your premise, keep it where it originated. >> Unless you need to combine it. But that's a new origination point. >> Then you're taking subsets of that data and then combining that up for itself. So that would be my first point. So organizations are going to need to put together what George was talking about, this metadata of all the data, how it interconnects, how it's being used. The flow of data through the organization, it's amazing to me that when you go to an IT shop they cannot define for you how the data flows through that data center or that organization. That's the requirement that you have to have and AI is going to be part of that solution, of looking at all of the applications and the data and telling you where it's going and how it's working together. >> So the second thing would be companies that are able to build or conceive of networks as data. Will also have an advantage. And I think I'd add a third one. Companies that demonstrate perennial observations, a real understanding of the unbelievable change that's required you can't just say, oh Facebook wants this therefore everybody's going to want it. There's going to be a lot of push marketing that goes on at the technology side. Alright so let's get to some Action Items. David Vellante, I'll start with you. Action Item. >> Well the future's going to be one where systems see, they talk, they sense, they recognize, they control, they optimize. It may be tempting to say, you know what I'm going to wait, I'm going to sit back and wait to figure out how I'm going to close that machine intelligence gap. I think that's a mistake. I think you have to start now, and you have to start with your data model. >> George Gilbert, Action Item. >> I think you have to keep in mind the guardrails related to governance, and trust, when you're building applications on the new data fabric. And you can take the approach of a platform-oriented one where you're plugging into an API, like Apache Atlas, that Hortonworks is driving, or a discovery-oriented one as David was talking about which would be something like Alation, using machine learning. But if, let's say the use case starts out as an IOT, edge analytics and cloud inferencing, that data science pipeline itself has to now be part of this fabric. Including the output of the design time. Meaning the models themselves, so they can be managed. >> Excellent. Jim Kobielus, you've been pretty quiet but I know you've got a lot to offer. Action Item, Jim. >> I'll be very brief. What you need to do is protect your data science knowledge base. That's the way to de-risk this entire process. And that involves more than just a data catalog. You need a data science expertise registry within your distributed value chain. And you need to manage that as a very human asset that needs to grow. That is your number one asset going forward. >> Ralph Finos, you've also been pretty quiet. Action Item, Ralph. >> Yeah, I think you've got to be careful about what you're trying to get done. Whether it's, it depends on your industry, whether it's finance or whether it's the entertainment business, there are different requirements about data in those different environments. And you need to be cautious about that and you need leadership on the executive business side of things. The last thing in the world you want to do is depend on data scientists to figure this stuff out. >> And I'll give you the second to last answer or Action Item. Neil Raden, Action Item. >> I think there's been a lot of progress lately in creating tools for data scientists to be more efficient and they need to be, because the big digital giants are draining them from other companies. So that's very encouraging. But in general I think becoming a data-driven, a digital transformation company for most companies, is a big job and I think they need to it in piece parts because if they try to do it all at once they're going to be in trouble. >> Alright, so that's great conversation guys. Oh, David Floyer, Action Item. David's looking at me saying, ah what about me? David Floyer, Action Item. >> (laughing) So my Action Item comes from an Irish proverb. Which if you ask for directions they will always answer you, "I wouldn't start from here." So the Action Item that I have is, if somebody is coming in saying you have to re-do all of your applications and re-write them from scratch, and start in a completely different direction, that is going to be a 20-year job and you're not going to ever get it done. So you have to start from what you have. The digital assets that you have, and you have to focus on improving those with additional applications, additional data using that as the foundation for how you build that business with a clear long-term view. And if you look at some of the examples that were given early, particularly in the insurance industries, that's what they did. >> Thank you very much guys. So, let's do an overall Action Item. We've been talking today about the challenges of de-risking digital business which ties directly to the overall understanding of the role of data assets play in businesses and the technology's ability to move from just protecting data, restoring data, to actually restoring the relationships in the data, the structures of the data and very importantly the models that are resident in the data. This is going to be a significant journey. There's clear evidence that this is driving a new valuation within the business. Folks talk about data as the new oil. We don't necessarily see things that way because data, quite frankly, is a very very different kind of asset. The cost could be shared because it doesn't suffer the same limits on scarcity. So as a consequence, what has to happen is, you have to start with where you are. What is your current value proposition? And what data do you have in support of that value proposition? And then whiteboard it, clean slate it and say, what data would we like to have in support of the activities that we perform? Figure out what those gaps are. Find ways to get access to that data through piecemeal, piece-part investments. That provide a roadmap of priorities looking forward. Out of that will come a better understanding of the fundamental data assets that are being created. New models of how you engage customers. New models of how operations works in the shop floor. New models of how financial services are being employed and utilized. And use that as a basis for then starting to put forward plans for bringing technologies in, that are capable of not just supporting the data and protecting the data but protecting the overall organization of data in the form of these models, in the form of these relationships, so that the business can, as it creates these, as it throws off these new assets, treat them as the special resource that the business requires. Once that is in place, we'll start seeing businesses more successfully reorganize, reinstitutionalize the work around data, and it won't just be the big technology companies who have, who people call digital native, that are well down this path. I want to thank George Gilbert, David Floyer here in the studio with me. David Vellante, Ralph Finos, Neil Raden and Jim Kobelius on the phone. Thanks very much guys. Great conversation. And that's been another Wikibon Action Item. (upbeat music)

Published Date : Mar 16 2018

SUMMARY :

I'm joined here in the studio has been that the difference and importantly in the context are going to have absolute consequential impacts and provide access to the data model, the ability of organizations to really implement this but is bigger than the role of technology. that is the right thing to do. Why is it that the most valuable companies in the world human expertise is at the center and the data lives in silos in other businesses, the buildings. the bottling plant et cetera, to data. Yeah we are, we are. an example from the news. and take the assets that you have. One of the primary reasons why is going to enable you to be able to build At the end of the day you look at your value proposition And then you need to put together a plan once again, talking about the Uberization of things, to erase my data, can you say with extreme confidence There's not a lot of companies on the planet and the only way you can be a trusted steward of that That's an example of another, one of the properties I mentioned the big five, but if you look at Spotify, and I'm going to come to you in a second And so really, what you need to do is, of protecting the full organization of those data assets. and use AI if you like to actually understand and it's not just the metadata, And the ability to continue to upgrade that and the technology companies also have to step up It's clear that you should keep data where it is. are going to have an advantage. So if it originates in the cloud, keep it in the cloud. Unless you need to combine it. That's the requirement that you have to have that goes on at the technology side. Well the future's going to be one where systems see, I think you have to keep in mind the guardrails but I know you've got a lot to offer. that needs to grow. Ralph Finos, you've also been pretty quiet. And you need to be cautious about that And I'll give you the second to last answer and they need to be, because the big digital giants David's looking at me saying, ah what about me? that is going to be a 20-year job and the technology's ability to move from just

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Wrap | Machine Learning Everywhere 2018


 

>> Narrator: Live from New York, it's theCUBE. Covering machine learning everywhere. Build your ladder to AI. Brought to you by IBM. >> Welcome back to IBM's Machine Learning Everywhere. Build your ladder to AI, along with Dave Vellante, John Walls here, wrapping up here in New York City. Just about done with the programming here in Midtown. Dave, let's just take a step back. We've heard a lot, seen a lot, talked to a lot of folks today. First off, tell me, AI. We've heard some optimistic outlooks, some, I wouldn't say pessimistic, but some folks saying, "Eh, hold off." Not as daunting as some might think. So just your take on the artificial intelligence conversation we've heard so far today. >> I think generally, John, that people don't realize what's coming. I think the industry, in general, our industry, technology industry, the consumers of technology, the businesses that are out there, they're steeped in the past, that's what they know. They know what they've done, they know the history and they're looking at that as past equals prologue. Everybody knows that's not the case, but I think it's hard for people to envision what's coming, and what the potential of AI is. Having said that, Jennifer Shin is a near-term pessimist on the potential for AI, and rightly so. There are a lot of implementation challenges. But as we said at the open, I'm very convinced that we are now entering a new era. The Hadoop big data industry is going to pale in comparison to what we're seeing. And we're already seeing very clear glimpses of it. The obvious things are Airbnb and Uber, and the disruptions that are going on with Netflix and over-the-top programming, and how Google has changed advertising, and how Amazon is changing and has changed retail. But what you can see, and again, the best examples are Apple getting into financial services, moving into healthcare, trying to solve that problem. Amazon buying a grocer. The rumor that I heard about Amazon potentially buying Nordstrom, which my wife said is a horrible idea. (John laughs) But think about the fact that they can do that is a function of, that they are a digital-first company. Are built around data, and they can take those data models and they can apply it to different places. Who would have thought, for example, that Alexa would be so successful? That Siri is not so great? >> Alexa's become our best friend. >> And it came out of the blue. And it seems like Google has a pretty competitive piece there, but I can almost guarantee that doing this with our thumbs is not the way in which we're going to communicate in the future. It's going to be some kind of natural language interface that's going to rely on artificial intelligence and machine learning and the like. And so, I think it's hard for people to envision what's coming, other than fast forward where machines take over the world and Stephen Hawking and Elon Musk say, "Hey, we should be concerned." Maybe they're right, not in the next 10 years. >> You mentioned Jennifer, we were talking about her and the influencer panel, and we've heard from others as well, it's a combination of human intelligence and artificial intelligence. That combination's more powerful than just artificial intelligence, and so, there is a human component to this. So, for those who might be on the edge of their seat a little bit, or looking at this from a slightly more concerning perspective, maybe not the case. Maybe not necessary, is what you're thinking. >> I guess at the end of the day, the question is, "Is the world going to be a better place with all this AI? "Are we going to be more prosperous, more productive, "healthier, safer on the roads?" I am an optimist, I come down on the side of yes. I would not want to go back to the days where I didn't have GPS. That's worth it to me. >> Can you imagine, right? If you did that now, you go back five years, just five years from where we are now, back to where we were. Waze was nowhere, right? >> All the downside of these things, I feel is offset by that. And I do think it's incumbent upon the industry to try to deal with the problem, especially with young people, the blue light problem. >> John: The addictive issue. >> That's right. But I feel like those downsides are manageable, and the upsides are of enough value that society is going to continue to move forward. And I do think that humans and machines are going to continue to coexist, at least in the near- to mid- reasonable long-term. But the question is, "What can machines "do that humans can't do?" And "What can humans do that machines can't do?" And the answer to that changes every year. It's like I said earlier, not too long ago, machines couldn't climb stairs. They can now, robots can climb stairs. Can they negotiate? Can they identify cats? Who would've imagined that all these cats on the Internet would've led to facial recognition technology. It's improving very, very rapidly. So, I guess my point is that that is changing very rapidly, and there's no question it's going to have an impact on society and an impact on jobs, and all those other negative things that people talk about. To me, the key is, how do we embrace that and turn it into an opportunity? And it's about education, it's about creativity, it's about having multi-talented disciplines that you can tap. So we talked about this earlier, not just being an expert in marketing, but being an expert in marketing with digital as an understanding in your toolbox. So it's that two-tool star that I think is going to emerge. And maybe it's more than two tools. So that's how I see it shaping up. And the last thing is disruption, we talked a lot about disruption. I don't think there's any industry that's safe. Colin was saying, "Well, certain industries "that are highly regulated-" In some respects, I can see those taking longer. But I see those as the most ripe for disruption. Financial services, healthcare. Can't we solve the HIPAA challenge? We can't get access to our own healthcare information. Well, things like artificial intelligence and blockchain, we were talking off-camera about blockchain, those things, I think, can help solve the challenge of, maybe I can carry around my health profile, my medical records. I don't have access to them, it's hard to get them. So can things like artificial intelligence improve our lives? I think there's no question about it. >> What about, on the other side of the coin, if you will, the misuse concerns? There are a lot of great applications. There are a lot of great services. As you pointed out, a lot of positive, a lot of upside here. But as opportunities become available and technology develops, that you run the risk of somebody crossing the line for nefarious means. And there's a lot more at stake now because there's a lot more of us out there, if you will. So, how do you balance that? >> There's no question that's going to happen. And it has to be managed. But even if you could stop it, I would say you shouldn't because the benefits are going to outweigh the risks. And again, the question we asked the panelists, "How far can we take machines? "How far can we go?" That's question number one, number two is, "How far should we go?" We're not even close to the "should we go" yet. We're still on the, "How far can we go?" Jennifer was pointing out, I can't get my password reset 'cause I got to call somebody. That problem will be solved. >> So, you're saying it's more of a practical consideration now than an ethical one, right now? >> Right now. Moreso, and there's certainly still ethical considerations, don't get me wrong, but I see light at the end of the privacy tunnel, I see artificial intelligence as, well, analytics is helping us solve credit card fraud and things of that nature. Autonomous vehicles are just fascinating, right? Both culturally, we talked about that, you know, we learned how to drive a stick shift. (both laugh) It's a funny story you told me. >> Not going to worry about that anymore, right? >> But it was an exciting time in our lives, so there's a cultural downside of that. I don't know what the highway death toll number is, but it's enormous. If cell phones caused that many deaths, we wouldn't be using them. So that's a problem that I think things like artificial intelligence and machine intelligence can solve. And then the other big thing that we talked about is, I see a huge gap between traditional companies and these born-in-the-cloud, born-data-oriented companies. We talked about the top five companies by market cap. Microsoft, Amazon, Facebook, Alphabet, which is Google, who am I missing? >> John: Apple. >> Apple, right. And those are pretty much very much data companies. Apple's got the data from the phones, Google, we know where they get their data, et cetera, et cetera. Traditional companies, however, their data resides in silos. Jennifer talked about this, Craig, as well as Colin. Data resides in silos, it's hard to get to. It's a very human-driven business and the data is bolted on. With the companies that we just talked about, it's a data-driven business, and the humans have expertise to exploit that data, which is very important. So there's a giant skills gap in existing companies. There's data silos. The other thing we touched on this is, where does innovation come from? Innovation drives value drives disruption. So the innovation comes from data. He or she who has the best data wins. It comes from artificial intelligence, and the ability to apply artificial intelligence and machine learning. And I think something that we take for granted a lot, but it's cloud economics. And it's more than just, and somebody, one of the folks mentioned this on the interview, it's more than just putting stuff in the cloud. It's certainly managed services, that's part of it. But it's also economies of scale. It's marginal economics that are essentially zero. It's speed, it's low latency. It's, and again, global scale. You combine those things, data, artificial intelligence, and cloud economics, that's where the innovation is going to come from. And if you think about what Uber's done, what Airbnb have done, where Waze came from, they were picking and choosing from the best digital services out there, and then developing their own software from this, what I say my colleague Dave Misheloff calls this matrix. And, just to repeat, that matrix is, the vertical matrix is industries. The horizontal matrix are technology platforms, cloud, data, mobile, social, security, et cetera. They're building companies on top of that matrix. So, it's how you leverage the matrix is going to determine your future. Whether or not you get disrupted, whether your the disruptor or the disruptee. It's not just about, we talked about this at the open. Cloud, SaaS, mobile, social, big data. They're kind of yesterday's news. It's now new artificial intelligence, machine intelligence, deep learning, machine learning, cognitive. We're still trying to figure out the parlance. You could feel the changes coming. I think this matrix idea is very powerful, and how that gets leveraged in organizations ultimately will determine the levels of disruption. But every single industry is at risk. Because every single industry is going digital, digital allows you to traverse industries. We've said it many times today. Amazon went from bookseller to content producer to grocer- >> John: To grocer now, right? >> To maybe high-end retailer. Content company, Apple with Apple Pay and companies getting into healthcare, trying to solve healthcare problems. The future of warfare, you live in the Beltway. The future of warfare and cybersecurity are just coming together. One of the biggest issues I think we face as a country is we have fake news, we're seeing the weaponization of social media, as James Scott said on theCUBE. So, all these things are coming together that I think are going to make the last 10 years look tame. >> Let's just switch over to the currency of AI, data. And we've talked to, Sam Lightstone today was talking about the database querying that they've developed with the Plex product. Some fascinating capabilities now that make it a lot richer, a lot more meaningful, a lot more relevant. And that seems to be, really, an integral step to making that stuff come alive and really making it applicable to improving your business. Because they've come up with some fantastic new ways to squeeze data that's relevant out, and get it out to the user. >> Well, if you think about what I was saying earlier about data as a foundational core and human expertise around it, versus what most companies are, is human expertise with data bolted on or data in silos. What was interesting about Queryplex, I think they called it, is it essentially virtualizes the data. Well, what does that mean? That means i can have data in place, but I can have access to that data, I can democratize that data, make it accessible to people so that they can become data-driven, data is the core. Now, what I don't know, and I don't know enough, just heard about it today, I missed that announcement, I think they announced it a year ago. He mentioned DB2, he mentioned Netezza. Most of the world is not on DB2 and Netezza even though IBM customers are. I think they can get to Hadoop data stores and other data stores, I just don't know how wide that goes, what the standards look like. He joked about the standards as, the great thing about standards is- >> There are a lot of 'em. (laughs) >> There's always another one you can pick if this one fails. And he's right about that. So, that was very interesting. And so, this is again, the question, can traditional companies close that machine learning, machine intelligence, AI gap? Close being, close the gap that the big five have created. And even the small guys, small guys like Uber and Airbnb, and so forth, but even those guys are getting disrupted. The Airbnbs and the Ubers, right? Again, blockchain comes in and you say, "Why do I need a trusted third party called Uber? "Why can't I do this on the blockchain?" I predict you're going to see even those guys get disrupted. And I'll say something else, it's hard to imagine that a Google or a Facebook can be unseated. But I feel like we may be entering an era where this is their peak. Could be wrong, I'm an Apple customer. I don't know, I'm not as enthralled as I used to be. They got trillions in the bank. But is it possible that opensource and blockchain and the citizen developer, the weekend and nighttime developers, can actually attack that engine of growth for the last 10 years, 20 years, and really break that monopoly? The Internet has basically become an oligopoly where five companies, six companies, whatever, 10 companies kind of control things. Is it possible that opensource software, AI, cryptography, all this activity could challenge the status quo? Being in this business as long as I have, things never stay the same. Leaders come, leaders go. >> I just want to say, never say never. You don't know. >> So, it brings it back to IBM, which is interesting to me. It was funny, I was asking Rob Thomas a question about disruption, and I think he misinterpreted it. I think he was thinking that I was saying, "Hey, you're going to get disrupted by all these little guys." IBM's been getting disrupted for years. They know how to reinvent. A lot of people criticize IBM, how many quarters they haven't had growth, blah, blah, blah, but IBM's made some big, big bets on the future. People criticizing Watson, but it's going to be really interesting to see how all this investment that IBM has made is going to pay off. They were early on. People in the Valley like to say, "Well, the Facebooks, and even Amazon, "Google, they got the best AI. "IBM is not there with them." But think about what IBM is trying to do versus what Google is doing. They're very consumer-oriented, solving consumer problems. Consumers have really led the consumerization of IT, that's true, but none of those guys are trying to solve cancer. So IBM is talking about some big, hairy, audacious goals. And I'm not as pessimistic as some others you've seen in the trade press, it's popular to do. So, bringing it back to IBM, I saw IBM as trying to disrupt itself. The challenge IBM has, is it's got a lot of legacy software products that have purchased over the years. And it's got to figure out how to get through those. So, things like Queryplex allow them to create abstraction layers. Things like Bluemix allow them to bring together their hundreds and hundreds and hundreds of SaaS applications. That takes time, but I do see IBM making some big investments to disrupt themselves. They've got a huge analytics business. We've been covering them for quite some time now. They're a leader, if not the leader, in that business. So, their challenge is, "Okay, how do we now "apply all these technologies to help "our customers create innovation?" What I like about the IBM story is they're not out saying, "We're going to go disrupt industries." Silicon Valley has a bifurcated disruption agenda. On the one hand, they're trying to, cloud, and SaaS, and mobile, and social, very disruptive technologies. On the other hand, is Silicon Valley going to disrupt financial services, healthcare, government, education? I think they have plans to do so. Are they going to be able to execute that dual disruption agenda? Or are the consumers of AI and the doers of AI going to be the ones who actually do the disrupting? We'll see, I mean, Uber's obviously disrupted taxis, Silicon Valley company. Is that too much to ask Silicon Valley to do? That's going to be interesting to see. So, my point is, IBM is not trying to disrupt its customers' businesses, and it can point to Amazon trying to do that. Rather, it's saying, "We're going to enable you." So it could be really interesting to see what happens. You're down in DC, Jeff Bezos spent a lot of time there at the Washington Post. >> We just want the headquarters, that's all we want. We just want the headquarters. >> Well, to the point, if you've got such a growing company monopoly, maybe you should set up an HQ2 in DC. >> Three of the 20, right, for a DC base? >> Yeah, he was saying the other day that, maybe we should think about enhancing, he didn't call it social security, but the government, essentially, helping people plan for retirement and the like. I heard that and said, "Whoa, is he basically "telling us he's going to put us all out of jobs?" (both laugh) So, that, if I'm a customer of Amazon's, I'm kind of scary. So, one of the things they should absolutely do is spin out AWS, I think that helps solve that problem. But, back to IBM, Ginni Rometty was very clear at the World of Watson conference, the inaugural one, that we are not out trying to compete with our customers. I would think that resonates to a lot of people. >> Well, to be continued, right? Next month, back with IBM again? Right, three days? >> Yeah, I think third week in March. Monday, Tuesday, Wednesday, theCUBE's going to be there. Next week we're in the Bahamas. This week, actually. >> Not as a group taking vacation. Actually a working expedition. >> No, it's that blockchain conference. Actually, it's this week, what am I saying next week? >> Although I'm happy to volunteer to grip on that shoot, by the way. >> Flying out tomorrow, it's happening fast. >> Well, enjoyed this, always good to spend time with you. And good to spend time with you as well. So, you've been watching theCUBE, machine learning everywhere. Build your ladder to AI. Brought to you by IBM. Have a good one. (techno music)

Published Date : Feb 27 2018

SUMMARY :

Brought to you by IBM. talked to a lot of folks today. and they can apply it to different places. And so, I think it's hard for people to envision and so, there is a human component to this. I guess at the end of the day, the question is, back to where we were. to try to deal with the problem, And the answer to that changes every year. What about, on the other side of the coin, because the benefits are going to outweigh the risks. of the privacy tunnel, I see artificial intelligence as, And then the other big thing that we talked about is, And I think something that we take that I think are going to make the last 10 years look tame. And that seems to be, really, an integral step I can democratize that data, make it accessible to people There are a lot of 'em. The Airbnbs and the Ubers, right? I just want to say, never say never. People in the Valley like to say, We just want the headquarters, that's all we want. Well, to the point, if you've got such But, back to IBM, Ginni Rometty was very clear Monday, Tuesday, Wednesday, theCUBE's going to be there. Actually a working expedition. No, it's that blockchain conference. to grip on that shoot, by the way. And good to spend time with you as well.

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Kickoff John Walls and Dave Vellante | Machine Learning Everywhere 2018


 

>> Announcer: Live from New York, it's theCUBE! Covering Machine Learning Everywhere: Build Your Ladder To AI. Brought to you by IBM. >> Well, good morning! Welcome here on theCUBE. Along with Dave Vellante, I'm John Walls. We're in Midtown New York for IBM's Machine Learning Everywhere: Build Your Ladder To AI. Great lineup of guests we have for you today, looking forward to bringing them to you, including world champion chess master Garry Kasparov a little bit later on. It's going to be fascinating. Dave, glad you're here. Dave, good to see you, sir. >> John, always a pleasure. >> How you been? >> Up from DC, you know, I was in your area last week doing some stuff with John Furrier, but I've been great. >> Stopped by the White House, drop in? >> You know, I didn't this time. No? >> No. >> Dave: My son, as you know, goes to school down there, so when I go by my hotel, I always walk by the White House, I wave. >> Just in case, right? >> No reciprocity. >> Same deal, we're in the same boat. Let's talk about what we have coming up here today. We're talking about this digital transformation that's going on within multiple industries. But you have an interesting take on it that it's a different wave, and it's a bigger wave, and it's an exciting wave right now, that digital is creating. >> Look at me, I've been around for a long time. I think we're entering a new era. You know, the great thing about theCUBE is you go to all these events, you hear the innovations, and we started theCUBE in 2010. The Big Data theme was just coming in, and it appeared, everybody was very excited. Still excited, obviously, about the data-driven concept. But we're now entering a new era. It's like every 10 years, the parlance in our industry changes. It was cloud, Big Data, SaaS, mobile, social. It just feels like, okay, we're here. We're doing that now. That's sort of a daily ritual. We used to talk about how it's early innings. It's not anymore. It's the late innings for those. I think the industry is changing. The describers of what we're entering are autonomous, pervasive, self-healing, intelligent. When you infuse artificial intelligence, I'm not crazy about that name, but when you infuse that throughout the landscape, things start to change. Data is at the center of it, but I think, John, we're going to see the parlance change. IBM, for example, uses cognitive. People use artificial intelligence. I like machine intelligence. We're trying to still figure out the names. To me, it's an indicator that things are changing. It's early innings now. What we're seeing is a whole new set of opportunities emerging, and if you think about it, it's based on this notion of digital services, where data is at the center. That's something that I want to poke at with the folks at IBM and our guests today. How are people going to build new companies? You're certainly seeing it with the likes of Uber, Airbnb, Waze. It's built on these existing cloud and security, off-the-shelf, if you will, horizontal technologies. How are new companies going to be built, what industries are going to be disruptive? Hint, every industry. But really, the key is, how will existing companies keep pace? That's what I really want to understand. >> You said, every industry's going to be disrupted, which is certainly, I think, an exciting prospect in some respects, but a little scary to some, too, right? Because they think, "No, we're fat and happy "and things are going well right now in our space, "and we know our space better than anybody." Some of those leaders might be thinking that. But as you point out, digital technology has transformed to the extent now that there's nobody safe, because you just slap this application in, you put this technology in, and I'm going to change your business overnight. >> That's right. Digital means data, data is at the center of this transformation. A colleague of mine, David Moschella, has come up with this concept of the matrix, and what the matrix is is a set of horizontal technology services. Think about cloud, or SaaS, or security, or mobile, social, all the way up the stack through data services. But when you look at the companies like Airbnb and Uber and, certainly, what Google is doing, and Facebook, and others, they're building services on top of this matrix. The matrix is comprised of vertical slices by industry and horizontal slices of technology. Disruptors are cobbling together through software and data new sets of services that are disrupting industries. The key to this, John, in my view, anyway, is that, historically, within healthcare or financial services, or insurance, or manufacturing, or education, those were very siloed. But digital and data allows companies and disruptors to traverse silos like never before. Think about it. Amazon buying Whole Foods. Apple getting into healthcare and financial services. You're seeing these big giants disrupt all of these different industries, and even smaller guys, there's certainly room for startups. But it's all around the data and the digital transformation. >> You spoke about traditional companies needing to convert, right? Needing to get caught up, perhaps, or to catch up with what's going on in that space. What do you do with your workforce in that case? You've got a bunch of great, hardworking people, embedded legacy. You feel good about where you are. And now you're coming to that workforce and saying, "Here's a new hat." >> I think that's a great question. I think the concern that one would have for traditional companies is, data is not foundational for most companies. It's not at their core. The vast majority of companies, the core are the people. You hear it all the time. "The people are our greatest asset." That, I hate to say it, but it's somewhat changing. If you look at the top five companies by market cap, their greatest asset is their data, and the people are surrounding that data. They're very, very important because they know how to leverage that data. But if you look at most traditional companies, people are at their core. Data is kind of, "Oh, we got this bolt-on," or it's in a bunch of different silos. The big question is, how do they close that gap? You're absolutely right. The key is skillsets, and the skills have to be, you know, we talk about five-tool baseball players. You're a baseball fan, as am I. Well, you need multi-tool players, those that understand not only the domain of whether it's marketing or sales or operational expertise or finance, but they also require digital expertise. They know, for example, if you're a marketing professional, they know how to do hypertargeting. They know how to leverage social. They know how to do SEO, all these digital skills, and they know how to get information that's relevant and messaging out into the marketplace and permeate that. And so, we're entering, again, this whole new world that's highly scalable, highly intelligent, pervasive, autonomous. We're going to talk about that today with a lot of their guests, with a lot of our guests, that really are kind of futurists and have thought through, I think, the changes that are coming. >> You can't have a DH anymore, right, that's what you're saying? You need a guy that can play the field. >> Not only play the field, not only a utility player, but somebody who's a utility player, but great. Best of breed at all these different skillsets. >> Machine learning, we haven't talked much about that, and another term, right, that certainly has different definitions, but certainly real specific applications to what's going on today. We'll talk a lot about ML today. Your thoughts about that, and how that squares into the artificial intelligence picture, and what we're doing with all those machines out there that are churning 24/7. >> Yeah, so, real quick, I know we're tight on time here. Artificial intelligence to me is the umbrella. Machine learning is the application of math and algorithms to solve a particular problem or answer a particular question. And then there's deep learning, which is highly focused neural networks that go deeper and deeper and deeper, and become auto-didactic, self-learning, in a manner. Those are just the very quick and rudimentary description. Machine learning to me is the starting point, and that's really where organizations really want to start to learn and begin to close the gap. >> A lot of ground to cover, and we're going to do that for you right here on theCUBE as we continue our coverage of Machine Learning Everywhere: Your Ladder To AI, coming up here, IBM hosting us in Midtown, New York. Back with more here on theCUBE in just a bit. (fast electronic music)

Published Date : Feb 27 2018

SUMMARY :

Brought to you by IBM. Great lineup of guests we have for you today, Up from DC, you know, I was in your area last week You know, I didn't this time. I always walk by the White House, I wave. But you have an interesting take on it that and if you think about it, and I'm going to change your business overnight. But when you look at the companies like Airbnb or to catch up with what's going on in that space. and the skills have to be, You need a guy that can play the field. Not only play the field, and what we're doing with all those machines out there of math and algorithms to solve a particular problem and we're going to do that for you right here on theCUBE

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Jonathan Ebinger, BRV | CUBE Conversations Jan 2018


 

(orchestral music) >> Hello everyone. Welcome to the special CUBE conversation here in theCUBE's Palo Alto studio. I'm John Furrier. Where conversation around venture capital, entrepreneurship, crypto currencies, block chain, and more, Jonathan Ebinger our friend with BRV, formerly Blue Run Ventures, but BRV for short, sounds better, welcome to theCUBE. >> Thanks John, looking forward to it. >> Great to see you, we've known each other for a long time and you've been a great investor, your firm has done a lot of great stuff, deals are really famous deals, but also you dig into the companies and you really stand by your portfolio companies, but you've also done a lot of work in China. >> Yes. >> So you have a good landscape of what's going on. What's the, what's going on in China? >> Well China is really expanding in ways which we had not foreseen when we first started investing there almost 15 years ago. We were really active for five to 10 years, investing in companies that initially were considered copycat companies, you can't really use that term anymore. In fact what's happening more and more, you're seeing Chinese ideas coming to the United States. Businesses like We Chat are being copied as fast as they can, you're seeing Snapchat, Messenger and so forth, they're quickly trying to amalgamate as many assets as they can within their viewership much like we're seeing in a lot of the other Chinese analogs over there. It's exciting to see, it's very much an arms race. >> It's been interesting to watch. We were at the Ali Baba Cloud Conference last year, at the end of last year, it's interesting the innovation and entrepreneurial thirst has really changed. If you go back just 10 years ago when you guys were first getting in there, I remember the conversations were what's going on in China, it's very developmental but what's going on 10 years ago, they are dominating the mobile space, they're mobile usage is really much different makeup in how they do startups, the apps. How much of that has influenced some of their success just the demand? >> Always on, location always available, it opens up a whole new level of communication services. The idea of the larger screen format, people used to think in the United States, these large devices coming out of Korea first and then China, we thought these would never play in the United States, now Apple 10, larger screen size, it makes sense, it's mobile first right from the get go for a now billion plus users. >> So BRV, how many active portfolio companies do you guys have and what's the profile that you're looking for for entrepreneurs, what are some of the kind of companies? >> We're about 45 active companies right now. We're putting about, we're putting money in about 10 new companies a year at this point. We have a very disciplined approach of investing in Series A style companies, Series A of course means a lot of different things to people, but generally, we like to put $3 to $5 million to work early on and then follow on. >> How much do take for that, just a third? >> Typical in the 20%-25% range. There's a lot of companies out there that still fit that profile. Of course you're seeing some super sized Series A's that happen, we don't play in those but for the traditional software companies, evaluations are really right in our sweet spot. >> How big is the fund now, just what's the number in terms of capital? >> We're in fund six, we're just over $150 million. >> And you got to save some for follow on rounds. >> Exactly. >> Talk about the changes in venture capital because what's interesting, I had a conversation with Greg Sands with Costanoa Ventures, another great investor, formerly I think the first employee of Netscape I think or the business plan. Great guy, he talked about the dynamics of, you don't need that much cash anymore because if you can get unit economic visibility into what the business is working, you can do so much more with that and I'm calling it the hourglass effect, you get through that visibility, you're in control, you own your own destiny, versus the old Silicon Valley model which seems to be fading away, which is hey, what do you need? $40 million, or here's $100 million. That really limits your exit options and sometimes you can drown in your own capital. Talk about that dynamic. >> You're seeing the $40 million rounds with businesses that are much more capital intensive and that's coming back in vogue now but for the most part, I agree with what Greg's saying and this whole advent of seed funds and super seed funds and angel funds and so forth has been really great for the traditional series A investor. A lot of that early fundamental and foundational work is being done and then when the series A comes, it's more about expansion so we're effectively getting what was a Series B type stage company now we're investing in Series A. We're saying hey, this product works, there's product market fit, let's put dollars to work to really grow the market. >> So you're saying Series B was a kind of prove the business model, shifted down to the A because the cost to get there is lower and hence that's opened up a seed round lower in numbers, so it just shifts down a little bit. >> It really has, it really has and that plays into our sweet spot. We really like working on business models, distribution strategies, things like that. >> And what kind of startups do you want to invest in? What are some of the categories? >> Love financial services, we like health tech, we're doing education, we're really pretty omnivorous when it comes to the sector. What we're looking for is really businesses that are using data, real time data to disrupt the numbers. >> So you're not sector driven, you're disruption oriented. >> That's right. >> Okay let's talk about disruption, my favorite trend. Obviously I love the China dynamic because you're not sure what it is, but it's really doing well so you can't ignore it and they're innovative and they're hustling hard and they've got massive numbers. Block chain, we're super excited about, we love crypto, we think it's the biggest wave coming out there, so a lot of my smart, entrepreneurial friends are jumping on their surfboards literally and jumping out into those waves and there's a lot of action there. At the same time, people are saying, stay away from that crypto thing, it's a scam. Kind of a different perspective, what's your thoughts on that? >> If you look at, you separate the cryptocurrencies from block chain, I think it becomes a lot more clear. Block chain is for real. Tracking provenance on transactions, real estate transactions, multinational transactions, makes a lot of sense, dovetails nicely with security, so there's a real business there. You saw the announcement with IBM and Mersk the other day, what they are taking enterprise level block chain into their whole supply chain. I think that's really important. We have a company in the category called pay stand which is doing the same sort of thing with smaller size businesses, just accelerating the whole process on accounts receivable, taking working capital. >> And they're doing block chain for that? >> Yes block chain is an option, we're not forcing people onto block chain, but the idea of hey, let's give people more cost effective ways to transact, get rid of the paper checks, get rid of the invoicing and just join the modern world, much like you use Venmo if you and I are going to exchange money. >> That's pay stand, that's one of your hot companies. >> Yeah it is, absolutely. >> So are they using block chain or not? >> They are, yes. >> Okay, because it's a physical asset, it's kind of a supply chain thing? >> They use it to track the funds themselves, unlike a credit card where you have to pay a big fee or ACH which you can't really get proof of funds, with their block chain technology, you can be sure that you have the funds available and you get it instantly. >> Let's talk about use cases that you think out there, I'd like you to just weigh in on use cases for block chain that a mainstream person that's not in the tech business would understand, because they say, is it real or not? I agree block chain is legit, what are some use cases that would highlight that? >> I think if you've ever been involved in real estate, bought a home, things like that, just tracking title insurance, you're going all the way back if you live in California, you're going all the way back to pre-statehood days, you have to track the provenance of that land all the way through. You're paying title insurance, title insurance is a business you don't really need if you have accurate provenance tracking through block chain. I think that's one most of us can understand. Obviously bills of weighting with things coming over on ships. That's natural and right now things get held up in port because people are trying to find a clipboard before you can sign off on who, is this bill of weighting actually clean, that stuff can be done automatically with 2D barcodes, block chain usage. >> Certainly with perishable goods too, we learned that with IBM's example. >> Sure. >> Okay let's get into the hot companies you got going on. Name some of the hot investments that you've done. >> Sure, well I talked about pay stand a minute ago, really excited about them, another one we really like is a company called aerobotics. I know you're a fan of autonomous flying. If you think about drones and everyone knows DJI and they're a great company, that's one to one, one person flying one drone, that's not scalable obviously, it scales at one to one. With autonomous flying, you can have a whole army of drones out doing your business, whether they're doing site exploration, checking for chemical spills, looking at traffic and so forth. The company is now operating in three continents, it's just, if you think about what a drone is, effectively it's a flying cell phone. It's a cell phone that goes around, takes pictures, transmits data back, we know something about cell phones at BRV, we've been investing in this category for a long time so when we say aerobotics come along, we said this is just a natural extension of real time data, cellular technology, and location based services. >> You guys don't get a lot of credit as much as you should, in my opinion on that, you guys were very early on the mobile, mobile connectivity side and mobile footprint and device and software. That's playing well into the hottest trend that we see, that's not the sexiest trend, that's IOT. >> Absolutely. >> Because drones are certainly, industrial IOT is a big one. Instrumenting physical plants, equipment, and IOT in general the edge of the network. What's your thoughts on IOT and how would you, how do you see that evolving? It's more than just the edge of the network issue, it's bigger. >> It is, well of course the devices and sensors are important. I think a lot of that's been commoditized. The business that we've been seeing develop and there's a lot of folks, they've moved from analytics of the web to analytics of IOT, so there's a lot of interesting companies coming in the analytic space. We're not playing in that as much, we tend to like to invest in companies that are big enough that you need to have analytics for them. We like companies that have proprietary control of analytics versus necessarily running analytics for company X. >> So you're not poopooing IOT per se, just that from an investment thesis standpoint, it's not on your radar yet. >> That's right, they're either too capital intensive for us as a firm or you're basically managing someone else's data. I want to be in companies that we're managing our own data for a proprietary advantage. >> That's really what I was going to get to next, the role of data driven, so we've lived in dupe world, theCUBE started in 2010 in the offices of Cloud Air actually and people don't know the history and it's been interesting, Hadoop was supposed to save the world, the data, but it really started the data trend, the data driven trend, Mike Olsen, Amar Omadala and the team over there really nailed it but it didn't turn into be just Hadoop, it's everything so we're seeing that now become a bumper sticker, data driven marketer, I'm a data driven executive, I'm a data driven interviewer, all that stuff, what does it actually mean? What does data driven mean to you? >> Data is, there's big data and then there's actionable data obviously people talk about exhaust, the data coming off, we really got started with, as you know, we were investors in Waze, awful lot of data coming out of your cell phone, extracting just the important pieces of it are really what's important. We're investors in a company called Cabbage which looks at every transaction a small business makes to determine their credit worthiness. It's really the science. People talk about data scientists, what do they actually do? What they're actually doing is separating out the wheat from the chaff because it's just a crush of data. I saw your interview with Andy Jazzy to other day from AWS, the amount of data that's being stored, it's almost unfathomable but the important people. >> They have a lot of data. You'd like to invest in them now. >> Exactly, but that's really the thing, it's being able to separate the good data from the bad. >> You look at Amazon, I was talking to Jesse and he didn't really go there because he was kind of on message but when I talked with Swami who runs the AI group over there, we were talking about, I said to him straight up, I'm like, you're running a lot of workloads on your cloud, I'm sure you have data on those workloads. Just the impact of what they could do with that data. This is the virtuous cycle that their business model is made up of, but it's changing the game for what they can become. The thing that we're seeing in the data world is, sometimes the outcome might not be what you think because if you can use the data effectively, it's a competitive advantage, not a department. >> Right and you have to really stay true to your commitment to data. What we've seen happen is when companies, if you've been around for 10 years or so, you start to trust your gut, that's important, but it can also not lead you to see obvious conclusions because the world changes. >> And also committing to data also means from a practitioner's standpoint, investing in the tech, investing in things to be data driven, not just to say it. >> Exactly. >> Okay so what's the future for you guys? What are you looking at next year, what are some of the things you'd like to accomplish for investment opportunities, besides getting all the hot deals, you did Waze, that was an amazing deal, one of my favorite products, how did that go down? How many people passed on Waze? >> I don't know how many people passed, but we were lucky, they wanted to bring us in to the initial syndicate, they wanted to have some folks who understood. >> But it wasn't that obvious though at the beginning. What was the original pitch? >> The initial pitch was that they were going to have folks have the dash devices, the product would sit on your dashboard and they were going to be using it to map Eastern Europe because Eastern Europe was just coming into the Western world and they didn't really have good roads and good maps. We thought, that's interesting but they probably also don't have smartphones, so why don't we come across the Atlantic and let's make this thing work in the US and then from there, the rest took off country by country we were the number one navigation app in I think 150 countries at one point. >> What's the biggest thing that you've learned over the past few years in the industry that's different now I mean obviously there's some context that I'll share which is obviously the big cloud players are becoming bigger, scale's a big thing, you got Google, you got Microsoft and Amazon, you've got Facebook's out there as well. Then you get the political climate. You go to Washington D.C. and New York, Silicon Valley is not really talked highly about these days on the hill in Washington, yet GovCloud is completely changing the game of how the government is going to work with massive innovations and efficiencies, literally overnight, it's almost weird. >> It is and it isn't. If you look at it through a longer term horizon, Silicon Valley is again at the forefront, we're really the first ones with more transparency in the industry, all the different movements which are really important and all the conversations that are happening are important and they're happening here first. I think you're starting to see a ripple effect, you're seeing it going through entertainment, you're going to see it in the government, industry after industry I think is going to start to have to be more open as Silicon Valley has led the way on that. >> That's a great point. Take a minute to describe the folks out there watching that aren't from here, what is Silicon Valley about in your opinion? >> Silicon Valley is, of course it's more than a mindset, but folks who are here are here on purpose. They come here intentionally. There are very few people that I know who were born and raised here, so they're coming here because they want to be part of a shared ethos around success, around success, around shared values and competition so it's a very healthy environment, I came, I used to live in Washington D.C. and I couldn't be happier to be 3000 miles away. >> If you're a technology entrepreneur, this is where all the sports and action is, as I always say, we always love sports analogies. Okay, I got to ask you about the VC situation around ICOs, initial coin offerings are being talked about as an alternative to fundraising, there's some security options on token sales as a utility, the SEC has started to put some guidelines down on what that looks like, but the general sentiment is, it's a new way to raise money and some people are doing private rounds with venture capital and doing token sales through ICOs. You see some hybrids, but for the most part, the hard core I don't want to say right or left wing, is there a wing of the political spectrum, but the hard core ICO guys are like, this is all about disrupting the VC community and you're a VC, so you got to take that a little bit personal but the point is, what do you think about that? Is that talked about? >> I think that's good salesmanship. The VC industry such as it is, you can fit every VC into one section of Stanford stadium. There just aren't that many VCs to really go after. We're a small group of folks. I think that going after maybe disrupting the way folks are raising money through Kickstarter and things like that, that's all great. We're not going to stop it, we're going to embrace it. I think that there's plenty of different ways to raise capital, I have no compunction about those things. >> Do you think it's more of a democratization trend or a new asset class, so you don't see it disrupting the VCs per se, but if it's only a handful of VCs that could fit into Stanford Stadium, for instance, then certainly there's more options, it's a dilution. >> I think you look at it as it's just an alternative financing method, do I take debt, do I take equity, do I take venture, do I take friends and family? It's just one more arrow in the quiver of the entrepreneur, I think you have to be smart about it because thinking that you're going to get the same level of attention from an investor in your ICO that you are going to get from a series A investor who owns 20% of your company, those are two very different value propositions. >> So you see a lot of pitches and sometimes, you have to say no a lot and that's the way the game is, but a lot of times, you want the best deals. But the founders' side of the table, they're looking at the VC, I need money. So that's one of the options, what they really want is a value added partner, so what's your current take on what that means these days? Sometimes it means a firm, sometimes it means a partner, sometimes it means the community. How are you guys looking at BRV as value add versus the worst case scenario which is value subtract, you just want to have that be positive. >> I see that written about venture too. >> I know, some people experienced it. >> I think it helps that we've been around now for almost 20 years, we got started in '98 so you have to look at our body of work and the continuum of investments and founders and CEOs and CTOs that we've invested in. There's hundreds and hundreds of people who have taken money from BRV, and so that's one of the real positives about this current state we're in is that there's so much transparency. The fact that we are, I like to think we're good actors and have been for a long time, that comes out, now through our words but through the words of. >> What would they say about you guys? What would your entrepreneurs say about BRV? >> Aside from using buzzwords like value add, they say, they know their industry, they're not afraid to ask for help, they try to call problems when they see it, things like that. >> You stand by your companies. >> Absolutely. >> Awesome, well what's your favorite trend that you're personally interested in? >> I think you have to go after health care right now. It is just such a big market right now. People have been nibbling all different sides of it right now, there's been folks who are trying to expedite processing, there's actual innovations happening on the medical side, I think there is just, technology is just now starting to get into that, technology has gotten into education. >> How about the startup you guys funded that's related to the health care field. >> Yes, we're in a company called Hello Heart which is really at the confluence of a number of trends. It starts off, what Hello Heart is, it's a personal blood pressure cuff for you as an employee of a big company, more and more companies are starting to self insure. If you're a big enough company, 10,000 plus employees or even fewer, you're going to want to self insure to save money but also, your employees get very much more comfortable with you as an employer, you care about my well being, so it's a very virtuous cycle for the employees. >> So companies themselves insuring their own employees. >> Absolutely. >> They have to be super big, this company. >> This is just one component of a self insured business. You also, of course you still have access to doctors and stuff, I'm not making the pitch for being self insured as a company, I'm just saying that. >> But that's a trend. >> It's absolutely a trend and you're seeing a lot of what I would call point solutions stepping in, whether it's psychiatric, whether it's opioid help, whether it's working on heart conditions, these are all different point solutions which are being amalgamated together to help companies which are self insuring. >> So is Hello Heart for consumers or for business? >> It's sold to businesses but individual employees have it so they can keep track of their blood pressure. >> But I can't buy one if I wanted one? >> Not today, but I'll make sure I can get one to you. >> I need one, get all of our employees instrumented. >> Exactly. >> Drug tested all that stuff going on. People worry about the privacy, that's something I would be concerned with, putting. >> That's taken a really fast pendulum swing. A few years ago, Generation X was privacy, there is no privacy, the default was, location is always on, that's just flipped 180 degrees in the last few years. >> Well Jonathan, thanks for coming into this CUBE conversation, I want to ask you one final question, one thing we're passionate about is women in tech and underserved minorities, obviously Silicon Valley has to do a better job, it's out on the table, and it's working but we're still seeing a lot more work to be done, we're seeing titles not being at the right level, but pay's getting there in some places but titles aren't, some paying still below for women, still a lot more to do, what are you guys doing for the women in tech trend, how are you guys looking at that? Certainly it's a sensitive topic these days, but more importantly, it's one that's super important to society. >> It is, I think like a lot of things that have long term value, it's really about your actions versus your words, so our firm has two out of the five investment professionals are female, one of the last three CEO's we've founded is a female CEO, we have technologists, we have marketing people, we have CEO's that are females it's very much of a cross the board, sex, race and so forth. >> You guys are indiscriminate, a good deal's a good deal. >> Exactly right. >> It's about making money, VC's are in the business of making money, a lot of people don't understand, you guys have a job to do but you do a good job. >> We're in the business of making money but our investors for the most part are not for profits. Large universities, our biggest investor is the Red Cross, so when we do well, the Red Cross does well and the country does well. >> You're mission driven at this point. >> Exactly. >> Is that by design or is that just, your selection? >> We're delighted with our LP's, it's important that we have synergies aside from just finances with our investors. >> That's super well, I appreciate you coming on, I think it's super great that you're tying society benefits into money making and entrepreneurship, great stuff Jonathan Ebinger here on theCUBE, BRV check them out, great VC firm here in Silicon Valley. It's a CUBE conversation, we're talking about startups and entrepreneurship I'm John Furrier, thanks for watching. (dramatic music)

Published Date : Jan 18 2018

SUMMARY :

and more, Jonathan Ebinger our friend with BRV, and you really stand by your portfolio companies, So you have a good landscape of what's going on. in a lot of the other Chinese analogs over there. at the end of last year, it's interesting the innovation The idea of the larger screen format, a lot of different things to people, but generally, but for the traditional software companies, and sometimes you can drown in your own capital. for the traditional series A investor. prove the business model, shifted down to the A and that plays into our sweet spot. that are using data, real time data to disrupt the numbers. but it's really doing well so you can't ignore it We have a company in the category called pay stand people onto block chain, but the idea of hey, that you have the funds available and you get it instantly. of that land all the way through. we learned that with IBM's example. Okay let's get into the hot companies you got going on. and they're a great company, that's one to one, You guys don't get a lot of credit as much as you should, and IOT in general the edge of the network. that you need to have analytics for them. it's not on your radar yet. I want to be in companies that we're managing It's really the science. They have a lot of data. Exactly, but that's really the thing, sometimes the outcome might not be what you think Right and you have to really from a practitioner's standpoint, investing in the tech, to the initial syndicate, they wanted to have What was the original pitch? the product would sit on your dashboard changing the game of how the government is going to work in the industry, all the different movements which Take a minute to describe the folks and I couldn't be happier to be 3000 miles away. but the point is, what do you think about that? There just aren't that many VCs to really go after. or a new asset class, so you don't see it disrupting of the entrepreneur, I think you have to be smart about it So that's one of the options, what they really want and so that's one of the real positives they're not afraid to ask for help, they try I think you have to go after health care right now. How about the startup you guys funded more comfortable with you as an employer, You also, of course you still have access to doctors to help companies which are self insuring. It's sold to businesses but individual employees Drug tested all that stuff going on. that's just flipped 180 degrees in the last few years. still a lot more to do, what are you guys doing for the one of the last three CEO's we've founded you guys have a job to do but you do a good job. and the country does well. it's important that we have synergies That's super well, I appreciate you coming on,

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Kickoff | IBM CDO Strategy Summit 2017


 

>> Live from Boston, Massachusetts, it's the CUBE, covering IBM Chief Data Officer Summit, brought to you by IBM. (soft electronic music) >> Welcome to theCUBE's coverage of IBM Chief Data Strategy Officer Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, co-hosting here today with Dave Vellante. >> Hey, Rebecca. >> Great to be working with you again. >> Good to see you again. It's been a while. >> It has. >> Last summer, in the heat of New York. >> That's right, and now here we are in the dreariness of Boston. Dave, we just finished up the keynote. As you said, it's a meaty keynote. It's a seminal time for Chief Data Officers at companies. What did you hear? What most interested you about what Joe Kavanaugh said? >> Well, a couple things. I think it's worthwhile going back a few years. The ascendancy of the Chief Data Officer as a role and a title kind of emerged from the back-office records management side of the house. It really started in regulated industries. Financial services, healthcare, and government. For obvious reasons. These are data-oriented companies. They're highly regulated. There's a lot of risk. So, there's really sort of a risk-first approach. Then, that sort of coincided with the big data meme exploding. Then, this whole discussion of is data an asset or a liability? Increasingly, organizations are looking at it, as we know, as an asset. So, the Chief Data Officer has emerged as the individual who is responsible for the data architecture of the company, trying to figure out how to monetize data. Not necessarily monetize explicitly the data, but how data contributes to the monetization of the organization. That has a lot of ripple effects, Rebecca, in terms of technology implications, skillsets, obviously security, relationships with line of business, and fundamentally the organization and the mission of the company. So, IBM has been pretty leading and aggressive about going after the Chief Data Officer role, and has events like this, the Chief Data Officer Summit. They do them, kind of signature moments, and these little its and bit events. I don't know how many people you think are here. >> 150, I think. >> 150? Okay. And they're the data-rowdy of the Boston community. They're chartered with figuring out what the data strategy is. How to value data and how to put data front and center. Everybody talks about being a data-driven organization, but most organizations-- Everybody talks about becoming a digital business, but a digital business means that you are data driven. The data is first. You understand how to monetize data. You know how to value data. Your decisions are data-driven. I would say that less than 10% of the organizations that we work with are of that ilk. So, it's early days still. What was interesting about what Jim Kavanaugh says, they put forth this cognitive blueprint that Inderpal Bhandari, who'll be on theCUBE later, envisioned and has brought to life in his two years as the Chief Data Officer here at IBM. Now, what I like about what IBM is doing is they're sharing their dog food experience with their clients. He talked about that enterprise blueprint architecture but he also talked about what IBM is doing to transform. So, James Kavanaugh is the Senior Vice President of Transformation at IBM, and works directly for Jenny Remetti. He fundamentally talked about IBM as an organization that is data-first, cloud, and consumerization was the other big trend. Now, I don't know if IBM's hit on all three of those yet but they're certainly working to get there. The other thing that was interesting is they talked about the data warehouse as the former king, and now process is king. What I think is interesting about that, I want to explore this with those guys, is that technology largely is well known today. People have access to technology. You can get security from-- You can log in with Twitter linked in our Facebook. You can-- Look at Uber and Waze. They're really software companies but they're built on other platforms, like the cloud, for example. These horizontal platforms. It's the processes that are new and unknown. You know, when you look at these emerging companies like Air BnB and Uber and Waze, and so forth, the processes by which consumers interact with businesses are totally changed. >> Exactly. That is what Jim and James and Inderpal were saying is that this explosion in data is really forcing companies to rethink their business models. And it's-- Their reporting structures, how they innovate, the kinds of things that they're working on, the kinds of risks that are keeping them up at night. >> Yeah, Kavanaugh cited a study for 4,000 CXOs and they said the number one factor impacting business sustainability in the next five years are technology-related. Which again, I want to poke at that a little bit, because to me technology is not the problem. It's process and skill sets and people are the really big challenges. But, I think really what I interpret from that data, what the CXOs are saying, the challenge is applying technology to create a business capability that involves all the process changes, the organizational changes, the people and skills set issues. Of course, they threw in a little fear, uncertainty, and doubt with GDPR, the recent breaches. The other big thing that you hear from IBM at these events is that IBM is a steward of your data. That it's your data, we're not going to-- They have this notion of data responsibility. He didn't mention-- He said the unnamed west coast companies. Of course, he's talking about Google and Amazon, who are sucking in our data and then advertising to us and telling us, hey there's a special and what to buy and what movie to watch, and so forth. That's not IBM's business. But, there's a nuance there that again, I want to explore with these guys if we have time is, while IBM is not taking your data and then turning it into business through advertising, IBM is training models. I'm interested in hearing IBM's response about where's the dividing line between the model-- sorry, the data, and the model. If the data is informing the model, the model then becomes IP. What happens to that IP? Does it get shared across the client base within an industry? So, I really want to understand that better. >> Right, and that is one thing that Jim Kavanaugh will talk about, definitely, is the responsibility that IBM has in terms of our data and protecting it and keeping it private. >> Yeah, so what I like about these events is they're intimate. We get into it with the CDOs. We got CDOs at banks, we have the influencer panel coming on, a lot are data practitioners. And, so much has changed over the last three or four years that we're happy to be here with the CUBE. >> It is. It's going to be a great day. So, we will have much more here at the IBM Chief Data Officer Strategy Summit. I'm Rebecca Knight for Dave Vallante. Stay tuned. (soft electronic music)

Published Date : Oct 26 2017

SUMMARY :

it's the CUBE, Welcome to theCUBE's coverage with you again. Good to see you again. in the dreariness of Boston. The ascendancy of the Chief Data Officer of the Boston community. the kinds of risks that are is not the problem. is the responsibility the last three or four years It's going to be a great day.

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Keegan Riley, HPE | VMworld 2017


 

>> Announcer: Live from Las Vegas it's theCUBE covering VMWorld 2017. Brought to you by VMware and its ecosystem partners. >> Okay, welcome back everyone. Live CUBE coverage here at VMWorld 2017. Three days, we're on our third day of VMWorld, always a great tradition, our eighth year. I'm John Furrier with theCUBE co-hosted by Dave Vellante of Wikibon and our next guest is Keegan Riley, vice president and general manager of North American storage at HP Enterprise. Welcome to theCUBE. >> Thank you, thanks for having me. >> Thanks for coming on, love the pin, as always wearin' that with flair. Love the logo, always comment on that when I, first I was skeptical on it, but now I love it, but, HP doing great in storage with acquisitions of SimpliVity and Nimble where you had a good run there. >> Keegan: Absolutely. >> We just had a former HPE entrepreneur now on doing a storage startup, so we're familiar with he HPE storage. Good story. What's the update now, you got Discover in the books, now you got the Madrid coming up event. Software to find storage that pony's going to run for a while. What's the update? >> Yeah, so appreciate the time, appreciate you having me on. You know, the way that we're thinking about HPE's storage it's interesting, it's the company is so different, and mentioned to you guys when we were talking before that I actually left HP to come to Nimble, so in some ways I'm approaching the gold pin for a 10 year anniversary at HP. But the-- >> And they retro that so you get that grand floated in. >> Oh, absolutely, absolutely, vacation time carries over it's beautiful. But the HPE storage that I'm now leading is in some ways very different from the HP storage that I left sic years ago and the vision behind HPE's storage is well aligned with the overall vision of Hewlett-Packard Enterprise, which is we make hybrid IT simple, we power the intelligent edge, and we deliver the services to empower organizations to do this. And the things that we were thinking about at Nimble and the things that we're thinking about as kind of a part of HPE are well aligned with this. So, our belief is everyone at this conference cares about whether it's software defined, whether it's hybrid converge, whether it's all flash so on and so forth, but in the real world what clients tend to care about is kind of their experience and we've seen this really fundamental shift in how consumers think about interacting with IT in general. The example I always give is you know I've been in sales my whole career, I've traveled a lot and historically 15 years ago when I would go to a new city, you know, I would land and I would jump on a airport shuttle to go rent a car and then I would pull out a Thomas Guide and I would go to cell C3 and map out my route to the client and things like that. And so I just expected that if I had a meeting at 2:00 p.m., I needed to land at 10:00 a.m., to make my way to, that was just my experience. Cut to today, you know, I land and I immediately pull out my iPhone and hail an Uber and you know reserve an Airbnb when I get there and I, for a 2:00 p.m. meeting I can land at 1:15 and I know Waze is going to route me around traffic to get there. So, my experience as a consumer has fundamentally changed and that's true of IT organizations and consumers within those organizations. So, IT departments have to adapt to that, right? And so a kind of powering this hybrid IT experience and servicing clients that expect immediacy is what we're all about. >> Okay, so I love that analogy. In fact when we were at HP Discover we kind of had this conversation, so as you hailed that Uber, IT wants self driving storage. >> Keegan: Absolutely. >> So, bring that, tie that back, things that we talk a lot about in kind of a colorful joking way, but that is the automation goal of storage is to be available. We talk about edge, unstructured data, moving compute to the edge, it's nuanced now, storage and compute all this where they go through software. Self driving storage means something, and it's kind of a joke on one hand, but what does it actually mean for an IT guy? >> No, that's a great question and this is exactly the way that we think about it. An the self driving car analogy is a really powerful one, right? And so the way we think about this, we're delivering a predictive cloud platform overall and notice that's not a predictive cloud storage conversation and it's a big part of why it made a ton of sense for Nimble storage to become a part of HPE. We brought to bear a product called InfoSight that you might be familiar with. The idea behind InfoSight is in a cloud connected world the client should never know about what's going on in their infrastructure than we do. So, we view every system as being at the edge of our network and for about seven years now we've been collecting a massive amount of information about infrastructure, about 70 million telemetry points per day per system that's coming back to us. So, we have a massive anonymized dataset about infrastructure. So, we've been collecting all of the sensor data in the same way that say Uber or Tesla has been collecting sensor data from cars, right, and the next step kind of the next wave of innovation, if you will, is, okay it's great that you've collected this sensor data, now what do you do with it? Right? And so we're starting to think about how do you put actuators in place so that you can have an actual self managing data center. How can you apply a machine learning and global kind of corelation in a way that actually applies artificial intelligence to the data center and makes it truly touchless and self managing and self healing and so on and so forth. >> So, that vision alone is when, well, I'm sure when you pitched that to Meg, she was like,"Okay, that sounds good, "let's buy the company." But as well, there was another factor, which was the success that Nimble was having. A major shift in the storage market and you can see it walking around here is that over the last five, seven years there's been a shift from the storage specialist expert at managing LUNs and deploying and tuning, to the sort of generalist because people realize, look, there's no competitive advantage. So, talk about that and how the person to whom you've sold and your career has changed. >> Yeah, no, absolutely, it's a great point. And I think it's in a lot of ways it goes to, you're right, obviously Meg and Antonio saw a lot of value in Nimble Storage. The value that we saw as Nimble Storage is as a standalone storage company with kind of one product to sell. You know there's a saying in sales that if you're a hammer everything looks like a nail, right. And so, it's really cool that we could go get on a whiteboard and explain why the Castle file system is revolutionary and delivers superior IOPs and so on and so forth, but the conversation is shifting to more of a solutions conversation. It moves to how do I deliver actual value and how do I help organizations drive revenue and help them distinguish themselves from their competitors leveraging digital transformation. So, being a part of a company that has a wide portfolio and applying a solutions sales approach it's game changing, right. Our ability to go in and say, "I don't want to tell you about the Nimble OS, "I want to hear from you what your challenges are "and then I'm going to come back to you with a proposal "to help you solve those challenges." It's exciting for our sales teams, frankly, because it changes our conversations that makes us more consultative. >> Alright, talk about the some of the-- >> Value conversations. >> Talk about the sales engagement dynamic with the buyer of storage, especially you mentioned in the old days, now new days. A new dynamic's emerging we've identified on theCUBE past couple days and I'll just kind of lay it out for you and I want you to get a reaction. I'm the storage buyer of old, now I'm the modern guy, I got to know all the ins and outs of speeds and feeds against all the competitors, but now there's a new overlay on top of which is a broader picture across the organization that has compute, that has edge, so I feel more, not deluded from storage, but more holistic around other things, so I have to balance both worlds. I got to balance the, I got to know and nail the storage equation. >> Yeah. >> Okay, at well as know the connection points with how it all works, kind of almost as an OS. How do you engage in that conversation? 'Cause it's hard, right? 'Cause storage you go right into the weeds, speeds and feeds under the hood, see our numbers, we're great, we do all this stuff. But now you got to say wait a minute, but in a VM environment it's this, in a cloud it's like this and there's a little bit of bigger picture, HCI or whatever that is. How do you deal with that? >> No, absolutely, and I think that's well said. I mean, I think the storage market historically has always been sort of, alright, do you want Granny Smith apples or red delicious apples? It always sort of looked the same and it was just about I can deliver x number of IOPs and it became a speeds and feeds conversation. Today, it's not just not apples to apples, it's like you prefer apples, pineapples, or vacuum cleaners. Like, there's so many different ways to solve these challenges and so you have to take the conversation to a higher level, right. It has to be a conversation about how do you deliver value to businesses? And I think, I hear-- >> It gets confusing to the buyers, too, because they're being bombarded with a lot of fudd and they still got to check the boxes on all the under the hood stuff, the engine's got to work. >> And they come to VMWorld and every year there's 92 new companies that haven't heard of before that are pitching them on, hey, I solve your problems. I think what I'm hearing from clients a lot is they don't necessarily want to think about the storage, they don't want to think about do I provision RAID 10 or RAID five and do I manage this aggregate in this way or that way, they don't want to think about, right. So, I think this is why you're seeing the success of these next generation platforms that are radically simple to implement, right, and in some ways at Nimble, wen we were talking to some of these clients to have sort of a legacy approach to storage where you got like a primary LUN administrator, there's nothing wrong with that job, it's a great job and I have friends who do that job, but a lot of companies are now shifting to more of a generalist, I manage applications and I manage you know-- >> John: You manage a dashboard console. >> Exactly, yeah, so you have to make it simple and you have to make it you don't have to think about those things anymore. >> So, in thinking about your relationship over the years with VMware, as HP, you are part of the cartel I call it, the inner circle, you got all the APIs early, all the, you know, the CDKs or SDKs early. You know, you were one of the few. You, of course EMC, NetApp, all the big storage players, couple of IBM, couple others. Okay, and then you go to Nimble, you're a little guy, and it's like c'mon hey let's partner! Okay and so much has changed now that you're back at HPE, how has that, how is it VMware evolved from a ecosystem partner standpoint and then specifically where you are today with HPE? >> That's a great question and I remember the early days at Nimble when you know we were knocking on the door and they were like, "Who are you again? "Nimble who?" And we're really proud of sort of the reputation that we've earned inside of VMware, they're a great partner and they've built such a massive ecosystem, and I mean this show is incredible, right. They're such a core part of our business. At Nimble I feel like we earned sort of a seat at that table in some ways through technology differentiation and just grit and hustle, right. We kind of edged our way into those conversations. >> Dave: Performance. >> And performance. And we started to get interesting to them from a strategic perspective as just Nimble Storage. Now, as a part of HPE, HPE was, and in some ways as a part of HPE you're like, "Oh, that was cute." We thought we were strategic to VMware, now we actually are very strategic to VMware and the things that we're doing with them. From an innovation perspective it's like just throwing fuel on the fire, right. So, we're doubling down on some of the things we're doing around like VM Vision and InfoSight, our partnership with Visa and on ProLiant servers, things like that, it's a great partnership. And I think the things that VMware's announced this week are really exciting. >> Thank you, great to see you, and great to have you on theCUBE. >> Thank you so much. >> I'll give you the last word. What's coming up for you guys and HP storage as the vice president general manager, you're out there pounding the pavement, what should customers look for from you guys? >> No, I appreciate that. There's a couple things. So, first and foremost are R&D budget just got a lot bigger specifically around InfoSight. So, you'll see InfoSight come to other HPE products, 3PAR, ProLiant servers so on and so forth and InfoSight will become a much more interesting cloud based management tool for proactive wellness in the infrastructure. Second, you'll see us double down on our channel, right. So, the channel Nimble's always 100% channel, SimpliVity was 100% channel, HPE Storage is going to get very serious about embracing the channel. And third, we're going to ensure that the client experience remains top notch. The NPS score of 85 that Nimble delivered we're really proud of that and we're going to make sure we don't mess that up for our clients. >> You know it's so funny, just an observation, but I worked at HP for nine years in the late '80s, early '90s and then I watched and been covering theCUBE for over seven years now, storage is always like the power engine of HPE and no matter what's happening it comes back down to storage, I mean, the earnings, the results, the client engagements, storage has moved from this corner kind of function to really strategic. And it continues that way. Congratulations. >> Thank you so much. Appreciate the time. >> Alright, it's theCUBE. Coming up Pat Gelsinger on theCUBE at one o'clock. Stay with us. Got all the great guests and alumni and also executives from VMware coming on theCUBE. I'm John Furrier, Dave Vellante. We'll be right back with more live coverage after this short break.

Published Date : Aug 30 2017

SUMMARY :

Brought to you by VMware and its ecosystem partners. Welcome to theCUBE. of SimpliVity and Nimble where you had a good run there. What's the update now, you got Discover in the books, and mentioned to you guys when we were talking before and the things that we're thinking about as kind of conversation, so as you hailed that Uber, and it's kind of a joke on one hand, actuators in place so that you can have an actual self So, talk about that and how the person to whom you've "and then I'm going to come back to you with a proposal and I want you to get a reaction. 'Cause storage you go right into the weeds, It has to be a conversation about how do you deliver and they still got to check the boxes on all of a legacy approach to storage where you got like and you have to make it you don't have to think Okay, and then you go to Nimble, you're a little guy, and they were like, "Who are you again? and the things that we're doing with them. and great to have you on theCUBE. I'll give you the last word. and we're going to make sure we don't mess that up corner kind of function to really strategic. Thank you so much. and also executives from VMware coming on theCUBE.

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Marc Altshuller, IBM - IBM Fast Track Your Data 2017


 

>> Announcer: Live from Munich, Germany; it's The Cube! Covering IBM Fast Track Your Data, brought to you by IBM. >> Welcome back to Munich, Germany everybody. This is The Cube, the leader in live tech coverage. We're covering Fast Track Your Data, IBM's signature moment here in Munich. Big themes around GDPR, data science, data science being a team sport. I'm Dave Vellante, I'm here with my co-host Jim Kobielus. Marc Altshuller is here, he's the general manager of IBM Business Analytics. Good to see you again Marc. >> Hey, always great to see you. Welcome, it's our first time together. >> Okay so we heard your key note, you were talking about the caveats of correlations, you were talking about rear view mirror analysis versus sort of looking forward, something that I've been sort of harping on for years. You know, I mean I remember the early days of "decision support" and the promises of 360 degree views of the customer, and predictive analytics, and I've always said it, "DSS really never lived up to that", y'know? "Will big data live up to that?" and we're kind of living that now, but what's your take on where we're at in this whole databean? >> I mean look, different customers are at different ends of the spectrum, but people are really getting value. They're becoming these data driven businesses. I like what Rob Thomas talked about on stage, right. Visiting companies a few years ago where they'd say "I'm not a technology company.". Now, how can you possibly say you're not a technology company, regardless of the industry. Your competitors will beat you if they are using data and you're not. >> Yeah, and everybody talks about digital transformation. And you hear that a lot at conferences, you guys haven't been pounding that theme, other than, y'know below the surface. And to us, digital means data, right? And if you're going to transform digitally, then it's all about the data, you mentioned data driven. What are you seeing, I mean most organizations in our view aren't "data driven" they're sort of reactive. Their CEO's maybe want to be data driven, maybe they're aboard conversations as to how to get there, but they're mostly focused on "Alright, how do we keep "the lights on, how do we meet our revenue targets, "how do we grow a little bit, and then whatever money "we have leftover we'll try to, y'know transform." What are you seeing? Is that changing? >> I would say, look I can give you an example right from my own space, the software space. For years we would have product managers, offering managers, maybe interviewing clients, on gut feel deciding what features to put at what priority within the next release. Now we have all these products instrumented behind the scenes with data, so we can literally see the friction points, the exit points, how frequently they come back, how long they're sessions are, we can even see them effectively graduating within the system where they continue to learn, and where they had shorter sessions, they're now going the longer sessions. That's really, really powerful for us in terms of trying to maximize our outcome from a software perspective. So that's where we kind of like, drink our own champagne. >> I got to ask you, so in around 2003, 2004 HBR had an article, front page y'know cover article of how "gut feel beats data and analytics", now this is 2003, 2004, software development as you know it's a lot of art involved, so my question is how are you doing? Is the data informing you in ways that are nonintuitive? And is it driving y'know, business outcomes for IBM? >> It is, look you see, I'll see like GM's of sports teams talking about maybe pushing back a little bit on the data. It's not all data driven, there's a little bit of gut, like is the guy going to, is he a checker in hockey or whatever that happens to be, and I would say, when you actually look at what's going on within baseball, and you look at the data, when you watch baseball growing up, the commentator might say something along the lines of "the pitcher has their stuff" right? "Does the pitcher have their stuff or not?". Now they literally know, the release point based on elevation, IOT within the state of the release point, the spin velocity of the ball, where they mathematically know "does the pitcher have their stuff?", are they hitting their locations? So all that stuff has all become data driven, and if you don't want to embrace it, you get beat, right? I mean even in baseball, I remember talking to one of these Moneyball type guys where I said like "Doesn't weather impact baseball?" And they're like "Yeah, we've looked at that, it absolutely impacts it." 'Cause you always hear of football and remember the old Peyton Manning thing? Don't play Peyton Manning in cold weather, don't bet on Peyton Manning in cold weather. So "I'm like isn't the same in baseball?", And he's like, absolutely it's the same in baseball, players preform different based on the climate. Do any mangers change their lineup based on that? Never. >> Speaking of HBR, I mean in the last few years there was also an article or two by Michael Shrage about the whole notion of real world experimentation and e-commerce, driven by data, y'know in line, to an operational process, like tuning the design iteratively of say, a shopping cart within your e-commerce environment, based on the stats on what work and what does not work. So, in many ways I mean AB testing, real world experimentation thrives on data science. Do you see AB testing becoming a standard business practice everywhere, or only in particular industries like you know, like the Wal-marts of the world? >> Yeah, look so, AB testing, multi-variant testing, they're pervasive, pretty much anyone who has a website ought to be doing this if they're not doing it already. Maybe some startups aren't quite into it. They prioritized in different spots, but mainstream fortune 500 companies are doing this, the tools have made it really easy. I would say, maybe the Achilles heel or the next frontier is, that is effectively saying, kind of creating one pattern of user, putting everyone in a single bucket, right? "Does this button perform better "when it's orange or when it's green? "Oh, it performs better orange." Really, does it perform well for every segmentation orange better than green or is it just a certain segmentation? So that next kind of frontier is going to be, how do we segment it, know a little bit more about you when you're coming in so that AB testing starts to build these kind of sub-profiles, sub-segmentation. >> Micro-segmentation, and of course, the end extreme of that dynamic is one-to-one personalization of experiences and engagements based on knowing 360 degrees about you and what makes you tick as well, so yeah. >> Altshuller: And add onto that context, right? You have your business, let's even keep it really simple, right, you've got your business life, you've got your social life, and your profile of what you're looking for when you're shopping your social life or something is very different than when you're shopping your business life. We have to personalize it to the idea where, I don't want to say schizophrenic but you do have multiple personalities from an online perspective, right? From a digital perspective it all depends in the moment, what is it that you're actually doing, right? And what are you, who are you acting for? >> Marc, I want to ask you, you're homies, your peeps are the business people. >> Yes. >> That's where you spend your time. I'm interested in the relationship between those business people and the data science teams. They're all, we all hear about how data science and unicorns are hard to find, difficult to get the skills, citizen data science is sort of a nirvana. But, how are you seeing businesses bring the domain expertise of the business and blending that with data science? >> So, they do it, I have some cautionary tales that I've experienced in terms of how they're doing it. They feel like, let's just assign the subject matter expert, they'll work with the data scientist, they'll give them context as they're doing their project, but unfortunately what I've seen time and time again, is that subject matter expert right out of the gate brings a tremendous amount of bias based on the types of analysis they've done in the past. >> Vellante: That's not how we do it here. >> Yeah, exactly, like "did you test this?". "Oh yeah, there's no correlation there, we've tried it." Well, just because there's no correlation, as I talked about onstage, doesn't mean it's not part of the pattern in terms of, like you don't want someone in there right off the bat dismissing things. So I always coach, when the business user subject matter experts become involved early, they have to be tremendously open-minded and not all of them can be. I like bringing them in later, because that data scientist, they are unbiased, like they see this data set, it doesn't mean anything to them, they're just numerically telling you what the data set says. Now the business user can then add some context, maybe they grabbed a field that really is an irrelevant field and they can give them that context afterwards. But we just don't want them shutting down, kind of roots, too early in the process. >> You know, we've been talking for a couple of years now within our community about this digital matrix, this digital fabric that's emerged and you're seeing these horizontal layers of technology, whether it's cloud or, you know, security, you all OAuth in with LinkedIn, Facebook, and Twitter. There's a data fabric that's emerging and you're seeing all these new business models, whether it's Uber or Airbnb or WAZE, et cetera, and then you see this blockbuster announcement last week, Amazon buying Whole Foods. And it's just fascinating to us and it's all about the data that a company like an Amazon can be a content company, could be a retail company, now it's becoming a grocer, you see Apple getting into financial services. So, you're seeing industries being able to traverse or companies being able traverse industries and it's all because of the data, so these conversations absolutely are going on in boardrooms. It's all about the digital transformation, the digital disruption, so how do you see, you know, your clients trying to take advantage of that or defend against that? >> Yeah look, I mean, you have to be proactive. You have to be willing to disrupt yourself in all these tech industries, it's just moving too quickly. I read a similar story, I think yesterday, around potentially Blockchain disrupting ridesharing programs, right? Why do you need the intermediary if you have this open ledger and these secure transactions you can do back and forth with this ecosystem. So there's another interesting disruption. Now do the ridesharing guys proactively get into that and promote it, or do they almost in slow motion, get replaced by that at some point. So yeah I think it's a come-on on all of us, like you don't remain a market lead, every market leader gets destructive at some point, the key is, do you disrupt yourself and you remain the market leader, or do you let someone else disrupt you. And if you get disrupted, how quickly can you recover. >> Well you know, you talked to banking executives and they're all talking Blockchain. Blockchain is the future, Bitcoin was designed to disintermediate the bank, so they're many, many banks are embracing it and so it comes back to the data. So my question I have, the discussion I'd like to have is how organizations are valuing data. You can't put data as a value on, y'know an asset on your balance sheet. The accounting industry standards don't exist. They probably won't for decades. So how are companies, y'know crocking data value, is it limiting their ability to move toward a data driven economy, is it a limiting factor that they don't have a good way to value their data, and understand how to monetize it. >> So I have heard of cases where companies have but data on their balance sheet, it's not mainstream at this point, but I mean you've seen it sometimes, and even some bankruptcy proceedings, their industry that's being in a bankruptcy protection where they say "Hey, but this data asset "is really where the value is." >> Vellante: And it's certainly implicit in valuations. >> Correct, I mean you see bios all the time based on the actual data sets, so yeah that data set, they definitely treasure it, and they realize that a lot of their answers are within that data set. And they also I think, understand that they're is a lot of peeling the onion that goes on when you're starting to work through that data, right? You have your initial thoughts, then you correct something based on what the data told you to do, and then the new data comes in based on what your new experience is, and then all of a sudden you have, you see what your next friction point is. You continue to knock down these things, so it is also very iterative working with that data asset. But yeah, these companies are seeing it's very value when they collect the data, but the other thing is the signal of what's driving your business may not be in your data, more and more often it may be in market data that's out there. So you think about social media data, you think about weather data and being able to go and grab that information. I remember watching the show Millions, where they talk about the hedge fund guys running satellites over like Wal-mart parking lots to try to predict the redux for the quarter, right? Like, you're collecting all this data but it's out there. >> Or maybe the value is not so much in the data itself, but in what it enables you to develop as a derivative asset, meaning a statistical predictive model or machine learning model that shows the patterns that you can then drive into, recommendation engines, and your target marketing y'know applications. So you see any clients valuate, doing their valuation of data on those derivative assets? >> Altshuller: Yeah. >> In lieu of... >> In these new business models I see within corporations that have been around for decades, it's actual data offers that they make to maybe their ecosystem, their channel. "Here's data we have, here's how you interpret it, "we'll continue to collect it, we'll continue to curate it, "we'll make it available." And this is really what's driving your business. So yeah, data assets become something that, companies are figuring out how to monetize their data assets. >> Of course those derived assets will decay if those models of, for example machine learning models are not trained with fresh, y'know data from the sources. >> And if we're not testing for new variable too, right? Like if the variable was never in the model, you still have to have this discovery process, that's always going on the see what new variables might be out there, what new data set, right. Like if a new IOT sensor in the baseball stadium becomes available, maybe that one I talked about with elevation of the pitcher, like until you have that you can't use it, but once you have it you have to figure out how to use it. >> Alright lets bring it back to your business, what can I buy from you, what do sell, what are your products? >> Yeah so after being in business analytics is Cognos analytics, Watson analytics, Watts analytics for social media, and planning analytics. Cognos is the "what", what's going on in my business. Watts analytics is the "why", planning analytics is "what do we think is going to happen?". We're starting to do more and more smarter, what do we think's going to happen based on these predictive models instead of just guessing what's going to happen. And then social media really gets into this idea of trying to find the signal, the sentiment. Not just around your own brand, it could be a competitor recall, and what now the intent is of that customer, are they going to now start buying other products, or are they going to stick with the recall company. >> Vellante: Okay so the starting point of your business having Cognos, one of the largest acquisitions ever in IBM's history, and of course it was all about CFO's and reporting and Sarbanes-Oxley was a huge boom to that business, but as I was saying before it, it never really got us to that predictive era. So you're layering those predictive pieces on top. >> That's what you saw on stage. >> Yes, that's right, what, so we saw on stage, and then are you selling to the same constituencies? Or how is constituency that you sell to changing? >> Yeah, no it's actually the same. Well Cognos BI, historically was selling to IT, and Cognos Analytics is selling to the business. But if we take that leap forward then we're now in the market, we have been for a few years now at Cognos Analytics. Yeah, that capability we showed onstage where we talked about not only what's going on, why it's going on, what will happen next, and what we ought to do about it. We're selling that capability for them, the business user, the dashboard becomes like a piece of glass to them. And that glass is able to call services that they don't have to be proficient in, they just want to be able to use them. It calls the weather service, it calls the optimization service, it calls the machine learning data sign service, and it actually gives them information that's forward looking and highly accurate, so they love it, 'cause it's cool they haven't had anything like that before. >> Vellante: Alright Marc Altshuller, thanks very much for coming back on The Cube, it's great to see you. >> Thank you. >> "You can't measure heart" as we say in boston, but you better start measuring. Alright keep right there everybody, Jim and I will right back after this short break. This is The Cube, we're live from Fast Track Your Data in Munich. We'll be right back. (upbeat jingle) (thoughtful music)

Published Date : Jun 24 2017

SUMMARY :

Covering IBM Fast Track Your Data, brought to you by IBM. Good to see you again Marc. Hey, always great to see you. about the caveats of correlations, you were talking about of the spectrum, but people are really getting value. And you hear that a lot at conferences, the exit points, how frequently they come back, and if you don't want to embrace it, you get beat, right? based on the stats on what work and what does not work. how do we segment it, know a little bit more about you Micro-segmentation, and of course, the end extreme I don't want to say schizophrenic but you do have your peeps are the business people. That's where you spend your time. based on the types of analysis they've done in the past. part of the pattern in terms of, like you don't want and it's all because of the data, so these conversations the key is, do you disrupt yourself So my question I have, the discussion I'd like to have So I have heard of cases where companies based on what the data told you to do, but in what it enables you to develop as a derivative asset, "Here's data we have, here's how you interpret it, are not trained with fresh, y'know data from the sources. that you can't use it, but once you have it Cognos is the "what", what's going on in my business. Vellante: Okay so the starting point of your business the dashboard becomes like a piece of glass to them. for coming back on The Cube, it's great to see you. but you better start measuring.

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